TY - JOUR
T1 - Predicting Opioid Overdose Deaths Using Prescription Drug Monitoring Program Data
AU - Ferris, Lindsey M.
AU - Saloner, Brendan
AU - Krawczyk, Noa
AU - Schneider, Kristen E.
AU - Jarman, Molly P.
AU - Jackson, Kate
AU - Lyons, B. Casey
AU - Eisenberg, Matthew D.
AU - Richards, Tom M.
AU - Lemke, Klaus W.
AU - Weiner, Jonathan P.
N1 - Funding Information:
This project was supported by Grant No. 2015-PM-BX-K002 awarded by the Bureau of Justice Assistance. The Bureau of Justice Assistance is a component of the Department of Justice's Office of Justice Programs, which also includes the Bureau of Justice Statistics, the National Institute of Justice, the Office of Juvenile Justice and Delinquency Prevention, the Office for Victims of Crime, and the SMART Office. The Maryland Department of Health (MDH), Behavioral Health Administration was awarded the grant in partnership with the Center for Population Health Information Technology at the Johns Hopkins Bloomberg School of Public Health for data analyses and subject matter expertise and the Chesapeake Regional Information Systems for our Patients (CRISP)—the Maryland Health Information Exchange—to link the data sets. Noa Krawczyk was also supported by the National Institute on Drug Abuse of the NIH (F31DA047021). Kristin Schneider was supported by the National Institute on Drug Abuse (5T32DA007292-25: KS supported, Principal Investigator: Maher). Points of view or opinions in this document are those of the author and do not necessarily represent the official position or policies of the U.S. Department of Justice, the MDH, or other partner agencies. Human Subjects approval for the study was provided by IRBs at the Johns Hopkins University (IRB00007542) and MDH. Author contributions: LF contributed to the conception of the study, drafted the manuscript, identified key study variables, and facilitated data linkage. JW and BS led and oversaw the research project, conceptualized the study question and variables, and revised the manuscript. KWL developed variables, created and performed model analysis, and revised the manuscript. TR generated the study variables and analytic database. BCL and KJ contributed to the conception of the study, secured databases for analysis and linkage, helped develop study variables, and revised the manuscript. NK, KS, MJ, and ME contributed to the conception of the study and revised the manuscript. No financial disclosures were reported by the authors of this paper.
Funding Information:
This project was supported by Grant No. 2015-PM-BX-K002 awarded by the Bureau of Justice Assistance . The Bureau of Justice Assistance is a component of the Department of Justice's Office of Justice Programs, which also includes the Bureau of Justice Statistics, the National Institute of Justice, the Office of Juvenile Justice and Delinquency Prevention, the Office for Victims of Crime, and the SMART Office. The Maryland Department of Health (MDH), Behavioral Health Administration was awarded the grant in partnership with the Center for Population Health Information Technology at the Johns Hopkins Bloomberg School of Public Health for data analyses and subject matter expertise and the Chesapeake Regional Information Systems for our Patients (CRISP)—the Maryland Health Information Exchange—to link the data sets. Noa Krawczyk was also supported by the National Institute on Drug Abuse of the NIH ( F31DA047021 ). Kristin Schneider was supported by the National Institute on Drug Abuse ( 5T32DA007292-25 : KS supported, Principal Investigator: Maher). Points of view or opinions in this document are those of the author and do not necessarily represent the official position or policies of the U.S. Department of Justice, the MDH, or other partner agencies. Human Subjects approval for the study was provided by IRBs at the Johns Hopkins University (IRB00007542) and MDH.
Publisher Copyright:
© 2019 American Journal of Preventive Medicine
PY - 2019/12
Y1 - 2019/12
N2 - Introduction: Prescription Drug Monitoring Program data can provide insights into a patient's likelihood of an opioid overdose, yet clinicians and public health officials lack indicators to identify individuals at highest risk accurately. A predictive model was developed and validated using Prescription Drug Monitoring Program prescription histories to identify those at risk for fatal overdose because of any opioid or illicit opioids. Methods: From December 2018 to July 2019, a retrospective cohort analysis was performed on Maryland residents aged 18–80 years with a filled opioid prescription (n=565,175) from January to June 2016. Fatal opioid overdoses were identified from the Office of the Chief Medical Examiner and were linked at the person-level with Prescription Drug Monitoring Program data. Split-half technique was used to develop and validate a multivariate logistic regression with a 6-month lookback period and assessed model calibration and discrimination. Results: Predictors of any opioid-related fatal overdose included male sex, age 65–80 years, Medicaid, Medicare, 1 or more long-acting opioid fills, 1 or more buprenorphine fills, 2 to 3 and 4 or more short-acting schedule II opioid fills, opioid days’ supply ≥91 days, average morphine milligram equivalent daily dose, 2 or more benzodiazepine fills, and 1 or more muscle relaxant fills. Model discrimination for the validation cohort was good (area under the curve: any, 0.81; illicit, 0.77). Conclusions: A model for predicting fatal opioid overdoses was developed using Prescription Drug Monitoring Program data. Given the recent national epidemic of deaths involving heroin and fentanyl, it is noteworthy that the model performed equally well in identifying those at risk for overdose deaths from both illicit and prescription opioids.
AB - Introduction: Prescription Drug Monitoring Program data can provide insights into a patient's likelihood of an opioid overdose, yet clinicians and public health officials lack indicators to identify individuals at highest risk accurately. A predictive model was developed and validated using Prescription Drug Monitoring Program prescription histories to identify those at risk for fatal overdose because of any opioid or illicit opioids. Methods: From December 2018 to July 2019, a retrospective cohort analysis was performed on Maryland residents aged 18–80 years with a filled opioid prescription (n=565,175) from January to June 2016. Fatal opioid overdoses were identified from the Office of the Chief Medical Examiner and were linked at the person-level with Prescription Drug Monitoring Program data. Split-half technique was used to develop and validate a multivariate logistic regression with a 6-month lookback period and assessed model calibration and discrimination. Results: Predictors of any opioid-related fatal overdose included male sex, age 65–80 years, Medicaid, Medicare, 1 or more long-acting opioid fills, 1 or more buprenorphine fills, 2 to 3 and 4 or more short-acting schedule II opioid fills, opioid days’ supply ≥91 days, average morphine milligram equivalent daily dose, 2 or more benzodiazepine fills, and 1 or more muscle relaxant fills. Model discrimination for the validation cohort was good (area under the curve: any, 0.81; illicit, 0.77). Conclusions: A model for predicting fatal opioid overdoses was developed using Prescription Drug Monitoring Program data. Given the recent national epidemic of deaths involving heroin and fentanyl, it is noteworthy that the model performed equally well in identifying those at risk for overdose deaths from both illicit and prescription opioids.
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U2 - 10.1016/j.amepre.2019.07.026
DO - 10.1016/j.amepre.2019.07.026
M3 - Article
C2 - 31753274
AN - SCOPUS:85074912489
SN - 0749-3797
VL - 57
SP - e211-e217
JO - American journal of preventive medicine
JF - American journal of preventive medicine
IS - 6
ER -