An Application Of Machine Learning Methods To The Derivation Of Exposure-Response Curves For Respiratory Outcomes
Year: 2013
Type: article
Abstract: Analyses of epidemiological studies of the association between short-term changes in air pollution and health outcomes have not sufficiently discussed the degree to which the statistical models chosen for these analyses reflect what is actually known about the true data-generating distribution. We present a method to estimate population-level ambient air pollution (NO2) exposure-health (wheeze in children with asthma) response functions that is not dependent on assumptions about the data-generating function that underlies the observed data and which focuses on a specific scientific parameter of interest (the marginal adjusted association of exposure on probability of wheeze, over a grid of possible exposure values). We show that this approach provides a more nuanced summary of the data than more typical statistical methods used in air pollution epidemiology and epidemiological studies in general.
Authors: Ekaterina Eliseeva, Alan Hubbard, Ira B. Tager
Institution: University of California, Berkeley
Language: English
Subfield: Health, Toxicology and Mutagenesis
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2217628282