Summary
Objectives
To assess the effect of poor sleep quality on Overweight/Obesity (Ow/Ob) in young subjects, and explore if this association is independent of sleep duration.
Methods
Pubmed, ...EMBASE, and MEDLINE databases were searched for papers on sleep quality and overweight/obesity, focusing on children, adolescents, and young adults. Studies based on subjects with medical/psychological problems or published in languages other than English were excluded. Quality effects model was used to pool studies for meta‐analysis.
Results
Findings from the systematic review suggest a link between poor sleep quality and Ow/Ob in young subjects. Pooled estimate (from 26,553 subjects) suggest a role of inadequate sleep (including both short duration and poor quality) in Ow/Ob (OR: 1.27 95% CI: 1.05‐1.53). Sub‐group‐analyses suggest considerably higher odds of Ow/Ob (OR = 1.46, 95% CI: 1.24‐1.72) in young subjects with poor sleep quality (independent of duration).
Conclusions
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.
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.
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.
Gestational weight gain (GWG) is considered one of the risk factors for future obesity in the offspring. However, the direction and strength of this association at different periods of offspring life ...is relatively unknown. This study investigates whether excess or inadequate maternal GWG during pregnancy influences the risk of offspring obesity at different stages in life. A systematic review of published articles was undertaken after a comprehensive search of different databases, and extracted data were meta‐analysed. To quantify offspring obesity estimates in relation to GWG, we stratified obesity estimates within three life stages of the offspring age: <5 years, 5 to <18 years and 18+ years. Our meta‐analysis showed that, compared with offspring of women with adequate GWG, offspring of women who gained inadequate gestational weight were at a decreased risk of obesity (relative risk RR: 0.86; 95% confidence interval CI: 0.78–0.94), and offspring of women who gained excess weight were at an increased risk of obesity (RR: 1.40; 95% CI: 1.23–1.59). These relationships were similar after stratification by life stage. Findings of this study therefore suggest that excess GWG does influence offspring obesity over the short‐ and long‐term, and should therefore be avoided.
The aim of the study was to investigate the effect of number of studies in a meta-analysis on the detection of publication bias using P value–driven methods.
The proportion of meta-analyses detected ...by Egger's, Harbord's, Peters', and Begg's tests to have asymmetry suggestive of publication bias were examined in 5,014 meta-analyses from Cochrane reviews. P values were also assessed in meta-analyses with varying number of studies, whereas symmetry was held constant. A simulation study was conducted to investigate if the above tests underestimate or overestimate the presence of publication bias.
The proportion of meta-analyses detected as asymmetrical via Egger's, Harbord's, Peters', and Begg's tests decreased by 42.6%, 41.1%, 29.3%, and 28.3%, respectively, when the median number of studies in the meta-analysis decreased from 87 to 14. P values decreased as the number of studies increased in the meta-analysis, despite the level of symmetry remaining constant. The simulation study confirmed that when publication bias is present, P value tests underestimate the presence of publication bias, particularly when study numbers are small.
P value–based tests used for the detection of publication bias–related asymmetry in meta-analysis require careful examination, as they underestimate asymmetry. Alternative methods not dependent on the number of studies are preferable.
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.
The inconsistency demonstrated across strata when using different scales has been attributed to quality scores, and stratification continues to be done using risk of bias domain judgments. This study ...examines if restricting primary meta-analyses to studies at low risk of bias or presenting meta-analyses stratified according to risk of bias is indeed the right approach to explore potential methodological bias.
Reanalysis of the impact of quality subgroupings in an existing meta-analysis based on 25 different scales.
We demonstrate that quality stratification itself is the problem because it induces a spurious association between effect size and precision within stratum. Studies with larger effects or lesser precision tend to be of lower quality—a form of collider-stratification bias (stratum being the common effect of the reasons for these two outcomes) that leads to inconsistent results across scales. We also show that the extent of this association determines the variability in effect size and statistical significance across strata when conditioning on quality.
We conclude that stratification by quality leads to a form of selection bias (collider-stratification bias) and should be avoided. We demonstrate consistent results with an alternative method that includes all studies.
We present a systematic review providing estimates of the overall and regional burden of infectious complications following prostate biopsy. A directly standardized prevalence estimate was used ...because it reflects the burden of disease more explicitly. Complications included sepsis, hospitalization, bacteraemia, bacteriuria, and acute urinary retention after biopsy. There were 165 articles, comprising 162 577 patients, included in the final analysis. Our findings demonstrate that transrectal biopsy was associated with a higher burden of hospitalization (1·1% vs. 0·9%) and sepsis (0·8% vs. 0·1%) compared to transperineal biopsy, while acute urinary retention was more prevalent after transperineal than transrectal biopsy (4·2% vs. 0·9%). The differences were statistically non-significant because of large heterogeneity across countries. We also demonstrate and discuss regional variations in complication rates, with Asian studies reporting higher rates of sepsis and hospitalization.
The quality of primary research is commonly assessed before inclusion in meta-analyses. Findings are discussed in the context of the quality appraisal by categorizing studies according to risk of ...bias. The impact of appraised risk of bias on study outcomes is typically judged by the reader; however, several methods have been developed to quantify this risk of bias assessment and incorporate it into the pooled results of meta-analysis, a process known as bias adjustment. The advantages, potential limitations, and applicability of these methods are not well defined.
Comparative evaluation of the applicability of the various methods and their limitations are discussed using two examples from the literature. These methods include weighting, stratification, regression, use of empirically based prior distributions, and elicitation by experts.
Use of the two examples from the literature suggest that all methods provide similar adjustment. Methods differed mainly in applicability and limitations.
Bias adjustment is a feasible process in meta-analysis with several strategies currently available. Quality effects modelling was found to be easily implementable with fewer limitations in comparison to other methods.
Dose-response meta-analysis has been widely employed in evidence-based decision-making. Currently, the most popular approach is the one or two-stage generalized least squares for trend model. This ...approach however has some drawbacks, and therefore, we compare the latter with a one-stage robust error meta-regression (REMR) model, based on inverse variance weighted least squares regression and cluster robust error variances for dealing with the synthesis of correlated dose-response data from different studies.
We apply both methods to three examples (alcohol and lung cancer, alcohol and colorectal cancer, and BMI and renal cancer). The analysis of the three datasets reveals that the one-stage REMR approach may result in better error estimation and a better visual fit to the data than the generalized least squares approach with the added benefit of not needing to impute covariances from the data.
The one-stage REMR approach is easily executed in Stata with codes given in this article. We therefore recommend that REMR models be considered for dose-response meta-analysis and suggest further comparison of these two methods in future studies to conclusively determine the benefits and pitfalls of each.
PDF
