Understanding the Impacts of Climate and Hydrologic Extremes on Diarrheal Diseases in Southwestern Amazon

Abstract:

Bacterial diarrheal diseases have a high incidence rate during and after flooding episodes. In the Brazilian Amazon, flood extreme events have become more frequent, leading to high incidence rates for infant diarrhea. In this study we aimed to find a statistical association between rainfall, river levels and diarrheal diseases in children under 5, in the river Acre basin, in the State of Acre (Brazil). We also aimed to identify the time-lag and annual season of extreme rainfall and flooding in different cities in the water basin. The results using Tropical Rainfall Measuring Mission (TRMM) Satellite rainfall data show robustness of these estimates against observational stations on-ground. The Pearson coefficient correlation results (highest 0.35) indicate a time-lag, up to 4 days in three of the cities in the water-basin. In addition, a correlation was also tested between monthly accumulated rainfall and the diarrheal incidence during the rainy season (DJF). Correlation results were higher, especially in Acrelândia (0.7) and Brasiléia and Epitaciolândia (0.5). The correlation between water level monthly averages and diarrheal diseases incidence was 0.3 and 0.5 in Brasiléia and Epitaciolândia. The time-lag evidence found in this paper is critical to inform stakeholders, local populations and civil defense authorities about the time available for preventive and adaptation measures between extreme rainfall and flooding events in vulnerable cities. This study was part of a pilot application in the state of Acre of the PULSE-Brazil project (http://www.pulse-brasil.org/tool/), an interface of climate, environmental and health data to support climate adaptation. The next step of this research is to expand the analysis to other climate variables on diarrheal diseases across the whole Brazilian Amazon Basin and estimate the relative risk (RR) of a child getting sick. A statistical model will estimate RR based on the observed values and seasonal forecasts (higher accuracy for the Amazon region) will be used so the government can be prepared for extreme climate events forecasted. It is expected that these results can be helpful during and post extreme events to improve health surveillance preparedness and better allocate available results in adapting vulnerable cities to climate extreme events.