Faculty Bibliography

PURPOSE/OBJECTIVE:In this article, we studied whether pathways in substance use disorder (SUD) treatment differ among people admitted to residential versus ambulatory settings. METHODS:We analyzed a retrospective cohort of 84,755 adults (ages ≥ 18) in Chilean SUD treatment during 2010-2019, creating a comparable sample of 11,226 pairs in ambulatory and residential treatment through cardinality matching. We used a nine-state multistate model, stratifying readmissions by baseline treatment outcome (i.e., completion vs. noncompletion) from admission to the third readmission. We estimated transition probabilities and lengths of stay in states at three-month, one-year, three-year, and five-year follow-ups. Sensitivity analyses tested different model specifications and estimated E-values. RESULTS:Patients in residential settings (vs. ambulatory) had greater treatment completion probabilities (difference at three months; 3.4% [95% CI: 2.9%, 3.9%]), and longer treatment retention (e.g., 1.6 days longer at three months, 95% CI: 0.8, 2.3). Patients in residential vs. ambulatory settings had higher first readmission probabilities regardless of baseline treatment outcome (e.g., three-month difference: 5.7% if completed baseline [95% CI: 4.4%, 7.0%] and 8.0% if did not complete baseline [95% CI: 6.7, 9.3%]). Third readmission probabilities were higher only among patients in residential settings with an incomplete baseline treatment (at least 3.7%; 95% CI: 0.2%, 7.3% at 1-year). CONCLUSION/CONCLUSIONS:Patients in residential settings at baseline were more likely to experience a second treatment and a third readmission among patients with incomplete treatments. Findings underscore the importance of completing initial SUD treatments to reduce readmissions. Residential treatments might require additional strategies to prevent readmissions.

BACKGROUND:Medications for opioid use disorder are associated with a lower risk of drug overdoses at the individual level. However, little is known about whether these effects translate to population-level reductions. We investigated whether county-level efforts to increase access to medication for opioid use disorder in 2012-2014 were associated with opioid overdose deaths in New York State during the first years of the synthetic opioid crisis. METHODS:We performed an ecologic county-level study including data from 60 counties (2010-2018). We calculated rates of people receiving medication for opioid use disorder among the population misusing opioids in 2012-2014 and categorized counties into quartiles of this exposure. We modeled synthetic and nonsynthetic opioid overdose death rates using Bayesian hierarchical models. RESULTS:Counties with higher rates of receiving medications for opioid use disorder in 2012-2014 had lower synthetic opioid overdose deaths in 2016 (highest vs. lowest quartile: rate ratio [RR] = 0.33, 95% credible interval [CrI] = 0.12, 0.98; and second-highest vs. lowest: RR = 0.20, 95% CrI = 0.07, 0.59) and 2017 (quartile second-highest vs. lowest: RR = 0.22, 95% CrI = 0.06, 0.83), but not 2018. There were no differences in nonsynthetic opioid overdose death rates comparing higher quartiles versus lowest quartile of exposure. CONCLUSIONS:A spatio-temporal modeling approach incorporating counts of the population misusing opioids provided information about trends and interventions in the target population. Higher rates of receiving medications for opioid use disorder in 2012-2014 were associated with lower rates of synthetic opioid overdose deaths early in the crisis.

BACKGROUND:Identifying the most effective state laws and provisions to reduce opioid overdose deaths remains critical. METHODS:Using expert ratings of opioid laws, we developed annual state scores for three domains: opioid prescribing restrictions, harm reduction, and Medicaid treatment coverage. We modeled associations of state opioid policy domain scores with opioid-involved overdose death counts in 3133 counties, and among racial/ethnic subgroups in 1485 counties (2013-2020). We modeled a second set of domain scores based solely on experts' highest 20 ranked provisions to compare with the all-provisions model. RESULTS:From 2013 to 2020, moving from non- to full enactment of harm reduction domain laws (i.e., 0 to 1 in domain score) was associated with reduced county-level relative risk (RR) of opioid overdose death in the subsequent year (adjusted RR = 0.84, 95 % credible interval (CrI): 0.77, 0.92). Moving from non- to full enactment of opioid prescribing restrictions and Medicaid treatment coverage domains was associated with higher overdose in 2013-2016 (aRR 1.69 (1.35, 2.11) and aRR 1.20 (1.11, 1.29) respectively); both shifted to the null in 2017-2020. Effect sizes and direction were similar across racial/ethnic groups. Results for experts' highest 20 ranked provisions did not differ from the all-provision model. CONCLUSIONS:More robust state harm reduction policy scores were associated with reduced overdose risk, adjusting for other policy domains. Harmful associations with opioid prescribing restrictions in 2013-2016 may reflect early unintended consequences of these laws. Medicaid coverage domain findings did not align with experts' perceptions, though data limitations precluded inclusion of several highly ranked Medicaid policies.

