Time- and Region-Specific Season of Birth Effects in Multiple Sclerosis in the United Kingdom.

IMPORTANCE

The reports of seasonal variation in the births of people who later develop multiple sclerosis (MS) have been challenged and attributed to the background pattern in the general population, resulting in a false association.

OBJECTIVE

To study the seasonality of MS births after adjusting for temporal and regional confounding factors.

DESIGN, SETTING, AND PARTICIPANTS: A study was conducted using case-control data from 8 MS-specialized centers from the United Kingdom, MS cases from a population-based study in the Lothian and Border regions of Scotland, and death records from the UK Registrar General. Participants included 21 138 patients with MS and control data from the UK Office of National Statistics and the UK government office regions. The seasonality of MS births was evaluated using the Walter and Elwood test, after adjusting for temporal and regional variations in the live births of the UK population. The study was conducted from January 16, 2014, to September 2, 2015.

MAIN OUTCOMES AND MEASURES

Diagnosis of multiple sclerosis.

RESULTS

Analysis of the general population indicated that seasonal differences are present across time and region in the United Kingdom, with both factors contributing to the monthly distribution of live births. We were able to demonstrate that, when adjusting for the temporal and regional variations in the live births of the UK population, there was a significant season of birth effect in patients with MS, with an increased risk of disease in the peak month (April) compared with the trough month (November) (odds ratio, 1.24; 95% CI, 1.10-1.41) and 15.68% fewer people who developed MS being born in November (observed to expected birth ratio, 0.840; 95% CI, 0.76-0.92).

CONCLUSIONS AND RELEVANCE

Season of birth is a risk factor for MS in the United Kingdom and cannot be attributed to the background pattern in the general population. The reasons for the variations in birth rates in the general population are unclear, but not taking them into consideration could lead to false-positive associations.