TY - JOUR
T1 - Predictive Modeling of Opioid Overdose Using Linked Statewide Medical and Criminal Justice Data
AU - Saloner, Brendan
AU - Chang, Hsien Yen
AU - Krawczyk, Noa
AU - Ferris, Lindsey
AU - Eisenberg, Matthew
AU - Richards, Thomas
AU - Lemke, Klaus
AU - Schneider, Kristin E.
AU - Baier, Michael
AU - Weiner, Jonathan P.
N1 - Funding Information:
Funding/Support: This project was supported by the Bureau of Justice Assistance (grant 2015-PM-BX-K002). This research was also supported by the National Institute on Drug Abuse (grants 5T32DA007292 [Drs Schneider and Krawczyk] and F31DA047021 [Dr Krawczyk]).
Funding Information:
reported grants from the US Department of Justice Bureau of Justice Assistance during the conduct of the study. Drs Chang, Saloner, and Eisenberg also reported grants from the National Institute on Drug Abuse during the conduct of the study. Dr Eisenberg also reported grants from the Agency for Healthcare Research and Quality and the Arnold Foundation outside the submitted work. Dr Saloner also reported grants from the Arnold Foundation outside the submitted work. No other disclosures were reported.
Publisher Copyright:
© 2020 American Medical Association. All rights reserved.
PY - 2020/11
Y1 - 2020/11
N2 - Importance: Responding 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. Objective: To develop a predictive risk model to identify opioid overdose using linked clinical and criminal justice data. Design, Setting, and Participants: A 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. Exposures: Controlled substance prescription fills and hospital, specialty behavioral health, or criminal justice encounters. Main Outcomes and Measures: Fatal opioid overdose defined by the state medical examiner and 1 or more nonfatal overdoses treated in Maryland hospitals during 2016. Results: There 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 Relevance: In 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..
AB - Importance: Responding 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. Objective: To develop a predictive risk model to identify opioid overdose using linked clinical and criminal justice data. Design, Setting, and Participants: A 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. Exposures: Controlled substance prescription fills and hospital, specialty behavioral health, or criminal justice encounters. Main Outcomes and Measures: Fatal opioid overdose defined by the state medical examiner and 1 or more nonfatal overdoses treated in Maryland hospitals during 2016. Results: There 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 Relevance: In 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..
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U2 - 10.1001/jamapsychiatry.2020.1689
DO - 10.1001/jamapsychiatry.2020.1689
M3 - Article
C2 - 32579159
AN - SCOPUS:85089739259
SN - 2168-622X
VL - 77
SP - 1155
EP - 1162
JO - JAMA psychiatry
JF - JAMA psychiatry
IS - 11
ER -