锦州市疾病预防控制中心

BackgroundAir pollution and meteorological factors exert complex nonlinear effects on acute symptoms in the population, with intricate interactions among these factors. Traditional statistical methods struggle to simultaneously address complex nonlinear relationships and multicollinearity issues.ObjectiveTo delineate the dynamic effects of air pollutants and meteorological parameters on acute symptoms in three distinct populations with the multicollinearity being addressed and to generate reliable scientific evidence for prevention and control of health risk factors.MethodsA time-series study design was employed to collect data on air pollution (daily mean temperature, daily precipitation, daily mean relative humidity, and daily mean wind speed), meteorological factors [Air Quality Index (AQI), fine particulate matter (PM2.5), inhalable particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and 8-hour maximum ozone (O3)], and acute symptoms such as fever, cough, and sore throat in Jinan from June to December 2023. Key variables were selected using least absolute shrinkage and selection operator (LASSO) regression, followed by generalized additive mixed modeling (GAMM) to analyze the health effects of combined environmental exposures to air pollution and meteorological factors. Linear variables were modeled using linear mixed-effects function, nonlinear variables were smoothed using thin-plate regression splines, and variables with interaction effects were smoothed using low-rank scale-invariant tensor product splines. Fluctuations in independent variables following a normal distribution were treated as sampling errors and incorporated as random effects in the GAMM.ResultsFor fever, the daily mean temperature, daily mean relative humidity, daily mean wind speed, and ambient SO2 were statistically significant (P<0.05), with daily mean wind speed being a linear influencing factor. When the daily mean temperature was below 3 °C, each 10 °C increase corresponded to a relative risk (RR) of 2.64 (95%CI: 2.50, 2.79). When the daily mean temperature was ≥3 °C, each 10 °C increase corresponded to an RR of 0.86 (95%CI: 0.83, 0.89). Each 10% increase in daily mean relative humidity was associated with an RR of 0.93 (95%CI: 0.89, 0.97). Each 1 m·s−1 increase in daily mean wind speed corresponded to an RR of 1.06 (95%CI: 1.02, 1.10). Within the concentration ranges of <10 μg·m−3, 10–<12.5 μg·m−3, and ≥12.5 μg·m−3, each 1 μg·m−3 increase in ambient SO2 corresponded to RR values of 1.01 (95%CI: 0.98, 1.05), 1.21 (95%CI: 1.17, 1.24), and 0.97 (95%CI: 0.94, 0.99), respectively. For cough, the daily mean temperature, daily mean relative humidity, PM10, and SO2 were statistically significant (P<0.001), with PM10 being a linear influencing factor. When the daily mean temperature was below 1 °C, each 10 °C increase corresponded to an RR of 1.47 (95%CI: 1.42, 1.52). When the daily mean temperature was ≥1 °C, each 10 °C increase corresponded to an RR of 0.85 (95%CI: 0.82, 0.87). Each 10% increase in daily mean relative humidity was associated with an RR of 0.95 (95%CI: 0.92, 0.98). Each 50 μg·m−3 increase in PM10 concentration corresponded to an RR of 1.05 (95%CI: 1.02, 1.08). Within the concentration ranges of <10 μg·m−3, 10–<12.5 μg·m−3, and ≥ 12.5 μg·m−3, each 1 μg·m−3 increase in ambient SO2 corresponded to RR values of 1.00 (95%CI: 0.97, 1.03), 1.12 (95%CI: 1.09, 1.16), and 0.98 (95%CI: 0.95, 1.00), respectively. For sore throat, the daily mean temperature, daily mean relative humidity, daily mean wind speed, PM10, and SO2 were statistically significant (P<0.05), with daily mean wind speed and PM10 being linear influencing factors. When the daily mean temperature was below 2 °C, each 10 °C increase corresponded to an RR of 1.82 (95%CI: 1.69, 1.96). When the daily mean temperature was ≥2 °C, each 10 °C increase corresponded to an RR of 0.81 (95%CI: 0.77, 0.87). Each 10% increase in daily mean relative humidity was associated with an RR of 0.94 (95%CI: 0.88, 1.00). Within the concentration ranges of <10 μg·m−3, 10–<12.5 μg·m−3, and ≥12.5 μg·m−3, each 1 μg·m−3 increase in ambient SO2 corresponded to RR values of 1.02 (95%CI: 0.97, 1.08), 1.13 (95%CI: 1.08, 1.19), and 0.98 (95%CI: 0.94, 1.02), respectively. Each 1 m·s−1 increase in daily mean wind speed and each 50 μg·m−3 increase in PM10 concentration were associated with RR values of 1.06 (95%CI: 1.00, 1.12) and 1.04 (95%CI: 0.98, 1.10), respectively. An interaction effect was observed between daily mean wind speed and PM10: increasing daily mean wind speed non-linearly reduced the impact of PM10, on sore throat whereas PM10 had no significant effect on wind speed.ConclusionThis study, by combining LASSO and GAMM, largely eliminates the multicollinearity among selected variables. It reveals complex non-linear effects and interactions between air pollutants, meteorological factors, and acute symptoms in different population groups in Jinan. The symptoms like fever, cough, and sore throat are non-linearly associated with daily mean temperature and SO2 concentration, while PM10 and wind speed show a linear relationship or interactive effects. These findings provide a new basis for the precise prevention and control of health risk factors.

