title: Hypothesis-Free Search for Connections between Birth Month and Disease Prevalence in Large, Geographically Varied Cohorts.
abstract: We have sought to replicate and extend the Season-wide Association Study (SeaWAS) of Boland, et al.1in identifying birth month-disease associations from electronic health records (EHRs). We used methodology similar to that implemented by Boland on three geographically distinct cohorts, for a total of 11.8 million individuals derived from multiple data sources. We were able to identify eleven out of sixteen literature-supported birth month associations as compared to seven of sixteen for SeaWAS. Of the nine novel cardiovascular birth month associations discovered by SeaWAS, we were able to replicate four. None of the novel non-cardiovascular associations discovered by SeaWAS emerged as significant relations in our study. We identified thirty birth month disease associations not previously reported; of those, only six associations were validated in more than one cohort. These results suggest that differences in cohort composition and location can cause consequential variation in results of hypothesis-free searches.
authors.affiliation: Explorys, an IBM Company, Cleveland, OH.
authors.forename: John P
authors.initials: JP
authors.lastname: Borsi
mesh_terms: D000465:Algorithms; D015988:Cohort Effect; D057225:Data Mining; D004194:Disease; D057286:Electronic Health Records; D004813:Epidemiology; D005260:Female; D062306:Geography, Medical; D006801:Humans; D008297:Male; D011247:Pregnancy; D015995:Prevalence; D012189:Retrospective Studies; D012621:Seasons