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Inequalities in multiple health outcomes by education, sex, and race in 93 US counties: Why we should measure them all
INTRODUCTION: Regular reporting of health inequalities is essential to monitoring progress of efforts to reduce health inequalities. While reporting of population health became increasingly common, reporting of a subpopulation group breakdown of each indicator of the health of the population is rare...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4070094/ https://www.ncbi.nlm.nih.gov/pubmed/24927805 http://dx.doi.org/10.1186/1475-9276-13-47 |
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author | Asada, Yukiko Whipp, Alyce Kindig, David Billard, Beverly Rudolph, Barbara |
author_facet | Asada, Yukiko Whipp, Alyce Kindig, David Billard, Beverly Rudolph, Barbara |
author_sort | Asada, Yukiko |
collection | PubMed |
description | INTRODUCTION: Regular reporting of health inequalities is essential to monitoring progress of efforts to reduce health inequalities. While reporting of population health became increasingly common, reporting of a subpopulation group breakdown of each indicator of the health of the population is rarely a standard practice. This study reports education-, sex-, and race-related inequalities in four health outcomes in each of the selected 93 counties in the United States in a systematic and comparable manner. METHODS: This study is a cross-sectional analysis of large, publicly available data, 2008, 2009, and 2010 Behavioral Risk Factor Surveillance System (BRFSS) Selected Metropolitan/Micropolitan Area Risk Trends (SMART) and 2008, 2009, and 2010 United States Birth Records from the National Vital Statistics System. The study population is American adults older than 25 years of age residing in the selected 93 counties, representing about 30% of the US population, roughly equally covering all geographic regions of the country. Main outcome measures are: (1) Attribute (group characteristic)-specific inequality: education-, sex-, or race-specific inequality in each of the four health outcomes (poor or fair health, poor physical health days, poor mental health days, and low birthweight) in each county; (2) Overall inequality: the average of these three attribute-specific inequalities for each health outcome in each county; and (3) Summary inequality in total morbidity: the weighted average of the overall inequalities across the four health outcomes in each county. RESULTS: The range of inequality across the counties differed considerably by health outcome; inequality in poor or fair health had the widest range and the highest median among inequalities in all health outcomes. In more than 70% of the counties, education-specific inequality was the largest in all health outcomes except for low birthweight. CONCLUSIONS: It is feasible to extend population health reporting to include reporting of a subpopulation group breakdown of each indicator of the health of the population at a small jurisdictional level using publicly available data. No single group characteristic or health outcome represents the whole picture of health inequalities in a population. Examining multiple group characteristics and outcomes in a comparable manner is essential in reporting health inequalities. |
format | Online Article Text |
id | pubmed-4070094 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-40700942014-06-26 Inequalities in multiple health outcomes by education, sex, and race in 93 US counties: Why we should measure them all Asada, Yukiko Whipp, Alyce Kindig, David Billard, Beverly Rudolph, Barbara Int J Equity Health Research INTRODUCTION: Regular reporting of health inequalities is essential to monitoring progress of efforts to reduce health inequalities. While reporting of population health became increasingly common, reporting of a subpopulation group breakdown of each indicator of the health of the population is rarely a standard practice. This study reports education-, sex-, and race-related inequalities in four health outcomes in each of the selected 93 counties in the United States in a systematic and comparable manner. METHODS: This study is a cross-sectional analysis of large, publicly available data, 2008, 2009, and 2010 Behavioral Risk Factor Surveillance System (BRFSS) Selected Metropolitan/Micropolitan Area Risk Trends (SMART) and 2008, 2009, and 2010 United States Birth Records from the National Vital Statistics System. The study population is American adults older than 25 years of age residing in the selected 93 counties, representing about 30% of the US population, roughly equally covering all geographic regions of the country. Main outcome measures are: (1) Attribute (group characteristic)-specific inequality: education-, sex-, or race-specific inequality in each of the four health outcomes (poor or fair health, poor physical health days, poor mental health days, and low birthweight) in each county; (2) Overall inequality: the average of these three attribute-specific inequalities for each health outcome in each county; and (3) Summary inequality in total morbidity: the weighted average of the overall inequalities across the four health outcomes in each county. RESULTS: The range of inequality across the counties differed considerably by health outcome; inequality in poor or fair health had the widest range and the highest median among inequalities in all health outcomes. In more than 70% of the counties, education-specific inequality was the largest in all health outcomes except for low birthweight. CONCLUSIONS: It is feasible to extend population health reporting to include reporting of a subpopulation group breakdown of each indicator of the health of the population at a small jurisdictional level using publicly available data. No single group characteristic or health outcome represents the whole picture of health inequalities in a population. Examining multiple group characteristics and outcomes in a comparable manner is essential in reporting health inequalities. BioMed Central 2014-06-13 /pmc/articles/PMC4070094/ /pubmed/24927805 http://dx.doi.org/10.1186/1475-9276-13-47 Text en Copyright © 2014 Asada et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Asada, Yukiko Whipp, Alyce Kindig, David Billard, Beverly Rudolph, Barbara Inequalities in multiple health outcomes by education, sex, and race in 93 US counties: Why we should measure them all |
title | Inequalities in multiple health outcomes by education, sex, and race in 93 US counties: Why we should measure them all |
title_full | Inequalities in multiple health outcomes by education, sex, and race in 93 US counties: Why we should measure them all |
title_fullStr | Inequalities in multiple health outcomes by education, sex, and race in 93 US counties: Why we should measure them all |
title_full_unstemmed | Inequalities in multiple health outcomes by education, sex, and race in 93 US counties: Why we should measure them all |
title_short | Inequalities in multiple health outcomes by education, sex, and race in 93 US counties: Why we should measure them all |
title_sort | inequalities in multiple health outcomes by education, sex, and race in 93 us counties: why we should measure them all |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4070094/ https://www.ncbi.nlm.nih.gov/pubmed/24927805 http://dx.doi.org/10.1186/1475-9276-13-47 |
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