research paper on drug control

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• Published: 13 November 2021

Risk and protective factors of drug abuse among adolescents: a systematic review

• Azmawati Mohammed Nawi 1 ,
• Rozmi Ismail 2 ,
• Fauziah Ibrahim 2 ,
• Mohd Rohaizat Hassan 1 ,
• Mohd Rizal Abdul Manaf 1 ,
• Noh Amit 3 ,
• Norhayati Ibrahim 3 &
• Nurul Shafini Shafurdin 2  

BMC Public Health volume  21 , Article number:  2088 ( 2021 ) Cite this article

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Drug abuse is detrimental, and excessive drug usage is a worldwide problem. Drug usage typically begins during adolescence. Factors for drug abuse include a variety of protective and risk factors. Hence, this systematic review aimed to determine the risk and protective factors of drug abuse among adolescents worldwide.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was adopted for the review which utilized three main journal databases, namely PubMed, EBSCOhost, and Web of Science. Tobacco addiction and alcohol abuse were excluded in this review. Retrieved citations were screened, and the data were extracted based on strict inclusion and exclusion criteria. Inclusion criteria include the article being full text, published from the year 2016 until 2020 and provided via open access resource or subscribed to by the institution. Quality assessment was done using Mixed Methods Appraisal Tools (MMAT) version 2018 to assess the methodological quality of the included studies. Given the heterogeneity of the included studies, a descriptive synthesis of the included studies was undertaken.

Out of 425 articles identified, 22 quantitative articles and one qualitative article were included in the final review. Both the risk and protective factors obtained were categorized into three main domains: individual, family, and community factors. The individual risk factors identified were traits of high impulsivity; rebelliousness; emotional regulation impairment, low religious, pain catastrophic, homework completeness, total screen time and alexithymia; the experience of maltreatment or a negative upbringing; having psychiatric disorders such as conduct problems and major depressive disorder; previous e-cigarette exposure; behavioral addiction; low-perceived risk; high-perceived drug accessibility; and high-attitude to use synthetic drugs. The familial risk factors were prenatal maternal smoking; poor maternal psychological control; low parental education; negligence; poor supervision; uncontrolled pocket money; and the presence of substance-using family members. One community risk factor reported was having peers who abuse drugs. The protective factors determined were individual traits of optimism; a high level of mindfulness; having social phobia; having strong beliefs against substance abuse; the desire to maintain one’s health; high paternal awareness of drug abuse; school connectedness; structured activity and having strong religious beliefs.

The outcomes of this review suggest a complex interaction between a multitude of factors influencing adolescent drug abuse. Therefore, successful adolescent drug abuse prevention programs will require extensive work at all levels of domains.

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Introduction

Drug abuse is a global problem; 5.6% of the global population aged 15–64 years used drugs at least once during 2016 [ 1 ]. The usage of drugs among younger people has been shown to be higher than that among older people for most drugs. Drug abuse is also on the rise in many ASEAN (Association of Southeast Asian Nations) countries, especially among young males between 15 and 30 years of age. The increased burden due to drug abuse among adolescents and young adults was shown by the Global Burden of Disease (GBD) study in 2013 [ 2 ]. About 14% of the total health burden in young men is caused by alcohol and drug abuse. Younger people are also more likely to die from substance use disorders [ 3 ], and cannabis is the drug of choice among such users [ 4 ].

Adolescents are the group of people most prone to addiction [ 5 ]. The critical age of initiation of drug use begins during the adolescent period, and the maximum usage of drugs occurs among young people aged 18–25 years old [ 1 ]. During this period, adolescents have a strong inclination toward experimentation, curiosity, susceptibility to peer pressure, rebellion against authority, and poor self-worth, which makes such individuals vulnerable to drug abuse [ 2 ]. During adolescence, the basic development process generally involves changing relations between the individual and the multiple levels of the context within which the young person is accustomed. Variation in the substance and timing of these relations promotes diversity in adolescence and represents sources of risk or protective factors across this life period [ 6 ]. All these factors are crucial to helping young people develop their full potential and attain the best health in the transition to adulthood. Abusing drugs impairs the successful transition to adulthood by impairing the development of critical thinking and the learning of crucial cognitive skills [ 7 ]. Adolescents who abuse drugs are also reported to have higher rates of physical and mental illness and reduced overall health and well-being [ 8 ].

The absence of protective factors and the presence of risk factors predispose adolescents to drug abuse. Some of the risk factors are the presence of early mental and behavioral health problems, peer pressure, poorly equipped schools, poverty, poor parental supervision and relationships, a poor family structure, a lack of opportunities, isolation, gender, and accessibility to drugs [ 9 ]. The protective factors include high self-esteem, religiosity, grit, peer factors, self-control, parental monitoring, academic competence, anti-drug use policies, and strong neighborhood attachment [ 10 , 11 , 12 , 13 , 14 , 15 ].

The majority of previous systematic reviews done worldwide on drug usage focused on the mental, psychological, or social consequences of substance abuse [ 16 , 17 , 18 ], while some focused only on risk and protective factors for the non-medical use of prescription drugs among youths [ 19 ]. A few studies focused only on the risk factors of single drug usage among adolescents [ 20 ]. Therefore, the development of the current systematic review is based on the main research question: What is the current risk and protective factors among adolescent on the involvement with drug abuse? To the best of our knowledge, there is limited evidence from systematic reviews that explores the risk and protective factors among the adolescent population involved in drug abuse. Especially among developing countries, such as those in South East Asia, such research on the risk and protective factors for drug abuse is scarce. Furthermore, this review will shed light on the recent trends of risk and protective factors and provide insight into the main focus factors for prevention and control activities program. Additionally, this review will provide information on how these risk and protective factors change throughout various developmental stages. Therefore, the objective of this systematic review was to determine the risk and protective factors of drug abuse among adolescents worldwide. This paper thus fills in the gaps of previous studies and adds to the existing body of knowledge. In addition, this review may benefit certain parties in developing countries like Malaysia, where the national response to drugs is developing in terms of harm reduction, prison sentences, drug treatments, law enforcement responses, and civil society participation.

This systematic review was conducted using three databases, PubMed, EBSCOhost, and Web of Science, considering the easy access and wide coverage of reliable journals, focusing on the risk and protective factors of drug abuse among adolescents from 2016 until December 2020. The search was limited to the last 5 years to focus only on the most recent findings related to risk and protective factors. The search strategy employed was performed in accordance with the Preferred Reporting Items for a Systematic Review and Meta-analysis (PRISMA) checklist.

A preliminary search was conducted to identify appropriate keywords and determine whether this review was feasible. Subsequently, the related keywords were searched using online thesauruses, online dictionaries, and online encyclopedias. These keywords were verified and validated by an academic professor at the National University of Malaysia. The keywords used as shown in Table  1 .

Selection criteria

The systematic review process for searching the articles was carried out via the steps shown in Fig.  1 . Firstly, screening was done to remove duplicate articles from the selected search engines. A total of 240 articles were removed in this stage. Titles and abstracts were screened based on the relevancy of the titles to the inclusion and exclusion criteria and the objectives. The inclusion criteria were full text original articles, open access articles or articles subscribed to by the institution, observation and intervention study design and English language articles. The exclusion criteria in this search were (a) case study articles, (b) systematic and narrative review paper articles, (c) non-adolescent-based analyses, (d) non-English articles, and (e) articles focusing on smoking (nicotine) and alcohol-related issues only. A total of 130 articles were excluded after title and abstract screening, leaving 55 articles to be assessed for eligibility. The full text of each article was obtained, and each full article was checked thoroughly to determine if it would fulfil the inclusion criteria and objectives of this study. Each of the authors compared their list of potentially relevant articles and discussed their selections until a final agreement was obtained. A total of 22 articles were accepted to be included in this review. Most of the excluded articles were excluded because the population was not of the target age range—i.e., featuring subjects with an age > 18 years, a cohort born in 1965–1975, or undergraduate college students; the subject matter was not related to the study objective—i.e., assessing the effects on premature mortality, violent behavior, psychiatric illness, individual traits, and personality; type of article such as narrative review and neuropsychiatry review; and because of our inability to obtain the full article—e.g., forthcoming work in 2021. One qualitative article was added to explain the domain related to risk and the protective factors among the adolescents.

PRISMA flow diagram showing the selection of studies on risk and protective factors for drug abuse among adolescents.2.2. Operational Definition

Drug-related substances in this context refer to narcotics, opioids, psychoactive substances, amphetamines, cannabis, ecstasy, heroin, cocaine, hallucinogens, depressants, and stimulants. Drugs of abuse can be either off-label drugs or drugs that are medically prescribed. The two most commonly abused substances not included in this review are nicotine (tobacco) and alcohol. Accordingly, e-cigarettes and nicotine vape were also not included. Further, “adolescence” in this study refers to members of the population aged between 10 to 18 years [ 21 ].

Data extraction tool

All researchers independently extracted information for each article into an Excel spreadsheet. The data were then customized based on their (a) number; (b) year; (c) author and country; (d) titles; (e) study design; (f) type of substance abuse; (g) results—risks and protective factors; and (h) conclusions. A second reviewer crossed-checked the articles assigned to them and provided comments in the table.

Quality assessment tool

By using the Mixed Method Assessment Tool (MMAT version 2018), all articles were critically appraised for their quality by two independent reviewers. This tool has been shown to be useful in systematic reviews encompassing different study designs [ 22 ]. Articles were only selected if both reviewers agreed upon the articles’ quality. Any disagreement between the assigned reviewers was managed by employing a third independent reviewer. All included studies received a rating of “yes” for the questions in the respective domains of the MMAT checklists. Therefore, none of the articles were removed from this review due to poor quality. The Cohen’s kappa (agreement) between the two reviewers was 0.77, indicating moderate agreement [ 23 ].

The initial search found 425 studies for review, but after removing duplicates and applying the criteria listed above, we narrowed the pool to 22 articles, all of which are quantitative in their study design. The studies include three prospective cohort studies [ 24 , 25 , 26 ], one community trial [ 27 ], one case-control study [ 28 ], and nine cross-sectional studies [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ]. After careful discussion, all reviewer panels agreed to add one qualitative study [ 46 ] to help provide reasoning for the quantitative results. The selected qualitative paper was chosen because it discussed almost all domains on the risk and protective factors found in this review.

A summary of all 23 articles is listed in Table  2 . A majority of the studies (13 articles) were from the United States of America (USA) [ 25 , 26 , 27 , 29 , 30 , 31 , 34 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ], three studies were from the Asia region [ 32 , 33 , 38 ], four studies were from Europe [ 24 , 28 , 40 , 44 ], and one study was from Latin America [ 35 ], Africa [ 43 ] and Mediterranean [ 45 ]. The number of sample participants varied widely between the studies, ranging from 70 samples (minimum) to 700,178 samples (maximum), while the qualitative paper utilized a total of 100 interviewees. There were a wide range of drugs assessed in the quantitative articles, with marijuana being mentioned in 11 studies, cannabis in five studies, and opioid (six studies). There was also large heterogeneity in terms of the study design, type of drug abused, measurements of outcomes, and analysis techniques used. Therefore, the data were presented descriptively.

After thorough discussion and evaluation, all the findings (both risk and protective factors) from the review were categorized into three main domains: individual factors, family factors, and community factors. The conceptual framework is summarized in Fig.  2 .

Conceptual framework of risk and protective factors related to adolescent drug abuse

DOMAIN: individual factor

Risk factors.

Almost all the articles highlighted significant findings of individual risk factors for adolescent drug abuse. Therefore, our findings for this domain were further broken down into five more sub-domains consisting of personal/individual traits, significant negative growth exposure, personal psychiatric diagnosis, previous substance history, comorbidity and an individual’s attitude and perception.

Personal/individual traits

Chuang et al. [ 29 ] found that adolescents with high impulsivity traits had a significant positive association with drug addiction. This study also showed that the impulsivity trait alone was an independent risk factor that increased the odds between two to four times for using any drug compared to the non-impulsive group. Another longitudinal study by Guttmannova et al. showed that rebellious traits are positively associated with marijuana drug abuse [ 27 ]. The authors argued that measures of rebelliousness are a good proxy for a youth’s propensity to engage in risky behavior. Nevertheless, Wilson et al. [ 37 ], in a study involving 112 youths undergoing detoxification treatment for opioid abuse, found that a majority of the affected respondents had difficulty in regulating their emotions. The authors found that those with emotional regulation impairment traits became opioid dependent at an earlier age. Apart from that, a case-control study among outpatient youths found that adolescents involved in cannabis abuse had significant alexithymia traits compared to the control population [ 28 ]. Those adolescents scored high in the dimension of Difficulty in Identifying Emotion (DIF), which is one of the key definitions of diagnosing alexithymia. Overall, the adjusted Odds Ratio for DIF in cannabis abuse was 1.11 (95% CI, 1.03–1.20).

Significant negative growth exposure

A history of maltreatment in the past was also shown to have a positive association with adolescent drug abuse. A study found that a history of physical abuse in the past is associated with adolescent drug abuse through a Path Analysis, despite evidence being limited to the female gender [ 25 ]. However, evidence from another study focusing at foster care concluded that any type of maltreatment might result in a prevalence as high as 85.7% for the lifetime use of cannabis and as high as 31.7% for the prevalence of cannabis use within the last 3-months [ 30 ]. The study also found significant latent variables that accounted for drug abuse outcomes, which were chronic physical maltreatment (factor loading of 0.858) and chronic psychological maltreatment (factor loading of 0.825), with an r 2 of 73.6 and 68.1%, respectively. Another study shed light on those living in child welfare service (CWS) [ 35 ]. It was observed through longitudinal measurements that proportions of marijuana usage increased from 9 to 18% after 36 months in CWS. Hence, there is evidence of the possibility of a negative upbringing at such shelters.

Personal psychiatric diagnosis

The robust studies conducted in the USA have deduced that adolescents diagnosed with a conduct problem (CP) have a positive association with marijuana abuse (OR = 1.75 [1.56, 1.96], p  < 0.0001). Furthermore, those with a diagnosis of Major Depressive Disorder (MDD) showed a significant positive association with marijuana abuse.

Previous substance and addiction history

Another study found that exposure to e-cigarettes within the past 30 days is related to an increase in the prevalence of marijuana use and prescription drug use by at least four times in the 8th and 10th grades and by at least three times in the 12th grade [ 34 ]. An association between other behavioral addictions and the development of drug abuse was also studied [ 29 ]. Using a 12-item index to assess potential addictive behaviors [ 39 ], significant associations between drug abuse and the groups with two behavioral addictions (OR = 3.19, 95% CI 1.25,9.77) and three behavioral addictions (OR = 3.46, 95% CI 1.25,9.58) were reported.

Comorbidity

The paper by Dash et al. (2020) highlight adolescent with a disease who needs routine medical pain treatment have higher risk of opioid misuse [ 38 ]. The adolescents who have disorder symptoms may have a risk for opioid misuse despite for the pain intensity.

Individual’s attitudes and perceptions

In a study conducted in three Latin America countries (Argentina, Chile, and Uruguay), it was shown that adolescents with low or no perceived risk of taking marijuana had a higher risk of abuse (OR = 8.22 times, 95% CI 7.56, 10.30) [ 35 ]. This finding is in line with another study that investigated 2002 adolescents and concluded that perceiving the drug as harmless was an independent risk factor that could prospectively predict future marijuana abuse [ 27 ]. Moreover, some youth interviewed perceived that they gained benefits from substance use [ 38 ]. The focus group discussion summarized that the youth felt positive personal motivation and could escape from a negative state by taking drugs. Apart from that, adolescents who had high-perceived availability of drugs in their neighborhoods were more likely to increase their usage of marijuana over time (OR = 11.00, 95% CI 9.11, 13.27) [ 35 ]. A cheap price of the substance and the availability of drug dealers around schools were factors for youth accessibility [ 38 ]. Perceived drug accessibility has also been linked with the authorities’ enforcement programs. The youth perception of a lax community enforcement of laws regarding drug use at all-time points predicted an increase in marijuana use in the subsequent assessment period [ 27 ]. Besides perception, a study examining the attitudes towards synthetic drugs based on 8076 probabilistic samples of Macau students found that the odds of the lifetime use of marijuana was almost three times higher among those with a strong attitude towards the use of synthetic drugs [ 32 ]. In addition, total screen time among the adolescent increase the likelihood of frequent cannabis use. Those who reported daily cannabis use have a mean of 12.56 h of total screen time, compared to a mean of 6.93 h among those who reported no cannabis use. Adolescent with more time on internet use, messaging, playing video games and watching TV/movies were significantly associated with more frequent cannabis use [ 44 ].

Protective factors

Individual traits.

Some individual traits have been determined to protect adolescents from developing drug abuse habits. A study by Marin et al. found that youth with an optimistic trait were less likely to become drug dependent [ 33 ]. In this study involving 1104 Iranian students, it was concluded that a higher optimism score (measured using the Children Attributional Style Questionnaire, CASQ) was a protective factor against illicit drug use (OR = 0.90, 95% CI: 0.85–0.95). Another study found that high levels of mindfulness, measured using the 25-item Child Acceptance and Mindfulness Measure, CAMM, lead to a slower progression toward injectable drug abuse among youth with opioid addiction (1.67 years, p  = .041) [ 37 ]. In addition, the social phobia trait was found to have a negative association with marijuana use (OR = 0.87, 95% CI 0.77–0.97), as suggested [ 31 ].

According to El Kazdouh et al., individuals with a strong belief against substance use and those with a strong desire to maintain their health were more likely to be protected from involvement in drug abuse [ 46 ].

DOMAIN: family factors

The biological factors underlying drug abuse in adolescents have been reported in several studies. Epigenetic studies are considered important, as they can provide a good outline of the potential pre-natal factors that can be targeted at an earlier stage. Expecting mothers who smoke tobacco and alcohol have an indirect link with adolescent substance abuse in later life [ 24 , 39 ]. Moreover, the dynamic relationship between parents and their children may have some profound effects on the child’s growth. Luk et al. examined the mediator effects between parenting style and substance abuse and found the maternal psychological control dimension to be a significant variable [ 26 ]. The mother’s psychological control was two times higher in influencing her children to be involved in substance abuse compared to the other dimension. Conversely, an indirect risk factor towards youth drug abuse was elaborated in a study in which low parental educational level predicted a greater risk of future drug abuse by reducing the youth’s perception of harm [ 27 , 43 ]. Negligence from a parental perspective could also contribute to this problem. According to El Kazdouh et al. [ 46 ], a lack of parental supervision, uncontrolled pocket money spending among children, and the presence of substance-using family members were the most common negligence factors.

While the maternal factors above were shown to be risk factors, the opposite effect was seen when the paternal figure equipped himself with sufficient knowledge. A study found that fathers with good information and awareness were more likely to protect their adolescent children from drug abuse [ 26 ]. El Kazdouh et al. noted that support and advice could be some of the protective factors in this area [ 46 ].

DOMAIN: community factors

• Risk factor

A study in 2017 showed a positive association between adolescent drug abuse and peers who abuse drugs [ 32 , 39 ]. It was estimated that the odds of becoming a lifetime marijuana user was significantly increased by a factor of 2.5 ( p  < 0.001) among peer groups who were taking synthetic drugs. This factor served as peer pressure for youth, who subconsciously had desire to be like the others [ 38 ]. The impact of availability and engagement in structured and unstructured activities also play a role in marijuana use. The findings from Spillane (2000) found that the availability of unstructured activities was associated with increased likelihood of marijuana use [ 42 ].

• Protective factor

Strong religious beliefs integrated into society serve as a crucial protective factor that can prevent adolescents from engaging in drug abuse [ 38 , 45 ]. In addition, the school connectedness and adult support also play a major contribution in the drug use [ 40 ].

