focus of analysis in case study

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, November 20). What Is a Case Study? | Definition, Examples & Methods. Scribbr. Retrieved April 16, 2024, from https://www.scribbr.com/methodology/case-study/

Is this article helpful?

Shona McCombes

Other students also liked, primary vs. secondary sources | difference & examples, what is a theoretical framework | guide to organizing, what is action research | definition & examples, what is your plagiarism score.

• Privacy Policy

Buy Me a Coffee

Home » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Questionnaire – Definition, Types, and Examples

Observational Research – Methods and Guide

Quantitative Research – Methods, Types and...

Qualitative Research Methods

Explanatory Research – Types, Methods, Guide

Survey Research – Types, Methods, Examples

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

Prevent plagiarism, run a free check.

Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2023, January 30). Case Study | Definition, Examples & Methods. Scribbr. Retrieved 15 April 2024, from https://www.scribbr.co.uk/research-methods/case-studies/

Is this article helpful?

Shona McCombes

Other students also liked, correlational research | guide, design & examples, a quick guide to experimental design | 5 steps & examples, descriptive research design | definition, methods & examples.

The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

Applications for case study research, what is a good case study, process of case study design, benefits and limitations of case studies.

• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Case studies

Case studies are essential to qualitative research , offering a lens through which researchers can investigate complex phenomena within their real-life contexts. This chapter explores the concept, purpose, applications, examples, and types of case studies and provides guidance on how to conduct case study research effectively.

Whereas quantitative methods look at phenomena at scale, case study research looks at a concept or phenomenon in considerable detail. While analyzing a single case can help understand one perspective regarding the object of research inquiry, analyzing multiple cases can help obtain a more holistic sense of the topic or issue. Let's provide a basic definition of a case study, then explore its characteristics and role in the qualitative research process.

Definition of a case study

A case study in qualitative research is a strategy of inquiry that involves an in-depth investigation of a phenomenon within its real-world context. It provides researchers with the opportunity to acquire an in-depth understanding of intricate details that might not be as apparent or accessible through other methods of research. The specific case or cases being studied can be a single person, group, or organization – demarcating what constitutes a relevant case worth studying depends on the researcher and their research question .

Among qualitative research methods , a case study relies on multiple sources of evidence, such as documents, artifacts, interviews , or observations , to present a complete and nuanced understanding of the phenomenon under investigation. The objective is to illuminate the readers' understanding of the phenomenon beyond its abstract statistical or theoretical explanations.

Characteristics of case studies

Case studies typically possess a number of distinct characteristics that set them apart from other research methods. These characteristics include a focus on holistic description and explanation, flexibility in the design and data collection methods, reliance on multiple sources of evidence, and emphasis on the context in which the phenomenon occurs.

Furthermore, case studies can often involve a longitudinal examination of the case, meaning they study the case over a period of time. These characteristics allow case studies to yield comprehensive, in-depth, and richly contextualized insights about the phenomenon of interest.

The role of case studies in research

Case studies hold a unique position in the broader landscape of research methods aimed at theory development. They are instrumental when the primary research interest is to gain an intensive, detailed understanding of a phenomenon in its real-life context.

In addition, case studies can serve different purposes within research - they can be used for exploratory, descriptive, or explanatory purposes, depending on the research question and objectives. This flexibility and depth make case studies a valuable tool in the toolkit of qualitative researchers.

Remember, a well-conducted case study can offer a rich, insightful contribution to both academic and practical knowledge through theory development or theory verification, thus enhancing our understanding of complex phenomena in their real-world contexts.

What is the purpose of a case study?

Case study research aims for a more comprehensive understanding of phenomena, requiring various research methods to gather information for qualitative analysis . Ultimately, a case study can allow the researcher to gain insight into a particular object of inquiry and develop a theoretical framework relevant to the research inquiry.

Why use case studies in qualitative research?

Using case studies as a research strategy depends mainly on the nature of the research question and the researcher's access to the data.

Conducting case study research provides a level of detail and contextual richness that other research methods might not offer. They are beneficial when there's a need to understand complex social phenomena within their natural contexts.

The explanatory, exploratory, and descriptive roles of case studies

Case studies can take on various roles depending on the research objectives. They can be exploratory when the research aims to discover new phenomena or define new research questions; they are descriptive when the objective is to depict a phenomenon within its context in a detailed manner; and they can be explanatory if the goal is to understand specific relationships within the studied context. Thus, the versatility of case studies allows researchers to approach their topic from different angles, offering multiple ways to uncover and interpret the data .

The impact of case studies on knowledge development

Case studies play a significant role in knowledge development across various disciplines. Analysis of cases provides an avenue for researchers to explore phenomena within their context based on the collected data.

This can result in the production of rich, practical insights that can be instrumental in both theory-building and practice. Case studies allow researchers to delve into the intricacies and complexities of real-life situations, uncovering insights that might otherwise remain hidden.

Types of case studies

In qualitative research , a case study is not a one-size-fits-all approach. Depending on the nature of the research question and the specific objectives of the study, researchers might choose to use different types of case studies. These types differ in their focus, methodology, and the level of detail they provide about the phenomenon under investigation.

Understanding these types is crucial for selecting the most appropriate approach for your research project and effectively achieving your research goals. Let's briefly look at the main types of case studies.

Exploratory case studies

Exploratory case studies are typically conducted to develop a theory or framework around an understudied phenomenon. They can also serve as a precursor to a larger-scale research project. Exploratory case studies are useful when a researcher wants to identify the key issues or questions which can spur more extensive study or be used to develop propositions for further research. These case studies are characterized by flexibility, allowing researchers to explore various aspects of a phenomenon as they emerge, which can also form the foundation for subsequent studies.

Descriptive case studies

Descriptive case studies aim to provide a complete and accurate representation of a phenomenon or event within its context. These case studies are often based on an established theoretical framework, which guides how data is collected and analyzed. The researcher is concerned with describing the phenomenon in detail, as it occurs naturally, without trying to influence or manipulate it.

Explanatory case studies

Explanatory case studies are focused on explanation - they seek to clarify how or why certain phenomena occur. Often used in complex, real-life situations, they can be particularly valuable in clarifying causal relationships among concepts and understanding the interplay between different factors within a specific context.

Intrinsic, instrumental, and collective case studies

These three categories of case studies focus on the nature and purpose of the study. An intrinsic case study is conducted when a researcher has an inherent interest in the case itself. Instrumental case studies are employed when the case is used to provide insight into a particular issue or phenomenon. A collective case study, on the other hand, involves studying multiple cases simultaneously to investigate some general phenomena.

Each type of case study serves a different purpose and has its own strengths and challenges. The selection of the type should be guided by the research question and objectives, as well as the context and constraints of the research.

The flexibility, depth, and contextual richness offered by case studies make this approach an excellent research method for various fields of study. They enable researchers to investigate real-world phenomena within their specific contexts, capturing nuances that other research methods might miss. Across numerous fields, case studies provide valuable insights into complex issues.

Critical information systems research

Case studies provide a detailed understanding of the role and impact of information systems in different contexts. They offer a platform to explore how information systems are designed, implemented, and used and how they interact with various social, economic, and political factors. Case studies in this field often focus on examining the intricate relationship between technology, organizational processes, and user behavior, helping to uncover insights that can inform better system design and implementation.

Health research

Health research is another field where case studies are highly valuable. They offer a way to explore patient experiences, healthcare delivery processes, and the impact of various interventions in a real-world context.

Case studies can provide a deep understanding of a patient's journey, giving insights into the intricacies of disease progression, treatment effects, and the psychosocial aspects of health and illness.

Asthma research studies

Specifically within medical research, studies on asthma often employ case studies to explore the individual and environmental factors that influence asthma development, management, and outcomes. A case study can provide rich, detailed data about individual patients' experiences, from the triggers and symptoms they experience to the effectiveness of various management strategies. This can be crucial for developing patient-centered asthma care approaches.

Other fields

Apart from the fields mentioned, case studies are also extensively used in business and management research, education research, and political sciences, among many others. They provide an opportunity to delve into the intricacies of real-world situations, allowing for a comprehensive understanding of various phenomena.

Case studies, with their depth and contextual focus, offer unique insights across these varied fields. They allow researchers to illuminate the complexities of real-life situations, contributing to both theory and practice.

Whatever field you're in, ATLAS.ti puts your data to work for you

Download a free trial of ATLAS.ti to turn your data into insights.

Understanding the key elements of case study design is crucial for conducting rigorous and impactful case study research. A well-structured design guides the researcher through the process, ensuring that the study is methodologically sound and its findings are reliable and valid. The main elements of case study design include the research question , propositions, units of analysis, and the logic linking the data to the propositions.

The research question is the foundation of any research study. A good research question guides the direction of the study and informs the selection of the case, the methods of collecting data, and the analysis techniques. A well-formulated research question in case study research is typically clear, focused, and complex enough to merit further detailed examination of the relevant case(s).

Propositions

Propositions, though not necessary in every case study, provide a direction by stating what we might expect to find in the data collected. They guide how data is collected and analyzed by helping researchers focus on specific aspects of the case. They are particularly important in explanatory case studies, which seek to understand the relationships among concepts within the studied phenomenon.

Units of analysis

The unit of analysis refers to the case, or the main entity or entities that are being analyzed in the study. In case study research, the unit of analysis can be an individual, a group, an organization, a decision, an event, or even a time period. It's crucial to clearly define the unit of analysis, as it shapes the qualitative data analysis process by allowing the researcher to analyze a particular case and synthesize analysis across multiple case studies to draw conclusions.

Argumentation

This refers to the inferential model that allows researchers to draw conclusions from the data. The researcher needs to ensure that there is a clear link between the data, the propositions (if any), and the conclusions drawn. This argumentation is what enables the researcher to make valid and credible inferences about the phenomenon under study.

Understanding and carefully considering these elements in the design phase of a case study can significantly enhance the quality of the research. It can help ensure that the study is methodologically sound and its findings contribute meaningful insights about the case.

Ready to jumpstart your research with ATLAS.ti?

Conceptualize your research project with our intuitive data analysis interface. Download a free trial today.

Conducting a case study involves several steps, from defining the research question and selecting the case to collecting and analyzing data . This section outlines these key stages, providing a practical guide on how to conduct case study research.

Defining the research question

The first step in case study research is defining a clear, focused research question. This question should guide the entire research process, from case selection to analysis. It's crucial to ensure that the research question is suitable for a case study approach. Typically, such questions are exploratory or descriptive in nature and focus on understanding a phenomenon within its real-life context.

Selecting and defining the case

The selection of the case should be based on the research question and the objectives of the study. It involves choosing a unique example or a set of examples that provide rich, in-depth data about the phenomenon under investigation. After selecting the case, it's crucial to define it clearly, setting the boundaries of the case, including the time period and the specific context.

Previous research can help guide the case study design. When considering a case study, an example of a case could be taken from previous case study research and used to define cases in a new research inquiry. Considering recently published examples can help understand how to select and define cases effectively.

Developing a detailed case study protocol

A case study protocol outlines the procedures and general rules to be followed during the case study. This includes the data collection methods to be used, the sources of data, and the procedures for analysis. Having a detailed case study protocol ensures consistency and reliability in the study.

The protocol should also consider how to work with the people involved in the research context to grant the research team access to collecting data. As mentioned in previous sections of this guide, establishing rapport is an essential component of qualitative research as it shapes the overall potential for collecting and analyzing data.

Collecting data

Gathering data in case study research often involves multiple sources of evidence, including documents, archival records, interviews, observations, and physical artifacts. This allows for a comprehensive understanding of the case. The process for gathering data should be systematic and carefully documented to ensure the reliability and validity of the study.

Analyzing and interpreting data

The next step is analyzing the data. This involves organizing the data , categorizing it into themes or patterns , and interpreting these patterns to answer the research question. The analysis might also involve comparing the findings with prior research or theoretical propositions.

Writing the case study report

The final step is writing the case study report . This should provide a detailed description of the case, the data, the analysis process, and the findings. The report should be clear, organized, and carefully written to ensure that the reader can understand the case and the conclusions drawn from it.

Each of these steps is crucial in ensuring that the case study research is rigorous, reliable, and provides valuable insights about the case.

The type, depth, and quality of data in your study can significantly influence the validity and utility of the study. In case study research, data is usually collected from multiple sources to provide a comprehensive and nuanced understanding of the case. This section will outline the various methods of collecting data used in case study research and discuss considerations for ensuring the quality of the data.

Interviews are a common method of gathering data in case study research. They can provide rich, in-depth data about the perspectives, experiences, and interpretations of the individuals involved in the case. Interviews can be structured , semi-structured , or unstructured , depending on the research question and the degree of flexibility needed.

Observations

Observations involve the researcher observing the case in its natural setting, providing first-hand information about the case and its context. Observations can provide data that might not be revealed in interviews or documents, such as non-verbal cues or contextual information.

Documents and artifacts

Documents and archival records provide a valuable source of data in case study research. They can include reports, letters, memos, meeting minutes, email correspondence, and various public and private documents related to the case.

These records can provide historical context, corroborate evidence from other sources, and offer insights into the case that might not be apparent from interviews or observations.

Physical artifacts refer to any physical evidence related to the case, such as tools, products, or physical environments. These artifacts can provide tangible insights into the case, complementing the data gathered from other sources.

Ensuring the quality of data collection

Determining the quality of data in case study research requires careful planning and execution. It's crucial to ensure that the data is reliable, accurate, and relevant to the research question. This involves selecting appropriate methods of collecting data, properly training interviewers or observers, and systematically recording and storing the data. It also includes considering ethical issues related to collecting and handling data, such as obtaining informed consent and ensuring the privacy and confidentiality of the participants.

Data analysis

Analyzing case study research involves making sense of the rich, detailed data to answer the research question. This process can be challenging due to the volume and complexity of case study data. However, a systematic and rigorous approach to analysis can ensure that the findings are credible and meaningful. This section outlines the main steps and considerations in analyzing data in case study research.

Organizing the data

The first step in the analysis is organizing the data. This involves sorting the data into manageable sections, often according to the data source or the theme. This step can also involve transcribing interviews, digitizing physical artifacts, or organizing observational data.

Categorizing and coding the data

Once the data is organized, the next step is to categorize or code the data. This involves identifying common themes, patterns, or concepts in the data and assigning codes to relevant data segments. Coding can be done manually or with the help of software tools, and in either case, qualitative analysis software can greatly facilitate the entire coding process. Coding helps to reduce the data to a set of themes or categories that can be more easily analyzed.

Identifying patterns and themes

After coding the data, the researcher looks for patterns or themes in the coded data. This involves comparing and contrasting the codes and looking for relationships or patterns among them. The identified patterns and themes should help answer the research question.

Interpreting the data

Once patterns and themes have been identified, the next step is to interpret these findings. This involves explaining what the patterns or themes mean in the context of the research question and the case. This interpretation should be grounded in the data, but it can also involve drawing on theoretical concepts or prior research.

Verification of the data

The last step in the analysis is verification. This involves checking the accuracy and consistency of the analysis process and confirming that the findings are supported by the data. This can involve re-checking the original data, checking the consistency of codes, or seeking feedback from research participants or peers.

Like any research method , case study research has its strengths and limitations. Researchers must be aware of these, as they can influence the design, conduct, and interpretation of the study.

Understanding the strengths and limitations of case study research can also guide researchers in deciding whether this approach is suitable for their research question . This section outlines some of the key strengths and limitations of case study research.

Benefits include the following:

• Rich, detailed data: One of the main strengths of case study research is that it can generate rich, detailed data about the case. This can provide a deep understanding of the case and its context, which can be valuable in exploring complex phenomena.
• Flexibility: Case study research is flexible in terms of design , data collection , and analysis . A sufficient degree of flexibility allows the researcher to adapt the study according to the case and the emerging findings.
• Real-world context: Case study research involves studying the case in its real-world context, which can provide valuable insights into the interplay between the case and its context.
• Multiple sources of evidence: Case study research often involves collecting data from multiple sources , which can enhance the robustness and validity of the findings.

On the other hand, researchers should consider the following limitations:

• Generalizability: A common criticism of case study research is that its findings might not be generalizable to other cases due to the specificity and uniqueness of each case.
• Time and resource intensive: Case study research can be time and resource intensive due to the depth of the investigation and the amount of collected data.
• Complexity of analysis: The rich, detailed data generated in case study research can make analyzing the data challenging.
• Subjectivity: Given the nature of case study research, there may be a higher degree of subjectivity in interpreting the data , so researchers need to reflect on this and transparently convey to audiences how the research was conducted.

Being aware of these strengths and limitations can help researchers design and conduct case study research effectively and interpret and report the findings appropriately.

Ready to analyze your data with ATLAS.ti?

See how our intuitive software can draw key insights from your data with a free trial today.

Organizing Your Social Sciences Research Assignments

• Annotated Bibliography
• Analyzing a Scholarly Journal Article
• Group Presentations
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• Types of Structured Group Activities
• Group Project Survival Skills
• Leading a Class Discussion
• Multiple Book Review Essay
• Reviewing Collected Works
• Writing a Case Analysis Paper
• Writing a Case Study
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Reflective Paper
• Writing a Research Proposal
• Generative AI and Writing
• Acknowledgments

A case study research paper examines a person, place, event, condition, phenomenon, or other type of subject of analysis in order to extrapolate  key themes and results that help predict future trends, illuminate previously hidden issues that can be applied to practice, and/or provide a means for understanding an important research problem with greater clarity. A case study research paper usually examines a single subject of analysis, but case study papers can also be designed as a comparative investigation that shows relationships between two or more subjects. The methods used to study a case can rest within a quantitative, qualitative, or mixed-method investigative paradigm.

Case Studies. Writing@CSU. Colorado State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010 ; “What is a Case Study?” In Swanborn, Peter G. Case Study Research: What, Why and How? London: SAGE, 2010.

How to Approach Writing a Case Study Research Paper

General information about how to choose a topic to investigate can be found under the " Choosing a Research Problem " tab in the Organizing Your Social Sciences Research Paper writing guide. Review this page because it may help you identify a subject of analysis that can be investigated using a case study design.

However, identifying a case to investigate involves more than choosing the research problem . A case study encompasses a problem contextualized around the application of in-depth analysis, interpretation, and discussion, often resulting in specific recommendations for action or for improving existing conditions. As Seawright and Gerring note, practical considerations such as time and access to information can influence case selection, but these issues should not be the sole factors used in describing the methodological justification for identifying a particular case to study. Given this, selecting a case includes considering the following:

• The case represents an unusual or atypical example of a research problem that requires more in-depth analysis? Cases often represent a topic that rests on the fringes of prior investigations because the case may provide new ways of understanding the research problem. For example, if the research problem is to identify strategies to improve policies that support girl's access to secondary education in predominantly Muslim nations, you could consider using Azerbaijan as a case study rather than selecting a more obvious nation in the Middle East. Doing so may reveal important new insights into recommending how governments in other predominantly Muslim nations can formulate policies that support improved access to education for girls.
• The case provides important insight or illuminate a previously hidden problem? In-depth analysis of a case can be based on the hypothesis that the case study will reveal trends or issues that have not been exposed in prior research or will reveal new and important implications for practice. For example, anecdotal evidence may suggest drug use among homeless veterans is related to their patterns of travel throughout the day. Assuming prior studies have not looked at individual travel choices as a way to study access to illicit drug use, a case study that observes a homeless veteran could reveal how issues of personal mobility choices facilitate regular access to illicit drugs. Note that it is important to conduct a thorough literature review to ensure that your assumption about the need to reveal new insights or previously hidden problems is valid and evidence-based.
• The case challenges and offers a counter-point to prevailing assumptions? Over time, research on any given topic can fall into a trap of developing assumptions based on outdated studies that are still applied to new or changing conditions or the idea that something should simply be accepted as "common sense," even though the issue has not been thoroughly tested in current practice. A case study analysis may offer an opportunity to gather evidence that challenges prevailing assumptions about a research problem and provide a new set of recommendations applied to practice that have not been tested previously. For example, perhaps there has been a long practice among scholars to apply a particular theory in explaining the relationship between two subjects of analysis. Your case could challenge this assumption by applying an innovative theoretical framework [perhaps borrowed from another discipline] to explore whether this approach offers new ways of understanding the research problem. Taking a contrarian stance is one of the most important ways that new knowledge and understanding develops from existing literature.
• The case provides an opportunity to pursue action leading to the resolution of a problem? Another way to think about choosing a case to study is to consider how the results from investigating a particular case may result in findings that reveal ways in which to resolve an existing or emerging problem. For example, studying the case of an unforeseen incident, such as a fatal accident at a railroad crossing, can reveal hidden issues that could be applied to preventative measures that contribute to reducing the chance of accidents in the future. In this example, a case study investigating the accident could lead to a better understanding of where to strategically locate additional signals at other railroad crossings so as to better warn drivers of an approaching train, particularly when visibility is hindered by heavy rain, fog, or at night.
• The case offers a new direction in future research? A case study can be used as a tool for an exploratory investigation that highlights the need for further research about the problem. A case can be used when there are few studies that help predict an outcome or that establish a clear understanding about how best to proceed in addressing a problem. For example, after conducting a thorough literature review [very important!], you discover that little research exists showing the ways in which women contribute to promoting water conservation in rural communities of east central Africa. A case study of how women contribute to saving water in a rural village of Uganda can lay the foundation for understanding the need for more thorough research that documents how women in their roles as cooks and family caregivers think about water as a valuable resource within their community. This example of a case study could also point to the need for scholars to build new theoretical frameworks around the topic [e.g., applying feminist theories of work and family to the issue of water conservation].

Eisenhardt, Kathleen M. “Building Theories from Case Study Research.” Academy of Management Review 14 (October 1989): 532-550; Emmel, Nick. Sampling and Choosing Cases in Qualitative Research: A Realist Approach . Thousand Oaks, CA: SAGE Publications, 2013; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Seawright, Jason and John Gerring. "Case Selection Techniques in Case Study Research." Political Research Quarterly 61 (June 2008): 294-308.

Structure and Writing Style

The purpose of a paper in the social sciences designed around a case study is to thoroughly investigate a subject of analysis in order to reveal a new understanding about the research problem and, in so doing, contributing new knowledge to what is already known from previous studies. In applied social sciences disciplines [e.g., education, social work, public administration, etc.], case studies may also be used to reveal best practices, highlight key programs, or investigate interesting aspects of professional work.

In general, the structure of a case study research paper is not all that different from a standard college-level research paper. However, there are subtle differences you should be aware of. Here are the key elements to organizing and writing a case study research paper.

I.  Introduction

As with any research paper, your introduction should serve as a roadmap for your readers to ascertain the scope and purpose of your study . The introduction to a case study research paper, however, should not only describe the research problem and its significance, but you should also succinctly describe why the case is being used and how it relates to addressing the problem. The two elements should be linked. With this in mind, a good introduction answers these four questions:

• What is being studied? Describe the research problem and describe the subject of analysis [the case] you have chosen to address the problem. Explain how they are linked and what elements of the case will help to expand knowledge and understanding about the problem.
• Why is this topic important to investigate? Describe the significance of the research problem and state why a case study design and the subject of analysis that the paper is designed around is appropriate in addressing the problem.
• What did we know about this topic before I did this study? Provide background that helps lead the reader into the more in-depth literature review to follow. If applicable, summarize prior case study research applied to the research problem and why it fails to adequately address the problem. Describe why your case will be useful. If no prior case studies have been used to address the research problem, explain why you have selected this subject of analysis.
• How will this study advance new knowledge or new ways of understanding? Explain why your case study will be suitable in helping to expand knowledge and understanding about the research problem.

Each of these questions should be addressed in no more than a few paragraphs. Exceptions to this can be when you are addressing a complex research problem or subject of analysis that requires more in-depth background information.

II.  Literature Review

The literature review for a case study research paper is generally structured the same as it is for any college-level research paper. The difference, however, is that the literature review is focused on providing background information and  enabling historical interpretation of the subject of analysis in relation to the research problem the case is intended to address . This includes synthesizing studies that help to:

• Place relevant works in the context of their contribution to understanding the case study being investigated . This would involve summarizing studies that have used a similar subject of analysis to investigate the research problem. If there is literature using the same or a very similar case to study, you need to explain why duplicating past research is important [e.g., conditions have changed; prior studies were conducted long ago, etc.].
• Describe the relationship each work has to the others under consideration that informs the reader why this case is applicable . Your literature review should include a description of any works that support using the case to investigate the research problem and the underlying research questions.
• Identify new ways to interpret prior research using the case study . If applicable, review any research that has examined the research problem using a different research design. Explain how your use of a case study design may reveal new knowledge or a new perspective or that can redirect research in an important new direction.
• Resolve conflicts amongst seemingly contradictory previous studies . This refers to synthesizing any literature that points to unresolved issues of concern about the research problem and describing how the subject of analysis that forms the case study can help resolve these existing contradictions.
• Point the way in fulfilling a need for additional research . Your review should examine any literature that lays a foundation for understanding why your case study design and the subject of analysis around which you have designed your study may reveal a new way of approaching the research problem or offer a perspective that points to the need for additional research.
• Expose any gaps that exist in the literature that the case study could help to fill . Summarize any literature that not only shows how your subject of analysis contributes to understanding the research problem, but how your case contributes to a new way of understanding the problem that prior research has failed to do.
• Locate your own research within the context of existing literature [very important!] . Collectively, your literature review should always place your case study within the larger domain of prior research about the problem. The overarching purpose of reviewing pertinent literature in a case study paper is to demonstrate that you have thoroughly identified and synthesized prior studies in relation to explaining the relevance of the case in addressing the research problem.

III.  Method

In this section, you explain why you selected a particular case [i.e., subject of analysis] and the strategy you used to identify and ultimately decide that your case was appropriate in addressing the research problem. The way you describe the methods used varies depending on the type of subject of analysis that constitutes your case study.

If your subject of analysis is an incident or event . In the social and behavioral sciences, the event or incident that represents the case to be studied is usually bounded by time and place, with a clear beginning and end and with an identifiable location or position relative to its surroundings. The subject of analysis can be a rare or critical event or it can focus on a typical or regular event. The purpose of studying a rare event is to illuminate new ways of thinking about the broader research problem or to test a hypothesis. Critical incident case studies must describe the method by which you identified the event and explain the process by which you determined the validity of this case to inform broader perspectives about the research problem or to reveal new findings. However, the event does not have to be a rare or uniquely significant to support new thinking about the research problem or to challenge an existing hypothesis. For example, Walo, Bull, and Breen conducted a case study to identify and evaluate the direct and indirect economic benefits and costs of a local sports event in the City of Lismore, New South Wales, Australia. The purpose of their study was to provide new insights from measuring the impact of a typical local sports event that prior studies could not measure well because they focused on large "mega-events." Whether the event is rare or not, the methods section should include an explanation of the following characteristics of the event: a) when did it take place; b) what were the underlying circumstances leading to the event; and, c) what were the consequences of the event in relation to the research problem.

If your subject of analysis is a person. Explain why you selected this particular individual to be studied and describe what experiences they have had that provide an opportunity to advance new understandings about the research problem. Mention any background about this person which might help the reader understand the significance of their experiences that make them worthy of study. This includes describing the relationships this person has had with other people, institutions, and/or events that support using them as the subject for a case study research paper. It is particularly important to differentiate the person as the subject of analysis from others and to succinctly explain how the person relates to examining the research problem [e.g., why is one politician in a particular local election used to show an increase in voter turnout from any other candidate running in the election]. Note that these issues apply to a specific group of people used as a case study unit of analysis [e.g., a classroom of students].

If your subject of analysis is a place. In general, a case study that investigates a place suggests a subject of analysis that is unique or special in some way and that this uniqueness can be used to build new understanding or knowledge about the research problem. A case study of a place must not only describe its various attributes relevant to the research problem [e.g., physical, social, historical, cultural, economic, political], but you must state the method by which you determined that this place will illuminate new understandings about the research problem. It is also important to articulate why a particular place as the case for study is being used if similar places also exist [i.e., if you are studying patterns of homeless encampments of veterans in open spaces, explain why you are studying Echo Park in Los Angeles rather than Griffith Park?]. If applicable, describe what type of human activity involving this place makes it a good choice to study [e.g., prior research suggests Echo Park has more homeless veterans].

If your subject of analysis is a phenomenon. A phenomenon refers to a fact, occurrence, or circumstance that can be studied or observed but with the cause or explanation to be in question. In this sense, a phenomenon that forms your subject of analysis can encompass anything that can be observed or presumed to exist but is not fully understood. In the social and behavioral sciences, the case usually focuses on human interaction within a complex physical, social, economic, cultural, or political system. For example, the phenomenon could be the observation that many vehicles used by ISIS fighters are small trucks with English language advertisements on them. The research problem could be that ISIS fighters are difficult to combat because they are highly mobile. The research questions could be how and by what means are these vehicles used by ISIS being supplied to the militants and how might supply lines to these vehicles be cut off? How might knowing the suppliers of these trucks reveal larger networks of collaborators and financial support? A case study of a phenomenon most often encompasses an in-depth analysis of a cause and effect that is grounded in an interactive relationship between people and their environment in some way.

NOTE:   The choice of the case or set of cases to study cannot appear random. Evidence that supports the method by which you identified and chose your subject of analysis should clearly support investigation of the research problem and linked to key findings from your literature review. Be sure to cite any studies that helped you determine that the case you chose was appropriate for examining the problem.

IV.  Discussion

The main elements of your discussion section are generally the same as any research paper, but centered around interpreting and drawing conclusions about the key findings from your analysis of the case study. Note that a general social sciences research paper may contain a separate section to report findings. However, in a paper designed around a case study, it is common to combine a description of the results with the discussion about their implications. The objectives of your discussion section should include the following:

Reiterate the Research Problem/State the Major Findings Briefly reiterate the research problem you are investigating and explain why the subject of analysis around which you designed the case study were used. You should then describe the findings revealed from your study of the case using direct, declarative, and succinct proclamation of the study results. Highlight any findings that were unexpected or especially profound.

Explain the Meaning of the Findings and Why They are Important Systematically explain the meaning of your case study findings and why you believe they are important. Begin this part of the section by repeating what you consider to be your most important or surprising finding first, then systematically review each finding. Be sure to thoroughly extrapolate what your analysis of the case can tell the reader about situations or conditions beyond the actual case that was studied while, at the same time, being careful not to misconstrue or conflate a finding that undermines the external validity of your conclusions.

Relate the Findings to Similar Studies No study in the social sciences is so novel or possesses such a restricted focus that it has absolutely no relation to previously published research. The discussion section should relate your case study results to those found in other studies, particularly if questions raised from prior studies served as the motivation for choosing your subject of analysis. This is important because comparing and contrasting the findings of other studies helps support the overall importance of your results and it highlights how and in what ways your case study design and the subject of analysis differs from prior research about the topic.

Consider Alternative Explanations of the Findings Remember that the purpose of social science research is to discover and not to prove. When writing the discussion section, you should carefully consider all possible explanations revealed by the case study results, rather than just those that fit your hypothesis or prior assumptions and biases. Be alert to what the in-depth analysis of the case may reveal about the research problem, including offering a contrarian perspective to what scholars have stated in prior research if that is how the findings can be interpreted from your case.

Acknowledge the Study's Limitations You can state the study's limitations in the conclusion section of your paper but describing the limitations of your subject of analysis in the discussion section provides an opportunity to identify the limitations and explain why they are not significant. This part of the discussion section should also note any unanswered questions or issues your case study could not address. More detailed information about how to document any limitations to your research can be found here .

Suggest Areas for Further Research Although your case study may offer important insights about the research problem, there are likely additional questions related to the problem that remain unanswered or findings that unexpectedly revealed themselves as a result of your in-depth analysis of the case. Be sure that the recommendations for further research are linked to the research problem and that you explain why your recommendations are valid in other contexts and based on the original assumptions of your study.

V.  Conclusion

As with any research paper, you should summarize your conclusion in clear, simple language; emphasize how the findings from your case study differs from or supports prior research and why. Do not simply reiterate the discussion section. Provide a synthesis of key findings presented in the paper to show how these converge to address the research problem. If you haven't already done so in the discussion section, be sure to document the limitations of your case study and any need for further research.

The function of your paper's conclusion is to: 1) reiterate the main argument supported by the findings from your case study; 2) state clearly the context, background, and necessity of pursuing the research problem using a case study design in relation to an issue, controversy, or a gap found from reviewing the literature; and, 3) provide a place to persuasively and succinctly restate the significance of your research problem, given that the reader has now been presented with in-depth information about the topic.

Consider the following points to help ensure your conclusion is appropriate:

• If the argument or purpose of your paper is complex, you may need to summarize these points for your reader.
• If prior to your conclusion, you have not yet explained the significance of your findings or if you are proceeding inductively, use the conclusion of your paper to describe your main points and explain their significance.
• Move from a detailed to a general level of consideration of the case study's findings that returns the topic to the context provided by the introduction or within a new context that emerges from your case study findings.

Note that, depending on the discipline you are writing in or the preferences of your professor, the concluding paragraph may contain your final reflections on the evidence presented as it applies to practice or on the essay's central research problem. However, the nature of being introspective about the subject of analysis you have investigated will depend on whether you are explicitly asked to express your observations in this way.

Problems to Avoid

Overgeneralization One of the goals of a case study is to lay a foundation for understanding broader trends and issues applied to similar circumstances. However, be careful when drawing conclusions from your case study. They must be evidence-based and grounded in the results of the study; otherwise, it is merely speculation. Looking at a prior example, it would be incorrect to state that a factor in improving girls access to education in Azerbaijan and the policy implications this may have for improving access in other Muslim nations is due to girls access to social media if there is no documentary evidence from your case study to indicate this. There may be anecdotal evidence that retention rates were better for girls who were engaged with social media, but this observation would only point to the need for further research and would not be a definitive finding if this was not a part of your original research agenda.

Failure to Document Limitations No case is going to reveal all that needs to be understood about a research problem. Therefore, just as you have to clearly state the limitations of a general research study , you must describe the specific limitations inherent in the subject of analysis. For example, the case of studying how women conceptualize the need for water conservation in a village in Uganda could have limited application in other cultural contexts or in areas where fresh water from rivers or lakes is plentiful and, therefore, conservation is understood more in terms of managing access rather than preserving access to a scarce resource.

Failure to Extrapolate All Possible Implications Just as you don't want to over-generalize from your case study findings, you also have to be thorough in the consideration of all possible outcomes or recommendations derived from your findings. If you do not, your reader may question the validity of your analysis, particularly if you failed to document an obvious outcome from your case study research. For example, in the case of studying the accident at the railroad crossing to evaluate where and what types of warning signals should be located, you failed to take into consideration speed limit signage as well as warning signals. When designing your case study, be sure you have thoroughly addressed all aspects of the problem and do not leave gaps in your analysis that leave the reader questioning the results.

Case Studies. Writing@CSU. Colorado State University; Gerring, John. Case Study Research: Principles and Practices . New York: Cambridge University Press, 2007; Merriam, Sharan B. Qualitative Research and Case Study Applications in Education . Rev. ed. San Francisco, CA: Jossey-Bass, 1998; Miller, Lisa L. “The Use of Case Studies in Law and Social Science Research.” Annual Review of Law and Social Science 14 (2018): TBD; Mills, Albert J., Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Putney, LeAnn Grogan. "Case Study." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE Publications, 2010), pp. 116-120; Simons, Helen. Case Study Research in Practice . London: SAGE Publications, 2009;  Kratochwill,  Thomas R. and Joel R. Levin, editors. Single-Case Research Design and Analysis: New Development for Psychology and Education .  Hilldsale, NJ: Lawrence Erlbaum Associates, 1992; Swanborn, Peter G. Case Study Research: What, Why and How? London : SAGE, 2010; Yin, Robert K. Case Study Research: Design and Methods . 6th edition. Los Angeles, CA, SAGE Publications, 2014; Walo, Maree, Adrian Bull, and Helen Breen. “Achieving Economic Benefits at Local Events: A Case Study of a Local Sports Event.” Festival Management and Event Tourism 4 (1996): 95-106.

