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J Carcinog. 2011;10:22. doi: 10.4103/1477-3163.85184. Epub 2011 Sep 21.

Toward the identification of communities with increased tobacco-associated cancer burden: Application of spatial modeling techniques.

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1
Department of Epidemiology and Public Health, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine.

Abstract

INTRODUCTION:

Smoking-attributable risks for lung, esophageal, and head and neck (H/N) cancers range from 54% to 90%. Identifying areas with higher than average cancer risk and smoking rates, then targeting those areas for intervention, is one approach to more rapidly lower the overall tobacco disease burden in a given state. Our research team used spatial modeling techniques to identify areas in Florida with higher than expected tobacco-associated cancer incidence clusters.

MATERIALS AND METHODS:

Geocoded tobacco-associated incident cancer data from 1998 to 2002 from the Florida Cancer Data System were used. Tobacco-associated cancers included lung, esophageal, and H/N cancers. SaTScan was used to identify geographic areas that had statistically significant (P<0.10) excess age-adjusted rates of tobacco-associated cancers. The Poisson-based spatial scan statistic was used. Phi correlation coefficients were computed to examine associations among block groups with/without overlapping cancer clusters. The logistic regression was used to assess associations between county-level smoking prevalence rates and being diagnosed within versus outside a cancer cluster. Community-level smoking rates were obtained from the 2002 Florida Behavioral Risk Factor Surveillance System (BRFSS). Analyses were repeated using 2007 BRFSS to examine the consistency of associations.

RESULTS:

Lung cancer clusters were geographically larger for both squamous cell and adenocarcinoma cases in Florida from 1998 to 2002, than esophageal or H/N clusters. There were very few squamous cell and adenocarcinoma esophageal cancer clusters. H/N cancer mapping showed some squamous cell and a very small amount of adenocarcinoma cancer clusters. Phi correlations were generally weak to moderate in strength. The odds of having an invasive lung cancer cluster increased by 12% per increase in the county-level smoking rate. Results were inconsistent for esophageal and H/N cancers, with some inverse associations. 2007 BRFSS data also showed a similar results pattern.

CONCLUSIONS:

Spatial analysis identified many nonoverlapping areas of high risk across both cancer and histological subtypes. Attempts to correlate county-level smoking rates with cancer cluster membership yielded consistent results only for lung cancer. However, spatial analyses may be most useful when examining incident clusters where several tobacco-associated cancer clusters overlap. Focusing on overlapping cancer clusters may help investigators identify priority areas for further screening, detailed assessments of tobacco use, and/or prevention and cessation interventions to decrease risk.

KEYWORDS:

Cancer cluster; cancer; spatial analysis; tobacco use

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