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

BackgroundExploring 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.MethodsWe 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.ResultsThe 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.ConclusionsWe 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.

[1]  R. Tibshirani,et al.  Generalized additive models for medical research , 1986, Statistical methods in medical research.

[2]  R. Hoover,et al.  Developing clues to environmental cancer: a stepwise approach with the use of cancer mortality data. , 1979, Environmental health perspectives.

[3]  C. Wild.  Complementing the Genome with an “Exposome”: The Outstanding Challenge of Environmental Exposure Measurement in Molecular Epidemiology , 2005, Cancer Epidemiology Biomarkers & Prevention.

[4]  J. Cerhan,et al.  Persistent organochlorine chemicals in plasma and risk of non-Hodgkin's lymphoma. , 2005, Cancer research.

[5]  Lance A. Waller,et al.  Detection of Clustering in Spatial Data , 2006 .

[6]  W. Cleveland,et al.  Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting , 1988 .

[7]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[8]  Geoffrey M Jacquez,et al.  Current practices in the spatial analysis of cancer: flies in the ointment , 2004, International journal of health geographics.

[9]  E. Barrett-Connor,et al.  The effect of response bias on the odds ratio. , 1981, American journal of epidemiology.

[10]  J. Cerhan,et al.  Residential proximity to industrial facilities and risk of non-Hodgkin lymphoma. , 2008, Environmental research.

[11]  Alan Y. Chiang,et al.  Generalized Additive Models: An Introduction With R , 2007, Technometrics.

[12]  Pierre Goovaerts,et al.  Global, local and focused geographic clustering for case-control data with residential histories , 2005, Environmental health : a global access science source.

[13]  S. Wood.  Generalized Additive Models: An Introduction with R , 2006 .

[14]  J. Kaldor,et al.  Original Contribution Occupational Exposure to Pesticides and Risk of Non-Hodgkin’s Lymphoma , 2005 .

[15]  Lance A Waller,et al.  The geography of power: statistical performance of tests of clusters and clustering in heterogeneous populations , 2006, Statistics in medicine.

[16]  C. Skibola,et al.  Genetic susceptibility to lymphoma. , 2007, Haematologica.

[17]  Patrick Arveux,et al.  Dioxin Emissions from a Solid Waste Incinerator and Risk of Non-Hodgkin Lymphoma , 2003, Epidemiology.

[18]  S. Khuder,et al.  Meta-analyses of non-Hodgkin's lymphoma and farming. , 1998, Scandinavian journal of work, environment & health.

[19]  W. Cleveland.  Robust Locally Weighted Regression and Smoothing Scatterplots , 1979 .

[20]  T. Mack.  Cancers in the urban environment : patterns of malignant disease in Los Angeles County and its neighborhoods , 2004 .

[21]  J. Ferlay,et al.  Global Cancer Statistics, 2002 , 2005, CA: a cancer journal for clinicians.

[22]  C. Vionnet.  [Environmental health]. , 1983, Krankenpflege. Soins infirmiers.

[23]  D. Weisenburger,et al.  A Case‐Control Study of Non‐Hodgkin's Lymphoma and the Herbicide 2,4‐Dichlorophenoxyacetic Acid (2, 4‐D) in Eastern Nebraska , 1990, Epidemiology.

[24]  J. Cerhan,et al.  Risk of non-Hodgkin's lymphoma and family history of lymphatic, hematologic, and other cancers. , 2004, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology.

[25]  Thomas Webster,et al.  Method for mapping population-based case-control studies: an application using generalized additive models , 2006, International journal of health geographics.

[26]  V. Novack,et al.  Non-Hodgkin's lymphoma and residential proximity to toxic industrial waste in southern Israel. , 2005, Haematologica.

[27]  J. Cerhan,et al.  Etiologic heterogeneity among non-Hodgkin lymphoma subtypes. , 2008, Blood.

[28]  L. Waller,et al.  Applied Spatial Statistics for Public Health Data , 2004 .

[29]  Al Ozonoff,et al.  Generalized additive models and inflated type I error rates of smoother significance tests , 2011, Comput. Stat. Data Anal..

[30]  P. Diggle,et al.  Non-parametric estimation of spatial variation in relative risk. , 1995, Statistics in medicine.

[31]  D. Wheeler.  A comparison of spatial clustering and cluster detection techniques for childhood leukemia incidence in Ohio, 1996 – 2003 , 2007, International journal of health geographics.

[32]  P. Rogerson,et al.  The Sage handbook of spatial analysis , 2009 .

[33]  J. Cerhan,et al.  Census and geographic differences between respondents and nonrespondents in a case-control study of non-Hodgkin lymphoma. , 2007, American journal of epidemiology.

[34]  P. Diggle,et al.  Spatial variation in risk of disease: a nonparametric binary regression approach , 2002 .

[35]  N. Dubrawsky.  Cancer statistics , 1989, CA: a cancer journal for clinicians.

[36]  L. Waller,et al.  Applied Spatial Statistics for Public Health Data: Waller/Applied Spatial Statistics , 2004 .

[37]  J. Cerhan,et al.  Organochlorines in Carpet Dust and Non-Hodgkin Lymphoma , 2005, Epidemiology.

[38]  M. Goldberg,et al.  Risks of developing cancer relative to living near a municipal solid waste landfill site in Montreal, Quebec, Canada. , 1999, Archives of environmental health.

[39]  Julian Besag,et al.  The Detection of Clusters in Rare Diseases , 1991 .

[40]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[41]  J F Bithell,et al.  Distribution of childhood leukaemias and non-Hodgkin's lymphomas near nuclear installations in England and Wales , 1994, BMJ.

[42]  J. Cerhan,et al.  Tumor necrosis factor (TNF) and lymphotoxin-alpha (LTA) polymorphisms and risk of non-Hodgkin lymphoma in the InterLymph Consortium. , 2010, American journal of epidemiology.

[43]  A. Jemal,et al.  Global Cancer Statistics , 2011 .

[44]  P. Diggle,et al.  Kernel estimation of relative risk , 1995 .

[45]  David Ozonoff,et al.  Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: An application of generalized additive models to case-control data , 2005, Environmental health : a global access science source.

[46]  A. Jemal,et al.  Global cancer statistics , 2011, CA: a cancer journal for clinicians.

[47]  A. Jemal,et al.  Cancer Statistics, 2004 , 2004, CA: a cancer journal for clinicians.

[48]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[49]  J. Weinberg,et al.  Spatial analysis of bladder, kidney, and pancreatic cancer on upper Cape Cod: an application of generalized additive models to case-control data , 2009, Environmental health : a global access science source.