Objective: To evaluate the health risk of drinking water in Ningbo City, Zhejiang Province from 2021 to 2022, so as to provide insights into ensuring the safety of drinking water. Methods: The monitoring data of drinking water from 2021 to 2022 in Ningbo City were collected from the Chinese Disease Prevention and Control Information System. The routine indicators and disinfectant indicators (radioactivity indicators were excluded) of drinking water were evaluated according to the reference limits issued by Standards for Drinking Water Quality (GB 5749-2006), and the qualification rates were calculated. The indicators with detection rate higher than 50% were selected, and assessed the carcinogenic and non-carcinogenic risks via drinking water using the risk assessment model recommended by the United States Environmental Protection Agency. Results: A total of 1 678 samples were monitored in Ningbo City from 2021 to 2022. Sodium hypochlorite was the main disinfectant among 1 558 samples from centralized water supply (1 079 samples, 64.30%), and none of the 120 samples from decentralized water supply underwent disinfection treatment. The qualification rate of 88.38%, and the pollutants with a detection rate higher than 50% were nitrate, fluoride, trichloromethane and aluminum. The median carcinogenic risk value of trichloromethane was 2.964×10-6 (interquartile range, 3.909×10-6), and the median hazard quotient values of nitrate, fluoride, trichloromethane and aluminum were 1.631×10-2 (interquartile range, 1.361×10-2), 3.955×10-2 (3.164×10-2), 2.231×10-2 (2.942×10-2) and 2.136×10-4 (6.573×10-4), respectively. Conclusion The carcinogenic and non-carcinogenic risks through drinking water for 17 pollutants in drinking water of Ningbo City from 2021 to 2022 were at low levels.

Objective To evaluate the total and added effects of heatwaves on years of life lost(YLL)in Ningbo city.Methods We obtained data of mortality,population,meteorological and air quality from 2013 to 2019 in Ningbo city,and calculated the daily YLL rate.We defined 15 types of heatwaves by combining percentiles of daily maximum temperature with duration.Time series analysis and distributed lag non-linear model were used to estimate the total and added effects of heatwaves on YLL rate,and stratified analyses were conducted by gender and age(＜65 years,≥65 years).Results In the study period,the daily total YLL rate was(19.74±3.14)/105,which were higher in heatwave periods than in non-heatwave periods.The total effects of heatwave increased with higher temperature and longer duration.When heatwave was defined by daily maximum temperature≥95th(37.2℃)and the duration≥2 d,the total effect of heatwave in lag 0-10d was the greatest,with an increased total YLL rate of 3.77(95%CI:2.25,5.29)/105.The results of stratified analyses showed that heatwave had a larger effect on male and≥65 years old.The added effects of heatwave on male and≥65 years old were statistically significant(P＜0.05).Conclusion Heatwave could elevate the level of YLL rate,with greater impacts on male and elderly people.The added effects of heatwave may only occur in sensitive populations such as male and elderly people.

Background Research on the relationship between ambient temperature and preterm birth has received increasing attention, but the conclusions of the previous literature are inconsistent. Objective To explore the impact of environmental temperature exposure in Ningbo on premature delivery of pregnant women. Methods The birth information, preterm birth data, and age of pregnant women from January 2016 to September 2020 were collected by the electronic medical record system of Ningbo Women’s and Children’s Hospital. Meteorological data for the same period were obtained through Ningbo Meteorological Bureau, including daily average temperature, daily average relative humidity, and daily average air pressure. Daily concentrations of SO₂, NO₂, and PM₁₀ were derived through the air quality real-time release system on the website of Ningbo Environmental Protection Bureau. A distributed lag nonlinear model was used to analyze the impact of environmental temperature on preterm birth by stratifying pregnant women’s age and birth delivery mode. Results The incidence rate of preterm birth in Ningbo from 2016 to 2020 was 5.91%. The exposure-response curve between environmental temperature and preterm birth presented a “U” shape. Taking 22.5 ℃ as a reference, the cumulative effect of 31 ℃ (the 95th percentile) and 32 ℃ (the 99th percentile) over a 21-day lag on preterm delivery was statistically significant, and the related RR (95%CI) values were 1.67 (1.05-2.65) and 1.85 (1.09-3.14) respectively. The results of stratified analysis showed that among pregnant women ≥30 years old, the 21-day cumulative effects of 31 ℃ and 32 ℃ on preterm delivery were statistically significant, and the related RR (95%CI) values were 2.09 (1.08-4.05) and 2.36 (1.11-5.03) respectively; among pregnant women with natural delivery, the 21-day cumulative effect of 32 ℃ on preterm delivery was statistically significant, and the RR (95%CI) was 1.95 (1.02-3.74). Conclusion Exposure of pregnant women to high temperature during pregnancy could increase the risk of preterm birth, and there is a delayed cumulative effect.

