Background/objectives During the 2019 coronavirus disease (COVID-19) outbreak, obesity may contribute to COVID-19 transmission and deterioration. In addition, many patients with COVID-19 infection have suffered liver damage which might contribute to a worse prognosis. We conducted a clinical epidemiological analysis to investigate the association of overweight/obesity and abnormal liver function (ALF) with hospitalized duration in patients infected with COVID-19. Subjects/methods Fifty-eight patients with diagnosed COVID-19 (22 women & 36 men; average age: 49.2 ± 13.1 yr) were included, and their clinical data were collected at The Second Affiliated and Yuying Children's Hospital of Wenzhou Medical University, Zhejiang. Overweight/obesity was determined as body mass index (BMI) ≥24 kg/m 2 , ALF was determined as alanine aminotransferase >40 U/L, and prolonged hospitalization was lasting more than the median value of the hospitalized days (19 days) in this population. Results The proportions of prolonged hospitalization were elevated in patients with overweight/obesity and ALF compared with those without overweight/obesity (62.1% versus 26.1%, P = 0.010) and those without ALF (70.6% versus 41.5%, P = 0.043). Kaplan-Meier analysis showed that the hospitalized duration was increased from the patients with neither overweight/obesity nor ALF to those with either overweight/obesity or ALF, and to those with both of overweight/obesity and ALF (mean with 95% confidence interval: 16.4 [14.5-18.3] versus 25.3 [21.6-29.1] versus 28.3 [24.6-32.0], P for trend = 0.001). Being discharged from hospital in time was inversely and independently associated with BMI (hazard ratio [HR] = 0.75, 95% CI: 0.63-0.90, P for trend = 0.002) and ALT (HR = 0.95, 95% CI: 0.92-0.99, P for trend = 0.007). Conclusions Present findings suggested that overweight/obesity and/or ALF contributed to predicting a probability of prolonged hospitalization in patients with COVID-19 infection, to whom extra attentions and precautions should be paid during clinical treatments.

Background This study applied the susceptible-exposed-infectious-removed (SEIR) model to analyze and simulate the transmission mechanisms of the coronavirus disease 2019 (COVID-19) in China. Methods The population migration was embedded in the SEIR model to simulate and analyze the effects of the amount of population inflow on the number of confirmed cases. Based on numerical simulations, this study used statistical data for the empirical validation of its theoretical deductions and discussed how to improve the effectiveness of epidemic prevention and control considering population migration variables. Statistics regarding the numbers of infected people in various provinces were obtained from the epidemic-related data reported by China’s National Health Commission. Results This study explored how the epidemic should be prevented and controlled from the perspective of population migration variables. It found that the combination of a susceptible population, an infected population, and transmission media were important routes affecting the number of infections and that the migration of a Hubei-related infected population played a key role in promoting epidemic spread. Epidemic prevention and control should focus on regions with better economic conditions than the epidemic region. Prevention and control efforts should focus on the more populated neighboring provinces having convenient transportation links with the epidemic region. To prevent and control epidemic spread, priority should be given to elucidating the destinations and directions of population migration from the domestic origin of infections, and then controlling population migration or human-to-human contact after such migration. Conclusions This study enriched and expanded on simulations of the effects of population migration on the COVID-19 epidemic and China-based empirical studies while offering an epidemic evaluation and warning mechanism to prevent and control similar public health emergencies in the future.

Background: Increased population aging is associated with increased incidence of depression among the elderly. Existing studies have shown that ill-advised fertility behaviors during their youth also affect the health of the elderly. However, insufficient attention has been paid to depression among elderly in China. This paper focuses on how fertility behaviors affect senile depression among parents by examining the heterogeneity of such effects and tests the applicability of existing theoretical findings in a Chinese sample.Methods: The effects of fertility behaviors on depression among the elderly were investigated using the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative dataset. The effects of early-age fertility behaviors on the degree of depression among the elderly were investigated using ordinary least squares and ordered probit models that adjusted for demographic and socioeconomic factors.Results: (1) The age of first childbirth, childbearing period, and number of births were significantly and positively correlated with the degree of depression among the elderly (particularly rural persons aged 50–70 and older womens). (2) Elderly persons with sons had no better mental health status than those without sons, thus indicating the inapplicability of the traditional concept of “more sons are equal to more happiness” to the actual mental health situation of the elderly in China today.Conclusion: Overall, multiple, late, and boy-oriented childbearing and overly long childbearing periods had negative effects on mental health among Chinese elderly persons. This study tested the applicability of existing theoretical inferences and empirical conclusions in China, thereby further expanding the current literature regarding the effects of fertility behaviors on depression among the elderly.

Background: This study applied the SEIR model to analyze and simulate the transmission mechanisms of the coronavirus disease 2019 (COVID-19) in China. Methods: The population migration was embedded in the SEIR model to simulate and analyze the effects of the amount of population inflow on the number of confirmed cases. Based on numerical simulations, this study used statistical data for the empirical validation of its theoretical deductions and discussed how to improve the effectiveness of epidemic prevention and control considering population migration variables. Statistics regarding the numbers of infected people in various provinces were obtained from the epidemic-related data reported by China’s National Health Commission.Results: This study explored how the epidemic should be prevented and controlled from the perspective of population migration variables. It found that a combination of a susceptible population, an infected population, and transmission media was an important route affecting the number of infections and that the migration of a Hubei-related infected population played a key role in promoting epidemic spread. Epidemic prevention and control should focus on regions with better economic conditions than the epidemic region. Prevention and control efforts should focus on the more populated neighboring provinces having convenient transportation links with the epidemic region. To prevent and control epidemic spread, priority should be given to elucidating the destinations and directions of population migration from the domestic origin of infections, and then stemming population migration or human-to-human contact after such migration. Conclusions: This study enriched and expanded on simulations of the effects of population migration on the COVID-19 epidemic and China-based empirical studies while offering an epidemic evaluation and warning mechanism to prevent and control similar public health emergencies in the future.

Does delayed retirement crowd out the welfare of the workforce? To answer this question, a dynamic optimization framework is established to simulate the impact of delayed retirement on the welfare of the working population over time. Simulations are conducted based on practical and feasible parameters. Delayed retirement was found to improve the welfare of the working population rather than crowding it out. Furthermore, the results are robust against changes in parameters and modes of supporting elderly individuals. In terms of policymaking, it is suggested that such facts be shared with the public and that a delayed retirement plan be introduced as soon as possible to manage the pension and retirement wave caused by post-1960s baby boomers. However, to ensure that the delayed retirement plan does not lead to a reduction in the welfare of the working population, increases in fertility costs and the pension replacement rate should be appropriately controlled.