Improving PMTCT outcomes for mother-infant pairs through community-facility linkage: Results from a mixed methods study in Malawi

To improve MIP care retention in Malawi, several prevention of mother-to-child transmission of HIV (PMTCT) care delivery models have emerged to strengthen community-facility linkage (CFL), a concept defined as any “formalized connection between a health facility and the communities it serves to support improved health outcomes.” Similar to other settings in sub-Saharan Africa, three models have been widely implemented to complement Malawi’s National PMTCT Programme: 1) mentor mothers; 2) expert clients; and 3) community health workers. While the rationale underpinning these models has been substantiated by evidence generated from general antiretroviral therapy and PMTCT programs across SSA, a clear and rigorous description of each model, including characterization of supervisory structures, training, and relationships with clinical services, is currently unavailable. Equally important, the comparative impact of these models and their components on MIP care retention and other health outcomes have not been well characterized, particularly in the era of test and start. In the USAID-funded Project SOAR “Maternal-Infant Retention Study” reported here, we have attempted to address these existing evidence gaps by rigorously characterizing these community-facility linkage models and comparing their impact against each other and the “traditional” standard of care according to routinely collected health outcomes for mother-infant pairs (MIPs), including maternal retention in care and viral suppression, and infant HIV-free survival. To this end, specific objectives of this study were to: (1) establish a clear typology for CFL models by describing the main components of, and patient and key stakeholder perspectives on, three such models in Malawi; (2) describe MIP health outcomes in each model, and compare outcomes across models; and (2a) examine associations between individual components of CFL models and MIP health outcomes, controlling for confounding