This study examines tuberculosis (TB) incidence among students in Jilin Province, China, focusing on spatial, temporal, and demographic dynamics in areas of social inequality. Variation in incidence rate of TB was analyzed using the joinpoint regression method. Spatial analyses techniques included the global and local Moran indices and Getis-Ord Gi* analysis. Demographic changes in new cases were analyzed descriptively, and the Geodetector method measured the influence of risk factors on student TB incidence. The analysis revealed a declining trend in TB cases, particularly among male students. TB incidence showed geographical heterogeneity, with lower rates in underdeveloped rural areas compared to urban regions. Significant spatial correlations were observed, with high-high clusters forming in central Jilin Province. Hotspots of student TB transmission were primarily concentrated in the southwestern and central regions from 2008 to 2018. Socio-economic factors exhibited nonlinear enhancement effects on incidence rates, with a dominant bifactor effect. High-risk zones were predominantly located in urban centers, with university and high school students showing higher incidences than other educational stages. The study revealed economic determinants as being especially important in affecting TB incidence among students, with these factors having nonlinear interacting effects on student TB incidence.

Injury poses heavy burden on public health, accounting for nearly 8% of all deaths globally, but little evidence on the role of climate change on injury exists. We collect data during 2013-2019 in six provinces of China to examine the effects of temperature on injury mortality, and to project future mortality burden attributable to temperature change driven by climate change based on the assumption of constant injury mortality and population scenario. The results show that a 0.50% (95% confident interval (CI): 0.13%–0.88%) increase of injury mortality risk for each 1 °C rise in daily temperature, with higher risk for intentional injury (1.13%, 0.55%–1.71%) than that for unintentional injury (0.40%, 0.04%–0.77%). Compared to the 2010s, total injury deaths attributable to temperature change in China would increase 156,586 (37,654–272,316) in the 2090 s under representative concentration pathways 8.5 scenario with the highest for transport injury (64,764, 8,517–115,743). Populations living in Western China, people aged 15–69 years, and male may suffer more injury mortality burden from increased temperature caused by climate change. Our findings may be informative for public health policy development to effectively adapt to climate change.

The current epidemiological status of the new coronary pneumonia epidemic in China is being explored to prevent and control the localized dissemination of aggregated outbreaks. This study analyzed the characteristics of new outbreaks of coronavirus disease 2019 (COVID-19) at three stages of aggregated outbreaks in Jilin Province, China, to provide a reference for the prevention and control of aggregated outbreaks. Case information were collected from all patients in Jilin Province from January 12, 2020 to the present. The epidemic was divided into three stages according to the time of onset. The first stage comprised 97 cases reported from January 12, 2020 to February 19, 2020, during which 17 aggregated outbreaks occurred. The second comprised 43 cases reported from April 25, 2020 and May 23, 2020, involving one aggregated outbreak. The third comprised 435 cases reported on January 10, 2021 and February 9, 2021, involving one aggregated outbreak. The relationship between aggregated and non-aggregated cases in the first phase of the outbreak and the difference between imported and local cases during the aggregated outbreak were assess using statistical analysis, and the differences in the baseline information between the three phases were analyzed. The incubation periods of the three phases were 10 days, 8 days, and 5 days. The number of aggregated epidemic events in Jilin Province tended to increase and then decrease over time. The clustered events in Jilin Province were divided into four categories: household contact (14 times, 51 cases); household contact and public places (one time, three cases); household contact, public places, and gatherings (one time, six cases); and household contact, public places, gatherings, and work (three times, 495 cases). Clustered events occurred mainly between January 22, 2020, and February 4, 2020. Among all cases in the first phase of the outbreak, the method of detection and the time from diagnosis to discharge were longer in aggregated cases than in non-aggregated cases, and that the source of infection and renewal cases were more frequent and more likely to be detected in the outpatient clinics during aggregated outbreaks than the imported cases. The second phase of the epidemic showed significant spatial variability (Moran's I<0, P<0.05). The third stage of the epidemic occurred in a higher proportion of individuals aged 50-90 years and within a shorter incubation period compared with the first two stages. The current focus of prevention and control of the COVID-19 epidemic in Jilin Province is to strictly implement the restrictions on gatherings and to perform timely screening and isolation of close contacts of infectious sources while strengthening the supervision of the inflow of people from outside the region. Simultaneously, more targeted prevention and control measures can be implemented for different age groups and occupations.