Writing Tip

At Least Five Misconceptions about Case Study Research

Social science case studies are often perceived as limited in their ability to create new knowledge because they are not randomly selected and findings cannot be generalized to larger populations. Flyvbjerg examines five misunderstandings about case study research and systematically "corrects" each one. To quote, these are:

Misunderstanding 1 :  General, theoretical [context-independent] knowledge is more valuable than concrete, practical [context-dependent] knowledge. Misunderstanding 2 :  One cannot generalize on the basis of an individual case; therefore, the case study cannot contribute to scientific development. Misunderstanding 3 :  The case study is most useful for generating hypotheses; that is, in the first stage of a total research process, whereas other methods are more suitable for hypotheses testing and theory building. Misunderstanding 4 :  The case study contains a bias toward verification, that is, a tendency to confirm the researcher’s preconceived notions. Misunderstanding 5 :  It is often difficult to summarize and develop general propositions and theories on the basis of specific case studies [p. 221].

While writing your paper, think introspectively about how you addressed these misconceptions because to do so can help you strengthen the validity and reliability of your research by clarifying issues of case selection, the testing and challenging of existing assumptions, the interpretation of key findings, and the summation of case outcomes. Think of a case study research paper as a complete, in-depth narrative about the specific properties and key characteristics of your subject of analysis applied to the research problem.

Flyvbjerg, Bent. “Five Misunderstandings About Case-Study Research.” Qualitative Inquiry 12 (April 2006): 219-245.

• << Previous: Writing a Case Analysis Paper
• Next: Writing a Field Report >>
• Last Updated: Mar 6, 2024 1:00 PM
• URL: https://libguides.usc.edu/writingguide/assignments

Research Writing and Analysis

• NVivo Group and Study Sessions
• SPSS This link opens in a new window
• Statistical Analysis Group sessions
• Using Qualtrics
• Dissertation and Data Analysis Group Sessions
• Defense Schedule - Commons Calendar This link opens in a new window
• Research Process Flow Chart
• Research Alignment This link opens in a new window
• Step 1: Seek Out Evidence
• Step 2: Explain
• Step 3: The Big Picture
• Step 4: Own It
• Step 5: Illustrate
• Annotated Bibliography
• Literature Review This link opens in a new window
• Systematic Reviews & Meta-Analyses
• How to Synthesize and Analyze
• Synthesis and Analysis Practice
• Synthesis and Analysis Group Sessions
• Problem Statement
• Purpose Statement
• Quantitative Research Questions
• Qualitative Research Questions
• Trustworthiness of Qualitative Data
• Analysis and Coding Example- Qualitative Data
• Thematic Data Analysis in Qualitative Design
• Dissertation to Journal Article This link opens in a new window
• International Journal of Online Graduate Education (IJOGE) This link opens in a new window
• Journal of Research in Innovative Teaching & Learning (JRIT&L) This link opens in a new window

Writing a Case Study

What is a case study?

A Case study is: 

• An in-depth research design that primarily uses a qualitative methodology but sometimes​​ includes quantitative methodology.
• Used to examine an identifiable problem confirmed through research.
• Used to investigate an individual, group of people, organization, or event.
• Used to mostly answer "how" and "why" questions.

What are the different types of case studies?

Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types. 

Who are your case study participants?

What is triangulation ? 

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

Was this resource helpful?

• << Previous: Thematic Data Analysis in Qualitative Design
• Next: Journal Article Reporting Standards (JARS) >>
• Last Updated: Apr 12, 2024 11:40 AM
• URL: https://resources.nu.edu/researchtools

15.7 Evaluation: Presentation and Analysis of Case Study

Learning outcomes.

By the end of this section, you will be able to:

• Revise writing to follow the genre conventions of case studies.
• Evaluate the effectiveness and quality of a case study report.

Case studies follow a structure of background and context , methods , findings , and analysis . Body paragraphs should have main points and concrete details. In addition, case studies are written in formal language with precise wording and with a specific purpose and audience (generally other professionals in the field) in mind. Case studies also adhere to the conventions of the discipline’s formatting guide ( APA Documentation and Format in this study). Compare your case study with the following rubric as a final check.

As an Amazon Associate we earn from qualifying purchases.

This book may not be used in the training of large language models or otherwise be ingested into large language models or generative AI offerings without OpenStax's permission.

Want to cite, share, or modify this book? This book uses the Creative Commons Attribution License and you must attribute OpenStax.

Access for free at https://openstax.org/books/writing-guide/pages/1-unit-introduction

• Authors: Michelle Bachelor Robinson, Maria Jerskey, featuring Toby Fulwiler
• Publisher/website: OpenStax
• Book title: Writing Guide with Handbook
• Publication date: Dec 21, 2021
• Location: Houston, Texas
• Book URL: https://openstax.org/books/writing-guide/pages/1-unit-introduction
• Section URL: https://openstax.org/books/writing-guide/pages/15-7-evaluation-presentation-and-analysis-of-case-study

© Dec 19, 2023 OpenStax. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution License . The OpenStax name, OpenStax logo, OpenStax book covers, OpenStax CNX name, and OpenStax CNX logo are not subject to the Creative Commons license and may not be reproduced without the prior and express written consent of Rice University.

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Guided Meditations
• Verywell Mind Insights
• 2023 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

What Is a Case Study?

Weighing the pros and cons of this method of research

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Cara Lustik is a fact-checker and copywriter.

Verywell / Colleen Tighe

• Pros and Cons

What Types of Case Studies Are Out There?

Where do you find data for a case study, how do i write a psychology case study.

A case study is an in-depth study of one person, group, or event. In a case study, nearly every aspect of the subject's life and history is analyzed to seek patterns and causes of behavior. Case studies can be used in many different fields, including psychology, medicine, education, anthropology, political science, and social work.

The point of a case study is to learn as much as possible about an individual or group so that the information can be generalized to many others. Unfortunately, case studies tend to be highly subjective, and it is sometimes difficult to generalize results to a larger population.

While case studies focus on a single individual or group, they follow a format similar to other types of psychology writing. If you are writing a case study, we got you—here are some rules of APA format to reference.  

At a Glance

A case study, or an in-depth study of a person, group, or event, can be a useful research tool when used wisely. In many cases, case studies are best used in situations where it would be difficult or impossible for you to conduct an experiment. They are helpful for looking at unique situations and allow researchers to gather a lot of˜ information about a specific individual or group of people. However, it's important to be cautious of any bias we draw from them as they are highly subjective.

What Are the Benefits and Limitations of Case Studies?

A case study can have its strengths and weaknesses. Researchers must consider these pros and cons before deciding if this type of study is appropriate for their needs.

One of the greatest advantages of a case study is that it allows researchers to investigate things that are often difficult or impossible to replicate in a lab. Some other benefits of a case study:

• Allows researchers to capture information on the 'how,' 'what,' and 'why,' of something that's implemented
• Gives researchers the chance to collect information on why one strategy might be chosen over another
• Permits researchers to develop hypotheses that can be explored in experimental research

On the other hand, a case study can have some drawbacks:

• It cannot necessarily be generalized to the larger population
• Cannot demonstrate cause and effect
• It may not be scientifically rigorous
• It can lead to bias

Researchers may choose to perform a case study if they want to explore a unique or recently discovered phenomenon. Through their insights, researchers develop additional ideas and study questions that might be explored in future studies.

It's important to remember that the insights from case studies cannot be used to determine cause-and-effect relationships between variables. However, case studies may be used to develop hypotheses that can then be addressed in experimental research.

Case Study Examples

There have been a number of notable case studies in the history of psychology. Much of  Freud's work and theories were developed through individual case studies. Some great examples of case studies in psychology include:

• Anna O : Anna O. was a pseudonym of a woman named Bertha Pappenheim, a patient of a physician named Josef Breuer. While she was never a patient of Freud's, Freud and Breuer discussed her case extensively. The woman was experiencing symptoms of a condition that was then known as hysteria and found that talking about her problems helped relieve her symptoms. Her case played an important part in the development of talk therapy as an approach to mental health treatment.
• Phineas Gage : Phineas Gage was a railroad employee who experienced a terrible accident in which an explosion sent a metal rod through his skull, damaging important portions of his brain. Gage recovered from his accident but was left with serious changes in both personality and behavior.
• Genie : Genie was a young girl subjected to horrific abuse and isolation. The case study of Genie allowed researchers to study whether language learning was possible, even after missing critical periods for language development. Her case also served as an example of how scientific research may interfere with treatment and lead to further abuse of vulnerable individuals.

Such cases demonstrate how case research can be used to study things that researchers could not replicate in experimental settings. In Genie's case, her horrific abuse denied her the opportunity to learn a language at critical points in her development.

This is clearly not something researchers could ethically replicate, but conducting a case study on Genie allowed researchers to study phenomena that are otherwise impossible to reproduce.

There are a few different types of case studies that psychologists and other researchers might use:

• Collective case studies : These involve studying a group of individuals. Researchers might study a group of people in a certain setting or look at an entire community. For example, psychologists might explore how access to resources in a community has affected the collective mental well-being of those who live there.
• Descriptive case studies : These involve starting with a descriptive theory. The subjects are then observed, and the information gathered is compared to the pre-existing theory.
• Explanatory case studies : These   are often used to do causal investigations. In other words, researchers are interested in looking at factors that may have caused certain things to occur.
• Exploratory case studies : These are sometimes used as a prelude to further, more in-depth research. This allows researchers to gather more information before developing their research questions and hypotheses .
• Instrumental case studies : These occur when the individual or group allows researchers to understand more than what is initially obvious to observers.
• Intrinsic case studies : This type of case study is when the researcher has a personal interest in the case. Jean Piaget's observations of his own children are good examples of how an intrinsic case study can contribute to the development of a psychological theory.

The three main case study types often used are intrinsic, instrumental, and collective. Intrinsic case studies are useful for learning about unique cases. Instrumental case studies help look at an individual to learn more about a broader issue. A collective case study can be useful for looking at several cases simultaneously.

The type of case study that psychology researchers use depends on the unique characteristics of the situation and the case itself.

There are a number of different sources and methods that researchers can use to gather information about an individual or group. Six major sources that have been identified by researchers are:

• Archival records : Census records, survey records, and name lists are examples of archival records.
• Direct observation : This strategy involves observing the subject, often in a natural setting . While an individual observer is sometimes used, it is more common to utilize a group of observers.
• Documents : Letters, newspaper articles, administrative records, etc., are the types of documents often used as sources.
• Interviews : Interviews are one of the most important methods for gathering information in case studies. An interview can involve structured survey questions or more open-ended questions.
• Participant observation : When the researcher serves as a participant in events and observes the actions and outcomes, it is called participant observation.
• Physical artifacts : Tools, objects, instruments, and other artifacts are often observed during a direct observation of the subject.

If you have been directed to write a case study for a psychology course, be sure to check with your instructor for any specific guidelines you need to follow. If you are writing your case study for a professional publication, check with the publisher for their specific guidelines for submitting a case study.

Here is a general outline of what should be included in a case study.

Section 1: A Case History

This section will have the following structure and content:

Background information : The first section of your paper will present your client's background. Include factors such as age, gender, work, health status, family mental health history, family and social relationships, drug and alcohol history, life difficulties, goals, and coping skills and weaknesses.

Description of the presenting problem : In the next section of your case study, you will describe the problem or symptoms that the client presented with.

Describe any physical, emotional, or sensory symptoms reported by the client. Thoughts, feelings, and perceptions related to the symptoms should also be noted. Any screening or diagnostic assessments that are used should also be described in detail and all scores reported.

Your diagnosis : Provide your diagnosis and give the appropriate Diagnostic and Statistical Manual code. Explain how you reached your diagnosis, how the client's symptoms fit the diagnostic criteria for the disorder(s), or any possible difficulties in reaching a diagnosis.

Section 2: Treatment Plan

This portion of the paper will address the chosen treatment for the condition. This might also include the theoretical basis for the chosen treatment or any other evidence that might exist to support why this approach was chosen.

• Cognitive behavioral approach : Explain how a cognitive behavioral therapist would approach treatment. Offer background information on cognitive behavioral therapy and describe the treatment sessions, client response, and outcome of this type of treatment. Make note of any difficulties or successes encountered by your client during treatment.
• Humanistic approach : Describe a humanistic approach that could be used to treat your client, such as client-centered therapy . Provide information on the type of treatment you chose, the client's reaction to the treatment, and the end result of this approach. Explain why the treatment was successful or unsuccessful.
• Psychoanalytic approach : Describe how a psychoanalytic therapist would view the client's problem. Provide some background on the psychoanalytic approach and cite relevant references. Explain how psychoanalytic therapy would be used to treat the client, how the client would respond to therapy, and the effectiveness of this treatment approach.
• Pharmacological approach : If treatment primarily involves the use of medications, explain which medications were used and why. Provide background on the effectiveness of these medications and how monotherapy may compare with an approach that combines medications with therapy or other treatments.

This section of a case study should also include information about the treatment goals, process, and outcomes.

When you are writing a case study, you should also include a section where you discuss the case study itself, including the strengths and limitiations of the study. You should note how the findings of your case study might support previous research. 

In your discussion section, you should also describe some of the implications of your case study. What ideas or findings might require further exploration? How might researchers go about exploring some of these questions in additional studies?

Need More Tips?

Here are a few additional pointers to keep in mind when formatting your case study:

• Never refer to the subject of your case study as "the client." Instead, use their name or a pseudonym.
• Read examples of case studies to gain an idea about the style and format.
• Remember to use APA format when citing references .

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach .  BMC Med Res Methodol . 2011;11:100.

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach . BMC Med Res Methodol . 2011 Jun 27;11:100. doi:10.1186/1471-2288-11-100

Gagnon, Yves-Chantal.  The Case Study as Research Method: A Practical Handbook . Canada, Chicago Review Press Incorporated DBA Independent Pub Group, 2010.

Yin, Robert K. Case Study Research and Applications: Design and Methods . United States, SAGE Publications, 2017.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Case Study Research in Software Engineering: Guidelines and Examples by Per Runeson, Martin Höst, Austen Rainer, Björn Regnell

Get full access to Case Study Research in Software Engineering: Guidelines and Examples and 60K+ other titles, with a free 10-day trial of O'Reilly.

There are also live events, courses curated by job role, and more.

DATA ANALYSIS AND INTERPRETATION

5.1 introduction.

Once data has been collected the focus shifts to analysis of data. It can be said that in this phase, data is used to understand what actually has happened in the studied case, and where the researcher understands the details of the case and seeks patterns in the data. This means that there inevitably is some analysis going on also in the data collection phase where the data is studied, and for example when data from an interview is transcribed. The understandings in the earlier phases are of course also valid and important, but this chapter is more focusing on the separate phase that starts after the data has been collected.

Data analysis is conducted differently for quantitative and qualitative data. Sections 5.2 – 5.5 describe how to analyze qualitative data and how to assess the validity of this type of analysis. In Section 5.6 , a short introduction to quantitative analysis methods is given. Since quantitative analysis is covered extensively in textbooks on statistical analysis, and case study research to a large extent relies on qualitative data, this section is kept short.

5.2 ANALYSIS OF DATA IN FLEXIBLE RESEARCH

5.2.1 introduction.

As case study research is a flexible research method, qualitative data analysis methods are commonly used [176]. The basic objective of the analysis is, as in any other analysis, to derive conclusions from the data, keeping a clear chain of evidence. The chain of evidence means that a reader ...

Get Case Study Research in Software Engineering: Guidelines and Examples now with the O’Reilly learning platform.

O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Don’t leave empty-handed

Get Mark Richards’s Software Architecture Patterns ebook to better understand how to design components—and how they should interact.

It’s yours, free.

Check it out now on O’Reilly

Dive in for free with a 10-day trial of the O’Reilly learning platform—then explore all the other resources our members count on to build skills and solve problems every day.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.24(6); 2019 Sep

Lessons learnt: examining the use of case study methodology for nursing research in the context of palliative care

Paula brogan.

School of Communication and Media, University of Ulster, Northern Ireland, UK

Felicity Hasson

Institute of Nursing Research, University of Ulster, Northern Ireland, UK

An empirical social research approach, facilitating in-depth exploration of complex, contemporary contextualised phenomena, case study research has been used internationally in healthcare studies across clinical settings, to explore systems and processes of care delivery. In the United Kingdom, case study methods have been championed by nurse researchers, particularly in the context of community nursing and palliative care provision, where its applicability is well established. Yet, dogged by conceptual confusion, case study remains largely underutilised as a research approach.

Drawing on examples from nursing and palliative care studies, this paper clarifies case study research, identifies key concepts and considers lessons learned about its potential for nursing research within the unique and complex palliative and end of life context.

A case study approach offers nurse researchers the opportunity for in-depth, contextualised understanding of the systems and processes which influence their role in palliative care delivery across settings. However, philosophical and conceptual understandings are needed and further training in case study methodology is required to enable researchers to articulate and conduct case study.

Introduction

An empirical social research approach, facilitating in-depth exploration of a contemporary phenomenon ( Yin, 2009 ), case study research has been used internationally in healthcare studies ( Anthony and Jack, 2009 ) to explore systems of palliative care ( Lalor et al., 2013 ), diverse contexts for palliative care delivery ( Sussman et al., 2011 ), roles of professional groups such as pharmacy ( O’Connor et al., 2011 ), the impact of services such as complementary therapy ( Maddalena et al., 2010 ) and nursing (Kaasalainen et al., 2013). In the United Kingdom, case study methods have been championed by nurse researchers ( Payne et al., 2006 ), particularly in the context of community nursing and palliative care provision ( Kennedy, 2005 ; Walshe et al., 2004 , 2008 ) and its applicability to palliative and end-of-life care research is established ( Goodman et al., 2012 ). Suited to the study of complex processes ( Walshe, 2011 ), case study methodology is embedded in professional guidance on the development of complex interventions ( Medical Research Council, 2008 ). Yet, case study is dogged by conceptual confusion (Flyvberg, 2006), and, despite sporadic use, remains underutilised as a research approach in healthcare settings ( Froggatt et al., 2003 ).

Illustrated by examples from nursing and palliative care studies, this paper aims to clarify conceptual understanding and identify key lessons for its application within these unique and complex contexts and, more broadly, for nursing research.

Origins and definitions

French sociologist Frederic Le Play (1806–1882) is associated with the origin of the case study approach ( Hamel et al., 1993 ). Using a purposive sample of working class families and fieldwork methods of observation and individual interview, he sought a contextualised and in-depth understanding of their individual experiences. Each family case study uncovered the unique experience of that family, but each additional family studied was another ‘ case of the lived experience’ of working class families in mid-18th century France. Thereby, Le Play used the lens of individual experience ( Yin, 2013 ) to build comparisons across families and enrich overall understanding of that complex society.

This early glimpse of the case study approach showed it to be a straightforward ‘field investigation’ ( Hamel et al., 1993 ); epistemologically pragmatic as it generated knowledge through data drawn from diverse sources, such as family members, and used the best available data collection methods then, to inform a holistic and contextualised understanding of how people operated within a complex social system ( Stake, 1995 ).

However, defining case study has become increasingly challenging since its expansion into North America in the 1800s ( Platt, 1992 ), and its use across a range of disciplines such as politics ( Gerring, 2004 ), social science ( George and Bennett, 2005 ), education ( Merriam, 1998 ) and healthcare ( Yin, 2013 ). Variously characterised as a case report, data collection method and methodology ( Anthony and Jack, 2009 ), the development of case histories as illustrations in health and social care and in education ( Merriam, 1998 ) has contributed to further confusion for researchers and readers of case study research ( Gomm et al., 2000 ). Critiques of case study note that it lacks a single definition, such that a plethora of discipline dependant interpretations ( Simons, 2009 ) and loose use of the term case study ( Tight, 2010 ) have contributed to confusion and undermined case study credibility. However, Simons ( 2009 , p. 63) advises researchers that case study must be seen within the complex nexus of political, methodological and epistemological convictions that constitute the field of enquiry, and variations of these may be glimpsed in Table 1 as definitions from four eminent and frequently cited case study authors illustrate philosophical and discipline-influenced differences in emphasis. Consequently, the case study definition selected, with its underpinning ontology and epistemology has important implications for the coherent outworking of the overall research design. It is therefore notable that many of the palliative care case studies contained in Table 2 fail to identify any such definition and this may have implications for interpretation of the quality of studies.

Definitions of case study by four key authors, showing the variation in meaning and interpretation.

Examples of Case Studies (CS) conducted in palliative care contexts.

Case study as a philosophy for the epistemology of knowledge generation

Although frequently linked to naturalistic inquiry ( Lincoln and Guba, 1986 ), interpretative/constructivist philosophy and qualitative methodology ( Stake, 1995 ), case study is not in fact bound to any single research paradigm ( Creswell, 2013 ). It is philosophically pragmatic, such that the case study design should reflect the ontological positions and epistemological considerations of the researchers and their topic of interest ( Luck et al., 2006 ). In practice, this means that case study research may pragmatically employ both qualitative and quantitative methods independently or together in order to respond to the research objectives ( Cooper et al., 2012 ; Simons, 1987 ; Stake, 2006 ). So whilst Table 2 shows that qualitative case studies are common in palliative care, epistemological variation is evident and reflects the study topic, purpose and context of the research. For example, Maddalena et al. (2010) used in-depth interview and discourse analysis to understand individual patient meaning-making; Brogan et al. (2017) used focus groups and thematic analysis as part of an embedded element of a multiple case study, to contrast the diverse perspectives of multi-disciplinary healthcare practitioners on end-of-life decision-making; Sussman et al. (2011) incorporated survey data into a mixed methods multiple case study which explored health system characteristics and quality of care delivery for cancer patients across four regions of Canada. Consequently, it is useful to ‘conceptualise (case study) as an approach to research rather than a methodology in its own right’ ( Rosenberg and Yates, 2007 , p. 448), so that a non-standardised approach exists and the case study design, its boundaries, numbers of cases and methods are guided by the stated underpinning ontological perspectives of the researcher and their topic of interest. The study then flexibly adopts the best methods to gain an in-depth, holistic and contextualised understanding of the phenomenon of interest – the latter objectives being at the core of any definition of case study research.

Key case study concepts and lessons for practice

When considering the utility of a case study approach, research conducted in complex palliative care contexts offers several insights into how central concepts translate to practice.

Contextualised understanding

Drawing on the definitions in Table 1 , Stake emphasised the particularity and intrinsic value of each individual case ( Stake, 1995 ), to emphasise the usefulness of multiple cases to increase insight ( Stake, 2006 ), analyse patterns ( Gerring, 2004 ; George and Bennett, 2005 ) and develop causal hypotheses ( Yin, 2013 ). Yet, whatever the purpose, all case studies are concerned with the crucial relationship between a phenomenon and the environment in which it has occurred. In practice therefore, case study researchers must be concerned with understanding the background systems, structures and processes that influence and interact with the phenomenon under study. This capacity for contextualised and holistic understanding is underpinned by use of multiple data collection methods, such as observation, interview and document review, used simultaneously or sequentially ( Stake, 2006 ; Scholz and Tietje, 2002 ), to mine multiple sources of data, such as participant experience ( Brogan et al., 2017 ; Kaasalainen et al., 2012 ), documents (Lalor et al., 2003) service evaluations ( Walshe et al., 2008 ), and diaries ( Skilbeck and Seymour, 2002 ). This is exemplified in a study by Walshe et al. (2011) , who investigated referral decisions made by community palliative care nurses in the UK, by capturing interview data on the self-reported perspectives of healthcare professionals, in combination with observed team meetings in which decisions were influenced, and review of the written referral policies, protocols and palliative healthcare strategies specific to those decisions. This comprehensive and complex data enabled comparison of decisional processes and their influencing factors both within and across three Primary Care Trusts, thus providing a contemporaneous understanding of the complex relationship between individual nurse's referral decisions and the impact of the organisational and professional systems that underpinned them. Enhancing rigor, such methodological triangulation importantly contributed to the richness of data analysis and the development of assertions which might be drawn from the findings ( Cooper et al., 2012 ; Stake, 2006 ).

Process-focused

Flexible data collection methods, linked to the research purpose, enables case study researchers to gather both historical and real-time data in a variety of ways. For example, Kennedy’s longitudinal case study ( Kennedy, 2002 ) observed snapshots of the initial and follow-up assessment conducted by 11 district nurses over the subsequent 12 months, enabling an exploration of the outcome and impact of their decision-making, demonstrating the usefulness of case study to understand complex roles and processes which are fluid and elusive ( Yin, 2013 ), or otherwise difficult to capture, particularly in the intimate interpersonal contexts where nursing happens.

Analytic frame

Palliative care studies reviewed frequently report the use of thematic analysis. However, whilst this approach is certainly useful to process data generated in qualitative case studies, the approach to analysis must be congruent with the research design and reflect the purpose of the research and methods used. Moreover, beyond decisions about use of thematic analysis or descriptive statistics etc., in case study, important decisions must be made about the analytic frame of the research. Gerring’s definition (2004) set out the analytic frame in which the cases studied might be understood, explaining that each unit of analysis (or case), sheds light on other units (or cases). Thus defined, an individual case offers intrinsically valuable information about a phenomenon ( Stake, 1995 ) and the purposeful selection of cases is central to case study design. This is because, viewed from a certain angle, each case is also a case of something else, such that the findings have broader implications ( Gerring, 2004 ; Simons, 2009 , 1987 ; Yin, 2013 ). In practice, this means that the case and what it is a case of, must be clearly identified and well defined at the outset of a study, since this has implications for the relevance of findings. This can be seen in a study by O’Connor et al., (2011) , who considered the perceived role of community pharmacists in palliative care teams in Australia. Each unique case included multi-disciplinary healthcare team members, such as pharmacists, doctors and nurses working in localities, whose perspectives were sought. Each locality group was a case of community pharmacy provision in palliative care settings in Australia, and findings had implications for the planning of community services overall. So, insight development was possible at an individual, group and organisational level, and inferences were made directly in relation to the parameters of that case study.

The addition of several carefully selected cases, as in multiple case studies, offers the opportunity to analyse data gained within and across cases ( Stake, 2006 ). Case selection may be made in order to explore similarities and contrasting perspectives ( Brogan et al., 2017 ), understand the various impacts of geographical differences ( Sussman et al., 2011 ), and different organisational influences ( Walshe et al., 2008 ). However, whilst repetition of data across cases may reinforce propositions made at the outset of a study, the purpose of increasing the number of cases in case study research is primarily about increasing insight development into the complexity of a phenomenon ( Stake, 2006 ). Since case study is the study of a boundaried phenomenon ( Yin, 2013 ), establishing the analytic frame then underpins the selection criteria for potentially useful cases. Such clarification is essential since it provides the lens through which to focus research ( Gerring, 2004 ; Scholz and Tietje, 2002 ; Stake, 2006 ) and permits key decisions to be made about data which may be included and that which is not applicable.

However, significantly, this information is rarely articulated within published case studies in palliative care. This is an important issue for the quality of case study research, since description of the process of refining case study parameters, establishing clear boundaries of the case, articulating propositions based on existing literature, identifying the sources of data (people, records, policies, etc.) and the ways in which data would be captured, establishes clarity and underpins a rigorous, systematic and comprehensive process ( Gibbert et al., 2008 ), which can usefully contribute to practice and policy development ( George and Bennett, 2005 ).

Shaped by organisational systems, intimate settings and significant life stage contexts, the interconnection between context and participant experience of palliative care is one example of a process of healthcare provision that is often complex, subtle and elusive ( Walshe et al., 2011 ). Case studies conducted in these swiftly changing contexts illustrate several characteristics of case study research, which make it an appropriate methodological option for nurse researchers, providing the opportunity for in-depth, contextualised understanding of the systems and processes which influence their role in palliative care delivery across settings ( Walshe et al., 2004 ) and many others who seek a contextualised, contemporaneous understanding of any complex role or process ( Yin, 2013 ; Simons, 2009 ). This fieldwork-based approach has the potential to achieve depth and breadth of insight through the pragmatic, but carefully planned and articulated, use of multiple methods of data collection in order to answer the research question ( Stake, 2006 ) when analysed systematically within a frame determined at the outset by the definition of the case and its boundaries ( Gerring, 2004 ). Yet, the methodological flexibility that is advantageous in complex contexts, may be misunderstood ( Hammersley, 2012 ), particularly where terminology is unclear ( Lather, 1996 ) or where description of the systematic and rigorous application of the approach is missing from the report ( Morrow, 2005 ). Taken as an example of one area of healthcare research, evidence suggests that palliative care studies that deal meaningfully with underpinning philosophical perspectives for their selected case study approach, or which articulate coherent links between the defined case, its boundaries and the analytical frame are rare. The impact of such omissions may be the perpetuation of confusion and out-dated perceptions about the personality and quality of case study research ( King et al., 1994 ), with implications for its wider adoption by nurses in healthcare research. Further training in case study methodology is required to promote philosophical and conceptual understanding, and to enable researchers to fully articulate, conduct and report case study, to underpin its credibility, relevance and future use ( Hammersley et al., 2000 ; Stake and Turnbull, 1982).

Key points for policy, practice and/or research

• Case study is well suited to nursing research in palliative care contexts, where in-depth understanding of participant experience, complex systems and processes of care within changing contexts is needed.
• Not bound to any single paradigm, nor defined by any methodology, case study’s pragmatism and flexibility makes it useful for studies in palliative care.
• Training is needed in the underpinning philosophical and conceptual basis of case study methodology, in order to articulate, conduct and report credible case study research, and take advantage of the opportunities it offers for the conduct of palliative and end-of-life care research.

Paula Brogan is a Lecturer in counselling and communication in the School of Communication and Media, and was recently appointed as Faculty Partnership Manager, University of Ulster. Dual qualified as a Registered Nurse with specialism in District Nursing and as a Counsellor/couple psychotherapist (Reg MBACPaccred), she has over 30 years’ clinical practice experience in community palliative care nursing and the provision of psychological care to patients and families dealing with palliative and chronic illness. Having worked across statutory, voluntary and private sectors, her PhD focused on multi-disciplinary decision-making at the end of life with patients and families in the community setting. Currently secretary of the Palliative Care Research Forum for Northern Ireland (PCRFNI), Paula’s ongoing research interests include communication and co-constructed decision-making in palliative and chronic illness, and the psychological support of individuals, couples, patient-family groups and multi-disciplinary staff responding to challenges of advanced progressive illness.

Felicity Hasson is a Senior Lecturer in the Institute of Nursing Research at the University of Ulster with 20 years’ experience in research. A social researcher by background, she has extensive experience and knowledge of qualitative, quantitative and mixed method research and has been involved in numerous research studies in palliative and end-of-life care. She completed her MSc in 1996 and her PhD from University of Ulster in 2012. Felicity sits on the Council of Partners for the All Ireland Institute of Hospice and the Palliative Care Palliative Care Research Network (PCRN) and is an executive board member for the UK Palliative Care Research Society. She holds an editorial board position on Futures and Foresight Science. Felicity has an established publication track recorded and successful history of grant applications. Her research interests include nurse and assistant workforce, workforce training, palliative care and chronic illness (malignant and non-malignant with patients, families and multi-disciplinary health care professionals) and public awareness of palliative care and end of life issues.

Sonja McIlfatrick is a Professor in Nursing and Palliative Care and has recently been appointed as the Head of School of Nursing at University of Ulster. She is an experienced clinical academic with experience in nursing and palliative care practice, education and research. She previously worked as the Head of Research for the All Ireland Institute of Hospice and Palliative Care (2011-2014) and led the establishment of the All Ireland Palliative Care Research Network (PCRN) and is the current Chair of the Strategic Scientific Committee for the PCRN (AIIHPC). Sonja is an Executive Board member for the UK, Palliative Care Research Society and is member of the Research Scientific Advisory Committee for Marie Curie, UK. Sonja holds an Editorial Board position on the International Journal of Palliative Nursing and Journal of Research in Nursing. Professor McIlfatrick has published widely in academic and professional journals focused on palliative care research and has a successful history of grant acquisition. Sonja has a keen interest in doctoral education and is the current President of the International Network of Doctoral Education in Nursing (INDEN). Her research interests include, palliative care in chronic illness, decision making at end of life; public awareness of palliative care and psychosocial support for family caregivers affected by advanced disease.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethics statement

Ethical permission was not required for this paper.

The author(s) received no financial support for the research, authorship, and/or publication of this article.

