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Using Population Health Measures to Evaluate the Environmental Burden of Cancer at the County Level
INTRODUCTION: Burden of disease is often defined by using epidemiologic measures. However, there may be latent aspects of disease burden that are not factored into these types of estimates. This study quantified environmental burden of disease by using population health indicators and exploratory fa...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Centers for Disease Control and Prevention
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6464050/ https://www.ncbi.nlm.nih.gov/pubmed/30974072 http://dx.doi.org/10.5888/pcd16.180530 |
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author | Scott, Lia C. Barker, Lawrence E. Richardson, Lisa C. |
author_facet | Scott, Lia C. Barker, Lawrence E. Richardson, Lisa C. |
author_sort | Scott, Lia C. |
collection | PubMed |
description | INTRODUCTION: Burden of disease is often defined by using epidemiologic measures. However, there may be latent aspects of disease burden that are not factored into these types of estimates. This study quantified environmental burden of disease by using population health indicators and exploratory factor analysis at the county level across the United States. METHODS: Ninety-nine variables drawn from public use data sets from 2010 to 2016 were used to create a multifactor index — the burden index. We applied principal components analysis with promax rotation to allow the factors to correlate. Correlation coefficients for each factor and the outcome of interest, age-adjusted cancer death rate, were calculated. We used both unadjusted and adjusted linear regression techniques. RESULTS: The final additive county-level index included 9 factors that explained 68.3% of the variance in the counties and county equivalents. The burden index had a moderate association with the age-adjusted cancer death rates (r =.48, P <.001), and adjusted linear regression with all 9 factors explained 34% of the variance in the age-adjusted cancer death rate. Results were mapped, and the geographic distribution of both the burden index and age-adjusted cancer mortality were assessed. There are distinct geospatial patterns for both. CONCLUSIONS: Results from this study show potential areas of need, as well as the importance of including environmental variables in the study of cancer etiology. Future studies can aim to validate these findings by quantifying burden as it relates to overall cancer mortality by using epidemiologic measures, along with other confirmatory statistical methods. |
format | Online Article Text |
id | pubmed-6464050 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Centers for Disease Control and Prevention |
record_format | MEDLINE/PubMed |
spelling | pubmed-64640502019-04-24 Using Population Health Measures to Evaluate the Environmental Burden of Cancer at the County Level Scott, Lia C. Barker, Lawrence E. Richardson, Lisa C. Prev Chronic Dis Original Research INTRODUCTION: Burden of disease is often defined by using epidemiologic measures. However, there may be latent aspects of disease burden that are not factored into these types of estimates. This study quantified environmental burden of disease by using population health indicators and exploratory factor analysis at the county level across the United States. METHODS: Ninety-nine variables drawn from public use data sets from 2010 to 2016 were used to create a multifactor index — the burden index. We applied principal components analysis with promax rotation to allow the factors to correlate. Correlation coefficients for each factor and the outcome of interest, age-adjusted cancer death rate, were calculated. We used both unadjusted and adjusted linear regression techniques. RESULTS: The final additive county-level index included 9 factors that explained 68.3% of the variance in the counties and county equivalents. The burden index had a moderate association with the age-adjusted cancer death rates (r =.48, P <.001), and adjusted linear regression with all 9 factors explained 34% of the variance in the age-adjusted cancer death rate. Results were mapped, and the geographic distribution of both the burden index and age-adjusted cancer mortality were assessed. There are distinct geospatial patterns for both. CONCLUSIONS: Results from this study show potential areas of need, as well as the importance of including environmental variables in the study of cancer etiology. Future studies can aim to validate these findings by quantifying burden as it relates to overall cancer mortality by using epidemiologic measures, along with other confirmatory statistical methods. Centers for Disease Control and Prevention 2019-04-11 /pmc/articles/PMC6464050/ /pubmed/30974072 http://dx.doi.org/10.5888/pcd16.180530 Text en https://creativecommons.org/licenses/by/4.0/This is a publication of the U.S. Government. This publication is in the public domain and is therefore without copyright. All text from this work may be reprinted freely. Use of these materials should be properly cited. |
spellingShingle | Original Research Scott, Lia C. Barker, Lawrence E. Richardson, Lisa C. Using Population Health Measures to Evaluate the Environmental Burden of Cancer at the County Level |
title | Using Population Health Measures to Evaluate the Environmental Burden of Cancer at the County Level |
title_full | Using Population Health Measures to Evaluate the Environmental Burden of Cancer at the County Level |
title_fullStr | Using Population Health Measures to Evaluate the Environmental Burden of Cancer at the County Level |
title_full_unstemmed | Using Population Health Measures to Evaluate the Environmental Burden of Cancer at the County Level |
title_short | Using Population Health Measures to Evaluate the Environmental Burden of Cancer at the County Level |
title_sort | using population health measures to evaluate the environmental burden of cancer at the county level |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6464050/ https://www.ncbi.nlm.nih.gov/pubmed/30974072 http://dx.doi.org/10.5888/pcd16.180530 |
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