The Impact of Comorbidities on COVID-19 Severity and Mortality in Egypt
Why this work is in the frame
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Bibliographic record
Abstract
Background Older persons and people of any age with certain underlying comorbidities such as diabetes mellitus, cardiovascular disease, lung disease, kidney disease, liver disease, and cancer are at a higher risk of severe disease course and death if they become infected with COVID-19. Identifying at-risk groups and risk factors for COVID-19 severity and mortality is important for guiding the efficient and appropriate prevention and management of patients with COVID-19. Objective This study aimed at describing the demographics and epidemiologic characteristics of confirmed COVID-19 cases in Egypt and determining the impact of different comorbidities on patients’ outcomes. Methods The data of all confirmed COVID-19 patients admitted to 408 governmental hospitals all over Egypt from February to May 2020 were collected retrospectively from the National Egyptian Disease Surveillance System. The cases were confirmed using RT-PCR. Results Overall, 28,415 patients (55% male and 45% female) were identified. Their median age was 44 years. Of those, 743 (2.6%) were admitted to ICU, 408 (1.4%) required ventilator, and 1045 (3.7%) died. Of the 21,617 (76.1%) patients with completed data, 4687 (21.7%) had comorbidities. Overall, 11.8% had diabetes, 5.3% cardiovascular disease, and 4.3% chronic obstructive pulmonary disease. Those who had 1 comorbidity were more likely to die (odds ratio 2.83), were admitted to ICU (odds ratio 6.36), and needed a ventilator (odds ratio 5.95) compared to patients with no comorbidities. Having multiple comorbidities increased the risk of mortality (odds ratio 3.53), ICU admission (odds ratio 8.62), and requiring a ventilator (odds ratio 9.06). Conclusions COVID-19 patients with comorbidities had a higher risk of disease severity and mortality. Multiple comorbidities further increase the risk to a higher extent. All necessary precautions should be taken for patients with comorbidities to avoid COVID-19 infection in order to prevent the worst prognosis.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it