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Highlights • This paper explores the effects of four dimensions of social exclusion on self-rated health and depression among older people in China. • Lower level of exclusion from social relationships was associated with less likely to report fair or poor SRH. • Lower level of subjective feelings of exclusion was associated with less likely to report fair or poor SRH. • Lower level of subjective feeling of exclusion was associated with less likely to report depression. • Lower level of exclusion from financial products was associated with less likely to report depression. Abstract Background It is well established that social exclusion is a key social determinant of health; however, such association between social exclusion and health outcomes among older people remain a relatively under-researched area. This paper explores the effects of four dimensions of social exclusion on self-rated health and depression among older people in China. Methods This paper includes 8038 individuals aged 60 and over from the first wave national multi-stage probability sample (2014) from the China Longitudinal Aging Social Survey (CLASS). Descriptive univariate information for individual variables and four dimensions of social exclusion are presented. Multinomial and binary logistic regression models are used to examine the associations between social exclusion and self-rated health and depression. Results Older people who were in the lower level of exclusion from social relationships or subjective feelings of exclusion were significantly less likely to report fair or poor self-rated health than people in the higher level of exclusion (lower level of exclusion from social activities was significantly associated with being less likely to report poor SRH only). Older people who were in the lower level of subjective feeling of exclusion or exclusion from financial products were significantly less likely to report depression. Conclusions Different dimensions of social exclusion have different effects on self-rated health and depression. Social policies need to reflect this and efforts of services could usefully be oriented to prevent multi-dimensions of social exclusion. Ultimately, such policies should have the potential to enhance the health of older people in China. [ABSTRACT FROM AUTHOR]

There is an increasing interest in using ordinal data collection methods, such as the best-worst scaling (BWS), to develop preference-based tariffs (value sets) for health-related quality of life instruments, yet the evidence on their performance is limited. This paper proposed to use an anchored BWS technique (in which the state of "death" served as an anchoring state) to directly develop a utility weight that lies on a scale anchored at 0 = death and 1 = full health for the Simplified Chinese version of the Short Form 6 Dimension version 2 (SF-6Dv2). An online panel from the general population of Mainland China completed an online survey between 20th July and 19th August, 2019 and 463 respondents were included in the main analysis. The Conditional Logit (CL) model, which assumes a homogeneous preference, as well as a Hierarchical Bayes (HB) model, which accounts for preference heterogeneity, were used to analyze the BWS data. The model performances were evaluated based on monotonicity and model-fit statistics. The majority of respondents indicated that the BWS questions were easy to understand and complete. Initial analyses suggested that the best and worst choices should not be pooled together. Based on model fit statistics of separated estimations and previous literature on health state valuation studies using BWS, the best choices were used for developing the final algorithm. The HB estimates were found to have better model performance than the CL estimates. This study provides an essential insight into using an anchored BWS approach in health state valuation. Furthermore, it demonstrates the advantage of using HB compared to the traditional CL model in producing preference values. • An anchored best-worst scaling technique has been used to develop preference weights directly. • Accounting for individual heterogeneity leads to a better model fit. • Incorporating respondents' views of attribute importance can improve model performance. • A Chinese online-panel-based scoring algorithm for SF-6Dv2 is developed. [ABSTRACT FROM AUTHOR]