Host genetic variability and pneumococcal disease: a systematic review and meta-analysis

Abstract Background Pneumonia, sepsis, meningitis, and empyema due to Streptococcus pneumoniae is a major cause of morbidity and mortality. We provide a systemic overview of genetic variants associated with susceptibility, phenotype and outcome of community acquired pneumococcal pneumonia (CAP) and invasive pneumococcal disease (IPD). Methods We searched PubMed for studies on the influence of host genetics on susceptibility, phenotype, and outcome of CAP and IPD between Jan 1, 1983 and Jul 4, 2018. We listed methodological characteristics and when genetic data was available we calculated effect sizes. We used fixed or random effect models to calculate pooled effect sizes in the meta-analysis. Results We identified 1219 studies of which 60 studies involving 15,358 patients were included. Twenty-five studies (42%) focused on susceptibility, 8 (13%) on outcome, 1 (2%) on disease phenotype, and 26 (43%) on multiple categories. We identified five studies with a hypothesis free approach of which one resulted in one genome wide significant association in a gene coding for lincRNA with pneumococcal disease susceptibility. We performed 17 meta-analyses of which two susceptibility polymorphisms had a significant overall effect size: variant alleles of MBL2 (odds ratio [OR] 1·67, 95% confidence interval [CI] 1·04–2·69) and a variant in CD14 (OR 1·77, 95% CI 1·18–2·66) and none of the outcome polymorphisms. Conclusions Studies have identified several host genetics factors influencing risk of pneumococcal disease, but many result in non-reproducible findings due to methodological limitations. Uniform case definitions and pooling of data is necessary to obtain more robust findings.

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The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution and Beyond

10.32942/osf.io/epqa7 ◽

2019 ◽

Author(s):

Shinichi Nakagawa ◽

Malgorzata Lagisz ◽

Rose E O'Dea ◽

Joanna Rutkowska ◽

Yefeng Yang ◽

...

Keyword(s):

Meta Analysis ◽

R Package ◽

Effect Sizes ◽

Forest Plot ◽

Point Estimates ◽

Aggregate Effect ◽

The Individual ◽

Meta Analyses ◽

Heterogeneous Effect ◽

Intuitive Interpretation

‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

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Identification of and Correction for Publication Bias: Comment

10.31222/osf.io/dh87m ◽

2019 ◽

Author(s):

Amanda Kvarven ◽

Eirik Strømland ◽

Magnus Johannesson

Keyword(s):

Publication Bias ◽

False Positive ◽

Large Scale ◽

Meta Analysis ◽

False Positive Rate ◽

Effect Sizes ◽

Replication Studies ◽

Moderate Reduction ◽

Positive Rate ◽

Meta Analyses

Andrews & Kasy (2019) propose an approach for adjusting effect sizes in meta-analysis for publication bias. We use the Andrews-Kasy estimator to adjust the result of 15 meta-analyses and compare the adjusted results to 15 large-scale multiple labs replication studies estimating the same effects. The pre-registered replications provide precisely estimated effect sizes, which do not suffer from publication bias. The Andrews-Kasy approach leads to a moderate reduction of the inflated effect sizes in the meta-analyses. However, the approach still overestimates effect sizes by a factor of about two or more and has an estimated false positive rate of between 57% and 100%.

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Medicine storage, wastage, and associated determinants among urban households: a systematic review and meta-analysis of household surveys

BMC Public Health ◽

10.1186/s12889-021-11100-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Adineh Jafarzadeh ◽

Alireza Mahboub-Ahari ◽

Moslem Najafi ◽

Mahmood Yousefi ◽

Koustuv Dalal

Keyword(s):

