The Impact COVID-19 Pandemic on Coronary Heart Disease Deaths: Using Bayesian Lorenz Curve and Gini-Index Distribution
Bibliographic record
Abstract
Aim: The aim to investigate and assess role of COVID-19 on Coronary Heart Disease (CHD) mortality using Bayesian Lorenz Curve and associated Gini Index Statistical Method: Bayesian estimation was applied to analyze CHD mortality rates, focusing on both gender and age group differences. Application: A total of 341,467 patients were treated during 2-year period from 2020 to 2021during COVID-19 in Turkey. 195,413 females and 146,054 males were diagnosed and 155,211 deaths where 88,824 were males and 66,387 were females with CHD, and hence were studied to evaluate whether female gender was an independent predictor for poor prognosis. Results: Mortality rates increase with age for females compared to males. The model suggests that males have higher risks or proportions across all groups compared to females, particularly in older age categories. The Lorenz curves for both genders show that a significant portion of deaths is concentrated in a relatively small subset of age groups, particularly older adults. The Gini Index regarding mortality for males is found to be 0.123 compared to value of 0.384 associated with female's age distribution. Meanwhile, the Gini Index regarding morbidity for males (0.146) and females (0.394) are very similar, suggesting that the patterns of inequality in morbidity distribution are comparable across genders. Conclusion: The study highlights the effectiveness of empirical Bayesian techniques in estimating CHD-related COVID-19 mortality rates across Turkey. It suggests that such statistical methods can help allocate resources more efficiently to high-risk areas and to ensure fair resource distribution and healthcare interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".