Modeling outbreaks of COVID-19 in China: The impact of vaccination and other control measures on curbing the epidemic.

This study aims to examine the development trend of COVID-19 in China and propose a model to assess the impacts of various prevention and control measures in combating the COVID-19 pandemic. Using COVID-19 cases reported by the National Health Commission of China from January 2, 2020, to January 2, 2022, we established a Susceptible-Exposed-Infected-Asymptomatic-Quarantined-Vaccinated-Hospitalized-Removed (SEIAQVHR) model to calculate the COVID-19 transmission rate and Rt effective reproduction number, and assess prevention and control measures. Additionally, we built a stochastic model to explore the development of the COVID-19 epidemic. We modeled the incidence trends in five outbreaks between 2020 and 2022. Some important features of the COVID-19 epidemic are mirrored in the estimates based on our SEIAQVHR model. Our model indicates that an infected index case entering the community has a 50%-60% chance to cause a COVID-19 outbreak. Wearing masks and getting vaccinated were the most effective measures among all the prevention and control measures. Specifically targeting asymptomatic individuals had no significant impact on the spread of COVID-19. By adjusting prevention and control parameters, we suggest that increasing the rates of effective vaccination and mask-wearing can significantly reduce COVID-19 cases in China. Our stochastic model analysis provides a useful tool for understanding the COVID-19 epidemic in China.

Hysteresis effects of different levels of storm flooding on susceptible enteric infectious diseases in a central city of China.

BACKGROUND: Recently, attention has focused on the impact of global climate change on infectious diseases. Storm flooding is an extreme weather phenomenon that not only impacts the health of the environment but also worsens the spread of pathogens. This poses a significant challenge to public health security. However, there is still a lack of research on how different levels of storm flooding affect susceptible enteric infectious diseases over time. METHODS: Data on enteric infectious diseases, storm flooding events, and meteorology were collected for Changsha, Hunan Province, between 2016 and 2020. The Wilcoxon Rank Sum Test was used to identify the enteric infectious diseases that are susceptible to storm flooding. Then, the lagged effects of different levels of storm flooding on susceptible enteric infectious diseases were analyzed using a distributed lag nonlinear model. RESULTS: There were eleven storm flooding events in Changsha from 2016 to 2020, concentrated in June and July. 37,882 cases of enteric infectious diseases were reported. During non-flooding days, the daily incidence rates of typhoid/paratyphoid and bacillary dysentery were 0.3/100,000 and 0.1/100,000, respectively. During flooding days, the corresponding rates increased to 2.0/100,000 and 0.8/100,000, respectively. The incidence rates of both diseases showed statistically significant differences between non-flooding and flooding days. Correlation analysis shows that the best lags for typhoid/paratyphoid and bacillary dysentery relative to storm flooding events may be 1 and 3 days. The results of the distributed lag nonlinear model showed that typhoid/paratyphoid had the highest cumulative RR values of 2.86 (95% CI: 1.71-4.76) and 8.16 (95% CI: 2.93-22.67) after 4 days of general flooding and heavy flooding, respectively; and bacillary dysentery had the highest cumulative RR values of 1.82 (95% CI: 1.40-2.35) and 3.31 (95% CI: 1.97-5.55) after 5 days of general flooding and heavy flooding, respectively. CONCLUSIONS: Typhoid/paratyphoid and bacillary dysentery are sensitive enteric infectious diseases related to storm flooding in Changsha. There is a lagging effect of storm flooding on the onset of typhoid/paratyphoid and bacillary dysentery, with the best lagging periods being days 1 and 3, respectively. The cumulative risk of typhoid/paratyphoid and bacillary dysentery was highest at 4/5 days lag, respectively. The higher of storm flooding, the higher the risk of disease, which suggests that the authorities should take appropriate preventive and control measures before and after storm flooding.

Spatial heterogeneity of socio-economic determinants of typhoid/paratyphoid fever in one province in central China from 2015 to 2019.

