Abstract Background COVID-19 has posed an enormous threat to public health around the world. Some severe and critical cases have bad prognoses and high case fatality rates, unraveling risk factors for severe COVID-19 are of significance for predicting and preventing illness progression, and reducing case fatality rates. Our study focused on analyzing characteristics of COVID-19 cases and exploring risk factors for developing severe COVID-19. Methods The data for this study was disease surveillance data on symptomatic cases of COVID-19 reported from 30 provinces in China between January 19 and March 9, 2020, which included demographics, dates of symptom onset, clinical manifestations at the time of diagnosis, laboratory findings, radiographic findings, underlying disease history, and exposure history. We grouped mild and moderate cases together as non-severe cases and categorized severe and critical cases together as severe cases. We compared characteristics of severe cases and non-severe cases of COVID-19 and explored risk factors for severity. Results The total number of cases were 12 647 with age from less than 1 year old to 99 years old. The severe cases were 1662 (13.1%), the median age of severe cases was 57 years [Inter-quartile range(IQR): 46–68] and the median age of non-severe cases was 43 years (IQR: 32–54). The risk factors for severe COVID-19 were being male [adjusted odds ratio (a OR ) = 1.3, 95% CI: 1.2–1.5]; fever (a OR = 2.3, 95% CI: 2.0–2.7), cough (a OR = 1.4, 95% CI: 1.2–1.6), fatigue (a OR = 1.3, 95% CI: 1.2–1.5), and chronic kidney disease (a OR = 2.5, 95% CI: 1.4–4.6), hypertension (a OR = 1.5, 95% CI: 1.2–1.8) and diabetes (a OR = 1.96, 95% CI: 1.6–2.4). With the increase of age, risk for the severity was gradually higher [20–39 years (a OR = 3.9, 95% CI: 1.8–8.4), 40–59 years (a OR = 7.6, 95% CI: 3.6–16.3), ≥ 60 years (a OR = 20.4, 95% CI: 9.5–43.7)], and longer time from symtem onset to diagnosis [3–5 days (a OR = 1.4, 95% CI: 1.2–1.7), 6–8 days (a OR = 1.8, 95% CI: 1.5–2.1), ≥ 9 days(a OR = 1.9, 95% CI: 1.6–2.3)]. Conclusions Our study showed the risk factors for developing severe COVID-19 with large sample size, which included being male, older age, fever, cough, fatigue, delayed diagnosis, hypertension, diabetes, chronic kidney diasease. Based on these factors, the severity of COVID-19 cases can be predicted. So cases with these risk factors should be paid more attention to prevent severity.
For providing evidences for further modification of China Infectious Diseases Automated-alert and Response System (CIDARS) by comparing the early-warning performance of the temporal model and temporal-spatial model in CIDARS.The application performance for outbreak detection of temporal model and temporal-spatial model simultaneously running among 208 pilot counties in 20 provinces from 2011 to 2013 was compared; the 16 infectious diseases were divided into two classes according to the disease incidence level; cases data in nationwide Notifiable Infectious Diseases Reporting Information System was combined with outbreaks reported to Public Health Emergency Reporting System, by adopting the index of the number of signals, sensitivity, false alarm rate and time for detection.The overall sensitivity of temporal model and temporal-spatial model for 16 diseases was 96.23% (153/159) and 90.57% (144/159) respectively, without significant difference (Z = -1.604, P = 0.109), and the false alarm rate of temporal model (1.57%, 57 068/3 643 279) was significantly higher than that of temporal-spatial model (0.64%, 23 341/3 643 279) (Z = -3.408, P = 0.001), while the median time for detection of these two models was not significantly different, which was 3.0 days and 1.0 day respectively (Z = -1.334, P = 0.182).For 6 diseases of type I which represent the lower incidence, including epidemic hemorrhagic fever,Japanese encephalitis, dengue, meningococcal meningitis, typhus, leptospirosis, the sensitivity was 100% for both models (8/8, 8/8), and the false alarm rate of both temporal model and temporal-spatial model was 0.07% (954/1 367 437, 900/1 367 437), with the median time for detection being 2.5 days and 3.0 days respectively. The number of signals generated by temporal-spatial model was reduced by 2.29% compared with that of temporal model.For 10 diseases of type II which represent the higher incidence, including mumps, dysentery, scarlet fever, influenza, rubella, hepatitis E, acute hemorrhagic conjunctivitis, hepatitis A, typhoid and paratyphoid, and other infectious diarrhea, the sensitivity of temporal model was 96.03% (145/151), and the sensitivity of temporal-spatial model was 90.07% (136/151), the number of signals generated by temporal-spatial model was reduced by 59.36% compared with that of temporal model. Compared to temporal model, temporal-spatial model reduced both the number of signals and the false alarm rate of all the type II diseases;and the median of outbreak detection time of temporal model and temporal-spatial model was 3.0 days and 1.0 day, respectively.Overall, the temporal-spatial model had better outbreak detection performance, but the performance of two different models varies for infectious diseases with different incidence levels, and the adjustment and optimization of the temporal model and temporal-spatial model should be conducted according to specific infectious disease in CIDARS.
