| Summary: | Disease maps are powerful tools for depicting spatial variations in disease risk and its underlying drivers. However, producing effective disease maps requires careful consideration of the statistical and spatial properties of the disease data. In fact, the choice of mapping method influences the resulting spatial pattern of the disease, as well as the understanding of its underlying population characteristics. New developments in mapping methods and software in addition to continuing improvements in data quality and quantity are requiring map-makers to make a multitude of decisions before a map of disease burdens can be created. The impact of such decisions on a map, including the choice of appropriate mapping method, not been addressed adequately in the literature. This research demonstrates how choice of mapping method and associated parameters influence the spatial pattern of disease. We use four different disease-mapping methods – unsmoothed choropleth maps, smoothed choropleth maps produced using the headbanging method, smoothed kernel density maps, and smoothed choropleth maps produced using spatial empirical Bayes methods and 5-years of zip code level HIV incidence (2007- 2011) data from Dallas and Tarrant Counties, Texas. For each map, the leading population characteristics and their relative importance with regards to HIV incidence is identified using a regression analysis of a CDC recommended list of socioeconomic determinants of HIV. Our results show that the choice of mapping method leads to different conclusions regarding the associations between HIV disease burden and the underlying demographic and socioeconomic characteristics. Thus, the choice of mapping method influences the patterns of disease we see or fail to see. Accurate depiction of areas of high disease burden is important for developing and targeting appropriate public health interventions.
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