SynthesisPain medicine (Malden, Mass.)2026
Evidence-based framework for identifying opioid use disorder in administrative data: A systematic review and methodological development study.
Synthesis in Pain medicine (Malden, Mass.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
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Who cites it
4 citing papers in PubMed.
- Access to Medications for Opioid Use Disorder Among Veterans With Homeless Experience in Permanent Supportive Housing.JAMA network open · 2026Article
- State Prior Authorization Prohibitions and Buprenorphine Retention Among Privately Insured Patients.JAMA health forum · 2026Article
- Algorithmic opacity in opioid risk scoring and the need for transparent AI regulation.NPJ digital medicine · 2026Article
- Criterion validity and divergent risk profiles of long-term opioid therapy across medicare and medicaid.PloS one · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectiveTo systematically evaluate existing approaches for identifying opioid use disorder (OUD) in administrative data sets and develop evidence-based recommendations for standardized identification methods.
designSystematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Scoping Review guidelines with comprehensive literature search and evidence synthesis for framework development.
settingAdministrative data sets including commercial claims, Medicaid, Medicare, and electronic health records. SUBJECTS: In brief, 169 studies using administrative codes to identify OUD, primarily from US healthcare systems (94.7%).
methodsSystematic search of EMBASE, MEDLINE, Google Scholar, and PubMed through February 2024. Three independent reviewers screened articles and extracted data using standardized tools. Study quality was assessed using modified Newcastle-Ottawa Scale. Framework development employed systematic integration of evidence-based components from high-quality studies.
resultsOur analysis of 169 studies revealed four distinct identification approaches: Direct diagnosis codes (36.7%), composite definitions (48.0%), overdose codes (10.1%), and medication-assisted treatment codes (1.2%). Commercial claims data predominated (60.4%), followed by Medicaid claims (10.1%) and electronic health records (7.7%). Multi-modal strategies incorporating both diagnostic and treatment codes showing superior theoretical foundation compared to single-method approaches. Substantial variation existed in reference periods, code requirements, and treatment verification approaches.
conclusionsAn evidence-based framework incorporating diagnosis codes, specific temporal requirements, validated indirect indicators, and treatment evidence provides theoretical foundation for standardized OUD identification protocols. The framework addresses known sources of misclassification while maintaining diagnostic specificity through clinical diagnostic alignment and systematic validation research programs. REGISTRATION: Prospero (CRD42023406173) and OSF (osf.io/ru4j3).
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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.