The goal of this review was to identify and classify the risks and protective factors that lead adolescents to drug abuse across the three important domains of the individual, family, and community. No findings conflicted with each other, as each of them had their own arguments and justifications. The findings from our review showed that individual factors were the most commonly highlighted. These factors include individual traits, significant negative growth exposure, personal psychiatric diagnosis, previous substance and addiction history, and an individual’s attitude and perception as risk factors.

Within the individual factor domain, nine articles were found to contribute to the subdomain of personal/ individual traits [ 27 , 28 , 29 , 37 , 38 , 39 , 40 , 43 , 44 ]. Despite the heterogeneity of the study designs and the substances under investigation, all of the papers found statistically significant results for the possible risk factors of adolescent drug abuse. The traits of high impulsivity, rebelliousness, difficulty in regulating emotions, and alexithymia can be considered negative characteristic traits. These adolescents suffer from the inability to self-regulate their emotions, so they tend to externalize their behaviors as a way to avoid or suppress the negative feelings that they are experiencing [ 41 , 47 , 48 ]. On the other hand, engaging in such behaviors could plausibly provide a greater sense of positive emotions and make them feel good [ 49 ]. Apart from that, evidence from a neurophysiological point of view also suggests that the compulsive drive toward drug use is complemented by deficits in impulse control and decision making (impulsive trait) [ 50 ]. A person’s ability in self-control will seriously impaired with continuous drug use and will lead to the hallmark of addiction [ 51 ].

On the other hand, there are articles that reported some individual traits to be protective for adolescents from engaging in drug abuse. Youth with the optimistic trait, a high level of mindfulness, and social phobia were less likely to become drug dependent [ 31 , 33 , 37 ]. All of these articles used different psychometric instruments to classify each individual trait and were mutually exclusive. Therefore, each trait measured the chance of engaging in drug abuse on its own and did not reflect the chance at the end of the spectrum. These findings show that individual traits can be either protective or risk factors for the drugs used among adolescents. Therefore, any adolescent with negative personality traits should be monitored closely by providing health education, motivation, counselling, and emotional support since it can be concluded that negative personality traits are correlated with high risk behaviours such as drug abuse [ 52 ].

Our study also found that a history of maltreatment has a positive association with adolescent drug abuse. Those adolescents with episodes of maltreatment were considered to have negative growth exposure, as their childhoods were negatively affected by traumatic events. Some significant associations were found between maltreatment and adolescent drug abuse, although the former factor was limited to the female gender [ 25 , 30 , 36 ]. One possible reason for the contrasting results between genders is the different sample populations, which only covered child welfare centers [ 36 ] and foster care [ 30 ]. Regardless of the place, maltreatment can happen anywhere depending on the presence of the perpetrators. To date, evidence that concretely links maltreatment and substance abuse remains limited. However, a plausible explanation for this link could be the indirect effects of posttraumatic stress (i.e., a history of maltreatment) leading to substance use [ 53 , 54 ]. These findings highlight the importance of continuous monitoring and follow-ups with adolescents who have a history of maltreatment and who have ever attended a welfare center.

Addiction sometimes leads to another addiction, as described by the findings of several studies [ 29 , 34 ]. An initial study focused on the effects of e-cigarettes in the development of other substance abuse disorders, particularly those related to marijuana, alcohol, and commonly prescribed medications [ 34 ]. The authors found that the use of e-cigarettes can lead to more severe substance addiction [ 55 ], possibly through normalization of the behavior. On the other hand, Chuang et al.’s extensive study in 2017 analyzed the combined effects of either multiple addictions alone or a combination of multiple addictions together with the impulsivity trait [ 29 ]. The outcomes reported were intriguing and provide the opportunity for targeted intervention. The synergistic effects of impulsiveness and three other substance addictions (marijuana, tobacco, and alcohol) substantially increased the likelihood for drug abuse from 3.46 (95%CI 1.25, 9.58) to 10.13 (95% CI 3.95, 25.95). Therefore, proper rehabilitation is an important strategy to ensure that one addiction will not lead to another addiction.

The likelihood for drug abuse increases as the population perceives little or no harmful risks associated with the drugs. On the opposite side of the coin, a greater perceived risk remains a protective factor for marijuana abuse [ 56 ]. However, another study noted that a stronger determinant for adolescent drug abuse was the perceived availability of the drug [ 35 , 57 ]. Looking at the bigger picture, both perceptions corroborate each other and may inform drug use. Another study, on the other hand, reported that there was a decreasing trend of perceived drug risk in conjunction with the increasing usage of drugs [ 58 ]. As more people do drugs, youth may inevitably perceive those drugs as an acceptable norm without any harmful consequences [ 59 ].

In addition, the total spent for screen time also contribute to drug abuse among adolescent [ 43 ]. This scenario has been proven by many researchers on the effect of screen time on the mental health [ 60 ] that leads to the substance use among the adolescent due to the ubiquity of pro-substance use content on the internet. Adolescent with comorbidity who needs medical pain management by opioids also tend to misuse in future. A qualitative exploration on the perspectives among general practitioners concerning the risk of opioid misuse in people with pain, showed pain management by opioids is a default treatment and misuse is not a main problem for the them [ 61 ]. A careful decision on the use of opioids as a pain management should be consider among the adolescents and their understanding is needed.

Within the family factor domain, family structures were found to have both positive and negative associations with drug abuse among adolescents. As described in one study, paternal knowledge was consistently found to be a protective factor against substance abuse [ 26 ]. With sufficient knowledge, the father can serve as the guardian of his family to monitor and protect his children from negative influences [ 62 ]. The work by Luk et al. also reported a positive association of maternal psychological association towards drug abuse (IRR 2.41, p  < 0.05) [ 26 ]. The authors also observed the same effect of paternal psychological control, although it was statistically insignificant. This construct relates to parenting style, and the authors argued that parenting style might have a profound effect on the outcomes under study. While an earlier literature review [ 63 ] also reported such a relationship, a recent study showed a lesser impact [ 64 ] with regards to neglectful parenting styles leading to poorer substance abuse outcomes. Nevertheless, it was highlighted in another study that the adolescents’ perception of a neglectful parenting style increased their odds (OR 2.14, p  = 0.012) of developing alcohol abuse, not the parenting style itself [ 65 ]. Altogether, families play vital roles in adolescents’ risk for engaging in substance abuse [ 66 ]. Therefore, any intervention to impede the initiation of substance use or curb existing substance use among adolescents needs to include parents—especially improving parent–child communication and ensuring that parents monitor their children’s activities.

Finally, the community also contributes to drug abuse among adolescents. As shown by Li et al. [ 32 ] and El Kazdouh et al. [ 46 ], peers exert a certain influence on other teenagers by making them subconsciously want to fit into the group. Peer selection and peer socialization processes might explain why peer pressure serves as a risk factor for drug-abuse among adolescents [ 67 ]. Another study reported that strong religious beliefs integrated into society play a crucial role in preventing adolescents from engaging in drug abuse [ 46 ]. Most religions devalue any actions that can cause harmful health effects, such as substance abuse [ 68 ]. Hence, spiritual beliefs may help protect adolescents. This theme has been well established in many studies [ 60 , 69 , 70 , 71 , 72 ] and, therefore, could be implemented by religious societies as part of interventions to curb the issue of adolescent drug abuse. The connection with school and structured activity did reduce the risk as a study in USA found exposure to media anti-drug messages had an indirect negative effect on substances abuse through school-related activity and social activity [ 73 ]. The school activity should highlight on the importance of developmental perspective when designing and offering school-based prevention programs [75].

Limitations

We adopted a review approach that synthesized existing evidence on the risk and protective factors of adolescents engaging in drug abuse. Although this systematic review builds on the conclusion of a rigorous review of studies in different settings, there are some potential limitations to this work. We may have missed some other important factors, as we only included English articles, and article extraction was only done from the three search engines mentioned. Nonetheless, this review focused on worldwide drug abuse studies, rather than the broader context of substance abuse including alcohol and cigarettes, thereby making this paper more focused.

Conclusions

This review has addressed some recent knowledge related to the individual, familial, and community risk and preventive factors for adolescent drug use. We suggest that more attention should be given to individual factors since most findings were discussed in relation to such factors. With the increasing trend of drug abuse, it will be critical to focus research specifically on this area. Localized studies, especially those related to demographic factors, may be more effective in generating results that are specific to particular areas and thus may be more useful in generating and assessing local control and prevention efforts. Interventions using different theory-based psychotherapies and a recognition of the unique developmental milestones specific to adolescents are among examples that can be used. Relevant holistic approaches should be strengthened not only by relevant government agencies but also by the private sector and non-governmental organizations by promoting protective factors while reducing risk factors in programs involving adolescents from primary school up to adulthood to prevent and control drug abuse. Finally, legal legislation and enforcement against drug abuse should be engaged with regularly as part of our commitment to combat this public health burden.

Data availability and materials

All data generated or analysed during this study are included in this published article.

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The authors acknowledge The Ministry of Higher Education Malaysia and The Universiti Kebangsaan Malaysia, (UKM) for funding this study under the Long-Term Research Grant Scheme-(LGRS/1/2019/UKM-UKM/2/1). We also thank the team for their commitment and tireless efforts in ensuring that manuscript was well executed.

Financial support for this study was obtained from the Ministry of Higher Education, Malaysia through the Long-Term Research Grant Scheme-(LGRS/1/2019/UKM-UKM/2/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Understanding the Demand for Illegal Drugs (2010)

Chapter: 1 introduction, 1 introduction.

A merica’s problem with illegal drugs seems to be declining, and it is certainly less in the news than it was 20 years ago. Surveys have shown a decline in the number of users dependent on expensive drugs (Office of National Drug Control Policy, 2001), an aging of the population in treatment (Trunzo and Henderson, 2007), and a decline in the violence related to drug markets (Pollack et al., 2010). Still, research indicates that illegal drugs remain a concern for the majority of Americans (Caulkins and Mennefee, 2009; Gallup Poll, 2009).

There is virtually no disagreement that the trafficking in and use of cocaine, heroin, and methamphetamine continue to cause great harm to the nation, particularly to vulnerable minority communities in the major cities. In contrast, there is disagreement about marijuana use, which remains a part of adolescent development for about half of the nation’s youth. The disagreement concerns the amount, source, and nature of the harms from marijuana. Some note, for example, that most of those who use marijuana use it only occasionally and neither incur nor cause harms and that marijuana dependence is a much less serious problem than dependence on alcohol or cocaine. Others emphasize the evidence of a potential for triggering psychosis (Arseneault et al., 2004) and the strengthening evidence for a gateway effect (i.e., an opening to the use of other drugs) (Fergusson et al., 2006). The uncertainty of the causal mechanism is reflected in the fact that the gateway studies cannot disentangle the effect of the drug itself from its status as an illegal good (Babor et al., 2010).

The federal government probably spends $20 billion per year on a wide array of interventions to try to reduce drug consumption in the United States, from crop eradication in Colombia to mass media prevention programs aimed at preteens and their parents. 1 State and local governments spend comparable amounts, mostly for law enforcement aimed at suppressing drug markets. 2 Yet the available evidence, reviewed in detail in this report, shows that drugs are just as cheap and available as they have ever been.

Though fewer young people are starting to use drugs than in some previous years, for each successive birth cohort that turns 21, approximately half have experimented with illegal drugs. The number of people who are dependent on cocaine, heroin, and methamphetamine is probably declining modestly, 3 and drug-related violence has appears to have declined sharply. 4 At the same time, injecting drug use is still a major vector for HIV transmission, and drug markets blight parts of many U.S. cities.

The declines in drug use that have occurred in recent years are probably mostly the natural working out of old epidemics. Policy measures— whether they involve prevention, treatment, or enforcement—have met with little success at the population level (see Chapter 4 ). Moreover, research on prevention has produced little evidence of any targeted interventions that make a substantial difference in initiation to drugs when implemented on a large scale. For treatment programs, there is a large body of evidence of effectiveness and cost-effectiveness (reviewed in Babor et al., 2010), but the supply of treatment facilities is inadequate and,

perversely, not enough of those who need treatment are persuaded to seek it (see Chapter 4 ). Efforts to raise the price of drugs through interdiction and other enforcement programs have not had the intended effects: the prices of cocaine and heroin have declined for more than 25 years, with only occasional upward blips that rarely last more than 9 months (Walsh, 2009).

STUDY PROJECT AND GOALS

Given the persistence of drug demand in the face of lengthy and expensive efforts to control the markets, the National Institute of Justice asked the National Research Council (NRC) to undertake a study of current research on the demand for drugs in order to help better focus national efforts to reduce that demand. In response to that request, the NRC formed the Committee on Understanding and Controlling the Demand for Illegal Drugs. The committee convened a workshop of leading researchers in October 2007 and held two follow-up meetings to prepare this report. The statement of task for this project is as follows:

An ad hoc committee will conduct a workshop-based study that will identify and describe what is known about the nature and scope of markets for illegal drugs and the characteristics of drug users. The study will include exploration of research issues associated with drug demand and what is needed to learn more about what drives demand in the United States. The committee will specifically address the following issues:

What is known about the nature and scope of illegal drug markets and differences in various markets for popular drugs?

What is known about the characteristics of consumers in different markets and why the market remains robust despite the risks associated with buying and selling?

What issues can be identified for future research? Possibilities include the respective roles of dependence, heavy use, and recreational use in fueling the market; responses that could be developed to address different types of users; the dynamics associated with the apparent failure of policy interventions to delay or inhibit the onset of illegal drug use for a large proportion of the population; and the effects of enforcement on demand reduction.

Drawing on commissioned papers and presentations and discussions at a public workshop that it will plan and hold, the committee will prepare a report on the nature and operations of the illegal drug market in the United States and the research issues identified as having potential for informing policies to reduce the demand for illegal drugs.

The committee drew on economic models and their supporting data, as well as other research, as one part of the evidentiary base for this

report. However, the context for and content of this report were informed as well by the general discussion and the presentations in the workshop. The committee was not able to fully address task 2 because research in that area is not strong enough to give an accurate description of consumers across different markets nor to address the questions about why markets remain robust despite the risks associated with buying and selling. The discussion at the workshop underscored the point that neither the available ethnographic research nor the limited longitudinal research on drug-seeking behavior is strong enough to inform these questions related to task 2. With regard to task 3, the committee benefitted considerably from the paper by Jody Sindelar that was presented at the workshop and its discussion by workshop participants.

This study was intended to complement Informing America’s Policy on Illegal Drugs: What We Don’t Know Keeps Hurting Us (National Research Council, 2001) by giving more attention to the sources of demand and assessing the potential of demand-side interventions to make a substantial difference to the nation’s drug problems. This report therefore refers to supply-side considerations only to the extent necessary to understand demand.

The charge to the committee was extremely broad. It could have included reviewing the literature on such topics as characteristics of substance users, etiology of initiation of use, etiology of dependence, drug use prevention programs, and drug treatments. Two considerations led to narrowing the focus of our work. The first was substantive. Each of the topics just noted involves a very large field of well-developed research, and each has been reviewed elsewhere. Moreover, each of these areas of inquiry is currently expanding as a result of new research initiatives 5 and new technologies (e.g., neuroimaging, genetics). The second consideration was practical: given the available resources, we could not undertake a complete review of the entire field.

Thus, we decided to focus our work and this report tightly on demand models in the field of economics and to evaluate the data needs for advancing this relatively undeveloped area of investigation. That is, this area has a relatively shorter history of accumulated findings than the more clinical, biological, and epidemiological areas of drug research. Yet it is arguably better situated to inform government policy at the national level. A report on economic models and supporting data seemed to us more timely than a report on drug consumers and drug interventions.

The rest of this chapter briefly lays out some concepts that provide a basis for understanding the committee’s work and the rest of the report.

Chapter 2 presents the economic framework that seems most useful for studying the phenomenon of drug demand. It emphasizes the importance of understanding the responsiveness of demand and supply to price, which is the intermediate variable targeted by the principal government programs in the United States, namely, drug law enforcement. Chapter 3 then examines changes in the consumption of drugs and assesses the various indicators that are available to measure that consumption. Chapter 4 turns to the program type that most focuses specifically on reducing drug demand, the treatment of dependent users. It considers how well these programs work and how the treatment system might be expanded to further reduce consumption. Finally, Chapter 5 presents our recommendations for how the data and research base might be built to improve understanding of the demand for drugs and policies to reduce it.

PROGRAM CONCEPTS

A standard approach to considering drug policy is to divide programs into supply side and demand side. This approach accepts that drugs, as commodities, albeit illegal ones, are sold in markets. Supply-side programs aim to reduce drug consumption by making it more expensive to purchase drugs through increasing costs to producers and distributors. Demand-side programs try to lower consumption by reducing the number of people who, at a given price, seek to buy drugs; the amount that the average user wishes to consume; or the nonmonetary costs of obtaining the drugs. This approach has value, but it also raises questions.

The value of this framework is that it allows systematic evaluation of programs. A successful supply-side program will raise the price of drugs, as well as reduce the quantity available, while a demand-side program will lower both the number of users and the quantity consumed, as well as eventually reducing the price. As noted above, this report is primarily focused on improving understanding of the sources of demand.

There are two basic objections to this approach. First, some programs have both demand- and supply-side effects. Since many dealers are themselves heavy users, drug treatment will reduce supply, just as incarceration of drug dealers lowers demand. Second, there is a collection of programs that do not attempt to reduce demand or supply; rather, their goal is to reduce the damage that drug use and drug markets cause society, which are generally referred to as “harm-reduction” programs (Iversen, 2005; National Institute on Drug Abuse, 2010). 6 Nonetheless, the classifi-

cation of interventions into demand reduction and supply reduction is a very helpful heuristic for policy purposes, as well as being written into the legislation under which the Office of National Drug Control Policy operates.

What determines the demand for drugs? Clearly, many different factors play a role: cultural, economic, and social influences are all important. At the individual level, a rich set of correlates have been explored, either in large-scale cross-sectional surveys (such as the National Survey on Drug Use and Health and the National Household Survey on Drug Abuse) or in small-scale longitudinal studies (see, e.g., Wills et al., 2005). Below we briefly summarize the complex findings of those studies.

Less has been done at the population level. It is known that rich western countries differ substantially in the extent of drug use, in ways that do not seem to reflect policy differences. For example, despite the relatively easy access to marijuana in the Netherlands, that nation has a prevalence rate that is in the middle of the pack for Europe, while Britain, despite what may be characterized as a pragmatic and relatively evidence-oriented drug policy, has Europe’s highest rates of cocaine and heroin addiction (European Monitoring Center for Drugs and Drug Addiction, 2007). There is only minimal empirical research that has attempted to explain those differences. Similarly, there is very little known about why epidemics of drug use occur at specific times. In the United States, for example, there is no known reason for the sudden spread of methamphetamine from its long-term West Coast concentration to the Midwest that began in the early 1990s. There are only the most speculative conjectures as to the proximate causes.

A DYNAMIC AND HETEROGENEOUS PROCESS

The committee’s starting point is that drug use is a dynamic phenomenon, both at the individual and community levels. In the United States there is a well-established progression of use of substances for individuals, starting with alcohol or cigarettes (or both) and proceeding through marijuana (at least until recently) possibly to more dangerous and expensive drugs (see, e.g., Golub and Johnson, 2001). Such a progression seems to be a common feature of drug use, although the exact sequence might not apply in other countries and may change over time. For example, cigarettes may lose their status as a gateway drug because of new restrictions on their use. 7 Recently, abuse of prescription drugs has emerged as a possible gateway, with high prevalence rates reported for youth aged 18-25;

however, because of limited economic research on this phenomenon, this report’s focus is on completely illegal drugs.

At the population level, there are epidemics, in which, like a fashion good, a new drug becomes popular rapidly in part because of its novelty and then, often just as rapidly, loses its appeal to those who have not tried it. For addictive substances (including marijuana but not hallucinogens, such as LSD), that leaves behind a cohort of users who experimented with the drug and then became habituated to it.