• Anthony S, Jack S. (2009) Qualitative case-study methodology in nursing research: An integrative review . Journal of Advanced Nursing 65 ( 6 ): 1171–1181. [ PubMed ] [ Google Scholar ]
• Brogan P, Hasson F, McIlfatrick S. (2017) Shared decision-making at the end of life: A focus group study exploring the perceptions and experiences of multi-disciplinary healthcare professionals working in a home setting . Palliative Medicine 32 ( 1 ): 123–132. [ PubMed ] [ Google Scholar ]
• Cooper B, Glaesser J, Gomm R, et al. (2012) Challenging the Qualitative-Quantitative Divide: Explorations in Case-focused Causal Analysis , London: Continuum. [ Google Scholar ]
• Creswell JW. (2013) Qualitative Inquiry and Research Design: Choosing among five approaches , 3rd ed. Thousand Oaks: SAGE. [ Google Scholar ]
• Flyvbjerg B. (2006) Five misunderstandings about case-study research . Qualitative Inquiry 12 ( 2 ): 219–245. [ Google Scholar ]
• Froggatt KA, Field D, Bailey C, et al. (2003) Qualitative research in palliative care 1990–1999: A descriptive review . International Journal of Palliative Nursing 9 ( 3 ): 98–104. [ PubMed ] [ Google Scholar ]
• George AL, Bennett A. (2005) Case Studies and Theory Development in the Social Sciences , Cambridge, MA: MIT Press. [ Google Scholar ]
• Gerring J. (2004) What is case-study and what is it good for? The American Political Science Review 98 ( 2 ): 341–354. [ Google Scholar ]
• Gibbert M, Ruigrok W, Wicki B. (2008) What passes as rigorous case-study? Strategic Management Journal 29 : 1465–1474. [ Google Scholar ]
• Gomm R, Hammersley M, Foster P. (2000) Case Study Method: Key Issues, Key Texts , London: SAGE. [ Google Scholar ]
• Goodman C, Froggatt KA, Mathie E. (2012) End-of-life Care. Methods Review 12 , London: National Institute for Health Research and School for Social Care Research. [ Google Scholar ]
• Hamel J, Defour S, Fortin D. (1993) Case-study Methods. Qualitative Research Methods Series. 32 , Newbury Park: SAGE. [ Google Scholar ]
• Hammersley M, Foster P, Gomm R. (2000) Case study and generalisation . In: Gomm R, Hammersley M, Foster P. (eds) Case Study Method: Key Issues, Key Texts , London: SAGE, pp. 98–115. [ Google Scholar ]
• Hammersley M. (2012) Troubling theory in case study research . Higher Education Research and Development 31 ( 3 ): 393–405. [ Google Scholar ]
• Kaasalainen S, Ploeg J, McAiney C, et al. (2012) Role of the nurse practitioners in providing palliative care in long-term care homes . International Journal of Palliative Nursing 19 ( 10 ): 477–485. [ PubMed ] [ Google Scholar ]
• Kennedy C. (2005) District nursing support for patients with cancer requiring palliative care . British Journal of Community Nursing 10 ( 12 ): 566–574. [ PubMed ] [ Google Scholar ]
• Kennedy C. (2002) The decision-making process in a district nursing assessment . British Journal of Community Nursing 7 ( 10 ): 505–513. [ PubMed ] [ Google Scholar ]
• King G, Keohane RO, Verba S. (1994) Designing Social Inquiry: Scientific Inference in Qualitative Research , Princeton: Princeton University Press. [ Google Scholar ]
• Lalor JG, Casey D, Elliott N, et al. (2013) Using case-study within a sequential explanatory design to evaluate the impact of specialist and advanced practice roles on clinical outcomes: The SCAPE study . BMC Medical Research Methodology 13 ( 1 ): 55. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lather P. (1996) Troubling clarity: The politics of accessible language . Harvard Educational Review 66 ( 3 ): 525–554. [ Google Scholar ]
• Lincoln YS, Guba EG. (1986) But is it rigorous? Trustworthiness and authenticity in naturalistic evaluation . In: Williams DD. (eds) Naturalistic Evaluation , San Francisco: Josey-Bass, pp. 73–84. [ Google Scholar ]
• Luck L, Jackson D, Usher K. (2006) Case-study: A bridge across paradigms . Nursing Inquiry 13 ( 2 ): 103–109. [ PubMed ] [ Google Scholar ]
• Maddalena VJ, Bernard WT, Etowa J, et al. (2010) Cancer care experiences and the use of complementary and alternative medicine at the end-of-life in Nova Scotia’s black communities . J Transcultural Nursing 21 ( 2 ): 114–122. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Medical Research Council (2008) Complex Interventions Guidance, www.mrc.ac.uk/complexinterventionsguidance (accessed 14 March 2014).
• Merriam SB. (1998) Qualitative research and case-study applications in education , San Francisco: Jossey-Bass. [ Google Scholar ]
• Morrow SL. (2005) Quality and trustworthiness in qualitative research in counselling psychology . Journal of Counseling Psychology 52 ( 2 ): 256–260. [ Google Scholar ]
• O’Connor M, Fisher C, French L, et al. (2011) Exploring the community pharmacist’s role in palliative care: Focusing on the person not just the prescription . Patient Education and Counseling 83 : 458–464. [ PubMed ] [ Google Scholar ]
• Payne S, Field D, Rolls L, et al. (2006) Case-study research methods in end-of-life care: Reflections on three studies . Journal of Advanced Nursing 58 ( 3 ): 236–245. [ PubMed ] [ Google Scholar ]
• Platt J. (1992) Case study in American methodological thought . Current Sociology 40 ( 1 ): 17–48. [ Google Scholar ]
• Rosenberg JP, Yates PM. (2007) Schematic representation of case-study research designs . Journal of Advanced Nursing 60 ( 4 ): 447–452. [ PubMed ] [ Google Scholar ]
• Scholz RW, Tietje O. (2002) Embedded Case Study Methods: Integrating Quantitative and Qualitative Knowledge , Los Angeles: SAGE. [ Google Scholar ]
• Simons H. (1987) The paradox of case-study . Cambridge Journal of Education 26 ( 2 ): 225–240. [ Google Scholar ]
• Simons H. (2009) Case-study Research in Practice , Thousand Oaks: SAGE. [ Google Scholar ]
• Skilbeck J, Seymour J. (2002) Meeting complex needs: And analysis of Macmillan nurses’ work with patients . International Journal of Palliative Nursing 8 ( 12 ): 574–582. [ PubMed ] [ Google Scholar ]
• Stake RE. (1995) The Art of Case-study Research , Thousand Oaks: SAGE. [ Google Scholar ]
• Stake RE, Trumbull DJ. (1987) Naturalistic generalizations . Review Journal of Philosophy and Social Science 7 : 1–12. [ Google Scholar ]
• Stake RE. (2006) Multiple case-study analysis , New York: The Guilford Press. [ Google Scholar ]
• Sussman J, Barbera L, Bainbridge D, et al. (2011) Health system characteristics and quality of care delivery: A comparative case-study examination of palliative care for cancer patients in four regions in Ontario, Canada . Palliative Medecine 26 ( 4 ): 322–335. [ PubMed ] [ Google Scholar ]
• Tight M. (2010) The curious case of case study: A viewpoint . International Journal of Social Research Methodology 13 ( 4 ): 329–339. [ Google Scholar ]
• Walshe C. (2011) The evaluation of complex interventions in palliative care: An exploration of the potential of case-study . Palliative Medicine 25 ( 8 ): 774–781. [ PubMed ] [ Google Scholar ]
• Walshe C, Chew-Graham C, Todd C, et al. (2008) What influences referrals within community palliative care services? A qualitative case-study . Social Science and Medicine 6 : 137–146. [ PubMed ] [ Google Scholar ]
• Walshe CE, Caress AL, Chew-Graham C, et al. (2004) Case studies: A research strategy appropriate for palliative care?’ . Palliative Medicine 18 : 677–684. [ PubMed ] [ Google Scholar ]
• Yin RK. (2013) Case-study Research: Design and Methods (Applied Social Research Methods) , Newbury Park: SAGE. [ Google Scholar ]
• Yin RK. (2009) Case-study Research: Design and Methods , 4th ed. Newbury Park: SAGE. [ Google Scholar ]

Case Study Analysis: Examples + How-to Guide & Writing Tips

A case study analysis is a typical assignment in business management courses. The task aims to show high school and college students how to analyze a current situation, determine what problems exist, and develop the best possible strategy to achieve the desired outcome.

Our specialists will write a custom essay specially for you!

Many students feel anxious about writing case analyses because being told to analyze a case study and provide a solution can seem like a big task. That is especially so when working with real-life scenarios. However, you can rest assured writing a case analysis paper is easier than you think. Just keep reading this article and you will find case study examples for students and the advice provided by Custom-writing experts!

• 👣 Main Steps
• 🕵 Preparing the Case

🔬 Analyzing the Case

• 📑 Format & Structure
• 🙅 Things to Avoid
• 🏁 Conclusion

🔗 References

👣 writing a case study analysis: main steps.

Business management is built on case analysis. Every single economic result shows that the methods and instruments employed were either well-timed and expedient, in the event of success, or not, in case of failure. These two options indicate whether the strategy is efficient (and should be followed) or requires corrections (or complete change). Such an approach to the case study will make your writing piece more proficient and valuable for the reader. The following steps will direct your plan for writing a case study analysis.

Step 1: Preliminary work

• Make notes and highlight the numbers and ideas that could be quoted.
• Single out as many problems as you can, and briefly mark their underlying issues. Then make a note of those responsible. In the report, you will use two to five of the problems, so you will have a selection to choose from.
• Outline a possible solution to each of the problems you found. Course readings and outside research shall be used here. Highlight your best and worst solution for further reference.

Step 2: Drafting the Case

• Provide a general description of the situation and its history.
• Name all the problems you are going to discuss.
• Specify the theory used for the analysis.
• Present the assumptions that emerged during the analysis, if any.
• Describe the detected problems in more detail.
• Indicate their link to, and effect on, the general situation.
• Explain why the problems emerged and persist.
• List realistic and feasible solutions to the problems you outlined, in the order of importance.
• Specify your predicted results of such changes.
• Support your choice with reliable evidence (i.e., textbook readings, the experience of famous companies, and other external research).
• Define the strategies required to fulfill your proposed solution.
• Indicate the responsible people and the realistic terms for its implementation.
• Recommend the issues for further analysis and supervision.

Step 3: Finalizing the Case

Like any other piece of writing, a case analysis requires post-editing. Carefully read it through, looking for inconsistencies and gaps in meaning. Your purpose is to make it look complete, precise, and convincing.

🕵 Preparing a Case for Analysis

Your professor might give you various case study examples from which to choose, or they may just assign you a particular case study. To conduct a thorough data analysis, you must first read the case study. This might appear to be obvious. However, you’d be surprised at how many students don’t take adequate time to complete this part.

Read the case study very thoroughly, preferably several times. Highlight, underline, flag key information, and make notes to refer to later when you are writing your analysis report.

Just in 1 hour! We will write you a plagiarism-free paper in hardly more than 1 hour

If you don’t have a complete knowledge of the case study your professor has assigned, you won’t conduct a proper analysis of it. Even if you make use of a business case study template or refer to a sample analysis, it won’t help if you aren’t intimately familiar with your case study.

You will also have to conduct research. When it comes to research, you will need to do the following:

• Gather hard, quantitative data (e.g. 67% of the staff participated in the meeting).
• Design research tools , such as questionnaires and surveys (this will aid in gathering data).
• Determine and suggest the best specific, workable solutions.

It would be best if you also learned how to analyze a case study. Once you have read through the case study, you need to determine the focus of your analysis. You can do this by doing the following:

Compare your chosen solutions to the solutions offered by the experts who analyzed the case study you were given or to online assignments for students who were dealing with a similar task. The experts’ solutions will probably be more advanced than yours simply because these people are more experienced. However, don’t let this discourage you; the whole point of doing this analysis is to learn. Use the opportunity to learn from others’ valuable experience, and your results will be better next time.

If you are still in doubt, the University of South Carolina offers a great guide on forming a case study analysis.

Receive a plagiarism-free paper tailored to your instructions. Cut 20% off your first order!

📑 Case Analysis Format & Structure

When you are learning how to write a case study analysis, it is important to get the format of your analysis right. Understanding the case study format is vital for both the professor and the student. The person planning and handing out such an assignment should ensure that the student doesn’t have to use any external sources .

In turn, students have to remember that a well-written case analysis provides all the data, making it unnecessary for the reader to go elsewhere for information.

Regardless of whether you use a case study template, you will need to follow a clear and concise format when writing your analysis report. There are some possible case study frameworks available. Still, a case study should contain eight sections laid out in the following format:

• Describe the purpose of the current case study;
• Provide a summary of the company;
• Briefly introduce the problems and issues found in the case study
• Discuss the theory you will be using in the analysis;
• Present the key points of the study and present any assumptions made during the analysis.
• Present each problem you have singled out;
• Justify your inclusion of each problem by providing supporting evidence from the case study and by discussing relevant theory and what you have learned from your course content;
• Divide the section (and following sections) into subsections, one for each of your selected problems.
• Present a summary of each problem you have identified;
• Present plausible solutions for each of the problems, keeping in mind that each problem will likely have more than one possible solution;
• Provide the pros and cons of each solution in a way that is practical.
• Conclusion . This is a summary of your findings and discussion.
• Decide which solution best fits each of the issues you identified;
• Explain why you chose this solution and how it will effectively solve the problem;
• Be persuasive when you write this section so that you can drive your point home;
• Be sure to bring together theory and what you have learned throughout your course to support your recommendations.
• Provide an explanation of what must be done, who should take action, and when the solution should be carried out;
• Where relevant, you should provide an estimate of the cost in implementing the solution, including both the financial investment and the cost in terms of time.
• References. While you generally do not need to refer to many external sources when writing a case study analysis, you might use a few. When you do, you will need to properly reference these sources, which is most often done in one of the main citation styles, including APA, MLA, or Harvard. There is plenty of help when citing references, and you can follow these APA guidelines , these MLA guidelines , or these Harvard guidelines .
• Appendices. This is the section you include after your case study analysis if you used any original data in the report. These data, presented as charts, graphs, and tables, are included here because to present them in the main body of the analysis would be disruptive to the reader. The University of Southern California provides a great description of appendices and when to make use of them.

When you’ve finished your first draft, be sure to proofread it. Look not only for potential grammar and spelling errors but also for discrepancies or holes in your argument.

You should also know what you need to avoid when writing your analysis.

Get an originally-written paper according to your instructions!

🙅 Things to Avoid in Case Analysis

Whenever you deal with a case study, remember that there are some pitfalls to avoid! Beware of the following mistakes:

• Excessive use of colloquial language . Even though it is a study of an actual case, it should sound formal.
• Lack of statistical data . Give all the important data, both in percentages and in numbers.
• Excessive details. State only the most significant facts, rather than drowning the reader in every fact you find.
• Inconsistency in the methods you have used . In a case study, theory plays a relatively small part, so you must develop a specific case study research methodology.
• Trivial means of research . It is critical that you design your own case study research method in whatever form best suits your analysis, such as questionnaires and surveys.

It is useful to see a few examples of case analysis papers. After all, a sample case study report can provide you with some context so you can see how to approach each aspect of your paper.

👀 Case Study Examples for Students

It might be easier to understand how a case study analysis works if you have an example to look at. Fortunately, examples of case studies are easy to come by. Take a look at this video for a sample case study analysis for the Coca-Cola Company.

If you want another example, then take a look at the one below!

Business Case Analysis: Example

CRM’s primary focus is customers and customer perception of the brand or the company. The focus may shift depending on customers’ needs. The main points that Center Parcs should consider are an increase in customer satisfaction and its market share. Both of these points will enhance customer perception of the product as a product of value. Increased customer satisfaction will indicate that the company provides quality services, and increased market share can reduce the number of switching (or leaving) customers, thus fostering customer loyalty.

Case Study Topics

• Equifax case study: the importance of cybersecurity measures . 
• Study a case illustrating ethical issues of medical research.  
• Examine the case describing the complications connected with nursing and residential care.  
• Analyze the competitive strategy of Delta Airlines . 
• Present a case study of an ethical dilemma showing the conflict between the spirit and the letter of the law.  
• Explore the aspects of Starbucks’ marketing strategyin a case study.  
• Research a case of community-based clinic organization and development.  
• Customer service of United Airlines: a case study . 
• Analyze a specific schizophrenia case and provide your recommendations.  
• Provide a case study of a patient with hyperglycemia.  
• Examine the growth strategy of United Healthcare. 
• Present a case study demonstrating ethical issues in business .  
• Study a case of the 5% shareholding rule application and its impact on the company.  
• Case study of post-traumatic stress disorder . 
• Analyze a case examining the issues of cross-cultural management .  
• Write a case study exploring the ethical issues the finance manager of a long-term care facility can face and the possible reaction to them.  
• Write a case study analyzing the aspects of a new president of a firm election. 
• Discuss the specifics of supply chain management in the case of Tehindo company. 
• Study a case of a life crisis in a family and the ways to cope with it.  
• Case study of Tea Leaves and More: supply chain issues .   
• Explore the case of ketogenic diet implementation among sportspeople.  
• Analyze the case of Webster Jewelry shop and suggest some changes.  
• Examine the unique aspects of Tea and More brand management .  
• Adidas case study: an ethical dilemma .  
• Research the challenges of Brazos Valley Food Bank and suggest possible solutions.  
• Describe the case of dark web monitoring for business.  
• Study a case of permissive parenting style .  
• Case study of Starbucks employees . 
• Analyze a case of workplace discrimination and suggest a strategy to avoid it.  
• Examine a case of the consumer decision-making process and define the factors that influence it.  
• Present a case study of Netflix illustrating the crucial role of management innovation for company development.  
• Discuss a case describing a workplace ethical issue and propose ways to resolve it.  
• Case study of the 2008 financial crisis: Graham’s value investing principles in the modern economic climate. 
• Write a case study analyzing the harmful consequences of communication issues in a virtual team .  
• Analyze a case that highlights the importance of a proper functional currency choice. 
• Examine the case of Hitachi Power Systems management.  
• Present a case study of medication research in a healthcare facility.  
• Study the case of Fiji Water and the challenges the brand faces.  
• Research a social problem case and suggest a solution.  
• Analyze a case that reveals the connection between alcohol use and borderline personality disorder .  
• Transglobal Airline case study: break-even analysis.   
• Examine the case of Chiquita Brands International from the moral and business ethics points of view.  
• Present a case study of applying for Social Security benefits. 
• Study the case of a mass hacker attack on Microsoft clients and suggest possible ways to prevent future attacks.  
• Case study of leadership effectiveness . 
• Analyze a case presenting a clinical moral dilemma and propose ways to resolve it. 
• Describe the case of Cowbell Brewing Company and discuss the strategy that made them successful.  
• Write a case study of WeWork company and analyze the strengths and weaknesses of its strategy.  
• Case study of medical ethical decision-making. 
• Study the case of The Georges hotel and suggest ways to overcome its managerial issues.  

🏁 Concluding Remarks

Writing a case study analysis can seem incredibly overwhelming, especially if you have never done it before. Just remember, you can do it provided you follow a plan, keep to the format described here, and study at least one case analysis example.

If you still need help analyzing a case study, your professor is always available to answer your questions and point you in the right direction. You can also get help with any aspect of the project from a custom writing company. Just tackle the research and hand over the writing, write a rough draft and have it checked by a professional, or completely hand the project off to an expert writer.

Regardless of the path you choose, you will turn in something of which you can be proud!

✏️ Case Study Analysis FAQ

Students (especially those who study business) often need to write a case study analysis. It is a kind of report that describes a business case. It includes multiple aspects, for example, the problems that exist, possible solutions, forecasts, etc.

There should be 3 main points covered in a case study analysis:

• The challenge(s) description,
• Possible solutions,
• Outcomes (real and/or foreseen).

Firstly, study some examples available online and in the library. Case study analysis should be a well-structured paper with all the integral components in place. Thus, you might want to use a template and/or an outline to start correctly.

A case study analysis is a popular task for business students. They typically hand it in the format of a paper with several integral components:

• Description of the problem
• Possible ways out
• Results and/or forecasts

Students sometimes tell about the outcome of their research within an oral presentation.

• Case Study: Academia
• Windows of vulnerability: a case study analysis (IEEE)
• A (Very) Brief Refresher on the Case Study Method: SAGE
• The case study approach: Medical Research Methodology
• Strengths and Limitations of Case Studies: Stanford University
• A Sample APA Paper: Radford University
• How to Write a Case Study APA Style: Seattle PI
• The Case Analysis: GVSU
• How to Outline: Purdue OWL
• Incorporating Interview Data: UW-Madison Writing Center
• Share to Facebook
• Share to Twitter
• Share to LinkedIn
• Share to email

If you are a student, you might need to learn how to write a literature review at some point. But don’t think it’s the same as the book review or other types of academic writing you had to do in high school! A literature review is a close examination of...

So, have you been recently assigned a research project? Or, even worse, is it already due soon? The following research paper hacks will help you do it in record time. In the article, you’ll see ten things you can do to conduct a study and compose a piece like a...

Eating a delicacy, watching a good movie, and proving a point to an audience are the three things that make life seem better. Today, you’ll deal with the last one. You’re about to become a professional at public speaking and attention grabbing. Here, you can learn how to write a...

What is a library research paper? It’s nothing more than an academic writing project that summarizes the information on a specific topic taken from primary and secondary sources. There are numerous library research examples you can find online. But to complete this assignment, you should simply follow these essential steps:...

Do you need to write a research analysis paper but have no idea how to do that? Then you’re in the right place. While completing this type of assignment, your key aim is to critically analyze a research article. An article from a serious scientific journal would be a good...

American Antiquity is a professional quarterly journal, which contains various papers on the American archeology. It is incredibly popular among archeologists and the students majoring in history. The organization adopted the rules of The Society for American Archaeology (SAA) citation style. As a result: The journal includes numerous references that...

The most tedious and time-consuming part of any school or college written assignment is the bibliography. Sometimes, it can even be challenging! For example, if you’re confused by the variety of citation styles. This is probably when the most students wonder “Is there someone who could complete my assignment?” That...

An appendix is the part of the paper that contains supplementary material. The information from an appendix in paper writing is not essential. If the readers ignore this part, they still have to get the paper’s idea. Appendices help the readers to understand the research better. They might be useful...

Writing an abstract is one of the skills you need to master to succeed in your studies. An abstract is a summary of an academic text. It contains information about the aims and the outcomes of the research. The primary purpose of an abstract is to help readers understand what...

So you have to write a literature review. You find your favorite novel and then start analyzing it. This is how it’s usually done, right? It’s not. You have to learn the elements of literature review and how to deal with them.

Only two words, but you already feel a chill down your spine. A research paper is no joke. It’s a super detailed piece of academic writing where you analyze a chosen issue in-depth. The main aim of such torture is to show how knowledgeable you are and that your opinion...

A research proposal is a text that suggests a topic or research problem, justifies the need to study it, and describes the ways and methods of conducting the study. Scholars usually write proposals to get funding for their research. In their turn, students might have to do that to get...

Quite an impressive piece The steps and procedures outlined here are well detailed and the examples facilitates understanding.

Thank you for the great feedback, Collins!

it was very helpful. I have an assessment to write where in I need to mention different effective components that are needed to compile a high quality case study assessment.

Glad you liked our article! Your opinion means so much for us!

It is very important and helpful.

Thanks for the feedback, Abraham! Your opinion is very important for us!

Thanks a lot. A knowledge shared with a structured template. Stay the course

Thanks for the feedback!

Thanks for this valuable knowledge.I loved this. keep sharing. to know more about click Air India Case Study – Why Air India failed ?

We are glad to hear your opinion! Thank you for your feedback!

This is going to be a great help in my monthly analysis requirements for my subject. Thank you so much.

Thank you very much for this insightful guidelines… It has really been a great tool for writing my project. Thanks once again.

We are so glad to hear that! Thank you for your feedback!

This article was very helpful, even though I’ll have a clearer mind only after I do the case study myself but I felt very much motivated after reading this, as now I can at least have a plan of what to do compared to the clueless me I was before I read it. I hope if I have any questions or doubts about doing a case study I can clear it out here.

Sarmistha, I’m happy you found the article helpful 🙂

Main Navigation

• Accept offer and enrol
• Current Students

Personalise your experience

Did you mean..., diploma of arts and social sciences, art/science collaboration wins waterhouse natural science art prize, unit of study busn6008 strategy and case analysis (online) (2025).

Future students: 1300 854 556 [email protected]

Current students: [email protected]

updated - DO NOT REMOVE THIS LINE 6:05 AM on Fri, 12 April

Show me unit information for year

Unit snapshot.

PG Coursework Unit

Credit points

Faculty & college.

Faculty of Business, Law and Arts

Anti-requisites

BUSN6005 - Strategy and Case Analysis

Unit description

Examines the key principles of strategic management, with particular focus on the strategy process and its individual components – analysis, choice, action and evaluation. This unit makes use of the case analysis method so that students can apply their understanding to real-world problems across a range of professional contexts.

Unit content

Part 1 – Strategy Development

• Outside-in: Strategy as positioning, Porter’s 5-forces and positional matrix, PESTEL
• Inside-out: Resource-based view of strategy, VIRO/VIRN, developing resources/capabilities
• Emergent view of strategy, experiments, strategy innovation, the three views and bringing them together

Part 2 – Strategic Analysis and Fit

• Analysis models and frameworks such as McKinsey7-Ss, MIT90s, CAGE, and how to use them
• Fit and sustainability, strategy-structure-processes, Miles and Snow, structural options

Part 3 – Strategy Implementation

• Strategy implementation and organisational change

Availabilities

Learning outcomes.

Unit Learning Outcomes express learning achievement in terms of what a student should know, understand and be able to do on completion of a unit. These outcomes are aligned with the graduate attributes . The unit learning outcomes and graduate attributes are also the basis of evaluating prior learning.

On completion of this unit, students should be able to:

demonstrate an understanding of strategic management tools, frameworks and processes

apply strategic management theories to the analysis of real-world case studies

critique the strategic management approach of a contemporary organisation

apply a formal strategy process to diagnose and address a strategic business problem.

Teaching and assessment

Scu online (term), prescribed learning resources.

• Prescribed text information is not currently available.
• Prescribed resources/equipment information is not currently available.

Prescribed Learning Resources may change in future Teaching Periods.

Fee information

Commonwealth Supported courses For information regarding Student Contribution Amounts please visit the Student Contribution Amounts .

Fee paying courses For postgraduate or undergraduate full-fee paying courses please check Domestic Postgraduate Fees OR Domestic Undergraduate Fees .

International

Please check the international course and fee list to determine the relevant fees.

Courses that offer this unit

Graduate diploma of information technology management (2024), graduate diploma of information technology management (2025), graduate diploma of project management (2024), graduate diploma of project management (2025), master of business administration (2025), master of business administration (2024), master of information technology management (2024), master of information technology management (2025), master of project management (2025), master of project management (2024), any questions we'd love to help.

Advertisement

Toward a framework for selecting indicators of measuring sustainability and circular economy in the agri-food sector: a systematic literature review

• LIFE CYCLE SUSTAINABILITY ASSESSMENT
• Published: 02 March 2022

Cite this article

• Cecilia Silvestri   ORCID: orcid.org/0000-0003-2528-601X 1 ,
• Luca Silvestri   ORCID: orcid.org/0000-0002-6754-899X 2 ,
• Michela Piccarozzi   ORCID: orcid.org/0000-0001-9717-9462 1 &
• Alessandro Ruggieri 1  

2866 Accesses

11 Citations

9 Altmetric

Explore all metrics

A Correction to this article was published on 24 March 2022

This article has been updated

The implementation of sustainability and circular economy (CE) models in agri-food production can promote resource efficiency, reduce environmental burdens, and ensure improved and socially responsible systems. In this context, indicators for the measurement of sustainability play a crucial role. Indicators can measure CE strategies aimed to preserve functions, products, components, materials, or embodied energy. Although there is broad literature describing sustainability and CE indicators, no study offers such a comprehensive framework of indicators for measuring sustainability and CE in the agri-food sector.

Starting from this central research gap, a systematic literature review has been developed to measure the sustainability in the agri-food sector and, based on these findings, to understand how indicators are used and for which specific purposes.

The analysis of the results allowed us to classify the sample of articles in three main clusters (“Assessment-LCA,” “Best practice,” and “Decision-making”) and has shown increasing attention to the three pillars of sustainability (triple bottom line). In this context, an integrated approach of indicators (environmental, social, and economic) offers the best solution to ensure an easier transition to sustainability.

Conclusions

The sample analysis facilitated the identification of new categories of impact that deserve attention, such as the cooperation among stakeholders in the supply chain and eco-innovation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Source: Authors’ elaboration. Notes: The graph shows the temporal distribution of the articles under analysis

Source: Authors’ elaborations. Notes: The graph shows the time distribution of articles from the three major journals

Source: Authors’ elaboration. Notes: The graph shows the composition of the sample according to the three clusters identified by the analysis

Source: Authors’ elaboration. Notes: The graph shows the distribution of articles over time by cluster

Source: Authors’ elaboration. Notes: The graph shows the network visualization

Source: Authors’ elaboration. Notes: The graph shows the overlay visualization

Source: Authors’ elaboration. Notes: The graph shows the classification of articles by scientific field

Source: Authors’ elaboration. Notes: Article classification based on their cluster to which they belong and scientific field

Source: Authors’ elaboration

Source: Authors’ elaboration. Notes: The graph shows the distribution of items over time based on TBL

Source: Authors’ elaboration. Notes: The graph shows the Pareto diagram highlighting the most used indicators in literature for measuring sustainability in the agri-food sector

Source: Authors’ elaboration. Notes: The graph shows the distribution over time of articles divided into conceptual and empirical

Source: Authors’ elaboration. Notes: The graph shows the classification of articles, divided into conceptual and empirical, in-depth analysis

Source: Authors’ elaboration. Notes: The graph shows the geographical distribution of the authors

Source: Authors’ elaboration. Notes: The graph shows the distribution of authors according to the continent from which they originate

Source: Authors’ elaboration. Notes: The graph shows the time distribution of publication of authors according to the continent from which they originate

Source: Authors’ elaboration. Notes: Sustainability measurement indicators and impact categories of LCA, S-LCA, and LCC tools should be integrated in order to provide stakeholders with best practices as guidelines and tools to support both decision-making and measurement, according to the circular economy approach

Similar content being viewed by others

The socio-economic performance of agroecology. A review

Ioanna Mouratiadou, Alexander Wezel, … Paolo Bàrberi

The Circular Economy: An Interdisciplinary Exploration of the Concept and Application in a Global Context

Alan Murray, Keith Skene & Kathryn Haynes

Food systems and rural wellbeing: challenges and opportunities

Jim Woodhill, Avinash Kishore, … Saher Hasnain

Change history

24 march 2022.

A Correction to this paper has been published: https://doi.org/10.1007/s11367-022-02038-9

Acero AP, Rodriguez C, Ciroth A (2017) LCIA methods: impact assessment methods in life cycle assessment and their impact categories. Version 1.5.6. Green Delta 1–23

Accorsi R, Versari L, Manzini R (2015) Glass vs. plastic: Life cycle assessment of extra-virgin olive oil bottles across global supply chains. Sustain 7:2818–2840. https://doi.org/10.3390/su7032818

Adjei-Bamfo P, Maloreh-Nyamekye T, Ahenkan A (2019) The role of e-government in sustainable public procurement in developing countries: a systematic literature review. Resour Conserv Recycl 142:189–203. https://doi.org/10.1016/j.resconrec.2018.12.001

Article   Google Scholar  

Aivazidou E, Tsolakis N, Vlachos D, Iakovou E (2015) Water footprint management policies for agrifood supply chains: a critical taxonomy and a system dynamics modelling approach. Chem Eng Trans 43:115–120. https://doi.org/10.3303/CET1543020

Alhaddi H (2015) Triple bottom line and sustainability: a literature review. Bus Manag Stud 1:6–10

Allaoui H, Guo Y, Sarkis J (2019) Decision support for collaboration planning in sustainable supply chains. J Clean Prod 229:761–774. https://doi.org/10.1016/j.jclepro.2019.04.367

Alshqaqeeq F, Amin Esmaeili M, Overcash M, Twomey J (2020) Quantifying hospital services by carbon footprint: a systematic literature review of patient care alternatives. Resour Conserv Recycl 154:104560. https://doi.org/10.1016/j.resconrec.2019.104560

Anwar F, Chaudhry FN, Nazeer S et al (2016) Causes of ozone layer depletion and its effects on human: review. Atmos Clim Sci 06:129–134. https://doi.org/10.4236/acs.2016.61011

Aquilani B, Silvestri C, Ruggieri A (2016). A Systematic Literature Review on Total Quality Management Critical Success Factors and the Identification of New Avenues of Research. https://doi.org/10.1108/TQM-01-2016-0003

Aramyan L, Hoste R, Van Den Broek W et al (2011) Towards sustainable food production: a scenario study of the European pork sector. J Chain Netw Sci 11:177–189. https://doi.org/10.3920/JCNS2011.Qpork8

Arfini F, Antonioli F, Cozzi E et al (2019) Sustainability, innovation and rural development: the case of Parmigiano-Reggiano PDO. Sustain 11:1–17. https://doi.org/10.3390/su11184978

Assembly UG (2005) Resolution adopted by the general assembly. New York, NY

Avilés-Palacios C, Rodríguez-Olalla A (2021) The sustainability of waste management models in circular economies. Sustain 13:1–19. https://doi.org/10.3390/su13137105

Azevedo SG, Silva ME, Matias JCO, Dias GP (2018) The influence of collaboration initiatives on the sustainability of the cashew supply chain. Sustain 10:1–29. https://doi.org/10.3390/su10062075

Bajaj S, Garg R, Sethi M (2016) Total quality management: a critical literature review using Pareto analysis. Int J Product Perform Manag 67:128–154

Banasik A, Kanellopoulos A, Bloemhof-Ruwaard JM, Claassen GDH (2019) Accounting for uncertainty in eco-efficient agri-food supply chains: a case study for mushroom production planning. J Clean Prod 216:249–256. https://doi.org/10.1016/j.jclepro.2019.01.153

Barth H, Ulvenblad PO, Ulvenblad P (2017) Towards a conceptual framework of sustainable business model innovation in the agri-food sector: a systematic literature review. Sustain 9. https://doi.org/10.3390/su9091620

Bastas A, Liyanage K (2018) Sustainable supply chain quality management: a systematic review

Beckerman W (1992) Economic growth and the environment: whose growth? Whose environment? World Dev 20:481–496. https://doi.org/10.1016/0305-750X(92)90038-W

Belaud JP, Prioux N, Vialle C, Sablayrolles C (2019) Big data for agri-food 4.0: application to sustainability management for by-products supply chain. Comput Ind 111:41–50. https://doi.org/10.1016/j.compind.2019.06.006

Bele B, Norderhaug A, Sickel H (2018) Localized agri-food systems and biodiversity. Agric 8. https://doi.org/10.3390/agriculture8020022

Bilali H El, Calabrese G, Iannetta M et al (2020) Environmental sustainability of typical agro-food products: a scientifically sound and user friendly approach. New Medit 19:69–83. https://doi.org/10.30682/nm2002e

Blanc S, Massaglia S, Brun F et al (2019) Use of bio-based plastics in the fruit supply chain: an integrated approach to assess environmental, economic, and social sustainability. Sustain 11. https://doi.org/10.3390/su11092475

Bloemhof JM, van der Vorst JGAJ, Bastl M, Allaoui H (2015) Sustainability assessment of food chain logistics. Int J Logist Res Appl 18:101–117. https://doi.org/10.1080/13675567.2015.1015508

Bonisoli L, Galdeano-Gómez E, Piedra-Muñoz L (2018) Deconstructing criteria and assessment tools to build agri-sustainability indicators and support farmers’ decision-making process. J Clean Prod 182:1080–1094. https://doi.org/10.1016/j.jclepro.2018.02.055

Bonisoli L, Galdeano-Gómez E, Piedra-Muñoz L, Pérez-Mesa JC (2019) Benchmarking agri-food sustainability certifications: evidences from applying SAFA in the Ecuadorian banana agri-system. J Clean Prod 236. https://doi.org/10.1016/j.jclepro.2019.07.054

Bornmann L, Haunschild R, Hug SE (2018) Visualizing the context of citations referencing papers published by Eugene Garfield: a new type of keyword co-occurrence analysis. Scientometrics 114:427–437. https://doi.org/10.1007/s11192-017-2591-8

Boulding KE (1966) The economics of the coming spaceship earth. New York, 1-17

Bracquené E, Dewulf W, Duflou JR (2020) Measuring the performance of more circular complex product supply chains. Resour Conserv Recycl 154:104608. https://doi.org/10.1016/j.resconrec.2019.104608

Burck J, Hagen U, Bals C et al (2021) Climate Change Performance Index

Calisto Friant M, Vermeulen WJV, Salomone R (2020) A typology of circular economy discourses: navigating the diverse visions of a contested paradigm. Resour Conserv Recycl 161:104917. https://doi.org/10.1016/j.resconrec.2020.104917

Campbell BM, Beare DJ, Bennett EM et al (2017) Agriculture production as a major driver of the earth system exceeding planetary boundaries. Ecol Soc 22. https://doi.org/10.5751/ES-09595-220408

Capitanio F, Coppola A, Pascucci S (2010) Product and process innovation in the Italian food industry. Agribusiness 26:503–518. https://doi.org/10.1002/agr.20239

Caputo P, Zagarella F, Cusenza MA et al (2020) Energy-environmental assessment of the UIA-OpenAgri case study as urban regeneration project through agriculture. Sci Total Environ 729:138819. https://doi.org/10.1016/j.scitotenv.2020.138819

Article   CAS   Google Scholar  

Chabowski BR, Mena JA, Gonzalez-Padron TL (2011) The structure of sustainability research in marketing, 1958–2008: a basis for future research opportunities. J Acad Mark Sci 39:55–70. https://doi.org/10.1007/s11747-010-0212-7

Chadegani AA, Salehi H, Yunus M et al (2017) A comparison between two main academic literature collections : Web of Science and Scopus databases. Asian Soc Sci 9:18–26. https://doi.org/10.5539/ass.v9n5p18

Chams N, Guesmi B, Gil JM (2020) Beyond scientific contribution: assessment of the societal impact of research and innovation to build a sustainable agri-food sector. J Environ Manage 264. https://doi.org/10.1016/j.jenvman.2020.110455

Chandrakumar C, McLaren SJ, Jayamaha NP, Ramilan T (2019) Absolute sustainability-based life cycle assessment (ASLCA): a benchmarking approach to operate agri-food systems within the 2°C global carbon budget. J Ind Ecol 23:906–917. https://doi.org/10.1111/jiec.12830

Chaparro-Africano AM (2019) Toward generating sustainability indicators for agroecological markets. Agroecol Sustain Food Syst 43:40–66. https://doi.org/10.1080/21683565.2019.1566192

Colicchia C, Strozzi F (2012) Supply chain risk management: a new methodology for a systematic literature review

Conca L, Manta F, Morrone D, Toma P (2021) The impact of direct environmental, social, and governance reporting: empirical evidence in European-listed companies in the agri-food sector. Bus Strateg Environ 30:1080–1093. https://doi.org/10.1002/bse.2672

Coppola A, Ianuario S, Romano S, Viccaro M (2020) Corporate social responsibility in agri-food firms: the relationship between CSR actions and firm’s performance. AIMS Environ Sci 7:542–558. https://doi.org/10.3934/environsci.2020034

