ASSOCIATION BETWEEN ETHNICITY AND SEVERE COVID-19 DISEASE: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
ABSTRACT Background Multiple reports suggest a disproportionate impact of Covid-19 on ethnic minorities. Whether ethnicity is an independent risk factor for severe Covid-19 disease is unclear. Purpose Review the association between ethnicity and poor outcomes including all-cause mortality, hospitalisation, critical care admission, respiratory and kidney failure. Data Sources MEDLINE, EMBASE, Cochrane COVID-19 Study Register, WHO COVID-19 Global Research Database up to 15/06/2020, and preprint servers. No language restriction. Study Selection All studies providing ethnicity-aggregated data on the pre-specified outcomes, except case reports or interventional trials. Data Extraction Pairs of investigators independently extracted data, assessed risk of bias using Newcastle-Ottawa scale (NOS), and rated certainty of evidence following GRADE framework. Data Synthesis Seventy-two articles (59 cohort studies with 17,950,989 participants; 13 ecological studies; 54 US-based and 15 UK-based; 41 peer-reviewed) were included for systematic review and 45 for meta-analyses. Risk of bias was low, with median NOS 7 of 9 (interquartile range 6-8). In the unadjusted analyses, compared to white ethnicity, all-cause mortality risk was similar in Black (RR:0.96 [95%CI: 0.83-1.08]), Asian (RR:0.99 [0.85-1.16]) but reduced in Hispanic ethnicity (RR:0.69 [0.57-0.84]). Age and sex-adjusted-risks were significantly elevated for Black (HR:1.38 [1.09-1.75]) and Asian (HR:1.42 [1.15-1.75]), but not for Hispanic (RR:1.14 [0.93-1.40]). Further adjusting for comorbidities attenuated these association to non-significance; Black (HR:0.95 [0.72-1.25]); Asian (HR:1.17 [0.84-1.63]); Hispanic (HR:0.94 [0.63-1.44]). Similar results were observed for other outcomes. In subgroup analysis, there was a trend towards greater disparity in outcomes for UK ethnic minorities, especially hospitalisation risk. Limitations Paucity of evidence on native ethnic groups, and studies outside the US and UK. Conclusions Currently available evidence cannot confirm ethnicity as an independent risk factor for severe Covid-19 illness, but indicates that disparity may be partially attributed to greater burden of comorbidities. Registration PROSPERO, CRD42020188421 Funding source none
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.031 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 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".