Objective To understand the level of occupational health literacy among workers in a ferroalloy enterprise and its influencing factors,providing a basis for formulating targeted health literacy intervention strategies and measures.Methods From August to September 2023,750 workers in this enterprise were surveyed to carry out occupational health literacy surveys by the"National Occupational Health Literacy Monitoring Questionnaire for Key Population,"and logistic regression models were used to analyze the influencing factors of their occupational health literacy levels.Results A total of 726 valid surveys were completed with a response rate of 96.8％.Of these workers,405(55.79％)had basic occupational health literacy,66.25％were aware of occupational health knowledge,49.59％had basic occupational health protection skills,and 57.99％had healthy working habits and behaviors.The individuals,who were female,married,had a college degree or above,were non-smokers,were not working night shifts,and had higher average monthly incomes,had higher awareness of occupational health knowledge(all P＜0.05).The individuals,who had a college degree or above,thought their health status was generally normal,didn't work night shifts,and had higher average monthly incomes had better basic occupational health protection skills(all P＜0.05).The individuals,who were female,aged 41-50 years,had a college degree or above,were non-smokers,and didn't work in night shifts,had healthier working habits and behaviors(all P＜0.05).Multivariate logistic regression analysis showed that compared to those with a college degree or above,those with a high school education or below were 1.735 times more likely to lack basic occupational health literacy(OR=1.735,P=0.003);workers on night shift were 2.079 times more likely to not have basic occupational health literacy compared to those who did not work night shift(OR=2.079,P＜0.001).Conclusions The occupational health literacy level of workers in this ferroalloy smelting enterprise was slightly higher than the national average level of key populations reported in 2022.However,effective strategies and measures should still be formulated to improve the shift work system and conduct accessible occupational health education,especially for frontline workers with relatively low educational levels,to improve their occupational health status.

[Background]Polycyclic aromatic hydrocarbons(PAHs),one of the main components of fine par-ticulate matter(PM2.5),have a certain impact on ambient air quality,and long-term exposure to PAHs may pose potential health risks to human beings. [Objective]To identify the distribution characteristics and sources of PAHs in atmospheric PM2.5 in a district of Taizhou City from 2019 to 2021,and to evaluate the health risks of PAHs to the population in the area through the inhalation pathway. [Methods]From 2019 to 2021,air PM2.5 sampling was carried out at a state-controlled surveillance point in a district of Taizhou City for 7 consecutive days on the 10th-16th of each month,the sampling time was 24 h·d-1,and the sampling flow rate was 100 L·min-1.PM2.5 mass concentration was calculated by gravimetric method.A total of 16 PAHs were determined by ultrasonic extrac-tion-liquid chromatography.Kruskal-Wallis H test was used to compare the distribution charac teristics of PAHs concentrations by years and seasons,characteristic ratio and principal component analysis(PCA)was used to analyze their sources,and a lifetime carcinogenic risk(ILCR)model was used to assess the health risk of PAHs. [Results]From 2019 to 2021,the annual average concentrations[M(P25,P75)]of ∑PAHs in atmospheric PM2.5 in the selected district of Taizhou City were 6.52(2.46,10.59),8.52(4.56,12.29),and 3.72(1.51,7.11)ng·m-3,respectively,and the annual benzo[a]pyrene(BaP)excess rates(national limit:1 ng·m-3)were 27.38%(23/84),47.62%(40/84),and 19.04%(16/84),respectively,both presenting 2020>2019>2021(P<0.001,P<0.05).The ∑PAHs concentration distribution showed a seasonal variation,with the highest value in winter and the lowest value in summer(P<0.05).Among the atmospheric PM2.5 samples,the proportion of 5-ring PAHs was the highest,the proportion of 2-3-ring PAHs was the lowest;the proportion of 2-4-ring PAHs showed a yearly upward trend,and the proportion of 5-6-ring PAHs showed yearly downward trend(P<0.05).The characteristic ratio and PCA results suggested that the sources of sampled PAHs were mainly mixed sources such as dust,fossil fuel(natural gas),coal combustion,industrial emissions,and motor vehicle exhaust emis-sions.The ILCR(RILCR)of PAHs by inhalation for men,women,and children were 1.83×10-6,2.35×10-6,and 2.04×10-6,respectively,and the annual average RILCR was 2.07×10-6,all greater than 1×10-6. [Conclusion]For the sampled time period,the main sources of PAHs pollution in atmospheric PM2.5 in the target district of Taizhou City are dust,fossil fuel(natural gas),coal combustion,industrial emissions,motor vehicle emissions,etc.,and PAHs may have a potential car-cinogenic risk to local residents.

