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Individual- and county-level determinants of high breast cancer incidence rates

BACKGROUND: Age-adjusted breast cancer rates vary across and within states. However, most statistical models inherently identify either individual- or area-level determinants to explain geographic disparities in breast cancer rates and ignore the effects of the other level of determinants. We presen...

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Autores principales: Schootman, Mario, Ratnapradipa, Kendra, Loux, Travis, McVay, Allese, Su, L. Joseph, Nelson, Erik, Kadlubar, Susan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8799299/
https://www.ncbi.nlm.nih.gov/pubmed/35117111
http://dx.doi.org/10.21037/tcr.2019.06.08
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author Schootman, Mario
Ratnapradipa, Kendra
Loux, Travis
McVay, Allese
Su, L. Joseph
Nelson, Erik
Kadlubar, Susan
author_facet Schootman, Mario
Ratnapradipa, Kendra
Loux, Travis
McVay, Allese
Su, L. Joseph
Nelson, Erik
Kadlubar, Susan
author_sort Schootman, Mario
collection PubMed
description BACKGROUND: Age-adjusted breast cancer rates vary across and within states. However, most statistical models inherently identify either individual- or area-level determinants to explain geographic disparities in breast cancer rates and ignore the effects of the other level of determinants. We present a micro-macro modelling approach that incorporates both levels of determinants to better explain this variability and to discover opportunities to reduce breast cancer rates. METHODS: Individual-level data about breast cancer risk factors from eligible Arkansas Rural Community Health (ARCH) study participants (n=13,554) was supplemented with publicly available county-level data using a novel micro-macro statistical approach. This model uses individual-level data to account for aggregation-induced biases, to predict county-level breast cancer incidence rates across Arkansas. RESULTS: County-level breast cancer incidence rates ranged from 80.9 to 161.6 per 100,000 population. The best-fit model, which included individual-level predicted risk based on the Gail/CARE models, county-level population density (log transformed), and lead exposure (log transformed), explained 14.1% of the county variance. CONCLUSIONS: Our results support theoretical models that maintain that area-level determinants of breast cancer incidence are key risk factors in addition to established individual risks.
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spelling pubmed-87992992022-02-02 Individual- and county-level determinants of high breast cancer incidence rates Schootman, Mario Ratnapradipa, Kendra Loux, Travis McVay, Allese Su, L. Joseph Nelson, Erik Kadlubar, Susan Transl Cancer Res Original Article BACKGROUND: Age-adjusted breast cancer rates vary across and within states. However, most statistical models inherently identify either individual- or area-level determinants to explain geographic disparities in breast cancer rates and ignore the effects of the other level of determinants. We present a micro-macro modelling approach that incorporates both levels of determinants to better explain this variability and to discover opportunities to reduce breast cancer rates. METHODS: Individual-level data about breast cancer risk factors from eligible Arkansas Rural Community Health (ARCH) study participants (n=13,554) was supplemented with publicly available county-level data using a novel micro-macro statistical approach. This model uses individual-level data to account for aggregation-induced biases, to predict county-level breast cancer incidence rates across Arkansas. RESULTS: County-level breast cancer incidence rates ranged from 80.9 to 161.6 per 100,000 population. The best-fit model, which included individual-level predicted risk based on the Gail/CARE models, county-level population density (log transformed), and lead exposure (log transformed), explained 14.1% of the county variance. CONCLUSIONS: Our results support theoretical models that maintain that area-level determinants of breast cancer incidence are key risk factors in addition to established individual risks. AME Publishing Company 2019-07 /pmc/articles/PMC8799299/ /pubmed/35117111 http://dx.doi.org/10.21037/tcr.2019.06.08 Text en 2019 Translational Cancer Research. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
spellingShingle Original Article
Schootman, Mario
Ratnapradipa, Kendra
Loux, Travis
McVay, Allese
Su, L. Joseph
Nelson, Erik
Kadlubar, Susan
Individual- and county-level determinants of high breast cancer incidence rates
title Individual- and county-level determinants of high breast cancer incidence rates
title_full Individual- and county-level determinants of high breast cancer incidence rates
title_fullStr Individual- and county-level determinants of high breast cancer incidence rates
title_full_unstemmed Individual- and county-level determinants of high breast cancer incidence rates
title_short Individual- and county-level determinants of high breast cancer incidence rates
title_sort individual- and county-level determinants of high breast cancer incidence rates
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8799299/
https://www.ncbi.nlm.nih.gov/pubmed/35117111
http://dx.doi.org/10.21037/tcr.2019.06.08
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