Meta-analysis is a method to obtain a weighted average of results from various studies. In addition to pooling effect sizes, meta-analysis can also be used to estimate disease frequencies, such as incidence and prevalence. In this article we present methods for the meta-analysis of prevalence. We discuss the logit and double arcsine transformations to stabilise the variance. We note the special situation of multiple category prevalence, and propose solutions to the problems that arise. We describe the implementation of these methods in the MetaXL software, and present a simulation study and the example of multiple sclerosis from the Global Burden of Disease 2010 project. We conclude that the double arcsine transformation is preferred over the logit, and that the MetaXL implementation of multiple category prevalence is an improvement in the methodology of the meta-analysis of prevalence.

This article examines an improved alternative to the random effects (RE) model for meta-analysis of heterogeneous studies. It is shown that the known issues of underestimation of the statistical error and spuriously overconfident estimates with the RE model can be resolved by the use of an estimator under the fixed effect model assumption with a quasi-likelihood based variance structure - the IVhet model. Extensive simulations confirm that this estimator retains a correct coverage probability and a lower observed variance than the RE model estimator, regardless of heterogeneity. When the proposed IVhet method is applied to the controversial meta-analysis of intravenous magnesium for the prevention of mortality after myocardial infarction, the pooled OR is 1.01 (95% CI 0.71-1.46) which not only favors the larger studies but also indicates more uncertainty around the point estimate. In comparison, under the RE model the pooled OR is 0.71 (95% CI 0.57-0.89) which, given the simulation results, reflects underestimation of the statistical error. Given the compelling evidence generated, we recommend that the IVhet model replace both the FE and RE models. To facilitate this, it has been implemented into free meta-analysis software called MetaXL which can be downloaded from www.epigear.com.

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.

Short sleep duration is considered a potential risk for overweight/obesity in childhood and adolescence. However, most of the evidence on this topic is obtained from cross-sectional studies; therefore, the nature and extent of the longitudinal associations are unclear. This study explores the prospective association between short sleep and overweight/obesity in young subjects. The MEDLINE, EMBASE, Pubmed, and CINAHL databases were searched for English-language articles, published until May 2014, reporting longitudinal association between sleep and body mass index (BMI) in children and adolescents. Recommendations of the Sleep Health Foundation were used to standardize reference sleep duration. Sleep category, with sleep duration less than the reference sleep, was considered as the short sleep category. Meta-analysis was conducted to explore the association between short sleep and overweight/obesity. A review of 22 longitudinal studies, with subjects from diverse backgrounds, suggested an inverse association between sleep duration and BMI. Meta-analysis of 11 longitudinal studies, comprising 24,821 participants, revealed that subjects sleeping for short duration had twice the risk of being overweight/obese, compared with subjects sleeping for long duration (odds ratio 2.15; 95% confidence interval: 1.64-2.81). This study provides evidence that short sleep duration in young subjects is significantly associated with future overweight/obesity.

Adolescent obesity and depression are increasingly prevalent and are currently recognised as major public health concerns worldwide. The aim of this study is to evaluate the bi-directional associations between obesity and depression in adolescents using longitudinal studies. A systematic literature search was conducted using Pubmed (including Medline), PsycINFO, Embase, CINAHL, BIOSIS Preview and the Cochrane Library databases. According to the inclusion criteria, 13 studies were found where seven studies evaluated depression leading to obesity and six other studies examined obesity leading to depression. Using a bias-adjusted quality effects model for the meta-analysis, we found that adolescents who were depressed had a 70% (RR 1.70, 95% CI: 1.40, 2.07) increased risk of being obese, conversely obese adolescents had an increased risk of 40% (RR 1.40, 95% CI: 1.16, 1.70) of being depressed. The risk difference (RD) of early adolescent depression leading to obesity is 3% higher risk than it is for obesity leading to depression. In sensitivity analysis, the association between depression leading to obesity was greater than that of obesity leading to depression for females in early adulthood compared with females in late adolescence. Overall, the findings of this study suggest a bi-directional association between depression and obesity that was stronger for female adolescents. However, this finding also underscores the importance of early detection and treatment strategies to inhibit the development of reciprocal disorders.

Poor sleep quality seems to be associated with Ow/Ob, and some studies indicate this association to be independent of duration. Therefore, considering only sleep duration might not help in disentangling sleep-obesity association. However, this review is mostly composed of cross-sectional studies. Therefore, a causal link or the stability of the sleep quality and Ow/Ob association could not be established.

We introduce a quality-effects approach that combines evidence from a series of trials comparing 2 interventions. This approach incorporates the heterogeneity of effects in the analysis of the overall interventional efficacy. However, unlike the random-effects model based on observed between-trial heterogeneity, we suggest adjustment based on measured methodological heterogeneity between studies. We propose a simple noniterative procedure for computing the combined effect size under this model and suggest that this could represent a more convincing alternative to the random effects model.

Gestational weight gain (GWG) is associated with postpartum weight retention (PPWR) in women. The strength of the association between GWG and long-term PPWR and body mass index (BMI), however, is still unclear. Publications from different databases were systematically extracted and the articles relevant to this study were reviewed to quantify the effect estimate of GWG on PPWR and BMI using a bias-adjusted method. The Institute of Medicine categories of "inadequate," "adequate," and "excess" were used to define GWG. The time span for PPWR was divided into three periods (<1 year, 1 year to 9 years, and ≥15 years) to determine outcome at different times postpartum. Twelve studies met the eligibility criteria and were included in the analyses. Women with an inadequate GWG had a significantly lower mean PPWR of -2.14 kg (95%CI, -2.61 to -1.66) than women with an adequate GWG, who had a mean PPWR of 3.15 kg (95%CI, 2.47 to 3.82) up to 21 years postpartum. Over the postpartum time span, a U-shaped relationship was observed between the weighted mean difference calculated for women with excess GWG and the weighted mean difference calculated for women with adequate GWG, and this relationship was time independent between these two groups. Postpartum BMI showed a similar relationship and magnitude of change, but the exact loss or gain was difficult to assess due to fewer studies (n = 5) with considerable heterogeneity of BMI measurements. The findings of this study suggest that GWG outside of the Institute of Medicine recommendations can lead to both short-term and long-term postpartum weight imbalance.