The study also indicated that asthma risk is greatest for babies born in July and October in the US.
Columbia University scientists developed a computational method to investigate the relationship between birth month and disease risk.
The researchers used this algorithm to examine New York City medical databases and found 55 diseases that correlated with the season of birth.
Researchers found that people born in May had the lowest disease risk, and those born in October the highest.
The researchers plan to replicate their study with data from several other locations in the US and abroad to see how results vary with the change of seasons and environmental factors in those places.
The study compared 1,688 diseases against the birth dates and medical histories of 1.7 million patients treated at NewYork-Presbyterian Hospital/CUMC between 1985 and 2013.
The study ruled out more than 1,600 associations and confirmed 39 links reported in the medical literature.
The researchers performed statistical tests to check that the 55 diseases for which they found associations did not arise by chance.
"It's important not to get overly nervous about these results because even though we found significant associations the overall disease risk is not that great," said Tatonetti.
"The risk related to birth month is relatively minor when compared to more influential variables like diet and exercise," Tatonetti said.
The new data are consistent with previous research on individual diseases. For example, the study authors found that asthma risk is greatest for July and October babies.
For ADHD, the Columbia data suggest that around one in 675 occurrences could relate to being born in New York in November. This result matches a Swedish study showing peak rates of ADHD in November babies.
The researchers also found a relationship between birth month and nine types of heart disease, with people born in March facing the highest risk for atrial fibrillation, congestive heart failure, and mitral valve disorder.
The study was published in the Journal of American Medical Informatics Association.
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