Faculty Bibliography

OBJECTIVE:The CDC recommends initiating opioids for pain treatment at the lowest effective dose and duration. We examine how interactions between dose, duration, and other medication factors (e.g., drug type) influence opioid use disorder (OUD) risk-a gap not considered by CDC guidelines. SUBJECTS/METHODS:Using Medicaid claims data (2016-2019) from 25 states, we analyzed opioid-naïve adults, newly diagnosed with musculoskeletal pain who initiated opioids within three months of diagnosis. A 6-month washout confirmed no prior opioid exposure or musculoskeletal diagnosis. METHODS:Initial opioids were categorized by "dose-days supplied" (low [>0-20 mg MME] to very high [>90 mg MME] dose, and short [1-7 days] to moderate [>7-30 days] supply), and by opioid type; physical therapy (PT) sessions were also recorded. Using Poisson regression models, we estimated the OUD risk associated with dose-days categories, adjusting for baseline demographics, clinical characteristics, and medications. We separately examined opioid dose-days and PT, and assessed PT's moderating effect on dose-days' impact. RESULTS:Among 30,536 patients, half initiated opioids at 20-50 MME for 1-7 days, and 20% received PT. OUD risk was 2-3 times higher for opioids initiated for >7-30 days compared to 1-7 days across doses, and 5.5 times higher for opioids initiated for >7-30 days at > 90 MME versus 1-7 days at < 20 MME. PT alone, neither affected OUD risk nor mitigated the increased risk from longer or higher-dose opioids. CONCLUSIONS:Our findings support the need for careful opioid prescribing and alternative pain management strategies, as the observed associations between initial prescription characteristics and OUD were not mitigated by adjunctive PT. PERSPECTIVE/CONCLUSIONS:This study demonstrated that initial opioid prescriptions of 7-30 days, especially above 90 MME/day, increased OUD risk in opioid-naïve patients with musculoskeletal pain; physical therapy did not mitigate the risk. Different opioids posed varied risks, even at the same dose and duration. Careful prescribing and alternative pain management are essential.

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.

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.

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.

BACKGROUND AND AIMS/OBJECTIVE:US regulatory changes allowed for additional methadone take-home doses following COVID-19 onset. How dispensing practices changed and which factors drove variation remains unexplored. We determined daily methadone dispensing trajectories over six months before and after regulatory changes due to COVID-19 using state sequence analysis and explored correlates. DESIGN/METHODS:Retrospective chart review of electronic health records. SETTINGS/METHODS:Nine opioid treatment programs (OTPs) across nine US states. PARTICIPANTS/METHODS:Adults initiating treatment in 2019 (n = 328) vs. initiating 1 month after the COVID-19 regulatory changes of March 2020 (n = 376). MEASUREMENTS/METHODS:Type of daily methadone medication encounter (in-clinic, weekend/holiday take-home, take-home, missed dose, discontinued) based on OTP clinic; cohort (pre vs. post-COVID-19); and patient substance use, clinical and sociodemographic characteristics. FINDINGS/RESULTS:Following COVID-19 regulatory changes, allotted methadone take-home doses increased from 3.5% to 13.8% of total person-days in treatment within the first 6 months in care. Clinic site accounted for the greatest variation in methadone dispensing (6.2% and 9.5% of the variation of discrepancy between sequences pre- and post-COVID-19, respectively). People who co-use methamphetamine had a greater increase in take-homes than people who did not use methamphetamine (from 3.7% pre-pandemic to 21.2% post-pandemic vs. 3.5% to 12.5%) and higher discontinuation (average 3.6 vs. 4.7 months among people who did not use methamphetamine pre-COVID-19; average 3.3 vs. 4.6 months post-COVID-19). In the post-COVID-19 cohort, females had a higher proportion of missed doses (17.2% vs. 11.9%) than males. People experiencing houselessness had a higher proportion of missed doses (19% vs. 12.3%) and shorter stays (average 3.5 vs. 4.5 months) when compared with those with stable housing. CONCLUSION/CONCLUSIONS:Daily methadone dispensing trajectories in the US both before and following COVID-19 regulatory changes appeared to depend more on the opioid treatment programs' practices than individual patient characteristics or response to treatment.

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.

