Abstract

ObjectiveSeveral groups have reported apparent association between month of birth and multiple sclerosis. We sought to test the extent to which such studies might be confounded by extraneous variables such as year and place of birth.MethodsUsing national birth statistics from 2 continents, we assessed the evidence for seasonal variations in birth rate and tested the extent to which these are subject to regional and temporal variation. We then established the age and regional origin distribution for a typical multiple sclerosis case collection and determined the false-positive rate expected when comparing such a collection with birth rates estimated by averaging population-specific national statistics.ResultsWe confirm that seasonality in birth rate is ubiquitous and subject to highly significant regional and temporal variations. In the context of this variation we show that birth rates observed in typical case collections are highly likely to deviate significantly from those obtained by the simple unweighted averaging of national statistics. The significant correlations between birth rates and both place (latitude) and time (year of birth) that characterize the general population indicate that the apparent seasonal patterns for month of birth suggested to be specific for multiple sclerosis (increased in the spring and reduced in the winter) are expected by chance alone.InterpretationIn the absence of adequate control for confounding factors, such as year and place of birth, our analyses indicate that the previous claims for association of multiple sclerosis with month of birth are probably false positives. ANN NEUROL 2013;73:714–720

Highlights

  • The significant correlations between birth rates and both place and time that characterize the general population indicate that the apparent seasonal patterns for month of birth suggested to be specific for multiple sclerosis are expected by chance alone

  • Considering the variation in birth rates seen for individual months and applying a Bonferroni correction, we found statistically significant evidence for seasonality, with excess births in either March, April, or May and=or reduced births in November, December, or January, mirroring the pattern described for multiple sclerosis,[13,14,15] in 97% of European and 48% of North American data sets

  • Testing and making compensation for structure has become an indispensable part of complex genetics but seems to have been largely ignored in efforts to explore the possible role of environmental factors in complex traits

Read more

Summary

Objective

Several groups have reported apparent association between month of birth and multiple sclerosis. We established the age and regional origin distribution for a typical multiple sclerosis case collection and determined the false-positive rate expected when comparing such a collection with birth rates estimated by averaging population-specific national statistics. The significant correlations between birth rates and both place (latitude) and time (year of birth) that characterize the general population indicate that the apparent seasonal patterns for month of birth suggested to be specific for multiple sclerosis (increased in the spring and reduced in the winter) are expected by chance alone. Bias may arise if the assumption of homogeneity is invalid, and subgroups that differ in the frequency of the candidate risk factor are concealed within the population from which cases and controls are drawn In this situation, differences in ascertainment may result in biased representation of the relevant subgroups among the samples of cases and controls, thereby creating an apparent difference in exposure and a falsepositive association. We review the evidence that month of birth varies significantly with geographical location[6,7,8] and over time[8,9,10,11] in the normal population and consider the implication of this variation for case–control studies considering month of birth as a risk factor for the development of multiple sclerosis

Subjects and Methods
Results
Discussion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call