Social region-specific characteristics and transition-differentiation of health capability-right-risk coupled drivers on multidimensional health poverty in China: a CHARLS and statistical data-based study
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摘要:
目的 了解中国社会区域多维健康贫困的空间分异格局及叠加耦合驱动特征，为下一阶段精准扶贫和健康贫困空间治理提供科学依据。 方法 收集2011、2013、2015和2018年中国健康与养老追踪调查（CHARLS），2012、2014、2016、2019年《中国统计年鉴》、《中国卫生和计划生育统计年鉴》和2019年全球疾病负担网络中的相关数据，以课题组构建的“能力 – 权利 – 风险”多维健康贫困理论模型为框架，最终纳入了健康能力、健康权利、健康风险3个维度的13个指标作为多维健康贫困的驱动因子，采用地理探测器实现单一及耦合驱动作用特征的捕捉，分析驱动因子的空间差异。 结果 与2011、2013和2015年比较，2018年健康能力维度的人均国内生产总值、城镇化率、居民年住院率的排位均有所上升，而健康权利维度的人均社会保障和就业支出、公众健康教育活动次数的排位均有所下降，权利赋能对于抵御健康贫困发生的效应增强。驱动因子空间差异研究结果显示，2011 — 2018年东部地区的风险驱动因子主要为城镇失业率，中部地区的风险驱动因子主要为调查前3年PM2.5浓度，而西部地区的多维健康贫困则是健康能力、健康权利和健康风险3个维度联合驱动的结果。从交互作用探测结果显示，健康能力维度的人均国内生产总值、城镇化率与健康权利维度的政府卫生支出占比、人均社会保障和就业支出、每千人口医疗卫生机构床位数的交互作用类型始终为双因子增强，健康能力与健康权利的联合作用会改善多维健康贫困的发生；调查前3年PM2.5浓度这一健康风险指标与居民年住院率、老年抚养比2011年、2013年、2018年的交互作用类型亦为双因子增强。 结论 多维健康贫困在中国社会区域空间存在时空分异格局，是多维因素耦合驱动的结果，而多维因素耦合对多维健康贫困发生的复杂机制则要求多领域、多部门、多机制、多阶段的综合健康治理。 Abstract:Objective To examine social region-specific characteristics and transition-differentiation of driving factors for multidimensional health poverty (MHP) in China for providing a reference to precise poverty alleviation and effective governance of health poverty. Methods We collected nationwide data from four rounds of the China Health and Retirement Longitudinal Survey (CHARLS) conducted in 2011, 2013, 2015, and 2018 and other relevant data from the China Statistical Yearbook and the China Health and Family Planning Statistical Yearbook of 2012, 2014, 2016, and 2019 and from the Global Burden of Disease Network in 2019. Based on a self-established theoretical framework of MHP associated with three dimensional components of health capability-right-risk, we selected 13 indicators covering the three dimensional components as the driving factors of MHP and used geographic detectors to explore the characteristics of independent and coupled effect of the driving factors and analyze differences in spatial distribution of the driving factors. Results Compared with those in 2011, 2013, and 2015, the ranking of three health capability dimension factors (gross domestic production [GDP] per capita, urbanization rate, and annual hospitalization rate of residents) increased in 2018; while the ranking of three health right dimension factors (per capita expenditure on both social security and employment and the number of public health education programs) decreased; the effect of health right empowerment on preventing the occurrence of health poverty increased. The analysis on differences in spatial distribution of driving factors during 2011 – 2018 showed that urban unemployment rate was a main driving factor for MHP in eastern region; while, the main driving factor for MHP in central region was the concentration of particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) three years ago and the MHP was driven jointly by multiple factors with regard to health capability-right-risk. The analyses on interactive effect of the driving factors revealed bivariate interactive enhancement effect on MHP among GDP per capita and urbanization rate as factors of health capability dimension and government health expenditure ratio, per capita expenditure on social security and employment, and the number of beds in health care institutions versus per 1 000 population as factors of health right dimension; the joint effect of driving factors regarding to health capability and health right reduced the occurrence MHP; the interaction between PM2.5 concentration three years ago (a health risk indicator) and two health capability indicators (annual hospitalization rate of residents and elderly dependency ratio in 2011/2013/2018) also showed bivariate interactive enhancement effect on MHP. Conclusion The prevalence of MHP is of spatially and temporally heterogeneous pattern across social regions in China and is driven by the coupling of multidimensional factors, suggesting that comprehensive strategies need to be developed for effective health poverty management. -
图 1 最优参数的选择
Figure 1. Selection of optimal parameters – analysis on the data from four rounds of China Health and Retirement Longitudinal Survey, 2011 – 2018
图 2 中国东部、中部和西部地区2011 — 2018年多维健康贫困驱动因子雷达图
Figure 2. Year-specific radar chart for contributions of driving factors to multidimensional health poverty by region – analysis on the data from four rounds of China Health and Retirement Longitudinal Survey, 2011 – 2018
图 3 2011 — 2018年各驱动因子交互作用探测结果
Figure 3. Year-specific schematic diagram for interactions among driving factors of multidimensional health poverty – analysis on the data from four rounds of China Health and Retirement Longitudinal Survey, 2011 – 2018
表 1 2011 — 2018年健康贫困指数的驱动因子解释力
Table 1. Explanatory power of driving factors for multidimensional health poverty index – analysis on the data from four rounds of China Health and Retirement Longitudinal Survey, 2011 – 2018
维度 指标 q 值 2011年 2013年 2015年 2018年 健康能力 人均国内生产总值（X1） 0.52 a 0.66 a 0.44 a 0.80 a 城镇化率（X2） 0.64 a 0.75 a 0.63 a 0.80 a 文盲率（X3） 0.47 a 0.83 a 0.47 a 0.75 a 居民年住院率（X4） 0.48 a 0.70 a 0.40 a 0.74 a 老年抚养比（X5） 0.55 a 0.62 a 0.44 a 0.44 a 健康权利 每千人口医疗卫生机构床位数（X6） 0.52 a 0.71 a 0.66 a 0.75 a 政府卫生支出占比（X7） 0.55 a 0.75 a 0.37 a 0.73 a 每千人注册护士数（X8） 0.33 a 0.62 a 0.37 a 0.44 a 公众健康教育活动次数（X9） 0.65 a 0.77 a 0.44 a 0.71 a 人均社会保障和就业支出（X10） 0.62 a 0.66 a 0.46 a 0.70 a 每万人图书馆面积（X11） 0.47 a 0.65 a 0.46 a 0.73 a 健康风险 城镇失业率（X12） 0.50 a 0.45 a 0.50 a 0.67 a 调查前3年PM2.5浓度（X13） 0.55 a 0.64 a 0.35 a 0.71 a 注：a P < 0.01。 
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