Corona B, Shen L, Reike D et al (2019) Towards sustainable development through the circular economy—a review and critical assessment on current circularity metrics. Resour Conserv Recycl 151:104498. https://doi.org/10.1016/j.resconrec.2019.104498

Correia MS (2019) Sustainability: An overview of the triple bottom line and sustainability implementation. Int J Strateg Eng 2:29–38.  https://doi.org/10.4018/IJoSE.2019010103

Coteur I, Marchand F, Debruyne L, Lauwers L (2019) Structuring the myriad of sustainability assessments in agri-food systems: a case in Flanders. J Clean Prod 209:472–480. https://doi.org/10.1016/j.jclepro.2018.10.066

CREA (2020) L’agricoltura italiana conta 2019

Crenna E, Sala S, Polce C, Collina E (2017) Pollinators in life cycle assessment: towards a framework for impact assessment. J Clean Prod 140:525–536. https://doi.org/10.1016/j.jclepro.2016.02.058

D’Eusanio M, Serreli M, Zamagni A, Petti L (2018) Assessment of social dimension of a jar of honey: a methodological outline. J Clean Prod 199:503–517. https://doi.org/10.1016/j.jclepro.2018.07.157

Dania WAP, Xing K, Amer Y (2018) Collaboration behavioural factors for sustainable agri-food supply chains: a systematic review. J Clean Prod 186:851–864

De Pascale A, Arbolino R, Szopik-Depczyńska K et al (2021) A systematic review for measuring circular economy: the 61 indicators. J Clean Prod 281. https://doi.org/10.1016/j.jclepro.2020.124942

De Schoenmakere M, Gillabel J (2017) Circular by design: products in the circular economy

Del Borghi A, Gallo M, Strazza C, Del Borghi M (2014) An evaluation of environmental sustainability in the food industry through life cycle assessment: the case study of tomato products supply chain. J Clean Prod 78:121–130. https://doi.org/10.1016/j.jclepro.2014.04.083

Del Borghi A, Strazza C, Magrassi F et al (2018) Life cycle assessment for eco-design of product–package systems in the food industry—the case of legumes. Sustain Prod Consum 13:24–36. https://doi.org/10.1016/j.spc.2017.11.001

Denyer D, Tranfield D (2009) Producing a systematic review. In: Buchanan B (ed) The sage handbook of organization research methods. Sage Publications Ltd, Cornwall, pp 671–689

Google Scholar  

Dietz T, Grabs J, Chong AE (2019) Mainstreamed voluntary sustainability standards and their effectiveness: evidence from the Honduran coffee sector. Regul Gov. https://doi.org/10.1111/rego.12239

Dixon-Woods M (2011) Using framework-based synthesis for conducting reviews of qualitative studies. BMC Med 9:9–10. https://doi.org/10.1186/1741-7015-9-39

do Canto NR, Bossle MB, Marques L, Dutra M, (2020) Supply chain collaboration for sustainability: a qualitative investigation of food supply chains in Brazil. Manag Environ Qual an Int J. https://doi.org/10.1108/MEQ-12-2019-0275

dos Santos RR, Guarnieri P (2020) Social gains for artisanal agroindustrial producers induced by cooperation and collaboration in agri-food supply chain. Soc Responsib J. https://doi.org/10.1108/SRJ-09-2019-0323

Doukidis GI, Matopoulos A, Vlachopoulou M, Manthou V, Manos B (2007) A conceptual framework for supply chain collaboration: empirical evidence from the agri‐food industry. Supply Chain Manag an Int Journal 12:177–186. https://doi.org/10.1108/13598540710742491

Durach CF, Kembro J, Wieland A (2017) A new paradigm for systematic literature reviews in supply chain management. J Supply Chain Manag 53:67–85. https://doi.org/10.1111/jscm.12145

Durán-Sánchez A, Álvarez-García J, Río-Rama D, De la Cruz M (2018) Sustainable water resources management: a bibliometric overview. Water 10:1–19. https://doi.org/10.3390/w10091191

Duru M, Therond O (2015) Livestock system sustainability and resilience in intensive production zones: which form of ecological modernization? Reg Environ Chang 15:1651–1665. https://doi.org/10.1007/s10113-014-0722-9

Edison Fondazione (2019) Le eccellenze agricole italiane. I primati europei e mondiali dell’Italia nei prodotti vegetali. Milan (IT)

Ehrenfeld JR (2005) The roots of sustainability. MIT Sloan Manag Rev 46(2)46:23–25

Elia V, Gnoni MG, Tornese F (2017) Measuring circular economy strategies through index methods: a critical analysis. J Clean Prod 142:2741–2751. https://doi.org/10.1016/j.jclepro.2016.10.196

Elkington J (1997) Cannibals with forks : the triple bottom line of 21st century business. Capstone, Oxford

Esposito B, Sessa MR, Sica D, Malandrino O (2020) Towards circular economy in the agri-food sector. A systematic literature review. Sustain 12. https://doi.org/10.3390/SU12187401

European Commission (2018) Agri-food trade in 2018

European Commission (2019) Monitoring EU agri-food trade: development until September 2019

Eurostat (2018) Small and large farms in the EU - statistics from the farm structure survey

FAO (2011) Biodiversity for food and agriculture. Italy, Rome

FAO (2012) Energy-smart food at FAO: an overview. Italy, Rome

FAO (2014) Food wastage footprint: fool cost-accounting

FAO (2016) The state of food and agriculture climate change, agriculture and food security. Italy, Rome

FAO (2017) The future of food and agriculture: trends and challenges. Italy, Rome

FAO (2020) The state of food security and nutrition in the world. Transforming Food Systems for Affordable Healthy Diets. Rome, Italy

Fassio F, Tecco N (2019) Circular economy for food: a systemic interpretation of 40 case histories in the food system in their relationships with SDGs. Systems 7:43. https://doi.org/10.3390/systems7030043

Fathollahi A, Coupe SJ (2021) Life cycle assessment (LCA) and life cycle costing (LCC) of road drainage systems for sustainability evaluation: quantifying the contribution of different life cycle phases. Sci Total Environ 776:145937. https://doi.org/10.1016/j.scitotenv.2021.145937

Ferreira VJ, Arnal ÁJ, Royo P et al (2019) Energy and resource efficiency of electroporation-assisted extraction as an emerging technology towards a sustainable bio-economy in the agri-food sector. J Clean Prod 233:1123–1132. https://doi.org/10.1016/j.jclepro.2019.06.030

Fiksel J (2006) A framework for sustainable remediation. JOM 8:15–22. https://doi.org/10.1021/es202595w

Flick U (2014) An introduction to qualitative research

Franciosi C, Voisin A, Miranda S et al (2020) Measuring maintenance impacts on sustainability of manufacturing industries : from a systematic literature review to a framework proposal. J Clean Prod 260:1–19. https://doi.org/10.1016/j.jclepro.2020.121065

Gaitán-Cremaschi D, Meuwissen MPM, Oude AGJML (2017) Total factor productivity: a framework for measuring agri-food supply chain performance towards sustainability. Appl Econ Perspect Policy 39:259–285. https://doi.org/10.1093/aepp/ppw008

Galdeano-Gómez E, Zepeda-Zepeda JA, Piedra-Muñoz L, Vega-López LL (2017) Family farm’s features influencing socio-economic sustainability: an analysis of the agri-food sector in southeast Spain. New Medit 16:50–61

Gallopín G, Herrero LMJ, Rocuts A (2014) Conceptual frameworks and visual interpretations of sustainability. Int J Sustain Dev 17:298–326. https://doi.org/10.1504/IJSD.2014.064183

Gallopín GC (2003) Sostenibilidad y desarrollo sostenible: un enfoque sistémico. Cepal, LATIN AMERICA

Garnett T (2013) Food sustainability: problems, perspectives and solutions. Proc Nutr Soc 72:29–39. https://doi.org/10.1017/S0029665112002947

Garofalo P, D’Andrea L, Tomaiuolo M et al (2017) Environmental sustainability of agri-food supply chains in Italy: the case of the whole-peeled tomato production under life cycle assessment methodology. J Food Eng 200:1–12. https://doi.org/10.1016/j.jfoodeng.2016.12.007

Gava O, Bartolini F, Venturi F et al (2018) A reflection of the use of the life cycle assessment tool for agri-food sustainability. Sustain 11. https://doi.org/10.3390/su11010071

Gazzola P, Querci E (2017) The connection between the quality of life and sustainable ecological development. Eur Sci J 7881:1857–7431

Geissdoerfer M, Savaget P, Bocken N, Hultink EJ (2017) The circular economy – a new sustainability paradigm ? The circular economy – a new sustainability paradigm ? J Clean Prod 143:757–768. https://doi.org/10.1016/j.jclepro.2016.12.048

Georgescu-Roegen N (1971) The entropy low and the economic process. Harward University Press, Cambridge Mass

Book   Google Scholar  

Gerbens-Leenes PW, Moll HC, Schoot Uiterkamp AJM (2003) Design and development of a measuring method for environmental sustainability in food production systems. Ecol Econ 46:231–248. https://doi.org/10.1016/S0921-8009(03)00140-X

Gésan-Guiziou G, Alaphilippe A, Aubin J et al (2020) Diversity and potentiality of multi-criteria decision analysis methods for agri-food research. Agron Sustain Dev 40. https://doi.org/10.1007/s13593-020-00650-3

Ghisellini P, Cialani C, Ulgiati S (2016) A review on circular economy: the expected transition to a balanced interplay of environmental and economic systems. J Clean Prod 114:11–32. https://doi.org/10.1016/j.jclepro.2015.09.007

Godoy-Durán Á, Galdeano- Gómez E, Pérez-Mesa JC, Piedra-Muñoz L (2017) Assessing eco-efficiency and the determinants of horticultural family-farming in southeast Spain. J Environ Manage 204:594–604. https://doi.org/10.1016/j.jenvman.2017.09.037

Gold S, Kunz N, Reiner G (2017) Sustainable global agrifood supply chains: exploring the barriers. J Ind Ecol 21:249–260. https://doi.org/10.1111/jiec.12440

Goucher L, Bruce R, Cameron DD et al (2017) The environmental impact of fertilizer embodied in a wheat-to-bread supply chain. Nat Plants 3:1–5. https://doi.org/10.1038/nplants.2017.12

Green A, Nemecek T, Chaudhary A, Mathys A (2020) Assessing nutritional, health, and environmental sustainability dimensions of agri-food production. Glob Food Sec 26:100406. https://doi.org/10.1016/j.gfs.2020.100406

Guinée JB, Heijungs R, Huppes G et al (2011) Life cycle assessment: past, present, and future †. Environ Sci Technol 45:90–96. https://doi.org/10.1021/es101316v

Guiomar N, Godinho S, Pinto-Correia T et al (2018) Typology and distribution of small farms in Europe: towards a better picture. Land Use Policy 75:784–798. https://doi.org/10.1016/j.landusepol.2018.04.012

Gunasekaran A, Patel C, McGaughey RE (2004) A framework for supply chain performance measurement. Int J Prod Econ 87:333–347. https://doi.org/10.1016/j.ijpe.2003.08.003

Gunasekaran A, Patel C, Tirtiroglu E (2001) Performance measures and metrics in a supply chain environment. Int J Oper Prod Manag 21:71–87. https://doi.org/10.1108/01443570110358468

Hamam M, Chinnici G, Di Vita G et al (2021) Circular economy models in agro-food systems: a review. Sustain 13

Harun SN, Hanafiah MM, Aziz NIHA (2021) An LCA-based environmental performance of rice production for developing a sustainable agri-food system in Malaysia. Environ Manage 67:146–161. https://doi.org/10.1007/s00267-020-01365-7

Harvey M, Pilgrim S (2011) The new competition for land: food, energy, and climate change. Food Policy 36:S40–S51. https://doi.org/10.1016/j.foodpol.2010.11.009

Hawkes C, Ruel MT (2006) Understanding the links between agriculture and health. DC: International Food Policy Research Institute. Washington, USA

Hellweg S, Milà i Canals L (2014) Emerging approaches, challenges and opportunities in life cycle assessment. Science (80)344:1109LP–1113. https://doi.org/10.1126/science.1248361

Higgins V, Dibden J, Cocklin C (2015) Private agri-food governance and greenhouse gas abatement: constructing a corporate carbon economy. Geoforum 66:75–84. https://doi.org/10.1016/j.geoforum.2015.09.012

Hill T (1995) Manufacturing strategy: text and cases., Macmillan

Hjeresen DD, Gonzales R (2020) Green chemistry promote sustainable agriculture?The rewards are higher yields and less environmental contamination. Environemental Sci Techonology 103–107

Horne R, Grant T, Verghese K (2009) Life cycle assessment: principles, practice, and prospects. Csiro Publishing, Collingwood, Australia

Horton P, Koh L, Guang VS (2016) An integrated theoretical framework to enhance resource efficiency, sustainability and human health in agri-food systems. J Clean Prod 120:164–169. https://doi.org/10.1016/j.jclepro.2015.08.092

Hospido A, Davis J, Berlin J, Sonesson U (2010) A review of methodological issues affecting LCA of novel food products. Int J Life Cycle Assess 15:44–52. https://doi.org/10.1007/s11367-009-0130-4

Huffman T, Liu J, Green M et al (2015) Improving and evaluating the soil cover indicator for agricultural land in Canada. Ecol Indic 48:272–281. https://doi.org/10.1016/j.ecolind.2014.07.008

Ilbery B, Maye D (2005) Food supply chains and sustainability: evidence from specialist food producers in the Scottish/English borders. Land Use Policy 22:331–344. https://doi.org/10.1016/j.landusepol.2004.06.002

Ingrao C, Faccilongo N, Valenti F et al (2019) Tomato puree in the Mediterranean region: an environmental life cycle assessment, based upon data surveyed at the supply chain level. J Clean Prod 233:292–313. https://doi.org/10.1016/j.jclepro.2019.06.056

Iocola I, Angevin F, Bockstaller C et al (2020) An actor-oriented multi-criteria assessment framework to support a transition towards sustainable agricultural systems based on crop diversification. Sustain 12. https://doi.org/10.3390/su12135434

Irabien A, Darton RC (2016) Energy–water–food nexus in the Spanish greenhouse tomato production. Clean Technol Environ Policy 18:1307–1316. https://doi.org/10.1007/s10098-015-1076-9

ISO 14040:2006 (2006) Environmental management — life cycle assessment — principles and framework

ISO 14044:2006 (2006) Environmental management — life cycle assessment — requirements and guidelines

ISO 15392:2008 (2008) Sustainability in building construction–general principles

Istat (2019) Andamento dell’economia agricola

Jaakkola E (2020) Designing conceptual articles : four approaches. AMS Rev 1–9. https://doi.org/10.1007/s13162-020-00161-0

Jin R, Yuan H, Chen Q (2019) Science mapping approach to assisting the review of construction and demolition waste management research published between 2009 and 2018. Resour Conserv Recycl 140:175–188. https://doi.org/10.1016/j.resconrec.2018.09.029

Johnston P, Everard M, Santillo D, Robèrt KH (2007) Reclaiming the definition of sustainability. Environ Sci Pollut Res Int 14:60–66. https://doi.org/10.1065/espr2007.01.375

Jorgensen SE, Burkhard B, Müller F (2013) Twenty volumes of ecological indicators-an accounting short review. Ecol Indic 28:4–9. https://doi.org/10.1016/j.ecolind.2012.12.018

Joshi S, Sharma M, Kler R (2020) Modeling circular economy dimensions in agri-tourism clusters: sustainable performance and future research directions. Int J Math Eng Manag Sci 5:1046–1061. https://doi.org/10.33889/IJMEMS.2020.5.6.080

Kamilaris A, Gao F, Prenafeta-Boldu FX, Ali MI (2017) Agri-IoT: a semantic framework for Internet of Things-enabled smart farming applications. In: 2016 IEEE 3rd World Forum on Internet of Things, WF-IoT 2016. pp 442–447

Karuppusami G, Gandhinathan R (2006) Pareto analysis of critical success factors of total quality management: a literature review and analysis. TQM Mag 18:372–385. https://doi.org/10.1108/09544780610671048

Kates RW, Parris TM, Leiserowitz AA (2005) What is sustainable development? Goals, indicators, values, and practice. Environ Sci Policy Sustain Dev 47:8–21. https://doi.org/10.1080/00139157.2005.10524444

Khounani Z, Hosseinzadeh-Bandbafha H, Moustakas K et al (2021) Environmental life cycle assessment of different biorefinery platforms valorizing olive wastes to biofuel, phosphate salts, natural antioxidant, and an oxygenated fuel additive (triacetin). J Clean Prod 278:123916. https://doi.org/10.1016/j.jclepro.2020.123916

Kitchenham B, Charters S (2007) Guidelines for performing systematic literature reviews in software engineering version 2.3. Engineering 45. https://doi.org/10.1145/1134285.1134500

Korhonen J, Nuur C, Feldmann A, Birkie SE (2018) Circular economy as an essentially contested concept. J Clean Prod 175:544–552. https://doi.org/10.1016/j.jclepro.2017.12.111

Kuisma M, Kahiluoto H (2017) Biotic resource loss beyond food waste: agriculture leaks worst. Resour Conserv Recycl 124:129–140. https://doi.org/10.1016/j.resconrec.2017.04.008

Laso J, Hoehn D, Margallo M et al (2018) Assessing energy and environmental efficiency of the Spanish agri-food system using the LCA/DEA methodology. Energies 11. https://doi.org/10.3390/en11123395

Lee KM (2007) So What is the “triple bottom line”? Int J Divers Organ Communities Nations Annu Rev 6:67–72. https://doi.org/10.18848/1447-9532/cgp/v06i06/39283

Lehmann RJ, Hermansen JE, Fritz M et al (2011) Information services for European pork chains - closing gaps in information infrastructures. Comput Electron Agric 79:125–136. https://doi.org/10.1016/j.compag.2011.09.002

León-Bravo V, Caniato F, Caridi M, Johnsen T (2017) Collaboration for sustainability in the food supply chain: a multi-stage study in Italy. Sustainability 9:1253

Lepage A (2009) The quality of life as attribute of sustainability. TQM J 21:105–115. https://doi.org/10.1108/17542730910938119

Li CZ, Zhao Y, Xiao B et al (2020) Research trend of the application of information technologies in construction and demolition waste management. J Clean Prod 263. https://doi.org/10.1016/j.jclepro.2020.121458

Lo Giudice A, Mbohwa C, Clasadonte MT, Ingrao C (2014) Life cycle assessment interpretation and improvement of the Sicilian artichokes production. Int J Environ Res 8:305–316. https://doi.org/10.22059/ijer.2014.721

Lueddeckens S, Saling P, Guenther E (2020) Temporal issues in life cycle assessment—a systematic review. Int J Life Cycle Assess 25:1385–1401. https://doi.org/10.1007/s11367-020-01757-1

Luo J, Ji C, Qiu C, Jia F (2018) Agri-food supply chain management: bibliometric and content analyses. Sustain 10. https://doi.org/10.3390/su10051573

Lynch J, Donnellan T, Finn JA et al (2019) Potential development of Irish agricultural sustainability indicators for current and future policy evaluation needs. J Environ Manage 230:434–445. https://doi.org/10.1016/j.jenvman.2018.09.070

MacArthur E (2013) Towards the circular economy. J Ind Ecol 2:23–44

MacArthur E (2017) Delivering the circular economy a toolkit for policymakers, The Ellen MacArthur Foundation

MacInnis DJ (2011) A framework for conceptual. J Mark 75:136–154. https://doi.org/10.1509/jmkg.75.4.136

Mangla SK, Luthra S, Rich N et al (2018) Enablers to implement sustainable initiatives in agri-food supply chains. Int J Prod Econ 203:379–393. https://doi.org/10.1016/j.ijpe.2018.07.012

Marotta G, Nazzaro C, Stanco M (2017) How the social responsibility creates value: models of innovation in Italian pasta industry. Int J Glob Small Bus 9:144–167. https://doi.org/10.1504/IJGSB.2017.088923

Martucci O, Arcese G, Montauti C, Acampora A (2019) Social aspects in the wine sector: comparison between social life cycle assessment and VIVA sustainable wine project indicators. Resources 8. https://doi.org/10.3390/resources8020069

Mayring P (2004) Forum : Qualitative social research Sozialforschung 2. History of content analysis. A Companion to Qual Res 1:159–176

McKelvey B (2002) Managing coevolutionary dynamics. In: 18th EGOS Conference. Barcelona, Spain, pp 1–21

McMichael AJ, Butler CD, Folke C (2003) New visions for addressing sustainability. Science (80- ) 302:1191–1920

Mehmood A, Ahmed S, Viza E et al (2021) Drivers and barriers towards circular economy in agri-food supply chain: a review. Bus Strateg Dev 1–17. https://doi.org/10.1002/bsd2.171

Mella P, Gazzola P (2011) Sustainability and quality of life: the development model. In: Kapounek S (ed) Enterprise and competitive environment. Mendel University: Brno, Czechia. 542–551

Merli R, Preziosi M, Acampora A (2018) How do scholars approach the circular economy ? A systematic literature review. J Clean Prod 178:703–722. https://doi.org/10.1016/j.jclepro.2017.12.112

Merli R, Preziosi M, Acampora A et al (2020) Recycled fibers in reinforced concrete: a systematic literature review. J Clean Prod 248:119207. https://doi.org/10.1016/j.jclepro.2019.119207

Miglietta PP, Morrone D (2018) Managing water sustainability: virtual water flows and economic water productivity assessment of the wine trade between Italy and the Balkans. Sustain 10. https://doi.org/10.3390/su10020543

Mitchell MGE, Chan KMA, Newlands NK, Ramankutty N (2020) Spatial correlations don’t predict changes in agricultural ecosystem services: a Canada-wide case study. Front Sustain Food Syst 4:1–17. https://doi.org/10.3389/fsufs.2020.539892

Moraga G, Huysveld S, Mathieux F et al (2019) Circular economy indicators: what do they measure?. Resour Conserv Recycl 146:452–461. https://doi.org/10.1016/j.resconrec.2019.03.045

Morrissey JE, Dunphy NP (2015) Towards sustainable agri-food systems: the role of integrated sustainability and value assessment across the supply-chain. Int J Soc Ecol Sustain Dev 6:41–58. https://doi.org/10.4018/IJSESD.2015070104

Moser G (2009) Quality of life and sustainability: toward person-environment congruity. J Environ Psychol 29:351–357. https://doi.org/10.1016/j.jenvp.2009.02.002

Muijs D (2010) Doing quantitative research in education with SPSS. London

Muller MF, Esmanioto F, Huber N, Loures ER (2019) A systematic literature review of interoperability in the green Building Information Modeling lifecycle. J Clean Prod 223:397–412. https://doi.org/10.1016/j.jclepro.2019.03.114

Muradin M, Joachimiak-Lechman K, Foltynowicz Z (2018) Evaluation of eco-efficiency of two alternative agricultural biogas plants. Appl Sci 8. https://doi.org/10.3390/app8112083

Naseer MA, ur R, Ashfaq M, Hassan S, et al (2019) Critical issues at the upstream level in sustainable supply chain management of agri-food industries: evidence from Pakistan’s citrus industry. Sustain 11:1–19. https://doi.org/10.3390/su11051326

Nattassha R, Handayati Y, Simatupang TM, Siallagan M (2020) Understanding circular economy implementation in the agri-food supply chain: the case of an Indonesian organic fertiliser producer. Agric Food Secur 9:1–16. https://doi.org/10.1186/s40066-020-00264-8

Nazari-Sharabian M, Ahmad S, Karakouzian M (2018) Climate change and eutrophication: a short review. Eng Technol Appl Sci Res 8:3668–3672. https://doi.org/10.5281/zenodo.2532694

Nazir N (2017) Understanding life cycle thinking and its practical application to agri-food system. Int J Adv Sci Eng Inf Technol 7:1861–1870. https://doi.org/10.18517/ijaseit.7.5.3578

Negra C, Remans R, Attwood S et al (2020) Sustainable agri-food investments require multi-sector co-development of decision tools. Ecol Indic 110:105851. https://doi.org/10.1016/j.ecolind.2019.105851

Newsham KK, Robinson SA (2009) Responses of plants in polar regions to UVB exposure: a meta-analysis. Glob Chang Biol 15:2574–2589. https://doi.org/10.1111/j.1365-2486.2009.01944.x

Niemeijer D, de Groot RS (2008) A conceptual framework for selecting environmental indicator sets. Ecol Indic 8:14–25. https://doi.org/10.1016/j.ecolind.2006.11.012

Niero M, Kalbar PP (2019) Coupling material circularity indicators and life cycle based indicators: a proposal to advance the assessment of circular economy strategies at the product level. Resour Conserv Recycl 140:305–312. https://doi.org/10.1016/j.resconrec.2018.10.002

Nikolaou IE, Tsagarakis KP (2021) An introduction to circular economy and sustainability: some existing lessons and future directions. Sustain Prod Consum 28:600–609. https://doi.org/10.1016/j.spc.2021.06.017

Notarnicola B, Hayashi K, Curran MA, Huisingh D (2012) Progress in working towards a more sustainable agri-food industry. J Clean Prod 28:1–8. https://doi.org/10.1016/j.jclepro.2012.02.007

Notarnicola B, Tassielli G, Renzulli PA, Monforti F (2017) Energy flows and greenhouses gases of EU (European Union) national breads using an LCA (life cycle assessment) approach. J Clean Prod 140:455–469. https://doi.org/10.1016/j.jclepro.2016.05.150

Opferkuch K, Caeiro S, Salomone R, Ramos TB (2021) Circular economy in corporate sustainability reporting: a review of organisational approaches. Bus Strateg Environ 1–22. https://doi.org/10.1002/bse.2854

Padilla-Rivera A, do Carmo BBT, Arcese G, Merveille N, (2021) Social circular economy indicators: selection through fuzzy delphi method. Sustain Prod Consum 26:101–110. https://doi.org/10.1016/j.spc.2020.09.015

Pagotto M, Halog A (2016) Towards a circular economy in Australian agri-food industry: an application of input-output oriented approaches for analyzing resource efficiency and competitiveness potential. J Ind Ecol 20:1176–1186. https://doi.org/10.1111/jiec.12373

Parent G, Lavallée S (2011) LCA potentials and limits within a sustainable agri-food statutory framework. Global food insecurity. Springer, Netherlands, Dordrecht, pp 161–171

Chapter   Google Scholar  

Pattey E, Qiu G (2012) Trends in primary particulate matter emissions from Canadian agriculture. J Air Waste Manag Assoc 62:737–747. https://doi.org/10.1080/10962247.2012.672058

Pauliuk S (2018) Critical appraisal of the circular economy standard BS 8001:2017 and a dashboard of quantitative system indicators for its implementation in organizations. Resour Conserv Recycl 129:81–92. https://doi.org/10.1016/j.resconrec.2017.10.019

Peano C, Migliorini P, Sottile F (2014) A methodology for the sustainability assessment of agri-food systems: an application to the slow food presidia project. Ecol Soc 19. https://doi.org/10.5751/ES-06972-190424

Peano C, Tecco N, Dansero E et al (2015) Evaluating the sustainability in complex agri-food systems: the SAEMETH framework. Sustain 7:6721–6741. https://doi.org/10.3390/su7066721

Pearce DW, Turner RK (1990) Economics of natural resources and the environment. Harvester Wheatsheaf, Hemel Hempstead, Herts

Pelletier N (2018) Social sustainability assessment of Canadian egg production facilities: methods, analysis, and recommendations. Sustain 10:1–17. https://doi.org/10.3390/su10051601

Peña C, Civit B, Gallego-Schmid A et al (2021) Using life cycle assessment to achieve a circular economy. Int J Life Cycle Assess 26:215–220. https://doi.org/10.1007/s11367-020-01856-z

Perez Neira D (2016) Energy sustainability of Ecuadorian cacao export and its contribution to climate change. A case study through product life cycle assessment. J Clean Prod 112:2560–2568. https://doi.org/10.1016/j.jclepro.2015.11.003

Pérez-Neira D, Grollmus-Venegas A (2018) Life-cycle energy assessment and carbon footprint of peri-urban horticulture. A comparative case study of local food systems in Spain. Landsc Urban Plan 172:60–68. https://doi.org/10.1016/j.landurbplan.2018.01.001

Pérez-Pons ME, Plaza-Hernández M, Alonso RS et al (2021) Increasing profitability and monitoring environmental performance: a case study in the agri-food industry through an edge-iot platform. Sustain 13:1–16. https://doi.org/10.3390/su13010283

Petti L, Serreli M, Di Cesare S (2018) Systematic literature review in social life cycle assessment. Int J Life Cycle Assess 23:422–431. https://doi.org/10.1007/s11367-016-1135-4

Pieroni MPP, McAloone TC, Pigosso DCA (2019) Business model innovation for circular economy and sustainability: a review of approaches. J Clean Prod 215:198–216. https://doi.org/10.1016/j.jclepro.2019.01.036

Polit DF, Beck CT (2004) Nursing research: principles and methods. Lippincott Williams & Wilkins, Philadelphia, PA

Porkka M, Gerten D, Schaphoff S et al (2016) Causes and trends of water scarcity in food production. Environ Res Lett 11:015001. https://doi.org/10.1088/1748-9326/11/1/015001

Prajapati H, Kant R, Shankar R (2019) Bequeath life to death: state-of-art review on reverse logistics. J Clean Prod 211:503–520. https://doi.org/10.1016/j.jclepro.2018.11.187

Priyadarshini P, Abhilash PC (2020) Policy recommendations for enabling transition towards sustainable agriculture in India. Land Use Policy 96:104718. https://doi.org/10.1016/j.landusepol.2020.104718

Pronti A, Coccia M (2020) Multicriteria analysis of the sustainability performance between agroecological and conventional coffee farms in the East Region of Minas Gerais (Brazil). Renew Agric Food Syst. https://doi.org/10.1017/S1742170520000332

Rabadán A, González-Moreno A, Sáez-Martínez FJ (2019) Improving firms’ performance and sustainability: the case of eco-innovation in the agri-food industry. Sustain 11. https://doi.org/10.3390/su11205590

Raut RD, Luthra S, Narkhede BE et al (2019) Examining the performance oriented indicators for implementing green management practices in the Indian agro sector. J Clean Prod 215:926–943. https://doi.org/10.1016/j.jclepro.2019.01.139

Recanati F, Marveggio D, Dotelli G (2018) From beans to bar: a life cycle assessment towards sustainable chocolate supply chain. Sci Total Environ 613–614:1013–1023. https://doi.org/10.1016/j.scitotenv.2017.09.187

Redclift M (2005) Sustainable development (1987–2005): an oxymoron comes of age. Sustain Dev 13:212–227. https://doi.org/10.1002/sd.281

Rezaei M, Soheilifard F, Keshvari A (2021) Impact of agrochemical emission models on the environmental assessment of paddy rice production using life cycle assessment approach. Energy Sources. Part A Recover Util Environ Eff 1–16

Rigamonti L, Mancini E (2021) Life cycle assessment and circularity indicators. Int J Life Cycle Assess. https://doi.org/10.1007/s11367-021-01966-2

Risku-Norja H, Mäenpää I (2007) MFA model to assess economic and environmental consequences of food production and consumption. Ecol Econ 60:700–711. https://doi.org/10.1016/j.ecolecon.2006.05.001

Ritzén S, Sandström GÖ (2017) Barriers to the circular economy – integration of perspectives and domains. Procedia CIRP 64:7–12. https://doi.org/10.1016/j.procir.2017.03.005

Rockström J, Steffen W, Noone K et al (2009) A safe operating space for humanity. Nature 461:472–475. https://doi.org/10.1038/461472a

Roos Lindgreen E, Mondello G, Salomone R et al (2021) Exploring the effectiveness of grey literature indicators and life cycle assessment in assessing circular economy at the micro level: a comparative analysis. Int J Life Cycle Assess. https://doi.org/10.1007/s11367-021-01972-4

Roselli L, Casieri A, De Gennaro BC et al (2020) Environmental and economic sustainability of table grape production in Italy. Sustain 12.  https://doi.org/10.3390/su12093670

Ross RB, Pandey V, Ross KL (2015) Sustainability and strategy in U.S. agri-food firms: an assessment of current practices. Int Food Agribus Manag Rev 18:17–48

Royo P, Ferreira VJ, López-Sabirón AM, Ferreira G. (2016) Hybrid diagnosis to characterise the energy and environmental enhancement of photovoltaic modules using smart materials. Energy 101:174–189. https://doi.org/10.1016/j.energy.2016.01.101

Ruggerio CA (2021) Sustainability and sustainable development: a review of principles and definitions. Sci Total Environ 786:147481. https://doi.org/10.1016/j.scitotenv.2021.147481

Ruiz-Almeida A, Rivera-Ferre MG (2019) Internationally-based indicators to measure agri-food systems sustainability using food sovereignty as a conceptual framework. Food Secur 11:1321–1337. https://doi.org/10.1007/s12571-019-00964-5

Ryan M, Hennessy T, Buckley C et al (2016) Developing farm-level sustainability indicators for Ireland using the Teagasc National Farm Survey. Irish J Agric Food Res 55:112–125. https://doi.org/10.1515/ijafr-2016-0011

Saade MRM, Yahia A, Amor B (2020) How has LCA been applied to 3D printing ? A systematic literature review and recommendations for future studies. J Clean Prod 244:118803. https://doi.org/10.1016/j.jclepro.2019.118803

Saitone TL, Sexton RJ (2017) Agri-food supply chain: evolution and performance with conflicting consumer and societal demands. Eur Rev Agric Econ 44:634–657. https://doi.org/10.1093/erae/jbx003

Salim N, Ab Rahman MN, Abd Wahab D (2019) A systematic literature review of internal capabilities for enhancing eco-innovation performance of manufacturing firms. J Clean Prod 209:1445–1460. https://doi.org/10.1016/j.jclepro.2018.11.105

Salimi N (2021) Circular economy in agri-food systems BT - strategic decision making for sustainable management of industrial networks. In: International S (ed) Rezaei J. Publishing, Cham, pp 57–70

Salomone R, Ioppolo G (2012) Environmental impacts of olive oil production: a life cycle assessment case study in the province of Messina (Sicily). J Clean Prod 28:88–100. https://doi.org/10.1016/j.jclepro.2011.10.004

Sánchez AD, Río DMDLC, García JÁ (2017) Bibliometric analysis of publications on wine tourism in the databases Scopus and WoS. Eur Res Manag Bus Econ 23:8–15. https://doi.org/10.1016/j.iedeen.2016.02.001

Saputri VHL, Sutopo W, Hisjam M, Ma’aram A (2019) Sustainable agri-food supply chain performance measurement model for GMO and non-GMO using data envelopment analysis method. Appl Sci 9. https://doi.org/10.3390/app9061199

Sassanelli C, Rosa P, Rocca R, Terzi S (2019) Circular economy performance assessment methods : a systematic literature review. J Clean Prod 229:440–453. https://doi.org/10.1016/j.jclepro.2019.05.019

Schiefer S, Gonzalez C, Flanigan S (2015) More than just a factor in transition processes? The role of collaboration in agriculture. In: Sutherland LA, Darnhofer I, Wilson GA, Zagata L (eds) Transition pathways towards sustainability in agriculture: case studies from Europe, CPI Group. Croydon, UK, pp. 83

Seuring S, Muller M (2008) From a literature review to a conceptual framework for sustainable supply chain management. J Clean Prod 16:1699–1710. https://doi.org/10.1016/j.jclepro.2008.04.020

Silvestri C, Silvestri L, Forcina A, et al (2021) Green chemistry contribution towards more equitable global sustainability and greater circular economy: A systematic literature review. J Clean Prod 294. https://doi.org/10.1016/j.jclepro.2021.126137

Smetana S, Schmitt E, Mathys A (2019) Sustainable use of Hermetia illucens insect biomass for feed and food: attributional and consequential life cycle assessment. Resour Conserv Recycl 144:285–296. https://doi.org/10.1016/j.resconrec.2019.01.042

Sonesson U, Berlin J, Ziegler F (2010) Environmental assessment and management in the food industry: life cycle assessment and related approaches. Woodhead Publishing, Cambridge

Soussana JF (2014) Research priorities for sustainable agri-food systems and life cycle assessment. J Clean Prod 73:19–23. https://doi.org/10.1016/j.jclepro.2014.02.061

Soylu A, Oruç C, Turkay M et al (2006) Synergy analysis of collaborative supply chain management in energy systems using multi-period MILP. Eur J Oper Res 174:387–403. https://doi.org/10.1016/j.ejor.2005.02.042

Spaiser V, Ranganathan S, Swain RB, Sumpter DJ (2017) The sustainable development oxymoron: quantifying and modelling the incompatibility of sustainable development goals. Int J Sustain Dev World Ecol 24:457–470. https://doi.org/10.1080/13504509.2016.1235624

Stewart R, Niero M (2018) Circular economy in corporate sustainability strategies: a review of corporate sustainability reports in the fast-moving consumer goods sector. Bus Strateg Environ 27:1005–1022. https://doi.org/10.1002/bse.2048

Stillitano T, Spada E, Iofrida N et al (2021) Sustainable agri-food processes and circular economy pathways in a life cycle perspective: state of the art of applicative research. Sustain 13:1–29. https://doi.org/10.3390/su13052472

Stone J, Rahimifard S (2018) Resilience in agri-food supply chains: a critical analysis of the literature and synthesis of a novel framework. Supply Chain Manag 23:207–238. https://doi.org/10.1108/SCM-06-2017-0201

Strazza C, Del Borghi A, Gallo M, Del Borghi M (2011) Resource productivity enhancement as means for promoting cleaner production: analysis of co-incineration in cement plants through a life cycle approach. J Clean Prod 19:1615–1621. https://doi.org/10.1016/j.jclepro.2011.05.014

Su B, Heshmati A, Geng Y, Yu X (2013) A review of the circular economy in China: moving from rhetoric to implementation. J Clean Prod 42:215–227. https://doi.org/10.1016/j.jclepro.2012.11.020

Suárez-Eiroa B, Fernández E, Méndez-Martínez G, Soto-Oñate D (2019) Operational principles of circular economy for sustainable development: linking theory and practice. J Clean Prod 214:952–961. https://doi.org/10.1016/j.jclepro.2018.12.271

Svensson G, Wagner B (2015) Implementing and managing economic, social and environmental efforts of business sustainability. Manag Environ Qual an Int Journal 26:195–213. https://doi.org/10.1108/MEQ-09-2013-0099

Tasca AL, Nessi S, Rigamonti L (2017) Environmental sustainability of agri-food supply chains: an LCA comparison between two alternative forms of production and distribution of endive in northern Italy. J Clean Prod 140:725–741. https://doi.org/10.1016/j.jclepro.2016.06.170

Tassielli G, Notarnicola B, Renzulli PA, Arcese G (2018) Environmental life cycle assessment of fresh and processed sweet cherries in southern Italy. J Clean Prod 171:184–197. https://doi.org/10.1016/j.jclepro.2017.09.227

Teixeira R, Pax S (2011) A survey of life cycle assessment practitioners with a focus on the agri-food sector. J Ind Ecol 15:817–820. https://doi.org/10.1111/j.1530-9290.2011.00421.x

Tobergte DR, Curtis S (2013) ILCD Handbook. J Chem Info Model. https://doi.org/10.278/33030

Tortorella MM, Di Leo S, Cosmi C et al (2020) A methodological integrated approach to analyse climate change effects in agri-food sector: the TIMES water-energy-food module. Int J Environ Res Public Health 17:1–21. https://doi.org/10.3390/ijerph17217703

Tranfield D, Denyer D, Smart P (2003) Towards a methodology for developing evidenceinformed management knowledge by means of systematic review. Br J Manag 14:207–222

Trivellas P, Malindretos G, Reklitis P (2020) Implications of green logistics management on sustainable business and supply chain performance: evidence from a survey in the greek agri-food sector. Sustain 12:1–29. https://doi.org/10.3390/su122410515

Tsangas M, Gavriel I, Doula M et al (2020) Life cycle analysis in the framework of agricultural strategic development planning in the Balkan region. Sustain 12:1–15. https://doi.org/10.3390/su12051813

Ülgen VS, Björklund M, Simm N (2019) Inter-organizational supply chain interaction for sustainability : a systematic literature review.

