BackgroundAir pollution constitutes a significant stimulus of asthma exacerbations; however, the impacts of exposure to major air pollutants on asthma-related hospital admissions and emergency room visits (ERVs) have not been fully determined.ObjectiveWe sought to quantify the associations between short-term exposure to air pollutants [ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter ≤10μm (PM10) and PM2.5] and the asthma-related emergency room visits (ERV) and hospitalizations.MethodsSystematic computerized searches without language limitation were performed. Pooled relative risks (RRs) and 95% confidence intervals (95%CIs) were estimated using the random-effect models. Sensitivity analyses and subgroup analyses were also performed.ResultsAfter screening of 246 studies, 87 were included in our analyses. Air pollutants were associated with significantly increased risks of asthma ERVs and hospitalizations [O3: RR(95%CI), 1.009 (1.006, 1.011); I2 = 87.8%, population-attributable fraction (PAF) (95%CI): 0.8 (0.6, 1.1); CO: RR(95%CI), 1.045 (1.029, 1.061); I2 = 85.7%, PAF (95%CI): 4.3 (2.8, 5.7); NO2: RR(95%CI), 1.018 (1.014, 1.022); I2 = 87.6%, PAF (95%CI): 1.8 (1.4, 2.2); SO2: RR(95%CI), 1.011 (1.007, 1.015); I2 = 77.1%, PAF (95%CI): 1.1 (0.7, 1.5); PM10: RR(95%CI), 1.010 (1.008, 1.013); I2 = 69.1%, PAF (95%CI): 1.1 (0.8, 1.3); PM2.5: RR(95%CI), 1.023 (1.015, 1.031); I2 = 82.8%, PAF (95%CI): 2.3 (1.5, 3.1)]. Sensitivity analyses yielded compatible findings as compared with the overall analyses without publication bias. Stronger associations were found in hospitalized males, children and elderly patients in warm seasons with lag of 2 days or greater.ConclusionShort-term exposures to air pollutants account for increased risks of asthma-related ERVs and hospitalizations that constitute a considerable healthcare utilization and socioeconomic burden.
Despite increased understanding of how viral infection is involved in asthma exacerbations, it is less clear which viruses are involved and to what extent they contribute to asthma exacerbations. Here, we sought to determine the prevalence of different respiratory viruses during asthma exacerbations. Systematic computerized searches of the literature up to June 2017 without language limitation were performed. The primary focus was on the prevalence of respiratory viruses, including AdV (adenovirus), BoV (bocavirus), CoV (coronavirus), CMV (cytomegalovirus), EnV (enterovirus), HSV (herpes simplex virus), IfV (influenza virus), MpV (metapneumovirus), PiV (parainfluenzavirus), RV (rhinovirus) and RSV (respiratory syncytial virus) during asthma exacerbations. We also examined the prevalence of viral infection stratified by age, geographic region, type of respiratory secretion, and detection method. Sixty articles were included in the final analysis. During asthma exacerbations, the mean prevalence of AdV, BoV, CoV, CMV, EnV, HSV, IfV, MpV, PiV, RV and RSV was 3.8%, 6.9%, 8.4%, 7.2%, 10.1%, 12.3%, 10.0%, 5.3%, 5.6%, 42.1% and 13.6%, respectively. EnV, MPV, RV and RSV were more prevalent in children, whereas AdV, BoV, CoV, IfV and PiV were more frequently present in adults. RV was the major virus detected globally, except in Africa. RV could be detected in both the upper and lower airway. Polymerase chain reaction was the most sensitive method for detecting viral infection. Our findings indicate the need to develop prophylactic polyvalent or polyvirus (including RV, EnV, IfV and RSV) vaccines that produce herd immunity and reduce the healthcare burden associated with virus-induced asthma exacerbations.
BackgroundDengue and chikungunya are co-circulating vector-borne diseases with substantial overlap in clinical presentations. It is important to differentiate between them during first presentation as their management, especially for dengue hemorrhagic fever (DHF), is different. This study compares their clinical presentation in Singapore adults to derive predictors to assist doctors in diagnostic decision-making.MethodsWe compared 117 patients with chikungunya infection diagnosed with reverse transcription-polymerase chain reaction (RT-PCR) with 917 dengue RT-PCR-positive adult patients (including 55 with DHF). We compared dengue fever (DF), DHF, and chikungunya infections by evaluating clinical characteristics of dengue and chikungunya; developing classification tools via multivariate logistic regression models and classification trees of disease etiology using clinical and laboratory factors; and assessing the time course of several clinical variables.FindingsAt first presentation to hospital, significantly more chikungunya patients had myalgia or arthralgia, and fewer had a sore throat, cough (for DF), nausea, vomiting, diarrhea, abdominal pain, anorexia or tachycardia than DF or DHF patients. From the decision trees, platelets <118×109/L was the only distinguishing feature for DF versus chikungunya with an overall correct classification of 89%. For DHF versus chikungunya using platelets <100×109/L and the presence of bleeding, the overall correct classification was 98%. The time course analysis supported platelet count as the key distinguishing variable.InterpretationThere is substantial overlap in clinical presentation between dengue and chikungunya infections, but simple clinical and laboratory variables can predict these infections at presentation for appropriate management.
BackgroundIt is possible that cross-over studies included in current systematic reviews are being inadequately assessed, because the current risk of bias tools do not consider possible biases specific to cross-over design. We performed this study to evaluate whether this was being done in cross-over studies included in Cochrane Systematic Reviews (CSRs).MethodsWe searched the Cochrane Library (up to 2013 issue 5) for CSRs that included at least one cross-over trial. Two authors independently undertook the study selection and data extraction. A random sample of the CSRs was selected and we evaluated whether the cross-over trials in these CSRs were assessed according to criteria suggested by the Cochrane handbook. In addition we reassessed the risk of bias of these cross-over trials by a checklist developed form the Cochrane handbook.ResultsWe identified 688 CSRs that included one or more cross-over studies. We chose a random sample of 60 CSRs and these included 139 cross-over studies. None of these CSRs undertook a risk of bias assessment specific for cross-over studies. In fact items specific for cross-over studies were seldom considered anywhere in quality assessment of these CSRs. When we reassessed the risk of bias, including the 3 items specific to cross-over trials, of these 139 studies, a low risk of bias was judged for appropriate cross-over design in 110(79%), carry-over effects in 48(34%) and for reporting data in all stages of the trial in 114(82%).Assessment of biases in cross-over trials could affect the GRADE assessment of a review’s findings.ConclusionThe current Cochrane risk of bias tool is not adequate to assess cross-over studies. Items specific to cross-over trials leading to potential risk of bias are generally neglected in CSRs. A proposed check list for the evaluation of cross-over trials is provided.
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