BACKGROUND: Typhoid fever and paratyphoid fever are one of the most criticial public health issues worldwide, especially in developing countries. The incidence of this disease may be closely related to socio-economic factors, but there is a lack of research on the spatial level of relevant determinants of typhoid fever and paratyphoid fever. METHODS: In this study, we took Hunan Province in central China as an example and collected the data on typhoid and paratyphoid incidence and socio-economic factors in 2015-2019. Firstly spatial mapping was made on the disease prevalence, and again using geographical probe model to explore the critical influencing factors of typhoid and paratyphoid, finally employing MGWR model to analysis the spatial heterogeneity of these factors. RESULTS: The results showed that the incidence of typhoid and paratyphoid fever was seasonal and periodic and frequently occurred in summer. In the case of total typhoid and paratyphoid fever, Yongzhou was the most popular, followed by Xiangxi Tujia and Miao Autonomous Prefecture, Huaihua and Chenzhou generally focused on the south and west. And Yueyang, Changde and Loudi had a slight increase trend year by year from 2015 to 2019. Moreover, the significant effects on the incidence of typhoid and paratyphoid fever from strong to weak were as follows: gender ratio(q = 0.4589), students in ordinary institutions of higher learning(q = 0.2040), per capita disposable income of all residents(q = 0.1777), number of foreign tourists received(q = 0.1697), per capita GDP(q = 0.1589), and the P values for these factors were less than 0.001. According to the MGWR model, gender ratio, per capita disposable income of all residents and Number of foreign tourists received had a positive effect on the incidence of typhoid and paratyphoid fever. In contrast, students in ordinary institutions of higher learning had a negative impact, and per capita GDP shows a bipolar change. CONCLUSIONS: The incidence of typhoid and paratyphoid fever in Hunan Province from 2015 to 2019 was a marked seasonality, concentrated in the south and west of Hunan Province. Attention should be paid to the prevention and control of critical periods and concentrated areas. Different socio-economic factors may show other directions and degrees of action in other prefecture-level cities. To summarize, health education, entry-exit epidemic prevention and control can be strengthened. This study may be beneficial to carry out targeted, hierarchical and focused prevention and control of typhoid fever and paratyphoid fever, and provide scientific reference for related theoretical research.

Factors Affecting Willingness on Sharing of Electronic Health Records Data: A Survey on Chinese Residents.

Background: China has been promoting sharing of Electronic Health Records (EHRs) data for several years. However, only a few studies have explored the views of Chinese residents on sharing personal health data, and the factors that affect sharing of EHRs have not been fully elucidated. This study sought to explore public attitudes toward sharing EHRs and the factors that affect sharing of personal health data among Chinese residents. Methods: A multi-stage stratified sampling design was adopted in this survey to select residents in Hunan province, resulting in 932 responses randomly. The investigation was carried out with the administration of a 19-item questionnaire. The measure includes items on demographics, willingness to share EHRs, experiences on EHRs, public acknowledgment of the benefits of sharing EHRs, and public awareness of potential risks of sharing EHRs. Results: The score of general willingness to share EHRs was 5.784 ± 2.031. Concerning the domain scores for the willingness, the willingness to share EHRs for research was 2.060 ± 0.942, whereas sharing anonymization EHRs for other nonmedical services was only 1.805 ± 0.877. Multiple linear regression showed that general willingness to share EHRs was related to job-related healthcare (ß = 0.520), experiences on EHRs (ß = 0.192), public awareness of potential risks of sharing EHRs (ß = -0.130), and public acknowledgment of the benefits of sharing EHRs (ß = 0.290). Conclusion: The willingness to share EHRs data with Chinese residents was not high. The willingness of Chinese residents towards data sharing in EHRs is influenced by several factors, primarily job-related to healthcare, experiences on EHRs, public acknowledgment of the benefits of sharing EHRs, and public awareness of potential risks of sharing EHRs. The results provide a basis for related research and provide information for designing public health strategies such as formulating policies to improve public acceptance of sharing EHRs and promoting EHRs-based public health services.

The development trend of influenza in China from 2010 to 2019.

In this study, we quantify and evaluate the transmission capacity of different types of influenza, and evaluate the flu vaccination effect. Taking the influenza cases reported by the National Influenza Center of China from 2010 to 2019 as the research object (http://www.chinaivdc.cn/cnic), we established the SEIABR model to calculate the influenza infection rate and R0 for each year from 2010 to 2019, and calculate the influenza A and B influenza infection rates. We further added vaccination measures to the SEIABR model, and analysis the impact of different vaccination rates on the spread of influenza. We find that the range of ß(infection rate) is 6.03×10-10 to 9.66×10-10, and the average is 7.95±1.27×10-10, the range of R0 is .98 to 1.47, and the average is 1.21. Simulation result suggest that vaccine coverage needed to reach 60%-80% to control the spread of influenza virus in China when the vaccine effectiveness was 20%-40%. When the vaccine effectiveness is 40%-60%, vaccine coverage needs to reach 40%-60% to control the spread of influenza virus in China. In China, the infection rate of influenza A is higher than influenza B, to better control the spread of the flu virus, we suggest that we also need to increase the number of people vaccinated or improve the efficiency of vaccines(the current vaccination coverage is probably less than 20%).

