中国中老年人群健康贫困及其影响因素时空特征分析

张曦宇; 赖勇强; 李叶; 吴群红; 吴冰; 苗雯青; 张晨溪; 刘馨蔚

doi:10.11847/zgggws1141548

中国中老年人群健康贫困及其影响因素时空特征分析

doi: 10.11847/zgggws1141548

1.
哈尔滨医科大学卫生管理学院卫生政策与医院管理研究中心，黑龙江哈尔滨 150076
2.
哈尔滨医科大学社会医学教研室

基金项目: 国家自然科学基金（72174047；71874045）

详细信息

作者简介:
张曦宇（1998 – ），男，辽宁阜新人，硕士在读，研究方向：时空分析方法在健康贫困中的应用

通讯作者:
李叶，E-mail：liye8459@163.com

中图分类号: R 197.1
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- 被引次数: 0
出版历程
- 接收日期: 2023-02-21
- 网络出版日期: 2023-04-21

Spatiotemporal characteristics of health poverty and its associates among middle-aged and elderly populations in China: a CHARLS and statistical data analysis

ZHANG Xiyu¹,
LAI Yongqiang¹,
LI Ye^{1
, ,},

1.
Research Center of Health Policy and Hospital Management, School of Health Management, Harbin Medical University, Harbin, Heilongjiang Province 150076, China