BACKGROUND:Wider availability of cannabis through medical and recreational legalization (MCL alone and RCL+MCL) has been hypothesized to contribute to reductions in opioid use, misuse, and related harms. We examined whether state adoption of cannabis laws was associated with changes in opioid outcomes overall and stratified by cannabis use. METHODS:Using National Survey on Drug Use and Health (NSDUH) data from 2015 to 2019, we estimated cannabis law associations with opioid (prescription opioid misuse and/or heroin use) misuse and use disorder. All logistic regression models (overall models and models stratified by cannabis use), included year and state fixed effects, individual level covariates, and opioid-related state policies. Stratified analyses were restricted to individuals who reported lifetime cannabis use prior to law adoption to reduce potential for collider bias. Estimates accounted for multiple comparisons using false discovery rate (FDR) corrections and sensitivity to unmeasured confounding using e-values. RESULTS:Overall, MCL and RCL adoption were not associated with changes in the odds of any opioid outcome. After restricting to respondents reporting past-year cannabis use, we observed decreased odds of past year opioid misuse (adjusted odds ratio [AOR]: 0.57 [95 % confidence interval [CI]: 0.38, 0.85]; FDR p-value: 0.07), among individuals in states with MCL compared to those in states without cannabis laws. RCLs were not associated with changes in the odds of any opioid outcome beyond MCL adoption. CONCLUSION/CONCLUSIONS:Comparing individuals in MCL alone states to those in states without such laws, we found an inconsistent pattern of decreased odds of opioid outcomes, which were more pronounced among people reporting cannabis use. The pattern did not hold for individuals in RCL states. In line with a substitution-oriented perspective, findings suggests that MCLs may be associated with reductions in opioid use among people using cannabis but additional work to replicate and expand on these findings is needed.

BACKGROUND:Recreational cannabis use is increasing in Mexico, where legalization is a possibility. The current area-level socioeconomic context of cannabis use has not been studied in the country, limiting our understanding and public health response. We aimed to analyze the association between the municipal socioeconomic environment and recreational cannabis use in Mexico. METHODS:We used data from the National Survey of Drug, Alcohol and Tobacco Consumption 2016-17, the National Health and Nutrition Survey 2023, the 2015 intercensal survey and the 2020 census to study the association of municipal income and municipal education with past-year recreational cannabis use. We fitted Poisson models with robust variance to obtain prevalence ratios and assessed for effect modification by individual-level sex and age, and household-level education. RESULTS:For every unit increase in municipal education, we observed a 1.5 % increase in the prevalence of recreational cannabis use in 2016-17, and a 2.9 % increase in 2023. For each unit increase in municipal income, we observed a 1.5 % increase in the prevalence of recreational cannabis use in 2016-17, and a 1.8 % increase in 2023. We found no effect modification except for a single age group (20- to 29-year-olds vs to 12- to 19-year-olds). CONCLUSION/CONCLUSIONS:Recreational cannabis use in Mexico is currently higher in more socioeconomically advantaged municipalities. Recreational cannabis use through socioeconomic areas should be monitored closely. Further research of the modifiable causes of this association could help inform current and future public health policies.