Early distinction of bipolar disorder (BD) from major depressive disorder (MDD) is difficult since no tools are available to estimate the risk of BD. In this study, we aimed to develop and validate a model of oxidative stress injury for predicting BD. Data were collected from 1252 BD and 1359 MDD patients, including 64 MDD patients identified as converting to BD from 2009 through 2018. 30 variables from a randomly-selected subsample of 1827 (70%) patients were used to develop the model, including age, sex, oxidative stress markers (uric acid, bilirubin, albumin, and prealbumin), sex hormones, cytokines, thyroid and liver function, and glycolipid metabolism. Univariate analyses and the Least Absolute Shrinkage and Selection Operator were applied for data dimension reduction and variable selection. Multivariable logistic regression was used to construct a model for predicting bipolar disorder by oxidative stress biomarkers (BIOS) on a nomogram. Internal validation was assessed in the remaining 784 patients (30%), and independent external validation was done with data from 3797 matched patients from five other hospitals in China. 10 predictors, mainly oxidative stress markers, were shown on the nomogram. The BIOS model showed good discrimination in the training sample, with an AUC of 75.1% (95% CI: 72.9%-77.3%), sensitivity of 0.66, and specificity of 0.73. The discrimination was good both in internal validation (AUC 72.1%, 68.6%-75.6%) and external validation (AUC 65.7%, 63.9%-67.5%). In this study, we developed a nomogram centered on oxidative stress injury, which could help in the individualized prediction of BD. For better real-world practice, a set of measurements, especially on oxidative stress markers, should be emphasized using big data in psychiatry.
Biomarkers/metabolism* ; Bipolar Disorder/metabolism* ; Depressive Disorder, Major/diagnosis* ; Early Diagnosis ; Humans ; Oxidative Stress

Objective: To identify the most appropriate meteorological variable for forecasting the health risk of high temperatures. Methods: The surveillance on causes of death, meteorological data and surveillance on air quality among registered residents in Ningbo City, Zhejiang Province during the period between May and October from 2013 to 2019 were collected. The association models of daily minimum temperature, average daily temperature, daily maximum temperature, daily minimum heat index, average daily heat index, daily maximum heat index, average daily apparent temperature and torridity index with deaths and years of life lost (YLL) were created using time series analysis and distributed lag non-linear models, and the model fitting effect was evaluated using the minimum Akaike information criterion (AIC) procedure. The most appropriate meteorological variable for forecasting gender-, age- and mortality-specific health risks of high temperatures was identified. Results: A total of 120 628 deaths were reported during the study period, with daily deaths of 94 cases, and daily YLL rate of 19.74 person-years/105. Except for daily minimum heat index and torridity index, the exposure-response relationships between other six meteorological variables and deaths and overall YLL rate all appeared a “J” shape. The lowest AIC values and the optimal model fitting effects were measured for the association models between average daily temperature and whole populations, females, subjects at ages of 65 years and older, and deaths and YLL rates due to circulatory diseases and respiratory diseases. Conclusion High model fitting effects are observed between average daily temperature and deaths and YLL rates, which are more suitable for forecasting the health risk of high temperature.

Objective : To evaluate the excess mortality risk related to heat wave in Ningbo, Zhejiang from 2013 to 2018, so as to provide a basis for formulating coping strategies for heat wave. Methods : The data of daily mortality, meteorological and air quality from May to October in Ningbo from 2013 to 2018 were obtained from Ningbo Center for Disease Control and Prevention, Ningbo Meteorological Bureau and Environmental Monitoring Center of Ningbo, respectively. The generalized linear model ( GLM ) and distributed lag non-linear model ( DLNM ) were used to estimate the associations between heat wave and cause-specific mortality. Results : Among 1 104 days of the study period, 18 heat waves occured and lasted for 132 days, accounting for 11.96%. A total of 102 954 deaths were reported in the same period. The risks of mortality in circulatory system diseases ( RR=1.09, 95%CI: 1.03-1.16 ), respiratory system diseases ( RR=1.14, 95%CI: 1.04-1.25 ), digestive system diseases ( RR=1.38, 95%CI: 1.15-1.65 ), nervous system diseases ( RR=1.32, 95%CI: 1.08-1.61 ), mental disorders ( RR=1.51, 95%CI: 1.12-2.03 ) and accidental injury ( RR=1.18, 95%CI: 1.06-1.32 ) and all causes ( RR=1.10, 95%CI: 1.06-1.14 ) increased at lag 0-1 day of heat wave. The total excess death related to heat wave was 1 218 ( 95%CI: 731-1 705 ) . The excess deaths of circulatory system diseases, respiratory system diseases, accidental injury, digestive system diseases, nervous system diseases, mental disorders, urinary system diseases and endocrine system diseases were 313 ( 95%CI: 104-556 ), 206 ( 95%CI: 59-368 ), 164 ( 95%CI: 55-292 ), 122 ( 95%CI: 48-208 ), 69 ( 95%CI: 17-131 ), 56 ( 95%CI: 13-113 ), 18 ( 95%CI: -15-64 ) and 3 ( 95%CI: -51-72 ). The excess deaths of urinary system and endocrine system diseases was not statistically significant ( P>0.05 ). Conclusion Heat wave can increase the mortality risk on the day and after a day in Ningbo from 2013 to 2018. Circulatory system diseases, respiratory system diseases and accidental injury rank top three in excess deaths.

Objective:To investigate a cluster epidemic of COVID-19 after a mass gathering activity in Ningbo of Zhejiang province and analyze the transmission chain and status of infection cases of different generations.Methods:The tracking of all the close contacts of the first COVID-19 case and epidemiological investigation were conducted on January 29, 2020 after a cluster epidemic of COVID-19 related with a Buddhism rally on January 19 (the 1.19 rally) in Ningbo occurred. The swabs of nose/throat of the cases and close contacts were collected and tested for nucleic acids by real-time fluorescence quantitative RT-PCR.Results:From January 26 to February 20, 2020, a total of 67 COVID-19 cases and 15 asymptomatic infection cases related with the 1.19 rally were reported in Ningbo. The initial case was the infection source who infected 29 second generation cases and 4 asymptomatic infection cases, in whom 23 second generation cases and 3 asymptomatic infection cases once took bus with the initial case, the attack rate was 33.82% (23/68) and the infection rate was 38.24% (26/68). The risks of suffering from COVID-19 and being infected were 28.91 times and 26.01 times higher in rally participants taking bus with initial case compared with those taking no bus with initial case. In this epidemic, 37 third+generation cases and 11 related asymptomatic infection cases occurred, the attack rate was 2.88% (37/1 283) and the infection rate was 4.76% (48/1 008). The main transmission routes included vehicle sharing and family transmission.Conclusion:It was a cluster epidemic of COVID-19 caused by a super spreader in a massive rally. The epidemic has been under effective control.

Objective:To describe the basic characteristics of clusters of coronavirus diseases 2019 (COVID-19) cases in Ningbo, Zhejiang province, and evaluate the generation time (Tg) and basic reproduction number ( R0) of COVID-19. Methods:The basic information and onset times of the clusters of COVID-19 cases in Ningbo were investigated, the inter-generational interval of the cases were fitted by using gamma distribution, and the R0 was calculated based on the SEIR model. Results:In the 15 clusters of COVID-19 cases, a total of 52 confirmed cases, 5 cases of nucleic acid-positive asymptomatic cases. The cases occurred from January 23 to February 4, the cases were mainly women. The incubation period was (6.11±3.38) days, and the median was 5 days. The Tg was (6.93±3.70) days. There were no significant differences in Tg between age group<60 years and age group 60 years and above, and between men and women ( P=0.551). According to the Tg calculated in this paper, the R0 of COVID-19 in Ningbo was 3.06 (95 %CI: 2.64- 3.51); according to the reported case transmission interval of 7.5 days in the literature, the R0 was 3.32 (95 %CI: 2.51-9.38). Conclusion:There is no age and gender specific differences in the Tg of clusters of COVID-19 cases in Ningbo, and COVID-19 has high infectivity and spreading power in early phase.

Objective: To explore the short-term effects of ambient PM_2.5 on the outpatient visits of chronic obstructive pulmonary disease (COPD) in Ningbo city. Methods: Through the regional health information platform, number of daily COPD outpatients from the four general hospitals in Ningbo was gathered. Related data on meteorological and air pollution from 2014 to 2016 was also collected. Generalized additive model (GAM) of Possion regression was used to estimate the impact of PM_2.5 pollution on COPD outpatients and the lagging effects. Results: In cold (November- April) or warm seasons (May-October), an 10 μg/m³ increase of PM_2.5 would result in the excessive number of COPD outpatients as 1.87% (95%CI: 0.98%-2.76%), 2.09% (95%CI: 1.11%-3.08%) and 2.56% (95%CI: 0.56%-4.59%), respectively. In terms of the short-term effects of PM_2.5 the strongest was seen in the days of warm season but without delay (P<0.05). The strongest effect appeared at day 4 in cold season and the effect was particularly significant seen in the over 65 year-old group or in the female population. After the introduction of PM₁₀, SO₂ and NO₂, the concentration of PM_2.5, did not show significant effect on the number of hospital visits due to COPD on the same day (P>0.05). The effect of COPD on the fourth day showed a slight change after the lagging, and the effect was statistically significant (P<0.05). Conclusion The increase of PM_2.5 concentration in Ningbo was related to the increase of COPD outpatient numbers. Effective prevention measures should be taken to protect the vulnerable population and to reduce the risk of COPD.

Objective: To explore the relationship between different temperature indicators and hospital admission for childhood pneumonia. Methods: The hospital admissions for pneumonia in children aged 0-14 years and meteorological data in Ningbo from 2015 to 2017 were collected. A distributed lag non-linear model combined with a generalized linear model was employed to analyze the exposure-response relationships between different temperature indicators（daily average,minimum and maximum temperature;the first percentile as low temperature and the 99th percentile as high temperature）and hospital admission for childhood pneumonia. Results: A total of 4 542 cases of childhood pneumonia were recruited. There were obvious seasonal fluctuations found in the inpatient volume of childhood pneumonia,which peaked in winter and bottomed in summer. After adjusting for potential confounding variables such as relative humidity,PM2.5,long term trend and seasonal trend,the results suggested that after exposed to whether low or high temperature,the inpatient volume of childhood pneumonia would increase. When the daily average temperature and daily minimum temperature were employed,the effect of high temperature on the increase of inpatient volume for childhood pneumonia was statistically significant and the cumulative relative risk for a lag of 0-7 days were 1.52（95%CI：1.04-2.23）and 1.59（95%CI：1.08-2.34）,respectively. When the daily maximum temperature was employed,the effect of low temperature on the increase of inpatient volume for childhood pneumonia was statistically significant and the cumulative relative risk for a lag of 0-7 days were 1.30（95%CI：1.02-1.66）. Conclusion Our findings suggested that an increased risk of hospital admission for childhood pneumonia was associated with both low and high temperature.