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What we know, and don't know, about the impact of state policy and systems-level interventions on prescription drug overdose
Open accessAbstract
Background Drug overdose deaths have been rising since the early 1990s and is the leading cause of injury death in the United States. Overdose from prescription opioids constitutes a large proportion of this burden. State policy and systems-level interventions have the potential to impact prescription drug misuse and overdose.Methods We searched the literature to identify evaluations of state policy or systems-level interventions using non-comparative, cross-sectional, before-after, time series, cohort, or comparison group designs or randomized/non-randomized trials. Eligible studies examined intervention effects on provider behavior, patient behavior, and health outcomes.Results Overall study quality is low, with a limited number of time-series or experimental designs. Knowledge and prescribing practices were measured more often than health outcomes (e.g., overdoses). Limitations include lack of baseline data and comparison groups, inadequate statistical testing, small sample sizes, self-reported outcomes, and short-term follow-up. Strategies that reduce inappropriate prescribing and use of multiple providers and focus on overdose response, such as prescription drug monitoring programs, insurer strategies, pain clinic legislation, clinical guidelines, and naloxone distribution programs, are promising. Evidence of improved health outcomes, particularly from safe storage and disposal strategies and patient education, is weak.Conclusions While important efforts are underway to affect prescriber and patient behavior, data on state policy and systems-level interventions are limited and inconsistent. Improving the evidence base is a critical need so states, regulatory agencies, and organizations can make informed choices about policies and practices that will improve prescribing and use, while protecting patient health.
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“Legislative efforts at the State level to close “pill mills” have had little discernable impact in reducing opioid use (35).”
From A Biopsychosocial Overview of the Opioid Crisis: Considering Nutrition and Gastrointestinal Health (2019) · cites this paper as [35]
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Metadata
- Authors
- Haegerich, T. M., Paulozzi, L. J., Manns, B. J., & Jones, C. M.
- Affiliations
- Centers for Disease Control and Prevention (US)
- United States Food and Drug Administration (US)
- Corresponding author
- Haegerich, Tamara M.
- Venue
- Drug and Alcohol Dependence
- Publisher
- Elsevier BV
- Published
- 2014-12
- Volume
- 145
- Pages
- 34-47
- ISSN
- 0376-8716 (ISSN-L)
- PMC
- PMC6557270
- OpenAlex
- W2035786417
- Cited by
- 272
- References
- 115
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 3 · 2023: 10 · 2022: 16 · 2021: 20 · 2020: 18 · 2019: 54 · 2018: 42 · 2017: 38 · 2016: 41 · 2015: 22
- FWCI
- 5.30
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Drug and Alcohol Dependence (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (CC999999)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Opioid Use Disorder Treatment
- Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
- Health Sciences › Medicine › Geriatrics and Gerontology › Pharmaceutical Practices and Patient Outcomes
- Keywords
- prescription drug overdose, prescription opioid misuse, state policy, system-level interventions, prescription drug monitoring programs, naloxone distribution, clinical guidelines, inappropriate prescribing, overdose response, patient education, health outcomes, provider behavior
- MeSH terms
- Analgesics, Opioid, Clinical Trials as Topic, Cross-Sectional Studies, Health Policy, Humans, United States, Cohort Studies, Prescription Drugs, Inappropriate Prescribing, Drug Overdose
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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