Faculty Bibliography

CONTEXT/BACKGROUND:Predictive modeling can identify neighborhoods at elevated risk of future overdose death and may assist community organizations' decisions about harm reduction resource allocation. In Rhode Island, PROVIDENT is a research initiative and randomized community intervention trial that developed and validated a machine learning model that predicts future overdose at a census block group (CBG) level. The PROVIDENT model prioritizes the top 20th percentile of CBGs at highest risk of future overdose death over the subsequent 6-month period. In CBGs assigned to the trial intervention arm, these predictions are then displayed for partnering community organizations via an interactive mapping dashboard. OBJECTIVE:To evaluate whether CBGs prioritized by the PROVIDENT model were associated with increased user engagement via an online dashboard for fatal overdose forecasting and resource planning. DESIGN/METHODS:We estimated prevalence ratios using modified Poisson regression models, adjusted for CBG-level characteristics that may confound the relationship between model predictions and dashboard engagement. SETTING/METHODS:We used CBG-level data in Rhode Island (N = 809) from November 2021 to July 2024. INTERVENTION/METHODS:Our exposure of interest was whether each CBG was prioritized by the PROVIDENT model and shown as prioritized on the interactive mapping dashboard. MAIN OUTCOME MEASURE/METHODS:Our primary outcome was whether a dashboard user from any partnering community organization engaged (eg, clicked, interacted with dashboard elements, or completed assessment or planning surveys) with each CBG on the interactive mapping dashboard. RESULTS:After adjusting for previous model predictions and dashboard engagement, nonfatal overdose counts, and distribution of race and ethnicity, poverty, unemployment, and rent burden, dashboard users were 1.0 to 2.4 times as likely to engage with CBGs prioritized by the PROVIDENT model that were shown as prioritized on the dashboard as compared to CBGs that were prioritized by the PROVIDENT model that were blinded on the dashboard. CONCLUSIONS:Interactive mapping tools with predictive modeling may be useful to support community-based harm reduction organizations in the allocation of resources to neighborhoods predicted to be at high risk of future overdose death.

IMPORTANCE/UNASSIGNED:As the primary facilities authorized to dispense methadone, opioid treatment programs (OTPs) are a critical access point for medications for opioid use disorder (MOUD). However, research is limited on the extent to which OTPs offer a broad range of MOUD and on the characteristics of programs that provide more comprehensive medication offerings. OBJECTIVE/UNASSIGNED:To assess the percentage of US OTPs offering all 3 forms of MOUD (methadone, buprenorphine, and naltrexone) and compare organizational and county characteristics of OTPs with different MOUD service offerings. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This longitudinal cross-sectional study used data on a panel of OTPs listed in the annual National Directory of Drug and Alcohol Use Treatment Facilities from 2017 to 2023. MAIN OUTCOMES AND MEASURES/UNASSIGNED:Measures included the percentage of OTPs offering buprenorphine, extended-release naltrexone, or all 3 MOUD from 2017 to 2023 (assuming all OTPs offered methadone). Descriptive statistics on organizational and county characteristics of OTPs by MOUD offerings were collected. Three longitudinal logistic regression models were used to estimate the odds of different MOUD offerings within OTPs, adjusting for organizational and county-level characteristics. RESULTS/UNASSIGNED:This analysis included 10 298 facility-year observations, ranging from 1211 in 2017 to 1421 in 2023. From 2017 to 2023, the percentage of OTPs offering MOUD beyond methadone increased (buprenorphine: 811 [67.0%] in 2017 to 1209 [85.1%] in 2023; naltrexone: 463 [38.2%] in 2017 to 749 [52.7%] in 2023; all 3 MOUD: 402 [33.2%] in 2017 to 639 [45.0%] in 2023). OTPs offering all 3 MOUD (3985 [38.7%]) had significantly higher odds of accepting Medicare (adjusted odds ratio [AOR], 2.14; 95% CI, 1.67-2.74); offering peer services (AOR, 1.63; 95% CI, 1.25-2.12), mental health services (AOR, 2.07; 95% CI, 1.53-2.80), and telemedicine services (AOR, 1.53; 95% CI, 1.22-1.92); and being private nonprofit (AOR, 7.45; 95% CI, 4.67-11.87) or government operated (AOR, 41.83; 95% CI, 19.71-88.75) compared with private for profit. CONCLUSIONS/UNASSIGNED:In this cross-sectional study of OTPs, although the availability of MOUD beyond methadone increased over time, most OTPs still did not offer all 3 forms of MOUD as of 2023. Specific organizational characteristics, such as being government operated and accepting Medicare, were associated with more comprehensive MOUD offerings. Future research should evaluate why OTPs vary in their MOUD offerings.

