A systematic review of models used to estimate undiagnosed HIV prevalence in high-income low-prevalence countries and territories

INTRODUCTION: Estimating undiagnosed HIV prevalence facilitates planning epidemic responses, and monitoring progress towards UNAIDS and national targets. We undertook a systematic review to identify models used to estimate undiagnosed HIV prevalence in overall populations in high-income low-HIV-prevalence countries and territories to inform model selection in New Zealand. METHODS: We searched Medline, EMBASE, Web of Science, CINAHL and Cochrane Database of Systematic Reviews to 5 March 2025. Two authors independently reviewed studies with conflicts resolved by a third. We assessed study quality against five key characteristics of good modelling practice. We undertook a grey literature search to identify modelling in HIV surveillance or monitoring reports. RESULTS: We identified 2147 unique citations, with 119 full text studies retrieved and 48 included. Forty-six studies described modelling undiagnosed HIV prevalence in 23 countries and territories, a further two for multiple countries. The most common methods used CD4 + back-calculation, with the ECDC model most frequently used (10 studies), followed by a clinical stage-based back-calculation model, a CD4 + depletion model and the Spectrum CSAVR model (eight, four and three studies, respectively). Almost all studies noted a full mathematical model description, included parameters, validation and uncertainty estimates. Only five articles estimated undiagnosed HIV by ethnicity, but estimates by gender and exposure were common. CONCLUSION: CD4 + back-calculation models, notably the online accessible ECDC model, have been most commonly used. These are well suited to surveillance systems like New Zealand’s, which collect demographic and exposure details and CD4 + cell counts at HIV diagnosis, but limited exposure group size and seroprevalence information

• Epidemiology and Determinants of Health
  • Epidemiology
• Population(s)
  • General HIV+ population
• Health Systems
  • Governance arrangements