OBJECTIVE:To evaluate the completeness and quality of Medicaid comprehensive managed care (CMC) data in national MAX/TAF research files. STUDY SETTING AND DESIGN/METHODS:This observational study compared CMC with fee-for-service (FFS) enrollee data in 2001-2019 Medicaid MAX/TAF inpatient, outpatient, and pharmacy files. Completeness was assessed as the proportion of enrollees with any claim and mean claims per enrollee with any claim. Quality was assessed as the proportion of inpatient and outpatient claims with primary diagnosis and procedure codes and the proportion of prescription drug claims with fill dates, National Drug Codes (NDC), days supplied, and quantity dispensed. Acceptable ranges for each study measure were defined as the national FFS mean ± 2 standard deviations. DATA SOURCES AND ANALYTIC SAMPLE/UNASSIGNED:We analyzed secondary data on 45 states from 2001 to 2013 (MAX) and 50 states and DC from 2014 to 2019 (TAF). The sample included adults aged 18-64 with continuous calendar-year enrollment who were eligible for full Medicaid benefits and ineligible for Medicare. We determined CMC enrollment rates and assessed data completeness and quality among state-years with ≥10% CMC penetration, comparing CMC with FFS enrollees. PRINCIPAL FINDINGS/RESULTS:Across 891 state-years, 194,364,647 enrollees met inclusion criteria. Of 540 state-years (60.6%) with ≥10% CMC enrollment, CMC data were largely comparable to national FFS distributions for all inpatient (n = 430; 79.6%), outpatient (n = 467, 86.5%), and prescription (n = 459, 85.0%) completeness criteria and for all inpatient (n = 449, 83.1%), outpatient (n = 511, 94.6%), and prescription (n = 528, 97.8%) quality criteria. Overall completeness (92.3%) and quality (84.6%) improved substantially by 2019. CONCLUSIONS:Completeness and quality of CMC data were largely comparable to FFS data, with increasing state-years meeting criteria over time. Further research on national Medicaid populations should assess and address differences in data completeness and quality by plan type across states, over time, and in relation to specific study samples and measures of interest.

INTRODUCTION/UNASSIGNED:Homelessness in the United States increased every year since 2016, with a 38% increase from 2023 to 2024. Much of the increase is attributable to rising home and rent costs, economic hardship caused by the recent pandemic, and the ending of protective legislation. Notably, people who experience homelessness have an increased risk of substance use disorders, HIV infection and poorer HIV outcomes than people who are stably housed. The iHouse model aims to develop feasible, effective, and cost-effective tailored approaches to improve health outcomes in this population including life expectancy, overdose, and HIV. METHODS AND ANALYSIS/UNASSIGNED:The study will employ Group Model Building methods and use insights from that process to develop an agent-based model simulating the dynamic processes contributing to HIV incidence and treatment, overdose, and life expectancy among people along the housing and homelessness continuum in Denver, CO and San Francisco, CA. The model will evaluate multiple outcomes from 4 conceptual dimensions: (1) movement along the housing continuum, (2) population health (overdose and HIV incidence and life expectancy), (3) budgetary impact, (4) economic value. ETHICS AND DISSEMINATION/UNASSIGNED:This study has been approved by the Colorado Institutional Review Board at the University of Colorado under protocol 24-0878. The data generated by this protocol, the methodologies used, and the findings will be made available in a timely manner to other researchers. iHOUSE code and parameter values will be published in Git Hub, such that all model analyses can be reproduced by independent investigators. Documentation of all parameter estimates and model results will be published for independent review and confirmation. In addition, supplemental materials and appendices for the model will be shared on a publicly available website.

