Seasonality of birth and implications for temporal studies of preterm birth

Abstract

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.

FIGURE 1

Average number of births per day by study month in the 20-county metropolitan Atlanta birth cohort, 1994–2004

FIGURE 2

Seasonality of A. births and B. estimated conceptions in the 20-county metropolitan Atlanta 1994–2004 birth cohort: average observed-to-expected counts per day by calendar month and monthly observed-to-expected counts per day for individual study years.

FIGURE 3

Model-based estimates of observed-to-expected births per day by calendar month, stratified by sociodemographic characteristics for the 20-county metropolitan Atlanta 1994–2004 birth cohort.

FIGURE 4

Week-specific conditional probabilities of birth for gestational weeks 20–36 for births in the 20-county metropolitan Atlanta 1994–2004 birth cohort (n=715,875), e.g., probability of birth during week 21 = p [21 weeks ≤ birth < 22 weeks | birth ≥ 21 weeks].

FIGURE 5

Proportion of all ongoing gestations 20–36 weeks that are in the 36th week, averaged by calendar month and rate ratios of preterm birth comparing expected rates in each calendar month relative to expected rates in May, based solely on the gestational age distribution of the risk set.

FIGURE 6

Proportion of conceptions that are non-Hispanic black, averaged by calendar month and rate ratios of preterm birth comparing expected rates of preterm birth for fetuses conceived in each month relative to expected rates for fetuses conceived in July, based solely on the racial composition of the risk set.