Clinical Characteristics and Outcomes of Patients Hospitalized for COVID-19 in Africa: Early Insights from the Democratic Republic of the Congo
Bibliographic record
Abstract
Little is known about the clinical features and outcomes of SARS-CoV-2 infection in Africa. We conducted a retrospective cohort study of patients hospitalized for COVID-19 between March 10, 2020 and July 31, 2020 at seven hospitals in Kinshasa, Democratic Republic of the Congo (DRC). Outcomes included clinical improvement within 30 days (primary) and in-hospital mortality (secondary). Of 766 confirmed COVID-19 cases, 500 (65.6%) were male, with a median (interquartile range [IQR]) age of 46 (34-58) years. One hundred ninety-one (25%) patients had severe/critical disease requiring admission in the intensive care unit (ICU). Six hundred twenty patients (80.9%) improved and were discharged within 30 days of admission. Overall in-hospital mortality was 13.2% (95% CI: 10.9-15.8), and almost 50% among those in the ICU. Independent risk factors for death were age < 20 years (adjusted hazard ratio [aHR] = 6.62, 95% CI: 1.85-23.64), 40-59 years (aHR = 4.45, 95% CI: 1.83-10.79), and 60 years (aHR = 13.63, 95% CI: 5.70-32.60) compared with those aged 20-39 years, with obesity (aHR = 2.30, 95% CI: 1.24-4.27), and with chronic kidney disease (aHR = 5.33, 95% CI: 1. 85-15.35). In marginal structural model analysis, there was no statistically significant difference in odds of clinical improvement (adjusted odds ratio [aOR] = 1.53, 95% CI: 0.88-2.67, P = 0.132) nor risk of death (aOR = 0.65, 95% CI: 0.35-1.20) when comparing the use of chloroquine/azithromycin versus other treatments. In this DRC study, the high mortality among patients aged < 20 years and with severe/critical disease is of great concern, and requires further research for confirmation and targeted interventions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".