Faculty Bibliography

OBJECTIVE:To examine nationwide trends in the prescribing of controlled medications to early adolescents, adolescents, and young adults enrolled in public insurance (Medicaid) from 2001 to 2019. METHODS:The study utilized US Medicaid data covering publicly insured enrollees from 43 states (2001-2019). Early adolescents (10-12y), adolescents (13-17y), and young adults (18-24y, 25-29y) with ≥ 10 months enrollment in each calendar year were included. Filled prescription for opioids, stimulants, benzodiazepines, Z-hypnotics, barbiturates, and gabapentin were identified. In each calendar year, annual proportions with 1 +  controlled medication, 2 +  classes of controlled medications, and each controlled medication were estimated. RESULTS:In 2019, the sample included 17.9 million enrollees (53 % female). The annual proportion prescribed any controlled medication peaked at 17.5 % in early adolescents (2003), 20.6 % in adolescents (2009), and 34.1 % (18-24y) and 47.0 % (25-29y) in young adults (2010). By 2019, the proportions declined to 11.7 % (early adolescents), 12.6 % (adolescents), 16.2 % (18-24y), and 23.9 % (25-29y). Trends varied by medication and age. The largest absolute decline was in the proportion with an opioid filled (2010 =29.8 %, 2019 =11.2 %, young adults 18-24y; 2003 =14.3 %, 2019 =4.4 %, adolescents). In contrast, the proportion with a stimulant fill increased, with eight-fold increases in young adults 25-29y (2001 =0.3 %, 2019 =2.6 %). Benzodiazepine and Z-hypnotic use peaked in 2010 and declined through 2019. CONCLUSIONS:In the past two decades, there were increases in stimulant prescriptions among young Medicaid enrollees. The declines in opioid, benzodiazepines, barbiturate and Z-hypnotic prescribing are encouraging and may indicate more cautious prescribing related to greater awareness of harms such as misuse and overdose, along with policy initiatives.

IMPORTANCE/UNASSIGNED:Since the start of the 21st century, more than 800 000 firearm deaths and more than 2 million firearm injuries have occurred in the US. All categories of firearm violence-homicide, suicide, unintentional-result in reverberating harms to individuals, families, communities, and society. The collective responsibility of society is to safeguard the health and safety of its members, including from firearm harms. The JAMA Summit on Firearm Violence convened 60 thought leaders from a wide array of disciplines to chart an innovations roadmap that will lead to substantial reductions in firearm harms by 2040. OBSERVATIONS/UNASSIGNED:The vision for 2040 is a country where firearm violence is substantially reduced and where all people and communities report feeling safe from firearm harms. The vision centers on practical solutions with an understanding of the country's constitutional protections for firearm ownership. Achieving the 2040 vision will require expansion of proven evidence-based strategies and the development of new, innovative approaches rooted in equity, accountability, and collective responsibility. Discussions centered on projecting a safer world, community violence interventions, technologic innovations, federal and state-level oversight of firearms, ethical considerations, and primordial prevention of firearm violence. The Summit charted a roadmap of 5 essential actions in the next 5 years to achieve this vision: (1) focus on communities and change fundamental structures that lead to firearm harms, (2) harness technological strengths responsibly, (3) change the narrative around firearm harms, (4) take a whole-government and whole-society approach, and (5) spark a research revolution on preventing firearm harms. CONCLUSIONS AND RELEVANCE/UNASSIGNED:A safer world will require investing in the discovery, implementation, and scaling of solutions that reduce firearm harms and center on the people and communities most affected by firearm violence.

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.

UNLABELLED:Policy Points State "must-query" prescription drug monitoring programs (PDMPs) were associated with increased overdose deaths, suggesting these policies may have unintended consequences. Black and Hispanic populations experienced disproportionately higher increases in overdose deaths following must-query PDMP adoption, highlighting that these policies may contribute to health disparities. Addressing systemic inequities in health care access and substance use treatment may help supplement the effective components of PDMPs, ensuring that these policies reduce rather than exacerbate overdose deaths. CONTEXT/BACKGROUND:Despite recent declines in national overdose deaths, these reductions have not been equitably experienced. Black and Hispanic communities continue to face rising rates of opioid-related mortality, even as overdose death rates among White individuals have begun to decline. One of the most widely implemented policy responses to the overdose crisis has been the adoption of prescription drug monitoring programs (PDMPs), particularly "must-query" mandates requiring prescribers to consult the PDMP before issuing controlled substances. However, limited research has examined whether the impact of these mandates varies by race and ethnicity. METHODS:We used restricted-use National Vital Statistics System data from 2013 to 2020 to estimate county-level overdose mortality stratified by drug type and race and ethnicity. We categorized deaths as follows: (1) all drug overdoses, (2) all opioid overdoses, and (3) natural/semisynthetic opioid overdoses. Exposure to must-query mandates was modeled as the proportion of the prior year during which mandates were in effect. Using Bayesian spatiotemporal models with county random effects and spatial autocorrelation, we estimated relative rates (RRs) for each outcome overall and by race and ethnicity, adjusting for state policies and sociodemographic characteristics. FINDINGS/RESULTS:Must-query mandates were associated with increases in overdose deaths across all groups, with the largest relative increases among Hispanic (RR = 1.32, 95% credible interval [CrI]: 1.21-1.44) and Black individuals (RR = 1.23, 95% CrI: 1.14-1.33) compared with White individuals (RR = 1.14, 95% CrI: 1.10-1.19). These increases were also observed among Black and Hispanic individuals for natural/semisynthetic opioid overdoses. CONCLUSIONS:PDMP must-query mandates are not uniformly protective across racial and ethnic groups. Increases in overdose mortality following adoption, particularly among Black and Hispanic populations, underscore the need to evaluate drug policies through an equity lens and consider broader structural determinants of health that shape their effectiveness.

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.

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.

PURPOSE/UNASSIGNED:Understanding the types of individuals and their position in the network may improve interventions for people who inject drugs (PWID). METHODS/UNASSIGNED:From the Transmission Reduction Intervention Project (TRIP), which enrolled PWID and their contacts in Athens, Greece, from 2013 to 2015, we extracted the largest connected component of the network (i.e., the largest group of connected individuals) and identified members who were in the top quartile of the distribution for three network centrality measures: closeness, betweenness, and eigenvector. Using logistic regression, we evaluated associations between high centrality measures and individual sociodemographic characteristics and behaviors. We also varied the definition for high centrality. RESULTS/UNASSIGNED:Among 231 individuals, 80% were male and between the ages of 25-40 years. Over half of the individuals injected at least once per day, compared to less than daily. Individuals who injected at least once per day were more likely to have high closeness (odds ratio (OR) = 3.36; 95% confidence interval (CI) = 1.57, 8.42), high betweenness (OR = 2.22; 95% CI = 1.06, 4.67), and eigenvector centrality (OR = 4.50; 95% CI = 1.89,10.68). Individuals who engaged in sex without a condom were less likely to have high closeness (OR = 0.18; 95% CI = 0.07, 0.45) or eigenvector (OR = 0.19; 95% CI = 0.07, 0.49) centrality. CONCLUSIONS/UNASSIGNED:Individual characteristics and behaviors were associated with centrality and may impact an individual's position in the network. These associations could be useful in identifying important community members to engage as part of public health initiatives.