Correlations between COVID-19 and burden of dementia: An ecological study and review of literature
Bibliographic record
Abstract
INTRODUCTION: Current evidence on the association between COVID-19 and dementia is sparse. This study aims to investigate the associations between COVID-19 caseload and the burden of dementia. METHODS: We gathered data regarding burden of dementia (disability-adjusted life years [DALYs] per 100,000), life expectancy, and healthy life expectancy (HALE) from the Global Burden of Disease (GBD) 2017 study. We obtained COVID-19 data from Our World in Data database. We analyzed the association of COVID-19 cases and deaths with the burden of dementia using Spearman's rank correlation coefficient. RESULTS: Globally, we found significant positive (p < .001) correlations between life expectancy (r = 0.60), HALE (r = 0.58), and dementia DALYs (r = 0.46) with COVID-19 caseloads. Likewise, we found similar correlations between life expectancy (r = 0.60), HALE (r = 0.58) and dementia DALYs (r = 0.54) with COVID-19 mortality. CONCLUSION: Health policymakers should clarify a targeted model of disease surveillance in order to reduce the dual burden of dementia and COVID-19.
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How this classification was reachedexpand
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".