Detection of publication and related biases remains suboptimal and threatens the validity and interpretation of meta-analytical findings. When bias is present, it usually differentially affects small and large studies manifesting as an association between precision and effect size and therefore visual asymmetry of conventional funnel plots. This asymmetry can be quantified and Egger's regression is, by far, the most widely used statistical measure for quantifying funnel plot asymmetry. However, concerns have been raised about both the visual appearance of funnel plots and the sensitivity of Egger's regression to detect such asymmetry, particularly when the number of studies is small. In this article, we propose a new graphical method, the Doi plot, to visualize asymmetry and also a new measure, the LFK index, to detect and quantify asymmetry of study effects in Doi plots. We demonstrate that the visual representation of asymmetry was better for the Doi plot when compared with the funnel plot. We also show that the diagnostic accuracy of the LFK index in discriminating between asymmetry due to simulated publication bias versus chance or no asymmetry was also better with the LFK index which had areas under the receiver operating characteristic curve of 0.74–0.88 with simulations of meta-analyses with five, 10, 15, and 20 studies. The Egger's regression result had lower areas under the receiver operating characteristic curve values of 0.58–0.75 across the same simulations. The LFK index also had a higher sensitivity (71.3–72.1%) than the Egger's regression result (18.5–43.0%). We conclude that the methods proposed in this article can markedly improve the ability of researchers to detect bias in meta-analysis.

Purpose: Differentiated thyroid cancer (DTC) incidence has been reported to have increased three- to 15-fold in the past few decades. It is unclear whether this represents overdiagnosis or a true increase in incidence. Therefore, the current study aimed to estimate the prevalence of incidental DTC in published autopsy series and determine whether this prevalence has been increasing over time. Materials and Methods: PubMed, Embase, and Web of Science were searched from inception to December 2015 for relevant studies. Two authors searched for all autopsy studies that had included patients with no known history of thyroid pathology and reported the prevalence of incidental DTC (iDTC). Two authors independently extracted the data, and discrepancies were resolved by another author. The pooled prevalence of iDTC was assessed using a fixed-effects meta-analysis model with robust error variance. The time effect was studied using an inverse-variance weighted logit-linear regression model with robust error variance and a time variable. Results: Thirty-five studies, conducted between 1949 and 2007, met the inclusion criteria and contributed 42 data sets and 12,834 autopsies. The prevalence of iDTC among the partial and whole examination subgroups was 4.1% (95% CI, 3.0% to 5.4%) and 11.2% (95% CI, 6.7% to 16.1%), respectively. Once the intensiveness of thyroid examination was accounted for in the regression model, the prevalence odds ratio stabilized from 1970 onward, and no time effect was observed. Conclusion: The current study confirms that iDTC is common, but the observed increasing incidence is not mirrored by prevalence within autopsy studies and, therefore, is unlikely to reflect a true population-level increase in tumorigenesis. This strongly suggests that the current increasing incidence of iDTC most likely reflects diagnostic detection increasing over time.

Background: Severe dengue is a life-threatening complication; rapid identification of these cases, followed by adequate management is crucial to improve the clinical prognosis. Therefore, this study aimed to identify risk factors and predictors of severe dengue. Methods: A literature search for studies reporting risk factors of severe dengue among individuals with dengue virus infection was conducted in PubMed, Scopus and Web of Science database from inception to December 31, 2020. Pooled odds ratios (ORs) for patients’ demographic characteristics, co-morbidities, and warning signs were estimated using an inverse variance heterogeneity model. Results: We included 143 articles in the meta-analysis from a total of 13 090 articles retrieved from the literature search. The risk factors of severe dengue were: being a child [OR = 1.96; 95% confidence interval (CI): 1.22–3.13], secondary infection (OR = 3.23; 95% CI: 2.28–4.57), and patients with pre-existing diabetes (OR = 2.88; 95% CI: 1.72–4.81) and renal disease (OR = 4.54; 95% CI: 1.55–13.31). Warning signs strongly associated with severe disease were increased haematocrit with a concurrent decrease in platelet count (OR = 5.13; 95% CI: 1.61–16.34), abdominal pain (OR = 2.00; 95% CI: 1.49–2.68), lethargy (OR = 2.73; 95% CI: 1.05–7.10), vomiting (OR = 1.80; 95% CI: 1.43–2.26), hepatomegaly (OR = 5.92; 95% CI: 3.29–10.66), ascites (OR = 6.30; 95% CI: 3.75–10.60), pleural effusion (OR = 5.72; 95% CI: 3.24–10.10) and melena (OR = 4.05; 95% CI: 1.64–10.00). Conclusions: Our meta-analysis identified children, secondary infection, diabetes and renal disease(s) as important predictors of severe dengue. Our finding also supports the predictive ability of the WHO warning signs to identify severe dengue. These findings are useful for clinicians to identify severe dengue for management and timely interventions.

