Validation of case-ascertainment algorithms using health administrative data to identify people who inject drugs in Ontario, Canada.

Objectives: Health administrative data can be used to improve the health of people who inject drugs by informing public health surveillance and program planning, monitoring, and evaluation. However, methodological gaps in the use of these data persist due to challenges in accurately identifying injection drug use (IDU) at the population level. In this study, we validated case-ascertainment algorithms for identifying people who inject drugs using health administrative data in Ontario, Canada.
Study Design and Setting: Data from cohorts of people with recent (past 12 months) IDU, including those participating in community-based research studies or seeking drug treatment, were linked to health administrative data in Ontario from 1992 to 2020. We assessed the validity of algorithms to identify IDU over varying look-back periods (ie, all years of data [1992 onwards] or within the past 1-5 years), including inpatient and outpatient physician billing claims for drug use, emergency department (ED) visits or hospitalizations for drug use or injection-related infections, and opioid agonist treatment (OAT).
Results: Algorithms were validated using data from 15,241 people with recent IDU (918 in community cohorts and 14,323 seeking drug treatment). An algorithm consisting of ≥1 physician visit, ED visit, or hospitalization for drug use, or OAT record could effectively identify IDU history (91.6% sensitivity and 94.2% specificity) and recent IDU (using 3-year look back: 80.4% sensitivity, 99% specificity) among community cohorts. Algorithms were generally more sensitive among people who inject drugs seeking drug treatment.
Conclusion: Validated algorithms using health administrative data performed well in identifying people who inject drugs. Despite their high sensitivity and specificity, the positive predictive value of these algorithms will vary depending on the underlying prevalence of IDU in the population in which they are applied.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr Jordan Feld has received research support from AbbVie Inc and Gilead Sciences for matters unrelated to the current study. Dr Eric Benchimol has acted as a consultant for McKesson Canada, the Dairy Farmers of Ontario and the Canadian Agency for Drugs and Technology in Health (CADTH) for matters unrelated to the topic of this research or to this study. Dr Sofia Bartlett has consulted, participated in advisory boards, and spoken for AbbVie Inc., Gilead Sciences Canada, Inc. and Cepheid Inc. for matters unrelated to the current study, and no personal payments were accepted. Dr Naveed Janjua has participated in advisory boards and has spoken for AbbVie and Gilead, for matters unrelated to the current study. All other coauthors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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