Search

Background: Drug overdose persists as a leading cause of death in the United States, but resources to address it remain limited. As a result, health authorities must consider where to allocate scarce resources within their jurisdictions. Machine learning offers a strategy to identify areas with increased future overdose risk to proactively allocate overdose prevention resources. This modeling study is embedded in a randomized trial to measure the effect of proactive resource allocation on statewide overdose rates in Rhode Island (RI). Methods: We used statewide data from RI from 2016 to 2020 to develop an ensemble machine learning model predicting neighborhoodlevel fatal overdose risk. Our ensemble model integrated gradient boosting machine and super learner base models in a moving window framework to make predictions in 6-month intervals. Our performance target, developed a priori with the RI Department of Health, was to identify the 20% of RI neighborhoods containing at least 40% of statewide overdose deaths, including at least one neighborhood per municipality. The model was validated after trial launch. Results: Our model selected priority neighborhoods capturing 40.2% of statewide overdose deaths during the test periods and 44.1% of statewide overdose deaths during validation periods. Our ensemble outperformed the base models during the test periods and performed comparably to the best-performing base model during the validation periods. Conclusions: We demonstrated the capacity for machine learning models to predict neighborhood-level fatal overdose risk to a degree of accuracy suitable for practitioners. Jurisdictions may consider predictive modeling as a tool to guide allocation of scarce resources. [ABSTRACT FROM AUTHOR]

Objective To assess whether chronic pain increases the risk of COVID-19 complications and whether opioid use disorder (OUD) differentiates this risk among New York State Medicaid beneficiaries. Design, Setting, and Subjects This was a retrospective cohort study of New York State Medicaid claims data. We evaluated Medicaid claims from March 2019 through December 2020 to determine whether chronic pain increased the risk of COVID-19 emergency department (ED) visits, hospitalizations, and complications and whether this relationship differed by OUD status. We included beneficiaries 18–64 years of age with 10 months of prior enrollment. Patients with chronic pain were propensity score-matched to those without chronic pain on demographics, utilization, and comorbidities to control for confounders and were stratified by OUD. Complementary log–log regressions estimated hazard ratios (HRs) of COVID-19 ED visits and hospitalizations; logistic regressions estimated odds ratios (ORs) of hospital complications and readmissions within 0–30, 31–60, and 61–90 days. Results Among 773 880 adults, chronic pain was associated with greater hazards of COVID-related ED visits (HR = 1.22 [95% CI: 1.16–1.29]) and hospitalizations (HR = 1.19 [95% CI: 1.12–1.27]). Patients with chronic pain and OUD had even greater hazards of hospitalization (HR = 1.25 [95% CI: 1.07–1.47]) and increased odds of hepatic- and cardiac-related events (OR = 1.74 [95% CI: 1.10–2.74]). Conclusions Chronic pain increased the risk of COVID-19 ED visits and hospitalizations. Presence of OUD further increased the risk of COVID-19 hospitalizations and the odds of hepatic- and cardiac-related events. Results highlight intersecting risks among a vulnerable population and can inform tailored COVID-19 management. [ABSTRACT FROM AUTHOR]

Prior applications of machine learning to population health have relied on conventional model assessment criteria, limiting the utility of models as decision support tools for public health practitioners. To facilitate practitioners' use of machine learning as a decision support tool for area-level intervention, we developed and applied 4 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–June 2020 (n  = 1,408) and neighborhood-level US Census data. We employed 2 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 describe the health equity implications of use of predictive modeling to guide interventions along the lines of urbanicity, racial/ethnic composition, and poverty. We then discuss considerations to complement predictive model evaluation criteria and inform the prevention and mitigation of spatially dynamic public health problems across the breadth of practice. This article is part of a Special Collection on Mental Health. [ABSTRACT FROM AUTHOR]

The article discusses research which examined the change in codeine and tramadol dispensing for treatment of pain to children aged <18 years after the 2017 Drug Safety Communication (DSC) issued by the U.S. Food and Drug Administration. Topics covered include the association between the DSC and the monthly number of patients dispensed codeine or tramadol prescription, and the association of the DSC to declines in codeine dispensing to children and adolescents.

