Are transient environmental agents involved in the cause of primary biliary cirrhosis? Evidence from space-time clustering analysis

Hepatology. 2009 Oct;50(4):1169-74. doi: 10.1002/hep.23139.

Abstract

The cause of primary biliary cirrhosis (PBC) is unclear. Both genetic and environmental factors are likely to contribute. Some studies have suggested that one or more infectious agents may be involved. To examine whether infections may contribute to the cause of PBC, we have analyzed for space-time clustering using population-based data from northeast England over a defined period (1987-2003). Space-time clustering is observed when excess cases of a disease are found within limited geographical areas at limited periods of time. If present, it is suggestive of the involvement of one or more environmental components in the cause of a disease and is especially supportive of infections. A second-order procedure based on K-functions was used to test for global space-time clustering using residential addresses at the time of diagnosis. The Knox method determined the spatiotemporal range over which global clustering was strongest. K-function tests were repeated using nearest neighbor thresholds to adjust for variations in population density. Individual space-time clusters were identified using Kulldorff's scan statistic. Analysis of 1015 cases showed highly statistically significant space-time clustering (P < 0.001). Clustering was most marked for cases diagnosed within 1-4 months of one another. A number of specific space-time clusters were identified. In conclusion, these novel results suggest that transient environmental agents may play a role in the cause of PBC.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bacterial Infections / complications*
  • Data Interpretation, Statistical
  • England / epidemiology
  • Environment*
  • Female
  • Humans
  • Incidence
  • Liver Cirrhosis, Biliary / epidemiology
  • Liver Cirrhosis, Biliary / etiology*
  • Male
  • Retrospective Studies
  • Space-Time Clustering
  • Time Factors
  • Virus Diseases / complications*