Systematic Review ◽

Meta Analysis ◽

Supply And Demand ◽

Random Effect ◽

Effect Sizes ◽

Household Surveys ◽

Affective Factors ◽

Urban Households ◽

Medical Needs ◽

Geographical Regions

Abstract Background Irrational household storage of medicines is a world-wide problem, which triggers medicine wastage as well as its associated harms. This study aimed to include all available evidences from literature to perform a focused examination of the prevalence and factors associated with medicine storage and wastage among urban households. This systematic review and meta-analysis mapped the existing literature on the burden, outcomes, and affective socio-economic factors of medicine storage among urban households. In addition, this study estimated pooled effect sizes for storage and wastage rates. Methods Household surveys evaluating modality, size, costs, and affective factors of medicines storage at home were searched in PubMed, EMBASE, OVID, SCOPUS, ProQuest, and Google scholar databases in 2019. Random effect meta-analysis and subgroup analysis were used to pool effect sizes for medicine storage and wastage prevalence among different geographical regions. Results From the 2604 initial records, 20 studies were selected for systematic review and 16 articles were selected for meta-analysis. An overall pooled-prevalence of medicine storage and real wastage rate was 77 and 15%, respectively. In this regard, some significant differences were observed between geographical regions. Southwest Asia region had the highest storage and wastage rates. The most common classes of medicines found in households belonged to the Infective agents for systemic (17.4%) and the Nervous system (16.4%). Moreover, income, education, age, the presence of chronic illness, female gender, and insurance coverage were found to be associated with higher home storage. The most commonly used method of disposal was throwing them in the garbage. Conclusions Factors beyond medical needs were also found to be associated with medicine storage, which urges effective strategies in the supply and demand side of the medicine consumption chain. The first necessary step to mitigate home storage is establishing an adequate legislation and strict enforcement of regulations on dispensing, prescription, and marketing of medicines. Patient’s pressure on excessive prescription, irrational storage, and use of medicines deserve efficient community-centered programs, in order to increase awareness on these issues. So, hazardous consequences of inappropriate disposal should be mitigated by different take back programs, particularly in low and middle income countries.

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A protocol for the systematic review and meta-analysis of thigmotactic behaviour in the open field test in rodent models associated with persistent pain

BMJ Open Science ◽

10.1136/bmjos-2020-100135 ◽

2021 ◽

Vol 5 (1) ◽

pp. e100135

Author(s):

Xue Ying Zhang ◽

Jan Vollert ◽

Emily S Sena ◽

Andrew SC Rice ◽

Nadia Soliman

Keyword(s):

Systematic Review ◽

Outcome Measure ◽

Meta Analysis ◽

Persistent Pain ◽

Effect Sizes ◽

Bias Assessment ◽

Animal Pain ◽

Trim And Fill ◽

Meta Analyses ◽

The Impact

ObjectiveThigmotaxis is an innate predator avoidance behaviour of rodents and is enhanced when animals are under stress. It is characterised by the preference of a rodent to seek shelter, rather than expose itself to the aversive open area. The behaviour has been proposed to be a measurable construct that can address the impact of pain on rodent behaviour. This systematic review will assess whether thigmotaxis can be influenced by experimental persistent pain and attenuated by pharmacological interventions in rodents.Search strategyWe will conduct search on three electronic databases to identify studies in which thigmotaxis was used as an outcome measure contextualised to a rodent model associated with persistent pain. All studies published until the date of the search will be considered.Screening and annotationTwo independent reviewers will screen studies based on the order of (1) titles and abstracts, and (2) full texts.Data management and reportingFor meta-analysis, we will extract thigmotactic behavioural data and calculate effect sizes. Effect sizes will be combined using a random-effects model. We will assess heterogeneity and identify sources of heterogeneity. A risk-of-bias assessment will be conducted to evaluate study quality. Publication bias will be assessed using funnel plots, Egger’s regression and trim-and-fill analysis. We will also extract stimulus-evoked limb withdrawal data to assess its correlation with thigmotaxis in the same animals. The evidence obtained will provide a comprehensive understanding of the strengths and limitations of using thigmotactic outcome measure in animal pain research so that future experimental designs can be optimised. We will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines and disseminate the review findings through publication and conference presentation.

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Effectiveness of Digital Health Interventions on Unintentional Injury, Violence, and Suicide: Meta-Analysis

Trauma Violence & Abuse ◽

10.1177/1524838020967346 ◽

2020 ◽

pp. 152483802096734

Author(s):

Mengtong Chen ◽

Ko Ling Chan

Keyword(s):

Interpersonal Violence ◽

Care Delivery ◽

Digital Health ◽

Meta Analysis ◽

Random Effect ◽

Unintentional Injury ◽

Health Interventions ◽

Effect Sizes ◽

Future Research ◽

Face To Face

Digital technologies are increasingly used in health-care delivery and are being introduced into work to prevent unintentional injury, violence, and suicide to reduce mortality. To understand the potential of digital health interventions (DHIs) to prevent and reduce these problems, we conduct a meta-analysis and provide an overview of their effectiveness and characteristics related to the effects. We searched electronic databases and reference lists of relevant reviews to identify randomized controlled trials (RCTs) published in or before March 2020 evaluating DHIs on injury, violence, or suicide reduction. Based on the 34 RCT studies included in the meta-analysis, the overall random effect size was 0.21, and the effect sizes for reducing suicidal ideation, interpersonal violence, and unintentional injury were 0.17, 0.24, and 0.31, respectively, which can be regarded as comparable to the effect sizes of traditional face-to-face interventions. However, there was considerable heterogeneity between the studies. In conclusion, DHIs have great potential to reduce unintentional injury, violence, and suicide. Future research should explore DHIs’ successful components to facilitate future implementation and wider access.

