Mapping late testers for HIV in Texas.

Understanding the spatial patterns of late testing for HIV infection is critically important for designing and evaluating intervention strategies to reduce the social and economic burdens of HIV/AIDS. Traditional mapping methods that rely on frequency counts or rates in predefined areal units are known to be problematic due to issues of small numbers and visual biases. Additionally, confidentiality requirements associated with health data further restrict the ability to produce cartographic representations at fine geographic scales. While kernel density estimation methods produce stable and geographically detailed patterns of the late testing burden, the resulting pattern depends critically on the definition of the at-risk population. Using three definitions of at risk groups, we examine the cartographic representation of HIV late testers in Texas and show that the resulting spatial patterns and the interpretation of disease burdens are different based on the choice of the at-risk population. Disease mappers should exercise considerable caution in selecting the denominator population for mapping.