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Spatial Analysis of Breast Cancer Mortality Rates in a Rural State
INTRODUCTION: Breast cancer affects 1 in 8 women in the US and is the most frequently diagnosed cancer in women. In South Dakota, 102 women die from breast cancer each year. We assessed which sociodemographic factors contributed to mortality rates in South Dakota and used spatial analysis to investi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Centers for Disease Control and Prevention
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9616131/ https://www.ncbi.nlm.nih.gov/pubmed/36265079 http://dx.doi.org/10.5888/pcd19.220113 |
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author | Schulz, Marisa Spors, Emma Bates, Kari Michael, Semhar |
author_facet | Schulz, Marisa Spors, Emma Bates, Kari Michael, Semhar |
author_sort | Schulz, Marisa |
collection | PubMed |
description | INTRODUCTION: Breast cancer affects 1 in 8 women in the US and is the most frequently diagnosed cancer in women. In South Dakota, 102 women die from breast cancer each year. We assessed which sociodemographic factors contributed to mortality rates in South Dakota and used spatial analysis to investigate how counties’ observed age-adjusted mortality rates compared with expected rates. METHODS: We computed standardized incidence ratios (SIRs) of all counties in South Dakota by using the age-adjusted mortality rates, the 2000 US standard population, and the South Dakota estimated population. We used a linear regression model to identify sociodemographic factors associated with breast cancer mortality rates and to compute a new SIR value, after controlling for relevant factors. RESULTS: Educational level and breast cancer incidence rates were significantly associated with breast cancer mortality rates at the county level. The SIR values based on age-adjusted counts showed which counties had more deaths due to breast cancer than what might be expected using South Dakota as the reference population. After controlling for sociodemographic factors, the range of SIR values decreased and had lower variability. CONCLUSION: The regression model helped identify factors associated with mortality and provided insights into which risk factors are at play in South Dakota. This information, in combination with the spatial distribution of mortality by county, can be used to help allocate resources to the counties in South Dakota that need them most. |
format | Online Article Text |
id | pubmed-9616131 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Centers for Disease Control and Prevention |
record_format | MEDLINE/PubMed |
spelling | pubmed-96161312022-11-03 Spatial Analysis of Breast Cancer Mortality Rates in a Rural State Schulz, Marisa Spors, Emma Bates, Kari Michael, Semhar Prev Chronic Dis Original Research INTRODUCTION: Breast cancer affects 1 in 8 women in the US and is the most frequently diagnosed cancer in women. In South Dakota, 102 women die from breast cancer each year. We assessed which sociodemographic factors contributed to mortality rates in South Dakota and used spatial analysis to investigate how counties’ observed age-adjusted mortality rates compared with expected rates. METHODS: We computed standardized incidence ratios (SIRs) of all counties in South Dakota by using the age-adjusted mortality rates, the 2000 US standard population, and the South Dakota estimated population. We used a linear regression model to identify sociodemographic factors associated with breast cancer mortality rates and to compute a new SIR value, after controlling for relevant factors. RESULTS: Educational level and breast cancer incidence rates were significantly associated with breast cancer mortality rates at the county level. The SIR values based on age-adjusted counts showed which counties had more deaths due to breast cancer than what might be expected using South Dakota as the reference population. After controlling for sociodemographic factors, the range of SIR values decreased and had lower variability. CONCLUSION: The regression model helped identify factors associated with mortality and provided insights into which risk factors are at play in South Dakota. This information, in combination with the spatial distribution of mortality by county, can be used to help allocate resources to the counties in South Dakota that need them most. Centers for Disease Control and Prevention 2022-10-20 /pmc/articles/PMC9616131/ /pubmed/36265079 http://dx.doi.org/10.5888/pcd19.220113 Text en https://creativecommons.org/licenses/by/4.0/Preventing Chronic Disease 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 Schulz, Marisa Spors, Emma Bates, Kari Michael, Semhar Spatial Analysis of Breast Cancer Mortality Rates in a Rural State |
title | Spatial Analysis of Breast Cancer Mortality Rates in a Rural State |
title_full | Spatial Analysis of Breast Cancer Mortality Rates in a Rural State |
title_fullStr | Spatial Analysis of Breast Cancer Mortality Rates in a Rural State |
title_full_unstemmed | Spatial Analysis of Breast Cancer Mortality Rates in a Rural State |
title_short | Spatial Analysis of Breast Cancer Mortality Rates in a Rural State |
title_sort | spatial analysis of breast cancer mortality rates in a rural state |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9616131/ https://www.ncbi.nlm.nih.gov/pubmed/36265079 http://dx.doi.org/10.5888/pcd19.220113 |
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