As the United States grapples with an ongoing overdose crisis, states and jurisdictions are adopting novel approaches to reduce overdose mortality. In one novel approach, public health researchers and leaders in Rhode Island leveraged the state's robust surveillance data and collaborations between government, academic, and community-based organizations (CBOs) to launch the PROVIDENT (PReventing OVerdose using Information and Data from the EnvironmeNT) project, a population-based randomized controlled research trial (NCT05096429) in December 2019. The PROVIDENT trial utilizes machine learning (ML) methods to identify neighborhoods at risk of future overdose deaths at the census-block-group level to inform community-level overdose prevention resource distribution. To disseminate the ML model predictions, our research team developed an interactive, online mapping dashboard in close collaboration with three statewide CBOs. We measured whether these organizations utilized the PROVIDENT dashboard to allocate harm-reduction services based on ML model predictions and collected information about their data-driven decision-making processes. This case study describes how we assembled and piloted this overdose forecasting dashboard for use by CBOs between November 2021 and August 2024. By measuring dashboard logins, completed surveys, and engagement with ongoing training, we illustrate how organizations utilized ML and surveillance data to inform their outreach efforts and generate valuable insights at a neighborhood level.

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.

We estimated the extent to which different pain management practices, considered together as well as individually, mediated the relationship between chronic pain or physical disability and new-onset opioid use disorder (OUD) in a large cohort of adult Medicaid patients. Considering the plausibility of the assumptions required to identify different mediational estimands, we estimated natural indirect effects when considering mediation through the group of mediators together and estimated interventional indirect effects when considering mediation through each pain management practice individually. We estimated each effect using a nonparametric one-step estimator. The pain management variables we examined mediated all of the total effect of chronic pain on OUD risk and nearly half of the total effect of physical disability on OUD risk. High-dose, long-duration opioid prescribing and co-prescription of opioids with benzodiazepines, gabapentinoids, and muscle relaxants each contributed substantially to the increased risk of OUD due to chronic pain (contributing to 10-37% of the overall effect) and more moderately to the increased risk of OUD due to physical disability (contributing to 3-19% of the overall effect). Antidepressant or anti-inflammatory prescribing and physical therapy generally did not contribute to increased OUD risk, and, in some cases, even contributed to small reductions in risk.

BACKGROUND AND AIM/OBJECTIVE:Sedative, hypnotic or anxiolytic use disorders (SHA-UD) are defined by significant impairment or distress caused by recurrent sedative, hypnotic or anxiolytic use. This study aimed to measure trends in the prevalence of SHA-UD diagnoses in adolescent and young adult US Medicaid enrollees from 2001 to 2019. DESIGN/METHODS:Annual, cross-sectional study, 2001-2019. SETTING/METHODS:Medicaid Analytic eXtracts (MAX) and Transformed Medicaid Analytic Files (TAF) from 42 US states with complete data. PARTICIPANTS/CASES/METHODS:Adolescents (13-17 years) and young adults (18-29 years) with ≥10 months Medicaid enrollment in the calendar year; analytic sample contained 5.7 (2001) to 13.2 (2019) million persons per year. MEASUREMENTS/METHODS:Annual prevalence of SHA-UD in adolescent and young adult Medicaid enrollees [defined as an inpatient or outpatient ICD code (304.1x, 305.4x, F13.1x, F13.2x) in the calendar year] was stratified by sex, race/ethnicity, receipt of a benzodiazepine, z-hypnotic or barbiturate prescription, and selected mental health diagnoses. Absolute and relative percent-changes from 2001 vs. 2019 were summarized. Secondary analyses were restricted to states with more consistent data capture. FINDINGS/RESULTS:The prevalence of SHA-UD diagnoses statistically significantly increased for adolescents (0.01% to 0.04%) and young adults (0.05% to 0.24%) from 2001 to 2019. Increasing trends were observed in sex and race/ethnicity subgroups, with greatest relative increases among Non-Hispanic Black (624%) and Hispanic (529%) young adults. The trend increased among those with and without a benzodiazepine, z-hypnotic or barbiturate prescription; i.e. young adults with (2001 = 0.39% to 2019 = 1.77%) and without (2001 = 0.03% to 2019 = 0.18%) a prescription. Most adolescents (76%) and young adults (91%) with a SHA-UD diagnosis in 2019 had a comorbid substance use disorder. CONCLUSIONS:Sedative, hypnotic or anxiolytic use disorders (SHA-UD) diagnoses increased 3- to 5-fold between 2001 and 2019 for adolescent and young adult US Medicaid enrollees, with prevalence remaining low in adolescents. The increase over two decades may be attributed to changes in the availability, use and misuse of sedative, hypnotic and anxiolytic medications and to increased detection, awareness and diagnosing of SHA-UD.