Using country-level variables to discover country clusters beyond traditional health policy and performance metrics: An unsupervised machine learning approach for HIV healthcare delivery and financing

HIV remains a major public health challenge, with 1.4 million new infections and 31.6 million people accessing antiretroviral treatment in 2024. In many countries, HIV services have been provided through vertical programs, which, although highly successful in bringing treatment to people living with HIV since the early 2000s, are unlikely to sustain progress beyond donor dependency. The increasing push towards Universal Health Coverage (UHC), while facing reduction in international assistance, are prompting countries search for innovative strategies to effectively deliver HIV services through national systems, supported by domestic financing. Developing country-tailored HIV financing and service delivery approaches will be critical to reaching the end of AIDS as a public health threat and sustaining gains by and beyond 2030. Our study aims to develop an innovative data-driven approach to identify clusters of countries with similar challenges in defining their HIV response sustainability pathways. These clusters provide a framework for mutual learning, allowing countries to co-develop context-specific solutions to HIV financing and service delivery. We utilized unsupervised machine learning (ML) methods, including partitional, hierarchical, spectral, and density-based algorithms, to identify clusters of countries based on HIV epidemic and response data among 134 LMICs. We pooled open-source data from repositories covering indicators related to HIV epidemic and response, UHC commitment and progress, legislation surrounding human rights and HIV response, wealth and equity. We identified four country clusters, which did not align with conventional global regions but instead cut across them, revealing more nuanced groupings. Clusters were located in (1) South Asia, East Africa, and Oceania; (2) Sub-Saharan Africa and the Caribbean; (3) Eastern Europe, Middle East, Latin and Southern Africa; (4) Latin America, Asia, Middle East, North Africa, Oceania and the Caribbean. Our study is an early example of how ML techniques can be applied to health policy and (public) health performance.