Brendan Saloner1,2, Hsien-Yen Chang1,3, Noa Krawczyk2,4, Lindsey Ferris1,5, Matthew Eisenberg1, Thomas Richards1,3, Klaus Lemke1,3, Kristin E Schneider2, Michael Baier6, Jonathan P Weiner1,3. 1. Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. 2. Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. 3. Center for Population Health IT, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. 4. Department of Population Health, New York University School of Medicine, New York. 5. Chesapeake Regional Information System for Our Patients, Columbia, Maryland. 6. Behavioral Health Administration, Maryland Department of Health, Baltimore.
Abstract
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 2 294 707 total individuals in the sample, of whom 42.3% were male (n = 970 019) and 53.0% were younger than 50 years (647 083 [28.2%] aged 18-34 years and 568 160 [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.
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 2 294 707 total individuals in the sample, of whom 42.3% were male (n = 970 019) and 53.0% were younger than 50 years (647 083 [28.2%] aged 18-34 years and 568 160 [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.
Authors: Eduardo R Butelman; Carina Y Chen; Kimberly J Lake; Kate G Brown; Mary Jeanne Kreek Journal: Exp Clin Psychopharmacol Date: 2020-10-29 Impact factor: 3.157
Authors: Robert C Schell; Bennett Allen; William C Goedel; Benjamin D Hallowell; Rachel Scagos; Yu Li; Maxwell S Krieger; Daniel B Neill; Brandon D L Marshall; Magdalena Cerda; Jennifer Ahern Journal: Am J Epidemiol Date: 2022-02-19 Impact factor: 5.363
Authors: Laura B Monico; Ariel Ludwig; Elizabeth Lertch; Robert P Schwartz; Marc Fishman; Shannon Gwin Mitchell Journal: J Subst Abuse Treat Date: 2021-12-12
Authors: Michael Ripperger; Sarah C Lotspeich; Drew Wilimitis; Carrie E Fry; Allison Roberts; Matthew Lenert; Charlotte Cherry; Sanura Latham; Katelyn Robinson; Qingxia Chen; Melissa L McPheeters; Ben Tyndall; Colin G Walsh Journal: J Am Med Inform Assoc Date: 2021-12-28 Impact factor: 7.942