Evidence map›Paper›PMID 30721165›Full record

ArticleEpidemiology (Cambridge, Mass.)2019

Prescription Drug Monitoring Programs and Opioid Overdoses: Exploring Sources of Heterogeneity.

Alvaro Castillo-Carniglia, William R Ponicki, Andrew Gaidus, Paul J Gruenewald, Brandon D L Marshall, David S Fink, Silvia S Martins, Ariadne Rivera-Aguirre, Garen J Wintemute, Magdalena Cerdá

Abstract read
In one paragraph

Article in Epidemiology (Cambridge, Mass.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 4 pooled it
–field-weighted citation impact
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

17 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
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

10 authors.

Alvaro Castillo-CarnigliaFrom the Violence Prevention Research Program, Department of Emergency Medicine, UC Davis School of Medicine, Sacramento, CA.
William R PonickiPrevention Research Center, Pacific Institute for Research and Evaluation, Berkeley, CA.
Andrew GaidusPrevention Research Center, Pacific Institute for Research and Evaluation, Berkeley, CA.
Paul J GruenewaldPrevention Research Center, Pacific Institute for Research and Evaluation, Berkeley, CA.
Brandon D L MarshallDepartment of Epidemiology, Brown University School of Public Health, Providence, RI.
David S FinkDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY.
Silvia S MartinsDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY.
Ariadne Rivera-AguirreFrom the Violence Prevention Research Program, Department of Emergency Medicine, UC Davis School of Medicine, Sacramento, CA.
Garen J WintemuteFrom the Violence Prevention Research Program, Department of Emergency Medicine, UC Davis School of Medicine, Sacramento, CA.
Magdalena CerdáFrom the Violence Prevention Research Program, Department of Emergency Medicine, UC Davis School of Medicine, Sacramento, CA.

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
Prescription Drug Monitoring Programs and Opioid-Related HarmR01DA039962 · NIDA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI CERDA, MAGDALENA · 2016 to 2018
$1.3M
NIDA NIH HHS R01 DA039962NIDA NIH HHS T32 DA031099
6 · The paper itself

Abstract

backgroundPrescription drug monitoring program are designed to reduce harms from prescription opioids; however, little is known about what populations benefit the most from these programs. We investigated how the relation between implementation of online prescription drug monitoring programs and rates of hospitalizations related to prescription opioids and heroin overdose changed over time, and varied across county levels of poverty and unemployment, and levels of medical access to opioids.

methodsEcologic county-level, spatiotemporal study, including 990 counties within 16 states, in 2001-2014. We modeled overdose counts using Bayesian hierarchical Poisson models. We defined medical access to opioids as the county-level rate of hospital discharges for noncancer pain conditions.

resultsIn 2010-2014, online prescription drug monitoring programs were associated with lower rates of prescription opioid-related hospitalizations (rate ratio 2014 = 0.74; 95% credible interval = 0.69, 0.80). The association between online prescription drug monitoring programs and heroin-related hospitalization was also negative but tended to increase in later years. Counties with lower rates of noncancer pain conditions experienced a lower decrease in prescription opioid overdose and a faster increase in heroin overdoses. No differences were observed across different county levels of poverty and unemployment.

conclusionsAreas with lower levels of noncancer pain conditions experienced the smallest decrease in prescription opioid overdose and the faster increase in heroin overdose following implementation of online prescription drug monitoring programs. Our results are consistent with the hypothesis that prescription drug monitoring programs are most effective in areas where people are likely to access opioids through medical providers.

Indexed as

Prescription Drug Monitoring ProgramsAdolescentAdultAgedAnalgesics, OpioidBayes TheoremDrug OverdoseDrug PrescriptionsFemaleHeroin DependenceHospitalizationHumansMaleMiddle AgedModels, TheoreticalPovertyAnalgesics, Opioid

Identifiers

PMID30721165
PMCPMC6437666

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.