Logo image
Back
Validation of case-ascertainment algorithms using health administrative data to identify people who inject drugs in Ontario, Canada
Journal article   Peer reviewed

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

Zoë R. Greenwald, Dan Werb, Jordan J. Feld, Peter C. Austin, Daniel Fridman, Ahmed M. Bayoumi, Tara Gomes, Claire E. Kendall, Lauren Lapointe-Shaw, Ayden I. Scheim, …
Journal of clinical epidemiology, v 170, 111332
Jun 2024
DOI: https://doi.org/10.1016/j.jclinepi.2024.111332
PMID: 38522754
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1016/j.jclinepi.2024.111332View
Published, Version of Record (VoR) Restricted CC BY-NC V4.0

Abstract

Case-ascertainment Health administrative data People who inject drugs Routinely collected health data Validation Epidemiology
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. 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). 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. 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. (up to 5 points, 85 characters including spaces per bullet point)•Health administrative data can support research among people who inject drugs•Validated algorithms have high sensitivity and specificity to identify injecting history and recent injecting•Accuracy of algorithms to identify injecting will vary by population group

Metrics

10 Record Views
11 citations in Web of Science

Details

UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

Source: SDGs in the Output

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Health Care Sciences & Services
Public, Environmental & Occupational Health
Logo image