UNEP S (2020) Guidelines for social life cycle assessment of products and organizations 2020.

UNEP/SETAC (2009) United Nations Environment Programme-society of Environmental Toxicology and Chemistry. Guidelines for social life cycle assessment of products. France

United Nations (2011) Guiding principles on business and human rights. Implementing the United Nations “protect, respect and remedy” framework

United Nations (2015) Transforming our world: the 2030 agenda for sustainable development. sustainabledevelopment.un.org

Van Asselt ED, Van Bussel LGJ, Van Der Voet H et al (2014) A protocol for evaluating the sustainability of agri-food production systems - a case study on potato production in peri-urban agriculture in the Netherlands. Ecol Indic 43:315–321. https://doi.org/10.1016/j.ecolind.2014.02.027

Van der Ploeg JD (2014) Peasant-driven agricultural growth and food sovereignty. J Peasant Stud 41:999–1030. https://doi.org/10.1080/03066150.2013.876997

van Eck NJ, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84:523–538. https://doi.org/10.1007/s11192-009-0146-3

Van Eck NJ, Waltman L (2019) Manual for VOSviwer version 1.6.10. CWTS Meaningful metrics 1–53

Vasa L, Angeloska A, Trendov NM (2017) Comparative analysis of circular agriculture development in selected Western Balkan countries based on sustainable performance indicators. Econ Ann 168:44–47. https://doi.org/10.21003/ea.V168-09

Verdecho MJ, Alarcón-Valero F, Pérez-Perales D et al (2020) A methodology to select suppliers to increase sustainability within supply chains. Cent Eur J Oper Res. https://doi.org/10.1007/s10100-019-00668-3

Vergine P, Salerno C, Libutti A et al (2017) Closing the water cycle in the agro-industrial sector by reusing treated wastewater for irrigation. J Clean Prod 164:587–596. https://doi.org/10.1016/j.jclepro.2017.06.239

WCED (1987) Our common future - call for action

Webster K (2013) What might we say about a circular economy? Some temptations to avoid if possible. World Futures 69:542–554

Wheaton E, Kulshreshtha S (2013) Agriculture and climate change: implications for environmental sustainability indicators. WIT Trans Ecol Environ 175:99–110. https://doi.org/10.2495/ECO130091

Wijewickrama MKCS, Chileshe N, Rameezdeen R, Ochoa JJ (2021) Information sharing in reverse logistics supply chain of demolition waste: a systematic literature review. J Clean Prod 280:124359. https://doi.org/10.1016/j.jclepro.2020.124359

Woodhouse A, Davis J, Pénicaud C, Östergren K (2018) Sustainability checklist in support of the design of food processing. Sustain Prod Consum 16:110–120. https://doi.org/10.1016/j.spc.2018.06.008

Wu R, Yang D, Chen J (2014) Social Life Cycle Assessment Revisited Sustain 6:4200–4226. https://doi.org/10.3390/su6074200

Yadav S, Luthra S, Garg D (2021) Modelling Internet of things (IoT)-driven global sustainability in multi-tier agri-food supply chain under natural epidemic outbreaks. Environ Sci Pollut Res 16633–16654. https://doi.org/10.1007/s11356-020-11676-1

Yee FM, Shaharudin MR, Ma G et al (2021) Green purchasing capabilities and practices towards Firm’s triple bottom line in Malaysia. J Clean Prod 307:127268. https://doi.org/10.1016/j.jclepro.2021.127268

Yigitcanlar T (2010) Rethinking sustainable development: urban management, engineering, and design. IGI Global

Zamagni A, Amerighi O, Buttol P (2011) Strengths or bias in social LCA? Int J Life Cycle Assess 16:596–598. https://doi.org/10.1007/s11367-011-0309-3

Download references

Author information

Authors and affiliations.

Department of Economy, Engineering, Society and Business Organization, University of “Tuscia, ” Via del Paradiso 47, 01100, Viterbo, VT, Italy

Cecilia Silvestri, Michela Piccarozzi & Alessandro Ruggieri

Department of Engineering, University of Rome “Niccolò Cusano, ” Via Don Carlo Gnocchi, 3, 00166, Rome, Italy

Luca Silvestri

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Cecilia Silvestri .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Communicated by Monia Niero

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original online version of this article was revised: a number of ill-placed paragraph headings were removed and the source indication "Authors' elaborations" was added to Tables 1-3.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 31 KB)

Rights and permissions.

Reprints and permissions

About this article

Silvestri, C., Silvestri, L., Piccarozzi, M. et al. Toward a framework for selecting indicators of measuring sustainability and circular economy in the agri-food sector: a systematic literature review. Int J Life Cycle Assess (2022). https://doi.org/10.1007/s11367-022-02032-1

Download citation

Received : 15 June 2021

Accepted : 16 February 2022

Published : 02 March 2022

DOI : https://doi.org/10.1007/s11367-022-02032-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Agri-food sector
• Sustainability
• Circular economy
• Triple bottom line
• Life cycle assessment
• Find a journal
• Publish with us
• Track your research

• Research article
• Open access
• Published: 15 April 2024

What is quality in long covid care? Lessons from a national quality improvement collaborative and multi-site ethnography

• Trisha Greenhalgh   ORCID: orcid.org/0000-0003-2369-8088 1 ,
• Julie L. Darbyshire 1 ,
• Cassie Lee 2 ,
• Emma Ladds 1 &
• Jenny Ceolta-Smith 3  

BMC Medicine volume  22 , Article number:  159 ( 2024 ) Cite this article

822 Accesses

55 Altmetric

Metrics details

Long covid (post covid-19 condition) is a complex condition with diverse manifestations, uncertain prognosis and wide variation in current approaches to management. There have been calls for formal quality standards to reduce a so-called “postcode lottery” of care. The original aim of this study—to examine the nature of quality in long covid care and reduce unwarranted variation in services—evolved to focus on examining the reasons why standardizing care was so challenging in this condition.

In 2021–2023, we ran a quality improvement collaborative across 10 UK sites. The dataset reported here was mostly but not entirely qualitative. It included data on the origins and current context of each clinic, interviews with staff and patients, and ethnographic observations at 13 clinics (50 consultations) and 45 multidisciplinary team (MDT) meetings (244 patient cases). Data collection and analysis were informed by relevant lenses from clinical care (e.g. evidence-based guidelines), improvement science (e.g. quality improvement cycles) and philosophy of knowledge.

Participating clinics made progress towards standardizing assessment and management in some topics; some variation remained but this could usually be explained. Clinics had different histories and path dependencies, occupied a different place in their healthcare ecosystem and served a varied caseload including a high proportion of patients with comorbidities. A key mechanism for achieving high-quality long covid care was when local MDTs deliberated on unusual, complex or challenging cases for which evidence-based guidelines provided no easy answers. In such cases, collective learning occurred through idiographic (case-based) reasoning , in which practitioners build lessons from the particular to the general. This contrasts with the nomothetic reasoning implicit in evidence-based guidelines, in which reasoning is assumed to go from the general (e.g. findings of clinical trials) to the particular (management of individual patients).

Not all variation in long covid services is unwarranted. Largely because long covid’s manifestations are so varied and comorbidities common, generic “evidence-based” standards require much individual adaptation. In this complex condition, quality improvement resources may be productively spent supporting MDTs to optimise their case-based learning through interdisciplinary discussion. Quality assessment of a long covid service should include review of a sample of individual cases to assess how guidelines have been interpreted and personalized to meet patients’ unique needs.

Study registration

NCT05057260, ISRCTN15022307.

Peer Review reports

The term “long covid” [ 1 ] means prolonged symptoms following SARS-CoV-2 infection not explained by an alternative diagnosis [ 2 ]. It embraces the US term “post-covid conditions” (symptoms beyond 4 weeks) [ 3 ], the UK terms “ongoing symptomatic covid-19” (symptoms lasting 4–12 weeks) and “post covid-19 syndrome” (symptoms beyond 12 weeks) [ 4 ] and the World Health Organization’s “post covid-19 condition” (symptoms occurring beyond 3 months and persisting for at least 2 months) [ 5 ]. Long covid thus defined is extremely common. In UK, for example, 1.8 million of a population of 67 million met the criteria for long covid in early 2023 and 41% of these had been unwell for more than 2 years [ 6 ].

Long covid is characterized by a constellation of symptoms which may include breathlessness, fatigue, muscle and joint pain, chest pain, memory loss and impaired concentration (“brain fog”), sleep disturbance, depression, anxiety, palpitations, dizziness, gastrointestinal problems such as diarrhea, skin rashes and allergy to food or drugs [ 2 ]. These lead to difficulties with essential daily activities such as washing and dressing, impaired exercise tolerance and ability to work, and reduced quality of life [ 2 , 7 , 8 ]. Symptoms typically cluster (e.g. in different patients, long covid may be dominated by fatigue, by breathlessness or by palpitations and dizziness) [ 9 , 10 ]. Long covid may follow a fairly constant course or a relapsing and remitting one, perhaps with specific triggers [ 11 ]. Overlaps between fatigue-dominant subtypes of long covid, myalgic encephalomyelitis and chronic fatigue syndrome have been hypothesized [ 12 ] but at the time of writing remain unproven.

Long covid has been a contested condition from the outset. Whilst long-term sequelae following other coronavirus (SARS and MERS) infections were already well-documented [ 13 ], SARS-CoV-2 was originally thought to cause a short-lived respiratory illness from which the patient either died or recovered [ 14 ]. Some clinicians dismissed protracted or relapsing symptoms as due to anxiety or deconditioning, especially if the patient had not had laboratory-confirmed covid-19. People with long covid got together in online groups and shared accounts of their symptoms and experiences of such “gaslighting” in their healthcare encounters [ 15 , 16 ]. Some groups conducted surveys on their members, documenting the wide range of symptoms listed in the previous paragraph and showing that whilst long covid is more commonly a sequel to severe acute covid-19, it can (rarely) follow a mild or even asymptomatic acute infection [ 17 ].

Early publications on long covid depicted a post-pneumonia syndrome which primarily affected patients who had been hospitalized (and sometimes ventilated) [ 18 , 19 ]. Later, covid-19 was recognized to be a multi-organ inflammatory condition (the pneumonia, for example, was reclassified as pneumonitis ) and its long-term sequelae attributed to a combination of viral persistence, dysregulated immune response (including auto-immunity), endothelial dysfunction and immuno-thrombosis, leading to damage to the lining of small blood vessels and (thence) interference with transfer of oxygen and nutrients to vital organs [ 20 , 21 , 22 , 23 , 24 ]. But most such studies were highly specialized, laboratory-based and written primarily for an audience of fellow laboratory researchers. Despite demonstrating mean differences in a number of metabolic variables, they failed to identify a reliable biomarker that could be used routinely in the clinic to rule a diagnosis of long covid in or out. Whilst the evidence base from laboratory studies grew rapidly, it had little influence on clinical management—partly because most long covid clinics had been set up with impressive speed by front-line clinical teams to address an immediate crisis, with little or no input from immunologists, virologists or metabolic specialists [ 25 ].

Studies of the patient experience revealed wide geographical variation in whether any long covid services were provided and (if they were) which patients were eligible for these and what tests and treatments were available [ 26 ]. An interim UK clinical guideline for long covid had been produced at speed and published in December 2020 [ 27 ], but it was uncertain about diagnostic criteria, investigations, treatments and prognosis. Early policy recommendations for long covid services in England, based on wide consultation across UK, had proposed a tiered service with “tier 1” being supported self-management, “tier 2” generalist assessment and management in primary care, “tier 3” specialist rehabilitation or respiratory follow-up with oversight from a consultant physician and “tier 4” tertiary care for patients with complications or complex needs [ 28 ]. In 2021, ring-fenced funding was allocated to establish 90 multidisciplinary long covid clinics in England [ 29 ]; some clinics were also set up with local funding in Scotland and Wales. These clinics varied widely in eligibility criteria, referral pathways, staffing mix (some had no doctors at all) and investigations and treatments offered. A further policy document on improving long covid services was published in 2022 [ 30 ]; it recommended that specialist long covid clinics should continue, though the long-term funding of these services remains uncertain [ 31 ]. To build the evidence base for delivering long covid services, major programs of publicly funded research were commenced in both UK [ 32 ] and USA [ 33 ].

In short, at the time this study began (late 2021), there appeared to be much scope for a program of quality improvement which would capture fast-emerging research findings, establish evidence-based standards and ensure these were rapidly disseminated and consistently adopted across both specialist long covid services and in primary care.

Quality improvement collaboratives

The quality improvement movement in healthcare was born in the early 1980s when clinicians and policymakers US and UK [ 34 , 35 , 36 , 37 ] began to draw on insights from outside the sector [ 38 , 39 , 40 ]. Adapting a total quality management approach that had previously transformed the Japanese car industry, they sought to improve efficiency, reduce waste, shift to treating the upstream causes of problems (hence preventing disease) and help all services approach the standards of excellence achieved by the best. They developed an approach based on (a) understanding healthcare as a complex system (especially its key interdependencies and workflows), (b) analysing and addressing variation within the system, (c) learning continuously from real-world data and (d) developing leaders who could motivate people and help them change structures and processes [ 41 , 42 , 43 , 44 ].

Quality improvement collaboratives (originally termed “breakthrough collaboratives” [ 45 ]), in which representatives from different healthcare organizations come together to address a common problem, identify best practice, set goals, share data and initiate and evaluate improvement efforts [ 46 ], are one model used to deliver system-wide quality improvement. It is widely assumed that these collaboratives work because—and to the extent that—they identify, interpret and implement high-quality evidence (e.g. from randomized controlled trials).

Research on why quality improvement collaboratives succeed or fail has produced the following list of critical success factors: taking a whole-system approach, selecting a topic and goal that fits with organizations’ priorities, fostering a culture of quality improvement (e.g. that quality is everyone’s job), engagement of everyone (including the multidisciplinary clinical team, managers, patients and families) in the improvement effort, clearly defining people’s roles and contribution, engaging people in preliminary groundwork, providing organizational-level support (e.g. chief executive endorsement, protected staff time, training and support for teams, resources, quality-focused human resource practices, external facilitation if needed), training in specific quality improvement techniques (e.g. plan-do-study-act cycle), attending to the human dimension (including cultivating trust and working to ensure shared vision and buy-in), continuously generating reliable data on both processes (e.g. current practice) and outcomes (clinical, satisfaction) and a “learning system” infrastructure in which knowledge that is generated feeds into individual, team and organizational learning [ 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ].

The quality improvement collaborative approach has delivered many successes but it has been criticized at a theoretical level for over-simplifying the social science of human motivation and behaviour and for adopting a somewhat mechanical approach to the study of complex systems [ 55 , 56 ]. Adaptations of the original quality improvement methodology (e.g. from Sweden [ 57 , 58 ]) have placed greater emphasis on human values and meaning-making, on the grounds that reducing the complexities of a system-wide quality improvement effort to a set of abstract and generic “success factors” will miss unique aspects of the case such as historical path dependencies, personalities, framing and meaning-making and micropolitics [ 59 ].

Perhaps this explains why, when the abovementioned factors are met, a quality improvement collaborative’s success is more likely but is not guaranteed, as a systematic review demonstrated [ 60 ]. Some well-designed and well-resourced collaboratives addressing clear knowledge gaps produced few or no sustained changes in key outcome measures [ 49 , 53 , 60 , 61 , 62 ]. To identify why this might be, a detailed understanding of a service’s history, current challenges and contextual constraints is needed. This explains our decision, part-way through the study reported here, to collect rich contextual data on participating sites so as to better explain success or failure of our own collaborative.

Warranted and unwarranted variation in clinical practice

A generation ago, Wennberg described most variation in clinical practice as “unwarranted” (which he defined as variation in the utilization of health care services that cannot be explained by variation in patient illness or patient preferences) [ 63 ]. Others coined the term “postcode lottery” to depict how such variation allegedly impacted on health outcomes [ 64 ]. Wennberg and colleagues’ Atlas of Variation , introduced in 1999 [ 65 ], and its UK equivalent, introduced in 2010 [ 66 ], described wide regional differences in the rates of procedures from arthroscopy to hysterectomy, and were used to prompt services to identify and address examples of under-treatment, mis-treatment and over-treatment. Numerous similar initiatives, mostly based on hospital activity statistics, have been introduced around the world [ 66 , 67 , 68 , 69 ]. Sutherland and Levesque’s proposed framework for analysing variation, for example, has three domains: capacity (broadly, whether sufficient resources are allocated at organizational level and whether individuals have the time and headspace to get involved), evidence (the extent to which evidence-based guidelines exist and are followed), and agency (e.g. whether clinicians are engaged with the issue and the effect of patient choice) [ 70 ].

Whilst it is clearly a good idea to identify unwarranted variation in practice, it is also important to acknowledge that variation can be warranted . The very act of measuring and describing variation carries great rhetorical power, since revealing geographical variation in any chosen metric effectively frames this as a problem with a conceptually simple solution (reducing variation) that will appeal to both politicians and the public [ 71 ]. The temptation to expose variation (e.g. via visualizations such as maps) and address it in mechanistic ways should be resisted until we have fully understood the reasons why it exists, which may include perverse incentives, insufficient opportunities to discuss cases with colleagues, weak or absent feedback on practice, unclear decision processes, contested definitions of appropriate care and professional challenges to guidelines [ 72 ].

Research question, aims and objectives

Research question.

What is quality in long covid care and how can it best be achieved?

To identify best practice and reduce unwarranted variation in UK long covid services.

To explain aspects of variation in long covid services that are or may be warranted.

Our original objectives were to:

Establish a quality improvement collaborative for 10 long covid clinics across UK.

Use quality improvement methods in collaboration with patients and clinic staff to prioritize aspects of care to improve. For each priority topic, identify best (evidence-informed) clinical practice, measure performance in each clinic, compare performance with a best practice benchmark and improve performance.

Produce organizational case studies of participating long covid clinics to explain their origins, evolution, leadership, ethos, population served, patient pathways and place in the wider healthcare ecosystem.

Examine these case studies to explain variation in practice, especially in topics where the quality improvement cycle proves difficult to follow or has limited impact.

The LOCOMOTION study

LOCOMOTION (LOng COvid Multidisciplinary consortium Optimising Treatments and services across the NHS) was a 30-month multi-site case study of 10 long covid clinics (8 in England, 1 in Wales and 1 in Scotland), beginning in 2021, which sought to optimise long covid care. Each clinic offered multidisciplinary care to patients referred from primary or secondary care (and, in some cases, self-referred), and held regular multidisciplinary team (MDT) meetings, mostly online via Microsoft Teams, to discuss cases. A study protocol for LOCOMOTION, with details of ethical approvals, management, governance and patient involvement has been published [ 25 ]. The three main work packages addressed quality improvement, technology-supported patient self-management and phenotyping and symptom clustering. This paper reports on the first work package, focusing mainly on qualitative findings.

Setting up the quality improvement collaborative

We broadly followed standard methodology for “breakthrough” quality improvement collaboratives [ 44 , 45 ], with two exceptions. First, because of geographical distance, continuing pandemic precautions and developments in videoconferencing technology, meetings were held online. Second, unlike in the original breakthrough model, patients were included in the collaborative, reflecting the cultural change towards patient partnerships since the model was originally proposed 40 years ago.

Each site appointed a clinical research fellow (doctor, nurse or allied health professional) funded partly by the LOCOMOTION study and partly with clinical sessions; some were existing staff who were backfilled to take on a research role whilst others were new appointments. The quality improvement meetings were held approximately every 8 weeks on Microsoft Teams and lasted about 2 h; there was an agenda and a chair, and meetings were recorded with consent. The clinical research fellow from each clinic attended, sometimes joined by the clinical lead for that site. In the initial meeting, the group proposed and prioritized topics before merging their consensus with the list of priority topics generated separately by patients (there was much overlap but also some differences).

In subsequent meetings, participants attempted to reach consensus on how to define, measure and achieve quality for each priority topic in turn, implement this approach in their own clinic and monitor its impact. Clinical leads prepared illustrative clinical cases and summaries of the research evidence, which they presented using Microsoft Powerpoint; the group then worked towards consensus on the implications for practice through general discussion. Clinical research fellows assisted with literature searches, collected baseline data from their own clinic, prepared and presented anonymized case examples, and contributed to collaborative goal-setting for improvement. Progress on each topic was reviewed at a later meeting after an agreed interval.

An additional element of this work package was semi-structured interviews with 29 patients, recruited from 9 of the 10 participating sites, about their clinic experiences with a view to feeding into service improvement (in the other site, no patient volunteered).

Our patient advisory group initially met separately from the quality improvement collaborative. They designed a short survey of current practice and sent it to each clinic; the results of this informed a prioritization exercise for topics where they considered change was needed. The patient-generated list was tabled at the quality improvement collaborative discussions, but patients were understandably keen to join these discussions directly. After about 9 months, some patient advisory group members joined the regular collaborative meetings. This dynamic was not without its tensions, since sharing performance data requires trust and there were some concerns about confidentiality when real patient cases were discussed with other patients present.

How evidence-informed quality targets were set

At the time the study began, there were no published large-scale randomized controlled trials of any interventions for long covid. We therefore followed a model used successfully in other quality improvement efforts where research evidence was limited or absent or it did not translate unambiguously into models for current services. In such circumstances, the best evidence may be custom and practice in the best-performing units. The quality improvement effort becomes oriented to what one group of researchers called “potentially better practices”—that is, practices that are “developed through analysis of the processes of care, literature review, and site visits” (page 14) [ 73 ]. The idea was that facilitated discussion among clinical teams, drawing on published research where available but also incorporating clinical experience, established practice and systematic analysis of performance data across participating clinics would surface these “potentially better practices”—an approach which, though not formally tested in controlled trials, appears to be associated with improved outcomes [ 46 , 73 ].

Adding an ethnographic component

Following limited progress made on some topics that had been designated high priority, we interviewed all 10 clinical research fellows (either individually or, in two cases, with a senior clinician present) and 18 other clinic staff (five individually plus two groups of 5 and 8), along with additional informal discussions, to explore the challenges of implementing the changes that had been agreed. These interviews were not audiotaped but detailed notes were made and typed up immediately afterwards. It became evident that some aspects of what the collaborative had deemed “evidence-informed” care were contested by front-line clinic staff, perceived as irrelevant to the service they were delivering, or considered impossible to implement. To unpack these issues further, the research protocol was amended to include an ethnographic component.

TG and EL (academic general practitioners) and JLD (a qualitative researcher with a PhD in the patient experience) attended a total of 45 MDT meetings in participating clinics (mostly online or hybrid). Staff were informed in advance that there would be an observer present; nobody objected. We noted brief demographic and clinical details of cases discussed (but no identifying data), dilemmas and uncertainties on which discussions focused, and how different staff members contributed.

TG made 13 in-person visits to participating long covid clinics. Staff were notified in advance; all were happy to be observed. Visits lasted between 5 and 8 h (54 h in total). We observed support staff booking patients in and processing requests and referrals, and shadowed different clinical staff in turn as they saw patients. Patients were informed of our presence and its purpose beforehand and given the opportunity to decline (three of 53 patients approached did). We discussed aspects of each case with the clinician after the patient left. When invited, we took breaks with staff and used these as an opportunity to ask them informally what it was like working in the clinic.

Ethnographic observation, analysis and reporting was geared to generating a rich interpretive account of the clinical, operational and interpersonal features of each clinic—what Van Maanen calls an “impressionist tales” [ 74 ]. Our work was also guided by the principles set out by Golden-Biddle and Locke, namely authenticity (spending time in the field and basing interpretations on these direct observations), plausibility (creating a plausible account through rich persuasive description) and criticality (e.g. reflexively examining our own assumptions) [ 75 ]. Our collection and analysis of qualitative data was informed by our own professional backgrounds (two general practitioners, one physical therapist, two non-clinicians).

In both MDTs and clinics, we took contemporaneous notes by hand and typed these up immediately afterwards.

Data management and analysis

Typed interview notes and field notes from clinics were collated in a set of Word documents, one for each clinic attended. They were analysed thematically [ 76 ] with attention to the literature on quality improvement and variation (see “ Background ”). Interim summaries were prepared on each clinic, setting out the narrative of how it had been established, its ethos and leadership, setting and staffing, population served and key links with other parts of the local healthcare ecosystem.

Minutes and field notes from the quality improvement collaborative meetings were summarized topic by topic, including initial data collected by the researchers-in-residence, improvement actions taken (or attempted) in that clinic, and any follow-up data shared. Progress or lack of it was interpreted in relation to the contextual case summary for that clinic.

Patient cases seen in clinic, and those discussed by MDTs, were summarized as brief case narratives in Word documents. Using the constant comparative method [ 77 ], we produced an initial synthesis of the clinical picture and principles of management based on the first 10 patient cases seen, and refined this as each additional case was added. Demographic and brief clinical and social details were also logged on Excel spreadsheets. When writing up clinical cases, we used the technique of composite case construction (in which we drew on several actual cases to generate a fictitious one, thereby protecting anonymity whilst preserving key empirical findings [ 78 ]); any names reported in this paper are pseudonyms.

Member checking

A summary was prepared for each clinic, including a narrative of the clinic’s own history and a summary of key quality issues raised across the ten clinics. These summaries included examples from real cases in our dataset. These were shared with the clinical research fellow and a senior clinician from the clinic, and amended in response to feedback. We also shared these summaries with representatives from the patient advisory group.

Overview of dataset

This study generated three complementary datasets. First, the video recordings, minutes, and field notes of 12 quality improvement collaborative meetings, along with the evidence summaries prepared for these meetings and clinic summaries (e.g. descriptions of current practice, audits) submitted by the clinical research fellows. This dataset illustrated wide variation in practice, and (in many topics) gaps or ambiguities in the evidence base.

Second, interviews with staff ( n  = 30) and patients ( n  = 29) from the clinics, along with ethnographic field notes (approximately 100 pages) from 13 in-person clinic visits (54 h), including notes on 50 patient consultations (40 face-to-face, 6 telephone, 4 video). This dataset illustrated the heterogeneity among the ten participating clinics.

Third, field notes (approximately 100 pages), including discussions on 244 clinical cases from the 45 MDT meetings (49 h) that we observed. This dataset revealed further similarities and contrasts among clinics in how patients were managed. In particular, it illustrated how, for the complex patients whose cases were presented at these meetings, teams made sense of, and planned for, each case through multidisciplinary dialogue. This dialogue typically began with one staff member presenting a detailed clinical history along with a narrative of how it had affected the patient’s life and what was at stake for them (e.g. job loss), after which professionals from various backgrounds (nursing, physical therapy, occupational therapy, psychology, dietetics, and different medical specialties) joined in a discussion about what to do.

The ten participating sites are summarized in Table  1 .

In the next two sections, we explore two issues—difficulty defining best practice and the heterogeneous nature of the clinics—that were key to explaining why quality, when pursued in a 10-site collaborative, proved elusive. We then briefly summarize patients’ accounts of their experience in the clinics and give three illustrative examples of the elusiveness of quality improvement using selected topics that were prioritized in our collaborative: outcome measures, investigation of palpitations and management of fatigue. In the final section of the results, we describe how MDT deliberations proved crucial for local quality improvement. Further detail on clinical priority topics will be presented in a separate paper.

“Best practice” in long covid: uncertainty and conflict

The study period (September 2021 to December 2023) corresponded with an exponential increase in published research on long covid. Despite this, the quality improvement collaborative found few unambiguous recommendations for practice. This gap between what the research literature offered and what clinical practice needed was partly ontological (relating what long covid is ). One major bone of contention between patients and clinicians (also evident in discussions with our patient advisory group), for example, was how far (and in whom) clinicians should look for and attempt to treat the various metabolic abnormalities that had been documented in laboratory research studies. The literature on this topic was extensive but conflicting [ 20 , 21 , 22 , 23 , 24 , 79 , 80 , 81 , 82 ]; it was heavy on biological detail but light on clinical application.

Patients were often aware of particular studies that appeared to offer plausible molecular or cellular explanations for symptom clusters along with a drug (often repurposed and off-label) whose mechanism of action appeared to be a good fit with the metabolic chain of causation. In one clinic, for example, we were shown an email exchange between a patient (not medically qualified) and a consultant, in which the patient asked them to reconsider their decision not to prescribe low-dose naltrexone, an opioid receptor antagonist with anti-inflammatory properties. The request included a copy of a peer-reviewed academic paper describing a small, uncontrolled pre-post study (i.e. a weak study design) in which this drug appeared to improve symptoms and functional performance in patients with long covid, as well as a mechanistic argument explaining why the patient felt this drug was a plausible choice in their own case.

This patient’s clinician, in common with most clinicians delivering front-line long covid services, considered that the evidence for such mechanism-based therapies was weak. Clinicians generally felt that this evidence, whilst promising, did not yet support routine measurement of clotting factors, antibodies, immune cells or other biomarkers or the prescription of mechanism-based therapies such as antivirals, anti-inflammatories or anticoagulants. Low-dose naltroxone, for example, is currently being tested in at least one randomized controlled trial (see National Clinical Trials Registry NCT05430152), which had not reported at the time of our observations.

Another challenge to defining best practice was the oft-repeated phrase that long covid is a “diagnosis by exclusion”, but the high prevalence of comorbidities meant that the “pure” long covid patient untainted by other potential explanations for their symptoms was a textbook ideal. In one MDT, for example, we observed a discussion about a patient who had had both swab-positive covid-19 and erythema migrans (a sign of Lyme disease) in the weeks before developing fatigue, yet local diagnostic criteria for each condition required the other to be excluded.

The logic of management in most participating clinics was pragmatic: prompt multidisciplinary assessment and treatment with an emphasis on obtaining a detailed clinical history (including premorbid health status), excluding serious complications (“red flags”), managing specific symptom clusters (for example, physical therapy for breathing pattern disorder), treating comorbidities (for example, anaemia, diabetes or menopause) and supporting whole-person rehabilitation [ 7 , 83 ]. The evidentiary questions raised in MDT discussions (which did not include patients) addressed the practicalities of the rehabilitation model (for example, whether cognitive therapy for neurocognitive complications is as effective when delivered online as it is when delivered in-person) rather than the molecular or cellular mechanisms of disease. For example, the question of whether patients with neurocognitive impairment should be tested for micro-clots or treated with anticoagulants never came up in the MDTs we observed, though we did visit a tertiary referral clinic (the tier 4 clinic in site H), whose lead clinician had a research interest in inflammatory coagulopathies and offered such tests to selected patients.

Because long covid typically produces dozens of symptoms that tend to be uniquely patterned in each patient, the uncertainties on which MDT discussions turned were rarely about general evidence of the kind that might be found in a guideline (e.g. how should fatigue be managed?). Rather they concerned particular case-based clinical decisions (e.g. how should this patient’s fatigue be managed, given the specifics of this case?). An example from our field notes illustrates this:

Physical therapist presents the case of a 39-year-old woman who works as a cleaner on an overnight ferry. Has had long covid for 2 years. Main symptoms are shortness of breath and possible anxiety attacks, especially when at work. She has had a course of physical therapy to teach diaphragmatic breathing but has found that focusing on her breathing makes her more anxious. Patient has to do a lot of bending in her job (e.g. cleaning toilets and under seats), which makes her dizzy, but Active Stand Test was normal. She also has very mild tricuspid incompetence [someone reads out a cardiology report—not hemodynamically significant].