Assess transmissibility of different influenza subtypes: Based on a SEIABR model.

OBJECTIVE: Influenza is a worldwide public health problem which causes a serious economic and health burden. In order to provide a scientific basis for improving the prevention and control level of influenza, using dynamic model to evaluate the infection rates of influenza different subtypes from 2010 to 2019 in China. METHODS: This article established SEIABR model based on influenza cases reported by China National Influenza Center from 2010 to 2019. And calculated the transmission rate and Re by combined the natural birth rate, natural death rate, infectious rate, proportion of asymptomatic patients, proportion of untreated patients, recovery rate and fatality rate. RESULTS: The average infection rate of influenza was (2.38 ± 0.59) × 10-10, and influenza A was (2.24 ± 0.51) × 10-10, influenza B was (2.21 ± 0.68) × 10-10. And average Re were 1.60, 1.51, 1.49. In addition, the infection rates of A /H1N1, A/H3N2, B/Yamagata and B/Victoria were (2.47 ± 0.51) × 10-10, (2.25 ± 0.48) × 10-10, (2.15 ± 0.61) × 10-10, and (2.30 ± 0.66) × 10-10 and average Re were 1.67, 1.52, 1.44, 1.56. CONCLUSION: Between each year, flu transmission capacity had fluctuation. Influenza A was more transmissible than influenza B, and during the major subtypes, influenza A/H1N1 was the most transmissible.

Ultra-short-term power forecast method for the wind farm based on feature selection and temporal convolution network.

The random fluctuation of wind energy is so strong that the output power cannot be predicted in time and accurately, which will influence the safety and stability of the power system. By analyzing the output power and meteorological data, the ultra-short-term power forecast method of the wind farm has been studied in this paper. Firstly, all the feature data are preprocessed and part of them with stronger correlation with the output power are obtained according to the eXtreme Gradient Boosting (XGBoost) algorithm. Then, with the reconstructed datasets and the Tree-structured Parzen Estimator (TPE) algorithm, the optimal temporal convolution network (TCN) is achieved to forecast the output power. Finally, with respect to a certain wind farm in China, ablation study and comparative experiments are conducted respectively. The ablation experiment results show that by adding the feature selection procedure into all the models, the indicators RMSE and MAE are obviously reduced as well as the running time of the model. Among them, our proposed method based on XGBoost and TCN performs best, which provides a new prospect for investigating the ultra-short-term wind power forecast problem.

The impact of meteorological factors and PM2.5 on COVID-19 transmission.

In this study, we analysed the relationship between meteorological factors and the number of patients with coronavirus disease 2019 (COVID-19). The study period was from 12 April 2020 to 13 October 2020, and daily meteorological data and the daily number of patients with COVID-19 in each state of the United States were collected. Based on the number of COVID-19 patients in each state of the United States, we selected four states (California, Florida, New York, Texas) for analysis. One-way analysis of variance ( ANOVA), scatter plot analysis, correlation analysis and distributed lag nonlinear model (DLNM) analysis were used to analyse the relationship between meteorological factors and the number of patients with COVID-19. We found that the significant influencing factors of the number of COVID-19 cases differed among the four states. Specifically, the number of COVID-19 confirmed cases in California and New York was negatively correlated with AWMD (P < 0.01) and positively correlated with AQI, PM2.5 and TAVG (P < 0.01) but not significantly correlated with other factors. Florida was significantly correlated with TAVG (positive) (P < 0.01) but not significantly correlated with other factors. The number of COVID-19 cases in Texas was only significantly negatively associated with AWND (P < 0.01). The influence of temperature and PM2.5 on the spread of COVID-19 is not obvious. This study shows that when the wind speed was 2 m/s, it had a significant positive correlation with COVID-19 cases. The impact of meteorological factors on COVID-19 may be very complicated. It is necessary to further explore the relationship between meteorological factors and COVID-19. By exploring the influence of meteorological factors on COVID-19, we can help people to establish a more accurate early warning system.

