Researchers increasingly use meta-analysis to synthesize the results of several studies in order to estimate a common effect. When the outcome variable is continuous, standard meta-analytic ...approaches assume that the primary studies report the sample mean and standard deviation of the outcome. However, when the outcome is skewed, authors sometimes summarize the data by reporting the sample median and one or both of (i) the minimum and maximum values and (ii) the first and third quartiles, but do not report the mean or standard deviation. To include these studies in meta-analysis, several methods have been developed to estimate the sample mean and standard deviation from the reported summary data. A major limitation of these widely used methods is that they assume that the outcome distribution is normal, which is unlikely to be tenable for studies reporting medians. We propose two novel approaches to estimate the sample mean and standard deviation when data are suspected to be non-normal. Our simulation results and empirical assessments show that the proposed methods often perform better than the existing methods when applied to non-normal data.
Objectives
Our objective was to assess the effects of mental health interventions for children, adolescents, and adults not quarantined or undergoing treatment due to COVID-19 infection.
Methods
We ...searched 9 databases (2 Chinese-language) from December 31, 2019, to March 22, 2021. We included randomised controlled trials of interventions to address COVID-19 mental health challenges among people not hospitalised or quarantined due to COVID-19 infection. We synthesized results descriptively due to substantial heterogeneity of populations and interventions and risk of bias concerns.
Results
We identified 9 eligible trials, including 3 well-conducted, well-reported trials that tested interventions designed specifically for COVID-19 mental health challenges, plus 6 other trials with high risk of bias and reporting concerns, all of which tested standard interventions (e.g., individual or group therapy, expressive writing, mindfulness recordings) minimally adapted or not specifically adapted for COVID-19. Among the 3 well-conducted and reported trials, 1 (N = 670) found that a self-guided, internet-based cognitive-behavioural intervention targeting dysfunctional COVID-19 worry significantly reduced COVID-19 anxiety (standardized mean difference SMD 0.74, 95% confidence interval CI, 0.58 to 0.90) and depression symptoms (SMD 0.38, 95% CI, 0.22 to 0.55) in Swedish general population participants. A lay-delivered telephone intervention for homebound older adults in the United States (N = 240) and a peer-moderated education and support intervention for people with a rare autoimmune condition from 12 countries (N = 172) significantly improved anxiety (SMD 0.35, 95% CI, 0.09 to 0.60; SMD 0.31, 95% CI, 0.03 to 0.58) and depressive symptoms (SMD 0.31, 95% CI, 0.05 to 0.56; SMD 0.31, 95% CI, 0.07 to 0.55) 6-week post-intervention, but these were not significant immediately post-intervention. No trials in children or adolescents were identified.
Conclusions
Interventions that adapt evidence-based strategies for feasible delivery may be effective to address mental health in COVID-19. More well-conducted trials, including for children and adolescents, are needed.
Abstract
Women and gender-diverse individuals have faced disproportionate socioeconomic burden during COVID-19. There have been reports of greater negative mental health changes compared to men based ...on cross-sectional research that has not accounted for pre-COVID-19 differences. We compared mental health changes from pre-COVID-19 to during COVID-19 by sex or gender. MEDLINE (Ovid), PsycINFO (Ovid), CINAHL (EBSCO), EMBASE (Ovid), Web of Science Core Collection: Citation Indexes, China National Knowledge Infrastructure, Wanfang, medRxiv (preprints), and Open Science Framework Preprints (preprint server aggregator) were searched to August 30, 2021. Eligible studies included mental health symptom change data by sex or gender. 12 studies (10 unique cohorts) were included, all of which reported dichotomized sex or gender data. 9 cohorts reported results from March to June 2020, and 2 of these also reported on September or November to December 2020. One cohort included data pre-November 2020 data but did not provide dates. Continuous symptom change differences were not statistically significant for depression (standardized mean difference SMD = 0.12, 95% CI -0.09–0.33; 4 studies, 4,475 participants; I
2
= 69.0%) and stress (SMD = − 0.10, 95% CI -0.21–0.01; 4 studies, 1,533 participants; I
2
= 0.0%), but anxiety (SMD = 0.15, 95% CI 0.07–0.22; 4 studies, 4,344 participants; I
2
= 3.0%) and general mental health (SMD = 0.15, 95% CI 0.12–0.18; 3 studies, 15,692 participants; I
2
= 0.0%) worsened more among females/women than males/men. There were no significant differences in changes in proportions above cut-offs: anxiety (difference = − 0.05, 95% CI − 0.20–0.11; 1 study, 217 participants), depression (difference = 0.12, 95% CI -0.03–0.28; 1 study, 217 participants), general mental health (difference = − 0.03, 95% CI − 0.09–0.04; 3 studies, 18,985 participants; I
2
= 94.0%), stress (difference = 0.04, 95% CI − 0.10–0.17; 1 study, 217 participants). Mental health outcomes did not differ or were worse by small amounts among women than men during early COVID-19.
