The project team is assessing whether there is a causal effect of adverse weather events on indicators of population health, and if water policy affects the strength of the link between adverse weather conditions and health. To conduct this research the investigators are creating a dataset that will include health measures, indices of drought and extreme temperature, and water policy measures, parts which will be publicly available for future research. Resulting evidence will encourage and inform water policy that considers the health consequences of drought and extreme temperature alongside other impacts in the development of wise and equitable plans for the distribution of scarce water resources.
Briefly, Model 1 shows the causal inference question the difference-in-difference design will answer. Model 2 shows the underlying research-supported mechanisms through which adverse weather events (AWEs) are thought to influence health, and Model 3 depicts the hypotheses about differential effects of AWEs on vulnerable populations. Finally Model 4 depicts the second major research questions concerning whether variation in water policy modifies the effect of AWEs on population health.
Spatial-temporal data on adverse weather events, agricultural productivity, and characteristics of water will be aggregated to the county level over the past 25 years. This data will be merged with individual-level health data on critical health indicators for both newborns and adults which are available within the same temporal (year) spatial (county) units as the drought and temperature relevant data.
A series of difference-in-difference models with the inclusion of time-varying socio-economic and demographic factors to gauge whether and to what extent changes in drought and extreme temperatures drives changes in health outcomes after controlling for other possible confounding factors.