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Open Access Research

Spatial-temporal analysis of non-Hodgkin lymphoma in the NCI-SEER NHL case-control study

David C Wheeler1*, Anneclaire J De Roos2, James R Cerhan3, Lindsay M Morton4, Richard Severson5, Wendy Cozen6 and Mary H Ward1

Author Affiliations

1 Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute (NCI), National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Bethesda, MD, USA

2 Fred Hutchinson Cancer Research Center and University of Washington, Seattle, WA

3 Mayo Clinic College of Medicine, Rochester, MN, USA

4 Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, NCI, NIH, DHHS, Bethesda, MD, USA

5 Department of Family Medicine and Karmanos Cancer Institute, Wayne State University, Detroit, MI, USA

6 Department of Preventive Medicine and Pathology, and Norris Comprehensive Cancer Center, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

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Environmental Health 2011, 10:63  doi:10.1186/1476-069X-10-63

Published: 30 June 2011

Abstract

Background

Exploring spatial-temporal patterns of disease incidence through cluster analysis identifies areas of significantly elevated or decreased risk, providing potential clues about disease risk factors. Little is known about the etiology of non-Hodgkin lymphoma (NHL), or the latency period that might be relevant for environmental exposures, and there are no published spatial-temporal cluster studies of NHL.

Methods

We conducted a population-based case-control study of NHL in four National Cancer Institute (NCI)-Surveillance, Epidemiology, and End Results (SEER) centers: Detroit, Iowa, Los Angeles, and Seattle during 1998-2000. Using 20-year residential histories, we used generalized additive models adjusted for known risk factors to model spatially the probability that an individual had NHL and to identify clusters of elevated or decreased NHL risk. We evaluated models at five different time periods to explore the presence of clusters in a time frame of etiologic relevance.

Results

The best model fit was for residential locations 20 years prior to diagnosis in Detroit, Iowa, and Los Angeles. We found statistically significant areas of elevated risk of NHL in three of the four study areas (Detroit, Iowa, and Los Angeles) at a lag time of 20 years. The two areas of significantly elevated risk in the Los Angeles study area were detected only at a time lag of 20 years. Clusters in Detroit and Iowa were detected at several time points.

Conclusions

We found significant spatial clusters of NHL after allowing for disease latency and residential mobility. Our results show the importance of evaluating residential histories when studying spatial patterns of cancer.