Faculty Bibliography

Overdose Good Samaritan Laws (GSLs) aim to reduce mortality by providing limited legal protections when an overdose bystander summons help. Most research into the impact of these laws is dated or potentially confounded by co-enacted naloxone access laws. Lack of awareness and trust in GSL protections, as well as fear of police involvement and legal repercussions, remain key deterrents of help-seeking. These barriers may be unequally distributed by race due to racist policing and drug policies, potentially producing racial disparities in the effectiveness of GSLs for reducing overdose mortality. We used 2015-2019 vital statistics data to estimate the effect of recent GSLs on overdose mortality, overall (eight states) and by Black/white race (four states). Given GSLs' near ubiquity, few unexposed states were available for comparison. We therefore proposed an "inverted" synthetic control method (SCM) to compare overdose mortality in new-GSL states to states with GSLs throughout the analytic period. The estimated relationships between GSLs and overdose mortality, both overall and stratified by Black/white race, were consistent with chance. An absence of effect could result from insufficient protection provided by the laws, insufficient awareness of them, and/or reticence to summon help not addressable by legal protections. The inverted SCM may be useful for evaluating other widespread policies.

Former industrial cities facing economic challenges and depopulation often experience high levels of firearm and other forms of violence. Within these cities, violent crime often clusters in neighborhoods affected by high levels of vacant and abandoned housing. This study estimates the effects of building demolition in Detroit, Michigan on the subsequent risk of violent crime using property-level data and longitudinal targeted maximum likelihood estimation. The primary outcome is violent Crime Index crimes (homicide, rape, robbery and aggravated assault). We estimate effects for this category of crimes as a whole and for the subset involving firearms. Drug and other lower-level crimes are included as secondary outcomes. We compare the risk of experiencing each crime type following building demolition in Census blocks and block groups to an estimate of the risk had there been no demolition in the 1-3 quarters prior in 2017. There were >2600 total demolitions in about 1700 blocks in 2017 in Detroit. Nearly all demolished buildings were sourced from tax foreclosures. Estimates suggest the risk for all crime types tested would have been statistically indistinguishable from the observed crime risk had demolitions in the prior 1-3 quarters of 2017 not occurred. Our results run counter to most previous research on this topic, which tends to show a protective effect of demolition on violent crime. Understanding why our results differ may provide important insights into the types of demolition programs with the greatest potential to reduce violent crime.

Violations of the positivity assumption (also called the common support condition) challenge health policy research and can result in significant bias, large variance, and invalid inference. We define positivity in the single- and multiple-timepoint (i.e., longitudinal) health policy evaluation setting, and discuss real-world threats to positivity. We show empirical evidence of the practical positivity violations that can result when attempting to estimate the effects of health policies (in this case, Naloxone Access Laws). In such scenarios, an alternative is to estimate the effect of a shift in law enactment (e.g., the effect if enactment had been delayed by some number of years). Such an effect corresponds to what is called a modified treatment policy, and dramatically weakens the required positivity assumption, thereby offering a means to estimate policy effects even in scenarios with serious positivity problems. We apply the approach to define and estimate the longitudinal effects of Naloxone Access Laws on opioid overdose rates.

Introduction/UNASSIGNED:Adolescent time use in recent cohorts is distinguished by large-scale changes, including shifts in parental monitoring, supervision, and adolescent activity patterns, that together may provide a more complete perspective on changing patterns of mental health than can be captured by single risk factors. Methods/UNASSIGNED:To determine whether patterns of adolescent time use explain recent increases in depressive and other internalizing symptoms, we first conducted latent profile analyses of 465,839 adolescents, grades 8/10, from annual, cross-sectional Monitoring the Future surveys, years:1991-2019, using twenty-one variables (e.g., frequency of attending parties) to identify groups based on patterns of time use. Most of the sample was female (51.0%), non-Hispanic white (58.8%), and in grade 8 (52.2%); mean age: 14.60 years (95% CI: 14.57, 14.64). We subsequently examined differences in depressive and other internalizing symptoms between these time use groups over time with survey-weighted logistic regressions producing odds ratios. Results/UNASSIGNED:Analyses derived six groups: part time workers, full time workers, and four groups based on levels of social activities (Low, medium, and high levels, with "High Social" split between those engaged in sports, academics, and community service and those who were not). Internalizing symptoms were predicted by lower socialization, low engagement in activities like sports, academics, and community service, and time spent at a paid job. Adolescents decreasingly engaged in social activities over time, though shifts in time use patterns did not account for much of the overall increase in depressive symptoms. Conclusion/UNASSIGNED:Shifts in adolescent time use do not explain increases in depressive and other internalizing symptoms, which increased across different patterns of time use. Levels of internalizing symptoms were highest among those with low socialization, low recreational engagement, and those working substantial hours. Encouraging socialization, engagement in recreational activities, and providing mental health resources for isolated adolescents may reduce internalizing symptom trends.

