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Am J Epidemiol. 2017 Dec 1;186(11):1281-1289. doi: 10.1093/aje/kwx184.

Potential for Bias When Estimating Critical Windows for Air Pollution in Children's Health.

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Department of Statistics, Colorado State University, Fort Collins, Colorado.
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York.
Kravis Children's Hospital, Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, New York.
Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, New York, New York.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.


Evidence supports an association between maternal exposure to air pollution during pregnancy and children's health outcomes. Recent interest has focused on identifying critical windows of vulnerability. An analysis based on a distributed lag model (DLM) can yield estimates of a critical window that are different from those from an analysis that regresses the outcome on each of the 3 trimester-average exposures (TAEs). Using a simulation study, we assessed bias in estimates of critical windows obtained using 3 regression approaches: 1) 3 separate models to estimate the association with each of the 3 TAEs; 2) a single model to jointly estimate the association between the outcome and all 3 TAEs; and 3) a DLM. We used weekly fine-particulate-matter exposure data for 238 births in a birth cohort in and around Boston, Massachusetts, and a simulated outcome and time-varying exposure effect. Estimates using separate models for each TAE were biased and identified incorrect windows. This bias arose from seasonal trends in particulate matter that induced correlation between TAEs. Including all TAEs in a single model reduced bias. DLM produced unbiased estimates and added flexibility to identify windows. Analysis of body mass index z score and fat mass in the same cohort highlighted inconsistent estimates from the 3 methods.


air pollution; children’s health; confounding bias; critical windows; distributed lag models; seasonality

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