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Activity Number: 403 - SPAAC Poster Competition
Type: Topic Contributed
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #306614
Title: A Causal Model to Estimate the Effect of Distance-Weighted Built Environment Exposures from Longitudinal Data
Author(s): Adam Peterson* and Brisa Sanchez
Companies: and Drexel University
Keywords: Built-Environment; Spatial Scale; Difference-in-Differences

Identifying health effects causally conferred by built environment exposures is challenging due to uncertainty about the spatial scale that is relevant for exposure assessment and confounding due to unmeasured person-level factors. We propose a difference-in-differences parameterization for the spatial temporal aggregated predictor (STAP) model to address the question of spatial scale and condition on unmeasured, time-invariant person-level confounders. As with STAP, the model uses the distances between study participants’ locations and environmental features (e.g., supermarkets) to define a weighted exposure count, where weights are a function of distance with parameters interpretable as the spatial scale. In addition, the predictor is written as the difference in exposure during the current visit from the person-level average exposure, such that the effect of interest is interpreted as the change in the outcome associated with person-level change in exposure, causal interpretation. Implemented using a custom No U-Turn Hamiltonian Monte Carlo Sampler in C++, the model is used to estimate the causal effect of healthy food availability and body mass index within an ageing cohort.

Authors who are presenting talks have a * after their name.

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