Can population differences explain the contrasting results of the Mwanza, Rakai, and Masaka HIV/ sexually transmitted disease intervention trials? A modeling study.

Background: Periodontitis was reported to be associated with inflammatory bowel disease (IBD). However, the association between them has not been firmly established in the existing literature. Therefore, this meta-analysis was conducted to evaluate the relationship between periodontitis and IBD. Methods: Electronic databases were searched for publications up to August 1, 2019 to include all eligible studies. The pooled odds ratios (ORs) and 95% confidence intervals (95% CIs) were estimated to determine the association between periodontal disease and IBD using a random or fixed effects model according to heterogeneity. Results: Six eligible studies involving 599 IBD patients and 448 controls were included. The pooled OR between periodontitis and IBD was 3.17 (95% CI: 2.09-4.8) with no heterogeneity observed (I 2 = 0.00%). The pooled ORs were 3.64 (95% CI: 2.33-5.67) and 5.37 (95% CI: 3.30-8.74) for the associations between periodontitis and the two subcategories of IBD, Crohn' s disease and ulcerative colitis, respectively. Conclusions: The results demonstrated that periodontitis was significantly associated with IBD. However, the mechanisms underlying periodontitis and IBD development are undetermined. Further studies are needed to elucidate this relationship.

Surgical site infection (SSI) is one of the most common surgical complications in the world, particularly in developing countries. This study aimed to estimate the incidence and distribution of SSI in mainland China. Eighty-four prospective observational studies (82 surveillance studies, 1 nested case control study, and 1 cohort study) were selected for inclusion in this meta-analysis. The average incidence of SSI in mainland China was 4.5% (95% CI: 3.1–5.8) from 2001 to 2012 and has decreased significantly in recent years. The remote western regions had a higher incidence of 4.6% (95% CI: 4.0–5.3). The most common surgical procedure was abdominal surgery (8.3%, 95% CI: 6.5–10.0). SSI occurred frequently in the elderly (5.1%, 95% CI: 2.2–8.0), patients confined to hospital for over 2 weeks (5.7%, 95% CI: 0.9–10.0), superficial incision wounds (5.6%, 95% CI: 4.4–6.8), dirty wounds (8.7%, 95% CI: 6.9–10.6), operations lasting for over 2 hours (7.3%, 95% CI: 4.9–9.7), general anaesthesia operations (4.7%, 95% CI: 2.7–6.6), emergency surgeries (5.9%, 95% CI: 4.2–7.7), and non-intra-medication operations (7.4%, 95% CI: 1.0–13.7).

BackgroundOutbreaks of hand-foot-mouth disease (HFMD) have been reported for many times in Asia during the last decades. This emerging disease has drawn worldwide attention and vigilance. Nowadays, the prevention and control of HFMD has become an imperative issue in China. Early detection and response will be helpful before it happening, using modern information technology during the epidemic.MethodIn this paper, a hybrid model combining seasonal auto-regressive integrated moving average (ARIMA) model and nonlinear auto-regressive neural network (NARNN) is proposed to predict the expected incidence cases from December 2012 to May 2013, using the retrospective observations obtained from China Information System for Disease Control and Prevention from January 2008 to November 2012.ResultsThe best-fitted hybrid model was combined with seasonal ARIMA and NARNN with 15 hidden units and 5 delays. The hybrid model makes the good forecasting performance and estimates the expected incidence cases from December 2012 to May 2013, which are respectively −965.03, −1879.58, 4138.26, 1858.17, 4061.86 and 6163.16 with an obviously increasing trend.ConclusionThe model proposed in this paper can predict the incidence trend of HFMD effectively, which could be helpful to policy makers. The usefulness of expected cases of HFMD perform not only in detecting outbreaks or providing probability statements, but also in providing decision makers with a probable trend of the variability of future observations that contains both historical and recent information.

Background: Patients with severe Coronavirus Disease 2019 (COVID-19) will progress rapidly to acute respiratory failure or death. We aimed to develop a quantitative tool for early predicting mortality risk of patients with COVID-19.