Chikungunya and dengue infections are spatio-temporally related. The current review aims to determine the geographic limits of chikungunya, dengue and the principal mosquito vectors for both viruses and to synthesise current epidemiological understanding of their co-distribution. Three biomedical databases (PubMed, Scopus and Web of Science) were searched from their inception until May 2015 for studies that reported concurrent detection of chikungunya and dengue viruses in the same patient. Additionally, data from WHO, CDC and Healthmap alerts were extracted to create up-to-date global distribution maps for both dengue and chikungunya. Evidence for chikungunya-dengue co-infection has been found in Angola, Gabon, India, Madagascar, Malaysia, Myanmar, Nigeria, Saint Martin, Singapore, Sri Lanka, Tanzania, Thailand and Yemen; these constitute only 13 out of the 98 countries/territories where both chikungunya and dengue epidemic/endemic transmission have been reported. Understanding the true extent of chikungunya-dengue co-infection is hampered by current diagnosis largely based on their similar symptoms. Heightened awareness of chikungunya among the public and public health practitioners in the advent of the ongoing outbreak in the Americas can be expected to improve diagnostic rigour. Maps generated from the newly compiled lists of the geographic distribution of both pathogens and vectors represent the current geographical limits of chikungunya and dengue, as well as the countries/territories at risk of future incursion by both viruses. These describe regions of co-endemicity in which lab-based diagnosis of suspected cases is of higher priority.

The epidemiology of Clostridium difficile infection (CDI) has changed over the past decades with the emergence of highly virulent strains. The role of asymptomatic C. difficile colonization as part of the clinical spectrum of CDI is complex because many risk factors are common to both disease and asymptomatic states. In this article, we review the role of asymptomatic C. difficile colonization in the progression to symptomatic CDI, describe the epidemiology of asymptomatic C. difficile colonization, assess the effectiveness of screening and intensive infection control practices for patients at risk of asymptomatic C. difficile colonization, and discuss the implications for clinical practice. A narrative review was performed in PubMed for articles published from January 1980 to February 2015 using search terms ‘Clostridium difficile’ and ‘colonization’ or ‘colonisation’ or ‘carriage’. There is no clear definition for asymptomatic CDI and the terms carriage and colonization are often used interchangeably. The prevalence of asymptomatic C. difficile colonization varies depending on a number of host, pathogen, and environmental factors; current estimates of asymptomatic colonization may be underestimated as stool culture is not practical in a clinical setting. Asymptomatic C. difficile colonization presents challenging concepts in the overall picture of this disease and its management. Individuals who are colonized by the organism may acquire protection from progression to disease, however they also have the potential to contribute to transmission in healthcare settings.

Background: Clostridium difficile infection (CDI) has been extensively described in healthcare settings; however, risk factors associated with community-acquired (CA) CDI remain uncertain. This study aimed to synthesize the current evidence for an association between commonly prescribed medications and comorbidities with CA-CDI.Methods: A systematic search was conducted in 5 electronic databases for epidemiologic studies that examined the association between the presence of comorbidities and exposure to medications with the risk of CA-CDI. Pooled odds ratios were estimated using 3 meta-analytic methods. Subgroup analyses by location of studies and by life stages were conducted.Results: Twelve publications (n=56,776 patients) met inclusion criteria. Antimicrobial (odds ratio, 6.18; 95% CI, 3.80–10.04) and corticosteroid (1.81; 1.15–2.84) exposure were associated with increased risk of CA-CDI. Among the comorbidities, inflammatory bowel disease (odds ratio, 3.72; 95% CI, 1.52–9.12), renal failure (2.64; 1.23–5.68), hematologic cancer (1.75; 1.02–5.68), and diabetes mellitus (1.15; 1.05–1.27) were associated with CA-CDI. By location, antimicrobial exposure was associated with a higher risk of CA-CDI in the United States, whereas proton-pump inhibitor exposure was associated with a higher risk in Europe. By life stages, the risk of CA-CDI associated with antimicrobial exposure greatly increased in adults older than 65 years.Conclusions: Antimicrobial exposure was the strongest risk factor associated with CA-CDI. Further studies are required to investigate the risk of CA-CDI associated with medications commonly prescribed in the community. Patients with diarrhea who have inflammatory bowel disease, renal failure, hematologic cancer, or diabetes are appropriate populations for interventional studies of screening.Infect Control Hosp Epidemiol 2014;00(0):1–10