Rehabilitation guidelines (e.g. WHO) recommend phased return to work (e.g. with reduced hours) and frequent breaks. “Tricky!” says someone. The job is intense and busy, and the patient can’t afford not to work. Discussion on whether all her symptoms can be attributed to tension and anxiety. Physical therapist who runs the breathing group says, “No, it’s long covid”, and describes severe initial covid-19 episode and results of serial chest X-rays which showed gradual clearing of ground glass shadows. Team discussion centers on how to negotiate reduced working hours in this particular job, given the overnight ferry shifts. --MDT discussion, Site D

This example raises important considerations about the nature of clinical knowledge in long covid. We return to it in the final section of the “ Results ” and in the “ Discussion ”.

Long covid clinics: a heterogeneous context for quality improvement

Most participating clinics had been established in mid-2020 to follow up patients who had been hospitalized (and perhaps ventilated) for severe acute covid-19. As mass vaccination reduced the severity of acute covid-19 for most people, the patient population in all clinics progressively shifted to include fewer “post-ICU [intensive care unit]” patients (in whom respiratory symptoms almost always dominated), and more people referred by their general practitioners or other secondary care specialties who had not been hospitalized for their acute covid-19 infection, and in whom fatigue, brain fog and palpitations were often the most troubling symptoms. Despite these similarities, the ten clinics had very different histories, geographical and material settings, staffing structures, patient pathways and case mix, as Table  1 illustrates. Below, we give more detail on three example sites.

Site C was established as a generalist “assessment-only” service by a general practitioner with an interest in infectious diseases. It is led jointly by that general practitioner and an occupational therapist, assisted by a wide range of other professionals including speech and language therapy, dietetics, clinical psychology and community-based physical therapy and occupational therapy. It has close links with a chronic fatigue service and a pain clinic that have been running in the locality for over 20 years. The clinic, which is entirely virtual (staff consult either from home or from a small side office in the community trust building), is physically located in a low-rise building on the industrial outskirts of a large town, sharing office space with various community-based health and social care services. Following a 1-h telephone consultation by one of the clinical leads, each patient is discussed at the MDT and then either discharged back to their general practitioner with a detailed management plan or referred on to one of the specialist services. This arrangement evolved to address a particular problem in this locality—that many patients with long covid were being referred by their general practitioner to multiple specialties (e.g. respiratory, neurology, fatigue), leading to a fragmented patient experience, unnecessary specialist assessments and wasteful duplication. The generalist assessment by telephone is oriented to documenting what is often a complex illness narrative (including pre-existing physical and mental comorbidities) and working with the patient to prioritize which symptoms or problems to pursue in which order.

Site E, in a well-regarded inner-city teaching hospital, had been set up in 2020 by a respiratory physician. Its initial ethos and rationale had been “respiratory follow-up”, with strong emphasis on monitoring lung damage via repeated imaging and lung function tests and in ensuring that patients received specialist physical therapy to “re-learn” efficient breathing techniques. Over time, this site has tried to accommodate a more multi-system assessment, with the introduction of a consultant-led infectious disease clinic for patients without a dominant respiratory component, reflecting the shift towards a more fatigue-predominant case mix. At the time of our fieldwork, each patient was seen in turn by a physician, psychologist, occupational therapist and respiratory physical therapist (half an hour each) before all four staff reconvened in a face-to-face MDT meeting to form a plan for each patient. But whilst a wide range of patients with diverse symptoms were discussed at these meetings, there remained a strong focus on respiratory pathology (e.g. tracking improvements in lung function and ensuring that coexisting asthma was optimally controlled).

Site F, one of the first long covid clinics in UK, was set up by a rehabilitation consultant who had been drafted to work on the ICU during the first wave of covid-19 in early 2020. He had a longstanding research interest in whole-patient rehabilitation, especially the assessment and management of chronic fatigue and pain. From the outset, clinic F was more oriented to rehabilitation, including vocational rehabilitation to help patients return to work. There was less emphasis on monitoring lung function or pursuing respiratory comorbidities. At the time of our fieldwork, clinic F offered both a community-based service (“tier 2”) led by an occupational therapist, supported by a respiratory physical therapist and psychologist, and a hospital-based service (“tier 3”) led by the rehabilitation consultant, supported by a wider MDT. Staff in both tiers emphasized that each patient needs a full physical and mental assessment and help to set and work towards achievable goals, whilst staying within safe limits so as to avoid post-exertional symptom exacerbation. Because of the research interest of the lead physician, clinic F adapted well to the growing numbers of patients with fatigue and quickly set up research studies on this cohort [ 84 ].

Details of the other seven sites are shown in Table  1 . Broadly speaking, sites B, E, G and H aligned with the “respiratory follow-up” model and sites F and I aligned with the “rehabilitation” model. Sites A and J had a high-volume, multi-tiered service whose community tier aligned with the “holistic GP assessment” model (site C above) and which also offered a hospital-based, rehabilitation-focused tier. The small service in Scotland (site D) had evolved from an initial respiratory focus to become part of the infectious diseases (ME/CFS) service; Lyme disease (another infectious disease whose sequelae include chronic fatigue) was also prevalent in this region.

The patient experience

Whilst the 10 participating clinics were very diverse in staffing, ethos and patient flows, the 29 patient interviews described remarkably consistent clinic experiences. Almost all identified the biggest problem to be the extended wait of several months before they were seen and the limited awareness (when initially referred) of what long covid clinics could provide. Some talked of how they cried with relief when they finally received an appointment. When the quality improvement collaborative was initially established, waiting times and bottlenecks were patients’ the top priority for quality improvement, and this ranking was shared by clinic staff, who were very aware of how much delays and uncertainties in assessment and treatment compounded patients’ suffering. This issue resolved to a large extent over the study period in all clinics as the referral backlog cleared and the incidence of new cases of long covid fell [ 85 ]; it will be covered in more detail in a separate publication.

Most patients in our sample were satisfied with the care they received when they were finally seen in clinic, especially how they finally felt “heard” after a clinician took a full history. They were relieved to receive affirmation of their experience, a diagnosis of what was wrong and reassurance that they were believed. They were grateful for the input of different members of the multidisciplinary teams and commented on the attentiveness, compassion and skill of allied professionals in particular (“she was wonderful, she got me breathing again”—patient BIR145 talking about a physical therapist). One or two patient participants expressed confusion about who exactly they had seen and what advice they had been given, and some did not realize that a telephone assessment had been an actual clinical consultation. A minority expressed disappointment that an expected investigation had not been ordered (one commented that they had not had any blood tests at all). Several had assumed that the help and advice from the long covid clinic would continue to be offered until they were better and were disappointed that they had been discharged after completing the various courses on offer (since their clinic had been set up as an “assessment only” service).

In the next sections, we give examples of topics raised in the quality improvement collaborative and how they were addressed.

Example quality topic 1: Outcome measures

The first topic considered by the quality improvement collaborative was how (that is, using which measures and metrics) to assess and monitor patients with long covid. In the absence of a validated biomarker, various symptom scores and quality of life scales—both generic and disease-specific—were mooted. Site F had already developed and validated a patient-reported outcome measure (PROM), the C19-YRS (Covid-19 Yorkshire Rehabilitation Scale) and used it for both research and clinical purposes [ 86 ]. It was quickly agreed that, for the purposes of generating comparative research findings across the ten clinics, the C19-YRS should be used at all sites and completed by patients three-monthly. A commercial partner produced an electronic version of this instrument and an app for patient smartphones. The quality improvement collaborative also agreed that patients should be asked to complete the EUROQOL EQ5D, a widely used generic health-related quality of life scale [ 87 ], in order to facilitate comparisons between long covid and other chronic conditions.

In retrospect, the discussions which led to the unopposed adoption of these two measures as a “quality” initiative in clinical care were somewhat aspirational. A review of progress at a subsequent quality improvement meeting revealed considerable variation among clinics, with a wide variety of measures used in different clinics to different degrees. Reasons for this variation were multiple. First, although our patient advisory group were keen that we should gather as much data as possible on the patient experience of this new condition, many clinic patients found the long questionnaires exhausting to complete due to cognitive impairment and fatigue. In addition, whilst patients were keen to answer questions on symptoms that troubled them, many had limited patience to fill out repeated surveys on symptoms that did not trouble them (“it almost felt as if I’ve not got long covid because I didn’t feel like I fit the criteria as they were laying it out”—patient SAL001). Staff assisted patients in completing the measures when needed, but this was time-consuming (up to 45 min per instrument) and burdensome for both staff and patients. In clinics where a high proportion of patients required assistance, staff time was the rate-limiting factor for how many instruments got completed. For some patients, one short instrument was the most that could be asked of them, and the clinician made a judgement on which one would be in their best interests on the day.

The second reason for variation was that the clinical diagnosis and management of particular features, complications and comorbidities of long covid required more nuance than was provided by these relatively generic instruments, and the level of detail sought varied with the specialist interest of the clinic (and the clinician). The modified C19-YRS [ 88 ], for example, contained 19 items, of which one asked about sleep quality. But if a patient had sleep difficulties, many clinicians felt that these needed to be documented in more detail—for example using the 8-item Epworth Sleepiness Scale, originally developed for conditions such as narcolepsy and obstructive sleep apnea [ 89 ]. The “Epworth score” was essential currency for referrals to some but not all specialist sleep services. Similarly, the C19-YRS had three items relating to anxiety, depression and post-traumatic stress disorder, but in clinics where there was a strong focus on mental health (e.g. when there was a resident psychologist), patients were usually invited to complete more specific tools (e.g. the Patient Health Questionnaire 9 [ 90 ], a 9-item questionnaire originally designed to assess severity of depression).

The third reason for variation was custom and practice. Ethnographic visits revealed that paper copies of certain instruments were routinely stacked on clinicians’ desks in outpatient departments and also (in some cases) handed out by administrative staff in waiting areas so that patients could complete them before seeing the clinician. These familiar clinic artefacts tended to be short (one-page) instruments that had a long tradition of use in clinical practice. They were not always fit for purpose. For example, the Nijmegen questionnaire was developed in the 1980s to assess hyperventilation; it was validated against a longer, “gold standard” instrument for that condition [ 91 ]. It subsequently became popular in respiratory clinics to diagnose or exclude breathing pattern disorder (a condition in which the normal physiological pattern of breathing becomes replaced with less efficient, shallower breathing [ 92 ]), so much so that the researchers who developed the instrument published a paper to warn fellow researchers that it had not been validated for this purpose [ 93 ]. Whilst a validated 17-item instrument for breathing pattern disorder (the Self-Evaluation of Breathing Questionnaire [ 94 ]) does exist, it is not in widespread clinical use. Most clinics in LOCOMOTION used Nijmegen either on all patients (e.g. as part of a comprehensive initial assessment, especially if the service had begun as a respiratory follow-up clinic) or when breathing pattern disorder was suspected.

In sum, the use of outcome measures in long covid clinics was a compromise between standardization and contingency. On the one hand, all clinics accepted the need to use “validated” instruments consistently. On the other hand, there were sometimes good reasons why they deviated from agreed practice, including mismatch between the clinic’s priorities as a research site, its priorities as a clinical service, and the particular clinical needs of a patient; the clinic’s—and the clinician’s—specialist focus; and long-held traditions of using particular instruments with which staff and patients were familiar.

Example quality topic 2: Postural orthostatic tachycardia syndrome (POTS)

Palpitations (common in long covid) and postural orthostatic tachycardia syndrome (POTS, a disproportionate acceleration in heart rate on standing, the assumed cause of palpitations in many long covid patients) was the top priority for quality improvement identified by our patient advisory group. Reflecting discussions and evidence (of various kinds) shared in online patient communities, the group were confident that POTS is common in long covid patients and that many cases remain undetected (perhaps misdiagnosed as anxiety). Their request that all long covid patients should be “screened” for POTS prompted a search for, and synthesis of, evidence (which we published in the BMJ [ 95 ]). In sum, that evidence was sparse and contested, but, combined with standard practice in specialist clinics, broadly supported the judicious use of the NASA Lean Test [ 96 ]. This test involves repeated measurements of pulse and blood pressure with the patient first lying and then standing (with shoulders resting against a wall).

The patient advisory group’s request that the NASA Lean Test should be conducted on all patients met with mixed responses from the clinics. In site F, the lead physician had an interest in autonomic dysfunction in chronic fatigue and was keen; he had already published a paper on how to adapt the NASA Lean Test for self-assessment at home [ 97 ]. Several other sites were initially opposed. Staff at site E, for example, offered various arguments:

The test is time-consuming, labor-intensive, and takes up space in the clinic which has an opportunity cost in terms of other potential uses;

The test is unvalidated and potentially misleading (there is a high incidence of both false negative and false positive results);

There is no proven treatment for POTS, so there is no point in testing for it;

It is a specialist test for a specialist condition, so it should be done in a specialist clinic where its benefits and limitations are better understood;

Objective testing does not change clinical management since what we treat is the patient’s symptoms (e.g. by a pragmatic trial of lifestyle measures and medication);

People with symptoms suggestive of dysautonomia have already been “triaged out” of this clinic (that is, identified in the initial telephone consultation and referred directly to neurology or cardiology);

POTS is a manifestation of the systemic nature of long covid; it does not need specific treatment but will improve spontaneously as the patient goes through standard interventions such as active pacing, respiratory physical therapy and sleep hygiene;

Testing everyone, even when asymptomatic, runs counter to the ethos of rehabilitation, which is to “de-medicalize” patients so as to better orient them to their recovery journey.

When clinics were invited to implement the NASA Lean Test on a consecutive sample of patients to resolve a dispute about the incidence of POTS (from “we’ve only seen a handful of people with it since the clinic began” to “POTS is common and often missed”), all but one site agreed to participate. The tertiary POTS centre linked to site H was already running the NASA Lean Test as standard on all patients. Site C, which operated entirely virtually, passed the work to the referring general practitioner by making this test a precondition for seeing the patient; site D, which was largely virtual, sent instructions for patients to self-administer the test at home.

The NASA Lean Test study has been published separately [ 98 ]. In sum, of 277 consecutive patients tested across the eight clinics, 20 (7%) had a positive NASA Lean Test for POTS and a further 28 (10%) a borderline result. Six of 20 patients who met the criteria for POTS on testing had no prior history of orthostatic intolerance. The question of whether this test should be used to “screen” all patients was not answered definitively. But the experience of participating in the study persuaded some sceptics that postural changes in heart rate could be severe in some long covid patients, did not appear to be fully explained by their previously held theories (e.g. “functional”, anxiety, deconditioning), and had likely been missed in some patients. The outcome of this particular quality improvement cycle was thus not a wholescale change in practice (for which the evidence base was weak) but a more subtle increase in clinical awareness, a greater willingness to consider testing for POTS and a greater commitment to contribute to research into this contested condition.

More generally, the POTS audit prompted some clinicians to recognize the value of quality improvement in novel clinical areas. One physician who had initially commented that POTS was not seen in their clinic, for example, reflected:

“ Our clinic population is changing. […] Overall there’s far fewer post-ICU patients with ECMO [extra-corporeal membrane oxygenation] issues and far more long covid from the community, and this is the bit our clinic isn’t doing so well on. We’re doing great on breathing pattern disorder; neuro[logists] are helping us with the brain fogs; our fatigue and occupational advice is ok but some of the dysautonomia symptoms that are more prevalent in the people who were not hospitalized – that’s where we need to improve .” -Respiratory physician, site G (from field visit 6.6.23)

Example quality topic 3: Management of fatigue

Fatigue was the commonest symptom overall and a high priority among both patients and clinicians for quality improvement. It often coexisted with the cluster of neurocognitive symptoms known as brain fog, with both conditions relapsing and remitting in step. Clinicians were keen to systematize fatigue management using a familiar clinical framework oriented around documenting a full clinical history, identifying associated symptoms, excluding or exploring comorbidities and alternative explanations (e.g. poor sleep patterns, depression, menopause, deconditioning), assessing how fatigue affects physical and mental function, implementing a program of physical and cognitive therapy that was sensitive to the patient’s condition and confidence level, and monitoring progress using validated patient-reported outcome measures and symptom diaries.

The underpinning logic of this approach, which broadly reflected World Health Organization guidance [ 99 ], was that fatigue and linked cognitive impairment could be a manifestation of many—perhaps interacting—conditions but that a whole-patient (body and mind) rehabilitation program was the cornerstone of management in most cases. Discussion in the quality improvement collaborative focused on issues such as whether fatigue was so severe that it produced safety concerns (e.g. in a person’s job or with childcare), the pros and cons of particular online courses such as yoga, relaxation and mindfulness (many were viewed positively, though the evidence base was considered weak), and the extent to which respiratory physical therapy had a crossover impact on fatigue (systematic reviews suggested that it may do, but these reviews also cautioned that primary studies were sparse, methodologically flawed, and heterogeneous [ 100 , 101 ]). They also debated the strengths and limitations of different fatigue-specific outcome measures, each of which had been developed and validated in a different condition, with varying emphasis on cognitive fatigue, physical fatigue, effect on daily life, and motivation. These instruments included the Modified Fatigue Impact Scale; Fatigue Severity Scale [ 102 ]; Fatigue Assessment Scale; Functional Assessment Chronic Illness Therapy—Fatigue (FACIT-F) [ 103 ]; Work and Social Adjustment Scale [ 104 ]; Chalder Fatigue Scale [ 105 ]; Visual Analogue Scale—Fatigue [ 106 ]; and the EQ5D [ 87 ]. In one clinic (site F), three of these scales were used in combination for reasons discussed below.

Some clinicians advocated melatonin or nutritional supplements (such as vitamin D or folic acid) for fatigue on the grounds that many patients found them helpful and formal placebo-controlled trials were unlikely ever to be conducted. But neurostimulants used in other fatigue-predominant conditions (e.g. brain injury, stroke), which also lacked clinical trial evidence in long covid, were viewed as inappropriate in most patients because of lack of evidence of clear benefit and hypothetical risk of harm (e.g. adverse drug reactions, polypharmacy).

Whilst the patient advisory group were broadly supportive of a whole-patient rehabilitative approach to fatigue, their primary concern was fatiguability , especially post-exertional symptom exacerbation (PESE, also known as “crashes”). In these, the patient becomes profoundly fatigued some hours or days after physical or mental exertion, and this state can last for days or even weeks [ 107 ]. Patients viewed PESE as a “red flag” symptom which they felt clinicians often missed and sometimes caused. They wanted the quality improvement effort to focus on ensuring that all clinicians were aware of the risks of PESE and acted accordingly. A discussion among patients and clinicians at a quality improvement collaborative meeting raised a new research hypothesis—that reducing the number of repeated episodes of PESE may improve the natural history of long covid.

These tensions around fatigue management played out differently in different clinics. In site C (the GP-led virtual clinic run from a community hub), fatigue was viewed as one manifestation of a whole-patient condition. The lead general practitioner used the metaphor of untangling a skein of wool: “you have to find the end and then gently pull it”. The underlying problem in a fatigued patient, for example, might be an undiagnosed physical condition such as anaemia, disturbed sleep, or inadequate pacing. These required (respectively) the chronic fatigue service (comprising an occupational therapist and specialist psychologist and oriented mainly to teaching the techniques of goal-setting and pacing), a “tiredness” work-up (e.g. to exclude anaemia or menopause), investigation of poor sleep (which, not uncommonly, was due to obstructive sleep apnea), and exploration of mental health issues.

In site G (a hospital clinic which had evolved from a respiratory service), patients with fatigue went through a fatigue management program led by the occupational therapist with emphasis on pacing, energy conservation, avoidance of PESE and sleep hygiene. Those without ongoing respiratory symptoms were often discharged back to their general practitioner once they had completed this; there was no consultant follow-up of unresolved fatigue.

In site F (a rehabilitation clinic which had a longstanding interest in chronic fatigue even before the pandemic), active interdisciplinary management of fatigue was commenced at or near the patient’s first visit, on the grounds that the earlier this began, the more successful it would be. In this clinic, patients were offered a more intensive package: a similar occupational therapy-led fatigue course as those in site G, plus input from a dietician to advise on regular balanced meals and caffeine avoidance and a group-based facilitated peer support program which centred on fatigue management. The dietician spoke enthusiastically about how improving diet in longstanding long covid patients often improved fatigue (e.g. because they had often lost muscle mass and tended to snack on convenience food rather than make meals from scratch), though she agreed there was no evidence base from trials to support this approach.

Pursuing local quality improvement through MDTs

Whilst some long covid patients had “textbook” symptoms and clinical findings, many cases were unique and some were fiendishly complex. One clinician commented that, somewhat paradoxically, “easy cases” were often the post-ICU follow-ups who had resolving chest complications; they tended to do well with a course of respiratory physical therapy and a return-to-work program. Such cases were rarely brought to MDT meetings. “Difficult cases” were patients who had not been hospitalized for their acute illness but presented with a months- or years-long history of multiple symptoms with fatigue typically predominant. Each one was different, as the following example (some details of which have been fictionalized to protect anonymity) illustrates.

The MDT is discussing Mrs Fermah, a 65-year-old homemaker who had covid-19 a year ago. She has had multiple symptoms since, including fluctuating fatigue, brain fog, breathlessness, retrosternal chest pain of burning character, dry cough, croaky voice, intermittent rashes (sometimes on eating), lips going blue, ankle swelling, orthopnoea, dizziness with the room spinning which can be triggered by stress, low back pain, aches and pains in the arms and legs and pins and needles in the fingertips, loss of taste and smell, palpitations and dizziness (unclear if postural, but clear association with nausea), headaches on waking, and dry mouth. She is somewhat overweight (body mass index 29) and admits to low mood. Functionally, she is mostly confined to the house and can no longer manage the stairs so has begun to sleep downstairs. She has stumbled once or twice but not fallen. Her social life has ceased and she rarely has the energy to see her grandchildren. Her 70-year-old husband is retired and generally supportive, though he spends most evenings at his club. Comorbidities include glaucoma which is well controlled and overseen by an ophthalmologist, mild club foot (congenital) and stage 1 breast cancer 20 years ago. Various tests, including a chest X-ray, resting and exercise oximetry and a blood panel, were normal except for borderline vitamin D level. Her breathing questionnaire score suggests she does not have breathing pattern disorder. ECG showed first-degree atrioventricular block and left axis deviation. No clinician has witnessed the blue lips. Her current treatment is online group respiratory physical therapy; a home visit is being arranged to assess her climbing stairs. She has declined a psychologist assessment. The consultant asks the nurse who assessed her: “Did you get a feel if this is a POTS-type dizziness or an ENT-type?” She sighs. “Honestly it was hard to tell, bless her.”—Site A MDT

This patient’s debilitating symptoms and functional impairments could all be due to long covid, yet “evidence-based” guidance for how to manage her complex suffering does not exist and likely never will exist. The question of which (if any) additional blood or imaging tests to do, in what order of priority, and what interventions to offer the patient will not be definitively answered by consulting clinical trials involving hundreds of patients, since (even if these existed) the decision involves weighing this patient’s history and the multiple factors and uncertainties that are relevant in her case. The knowledge that will help the MDT provide quality care to Mrs Fermah is case-based knowledge—accumulated clinical experience and wisdom from managing and deliberating on multiple similar cases. We consider case-based knowledge further in the “ Discussion ”.

Summary of key findings

This study has shown that a quality improvement collaborative of UK long covid clinics made some progress towards standardizing assessment and management in some topics, but some variation remained. This could be explained in part by the fact that different clinics had different histories and path dependencies, occupied a different place in the local healthcare ecosystem, served different populations, were differently staffed, and had different clinical interests. Our patient advisory group and clinicians in the quality improvement collaborative broadly prioritized the same topics for improvement but interpreted them somewhat differently. “Quality” long covid care had multiple dimensions, relating to (among other things) service set-up and accessibility, clinical provision appropriate to the patient’s need (including options for referral to other services locally), the human qualities of clinical and support staff, how knowledge was distributed across (and accessible within) the system, and the accumulated collective wisdom of local MDTs in dealing with complex cases (including multiple kinds of specialist expertise as well as relational knowledge of what was at stake for the patient). Whilst both staff and patients were keen to contribute to the quality improvement effort, the burden of measurement was evident: multiple outcome measures, used repeatedly, were resource-intensive for staff and exhausting for patients.

Strengths and limitations of this study

To our knowledge, we are the first to report both a quality improvement collaborative and an in-depth qualitative study of clinical work in long covid. Key strengths of this work include the diverse sampling frame (with sites from three UK jurisdictions and serving widely differing geographies and demographics); the use of documents, interviews and reflexive interpretive ethnography to produce meaningful accounts of how clinics emerged and how they were currently organized; the use of philosophical concepts to analyse data on how MDTs produced quality care on a patient-by-patient basis; and the close involvement of patient co-researchers and coauthors during the research and writing up.

Limitations of the study include its exclusive UK focus (the external validity of findings to other healthcare systems is unknown); the self-selecting nature of participants in a quality improvement collaborative (our patient advisory group suggested that the MDTs observed in this study may have represented the higher end of a quality spectrum, hence would be more likely than other MDTs to adhere to guidelines); and the particular perspective brought by the researchers (two GPs, a physical therapist and one non-clinical person) in ethnographic observations. Hospital specialists or organizational scholars, for example, may have noticed different things or framed what they observed differently.

Explaining variation in long covid care

Sutherland and Levesque’s framework mentioned in the “ Background ” section does not explain much of the variation found in our study [ 70 ]. In terms of capacity, at the time of this study most participating clinics benefited from ring-fenced resources. In terms of evidence, guidelines existed and were not greatly contested, but as illustrated by the case of Mrs Fermah above, many patients were exceptions to the guideline because of complex symptomatology and relevant comorbidities. In terms of agency, clinicians in most clinics were passionately engaged with long covid (they were pioneers who had set up their local clinic and successfully bid for national ring-fenced resources) and were generally keen to support patient choice (though not if the patient requested tests which were unavailable or deemed not indicated).

Astma et al.’s list of factors that may explain variation in practice (see “ Background ”) includes several that may be relevant to long covid, especially that the definition of appropriate care in this condition remains somewhat contested. But lack of opportunity to discuss cases was not a problem in the clinics in our sample. On the contrary, MDT meetings in each locality gave clinicians multiple opportunities to discuss cases with colleagues and reflect collectively on whether and how to apply particular guidelines.

The key problem was not that clinicians disputed the guidelines for managing long covid or were unaware of them; it was that the guidelines were not self-interpreting . Rather, MDTs had to deliberate on the balance of benefits and harms in different aspects of individual cases. In patients whose symptoms suggested a possible diagnosis of POTS (or who suspected themselves of having POTS), for example, these deliberations were sometimes lengthy and nuanced. Should a test result that is not technically in the abnormal range but close to it be treated as diagnostic, given that symptoms point to this diagnosis? If not, should the patient be told that the test excludes POTS or that it is equivocal? If a cardiology opinion has stated firmly that the patient does not have POTS but the cardiologist is not known for their interest in this condition, should a second specialist opinion be sought? If the gold standard “tilt test” [ 108 ] for POTS (usually available only in tertiary centres) is not available locally, does this patient merit a costly out-of-locality referral? Should the patient’s request for a trial of off-label medication, reflecting discussions in an online support group, be honoured? These are the kinds of questions on which MDTs deliberated at length.

The fact that many cases required extensive deliberation does not necessarily justify variation in practice among clinics. But taking into account the clinics’ very different histories, set-up, and local referral pathways, the variation begins to make sense. A patient who is being assessed in a clinic that functions as a specialist chronic fatigue centre and attracts referrals which reflect this interest (e.g. site F in our sample) will receive different management advice from one that functions as a telephone-only generalist assessment centre and refers on to other specialties (site C in our sample). The wide variation in case mix, coupled with the fact that a different proportion of these cases were highly complex in each clinic (and in different ways), suggests that variation in practice may reflect appropriate rather than inappropriate care.

Our patient advisory group affirmed that many of the findings reported here resonated with their own experience, but they raised several concerns. These included questions about patient groups who may have been missed in our sample because they were rarely discussed in MDTs. The decision to take a case to MDT discussion is taken largely by a clinician, and there was evidence from online support groups that some patients’ requests for their case to be taken to an MDT had been declined (though not, to our knowledge, in the clinics participating in the LOCOMOTION study).

We began this study by asking “what is quality in long covid care?”. We initially assumed that this question referred to a generalizable evidence base, which we felt we could identify, and we believed that we could then determine whether long covid clinics were following the evidence base through conventional audits of structure, process, and outcome. In retrospect, these assumptions were somewhat naïve. On the basis of our findings, we suggest that a better (and more individualized) research question might be “to what extent does each patient with long covid receive evidence-based care appropriate to their needs?”. This question would require individual case review on a sample of cases, tracking each patient longitudinally including cross-referrals, and also interviewing the patient.

Nomothetic versus idiographic knowledge

In a series of lectures first delivered in the 1950s and recently republished [ 109 ], psychiatrist Dr Maurice O’Connor Drury drew on the later philosophy of his friend and mentor Ludwig Wittgenstein to challenge what he felt was a concerning trend: that the nomothetic (generalizable, abstract) knowledge from randomized controlled trials (RCTs) was coming to over-ride the idiographic (personal, situated) knowledge about particular patients. Based on Wittgenstein’s writings on the importance of the particular, Drury predicted—presciently—that if implemented uncritically, RCTs would result in worse, not better, care for patients, since it would go hand-in-hand with a downgrading of experience, intuition, subjective judgement, personal reflection, and collective deliberation.

Much conventional quality improvement methodology is built on an assumption that nomothetic knowledge (for example, findings from RCTs and systematic reviews) is a higher form of knowing than idiographic knowledge. But idiographic, case-based reasoning—despite its position at the very bottom of evidence-based medicine’s hierarchy of evidence [ 110 ]—is a legitimate and important element of medical practice. Bioethicist Kathryn Montgomery, drawing on Aristotle’s notion of praxis , considers clinical practice to be an example of case-based reasoning [ 111 ]. Medicine is governed not by hard and fast laws but by competing maxims or rules of thumb ; the essence of judgement is deciding which (if any) rule should be applied in a particular circumstance. Clinical judgement incorporates science (especially the results of well-conducted research) and makes use of available tools and technologies (including guidelines and decision-support algorithms that incorporate research findings). But rather than being determined solely by these elements, clinical judgement is guided both by the scientific evidence and by the practical and ethical question “what is it best to do, for this individual, given these circumstances?”.

In this study, we observed clinical management of, and MDT deliberations on, hundreds of clinical cases. In the more straightforward ones (for example, recovering pneumonitis), guideline-driven care was not difficult to implement and such cases were rarely brought to the MDT. But cases like Mrs Fermah (see last section of “ Results ”) required much discussion on which aspects of which guideline were in the patient’s best interests to bring into play at any particular stage in their illness journey.

Conclusions

One systematic review on quality improvement collaboratives concluded that “ [those] reporting success generally addressed relatively straightforward aspects of care, had a strong evidence base and noted a clear evidence-practice gap in an accepted clinical pathway or guideline” (page 226) [ 60 ]. The findings from this study suggest that to the extent that such collaboratives address clinical cases that are not straightforward, conventional quality improvement methods may be less useful and even counterproductive.

The question “what is quality in long covid care?” is partly a philosophical one. Our findings support an approach that recognizes and values idiographic knowledge —including establishing and protecting a safe and supportive space for deliberation on individual cases to occur and to value and draw upon the collective learning that occurs in these spaces. It is through such deliberation that evidence-based guidelines can be appropriately interpreted and applied to the unique needs and circumstances of individual patients. We suggest that Drury’s warning about the limitations of nomothetic knowledge should prompt a reassessment of policies that rely too heavily on such knowledge, resulting in one-size-fits-all protocols. We also cautiously hypothesize that the need to centre the quality improvement effort on idiographic rather than nomothetic knowledge is unlikely to be unique to long covid. Indeed, such an approach may be particularly important in any condition that is complex, unpredictable, variable in presentation and clinical course, and associated with comorbidities.

Availability of data and materials

Selected qualitative data (ensuring no identifiable information) will be made available to formal research teams on reasonable request to Professor Greenhalgh at the University of Oxford, on condition that they have research ethics approval and relevant expertise. The quantitative data on NASA Lean Test have been published in full in a separate paper [ 98 ].

Abbreviations

Chronic fatigue syndrome

Intensive care unit

Jenny Ceolta-Smith

Julie Darbyshire

LOng COvid Multidisciplinary consortium Optimising Treatments and services across the NHS

Multidisciplinary team

Myalgic encephalomyelitis

Middle East Respiratory Syndrome

National Aeronautics and Space Association

Occupational therapy/ist

Post-exertional symptom exacerbation

Postural orthostatic tachycardia syndrome

Speech and language therapy

Severe Acute Respiratory Syndrome

Trisha Greenhalgh

United Kingdom

United States

World Health Organization

Perego E, Callard F, Stras L, Melville-JÛhannesson B, Pope R, Alwan N. Why the Patient-Made Term “Long Covid” is needed. Wellcome Open Res. 2020;5:224.

Article   Google Scholar  

Greenhalgh T, Sivan M, Delaney B, Evans R, Milne R: Long covid—an update for primary care. bmj 2022;378:e072117.

Centers for Disease Control and Prevention (US): Long COVID or Post-COVID Conditions (updated 16th December 2022). Atlanta: CDC. Accessed 2nd June 2023 at https://www.cdc.gov/coronavirus/2019-ncov/long-term-effects/index.html ; 2022.

National Institute for Health and Care Excellence (NICE) Scottish Intercollegiate Guidelines Network (SIGN) and Royal College of General Practitioners (RCGP): COVID-19 rapid guideline: managing the long-term effects of COVID-19, vol. Accessed 30th January 2022 at https://www.nice.org.uk/guidance/ng188/resources/covid19-rapid-guideline-managing-the-longterm-effects-of-covid19-pdf-51035515742 . London: NICE; 2022.

Organization WH: Post Covid-19 Condition (updated 7th December 2022), vol. Accessed 2nd June 2023 at https://www.who.int/europe/news-room/fact-sheets/item/post-covid-19-condition#:~:text=It%20is%20defined%20as%20the,months%20with%20no%20other%20explanation . Geneva: WHO; 2022.

Office for National Statistics: Prevalence of ongoing symptoms following coronavirus (COVID-19) infection in the UK: 31st March 2023. London: ONS. Accessed 30th May 2023 at https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/datasets/alldatarelatingtoprevalenceofongoingsymptomsfollowingcoronaviruscovid19infectionintheuk ; 2023.

Crook H, Raza S, Nowell J, Young M, Edison P: Long covid—mechanisms, risk factors, and management. bmj 2021;374.

Sudre CH, Murray B, Varsavsky T, Graham MS, Penfold RS, Bowyer RC, Pujol JC, Klaser K, Antonelli M, Canas LS. Attributes and predictors of long COVID. Nat Med. 2021;27(4):626–31.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Reese JT, Blau H, Casiraghi E, Bergquist T, Loomba JJ, Callahan TJ, Laraway B, Antonescu C, Coleman B, Gargano M: Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes. EBioMedicine 2023;87.

Thaweethai T, Jolley SE, Karlson EW, Levitan EB, Levy B, McComsey GA, McCorkell L, Nadkarni GN, Parthasarathy S, Singh U. Development of a definition of postacute sequelae of SARS-CoV-2 infection. JAMA. 2023;329(22):1934–46.

Brown DA, O’Brien KK. Conceptualising Long COVID as an episodic health condition. BMJ Glob Health. 2021;6(9): e007004.

Article   PubMed   Google Scholar  

Tate WP, Walker MO, Peppercorn K, Blair AL, Edgar CD. Towards a Better Understanding of the Complexities of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Long COVID. Int J Mol Sci. 2023;24(6):5124.

Ahmed H, Patel K, Greenwood DC, Halpin S, Lewthwaite P, Salawu A, Eyre L, Breen A, Connor RO, Jones A. Long-term clinical outcomes in survivors of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome coronavirus (MERS) outbreaks after hospitalisation or ICU admission: a systematic review and meta-analysis. J Rehabil Med. 2020;52(5):1–11.

Google Scholar  

World Health Organisation: Clinical management of severe acute respiratory infection (SARI) when COVID-19 disease is suspected: Interim guidance (13th March 2020). Geneva: WHO. Accessed 3rd January 2023 at https://t.co/JpNdP8LcV8?amp=1 ; 2020.

