Guillermo Douglass-Jaimes PhD, MA

guillermoGHES US Fellow 2016-2017

Fellowship Site: Universidade Federal Fluminense, Rio de Janeiro, Brazil
U.S. Institution: UC Berkeley

Project Title: Unpacking the slum divide: A socio-spatial examination of disease presence across the formal and informal urban divide in Niteroi, Rio de Janeiro

This research project seeks to better examine the social and spatial determinants of health in Brazil by relying on data from three disease tracking studies in Niteroi, Brazil and the surrounding areas.

As part of an ongoing project, Brazilian doctors and researchers have been documenting cases of Zika, tuberculosis, and vertical HIV transmission in and around Niteroi, Brazil. Each of these diseases differ in transmission mode and impacted communities. Each of these diseases differ in the degree to which they are promoted by prevailing urban conditions, yet it is undeniable that urban conditions can exacerbate how these diseases impact the people who contract them. There has been a growing  understanding that slum areas and non-slum areas differ in vectors of disease transmission, as well as health protective resources. While the Brazilian government has officially demarcated slum and non-slum areas through their census, the realities are that on-the-ground conditions make it difficult to discern these official distinctions. Thus a precise distinction between two categories of urban formation is insufficient for explaining how urban conditions may contribute to the prevalence and severity of disease. This project sets out to do the following: 1) Georeference existing cases of Zika, Tuberculosis, and vertical HIV transmission for on-going research projects. 2) Overlay these geo-referenced cases on whether they occur in slum or non-slum areas as defined by the federal government 3) Compare patient self-reported residence in slum and non-slum areas to official designation. 4) Develop and pilot supplementary patient geo-refencing questions and tools. The 2010 census is considered the most comprehensive to date and further demarcates between slum and non-slum communities. Overlaying census slum designations with disease presence can shed light on how strong the slum-divide helps explain disease presence and will serve as a baseline for comparison with new questions to be asked of patients that more accurately captures where they live and conditions of their household and neighborhood to add nuance to the slum-divide. Results of this study can help to answer questions on the effectiveness of government sponsored health and welfare interventions to reduce disease burdens, and provide a greater understanding on the spatial and social determinants of health.

Whereas others have found spatial health disparities across Rio de Janeiro (Bortz 2015), this work relies on areal data that misses out on the micro-scale differences that are present between slum/non-slum designations. Self-reported geo-referenced data will be linked with the census to both evaluate how well census classifications can be used as a proxy for socio-economic and built environment drivers for poor health. This project will link individual health demographic information and urban infrastructure data to test the extent to which official slum designations compared to slum conditions outside these designations can be used to predict the existence of health disparities. This work will compliment that of my colleagues (which assesses the benefits of the seventeen social programs encompassed by this data set) by better understanding how the built environment can exacerbate or mitigate disease burden.