Although studies have estimated the associations of PM2.5 with total mortality or cardiopulmonary mortality, few have comprehensively examined cause-specific mortality risk and burden caused by ambient PM2.5. Thus, this study investigated the association of short-term exposure to PM2.5 with cause-specific mortality using a death-spectrum wide association study (DWAS). Individual information of 5,450,764 deaths during 2013-2018 were collected from six provinces in China. Daily PM2.5 concentration in the case and control days were estimated by a random forest model. A time-stratified case-crossover study design was applied to estimate the associations (access risk, ER) of PM2.5 with cause-specific mortality, which was then used to calculate the population-attributable fraction (PAF) of mortality and the corresponding mortality burden caused by PM2.5. Each 10 μg/m3 increase in PM2.5 concentration (lag03) was associated with a 0.80 % [95 % confidence interval (CI): 0.73 %, 0.86 %] rise in total mortality. We found greater mortality effect at PM2.5 concentrations < 50 μg/m3. Stratified analyses showed greater ERs in females (1.01 %, 95 %CI: 0.91 %, 1.11 %), children ≤ 5 years (2.17 %, 95 %CI: 0.85 %, 3.51 %), and old people ≥ 70 years. We identified 33 specific causes (level 2) of death which had significant associations with PM2.5, including 16 circulatory diseases, 9 respiratory diseases, and 8 other causes. The PAF estimated based on the overall association between PM2.5 and total mortality was 3.16 % (95 %CI: 2.89 %, 3.40 %). However, the PAF was reduced to 2.88 % (95 %CI: 1.88 %, 3.81 %) using the associations of PM2.5 with 33 level 2 causes of death, based on which 250.15 (95 %CI: 163.29, 330.93) thousand deaths were attributable to short-term PM2.5 exposure across China in 2019. Overall, this study provided a comprehensive picture on the death-spectrum wide association between PM2.5 and morality in China. We observed robust positive cause-specific associations of PM2.5 with mortality risk, which may provide more precise basis in assessing the mortality burden of air pollution.

The association between temperature and mortality has been widely investigated, however, studies on the effects of central heating on mortality risk are sparse. We applied a variant of difference-in-differences (DID) approach to assess the effects of central heating on winter mortality in China, and further analyzed the modification effect of central heating on the temperature-mortality association. Central heating was negatively associated with winter mortality (OR: 0.42, 95% CI: 0.39, 0.46) with higher effects on females and the elderly. Compared with cardiovascular or cerebrovascular diseases, central heating had a much greater effect on respiratory diseases. Every week less in central heating duration was associated with a 3.32% (95% CI: 3.03%, 3.61%) increased risk in total mortality. We found the temperature effect was much more pronounced among the locations without central heating (ER: 5.01%, 95% CI: 4.68%, 5.34%) than those with central heating (ER: 0.01%, 95% CI: −0.74%, 0.75%). The analysis suggests that central heating significantly decreases winter mortality in northern China, which may partly be achieved by attenuating the mortality caused by temperature. Therefore, central heating should be developed in some regions of southern China to reduce the mortality risk of low temperature in winter.

