| 引用本文：董惠玲, 任桂芳, 宁佩, 吴炳义.基于地理加权回归的老年人口健康预期寿命影响因素分析[J].中国卫生政策研究,2020,13(2):73-80 |
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| 基于地理加权回归的老年人口健康预期寿命影响因素分析 |
| 投稿时间：2020-01-01 修订日期：2020-02-10 PDF全文浏览 HTML全文浏览 |
| 董惠玲1, 任桂芳2, 宁佩1, 吴炳义1 |
1. 潍坊医学院公共卫生与管理学院 山东潍坊 261053;
2. 潍坊市人民医院 山东潍坊 261053 |
| 摘要:目的：分析我国老年人口健康预期寿命的影响因素，并对相关因素的空间变异特征进行深入探讨。方法：以第六次人口普查数据和《2011年中国卫生统计年鉴》数据为资料来源，采用经典OLS回归和地理加权回归，分析男性和女性老年人口健康预期寿命及各影响因素间的区域差异。结果：经典OLS回归显示，人均可支配收入和平均受教育年限对男性和女性老年人口健康预期寿命均具有正向效应，而每万人拥有卫生机构床位数仅对男性老年人有负向效应。地理加权回归显示，老年人口健康预期寿命分布特征受经济、教育、卫生等非空间因素的影响，还与不同区域的地理分布有密切关系。结论：地理加权回归模型对具有空间自相关性的数据具有更优的拟合，可以较好地揭示空间因素在地区间作用的差异。我国政府制定区域人口老龄化健康政策，应科学把握不同地区对政策背后同一因素的不同反馈作用。 |
| 关键词:老年人 健康预期寿命 影响因素 地理加权回归 |
| 基金项目:国家社会科学基金面上项目（18BRK013） |
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| Analysison factors influencing healthy life expectancy of elderly population based on geographic weighted regression |
| DONG Hui-ling1, REN Gui-fang2, NING Pei1, WU Bing-yi1 |
1. School of Management, Weifang Medical University, Weifang Shandong 261053, China;
2. Weifang People's Hospital, Weifang Shandong 261053, China |
| Abstract:Objective: To explore the influencing factors and spatial heterogeneity of healthy life expectancy of elderly population at provincial level in China. Methods: Based on the data of the 6th Census and the China Health Statistics Yearbook of 2011, Classical OLS regression and geographical weighted regression were used to analyze the main factors influencing healthy life expectancy of elderly people, and regional differences induced by each influencing factor were depicted. Results: Classical OLS regression analysis showed that the per capita disposable income and average number of years of education had a positive effect on the health life expectancy of both the male and female elderly, while the number of beds per 10,000 people showed a negative effect on the male elderly only. Urban green space and forests in various areas positively affected on female elderly only. In addition to the non-spatial factors such as economy, education and healthcare, the geographically weighted regression analysis showed that the distribution of elderly's healthy life expectancy was closely related to the geographical positions of different regions.Conclusion: The geographical weighted regression model proved to be a better fit for the data with spatial autocorrelation, which can reveal the difference between spatial factors applied in different regions. In formulating the health policy and reforming the existing healthcare system for the regional aging population, different feedback performance of the same factors behind the policy, applied in different regions, should be grasped. |
| Key words:The elderly Healthy life expectancy Influencing factors Geographical weighted regression |
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