BACKGROUND:In the context of changing cannabis and other drug policy and regulation, concerns may arise regarding drug treatment access and use. We assessed cannabis/cocaine-related dependence and treatment in Argentina, Chile, and Uruguay. METHODS:Nationally representative cross-sectional household surveys of people ages 15-64 in Argentina (4 surveys, 2006-2017), Chile (7 surveys, 2006-2018), and Uruguay (4 surveys, 2006-2018) were harmonized. We estimated weighted prevalences of cannabis or cocaine-related (cocaine or cocaine paste) dependence, based on meeting 3+ past-year ICD-10 dependence criteria. We estimated weighted prevalences of past-year alcohol/drug treatment use (Argentina, Chile) or use/seeking (Uruguay) among people with past-year cannabis/cocaine-related dependence. We tested model-based prevalence trends over time and described individual-level treatment correlates by country. RESULTS:Cannabis/cocaine dependence prevalence increased in the region starting in 2010-2011, driven by cannabis dependence. Adjusted cannabis dependence prevalence increased from 0.7% in 2010 to 1.5% in 2017 in Argentina (aPD=0.8, 95% CI= 0.3, 1.2), from 0.8% in 2010 to 2.8% in 2018 in Chile (aPD=2.0, 95% CI= 1.4, 2.6), and from 1.4% in 2011 to 2.4% in 2018 in Uruguay (aPD=0.9, 95% CI= 0.2, 1.6). Cocaine-related dependence increased in Uruguay, decreased in Argentina, and remained stable in Chile. Among people with past-year cannabis/cocaine dependence, average alcohol/drug treatment use prevalence was 15.3% in Argentina and 6.0% in Chile, while treatment use/seeking was 14.7% in Uruguay. Alcohol/drug treatment prevalence was lower among people with cannabis dependence than cocaine-related dependence. Treatment correlates included older ages in all countries and male sex in Argentina only. CONCLUSION/CONCLUSIONS:Alcohol/drug treatment use among people with cannabis/cocaine-related dependence remained low, signaling an ongoing treatment gap in the context of growing cannabis dependence prevalence in the region. Additional resources may be needed to increase treatment access and uptake. Future studies should assess contributors of low treatment use, including perceived need, stigma, and service availability.

OBJECTIVES:To compare the characteristics of individual overdose decedents in Rhode Island, 2016-2020 to the neighborhoods where fatal overdoses occurred over the same time period. METHODS:We conducted a retrospective analysis of fatal overdoses occurring between January 1, 2016 and June 30, 2020. Using individual- and neighborhood-level data, we conducted descriptive analyses to explore the characteristics of individuals and neighborhoods most affected by overdose. RESULTS:Most overdose decedents during the study period were non-Hispanic White. Across increasingly more White and non-Hispanic neighborhoods, rates of fatal overdose per 100,000 person-years decreased. An opposite pattern was observed across quintiles of average neighborhood poverty. CONCLUSIONS:Rates of fatal overdose were higher in less White, more Hispanic, and poorer neighborhoods, suggesting modest divergence between the characteristics of individuals and the neighborhoods most severely affected. These impacts may not be uniform across space and may accrue differentially to more disadvantaged and racially/ethnically diverse neighborhoods.

BACKGROUND AND AIM/OBJECTIVE:In Uruguay, residents age 18 and older seeking legal cannabis must register with the government and choose one of three supply mechanisms: self-cultivation, non-profit cannabis clubs or pharmacies. This is the first paper to measure the association between type of legal cannabis supply mechanism and traffic crashes involving injuries. DESIGN/METHODS:Ecological study using ordinary least squares regression to examine how department-level variation in registrations (overall and by type) is associated with traffic crashes involving injuries. SETTING/METHODS:Uruguay. CASES/METHODS:532 department-quarters. MEASUREMENTS/METHODS:Quarterly cannabis registration counts at the department level and incident-level traffic crash data were obtained from government agencies. The analyses controlled for department-level economic and demographic characteristics and, as a robustness check, we included traffic violations involving alcohol for departments reporting this information. Department-level data on crashes, registrations and alcohol violations were denominated by the number of residents ages 18 and older. FINDINGS/RESULTS:From 2013 to 2019, the average number of registrations at the department-quarter level per 10 000 residents age 18 and older for self-cultivation, club membership and pharmacy purchasing were 17.7 (SD = 16.8), 3.6 (SD = 8.6), and 25.1 (SD = 50.4), respectively. In our multivariate regression analyses, we did not find a statistically significant association between the total number of registrations and traffic crashes with injuries (β = -0.007; P = 0.398; 95% CI = -0.023, 0.01). Analyses focused on the specific supply mechanisms found a consistent, positive and statistically significant association between the number of individuals registered as self-cultivators and the number of traffic crashes with injuries (β = 0.194; P = 0.008; 95% CI = 0.058, 0.329). Associations for other supply mechanisms were inconsistent across the various model specifications. CONCLUSIONS:In Uruguay, the number of people allowed to self-cultivate cannabis is positively associated with traffic crashes involving injuries. Individual-level analyses are needed to assess better the factors underlying this association.

