Evidence map›Paper›PMID 31627159›Full record

ArticleThe International journal on drug policy2019

Prescription drug monitoring programs operational characteristics and fatal heroin poisoning.

Silvia S Martins, William Ponicki, Nathan Smith, Ariadne Rivera-Aguirre, Corey S Davis, David S Fink, Alvaro Castillo-Carniglia, Stephen G Henry, Brandon D L Marshall, Paul Gruenewald and 1 more

Open access · greenAbstract read
In one paragraph

Article in The International journal on drug policy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 1 pooled it
9.1field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

34 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 8 institutions in 2 countries.

Silvia S MartinsDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, United States. Electronic address: ssm2183@cumc.columbia.edu.
William PonickiPrevention Research Center, Pacific Institute for Research and Evaluation, Berkeley, CA, United States.
Nathan SmithViolence Prevention Research Program, Department of Emergency Medicine, UC Davis School of Medicine, CA, United States.
Ariadne Rivera-AguirreViolence Prevention Research Program, Department of Emergency Medicine, UC Davis School of Medicine, CA, United States; Department of Population Health, NYU School of Medicine, New York, NY, United States.
Corey S DavisNetwork for Public Health Law, Los Angeles, CA, United States.
David S FinkDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, United States.
Alvaro Castillo-CarnigliaSociety and Health Research Center, Facultad de Humanidades, Universidad Mayor, Chile.
Stephen G HenryDepartment of Internal Medicine, University of California Davis, Sacramento, CA, United States.
Brandon D L MarshallDepartment of Epidemiology, Brown University School of Public Health, Providence, RI, United States.
Paul GruenewaldPrevention Research Center, Pacific Institute for Research and Evaluation, Berkeley, CA, United States.
Magdalena CerdáDepartment of Population Health, NYU School of Medicine, New York, NY, United States.
New York University · USPacific Institute For Research and Evaluation · USUniversity of California, Davis · USBrown University · USColumbia University · USColumbia University Irving Medical Center · USNational Health Law Program · USUniversidad Mayor · CL

Funding

Substance Abuse Epidemiology Training Program (SAETP) at Columbia UniversityT32DA031099 · NIDA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI DEBORAH S HASIN, Silvia Saboia Martins · 2012 to 2026
$6.1M
Examining the Synergistic Effects of Cannabis and Prescription Opioid Policies on Chronic Pain, Opioid Prescribing, and Opioid OverdoseR01DA045872 · NIDA · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI CERDA, MAGDALENA, MARTINS, SILVIA SABOIA · 2019 to 2023
$4.3M
Prescription Drug Monitoring Programs and Opioid-Related HarmR01DA039962 · NIDA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI CERDA, MAGDALENA · 2016 to 2018
$1.3M
A clinician training intervention to improve pain-related communication, pain management and opioid prescribing in primary careK23DA043052 · NIDA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI HENRY, STEPHEN G · 2017 to 2019
$591k
NIDA NIH HHS K23 DA043052NIDA NIH HHS R01 DA039962NIDA NIH HHS R01 DA045872NIDA NIH HHS T32 DA031099
6 · The paper itself

Abstract

backgroundPrescription drug monitoring programs (PDMP), by reducing access to prescribed opioids (POs), may contribute to a policy environment in which some people with opioid dependence are at increased risk for transitioning from POs to heroin/other illegal opioids. This study examines how PDMP adoption and changes in the characteristics of PDMPs over time contribute to changes in fatal heroin poisoning in counties within states from 2002 to 2016.

methodsLatent transition analysis to classify PDMPs into latent classes (Cooperative, Proactive, and Weak) for each state and year, across three intervals (1999-2004, 2005-2009, 2010-2016). We examined the association between probability of PDMP latent class membership and the rate of county-level heroin poisoning death.

resultsAfter adjustment for potential county-level confounders and co-occurring policy changes, adoption of a PDMP was significantly associated with increased heroin poisoning rates (22% increase by third year post-adoption). Findings varied by PDMP type. From 2010-2016, states with Cooperative PDMPs (those more likely to share data with other states, to require more frequent reporting, and include more drug schedules) had 19% higher heroin poisoning rates than states with Weak PDMPs (adjusted rate ratio [ARR] = 1.19; 95% CI = 1.14, 1.25). States with Proactive PDMPs (those more likely to report outlying prescribing and dispensing and provide broader access to law enforcement) had 6% lower heroin poisoning rates than states with No/Weak PDMPs (ARR = 0.94; 95% CI = 0.90, 0.98).

conclusionThere is a consistent, positive association between state PDMP adoption and heroin poisoning mortality. However, this varies by PDMP type, with Proactive PDMPs associated with a small reduction in heroin poisoning deaths. This raises questions about the potential for PDMPs to support efforts to decrease heroin overdose risk, particularly by using proactive alerts to identify patients in need of treatment for opioid use disorder. Future research on mechanisms explaining the reduction in heroin poisonings after enactment of Proactive PDMPs is merited.

Indexed as

Analgesics, OpioidDrug OverdoseFemaleHeroinHeroin DependenceHumansMaleOpioid-Related DisordersPrescription Drug MisusePrescription Drug Monitoring ProgramsUnited StatesAnalgesics, OpioidHeroinFatal heroin poisoningsLatent classesOverdosePrescription drug monitoring programs

Identifiers

PMID31627159
PMCPMC6897357
OpenAlexW2980963477

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.