Evidence map›Paper›PMID 34117957›Full record

ArticleCancer causes & control : CCC2021

Geographic clustering of cutaneous T-cell lymphoma in New Jersey: an exploratory analysis using residential histories.

Kevin A Henry, Daniel Wiese, Aniruddha Maiti, Gerald Harris, Slobodan Vucetic, Antoinette M Stroup

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Article in Cancer causes & control : CCC, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Kevin A HenryDepartment of Geography and Urban Studies, Temple University, Philadelphia, PA, USA. khenry1@temple.edu.
Daniel WieseDepartment of Geography and Urban Studies, Temple University, Philadelphia, PA, USA.
Aniruddha MaitiDepartment of Computer and Information Sciences, Temple University, Philadelphia, PA, USA.
Gerald HarrisDepartment of Health, New Jersey State Cancer Registry, Trenton, NJ, USA.
Slobodan VuceticDepartment of Computer and Information Sciences, Temple University, Philadelphia, PA, USA.
Antoinette M StroupDepartment of Health, New Jersey State Cancer Registry, Trenton, NJ, USA.

Funding

CDC HHS 5U58DP003931-02/NU58DP006279-02-00National Science Foundation 1560888NCI NIH HHS HHSN261201300021I
6 · The paper itself

Abstract

purposeCutaneous 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.

methodsCTCL 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.

resultsThe 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.

conclusionThere 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.

Indexed as

Lymphoma, T-Cell, CutaneousSkin NeoplasmsCluster AnalysisHumansIncidenceNew JerseyCTCLGeographic clusteringGeographic information systemsLymphomaNon-hodgkinResidential histories

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.