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中国潜江市医疗保险福利的地域差异:一项横断面研究。

Geographic variation in health insurance benefits in Qianjiang District, China: a cross-sectional study.

机构信息

School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, China.

出版信息

Int J Equity Health. 2018 Feb 5;17(1):20. doi: 10.1186/s12939-018-0730-3.

DOI:10.1186/s12939-018-0730-3
PMID:29402292
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5800004/
Abstract

BACKGROUND

Health insurance contributes to reducing the economic burden of disease and improving access to healthcare. In 2016, the Chinese government announced the integration of the New Cooperative Medical Scheme (NCMS) and Urban Resident Basic Medical Insurance (URBMI) to reduce system segmentation. Nevertheless, it was unclear whether there would be any geographic variation in health insurance benefits if the two types of insurance were integrated. The aim of this study was to identify the potential geographic variation in health insurance benefits and the related contributing factors.

METHODS

This cross-sectional study was carried out in Qianjiang District, where the NCMS and URBMI were integrated into Urban and Rural Resident Basic Medical Insurance Scheme (URRBMI) in 2010. All beneficiaries under the URRBMI were hospitalized at least once in 2013, totaling 445,254 persons and 65,877 person-times, were included in this study. Town-level data on health insurance benefits, healthcare utilization, and socioeconomic and geographical characteristics were collected through health insurance system, self-report questionnaires, and the 2014 Statistical Yearbook of Qianjiang District. A simplified Theil index at town level was calculated to measure geographic variation in health insurance benefits. Colored maps were created to visualize the variation in geographic distribution of benefits. The effects of healthcare utilization and socioeconomic and geographical characteristics on geographic variation in health insurance benefits were estimated with a multiple linear regression analysis.

RESULTS

Different Theil index values were calculated for different towns, and the Theil index values for compensation by person-times and amount were 2.5028 and 1.8394 in primary healthcare institutions and 1.1466 and 0.9204 in secondary healthcare institutions. Healthcare-seeking behavior and economic factors were positively associated with health insurance benefits in compensation by person-times significantly, meanwhile, geographical accessibility and economic factors had positive effects (p < 0.05).

CONCLUSIONS

The geographic variation in health insurance benefits widely existed in Qianjiang District and the distribution of health insurance benefits for insured inpatients in primary healthcare institutions was distinctly different from that in secondary healthcare institutions. When combining the NRCM and URMIS in China, the geographical accessibility, healthcare-seeking behavior and economic factors required significant attention.

摘要

背景

医疗保险有助于减轻疾病的经济负担并改善医疗服务的可及性。2016 年,中国政府宣布整合新型农村合作医疗制度(NCMS)和城镇居民基本医疗保险(URBMI),以减少制度分割。然而,如果将这两种保险整合,医疗保险福利是否会存在地域差异尚不清楚。本研究旨在确定医疗保险福利的潜在地域差异及其相关影响因素。

方法

本研究为横断面研究,在 2010 年将 NCMS 和 URBMI 整合为城乡居民基本医疗保险制度(URRBMI)的潜江市进行。所有在 2013 年至少住院一次的 URRBMI 参保人共 445254 人,65877 人次纳入本研究。通过医疗保险系统、自报问卷和潜江市 2014 年统计年鉴收集了乡镇级医疗保险福利、医疗服务利用以及社会经济和地理特征的数据。使用简化的 Theil 指数在乡镇层面衡量医疗保险福利的地域差异。制作彩色地图以可视化福利的地域分布变化。采用多元线性回归分析估计医疗服务利用以及社会经济和地理特征对医疗保险福利地域差异的影响。

结果

不同乡镇的 Theil 指数值不同,在基层医疗机构,按人头和按费用补偿的 Theil 指数值分别为 2.5028 和 1.8394,在二级医疗机构分别为 1.1466 和 0.9204。人头补偿的医疗服务利用和经济因素与医疗保险福利显著正相关,而地理可达性和经济因素也有正向影响(p<0.05)。

结论

潜江市医疗保险福利存在广泛的地域差异,参保住院患者在基层医疗机构的医疗保险福利分布与二级医疗机构明显不同。在中国整合新型农村合作医疗制度和城镇居民基本医疗保险时,需要关注地理可达性、医疗服务利用和经济因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a503/5800004/9e297e1639ff/12939_2018_730_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a503/5800004/e5d1c3fe0698/12939_2018_730_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a503/5800004/9e297e1639ff/12939_2018_730_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a503/5800004/e5d1c3fe0698/12939_2018_730_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a503/5800004/9e297e1639ff/12939_2018_730_Fig2_HTML.jpg

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