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Measuring inequalities in the public health workforce at county-level Centers for Disease Control and Prevention in China

BACKGROUND: The public health workforce (PHW) is a key component of a country’s public health system. Since the outbreak of SARS (severe acute respiratory syndrome) in 2003, the scale of PHW in China has been continuously expanding, but policymakers and researchers still focus on the distribution of...

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Autores principales: Cai, Weiqin, Li, Chengyue, Sun, Mei, Hao, Mo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6873429/
https://www.ncbi.nlm.nih.gov/pubmed/31752854
http://dx.doi.org/10.1186/s12939-019-1073-4
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author Cai, Weiqin
Li, Chengyue
Sun, Mei
Hao, Mo
author_facet Cai, Weiqin
Li, Chengyue
Sun, Mei
Hao, Mo
author_sort Cai, Weiqin
collection PubMed
description BACKGROUND: The public health workforce (PHW) is a key component of a country’s public health system. Since the outbreak of SARS (severe acute respiratory syndrome) in 2003, the scale of PHW in China has been continuously expanding, but policymakers and researchers still focus on the distribution of public health personnel, especially the regional inequality in such distribution. We aimed to identify the root cause of PHW inequality by decomposing different geographical units in China. METHODS: This study was based on data from a nationwide survey, which included 2712 county-level data. The distribution of the PHW in geographical units was evaluated by the Gini coefficient and Theil T index, and inequalities at regional, provincial, and municipal levels were decomposed to identify the root causes of inequalities in the PHW. Additionally, the contextual factors affecting the distribution of the PHW were determined through regression analysis. RESULTS: The overall inequality results show that health professional and field epidemiological investigators faced worse inequality than the staff. In particular, field epidemiological investigators had a Gini coefficient close to 0.4. Step decomposition showed that within-region inequalities accounted for 98.5% or more of overall inter-county inequality in the distribution of all PHW categories; provincial decomposition showed that at least 74% of inequality is still distributed within provinces; the overall contribution of within-municipal inequality and between-municipal inequality was basically the same. Further, the contextual factor that influenced between-municipality and within-municipality inequality for all three categories of PHWs was the agency building area per employee. Per capita GDP had a similar effect, except for between-municipality inequality of professionals and within-municipality inequality of field epidemiological investigators. CONCLUSIONS: The successive decomposition showed that inequality is mainly concentrated in counties at the within-province and within-municipal levels. This study clearly suggests that the government, especially the municipal government at the provincial level, should increase financial investment in Centers for Disease Control and Prevention (CDCs) with worse resource allocation in their jurisdiction through various ways of compensation and incentives, enhance their infrastructure, and improve the salary of personnel in these institutions, to attract more public health professionals to these institutions.
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spelling pubmed-68734292019-12-12 Measuring inequalities in the public health workforce at county-level Centers for Disease Control and Prevention in China Cai, Weiqin Li, Chengyue Sun, Mei Hao, Mo Int J Equity Health Research BACKGROUND: The public health workforce (PHW) is a key component of a country’s public health system. Since the outbreak of SARS (severe acute respiratory syndrome) in 2003, the scale of PHW in China has been continuously expanding, but policymakers and researchers still focus on the distribution of public health personnel, especially the regional inequality in such distribution. We aimed to identify the root cause of PHW inequality by decomposing different geographical units in China. METHODS: This study was based on data from a nationwide survey, which included 2712 county-level data. The distribution of the PHW in geographical units was evaluated by the Gini coefficient and Theil T index, and inequalities at regional, provincial, and municipal levels were decomposed to identify the root causes of inequalities in the PHW. Additionally, the contextual factors affecting the distribution of the PHW were determined through regression analysis. RESULTS: The overall inequality results show that health professional and field epidemiological investigators faced worse inequality than the staff. In particular, field epidemiological investigators had a Gini coefficient close to 0.4. Step decomposition showed that within-region inequalities accounted for 98.5% or more of overall inter-county inequality in the distribution of all PHW categories; provincial decomposition showed that at least 74% of inequality is still distributed within provinces; the overall contribution of within-municipal inequality and between-municipal inequality was basically the same. Further, the contextual factor that influenced between-municipality and within-municipality inequality for all three categories of PHWs was the agency building area per employee. Per capita GDP had a similar effect, except for between-municipality inequality of professionals and within-municipality inequality of field epidemiological investigators. CONCLUSIONS: The successive decomposition showed that inequality is mainly concentrated in counties at the within-province and within-municipal levels. This study clearly suggests that the government, especially the municipal government at the provincial level, should increase financial investment in Centers for Disease Control and Prevention (CDCs) with worse resource allocation in their jurisdiction through various ways of compensation and incentives, enhance their infrastructure, and improve the salary of personnel in these institutions, to attract more public health professionals to these institutions. BioMed Central 2019-11-21 /pmc/articles/PMC6873429/ /pubmed/31752854 http://dx.doi.org/10.1186/s12939-019-1073-4 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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
Cai, Weiqin
Li, Chengyue
Sun, Mei
Hao, Mo
Measuring inequalities in the public health workforce at county-level Centers for Disease Control and Prevention in China
title Measuring inequalities in the public health workforce at county-level Centers for Disease Control and Prevention in China
title_full Measuring inequalities in the public health workforce at county-level Centers for Disease Control and Prevention in China
title_fullStr Measuring inequalities in the public health workforce at county-level Centers for Disease Control and Prevention in China
title_full_unstemmed Measuring inequalities in the public health workforce at county-level Centers for Disease Control and Prevention in China
title_short Measuring inequalities in the public health workforce at county-level Centers for Disease Control and Prevention in China
title_sort measuring inequalities in the public health workforce at county-level centers for disease control and prevention in china
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6873429/
https://www.ncbi.nlm.nih.gov/pubmed/31752854
http://dx.doi.org/10.1186/s12939-019-1073-4
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