An important and underappreciated element of the demand for illegal drugs is its variation in many dimensions. For example, the demand for marijuana may be much more responsive to price changes than the demand for heroin because fewer of those who use marijuana are drug dependent (Iversen, 2005; National Institute on Drug Abuse, 2010). Users who are employed, married, and not poor may be more likely to desist than users of the same drug who are unemployed, not part of an intact household, and poor. There may be differences in the characteristics of demand associated with when the specific drug first became available in a particular community, that is, whether it is early or late in a national drug “epidemic.”

There are also unexplained long-term differences in the drug patterns in cities that are close to each other. In Washington, DC, in 1987 half of all those arrested for a criminal offense (not just for drugs) tested positive for phencyclidine, while in Baltimore, 35 miles away, the drug was almost unknown. Although the Washington rate had fallen to approximately 10 percent in 2009 (District of Columbia Pretrial Services Agency, 2009), it remains far higher than in other cities. More recently, the spread of methamphetamine has shown the same unevenness: in San Antonio only 2.3 percent of arrestees tested positive for methamphetamine in 2002; in Phoenix, the figure was 31.2 percent (National Institute of Justice, 2003). These differences had existed for more than 10 years.

The implication of this heterogeneity is that programs that work for a particular drug, user type, place, or period may be much less effective under other circumstances, which substantially complicates any research task. It is hard to know how general are findings on, say, the effectiveness of a prevention program aimed at methamphetamine use by adolescents in a city where the drug has no history. Will this program also be effective for trying to prevent cocaine use among young adults in cities that have long histories of that drug?

This report does not claim to provide the answers to such ambitious questions. It does intend, however, to equip policy officials and the public to understand what is known and what needs to be done to provide a more sound base for answering them.

Arseneault, L., M. Cannon, J. Witten, and R. Murray. (2004). Causal association between cannabis and psychosis: Examination of the evidence. British Journal of Psychiatry, 184 , 110-117.

Babor, T., J. Caulkins, G. Edwards, D. Foxcroft, K. Humphreys, M.M. Mora, I. Obot, J. Rehm, P. Reuter, R. Room, I. Rossow, and J. Strang. (2010). Drug Policy and the Public Good . New York: Oxford University Press.

Carnevale, J. (2009). Restoring the Integrity of the Office of National Drug Control Policy. Testimony at the hearing on the Office of National Drug Control Policy’s Fiscal Year 2010 National Drug Control Budget and the Policy Priorities of the Office of National Drug Control Policy Under the New Administration. The Domestic Policy Subcommittee of the House Committee on Oversight and Government Reform. May 19, 2009. Available: http://carnevaleassociates.com/Testimony%20of%20John%20Carnevale%20May%2019%20-%20FINAL.pdf [accessed August 2010].

Caulkins, J., and R. Mennefee. (2009). Is objective risk all that matters when it comes to drugs? Journal of Drug Policy Analysis , 2 (1), Art. 1. Available: http://www.bepress.com/jdpa/vol2/iss1/art1/ [accessed August 2010].

District of Columbia Pretrial Services Agency. (2009). PSA’s Electronic Reading Room—FOIA. Available: http://www.dcpsa.gov/foia/foiaERRpsa.htm [accessed May 2009].

European Monitoring Center for Drugs and Drug Addiction. (2007). 2007 Annual Report: The State of the Drug Problem in Europe. Lisbon, Portugal. Available: http://www.emcdda.europa.eu/publications/annual-report/2007 [accessed May 2009].

Fergusson, D.M., J.M. Boden, and L.J. Horwood. (2006). Cannabis use and other illicit drug use: Testing the cannabis gateway hypothesis. Addiction, 6 (101), 556-569.

Gallup Poll. (2009). Illegal Drugs . Available: http://www.gallup.com/poll/1657/illegal-drugs.aspx [accessed April 2010].

Golub, A., and B. Johnson. (2001). Variation in youthful risks of progression from alcohol and tobacco to marijuana and to hard drugs across generations. American Journal of Public Health, 91 (2), 225-232.

Iversen, L. (2005). Long-term effects of exposure to cannabis. Current Opinion in Pharmacology, 5 (1), 69-72. Available: http://www.safeaccessnow.org/downloads/long%20term%20cannabis%20effects.pdf [accessed July 2010].

National Institute of Justice. (2003). Preliminary Data on Drug Use & Related Matters Among Adult Arrestees & Juvenile Detainees 2002 . Washington, DC: U.S. Department of Justice.

National Institute on Drug Abuse. (2010). NIDA InfoFacts: Heroin . Available: http://www.drugabuse.gov/infofacts/heroin.html [accessed August 2010].

National Research Council. (2001). Informing America’s Policy on Illegal Drugs: What We Don’t Know Keeps Hurting Us. Committee on Data and Research for Policy on Illegal Drugs, C.F. Manski, J.V. Pepper, and C.V. Petrie (Eds.). Committee on Law and Justice and Committee on National Statistics. Commission on Behavioral and Social Sciences and Education. Washington, DC: National Academy Press.

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Pollack, H., P. Reuter., and P. Sevigny. (2010). If Drug Treatment Works So Well, Why Are So Many Drug Users in Prison? Paper presented at the meeting of the National Bureau of Economic Research on Making Crime Control Pay: Cost-Effective Alternatives to Incarceration, July, Berkeley, CA. Available: http://www.nber.org/chapters/c12098.pdf [accessed August 2010].

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Despite efforts to reduce drug consumption in the United States over the past 35 years, drugs are just as cheap and available as they have ever been. Cocaine, heroin, and methamphetamines continue to cause great harm in the country, particularly in minority communities in the major cities. Marijuana use remains a part of adolescent development for about half of the country's young people, although there is controversy about the extent of its harm.

Given the persistence of drug demand in the face of lengthy and expensive efforts to control the markets, the National Institute of Justice asked the National Research Council to undertake a study of current research on the demand for drugs in order to help better focus national efforts to reduce that demand.

This study complements the 2003 book, Informing America's Policy on Illegal Drugs by giving more attention to the sources of demand and assessing the potential of demand-side interventions to make a substantial difference to the nation's drug problems. Understanding the Demand for Illegal Drugs therefore focuses tightly on demand models in the field of economics and evaluates the data needs for advancing this relatively undeveloped area of investigation.

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• Open access
• Published: 31 May 2024

Overdose responses among rural people who use drugs: A multi-regional qualitative study

• Robin Baker 1 , 13 ,
• Rob J Fredericksen 2 ,
• Abby E Rudolph 3 ,
• Thomas J Stopka 4 ,
• Suzan M Walters 5 ,
• Monica Fadanelli 6 ,
• Rebecca S Bolinski 7 ,
• Adams L Sibley 8 ,
• Erin Stack 9 ,
• Heidi M Crane 10 ,
• P Todd Korthuis 11 &
• David W Seal 12  

Harm Reduction Journal volume  21 , Article number:  107 ( 2024 ) Cite this article

65 Accesses

Metrics details

Efforts to distribute naloxone have equipped more people with the ability to reverse opioid overdoses but people who use drugs are often reluctant to call 911 due to concerns for legal repercussions. Rural communities face unique challenges in reducing overdose deaths compared to urban communities, including limited access to harm reduction services as well as greater concerns about stigma and privacy.

The Rural Opioid Initiative was funded in 2017 to better understand the health-related harms associated with the opioid crisis in rural US communities and consists of eight studies spanning ten states and 65 counties. Each study conducted semi-structured qualitative interviews with people who use drugs to understand contextual factors influencing drug use and health behaviors. We analyzed qualitative data from seven studies with data available at the time of analysis to understand peer response to overdose.

Of the 304 participants interviewed, 55% were men, 70% were white, 80% reported current injection drug use, and 60% reported methamphetamine use. Similar to what has been found in studies focused on urban settings, people who use drugs in rural communities use a range of strategies to reverse overdoses, including non-evidence-based approaches. Several reported that multiple doses of naloxone are needed to reverse overdose. Three themes emerged around the willingness to call 911, including (1) hesitancy to call 911 for fear of legal consequences, (2) negative perceptions or experiences with law enforcement officers, and (3) efforts to obtain medical intervention while avoiding identification/law enforcement involvement.

People who use drugs employ multiple strategies to attempt overdose reversal, including non-evidence-based approaches. Greater education about the most effective and least harmful strategies is needed. Reluctance to call 911 is rooted in concerns about potential legal consequences as well as perceptions about law enforcement officers, which may be heightened in rural communities where people who use drugs are more easily identified by law enforcement. People who use drugs will go to great strides to connect their peers to needed medical services, suggesting that comprehensive interventions to reduce interactions with law enforcement officers and eliminate legal consequences for reporting overdoses are critical.

Introduction

Opioid-related overdose rates have steadily increased since 2000. Provisional data from the CDC’s National Center for Health Statistics indicate that there were an estimated 77,766 opioid overdose deaths in the US during the 12-month period ending in December 2021, an increase of 12.6% from the 69,061 deaths during the 12-month period ending in December 2020 [ 1 ]. Of the 77,766 opioid overdose deaths reported in 2021, almost 88% involved synthetic opioids, primarily fentanyl [ 1 ]. While rates of overdose deaths are higher in urban settings, rural communities face unique challenges related to opioid use disorders due to reduced access and greater distances to behavioral health services and providers, limited public transportation, greater experience with stigma, and more concerns about privacy [ 2 , 3 , 4 , 5 ]. People who use drugs are well positioned to serve as first responders in the event of overdose [ 6 , 7 ]. However, research also suggests that most people who use drugs do not call 911 when witnessing an overdose [ 8 , 9 ]. Common reasons include fear of law enforcement officer involvement [ 8 , 10 ] and the belief that emergency medical services (EMS) are not needed [ 8 ]. One study found that overdose location may be an important factor, with people who use drugs more likely to call 911 when overdoses occur in public settings versus private residences or locations with active drug dealing and open drug use [ 11 ].

As of 2021, 47 states and Washington, D.C., have Good Samaritan laws (GSL) in place, which aim to protect bystanders from arrest or conviction when calling 911 to report an overdose [ 12 ]. Studies demonstrate that GSL have addressed some of the obstacles associated with calling 911 to report overdoses; however, limitations of GSL protections exist [ 13 , 14 , 15 ]. These include the narrow scope of protection that some GSL provide, a lack of awareness and understanding of the law among both people who use drugs and law enforcement officers, perceptions of and experiences among people who use drug with law enforcement officers disregarding the law [ 10 , 13 , 14 , 15 ].

Efforts to increase the capacity of people who use drugs to respond appropriately to overdoses include overdose education and naloxone distribution (OEND) programs [ 16 ]. Commonly recommended strategies to rescue people from opioid overdoses include sternum rubs, rescue breathing, naloxone administration, placing the person in the recovery position, and calling 911 [ 16 ]. Calling 911 is important because once the reversal effects of naloxone wear off, overdoses can reoccur, particularly as fentanyl and its analogs become the leading cause of overdose [ 17 ]. The increasing availability of naloxone through pharmacies, harm reduction agencies, health departments, and other community-based programs can equip people who use drugs with the tools needed to reverse opioid overdoses [ 16 , 18 ]. While several studies have demonstrated that distributing naloxone and training people who use drugs to administer naloxone reduces overdose mortality in communities [ 19 , 20 , 21 ], reluctance or hesitancy to call 911 persists. Most studies on overdose responses have focused on urban settings [ 10 , 22 , 23 ].

Studies focused on rural communities have similarly found that rural people who use drugs fear the legal consequences of reporting overdose, even in states with GSL [ 24 ]. As with urban communities, people who use drugs often report experiencing stigma related to drug use in rural communities [ 11 , 25 , 26 , 27 ]. In addition, rural communities also have limited access to substance use and harm reduction resources [ 3 , 5 ] and a larger proportion of overdose deaths involving psychostimulants (e.g., cocaine, methamphetamine) [ 28 ]. Limited access to OEND programs and increased rates of psychostimulant-involved overdose deaths is particularly concerning as previous studies have found that some people who use drugs mistakenly believe that psychostimulants can reverse opioid overdoses [ 29 , 30 ]. These unique challenges and factors may influence how rural people who use drugs respond to overdoses. This qualitative study builds on the existing literature by characterizing overdose response patterns and the interplay of factors influencing EMS involvement in a geographically diverse multi-regional U.S. sample of people who use drugs.

Materials and methods

The Rural Opioid Initiative (ROI) was funded in 2017 to better understand the health-related harms associated with the opioid crisis in rural parts of the United States ( http://ruralopioidinitiative.org/ ). ROI consists of eight study regions spanning ten states and 65 counties [ 31 , 32 ]. The initiative included qualitative and quantitative data collection, with harmonized data collection instruments, as well as epidemiologic, policy, and legal scans [ 33 , 34 ]. Each study region conducted semi-structured individual qualitative interviews with people who use drugs to better understand contextual factors, life history, and circumstances influencing drug use and health behavior.

Study settings

This manuscript reports on findings from seven study regions: Illinois (IL), Kentucky (KY), North Carolina (NC), Northern New England (NE; Vermont, New Hampshire, Massachusetts), Ohio (OH), Oregon (OR), and Wisconsin (WI). West Virginia was excluded due to lack of complete data at the time of analysis. Each study region focused on rural communities with high rates of hepatitis C virus (HCV) infection and opioid overdose fatalities. All study regions included counties that were identified in 2016 by the Centers for Disease Control and Prevention (CDC) as experiencing or at risk of experiencing increases in HCV infection due to injection drug use and several included counties that were identified as among the most vulnerable counties in the US for HIV/HCV outbreaks linked to injection drug use, including Illinois, Kentucky, North Carolina, Northern New England, and Ohio [ 35 ]. Among the nine states included in this manuscript, all but Oregon had higher opioid overdose death rates than the national opioid overdose death rates in 2018 and 2020, and most saw an increase in opioid overdose death rates from 2018 to 2020 [ 36 ]. In 2019 and 2020, three of the nine states had higher estimated rates of past-year methamphetamine use among individuals aged 18 years and older compared to the national estimate of under 1%; Oregon had nearly double with over 2% [ 37 ]. Six states had higher estimates of past year cocaine use among individuals aged 18 years and older compared to the national estimate of just over 2%; New Hampshire and Vermont had almost 3% [ 37 ].

Interview guide development

The interview guide was developed collaboratively by ROI researchers with expertise in qualitative methods, representing all ROI study regions, who comprised the ROI Qualitative Methods Workgroup. The development of a standardized interview guide ensured uniformity of primary content across studies. The interviews included but were not limited to topics related to illegal opioid and other drug use and access to and use of harm reduction, substance use, mental health, and health care services. Of particular interest for this study were questions related to observations of overdose. Participants were asked, “Tell me about your most significant experience with someone else overdosing?” Common follow-up questions probed for the overdose location; other people’s responses; the participant’s response; whether 911 was called; who arrived first (i.e., law enforcement or EMS); whether the person went to the hospital; if naloxone was used, and if so, by whom; and which drugs were involved. Probes varied across studies to enable investigators within each study region to follow-up on issues specific and relevant to their location and communities. Each study received approval from a local institutional review board and participant privacy was protected by a federal certificate of confidentiality.

Participant recruitment and data collection

Between 2018 and early 2020, we recruited people who used drugs to participate in interviews ranging from 60 to 90 min. Across all studies, qualitative interview participants had to reside in the study area and be at least 18 years old. Most sites required participants to report opioid use “to get high” or injection drug use in the past 30 days. Other eligibility criteria varied across sites due to regional differences in drug use and drug-related harms. For example, Ohio specifically recruited people who recently transitioned to injection drug use and women with experiences of neonatal opioid withdrawal, North Carolina focused on people who injected painkillers or heroin, and Wisconsin recruited people who injected opioids in the past month. All studies recruited participants from community-based programs and in some cases used street outreach. Trained ualitative researchers, many of whom had extensive training and experience, conducted the interviews, which were digitally recorded and transcribed verbatim. Participants were consented per the study IRB protocols and compensated with an incentive ranging from $25–50 depending on the study.

Identifying information was redacted from the transcripts. Each transcript was assigned a unique identification number and uploaded to a qualitative software program for data management, coding, and analyses (Dedoose, Los Angeles, CA). The Qualitative Core of the University of Washington ROI Data Coordinating Center conducted preliminary coding to categorize data by interview topic areas and lines of inquiry to facilitate retrieval of relevant data from the larger multi-study dataset. Upon retrieval of the data related to overdose in the preliminary coding round, we developed a data-driven thematic coding scheme that was iteratively refined by the writing team following principles of grounded theory analysis [ 38 , 39 ]. We analyzed the data to identify recurring themes, focusing on better understanding overdose experiences and responses. The writing team held regular meetings to discuss new thematic categories or codes that emerged in the data and to ensure consistency in coding/thematic definitions and application. This iterative process continued until we achieved thematic saturation and stabilized the organization of the findings. Nonverbal utterances were removed to improve the readability of the quotes.

A total of 304 participants completed a qualitative interview. The mean age was 36 years and 55% of the participants were men. Among the 169 for whom race data were available, 70% were white. 32% had a high school diploma or GED, 20% had some college, and 18% had less than a high school diploma or GED. 80% reported current injection drug use and 60% reported methamphetamine use. See Table  1 for demographic characteristics of interview participants, by study.

Rural people who use drugs in our study reported using multiple strategies, and often in combination, to attempt overdose reversal. Participants reported sternum rubs, rescue breaths, naloxone, application of ice or cold water, CPR, chest compressions, and inflicting pain by slapping or hitting their peers.

He was just completely out. It was really crazy. We had to jump out and pull him out of the car and slap him a little bit and throw cold water on him. (28-year-old man, OR) Chest compressions…anything I could think of to do…blow in their mouth…CPR…I carried a 180-pound man into a shower before and threw him in cold water, and that’s what got him out. (23-year-old woman, NE)

Of particular concern, some participants also reported using psychostimulants to reverse overdose (i.e., methamphetamine in OR, OH, IL, and NC; crack or cocaine in NE). In many of these cases, participants indicated that they used psychostimulants after other overdose reversal strategies did not work.

I tried giving him CPR and it didn’t work…I took his needles and put methamphetamine in it and shot it in his hand. Within about, probably 20 to 30 s, he jumped up and ran out the door. I know for a fact that if I hadn’t shot that speed into him, his heart would have stopped, because [his] face [was] blue, lips white…and [he was] breathing real funny and puking on himself and stuff. (30-year-old man, OH)

Finally, while participants reported using naloxone to reverse overdoses, notably, several talked about the need for multiple doses of naloxone, sometimes as many as 3–7 doses.

We tried Narcan three times. It didn’t work. (Cries)…Hours passed and finally…I had to go ask for help, I didn’t know what to do. And they called the ambulance to come get her. We tried the cold shower, we tried everything…it didn’t work. (38-year-old woman, NE)

In addition, multiple themes emerged regarding the decision to call 911. These included (1) hesitancy to call 911 for fear of legal consequences, (2) negative perceptions of, or experiences with law enforcement officers, and (3) efforts to obtain medical intervention while avoiding identification/law enforcement involvement.

Hesitancy to call 911

Among those participants who considered calling 911 to report witnessing an overdose, they shared conflicted feelings about calling 911 because they were concerned about the potential legal consequences. For example, many participants discussed their fear of losing child custody, getting arrested, or going to jail. Several participants also reported that they did not call 911 because they were respecting the wishes of the person who overdosed. In some cases, participants indicated that their peers made it known that they did not want 911 called if they overdosed. In other cases, participants indicated that they wanted to call 911 after they revived their peer, but the peer said no.

We probably should have [called 911], but we didn’t…we were scared of getting into trouble. We were doing drugs, which is really not an excuse. We should’ve called 911, but luckily, I was able to bring her back. (29-year-old man, KY) I’m a single mom, and my daughter was in the other room… I was afraid to go to jail… I would’ve called 911 if we couldn’t have gotten him to come through…but I did avoid it because I didn’t want to lose my kid and I didn’t want to go to jail for attempting murder. (36-year-old woman, WI) I didn’t call 911 because he refused it. I told him I was going to call, but he refused. So I didn’t call. (26-year-old woman, KY)

Perceptions of law enforcement officers and emergency medical services in small towns

A few participants reported perceptions of law enforcement or EMS that decreased their willingness to call 911. For example, one respondent noted that the local law enforcement officers are corrupt, while another noted that their local EMS is less likely to respond in a timely fashion for overdoses.

