Industrial development has brought about not only rapid economic growth, but also serious environmental pollution in China, which has led to serious health problems and heavy economic burdens on healthcare. Therefore, the relationship between the industrial air pollution and health care expenditure (HCE) has attracted the attention of researchers, most of which used the traditional empirical methods, such as ordinary least squares (OLS), logistic and so on. By collecting the panel data of 30 provinces of China during 2005–2016, this paper attempts to use the Bayesian quantile regression (BQR) to reveal the impact of industrial air pollution represented by industrial waste gas emission (IWGE) on HCE in high-, middle-, low-income regions. It was found that double heterogeneity in the influence of IWGE on HCE was obvious, which revealed that people in high-, middle-, low-income regions have significantly different understandings of environmental pollution and health problems. In addition, the BQR method provided more information than the traditional empirical methods, which verified that the BQR method, as a new empirical method for previous studies, was applicable in this topic and expanded the discussion space of this research field.

As a result of China’s economic growth, air pollution, including carbon dioxide (CO₂) emission, has caused serious health problems and accompanying heavy economic burdens on healthcare. Therefore, the effect of carbon dioxide emission on healthcare expenditure (HCE) has attracted the interest of many researchers, most of which have adopted traditional empirical methods, such as ordinary least squares (OLS) or quantile regression (QR), to analyze the issue. This paper, however, attempts to introduce Bayesian quantile regression (BQR) to discuss the relationship between carbon dioxide emission and HCE, based on the longitudinal data of 30 provinces in China (2005–2016). It was found that carbon dioxide emission is, indeed, an important factor affecting healthcare expenditure in China, although its influence is not as great as the income variable. It was also revealed that the effect of carbon dioxide emission on HCE at a higher quantile was much smaller, which indicates that most people are not paying sufficient attention to the correlation between air pollution and healthcare. This study also proves the applicability of Bayesian quantile regression and its ability to offer more valuable information, as compared to traditional empirical tools, thus expanding and deepening research capabilities on the topic.

The rapid growth of population aging makes providing adequate long-term care (LTC) services for the elderly a serious social dilemma in China. Thus, it is necessary to carry out a theoretical discussion on the LTC service needs of the elderly and find out their influencing factors. With four regions of the middle and upper reaches of the Yangtze River as the sample case, this study aims to explore the factors that affect LTC service needs of the elderly in the frame of the latest Anderson Model, which added psychosocial factors to predisposing characteristics, enabling factors, and need factors in the old version. Some interesting results have been found, for example, self-image evaluation is composed of several factors such as general physical health, attitude towards life, or psychosocial states. Finally, sub-analyses—namely, by age, by gender, and by educational level—were carried out since the choice of different long-term care service patterns is related to different age/gender/education groups.

Objective: This study assessed the economic burden of senile chronic diseases (SCD) in China. Methods: Based on China Health and Retirement Longitudinal Survey data, we used propensity score matching to compare the economic burden of SCD for families with and without members with SCD. Results: We examined three aspects of economic burden: health service utilization, including health care utilization and expenditure, family daily consumption spending, and workforce participation of family members in labor markets. SCD-families showed a higher hospitalization rate and number of outpatient visits than the control group. An average of 35%–43% of SCD-families’ expenditure was spent on out-of-pocket inpatient and outpatient care. SCD-families also consumed less per capita. Additionally, the negative influence of SCD on total family income may be partially offset by increasing the labor force participation rate of healthy family members. Conclusions: Relevant results may provide insight into formulating health and labor policies by the Chinese government. The study significance consists of the selection of matching variables and robustness, whereas study limitations include the omission of other important variables.