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Birth Month Affects Lifetime Disease Risk: A Phenome-Wide Method

Objective: An individual’s birth month has a significant impact on the diseases they develop during their lifetime. Previous studies reveal relationships between birth month and several diseases including atherothrombosis, asthma, attention deficit hyperactivity disorder, and myopia, leaving most diseases completely unexplored. This retrospective population study systematically explores the relationship between seasonal affects at birth and lifetime disease risk for 1688 conditions.

Methods: We developed a hypothesis-free method that minimizes publication and disease selection biases by systematically investigating disease-birth month patterns across all conditions. Our dataset includes 1 749 400 individuals with records at New York-Presbyterian/Columbia University Medical Center born between 1900 and 2000 inclusive. We modeled associations between birth month and 1688 diseases using logistic regression. Significance was tested using a chi-squared test with multiplicity correction.

Results: We found 55 diseases that were significantly dependent on birth month. Of these 19 were previously reported in the literature (P < .001), 20 were for conditions with close relationships to those reported, and 16 were previously unreported. We found distinct incidence patterns across disease categories.
Conclusions; Lifetime disease risk is affected by birth month. Seasonally dependent early developmental mechanisms may play a role in increasing lifetime risk of disease.

Editor-in-Chief: Lucila Ohno-Machado, MD, PhD
Mary Regina Boland , Zachary Shahn , David Madigan , George Hripcsak , Nicholas P. Tatonetti
the full paper:
Oxford University Press - jamia.oxfordjournals.org
First published online: 3 June 2015

INTRODUCTION
Hippocrates described a connection between seasonality and disease nearly 2500 years ago, “for knowing the changes of the seasons … how each of them takes place, he [the clinician] will be able to know beforehand what sort of a year is going to ensue … for with the seasons the digestive organs of men undergo a change.”1 Following in footsteps laid more than 2 millennia ago, recent studies have linked birth month with neurological,2–4 reproductive,5–9 endocrine10 and immune/inflammatory disorders,11 and overall lifespan.12

Many disease-dependent mechanisms exist relating disease-risk to birth month. For example, evidence linking a subtype of asthma to birth month was presented in 1983.13 They found that individuals born in seasons with more abundant home dust mites had a 40% increased risk of developing asthma complicated by dust mite allergies. Their finding was corroborated later when it was found that sensitization to allergens during infancy increases lifetime risk of developing allergies.14 In addition, some neurological conditions may be associated with birth month because of seasonal variations in vitamin D and thymic output.15 Understanding disease birth month dependencies is challenging because of the diversity of seasonal affects and connections to disease-risk.

The recent adoption of electronic health records (EHRs) allows meaningful use16 of data recorded during the clinical encounter for high-throughput exploratory analyses.17,18 Using EHR data requires overcoming problems with definition discrepancies,19 data sparseness, data quality,20 bias,21 healthcare process effects,22 and privacy issues.23 Informatics methods overcome these challenges, e.g., standardized ontologies minimize definition discrepancies,24 concordance measured across integrated datasets allows for data sparseness and quality assessment,20 and statistical methods can minimize bias and healthcare process effects.25–27 Using informatics approaches, EHR discovery methods28 were developed with successful applications in diverse areas including: dentistry,29 genetics,30–32 and pharmacovigilance.33,34 Novel disease association patterns35,36 and seasonal dependencies37–39 have also been established using EHRs.

Advances in health informatics coupled with the availability of large clinical databases enable systematic investigation of birth month-disease dependencies. All previous disease-birth month association studies were hypothesis-driven and focused on popular diseases leaving rare diseases unstudied (selection bias). Also, in the literature there is a propensity to publish studies that find an association over those that fail to find a relationship, illustrating publication bias.26,27,40,41 In contrast, we developed a high-throughput, hypothesis-free algorithm that mines for disease-birth month associations across millions of records. We call our approach: Season-Wide Association Study (SeaWAS) as it finds all conditions associated with birth month. We show that SeaWAS detects diseases with seasonal components related to early development.