Identification of People with Low Prevalence Diseases in Administrative Healthcare Records: A Case Study of HIV in British Columbia, Canada

Introduction: Case-finding algorithms can be applied to administrative healthcare records to identify people with diseases, including people with HIV (PWH). When supplementing an existing registry of a low prevalence disease, near-perfect specificity helps minimize impacts of adding in algorithm-identified false positive cases. We evaluated the performance of algorithms applied to healthcare records to supplement an HIV registry in British Columbia (BC), Canada.

Methods: We applied algorithms based on HIV-related diagnostic codes to healthcare practitioner and hospitalization records. We evaluated 28 algorithms in a validation sub-sample of 7,124 persons with positive HIV tests (2,817 with a prior negative test) from the STOP HIV/AIDS data linkage-a linkage of healthcare, clinical, and HIV test records for PWH in BC, resembling a disease registry (1996-2020). Algorithms were primarily assessed based on their specificity-derived from this validation sub-sample-and their impact on the estimate of the total number of PWH in BC as of 2020.

Results: In the validation sub-sample, median age at positive HIV test was 37 years (Q1: 30, Q3: 46), 80.1% were men, and 48.9% resided in the Vancouver Coastal Health Authority. For all algorithms, specificity exceeded 97% and sensitivity ranged from 81% to 95%. To supplement the HIV registry, we selected an algorithm with 99.89% (95% CI: 99.76% - 100.00%) specificity and 82.21% (95% CI: 81.26% - 83.16%) sensitivity, requiring five HIV-related healthcare practitioner encounters or two HIV-related hospitalizations within a 12-month window, or one hospitalization with HIV as the most responsible diagnosis. Upon adding PWH identified by this highly-specific algorithm to the registry, 8,774 PWH were present in BC as of March 2020, of whom 333 (3.8%) were algorithm-identified.

Discussion: In the context of an existing low prevalence disease registry, the results of our validation study demonstrate the value of highly-specific case-finding algorithms applied to administrative healthcare records to enhance our ability to estimate the number of PWH living in BC.