The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Implementation Of Prescription Drug Monitoring Programs Associated With Reductions In Opioid-Related Death Rates
Open accessAbstract
Over the past two decades the number of opioid pain relievers sold in the United States rose dramatically. This rise in sales was accompanied by an increase in opioid-related overdose deaths. In response, forty-nine states (all but Missouri) created prescription drug monitoring programs to detect high-risk prescribing and patient behaviors. Our objectives were to determine whether the implementation or particular characteristics of the programs were effective in reducing opioid-related overdose deaths. In adjusted analyses we found that a state's implementation of a program was associated with an average reduction of 1.12 opioid-related overdose deaths per 100,000 population in the year after implementation. Additionally, states whose programs had robust characteristics-including monitoring greater numbers of drugs with abuse potential and updating their data at least weekly-had greater reductions in deaths, compared to states whose programs did not have these characteristics. We estimate that if Missouri adopted a prescription drug monitoring program and other states enhanced their programs with robust features, there would be more than 600 fewer overdose deaths nationwide in 2016, preventing approximately two deaths each day.
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Cites
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Self-citation
Cited by
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Today’s fentanyl crisis: Prohibition’s Iron Law, revisited
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Metadata
- Authors
- Patrick, S. W., Fry, C. E., Jones, T. F., & Buntin, M. B.
- Venue
- Health Affairs
- Publisher
- Health Affairs Publishing, LLC
- Published
- 2016-07
- Volume
- 35
- Issue
- 7
- Pages
- 1324-1332
- ISSN
- 0278-2715 (ISSN-L)
- 1544-5208
- OpenAlex
- W2466771592
- Cited by
- 299
- References
- 30
- FWCI
- 48.97
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.