Abstract
BACKGROUND: Innovative and efficient methods are needed to identify remaining people living with HIV unaware of their status. Routine health information system (RHIS) data, widely available in high-burden HIV settings, may help target areas of high risk to deliver timely prevention services. Often underused, RHIS data were leveraged at the facility level to predict changes in HIV test positivity in Malawi. METHODS: From District Health Information Software-2 from January 2017 to March 2023, we analysed sexually transmitted infection (STI) cases and HIV tests and test results across 563 health facilities in Malawi. A multilevel model was employed to determine whether changes in STI diagnoses were predictive of changes in HIV test positivity. We considered STI types and their incubation periods, and controlled for facility type, ownership, quarter, season, zonal HIV and STI prevalence (2016 Population-Based HIV Impact Assessment). RESULTS: Among 139 million HIV tests, overall positivity was 2.8%. Blantyre facilities had the highest positivity (6.0%) while those in the central-east zone had the lowest (1.8%). Key variables-changes in syndromic STI counts (lagged and cross-sectional)-showed weak or no associations with HIV positivity (OR: 1.01, CI: 1.01 to 1.01; OR: 1.00, CI: 1.00 to 1.00). However, contextual covariates, including zonal HIV prevalence (OR: 1.04, CI: 1.04 to 1.04), genital ulcers (OR: 1.16, CI: 1.16 to 1.16) and clinical STI diagnoses (OR: 1.29, CI: 1.29 to 1.29), were positively associated with HIV positivity. CONCLUSIONS: In settings with high STI screening uptake, RHIS data can be used to monitor changes in STI diagnoses and contextual factors to identify HIV hotspots and guide targeted testing, prevention and treatment services.