Abstract Background Anoplophora glabripennis (Motschulsky), commonly known as Asian longhorned beetle (ALB), is a wood-boring insect that can cause lethal infestation to multiple borer leaf trees. In Gansu Province, northwest China, ALB has caused a large number of deaths of a local tree species Populus gansuensis . The damaged area belongs to Gobi desert where every single tree is artificially planted and is extremely difficult to cultivate. Therefore, the monitoring of the ALB infestation at the individual tree level in the landscape is necessary. Moreover, the determination of an abnormal phenotype that can be obtained directly from remote-sensing images to predict the damage degree can greatly reduce the cost of field investigation and management. Methods Multispectral WorldView-2 (WV-2) images and 5 tree physiological factors were collected as experimental materials. One-way ANOVA of the tree’s physiological factors helped in determining the phenotype to predict damage degrees. The original bands of WV-2 and derived vegetation indices were used as reference data to construct the dataset of a prediction model. Variance inflation factor and stepwise regression analyses were used to eliminate collinearity and redundancy. Finally, three machine learning algorithms—Random Forest(RF), Support Vector Machine༈SVM༉, Classification And Regression Tree༈CART༉—were applied and compared to find the best classifier for predicting the damage stage of individual P. gansuensis . Results The confusion matrix of RF achieved the highest overall classification accuracy (86.2%) and the highest Kappa index value (0.804), indicating the potential of using WV-2 imaging to accurately detect damage stages of individual trees. In addition, the canopy color was found to be positively correlated with P. gansuensis ’ damage stages. Conclusions A novel method was developed by combining WV-2 and tree physiological index for semi-automatic classification of three damage stages of P. gansuensis infested with ALB. The canopy color was determined as an abnormal phenotype that could be directly assessed using remote-sensing images at the tree level to predict the damage degree. These tools are highly applicable for driving quick and effective measures to reduce damage to pure poplar forests in Gansu Province, China.
