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

<p>Abstract</p> <p>Background</p> <p>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 b...

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Main Authors: Tong Shilu, Barnett Adrian G, Strand Linn
Format: Article
Language:English
Published: BMC 2011-04-01
Series:BMC Medical Research Methodology
Online Access:http://www.biomedcentral.com/1471-2288/11/49
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spelling doaj-9bffe81b88fa4e428bf511cdbe469e962020-11-24T21:39:43ZengBMCBMC Medical Research Methodology1471-22882011-04-011114910.1186/1471-2288-11-49Methodological challenges when estimating the effects of season and seasonal exposures on birth outcomesTong ShiluBarnett Adrian GStrand Linn<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>Studies using retrospective birth cohorts should account for the fixed cohort bias by removing selected births to get unbiased estimates of seasonal health effects.</p> http://www.biomedcentral.com/1471-2288/11/49
collection DOAJ
language English
format Article
sources DOAJ
author Tong Shilu
Barnett Adrian G
Strand Linn
spellingShingle Tong Shilu
Barnett Adrian G
Strand Linn
Methodological challenges when estimating the effects of season and seasonal exposures on birth outcomes
BMC Medical Research Methodology
author_facet Tong Shilu
Barnett Adrian G
Strand Linn
author_sort Tong Shilu
title Methodological challenges when estimating the effects of season and seasonal exposures on birth outcomes
title_short Methodological challenges when estimating the effects of season and seasonal exposures on birth outcomes
title_full Methodological challenges when estimating the effects of season and seasonal exposures on birth outcomes
title_fullStr Methodological challenges when estimating the effects of season and seasonal exposures on birth outcomes
title_full_unstemmed Methodological challenges when estimating the effects of season and seasonal exposures on birth outcomes
title_sort methodological challenges when estimating the effects of season and seasonal exposures on birth outcomes
publisher BMC
series BMC Medical Research Methodology
issn 1471-2288
publishDate 2011-04-01
description <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>Studies using retrospective birth cohorts should account for the fixed cohort bias by removing selected births to get unbiased estimates of seasonal health effects.</p>
url http://www.biomedcentral.com/1471-2288/11/49
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AT strandlinn methodologicalchallengeswhenestimatingtheeffectsofseasonandseasonalexposuresonbirthoutcomes
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