Rushforth A, Ladds E, Wieringa S, Taylor S, Husain L, Greenhalgh T: Long Covid – the illness narratives. Under review for Sociology of Health and Illness 2021.

Russell D, Spence NJ. Chase J-AD, Schwartz T, Tumminello CM, Bouldin E: Support amid uncertainty: Long COVID illness experiences and the role of online communities. SSM-Qual Res Health. 2022;2: 100177.

Article   PubMed   PubMed Central   Google Scholar  

Ziauddeen N, Gurdasani D, O’Hara ME, Hastie C, Roderick P, Yao G, Alwan NA. Characteristics and impact of Long Covid: Findings from an online survey. PLoS ONE. 2022;17(3): e0264331.

Evans RA, McAuley H, Harrison EM, Shikotra A, Singapuri A, Sereno M, Elneima O, Docherty AB, Lone NI, Leavy OC. Physical, cognitive, and mental health impacts of COVID-19 after hospitalisation (PHOSP-COVID): a UK multicentre, prospective cohort study. Lancet Respir Med. 2021;9(11):1275–87.

Sykes DL, Holdsworth L, Jawad N, Gunasekera P, Morice AH, Crooks MG. Post-COVID-19 symptom burden: what is long-COVID and how should we manage it? Lung. 2021;199(2):113–9.

Altmann DM, Whettlock EM, Liu S, Arachchillage DJ, Boyton RJ: The immunology of long COVID. Nat Rev Immunol 2023:1–17.

Klein J, Wood J, Jaycox J, Dhodapkar RM, Lu P, Gehlhausen JR, Tabachnikova A, Greene K, Tabacof L, Malik AA et al : Distinguishing features of Long COVID identified through immune profiling. Nature 2023.

Chen B, Julg B, Mohandas S, Bradfute SB. Viral persistence, reactivation, and mechanisms of long COVID. Elife. 2023;12: e86015.

Wang C, Ramasamy A, Verduzco-Gutierrez M, Brode WM, Melamed E. Acute and post-acute sequelae of SARS-CoV-2 infection: a review of risk factors and social determinants. Virol J. 2023;20(1):124.

Cervia-Hasler C, Brüningk SC, Hoch T, Fan B, Muzio G, Thompson RC, Ceglarek L, Meledin R, Westermann P, Emmenegger M et al Persistent complement dysregulation with signs of thromboinflammation in active Long Covid Science 2024;383(6680):eadg7942.

Sivan M, Greenhalgh T, Darbyshire JL, Mir G, O’Connor RJ, Dawes H, Greenwood D, O’Connor D, Horton M, Petrou S. LOng COvid Multidisciplinary consortium Optimising Treatments and servIces acrOss the NHS (LOCOMOTION): protocol for a mixed-methods study in the UK. BMJ Open. 2022;12(5): e063505.

Rushforth A, Ladds E, Wieringa S, Taylor S, Husain L, Greenhalgh T. Long covid–the illness narratives. Soc Sci Med. 2021;286: 114326.

National Institute for Health and Care Excellence: COVID-19 rapid guideline: managing the long-term effects of COVID-19, vol. Accessed 4th October 2023 at https://www.nice.org.uk/guidance/ng188/resources/covid19-rapid-guideline-managing-the-longterm-effects-of-covid19-pdf-51035515742 . London: NICE 2020.

NHS England: Long COVID: the NHS plan for 2021/22. London: NHS England. Accessed 2nd August 2022 at https://www.england.nhs.uk/coronavirus/documents/long-covid-the-nhs-plan-for-2021-22/ ; 2021.

NHS England: NHS to offer ‘long covid’ sufferers help at specialist centres. London: NHS England. Accessed 10th October 2020 at https://www.england.nhs.uk/2020/10/nhs-to-offer-long-covid-help/ ; 2020 (7th October).

NHS England: The NHS plan for improving long COVID services, vol. Acessed 4th February 2024 at https://www.england.nhs.uk/publication/the-nhs-plan-for-improving-long-covid-services/ .London: Gov.uk; 2022.

NHS England: Commissioning guidance for post-COVID services for adults, children and young people, vol. Accessed 6th February 2024 at https://www.england.nhs.uk/long-read/commissioning-guidance-for-post-covid-services-for-adults-children-and-young-people/ . London: gov.uk; 2023.

National Institute for Health Research: Researching Long Covid: Adressing a new global health challenge, vol. Accessed 9.8.23 at https://evidence.nihr.ac.uk/collection/researching-long-covid-addressing-a-new-global-health-challenge/ . London: NIHR; 2022.

Subbaraman N. NIH will invest $1 billion to study long COVID. Nature. 2021;591(7850):356–356.

Article   CAS   PubMed   Google Scholar  

Donabedian A. The definition of quality and approaches to its assessment and monitoring. Ann Arbor: Michigan; 1980.

Laffel G, Blumenthal D. The case for using industrial quality management science in health care organizations. JAMA. 1989;262(20):2869–73.

Maxwell RJ. Quality assessment in health. BMJ. 1984;288(6428):1470.

Berwick DM, Godfrey BA, Roessner J. Curing health care: New strategies for quality improvement. The Journal for Healthcare Quality (JHQ). 1991;13(5):65–6.

Deming WE. Out of the Crisis. Cambridge, MA: MIT Press; 1986.

Argyris C: Increasing leadership effectiveness: New York: J. Wiley; 1976.

Juran JM: A history of managing for quality: The evolution, trends, and future directions of managing for quality: Asq Press; 1995.

Institute of Medicine (US): Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press; 2001.

McNab D, McKay J, Shorrock S, Luty S, Bowie P. Development and application of ‘systems thinking’ principles for quality improvement. BMJ Open Qual. 2020;9(1): e000714.

Sampath B, Rakover J, Baldoza K, Mate K, Lenoci-Edwards J, Barker P. ​Whole-System Quality: A Unified Approach to Building Responsive, Resilient Health Care Systems. Boston: Institute for Healthcare Immprovement; 2021.

Batalden PB, Davidoff F: What is “quality improvement” and how can it transform healthcare? In . , vol. 16: BMJ Publishing Group Ltd; 2007: 2–3.

Baker G. Collaborating for improvement: the Institute for Healthcare Improvement’s breakthrough series. New Med. 1997;1:5–8.

Plsek PE. Collaborating across organizational boundaries to improve the quality of care. Am J Infect Control. 1997;25(2):85–95.

Ayers LR, Beyea SC, Godfrey MM, Harper DC, Nelson EC, Batalden PB. Quality improvement learning collaboratives. Qual Manage Healthcare. 2005;14(4):234–47.

Brandrud AS, Schreiner A, Hjortdahl P, Helljesen GS, Nyen B, Nelson EC. Three success factors for continual improvement in healthcare: an analysis of the reports of improvement team members. BMJ Qual Saf. 2011;20(3):251–9.

Dückers ML, Spreeuwenberg P, Wagner C, Groenewegen PP. Exploring the black box of quality improvement collaboratives: modelling relations between conditions, applied changes and outcomes. Implement Sci. 2009;4(1):1–12.

Nadeem E, Olin SS, Hill LC, Hoagwood KE, Horwitz SM. Understanding the components of quality improvement collaboratives: a systematic literature review. Milbank Q. 2013;91(2):354–94.

Shortell SM, Marsteller JA, Lin M, Pearson ML, Wu S-Y, Mendel P, Cretin S, Rosen M: The role of perceived team effectiveness in improving chronic illness care. Medical Care 2004:1040–1048.

Wilson T, Berwick DM, Cleary PD. What do collaborative improvement projects do? Experience from seven countries. Joint Commission J Qual Safety. 2004;30:25–33.

Schouten LM, Hulscher ME, van Everdingen JJ, Huijsman R, Grol RP. Evidence for the impact of quality improvement collaboratives: systematic review. BMJ. 2008;336(7659):1491–4.

Hulscher ME, Schouten LM, Grol RP, Buchan H. Determinants of success of quality improvement collaboratives: what does the literature show? BMJ Qual Saf. 2013;22(1):19–31.

Dixon-Woods M, Bosk CL, Aveling EL, Goeschel CA, Pronovost PJ. Explaining Michigan: developing an ex post theory of a quality improvement program. Milbank Q. 2011;89(2):167–205.

Bate P, Mendel P, Robert G: Organizing for quality: the improvement journeys of leading hospitals in Europe and the United States: CRC Press; 2007.

Andersson-Gäre B, Neuhauser D. The health care quality journey of Jönköping County Council. Sweden Qual Manag Health Care. 2007;16(1):2–9.

Törnblom O, Stålne K, Kjellström S. Analyzing roles and leadership in organizations from cognitive complexity and meaning-making perspectives. Behav Dev. 2018;23(1):63.

Greenhalgh T, Russell J. Why Do Evaluations of eHealth Programs Fail? An Alternative Set of Guiding Principles. PLoS Med. 2010;7(11): e1000360.

Wells S, Tamir O, Gray J, Naidoo D, Bekhit M, Goldmann D. Are quality improvement collaboratives effective? A systematic review. BMJ Qual Saf. 2018;27(3):226–40.

Landon BE, Wilson IB, McInnes K, Landrum MB, Hirschhorn L, Marsden PV, Gustafson D, Cleary PD. Effects of a quality improvement collaborative on the outcome of care of patients with HIV infection: the EQHIV study. Ann Intern Med. 2004;140(11):887–96.

Mittman BS. Creating the evidence base for quality improvement collaboratives. Ann Intern Med. 2004;140(11):897–901.

Wennberg JE. Unwarranted variations in healthcare delivery: implications for academic medical centres. BMJ. 2002;325(7370):961–4.

Bungay H. Cancer and health policy: the postcode lottery of care. Soc Policy Admin. 2005;39(1):35–48.

Wennberg JE, Cooper MM: The Quality of Medical Care in the United States: A Report on the Medicare Program: The Dartmouth Atlas of Health Care 1999: The Center for the Evaluative Clinical Sciences [Internet]. 1999.

DaSilva P, Gray JM. English lessons: can publishing an atlas of variation stimulate the discussion on appropriateness of care? Med J Aust. 2016;205(S10):S5–7.

Gray WK, Day J, Briggs TW, Harrison S. Identifying unwarranted variation in clinical practice between healthcare providers in England: Analysis of administrative data over time for the Getting It Right First Time programme. J Eval Clin Pract. 2021;27(4):743–50.

Wabe N, Thomas J, Scowen C, Eigenstetter A, Lindeman R, Georgiou A. The NSW Pathology Atlas of Variation: Part I—Identifying Emergency Departments With Outlying Laboratory Test-Ordering Practices. Ann Emerg Med. 2021;78(1):150–62.

Jamal A, Babazono A, Li Y, Fujita T, Yoshida S, Kim SA. Elucidating variations in outcomes among older end-stage renal disease patients on hemodialysis in Fukuoka Prefecture, Japan. PLoS ONE. 2021;16(5): e0252196.

Sutherland K, Levesque JF. Unwarranted clinical variation in health care: definitions and proposal of an analytic framework. J Eval Clin Pract. 2020;26(3):687–96.

Tanenbaum SJ. Reducing variation in health care: The rhetorical politics of a policy idea. J Health Polit Policy Law. 2013;38(1):5–26.

Atsma F, Elwyn G, Westert G. Understanding unwarranted variation in clinical practice: a focus on network effects, reflective medicine and learning health systems. Int J Qual Health Care. 2020;32(4):271–4.

Horbar JD, Rogowski J, Plsek PE, Delmore P, Edwards WH, Hocker J, Kantak AD, Lewallen P, Lewis W, Lewit E. Collaborative quality improvement for neonatal intensive care. Pediatrics. 2001;107(1):14–22.

Van Maanen J: Tales of the field: On writing ethnography: University of Chicago Press; 2011.

Golden-Biddle K, Locke K. Appealing work: An investigation of how ethnographic texts convince. Organ Sci. 1993;4(4):595–616.

Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101.

Glaser BG. The constant comparative method of qualitative analysis. Soc Probl. 1965;12:436–45.

Willis R. The use of composite narratives to present interview findings. Qual Res. 2019;19(4):471–80.

Vojdani A, Vojdani E, Saidara E, Maes M. Persistent SARS-CoV-2 Infection, EBV, HHV-6 and other factors may contribute to inflammation and autoimmunity in long COVID. Viruses. 2023;15(2):400.

Choutka J, Jansari V, Hornig M, Iwasaki A. Unexplained post-acute infection syndromes. Nat Med. 2022;28(5):911–23.

Connors JM, Ariëns RAS. Uncertainties about the roles of anticoagulation and microclots in postacute sequelae of severe acute respiratory syndrome coronavirus 2 infection. J Thromb Haemost. 2023;21(10):2697–701.

Patel MA, Knauer MJ, Nicholson M, Daley M, Van Nynatten LR, Martin C, Patterson EK, Cepinskas G, Seney SL, Dobretzberger V. Elevated vascular transformation blood biomarkers in Long-COVID indicate angiogenesis as a key pathophysiological mechanism. Mol Med. 2022;28(1):122.

Greenhalgh T, Sivan M, Delaney B, Evans R, Milne R: Long covid—an update for primary care. bmj 2022, 378.

Parkin A, Davison J, Tarrant R, Ross D, Halpin S, Simms A, Salman R, Sivan M. A multidisciplinary NHS COVID-19 service to manage post-COVID-19 syndrome in the community. J Prim Care Commun Health. 2021;12:21501327211010990.

NHS England: COVID-19 Post-Covid Assessment Service, vol. Accessed 5th March 2024 at https://www.england.nhs.uk/statistics/statistical-work-areas/covid-19-post-covid-assessment-service/ . London: NHS England; 2024.

Sivan M, Halpin S, Gee J, Makower S, Parkin A, Ross D, Horton M, O'Connor R: The self-report version and digital format of the COVID-19 Yorkshire Rehabilitation Scale (C19-YRS) for Long Covid or Post-COVID syndrome assessment and monitoring. Adv Clin Neurosci Rehabil 2021;20(3).

The EuroQol Group. EuroQol-a new facility for the measurement of health-related quality of life. Health Policy. 1990;16(3):199–208.

Sivan M, Preston NJ, Parkin A, Makower S, Gee J, Ross D, Tarrant R, Davison J, Halpin S, O’Connor RJ, et al. The modified COVID-19 Yorkshire Rehabilitation Scale (C19-YRSm) patient-reported outcome measure for Long Covid or Post-COVID syndrome. J Med Virol. 2022;94(9):4253–64.

Johns MW. A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep. 1991;14(6):540–5.

Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13.

Van Dixhoorn J, Duivenvoorden H. Efficacy of Nijmegen Questionnaire in recognition of the hyperventilation syndrome. J Psychosom Res. 1985;29(2):199–206.

Evans R, Pick A, Lardner R, Masey V, Smith N, Greenhalgh T: Breathing difficulties after covid-19: a guide for primary care. BMJ 2023;381.

Van Dixhoorn J, Folgering H: The Nijmegen Questionnaire and dysfunctional breathing. In . , vol. 1: Eur Respiratory Soc; 2015.

Courtney R, Greenwood KM. Preliminary investigation of a measure of dysfunctional breathing symptoms: The Self Evaluation of Breathing Questionnaire (SEBQ). Int J Osteopathic Med. 2009;12(4):121–7.

Espinosa-Gonzalez A, Master H, Gall N, Halpin S, Rogers N, Greenhalgh T. Orthostatic tachycardia after covid-19. BMJ (Clinical Research ed). 2023;380:e073488–e073488.

PubMed   Google Scholar  

Bungo M, Charles J, Johnson P Jr. Cardiovascular deconditioning during space flight and the use of saline as a countermeasure to orthostatic intolerance. Aviat Space Environ Med. 1985;56(10):985–90.

CAS   PubMed   Google Scholar  

Sivan M, Corrado J, Mathias C. The Adapted Autonomic Profile (Aap) Home-Based Test for the Evaluation of Neuro-Cardiovascular Autonomic Dysfunction. Adv Clin Neurosci Rehabil. 2022;3:10–13. https://doi.org/10.47795/QKBU46715 .

Lee C, Greenwood DC, Master H, Balasundaram K, Williams P, Scott JT, Wood C, Cooper R, Darbyshire JL, Gonzalez AE. Prevalence of orthostatic intolerance in long covid clinic patients and healthy volunteers: A multicenter study. J Med Virol. 2024;96(3): e29486.

World Health Organization: Clinical management of covid-19 - living guideline. Geneva: WHO. Accessed 4th October 2023 at https://www.who.int/publications/i/item/WHO-2019-nCoV-clinical-2021-2 ; 2023.

Ahmed I, Mustafaoglu R, Yeldan I, Yasaci Z, Erhan B: Effect of pulmonary rehabilitation approaches on dyspnea, exercise capacity, fatigue, lung functions and quality of life in patients with COVID-19: A Systematic Review and Meta-Analysis. Arch Phys Med Rehabil 2022.

Dillen H, Bekkering G, Gijsbers S, Vande Weygaerde Y, Van Herck M, Haesevoets S, Bos DAG, Li A, Janssens W, Gosselink R, et al. Clinical effectiveness of rehabilitation in ambulatory care for patients with persisting symptoms after COVID-19: a systematic review. BMC Infect Dis. 2023;23(1):419.

Learmonth Y, Dlugonski D, Pilutti L, Sandroff B, Klaren R, Motl R. Psychometric properties of the fatigue severity scale and the modified fatigue impact scale. J Neurol Sci. 2013;331(1–2):102–7.

Webster K, Cella D, Yost K. The Functional Assessment of Chronic Illness T herapy (FACIT) Measurement System: properties, applications, and interpretation. Health Qual Life Outcomes. 2003;1(1):1–7.

Mundt JC, Marks IM, Shear MK, Greist JM. The Work and Social Adjustment Scale: a simple measure of impairment in functioning. Br J Psychiatry. 2002;180(5):461–4.

Chalder T, Berelowitz G, Pawlikowska T, Watts L, Wessely S, Wright D, Wallace E. Development of a fatigue scale. J Psychosom Res. 1993;37(2):147–53.

Shahid A, Wilkinson K, Marcu S, Shapiro CM: Visual analogue scale to evaluate fatigue severity (VAS-F). In: STOP, THAT and one hundred other sleep scales . edn.: Springer; 2011:399–402.

Parker M, Sawant HB, Flannery T, Tarrant R, Shardha J, Bannister R, Ross D, Halpin S, Greenwood DC, Sivan M. Effect of using a structured pacing protocol on post-exertional symptom exacerbation and health status in a longitudinal cohort with the post-COVID-19 syndrome. J Med Virol. 2023;95(1): e28373.

Kenny RA, Bayliss J, Ingram A, Sutton R. Head-up tilt: a useful test for investigating unexplained syncope. The Lancet. 1986;327(8494):1352–5.

Drury MOC: Science and Psychology. In: The selected writings of Maurice O’Connor Drury: On Wittgenstein, philosophy, religion and psychiatry. edn.: Bloomsbury Publishing; 2017.

Concato J, Shah N, Horwitz RI. Randomized, controlled trials, observational studies, and the hierarchy of research designs. N Engl J Med. 2000;342(25):1887–92.

Mongtomery K: How doctors think: Clinical judgment and the practice of medicine: Oxford University Press; 2005.

Download references

Acknowledgements

We are grateful to clinic staff for allowing us to study their work and to patients for allowing us to sit in on their consultations. We also thank the funder of LOCOMOTION (National Institute for Health Research) and the patient advisory group for lived experience input.

This research is supported by National Institute for Health Research (NIHR) Long Covid Research Scheme grant (Ref COV-LT-0016).

Author information

Authors and affiliations.

Nuffield Department of Primary Care Health Sciences, University of Oxford, Woodstock Rd, Oxford, OX2 6GG, UK

Trisha Greenhalgh, Julie L. Darbyshire & Emma Ladds

Imperial College Healthcare NHS Trust, London, UK

LOCOMOTION Patient Advisory Group and Lived Experience Representative, London, UK

You can also search for this author in PubMed   Google Scholar

Contributions

TG conceptualized the overall study, led the empirical work, supported the quality improvement meetings, conducted the ethnographic visits, led the data analysis, developed the theorization and wrote the first draft of the paper. JLD organized and led the quality improvement meetings, supported site-based researchers to collect and analyse data on their clinic, collated and summarized data on quality topics, and liaised with the patient advisory group. CL conceptualized and led the quality topic on POTS, including exploring reasons for some clinics’ reluctance to conduct testing and collating and analysing the NASA Lean Test data across all sites. EL assisted with ethnographic visits, data analysis, and theorization. JCS contributed lived experience of long covid and also clinical experience as an occupational therapist; she liaised with the wider patient advisory group, whose independent (patient-led) audit of long covid clinics informed the quality improvement prioritization exercise. All authors provided extensive feedback on drafts and contributed to discussions and refinements. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Trisha Greenhalgh .

Ethics declarations

Ethics approval and consent to participate.

LOng COvid Multidisciplinary consortium Optimising Treatments and servIces acrOss the NHS study is sponsored by the University of Leeds and approved by Yorkshire & The Humber—Bradford Leeds Research Ethics Committee (ref: 21/YH/0276) and subsequent amendments.

Patient participants in clinic were approached by the clinician (without the researcher present) and gave verbal informed consent for a clinically qualified researcher to observe the consultation. If they consented, the researcher was then invited to sit in. A written record was made in field notes of this verbal consent. It was impractical to seek consent from patients whose cases were discussed (usually with very brief clinical details) in online MDTs. Therefore, clinical case examples from MDTs presented in the paper are fictionalized cases constructed from multiple real cases and with key clinical details changed (for example, comorbidities were replaced with different conditions which would produce similar symptoms). All fictionalized cases were checked by our patient advisory group to check that they were plausible to lived experience experts.

Consent for publication

No direct patient cases are reported in this manuscript. For details of how the fictionalized cases were constructed and validated, see “Consent to participate” above.

Competing interests

TG was a member of the UK National Long Covid Task Force 2021–2023 and on the Oversight Group for the NICE Guideline on Long Covid 2021–2022. She is a member of Independent SAGE.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Greenhalgh, T., Darbyshire, J.L., Lee, C. et al. What is quality in long covid care? Lessons from a national quality improvement collaborative and multi-site ethnography. BMC Med 22 , 159 (2024). https://doi.org/10.1186/s12916-024-03371-6

Download citation

Received : 04 December 2023

Accepted : 26 March 2024

Published : 15 April 2024

DOI : https://doi.org/10.1186/s12916-024-03371-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Post-covid-19 syndrome
• Quality improvement
• Breakthrough collaboratives
• Warranted variation
• Unwarranted variation
• Improvement science
• Ethnography
• Idiographic reasoning
• Nomothetic reasoning

BMC Medicine

ISSN: 1741-7015

• Submission enquiries: [email protected]
• General enquiries: [email protected]

• Open access
• Published: 13 April 2024

Associations between transport modes and site-specific cancers: a systematic review and meta-analysis

• Win Thu 1 ,
• Alistair Woodward 1 ,
• Alana Cavadino 1 &
• Sandar Tin Tin 1 , 2  

Environmental Health volume  23 , Article number:  39 ( 2024 ) Cite this article

164 Accesses

Metrics details

Physical inactivity is a global public health problem. A practical solution would be to build physical activity into the daily routine by using active modes of transport. Choice of transport mode can influence cancer risk through their effects on levels of physical activity, sedentary time, and environmental pollution. This review synthesizes existing evidence on the associations of specific transport modes with risks of site-specific cancers.

Relevant literature was searched in PubMed, Embase, and Scopus from 1914 to 17th February 2023. For cancer sites with effect measures available for a specific transport mode from two or more studies, random effects meta-analyses were performed to pool relative risks (RR) comparing the highest vs. lowest activity group as well as per 10 Metabolic Equivalent of Task (MET) hour increment in transport-related physical activity per week ( ∼ 150 min of walking or 90 min of cycling).

27 eligible studies (11 cohort, 15 case-control, and 1 case-cohort) were identified, which reported the associations of transport modes with 10 site-specific cancers. In the meta-analysis, 10 MET hour increment in transport-related physical activity per week was associated with a reduction in risk for endometrial cancer (RR: 0.91, 95% CI: 0.83–0.997), colorectal cancer (RR: 0.95, 95% CI: 0.91–0.99) and breast cancer (RR: 0.99, 95% CI: 0.89–0.996). The highest level of walking only or walking and cycling combined modes, compared to the lowest level, were significantly associated with a 12% and 30% reduced risk of breast and endometrial cancers respectively. Cycling, compared to motorized modes, was associated with a lower risk of overall cancer incidence and mortality.

Active transport appears to reduce cancer risk, but evidence for cancer sites other than colorectum, breast, and endometrium is currently limited.

Peer Review reports

Introduction

Physical inactivity is a global public health problem, contributing to substantial disease and economic burden worldwide [ 1 , 2 ]. With rapid changes in technology, lifestyle, and habitual environment, people have been less active and more sedentary over the past few decades. Globally, about 1 in 4 adults were not active, i.e., did not meet the World Health Organization (WHO) recommendation of engaging at least 150–300 min of moderate-intensity or 75–150 min of vigorous-intensity aerobic physical activity per week [ 3 ], but the prevalence varied widely within and across countries [ 4 ]. If the current trends continue, it is unlikely that the WHO’s target to reduce physical inactivity by 10% in 2025 will be met.

One practical solution would be to build physical activity into the daily routine by using active modes of transport [ 5 ]. Walking and cycling have been shown to improve health (mainly all-cause mortality, cardiovascular disease, diabetes, and cancer) [ 6 ] and also provide social, economic and environmental benefits [ 7 , 8 ]. Car use, on the other hand, contributes to a significant proportion of daily sedentary time, and the situation is worsening with increasing traffic congestion/delays [ 9 ]; it has been associated with an increased risk of obesity and related outcomes [ 10 ]. Further, exposure to environmental pollutants such as nitrogen dioxide and/or particulate matter could differ across different road users [ 11 ], while it has been shown to increase the risk of certain cancers, particularly lung cancer [ 12 ].

While there is ample evidence linking leisure time physical activity or physical activity in general with a reduced risk for a number of cancer sites [ 13 , 14 ], and sedentary behavior in general with an increased risk [ 15 ], the findings may not be directly applicable to transport-related activity because the context and correlates of activity as well as its frequency, duration and intensity are likely to be different across different domains. We therefore reviewed the existing literature that reported the associations between transport modes and risks of site-specific cancers.

A systematic literature review and meta-analysis was conducted and reported according to the PRISMA guideline (Supplementary file S1 ). The review was not registered.

Search strategy and study selection

Relevant literature was searched from 1914 to 17th February 2023 in PubMed, Scopus, and Embase databases using the relevant search terms such as walking, cycling, car, public transport, commute and cancers. Site-specific cancers known to be associated with physical activity and body weight such as breast, colon, liver, esophageal adenocarcinoma and those associated with environmental factor such as lung and melanoma of skin were also searched (Supplementary file S2 ). The reference lists of systematic reviews on physical activity and cancers were also reviewed. Studies were included if they (1) used cohort, case-control, case-cohort or experimental design, (2) assessed transport modes such as walking, cycling, public transport or car use as the exposures of interest, (3) investigated one or more site-specific cancers, overall cancer incidence and/or mortality as the outcome(s), (4) reported effect measures associated with transport modes, and (5) published the full article in English. Studies that used cross-sectional design or mathematical modeling to estimate health impacts at the population level were excluded. Details of excluded studies after full text review, together with the reasons for exclusion, were provided in the Supplementary file S3 . WT conducted the search and selection, and STT oversaw the process.

Data extraction and study quality assessment

Information about title, first author, year of publication, study name (if available), country, study design, sample size, age range of the participants, follow-up duration (for cohort and case-cohort studies), data collection tool, measurement units for exposure(s), data sources for outcome(s), site-specific cancer assessed, effect measures, and confounders adjusted were extracted in a standardized data collection spreadsheet. The study quality was evaluated using the Newcastle-Ottawa Scale (NOS) [ 16 ], which scores the cohort and case-control studies based on three domains: selection of study groups, comparability of the groups and ascertainment of exposure (case-control studies) or outcome (cohort studies). For the second domain, a point was awarded for adjustment of Body Mass Index (BMI) - to evaluate the direct vs. indirect (through BMI) effect of physical activity on cancer risk, and another point for adjustment of physical activities from other domains - to isolate the effects of transport-related physical activity from other activities. A maximum of nine points were awarded, with a higher score indicating better quality [ 16 ]. For case-cohort studies, the NOS scale for cohort studies was used. WT conducted the data extraction and quality assessment, and STT oversaw the process.

Data analysis

For cancer sites with effect measures available for a specific transport mode from two or more studies, meta-analyses were performed using random effects models. The analyses compared the highest level of active transport such as walking, cycling or mixed mode with the lowest level as reported in the individual studies. Where necessary, the reference category for exposure was changed to the lowest group to facilitate pooling of the risks [ 17 ]. The pooled relative risks (RRs) and 95% CI were presented for breast, endometrial, colorectal and testicular cancers, and overall cancer mortality.

For studies that reported time or MET as measurement units, the dose-response effects were estimated using the trend estimation method proposed by Greenland and Longnecker [ 18 ]. The reported time spent for each mode/category was converted to MET hours (see Supplementary file S4 for conversion values and formulas used). For studies that only reported estimates for categorical exposures, study-specific slopes were calculated from the natural logs of the reported risk estimates across categories and risk estimates per unit change were then estimated. The pooled results were presented per 10 Metabolic Equivalent of Task (MET) hour increment in transport-related physical activity per week ( ∼ 150 min of walking or 90 min of cycling) to align with the WHO’s physical activity recommendation [ 3 ]. This approach enabled us to pool risk estimates from a large number of studies irrespective of how the exposures were assessed (e.g., walking and cycling separately or combined) or categorised. The results were presented for breast, endometrial, colorectal, prostate cancers, and overall cancer mortality.

Meta-analysis was not conducted for the studies that compared active and non-active modes in relation to overall cancer incidence and mortality due to the potential overlap of the study samples.

For meta-analyses involving four or more studies, publication bias was assessed through the visual inspection of funnel plots, Begg’s rank correlation test, and Egger’s regression test for asymmetry. If significant associations were observed, sensitivity analyses were conducted by removing one study at a time from the initial meta-analysis to test the robustness of the results. Where possible, sub-group analyses were performed to assess variability of summary effects across population groups (Western vs. Asian), study design (cohort vs. case-control), measurement units (time vs. MET) and adjustment for BMI (yes vs. no). Metafor [ 19 ] and dosresmeta [ 20 ] R packages were used for meta-analysis and trend estimation. All authors have access to the data.

Of the 11,829 records identified, 27 unique studies (total 34 publications) were included, of which 22 studies (28 records) contributed to the meta-analyses (Fig.  1 ). There were four publications from the Netherlands Cohort Study which reported endometrial [ 21 ], ovarian [ 22 ], prostate [ 23 ], and colorectal [ 24 ] cancers, three publications from United Kingdom Biobank which reported lung [ 25 ], breast and colon [ 26 ], and overall cancer incidence and mortality [ 27 ], two publications from Shanghai Women’s Health Study which reported breast [ 28 ] and overall cancer mortality [ 29 ], and two publications from National Institutes of Health - American Association of Retired Persons Diet and Health Study which reported breast [ 30 ] and endometrial [ 31 ] cancers. Of the included studies, 20 compared the risks between the highest and lowest levels of active transport (e.g., walking, cycling, walking and cycling) and two compared the risk between active and non-active commuting modes. The majority used case-control design ( n  = 15), followed by cohort ( n  = 11) and case-cohort ( n  = 1) designs. Most of the studies were conducted in North America, mainly in the United States (US) ( n  = 7), followed by Europe ( n  = 5), China ( n  = 5), United Kingdom ( n  = 4), Australia ( n  = 2) and the remaining four studies were from India, Iran, Brazil and Nigeria. (Table  1 )

Almost half of the studies assessed walking and cycling combined, i.e., did not provide the risk estimates for each mode ( n  = 13), while others assessed walking and cycling separately ( n  = 8), or assessed only one mode (walking: n  = 3 and cycling: n  = 3). Most studies quantified active transport in terms of time spent (e.g., minutes per day, hours per week) ( n  = 14) or MET ( n  = 7), but others assessed it in terms of activity status (e.g., yes, no) ( n  = 3), or in comparison to car or motorized mode ( n  = 2), and distance ( n  = 1) (Supplementary file S5 ). The studies reported the risks associated with ten site-specific cancers, most commonly breast ( n  = 12), endometrial ( n  = 5), and colorectal ( n  = 4) cancers (Fig.  2 ). Cancer cases were identified through cancer registries, death registries, pathological reports, or hospital or medical records (Supplementary file S5 ). The NOS score for cohort studies ranged from 5 to 9, with an average score of 6.5, and the score for case-control studies ranged from 4 to 7, with an average score of 5.6 (Table  1 , detailed scoring in Supplementary file S6 , S7 ).

Flow diagram for study selection

Cancers reported in the studies

Active transport studies

The pooled results were presented for breast, endometrial, colorectal, testicular and prostate cancers, and overall cancer mortality (Fig.  3 ). For other cancers where only one study was identified, the results from the individual study were presented.

Breast cancer

In the meta-analysis of six studies comparing the highest vs. lowest activity group, an inverse association was observed for walking (RR: 0.88, 95% CI: 0.78–0.98), a borderline inverse association for cycling (RR: 0.90, 95% CI: 0.77–1.05) and no significant association for walking and cycling combined (RR: 0.97, 95% CI: 0.84–1.12). 10 MET hour increment in transport-related physical activity per week ( ∼ 150 min of walking or 90 min of cycling) was associated with a marginally reduced risk (RR: 0.99, 95% CI: 0.97–0.996). (Fig. 3 , detailed forest plots in the supplementary file S9 )

Endometrial cancer

The meta-analysis of four studies indicated that walking and cycling combined was associated with a reduced risk of endometrial cancer (RR comparing highest vs. lowest: 0.70, 95% CI: 0.56–0.87; RR per 10 MET hour increment in activity per week: 0.91, 95% CI: 0.83–0.997). (Fig.  3 , detailed forest plots in S9 )

Colorectal cancer

In the meta-analysis of two studies, walking and cycling combined was associated with a reduced risk of colorectal cancer (RR comparing highest vs. lowest: 0.89, 95% CI: 0.78–1.01; RR per 10 MET hour increment in activity per week: 0.95, 95% CI: 0.91–0.99) (Fig.  3 , detailed forest plot in S9 ).

Testicular cancer

In the meta-analysis of two studies, there was no significant association between cycle commuting in adolescence and testicular germ cell cancer (RR comparing highest vs. lowest: 1.23, 95% CI: 0.71–2.13). (Fig.  3 , detailed forest plot in S9 )

Prostate cancer

10 MET hour increment per week for transport related physical activity was associated with a reduced risk of prostate cancer (RR: 0.96, 95% CI: 0.88–1.04) (Fig.  3 , detailed forest plot in S9 ).

Ovarian cancer

Only a case-cohort study assessed the relationship of walking and cycling combined mode with ovarian cancer risk, and reported no significant association (Supplementary file S5 ) [ 22 ].

Liver, gallbladder and biliary tract cancers

A cohort study reported a significant association of commuting physical activity with a reduced risk of gallbladder and biliary tract cancers in women (HR: 0.51, 95% CI: 0.28–0.94) but not in men (HR: 0.92, 95% CI: 0.61–1.37); there was no significant association with liver cancer in both sexes (supplementary file S5 ) [ 49 ].

Renal cancer

A case-control study assessed the association of walking and cycling with risk of renal cell carcinoma in white and black participants in the ages of 20s and 50s, and reported a significant association in the white participants in their 20s (OR comparing lowest vs. highest: 1.42, 95% CI: 1.10–1.83) but not in the black counterparts; the associations were also not significant in both groups in their 50s. (Supplementary file S5 ) [ 50 ].

