Spatial analysis of bladder, kidney, and pancreatic cancer on upper Cape Cod: an application of generalized additive models to case-control data

doi:10.1186/1476-069X-8-3

Environmental Health

Volume 8

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Research

Spatial analysis of bladder, kidney, and pancreatic cancer on upper Cape Cod: an application of generalized additive models to case-control data

Verónica Vieira¹ , Thomas Webster¹ , Janice Weinberg² and Ann Aschengrau³

¹Department of Environmental Health, Boston University School of Public Health, 715 Albany Street, Boston, MA 02118, USA

²Department of Biostatistics, Boston University School of Public Health, 715 Albany Street, Boston, MA 02118, USA

³Department of Epidemiology, Boston University School of Public Health, 715 Albany Street, Boston, MA 02118, USA

author email corresponding author email

Environmental Health 2009, 8:3doi:10.1186/1476-069X-8-3


Published:	10 February 2009

Abstract

Background

In 1988, elevated cancer incidence in upper Cape Cod, Massachusetts prompted a large epidemiological study of nine cancers to investigate possible environmental risk factors. Positive associations were observed, but explained only a portion of the excess cancer incidence. This case-control study provided detailed information on individual-level covariates and residential history that can be spatially analyzed using generalized additive models (GAMs) and geographical information systems (GIS).

Methods

We investigated the association between residence and bladder, kidney, and pancreatic cancer on upper Cape Cod. We estimated adjusted odds ratios using GAMs, smoothing on location. A 40-year residential history allowed for latency restrictions. We mapped spatially continuous odds ratios using GIS and identified statistically significant clusters using permutation tests.

Results

Maps of bladder cancer are essentially flat ignoring latency, but show a statistically significant hot spot near known Massachusetts Military Reservation (MMR) groundwater plumes when 15 years latency is assumed. The kidney cancer map shows significantly increased ORs in the south of the study area and decreased ORs in the north.

Conclusion

Spatial epidemiology using individual level data from population-based studies addresses many methodological criticisms of cluster studies and generates new exposure hypotheses. Our results provide evidence for spatial clustering of bladder cancer near MMR plumes that suggest further investigation using detailed exposure modeling.