               The cops are shit…they treat people like they’re dirt, you know. (60-year-old woman, IL)

They [police] are involved in a lot of the stuff that goes on…Prostitution and stuff like that…I just don’t trust anyone down there. That is another reason why like I would be scared to call the law if someone overdosed. I mean, I would do it, I would, but I understand why people are scared because of that. (33-year-old woman, OH) The few times that EMS has been called, they take their time. We’re told not to even tell them that it’s an overdose. Tell them there’s an emergency, but don’t tell them that, because they’ll take their time. Small towns are like that. It is a very who you know and who you are, and if you do drugs, you’re automatically same category as a murder. (38-year-old woman, NC)

Obtaining medical intervention while avoiding identification and law enforcement involvement

Despite expressing a reluctance to call 911, several participants reported efforts to connect their peers with medical intervention while minimizing the chance of law enforcement involvement. These included driving to a public place before calling 911, driving the person to the hospital, or calling 911 and leaving the vicinity.

I called the ambulance and took off…I called them and said that there’s somebody sitting in there and I didn’t know what to do. I told them her name…told them she was alone in her house and she needs somebody to come and I was gone. (43-year-old woman, NC) Her mother overdosed, and…the guy that owned the house, was like, ‘get him out of here.’ We’re going to call the cops and we’re going to call the ambulance. [They said, ] ‘Oh, no you’re not,’ so we carried this woman outside and put her in a car and I drove to a bar that was a block away and…I called the cops and I called the ambulance. (44-year-old man, WI)

Interviews with people who use drugs in rural communities revealed a range of barriers to accessing emergency care and a range of strategies to reverse overdoses, including naloxone, slapping or hitting, ice or cold water, psychostimulants, CPR, chest compressions, and rescue breathing. Complex and interconnected factors influenced whether and how people responded. Hesitancy to call 911 or to remain with the peer after calling 911 was common, largely due to fear of legal repercussions, particularly among those with prior criminal justice involvement. Another factor that influenced whether people called 911 was negative perceptions of or prior interactions with local law enforcement or EMS. As a result, some participants reported workarounds to connect their peers with medical services while reducing law enforcement involvement.

The use of multiple strategies demonstrates that rural people who use drugs proactively respond to reverse an overdose and persist if the first intervention is not successful. It is important to note that there are some variations in recommendations for overdose response. SAMHSA and the World Health Association recommend rescue-breathing, while the American Heart Association does not [ 40 ]. These differences in recommendations are, in part, based on differing perceptions of the ability of laypersons to correctly identify overdose, respiratory or cardiac arrest symptoms, and carry out the appropriate intervention [ 40 ]. However, some strategies reported by participants, such as inflicting pain and using ice or water, are not recommended and can either cause additional injury or delay the implementation of safer and potentially more effective overdose strategies [ 22 ]. The reported folk method of using methamphetamine, crack, and cocaine to reverse overdoses highlights persistent system failures in ensuring that people have access to comprehensive overdose reversal education and access to naloxone. In 2018, nearly three-quarters of all cocaine overdose deaths and half of all methamphetamine overdose deaths also involved opioids [ 41 ], and co-use is associated with an increase in the risk of overdose in rural communities [ 42 ]. Expanding OEND programs and partnering with trusted community-based organizations and people who use drugs in rural communities may be an effective way to disseminate evidence-based overdose strategies directly to people actively using drugs. Further research is needed to understand the underlying systemic issues contributing to the use of psychostimulants to reverse overdoses and to identify strategies to increase overdose reversal education and the distribution of naloxone.

Many participants reported that multiple doses of naloxone were often needed to reverse overdose. Several studies have found that higher doses of naloxone are needed to reverse overdose caused by synthetic opioids [ 43 , 44 , 45 ]. Given the rise in overdose deaths attributed to fentanyl and related analogs, further overdose reversal research is warranted while counseling people to continue dosing naloxone as needed and call 911 [ 46 ]. The method of administration may make a difference. While intranasal naloxone has become popular because of ease of administration and increased accessibility, a randomized clinical trial found that clients at a Canadian overdose prevention site who received injectable naloxone were significantly less likely to need an additional rescue dose than clients who received intranasal naloxone [ 47 ]. One possible strategy to explore is the distribution of injectable naloxone both to people who use drugs who are comfortable with injections and EMS personnel. This could provide a cost-effective method and facilitate more efficient single doses of naloxone with a decreased need for additional doses. It is also critical to increase the quantity of naloxone, regardless of the mode of administration, distributed to rural people who use drugs and EMS and wherever fentanyl or other analogs are prevalent. Given reports among participants that it sometimes takes more than three doses of naloxone to reverse overdose, it is important that people have access to multiple naloxone kits so that they are better equipped to respond to an overdose. In addition, in rural communities where there may be long driving distances between the overdose location and the closest hospital [ 48 ], multiple doses of naloxone may be necessary in case the reversal effects of naloxone wear off before the person is connected to EMS.

These findings highlight the impact that fear of law enforcement involvement has on responses among those witnessing overdose. Concerns about legal consequences inhibit the ability of rural people who use drugs to respond appropriately. These concerns may be compounded by the relative lack of anonymity in rural communities [ 49 ]. The likelihood that rural people who use drugs are well known and easily identified by law enforcement officers is substantially increased compared to urban communities due to tighter interconnected social networks, lower population density, and smaller population counts [ 26 ]. These concerns may be heightened in small towns, as some participants shared perceptions of their local law enforcement officers and EMS that decreased their willingness to call 911, such as the perception that GSL would not be enforced.

While 47 states and Washington D.C. have GSL, there is significant variation in the protections they offer [ 10 ]. For example, of the nine states included in our study, only Ohio offers protections against the arrest, charge, and conviction for possession of illegal substances, and it limits immunity to only two instances, and only three states (Illinois, Massachusetts, and Vermont) provide provisions that allow reporting an overdose to be considered a mitigating factor in sentencing with significant variation in which crimes are permitted for mitigation [ 50 ]. In Ohio, the parole board or court has the discretion to mitigate the penalty [ 50 ]. However, ROI participants reported fear of legal repercussions across all studies, regardless of the presence or absence of GSL or the scope of protections. More research partnering with rural people who use drugs and law enforcement officers regarding awareness of existing GSLs and understanding of the protections and limitations of the GSL across settings is needed. Given that several participants shared the perception that small-town law enforcement officers are corrupt, it is possible that even if law enforcement officers are aware of an existing GSL, people who use drugs in rural communities may worry that the law would not be enforced. Persistent fear of legal repercussions and limitations of GSL protections underscore the need to explore policies that decrease the threat of legal action for consequences of drug use. Given recent efforts in other countries, such as Canada [ 51 , 52 , 53 , 54 ], to prevent and reduce overdose death, additional research on interventions such as overdose prevention sites and safer supply on overdose outcomes is needed.

Finally, another strategy could include the development of dedicated overdose call-in centers or emergency phone lines, like those established for suicide prevention [ 55 ], and potentially modeled after the “never use alone” crisis hotline ( https://neverusealone.com/ ) [ 56 , 57 ]. This could eliminate or significantly reduce law enforcement involvement with overdoses and directly connect people with needed medical services for overdoses. This would necessitate infrastructure development to ensure consistent Wi-Fi and cellular service, particularly in rural US communities [ 58 , 59 ].

Our findings demonstrate that there is need for greater access to existing evidence-based interventions such as syringe exchange and drug checking services, distribution of naloxone, and medications for opioid use disorders. Overdose prevention requires a multi-faceted approach that includes increasing overdose reversal education, eliminating legal consequences of reporting an overdose, and providing direct pathways for people who use drugs to access medical assistance. Integrating harm reduction principles into the design and execution of these strategies could empower people who use drugs, often the true first responders, to save lives and significantly reduce overdose.

Limitations & strengths

The study’s large sample size, and its diversity in geography (9 states and 58 counties) and age, offers the most comprehensive qualitative data on overdose responses among people who use drugs known to date. That said, our sample is largely white and cisgender, and the limited racial and gender diversity may limit the transferability of our findings to other settings and populations. In addition, study differences in drugs used, mode of drug administration, recruitment criteria, and follow-up probing questions may have influenced variation in responses. For example, the prevalence of methamphetamine use in some study locations may have provoked the use of methamphetamine as an overdose reversal strategy. Furthermore, while GSLs were in effect in all states included in the study at the time of data collection, participants’ retrospective recalls of overdose experiences may include overdoses that predate these laws, and such past decisions around medical intervention may have occurred in circumstances where legal protections were not yet afforded. Finally, this sample of rural people who use drugs is comprised mostly of persons who are engaged in harm reduction, who may have experiences that are unique from their peers who are not connected to such services.

Conclusions

People who use drugs are well-situated to serve as first responders to reduce overdose deaths. This study confirmed that like people who use drugs in urban communities, the responses when witnessing overdoses in rural communities may be impacted by widespread fear of legal consequences, which heavily influences decision-making regarding whether, how, and when to access emergency care. In the face of this fear, and in the urgency of the moment, people who use drugs employ a wide range of strategies to attempt overdose reversal. Greater education is needed to ensure that people are well-informed about the most effective and least harmful opioid overdose reversal strategies. Access to naloxone should be increased among people who use drugs and first responders, including EMS and fire and police departments, particularly in rural communities. However, it is critical to remove barriers that prevent people who experience an overdose from receiving appropriate medical care. One of the main barriers, hesitancy to call 911, is rooted in concerns about the potential legal consequences of law enforcement involvement. These concerns may be heightened in rural communities where people who use drugs may be more easily identified by law enforcement officers. Our findings demonstrate the great strides people who use drugs in rural communities will take to reverse overdoses and save lives as well as the need for comprehensive interventions that eliminate legal consequences and expand harm reduction strategies for responding to overdose.

Data availability

To respect the confidentiality of participants in this study, data is not publicly available. However, we welcome collaboration and encourage mentorship and the use of the ROI data stripped of all protected health information (PHI) to enable early investigators to address meaningful questions with support to help ensure their success. Additional information can be obtained at the ROI website: ruralopioidinitiative.org or by contacting the ROI DCC at [email protected]. Follow the Rural Opioid Initiative on Twitter @ruralopioids.

Abbreviations

Good Samaritan laws

Emergency medical services

Opioid Education and Naloxone Distribution

Rural Opioid Initiative

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Acknowledgements

This publication is based upon data collected and/or methods developed as part of the Rural Opioid Initiative (ROI), a multi-site study with a common protocol that was developed collaboratively by investigators at eight research institutions and at the National Institute of Drug Abuse (NIDA), the Appalachian Regional Commission (ARC), the Centers for Disease Control and Prevention (CDC), and the Substance Abuse and Mental Health Services Administration (SAMHSA). The views and opinions expressed in this manuscript are those of the authors only and do not necessarily represent the views, official policy or position of the U.S. Department of Health and Human Services or any of its affiliated institutions or agencies, or those of the Appalachian Regional Commission. The authors thank the other ROI investigators and their teams, community and state partners, and the participants of the individual ROI studies for their valuable contributions. A full list of participating ROI institutions and other resources can be found at http://ruralopioidinitiative.org .

Research presented in this manuscript is the result of data harmonization and was supported by grant U24DA048538 (Crane, Tsui) from NIDA. This research was funded by the National Institute on Drug Abuse (NIDA), in partnership with the Appalachian Regional Commission (ARC), the Centers for Disease Control and Prevention (CDC) and the Substance Abuse and Mental Health Services Administration (SAMHSA), grant numbers: UG3DA044830, UH3DA044830 (Friedmann, Stopka); K01DA053159 (Walters); P30DA01104 (Hagan); UG3DA044829, UH3DA044829 (Pho, Jenkins); UG3DA044825, UH3DA044825 (Feinberg, Smith); UG3DA044831, UH3DA044831 (Korthuis); UG3DA044822, UH3DA044822 (Miller, Go); UG3DA044826, UH3DA044826 (Westergaard, Seal); UG3DA044798, UH3DA044798 (Cooper, Young); UG3DA044823, UH3DA044823 (Zule); and U24DA044801 (Crane, Tsui). Data collection at the Oregon site was supported by UL1TR002369.

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Robin Baker

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Rob J Fredericksen

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Abby E Rudolph

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D.W.S, H.C., P.T.K devised and supervised the methodology; R.B., R.J.F., A.E.R. conceptualized the paper; R.J.F. prepared the table; R.B., R.J.F, A.E.R. led the formal qualitative analysis, R.B., R.J.F., A.E.R. wrote the original draft; R.J.F., A.E.R, T.J.S., S.M.W., R.S.B., A.L.S., E.S., D.W.S., H.C., P.T.K. provided review and substantial editing.

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Baker, R., Fredericksen, R.J., Rudolph, A.E. et al. Overdose responses among rural people who use drugs: A multi-regional qualitative study. Harm Reduct J 21 , 107 (2024). https://doi.org/10.1186/s12954-024-01007-9

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DOI : https://doi.org/10.1186/s12954-024-01007-9

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Evaluating the impact of price regulation (Drug Price Control Order 2013) on antibiotic sales in India: a quasi-experimental analysis, 2008–2018

• Sakthivel Selvaraj 1 ,
• Habib Hasan Farooqui 2 ,
• Aashna Mehta 3 &
• Manu Raj Mathur   ORCID: orcid.org/0000-0001-5518-1935 3 , 4  

Journal of Pharmaceutical Policy and Practice volume  15 , Article number:  68 ( 2022 ) Cite this article

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In India, due to a lack of population-level financial risk protection mechanisms, the expenditure on healthcare is primarily out-of-pocket in nature. Through Drug Price Control Orders (DPCOs), the Indian Government attempts to keep medicine prices under check. The aim of this study was to measure the potential impact of DPCO 2013 on the utilization of antibiotics under price regulation in India using large nationally representative pharmaceutical sales data.

We used interrupted time series analysis, a quasi-experimental research design to estimate the impact of DPCO 2013 on the utilization of antibiotics in the private sector in India. Indian pharmaceutical sales data set, PharmaTrac from a market research company—All Indian Origin Chemists and Distributors Limited—was used for the study. The data are collected from a panel of around 18,000 stockists across 23 different regions of the country. The primary outcome measure is the percentage change (increase or decrease) in the sales volume of the antibiotics under DPCO 2013, measured in standard units (SUs).

Our estimates suggest that post-intervention (after notification of DPCO 2013) there was an immediate reduction (level change) in the sales of antibiotics under DPCO 2013 by 3.7% ( P  > 0.05), followed by a sustained decline (trend change) of 0.3% ( P  > 0.05) as compared to the pre-intervention trend at the molecule level, but both changes were statistically insignificant. However, in terms of ‘average monthly market share,’ the DPCO 2013 notification resulted in a sharp reduction of 579% ( P  < 0.05) (level change) followed by a sustained increase of 9.5% ( P  > 0.05) (trend change) in the ‘market share of antibiotics under DPCO’ as compared to pre-intervention trend.

Conclusions

The impact of DPCO 2013 in terms of the overall increase in the utilization of antibiotics under price regulation was limited but there was a switch from non-price controlled antibiotics to price regulated antibiotics (notified under DPCO 2013). We argue that policies on price control need to be complemented with continuous monitoring of market behavior to have a measurable and long-term impact.

The World Health Assembly approved a resolution for transparency in medicine prices in 2019 urging governments to monitor the impact of pricing policies on the affordability and accessibility of medical products[ 1 ]. In LMICs, medicines account for 20–60% of total health care expenditure, with nearly 90% of the population purchasing medicines through out-of-pocket payments [ 2 ]. In countries with a dominant private market, the availability of essential drugs per se is a relatively smaller issue, but affordability continues to be a major challenge owing to the high prices of medicines targeting both acute and chronic conditions. A large segment of the population in LMICs was observed to be unable to afford monthly treatment costs for medicines meant for three common non-communicable diseases (NCD) conditions in the private sector [ 3 ]. Another study of cardiovascular disease (CVD) medicines in 18 countries suggested that medicines were unaffordable for some patients who purchased them from a private pharmacy [ 4 ]. India is a leading producer and exporter of generic medicines and has one of the lowest medicine prices in the world. Yet, due to a lack of financial risk protection mechanisms at the country level, the share of households’ in drug spending is as high as 90%, whereas the governments contribute only 10% [ 5 ].

National governments adopt various policy instruments to address market imperfections to control medicine prices and expenditures. Price regulation is often employed to bring down pharmaceutical prices. The approaches for regulating medicine prices include controlling mark-ups, reference pricing, price negotiations, cost-plus-based pricing, value-based pricing, pricing through tenders and pooled procurement [ 2 ]. The choice of one approach over the other is generally made by a country after carefully considering national priorities, health financing mechanisms, regulatory landscape and health system context within the country. Most countries adopt a mix of policy instruments to strike a balance between health policy and industrial policy goals. India has a long history of price regulation (since 1962), through the Drug Price Control Orders (DPCO).

The latest DPCO, 2013 [ 6 ] was notified on 15 May 2013 for the implementation of the National Pharmaceutical Pricing Policy (NPPP), 2012 [ 7 ]. The NPPP’s objective was ‘to put in place a regulatory framework for pricing of drugs to ensure availability of essential medicines at reasonable prices even while offering adequate opportunity for innovation and competition to support the pharmaceutical industry. As per the DPCO, 2013 all the drugs under the National List of Essential Medicines ( n  = 348) notified by the Ministry of Health and Family Welfare were brought under price control. DPCO 2013 set the price ceilings for these medicines averaging the existing market prices (of all brands that have a market share of 1% or greater) and adding a 16 per cent retailer’s margin to it. In addition, the brands priced below the ceiling price were required to maintain the prices at current levels, whereas the brands priced above the price cap had to reduce their prices.

Previous research from India on an antihyperlipidemic drug—atorvastatin—the price for which was regulated under DPCO 2013 suggested that bringing the drug under price control improved the relative sales of atorvastatin in the country [ 8 ] in comparison to non-price controlled statins. However, Meng et al. reported that implementation of retail price controls in isolation was not effective in controlling drug expenditures in Korea, as medicine utilization was the determining factor for achieving the same, more than the prices [ 9 ]. Similarly, Bhaskarabhatla et al. reported that price control of metformin—an antidiabetic medicine—notified under DPCO 2013 resulted in a modest improvement in its sales [ 10 ].

We argue that a majority of medicines are therapeutically substitutable at the formulation level within a therapeutic segment depending upon the clinical condition, and hence any policy measure that selectively targets some and not all medicines within a therapeutic segment may lead to unintended consequences. For example, Emma [ 11 ] reported an exit of local firms from the price-controlled molecule market on account of the DPCO 2013 in India, though they continued to produce non-price-controlled formulations of the same molecule.

We also recognize that much of the previous research on the impact of DPCO 2013 on medicine sales have been restricted to single formulations [ 10 , 12 ] and over shorter time frames. The core objective of this research was to measure the long-term impact of DPCO 2013 on an entire therapeutic segment of systemic antibiotics, using a quasi-experimental research design and utilizing the latest available private sector pharmaceutical sales data. We chose the segment systemic antibiotics as India has a high burden of infectious diseases and a huge market for antibiotics.

We utilised the Indian pharmaceutical sales data set PharmaTrac [ 13 ] which is collected by a market research company AIOCD-AWACS which is a joint venture between All Indian Origin Chemists and Distributors Ltd (AIOCD Ltd) and Trikaal Mediinfotech Pvt Ltd. from a panel of around 18,000 stockists spread across 23 different regions in India. The stockists are selected after carrying out a census to understand the total number of pharmaceutical companies in the state and then selecting those stockists that account for at least 25 per cent of their turnover. The pharmaceutical sales data are compiled and extracted every month from the computers of the selected stockists using the software. These data are then extrapolated to reflect the overall medicine sales in the private sector in India using companywise and statewise projection factors.

