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What limits the utilization of health services among china labor force? analysis of inequalities in demographic, socio-economic and health status
BACKGROUND: Inequalities in demographic, socio-economic and health status for China labor force place them at greater health risks, and marginalized them in the utilization of healthcare services. This paper identifies the inequalities which limit the utilization of health services among China labor...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5289053/ https://www.ncbi.nlm.nih.gov/pubmed/28148264 http://dx.doi.org/10.1186/s12939-017-0523-0 |
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author | Lu, Liming Zeng, Jingchun Zeng, Zhi |
author_facet | Lu, Liming Zeng, Jingchun Zeng, Zhi |
author_sort | Lu, Liming |
collection | PubMed |
description | BACKGROUND: Inequalities in demographic, socio-economic and health status for China labor force place them at greater health risks, and marginalized them in the utilization of healthcare services. This paper identifies the inequalities which limit the utilization of health services among China labor force, and provides a reference point for health policy. METHODS: Data were collected from 23,505 participants aged 15 to 65, from the 2014 China Labor Force Dynamic Survey (a nationwide cross-sectional survey covering 29 provinces with a multi-stage cluster, and stratified, probability sampling strategy) conducted by Sun Yat-sen University. Logistic regression models were used to study the effects of demographic (age, gender, marital status, type of hukou and migration status), socio-economic (education, social class and insurance) and health status (self-perceived general health and several chronic illnesses) variables on the utilization of health services (two-week visiting and hospitalization during the past 12 months). Goodness of fit was assessed using Hosmer-Lemeshow test. Discrimination ability was assessed based on the area under the receiver operating curve (AUC). RESULTS: Migrants with more than 1 (OR 2.80, 95% CI 1.01 ~ 7.82) or none chronic illnesses (OR 1.26, 95% CI 1.01 ~ 7.82) are more likely to be two week visiting to the clinic than non-migrants; migrants with none chronic illnesses (OR 0.61, 95% CI 0.45 ~ 0.82) are less likely to be in hospitalization during the past 12 months than non-migrants. Female, elder, hukou of non-agriculture, higher education level, higher social class, purchasing more insurance and poorer self-perceived health were predictors for more utilization of health service. More insurance benefited more two-week visiting (OR 1.12, 95% CI 1.06 ~ 1.17) and hospitalization during the past 12 months (OR 1.12, 95% CI 1.07 ~ 1.18) for individuals with none chronic illness but not ≥1 chronic illnesses. All models achieved good calibration (Hosmer-Lemeshow test’s P range of 0.258-0.987) and discrimination (AUC range of 0.626-0.725). CONCLUSIONS: This study has shown that there are inequalities of demographic, socio-economic and health status in the utilization of health services for China labor force. Prudent health policy with equitable utilization of health services eliminating mentioned inequalities should be a priority in shaping China’s healthcare system reform. |
format | Online Article Text |
id | pubmed-5289053 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-52890532017-02-09 What limits the utilization of health services among china labor force? analysis of inequalities in demographic, socio-economic and health status Lu, Liming Zeng, Jingchun Zeng, Zhi Int J Equity Health Research BACKGROUND: Inequalities in demographic, socio-economic and health status for China labor force place them at greater health risks, and marginalized them in the utilization of healthcare services. This paper identifies the inequalities which limit the utilization of health services among China labor force, and provides a reference point for health policy. METHODS: Data were collected from 23,505 participants aged 15 to 65, from the 2014 China Labor Force Dynamic Survey (a nationwide cross-sectional survey covering 29 provinces with a multi-stage cluster, and stratified, probability sampling strategy) conducted by Sun Yat-sen University. Logistic regression models were used to study the effects of demographic (age, gender, marital status, type of hukou and migration status), socio-economic (education, social class and insurance) and health status (self-perceived general health and several chronic illnesses) variables on the utilization of health services (two-week visiting and hospitalization during the past 12 months). Goodness of fit was assessed using Hosmer-Lemeshow test. Discrimination ability was assessed based on the area under the receiver operating curve (AUC). RESULTS: Migrants with more than 1 (OR 2.80, 95% CI 1.01 ~ 7.82) or none chronic illnesses (OR 1.26, 95% CI 1.01 ~ 7.82) are more likely to be two week visiting to the clinic than non-migrants; migrants with none chronic illnesses (OR 0.61, 95% CI 0.45 ~ 0.82) are less likely to be in hospitalization during the past 12 months than non-migrants. Female, elder, hukou of non-agriculture, higher education level, higher social class, purchasing more insurance and poorer self-perceived health were predictors for more utilization of health service. More insurance benefited more two-week visiting (OR 1.12, 95% CI 1.06 ~ 1.17) and hospitalization during the past 12 months (OR 1.12, 95% CI 1.07 ~ 1.18) for individuals with none chronic illness but not ≥1 chronic illnesses. All models achieved good calibration (Hosmer-Lemeshow test’s P range of 0.258-0.987) and discrimination (AUC range of 0.626-0.725). CONCLUSIONS: This study has shown that there are inequalities of demographic, socio-economic and health status in the utilization of health services for China labor force. Prudent health policy with equitable utilization of health services eliminating mentioned inequalities should be a priority in shaping China’s healthcare system reform. BioMed Central 2017-02-02 /pmc/articles/PMC5289053/ /pubmed/28148264 http://dx.doi.org/10.1186/s12939-017-0523-0 Text en © The Author(s). 2017 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 Lu, Liming Zeng, Jingchun Zeng, Zhi What limits the utilization of health services among china labor force? analysis of inequalities in demographic, socio-economic and health status |
title | What limits the utilization of health services among china labor force? analysis of inequalities in demographic, socio-economic and health status |
title_full | What limits the utilization of health services among china labor force? analysis of inequalities in demographic, socio-economic and health status |
title_fullStr | What limits the utilization of health services among china labor force? analysis of inequalities in demographic, socio-economic and health status |
title_full_unstemmed | What limits the utilization of health services among china labor force? analysis of inequalities in demographic, socio-economic and health status |
title_short | What limits the utilization of health services among china labor force? analysis of inequalities in demographic, socio-economic and health status |
title_sort | what limits the utilization of health services among china labor force? analysis of inequalities in demographic, socio-economic and health status |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5289053/ https://www.ncbi.nlm.nih.gov/pubmed/28148264 http://dx.doi.org/10.1186/s12939-017-0523-0 |
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