Public housing was developed to provide stable housing for low-income families. It has since become synonymous with concentrated poverty. Programs to reduce poverty have focused on dismantling public housing to be replaced, if at all, by mixed income design alternatives. Studies have shown that low-income residents living in public housing have deleterious health outcomes. It is not clear, however, how public housing residents fare on a number of cardiovascular health indicators compared to those with an alternate low-income housing arrangement. Mechanistic models that incorporate housing with neighborhood access to resources and individual behaviors are poorly defined and understood. However, examining individual- and neighborhood- level data in synergistic ways can provide an important contribution to existing health campaigns to address cardiovascular disease in low-income populations. The current proposal uses a novel geocoding approach to analyze primary care electronic clinical record data in relation to housing and neighborhood level data from various sources in order to track health outcomes. This analysis approach focuses on individual level data within the context of the patient's housing and neighborhood environment. This K 01 award is designed to provide the foundation both conceptual and technical for Dr. Chambers to develop a career trajectory that combines prior experience in social and built environmental determinants of health with new knowledge in socio-ecologic models, graphical science, multi-level statistical modeling, and research informatics. The long-term goal is to develop clinical and public health partnerships that will use multi- source data, including housing and neighborhood environment, to the develop interventions that target vulnerable patient populations.
Among urban, low-income patients, it is not clear how public housing residents fare on a number of cardiovascular health indicators compared to those with an alternate low-income housing arrangement. The current proposal will provide the socio-ecologic, content, and analytic training to develop and test theoretic models. These models will integrate multi-source data to evaluate how blood pressure, BMI, and lifestyle behaviors are influenced by access to material (i.e. supermarkets, parks) and social (i.e. safety, deprivation) resources in the neighborhoods where patients live.