Pharmaceuticals in the data set are organised according to the anatomical therapeutic chemical (ATC) classification of the European Pharmaceutical Market Research Association (EphMRA). This classification was used to identify the private market for systemic antibiotics in the country. A total of 54 unique strengths and dosage forms of antibiotics were considered for the policy impact evaluation (see Additional file 1 : Table S1) which were notified for price regulation under DPCO 2013. The notifications for the price ceiling were staggered between June 2013 (first) and December 2014 (final). The data do not capture medicines consumed in the government facilities, our analysis, therefore, focuses exclusively on the impact of the DPCO 2013 on private sector antibiotic utilisation.

Intervention

The intervention under study is the Drug Price Control Order 2013 (DPCO, 2013) [ 6 ] which was notified on 15th May 2013 for the implementation of the National Pharmaceutical Pricing Policy (NPPP), 2012 [ 7 ] by the National Pharmaceutical Pricing Authority (NPPA), Ministry of Chemical and Fertilizers. The National Pharmaceutical Pricing Policy (NPPP), 2012 laid down three criteria for price control: (1) regulation of prices based on ‘essentiality of drugs’ (i.e., formulations as listed under the National List of Essential Medicines (NLEM) [ 14 ] notified by the Ministry of Health and Family Welfare, (2) control of formulation prices only and (3) market-based pricing.

The DPCO 2013 notifications for price control of antibiotics based on NLEM 2011 were staggered over a period of 19 months—the first notification was released in June 2013 and the final one in December 2014. However, the manufacturers were allowed a period of 45 days to comply with the notification and modify maximum retail prices (MRP) on the packs of medicine under notification for implementation reasons. We, therefore, considered the period from June 2013 to January 2015 as the implementation period.

It may be noted that the Government of India released another set of notifications in 2016 based on a new NLEM, 2015. In addition, the Government notified another policy to bring some of the antibiotics under a new clause ‘H1’ underlying the Indian Drugs and Cosmetics Rules, 1945 which required pharmacists to dispense these antibiotics only upon the production of a prescription from a Registered Medical Practitioner. However, the present analysis is confined only to the 2013 policy intervention. All antibiotic formulations that were influenced by the latter two interventions were excluded from the current analysis to avoid the confounding effects of multiple interventions. The analysis was, therefore, limited to 54 antibiotic formulations. All the antibiotic formulations included in the analysis along with their price ceiling notifications are provided in Additional file 1 : Table S1. Another advantage of limiting the scope of the present analysis to the 2013 intervention was that we had sufficient data points available to us in both the pre- and post-intervention periods, independent of other interventions targeted at the medicines under study and their confounding impacts thereof.

Outcome measures

The primary outcome measure is the percentage change (increase or decrease) in the sales volume of the antibiotics under DPCO 2013, measured in standard units (SUs). SU is defined as the smallest dose of formulation (one tablet or capsule for oral solids, one vial or ampoule for injectable). We computed sales volume in SUs of all dosage forms and strengths of antibiotics under the price regulation. We used the logarithmic form of the sales volumes to examine changes in sales volumes before and after the implementation of the DPCO 2013. Sales volume was considered as a proxy for antibiotic utilisation.

Research design

We used interrupted time series, a quasi-experimental research design to capture the impact of price regulation on the utilization of antibiotics (notified under DPCO in June 2013) [ 3 ].

Statistical analysis

We used interrupted time series (ITS) analysis, a quasi-experimental research design for the present study. ITS is commonly used to study the impact of policies by comparing pre-intervention trends with post-intervention trends, especially for health-related interventions [ 15 , 16 , 17 , 18 ].

We performed Interrupted Time Series Analysis (ITS) on the data spanning 132 month period that was distributed into two segments, pre-intervention and post-intervention period, based on notifications. The pre-intervention period referred to the period from January 2008 to May 2013 and the post-intervention period referred to the period from February 2014 to December 2018. The period of price ceiling notifications from June 2013 to January 2014 was considered the implementation period and, therefore, excluded from the analysis. Interrupted time series analysis was undertaken to detect the (a) pre-intervention level and trend, (b) post-intervention level and trend change in antibiotics utilisation.

The dependent variable (Y t ) was the ‘logarithm of sales volume’ of all antibiotics under price control. The ‘Time’ factor appeared as an independent variable. We fitted a least square regression line to the two segments of the continuous variable time and introduced two variables to estimate the immediate level change after the intervention (variable name: intervention) and the trend change (variable name: time after intervention), respectively (see Eq.  1 ). The variable ‘intervention’ was ‘0’ for the pre-intervention period and ‘1’ for the post-intervention. Time after the intervention was incorporated as a continuous variable in the post-intervention period (model 1).

The interrupted time series analysis helped us to statistically determine the change in the intercept and the slope coefficients between the pre- and post-intervention period (α: measures the base level of the outcome at the beginning of the series; β1: estimates the base trend; β2: estimates the change in level in the post‐intervention segment; β3: estimates the change in trend in the post‐intervention segment).

A dummy ( d ) was introduced to factor in the seasonality of antibiotic use. The variable took the value 1 underpinning 3 months period—August–September–October each year. The choice of the seasonal dummy is consistent with the findings of earlier studies [ 19 ]:

Furthermore, a counterfactual was introduced into the model to assess the outcome in absence of the intervention under study (DPCO 2013). It was assumed that in the absence of the price ceiling notification, the pre-intervention trend of antibiotic consumption would have remained unchanged in the post-intervention period (represented in the figure by a dotted line).

Since the antibiotic sales data were time-series in nature, we checked the regression model for autocorrelation using Durbin–Watson statistic, autocorrelation (ac) and partial autocorrelation (pac) estimates, and plots of the residuals (see Additional file 2 ). We detected first-order autocorrelation in our model and, therefore, altered it to the Prais–Winsten model (model 2) which makes use of the generalized least-squares method to estimate the parameters.

As part of sensitivity analysis, we ran another model using ‘market share’ of antibiotics under DPCO 2013 to the total antibiotic market as an outcome measure to understand the relative change in the market share of antibiotics under ‘DPCO 2013’ versus ‘not under DPCO 2013’ (model 3) to examine if there had been a switch in sales between price regulated and non-price regulated antibiotic formulations in response to the policy intervention. Prais–Winsten model was also used and reported in model 3, since first-order autocorrelation was detected. All analyses were carried out using Stata software version 14.

The study did not require primary data collection. It uses secondary data on pharmaceuticals and, therefore, did not require ethical clearances.

Descriptive statistics

In absolute terms, India’s antibiotic consumption doubled between 2008 and 2018 (Table 1 ). In 2018, around 535 companies produced antibiotics worth INR 140 billion and the share of antibiotics as a proportion of overall medicine sales was nearly 15 percent.

Interrupted time series analysis results

The results from the interrupted time series analysis (Model 1, Table 2 ), suggest that in the pre-intervention period, the average monthly sale of antibiotics under DPCO 2013 increased by 0.4 per cent ( p  < 0.01), whereas post-intervention there was an immediate increase (level change) of 0.8 per cent ( p  > 0.05) and a sustained decline (trend change) of 0.3 per cent ( p  < 0.01) in comparison to the pre-intervention level and trend. However, we detected first-order auto-correlation in Model 1 (Additional file 2 : Figs. S1, S2). We used Prais–Winsten model (Model 2, Table 2 ) to correct autocorrelation. The revised estimates suggest that post-intervention there was an immediate reduction (level change) in the sales of antibiotics under DPCO 2013 by 3.7% ( P  > 0.05), followed by a sustained decline (trend change) of 0.3% ( P  > 0.05) as compared to the pre-intervention scenario, but both changes were statistically insignificant.

Furthermore, as part of the sensitivity analysis, we used the ‘market share’ of antibiotics under DPCO 2013 in the overall antibiotics market as the outcome variable to examine whether there was a switch in antibiotic sales between non-price controlled antibiotics and those under DPCO 2013 (Model 3, Table 2 , Fig.  1 ). Results from model 3 suggest that post-intervention the average monthly market share of the antibiotic under DPCO 2013 fell by more than 579% ( P  < 0.05) (level change) followed by a sustained increase of 9.5% ( P  > 0.05) (trend change) as compared with the pre-intervention trend.

Fitted values of market share of price-regulated antibiotics—actual and counterfactual

Table 3 highlights results from interrupted time series analysis conducted on subtherapeutic categories of the antibiotics under DPCO 2013. The estimates suggest that post-intervention, antibiotics belonging to the therapeutic class broad-spectrum penicillins, cephalosporins, macrolides, trimethoprim and other antibacterials witnessed a sustained reduction in sales while aminoglycoside, narrow spectrum penicillin and fluoroquinolones witnessed increased sales in comparison to the pre-intervention trend. However, the trend change was observed to be statistically significant only for the segments—macrolides, trimethoprim and other antibacterials.

To the best of our knowledge, this is the first report on the impact of DPCO 2013 on antibiotic utilization in India. Our analyses reveal that the effect of price control on antibiotic utilization was modest and limited. Post-intervention (after implementation of DPCO 2013) there was a reduction in the sales of antibiotics under DPCO 2013 by 3.7% which was followed by a sustained decline of 0.3%, as compared to the pre-intervention level and trend. However, both changes were statistically insignificant.

The limited impact on overall antibiotic sales could be explained through the differential impact of DCPO 2013 on different therapeutic categories of antibiotics available in the market. For example, we observed that antibiotics belonging to the therapeutic classes broad-spectrum penicillin, cephalosporins, macrolides, trimethoprim and other antibacterials witnessed a sustained reduction in sales in the post-intervention period, whereas narrow spectrum penicillin, fluoroquinolones and aminoglycosides saw increased sales, though statistically non-significant. Another possible explanation could be the design of the policy and the implementation challenges associated with it. The DPCO 2013 was based on the Essential Medicine List 2011 notified by the Ministry of Health and Family Welfare which has a limited number of antibiotic formulations, which are not necessarily the most prescribed antibiotics in the country’s private sector. In addition, the demand for antibiotics in the market is not only driven by the changing epidemiological pattern but also influenced by market imperfections. For instance, manufacturers can influence prescriber behavior through medical representatives and can shift antibiotic prescriptions toward molecules that are not under price regulation [ 20 , 21 ].

Earlier, using a different methodological approach Sahay et al. reported that DPCO 2013 had a variable impact on the sales volume of medicines under price control. They reported that for a majority of the molecules (52) price regulation had a negative impact on their sales volume, while a few molecules (37) witnessed an increase in sales especially those prescribed for chronic illnesses [ 22 ]. Another independent evaluation reported that DPCO 2013 had resulted in reduced sales of medicines manufactured by small local generics manufacturers. Such medicines were observed to have had around a 14.5 per cent reduction in the market share and around a 5.3 per cent decline in sales [ 11 ].

However, these studies did not examine the dynamic changes in medicine utilisation resulting from the price regulation on a therapeutic segment where medicines are highly substitutable such as antibiotics. We postulate that since a majority of antibiotics are substitutable within a therapeutic class, price controls or price reductions should potentially lead prescribers and consumers to switch toward price-controlled antibiotics (cheaper formulations) from non-price-controlled ones (expensive formulations). Our sensitivity analysis using the ‘market share’ of antibiotics under DPCO 2013 to the overall antibiotics market as the outcome variable (model 3, Table 2 ) demonstrates the switch in sales toward price-regulated antibiotics from non-price-regulated antibiotics. Our estimates from model 3 suggest that the average monthly market share of antibiotics under DPCO 2013 increased by 9.5% in comparison to the pre-intervention period over the duration of the study. This empirical evidence also reflects the clinical practice and market behavior, highlighting that patients and prescribers switch toward cheaper alternatives. Our previous research on statins also found that price regulation led to a relative increase in the sales of the regulated Atorvastatin in the statins market in India [ 12 ].

However, price regulation alone does not guarantee a reduction in the overall expenditure on medicines or treatment costs. Previous research suggests that there could be unintended effects of price control policies. For example, Yoo et al. reported that the implementation of the drug price control policy, did reduce the expenditure by US$ − 1.51, (− 10.2 per cent) in Korea, but it also led to an increased average number of drugs prescribed per month, leading to overutilization and use of prohibited combinations [ 18 ].

Our study has some limitations. We did not assess the effect of price regulation on medicines other than antibiotics hence our findings are not representative of other therapeutic segments under price control. In addition, our study did not quantify the extent of price reduction on antibiotics. Finally, the scope of the research was limited to the private sector market and we did not evaluate the impact of DPCO 2013 on the public sector antibiotic utilization because of a lack of nationally representative public sector data on antibiotics. This is, however, not a particularly significant issue as 85–90 percent of prescriptions in the country occur in the private sector [ 23 ].

Our analysis suggests that DPCO 2013 had a limited impact in increasing utilization of all antibiotics under price regulation but there was a switch from non-price controlled antibiotics to price regulated antibiotics. It may be argued that in India and other market-oriented low and middle-income countries, where a significant proportion of the population seeks care in the private sector, price regulation is critical to contain pharmaceutical expenditure to ensure affordable healthcare. However, price regulation is not without unintended effects, hence continuous monitoring of sales and marketing practices of the manufacturers, and prescribers’ behavior are equally important. Further research is needed to investigate the impact of DPCO 2013 on other therapeutic markets.

Data availability

The data that support the findings of this study are available from AIOCD AWACS a pharmaceutical market research organization but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of AIOCD AWACS https://aiocdawacs.com/(S(1it2biqinendln3ovlglqu3m))/ProductDetail.aspx .

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SS and HHF conceived the idea. HHF and AM designed the analysis, AM conducted the data analysis. SS, HHF, AM and MM conducted the literature review and drafted the manuscript; SS, HHF, AM and MM critically revised the manuscript for intellectual content. All authors read and approved the final manuscript.

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Supplementary Information

Additional file 1: table s1..

Antibiotics in NLEM, 2011 and their price-ceiling notification dates by NPPA.

Additional file 2.

Autocorrelation (AC) and Partial Autocorrelation (PAC) plots of residuals for model 1.

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Selvaraj, S., Farooqui, H.H., Mehta, A. et al. Evaluating the impact of price regulation (Drug Price Control Order 2013) on antibiotic sales in India: a quasi-experimental analysis, 2008–2018. J of Pharm Policy and Pract 15 , 68 (2022). https://doi.org/10.1186/s40545-022-00466-4

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Next Generation Research Uses Real-World Data to Identify Most Effective Hypertension Drugs for Patients

Listen to "next generation research uses real-world data to identify most effective hypertension drugs for patients".

More than 100 million U.S. adults have been diagnosed with hypertension , one of the leading risk factors for cardiovascular disease. While more than 70% of people with hypertension cannot achieve adequate blood pressure control with just one drug, current guidelines only make recommendations for first-line therapy.

“The question is: when the first drug is not enough, what is the optimal second drug to add?” said Yuan Lu, ScD , assistant professor of medicine (cardiology) and assistant professor of biomedical informatics and data science and of epidemiology (chronic disease). “There are more than 50 drugs across five major classes available for treating hypertension. Conducting clinical trials to compare every possible drug and combination thereof is impractical; it would be incredibly time-consuming and costly. Consequently, this creates a significant gap in evidence.”

Lu recently received a Research Project Grant (R01) from the National Institutes of Health (NIH) for the project, “Real-World Evidence to Inform Decisions for Hypertension Treatment Escalation,” to help address this question.

Lu and her team will analyze real-world data that is routinely collected by clinicians in health care settings to compare the effectiveness of second antihypertensive agents on major cardiovascular events as well as their comparative risk on potential drug-related adverse events. This study will also look at the effectiveness and safety of each second hypertensive agent when used in different patient subgroups defined by age, sex, race, ethnicity, and comorbidities, which Lu hopes will help address disparities for patients with hypertension. This is the first research study of its kind that uses real-world data assets and reproducible methods to comprehensively evaluate the safety and effectiveness of second anti-hypertensive drugs added after monotherapy.

“Clinicians often face this important patient scenario and lack comprehensive, high-quality evidence on how best to guide the implementation of the available drug options for patients into real-world practice,” said Eric Velazquez, MD , Robert W. Berliner Professor of Medicine and chief of Yale Cardiovascular Medicine. “Hypertension impacts nearly every family in the world. It has been a substantial frustration for me that randomized clinical trials such as ACCOMPLISH , which we completed over 15 years ago, have not been adequately integrated into everyday care. Yuan’s work is pivotal to ensure our research meets its potential to improve the lives of millions of people living with hypertension.”

The study will analyze data from more than 100 million patients in the United States in five electronic health record (EHR) databases. Lu and her team are collaborating with the Observational Health Data Science and Informatics (OHDSI) , a multi-stakeholder, international organization that aims to use systematic approaches to improve observational study. OHDSI created the OMOP Common Data Model , which is an open community data standard that allows institutions to efficiently share data for analysis.

“By mapping EHR data into a common data model, we can now combine the power of computing, data science, and clinical knowledge to generate new evidence to address these important clinical questions,” said Lu. “We hope our research will inform the prioritization of future clinical trials, assisting investigators in selecting the most promising drug combinations for testing.”

Lu joined Yale in 2015 after receiving her ScD in Global Health and Population at the Harvard School of Public Health. “I was intrigued by this area of study because instead of a doctor, who can only treat 20 or 30 patients a day, I would have the opportunity to impact health at the population level,” she said.

She hopes that this research will inform the development of clinical guidelines. Even though clinical trials provide the highest quality of evidence, real-world data from observational studies can provide important evidence to complement clinical trials and support guideline development, especially when clinical trials are too expensive or unethical to conduct.

“Physicians can’t just wait for clinical trials to end before they help their patients. They need to keep treating people using the best available information and practices,” said Lu.

Eventually, Lu and her team plan to develop a clinical decision support tool that would incorporate the knowledge gained from this project. The tool would help doctors quickly and easily see recommendations about the types of combination therapies that may work best for their individual patients. “It’s often said that it takes about 17 years to translate about 14% of research findings to be implemented into routine clinical practice. It’s a long time. I want to try to reduce the time it takes to get research into clinical practice and increase the percentage of knowledge translation," she said.

The research team is beginning to refine their protocol for the study, which they aim to publish online via GitHub so that anyone interested in this work can read the proposal and provide feedback to help make improvements. Lu sees tremendous potential for this type of study in other areas of medicine, including diabetes, obesity, and other common health conditions. She and other team members are already beginning work on other projects using real-world data.

For example, Lu, along with other researchers from Yale and colleagues at Sentara Health, recently published a paper in the Journal of the American Heart Association (JAHA) , which used real-world EHR data to identify the prevalence, control rates, and diagnostic codes used in a large patient population. The study found that prevalence is increasing, a quarter of patients’ hypertension was not controlled, and there were marked disparities between non-Hispanic Black patients and other racial and ethnic groups. Lu and the study authors say other regional health systems could emulate this study to better understand their hypertension prevalence and control rates and to inform strategies to improve hypertension care. Other study authors include: Yuntian Liu, MPH , Shu-Xia Li, PhD , Mitsuaki Sawano, MD , Patrick Young, PhD , Wade Schulz, MD , and Harlan Krumholz, MD, SM , from Yale, and John E. Brush, Jr., MD, Jordan R. Asher, MD, MS, Mark Anderson, AS, and John S. Burrows, MBA.

“I feel so fortunate that I decided to come to Yale. As an investigator, it can sometimes seem like the only deliverable is a paper. But at Yale, I’m able to work closely with clinicians and see how this knowledge can inform their clinical practice or help them do their job better,” Lu said. “I’m excited to come to work every day.”