Even the term ‘data standardisation’ is ambiguous, as it can have a variety of meanings – e.g. there are standards for new data collection, data storage and analysis, and for mapping existing data to ...a standard. By focusing on these selected standards and related themes, we aim to enhance the recognition and understanding of data standardisation among academic researchers, as well as highlight the diverse yet interconnected landscape of data standardisation tools that underpin modern health care research and practice. WHY DATA STANDARDISATION MATTERS IN PUBLIC HEALTH The power of data pooling Pooling data from multiple sources is a cornerstone of robust population health research. The LOINC Committee, organised by Indianapolis-based non-profit medical research Regenstrief Institute, associated with Indiana University, developed a common language for laboratory and clinical observations to help with this issue. Since the observations and measurements that are recorded as part of laboratory test results still tend to contain local and institution-specific codes that are difficult for a receiving external care institution to decipher seamlessly, LOINC provides universal codes for identifying tests and observations.
To synthesise results of mental health outcomes in cohorts before and during the covid-19 pandemic.
Systematic review.
Medline, PsycINFO, CINAHL, Embase, Web of Science, China National Knowledge ...Infrastructure, Wanfang, medRxiv, and Open Science Framework Preprints.
Studies comparing general mental health, anxiety symptoms, or depression symptoms assessed from 1 January 2020 or later with outcomes collected from 1 January 2018 to 31 December 2019 in any population, and comprising ≥90% of the same participants before and during the covid-19 pandemic or using statistical methods to account for missing data. Restricted maximum likelihood random effects meta-analyses (worse covid-19 outcomes representing positive change) were performed. Risk of bias was assessed using an adapted Joanna Briggs Institute Checklist for Prevalence Studies.
As of 11 April 2022, 94 411 unique titles and abstracts including 137 unique studies from 134 cohorts were reviewed. Most of the studies were from high income (n=105, 77%) or upper middle income (n=28, 20%) countries. Among general population studies, no changes were found for general mental health (standardised mean difference (SMD)
0.11, 95% confidence interval -0.00 to 0.22) or anxiety symptoms (0.05, -0.04 to 0.13), but depression symptoms worsened minimally (0.12, 0.01 to 0.24). Among women or female participants, general mental health (0.22, 0.08 to 0.35), anxiety symptoms (0.20, 0.12 to 0.29), and depression symptoms (0.22, 0.05 to 0.40) worsened by minimal to small amounts. In 27 other analyses across outcome domains among subgroups other than women or female participants, five analyses suggested that symptoms worsened by minimal or small amounts, and two suggested minimal or small improvements. No other subgroup experienced changes across all outcome domains. In three studies with data from March to April 2020 and late 2020, symptoms were unchanged from pre-covid-19 levels at both assessments or increased initially then returned to pre-covid-19 levels. Substantial heterogeneity and risk of bias were present across analyses.
High risk of bias in many studies and substantial heterogeneity suggest caution in interpreting results. Nonetheless, most symptom change estimates for general mental health, anxiety symptoms, and depression symptoms were close to zero and not statistically significant, and significant changes were of minimal to small magnitudes. Small negative changes occurred for women or female participants in all domains. The authors will update the results of this systematic review as more evidence accrues, with study results posted online (https://www.depressd.ca/covid-19-mental-health).