Hypertension is a clinically important risk factor for critical illness and mortality in COVID-19: A meta-analysis.

AIMS: As reported, hypertension may play an important role in adverse outcomes of coronavirus disease-2019 (COVID-19), but it still had many confounding factors. The aim of this study was to explore whether hypertension is an independent risk factor for critical COVID-19 and mortality. DATA SYNTHESIS: The Medline, PubMed, Embase, and Web of Science databases were systematically searched until November 2020. Combined odds ratios (ORs) with their 95% confidence interval (CIs) were calculated by using random-effect models, and the effect of covariates was analyzed using the subgroup analysis and meta-regression analysis. A total of 24 observational studies with 99,918 COVID-19 patients were included in the meta-analysis. The proportions of hypertension in critical COVID-19 were 37% (95% CI: 0.27 -0.47) when compared with 18% (95% CI: 0.14 -0.23) of noncritical COVID-19 patients, in those who died were 46% (95%CI: 0.37 -0.55) when compared with 22% (95% CI: 0.16 -0.28) of survivors. Pooled results based on the adjusted OR showed that patients with hypertension had a 1.82-fold higher risk for critical COVID-19 (aOR: 1.82; 95% CI: 1.19 - 2.77; P = 0.005) and a 2.17-fold higher risk for COVID-19 mortality (aOR: 2.17; 95% CI: 1.67 - 2.82; P < 0.001). Subgroup analysis results showed that male patients had a higher risk of developing to the critical condition than female patients (OR: 3.04; 95%CI: 2.06 - 4.49; P < 0.001) and age >60 years was associated with a significantly increased risk of COVID-19 mortality (OR: 3.12; 95% CI: 1.93 - 5.05; P < 0.001). Meta-regression analysis results also showed that age (Coef. = 2.3×10-2, P = 0.048) had a significant influence on the association between hypertension and COVID-19 mortality. CONCLUSIONS: Evidence from this meta-analysis suggested that hypertension was independently associated with a significantly increased risk of critical COVID-19 and inhospital mortality of COVID-19.

Association of body mass index (BMI) with critical COVID-19 and in-hospital mortality: A dose-response meta-analysis.

BACKGROUND AND PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic presents an unprecedented health crisis to the entire world. As reported, the body mass index (BMI) may play an important role in COVID-19; however, this still remains unclear. The aim of this study was to explore the association between BMI and COVID-19 severity and mortality. METHODS: The Medline, PubMed, Embase and Web of science were systematically searched until August 2020. Random-effects models and dose-response meta-analysis were used to synthesize the results. Combined odds ratios (ORs) with their 95% confidence intervals (CIs) were calculated, and the effect of covariates were analyzed using subgroup analysis and meta-regression analyses. RESULTS: A total of 16 observational studies involving 109,881 patients with COVID-19 were included in the meta-analysis. The pooled results showed that patients with a BMI ≥ 30 kg/m2 had a 2.35-fold risk (OR = 2.35, 95%CI = 1.64-3.38, P < 0.001) for critical COVID-19 and a 2.68-fold risk for COVID-19 mortality (OR = 2.68, 95%CI = 1.65-4.37, P < 0.001) compared with patients with a BMI <30 kg/m2. Subgroup analysis results showed that patients with obesity and age > 60 years was associated with a significantly increased risk of critical COVID-19 (OR = 3.11, 95%CI = 1.73-5.61, P < 0.001) and COVID-19 mortality (OR = 3.93, 95%CI = 2.18-7.09, P < 0.001). Meta-regression analysis results also showed that age had a significant influence on the association between BMI and COVID-19 mortality (Coef. = 0.036, P = 0.048). Random-effects dose-response meta-analysis showed a linear association between BMI and both critical COVID-19(Pnon-linearity = 0.242) and mortality (Pnon-linearity = 0.116). The risk of critical COVID-19 and mortality increased by 9%(OR = 1.09, 95%CI = 1.04-1.14, P < 0.001) and 6%(OR = 1.06, 95%CI = 1.02-1.10, P = 0.002) for each 1 kg/m2 increase in BMI, respectively. CONCLUSIONS: Evidence from this meta-analysis suggested that a linear dose-response association between BMI and both COVID-19 severity and mortality. Further, obesity (BMI ≥ 30 kg/m2) was associated with a significantly increased risk of critical COVID-19 and in-hospital mortality of COVID-19.

Chili Consumption and Risk of Gastric Cancer: A Meta-Analysis.

Chili peppers are loved by people all over the world and have been indispensable vegetable for three meals a day. However, reports about the association between chili consumption and gastric cancer (GC) risk have been conflicting. So, we carried out this meta-analysis to evaluate the effect of chili consumption on the risk of GC. Medline, PubMed, Web of science, Embase, Cochrane Library databases were systematically searched until May 2019. Heterogeneity among studies was examined using Q and I2 statistics. Combined odds ratio (OR) with their 95% confidence interval (CI) were calculated using a random- or fixed-effects model. All data were analyzed using STATA 15.1 software. 13 studies (3,095 cases and 4,761 controls) were included in the meta-analysis. A 1.96-fold increased risk of GC was shown for the moderate-high chili consumption (OR = 1.96, 95%CI =1.59-2.42). Dose-response analysis showed a significant nonlinear association of GC risk with capsaicin intake (pnon-linearity <0.05) and suggested a significant positive association between high chili consumption and GC risk (OR = 2.28, 95%CI = 1.76-2.96) but not moderate chili consumption (OR = 0.72, 95%CI = 0.36-1.41). Sensitivity analysis and publication bias test results indicated that no publication bias and the results were reliable (Egger's: P = 0.288). Evidence from this meta-analysis suggested that a higher level of chili consumption may be associated with an increased incidence of GC. More studies are warranted to confirm the association between chili consumption and the risk of GC.

Effect of coffee consumption on dyslipidemia: A meta-analysis of randomized controlled trials.

BACKGROUND AND AIM: Dyslipidemia is a common metabolic disease worldwide and also an important predisposing factor for cardiovascular diseases (CVDs). Coffee is loved by people all over the world; however, the association between coffee consumption and blood lipids has yielded inconsistent results. So we carried this meta-analysis to explore the effects of coffee consumption on blood lipids. METHODS AND RESULTS: Medline, PubMed, Web of science, Embase, and Cochrane Library databases were systematically searched until April 2020. Combined weighted mean differences (WMD) with their 95% confidence interval (CI) were calculated using random-effects models, and between-study heterogeneity was assessed by Cochran's Q test and I2 statistics. Subgroup analysis and meta-regression analysis were also conducted to explore the potential heterogeneity. A total of 12 RCT studies involving the association between coffee consumption and blood lipid levels were included in the meta-analysis. The pooled results showed that coffee consumption significantly increased total cholesterol (TC) (WMD: 0.21 mmol/L, 95% CI: 0.04; 0.39, P = 0.017), triglyceride (TG) (WMD: 0.12 mmol/L, 95% CI: 0.03; 0.20, P = 0.006) and low-density lipoprotein (LDL-C) (WMD: 0.14 mmol/L, 95% CI: 0.05; 0.24, P = 0.003) while had no significant effect on high-density lipoprotein (HDL-C) (WMD: -0.01 mmol/L, 95% CI: -0.06; 0.04, P = 0.707). Dose-response analysis results revealed significant positive nonlinear associations between coffee consumption and the increase in TC, LDL-C, and TG levels. CONCLUSIONS: Evidence from this meta-analysis suggested that coffee consumption may be associated with an elevated risk for dyslipidemia and CVDs. So a reasonable habit of coffee consumption (<3 cups/d) is essential for the prevention of dyslipidemia.

Effects of meteorological factors on the incidence of mumps and models for prediction, China.

BACKGROUND: Mumps is an acute respiratory infectious disease with obvious regional and seasonal differences. Exploring the impact of climate factors on the incidence of mumps and predicting its incidence trend on this basis could effectively control the outbreak and epidemic of mumps. METHODS: Considering the great differences of climate in the vast territory of China, this study divided the Chinese mainland into seven regions according to the administrative planning criteria, data of Mumps were collected from the China Disease Prevention and Control Information System, ARIMA model and ARIMAX model with meteorological factors were established to predict the incidence of mumps. RESULTS: In this study, we found that precipitation, air pressure, temperature, and wind speed had an impact on the incidence of mumps in most regions of China and the incidence of mumps in the north and southwest China was more susceptible to climate factors. Considering meteorological factors, the average relative error of ARIMAX model was 10.87%, which was lower than ARIMA model (15.57%). CONCLUSIONS: Meteorology factors were the important factors which can affect the incidence of mumps, ARIMAX model with meteorological factors could better simulate and predict the incidence of mumps in China, which has certain reference value for the prevention and control of mumps.

