Mapping the spatial interdependence of adverse health outcomes and neighborhood socioeconomic conditions in Baltimore

Studies on health disparities in Baltimore, Maryland, have enriched scholarship on the social determinants of health. Yet, questions remain about whether a composite measure of disease prevalence provides greater analytical leverage over multiple measures when examining spatial patterns in health outcomes. We answer these questions by employing spatial and analytical approaches to examining census tract level data on chronic disease prevalence in Baltimore. First, we employ k-means cluster analysis to illustrate variation in health outcomes and to map said variation. Second, we compute a normalized composite measure of unhealthiness, and we plot the relationship between our composite measure and proxies for socioeconomic vulnerability. Results indicate that tracts high on unhealthiness had large proportions of residents who were more likely to be extremely vulnerable, unemployed, and of minority status. The spatial distribution of adverse health outcomes in Baltimore reflects the interlocking effects of urban segregation and socioeconomic conditions on personal well-being.