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Full-Text Articles in Environmental Public Health
Estimating Causal Effects In The Presence Of Spatial Interference, Keith W. Zirkle
Estimating Causal Effects In The Presence Of Spatial Interference, Keith W. Zirkle
Theses and Dissertations
Environmental epidemiologists are increasingly interested in establishing causality between exposures and health outcomes. A popular model for causal inference is the Rubin Causal Model (RCM), which typically seeks to estimate the average difference in study units' potential outcomes. If the exposure Z is binary, then we may express this as E[Y(Z=1)-Y(Z=0)]. An important assumption under RCM is no interference; that is, the potential outcomes of one unit are not affected by the exposure status of other units. The no interference assumption is violated if we expect spillover or diffusion of exposure effects based on units' proximity to other units and …
Japanese Encephalitis: Assessing Disease Risk Due To Landscape Factors At Multiple Scales, Julia E. Metelka
Japanese Encephalitis: Assessing Disease Risk Due To Landscape Factors At Multiple Scales, Julia E. Metelka
Theses and Dissertations (Comprehensive)
Japanese Encephalitis is a mosquito-borne disease and is the leading cause of viral encephalitis in Asia. In many Asian countries, the geographical distribution of JE is dependent on a variety of human-environment interactions that can be conceptualized as a complex social-ecological system. The JE transmission cycle is influenced by a few primary human-landscape factors; the abundance and the spatial configuration of rice paddy fields (which provide habitat for the vector), the distribution of pig farms (which position the virus' amplifying host), and the location of a susceptible human population. Our models integrate population dynamics, landscape characteristics, and weather variables that …