The Association Between Healthcare Resources, Non-communicable Diseases, and Covid-19 Mortality: An Epidemiological Study of 139 Countries
Bibliographic record
Abstract
Abstract Background: To provide an overview of the worldwide association between hospital beds, the burden of non-communicable diseases (NCDs), and COVID-19 mortality.Methods: Data was extracted regarding COVID-19 deaths and cases from the Our World in Data as of March 23, 2021. The following data was obtained:1) NCDs disability-adjusted life years (DALYs), health-adjusted life expectancy, and the health access and quality index from the Global Burden of Disease study; 2) the number of hospital beds, physicians, nurses and midwives per population, and out-of-pocket payments from the WHO website. Using the multilevel generalized linear model, these variables’ independent associations with COVID-19 mortality rate ratio (MRR) was examined.Results: Hospital beds were associated with reduced COVID-19 mortality (MRR=0.47; 95% CI: 0.44 to 0.5) globally. During COVID-19 peak periods, despite a decreasing trend in COVID-19 MRR with increasing beds in high-income countries, the odds of mortality remained high even within the highest percentile of hospital beds (MRR=1.54 for 20th-40th and 1.06 for >60th bed percentile, respectively). On the contrary, in middle-income countries, an inverse association was observed between the number of hospital beds and COVID-19 mortality in both periods. NCD DALYs were associated with increased COVID-19 deaths, particularly during peak mortality periods in high-income countries. Death-to-case ratio increased by approximately two times during the peak vs non-peak mortality periods.Conclusions: COVID-19 is a syndemic interacting with non-communicable diseases and not only a pandemic. A comprehensive national healthcare plan against COVID-19 spread should include adequate measures to protect vulnerable patients with pre-existing chronic conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 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".