摘要

摘要: 目的了解中国中老年人群健康贫困及其影响因素之间作用在时空上可能存在的非平稳性特征，为多元协同治理网络下区域健康减贫行政效率的提升提供时空特异性证据。方法收集2011、2013、2015和2018年中国健康与养老追踪调查（CHARLS）中中国28个省（自治区、直辖市）37296户 ≥ 45岁中老年家庭的相关数据，基于自行开发的多维健康贫困指数测度工具分别采用普通最小二乘模型（OLS）、地理加权回归模型（GWR）、时间加权回归模型（TWR）和时空加权回归模型（GTWR）对2011 — 2018年中国中老年家庭健康贫困及其影响因素之间的作用进行建模，并进一步对最优模型估计系数的时空特征进行了勾勒。结果在控制了年份和地区固定效应后，全局OLS模型回归分析结果显示，随着慢性病患病率、残疾患病率、人均国内生产总值、平均医疗自付费用和调查前3年年均PM_2.5浓度的提高，多维健康贫困指数随之增长；而随着人口密度、基本医疗保险参保率和每万人口护士数的提高，多维健康贫困指数则随之降低。在所有的局部模型中，GWR模型的表现最优，与全局OLS模型相比，其调整后R²值从70.57%上升至88.68%，残差平方和从797.791下降至585.277。GWR模型中慢性病患病率、残疾患病率、人口密度、人均国内生产总值、基本医疗保险参保率、平均医疗自付费用、每万人口护士数和调查前3年年均PM_2.5浓度8个协变量与响应变量的作用均存在非平稳性。GWR模型分析结果显示，慢性病患病率对多维健康贫困指数的正向促进作用在内蒙古自治区、河北省、山西省、云南省和贵州省达到最强；残疾患病率对多维健康贫困指数的正向促进作用在江苏省、安徽省和河南省达到最强；人口密度对多维健康贫困指数的负向抑制作用在甘肃省、内蒙古自治区、河北省和北京市达到最强，在黑龙江省、吉林省、辽宁省、山东省和山西省也表现为相对较强；基本医疗保险参保率和每万人口护士数对多维健康贫困指数的负向抑制作用以及人均国内生产总值对多维健康贫困指数的正向促进作用均在新疆维吾尔自治区、青海省和甘肃省达到最强；平均医疗自付费用对多维健康贫困指数的正向促进作用在河南省、安徽省和江苏省达到最强；调查前3年年均PM_2.5浓度对多维健康贫困指数的正向促进作用以北京市、河北省和内蒙古自治区为中心向外减弱，辐射了整个环渤海区域。结论健康贫困与其影响因素间的时空特征存在着空间非平稳性，引入局部加权回归技术为提供有助于提升区域健康减贫行政效率的时空特异性证据奠定了技术基础。
- 健康贫困 /
- 影响因素 /
- 时空特征 /
- 中老年人群 /
- 中国
Abstract: Objective To explore spatiotemporal non-stationarity in health poverty prevalence and its influencing factors among middle-aged and elderly residents in China for providing evidence to promote comprehensive and effective management on health poverty alleviation in the populations. Methods The data of the analysis were extracted from four rounds of China Health and Retirement Longitudinal Survey (CHARLS) conducted in 2011, 2013, 2015, and 2018 across China, which collected the information on 37 296 households with family members aged ≥ 45 years. Other relevant data were extracted from the China Statistical Yearbook of 2012, 2014, 2016, and 2019, the China Health and Family Planning Statistical Yearbook of 2012 and 2016, the China Tertiary Industry Statistical Yearbook of 2014 and 2019, and from the Global Burden of Disease Network in 2019. Based on the self-developed indexes for measurement of multidimensional health poverty (MHP), we used the ordinary least squares model (OLS), the geographically weighted regression model (GWR), the temporally weighted regression model (TWR), and the geographically and temporally weighted regression model (GTWR) to model the impacts of influencing factors on health poverty among the middle-aged and elderly residents during the period. The spatiotemporal modes of the estimation coefficients in the optimal model were further described. Results After adjusting for fixed effects of year and region, the global OLS regression results showed that the MHP index increased with the increment of the prevalence of non-communicable diseases (NCDs) and disabilities, gross domestic production (GDP) per capita, average out-of-pocket medical expenditure, and mean annual concentration of particulate matter less than 2.5 μm in aerodynamic diameter (PM_2.5) in previous 3 years; but the index decreased with the increment of population density, the coverage of basic medical insurance, and the number of nurses per 10 000 population. Among all partial regression models constructed, GWR fitted the survey data best, with the increased R² from 70.57% to 88.68% and the decreased sum of squares of the residuals from 797.791 to 585.277 compared with the global OLS regression model. The impacts of the eight covariates mentioned above on MHP index were non-stationary based on the results of GWR modeling; the aggravating impact of NCDs prevalence rate on MHP index was much stronger for Inner Mongolia Autonomous Region (Inner Mongolia) and the four provinces including Hebei, Shanxi, Yunnan, and Guizhou; the aggravating impact of disability prevalence rate on MHP index was much more stronger for Jiangsu, Anhui, and Henan province; the alleviating impact of population density on MHP index was stronger for the five provinces of Heilongjiang, Jilin, Liaoning, Shandong, and Shanxi and much more stronger for Inner Mongolia, Gansu and Hebei provinces, and Beijing municipality; both the alleviating impact of the coverage rate of basic medical insurance and the number of nurses versus 10 000 population and the aggravating impact of GDP per capita on MHP index were the strongest for Xinjiang Uygur Autonomous Region, Qinghai province, and Gansu province; the aggravating impact of average out-of-pocket medical expenditure on MHP index was much more stronger for Henan, Anhui and Jiangsu province; the aggravating impact of mean annual PM_2.5 concentration in previous 3 years on MHP index attenuated gradually from a central region including Beijing municipality, Hebei province and Inner Mongolia to the surrounding area of whole Bohai rim region. Conclusion There is a spatial non-stationarity for the impacts of multiple factors on health poverty among middle-aged and elderly residents in China. Partial weighted regression analysis could be adopted to examine the non-stationarity in studies on improving administrative efficiency of regional health poverty reduction.
- health poverty /
- influencing factor /
- spatiotemporal characteristics /
- middle-aged and elderly population /
- China

HTML全文

图 1 中国中老年人群2011 — 2018年多维健康贫困指数影响因素GWR模型分析中的协变量回归系数

注：基于国家自然资源部地图技术审查中心标准地图服务网站下载的审图号为GS（2019）1831号的标准地图制作。

下载: 全尺寸图片幻灯片

表 1 中国中老年人群2011 — 2018年多维健康贫困指数影响因素全局OLS模型分析

因素	β	$S_{\bar x}$	β'	t 值	P 值
慢性病患病率	0.016	0.063	0.028	0.25	0.803
残疾患病率	0.188	0.097	0.137	1.94	0.056
人口密度	– 43.352	8.028	– 0.453	– 5.40	< 0.001
基本医疗保险参保率	– 0.306	0.112	– 0.369	– 2.73	0.008
人均国内生产总值	22.481	33.037	0.072	0.68	0.498
平均医疗自付费用	4.618	25.240	0.014	0.18	0.855
每万人口护士数	– 0.342	0.072	– 0.426	– 4.77	< 0.001
调查前3年年均PM_2.5浓度	0.054	0.034	0.114	1.57	0.120

下载: 导出CSV

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