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. 2020 Jan;31(1):32-42.
doi: 10.1097/EDE.0000000000001123.

Measuring Relationships Between Proactive Reporting State-level Prescription Drug Monitoring Programs and County-level Fatal Prescription Opioid Overdoses

Magdalena Cerdá  1   2 , William R Ponicki  3 , Nathan Smith  2 , Ariadne Rivera-Aguirre  1   2 , Corey S Davis  4 , Brandon D L Marshall  5 , David S Fink  6 , Stephen G Henry  7 , Alvaro Castillo-Carniglia  2   8 , Garen J Wintemute  2 , Andrew Gaidus  3 , Paul J Gruenewald  3 , Silvia S Martins  6
Affiliations

Measuring Relationships Between Proactive Reporting State-level Prescription Drug Monitoring Programs and County-level Fatal Prescription Opioid Overdoses

Magdalena Cerdá et al. Epidemiology. 2020 Jan.

Abstract

Background: Prescription drug monitoring programs (PDMPs) that collect and distribute information on dispensed controlled substances have been adopted by nearly all US states. We know little about program characteristics that modify PDMP impact on prescription opioid (PO) overdose deaths.

Methods: We measured associations between adoption of any PDMP and changes in fatal PO overdoses in 2002-2016 across 3109 counties in 49 states and D.C. We then measured changes related to the adoption of "proactive PDMPs," which report outlying prescribing/dispensing patterns and provide broader access to PDMP data by law enforcement. Comparisons were made within 3 time intervals that broadly represent the evolution of PDMPs (2002-2004, 2005-2009, and 2010-2016). We modeled overdoses using Bayesian space-time models.

Results: Adoption of electronic PDMP access was associated with 9% lower rates of fatal PO overdoses after three years (rate ratio [RR] = 0.91, 95% credible interval [CI]: 0.88-0.93) with well-supported effects for methadone (RR = 0.86,95% CI: 0.82-0.90) and other synthetic opioids (RR = 0.82, 95% CI: 0.77-0.86). Compared with states with no/weak PDMPs, proactive PDMPs were associated with fewer deaths attributed to natural/semi-synthetic opioids (2002-2004: RR = 0.72 [0.66-0.78]; 2005-2009: RR = 0.93 [0.90-0.97]; 2010-2016: 0.89 [0.86-0.92]) and methadone (2002-2004: RR = 0.77 [0.69-0.85]; 2010-2016: RR = 0.90 [0.86-0.94]). Unintended effects were observed for synthetic opioids other than methadone (2005-2009: RR = 1.29 [1.21-1.38]; 2010-2016: RR = 1.22 [1.16-1.29]).

Conclusions: State adoption of PDMPs was associated with fewer PO deaths overall while proactive PDMPs alone were associated with fewer deaths related to natural/semisynthetic opioids and methadone, the specific targets of these programs. See video abstract at, http://links.lww.com/EDE/B619.

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Conflict of interest statement

Conflict of interest: None

Figures

Figure 1:
Figure 1:
Relationships between Prescription Drug Monitoring Program (PDMP) features and county-level log relative rates of overdose deaths from all opioids, 2002-2016, United Statesa aResults based on models that adjust for annual county-level age, race/ethnicity, sex, and socioeconomic composition, population density, and overall mortality rate; annual state-level medical marijuana laws, Good Samaritan laws, and naloxone overdose prevention laws; calendar year; state-level fixed effects; county unit and trend random effects.
Figure 2:
Figure 2:
Relationships between Prescription Drug Monitoring Program (PDMP) features and county-level log relative rates of overdose death from natural/semi-synthetic opioids, 2002-2016, United Statesa aResults based on models that adjust for annual county-level age, race/ethnicity, sex, and socioeconomic composition, population density, and overall mortality rate; annual state-level medical marijuana laws, Good Samaritan laws, and naloxone overdose prevention laws; calendar year; state-level fixed effects; county unit and trend random effects.
Figure 3:
Figure 3:
Relationships between Prescription Drug Monitoring Program (PDMP) features and the county-level log relative rates of overdose death from non-methadone synthetic opioids, 2002-2016, United Statesa aResults based on models that adjust for annual county-level age, race/ethnicity, sex, and socioeconomic composition, population density, and overall mortality rate; annual state-level medical marijuana laws, Good Samaritan laws, and naloxone overdose prevention laws; calendar year; state-level fixed effects; county unit and trend random effects.
Figure 4:
Figure 4:
Relationships between Prescription Drug Monitoring Program (PDMP) features and county-level log relative rates of overdose deaths from methadone, 2002-2016, United Statesa aResults based on models that adjust for annual county-level age, race/ethnicity, sex, and socioeconomic composition, population density, and overall mortality rate; annual state-level medical marijuana laws, Good Samaritan laws, and naloxone overdose prevention laws; calendar year; state-level fixed effects; county unit and trend random effects.
See this image and copyright information in PMC

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