To end the HIV/AIDS epidemic, innovative strategies are needed to improve outcomes along the HIV care continuum. Data-to-Care is a public health strategy whereby HIV surveillance data are used to identify people living with HIV/AIDS for linkage to, or re-engagement in HIV medical care. Three main approaches to Data-to-Care are defined by where persons out of care are identified and where outreach activities are initiated: the Health Department level, the Healthcare Provider level, or a combination of the two (Combination Model). The purpose of this evaluation was to compare successes and challenges for two Data-to-Care models implemented in New York State between 1 January 2015 and 1 September 2016: a Health Department Model, and a Combination Model. The Health Department Model identifies persons presumed to be out of care based on an absence of HIV laboratory tests within the states surveillance system alone, and the Combination Model identifies individuals based on both an absence of a medical provider visit at a partnering health center, and an absence of HIV laboratory tests in the surveillance system. Only counties served by partnering health centers were included in this evaluation. In the Health Department Model, 348 out of 1352 (26%) surveillance identified individuals were truly out of care; of those, re-linkage success was 78%. In the Combination Model, 19 out of 51 (37%) individuals were truly out of care; of those, re-linkage success was 63%. The proportion of cases truly out of care was significantly higher for the Combination Model than the Health Department Model (p-value: 0.08). Both models were successful in re-linking a high proportion of individuals back to care, though the efficiency of identifying individuals who are truly out of care remains an area in need of further refinement for both models.
Keywords: Data-to-care; HIV care continuum; HIV/AIDS; high impact prevention; linkage to care.