Zelalem Negeri
A5010867753- Maternal Mental Health During Pregnancy and Postpartum
- Mental Health Treatment and Access
- Systemic Sclerosis and Related Diseases
- Meta-analysis and systematic reviews
- Mental Health Research Topics
- Statistical Methods and Bayesian Inference
- Body Image and Dysmorphia Studies
- Maternal and Perinatal Health Interventions
- Child and Adolescent Psychosocial and Emotional Development
- Health disparities and outcomes
- COVID-19 and healthcare impacts
- Cardiac Health and Mental Health
- Health Systems, Economic Evaluations, Quality of Life
- Grief, Bereavement, and Mental Health
- Statistical Methods in Clinical Trials
- Delphi Technique in Research
- demographic modeling and climate adaptation
- Infant Development and Preterm Care
- Health, psychology, and well-being
- Obstructive Sleep Apnea Research
- Oral and gingival health research
- Seedling growth and survival studies
- Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Antifungal resistance and susceptibility
- Machine Learning in Healthcare
University of Waterloo
2024
Jewish General Hospital
2020-2023
McGill University
2020-2021
McMaster University
2018-2020
( BMJ . 2020;371:m4022) Depression in pregnant and postpartum women is common rates of detection management could potentially improve with screening. Self-report depression symptom questionnaires such as the Edinburgh Postnatal Scale (EPDS) be used part a full assessment when suspected. This study individual participant data meta-analysis to assess accuracy EPDS screening whether differs by timing screen, patient age, residence.
Abstract Background Selective reporting of results from only well-performing cut-offs leads to biased estimates accuracy in primary studies questionnaire-based screening tools and meta-analyses that synthesize results. Individual participant data meta-analysis (IPDMA) sensitivity specificity at each cut-off via bivariate random-effects models (BREMs) can overcome this problem. However, IPDMA is laborious depends on the ability successfully obtain datasets, BREMs ignore correlation between...
Abstract Background A Generalized Linear Mixed Model (GLMM) is recommended to meta-analyze diagnostic test accuracy studies (DTAs) based on aggregate or individual participant data. Since a GLMM does not have closed-form likelihood function parameter solutions, computational methods are conventionally used approximate the likelihoods and obtain estimates. The most commonly Iteratively Reweighted Least Squares (IRLS), Laplace approximation (LA), Adaptive Gauss-Hermite quadrature (AGHQ)....
Importance Test accuracy studies often use small datasets to simultaneously select an optimal cutoff score that maximizes test and generate estimates. Objective To evaluate the degree which using data-driven methods Patient Health Questionnaire-9 (PHQ-9) estimate yields (1) scores differ from population-level (2) biased Design, Setting, Participants This study used cross-sectional data existing individual participant meta-analysis (IPDMA) database on PHQ-9 screening represent a hypothetical...
Abstract Diagnostic or screening tests are widely used in medical fields to classify patients according their disease status. Several statistical models for meta‐analysis of diagnostic test accuracy studies have been developed synthesize sensitivity and specificity a interest. Because the correlation between specificity, modeling two measures using bivariate model is recommended. In this paper, we extend current standard linear mixed (LMM) by proposing variance‐stabilizing transformations:...
Bivariate random-effects models are currently widely used to synthesize pairs of test sensitivity and specificity across studies. Inferences drawn based on these may be distorted in the presence outlying or influential Currently, subjective methods such as inspection forest plots identify studies meta-analysis diagnostic accuracy We proposed objective solid statistical reasoning for identifying and/or The have been validated using simulation study illustrated two published data. Our...
Objective: People with chronic medical conditions are at risk of complications and mortality during the COVID-19 outbreak. Understanding mental health implications, including effects isolation movement restrictions, should consider specific fears level fear experienced. No questionnaires or scales, however, have been developed for this purpose. We sought input from people living autoimmune disease systemic sclerosis (SSc; scleroderma) on items inclusion in a preliminary version Fears...