Objective: To evaluate the effectiveness of varicella vaccine in varicella outbreaks and to analyze the influencing factors, and to provide a reference for making the targeted prevention and controlling measures. Methods: A total of 3 888 students with no history of varicella were selected from 2 schools with varicella outbreak in Guangdong Province in 2021, a retrospective cohort study was conducted by using questionnaire survey, rate ratio ( RR ) and vaccine effectiveness ( VE ) values were calculated and Logistic regression was uses to analyze the factors influencing the protective effect of varicella. Results: There were 138 confirmed cases of varicella among the participants. There was no significant sex difference in the vaccination rate( χ 2=1.36, P =0.51), but there was significant difference in the vaccinattion rate of different age groups( χ 2=555.82, P <0.01). The overall protective effect of VarV was 66.94%(95% CI =56.17%-77.71%), and the protective effect of 2 doses of vaccine( VE = 90.02% , 95% CI =83.13%-96.90%) was higher than that of 1 dose( VE =49.40%, 95% CI =32.36%-66.44%)( χ 2=24.93, P < 0.01 ). The high fever rates in the vaccinated and unvaccinated groups were 7.69% and 25.81%, with significant difference( χ 2= 6.29 , P <0.05). The rates of moderate and severe skin lesions of vaccinated and unvaccinated groups was 20.00% and 50.00%, respectively, and the difference was statistically significant( χ 2=11.32, P <0.01). The protective effects of varicella vaccine against high fever and moderate to severe rash were 70.19%(95% CI =42.11%-98.27%) and 60.00%(95% CI =38.15%-81.85%). Stratified analysis showed that there were significant differences in different years of vaccination( χ 2=37.87, P <0.05), while there were no significant differences in age of vaccination and vaccine manufacturer ( P >0.05). Conclusion Varicella vaccination can prevent chickenpox infection and reduce the severity of the disease. However, the efficacy of varicella vaccine was affected by vaccination years. It is recommended to improve the vaccination coverage of varicella vaccine to prevent the outbreak of the epidemic.

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.

Objective:To investigate the association between oxygen saturation (SpO 2) and risk of 3-year all-cause mortality among Chinese older adults aged 65 or over. Methods:The participants were enrolled from Healthy Aging and Biomarkers Cohort Study in year of 2012 to 2014 in 9 longevity areas in China. In this prospective cohort study, 2 287 participants aged 65 or over were enrolled. Data on SpO 2 and body measurements were collected at baseline in 2012, and data on survival outcome and time of mortality were collected at the follow-up in 2014. Participants were divided into two groups according to whether SpO 2 was abnormal (SpO 2<94% was defined as abnormal). Results:The 2 287 participants were (86.5±12.2) years old, 1 006 were males (44.0%), and 315 (13.8%) were abnormal in SpO 2. During follow-up in 2014, 452 were died, 1 434 were survived, and 401 were lost to follow-up. The all-cause mortality rate was 19.8%, and the follow-up rate was 82.5%. The mortality rate of SpO 2 in normal group was 21.1%, and that of abnormal group was 41.6% ( P<0.001). After adjusting for confounding factors, compared to participants with normal SpO 2, participants with abnormal SpO 2 had increased risk of all-cause mortality with HR (95% CI) of 1.62 (1.31-2.02); HR (95 % CI) was 1.49 (0.98-2.26) for males and 1.71 (1.30-2.26) for females in abnormal SpO 2group, respectively; HR (95% CI) was 2.70 (0.98-7.44) for aged 65-79 years old, 1.22 (0.63-2.38) for aged 80-89 years old, and 1.72 (1.35-2.19) for aged over 90 years old in abnormal SpO 2 group, respectively. Conclusion:Abnormal SpO 2 was responsible for increased risk of 3-year all-cause mortality among Chinese elderly adults.

Objective:To investigate the association between oxygen saturation (SpO 2) and risk of 3-year all-cause mortality among Chinese older adults aged 65 or over. Methods:The participants were enrolled from Healthy Aging and Biomarkers Cohort Study in year of 2012 to 2014 in 9 longevity areas in China. In this prospective cohort study, 2 287 participants aged 65 or over were enrolled. Data on SpO 2 and body measurements were collected at baseline in 2012, and data on survival outcome and time of mortality were collected at the follow-up in 2014. Participants were divided into two groups according to whether SpO 2 was abnormal (SpO 2<94% was defined as abnormal). Results:The 2 287 participants were (86.5±12.2) years old, 1 006 were males (44.0%), and 315 (13.8%) were abnormal in SpO 2. During follow-up in 2014, 452 were died, 1 434 were survived, and 401 were lost to follow-up. The all-cause mortality rate was 19.8%, and the follow-up rate was 82.5%. The mortality rate of SpO 2 in normal group was 21.1%, and that of abnormal group was 41.6% ( P<0.001). After adjusting for confounding factors, compared to participants with normal SpO 2, participants with abnormal SpO 2 had increased risk of all-cause mortality with HR (95% CI) of 1.62 (1.31-2.02); HR (95 % CI) was 1.49 (0.98-2.26) for males and 1.71 (1.30-2.26) for females in abnormal SpO 2group, respectively; HR (95% CI) was 2.70 (0.98-7.44) for aged 65-79 years old, 1.22 (0.63-2.38) for aged 80-89 years old, and 1.72 (1.35-2.19) for aged over 90 years old in abnormal SpO 2 group, respectively. Conclusion:Abnormal SpO 2 was responsible for increased risk of 3-year all-cause mortality among Chinese elderly adults.

