Go to content

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


Objective — 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 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 month) injection drug use, including those participating in community-based research studies or seeking drug treatment were linked to health administrative data in Ontario from 1992–2020. We assessed the validity of algorithms to identify injection drug use over varying lookback periods (i.e., 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 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, 14,323 seeking drug treatment). An algorithm consisting of ≥1 physician visit, emergency department visit or hospitalization for drug use, or OAT record could effectively identify IDU history (91.6% sensitivity, 94.2% specificity) and recent IDU (using 3 years lookback: 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 high sensitivity and specificity, the positive predictive value of these algorithms will vary depending on the underlying prevalence of injection drug use in the population in which they are applied.



Greenwald ZR, Werb D, Feld JJ, Austin PC, Fridman D, Bayoumi AM, Gomes T, Kendall CE, Lapointe-Shaw L, Scheim AI, Bartlett SR, Benchimol EI, Bouck Z, Boucher LM, Greenaway C, Janjua NZ, Leece P, Wong WWL, Sander B, Kwong JC. J Clin Epidemiol. 2024; Mar 22 [Epub ahead of print].

View Source

Associated Sites