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. [ABSTRACT FROM AUTHOR]

The association between recent release from incarceration and dramatically increased risk of fatal overdose is well-established at the individual level. Fatal overdose and. arrest/release are spatially clustered, suggesting that this association may persist at the neighborhood level. We analyzed multicomponent data from Rhode Island, 2016–2020, and observed a modest association at the census tract level between rates of release per 1000 population and fatal overdose per 100,000 person-years, adjusting for spatial autocorrelation in both the exposure and outcome. Our results suggest that for each additional person released to a given census tract per 1000 population, there is a corresponding increase in the rate of fatal overdose by 2 per 100,000 person years. This association is more pronounced in suburban tracts, where each additional release awaiting trial is associated with an increase in the rate of fatal overdose of 4 per 100,000 person-years and 6 per 100,000 person-years for each additional release following sentence expiration. This association is not modified by the presence or absence of a licensed medication for opioid use disorder (MOUD) treatment provider in the same or surrounding tracts. Our results suggest that neighborhood-level release rates are moderately informative as to tract-level rates of fatal overdose and underscore the importance of expanding pre-release MOUD access in correctional settings. Future research should explore risk and resource environments particularly in suburban and rural areas and their impacts on overdose risk among individuals returning to the community. • Release from incarceration is associated with fatal overdose at the individual level. • This association is also apparent at the neighborhood level. • Release is more strongly associated with fatal overdose in suburban neighborhoods. • Treatment provider presence does not modify the neighborhood-level association. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic's impact on utilization of medications for opioid use disorder (MOUD) among patients with opioid use disorder (OUD) and chronic pain is unclear. We analyzed New York State (NYS) Medicaid claims from pre-pandemic (August 2019-February 2020) and pandemic (March 2020-December 2020) periods for beneficiaries with and without chronic pain. We calculated monthly proportions of patients with OUD diagnoses in 6-month-lookback windows utilizing MOUD and proportions of treatment-naïve patients initiating MOUD. We used interrupted time series to assess changes in MOUD utilization and initiation rates by medication type and by race/ethnicity. Among 20,785 patients with OUD and chronic pain, 49.3% utilized MOUD (versus 60.3% without chronic pain). The pandemic did not affect utilization in either group but briefly disrupted initiation among patients with chronic pain (β=−0.009; 95% CI [−0.015, −0.002]). Overall MOUD utilization was not affected by the pandemic for any race/ethnicity but opioid treatment program (OTP) utilization was briefly disrupted for non-Hispanic Black individuals (β=−0.007 [−0.013, −0.001]). The pandemic disrupted overall MOUD initiation in non-Hispanic Black (β=−0.007 [−0.012, −0.002]) and Hispanic individuals (β=−0.010 [−0.019, −0.001]). Adults with chronic pain who were enrolled in NYS Medicaid before the COVID-19 pandemic had lower MOUD utilization than those without chronic pain. MOUD initiation was briefly disrupted, with disparities especially in racial/ethnic minority groups. Flexible MOUD policy initiatives may have maintained overall treatment utilization, but disparities in initiation and care continuity remain for patients with chronic pain, and particularly for racial/ethnic minoritized subgroups. • NYS Medicaid beneficiaries with chronic pain had lower MOUD access than those without chronic pain. • The COVID-19 pandemic did not affect overall MOUD access in patients with chronic pain. • The pandemic briefly disrupted treatment initiation only for patients with chronic pain. • COVID-19 led to brief MOUD disruptions among minoritized subgroups. • In the early pandemic months, MUD trends stabilized in NYS compared to pre-pandemic levels. [ABSTRACT FROM AUTHOR]