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 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".