Data-crunching scientists have run New York City medical databases through an algorithm to find that 55 diseases show a correlation with the season of birth, concluding that the worst time for offspring lifetime risks is a pregnancy that produces childbirth in October.

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Out of 1,688 conditions scanned, 55 were found to have dependence on the month of birth.

Researchers from Columbia University used a computational method to mine some 1,688 diseases for correlations with birth dates.

The medical histories scanned came from 1.7 million patients treated between 1985 and 2013 at New York-Presbyterian Hospital/Columbia University Medical Center (CUMC).

The results have been published in the Journal of the American Medical Informatics Association, and they reveal that the best chances against lifetime diseases are for babies born in May.

The study’s senior author says the data findings are important to understanding disease risks, but also need to be viewed with a sense of perspective.

“This data could help scientists uncover new disease risk factors,” said Nicholas Tatonetti, PhD, an assistant professor of biomedical informatics at CUMC.

He adds:

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.

The risk related to birth month is relatively minor when compared to more influential variables like diet and exercise.”

The high-throughput algorithm employed by the researchers was called SeaWAS, and provides a novel way of investigating birth-month dependencies across all diseases in a large electronic health records database.

What the method has uncovered, conclude the authors, “confirms many known connections between birth month and disease,” including the following conditions:

  • Reproductive performance (‘output’ in terms of numbers of babies born)
  • Attention deficit hyperactivity disorder (ADHD)
  • Asthma
  • Colitis
  • Eye conditions
  • Otitis media (ear infection)
  • Respiratory syncytial virus (RSV).

In addition to confirming those links, the scientists ruled out more than 1,600 associations and “discovered 16 associations with birth month that have never been explicitly studied previously.”

What does your birth month mean? A “data visualization map” can be used to find out the conditions that are at a slightly higher risk for a particular birth month. Dr. Tatonetti produced the calendar wheel at the website of the university’s Data Science Institute

About the 16 newly discovered links, the authors conclude: “Nine of these associations were related to cardiovascular conditions, strengthening the link between cardiac conditions, early development and vitamin D.”

This link showed that people born in March faced the highest risk for atrial fibrillation, congestive heart failure, and mitral valve disorder.

The seasonal effects on a March birth are estimated by the researchers to possibly influence one in every 40 cases of atrial fibrillation.

The findings add to a previous study analyzing patient records from Austria and Denmark, which found that babies born in the months March through June had higher rates of heart disease and shorter life spans.

Lead author and Columbia graduate student Mary Regina Boland says:

Faster computers and electronic health records are accelerating the pace of discovery. We are working to help doctors solve important clinical problems using this new wealth of data.”

On the links with asthma and ADHD, the new data are also consistent with previous research on these individual diseases.

The authors found that asthma risk is greatest for babies born in NYC in July or October, which mirrors the observation of earlier research finding that the peak risk in Denmark was in the equivalent 2 months when that country’s sunlight levels were similar.

For ADHD, the data from Columbia University suggest that around q in every 675 occurrences could relate to being born in New York City in November, a result that “matches a Swedish study showing peak rates of ADHD in November babies.”

The researchers have produced a video giving some of their key findings: