Physical attractiveness has been linked to better economic, dyadic, and health outcomes but is understudied. We focus here on the gendered implications of attractiveness for one component of social well-being, access to intimate partnership and sexuality, among older adults. And we examine the role of body size as measured and as rated by an observer in evaluations of attractiveness and the diverging consequences for women and men. We use data from Rounds 1 (2005/06) and 2 (2010/11) of the National Social Life, Health, and Aging Project (N=2144) to explore the association of two measures of body size, weight relative to height (BMI), and interviewer assessments of body size, with sexual behavior that requires a partner and with sexual behavior that does not. We find that at larger body sizes as reflected in both BMI and rated body shape, women, but not men, face a lower probability of having a partner and engaging in partnered sex, and a lower frequency of vaginal intercourse and receiving sexual touch. These associations are mediated by physical functioning for BMI and by attractiveness as rated by the interviewer for rated body shape. We also find that women, but not men, are more likely to report finding sex not pleasurable at a higher BMI, which partly operates through the mechanism of functional limitations. We suggest that these findings reflect different attractiveness standards for men and women, which reduce women's access to partners and partnered sex but not solitary sex, such as masturbation.

Given the prevalence of depressive mental health symptoms among Chinese adults of grandpar-enting age in recent decades, a better understanding of how the depression and life satisfaction among mid-aged and older adults in China are affected by their role as grandparents is called for. This study examines the relationship between grandparenting and depression and life satisfaction among Chinese adults using multilevel regression models based on a multilevel matching dataset formulated from the 2018 China Health and Retirement Longitudinal Study (CHARLS) and the 2018 China City Statistical Yearbook. The results show that for adults who take care of their grandchildren, living with their children can significantly reduce depression. Meanwhile, whereas spending more time taking care of grandchildren can lower life satisfaction, taking care of more grandchildren is related to higher life satisfaction. The findings of this study should help policymakers improve the quality of life of Chinese adults through better-targeted approaches.

Social contagion is a key mechanism that shapes health behaviors, but few studies have applied this approach at the regional level to examine how vaccination beliefs and rates vary and diffuse across geographic areas. Through modifying the traditional SIR model, this paper addresses this gap by applying social network theory to a new compartmental model to simulate regional contagion in COVID-19 vaccination rates in England, using panel data of new and accumulated vaccination numbers from December 2020 to June 2022. The model estimates each region's initial and changing vaccination beliefs and their mutual influence on each other. The results reveal that Southeastern regions in England had higher initial vaccination beliefs and stronger spillover effects on other regions than northwestern regions. The paper suggests that policies to increase vaccination rates should consider the heterogeneity and peer effects among regions and other factors that may affect vaccination beliefs. The paper also discusses the limitations of the network model and directions for future research.

The disruption of China's care systems during and immediately post-pandemic is not unique in the global context. Most countries continue to experience significant health systems challenges. However, the size of China's population and its ageing demographic, makes it a particular health system challenge from which important global lessons might be learned. China's case evidences the consequences of lack of integration of primary care practitioners into high-level decision-making, and the lack of investment in primary healthcare. The government must allow for transparent and meaningful integration of evidence and knowledge from researchers, healthcare providers, and practitioners at different tiers in the healthcare system when designing policies. In this way, the government can better allocate scarce medical resources for testing, medical treatment, and vaccination such that the healthcare system's capacity to face the healthcare challenges of COVID-19 and future pandemics can be improved. Payment systems should encourage the use of cost-effective primary care and primary care funds must be protected from the risk of reallocation into secondary and tertiary care - even in times of crisis. There is an urgent need for the Chinese government to rethink the payment mechanism and the allocation of medical resources, thus avoiding overcrowding at tertiary hospitals, enhancing primary healthcare capacity, and utilising the perspectives of grassroots-level health workers and researchers who better understand the needs of the population.

A year following the initial COVID-19 outbreak in China, many countries have approved emergency vaccines. Public-health practitioners and policymakers must understand the predicted populational willingness for vaccines and implement relevant stimulation measures. This study developed a framework for predicting vaccination uptake rate based on traditional clinical data - involving an autoregressive model with autoregressive integrated moving average (ARIMA) - and innovative web search queries - involving a linear regression with ordinary least squares/least absolute shrinkage and selection operator, and machine-learning with boost and random forest. For accuracy, we implemented a stacking regression for the clinical data and web search queries. The stacked regression of ARIMA (1,0,8) for clinical data and boost with support vector machine for web data formed the best model for forecasting vaccination speed in the US. The stacked regression provideda more accurate forecast. These results can help governments and policymakers predict vaccine demand and finance relevant programs.