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Featured in this article

• Yuan Lu, ScD Assistant Professor of Medicine (Cardiology) and of Biomedical Informatics and Data Science and of Epidemiology (Chronic Diseases)
• Yuntian Liu Statistician I
• Shu-Xia Li, PhD Staff Affiliate - YNHH; Associate Director, Data Management & Analytics, Center for Outcomes Research & Evaluation (CORE)
• Mitsuaki Sawano, MD, PhD Associate Research Scientist
• Wade Schulz, MD, PhD Assistant Professor; Director of Informatics, Laboratory Medicine; Director, CORE Center for Computational Health, Center for Outcomes Research & Evaluation (CORE)
• Patrick Young, PhD Associate Research Scientist
• Harlan Krumholz, MD, SM Harold H. Hines, Jr. Professor of Medicine (Cardiology) and Professor in the Institute for Social and Policy Studies, of Investigative Medicine and of Public Health (Health Policy); Founder, Center for Outcomes Research and Evaluation (CORE)
• Eric Velazquez, MD Robert W. Berliner Professor of Medicine (Cardiology); Chief, Cardiovascular Medicine; Chief, Cardiovascular Medicine, Yale New Haven Hospital; Physician-in-Chief, Heart and Vascular Center, Yale New Haven Health System; Deputy Director, Clinical Trials Innovation, Yale Center for Clinical Investigation (YCCI); Co-Chair, Clinical and Translational Research Oversight Committee; President’s contingency planning committee, Clinical Practice/Clinical Research Subcommittee

Related Links

• Digital Health Tools Help Manage Hypertension for Populations Experiencing Health Disparities
• Enhancing Treatment for Persistent Hypertension: Unleashing the Power of Actionable Taxonomy from EHR Data for Precision Medicine
• The Unmet Potential of Clinical Decision Support Tools in Cardiology

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Physical Activity Guidelines for Americans, 2nd Edition

The Physical Activity Guidelines for Americans, 2nd Edition, were published in the fall of 2018. Learn what the recommendations are here.

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• The U.S. Physical Activity Guidelines Advisory Committee Report—Introduction
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• Physical Activity, Injurious Falls and Physical Function in Aging: An Umbrella Review
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• Quantity and Quality of Exercise for Developing and Maintaining Cardiorespiratory, Musculoskeletal, and Neuromotor Fitness in Apparently Healthy Adults: Guidance for Prescribing Exercise

Guidelines for Physical Activity and Health: Evolution Over 50 Years

Presented as the D.B. Dill Historical Lecture at the 2019 ACSM Annual Meeting, William Haskell, PhD, FACSM, and ACSM past president, presented a timeline of the developing science behind the Physical Activity Guidelines for Americans.

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Title: reach-avoid control synthesis for a quadrotor uav with formal safety guarantees.

Abstract: Reach-avoid specifications are one of the most common tasks in autonomous aerial vehicle (UAV) applications. Despite the intensive research and development associated with control of aerial vehicles, generating feasible trajectories though complex environments and tracking them with formal safety guarantees remain challenging. In this paper, we propose a control framework for a quadrotor UAV that enables accomplishing reach-avoid tasks with formal safety guarantees. In this proposed framework, we integrate geometric control theory for tracking and polynomial trajectory generation using Bezier curves, where tracking errors are accounted for in the trajectory synthesis process. To estimate the tracking errors, we revisit the stability analysis of the closed-loop quadrotor system, when geometric control is implemented. We show that the tracking error dynamics exhibit local exponential stability when geometric control is implemented with any positive control gains, and we derive tight uniform bounds of the tracking error. We also introduce sufficient conditions to be imposed on the desired trajectory utilizing the derived uniform bounds to ensure the well-definedness of the closed-loop system. For the trajectory synthesis, we present an efficient algorithm that enables constructing a safe tube by means of sampling-based planning and safe hyper-rectangular set computations. Then, we compute the trajectory, given as a piecewise continuous Bezier curve, through the safe tube, where a heuristic efficient approach that utilizes iterative linear programming is employed. We present extensive numerical simulations with a cluttered environment to illustrate the effectiveness of the proposed framework in reach-avoid planning scenarios.

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New Aspects of Diabetes Research and Therapeutic Development

Both type 1 and type 2 diabetes mellitus are advancing at exponential rates, placing significant burdens on health care networks worldwide. Although traditional pharmacologic therapies such as insulin and oral antidiabetic stalwarts like metformin and the sulfonylureas continue to be used, newer drugs are now on the market targeting novel blood glucose–lowering pathways. Furthermore, exciting new developments in the understanding of beta cell and islet biology are driving the potential for treatments targeting incretin action, islet transplantation with new methods for immunologic protection, and the generation of functional beta cells from stem cells. Here we discuss the mechanistic details underlying past, present, and future diabetes therapies and evaluate their potential to treat and possibly reverse type 1 and 2 diabetes in humans.

Significance Statement

Diabetes mellitus has reached epidemic proportions in the developed and developing world alike. As the last several years have seen many new developments in the field, a new and up to date review of these advances and their careful evaluation will help both clinical and research diabetologists to better understand where the field is currently heading.

I. Introduction

Diabetes mellitus, a metabolic disease defined by elevated fasting blood glucose levels due to insufficient insulin production, has reached epidemic proportions worldwide (World Health Organization, 2020 ). Type 1 and type 2 diabetes (T1D and T2D, respectively) make up the majority of diabetes cases with T1D characterized by autoimmune destruction of the insulin-producing pancreatic beta cells. The much more prevalent T2D arises in conjunction with peripheral tissue insulin resistance and beta cell failure and is estimated to increase to 21%–33% of the US population by the year 2050 (Boyle et al., 2010 ). To combat this growing health threat and its cardiac, renal, and neurologic comorbidities, new and more effective diabetes drugs and treatments are essential. As the last several years have seen many new developments in the field of diabetes pharmacology and therapy, we determined that a new and up to date review of these advances was in order. Our aim is to provide a careful evaluation of both old and new therapies ( Fig. 1 ) in a manner that we hope will be of interest to both clinical and bench diabetologists. Instead of the usual encyclopedic approach to this topic, we provide here a targeted and selective consideration of the underlying issues, promising new treatments, and a re-examination of more traditional approaches. Thus, we do not discuss less frequently used diabetes agents, such as alpha-glucosidase inhibitors; these were discussed in other recent reviews (Hedrington and Davis, 2019 ; Lebovitz, 2019 ).

Pharmacologic targeting of numerous organ systems for the treatment of diabetes. Treatment of diabetes involves targeting of various organ systems, including the kidney by SGLT2 inhibitors; the liver, gut, and adipose tissue by metformin; and direct actions upon the pancreatic beta cell. Beta cell compounds aim to increase secretion or mass and/or to protect from autoimmunity destruction. Ultimately, insulin therapy remains the final line of diabetes treatment with new technologies under development to more tightly regulate blood glucose levels similar to healthy beta cells. hESC, human embryonic stem cell.

II. Diabetes Therapies

A. metformin.

Metformin is a biguanide originally based on the natural product galegine, which was extracted from the French lilac (Bailey, 1992 ; Rojas and Gomes, 2013 ; Witters, 2001 ). A closely related biguanide, phenformin, was also used initially for its hypoglycemic actions. Based on its successful track record as a safe, effective, and inexpensive oral medication, metformin has become the most widely prescribed oral agent in the world in treating T2D (Rojas and Gomes, 2013 ; He and Wondisford, 2015 ; Witters, 2001 ), whereas phenformin has been largely bypassed due to its unacceptably high association with lactic acidosis (Misbin, 2004 ). Unlike sulfonylureas, metformin lowers blood glucose without provoking hypoglycemia and improves insulin sensitivity (Bailey, 1992 ). Despite these well known beneficial metabolic actions, metformin’s mechanism of action and even its main target organ remain controversial. In fact, metformin has multiple mechanisms of action at the organ as well as the cellular level, which has hindered our understanding of its most important molecular effects on glucose metabolism (Witters, 2001 ). Adding to this, a specific receptor for metformin has never been identified. Metformin has actions on several tissues, although the primary foci of most studies have been the liver, skeletal muscle, and the intestine (Foretz et al., 2014 ; Rena et al., 2017 ). Metformin and phenformin clearly suppress hepatic glucose production and gluconeogenesis, and they improve insulin sensitivity in the liver and elsewhere (Bailey, 1992 ). The hepatic actions of metformin have been the most exhaustively studied to date, and there is little doubt that these actions are of some importance. However, several of the studies remain highly controversial, and there are still open questions.

One of the first reported specific molecular targets of metformin was mitochondrial complex I of the electron transport chain. Inhibition of this complex results in reduced oxidative phosphorylation and consequently decreased hepatic ATP production (El-Mir et al., 2008 ; Evans et al., 2005 ; Owen et al., 2000 ). As is the case in many other studies of metformin, however, high concentrations of the drug were found to be necessary to depress metabolism at this site (El-Mir et al., 2000 ; He and Wondisford, 2015 ; Owen et al., 2000 ). Also controversial is whether metformin works by activating 5′ AMP-activated protein kinase (AMPK), a molecular energy sensor that is known to be a major metabolic sensor in cells, or if not AMPK directly, then one of its upstream regulators such as liver kinase B2 (Zhou et al., 2001 ). Although metformin was shown to activate AMPK in several excellent studies, other studies directly contradicted the AMPK hypothesis. Most dramatic were studies showing that metformin’s actions to suppress hepatic gluconeogenesis persisted despite genetic deletion of the AMPK’s catalytic domain (Foretz et al., 2010 ). More recent studies identified additional or alternative targets, such as cAMP signaling in the liver (Miller et al., 2013 ) or glycogen synthase kinase-3 (Link, 2003 ). Other work showed that the phosphorylation of acetyl-CoA carboxylase and acetyl-CoA carboxylase 2 are involved in regulating lipid homeostasis and improving insulin sensitivity after exposure to metformin (Fullerton et al., 2013 ).

Although there are strong data to support each of these pathways, it is not entirely clear which signaling pathway(s) is most essential to the actions of metformin in hepatocytes. Metformin clearly inhibits complex I and concomitantly decreases ATP and increases AMP. The latter results in AMPK activation, reduced fatty acid synthesis, and improved insulin receptor activation, and increased AMP has been shown to inhibit adenylate cyclase to reduce cAMP and thus protein kinase A activation. Downstream, this reduces the expression of phosphoenolpyruvate carboxykinase and glucose 6-phosphatase via decreased cAMP response element-binding protein, the cAMP-sensitive transcription factor. Decreased PKA also promotes ATP-dependent 6-phosphofructokinase, liver type activity via fructose 2,6-bisphosphate and reduces gluconeogenesis, as fructose-bisphosphatase 1 is inhibited by fructose 2,6-bisphosphate, along with other mechanisms (Rena et al., 2017 ; Pernicova and Korbonits, 2014 ).

More recent work has shown that metformin at pharmacological rather than suprapharmacological doses increases mitochondrial respiration and complex 1 activity and also increases mitochondrial fission, now thought to be critical for maintaining proper mitochondrial density in hepatocytes and other cells. This improvement in respiratory activity occurs via AMPK activation (Wang et al., 2019 ).

Although the liver has historically been the major suspected site of metformin action, recent studies have suggested that the gut instead of the liver is a major target, a concept supported by the increased efficacy of extended-release formulations of metformin that reside for a longer duration in the gut after their administration (Buse et al., 2016 ). An older, but in our view an important observation, is that the intravenous administration of metformin has little or no effect on blood glucose, whereas, in contrast, orally administered metformin is much more effective (Bonora et al., 1984 ). Recent imaging studies using labeled glucose have shown directly that metformin stimulates glucose uptake by the gut in patients with T2D to reduce plasma glucose concentrations (Koffert et al., 2017 ; Massollo et al., 2013 ). Additionally, it is possible that metformin may exert its effect in the gut by inducing intestinal glucagon-like peptide-1 (GLP-1) release (Mulherin et al., 2011 ; Preiss et al., 2017) to potentiate beta cell insulin secretion and by stimulating the central nervous system (CNS) to exert control over both blood glucose and liver function. Indeed, CNS effects produced by metformin have been proposed to occur via the local release of GLP-1 to activate intestinal nerve endings of ascending nerve pathways that are involved in CNS glucose regulation (Duca et al., 2015 ). Lastly, several papers have now implicated that metformin may act by altering the gut microbiome, suggesting that changes in gut flora may be critical for metformin’s actions (McCreight et al., 2016 ; Wu et al., 2017 ; Devaraj et al., 2016 ). A new study proposed that activation of the intestinal farnesoid X receptor may be the means by which microbiota alter hyperglycemia (Sun et al., 2018 ). However, these studies will require more mechanistic detail and confirmation before they can be fully accepted by the field. In addition to the action of metformin on gut flora, the production of imidazole propionate by gut microbes in turn has been shown to interfere with metformin action through a p38-dependent mechanism and AMPK inhibition. Levels of imidazole propionate are especially higher in patients with T2D who are treated with metformin (Koh et al., 2020 ).

In summary, the combined contribution of these various effects of metformin on multiple cellular targets residing in many tissues may be key to the benefits of metformin treatment on lowering blood glucose in patients with type 2 diabetes (Foretz et al., 2019 ). In contrast, exciting new work showing metformin leads to weight loss by increasing circulating levels of the peptide hormone growth differentiation factor 15 and activation of brainstem glial cell-derived neurotropic factor family receptor alpha like receptors to reduce food intake and energy expenditure works independently of metformin’s glucose-lowering effect (Coll et al., 2020 ).

B. Sulfonylureas and Beta Cell Burnout

The class of compounds known as sulfonylureas includes one of the oldest oral antidiabetic drugs in the pharmacopoeia: tolbutamide. Tolbutamide is a “first generation” oral sulfonylurea secretagogue whose clinical usefulness is due to its prompt stimulation of insulin release from pancreatic beta cells. “Second generation” sulfonylureas include drugs such as glyburide, gliclazide, and glipizide. Sulfonylureas act by binding to a high affinity sulfonylurea binding site, the sulfonylurea receptor 1 subunit of the K(ATP) channel, which closes the channel. These drugs mimic the physiologic effects of glucose, which closes the K(ATP) channel by raising cytosolic ATP/ADP. This in turn provokes beta cell depolarization, resulting in increased Ca 2+ influx into the beta cell (Ozanne et al., 1995 ; Ashcroft and Rorsman, 1989 ; Nichols, 2006 ). Importantly, sulfonylureas, and all drugs that directly increase insulin secretion, are associated with hypoglycemia, which can be severe, and which limits their widespread use in the clinic (Yu et al., 2018 ). Meglitinides are another class of oral insulin secretagogues that, like the sulfonylureas, bind to sulfonylurea receptor 1 and inhibit K(ATP) channel activity (although at a different site of action). The rapid kinetics of the meglitinides enable them to effectively blunt the postprandial glycemic excursions that are a hallmark (along with elevated fasting glucose) of T2D (Rosenstock et al., 2004). However, the need for their frequent dosing (e.g., administration before each meal) has limited their appeal to patients.

The efficacy of sulfonylureas is known to decrease over time, leading to failure of the class for effective long-term treatment of T2D (Harrower, 1991 ). More broadly, it is now widely accepted that the number of functional beta cells in humans declines during the progression of T2D. Thus, one would expect that due to this decline, all manner of oral agents intended to target the beta cell and increase its cell function (and especially insulin secretion) will fail over time (RISE Consortium, 2019 ), a process referred to as “beta cell failure” (Prentki and Nolan, 2006 ). Currently, treatments that can expand beta cell mass or improve beta cell function or survival over time are not yet available for use in the clinic. As a result, treatments that may be able to help patients cope with beta cell burnout such as islet cell transplantation, insulin pumps, or stem cell therapy are alternatives that will be discussed below.

C. Ca 2+ Channel Blockers and Type 1 Diabetes

Strategies to treat and prevent T1D have historically focused on ameliorating the toxic consequences of immune dysregulation resulting in autoimmune destruction of pancreatic beta cells. More recently, a concerted focus on alleviating the intrinsic beta cell defects (Sims et al., 2020 ; Soleimanpour and Stoffers, 2013 ) that also contribute to T1D pathogenesis have been gaining traction at both the bench and the bedside. Several recent preclinical studies suggest that Ca 2+ -induced metabolic overload induces beta cell failure (Osipovich et al., 2020 ; Stancill et al., 2017 ; Xu et al., 2012 ), with the potential that excitotoxicity contributes to beta cell demise in both T1D and T2D, similar to the well known connection between excitotoxicity and, concomitantly, increased Ca 2+ loading of the cells and neuronal dysfunction. Indeed, the use of the phenylalkylamine Ca 2+ channel blocker verapamil has been successful in ameliorating beta cell dysfunction in preclinical models of both T1D and T2D (Stancill et al., 2017 ; Xu et al., 2012 ). Verapamil is a well known blocker of L-type Ca 2+ channels, and, in normally activated beta cells, it limits Ca 2+ entry into the beta cell (Ohnishi and Endo, 1981 ; Vasseur et al., 1987 ). This would be expected to, in turn, alter the expression of many Ca 2+ influx–dependent beta cell genes (Stancill et al., 2017 ), and the evidence to date suggests it is likely that verapamil preserves beta cell function in diabetes models by repressing thioredoxin-interacting protein (TXNIP) expression and thus protecting the beta cell. This is somewhat surprising given the physiologic role of Ca 2+ is to acutely trigger insulin secretion; this process would be expected to be inhibited by L-type Ca 2+ channel blockers (Ashcroft and Rorsman, 1989 ; Satin et al., 1995 ).

Hyperglycemia is a well known inducer of TXNIP expression, and a lack of TXNIP has been shown to protect against beta cell apoptosis after inflammatory stress (Chen et al., 2008a ; Shalev et al., 2002 ; Chen et al., 2008b ). Excitingly, the use of verapamil in patients with recent-onset T1D improved beta cell function and improved glycemic control for up to 12 months after the initiation of therapy, suggesting there is indeed promise for targeting calcium and TXNIP activation in T1D. Use of verapamil for a repurposed indication in the preservation of beta cell function in T1D is attractive due its well known safety profile as well as its cardiac benefits (Chen et al., 2009 ). Although the long-term efficacy of verapamil to maintain beta cell function in vivo is unclear, a recently described TXNIP inhibitor may also show promise in suppressing the hyperglucagonemia that also contributes to glucose intolerance in T2D (Thielen et al., 2020 ). As there is a clear need for increased Ca 2+ influx into the beta cell to trigger and maintain glucose-dependent insulin secretion (Ashcroft and Rorsman, 1990 ; Satin et al., 1995 ), it remains to be seen how well regulated insulin secretion is preserved in the presence of L-type Ca 2+ channel blockers like verapamil in the system. One might speculate that reducing but not fully eliminating beta cell Ca 2+ influx might reduce TXNIP levels while preserving enough influx to maintain glucose-stimulated insulin release. Alternatively, these two phenomena may operate on entirely different time scales. At present, these issues clearly will require further investigation.

D. GLP-1 and the Incretins

Studies dating back to the 1960s revealed that administering glucose in equal amounts via the peripheral circulation versus the gastrointestinal tract led to dramatically different amounts of glucose-induced insulin secretion (Elrick et al., 1964 ; McIntyre et al., 1964 ; Perley and Kipnis, 1967 ). Gastrointestinal glucose administration greatly increased insulin secretion versus intravenous glucose, and this came to be known as the “incretin effect” (Nauck et al., 1986a ; Nauck et al., 1986b ). Subsequent work showed that release of the gut hormone GLP-1 mediated this effect such that food ingestion induced intestinal cell hormone secretion. GLP-1 so released would then circulate to the pancreas via the blood to prime beta cells to secrete more insulin when glucose became elevated because these hormones stimulated beta cell cAMP formation (Drucker et al., 1987 ). The discovery that a natural peptide corresponding to GLP-1 could be found in the saliva of the Gila monster, a desert lizard, hastened progress in the field, and ample in vitro studies subsequently confirmed that GLP-1 potentiated insulin secretion in a glucose-dependent manner. GLP-1 has little or no significant action on insulin secretion in the absence of elevated glucose (such as might typically correspond to the postprandial case or during fasting), thus minimizing the likelihood of hypoglycemia provoked by GLP-1 in treated patients (Kreymann et al., 1987 ). Although not completely understood, the glucose dependence of GLP-1 likely reflects the requirement for adenine nucleotides to close glucose-inhibited K(ATP) channels and thus subsequently activate Ca 2+ influx–dependent insulin exocytosis. Besides potentiating GSIS at the level of the beta cell, glucagon-like peptide-1 receptor (GLP-1R) agonists also decrease glucagon secretion from pancreatic islet alpha cells, reduce gastric emptying, and may also increase beta cell proliferation, among other cellular actions (reviewed in Drucker, 2018 ; Muller et al., 2019).