PROSPERO CRD42020179703.
Our hypothesis is that machine learning (ML) analysis of whole exome sequencing (WES) data can be used to identify individuals at high risk for schizophrenia (SCZ). This study applies ML to WES data ...from 2,545 individuals with SCZ and 2,545 unaffected individuals, accessed via the database of genotypes and phenotypes (dbGaP). Single nucleotide variants and small insertions and deletions were annotated by ANNOVAR using the reference genome hg19/GRCh37. Rare (predicted functional) variants with a minor allele frequency ≤1% and genotype quality ≥90 including missense, frameshift, stop gain, stop loss, intronic, and exonic splicing variants were selected. A file containing all cases and controls, the names of genes with variants meeting our criteria, and the number of variants per gene for each individual, was used for ML analysis. The supervised machine‐learning algorithm used the patterns of variants observed in the different genes to determine which subset of genes can best predict that an individual is affected. Seventy percent of the data was used to train the algorithm and the remaining 30% of data (n = 1,526) was used to evaluate its efficiency. The supervised ML algorithm, gradient boosted trees with regularization (eXtreme Gradient Boosting implementation) was the best performing algorithm yielding promising results (accuracy: 85.7%, specificity: 86.6%, sensitivity: 84.9%, area under the receiver‐operator characteristic curve: 0.95). The top 50 features (genes) of the algorithm were analyzed using bioinformatics resources for new insights about the pathophysiology of SCZ. This manuscript presents a novel predictor which could potentially enable studies exploring disease‐modifying intervention in the early stages of the disease.
Introduction
The benefits of sharing participant-level data from biomedical studies have been widely touted and may be taken for granted. As investments in data sharing and reuse efforts continue to ...grow, understanding the cost and positive and negative effects of data sharing for research participants, the general public, individual researchers, research and development, clinical practice, and public health is of growing importance. In this scoping review, we will identify and summarize existing evidence on the positive and negative impacts and costs of data sharing and how they are measured.
Methods and analysis
Eligible studies will report on qualitative or quantitative approaches for measuring the cost of data sharing or its impact on participant privacy, individual or public health, researcher’s careers, clinical or public health practice, or research or development. The systematic search strategy uses MeSH and text terms and is tailored for Ovid Medline, Cumulative Index to Nursing and Allied Health Literature, and Web of Science. We will apply the Arskey and O’Malley scoping review methodology. We selected a scoping rather than a systematic review approach to address multiple related questions and provide guidance related to an emerging field. Two reviewers will conduct the title-abstract and full-text screening and data charting independently. Discrepancies will be resolved through consensus and results will be summarized in a narrative form.
Conclusion
Research participants, investigators, regulatory groups, ethics review committees, data protection officers, and funders cannot make informed decisions or policies about data reuse without appropriate means of measuring the effects, positive or negative, and cost of data sharing.
To examine the proportion of eligible primary studies that contributed data, study characteristics associated with data contribution, and reasons for noncontribution using diagnostic test accuracy ...Individual Participant Data Meta-Analysis (IPDMA) data sets from the DEPRESsion Screening Data project.
We reviewed data set contributions from four IPDMAs. A multivariable logistic regression model was fitted to evaluate study factors associated with data contribution.
Of 456 eligible studies from four included IPDMAs, 295 (65%) contributed data. More recent year of publication and higher journal impact factor were associated with greater odds of data contribution. Studies conducted in Europe (excluding the United Kingdom), Oceania, Canada, the Middle East, Africa, and Central or South America (reference = the United States), that have recruitment from inpatient care or nonmedical settings (reference = outpatient), that reported screening accuracy results, or that drew negative conclusions (reference = positive conclusions) were more likely to contribute data. Studies of the Geriatric Depression Scale (reference = the Patient Health Questionnaire) or lacking funding information were negatively associated with data contribution. Over 80% of noncontributions were due to authors being unreachable or data being unavailable.
The study identified factors associated with data contribution that may support future research to promote data contribution to IPDMAs.
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