Policy interplay among social health insurance system, pension system, delayed retirement initiative and implications for the self-rated health status of older workers.

Delayed retirement initiative proposed in China attaches greater importance to the sustainability of pension systems and the labour shortage, but less to the health status of older people. The existing social health insurance and pension system are not well established to match this initiative. This study investigates the policy mix of delayed retirement, employment-based social health insurance, social pension participation for health status of older people. Results of the data from the China Health and Retirement Longitudinal Study (CHARLS-2018) show that late retirement could benefit health status among older adults. Moreover, such effect of late retirement appears more salient for those uninsured by employment-based social health insurance and those still in the pension contribution phase upon reaching the statutory retirement age. Hence, in countries with inadequate health insurance and pension systems, such as China, delayed retirement may serve as an important alternative to social security for the health of older people.

Longstanding health risk across the life course: The influence of early-life experience on health status throughout the life span.

This study tracked the longstanding effect of childhood adversities on health status over the course of a life. This study used the data from China Health and Retirement Longitudinal Study which was a nationally representative survey and documented the generation who had arrived in the middle- and old-age phase and experienced the difficult time in the early founding of PR China in their childhood. Results shown the significant associations between multiple forms of children adversities (economic distress, child neglect, child abuse, lack of friends, parental mental health problems) and health status in adolescence (from 0.068 to 0.102, p<0.01), and health status in mid and late adulthood, including self-rated general health problems (from 0.039 to 0.061, p<0.01), chronic conditions (from 0.014 to 0.120, p<0.01 except for lack of friends), body aches (from 0.016 to 0.062, p<0.01 except for child neglect), and depression (from 0.047 to 0.112, p<0.01). Meanwhile, results also shown an underlying pathway (i.e., health status in adolescence) linking childhood adversities and health status in mid and late adulthood. Results suggested that the experience of multiple forms of adversities in childhood represented a substantial source of health risk throughout life.

Turning Point of Cognitive Decline for Chinese Older Adults from a Longitudinal Analysis: Protective Factors and Risk Factors.

OBJECTIVES: To explore the turning point of cognitive decline in Chinese older adults and to explore the influencing factors including covariates. PARTICIPANTS: Aged 65 and older whose cognitive function was normal at their first test. METHODS: a secondary analysis that identified participants from the database of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Cohort-sequential design was used to categorize the data by age (rather than study wave), including the follow-up data of Chinese older adults aged 65-79 years and spanning 14 years. Cognitive function in 1278 participants was assessed using the Chinese Mini-Mental State Examination (CMMSE) in five waves over 14 years. Piecewise latent growth curve modeling was used to analyze the data. RESULTS: (1) The turning point of cognitive decline in Chinese older adults occurs between the ages of 68 and 70. (2) There are statistically significant individual differences in the initial level of cognitive function and the growth rate of cognitive function before and after the transition stage. (3) Factors influencing cognitive function include residence, education level, smoking, drinking, exercise, leisure activities, social activities, Activities of Daily Living (ADL), and Instrumental Activities for Daily Living (IADL). (4) Exercise and ADL are the main protective factors, while smoking and drinking are the main risk factors. CONCLUSIONS: There is a transition stage (68-70) in the decline of cognitive function in Chinese older adults and four main factors (such as smoking, drinking, exercise and ADL) have impacts on the cognitive decline. We should strengthen these protective factors (exercise and ADL) for the cognitive decline of older adults and avoid these risk factors (smoking, drinking). To prevent the decline of the cognitive function of older adults, the government should build more places conducive to activities for older adults and actively encourage older adults to improve their physical activity level. Given our findings, public health interventions centered on alcohol and tobacco cessation in older adults should be governmentally endorsed.

A Novel Nonparametric Feature Selection Approach Based on Mutual Information Transfer Network.

The filter feature selection algorithm is habitually used as an effective way to reduce the computational cost of data analysis by selecting and implementing only a subset of original features into the study. Mutual information (MI) is a popular measurement adopted to quantify the dependence among features. MI-based greedy forward methods (MIGFMs) have been widely applied to escape from computational complexity and exhaustion of high-dimensional data. However, most MIGFMs are parametric methods that necessitate proper preset parameters and stopping criteria. Improper parameters may lead to ignorance of better results. This paper proposes a novel nonparametric feature selection method based on mutual information and mixed-integer linear programming (MILP). By forming a mutual information network, we transform the feature selection problem into a maximum flow problem, which can be solved with the Gurobi solver in a reasonable time. The proposed method attempts to prevent negligence on obtaining a superior feature subset while keeping the computational cost in an affordable range. Analytical comparison of the proposed method with six feature selection methods reveals significantly better results compared to MIGFMs, considering classification accuracy.