Epidemiology of COVID‐19 mortality in Nepal: An analysis of the National Health Emergency Operation Center data
Bibliographic record
Abstract
Introduction: COVID-19 had caused nearly 12,000 deaths in Nepal by March 2023. In this study, we compare COVID-19-associated mortality in the first (September 15 to November 30, 2020) and second (April 15 to June 30, 2021) waves of the pandemic in Nepal and investigate the associated epidemiological factors. Methods: We disaggregated the COVID-19-related deaths between the first and second waves of the pandemic using the national COVID-19 database and evaluated the association of independent variables with the deaths in the first versus second waves. Results: Out of 8133 deaths, 25% died in the first wave and 75% in the second. Overall, 33.5% of the deceased were female, and 52% of the deaths were in those 60 years or older. A vast majority (92%) of deaths occurred in hospitals. Geographically, the middle "Hill" region (58.3%) witnessed the most significant number of deaths. About two thirds (64%) had at least one comorbid condition. Multivariable logistic regression showed a difference in the reported deaths by province (state) and geography (ecological region) between the first and second waves. Those in the age groups "19-39 years" and "40-59 years" were more likely to die in the second wave than in the first wave compared to the younger age group. Conclusions: Overall, deaths were concentrated among older age groups, males, in the Hill regions, in the western provinces, and those with comorbidities. Therefore, the country must focus on these areas to ensure an efficient and effective pandemic response in the future.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".