Overall cancer mortality

In the meta-analyses of two studies, there was an inverse association for cycling only (RR comparing highest vs. lowest: 0.60, 95% CI: 0.34–1.04) and walking and cycling combined (RR: 0.98, 95% CI: 0.86–1.12), and also per 10 MET hour increment in activity per week (RR: 0.97, 95% CI: 0.92–1.01). (Fig.  3 , detailed forest plots in S9 )

In sub-group analyses, similar associations were observed between walking and breast cancer risk in terms of study design (cohort, case-control), population (western vs. Asian), measurement unit (time vs. MET), menopausal status (premenopausal and postmenopausal) and adjustment of BMI (yes vs. no); however, the associations were stronger in studies that adjusted for physical activity from other domains (Supplementary file S10 ). In the leave-one-out analyses assessing walking and breast cancer risk, the results were sensitive to effect sizes from some studies, but this was not the case for walking and cycling combined mode and endometrial cancer (Supplementary file S11 ). There was no evidence for funnel plot asymmetry; Egger’s regression tests and Begg’s ranks correlation tests were not significant (Supplementary file S12 ).

Results of meta-analysis for active transport studies. RE = a random-effects model, MET = Metabolic Equivalent of Task, I 2  = I 2 statistics for heterogeneity, RR = Summary relative risk

Studies comparing active vs. non-active modes for commuting

Four eligible publications were identified, of which three used the data from UK Biobank [ 25 , 26 , 27 ], one used the UK census data [ 51 ]. Three reported the associations for overall cancer incidence and mortality, and one reported the risk associated with lung cancer (Fig.  4 ). In the study that assessed lung cancer using the data from UK Biobank, when compared to automobile only mode, active modes did not show a significant association whereas frequent use of public transport (≥ 5 trips per week) was associated with an increased risk of lung cancer (HR: 1.58, 95% CI: 1.08–2.33) [ 25 ] (Fig.  4 ). In another UK Biobank study, no significant associations were observed for breast and colon cancers, and overall cancer incidence and mortality when more active patterns of commuting (walking, cycling, public transport, either alone or in combination with car) were compared to car only mode [ 26 ].

The results of two studies [ 27 , 51 ] that assessed overall cancer incidence and mortality were not combined as the outcome data was extracted from the same national cancer registry with an overlapped time frame (1991–2011 and 2007–2014), although the exposure information came from different sources (census and UK Biobank). In these studies, compared to private motorized mode or non-active mode, cycling was inversely associated with overall cancer incidence and mortality. Walking and public transport were also inversely associated with overall cancer incidence in the study that used the census data [ 51 ].

Results of the individual studies comparing active vs. non-active modes for commuting. Private motorized mode = car or motorcycle, Non-active = car or public transport, Active patterns of commuting = any other patterns including walking, cycling, public transport, either alone or in combination with car, HR = Hazard Ratio, regular:1–4, often: ≥5 work-bound trips/week

This review identified 27 studies (34 publications) that reported the associations of specific transport modes, mainly active transport modes, with risks of ten site-specific cancers along with overall cancer incidence and mortality. The most frequently studied cancer sites were breast, endometrium, and colorectum; our meta-analysis showed a reduction in risk of these cancers (1%, 9% and 5%, respectively) per 10 MET hour per week increment in transport-related physical activity ( ∼ 150 min of walking or 90 min of cycling).

We found an inverse association between active transport and risks of breast and endometrial cancers, with similar magnitude of risk reduction observed in previous systematic reviews on physical activity in general [ 55 , 56 ]. While obesity is known to increase post-menopausal but not pre-menopausal breast cancer risk [ 57 ], we found similar results by menopausal status. In contrast, an earlier review did not find any significant association between walking in general and risk of pre- or post-menopausal breast cancer [ 58 ], possibly because compared to walking for transport, walking for leisure or at home generally uses lower energy [ 59 ], and therefore may have less effect on body weight.

The inverse association of active transport with colorectal cancer risk observed in this review is also consistent with the findings from existing reviews on transport-related physical activity [ 60 ] as well as physical activity in general [ 61 ]. While physical activity in general or for leisure has also been associated with a reduced risk of many other cancer sites including liver, gastric, renal and lung [ 13 , 14 ], the evidence related to transport-related physical activity is currently limited.

Mechanisms linking physical activity with specific cancer sites have been proposed, including its effects on sex hormones (breast, endometrial and prostate cancers), insulin sensitivity, glucose metabolism and adipokines (obesity-related cancers), and inflammation and immune function (most cancers) [ 62 ]. For colorectal cancer, another potential mechanism is reduced contact time between carcinogens and bowel mucosa cells due to exercise-induced intestinal mobility [ 63 ].

The overall quality of the included studies, evaluated by NOS score, ranged from 4 to 9, and in general, cohort studies tend to have higher scores compared to case-control studies. The common criteria the studies did not meet include: inadequate exposure assessment, loss to follow-up (cohort studies) and low response rates (case-control studies). While we were not able to undertake subgroup analyses by NOS score due to the limited number of studies available, our subgroup analyses by study design showed similar associations between walking and breast cancer in cohort vs. case-control studies.

To our knowledge, this review represents the first systematic attempt to synthesize the existing evidence on specific transport modes and site-specific cancers. We provided mode-specific summary effects where possible and calculated the dose-response effects for transport-related physical activity, in line with WHO physical activity recommendation. When interpreting the findings, some limitations need to be considered. First, the review may not have included some eligible studies published in languages other than English. Second, due to the limited number of available studies, we were not able to pool the results separately for cohort and case-control studies; however, we conducted sub-group analyses by study design where possible. We were not able to evaluate the non-linear relationship between transport-related physical activity and the risks of site-specific cancers. While a recent systematic review on breast and colon cancers reported a linear relationship with physical activity [ 64 ], others suggested a non-linear relationship between physical activity and cancer risk [ 65 , 66 ]. Further, variations in measurement and categorization of the exposure across the studies make direct comparison of the results between different modes (e.g., walking vs. cycling) difficult. Finally, the majority of the studies included were conducted in high income countries in Europe, UK, and North America, limiting the generalizability of the findings to other populations and low and middle income countries where urbanization and motorization are mainly taking place [ 67 ].

Our findings suggest that transport choices may influence cancer risk, particularly of obesity-related cancers such as breast, colon and endometrial cancers. Breast cancer is the most common cancer in women globally, with an estimated over 2 million new cases (11.7% of all new cases) in 2020, while colon cancer stood at fourth place (over 1 million cases, 6% of total cases) [ 68 ]. The incidence of endometrial cancer also seems to be increasing in many countries particularly in younger women. Our findings indicate that the risks of these cancers can be reduced by meeting the WHO physical activity recommendation through active commuting ( ∼ 150 min of walking or 90 min of cycling per week). Yet, the current evidence is limited in relation to other cancer sites, underlying mechanisms, and potential environmental influences, requiring further exploration.

Given heterogeneity in exposure measurements in the existing studies, harmonizing choice of the assessment tool (e.g., using International Physical Activity Questionnaires that can capture information about all four physical activity domains including transport modes), and reporting the dose-response estimates for each transport mode such as walking and cycling separately rather than a combined mode would enhance comparability of results and provide mode-specific effects. Repeated or regular assessments of exposures/transport modes used throughout the study duration would capture changes and their potential impact on outcomes in cohort studies. Importantly, more research is needed in low and middle-income settings to generate context-specific evidence.

In conclusion, active transport modes appear to reduce cancer risk, but evidence for cancer sites other than colorectum, breast and endometrium is currently limited.

Data availability

No datasets were generated or analysed during the current study.

Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380(9838):219–29. https://doi.org/10.1016/S0140-6736(12)61031-9 .

Article   Google Scholar  

Ding D, Lawson KD, Kolbe-Alexander TL, et al. The economic burden of physical inactivity: a global analysis of major non-communicable diseases. Lancet. 2016;388(10051):1311–24. https://doi.org/10.1016/S0140-6736(16)30383-X .

World Health Organization. Global status report on physical activity 2022.; 2022.

Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants. Lancet Global Health. 2018;6(10):e1077–86. https://doi.org/10.1016/S2214-109X(18)30357-7 .

Berrigan D, Troiano RP, McNeel T, DiSogra C, Ballard-Barbash R. Active transportation increases adherence to activity recommendations. Am J Prev Med. 2006;31(3):210–6. https://doi.org/10.1016/j.amepre.2006.04.007 .

Dinu M, Pagliai G, Macchi C, Sofi F. Active commuting and multiple Health outcomes: a systematic review and Meta-analysis. Sports Med. 2019;49(3):437–52. https://doi.org/10.1007/s40279-018-1023-0 .

Boniface S, Scantlebury R, Watkins SJ, Mindell JS. Health implications of transport: evidence of effects of transport on social interactions. J Transp Health. 2015;2(3):441–6. https://doi.org/10.1016/j.jth.2015.05.005 .

Higgins PAT. Exercise-based transportation reduces oil dependence, carbon emissions and obesity. Envir Conserv. 2005;32(3):197–202. https://doi.org/10.1017/S037689290500247X .

Article   CAS   Google Scholar  

World Health Organization. Global Status Report on Road Safety 2018: Summary.; 2018.

Sugiyama T, Chandrabose M, Homer AR, Sugiyama M, Dunstan DW, Owen N. Car use and cardiovascular disease risk: systematic review and implications for transport research. J Transp Health. 2020;19:100930. https://doi.org/10.1016/j.jth.2020.100930 .

Panchal R, Panagi M, May HR, et al. Personal air pollution exposure during morning commute car and active transport journeys. J Transp Health. 2022;26:101365. https://doi.org/10.1016/j.jth.2022.101365 .

Turner MC, Andersen ZJ, Baccarelli A, et al. Outdoor air pollution and cancer: an overview of the current evidence and public health recommendations. CA Cancer J Clin. 2020;70(6):460–79. https://doi.org/10.3322/caac.21632 .

Moore SC, Lee IM, Weiderpass E, et al. Association of leisure-time physical activity with risk of 26 types of Cancer in 1.44 million adults. JAMA Intern Med. 2016;176(6):816. https://doi.org/10.1001/jamainternmed.2016.1548 .

Rezende LFMD, Sá THD, Markozannes G, et al. Physical activity and cancer: an umbrella review of the literature including 22 major anatomical sites and 770 000 cancer cases. Br J Sports Med. 2018;52(13):826–33. https://doi.org/10.1136/bjsports-2017-098391 .

Hermelink R, Leitzmann MF, Markozannes G, et al. Sedentary behavior and cancer–an umbrella review and meta-analysis. Eur J Epidemiol. 2022;37(5):447–60. https://doi.org/10.1007/s10654-022-00873-6 .

Wells G, Shea B, O’Connell D et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp .

Taylor K. Wanting a particular reference category in categorical risk data. Data extraction tips for meta-analysis. https://www.cebm.ox.ac.uk/resources/data-extraction-tips-meta-analysis/reference-category-risk-data .

Greenland S, Longnecker MP. Methods for Trend Estimation from Summarized Dose-Response Data, with applications to Meta-Analysis. Am J Epidemiol. 1992;135(11):1301–9. https://doi.org/10.1093/oxfordjournals.aje.a116237 .

Viechtbauer W. Conducting Meta-analyses in R with the metafor Package. J Stat Soft. 2010;36(3). https://doi.org/10.18637/jss.v036.i03 .

Crippa A, Orsini N. Multivariate Dose-Response Meta-Analysis: The dosresmeta R Package. J Stat Soft. 2016;72(Code Snippet 1). https://doi.org/10.18637/jss.v072.c01 .

Schouten LJ, Goldbohm RA, van den Brandt PA, Anthropometry. Physical activity, and Endometrial Cancer Risk: results from the Netherlands Cohort Study. JNCI J Natl Cancer Inst. 2004;96(21):1635–8. https://doi.org/10.1093/jnci/djh291 .

Biesma RG, Schouten LJ, Dirx MJM, Goldbohm RA, van den Brandt PA. Physical activity and risk of ovarian Cancer: results from the Netherlands Cohort Study (the Netherlands). Cancer Causes Control. 2006;17(1):109–15. https://doi.org/10.1007/s10552-005-0422-3 .

Zeegers MPA, Dirx MJM, van den Brandt PA. Physical activity and the risk of prostate Cancer in the Netherlands Cohort Study, results after 9.3 years of follow-up. Cancer Epidemiol Biomarkers Prev. 2005;14(6):1490–5. https://doi.org/10.1158/1055-9965.EPI-04-0771 .

Simons CCJM, Hughes LAE, van Engeland M, Goldbohm RA, van den Brandt PA, Weijenberg MP. Physical activity, occupational sitting time, and Colorectal Cancer Risk in the Netherlands Cohort Study. Am J Epidemiol. 2013;177(6):514–30. https://doi.org/10.1093/aje/kws280 .

Wong JYY, Jones RR, Breeze C, et al. Commute patterns, residential traffic-related air pollution, and lung cancer risk in the prospective UK Biobank cohort study. Environ Int. 2021;155:106698. https://doi.org/10.1016/j.envint.2021.106698 .

Panter J, Mytton O, Sharp S, et al. Using alternatives to the car and risk of all-cause, cardiovascular and cancer mortality. Heart. 2018;104(21):1749–55. https://doi.org/10.1136/heartjnl-2017-312699 .

Celis-Morales CA, Lyall DM, Welsh P, et al. Association between active commuting and incident cardiovascular disease, cancer, and mortality: prospective cohort study. BMJ Published Online April. 2017;19:j1456. https://doi.org/10.1136/bmj.j1456 .

Pronk A, Ji BT, Shu XO, et al. Physical activity and breast cancer risk in Chinese women. Br J Cancer. 2011;105(9):1443–50. https://doi.org/10.1038/bjc.2011.370 .

Matthews CE, Jurj AL, Shu Xo, et al. Influence of Exercise, walking, Cycling, and overall nonexercise physical activity on Mortality in Chinese women. Am J Epidemiol. 2007;165(12):1343–50. https://doi.org/10.1093/aje/kwm088 .

George SM, Irwin ML, Matthews CE, et al. Beyond recreational physical activity: examining Occupational and Household Activity, Transportation Activity, and sedentary behavior in relation to postmenopausal breast Cancer Risk. Am J Public Health. 2010;100(11):2288–95. https://doi.org/10.2105/AJPH.2009.180828 .

Gierach GL, Chang SC, Brinton LA, et al. Physical activity, sedentary behavior, and endometrial cancer risk in the NIH-AARP Diet and Health Study. Int J Cancer. 2009;124(9):2139–47. https://doi.org/10.1002/ijc.24059 .

Luoto R, Latikka P, Pukkala E, Hakulinen T, Vihko V. The effect of physical activity on breast cancer risk: a cohort study of 30,548 women. Eur J Epidemiol. 2000;16(10):973–80. https://doi.org/10.1023/A:1010847311422 .

Gomes MLB, Pinto SS, Domingues MR. Physical activity and breast Cancer: a case-control study in Southern Brazil. Nutr Cancer. 2022;74(1):149–57. https://doi.org/10.1080/01635581.2021.1880607 .

Azubuike SO, Hayes L, Sharp L, Alabi A, Oyesegun RA, McNally R. Physical activity and the risk of breast cancer among Nigerian women. Cancer Epidemiol. 2022;78:102163. https://doi.org/10.1016/j.canep.2022.102163 .

Si S, Boyle T, Heyworth J, Glass DC, Saunders C, Fritschi L. Lifetime physical activity and risk of breast cancer in pre-and post-menopausal women. Breast Cancer Res Treat. 2015;152(2):449–62. https://doi.org/10.1007/s10549-015-3489-x .

Mathew A, Gajalakshmi V, Rajan B, et al. Physical activity levels among urban and rural women in south India and the risk of breast cancer: a case–control study. Eur J Cancer Prev. 2009;18(5):368–76. https://doi.org/10.1097/CEJ.0b013e32832e1c46 .

Steindorf K. Case-control study of physical activity and breast Cancer risk among Premenopausal women in Germany. Am J Epidemiol. 2003;157(2):121–30. https://doi.org/10.1093/aje/kwf181 .

John EM, Horn-Ross PL, Koo J. Lifetime physical activity and breast cancer risk in a multiethnic population: the San Francisco Bay area breast cancer study. Cancer Epidemiol Biomarkers Prev. 2003;12(11 Pt 1):1143–52.

Google Scholar  

Matthews CE, Shu XO, Jin F, et al. Lifetime physical activity and breast cancer risk in the Shanghai breast Cancer Study. Br J Cancer. 2001;84(7):994–1001. https://doi.org/10.1054/bjoc.2000.1671 .

Marcus PM, Newman B, Moorman PG, et al. Physical activity at age 12 and adult breast cancer risk (United States). Cancer Causes Control. 1999;10(4):293–302. https://doi.org/10.1023/A:1008971417282 .

Friberg E, Mantzoros CS, Wolk A. Physical activity and risk of Endometrial Cancer: a Population-based prospective cohort study. Cancer Epidemiol Biomarkers Prev. 2006;15(11):2136–40. https://doi.org/10.1158/1055-9965.EPI-06-0465 .

John EM, Koo J, Horn-Ross PL. Lifetime physical activity and risk of Endometrial Cancer. Cancer Epidemiol Biomarkers Prev. 2010;19(5):1276–83. https://doi.org/10.1158/1055-9965.EPI-09-1316 .

Matthews CE, Xu WH, Zheng W, et al. Physical activity and risk of Endometrial Cancer: a report from the Shanghai Endometrial Cancer Study. Cancer Epidemiol Biomarkers Prev. 2005;14(4):779–85. https://doi.org/10.1158/1055-9965.EPI-04-0665 .

Mahmood S, English DR, MacInnis RJ, et al. Domain-specific physical activity and the risk of colorectal cancer: results from the Melbourne Collaborative Cohort Study. BMC Cancer. 2018;18(1):1063. https://doi.org/10.1186/s12885-018-4961-x .

Hou L. Commuting physical activity and risk of Colon cancer in Shanghai, China. Am J Epidemiol. 2004;160(9):860–7. https://doi.org/10.1093/aje/kwh301 .

Littman AJ, Doody DR, Biggs ML, Weiss NS, Starr JR, Schwartz SM. Physical activity in adolescence and testicular germ cell cancer risk. Cancer Causes Control. 2009;20(8):1281–90. https://doi.org/10.1007/s10552-009-9347-6 .

Coldman AJ, Elwood JM, Gallagher RP. Sports activities and risk of testicular cancer. Br J Cancer. 1982;46(5):749–56. https://doi.org/10.1038/bjc.1982.267 .

Hosseini M, SeyedAlinaghi S, Mahmoudi M, McFarland W. A case-control study of risk factors for prostate cancer in Iran. Acta Med Iran. 2010;48(1):61–6.

Pang Y, Lv J, Kartsonaki C, et al. Association of physical activity with risk of hepatobiliary diseases in China: a prospective cohort study of 0.5 million people. Br J Sports Med. 2021;55(18):1024–33. https://doi.org/10.1136/bjsports-2020-102174 .

Xiao Q, Liao L, Matthews CE, et al. Physical activity and renal cell carcinoma among black and white americans: a case-control study. BMC Cancer. 2014;14(1):707. https://doi.org/10.1186/1471-2407-14-707 .

Patterson R, Panter J, Vamos EP, Cummins S, Millett C, Laverty AA. Associations between commute mode and cardiovascular disease, cancer, and all-cause mortality, and cancer incidence, using linked Census data over 25 years in England and Wales: a cohort study. Lancet Planet Health. 2020;4(5):e186–94. https://doi.org/10.1016/S2542-5196(20)30079-6 .

Sahlqvist S, Goodman A, Simmons RK, et al. The association of cycling with all-cause, cardiovascular and cancer mortality: findings from the population-based EPIC-Norfolk cohort. BMJ Open. 2013;3(11):e003797. https://doi.org/10.1136/bmjopen-2013-003797 .

Autenrieth CS, Baumert J, Baumeister SE, et al. Association between domains of physical activity and all-cause, cardiovascular and cancer mortality. Eur J Epidemiol. 2011;26(2):91–9. https://doi.org/10.1007/s10654-010-9517-6 .

Batty GD, Shipley MJ, Marmot M, Smith GD. Physical activity and cause-specific mortality in men: further evidence from the Whitehall study. Eur J Epidemiol. 2001;17(9):863–9. https://doi.org/10.1023/A:1015609909969 .

Wu Y, Zhang D, Kang S. Physical activity and risk of breast cancer: a meta-analysis of prospective studies. Breast Cancer Res Treat. 2013;137(3):869–82. https://doi.org/10.1007/s10549-012-2396-7 .

Moore SC, Gierach GL, Schatzkin A, Matthews CE. Physical activity, sedentary behaviours, and the prevention of endometrial cancer. Br J Cancer. 2010;103(7):933–8. https://doi.org/10.1038/sj.bjc.6605902 .

García-Estévez L, Cortés J, Pérez S, Calvo I, Gallegos I, Moreno-Bueno G. Obesity and breast Cancer: a paradoxical and controversial relationship influenced by Menopausal Status. Front Oncol. 2021;11:705911. https://doi.org/10.3389/fonc.2021.705911 .

Chan DSM, Abar L, Cariolou M, et al. World Cancer Research Fund International: continuous update project—systematic literature review and meta-analysis of observational cohort studies on physical activity, sedentary behavior, adiposity, and weight change and breast cancer risk. Cancer Causes Control. 2019;30(11):1183–200. https://doi.org/10.1007/s10552-019-01223-w .

Compedium of Physical Activities. 17-Walking. Accessed August 3. 2023. https://sites.google.com/site/compendiumofphysicalactivities/Activity-Categories/walking?authuser=0 .

Mahmood S, MacInnis RJ, English DR, Karahalios A, Lynch BM. Domain-specific physical activity and sedentary behaviour in relation to colon and rectal cancer risk: a systematic review and meta-analysis. Int J Epidemiol. 2017;46(6):1797–813. https://doi.org/10.1093/ije/dyx137 .

Samad AKA, Taylor RS, Marshall T, Chapman MAS. A meta-analysis of the association of physical activity with reduced risk of colorectal cancer. Colorect Dis. 2005;7(3):204–13. https://doi.org/10.1111/j.1463-1318.2005.00747.x .

McTiernan A. Mechanisms linking physical activity with cancer. Nat Rev Cancer. 2008;8(3):205–11. https://doi.org/10.1038/nrc2325 .

Peters HPF. Potential benefits and hazards of physical activity and exercise on the gastrointestinal tract. Gut. 2001;48(3):435–9. https://doi.org/10.1136/gut.48.3.435 .

Diao X, Ling Y, Zeng Y, et al. Physical activity and cancer risk: a dose-response analysis for the global burden of Disease Study 2019. Cancer Commun. 2023;43(11):1229–43. https://doi.org/10.1002/cac2.12488 .

Garcia L, Pearce M, Abbas A, et al. Non-occupational physical activity and risk of cardiovascular disease, cancer and mortality outcomes: a dose–response meta-analysis of large prospective studies. Br J Sports Med. 2023;57(15):979–89. https://doi.org/10.1136/bjsports-2022-105669 .

Li T, Wei S, Shi Y, et al. The dose–response effect of physical activity on cancer mortality: findings from 71 prospective cohort studies. Br J Sports Med. 2016;50(6):339–45. https://doi.org/10.1136/bjsports-2015-094927 .

Unite, Nations. Department of Economic and Social Affair. World urbanization prospects: the 2018 revision. United Nations; 2019.

Sung H, Ferlay J, Siegel RL, Cancer, et al. J Clin. 2021;71(3):209–49. https://doi.org/10.3322/caac.21660 .

Download references

Acknowledgements

STT is supported by Sir Charles Hercus Health Research Fellowship from the Health Research Council of New Zealand (Ref: 23/051).

STT is supported by Sir Charles Hercus Health Research Fellowship from the Health Research Council of New Zealand (Ref: 23/051). The funder had no role in the study design, data collection, data analysis, data interpretation, writing of the report, approval of the manuscript, or decision to submit the manuscript for publication.

Author information

Authors and affiliations.

Epidemiology and Biostatistics, School of Population Health, University of Auckland, Auckland, New Zealand

Win Thu, Alistair Woodward, Alana Cavadino & Sandar Tin Tin

Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, UK

Sandar Tin Tin

You can also search for this author in PubMed   Google Scholar

Contributions

STT, AW, WT designed the study. STT supervised the study. WT conducted literature search, data analysis and wrote the original draft with critical inputs from STT, AC, and AW. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication. More than one author has directly accessed and verified the underlying data reported in the manuscript.

Corresponding author

Correspondence to Sandar Tin Tin .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

: Supplementary file S1 PRISMA checklist. Supplementary file S2 Literature search strategy. Supplementary file S3 List of excluded full texts with reasons. Supplementary file S4 Metabolic Equivalent of Task (MET) values used and MET hour per week conversion formulas. Supplementary file S5 Measurement units, effect measures and covariates included in the studies. Supplementary file S6 Newcastle-Ottawa Score of the studies (cohort studies). Supplementary file S7 Newcastle-Ottawa Score of the studies (case control studies). Supplementary file S8 Risks estimates used in the meta-analyses (separate excel sheet). Supplementary file S9 Forest plots. Supplementary file S10 Sub-group and covariates adjustment analyses. Supplementary file S11 Sensitivity analysis. Supplementary file S12 Funnel plots

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Thu, W., Woodward, A., Cavadino, A. et al. Associations between transport modes and site-specific cancers: a systematic review and meta-analysis. Environ Health 23 , 39 (2024). https://doi.org/10.1186/s12940-024-01081-3

Download citation

Received : 20 November 2023

Accepted : 08 April 2024

Published : 13 April 2024

DOI : https://doi.org/10.1186/s12940-024-01081-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Transport modes
• Active transport
• Site-specific cancers
• Systematic review
• Meta-analysis

Environmental Health

ISSN: 1476-069X

• General enquiries: [email protected]

Asking the better questions that unlock new answers to the working world's most complex issues.

Trending topics

AI insights

EY podcasts

EY webcasts

Operations leaders

Technology leaders

Marketing and growth leaders

Cybersecurity and privacy leaders

Risk leaders

EY Center for Board Matters

EY helps clients create long-term value for all stakeholders. Enabled by data and technology, our services and solutions provide trust through assurance and help clients transform, grow and operate.

Artificial Intelligence (AI)

Strategy, transaction and transformation consulting

Technology transformation

Tax function operations

Climate change and sustainability services

EY Ecosystems

Supply chain and operations

EY Partner Ecosystem

Explore Services

We bring together extraordinary people, like you, to build a better working world.

Experienced professionals

MBA and advanced-degree students

Student and entry level programs

Contract workers

EY-Parthenon careers

Discover how EY insights and services are helping to reframe the future of your industry.

Case studies

Energy and resources

How data analytics can strengthen supply chain performance

13-Jul-2023 Ben Williams

How Takeda harnessed the power of the metaverse for positive human impact

26-Jun-2023 Edwina Fitzmaurice

Banking and Capital Markets

How cutting back infused higher quality in transaction monitoring

11-Jul-2023 Ron V. Giammarco

At EY, our purpose is building a better working world. The insights and services we provide help to create long-term value for clients, people and society, and to build trust in the capital markets.

EY is now carbon negative

19-Sep-2022 Carmine Di Sibio

Our commitment to audit quality

13-Nov-2023 Julie A. Boland

No results have been found

Recent Searches

BEPS 2.0: as policies evolve, engagement is key

It remains to be seen whether the US will align its tax law with the OECD/G20’s global BEPS 2.0 rules. MNEs will feel the impact in 2024. Learn more.

How GenAI strategy can transform innovation

Companies considering or investing in a transformative GenAI strategy should tie generative artificial intelligence use cases to revenue, cost and expense. Learn more

Top five private equity trends for 2024

Read about the five key trends private equity firms will emphasize in 2024 as they create value

Select your location

close expand_more

How federal agencies are reducing cybersecurity risk from suppliers

Building and streamlining C-SCRM capabilities for a hyper-federated federal organization through the lens of people, process and technology

• 1. Better question
• 2. Better answer
• 3. Better working world

How EY can help

The better the question

What risks are suppliers introducing to your organization?

Early detection of supplier risks will enable risk-informed decisions.

New technologies are accelerating the pace of digital change and the broad-scale use of automation, data analytics and the cloud. Government and public sector organizations are increasingly concerned about their resiliency capabilities and are looking to provide a safer, more secure and affordable approach to managing their systems and data. A breach from a supplier or third party could be one of the greatest risks they face.

A federal agency engaged Ernst & Young LLP (EY US) to establish its enterprise cyber supply chain risk management (C-SCRM) program, focusing on helping leaders make risk-informed supplier decisions, reduce supplier risks and meet multiple federal compliance requirements. Doing business with a supplier that provides services, products or software and – knowingly or unknowingly – could be breached presents a major risk. This may result in financial loss, intelligence leaks, stolen intellectual property or reputational harm.

With a focus on information, communication and technology (ICT) and other department services and products, the federal agency wanted a better understanding of the potential risk that a supplier could pose to the organization across multiple risk lenses, such as cybersecurity and foreign interest. By understanding the potential risk, a leader will be able to make a risk-informed decision prior to securing a procurement.

In addition to the risk elements, there are many regulations and executive actions designed to protect organizations from breaches or supply chain attacks. Both risk and regulatory compliance have driven C-SCRM to be a top concern for this entity and other federal agencies today.

The better the answer

Enterprise C-SCRM services drive results and reduce supplier risk

Establish the processes. Train people. Apply technology and the right tools. Focus on what’s important.

EY US is recognized for its extensive risk and regulatory experience, system implementation and superior service delivery in SCRM services. It brought these capabilities to its engagement with a federal agency.

The EY approach helped the entity establish the processes and implement a C-SCRM program by driving innovation and leveraging two leading-edge technology platforms:

Supplier assessment platform

Utilizing commercial best practices honed from 15+ years of experience, EY teams developed tailored processes and implemented a supporting technology solution for this federal agency. The SCRM processes are risk-based so that the level of diligence conducted increases based on the risk the supplier presents to the organization. The established process is accompanied by the ServiceNow Vendor Risk Management application, which EY teams tailored to:

• Meet federal requirements while driving efficiency through an easy-to-use process
• Enable entity customization to meet the needs of a hyper-federated federal department made up of more than 50 independent entities
• Establish a centralized data set of supplier information providing enterprise visibility

The combination of best-in-class processes and a tailored technology solution set a new standard that government agencies are able to use to establish a leading-class C-SCRM program.

Business Relationship Economic and Threat Analysis

Business Relationship Economic and Threat Analysis (BRETA) is an automated tool that enables risk scoring by culling publicly and commercially available data sources from government and public sector resources to assess risks. It provides a multidimensional overview of threats in business relationships across six categories: financial, cybersecurity, geopolitical, technical, supply chain, and regulatory and compliance.

Working with the federal agency, EY teams used BRETA in combination with government data sources and analysts, conducting a detailed C-SCRM assessment to identify potential threats, monitor suppliers and offer actionable insights for risk mitigation. 

A more resilient suppler ecosystem

From a risk perspective, the federal agency is now able to:

• Make informed, risk-based decisions. Prior to acquiring products and services, the agency can now assess suppliers against multiple risk levels while addressing the impact on the organization.
• Reduce risk and provide secure, quality services. By making risk-based decisions, ongoing supplier monitoring and risk remediation, the agency can negate working with suppliers that present an unacceptable risk to the enterprise. This helps improve supplier security and negate potential supplier breaches that could have a large-scale impact on the department.
• Meet federal compliance. Due to recent breaches in the supply chain, regulators step in and introduce new regulatory requirements. The C-SCRM program is helping this federal agency meet and exceed numerous SCRM federal laws, regulations and standards while promoting transparency and better decision-making in governance, risk and compliance. 

SCRM programs are essential to improving the overall cyber posture of an organization – what makes this solution so compelling is the speed to market to stand up a program, the unique risk assessment capability of the BRETA tool, the alignment to NIST compliance standards and ultimately the ongoing monitoring capability to capture new risks over time.

Scott Smith

Principal, EY US Government and Public Sector Cybersecurity, Ernst & Young LLP

The better the world works

Understand the threats. Measure the risks.

Delivering an enterprise service that integrates risk assessment and risk treatment into the supplier management life cycle

EY teams worked with a federal agency to define what would be its end process: risk-based assessment process, entity onboarding, monitoring activities, mitigating identified risks, understanding how to handle new requests for suppliers, training users and suppliers, and integrating the service with other entities outside of the CISO. The integration helped with the change management efforts by embedding the C-SCRM services into existing procurement approval processes (e.g., the  Federal Information Technology Acquisition Reform Act ) and helped enrich the assessments by enabling intelligence teams to conduct a classified evaluation of high-risk suppliers.

After more than two years supporting this agency, EY teams has assessed over 2,000 supplier ICT or other department products and services. The C-SCRM assessments have helped change behavior while meeting government C-SCRM program requirements. Before the C-SCRM program, entities would have limited ability to assess a supplier and essentially would be “accepting” the risks present within the supplier – now leaders can make risk-informed decisions.

This was one of the first federal government agencies to stand up a C-SCRM program, and it is helping to set a standard for other government entities as they build their own programs to manage supplier cybersecurity risks at an enterprise level.

Our clients are facing increased uncertainty and disruptions, which is driving them to reassess their strategic imperatives and think differently about their operating environments. EY BRETA, coupled with our people, skills and experience, enables them to assess and monitor potential threats to their businesses and transform their decision-making from reactive to proactive.

Managing Director, Advisory Services, Ernst & Young LLP

Government & Public Sector services

Our Government & Public Sector practice provides a full range of consulting, strategy, tax and audit services.

Federal Government Consulting

Our Government & Public Sector team can help your federal agency achieve mission success. Find out how.

Digital Government

Our Government & Public Sector teams help governments transform their digital approach to improve citizens’ experiences. Learn more.

Related topics

Author on topics such as identity and access management, security analytics and machine learning. Long history advising government clients to successfully deliver long-lasting, transformative impacts.

Cybersecurity and technology leader. Retired Army Colonel with a son currently attending West Point. Distance runner and Boy Scout Council Advisory Board Member.

• Connect with us
• Our locations
• Do Not Sell or Share My Personal Information
• Legal and privacy
• Accessibility
• Open Facebook profile
• Open X profile
• Open LinkedIn profile
• Open Youtube profile

EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients.

ORIGINAL RESEARCH article

This article is part of the research topic.

Large Language Models in Work and Business

Evaluating ChatGPT-4's Historical Accuracy: A Case Study on the Origins of SWOT Provisionally Accepted

• 1 University of Twente, Netherlands
• 2 University of South-Eastern Norway (USN), Norway

The final, formatted version of the article will be published soon.

In this study we test ChatGPT-4's ability to provide accurate information about the origins and evolution of SWOT analysis, perhaps the most widely used strategy tool in practice worldwide.ChatGPT-4 is tested for historical accuracy and hallucinations. Our findings present a nuanced view of ChatGPT-4's capabilities. We observe that while ChatGPT-4 demonstrates a high level of proficiency in describing and outlining the general concept of SWOT analysis, there are notable discrepancies when it comes to detailing its origins and evolution. These inaccuracies range from minor factual errors to more serious hallucinations that deviate from evidence in scholarly publications. However, we also find that ChatGPT-4 comes up with spontaneous historically accurate facts. Our interpretation of the result is that ChatGPT is largely trained on easily available websites and to a very limited extent has been trained on scholarly publications on SWOT analysis, especially when these are behind a paywall. We conclude with four propositions for future research.

Keywords: ChatGPT, SWOT Analysis, Historical analysis of management concepts, AI in management research, strategy tools

Received: 16 Mar 2024; Accepted: 15 Apr 2024.

Copyright: © 2024 Puyt and Madsen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Richard W. Puyt, University of Twente, Enschede, Netherlands Prof. Dag Øivind Madsen, University of South-Eastern Norway (USN), Kongsberg, 3603, Vestfold, Norway

People also looked at

• Study Guides
• Homework Questions

Case Study - Risk Analysis and Mitigation Plans by Group#