Spatial-temporal analysis and visualization of scarlet fever in mainland China from 2004 to 2017.

This study retrospectively analyzed the spatio-temporal distribution and spatial clustering of scarlet fever in mainland China from 2004 to 2017. In recent years, the incidence of scarlet fever is increasing. Previous studies on the spatial distribution of scarlet fever in China are mainly focused at the provincial and municipal levels, and there is few systematic report on the spatial and temporal distribution characteristics of scarlet fever on the national level. Based on the incidence information of scarlet fever in mainland China between 2004 and 2017 collected from the China Center for Disease Control, this paper systematically explored the Spatio-temporal distribution of scarlet fever by three methods, contains spatial autocorrelation analysis, Spatio-temporal scanning analysis, and trend surface analysis. The results demonstrate that the incidence of scarlet fever varies by seasons, which is in line with double-peak distribution.The first peak generally occurs from May to June and the second one from November to December, while February and August is the lowest period of incidence. Trend surface analysis indicates that the incidence of scarlet fever in northern China is higher than the south, slightly higher in western compared to the east, and lower in the central part. Additionally, the results show that the clustering regions of scarlet fever centrally distributed in the northeast, northwest, north china and some provinces in the east, such as Zhejiang, Shanghai, Shandong, and Jiangsu.

Research about the optimal strategies for prevention and control of varicella outbreak in a school in a central city of China: based on an SEIR dynamic model.

Varicella is an acute respiratory infectious diseases, with high transmissibility and quick dissemination. In this study, an SEIR (susceptible-exposed-infected-recovered) dynamic model was established to explore the optimal prevention and control measures according to the epidemiological characteristics about varicella outbreak in a school in a central city of China. Berkeley Madonna 8.3.18 and Microsoft Office Excel 2010 software were employed for the model simulation and data management, respectively. The result showed that the simulated result of SEIR model agreed well with the reported data when ß (infected rate) equal to 0.067. Models showed that the cumulative number of cases was only 13 when isolation adopted when the infected individuals were identified (assuming isolation rate was up to 100%); the cumulative number of cases was only two and the TAR (total attack rate) was 0.56% when the vaccination coefficient reached 50%. The cumulative number of cases did not change significantly with the change of efficiency of ventilation and disinfection, but the peak time was delayed; when δ (vaccination coefficient) = 0.1, m (ventilation efficiency) = 0.7 or δ = 0.2, m = 0.5 or δ = 0.3, m = 0.1 or δ = 0.4 and above, the cumulative number of cases would reduce to one case and TAR would reduce to 0.28% with combined interventions. Varicella outbreak in school could be controlled through strict isolation or vaccination singly; combined interventions have been adopted when the vaccination coefficient was low.

The study on the early warning period of varicella outbreaks based on logistic differential equation model.

Chickenpox is a common acute and highly contagious disease in childhood; moreover, there is currently no targeted treatment. Carrying out an early warning on chickenpox plays an important role in taking targeted measures in advance as well as preventing the outbreak of the disease. In recent years, the infectious disease dynamic model has been widely used in the research of various infectious diseases. The logistic differential equation model can well demonstrate the epidemic characteristics of epidemic outbreaks, gives the point at which the early epidemic rate changes from slow to fast. Therefore, our study aims to use the logistic differential equation model to explore the epidemic characteristics and early-warning time of varicella. Meanwhile, the data of varicella cases were collected from first week of 2008 to 52nd week of 2017 in Changsha. Finally, our study found that the logistic model can be well fitted with varicella data, besides the model illustrated that there are two peaks of varicella at each year in Changsha City. One is the peak in summer-autumn corresponding to the 8th-38th week; the other is in winter-spring corresponding to the time from the 38th to the seventh week next year. The 'epidemic acceleration week' average value of summer-autumn and winter-spring are about the 16th week (ranging from the 15th to 17th week) and 45th week (ranging from the 44th to 47th week), respectively. What is more, taking warning measures during the acceleration week, the preventive effect will be delayed; thus, we recommend intervene during recommended warning weeks which are the 15th and 44th weeks instead.

