title: Methodological challenges when estimating the effects of season and seasonal exposures on birth outcomes.
abstract: BACKGROUND
  Many previous studies have found seasonal patterns in birth outcomes, but with little agreement about which season poses the highest risk. Some of the heterogeneity between studies may be explained by a previously unknown bias. The bias occurs in retrospective cohorts which include all births occurring within a fixed start and end date, which means shorter pregnancies are missed at the start of the study, and longer pregnancies are missed at the end. Our objective was to show the potential size of this bias and how to avoid it.
  
  
  METHODS
  To demonstrate the bias we simulated a retrospective birth cohort with no seasonal pattern in gestation and used a range of cohort end dates. As a real example, we used a cohort of 114,063 singleton births in Brisbane between 1 July 2005 and 30 June 2009 and examined the bias when estimating changes in gestation length associated with season (using month of conception) and a seasonal exposure (temperature). We used survival analyses with temperature as a time-dependent variable.
  
  
  RESULTS
  We found strong artificial seasonal patterns in gestation length by month of conception, which depended on the end date of the study. The bias was avoided when the day and month of the start date was just before the day and month of the end date (regardless of year), so that the longer gestations at the start of the study were balanced by the shorter gestations at the end. After removing the fixed cohort bias there was a noticeable change in the effect of temperature on gestation length. The adjusted hazard ratios were flatter at the extremes of temperature but steeper between 15 and 25°C.
  
  
  CONCLUSIONS
  Studies using retrospective birth cohorts should account for the fixed cohort bias by removing selected births to get unbiased estimates of seasonal health effects.
authors.affiliation: School of Public Health and Institute of Health and Biomedical Innovation, Queensland University of Technology, Australia.
authors.forename: Linn Beate
authors.initials: LB
authors.lastname: Strand
authors.forename: Adrian G
authors.initials: AG
authors.lastname: Barnett
authors.forename: Shilu
authors.initials: S
authors.lastname: Tong
mesh_terms: D015982:Bias; D003627:Data Interpretation, Statistical; D004781:Environmental Exposure / Q000009:adverse effects*; D005260:Female; D005865:Gestational Age; D006801:Humans; D007231:Infant, Newborn; D008297:Male; D008962:Models, Theoretical; D011247:Pregnancy; D011256:Pregnancy Outcome / Q000453:epidemiology*; D016016:Proportional Hazards Models; D012621:Seasons; D013696:Temperature