BACKGROUND:Firearm violence remains a leading cause of death and injury in the United States. Prior research supports that alcohol exposures, including individual-level alcohol use and alcohol control policies, are modifiable risk factors for firearm violence, yet additional research is needed to support prevention efforts. OBJECTIVES/OBJECTIVE:This scoping review aims to update a prior 2016 systematic review on the links between alcohol exposure and firearm violence to examine whether current studies indicate causal links between alcohol use, alcohol interventions, and firearm violence-related outcomes. ELIGIBILITY CRITERIA/METHODS:Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines, a comprehensive search of published studies was conducted, replicating the search strategy of the prior review but focusing on studies published since 2015. The review included published studies of humans, conducted in general populations of any age, gender, or racial/ethnic group, that examined the relationship between an alcohol-related exposure and an outcome involving firearm violence or risks for firearm violence. Excluded were small studies restricted to special populations, forensic or other technical studies, non-original research articles such as reviews, and studies that relied solely on descriptive statistics or did not adjust for confounders. SOURCES OF EVIDENCE/METHODS:The review included published studies indexed in PubMed, Web of Science, and Scopus. Eligible articles were published on or after January 1, 2015. The latest search was conducted on December 15, 2023. CHARTING METHODS/METHODS:Using a structured data collection instrument, data were extracted on the characteristics of each study, including the dimension of alcohol exposure, the dimension of firearm violence, study population, study design, statistical analysis, source of funding, main findings, and whether effect measure modification was assessed and, if so, along what dimensions. Two authors independently conducted title/abstract screening, full-text screening, and data extraction until achieving 95% agreement, with discrepancies resolved through discussion. RESULTS:The search yielded 797 studies. Of these, 754 were excluded and 43 met the final inclusion criteria. Studies addressed a range of alcohol exposures and firearm violence-related outcomes, primarily with cross-sectional study designs; 40% considered effect measure modification by any population characteristic. Findings from the 21 studies examining the relationship of individual-level alcohol use or alcohol use disorder (AUD) with firearm ownership, access, unsafe storage, or carrying indicated a strong and consistent positive association. Seven studies examined associations of individual-level alcohol use or AUD with firearm injury or death; these also indicated a pattern of positive associations, but the magnitude and precision of the estimates varied. Eight studies examined the impact of neighborhood proximity or density of alcohol outlets and found mixed results that were context- and study design-dependent. Two studies linked prior alcohol-related offenses to increased risk of firearm suicide and perpetration of violent firearm crimes among a large cohort of people who purchased handguns, and two studies linked policies prohibiting firearm access among individuals with a history of alcohol-related offenses to reductions in firearm homicide and suicide. Finally, four studies examined alcohol control policies and found that greater restrictiveness was generally associated with reductions in firearm homicide or firearm suicide. CONCLUSIONS:Findings from this scoping review continue to support a causal relationship between alcohol exposures and firearm violence that extends beyond acute alcohol use to include AUD and alcohol-related policies. Policies controlling the availability of alcohol and prohibiting firearm access among individuals with alcohol-related offense histories show promise for the prevention of firearm violence. Additional research examining differential impacts by population subgroup, alcohol use among perpetrators of firearm violence, policies restricting alcohol outlet density, and randomized or quasi-experimental study designs with longitudinal follow-up would further support inferences to inform prevention efforts.

BACKGROUND/UNASSIGNED:Rates of cannabis use disorder (CUD) have increased disproportionately among Veterans Administration (VA) patients with psychiatric disorders compared to patients with no disorder. However, VA patient samples are not representative of all U.S. adults, so results on disproportionate increases in CUD prevalence could have been biased. To address this concern, we investigated whether disproportionate increases in the prevalence of cannabis outcomes among those with psychiatric disorders would replicate in nationally representative samples of U.S. adults. METHODS/UNASSIGNED: = 36,309). Outcomes were any past-year non-medical cannabis use, frequent non-medical use (≥3 times weekly), and DSM-IV CUD. Psychiatric disorders included mood, anxiety and antisocial personality disorders. Logistic regression was used to generate predicted prevalences of the outcomes, prevalence differences calculated and additive interactions compared differences between those with and without psychiatric disorders. RESULTS/UNASSIGNED:Cannabis outcomes increased more among those with psychiatric disorders. The difference in prevalence differences included any past-year non-medical cannabis use, 2.45% (95%CI = 1.29-3.62); frequent non-medical cannabis use, 1.58% (95%CI = 0.83-2.33); CUD, 1.40% (95%CI = 0.58-2.21). For most specific disorders, prevalences increased more among those with the disorder. CONCLUSIONS/UNASSIGNED:In the U.S. general population, rates of cannabis use and CUD increased more among adults with psychiatric disorders than other adults, similar to findings from VA patient samples. Results suggest that although VA patients are not representative of all U.S. adults, findings from this important patient group can be informative. Greater clinical and policy attention to CUD is warranted for adults with psychiatric disorders.