Validation of algorithms for identifying people living with HIV in French medico-administrative databases: implications for HIV surveillance

BACKGROUND: French medico-administrative databases (Système National des Données de Santé, SNDS) are increasingly used to study the epidemiology of human immunodeficiency virus (HIV) infections. However, algorithms used to target HIV-positive patient populations have not yet been evaluated. This study aimed to validate algorithms for identifying people living with HIV in French medico-administrative databases and thus estimate diagnosed HIV prevalence and incidence. METHODS: Two algorithms were evaluated to detect diagnosed HIV cases. A gold-standard cohort was created by matching clinical data from a hospital data warehouse to the SNDS. The performance of the algorithms was assessed using Bayesian models, then calculated adjusted national HIV prevalence and incidence were estimated on the entire SNDS based on algorithm performance. RESULTS: For the prevalently diagnosed HIV population, the algorithms showed high sensitivity and specificity (> 99%). For new diagnoses, the sensitivity was 82–95% with a specificity > 99.9%. The positive predictive values varied substantially among the algorithms. The adjusted 2020 prevalence estimates ranged from 135,448 − 145,704 individuals diagnosed with HIV. For 2017–2023, the algorithms estimated 27,283 − 45,346 new diagnoses, compared to 40,912 from mandatory notifications. CONCLUSION: Medico-administrative data can provide useful automated HIV surveillance, but algorithm performance must be considered when interpreting the results. More sophisticated algorithms may improve the accuracy. These methods could complement existing HIV monitoring systems in France.