  The Clean Air Act mandates that the National Ambient Air Quality Standards
(NAAQS) must be routinely assessed to protect populations based on the latest
science. Therefore, researchers should continue to address whether exposure to
levels of air pollution below the NAAQS is harmful to human health. The
contentious nature surrounding environmental regulations urges us to cast this
question within a causal inference framework. Parametric and semi-parametric
regression approaches have been used to estimate the exposure-response (ER)
curve between ambient air pollution and health outcomes. Most of these
approaches are not formulated within a causal framework, adjust for the same
covariates across all levels of exposure, and do not account for model
uncertainty. We introduce a Bayesian framework for the estimation of a causal
ER curve called LERCA (Local Exposure Response Confounding Adjustment), which
allows for different confounders and different strength of confounding at the
different exposure levels; and propagates uncertainty regarding confounders'
selection and the shape of the ER. LERCA provides a principled way of assessing
the covariates' confounding importance at different exposure levels, providing
researchers with information regarding the variables to adjust for in
regression models. Using simulations, we show that state of the art approaches
perform poorly in estimating the ER curve in the presence of local confounding.
LERCA is used to evaluate the relationship between exposure to ambient PM2.5
and cardiovascular hospitalizations for 5,362 zip codes in the US, while
adjusting for a potentially varying set of confounders across the exposure
range. Ambient PM2.5 leads to an increase in cardiovascular hospitalization
rates when focusing at the low exposure range. Our results indicate that there
is no threshold for the effect of PM2.5 on cardiovascular hospitalizations.