Background Recently, despite the steady decline in the tuberculosis (TB) epidemic globally, school TB outbreaks have been frequently reported in China. This study aimed to quantify the transmissibility of Mycobacterium tuberculosis (MTB) among students and non-students using a mathematical model to determine characteristics of TB transmission. Methods We constructed a dataset of reported TB cases from four regions (Jilin Province, Xiamen City, Chuxiong Prefecture, and Wuhan City) in China from 2005 to 2019. We classified the population and the reported cases under student and non-student groups, and developed two mathematical models [nonseasonal model (Model A) and seasonal model (Model B)] based on the natural history and transmission features of TB. The effective reproduction number ( R eff ) of TB between groups were calculated using the collected data. Results During the study period, data on 456,423 TB cases were collected from four regions: students accounted for 6.1% of cases. The goodness-of-fit analysis showed that Model A had a better fitting effect ( P < 0.001). The average R eff of TB estimated from Model A was 1.68 [interquartile range (IQR): 1.20–1.96] in Chuxiong Prefecture, 1.67 (IQR: 1.40–1.93) in Xiamen City, 1.75 (IQR: 1.37–2.02) in Jilin Province, and 1.79 (IQR: 1.56–2.02) in Wuhan City. The average R eff of TB in the non-student population was 23.30 times (1.65/0.07) higher than that in the student population. Conclusions The transmissibility of MTB remains high in the non-student population of the areas studied, which is still dominant in the spread of TB. TB transmissibility from the non-student-to-student-population had a strong influence on students. Specific interventions, such as TB screening, should be applied rigorously to control and to prevent TB transmission among students. Graphical Abstract

Background There is still a relatively serious disease burden of infectious diseases and the warning time for different infectious diseases before implementation of interventions is important. The logistic differential equation models can be used for predicting early warning of infectious diseases. The aim of this study is to compare the disease fitting effects of the logistic differential equation (LDE) model and the generalized logistic differential equation (GLDE) model for the first time using data on multiple infectious diseases in Jilin Province and to calculate the early warning signals for different types of infectious diseases using these two models in Jilin Province to solve the disease early warning schedule for Jilin Province throughout the year. Methods Collecting the incidence of 22 infectious diseases in Jilin Province, China. The LDE and GLDE models were used to calculate the recommended warning week (RWW), the epidemic acceleration week (EAW) and warning removed week (WRW) for acute infectious diseases with seasonality, respectively. Results Five diseases were selected for analysis based on screening principles: hemorrhagic fever with renal syndrome (HFRS), shigellosis, mumps, Hand, foot and mouth disease (HFMD), and scarlet fever. The GLDE model fitted the above diseases better (0.80 ≤ R 2 ≤ 0.94, P < 0. 005) than the LDE model. The estimated warning durations (per year) of the LDE model for the above diseases were: weeks 12–23 and 40–50; weeks 20–36; weeks 15–24 and 43–52; weeks 26–34; and weeks 16–25 and 41–50. While the durations of early warning (per year) estimated by the GLDE model were: weeks 7–24 and 36–51; weeks 13–37; weeks 11–26 and 39–54; weeks 23–35; and weeks 12–26 and 40–50. Conclusions Compared to the LDE model, the GLDE model provides a better fit to the actual disease incidence data. The RWW appeared to be earlier when estimated with the GLDE model than the LDE model. In addition, the WRW estimated with the GLDE model were more lagged and had a longer warning time.

The short-term effects of ambient temperature on mortality have been widely investigated. However, the epidemiological evidence on the long-term effects of temperature on mortality is rare. In present study, we conducted a nationwide quasi-experimental design, which based on a variant of difference-in-differences (DID) approach, to examine the association between long-term exposure to ambient temperature and mortality risk in China, and to analyze the effect modification of population characteristics and socioeconomic status. Data on mortality were collected from 364 communities across China during 2006 2017, and environmental data were obtained for the same period. We estimated a 2.93 % (95 % CI: 2.68 %, 3.18 %) increase in mortality risk per 1 degrees C decreases in annual temperature, the greater effects were observed on respiratory diseases (5.16 %, 95 % CI: 4.53 %, 5.79 %) than cardiovascular diseases (3.43 %, 95 % CI: 3.06 %, 3.80 %), and on younger people (4.21 %, 95 % CI: 3.73 %, 4.68 %) than the elderly (2.36 %, 95 % CI: 2.06 %, 2.65 %). In seasonal analysis, per 1 degrees C decreases in average temperature was associated with 1.55 % (95 % CI: 1.23 %, 1.87 %), -0.53 % (95 % CI: -0.89 %, -0.16 %), 2.88 % (95 % CI: 2.45 %, 3.31 %) and 4.21 % (95 % CI: 3.98 %, 4.43 %) mortality change in spring, summer, autumn and winter, respectively. The effects of long-term temperature on total mortality were more pronounced among the communities with low urbanization, low education attainment, and low GDP per capita. In total, the decrease of average temperature in summer decreased mortality risk, while increased mortality risk in other seasons, and the associations were modified by demographic characteristics and socioeconomic status. Our findings suggest that populations with disadvantaged characteristics and socioeconomic status are vulnerable to long-term exposure of temperature, and targeted policies should be formulated to strengthen the response to the health threats of temperature exposure.

