Predictive Modeling Of Opioid Overdose Using Linked Statewide Medical And Criminal Justice Data
JAMA PSYCHIATRY(2020)
摘要
Key PointsQuestionWhat factors most strongly predict opioid overdose in a linked statewide administrative data set? FindingsIn this predictive modeling study of 4 statewide Maryland databases with data from 2.2 million individuals, fatal opioid overdose in the next 12 months could be predicted with an area under the curve as high as 0.89. The factors most strongly associated with the baseline year (by odds ratio) included male sex, use of addiction treatment, at least 1 nonfatal overdose, and release from prison. MeaningPublic health efforts to prioritize lifesaving interventions should consider the relative risk of overdose across different population groups.This cohort study combines data from 4 statewide databases from clinical and criminal justice sources in Maryland to develop a predictive model for identifying opioid overdose.ImportanceResponding to the opioid crisis requires tools to identify individuals at risk of overdose. Given the expansion of illicit opioid deaths, it is essential to consider risk factors across multiple service systems. ObjectiveTo develop a predictive risk model to identify opioid overdose using linked clinical and criminal justice data. Design, Setting, and ParticipantsA cross-sectional sample was created using 2015 data from 4 Maryland databases: all-payer hospital discharges, the prescription drug monitoring program (PDMP), public-sector specialty behavioral treatment, and criminal justice records for property or drug-associated offenses. Maryland adults aged 18 to 80 years with records in any of 4 databases were included, excluding individuals who died in 2015 or had a non-Maryland zip code. Logistic regression models were estimated separately for risk of fatal and nonfatal opioid overdose in 2016. Model performance was assessed using bootstrapping. Data analysis took place from February 2018 to November 2019. ExposuresControlled substance prescription fills and hospital, specialty behavioral health, or criminal justice encounters. Main Outcomes and MeasuresFatal opioid overdose defined by the state medical examiner and 1 or more nonfatal overdoses treated in Maryland hospitals during 2016. ResultsThere were 2294707 total individuals in the sample, of whom 42.3% were male (n=970019) and 53.0% were younger than 50 years (647083 [28.2%] aged 18-34 years and 568160 [24.8%] aged 35-49 years). In 2016, 1204 individuals (0.05%) in the sample experienced fatal opioid overdose and 8430 (0.37%) experienced nonfatal opioid overdose. In adjusted analysis, the factors mostly strongly associated with fatal overdose were male sex (odds ratio [OR], 2.40 [95% CI, 2.08-2.76]), diagnosis of opioid use disorder in a hospital (OR, 2.93 [95% CI, 2.17-3.80]), release from prison in 2015 (OR, 4.23 [95% CI, 2.10-7.11]), and receiving opioid addiction treatment with medication (OR, 2.81 [95% CI, 2.20-3.86]). Similar associations were found for nonfatal overdose. The area under the curve for fatal overdose was 0.82 for a model with hospital variables, 0.86 for a model with both PDMP and hospital variables, and 0.89 for a model that further added behavioral health and criminal justice variables. For nonfatal overdose, the area under the curve using all variables was 0.85. Conclusions and RelevanceIn this analysis, fatal and nonfatal opioid overdose could be accurately predicted with linked administrative databases. Hospital encounter data had higher predictive utility than PDMP data. Model performance was meaningfully improved by adding PDMP records. Predictive models using linked databases can be used to target large-scale public health programs.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络