Neighborhood conditions and celiac disease risk among children in Sweden

Scand J Public Health. 2014 Nov;42(7):572-80. doi: 10.1177/1403494814550173. Epub 2014 Sep 23.


Aim: To investigate celiac disease (CD) clustering at different geographical levels and to examine the association between neighborhood demographic and socioeconomic conditions and the risk of neighborhood CD.

Methods: We included 2080 children diagnosed with CD between 1998 and 2003, identified from 43 of the 47 reporting hospitals in Sweden. A total of 8036 small area market statistics (SAMS) areas were included; these were nested in 253 municipalities that were further nested into eight 'nomenclature of territorial units for statistics' (NUTS) 2 regions. We performed multilevel logistic regression analyses.

Results: We found the highest geographical variation in CD incidence at the municipality level, compared to the region level. The probability of having CD increased in the statistical areas of (SAMS) areas with higher average annual work income, with an odds ratio (OR) of 2.24 and 95% CI of 1.76-2.85. Reduced CD risk in neighborhoods was associated with higher average age (OR 0.96; 95% CI 0.95-0.97), higher proportion of residents with a university education (OR 0.98; 95% CI 0.97-0.99), and higher level of industrial and commercial activity (OR 0.59; 95% CI 0.44-0.82). We found no significant association between CD risk and population density, proportion of Nordic to non-Nordic inhabitants, nor share of the population with only a compulsory education.

Conclusions: Neighborhood composition influences cd risk this is one of the first attempts to identify factors explaining geographical variation in CD.

Keywords: Celiac disease; Sweden; demographics; education; environmental risk factors; geography; income; neighborhoods; socioeconomic conditions.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Celiac Disease / epidemiology*
  • Child
  • Child, Preschool
  • Cluster Analysis
  • Humans
  • Infant
  • Logistic Models
  • Multilevel Analysis
  • Registries
  • Residence Characteristics / statistics & numerical data*
  • Risk Factors
  • Socioeconomic Factors
  • Sweden / epidemiology