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Pre-post effect sizes should be avoided in meta-analyses

Epidemiology and Psychiatric Sciences ◽

10.1017/s2045796016000809 ◽

2016 ◽

Vol 26 (4) ◽

pp. 364-368 ◽

Cited By ~ 72

Author(s):

P. Cuijpers ◽

E. Weitz ◽

I. A. Cristea ◽

J. Twisk

Keyword(s):

Comparison Group ◽

Meta Analysis ◽

Effect Sizes ◽

Behaviour Therapy ◽

Natural Processes ◽

Cognitive Behaviour ◽

Post Test ◽

The Difference ◽

Meta Analyses ◽

Highly Cited

AimsThe standardised mean difference (SMD) is one of the most used effect sizes to indicate the effects of treatments. It indicates the difference between a treatment and comparison group after treatment has ended, in terms of standard deviations. Some meta-analyses, including several highly cited and influential ones, use the pre-post SMD, indicating the difference between baseline and post-test within one (treatment group).MethodsIn this paper, we argue that these pre-post SMDs should be avoided in meta-analyses and we describe the arguments why pre-post SMDs can result in biased outcomes.ResultsOne important reason why pre-post SMDs should be avoided is that the scores on baseline and post-test are not independent of each other. The value for the correlation should be used in the calculation of the SMD, while this value is typically not known. We used data from an ‘individual patient data’ meta-analysis of trials comparing cognitive behaviour therapy and anti-depressive medication, to show that this problem can lead to considerable errors in the estimation of the SMDs. Another even more important reason why pre-post SMDs should be avoided in meta-analyses is that they are influenced by natural processes and characteristics of the patients and settings, and these cannot be discerned from the effects of the intervention. Between-group SMDs are much better because they control for such variables and these variables only affect the between group SMD when they are related to the effects of the intervention.ConclusionsWe conclude that pre-post SMDs should be avoided in meta-analyses as using them probably results in biased outcomes.

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Meta-analysis of effect sizes reported at multiple time points: A multivariate approach

Clinical Trials ◽

10.1177/1740774512453218 ◽

2012 ◽

Vol 9 (5) ◽

pp. 610-620 ◽

Cited By ~ 28

Author(s):

Thomas A Trikalinos ◽

Ingram Olkin

Keyword(s):

Random Effects ◽

Treatment Effects ◽

Multivariate Analyses ◽

Meta Analysis ◽

Effect Sizes ◽

Multiple Time ◽

Time Points ◽

Fixed And Random Effects ◽

Multiple Time Points ◽

Meta Analyses

Background Many comparative studies report results at multiple time points. Such data are correlated because they pertain to the same patients, but are typically meta-analyzed as separate quantitative syntheses at each time point, ignoring the correlations between time points. Purpose To develop a meta-analytic approach that estimates treatment effects at successive time points and takes account of the stochastic dependencies of those effects. Methods We present both fixed and random effects methods for multivariate meta-analysis of effect sizes reported at multiple time points. We provide formulas for calculating the covariance (and correlations) of the effect sizes at successive time points for four common metrics (log odds ratio, log risk ratio, risk difference, and arcsine difference) based on data reported in the primary studies. We work through an example of a meta-analysis of 17 randomized trials of radiotherapy and chemotherapy versus radiotherapy alone for the postoperative treatment of patients with malignant gliomas, where in each trial survival is assessed at 6, 12, 18, and 24 months post randomization. We also provide software code for the main analyses described in the article. Results We discuss the estimation of fixed and random effects models and explore five options for the structure of the covariance matrix of the random effects. In the example, we compare separate (univariate) meta-analyses at each of the four time points with joint analyses across all four time points using the proposed methods. Although results of univariate and multivariate analyses are generally similar in the example, there are small differences in the magnitude of the effect sizes and the corresponding standard errors. We also discuss conditional multivariate analyses where one compares treatment effects at later time points given observed data at earlier time points. Limitations Simulation and empirical studies are needed to clarify the gains of multivariate analyses compared with separate meta-analyses under a variety of conditions. Conclusions Data reported at multiple time points are multivariate in nature and are efficiently analyzed using multivariate methods. The latter are an attractive alternative or complement to performing separate meta-analyses.