Objective: To understand the relationship between visual impairment and risk of all-cause mortality in the elderly aged 65 years and older in 8 longevity areas in China. Methods: The data of the elderly aged 65 years and older in the project in 2012 were obtained from Healthy Aging and Biomarkers Cohort Study, a sub-cohort of the Chinese Longitudinal Healthy Longevity Survey, including physical measurement and survival status, and a follow-up for survival outcomes were conducted in 2014 and 2017 respectively. Cox proportional hazard regression model was used to analyze the influence of visual impairment on mortality. Gender and age specific analysis was conducted. Results: A total of 1 736 elderly adults were included. A total of 943 deaths occurred during the 5-year follow-up period with a 5-year mortality rate of 54.3%. The 5-year mortality rate was 76.7% in the group with visual impairment, and 47.6% in the group without visual impairment (P<0.001). After adjusting for demographic information, life style and some disease factors, the risk of 5-year mortality in the group with visual impairment group was 1.30 times higher than that in the group without visual impairment (HR=1.30, 95%CI: 1.09-1.55). In the females, the risk for mortality in the group with visual impairment was 1.48 times higher than that in the group without visual impairment (HR=1.48, 95%CI:1.20-1.84). However, vision status was not associated with the risk for mortality in males (HR=1.02, 95%CI: 0.72-1.43). The risk for mortality in the group with visual impairment was 1.39 times higher than that in the group without visual impairment in the elderly aged over 90 years (HR=1.39, 95%CI: 1.13-1.70). Vision status was not associated with mortality risk in the elderly aged 65-79 years and 80-89 years (HR=1.37, 95%CI: 0.61-3.07; HR=0.95, 95%CI: 0.61-1.48). Conclusion In the elderly people in China, visual impairment is a risk factor for mortality.

Objective: To investigate the association between estimated glomerular filtration rate (eGFR) and all-cause mortality in the elderly aged 65 years and older in longevity areas in China. Methods: Data used in this study were obtained from Healthy Aging and Biomarkers Cohort Study, a sub-cohort of the Chinese Longitudinal Healthy Longevity Survey, 1 802 elderly adults were collected in the study during 2012-2017/2018. In this study, the elderly were classified into 4 groups, moderate-to-severe group [<45 ml·min^-1·(1.73 m²)^-1], mild-to-moderate group [45- ml·min^-1·(1.73 m²)^-1], mild group [60- ml·min^-1·(1.73 m²)^-1] and normal group [≥90 ml·min^-1·(1.73 m²)^-1] according to their eGFR levels. Results: After 6 years of follow-up, 852 participants died, with a mortality rate of 47.3%. Multivariate Cox regression analysis showed that the levels of eGFR were negatively correlated with all-cause mortality risk in the elderly (the HR of elderly was 0.993 and the 95%CI was 0.989-0.997 for every unit of eGFR increased, P=0.001), while compared with the group with normal eGFR, the HRs (95%CI) of the elderly in the moderate-to-severe group, mild-to-moderate group, and mild group were 1.690 (1.224-2.332, P=0.001), 1.312 (0.978-1.758, P=0.070), 1.349 (1.047-1.737, P=0.020) respectively [trend test P<0.001]. Conclusion The decrease in eGFR was associated with higher mortality risk among the elderly in longevity areas in China.

Randomization is the key to ensure the balance of confounders between the comparison groups in clinical trials, and it is the statistical basis for making the study results comparable. A simple randomization in a clinical trial with large sample-size allows for a balanced comparison of the number of subjects and confounding factors between groups, but in a clinical trial with small sample-size, it is necessary to use a restricted randomization method (the blocked randomization). Block randomization ensures that the number of subjects between groups is basically equal, maximize the effectiveness of clinical trials as the standard error of the treatment-effect estimate is decreased, which affords big rewards in scientific accuracy and credibility.

The hazard ratio and median survival time are the routine indicators in survival analysis. We briefly introduced the relationship between hazard ratio and median survival time and the role of proportional hazard assumption. We compared 110 pairs of hazard ratio and median survival time ratio in 58 articles and demonstrated the reasons for the difference by examples. The results showed that the hazard ratio estimated by the Cox regression model is unreasonable and not equivalent to median survival time ratio when the proportional hazard assumption is not met. Therefore, before performing the Cox regression model, the proportional hazard assumption should be tested first. If proportional hazard assumption is met, Cox regression model can be used; if proportional hazard assumption is not met, restricted mean survival times is suggested.

The hazard ratio and median survival time are the routine indicators in survival analysis. We briefly introduced the relationship between hazard ratio and median survival time and the role of proportional hazard assumption. We compared 110 pairs of hazard ratio and median survival time ratio in 58 articles and demonstrated the reasons for the difference by examples. The results showed that the hazard ratio estimated by the Cox regression model is unreasonable and not equivalent to median survival time ratio when the proportional hazard assumption is not met. Therefore,before performing the Cox regression model, the proportional hazard assumption should be tested first. If proportional hazard assumption is met, Cox regression model can be used; if proportional hazard assumption is not met, restricted mean survival times is suggested.

The hazard ratio and median survival time are the routine indicators in survival analysis. We briefly introduced the relationship between hazard ratio and median survival time and the role of proportional hazard assumption. We compared 110 pairs of hazard ratio and median survival time ratio in 58 articles and demonstrated the reasons for the difference by examples. The results showed that the hazard ratio estimated by the Cox regression model is unreasonable and not equivalent to median survival time ratio when the proportional hazard assumption is not met. Therefore,before performing the Cox regression model, the proportional hazard assumption should be tested first. If proportional hazard assumption is met, Cox regression model can be used; if proportional hazard assumption is not met, restricted mean survival times is suggested.