Measurement and decomposition of income-related inequality in self-rated health among the elderly in China - Hai Gu; Yun Kou; Hua You; Xinpeng Xu; Nichao Yang; Jing Liu; Xiyan Liu; Jinghong Gu; Xiaolu Li (BMC, 2019)
Abstract Background Population ageing in China has brought increasing attention to the health inequalities of the elderly. The purpose of this paper is to measure income-related health inequality among the elderly in China and decompose its causes. Methods The data are from the China Health and Retirement Longitudinal Study (CHARLS) survey in 2013, which contains 6176 individuals aged 60 years and above. A multiple linear regression model was used to analyze the influencing factors of self-rated health (SRH) among the elder people. Furthermore, the corrected concentration index were used to measure income-related health inequality. Wagstaff-type decomposition analysis was employed to explore the cause of inequality. The measurement and decomposition of health inequality was also performed separately in the male and female subgroups. Results Most elderly declared their health status as "fair" (51.33%) or "poor" (21.88%). Income, gender, residence, region, health insurance and other factors had significant association with SRH (P < 0.05). The corrected concentration index (CCI) was 0.06, indicating pro-rich inequality in health among the elderly. Decomposition analyses revealed that the main contributors to health inequality included income, residence, region, health insurance, and employment. For female elderly, most of the inequality was due to residence (50.78%) and income (49.51%); for male elderly, most of the inequality was due to insurance (38.65%) and income (22.26%); for the total sample, employment had a negative contribution to health inequality (− 25.83%). Conclusion The findings confirm a high proportion of elderly with poor SRH, and health inequality in the Chinese. Some socioeconomic strategies should be conducted to reduce this health inequality among the elderly, such as reducing income disparities, consolidating health insurance schemes, and narrowing urban-rural and regional gaps. Older females with low incomes in rural areas are a vulnerable subgroup and warrant targeted policy attention.