Abstract Background Certain bacterial infectious diseases are categorized as notifiable infectious diseases in China. Understanding the time-varying epidemiology of bacterial infections diseases can provide scientific evidence to inform prevention and control measures. Methods Yearly incidence data for all 17 major notifiable bacterial infectious diseases (BIDs) at the province level were obtained from the National Notifiable Infectious Disease Reporting Information System in China between 2004 and 2019. Of them 16 BIDs are divided into four categories, respiratory transmitted diseases (RTDs, 6 diseases), direct contact/fecal-oral transmitted diseases (DCFTDs, 3 diseases), blood-borne/sexually transmitted diseases (BSTDs, 2 diseases), and zoonotic and vector-borne diseases (ZVDs, 5 diseases), and neonatal tetanus is excluded in the analysis. We characterized the demographic, temporal, and geographical features of the BIDs and examined their changing trends using a joinpoint regression analysis. Results During 2004‒2019, 28 779 thousand cases of BIDs were reported, with an annualized incidence rate of 134.00 per 100 000. RTDs were the most commonly reported BIDs, accounting for 57.02% of the cases (16 410 639/28 779 000). Average annual percent changes (AAPC) in incidence were − 1.98% for RTDs, − 11.66% for DCFTDs, 4.74% for BSTDs, and 4.46% for ZVDs. Females had a higher incidence of syphilis than males, and other BIDs were more commonly reported in males. Among 0-5-year-olds, the diseases with the largest increases in incidence were pertussis (15.17% AAPC) and scarlet fever (12.05%). Children and students had the highest incidence rates of scarlet fever, pertussis, meningococcal meningitis, and bacillary dysentery. Northwest China had the highest incidence of RTDs, while South and East China had the highest incidences of BSTDs. Laboratory confirmation of BIDs increased from 43.80 to 64.04% during the study period. Conclusions RTDs and DCFTDs decreased from 2004 to 2019 in China, while BSTDs and ZVDs increased during the same period. Great attention should be paid to BSTDs and ZVDs, active surveillance should be strengthened, and timely control measures should be adopted to reduce the incidence.
Abstract Background We compared the clinical characteristics of infections caused by different pathogens and established a viral/bacterial infection prediction model to guide early clinical identification of pathogens among inpatients with community-acquired pneumonia (CAP). Methods Data were analysed to establish a prediction model for the early treatment of bacterial or viral infections. Basic data, clinical symptoms, laboratory examinations, and imaging of patients were collected and compared, and the virus/bacteria prediction equation was established. Results The proportion of patients with muscle soreness and headaches was significantly higher in the viral infection group than in the bacterial infection group. Procalcitonin (PCT) levels, erythrocyte sedimentation rate (ESR), and neutrophil alkaline phosphatase (NAP) scores were significantly higher in the bacterial infection group than in the viral infection group. The creatine kinase level was significantly higher in the viral infection group than in the bacterial infection group ( P < 0.05). More patients in the atypical pathogen infection group (up to 52.0%) had real lung degeneration, and the difference was statistically significant compared with other groups ( P < 0.005). Patchy shadows were more common in the viral infection group than in other groups (up to 92.5%). There were significant differences in the PCT levels and the presence of fever or muscle soreness between the groups. A binary logistic regression equation was obtained, which could predict the probability of viral infection (sensitivity 57.5%, specificity 67.7%, area under the ROC curve 0.651). Conclusions Adult CAP patients with viral infection are more likely to have headaches and muscle soreness than those with bacterial infection. An elevated PCT level, NAP score, and ESR indicated a high possibility of bacterial infection. Accordingly, a viral and bacterial infection prediction model was established.
BACKGROUND Influenza vaccination is recommended for nurses in China but is not mandatory or offered free of charge. Identifying factors that impact seasonal influenza vaccine acceptance among nurses in China may inform strategies to increase vaccination coverage in this high priority group. OBJECTIVE To determine influenza vaccination coverage and the principal factors influencing influenza vaccination among nurses in China. METHODS During March 22-April 1, 2018, we conducted an opt-in internet panel survey among registered nurses in China. Respondents were recruited from an internet-based training platform for nurses. We assessed influenza vaccination status and factors influencing influenza vaccine acceptance and refusal. RESULTS Among 22,888 nurses invited to participate, 4,706 responded, and 4,153 were valid respondents. Overall, 257 (6%) nurses reported receiving the seasonal influenza vaccine during the 2017/18 season. Vaccination coverage was highest among nurses working in Beijing (10%, P<.001) and nurses working in primary care (12%, P=.023). The top three reasons for not being vaccinated were lack of time (28%), not knowing where and when to get vaccinated (14%), and lack of confidence in the vaccine’s effectiveness (12%). Overall, 41% of nurses reported experiencing at least one episode of influenza-like illness (ILI) during the 2017/18 season; 87% of nurses kept working while sick, and 25% of nurses reported ever recommending influenza vaccination to patients. Compared with nurses who did not receive influenza vaccination in the 2017/18 season, nurses who received influenza vaccination were more likely to recommend influenza vaccination to patients (67% vs. 22%, P<.001). CONCLUSIONS Influenza vaccination coverage among nurses was low, and only a small proportion recommended influenza vaccine to patients. Our findings highlight the need for a multi-pronged strategy to increase influenza vaccination among nurses in China.