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Mindfulness- and Acceptance-Based Interventions for Stroke Survivors: A Systematic Review and Meta-Analysis

Rehabilitation Counseling Bulletin ◽

10.1177/00343552211043257 ◽

2021 ◽

pp. 003435522110432

Author(s):

Areum Han

Keyword(s):

Systematic Review ◽

Depressive Symptoms ◽

Meta Analysis ◽

Random Effect ◽

Random Effect Model ◽

Mental Fatigue ◽

Stroke Survivors ◽

Positive Effects ◽

Meta Analyses

Objective: Mindfulness- and acceptance-based intervention (MABI) is an emerging evidenced-based practice, but no systematic review incorporating meta-analyses for MABIs in stroke survivors has been conducted. The objective of this systematic review was to measure the effectiveness of MABIs on outcomes in people with stroke. Method: Three electronic databases, including PubMed, CINAHL, and PsycINFO, were searched to identify relevant studies published in peer-reviewed journals. The methodological quality of the included studies was assessed. Data were extracted and combined in a meta-analysis with a random-effect model to compute the size of the intervention effect. Results: A total of 11 studies met the eligibility criteria. Meta-analyses found a small-to-moderate effect of MABIs on depressive symptoms (standardized mean difference [SMD] = 0.39, 95% confidence interval [CI] = [0.12, 0.66]) and a large effect on mental fatigue (SMD = 1.22, 95% CI = [0.57, 1.87]). No statistically significant effect of MABIs on anxiety, quality of life, and mindfulness was found, but there was a trend in favor of MABIs overall. Conclusions: This meta-analysis found positive effects of MABIs on depressive symptoms and mental fatigue in stroke survivors, but future high-quality studies are needed to guarantee treatment effects of MABIs on varied outcomes in stroke survivors.

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A Statistical Method for Synthesizing Meta-Analyses

Computational and Mathematical Methods in Medicine ◽

10.1155/2013/732989 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Liansheng Larry Tang ◽

Michael Caudy ◽

Faye Taxman

Keyword(s):

Statistical Method ◽

Effect Size ◽

Meta Analysis ◽

Effect Sizes ◽

Weighted Mean ◽

Summary Effect ◽

Different Types ◽

Meta Analyses ◽

Size Estimates ◽

Similar Search

Multiple meta-analyses may use similar search criteria and focus on the same topic of interest, but they may yield different or sometimes discordant results. The lack of statistical methods for synthesizing these findings makes it challenging to properly interpret the results from multiple meta-analyses, especially when their results are conflicting. In this paper, we first introduce a method to synthesize the meta-analytic results when multiple meta-analyses use the same type of summary effect estimates. When meta-analyses use different types of effect sizes, the meta-analysis results cannot be directly combined. We propose a two-step frequentist procedure to first convert the effect size estimates to the same metric and then summarize them with a weighted mean estimate. Our proposed method offers several advantages over existing methods by Hemming et al. (2012). First, different types of summary effect sizes are considered. Second, our method provides the same overall effect size as conducting a meta-analysis on all individual studies from multiple meta-analyses. We illustrate the application of the proposed methods in two examples and discuss their implications for the field of meta-analysis.

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When the Bough Breaks: A Systematic Review and Meta-Analysis of Mental Health Symptoms in Mothers of Young Children during the COVID-19 Pandemic

10.31234/osf.io/u8pnh ◽

2021 ◽

Author(s):

Nicole Racine ◽

Rachel Eirich ◽

Jessica Cookee ◽

Jenney Zhu ◽

Paolo Pador ◽

...

Keyword(s):

Mental Health ◽

Young Children ◽

Maternal Mental Health ◽

Meta Analysis ◽

Random Effect ◽

Well Being ◽

Anxiety Symptoms ◽

Study Quality ◽

Depression And Anxiety ◽

Meta Analyses

Parents have experienced considerable challenges and stress during the COVID-19 pandemic, which may impact their well-being. This meta-analysis sought to identify: 1) the prevalence of depression and anxiety in parents of young children (< age 5) during the COVID-19 pandemic, and 2) sociodemographic (e.g., parent age, minority status) and methodological moderators (e.g., study quality) that explain heterogeneity among studies. A systematic search was conducted across four databases from January 1st, 2020 to March 3st, 2021. A total of 18 non-overlapping studies (9,101 participants), all focused on maternal mental health, met inclusion criteria. Random-effect meta-analyses were conducted. Pooled prevalence estimates for clinically significant depression and anxiety symptoms for mothers of young children during the COVID-19 pandemic were 27.4% (95% CI: 21.5-34.3) and 43.5% (95% CI:27.5-60.9), respectively. Prevalence of clinically elevated depression and anxiety symptoms were higher in Europe and North America and among older mothers. Clinically elevated depressive symptoms were lower in studies with a higher percentage of racial and ethnic minority individuals. In comparison, clinically elevated anxiety symptoms were higher among studies of low study quality and in samples with highly educated mothers. Policies and resources targeting improvements in maternal mental health are essential.

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