Associations of diabetes, hypertension and obesity with COVID-19 mortality: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction Despite a growing body of scholarly research on the risks of severe COVID-19 associated with diabetes, hypertension and obesity, there is a need for estimating pooled risk estimates with adjustment for confounding effects. We conducted a systematic review and meta-analysis to estimate the pooled adjusted risk ratios of diabetes, hypertension and obesity on COVID-19 mortality. Methods We searched 16 literature databases for original studies published between 1 December 2019 and 31 December 2020. We used the adapted Newcastle-Ottawa Scale to assess the risk of bias. Pooled risk ratios were estimated based on the adjusted effect sizes. We applied random-effects meta-analysis to account for the uncertainty in residual heterogeneity. We used contour-funnel plots and Egger’s test to assess possible publication bias. Results We reviewed 34 830 records identified in literature search, of which 145 original studies were included in the meta-analysis. Pooled adjusted risk ratios were 1.43 (95% CI 1.32 to 1.54), 1.19 (95% CI 1.09 to 1.30) and 1.39 (95% CI 1.27 to 1.52) for diabetes, hypertension and obesity (body mass index ≥30 kg/m 2 ) on COVID-19 mortality, respectively. The pooled adjusted risk ratios appeared to be stronger in studies conducted before April 2020, Western Pacific Region, low- and middle-income countries, and countries with low Global Health Security Index scores, when compared with their counterparts. Conclusions Diabetes, hypertension and obesity were associated with an increased risk of COVID-19 mortality independent of other known risk factors, particularly in low-resource settings. Addressing these chronic diseases could be important for global pandemic preparedness and mortality prevention. PROSPERO registration number CRD42021204371.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.018 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".