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A methodological framework for constructing opioid agonist therapy episodes in administrative health data.

Kiana Yazdani1,2, Cassidy Tam1, Christopher C Fisher1

  • 1Epidemiology and Population Health Program, British Columbia Centre for Excellence in HIV/AIDS, 608-1081 Burrard Street, Vancouver, BC V6Z 1Y6 Canada.

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Summary

A new framework using interval algebra precisely defines Opioid Agonist Therapy (OAT) episodes in health data, improving accuracy for overdose risk studies. This method enhances understanding of OAT use, including medication switches and concurrent therapies.

Keywords:
Administrative health dataAllen’s relationsMedication episode constructionOpioid agonist therapyOpioid use disorderPermissible gapTemporal margin

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Area of Science:

  • Health Informatics
  • Pharmacovigilance
  • Data Science

Background:

  • Opioid Agonist Therapy (OAT) is crucial for reducing overdose risk.
  • Administrative health data are increasingly used to study OAT outcomes.
  • Existing methods for OAT episode construction have limitations in capturing treatment complexity.

Purpose of the Study:

  • To develop a robust framework for constructing OAT episodes in administrative health data.
  • To more accurately reflect real-world OAT use, including medication switches and concurrent therapies.
  • To improve the precision of OAT outcome studies using health records.

Main Methods:

  • Utilized Allen's interval algebra, temporal margins, and drug-specific permissible gaps to define OAT episodes.
  • Analyzed OAT dispensations from people living with HIV in British Columbia (2010-2020).
  • Characterized temporal relations between dispensations (e.g., meets, before, overlaps) to differentiate monotherapy, transitions, and multitherapy.

Main Results:

  • The 'before' relation was most common for OAT, especially methadone (99.43%).
  • The 'equals' relation was notable for buprenorphine (21.46%) and slow-release oral morphine (20.87%).
  • Identified 10,386 episodes: 93.53% monotherapy, 3.72% transition, 2.27% multitherapy, and <1% transition-multitherapy.

Conclusions:

  • Proposed a novel episode-building framework using interval algebra, temporal margins, and permissible gaps for OAT data.
  • This framework enhances the classification of OAT use in administrative health data.
  • The method is adaptable for use in other settings and populations with parameter adjustments.