Background The Spring Festival is one of the most important traditional festivals in China. This study aimed to estimate the mortality risk attributable to the Spring Festival. Methods Between 2013 and 2017, daily meteorological, air pollution, and mortality data were collected from 285 locations in China. The Spring Festival was divided into three periods: pre-Spring Festival (16 days before Lunar New Year's Eve), mid-Spring Festival (16 days from Lunar New Year's Eve to Lantern Festival), and post-Spring Festival (16 days after Lantern Festival). The mortality risk attributed to the Spring Festival in each location was first evaluated using a distributed lag nonlinear model (DLNM), and then it was pooled using a meta-analysis model. Results We observed a dip/rise mortality pattern during the Spring Festival. Pre-Spring Festival was significantly associated with decreased mortality risk (ER: −1.58%, 95%CI: −3.09% to −0.05%), and mid-Spring Festival was unrelated to mortality risks, while post-Spring Festival was significantly associated with increased mortality risk (ER: 3.63%, 95%CI: 2.15–5.12%). Overall, a 48-day Spring Festival period was associated with a 2.11% (95%CI: 0.91–3.33%) increased mortality. We also found that the elderly aged over 64 years old, women, people with cardiovascular disease (CVD), and people living in urban areas were more vulnerable to the Spring Festival. Conclusion Our study found that the Spring Festival significantly increased the mortality risk in China. These findings suggest that it is necessary to develop clinical and public health policies to alleviate the mortality burden associated with the Spring Festival.

Objectives Despite the adoption of a new childhood immunization program in China, the incidence of mumps remains high. This study aimed to describe the epidemiological characteristics of mumps in Jilin Province from 2005 to 2019 and to assess the transmissibility of mumps virus among the whole population and different subgroups by regions and age groups. Methods The Non-age-specific and age-specific Susceptible–Exposed–Pre-symptomatic–Infectious–Asymptomatic–Recovered (SEPIAR) models were fitted to actual mumps incidence data. The time-varying reproduction number ( R t ) was used to evaluate and compare the transmissibility. Results From 2005 to 2019, a total of 57,424 cases of mumps were reported in Jilin Province. The incidence of mumps was the highest in people aged 5 to 9 years (77.37 per 100,000). The two SEPIAR models fitted the reported data well ( P < 0.01). The median transmissibility ( R t ) calculated by the two SEPIAR models were 1.096 (range: 1.911 × 10 −5 –2.192) and 1.074 (range: 0.033–2.114) respectively. The age-specific SEPIAR model was more representative of the actual epidemic of mumps in Jilin Province from 2005–2019. Conclusions For mumps control, it is recommended that mumps-containing vaccines (MuCV) coverage be increased nationwide in the 5–9 years age group, either by a mumps vaccine alone or by a combination of vaccines such as measles-mumps-rubella (MMR) vaccine. The coverage of vaccines in Jilin Province should be continuously expanded to establish solid immunity in the population. China needs to redefine the optimal time interval for MuCV immunization.

目的 通过对2007-2018吉林省肾综合征出血热病例进行流行特征描述,使用时间序列分析,预测吉林省未来的肾综合征出血热发病趋势.方法 通过建立ARIMA模型,对2007-2018年间吉林省肾综合征出血热发病数进行拟合,使用2019-2021年发病数与预测结果之间进行拟合优度检验来验证拟合效果.结果 2007-2018年间,吉林省共上报肾综合征出血热8 844例.总体来说吉林省肾综合征出血热发病率呈现缓慢下降趋势,时间序列分析结果为:ARIMA(1,0,0)(0,1,2)12较好地拟合和预测吉林省肾综合征出血热发病数(R2=0.66,P＜0.05).2019年至2021年预测肾综合征出血热发病数分别为510人、449人和513人.将2019-2021年HFRS实际病例数并与ARIMA模型预测数据进行比较,计算相关系数R2=0.85,P＜0.05.结论 ARIMA模型能较好地拟合和预测吉林省HFRS发病数.

