Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose.

BACKGROUND: This systematic review summarizes the development, accuracy, quality, and clinical utility of predictive models to assess the risk of opioid use disorder (OUD), persistent opioid use, and opioid overdose. METHODS: In accordance with Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines, 8 electronic databases were searched for studies on predictive models and OUD, overdose, or persistent use in adults until June 25, 2023. Study selection and data extraction were completed independently by 2 reviewers. Risk of bias of included studies was assessed independently by 2 reviewers using the Prediction model Risk of Bias ASsessment Tool (PROBAST). RESULTS: The literature search yielded 3130 reports; after removing 199 duplicates, excluding 2685 studies after abstract review, and excluding 204 studies after full-text review, the final sample consisted of 41 studies that developed more than 160 predictive models. Primary outcomes included opioid overdose (31.6% of studies), OUD (41.4%), and persistent opioid use (17%). The most common modeling approach was regression modeling, and the most common predictors included age, sex, mental health diagnosis history, and substance use disorder history. Most studies reported model performance via the c statistic, ranging from 0.507 to 0.959; gradient boosting tree models and neural network models performed well in the context of their own study. One study deployed a model in real time. Risk of bias was predominantly high; concerns regarding applicability were predominantly low. CONCLUSIONS: Models to predict opioid-related risks are developed using diverse data sources and predictors, with a wide and heterogenous range of accuracy metrics. There is a need for further research to improve their accuracy and implementation.

Trends in Firearm Injury Prevention Research Funding, Clinical Trials, and Publications in the US, 1985-2022.

This cross-sectional study examines federal funding, registered clinical trials, and publications to quantify trends in firearm injury prevention research in the US from 1985 to 2022.

Firearm-Related Lead Exposure and Child Lead Levels in the United States, 2012-2018.

OBJECTIVE: To determine if firearm ownership is positively related to elevated child lead levels at a state-level, even when accounting for other sources of lead. STUDY DESIGN: For this cross-sectional ecological study, we investigated whether household firearm ownership rates (a proxy for firearm-related lead exposure) was associated with the prevalence of elevated child blood lead levels in 44 US States between 2012 and 2018. To account for potential confounding, we adjusted for other known lead exposures, poverty rate, population density, race, and calendar year. To address missing data, we used multiple imputation by chained equations. RESULTS: Prevalence of elevated child blood lead positively correlated with household firearm ownership and established predictors of lead exposure. In fully adjusted negative binomial regression models, child blood lead was positively associated with household firearm ownership and older housing; each IQR (14%) increase in household firearm ownership rate was associated with a 41% higher prevalence of childhood elevated blood lead (prevalence ratio: 1.41, 95% CI: 1.11-1.79). CONCLUSION: These data provide state-level evidence that firearms may be an important source of child lead exposure. More research is needed to substantiate this relationship and identify modifiable pathways of exposure at the individual level.