ArticleFrontiers in public health2022
Medical expenditure and its inequity for people with disabilities: Evidence from the CHARLS 2018 data.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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6 authors.
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Abstract
Introduction: Disabilities may raise heavy medical expenses and rich-poor inequalities. However, data is lacking for the Chinese older populations. This study aimed to measure socioeconomic inequalities in medical expenses amongst the Chinese adult 45 years or older, and explored the main determinants among different disability categories. Method: Data from the 2018 China Health and Retirement Longitudinal Study (CHARLS) were used. Disabilities were divided into five categories: physical disabilities, intellectual disability, vision problems, hearing problems, and multiple disabilities. The two-part model was employed to identify the factors that are associated with medical expenditures. Socioeconomic inequalities were measured by the concentration index (CI), and the horizontal inequity index (HI) which adjusts for health needs. Decomposition analysis was further applied to evaluate the contribution of each determinant. Results: Two thousand four hundred nineteen people were included in this study. The CIs and HIs of the expenditure were both positive. Amongst the varied types of medical expenses, the highest CIs were found for self-treatment expenses (0.0262). Amongst the five categories of disabilities, the group with vision problem disability reported the highest CIs and HIs for outpatient expenses (CI = 0.0843, HI = 0.0751), self-treatment expenses (CI = 0.0958, HI = 0.1119), and total expenses (CI = 0.0622, HI = 0.0541). The group of intellectual disability reported the highest CI and HI (CI = 0.0707, HI = 0.0625). The decomposition analysis showed that income (80.32%), education (25.14%) and living in the rural areas (13.96%) were the main determinants of medical expenses for HI amongst all types of disabilities. Conclusion: For five types of disabilities, our data shows that medical expenses concentrated in the richer groups in China. Income, education, and rural areas factors were the main contributors to the economic-related inequalities. Health policies to improve the affordability of medical care are needed to decrease inequity of medical expenditures for people with disabilities.
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