ObjectivesThis study aims to explore the interaction of different pathogens in Hand, foot and mouth disease (HFMD) by using a mathematical epidemiological model and the reported data in five regions of China.MethodsA cross-regional dataset of reported HFMD cases was built from four provinces (Fujian Province, Jiangsu province, Hunan Province, and Jilin Province) and one municipality (Chongqing Municipality) in China. The subtypes of the pathogens of HFMD, including Coxsackievirus A16 (CV-A16), enteroviruses A71 (EV-A71), and other enteroviruses (Others), were included in the data. A mathematical model was developed to fit the data. The effective reproduction number (Reff) was calculated to quantify the transmissibility of the pathogens.ResultsIn total, 3,336,482 HFMD cases were collected in the five regions. In Fujian Province, the Reff between CV-A16 and EV-A71&CV-A16, and between CV-A16 and CV-A16&Others showed statistically significant differences (P < 0.05). In Jiangsu Province, there was a significant difference in Reff (P < 0.05) between the CV-A16 and Total. In Hunan Province, the Reff between CV-A16 and EV-A71&CV-A16, between CV-A16 and Total were significant (P < 0.05). In Chongqing Municipality, we found significant differences of the Reff (P < 0.05) between CV-A16 and CV-A16&Others, and between Others and CV-A16&Others. In Jilin Province, significant differences of the Reff (P < 0.05) were found between EV-A71 and Total, and between Others and Total.ConclusionThe major pathogens of HFMD have changed annually, and the incidence of HFMD caused by others and CV-A16 has surpassed that of EV-A71 in recent years. Cross-regional differences were observed in the interactions between the pathogens.

目的 描述2010-2019年吉林省细菌性痢疾流行特征,探讨发病率变化趋势,为防控工作提供科学依据. 方法 采用描述流行病学方法分析2010-2019年吉林省细菌性痢疾疫情情况,数据来源于中国疾病预防控制信息系统.结果 2010-2019年问,吉林省细菌性痢疾2011年发病率最高,为14.71/10万,2012年以后发病率逐年下降.2010-2019年7、8月份累计报告5 383例,占总报告病例数的29.55％.男性10 483例,女性7 735例,性别比1.36∶1.3岁以下病例占总发病数的39.48％.人群分类中散居儿童所占比例最多(44.58％). 结论 2012年以后吉林省细菌性痢疾报告发病率呈下降趋势.应在夏季高发季节前针对重点人群积极开展防控工作,并防范聚集性疫情的发生.

目的 对吉林省肾综合征出血热病例进行流行特征描述,分析影响肾综合征出血热发病的流行病学因素.对吉林省气象因素与发病数据的关系进行讨论,探究影响发病的气象因素.方法 采用卡方检验对2007-2019年间吉林省不同月份、不同地区、不同性别、不同年龄组以及不同职业的发病比例进行差异比较.使用类泊松回归对2007-2017年间气象数据与发病数进行拟合,得到气象因素对发病数的影响.结果 2007-2019年间,吉林省共上报肾综合征出血热9317例.吉林省肾综合征出血热呈现流行特征为:发病率呈现缓慢下降趋势,每年5-6月以及11月会出现两个发病高峰,病例以男性农业人口为主,经济相对落后地区发病率高.气象因素分析发现,气温、降水与日照的改变会影响肾综合征出血热发病数(P＜0.05),回归系数分别为1.002、1.000、0.999.结论 可针对吉林省肾综合征出血热高发地区和高发人群进行重点防控,并在发病高峰来临前做好防治准备.