Health care and risk of adverse pregnancy outcomes among diabetic women: an updated meta-analysis.

PURPOSE: Diabetic women appear to have adverse pregnancy outcomes. Although there were two meta-analyzes that examined the association between health care and adverse pregnancy outcomes, their results were limited because they only included congenital anomaly and perinatal mortality, and they did not clarify the detailed situations of diabetes and health care. This meta-analysis aims to completely evaluate the effects of health care in improving adverse pregnancy outcomes among diabetic mothers. METHODS: CNKI, EMBASE, Web of Science, and PubMed databases were searched for eligible studies up to December 2017, without any restrictions. Relevant cohort studies characterizing the relationship between health care and adverse pregnancy outcomes were selected for inclusion in the meta-analysis. We also screened the reference list of relevant studies. The fixed-effect models or random-effect models were used to calculate the risk estimates. The potential sources of heterogeneity were explored by stratified and sensitivity analyzes. RESULTS: Twenty-one studies with 6685 cases were included in our analysis. Health care was associated with significantly decreased risk of congenital anomaly (RR 0.237; 95% CI 0.166-0.338), perinatal death (RR 0.457; 95% CI 0.294-0.712), large for gestational age (LGA) (RR 0.794; 95% CI 0.640-0.986), and neonatal hypoglycemia (RR 0.672; 95% CI 0.486-0.929). Publication bias was not found in most results, with the exception of congenital anomaly and small for gestational age (SGA). CONCLUSION: Health care is associated with decreased risk of congenital anomaly, perinatal death, LGA, neonatal hypoglycemia.

Possible Epithelial Ovarian Cancer Association with HPV18 or HPV33 Infection.

The present study was conducted to investigate the prevalence of HPV infection in epithelial ovarian cancer (EOC) in Hunan province. DNA samples were collected from paraffin embedded ovarian tissue from 322 patients with EOC, 99 with ovarian benign tumors and 199 normal persons. The polymerase chain reaction and direct sequencing were used to identify the HPV types in the samples. The relationship between the infection of human papillomavirus (HPV) and the epithelial ovarian carcinoma (EOC) was investigated combined with clinical data. The prevalence of HPV18 and HPV33 in EOC group and benign group was higher than in the normal group. HPV18 and HPV33 may play a role in the development of both EOC and ovarian benign tumor and may participate in the development of EOC with traditional risk factors, family history and abortion, possibly exerting synergistic effects..

[Characteristic of spatial-temporal distribution of hepatitis E in Hunan province, 2006-2014].

OBJECTIVE: To analyze the spatial-temporal distribution of Hepatitis E (HEV) in Hunan province from 2006 to 2014. METHODS: Data related to HEV cases in Hunan province from 2006 to 2014 were collected from the Infectious Diseases Reporting Information System in the formation System of Disease Prevention and Control of China. Based on ArcGIS (10.2) and SaTScan(version 9.1), spatial autocorrelation analysis and space-time clustering analysis were used to study the prevalence on HEV. RESULTS: A total of 7 124 HEV cases were reported with 3 deaths during this period. The average annual incidence rate was 1.22/10(5). Most of the cases were over 55 years old and the majority of them (54.15%) were farmers. The distribution of HEV showed differences on locations and the regions with high incidence seen in northern and western areas of Hunan. However the regions with low incidence appeared in central or southern parts of Hunan. Data from the global spatial autocorrelation analysis showed that there was space autocorrelation on the HEV incidence rates in counties (cities, districts) (Moran'I was positive,P<0.05). A total of 31 countries were found in the high-high region with most of the clusters located in northern and western Hunan. According to local indication of spatial autocorrelation analysis, 31 countries in high-high region all showed statistically significant differences (P<0.05). RESULTS from the space-time scan showed 7 space-time clustering areas, including those most likely in the western Hunan area (2012-2014); the secondary clusters in northern Hunan areas (2011-2014). CONCLUSIONS: Significant cluster pattern was found in the distribution of HEV in Hunan province. Clusters found in northern and western of Hunan province were seen more than in other regions.