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Methodological challenges when estimating the effects of season and seasonal exposures on birth outcomes.

Strand LB, Barnett AG, Tong S - BMC Med Res Methodol (2011)

Bottom Line: Many previous studies have found seasonal patterns in birth outcomes, but with little agreement about which season poses the highest risk.We found strong artificial seasonal patterns in gestation length by month of conception, which depended on the end date of the study.The adjusted hazard ratios were flatter at the extremes of temperature but steeper between 15 and 25°C.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Public Health and Institute of Health and Biomedical Innovation, Queensland University of Technology, Australia.

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.

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Related in: MedlinePlus

Estimated seasonal pattern in gestation length by conception month for the Brisbane cohort. Each panel shows the estimated seasonal pattern for a different end date. The black lines show the estimated seasonal pattern ignoring the fixed cohort bias. The grey lines show the estimated seasonal pattern after adjusting for the fixed cohort bias. Mean hazard ratios (dots) and 95% confidence intervals (vertical lines).
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Figure 4: Estimated seasonal pattern in gestation length by conception month for the Brisbane cohort. Each panel shows the estimated seasonal pattern for a different end date. The black lines show the estimated seasonal pattern ignoring the fixed cohort bias. The grey lines show the estimated seasonal pattern after adjusting for the fixed cohort bias. Mean hazard ratios (dots) and 95% confidence intervals (vertical lines).

Mentions: We further illustrate the fixed cohort bias using the Brisbane cohort in Figure 4. The estimated seasonal pattern in gestation length was strongly dependent on the end date of the study. For an end date of September 2008 the hazard ratios for many conception months were significantly lower than January (e.g., the hazard ratio for June relative to January was 0.95, 95% CI: 0.92, 0.98) meaning that gestations were longer for June conceptions. Conversely for an end date of March 2009 the hazard ratio for June relative to January was 1.04, 95% CI: 1.01, 1.07, meaning that gestations were shorter for June conceptions. After adjusting the cohort by removing births conceived more than 19 weeks before the cohort started, and less than 43 weeks before the cohort ended, the estimated seasonal pattern remained almost the same regardless of the study end date. The estimated seasonal pattern using the adjusted cohort was very close to that shown for an end date of June 2009, an end date which avoids the fixed cohort bias for this analysis because the end day and month are just before the start day and month (regardless of year).


Methodological challenges when estimating the effects of season and seasonal exposures on birth outcomes.

Strand LB, Barnett AG, Tong S - BMC Med Res Methodol (2011)

Estimated seasonal pattern in gestation length by conception month for the Brisbane cohort. Each panel shows the estimated seasonal pattern for a different end date. The black lines show the estimated seasonal pattern ignoring the fixed cohort bias. The grey lines show the estimated seasonal pattern after adjusting for the fixed cohort bias. Mean hazard ratios (dots) and 95% confidence intervals (vertical lines).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3102035&req=5

Figure 4: Estimated seasonal pattern in gestation length by conception month for the Brisbane cohort. Each panel shows the estimated seasonal pattern for a different end date. The black lines show the estimated seasonal pattern ignoring the fixed cohort bias. The grey lines show the estimated seasonal pattern after adjusting for the fixed cohort bias. Mean hazard ratios (dots) and 95% confidence intervals (vertical lines).
Mentions: We further illustrate the fixed cohort bias using the Brisbane cohort in Figure 4. The estimated seasonal pattern in gestation length was strongly dependent on the end date of the study. For an end date of September 2008 the hazard ratios for many conception months were significantly lower than January (e.g., the hazard ratio for June relative to January was 0.95, 95% CI: 0.92, 0.98) meaning that gestations were longer for June conceptions. Conversely for an end date of March 2009 the hazard ratio for June relative to January was 1.04, 95% CI: 1.01, 1.07, meaning that gestations were shorter for June conceptions. After adjusting the cohort by removing births conceived more than 19 weeks before the cohort started, and less than 43 weeks before the cohort ended, the estimated seasonal pattern remained almost the same regardless of the study end date. The estimated seasonal pattern using the adjusted cohort was very close to that shown for an end date of June 2009, an end date which avoids the fixed cohort bias for this analysis because the end day and month are just before the start day and month (regardless of year).

Bottom Line: Many previous studies have found seasonal patterns in birth outcomes, but with little agreement about which season poses the highest risk.We found strong artificial seasonal patterns in gestation length by month of conception, which depended on the end date of the study.The adjusted hazard ratios were flatter at the extremes of temperature but steeper between 15 and 25°C.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Public Health and Institute of Health and Biomedical Innovation, Queensland University of Technology, Australia.

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.

Show MeSH
Related in: MedlinePlus