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Cancer Incidence and Multilevel Measures of Residential Economic and Racial Segregation for Cancer Registries

BACKGROUND: The handful of studies (<30) on cancer and residential segregation have focused on racial segregation, primarily at the city/town level. We tested a priori hypotheses about choice of measure and level by extending use of the Index of Concentration at the Extremes (ICE) to quantify bot...

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Autores principales: Krieger, Nancy, Feldman, Justin M, Kim, Rockli, Waterman, Pamela D
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6649696/
https://www.ncbi.nlm.nih.gov/pubmed/31360840
http://dx.doi.org/10.1093/jncics/pky009
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author Krieger, Nancy
Feldman, Justin M
Kim, Rockli
Waterman, Pamela D
author_facet Krieger, Nancy
Feldman, Justin M
Kim, Rockli
Waterman, Pamela D
author_sort Krieger, Nancy
collection PubMed
description BACKGROUND: The handful of studies (<30) on cancer and residential segregation have focused on racial segregation, primarily at the city/town level. We tested a priori hypotheses about choice of measure and level by extending use of the Index of Concentration at the Extremes (ICE) to quantify both economic and racial residential segregation, singly and combined, and conducted analyses for the total population and stratified by race/ethnicity. METHODS: Outcomes comprised Massachusetts incidence rates (2010–2014) for invasive breast, cervical, and lung cancer, analyzed in relation to census tract and city/town ICE measures for income, race/ethnicity, race/ethnicity + income, and the federal poverty line. Multilevel Poisson regression modeled observed counts of incident cases. RESULTS: Both choice of metric and level mattered. As illustrated by cervical cancer, in models including both the census tract and city/town levels, the rate ratio for the worst to best quintile for the total population was greatest at the census tract level for the ICE for racialized economic segregation (3.0, 95% confidence interval [CI] = 2.1 to 4.3) and least for the poverty measure (1.9, 95% CI = 1.4 to 2.6), with null associations at the city/town level. In analogous models with both levels for lung cancer, however, for the non-Hispanic black and Hispanic populations, the rate ratios for, respectively, the ICE and poverty measures, were larger (and excluded 1) at the city/town compared with the census tract level. CONCLUSIONS: Our study suggests that the ICE for racialized economic segregation, at multiple levels, can be used to improve monitoring and analysis of cancer inequities.
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spelling pubmed-66496962019-07-29 Cancer Incidence and Multilevel Measures of Residential Economic and Racial Segregation for Cancer Registries Krieger, Nancy Feldman, Justin M Kim, Rockli Waterman, Pamela D JNCI Cancer Spectr Article BACKGROUND: The handful of studies (<30) on cancer and residential segregation have focused on racial segregation, primarily at the city/town level. We tested a priori hypotheses about choice of measure and level by extending use of the Index of Concentration at the Extremes (ICE) to quantify both economic and racial residential segregation, singly and combined, and conducted analyses for the total population and stratified by race/ethnicity. METHODS: Outcomes comprised Massachusetts incidence rates (2010–2014) for invasive breast, cervical, and lung cancer, analyzed in relation to census tract and city/town ICE measures for income, race/ethnicity, race/ethnicity + income, and the federal poverty line. Multilevel Poisson regression modeled observed counts of incident cases. RESULTS: Both choice of metric and level mattered. As illustrated by cervical cancer, in models including both the census tract and city/town levels, the rate ratio for the worst to best quintile for the total population was greatest at the census tract level for the ICE for racialized economic segregation (3.0, 95% confidence interval [CI] = 2.1 to 4.3) and least for the poverty measure (1.9, 95% CI = 1.4 to 2.6), with null associations at the city/town level. In analogous models with both levels for lung cancer, however, for the non-Hispanic black and Hispanic populations, the rate ratios for, respectively, the ICE and poverty measures, were larger (and excluded 1) at the city/town compared with the census tract level. CONCLUSIONS: Our study suggests that the ICE for racialized economic segregation, at multiple levels, can be used to improve monitoring and analysis of cancer inequities. Oxford University Press 2018-04-25 /pmc/articles/PMC6649696/ /pubmed/31360840 http://dx.doi.org/10.1093/jncics/pky009 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Article
Krieger, Nancy
Feldman, Justin M
Kim, Rockli
Waterman, Pamela D
Cancer Incidence and Multilevel Measures of Residential Economic and Racial Segregation for Cancer Registries
title Cancer Incidence and Multilevel Measures of Residential Economic and Racial Segregation for Cancer Registries
title_full Cancer Incidence and Multilevel Measures of Residential Economic and Racial Segregation for Cancer Registries
title_fullStr Cancer Incidence and Multilevel Measures of Residential Economic and Racial Segregation for Cancer Registries
title_full_unstemmed Cancer Incidence and Multilevel Measures of Residential Economic and Racial Segregation for Cancer Registries
title_short Cancer Incidence and Multilevel Measures of Residential Economic and Racial Segregation for Cancer Registries
title_sort cancer incidence and multilevel measures of residential economic and racial segregation for cancer registries
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6649696/
https://www.ncbi.nlm.nih.gov/pubmed/31360840
http://dx.doi.org/10.1093/jncics/pky009
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