Objective To analyze the epidemiological characteristics of an acute gastroenteritis outbreak caused by norovirus and identify the risk factors in a school of Wuzhou, Guangxi Zhuang Autonomous Region, in 2021, evaluate the performance of the epidemic response and suggest the targeted prevention and control measures. Methods Field epidemiology survey was carried out. The clinical manifestations and the distributions of the acute gastroenteritis cases were analyzed with descriptive epidemiological method, and a case-control study was conducted to analyze the cause of this outbreak. Samples, such as anal swab, self-supplied well water, tank water, terminal water of public water supply system,object surface swab, and food sample, were collected from the typical cases for the detection of nucleic acid of norovirus and other common pathogens. Results A total of 142 acute gastroenteritis cases were reported, with an attack rate of 5.55%（142/2 559）. The main clinical symptoms were vomiting（83.10%）, abdominal pain（83.10%）, fever（67.61%）, nausea（63.38%）and diarrhea（50.70%）. There was a statistical difference in the attack rate among students in different dormitory buildings（χ2=36.978, P＜0.001）. The results of the case-control study showed that gargle with self-supplied well water（OR=3.78,95%CI: 1.736-8.234） and contact with acute gastroenteritis case（OR=2.97, 95% CI : 1.465-5.312） were the main risk factors.There was a dose-response relationship between the frequency of gargle with self-supplied well water and the attack rate（χ2=14.935, P=0.002）. Norovirus GⅡ was detected in 8 anal swabs, 1 vomitus sample and water samples from well A and tanks. Conclusion This outbreak was caused by the infection norovirus GⅡ, possibly resulting from well water pollution in environment and human-to-human transmission. Gargle with self-supplied well water and exposure to acute gastroenteritis case were the major risk factors. The lack of appropriate water treatment equipment and the failure to isolate acute gastroenteritis cases in time were the major reasons for the spread of the epidemic. It is recommended to strengthen the hygienic management of self-supplied well water to reduce the risk of outbreaks. Otherwise, we suggest schools and child-care settings to strengthen the management of infectious disease and conduct timely reporting and response of outbreaks to prevent the spread of the epidemic.

Objective To estimate the transmission dynamics parameters of Omicron BA.5.2 variant and provide a scientific evidence for understanding the infectivity of Omicron BA.5.2. Method The data of infection time， onset time and first positive sampling time of the infection cases with clear infection relationship in two outbreaks in Jingzhou were collected by using self-designed forms. The obtained data were fitted with log-normal distribution， gamma distribution and gamma distribution to calculate incubation period， generation time （GT）， and serial interval （SI）. The exponential growth model （EG） and the maximum likelihood estimation method （ML） were used to calculate R0， respectively. The fitted value with the larger R 2 was used as the R0 estimate. Results In the 2 outbreaks in Jingzhou， outbreak A was caused by the imported virus spread in local population mainly through people’s living， dining， amusing and working together， showing a common social transmission pattern. Outbreak B occurred in a wholesale market， which was caused by an infection case who had stay in the market for 3 hours and subsequent rapid transmission in market workers and their close contacts， showing a clustered transmission pattern. A total of 52 cases in 39 pairs of primary and secondary cases in the 2 outbreaks were included for the estimations of incubation period， GT and SI. The median of incubation period was 2.52 days （44 cases， 1.32-4.84 days）， the median of SI was 2.13 days （37 cases， 1.63-2.64 days）， and the median of GT was 1.91 days （21 cases， 1.05-3.15 days）. The optimal fitting models of outbreak A and outbreak B were all EG models， and the corresponding R 2 were 0.62 and 0.88， respectively. The fitted R0s of outbreak A and outbreak B were 5.33 （95% CI: 2.08～13.44） and 22.58 （95% CI: 7.23～91.36）， respectively. In some cases， the shortest interval （20 - 21 hours） between exposure and detection was observed. Conclusion Omicron BA.5.2 variant has the characteristics of short incubation period， rapid transmission speed and strong infectivity. In crowded places， Omicron BA.5.2 variant has very strong transmission ability.