Duration of fever ≥ 3 days, body temperature ≥ 37.5°C, lethargy, hyperglycemia, vomiting, increased neutrophil count, EV71 infection, and young age are risk factors for severe HFMD. A confirmed diagnosis at first visit to hospital can significantly decrease the risk of severe HFMD.

Objective Scrub typhus is an important febrile disease in Asia, and antibiotics have been used to treat this disease. The purpose of this study was to generate large-scale evidence of the efficacy of different antibiotic regimens for treating scrub typhus using a meta-analysis. Methods PubMed, Elsevier ScienceDirect, Chinese Biomedical Literature Database (CBM), China National Knowledge Infrastructure (CNKI), and Wanfang (Chinese) were searched to identify relevant articles. The data from eligible citations were extracted by two reviewers. All analyses were performed using the Cochrane Collaboration Review Manager 4.2 and Stata 10.0 software programs. Results We conducted a meta-analysis of 17 separate studies that evaluated the efficacy of treatment with the different antibiotic regimens for scrub typhus. The median time (h) to clearance of fever in the azithromycin-treated group was longer than that in the chloramphenicol-treated group (weighted mean difference [WMD] = 12.66, 95% confidence interval [CI]: 2.26,23.06). Adverse events were 2.95 (95%CI: 1.32, 6.61) times more likely to occur in the azithromycin-treated group than in the chloramphenicol-treated group. The clearance time (days) for the main symptoms (including fever, headache, rash and lymphadenectasis) in the doxycycline-treated group was shorter than that in the chloramphenicol-treated group (WMD = -0.4, 95%CI: -0.53, -0.26) in five trials. Adverse drug events occurred significantly less frequently in the azithromycin-treated group than in the doxycycline-treated group (relative risk [RR] = 0.47, 95%CI: 0.31,0.71). Conclusion Doxycycline was found to act more quickly, but more adverse drug events occur when using this regimen compared to azithromycin and chloramphenicol.

No abstract

No abstract

BackgroundIn February 2009, a high school student was diagnosed with sputum-smear positive pulmonary tuberculosis (TB). One year later, 2 other students in the same grade developed sputum-smear positive TB.MethodsWe used tuberculin skin testing (TST), chest radiography, sputum smear, and symptomatology for case identification. We defined latent TB infection (LTBI) as a TST induration of 15 mm or larger, probable TB as a chest radiograph indicative of TB plus productive cough/hemoptysis for at least 2 weeks or TST induration of 15 mm or larger, and confirmed TB as 2 or more positive sputum smears or 1 positive sputum smear plus a chest radiograph indicative of TB.ResultsOf students in the same grade as the primary case-student, 26% (122/476) had LTBI and 4.8% (23/476) had probable/confirmed TB. Of teachers, 43% (18/42) had LTBI and none had probable/confirmed TB. Sharing a classroom with the primary case-student increased risk for LTBI (rate ratio = 2.5; 95% CI: 1.9–3.4) and probable/confirmed TB (rate ratio = 17, 95% CI: 7.8–39). Of students with LTBI in February 2009 who refused prophylaxis, 50% (11/22) had probable/confirmed TB in April 2010.ConclusionsThis TB outbreak was likely started by delayed diagnosis of TB in the case-student and was facilitated by lack of post-exposure chemoprophylaxis. Post-exposure prophylaxis is strongly recommended for all TST-positive students.

Behavioral interventions have been shown to both promote and change many health-related behaviors and issues. This meta-analysis was performed to assess whether behavioral interventions have the potential to increase condom use and HIV testing uptake among men who have sex with men (MSM) in China. PubMed, Elsevier Science Direct, Chinese Biomedical Literature Database (CBM), China National Knowledge Infrastructure (CNKI), and Wanfang (Chinese) were searched to June 2011 to identify relevant articles. Data of eligible citations were extracted by two reviewers. Sixteen studies were identified. Aggregated findings indicated that interventions were associated with a significant increase in condom use between MSM and male sex partners in the last anal sex act (RR=1.17, 95% CI=1.05-1.29) and consistent condom use between MSM and male sex partners in the past 6 months (RR=1.36, 95% CI=1.15-1.60) and HIV testing (RR=2.22, 95% CI=1.72-2.88). However, no significant increase was detected in condom use over the course of the intervention among MSM engaging in sex with women. In the subgroup analyses, the positive effects were not detected in some subgroups such as anal sex with casual partners and intervention interval less than or equal to 6 months. The sensitivity analysis showed that these estimates were unchanged after removal of the study that had the biggest sample or the studies that had the most rigorous study design. This meta-analysis can inform future intervention design and implementation in terms of sample size, target populations, settings, goals for process measures, and intervention interval.

Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. Here, we have recruited a 144 COVID-19 patient cohort consisting of training, validation, and internal test sets, longitudinally recorded 124 routine clinical and laboratory parameters, and built a machine learning model to predict the disease progression based on measurements from the first 12 days since the disease onset when no patient became severe. A panel of 11 routine clinical factors, including oxygenation index, basophil counts, aspartate aminotransferase, gender, magnesium, gamma glutamyl transpeptidase, platelet counts, activated partial thromboplastin time, oxygen saturation, body temperature and days after symptom onset, constructed a classifier for COVID-19 severity prediction, achieving accuracy of over 94%. Validation of the model in an independent cohort containing 25 patients achieved accuracy of 80%. The overall sensitivity, specificity, PPV and NPV were 0.70, 0.99, 0.93 and 0.93, respectively. Our model captured predictive dynamics of LDH and CK while their levels were in the normal range. This study presents a practical model for timely severity prediction and surveillance for COVID-19, which is freely available at webserver https://guomics.shinyapps.io/covidAI/.

SUMMARY: This study aimed to assess the likelihood of an outbreak or epidemic of emerging infectious diseases (EIDs) in Shaoxing city, China, and its resulting impact to provide decision makers with quantitative, directive results. Factors related to the risk of EIDs were selected through meeting with experts and were arranged in a hierarchical structure. These evaluation factors were also weighted to allow the use of a point system for evaluation. As a result, 14 evaluation factors comprising a 3-layer hierarchy were generated. The riskiest top 10 EIDs were HIV/AIDS (consistency index [CI] ＝ 3.206), cholera (CI ＝ 3.103), SARS (CI ＝ 2.804), acute schistosomiasis (CI ＝ 2.784), malaria (CI ＝ 2.777), legionellosis (CI ＝ 2.743), avian influenza A/H5N1 (CI ＝ 2.734), dengue fever (CI ＝ 2.702), Escherichia coli O157:H7 enteritis (CI ＝ 2.593), and plague (CI ＝ 2.553). The risk assessment was specifically intended to support local and national government agencies in the management of high risk EIDs in their efforts to (i) make resource allocation decisions, (ii) make high-level planning decisions, and (iii) raise public awareness of the EID risk. The results showed that the EID risk in Shaoxing could be effectively assessed through an analytic hierarchy process.