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Substance use disorders involve a pattern of using drugs more extensively than intended and continuing use despite harmful consequences. This includes legal substances like alcohol and nicotine, as well as illegal drugs. These disorders often involve both physical and psychological dependence, reflecting compulsive use of substances that significantly alter thoughts, feelings, and behaviors, contributing to a major public health issue.
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Evidence-based framework for identifying opioid use disorder in administrative data: A systematic review and

Robert W Hurley1,2,3,4, Khadijah T Bland5, Mira D Chaskes6

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Standardized methods for identifying opioid use disorder (OUD) in administrative data are crucial. This review proposes an evidence-based framework using diagnosis codes, temporal requirements, and treatment data for accurate OUD identification.

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ICD-10administrative datadiagnostic codinghealth services researchopioid use disordersystematic review

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Area of Science:

  • Health Services Research
  • Public Health
  • Data Science

Background:

  • Opioid use disorder (OUD) is a significant public health crisis.
  • Accurate identification of OUD in administrative datasets is essential for surveillance, research, and intervention.
  • Existing methods for identifying OUD in administrative data lack standardization, leading to inconsistencies.

Purpose of the Study:

  • To systematically evaluate current approaches for identifying OUD in administrative datasets.
  • To develop evidence-based recommendations for standardized OUD identification methods.
  • To propose a framework for improving the accuracy and consistency of OUD identification.

Main Methods:

  • Systematic review following PRISMA-Scoping Review guidelines.
  • Comprehensive literature search of EMBASE, MEDLINE, Google Scholar, and PubMed.
  • Evidence synthesis and framework development integrating components from 169 studies.

Main Results:

  • Four main approaches for OUD identification were identified: direct diagnosis codes, composite definitions, overdose codes, and medication-assisted treatment codes.
  • Commercial claims data were most frequently used, followed by Medicaid claims and electronic health records.
  • Multi-modal strategies combining diagnostic and treatment codes demonstrated a stronger theoretical foundation than single-method approaches.

Conclusions:

  • An evidence-based framework incorporating diagnosis codes, temporal requirements, indirect indicators, and treatment evidence is proposed.
  • This framework aims to standardize OUD identification protocols and address misclassification issues.
  • The framework emphasizes clinical diagnostic alignment and systematic validation for enhanced specificity.