Venous thromboembolism in Black COVID-19 patients in a minority context compared to White, Asian and other racialized patients: A systematic review and meta-analysis
Bibliographic record
Abstract
IMPORTANCE: COVID-19 has disproportionately affected racialized populations, with particular impact among individuals of Black individuals. However, it is unclear whether disparities in venous thromboembolic (VTE) complications exist between Black individuals and those belonging to other racial groups with confirmed SARS-CoV2 infections. OBJECTIVE: To summarize the prevalence and moderators associated with VTE among Black COVID-19 patients in minoritized settings, and to compare this to White and Asian COVID-19 patients according to sex, age, and comorbid health conditions (heart failure, cancer, obesity, hypertension). DESIGN SETTING, AND PARTICIPANTS: A systematic search of MEDLINE, Embase, CINAHL and CENTRAL for articles or reports published from inception to February 15, 2023. STUDY SELECTION: Reports on VTE among Black individuals infected with SARS-CoV2, in countries where Black people are considered a minority population group. DATA EXTRACTION AND SYNTHESIS: Study characteristics and results of eligible studies were independently extracted by 2 pairs of reviewers. VTE prevalence was extracted, and risk of bias was assessed. Prevalence estimates of VTE prevalence among Black individuals with COVID19 in each study were pooled. Where studies provided race-stratified VTE prevalence among COVID19 patients, odds ratios were generated using a random-effects model. MAIN OUTCOMES AND MEASURES: Prevalence of VTE, comprising of deep vein thrombosis and pulmonary embolism. RESULTS: Ten studies with 66,185 Black individuals reporting the prevalence of COVID-19 associated VTE were included. Weighted median age of included studies was 47.60. Pooled prevalence of COVID-19 associated VTE was 7.2 % (95 % CI, 3.8 % - 11.5 %) among Black individuals. Among individuals with SARS-CoV2 infections, Black population had higher risks of VTE compared to their White (OR = 1.79, [95 % CI 1.28-2.53], p < .001) or Asian (OR = 2.01, [95 % CI, 1.14-3.60], p = .017) counterparts, or patients with other racial identities (OR = 2.01, [95 % CI, 1.39, 2.92]; p < .001). CONCLUSIONS AND RELEVANCE: Black individuals with COVID-19 had substantially higher risk of VTE compared to White or Asian individuals. Given racial disparities in thrombotic disease burden related to COVID-19, medical education, research, and health policy interventions are direly needed to ensure adequate disease awareness among Black individuals, to facilitate appropriate diagnosis and treatment among Black patients with suspected and confirmed VTE, and to advocate for culturally safe VTE prevention strategies, including pre-existing inequalities to the COVID-19 pandemic that persist after the crisis.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".