Challenges of Assessing Spatiotemporal Patterns of Environmentally Driven Infectious Diseases in Resource-Poor Settings

Abstract

National data repositories, such as census, surveys, clinical records, and surveillance systems, provide incredibly valuable information to establish links between health outcomes and environmental exposures, as well as to guide national, local, and global environmental and health policies to achieve the Sustainable Development Goals (SDGs). The SDGs, under the 2030 Agenda for Sustainable Development, call for improving health and education, reducing inequality, increasing economic growth, and creating sustainable environmental policies and practices. Using the SDGs as a global model, environmental health research can be focused on regions that have not yet met the goals. Modern geospatial tools enable a better understanding of spatiotemporal patterns of environmentally driven, climate-sensitive, and often preventable infections. Current research highlights the untapped potential of national repositories of medical claims, laboratory-confirmed surveillance records, and census data to quantify and predict the impact of environmental exposure and the complexity of interrelationships between spatiotemporal clustering of infections and water quality and quantity, extreme weather, and climate change. A systematic approach to quantify and depict an outcome, exposure, and the relationship between them, along with relevant uncertainties, in a given location at a given time, is essential for effective prevention. To build national and global geospatial capacities, accurate information at refined spatial and temporal scales is needed. However, the continuous monitoring of health and environmental data, especially in resource-poor settings of low-income countries, is prohibitively expensive and prone to underreporting and biases. Even in data-rich, high-income environments, advanced surveillance and public health records contain inaccuracies and inconsistencies that can cause costly errors of displacing or not accounting for ever-changing exposures. Aiming to meet the SDGs, many countries are strengthening their efforts to establish robust national data infrastructure. In this chapter, we illustrate such efforts and highlight ongoing challenges that arise in compiling the essential demographic, socioeconomic, environmental, and health records in Ghana to explore spatiotemporal patterns in climate-sensitive disease caused by Schistosoma haematobium and diarrheal infections. We recommend further investment in building a robust data infrastructure by technological leapfrogging with the support of novel geospatial tools.