Prevalence and moderators of depression symptoms among Black individuals in Western Countries: a systematic review and meta-analysis among 1.3 million people in 421 studies
Bibliographic record
Abstract
Background: Black people living in Western countries face a range of structural challenges and disparities (e.g. difficult socio-economic conditions, historical and intergenerational trauma, police brutality, racism) that adversely affect their mental health. This study assesses depression prevalence among Black individuals in minority contexts, examining sociodemographic factors, study type, evaluation period, publication year, and measures; and differences in depression rates between Black individuals and other racial groups (Asian, Indigenous, Latinx, White). Methods: To identify studies, a comprehensive search strategy was developed and executed on September 30, 2022 across six databases (Allied and Complementary Medicine Database, APA PsycInfo, CINAHL. Cochrane CENTRAL, Embase, MEDLINE). The meta-analysis protocol was registered with PROSPERO (CRD42020155634). A random-effects meta-analysis estimated depression prevalence among Black individuals. Meta-regression tested differences by racial background, gender, sample type, evaluation method, age group, and publication year, reporting Odd ratios (ORs) with Confidence intervals (CIs). Findings: = 0.004, 95% CI: 0.90, 0.98) compared to Black individuals. Pooled prevalence was 26.6% for the past week (95% CI: 24.6%-28.6%), 22.1% (95% CI: 19.2-23.1) for the past two weeks, 21.6% (95% CI: 11.6-33.5) for the past month, 9.1% (95% CI: 7.7%-10.7%) for the past year, and 16.6 (95% CI: 12.9-20.8) for lifetime. Depression prevalence was higher among Black women (24.3%; 95% CI: 21.3-27.4) and in North America (20.3%; 95% CI: 18.8-21.9). Depression prevalence was higher in 2000-2009 (23.5%; 95% CI: 20.9-26.2), decreased in 2010-2019 (17.7%; 95% CI: 15.6-19.9) and increased since 2020 (20.6%; 95% CI: 17.5-23.8). Interpretation: As depression constitutes a burden among Black individuals in the West, it is urgent to mobilize public health agencies, research funding agencies and clinicians to develop and implement antiracist and culturally adapted prevention and intervention programs. Funding: Public Health Agency of Canada, (grant number 1920-HQ-000053), the Social Sciences and Humanities Research Council (SSHRC) and Canadian Institutes of Health Research (CIHR) (grant number 469050).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".