Objective: Based on differences in populations and prevention and control measures, the spread of new coronary pneumonia in different countries and regions also differs. This study aimed to calculate the transmissibility of coronavirus disease 2019 (COVID-19), and to evaluate the effectiveness of countermeasures to control the disease in Jilin Province, China. Methods: The data of reported COVID-19 cases were collected, including imported and local cases from Jilin Province as of March 14, 2019. A Susceptible–Exposed–Infectious–Asymptomatic–Recovered (SEIAR) model was developed to fit the data, and the effective reproduction number ( R eff ) was calculated at different stages in the province. Finally, the effectiveness of the countermeasures was assessed. Results: A total of 97 COVID-19 infections were reported in Jilin Province, among which 45 were imported infections (including one asymptomatic infection) and 52 were local infections (including three asymptomatic infections). The model fit well with the reported data ( R 2 = 0.593, P < 0.001). The R eff of COVID-19 before and after February 1, 2020 was 1.64 and 0.05, respectively. Without the intervention taken on February 1, 2020, the predicted cases would reach a peak of 177,011 on October 22, 2020 (284 days from the first case). The projected number of cases until the end of the outbreak (on October 9, 2021) would be 17,129,367, with a total attack rate of 63.66%. Based on the comparison between the predicted incidence of the model and the actual incidence, the comprehensive intervention measures implemented in Jilin Province on February 1 reduced the incidence of cases by 99.99%. Therefore, according to the current measures and implementation efforts, Jilin Province can achieve good control of the virus’s spread. Conclusions: COVID-19 has a moderate transmissibility in Jilin Province, China. The interventions implemented in the province had proved effective, increasing social distancing and a rapid response by the prevention and control system will help control the spread of the disease.

Although studies have investigated the associations between PM2.5 and mortality risk, evidence from rural areas is scarce. We aimed to compare the PM2.5-mortality associations between urban cities and rural areas in China. Daily mortality and air pollution data were collected from 215 locations during 2014-2017 in China. A two-stage approach was employed to estimate the location-specific and combined cumulative associations between short-term exposure to PM2.5 (lag 0-3 days) and mortality risks. The excess risks (ER) of all-cause, respiratory disease (RESP), cardiovascular disease (CVD), and cerebrovascular disease (CED) mortality for each 10 mu g/m(3) increment in PM2.5 across all locations were 0.54% (95% confidence interval [CI]: 0.38%, 0.70%), 0.51% (0.10%, 0.93%), 0.74% (0.50%, 0.97%), and 0.52% (0.20%, 0.83%), respectively. Slightly stronger associations for CVD (0.80% versus 0.60%) and CED (0.61% versus 0.26%) mortality were observed in urban cities than in rural areas, and slightly greater associations for RESP mortality (0.51% versus 0.43%) were found in rural areas than in urban cities. A mean of 2.11% (attributable fraction [AF], 95% CI: 1.48%, 2.76%) of allcause mortality was attributable to PM2.5 exposure in China, with a larger AF in urban cities (2.89% [2.12%, 3.67%]) than in rural areas (0.61% [-0.60%, 1.84%]). Disparities in PM2.5-mortality associations between urban cities and rural areas were also found in some subgroups classified by sex and age. This study provided robust evidence on the associations of PM2.5 with mortality risks in China and demonstrated urban-rural disparities of PM2.5-mortality associations for various causes of death.

目的掌握现阶段吉林省各级各类医疗机构法定传染病报告质量,为进一步改进工作提供参考.方法采用多阶段分层随机抽样,调查分析医疗机构的法定传染病报告质量.结果本次抽取35家医疗机构,调查病例884例,进行网络直报868例,报告率98.19％.不同级别医疗机构传染病报告率的总体差异有统计学意义(x2= 17.632,P<0.001).报告及时率、纸质报告卡填写完整率、纸质报告卡填写准确率、网络报告一致率和有效证件号填写完整率分别为100.00％、100.00％、96.54％、92.74％和100.00％,不同级别医疗机构的纸质报告卡填写准确率、网络报告一致率总体差异均有统计学意义(x2= 3.100,P<0.001;x2=6.768,P<0.001).结论2019年吉林省传染病报告质量总体情况较好,但仍存在漏报和报告不规范等问题,应进一步规范管理,提升传染病报告质量.

