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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2019
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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. |
format | Online Article Text |
id | pubmed-8799299 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
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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