Inequalities in health-related quality of life and the contribution from socioeconomic status: evidence from Tibet, China

JieAnNaMu,; Xu, Xinpeng; You, Hua; Gu, Hongtao; Gu, Jinghong; Li, Xiaolu; Cui, Nan; Kou, Yun

doi:10.1186/s12889-020-08790-7

Cited by 19 publications

(16 citation statements)

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“…The CIs of education and income were slightly larger than those of the composite SEP score, which could suggest that the combination of education and income somewhat compensates for differential variation in these two SEP variables. The order of magnitude of these results is comparable to other studies investigating inequalities in HRQoL [ 48 , 56 ].…”

Section: Discussionsupporting

confidence: 86%

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Combining education and income into a socioeconomic position score for use in studies of health inequalities

et al. 2022

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Background In studies of social inequalities in health, there is no consensus on the best measure of socioeconomic position (SEP). Moreover, subjective indicators are increasingly used to measure SEP. The aim of this paper was to develop a composite score for SEP based on weighted combinations of education and income in estimating subjective SEP, and examine how this score performs in predicting inequalities in health-related quality of life (HRQoL). Methods We used data from a comprehensive health survey from Northern Norway, conducted in 2015/16 (N = 21,083). A composite SEP score was developed using adjacent-category logistic regression of subjective SEP as a function of four education and four household income levels. Weights were derived based on these indicators’ coefficients in explaining variations in respondents’ subjective SEP. The composite SEP score was further applied to predict inequalities in HRQoL, measured by the EQ-5D and a visual analogue scale. Results Education seemed to influence SEP the most, while income added weight primarily for the highest income category. The weights demonstrated clear non-linearities, with large jumps from the middle to the higher SEP score levels. Analyses of the composite SEP score indicated a clear social gradient in both HRQoL measures. Conclusions We provide new insights into the relative contribution of education and income as sources of SEP, both separately and in combination. Combining education and income into a composite SEP score produces more comprehensive estimates of the social gradient in health. A similar approach can be applied in any cohort study that includes education and income data.

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Section: Discussionsupporting

confidence: 86%

“…The CI's range is [-1,1], with the value 0 indicating perfect equality. A positive (negative) value indicates that the distribution of HRQoL is ‘pro-high SEP’ (‘pro-low SEP’) [ 48 ]. We compared CIs using the SEP score as the variable from which to rank individuals, with education and income.…”

Section: Methodsmentioning

confidence: 99%

Combining education and income into a socioeconomic position score for use in studies of health inequalities

et al. 2022

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“…The impact of SES highlighted the importance of graded socioeconomic differences in HRQoL. A recent study in China regarded SES as the main contributor to health inequality measured by the EQ-5D (41). Previous studies also discovered that the indicators of SES including education level, monthly income, and occupation were independently associated with HRQoL.…”

Section: Discussionmentioning

confidence: 99%

Health-related quality of life and its association with socioeconomic status and diet diversity in Chinese older adults

Zhang

Xiao

et al. 2023

Front. Public Health

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ObjectivesThe study aimed at examining the combined association of socioeconomic status (SES) and diet diversity (DD) with health-related quality of life (HRQoL) and exploring whether DD played a mediating role in the relationship between varied SES and HRQoL among Chinese older persons.MethodA multi-stage random sampling method was conducted in Shanxi Province of China, with 3,250 older adults participating in this cross-sectional survey. SES was divided into groups by quartiles and DD by means, and these variable groups were combined in pairs to generate a total of eight combinations. The PROCESS macro developed by Hayes was employed for the simple mediation analysis.ResultsCompared with the reference group (those with both high SES and high DD), older adults who were classified to have lower SES or DD had elevated odds of having worse HRQoL: low SES/ low DD (OR = 1.65, 95% CI 1.41–2.92); low SES/ high DD (OR = 1.45, 95% CI 1.17–1.80); middle low SES/ low DD (OR = 1.43, 95% CI 1.24–1.65); middle low SES/ high DD (OR = 1.23, 95% CI 1.03–1.47); upper high SES/ low DD (OR = 1.41, 95% CI 1.21–1.65); and high SES/ low DD (OR = 1.30, 95%CI 1.10–1.53). The mediation analysis revealed that DD mediated the relationship between SES and HRQoL (B=0.011, 95% CI 0.008–0.013), with its indirect effects accounting for 39.29% of the total effects.ConclusionsThese findings highlighted the role of DD as a mediator of the relationship between SES and HRQoL. As DD could be protective, modifiable, and easy for older adults to understand and implement, village clinics and community health stations should work collaboratively to design proper DD intervention measures for better HRQoL.

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“…In fact, some articles are devoted to the economic, social, and environmental drivers of health inequities, but many of the inequities in health in most regions are determined by factors beyond the health sector [30,31]. Different geographical locations, economic development status, health service talents and service facilities, and even local transportation convenience and national support have all affected the development of regional health services.…”

Section: Discussionmentioning

confidence: 99%

A Study of the Spatial Network Structure of the Health Economy in China: Benefits and Spillover Effects on Patient Mobility

Kaibo

Feng

et al. 2020

Preprint

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Abstract Background: The distribution of medical resources in China is seriously imbalanced due to imbalanced economic development in the country, and unbalanced distribution of medical resources makes patients try to seek better health services. Against this backdrop, this study aims to analyse the spatial network characteristics and spatial effects of China's health economy ,and then find evidence that affects patient mobility. Methods: Data for this study were drawn from the China Health Statistical Yearbooks and China Statistical Books. The gravitational value of China’s health spatial network was calculated to establish a network of gravitational relationships. The social network analysis method is used for centrality analysis and spillover effect analysis. Based on the gravitational value, we used indicators for visits, health expenditure, GDP and geographical distance, as well as an illustrative index for the population, bed utilization, and burden of consultations per day. Results: A gravity correlation matrix was constructed among provinces by calculating the gravitational value, indicating the spatial relationships of different provinces in the health economic network. Economically developed provinces, such as Shanghai and Jiangsu, are at the centre of the health economic network (centrality degree=93.333). These provinces also play a strong intermediary role in the network and have connections with other provinces. In the CONCOR analysis, 31 provinces are divided into four blocks. In the traditional meaning analysis, the gap between eastern and western China is still obvious. In comparing the northern and southern regions, this study finds that the concentration of medical resources in northern provinces seems to be higher. The spillover effect of the blocks indicates provinces with medical resource centres have beneficial effects, while provinces with insufficient resources have obvious spillover effects. Conclusion: There is a significant gap in the geographical distribution of medical resources, and the health economic spatial network structure needs to be improved. Most medical resources are concentrated in economically developed provinces, and these provinces’ positions in the health economic spatial network are becoming more centralized. By contrast, economically underdeveloped regions are at the edge of the network, causing patients to move to provinces with medical resource centres. There are health risks of the increasing

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Inequalities in health-related quality of life and the contribution from socioeconomic status: evidence from Tibet, China

Cited by 19 publications

References 50 publications

Combining education and income into a socioeconomic position score for use in studies of health inequalities

Combining education and income into a socioeconomic position score for use in studies of health inequalities

Health-related quality of life and its association with socioeconomic status and diet diversity in Chinese older adults

A Study of the Spatial Network Structure of the Health Economy in China: Benefits and Spillover Effects on Patient Mobility

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