Background: The disease burden of hepatitis E remains high. We used a new method (richness, diversity, evenness, and similarity analyses) to classify cities according to the occupational classification of hepatitis E patients across regions in China and compared the results of cluster analysis. Methods: Data on reported hepatitis E cases from 2008 to 2018 were collected from 24 cities (9 in Jilin Province, 13 in Jiangsu Province, Xiamen City, and Chuxiong Yi Autonomous Prefecture). Traditional statistical methods were used to describe the epidemiological characteristics of hepatitis E patients, while the new method and cluster analysis were used to classify the cities by analyzing the occupational composition across regions. Results: The prevalence of hepatitis E in eastern China (Jiangsu Province) was similar to that in the south (Xiamen City) and southwest of China (Chuxiong Yi Autonomous Prefecture), but higher than that in the north (Jilin Province). The age of hepatitis E patients was concentrated between 41 and 60 years, and the sex ratio ranged from 1:1.6 to 1:3.4. Farming was the most highly prevalent occupation; other sub-prevalent occupations included retirement, housework and unemployment. The incidence of occupations among migrant workers, medical staff, teachers, and students was moderate. There were several occupational types with few or no records, such as catering industry, caregivers and babysitters, diaspora children, childcare, herders, and fishing (boat) people. The occupational similarity of hepatitis E was high among economically developed cities, such as Nanjing, Wuxi, Baicheng, and Xiamen, while the similarity was small among cities with large economic disparities, such as Nanjing and Chuxiong Yi Autonomous Prefecture. A comparison of the classification results revealed more similarities and some differences when using these two methods. Conclusion: In China, the factors with the greatest influence on the prevalence of hepatitis E are living in the south, farming as an occupation, being middle-aged or elderly, and being male. The 24 cities we studied were highly diverse and moderately similar in terms of the occupational distribution of patients with hepatitis E. We confirmed the validity of the new method on in classifying cities according to their occupational composition by comparing it with the clustering method.

Objective: To study the association between apparent temperature (AT) and the incidence of hand,foot, and mouth disease (HFMD) and its spatial heterogeneity in 46 cities in Guangdong, Anhui and Jilin provinces, and provide scientific evidence for the early warning of HFMD. Methods: The data of HFMD incidence and meteorological factors from 2009 to 2018 in Guangdong province, 2009 to 2015 in Anhui province, and 2013 to 2018 in Jilin province were collected. Distributed lag non-linear models were constructed to investigate the association between AT and the incidence of HFMD in 46 cities from three provinces in China. Meta-analysis was used to pool the city-specific estimates, and Meta-regression was applied to analyze the factors that may cause spatial heterogeneity. Results: The relationship between daily AT and the incidence of HFMD in 46 cities appeared nonlinear. The association in Guangdong was similar to that in Jilin, and the risk of HFMD increased with the increase of AT. While the risk of HFMD in Anhui first increased with the increase of AT, and peaked at 18.1 ℃ and then went down. AT on different levels showed different lag impacts and the higher AT showed greater and longer lag impact. The spatial heterogeneity of associations may have been caused by latitude, longitude, average temperature, and average sunshine hours. Conclusions: AT is a comprehensive index to evaluate the association between temperature, relative humidity and wind speed and the incidence of HFMD. Higher AT may increase the risk of HFMD. The AT and HFMD relationship across spatial heterogeneity varies depending on geographic location and meteorological conditions.

As of 16∶00 on January 13, 2021, 95 positive cases of coronavirus disease 2019 were reported in Suihua City of Heilongjiang Province and an additional 35 cases have been linked across 7 cities in 3 provinces: Qiqihar City (7 cases), Mudanjiang City (4 cases), Harbin City (3 cases), and Yichun City (1 case) of Heilongjiang Province; Changchun City (9 cases) and Tonghua City (6 cases) of Jilin Province; and Weihai City of Shandong Province (1 case).On January 10, 2021, the first asymptomatic infections in Changchun City of Jilin Province were found in couple recently traveling from Suihua City on January 5 (Suihua A and B; 58-year-old male and 57year-old female, respectively).After hearing of the outbreak in Suihua City, the couple voluntarily sought testing at the Second Hospital of Jilin University and returned positive COVID-19 tests.The initial investigation of the couple patients indicated that they had traveled from Suihua City to Changchun by the K350 Train on January 5.This launched an epidemiological investigation of close contacts and for other passengers of the K350 train on January 5.