AIMS/HYPOTHESIS: Numerous epidemiological studies have shown differences in seasonality of birth patterns between the general population and the group who develop type 1 diabetes mellitus. This finding indicates that environmental factors operating during pre- and/or postnatal development could be aetiologically important. We examined whether the pattern of month of birth for type 1 diabetes patients in Ukraine differs from that for total live births. METHODS: Data consist of prevalent cases of type 1 diabetes in Ukraine by the end of 2003. Cases are restricted to persons born after 1 January 1960, diagnosed with type 1 diabetes before the age of 30 years (n = 20,117). People born during the same time in the general population (n = 29,105,560) were the reference standard. Seasonal patterns were estimated using logistic regression with harmonic terms. RESULTS: We found a strongly significant seasonal pattern of type 1 diabetes incidence rates (p < 0.001), with the lowest rates in December and the highest in April. The rate ratio between the extremes was 1.32 (95% CI 1.27-1.39). Tests for seasonal patterns in subgroups defined by sex and age or by sex and date of birth were all significant with p values less than 0.02. We found no interactions with sex (p = 0.142) or age at diagnosis (p = 0.207), but found a strong interaction with period of birth (p < 0.0001). CONCLUSIONS/ INTERPRETATION: The results obtained indicate that early-life factors linked to seasons may influence type 1 diabetes risk later in life.
AIMS/HYPOTHESIS: Numerous epidemiological studies have shown differences in seasonality of birth patterns between the general population and the group who develop type 1 diabetes mellitus. This finding indicates that environmental factors operating during pre- and/or postnatal development could be aetiologically important. We examined whether the pattern of month of birth for type 1 diabetespatients in Ukraine differs from that for total live births. METHODS: Data consist of prevalent cases of type 1 diabetes in Ukraine by the end of 2003. Cases are restricted to persons born after 1 January 1960, diagnosed with type 1 diabetes before the age of 30 years (n = 20,117). People born during the same time in the general population (n = 29,105,560) were the reference standard. Seasonal patterns were estimated using logistic regression with harmonic terms. RESULTS: We found a strongly significant seasonal pattern of type 1 diabetes incidence rates (p < 0.001), with the lowest rates in December and the highest in April. The rate ratio between the extremes was 1.32 (95% CI 1.27-1.39). Tests for seasonal patterns in subgroups defined by sex and age or by sex and date of birth were all significant with p values less than 0.02. We found no interactions with sex (p = 0.142) or age at diagnosis (p = 0.207), but found a strong interaction with period of birth (p < 0.0001). CONCLUSIONS/ INTERPRETATION: The results obtained indicate that early-life factors linked to seasons may influence type 1 diabetes risk later in life.
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