Is COVID-19 Another Case of Obesity Paradox? - Results from An International Ecological Study on behalf of the REPROGRAM Consortium Obesity Study Group
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction: Obesity has emerged as one of the major risk factors of severe morbidity and cause-specific mortality among severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infected individuals. Patients with obesity also have overlapping cardiovascular diseases and diabetes, which make them increasingly vulnerable. This novel ecological study examines the impact of obesity and/or body mass index (BMI) on rates of population-adjusted cases and deaths due to coronavirus disease 2019 (COVID-19). Material and methods: Publicly available datasets were used to obtain relevant data on COVID-19, obesity and ecological variables. Group-wise comparisons and multivariate logistic regression analyses were performed. The receiver operating characteristic curve (ROC) was plotted to compute the area under the curve. Results: We found that male BMI is an independent predictor of cause-specific (COVID-19) mortality, and not of the caseload per million population. Countries with obesity rates of 20-30% had a significantly higher (approximately double) number of deaths per million population to both those in < 20% and > 30% slabs. We postulate that there may be a U-shaped paradoxical relationship between obesity and COVID-19 with the cause-specific mortality burden more pronounced in the countries with 20-30% obesity rates. These findings are novel along with the methodological approach of doing ecological analyses on country-wide data from publicly available sources. Conclusions: We anticipate, in light of our findings, that appropriate targeted public health approaches or campaigns could be developed to minimize the risk and cause-specific morbidity burden due to COVID-19 in countries with nationwide obesity rates of 20-30%.
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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 | 0.005 | 0.141 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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