Burden and Associated Factors of Coronavirus Disease (COVID-19) in Al-Buraimi Governorate, Oman
Bibliographic record
Abstract
The Coronavirus disease 2019, is a global pandemic that has brought a significant health challenge all over the world. Oman reported its first case of COVID-19 on 24 February 2020. Understanding patient characteristics and demand on the healthcare system is essential to ensuring Oman can continue to provide high quality care. The aim of this study is to describe the burden of COVID-19 and associated factors for more severe disease in Al-Buraimi Governorate, Oman. We retrieved demographic and clinical data from electronic medical records for all COVID-19 laboratory-confirmed patients in Al-Buraimi Governorate from February 1 to August 31, 2020. We assessed the factors for hospitalization and outcome (recovery/death) using descriptive statistics, chi-square test/fisher exact test, spearman’s correlation, and multivariable logistic regression model in Epi info 7, Microsoft excel and SPSS software (p ≤ 0.05 significance level). A total of 977 COVID-19 patients were identified, with a prevalence rate of 8.4 per 1000 in Al-Buraimi Governorate. The male: female ratio was 3.1:1. Of COVID-19 patients, 11.7% were hospitalized, and 1.5% died. Diabetes (12.2%) and hypertension (10.8%) were the most prevalent chronic conditions among COVID-19 patients. Older patients (>60 years old) and those with comorbidities (chronic kidney disease, diabetes, heart disease, hypertension) were prone to hospitalization (p <0.001), intensive care (p <0.001), and death (p <0.001). Multivariate logistic regression analysis found that these risk factors were significantly associated with hospital admission (OR= 5.905, 95% CI 3.923–8.889; p <0.001), ICU admission (OR= 4.363, 95% CI 1.952–9.750; p <0.001), and death (OR= 6.785, 95% CI 2.295–20.062; p<0.001). A higher incidence of cases were observed among men and Omanis. Public health messaging for COVID-19 prevention should be tailored to inform these groups to slow the spread. Our findings are consistent with other studies, and local healthcare providers should be informed of the risk for severe disease among older patients and those with comorbidities, importance of early diagnosis, and prompt treatment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".