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Monitoring subnational regional inequalities in health: measurement approaches and challenges

BACKGROUND: Monitoring inequalities based on subnational regions is a useful practice to unmask geographical differences in health, and deploy targeted, equity-oriented interventions. Our objective is to describe, compare and contrast current methods of measuring subnational regional inequality. We...

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Autores principales: Hosseinpoor, Ahmad Reza, Bergen, Nicole, Barros, Aluisio J. D., Wong, Kerry L. M., Boerma, Ties, Victora, Cesar G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4730638/
https://www.ncbi.nlm.nih.gov/pubmed/26822991
http://dx.doi.org/10.1186/s12939-016-0307-y
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author Hosseinpoor, Ahmad Reza
Bergen, Nicole
Barros, Aluisio J. D.
Wong, Kerry L. M.
Boerma, Ties
Victora, Cesar G.
author_facet Hosseinpoor, Ahmad Reza
Bergen, Nicole
Barros, Aluisio J. D.
Wong, Kerry L. M.
Boerma, Ties
Victora, Cesar G.
author_sort Hosseinpoor, Ahmad Reza
collection PubMed
description BACKGROUND: Monitoring inequalities based on subnational regions is a useful practice to unmask geographical differences in health, and deploy targeted, equity-oriented interventions. Our objective is to describe, compare and contrast current methods of measuring subnational regional inequality. We apply a selection of summary measures to empirical data from four low- or middle-income countries to highlight the characteristics and overall performance of the different measures. METHODS: We use data from Demographic and Health Surveys conducted in Bangladesh, Egypt, Ghana and Zimbabwe to calculate subnational regional inequality estimates for reproductive, maternal, newborn, and child health services generated from 11 summary measures: pairwise measures included high to low absolute difference, high to low relative difference, and high to low ratio; complex measures included population attributable risk, weighted variance, absolute weighted mean difference from overall mean, index of dissimilarity, Theil index, population attributable risk percentage, coefficient of variation, and relative weighted mean difference from overall mean. Four of these summary measures (high to low absolute difference, high to low ratio, absolute weighted mean difference from overall mean, and relative weighted mean difference from overall mean) were selected to compare their performance in measuring trend over time in inequality for one health indicator. RESULTS: Overall, the 11 different measures were more remarkable for their similarities than for their differences. Pairwise measures tended to support the same conclusions as complex summary measures–that is, by identifying same best and worst coverage indicators in each country and indicating similar time trends. Complex measures may be useful to illustrate more nuanced results in countries with a great number of subnational regions. CONCLUSIONS: When pairwise and complex measures lead to the same conclusions about the state of subnational regional inequality, pairwise measures may be sufficient for reporting inequality. In cases where complex measures are required, mean difference from mean measures can be easily communicated to non-technical audiences.
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spelling pubmed-47306382016-01-29 Monitoring subnational regional inequalities in health: measurement approaches and challenges Hosseinpoor, Ahmad Reza Bergen, Nicole Barros, Aluisio J. D. Wong, Kerry L. M. Boerma, Ties Victora, Cesar G. Int J Equity Health Research BACKGROUND: Monitoring inequalities based on subnational regions is a useful practice to unmask geographical differences in health, and deploy targeted, equity-oriented interventions. Our objective is to describe, compare and contrast current methods of measuring subnational regional inequality. We apply a selection of summary measures to empirical data from four low- or middle-income countries to highlight the characteristics and overall performance of the different measures. METHODS: We use data from Demographic and Health Surveys conducted in Bangladesh, Egypt, Ghana and Zimbabwe to calculate subnational regional inequality estimates for reproductive, maternal, newborn, and child health services generated from 11 summary measures: pairwise measures included high to low absolute difference, high to low relative difference, and high to low ratio; complex measures included population attributable risk, weighted variance, absolute weighted mean difference from overall mean, index of dissimilarity, Theil index, population attributable risk percentage, coefficient of variation, and relative weighted mean difference from overall mean. Four of these summary measures (high to low absolute difference, high to low ratio, absolute weighted mean difference from overall mean, and relative weighted mean difference from overall mean) were selected to compare their performance in measuring trend over time in inequality for one health indicator. RESULTS: Overall, the 11 different measures were more remarkable for their similarities than for their differences. Pairwise measures tended to support the same conclusions as complex summary measures–that is, by identifying same best and worst coverage indicators in each country and indicating similar time trends. Complex measures may be useful to illustrate more nuanced results in countries with a great number of subnational regions. CONCLUSIONS: When pairwise and complex measures lead to the same conclusions about the state of subnational regional inequality, pairwise measures may be sufficient for reporting inequality. In cases where complex measures are required, mean difference from mean measures can be easily communicated to non-technical audiences. BioMed Central 2016-01-28 /pmc/articles/PMC4730638/ /pubmed/26822991 http://dx.doi.org/10.1186/s12939-016-0307-y Text en © World Health Organization; licensee BioMed Central. 2016 This is an open access article distributed under the terms of the Creative Commons Attribution IGO License (http://creativecommons.org/licenses/by/3.0/igo/legalcode), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In any reproduction of this article there should not be any suggestion that WHO or this article endorse any specific organisation or products. The use of the WHO logo is not permitted. This notice should be preserved along with the article's original URL.
spellingShingle Research
Hosseinpoor, Ahmad Reza
Bergen, Nicole
Barros, Aluisio J. D.
Wong, Kerry L. M.
Boerma, Ties
Victora, Cesar G.
Monitoring subnational regional inequalities in health: measurement approaches and challenges
title Monitoring subnational regional inequalities in health: measurement approaches and challenges
title_full Monitoring subnational regional inequalities in health: measurement approaches and challenges
title_fullStr Monitoring subnational regional inequalities in health: measurement approaches and challenges
title_full_unstemmed Monitoring subnational regional inequalities in health: measurement approaches and challenges
title_short Monitoring subnational regional inequalities in health: measurement approaches and challenges
title_sort monitoring subnational regional inequalities in health: measurement approaches and challenges
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4730638/
https://www.ncbi.nlm.nih.gov/pubmed/26822991
http://dx.doi.org/10.1186/s12939-016-0307-y
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