Background: A strength of time-series analyses is the inherent control of individual-level risk factors that do not vary temporally. However, in studies of adverse pregnancy outcomes, risk factors considered time-invariant at the individual level may vary seasonally when aggregated into a pregnancy risk set. To illustrate, we describe the seasonal patterns of birth in Atlanta and demonstrate how these patterns could lead to confounding in time-series studies of seasonally-varying exposures and preterm birth.
Methods: The study cohort included all births in 20-county metropolitan Atlanta delivered during the period 1994–2004 (n = 715,875). We assessed the seasonal patterns of estimated conception and birth for the full cohort and for subgroups stratified by sociodemographic factors. Based on the observed patterns, we quantified the degree of potential confounding created by (1) differences in the gestational age distribution in the risk set across calendar months and (2) differences in the sociodemographic composition of the risk set across calendar months.
Results: The overall seasonal pattern of birth was characterized by a peak in August–September and troughs in April–May and November–January. Seasonal patterns differed among racial and ethnic groups, maternal education levels, and marital status. As a consequence of these seasonal patterns, systematic seasonal differences in the gestational age distribution and the sociodemographic composition of the risk set led to differences in expected rates of preterm birth across calendar months.
Conclusions: Time-series investigations of seasonally-varying exposures and adverse pregnancy outcomes should consider the potential for bias due to seasonal heterogeneity in the risk set.
From the aRollins School of Public Health, Emory University, Atlanta, Georgia; and bNational Center for Birth Defects and Developmental Disabilities, Centers for Disease Control, Atlanta, Georgia.
Submitted 23 June 2008; accepted 16 October 2008; posted 17 June 2009.
Supported by National Institute of Environmental Health Sciences, NIH for the STAR Fellowship Program of the United States Environmental Protection Agency grants (R01-ES-012967-02S2A1).
The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention or the United States Environmental Protection Agency.
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Editors’ note: A related article appears on page 706.
Correspondence: Lyndsey Darrow, Department of Environmental and Occupational Health, 1518 Clifton Road NE, Atlanta, Georgia 30322. E-mail: firstname.lastname@example.org.