AIM/OBJECTIVE:To quantify the trends in frequent and occasional cannabis vaping, demographic differences and concurrent nicotine and alcohol use. DESIGN/METHODS:Observational study. Survey-weighted multinomial logistic regression models assessed trends and disparities in past 30-day cannabis use. Trends were assessed overall and by sex, race/ethnicity, parental education and urbanicity. Multinomial logistic regression models also estimated associations of cannabis use (none, use without vaping, use with vaping) with past 2-week binge drinking and past 30-day nicotine/tobacco use. SETTING/METHODS:United States, 2017-19. PARTICIPANTS/METHODS:Participants in the national Monitoring the Future (n = 51 052) survey. MEASUREMENTS/METHODS:Past 30-day frequent cannabis use (six or more times/30 days) and past 30-day occasional use (one to five times/30 days), with and without vaping. FINDINGS/RESULTS:Past 30-day frequent cannabis use with vaping and occasional use with vaping rose from 2017 to 2019. Past 30-day frequent and occasional cannabis use without vaping declined. Certain groups, such as Hispanic/Latino or lower socio-economic status adolescents, experienced particularly notable increases in frequent cannabis use with vaping (e.g. prevalence among Hispanic/Latino adolescents). Adolescents who reported smoking and vaping nicotine, and 10+ occasions of binge drinking, were 42.28 [95% confidence interval (CI) = 33.14-53.93] and 10.09 (95% CI = 4.51-22.53) times more likely to report past 30-day cannabis use with vaping, respectively, compared with no use. DISCUSSION/CONCLUSIONS:Cannabis use without vaping appears to be declining among adolescents in the United States, while cannabis use with vaping is accelerating; frequent cannabis vaping is especially increasing, with consistent increases across almost all adolescent demographic groups. Cannabis use among US adolescents remains highly associated with other substance use.

BACKGROUND:States have enacted multiple types of laws, with a variety of constituent provisions, in response to the opioid epidemic, often simultaneously. This temporal proximity and variation in state-to-state operationalization has resulted in significant challenges for empirical research on their effects. Thus, expert consensus can be helpful to classify laws and their provisions by their degree of helpfulness and impact. METHODS:We conducted a four-stage modified policy Delphi process to identify the top 10 most helpful and 5 most harmful provisions from eight opioid-related laws. This iterative consultation with six types of opioid experts included a preliminary focus group (n=12), two consecutive surveys (n=56 and n=40, respectively), and a final focus group feedback session (n=5). RESULTS:On a scale of very harmful (0) to very helpful (4), overdose Good Samaritan laws received the highest average helpfulness rating (3.62, 95% CI: 3.48-3.75), followed by naloxone access laws (3.37, 95% CI: 3.22-3.51), and pain management clinic laws (3.08, 95% CI: 2.89-3.26). Drug-induced homicide (DIH) laws were rated the most harmful (0.88, 95% CI: 0.66-1.11). Impact ratings aligned similarly, although Medicaid laws received the second highest overall impact rating (3.71, 95% CI: 3.45, 3.97). The two most helpful provisions were naloxone standing orders (3.94, 95% CI: 3.86-4.02) and Medicaid coverage of medications for opioid use disorder (MOUD) (3.89, 95% CI: 3.82). Mandatory minimum DIH laws were the most harmful provision (0.73, 95% CI 0.53-0.93); followed by requiring prior authorization for Medicaid coverage of MOUD (1.00 95% CI: 0.72-1.27). CONCLUSION/CONCLUSIONS:Overall, experts rated laws and provisions that facilitated harm reduction efforts and access to MOUD as most helpful. Laws and provisions rated as most harmful criminalized substance use and placed restrictions on access to MOUD. These ratings provide a foundation for evaluating the overall overdose policy environment for each state.