Intense interest in the incretins by basic scientists, clinicians, and the pharma community led to the rapid development of new drugs for treating primarily T2D. These drugs include a range of GLP-1R agonists and inhibitors of the incretin hormone degrading enzyme dipeptidyl peptidase 4 (DPP4), whose targeting increases the half-lives of GLP-1 and gastric inhibitory polypeptide (GIP) and thereby increases protein hormone levels in plasma. GLP-1R agonists have been associated with not only a lowering of plasma glucose but also weight loss, decreased appetite, reduced risk of cardiovascular events, and other favorable outcomes (Gerstein et al., 2019; Hernandez et al., 2018; Husain et al., 2019; Marso et al., 2016a; Marso et al., 2016b ; Buse et al., 2004). Regarding their untoward actions, although hypoglycemia is not a major concern, there have been reports of pancreatitis and pancreatic cancer from use of GLP-1R agonists. However, a recent meta-analysis covering four large-scale clinical trials and over 33,000 participants noted no significantly increased risk for pancreatitis/pancreatic cancer in patients using GLP-1R agonists (Bethel et al., 2018).

Ongoing and future developments in the use of proglucagon-derived peptides such as GLP-1 and glucagon include the use of combined GLP-1/GIP, glucagon/GLP-1, and agents targeting all three peptides in combination (reviewed in Alexiadou and Tan, 2020 ). Although short-term infusions of GLP-1 with GIP failed to yield metabolic benefits beyond those seen with GLP-1 alone (Bergmann et al., 2019 ), several GLP-1/GIP dual agonists are currently in development and have shown promising metabolic results in clinical trials (Frias et al., 2017 ; Frias et al., 2020 ; Frias et al., 2018 ). At the level of the pancreatic islet, beneficial effects of dual GLP-1/GIP agonists may be related to imbalanced and biased preferences of these agonists for the gastric inhibitory polypeptide receptor over the GLP-1R (Willard et al., 2020 ) and possibly were not simply to dual hormone agonism in parallel. Dual glucagon/GLP-1 agonist therapy has also been shown to have promising metabolic effects in humans (Ambery et al., 2018 ; Tillner et al., 2019 ). Oxyntomodulin is a natural dual glucagon/GLP-1 receptor agonist and proglucagon cleavage product that is also secreted from intestinal enteroendocrine cells, which has beneficial effects on insulin secretion, appetite regulation, and body weight in both humans and rodents (Cohen et al., 2003 ; Dakin et al., 2001 ; Dakin et al., 2002 ; Shankar et al., 2018 ; Wynne et al., 2005 ). Interestingly, alpha cell crosstalk to beta cells through the combined effects of glucagon and GLP-1 is necessary to obtain optimal glycemic control, suggesting a potential pathway for therapeutic dual glucagon/GLP-1 agonism within the islets of patients with T2D (Capozzi et al., 2019a ; Capozzi et al., 2019b ). Although the early results appear promising, more studies will be necessary to better understand the mechanistic and clinical impacts of these multiagonist agents.

E. DPP4 Inhibitors

Inhibition of DPP4, the incretin hormone degrading enzyme, is one of the most common T2D treatments to increase GLP-1 and GIP plasma hormone levels. These DPP4 inhibitors or “gliptins” are generally used in conjunction with other T2D drugs such as metformin or sulfonylureas to obtain the positive benefits discussed above (Lambeir et al., 2008 ). DPP4 is a primarily membrane-bound peptidase belonging to the serine peptidase/prolyl oligopeptidase gene family, which cleaves a large number of substrates in addition to the incretin hormones (Makrilakis, 2019 ). DPP4 inhibitors provide glucose-lowering benefits while being generally well tolerated, and the variety of available drugs (including sitagliptin, saxagliptin, vildagliptin, alogliptin, and linagliptin) with slightly different dosing frequency, half-life, and mode of excretion/metabolism allows for use in multiple patient populations (Makrilakis, 2019 ). This includes the elderly and individuals with renal or hepatic insufficiency (Makrilakis, 2019 ).

Although hypoglycemia is not a concern for DPP4 inhibitor use, other considerations should be made. DPP4 inhibitors tend to be more expensive than metformin or other second-line oral drugs in addition to having more modest glycemic effects than GLP-1R agonists (Munir and Lamos, 2017 ). Finally, meta-analysis of randomized and observational studies concluded that heart failure in patients with T2D was not associated with use of DPP4 inhibitors; however, this study was limited by the short follow-up and lack of high-quality data (Li et al., 2016 ). Thus, the US Food and Drug Administration (FDA) did recommend assessing risk of heart failure hospitalization in patients with pre-existing cardiovascular disease, prior heart failure, and chronic kidney disease when using saxagliptin and alogliptin (Munir and Lamos, 2017 ).

F. Sodium Glucose Cotransporter 2 Inhibitors

A recent development in the field of T2D drugs are sodium glucose cotransporter 2 (SGLT2) inhibitors, which have an interesting and very different mechanism of action. Within the proximal tubule of the nephron, SGLT2 transports ingested glucose into the lumen of the proximal tubule between the epithelial layers, thereby reclaiming glucose by this reabsorption process (reviewed in Vallon, 2015 ). SGLT2 inhibitors target this transporter and increase glucose in the tubular fluid and ultimately increase it in the urine. In patients with diabetes, SGLT2 inhibition results in a lowering of plasma glucose with urine glucose content rising substantially (Adachi et al., 2000 ; Vallon, 2015 ). These drugs, although they are relatively new, have become an area of great interest for not only patients with T2D (Grempler et al., 2012 ; Imamura et al., 2012 ; Meng et al., 2008 ; Nomura et al., 2010 ) but also for patients with T1D (Luippold et al., 2012 ; Mudaliar et al., 2012 ). Part of their appeal also rests on reports that their use can lead to a statistically significant decline in cardiac events that are known to occur secondarily to diabetes, possibly independently of plasma glucose regulation (reviewed in Kurosaki and Ogasawara, 2013 ). Although the long-term consequences of their clinical use cannot yet be determined, raising the glucose content of the urogenital tract leads to an increased risk of urinary tract infections and other related infections in some patients (Kurosaki and Ogasawara, 2013 ).

Another recent concern about the use of SGLT2 inhibitors has been the development of normoglycemic diabetic ketoacidosis (DKA). Despite the efficacy of SGLT2 inhibitors, observations of hyperglucagonemia in patients with euglycemic DKA has led to a number of recent studies focused on SGLT2 actions on pancreatic islets. Initial studies of isolated human islets treated with small interfering RNA directed against SGLT2 and/or SGLT2 inhibitors demonstrated increased glucagon release. These studies were complemented by the finding of elevations in glucagon release in mice that were administered SGLT2 inhibitors in vivo (Bonner et al., 2015 ). Insights into the possible mechanistic links between SGLT2 inhibition, DKA frequency, and glucagon secretion in humans may relate to the observation of heterogeneity in SGLT2 expression, as SGLT2 expression appears to have a high frequency of interdonor and intradonor variability (Saponaro et al., 2020 ). More recently, both insulin and GLP-1 have been demonstrated to modulate SGLT2-dependent glucagon release through effects on somatostatin release from delta cells (Vergari et al., 2019 ; Saponaro et al., 2019 ), suggesting potentially complex paracrine effects that may affect the efficacy of these compounds.

On the other hand, several recent studies question that the development of euglycemic DKA after SGLT2 inhibitor therapy may be through alpha cell–dependent mechanisms. Three recent studies found no effect of SGLT2 inhibitors to promote glucagon secretion in mouse and/or rat models and could not detect SGLT2 expression in human alpha cells (Chae et al., 2020 ; Kuhre et al., 2019 ; Suga et al., 2019 ). A fourth study demonstrated only a brief transient effect of SGLT2 inhibition to raise circulating glucagon concentrations in immunodeficient mice transplanted with human islets, which returned to baseline levels after longer exposures to SGLT2 inhibitors (Dai et al., 2020 ). Furthermore, SGLT2 protein levels were again undetectable in human islets (Dai et al., 2020 ). These results could suggest alternative islet-independent mechanisms by which patients develop DKA, including alterations in ketone generation and/or clearance, which underscore the additional need for further studies both in molecular models and at the bedside. Nevertheless, SGLT2 inhibitors continue to hold promise as a valuable therapy for T2D, especially in the large segment of patients who also have superimposed cardiovascular risk (McMurray et al., 2019; Wiviott et al., 2019; Zinman et al., 2015).

G. Thiazolidinediones

Once among the most commonly used oral agents in the armamentarium to treat T2D, thiazolidinediones (TZDs) were clinically popular in their utilization to act specifically as insulin sensitizers. TZDs improve peripheral insulin sensitivity through their action as peroxisome proliferator-activated receptor (PPAR) γ agonists, but their clinical use fell sharply after studies suggested a connection between cardiovascular toxicity with rosiglitazone and bladder cancer risk with pioglitazone (Lebovitz, 2019 ). Importantly, an FDA panel eventually removed restrictions related to cardiovascular risk with rosiglitazone in 2013 (Hiatt et al., 2013 ). Similarly, concerns regarding use of bladder cancer risk with pioglitazone were later abated after a series of large clinical studies found that pioglitazone did not increase bladder cancer (Lewis et al., 2015 ; Schwartz et al., 2015 ). However, usage of TZDs had already substantially decreased and has not since recovered.

Although concerns regarding edema, congestive heart failure, and fractures persist with TZD use, there have been several studies suggesting that TZDs protect beta cell function. In the ADOPT study, use of rosiglitazone monotherapy in patients newly diagnosed with T2D led to improved glycemic control compared with metformin or sulfonylureas (Kahn et al., 2006). Later analyses revealed that TZD-treated subjects had a slower deterioration of beta cell function than metformin- or sulfonylurea-treated subjects (Kahn et al., 2011). Furthermore, pioglitazone use improved beta cell function in the prevention of T2D in the ACT NOW study (Defronzo et al., 2013; Kahn et al., 2011). Mechanistically, it is unclear if TZDs lead to beneficial beta cell function through direct effects or through indirect effects of reduced beta cell demand due to enhanced peripheral insulin sensitivity. Indeed, a beta cell–specific knockout of PPAR γ did not impair glucose homeostasis, nor did it impair the antidiabetic effects of TZD use in mice (Rosen et al., 2003 ). However, other reports demonstrated PPAR-responsive elements within the promoters of both glucose transporter 2 and glucokinase that enhance beta cell glucose sensing and function, which could explain beta cell–specific benefits for TZDs (Kim et al., 2002 ; Kim et al., 2000 ). Furthermore, TZDs have been shown to improve beta cell function by upregulating cholesterol transport (Brunham et al., 2007 ; Sturek et al., 2010 ). Additionally, use of TZDs in the nonobese diabetic (NOD) mouse model of T1D augmented the beta cell unfolded protein response and prevented beta cell death, suggesting potential benefits for TZDs in both T1D and T2D (Evans-Molina et al., 2009 ; Maganti et al., 2016 ). With a now refined knowledge of demographics in which to avoid TZD treatment due to adverse effects, together with genetic approaches to identify candidates more likely to respond effectively to TZD therapy (Hu et al., 2019 ; Soccio et al., 2015 ), it remains to be seen if TZD therapy will return to more prominent use in the treatment of diabetes.

H. Insulin and Beyond: The Use of “Smart” Insulin and Closed Loop Systems in Diabetes Treatment

Due to recombinant DNA technology, numerous insulin analogs are now available in various forms ranging from fast acting crystalline insulin to insulin glargine; all of these analogs exhibit equally effective insulin receptor binding. Most are generated by altering amino acids in the B26–B30 region of the molecule (Kurtzhals et al., 2000 ). The American Diabetes Association delineates these insulins by their 1) onset or time before insulin reaches the blood stream, 2) peak time or duration of maximum blood glucose–lowering efficacy, and 3) the duration of blood glucose–lowering time. Insulin administration is independent of the residuum of surviving and/or functioning beta cells in the patient and remains the principal pharmacological treatment of both T1D and T2D. The availability of multiple types of delivery methods, i.e., insulin pens, syringes, pumps, and inhalants, provides clinicians with a solid and varied tool kit with which to treat diabetes. The downsides, however, are that 1) hypoglycemia is a constant threat, 2) proper insulin doses are not trivial to calculate, 3) compliance can vary especially in children and young adults, and 4) there can be side effects of a variety of types. Nonetheless, insulin therapy remains a mainstay treatment of diabetes.

To eliminate the downsides of insulin therapy, research in the past several decades has worked toward generating glucose-sensitive or “smart” insulin molecules. These molecules change insulin bioavailability and become active only upon high blood glucose using glucose-binding proteins such as concanavalin A, glucose oxidase to alter pH sensitivity, and phenylboronic acid (PBA), which forms reversible ester linkages with diol-containing molecules including glucose itself (reviewed in Rege et al., 2017 ). Indeed, promising recent studies included various PBA moieties covalently bonded to an acylated insulin analog (insulin detemir, which contains myristic acid coupled to Lys B29 ). The detemir allows for binding to serum albumin to prolong insulin’s half-life in the circulation, and PBA provided reversible glucose binding (Chou et al., 2015 ). The most promising of the PBA-modified conjugates showed higher potency and responsiveness in lowering blood glucose levels compared with native insulin in diabetic mouse models and decreased hypoglycemia in healthy mice, although the molecular mechanisms have not yet been determined (Chou et al., 2015 ).

An additional active area of research includes structurally defining the interaction between insulin and the insulin receptor ectodomain. Importantly, a major conformational change was discovered that may be exploited to impair insulin receptor binding under hypoglycemic conditions (Menting et al., 2013 ; Rege et al., 2017 ). Challenges in the design, testing, and execution of glucose-responsive insulins may be overcome by the adaptation of novel modeling approaches (Yang et al., 2020 ), which may allow for more rapid screening of candidate compounds.

Technologies have also progressed in the field of artificial pancreas design and development. Currently two “closed loop” systems are now available: Minimed 670G from Medtronic and Control-IQ from Tandem Diabetes Care. Both systems use a continuous glucose monitor, insulin pump, and computer algorithm to predict correct insulin doses and administer them in real time. Such algorithm systems also take into account insulin potency, the rate of blood glucose increase, and the patient’s heart rate and temperature to adjust insulin delivery levels during exercise and after a meal. In addition, so-called “artificial pancreas” systems have also been clinically tested, which use both insulin and glucagon and as such result in fewer reports of hypoglycemic episodes (El-Khatib et al., 2017 ). These types of systems will continue to become more popular as the development of room temperature–stable glucagon analogs continue, such as GVOKE by Xeris Pharmaceuticals (currently available in an injectable syringe) and Baqsimi, a nasally administered glucagon from Eli Lilly.

I. Present and Future Therapies: Beta Cell Transplantation, Replication, and Immune Protection

1. islet transplantation.

The idea to use pancreatic allo/xenografts to treat diabetes remarkably dates back to the late 1800s (Minkowski, 1892 ; Pybus, 1924 ; Williams, 1894 ). Before proceeding to the discovery of insulin (together with Best, MacLeod, and Collip), Frederick Banting also postulated the potential for transplantation of pancreatic tissue emulsions to treat diabetes in dog models in a notebook entry in 1921 (Bliss, 1982 ). Decades later, Paul Lacy, David Scharp, and colleagues successfully isolated intact functional pancreatic islets and transplanted them into rodent models (Kemp et al., 1973 ). These studies led to the initial proof of concept studies for humans, with the first successful islet transplant in a patient with T1D occurring in 1977 (Sutherland et al., 1978 ). A rapid expansion of islet transplantation, inspired by these original studies led to key observations of successfully prolonged islet engraftment by the “Edmonton protocol” whereby corticosteroid-sparing immunosuppression was applied, and islets from at least two allogeneic donors were used to achieve insulin independence (Shapiro et al., 2000 ). More recent work has focused on improving upon the efficiency and long-term engraftment of allogeneic transplants leading to more prolonged graft function (to the 5-year mark) and successful transplantation from a single islet donor (Hering et al., 2016; Hering et al., 2005 ; Rickels et al., 2013 ). Critical to these efforts to improve the success rate was the recognition that the earlier generation of immunosuppressive agents to counter tissue rejection was toxic to islets (Delaunay et al., 1997 ; Paty et al., 2002 ; Soleimanpour et al., 2010 ) and that more appropriate and less toxic agents were needed (Hirshberg et al., 2003 ; Soleimanpour et al., 2012 ).

Certainly, islet transplantation as a therapeutic approach for patients with T1D has been scrutinized due to several challenges, including (but not limited to) the lack of available donor supply to contend with demand, limited long-term functional efficacy of islet allografts, the potential for re-emergence of autoimmune islet destruction and/or metabolic overload-induced islet failure, and significant adverse effects of prolonged immunosuppression (Harlan, 2016 ). Furthermore, although islet transplantation is not currently available for individuals with T2D, simultaneous pancreas-kidney transplantation in T2D had similar favorable outcomes to simultaneous pancreas-kidney transplantation in T1D; therefore, islet-kidney transplantation may eventually be a feasible option to treat T2D, as patients will already be on immunosuppressors (Sampaio et al., 2011 ; Westerman et al., 1983 ). An additional significant obstacle is the tremendous expense associated with islet transplantation therapy. Indeed, the maintenance, operation, and utilization of an FDA-approved and Good Manufacturing Practice–compliant islet laboratory can lead to operating costs at nearly $150,000 per islet transplant, which is not cost effective for the vast majority of patients with T1D (Naftanel and Harlan, 2004 ; Wallner et al., 2016 ). At present, the focus has been to obtain FDA approval for islet allo-transplantation as a therapy for T1D to allow for insurance compensation (Hering et al., 2016; Rickels and Robertson, 2019 ). In the interim, the islet biology, stem cell, immunology, and bioengineering communities have continued the development of cell-based therapies for T1D by other approaches to overcome the challenges identified during the islet transplantation boom of the 1990s and 2000s.

2. Pharmacologic Induction of Beta Cell Replication

Besides transplantation, progress in islet cell biology and especially in developmental biology of beta cells over several decades raised the additional possibility that beta cell mass reduction in diabetes might be countered by increasing beta cell number through mitogenic means. A key method to expand pancreatic beta cell mass is through the enhancement of beta cell replication. Although the study of pancreatic beta cell replication has been an area of intense focus in the beta cell biology field for several decades, only recently has this seemed truly feasible. Seminal studies identified that human beta cells are essentially postmitotic, with a rapid phase of growth occurring in the prenatal period that dramatically tapers off shortly thereafter (Gregg et al., 2012 ; Meier et al., 2008 ). The plasticity of rodent beta cells is considerably higher than that of human beta cells (Dai et al., 2016 ), which has led to a renewed focus on validation of pharmacologic agents to enhance rodent beta cell replication using isolated and/or engrafted human islets (Bernal-Mizrachi et al., 2014 ; Kulkarni et al., 2012 ; Stewart et al., 2015 ). Indeed, a large percentage of agents that were successful when applied to rodent systems were largely unsuccessful at inducing replication in human beta cells (Bernal-Mizrachi et al., 2014 ; Kulkarni et al., 2012 ; Stewart et al., 2015 ). However, several recent studies have begun to make significant progress on successfully pushing human beta cells to replicate.

