Socioeconomic conditions and type 1 diabetes in childhood in North Rhine-Westphalia, Germany.

AIMS/HYPOTHESIS: The aim of the study was to investigate the relationships between childhood type 1 diabetes and socioeconomic conditions, which might provide clues to the aetiology of the disease.

MATERIALS AND METHODS

In an ecological study, we investigated the relationships between socioeconomic conditions and the incidence of type 1 diabetes incidence among children aged 0-14 years in North Rhine-Westphalia (NRW), Germany, between 1996 and 2000 at the level of the 33 districts. Incidence data were obtained from the population-based NRW diabetes register and regional socioeconomic data from official statistics. Associations were assessed by Poisson regression models and Bayesian conditionally autoregressive regression models (CAR).

RESULTS

In simple Poisson regression, population density, proportion of non-German nationals in the population, measures of income, education and professional training, and deprivation scores were significantly associated with diabetes risk (p < 0.01). An increase of about one interquartile range (IQR) in population density, proportion of non-German nationals or household income was associated with a 9-12% decrease in diabetes incidence. A rise of about one IQR in income ratio, measures of education and professional training, or in deprivation score (high values correspond to high deprivation) was associated with an 8-12% incidence increase. There was a significantly 'linear' increasing incidence trend across five deprivation classes (relative risk: 1.06; 95% CI: 1.03-1.09). All associations were confirmed when overdispersion and spatial autocorrelation were accounted for in Poisson and CAR models.

CONCLUSIONS/INTERPRETATIONS: The results raise the possibility that the risk for type 1 diabetes is higher for children living in socially deprived and less densely populated areas. Subsequent investigations are necessary to verify the observed ecological relations at the individual level and to identify the causal factors behind the socioeconomic indicators.