Prevalence of Diabetes and Hypertension and Their Associated Risks for Poor Outcomes in Covid-19 Patients
Bibliographic record
Abstract
Abstract Coronavirus disease 2019 (Covid-19) has affected millions of people and may disproportionately affect those with hypertension and diabetes. Because of inadequate methods in published systematic reviews, the prevalence of diabetes and hypertension and associated risks of poor outcomes in Covid-19 patients are unknown. We searched databases from December 1, 2019, to April 6, 2020, and selected observational peer-reviewed studies in English of patients with Covid-19. Independent reviewers extracted data on study participants, interventions, and outcomes and assessed risk of bias, and the certainty of evidence. We included 65 (15 794 participants) observational studies at moderate to high risk of bias. Overall prevalence of diabetes and hypertension was 12% (95% confidence interval [CI], 10-15; n = 12 870; I2: 89%), and 17% (95% CI, 13-22; n = 12 709; I2: 95%), respectively. In severe Covid-19, the prevalence of diabetes and hypertension were 18% (95% CI, 16-20; n = 1099; I2: 0%) and 32% (95% CI, 16-54; n = 1078; I2: 63%), respectively. Unadjusted relative risk for intensive care unit admission and mortality were 1.96 (95% CI, 1.19-3.22; n = 8890; I2: 80%; P = .008) and 2.78 (95% CI, 1.39-5.58; n = 2058; I2: 75%; P = .0004) for diabetics; and 2.95 (95% CI, 2.18-3.99; n = 1737; I2: 0%; P < .001) and 2.39 (95% CI, 1.54-3.73; n = 3107; I2: 66%; P < .001) for hypertensives. Neither diabetes (1.50; 95% CI, 0.90-2.50; n = 1991; I2: 74%; P = .119) nor hypertension (1.48; 95% CI, 0.99-2.23; n = 2023; I2: 69%; P = .058) was associated with severe Covid-19. In conclusion, the risk of intensive care unit admission and mortality for patients with diabetes or hypertension who developed Covid-19 is increased compared with those without these comorbidities. PROSPERO registration number CRD42020176582.
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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 | 0.029 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".