Kevin A Henry1,2, Daniel Wiese3, Aniruddha Maiti4, Gerald Harris5,6,7, Slobodan Vucetic4, Antoinette M Stroup5,6,7. 1. Department of Geography and Urban Studies, Temple University, Philadelphia, PA, USA. khenry1@temple.edu. 2. Division of Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, PA, USA. khenry1@temple.edu. 3. Department of Geography and Urban Studies, Temple University, Philadelphia, PA, USA. 4. Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA. 5. Department of Health, New Jersey State Cancer Registry, Trenton, NJ, USA. 6. Rutgers Cancer Institute of New Jersey, Rutgers Biomedical and Health Sciences, New Brunswick, NJ, USA. 7. Department of Biostatitics and Epidemiology, Rutgers School of Public Health, Piscataway, NJ, USA.
Abstract
PURPOSE: Cutaneous T-cell lymphoma (CTCL) is a rare type of non-Hodgkin lymphoma. Previous studies have reported geographic clustering of CTCL based on the residence at the time of diagnosis. We explore geographic clustering of CTCL using both the residence at the time of diagnosis and past residences using data from the New Jersey State Cancer Registry. METHODS: CTCL cases (n = 1,163) diagnosed between 2006-2014 were matched to colon cancer controls (n = 17,049) on sex, age, race/ethnicity, and birth year. Jacquez's Q-Statistic was used to identify temporal clustering of cases compared to controls. Geographic clustering was assessed using the Bernoulli-based scan-statistic to compare cases to controls, and the Poisson-based scan-statisic to compare the observed number of cases to the number expected based on the general population. Significant clusters (p < 0.05) were mapped, and standard incidence ratios (SIR) reported. We adjusted for diagnosis year, sex, and age. RESULTS: The Q-statistic identified significant temporal clustering of cases based on past residences in the study area from 1992 to 2002. A cluster was detected in 1992 in Bergen County in northern New Jersey based on the Bernoulli (1992 SIR 1.84) and Poisson (1992 SIR 1.86) scan-statistics. Using the Poisson scan-statistic with the diagnosis location, we found evidence of an elevated risk in this same area, but the results were not statistically significant. CONCLUSION: There is evidence of geographic clustering of CTCL cases in New Jersey based on past residences. Additional studies are necessary to understand the possible reasons for the excess of CTCL cases living in this specific area some 8-14 years prior to diagnosis.
PURPOSE:Cutaneous T-cell lymphoma (CTCL) is a rare type of non-Hodgkin lymphoma. Previous studies have reported geographic clustering of CTCL based on the residence at the time of diagnosis. We explore geographic clustering of CTCL using both the residence at the time of diagnosis and past residences using data from the New Jersey State Cancer Registry. METHODS: CTCL cases (n = 1,163) diagnosed between 2006-2014 were matched to colon cancer controls (n = 17,049) on sex, age, race/ethnicity, and birth year. Jacquez's Q-Statistic was used to identify temporal clustering of cases compared to controls. Geographic clustering was assessed using the Bernoulli-based scan-statistic to compare cases to controls, and the Poisson-based scan-statisic to compare the observed number of cases to the number expected based on the general population. Significant clusters (p < 0.05) were mapped, and standard incidence ratios (SIR) reported. We adjusted for diagnosis year, sex, and age. RESULTS: The Q-statistic identified significant temporal clustering of cases based on past residences in the study area from 1992 to 2002. A cluster was detected in 1992 in Bergen County in northern New Jersey based on the Bernoulli (1992 SIR 1.84) and Poisson (1992 SIR 1.86) scan-statistics. Using the Poisson scan-statistic with the diagnosis location, we found evidence of an elevated risk in this same area, but the results were not statistically significant. CONCLUSION: There is evidence of geographic clustering of CTCL cases in New Jersey based on past residences. Additional studies are necessary to understand the possible reasons for the excess of CTCL cases living in this specific area some 8-14 years prior to diagnosis.
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