Faculty Bibliography

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:The United States overdose crisis continues unabated. Despite efforts to increase capacity for treating opioid use disorder (OUD) in the U.S., how actual treatment receipt compares to need remains unclear. In this cross-sectional study, we estimate progress in addressing the gap between OUD prevalence and OUD treatment receipt at the national and state levels from 2010 to 2019. METHODS:We estimated past-year OUD prevalence rates based on the U.S. National Survey on Drug Use and Health (NSDUH), using adjustment methods that attempt to account for OUD underestimation in national household surveys. We used data from specialty substance use treatment records and outpatient pharmacy claims to estimate the gap between OUD prevalence and number of persons receiving medications for opioid use disorder (MOUD) during the past decade. RESULTS:Adjusted estimates suggest past-year OUD affected 7,631,804 individuals in the U.S. in (2,773 per 100,000 adults 12+), relative to only 1,023,959 individuals who received MOUD (365 per 100,000 adults 12+). This implies approximately 86.6% of individuals with OUD nationwide who may benefit from MOUD treatment do not receive it. MOUD receipt increased across states over the past decade, but most regions still experience wide gaps between OUD prevalence and MOUD receipt. CONCLUSIONS:Despite some progress in expanding access to MOUD, a substantial gap between OUD prevalence and treatment receipt highlights the critical need to increase access to evidence-based services.

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.

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.

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.

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:Substance use disorders (SUD) elevate the risk for COVID-19 hospitalization, but studies are inconsistent on the relationship of SUD to COVID-19 mortality. METHODS:Veterans Health Administration (VHA) patients treated in 2019 and evaluated in 2020 for COVID-19 (n=5,556,315), of whom 62,303 (1.1%) tested positive for COVID-19 (COVID-19+). Outcomes were COVID-19+ by 11/01/20, hospitalization, ICU admission, or death within 60 days of a positive test. Main predictors were any ICD-10-CM SUDs, with substance-specific SUDs (cannabis, cocaine, opioid, stimulant, sedative) explored individually. Logistic regression produced unadjusted and covariate-adjusted odds ratios (OR; aOR). RESULTS:Among COVID-19+ patients, 19.25% were hospitalized, 7.71% admitted to ICU, and 5.84% died. In unadjusted models, any SUD and all substance-specific SUDs except cannabis use disorder were associated with COVID-19+(ORs=1.06-1.85); adjusted models produced similar results. Any SUD and all substance-specific SUDs were associated with hospitalization (aORs: 1.24-1.91). Any SUD, cocaine and opioid disorder were associated with ICU admission in unadjusted but not adjusted models. Any SUD, cannabis, cocaine, and stimulant disorders were inversely associated with mortality in unadjusted models (OR=0.27-0.46). After adjustment, associations with mortality were no longer significant. In ad hoc analyses, adjusted odds of mortality were lower among the 49.9% of COVID-19+ patients with SUD who had SUD treatment in 2019, but not among those without such treatment. CONCLUSIONS:In VHA patients, SUDs are associated with COVID-19 hospitalization but not COVID-19 mortality. SUD treatment may provide closer monitoring of care, ensuring that these patients received needed medical attention, enabling them to ultimately survive serious illness.

OBJECTIVE:Suicidal behavior and bullying victimization are important indicators of adolescent psychological distress, and are patterned by sex, race/ethnicity and sexual identity. This study aimed to estimate trends and disparities in these factors along key demographics. METHOD/METHODS:Youth Risk Behavior Survey data (2015-2019, N=44,066) were collected biennially through national cross-sectional surveys of US school-attending adolescents. Survey-weighted logistic regressions examined disparities in past-year bullying and suicidal behavior, overall and by demographics. RESULTS:Bullying in 2019 was highest for female (vs. male) students (OR=1.82, 95% CI:[1.62, 2.06]), American Indian/Alaskan Native (vs White) students (OR= 1.48, [0.91, 2.41], p>.05), and gay/lesbian (vs heterosexual) students (OR= 2.81, [2.07, 3.81]). Suicidal behavior disparities affected similar groups. There was minimal evidence for shifts in disparities since 2015, with the exception of bullying for gay/lesbian adolescents. The prevalence of bullying victimization among gay and lesbian adolescents went from 31.6% to 44.5% between 2015 and 2019, surpassing the bisexual and "Not Sure" groups to be the sexual identity group with the highest rate of bullying victimization. CONCLUSION/CONCLUSIONS:Interventions that operate on multiple structural levels and empower marginalized youth are needed.