Abstract Objectives To outline which infectious diseases in the pre-covid-19 era persist in children and adolescents in China and to describe recent trends and variations by age, sex, season, and province. Design National surveillance studies, 2008-17. Setting 31 provinces in mainland China. Participants 4 959 790 Chinese students aged 6 to 22 years with a diagnosis of any of 44 notifiable infectious diseases. The diseases were categorised into seven groups: quarantinable; vaccine preventable; gastrointestinal and enteroviral; vectorborne; zoonotic; bacterial; and sexually transmitted and bloodborne. Main outcome measures Diagnosis of, and deaths from, 44 notifiable infectious diseases. Results From 2008 to 2017, 44 notifiable infectious diseases were diagnosed in 4 959 790 participants (3 045 905 males, 1 913 885 females) and there were 2532 deaths (1663 males, 869 females). The leading causes of death among infectious diseases shifted from rabies and tuberculosis to HIV/AIDS, particularly in males. Mortality from infectious diseases decreased steadily from 0.21 per 100 000 population in 2008 to 0.07 per 100 000 in 2017. Quarantinable conditions with high mortality have effectively disappeared. The incidence of notifiable infectious diseases in children and adolescents decreased from 280 per 100 000 in 2008 to 162 per 100 000 in 2015, but rose again to 242 per 100 000 in 2017, largely related to mumps and seasonal influenza. Excluding mumps and influenza, the incidence of vaccine preventable diseases fell from 96 per 100 000 in 2008 to 7 per 100 000 in 2017. The incidence of gastrointestinal and enterovirus diseases remained constant, but typhoid, paratyphoid, and dysentery continued to decline. Vectorborne diseases all declined, with a particularly noticeable reduction in malaria. Zoonotic infections remained at low incidence, but there were still unpredictable outbreaks, such as pandemic A/H1N1 2009 influenza. Tuberculosis remained the most common bacterial infection, although cases of scarlet fever doubled between 2008 and 2017. Sexually transmitted diseases and bloodborne infections increased significantly, particularly from 2011 to 2017, among which HIV/AIDS increased fivefold, particularly in males. Difference was noticeable between regions, with children and adolescents in western China continuing to carry a disproportionate burden from infectious diseases. Conclusions China’s success in infectious disease control in the pre-covid-19 era was notable, with deaths due to infectious diseases in children and adolescents aged 6-22 years becoming rare. Many challenges remain around reducing regional inequalities, scaling-up of vaccination, prevention of further escalation of HIV/AIDS, renewed efforts for persisting diseases, and undertaking early and effective response to highly transmissible seasonal and unpredictable diseases such as that caused by the novel SARS-CoV-2 virus.
Abstract Severe acute radiation injuries are both very lethal and exceptionally difficult to treat. Though the radioresistant bacterium D. radiodurans was first characterized in 1956, genes and proteins key to its radioprotection have not yet to be applied in radiation injury therapy for humans. In this work, we express the D. radiodurans protein PprI in Pichia pastoris yeast cells transfected with the designed vector plasmid pHBM905A- pprI . We then treat human umbilical endothelial vein cells and BALB/c mouse cells with the yeast-derived PprI and elucidate the radioprotective effects the protein provides upon gamma irradiation. We see that PprI significantly increases the survival rate, antioxidant viability and DNA-repair capacity in irradiated cells and decreases concomitant apoptosis rates and counts of damage-indicative γH2AX foci. Furthermore, we find that PprI reduces mortality and enhances bone marrow cell clone formation and white blood cell and platelet counts in irradiated mice. PprI also seems to alleviate pathological injuries to multiple organs and improve antioxidant viability in some tissues. Our results thus suggest that PprI has crucial radioprotective effects on irradiated human and mouse cells.
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