Sociodemographic determinants of spatial disparities in early childhood caries: An ecological analysis in Braunschweig, Germany.

OBJECTIVES

To identify spatial disparities in dental caries experience (measured by dmft (decayed missing filled teeth) index) of children in the city of Braunschweig and to evaluate whether these disparities can be explained by sociodemographic characteristics.

METHODS

We examined the dental health of children aged 3-6 years visiting a daycare centre (DCC) in the metropolitan area of Braunschweig between 2009 and 2014 by combining data on dental health from the annual visits of the local health service with aggregated data on sociodemographic factors for Braunschweig's city districts. We assessed longitudinal patterns of change in average dmft index at district level from 2009 to 2014 using a finite mixture model. We analysed spatial autocorrelation of the district's average dmft indices by Moran's I to identify spatial clusters. With a spatial lag model, we evaluated whether sociodemographic risk factors (data from 2012) were associated with high dmft scores (data from 2014) and whether spatial disparities remained after adjusting for these sociodemographic characteristics.

RESULTS

The average dmft index decreased slightly (β=-0.048; P<.03; CI 95% [-0.079; -0.017]) from 2009 to 2014. The finite mixture model resulted in four different groups of trajectories over time. While three groups showed a decrease in dmft score, one group showed an increase from 2009 to 2014. Moran's I test statistic showed strong evidence for spatial clustering (Moran's I 0.30, P=.002). A cluster of districts with high dmft values was identified in the centre of the city. The spatial lag model showed that both the proportion of unemployed persons (aged 16-65) and the proportion of persons with migration background were associated with the dmft values at district level. After adjusting for these, no further spatial heterogeneity was observed.

CONCLUSION

We identified regional clusters for poor dental health in a German city and showed that these clusters can be explained by sociodemographic characteristics. The findings support the need of targeted interventions and prevention measures in regions with less favourable sociodemographic characteristics.