The long-term aims of this R21 development grant proposal are to provide a comprehensive understanding of: 1) how the impacts of environmental change impact human health; 2) how socio-economic factors buffer environmental impacts on health; and 3) how social organization affects the delivery of prevention and treatment programs. The synthesis and insights from this developmental research award will establish the foundations for planning and strengthening a long-term research project grant (R01). To understand how socio-economic conditions mediate the dynamics of health-environment relationships, the proposed two-year developmental grant (R21) will investigate two basic questions: 1) How do land use/cover changes in frontier areas of Brazilian Amazonia impact human health? Specifically, what are the risks of malaria transmission related to specific changes in the landscape, and how do these risks vary over time and across space? 2) How do social networks, organizational structure, social stratification, and government programs influence the effectiveness of health care providers in dealing with regional health problems like malaria? To answer these questions the use of primary data on health, development, and environmental change in Brazilian Amazonia will be expanded, especially in the area of Machadinho D'Oeste, RondSnia, the site of extensive field research since 1984. Socio-demographic data for the R21 project includes a 1984 socioeconomic assessment of migrants in Machadinho and four household surveys (1985/86/87/95). A geo-referenced database on land cover (1986/94/99) allows for identification of land cover changes for all parcels in the study site. The main response variable for the analyses is the exposure weighted malaria illness rate. The project will integrate socio-demographic data and geo-referenced land use/land cover data into a unified geo-referenced database. The variety of data available for this project creates the potential for multi-level and spatial analyses of the incidence of malaria. Aggregate measures will be linked to the 1995 individual survey, and a series of hierarchical linear models will be estimated. The magnitude of land cover change will be related to rates of malaria transmission to test for and map spatial patterns of malaria. Spatial analysis of various land cover patterns will be related to environmental and socioeconomic indicators of health status. This comprehensive analysis of malaria risks will allow us to assess organizational responses and their effectiveness in prevention and treatment. The results of our analyses will enable us to identify the vulnerability of different population segments to malaria and other health risks and to make policy recommendations on how to most effectively target prevention and treatment delivery.