Several groups have reported successful human beta cell proliferation, both in vitro and in vivo, in response to inhibitors of the dual specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A). These inhibitors include harmine, INDY, GNF4877, 5-iodotubericidin, leucettine-42, TG003, AZ191, CC-401, and more specific, recently developed DYRK1A inhibitors (Ackeifi et al., 2020 ). Although DYRK1A is conclusively established as the important mediator of human beta cell proliferation, comprehensively determining other cellular targets and if additional gene inhibition amplifies the proliferative response is still in process. New evidence from Wang and Stewart shows dual specificity tyrosine phosphorylation-regulated kinase 1B to be an additional mitogenic target and also describes variability in the range of activated kinases within cells and/or levels of inhibition for the many DYRK1A inhibitors listed above (Ackeifi et al., 2020 ). Interestingly, opposite to these human studies, earlier mouse studies from the Scharfmann group demonstrated that Dyrk1a haploinsufficiency leads to decreased proliferation and loss of beta cell mass (Rachdi et al., 2014b ). In addition, overexpression of Dyrk1a in mice led to beta cell mass expansion with increased glucose tolerance (Rachdi et al., 2014a ).

Although important differences in beta cell proliferative capacity have been shown between human and rodent species, there are also significant differences in the mitogenic capacity of beta cells from juvenile, adult, and pregnant individuals. This demonstrates that proliferative stimuli appear to act within the complex islet, pancreas, and whole-body environments unique to each time point. For example, the administration of the hormones platelet-derived growth factor alpha or GLP-1 result in enhanced proliferation in juvenile human beta cells yet are ineffective in adult human beta cells (Chen et al., 2011 ; Dai et al., 2017 ). This has been shown to be due to a loss of platelet-derived growth factor alpha receptor expression as beta cells age but appears to be unrelated to GLP-1 receptor expression levels (Chen et al., 2011 ). Indeed, the GLP-1 receptor is highly expressed in adult beta cells, and GLP-1 secretion increases insulin secretion, as detailed previously; however, the induction of proliferative factors such as nuclear factor of activated T cells, cytoplasmic 1; forkhead box protein 1; and cyclin A1 is only seen in juvenile islets (Dai et al., 2017 ). Human studies using cadaveric pancreata from pregnant donors also showed increased beta cell mass, yet lactogenic hormones from the pituitary or placenta (prolactin, placental lactogen, or growth hormone) are unable to stimulate proliferation in human beta cells despite their ability to produce robust proliferation in mouse beta cells (reviewed in Baeyens et al., 2016 ). Experiments overexpressing mouse versus human signal transducer and activator of transcription 5, the final signaling factor inducing beta cell adaptation, in human beta cells allows for prolactin-mediated proliferation revealing fundamental differences in prolactin pathway competency in human (Chen et al., 2015 ). Overcoming the barrier of recapitulating human pregnancy’s effect on beta cells through isolating placental cells or blood serum during pregnancy may result in the discovery of a factor(s) that facilitates the increase in beta cell mass observed during human pregnancy.

Mechanisms that stimulate beta cell proliferation have also been discovered from studying genetic mutations that result in insulinomas, spontaneous insulin-producing beta cell adenomas. The most common hereditary mutation occurs in the multiple endocrine neoplasia type 1 (MEN1) gene. Indeed, administration of a MEN1 inhibitor in addition to a GLP-1 agonist (which cannot induce proliferation alone) is able to increase beta cell proliferation in isolated human islets through synergistic activation of KRAS proto-oncogene, GTPase downstream signals (Chamberlain et al., 2014 ). Interestingly, MEN1 mutations are uncommon in sporadic insulinomas, yet assaying genomic and epigenetic changes in a large cohort of non-MEN1 insulinomas found alterations in trithorax and polycomb chromatin modifying genes that were functionally related to MEN1 (Wang et al., 2017 ). Stewart and colleagues hypothesized that changes in histone 3 lysine 27 and histone 3 lysine 4 methylation status led to increased enhancer of zeste homolog 2 and lysine demethylase 6A, decreased cyclin-dependent kinase inhibitor 1C, and thereby increased beta cell proliferation, among other phenotypes. They also proposed that these findings help to explain why increased proliferation always occurs despite broad heterogeneity of mutations found between individual insulinomas (Wang et al., 2017 ).

Although factors that induce proliferation are continuing to be discovered, there are drawbacks that still limit their clinical application. Harmine and other DYRK1A inhibitors are not beta cell specific, nor have all their cellular targets been determined (Ackeifi et al., 2020 ). Targeting other pathways to induce human beta cell proliferation such as modulation of prostaglandin E2 receptors (i.e., inhibition of prostaglandin E receptor 3 alone or in combination with prostaglandin E receptor 4 activation) showed promising increases in proliferative rate yet suffers from the same lack of specificity (Carboneau et al., 2017 ). Induction of proliferation may also come at the expense of glucose sensing as in insulinomas, which have an increased expression of “disallowed genes” and alterations in glucose transporter and hexokinase expression (Wang et al., 2017 ). A further untoward consequence that must be avoided is the production of cancerous cells through unchecked proliferation. Finally, increasing beta cell mass through low rates of proliferation may increase the pool of functional insulin-secreting cells in T2D, but without additional measures, these beta cells will still ultimately be targeted for immune cell destruction in T1D.

3. Beta Cell Stress Relieving Therapies

Metabolic, inflammatory, and endoplasmic reticulum (ER) stress contribute to beta cell dysfunction and failure in both T1D and T2D. Although reduction of metabolic overload of beta cells by early exogenous insulin therapy or insulin sensitizers can temporarily reduce loss of beta cell mass/function early in diabetes, a focus on relieving ER and inflammatory stress is also of interest to preserve beta cell health.

ER stress is a well known contributor to beta cell demise both in T1D and T2D (Laybutt et al., 2007 ; Marchetti et al., 2007 ; Marhfour et al., 2012 ; Tersey et al., 2012 ) and a target of interest in the prevention of beta cell loss in both diseases. Preclinical studies suggest that the use of chemical chaperones, including 4-phenylbutyric acid and tauroursodeoxycholic acid (TUDCA), to alleviate ER stress improves beta cell function and insulin sensitivity in mouse models of T2D (Cnop et al., 2017 ; Ozcan et al., 2006 ). Furthermore, TUDCA has been shown to preserve beta cell mass and reduce ER stress in mouse models of T1D (Engin et al., 2013 ). Interestingly, TUDCA has shown promise at improving insulin action in obese nondiabetic human subjects, yet beta cell function and insulin secretion were not assessed (Kars et al., 2010 ). A clinical trial regarding the use of TUDCA for humans with new-onset T1D is also ongoing ( {"type":"clinical-trial","attrs":{"text":"NCT02218619","term_id":"NCT02218619"}} NCT02218619 ). However, a note of caution regarding use of ER chaperones is that they may prevent low level ER stress necessary to potentiate beta cell replication during states of increased insulin demand (Sharma et al., 2015 ), suggesting that the broad use of ER chaperone therapies should be carefully considered.

The blockade of inflammatory stress has long been an area of interest for treatments of both T1D and T2D (Donath et al., 2019 ; Eguchi and Nagai, 2017 ). Indeed, use of nonsteroidal anti-inflammatory drugs (NSAIDs), which block cyclooxygenase, have been observed to improve metabolic control in patients with diabetes since the turn of the 20th century (Williamson, 1901 ). Salicylates have been shown to improve insulin secretion and beta cell function in both obese human subjects and those with T2D (Fernandez-Real et al., 2008; Giugliano et al., 1985 ). However, another NSAID, salsalate, has not been shown to improve beta cell function while improving other metabolic outcomes (Kim et al., 2014 ; Penesova et al., 2015 ), possibly suggesting distinct mechanisms of action for anti-inflammatory compounds. The regular use of NSAIDs to enhance metabolic outcomes is also often limited to the tolerability of long-term use of these agents due to adverse effects. Recently, golilumab, a monoclonal antibody against the proinflammatory cytokine tumor necrosis factor alpha, was demonstrated to improve beta cell function in new-onset T1D, suggesting that targeting the underlying inflammatory milieu may have benefits to preserve beta cell mass and function in T1D (Quattrin et al., 2020). Taken together, both new and old approaches to target beta cell stressors still remain of long-term interest to improve beta cell viability and function in both T1D and T2D.

3. New Players to Induce Islet Immune Protection

Countless researchers have expended intense industry to determine T1D disease etiology and treatments focused on immunotherapy and tolerogenic methods. Multiple, highly comprehensive reviews are available describing these efforts (Goudy and Tisch, 2005 ; Rewers and Gottlieb, 2009 ; Stojanovic et al., 2017 ). Here we will focus on the protection of beta cells through programmed cell death protein-1 ligand (PD-L1) overexpression, major histocompatibility complex class I, A, B, C (HLA-A,B,C) mutated human embryonic stem cell–derived beta cells, and islet encapsulation methods.

Cancer immunotherapies that block immune checkpoints are beneficial for treating advanced stage cancers, yet induction of autoimmune diseases, including T1D, remains a potential side effect (Stamatouli et al., 2018 ; Perdigoto et al., 2019 ). A subset of these drugs target either the programmed cell death-1 protein on the surface of activated T lymphocytes or its receptor PD-L1 (Stamatouli et al., 2018 ; Perdigoto et al., 2019 ). PD-L1 expression was found in insulin-positive beta cells from T1D but not insulin-negative islets or nondiabetic islets, leading to the hypothesis that PD-L1 is upregulated in an attempt to drive immune cell attenuation (Osum et al., 2018 ; Colli et al., 2018 ). Adenoviral overexpression of PD-L1 specifically in beta cells rescued hyperglycemia in the NOD mouse model of T1D, but these animals eventually succumbed to diabetes by the study’s termination (El Khatib et al., 2015 ). A more promising report from Ben Nasr et al. ( 2017 ) demonstrated that pharmacologically or genetically induced overexpression of PD-L1 in hematopoietic stem and progenitor cells inhibited beta cell autoimmunity in the NOD mouse as well as in vitro using human hematopoietic stem and progenitor cells from patients with T1D.

As mentioned above, islet transplantation to treat T1D is limited by islet availability, cost, and the requirement for continuous immunosuppression. Islet cells generated by differentiating embryonic or induced pluripotent stem (iPS) cells could circumvent these limitations. Ideally, iPS-derived beta cells could be manipulated to eliminate the expression of polymorphic HLA-A,B,C molecules, which were found to be upregulated in T1D beta cells (Bottazzo et al., 1985 ; Richardson et al., 2016 ). These molecules allow peptide presentation to CD8+ T cells or cytotoxic T lymphocytes and may lead to beta cell removal. Interestingly, remaining insulin-positive cells in T1D donor pancreas are not HLA-A,B,C positive (Nejentsev et al., 2007; Rodriguez-Calvo et al., 2015 ). However, current differentiation protocols are still limited in their ability to produce fully glucose-responsive beta cells without transplantation into animal models to induce mature characteristics. Additionally, use of iPS-derived beta cells will still lead to concerns regarding DNA mutagenesis resulting from the methods used to obtain pluripotency or teratoma formation from cells that have escaped differentiation.

Encapsulation devices would protect islets or stem cells from immune cell infiltration while allowing for the proper exchange of nutrients and hormones. Macroencapsulation uses removable devices that would help assuage fears surrounding mutation or tumor formation; indeed, the first human trial using encapsulated hESC-derived beta cells will be completed in January 2021 ( {"type":"clinical-trial","attrs":{"text":"NCT02239354","term_id":"NCT02239354"}} NCT02239354 ). Macroencapsulation of islets prior to transplantation using various alginate-based hydrogels has historically been impeded by a strong in vivo foreign body immune response (Desai and Shea, 2017 ; Doloff et al., 2017 ; Pueyo et al., 1993 ). More recently, chemically modified forms of alginate that avoid macrophage recognition and fibrous deposition have been successfully used in rodents and for up to 6 months in nonhuman primates (Vegas et al., 2016 ). Indeed, Bochenek et al. ( 2018 ) successfully transplanted alginate protected islets for 4 months without immunosuppression in the bursa omentalis of nonhuman primates demonstrating the feasibility for this approach to be extended to humans. It remains to be seen if these devices will be successful for long-term use, perhaps decades, in patients with diabetes.

III. Summary

Although existing drug therapies using classic oral antidiabetic drugs like sulfonylureas and metformin or injected insulin remain mainstays of diabetes treatment, newer drugs based on incretin hormone actions or SGLT2 inhibitors have increased the pharmacological armamentarium available to diabetologists ( Fig. 1 ). However, the explosion of progress in beta cell biology has identified potential avenues that can increase beta cell mass in sophisticated ways by employing stem cell differentiation or enhancement of beta cell proliferation. Taken together, there should be optimism that the increased incidence of both T1D and T2D is being matched by the creativity and hard work of the diabetes research community.

Abbreviations

Authorship contributions.

Wrote and contributed to the writing of the manuscript: Satin, Soleimanpour, Walker

This work was supported by the National Institutes of Health National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) [Grant R01-DK46409] (to L.S.S.), [Grant R01-DK108921] (to S.A.S.), and [Grant P30-DK020572 pilot and feasibility grant] (to S.A.S.), the Juvenile Diabetes Research Foundation (JDRF) [Grant CDA-2016-189] (to L.S.S. and S.A.S.), [Grant SRA-2018-539] (to S.A.S.), and [Grant COE-2019-861] (to S.A.S.), and the US Department of Veterans Affairs [Grant I01 BX004444] (to S.A.S.). The JDRF Career Development Award to S.A.S. is partly supported by the Danish Diabetes Academy and the Novo Nordisk Foundation.

https://doi.org/10.1124/pharmrev.120.000160

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Modular, scalable hardware architecture for a quantum computer

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Quantum computers hold the promise of being able to quickly solve extremely complex problems that might take the world’s most powerful supercomputer decades to crack.

But achieving that performance involves building a system with millions of interconnected building blocks called qubits. Making and controlling so many qubits in a hardware architecture is an enormous challenge that scientists around the world are striving to meet.

Toward this goal, researchers at MIT and MITRE have demonstrated a scalable, modular hardware platform that integrates thousands of interconnected qubits onto a customized integrated circuit. This “quantum-system-on-chip” (QSoC) architecture enables the researchers to precisely tune and control a dense array of qubits. Multiple chips could be connected using optical networking to create a large-scale quantum communication network.

By tuning qubits across 11 frequency channels, this QSoC architecture allows for a new proposed protocol of “entanglement multiplexing” for large-scale quantum computing.

The team spent years perfecting an intricate process for manufacturing two-dimensional arrays of atom-sized qubit microchiplets and transferring thousands of them onto a carefully prepared complementary metal-oxide semiconductor (CMOS) chip. This transfer can be performed in a single step.

“We will need a large number of qubits, and great control over them, to really leverage the power of a quantum system and make it useful. We are proposing a brand new architecture and a fabrication technology that can support the scalability requirements of a hardware system for a quantum computer,” says Linsen Li, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this architecture.

Li’s co-authors include Ruonan Han, an associate professor in EECS, leader of the Terahertz Integrated Electronics Group, and member of the Research Laboratory of Electronics (RLE); senior author Dirk Englund, professor of EECS, principal investigator of the Quantum Photonics and Artificial Intelligence Group and of RLE; as well as others at MIT, Cornell University, the Delft Institute of Technology, the U.S. Army Research Laboratory, and the MITRE Corporation. The paper appears today in Nature .

Diamond microchiplets

While there are many types of qubits, the researchers chose to use diamond color centers because of their scalability advantages. They previously used such qubits to produce integrated quantum chips with photonic circuitry.

Qubits made from diamond color centers are “artificial atoms” that carry quantum information. Because diamond color centers are solid-state systems, the qubit manufacturing is compatible with modern semiconductor fabrication processes. They are also compact and have relatively long coherence times, which refers to the amount of time a qubit’s state remains stable, due to the clean environment provided by the diamond material.

In addition, diamond color centers have photonic interfaces which allows them to be remotely entangled, or connected, with other qubits that aren’t adjacent to them.

“The conventional assumption in the field is that the inhomogeneity of the diamond color center is a drawback compared to identical quantum memory like ions and neutral atoms. However, we turn this challenge into an advantage by embracing the diversity of the artificial atoms: Each atom has its own spectral frequency. This allows us to communicate with individual atoms by voltage tuning them into resonance with a laser, much like tuning the dial on a tiny radio,” says Englund.

This is especially difficult because the researchers must achieve this at a large scale to compensate for the qubit inhomogeneity in a large system.

To communicate across qubits, they need to have multiple such “quantum radios” dialed into the same channel. Achieving this condition becomes near-certain when scaling to thousands of qubits. To this end, the researchers surmounted that challenge by integrating a large array of diamond color center qubits onto a CMOS chip which provides the control dials. The chip can be incorporated with built-in digital logic that rapidly and automatically reconfigures the voltages, enabling the qubits to reach full connectivity.

“This compensates for the in-homogenous nature of the system. With the CMOS platform, we can quickly and dynamically tune all the qubit frequencies,” Li explains.

Lock-and-release fabrication

To build this QSoC, the researchers developed a fabrication process to transfer diamond color center “microchiplets” onto a CMOS backplane at a large scale.

They started by fabricating an array of diamond color center microchiplets from a solid block of diamond. They also designed and fabricated nanoscale optical antennas that enable more efficient collection of the photons emitted by these color center qubits in free space.

Then, they designed and mapped out the chip from the semiconductor foundry. Working in the MIT.nano cleanroom, they post-processed a CMOS chip to add microscale sockets that match up with the diamond microchiplet array.

They built an in-house transfer setup in the lab and applied a lock-and-release process to integrate the two layers by locking the diamond microchiplets into the sockets on the CMOS chip. Since the diamond microchiplets are weakly bonded to the diamond surface, when they release the bulk diamond horizontally, the microchiplets stay in the sockets.

“Because we can control the fabrication of both the diamond and the CMOS chip, we can make a complementary pattern. In this way, we can transfer thousands of diamond chiplets into their corresponding sockets all at the same time,” Li says.

The researchers demonstrated a 500-micron by 500-micron area transfer for an array with 1,024 diamond nanoantennas, but they could use larger diamond arrays and a larger CMOS chip to further scale up the system. In fact, they found that with more qubits, tuning the frequencies actually requires less voltage for this architecture.

“In this case, if you have more qubits, our architecture will work even better,” Li says.

The team tested many nanostructures before they determined the ideal microchiplet array for the lock-and-release process. However, making quantum microchiplets is no easy task, and the process took years to perfect.

“We have iterated and developed the recipe to fabricate these diamond nanostructures in MIT cleanroom, but it is a very complicated process. It took 19 steps of nanofabrication to get the diamond quantum microchiplets, and the steps were not straightforward,” he adds.

Alongside their QSoC, the researchers developed an approach to characterize the system and measure its performance on a large scale. To do this, they built a custom cryo-optical metrology setup.

Using this technique, they demonstrated an entire chip with over 4,000 qubits that could be tuned to the same frequency while maintaining their spin and optical properties. They also built a digital twin simulation that connects the experiment with digitized modeling, which helps them understand the root causes of the observed phenomenon and determine how to efficiently implement the architecture.

In the future, the researchers could boost the performance of their system by refining the materials they used to make qubits or developing more precise control processes. They could also apply this architecture to other solid-state quantum systems.

This work was supported by the MITRE Corporation Quantum Moonshot Program, the U.S. National Science Foundation, the U.S. Army Research Office, the Center for Quantum Networks, and the European Union’s Horizon 2020 Research and Innovation Program.

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• Research Laboratory of Electronics
• Microsystems Technology Laboratories
• Department of Electrical Engineering and Computer Science

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• Computer science and technology
• Quantum computing
• Electronics
• Semiconductors
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