Policy Points There have been significant advancements in expanding care for opioid use disorder and suicide in general medical settings in the first quarter of the 21st century. Incessant barriers in the US health system continue to hinder progress in sufficiently scaling up evidence-based behavioral health interventions and getting them to those at highest risk. State policymakers have multiple levers available to make significant improvements to address ongoing challenges and improve access to evidence-based behavioral health services in emergency and primary care settings.

IMPORTANCE/UNASSIGNED:In the context of the US opioid crisis, factors associated with the prevalence of opioid use disorder (OUD) must be identified to aid prevention and treatment. State medical cannabis laws (MCL) and recreational cannabis laws (RCL) are potential factors associated with OUD prevalence. OBJECTIVE/UNASSIGNED:To examine changes in OUD prevalence associated with MCL and RCL enactment among veterans treated at the Veterans Health Administration (VHA) and whether associations differed by age or chronic pain. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:Using VHA electronic health records from January 2005 to December 2022, adjusted yearly prevalences of OUD were calculated, controlling for sociodemographic characteristics, receipt of prescription opioids, other substance use disorders, and time-varying state covariates. Staggered-adoption difference-in-difference analyses were used for estimates and 95% CIs for the relationship between MCL and RCL enactment and OUD prevalence. The study included VHA patients aged 18 to 75 years. The data were analyzed in December 2023. MAIN OUTCOME AND MEASURES/UNASSIGNED:International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) or International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) OUD diagnoses. RESULTS/UNASSIGNED:From 2005 to 2022, most patients were male (86.7.%-95.0%) and non-Hispanic White (70.3%-78.7%); the yearly mean age was 61.9 to 63.6 years (approximately 3.2 to 4.5 million patients per year). During the study period, OUD decreased from 1.12% to 1.06% in states without cannabis laws, increased from 1.13% to 1.19% in states that enacted MCL, and remained stable in states that also enacted RCL. OUD prevalence increased significantly by 0.06% (95% CI, 0.05%-0.06%) following MCL enactment and 0.07% (95% CI, 0.06%-0.08%) after RCL enactment. In patients aged 35 to 64 years and 65 to 75 years, MCL and RCL enactment was associated with increased OUD, with the greatest increase after RCL enactment among older adults (0.12%; 95% CI, 0.11%-0.13%). Patients with chronic pain had even larger increases in OUD following MCL (0.08%; 95% CI, 0.07%-0.09%) and RCL enactment (0.13%; 95% CI, 0.12%-0.15%). Consistent with overall findings, the largest increases in OUD occurred among patients with chronic pain aged 35 to 64 years following the enactment of MCL and RCL (0.09%; 95% CI, 0.07%-0.11%) and adults aged 65 to 75 years following RCL enactment (0.23%; 95% CI, 0.21%-0.25%). CONCLUSIONS AND RELEVANCE/UNASSIGNED:The results of this cohort study suggest that MCL and RCL enactment was associated with greater OUD prevalence in VHA patients over time, with the greatest increases among middle-aged and older patients and those with chronic pain. The findings did not support state cannabis legalization as a means of reducing the burden of OUD during the ongoing opioid epidemic.

An increasing number of U.S. states have legalized cannabis, but the effect on adolescent and young adult psychosis-related hospitalizations remains under-studied. Using data from Denver Health between 2005 and 2020, we examined associations between implementation of the Ogden Memo (expanding use of medical cannabis in Colorado, October 2009) and Amendment 64 (legalizing adult-use cannabis in Colorado, November 2012) and trends in psychosis-related emergency department and hospital visits with and without cannabis use disorder (CUD) among youth aged 10-29. Patients with psychosis hospitalizations were predominately male (68 %), white (53 %), and Medicaid recipients (59 %). Significant increases (p < 0.05) were observed in the monthly average rate of psychosis hospitalizations between pre-Ogden memo (21.9 per 100,000) and post-Ogden memo pre-legalization (28.0 per 100,000) and post-legalization (32.3 per 100,000). Similarly, significant increases (p < 0.05) were observed in the monthly average rate of psychosis hospitalizations involving CUD between pre-Ogden memo (2.0 per 100,000), post-Ogden memo and pre-legalization (3.4 per 100,000), and post-legalization (8.5 per 100,000). Interrupted time series modeling found a significant difference in the trends for psychosis hospitalizations involving CUD following recreational legalization (change in average monthly rate went from 0.02/100,000 (95 % CI -0.02, 0.06) to 0.11/100,000 (95 % CI 0.09, 0.13), (difference (0.09 (95 %CI 0.05, 0.14)). Findings suggest an increase in overall hospital encounters for psychosis among youth after the legalization of recreational cannabis. Given the adoption of increasingly permissive cannabis laws, there is a need to plan effective public health responses that could mitigate unintended consequences related to cannabis use.

