Objective To understand the prevalence of senile dementia in the elderly aged ≥ 60 years in China between 2000 and 2015.Methods Publications between 2000 and 2015 were retrieved from China National Knowledge Infrastructure (CNKI),Wanfang Databases,Chinese Science and Technology Journal Databases (VIP) and PubMed.Observational studies on prevalence of senile dementia were included.Meta-analysis was carried out by using R software.Results A total of 25 papers were included in this study.The total number of participants investigated was 76 980,with 4 295 dementia cases identified.The prevalence of senile dementia in the elderly was 5.15％ (95％CI:4.21％-6.09％);Subgroup analysis showed that the women had a higher prevalence (6.08％) than the men (4.10％);and the prevalence was significant increased with age (The senile dementia prevalence was 1.01％ in age groups 60-64 years,but 23.60％ in age group ≥85 years).The prevalence of Alzheimer's disease (3.56％) was higher than that of cerebral vascular dementia (1.11％).The senile dementia prevalence in the illiterate was 8.74％,higher than 3.17％ in the literate.The prevalence of senile dementia in the unmarried was 6.83％,higher than that in the married (3.95％).Conclusion The prevalence of senile dementia was high in the population aged ≥60 years in China.Age,sex,education level and marital status might influence the prevalence of senile dementia.

Objective:To identify the threshold of a health warning system based on the association of apparent temperature and years of life lost (YLL).Methods:Daily mortality records and meteorological data were collected from 364 Chinese counties for 2006-2017. Distributed lag nonlinear model and multivariate Meta-analyses were applied to estimate the association between the apparent temperature and YLL rate. A regression tree model was employed to estimate the warning thresholds of the apparent temperature. Stratified analyses were further conducted by age and cause of death.Results:The daily YLL rate was 23.6/10 5. The mean daily apparent temperature was 15.7 ℃. U-shaped nonlinear associations were observed between apparent temperature and YLL rate. The actual temperature-caused YLL rate for the elderly was higher than the young population. The daily excess deaths rate increased with the higher effect levels. Conclusions:Regression tree model was employed to define the warning threshold for meteorological health risk. The present study provides theoretical support for the weather-related health warning system.

Objective: To investigate the association between sleep duration and frailty among people aged 50 years and over. Methods: Cross-sectional data was collected from the first wave of World Health Organization Study on global AGEing and adult health in China. Frailty index was constructed on the proportion of deficits, out of the 40 variables. A two-level (individual level and community level) linear model was performed to identify the related factors on frailty. All the models were stratified by age, gender, residence (urban/rural). Restricted cubic spline was performed to graphically evaluate the dose-response association between self-reported sleep duration and frailty. Results: A total of 13 175 individuals aged 50 years and over participated in this study. Without adjusting on any confounding factors, shorter or longer sleep duration significantly increased the risk of weakness compared with normal sleep time (OR=2.05, 95%CI: 1.71-2.44; OR=1.35, 95%CI: 1.12-1.63). After adjusting for confounding factors such as gender, age, residence, education, family assets, vegetable, smoking, drinking and physical activity, a positive association between short sleep duration and frailty was noticed compared with normal sleep time (aOR=1.60, 95%CI: 1.27-2.01). The results of stratified analysis on sex, age and urban and rural areas showed that, after adjusting for gender, age, residence, education level, family assets, intake of vegetables and fruits, smoking, drinking and physical activity, only shorter sleep duration was positively correlated with the risk of weakness. In addition, among people aged 65 years and over, adjusted for confounding factors, the risk of weakness increased by 91%, compared with normal sleep time (aOR=1.91, 95%CI: 1.46-2.49). The dose-response curve also showed that the sleep duration and frailty present an approximate "U" shaped relationship. Conclusion Short sleep duration might be associated with frailty.

Background Air pollution is a major public health concern. Air Quality Health Index (AQHI) is a very important air quality risk communication tool. However, AQHI is usually constructed by single-pollutant model, which has obvious disadvantages. Objective To construct an AQHI based on the joint effects of multiple air pollutants (J-AQHI), and to provide a scientific tool for health risk warning and risk communication of air pollution. Methods Data on non-accidental deaths in Yunnan, Guangdong, Hunan, Zhejiang, and Jilin provinces from January 1, 2013 to December 31, 2018 were obtained from the corresponding provincial disease surveillance points systems (DSPS), including date of death, age, gender, and cause of death. Daily meteorological (temperature and relative humidity) and air pollution data (SO₂, NO₂, CO, PM_2.5, PM₁₀, and maximum 8 h O₃ concentrations) at the same period were respectively derived from China Meteorological Data Sharing Service System and National Urban Air Quality Real-time Publishing Platform. Lasso regression was first applied to select air pollutants, then a time-stratified case-crossover design was applied. Each case was matched to 3 or 4 control days which were selected on the same days of the week in the same calendar month. Then a distributed lag nonlinear model (DLNM) was used to estimate the exposure-response relationship between selected air pollutants and mortality, which was used to construct the AQHI. Finally, AQHI was classified into four levels according to the air pollutant guidance limit values from World Health Organization Global Air Quality Guidelines (AQG 2021), and the excess risks (ERs) were calculated to compare the AQHI based on single-pollutant model and the J-AQHI based on multi-pollutant model. Results PM_2.5, NO₂, SO₂, and O₃ were selected by Lasso regression to establish DLNM model. The ERs for an interquartile range (IQR) increase and 95% confidence intervals (CI) for PM_2.5, NO₂, SO₂ and O₃ were 0.71% (0.34%–1.09%), 2.46% (1.78%–3.15%), 1.25% (0.9%–1.6%), and 0.27% (−0.11%–0.65%) respectively. The distribution of J-AQHI was right-skewed, and it was divided into four levels, with ranges of 0-1 for low risk, 2-3 for moderate risk, 4-5 for high health risk, and ≥6 for severe risk, and the corresponding proportions were 11.25%, 64.61%, 19.33%, and 4.81%, respectively. The ER (95%CI) of mortality risk increased by 3.61% (2.93–4.29) for each IQR increase of the multi-pollutant based J-AQHI , while it was 3.39% (2.68–4.11) for the single-pollutant based AQHI . Conclusion The J-AQHI generated by multi-pollutant model demonstrates the actual exposure health risk of air pollution in the population and provides new ideas for further improvement of AQHI calculation methods.