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In 2018, the pre-exposure prophylaxis (PrEP) program was initiated in British Columbia (BC), Canada, providing PrEP at no cost to qualifying residents. This observational study discussed the steps to develop key evidence-based monitoring indicators and their calculation using real-time data. The indicators were conceptualized, developed, assessed and approved by the Technical Monitoring Committee of representatives from five health authority regions in BC, the BC Ministry of Health, the BC Centre for Disease Control, and the BC Centre for Excellence in HIV/AIDS. Indicator development followed the steps adopted from the United States Centers for Disease Control and Prevention framework for program evaluation in public health. The assessment involved eight selection criteria: data quality, indicator validity, existing scientific evidence, indicator informativeness, indicator computing feasibility, clients’ confidentiality maintenance capacity, indicator accuracy, and administrative considerations. Clients’ data from the provincial-wide PrEP program (January 2018—December 2020) shows the indicators’ calculation. The finalized 14 indicators included gender, age, health authority, new clients enrolled by provider type and by the health authority, new clients dispensed PrEP, clients per provider, key qualifying HIV risk factor(s), client status, PrEP usage type, PrEP quantity dispensed, syphilis and HIV testing and incident cases, and adverse drug reaction events. Cumulative clients’ data (n = 6966; 99% cis-gender males) identified an increased new client enrollment and an unexpected drop during the COVID-19 pandemic. About 80% dispensed PrEP from the Vancouver Coastal health authority. The HIV incidence risk index for men who have sex with men score ≥10 was the most common qualifying risk factor. The framework we developed integrating indicators was applied to monitor our PrEP program, which could help reduce the public health impact of HIV.

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.