Policy Points We can leverage data science and artificial intelligence to inform state and local resource allocation for overdose prevention. Data science and artificial intelligence can help us answer four questions: (1) What is the impact of laws on access to interventions and overdose risk? (2) Where should interventions be targeted? (3) Which types of demographic subgroups benefit the most and the least from interventions? and (4) Which types of interventions should they invest in for each setting and population? Advances in data science and artificial intelligence can accelerate the pace at which we can answer these critical questions and help inform an effective overdose prevention response.

Between May 2020 and December 2021, there were 159,872 drug overdose deaths in the US. Higher eviction rates have been associated with higher overdose mortality. Amid the economic turmoil caused by the COVID-19 pandemic, 43 states and Washington, DC, implemented eviction moratoria of varying durations. These moratoria reduced eviction filing rates, but their impact on fatal drug overdoses remains unexplored. We evaluated the effect of these policies on county-level overdose death rates by focusing on the dates the state eviction moratoria were lifted. We obtained mortality data from NCHS and eviction moratoria dates from the COVID-19 US State Policy Database. We employed a longitudinal targeted minimum-loss-based estimation with Super Learner to flexibly estimate the average treatment effect (ATE) of never lifting the moratoria. Lifting state eviction moratoria was associated with a 0.14 per 100,000 higher rate of monthly overdose mortality (95%CI: -0.03, 0.32), although confidence intervals were wide and included zero. Eviction moratoria may not be sufficient to prevent overdose mortality during crises such as the COVID-19 pandemic.

BACKGROUND AND OBJECTIVE/OBJECTIVE:Youth overdose deaths have remained elevated in recent years as the illicit drug supply has become increasingly contaminated with fentanyl and other synthetics. There is a need to better understand fatal drug combinations and how trends have changed over time and across sociodemographic groups in this age group. METHODS:We used the National Vital Statistics System's multiple cause of death datasets to examine trends in overdose deaths involving combinations of synthetic opioids with benzodiazepine, cocaine, heroin, prescription opioids, and other stimulants among US youth aged 15 to 24 years from 2018 to 2022 across age, sex, race and ethnicity, and region. RESULTS:Overdose death counts rose from 4652 to 6723 (10.85 to 15.16 per 100 000) between 2018 and 2022, with a slight decrease between 2021 and 2022. The largest increases were deaths involving synthetic opioids only (1.8 to 4.8 deaths per 100 000). Since 2020, fatal synthetic opioid-only overdose rates were higher than polydrug overdose rates involving synthetic opioids, regardless of race, ethnicity, or sex. In 2022, rates of synthetic-only overdose deaths were 2.49-times higher among male youths compared with female youths and 2.15-times higher among those aged 20 to 24 years compared with those aged 15 to 19 years. CONCLUSIONS:Polydrug combinations involving synthetic opioids continue to contribute to fatal youth overdoses, yet deaths attributed to synthetic opioids alone are increasingly predominant. These findings highlight the changing risks of the drug supply and the need for better access to harm-reduction services to prevent deaths among youth.

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.

To meet the needs of diverse communities, public health authorities are increasingly reliant on hyperlocal interventions targeting specific health issues and distinct populations. To facilitate epidemiological evaluation of hyperlocal interventions on community-level outcomes, we developed a framework of six practice-based considerations for researchers: spatial zone of impact, temporal resolution of impact, outcome of interest, definition of a plausible comparison group, micro vs. macro impacts, and practitioner engagement. We applied this framework to a case study of an impact evaluation of the New York City (NYC) overdose prevention centers (OPCs) on neighborhood-level drug-related arrests. We used drug arrest data from NYC from January 1, 2014, to September 30, 2023 and US Census data to conduct synthetic control modeling, comparing pre- and post-OPC arrests in the neighborhoods surrounding the two NYC OPCs (East Harlem and Washington Heights). We conducted sensitivity analyses to validate our results and compare our findings with those from a prior published study. Our findings indicate no significant change in drug-related arrests following the OPC openings. The mean absolute differences in daily drug-related arrests between the OPCs and their synthetic controls were 0.63 (p = 0.19) in East Harlem and 0.14 (p = 0.22) in Washington Heights. Sensitivity analyses corroborated our main results. Overall, findings demonstrate how our framework can be used to guide future epidemiological evaluations of diverse, hyperlocal public health interventions.