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; Sofia R Bartlett; Eric I Benchimol; Zachary Bouck; Lisa M Boucher; Christina Greenaway; Naveed Z Janjua; Pamela Leece; William W L Wong; Beate Sander; Jeffrey C Kwong

doi:10.1016/j.jclinepi.2024.111332

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

J Clin Epidemiol. 2024 Mar 24:170:111332. doi: 10.1016/j.jclinepi.2024.111332. Online ahead of print.

Authors

Zoë R Greenwald¹, Dan Werb², Jordan J Feld³, Peter C Austin⁴, Daniel Fridman⁵, Ahmed M Bayoumi⁶, Tara Gomes⁷, Claire E Kendall⁸, Lauren Lapointe-Shaw⁹, Ayden I Scheim¹⁰, Sofia R Bartlett¹¹, Eric I Benchimol¹², Zachary Bouck¹³, Lisa M Boucher¹⁴, Christina Greenaway¹⁵, Naveed Z Janjua¹⁶, Pamela Leece¹⁷, William W L Wong¹⁸, Beate Sander¹⁹, Jeffrey C Kwong²⁰

Affiliations

¹ Dalla Lana School of Public Health, University of Toronto, Toronto, Canada; ICES, Toronto, Canada; Centre on Drug Policy Evaluation, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada.
² Centre on Drug Policy Evaluation, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada; Division of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, USA.
³ Department of Medicine, University of Toronto, Toronto, Canada; Toronto Centre for Liver Disease, Toronto General Hospital, Toronto, Canada; University Health Network, Toronto, Canada.
⁴ ICES, Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada.
⁵ ICES, Toronto, Canada.
⁶ ICES, Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada; Department of Medicine, University of Toronto, Toronto, Canada; Division of General Internal Medicine, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada; MAP Centre for Urban Health Solutions, St. Michael's Hospital, Unity Health Toronto, Toronto Canada.
⁷ ICES, Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada; MAP Centre for Urban Health Solutions, St. Michael's Hospital, Unity Health Toronto, Toronto Canada; Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Canada; Ontario Drug Policy Research Network, Toronto, Canada.
⁸ ICES, Toronto, Canada; Bruyère Research Institute, Ottawa, Canada; Department of Family Medicine, University of Ottawa, Ottawa, Canada.
⁹ ICES, Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada; Department of Medicine, University of Toronto, Toronto, Canada; University Health Network, Toronto, Canada.
¹⁰ Centre on Drug Policy Evaluation, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada; Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, USA; Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Canada.
¹¹ British Columbia Centre for Disease Control, Vancouver, Canada; School of Population and Public Health, University of British Columbia, Vancouver, Canada.
¹² ICES, Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada; Child Health Evaluative Sciences, SickKids Research Institute, The Hospital for Sick Children, Toronto, Canada; Department of Paediatrics, University of Toronto, Toronto, Canada; Division of Gastroenterology, Hepatology and Nutrition, The Hospital for Sick Children, Toronto, Canada.
¹³ Centre on Drug Policy Evaluation, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada; MAP Centre for Urban Health Solutions, St. Michael's Hospital, Unity Health Toronto, Toronto Canada; Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Canada.
¹⁴ Bruyère Research Institute, Ottawa, Canada.
¹⁵ Division of Infectious Diseases, Jewish General Hospital, Montreal, Canada; Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, Canada; Department of Epidemiology and Biostatistics and Occupational Health, McGill University, Montreal, Canada.
¹⁶ British Columbia Centre for Disease Control, Vancouver, Canada; School of Population and Public Health, University of British Columbia, Vancouver, Canada; Centre for Health Evaluation & Outcome Sciences, St Paul's Hospital Vancouver, Vancouver, Canada.
¹⁷ Public Health Ontario, Toronto, Canada; Department of Family and Community Medicine, University of Toronto, Toronto, Canada.
¹⁸ ICES, Toronto, Canada; School of Pharmacy, University of Waterloo, Kitchener, Canada; Toronto Health Economics and Technology Assessment Collaborative, Toronto, Canada.
¹⁹ ICES, Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada; University Health Network, Toronto, Canada; Public Health Ontario, Toronto, Canada; Toronto Health Economics and Technology Assessment Collaborative, Toronto, Canada.
²⁰ Dalla Lana School of Public Health, University of Toronto, Toronto, Canada; ICES, Toronto, Canada; University Health Network, Toronto, Canada; Public Health Ontario, Toronto, Canada; Department of Family and Community Medicine, University of Toronto, Toronto, Canada. Electronic address: Jeff.kwong@utoronto.ca.

PMID: 38522754
DOI: 10.1016/j.jclinepi.2024.111332

Abstract

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.

Keywords: Case-ascertainment; Epidemiology; Health administrative data; People who inject drugs; Routinely collected health data; Validation.