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IMPORTANCE Prescription opioids are involved in more than half of opioid overdoses among younger persons. Understanding opioid prescribing practices is essential for developing appropriate interventions for this population. OBJECTIVE To examine temporal trends in opioid prescribing practices in children, adolescents, and younger adults in the US from 2006 to 2018. DESIGN, SETTING, AND PARTICIPANTS A population-based, cross-sectional analysis of opioid prescription data was conducted from January 1, 2006, to December 31, 2018. Longitudinal data on retail pharmacy-dispensed opioids for patients younger than 25 years were used in the analysis. Data analysis was performed from December 26, 2019, to July 8, 2020. MAIN OUTCOMES AND MEASURES Opioid dispensing rate, mean amount of opioid dispensed in morphine milligram equivalents (MME) per day (individuals aged 15-24 years) or MME per kilogram per day (age <15 years), duration of prescription (mean, short [Յ3 days], and long [Ն30 days] duration), high-dosage prescriptions, and extended-release or long-acting (ER/LA) formulation prescriptions. Outcomes were calculated for age groups: 0 to 5, 6 to 9, 10 to 14, 15 to 19, and 20 to 24 years. Joinpoint regression was used to examine opioid prescribing trends.RESULTS From 2006 to 2018, the opioid dispensing rate for patients younger than 25 years decreased from 14.28 to 6.45, with an annual decrease of 15.15% (95% CI, −17.26% to −12.99%) from 2013 to 2018. The mean amount of opioids dispensed and rates of short-duration and high-dosage prescriptions decreased for all age groups older than 5 years, with the largest decreases in individuals aged 15 to 24 years. Mean duration per prescription increased initially for all ages, but then decreased for individuals aged 10 years or older. The duration remained longer than 5 days across all ages. The rate of long-duration prescriptions increased for all age groups younger than 15 years and initially increased, but then decreased after 2014 for individuals aged 15 to 24 years. For children aged 0 to 5 years dispensed an opioid, annual increases from 2011 to 2014 were noted for the mean amount of opioids dispensed (annual percent change [APC], 10.58%; 95% CI, 1.77% to 20.16%) and rates of long-duration (APC, 30.42%; 95% CI, 14.13% to 49.03%), high-dosage (APC, 31.27%; 95% CI, 16.81% to 47.53%), and ER/LA formulation (APC, 27.86%; 95% CI, 12.04% to 45.91%) prescriptions, although the mean amount dispensed and rate of high-dosage prescriptions decreased from 2014 to 2018.CONCLUSIONS AND RELEVANCE These findings suggest that opioid dispensing rates decreased for patients younger than 25 years, with decreasing rates of high-dosage and long-duration prescriptions for adolescents and younger adults. However, opioids remain readily dispensed, and possible high-risk prescribing practices appear to be common, especially in younger children.

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Prior applications of machine learning to population health have relied on conventional model assessment criteria, limiting the utility of models as decision supports for public health practitioners. To facilitate practitioner use of machine learning as decision support for area-level intervention, this study developed and applied four practice-based predictive model evaluation criteria (implementation capacity, preventive potential, health equity, and jurisdictional practicalities). We used a case study of overdose prevention in Rhode Island to illustrate how these criteria could inform public health practice and health equity promotion. We used Rhode Island overdose mortality records from January 2016 to June 2020 (N=1,408) and neighborhood-level Census data. We learned two disparate machine learning models, Gaussian process and random forest, to illustrate the comparative utility of our criteria to guide interventions. Our models predicted 7.5-36.4% of overdose deaths during the test period, illustrating the preventive potential of overdose interventions assuming 5-20% statewide implementation capacities for neighborhood-level resource deployment. We described the health equity implications of predictive modeling to guide interventions along urbanicity, racial/ethnic composition, and poverty. In sum, our study discussed considerations to complement predictive model evaluation criteria and inform the prevention and mitigation of spatially dynamic public health problems across the breadth of practice.

Objective: Availability of medications for opioid use disorder (MOUD) remains sparse. To date, there has been no national, state-by-state comparison of patient MOUD utilization relative to treatment availability and burden of overdose deaths. We aimed to quantify, for each state, the number of MOUD patients relative to (1) office-based buprenorphine providers and opioid treatment programs (OTPs) and (2) overdose deaths. Methods: We conducted a spatial analysis of patients receiving MOUD from OTPs or buprenorphine providers in March 2017 across all 50 states and Washington, DC. For each state, we calculated the number of patients receiving MOUD from OTPs and buprenorphine prescriptions, relative to available OTPs and buprenorphine providers; as well as ratios of number of patients receiving MOUD relative to overdose deaths. Results: In March 2017, 942,368 patients attended an OTP (410,288) or received a buprenorphine prescription (486,318). Patient to OTP ratio was highest in West Virginia, Delaware, Washington, DC, New Jersey, New Hampshire, Connecticut and Ohio, ranging from 91 to 193 patients per OTP in the first quintile to 430 to 648 in the fifth. Patient to buprenorphine provider ratio was highest in Kentucky and West Virginia, ranging from 3 to 7 patients per provider in the first quintile to 19 to 28 in the fifth. Median MOUD patients per overdose death was 21 (IQR:14.9-28.2). Of high overdose states, Washington, DC, New Jersey, and Ohio had the smallest number of patients on MOUD relative to deaths.Conclusions: High patient volume relative to treatment availability in overdose-burdened areas may indicate strain on MOUD providers and OTPs. Promoting greater utilization while expanding MOUD providers and programs is critical.