Prevalence of Dementia and Associated Factors among Older Adults in Latin America during the COVID-19 Pandemic
Bibliographic record
Abstract
Background: The COVID-19 pandemic has had a great impact on cognitive health in Latin American older adults, increasing the risk of cognitive impairment and dementia. Our objective was to analyze the prevalence of dementia and the associated factors in Latin American older adults during SARS-CoV-2 pandemic. Methods: A multicentric first phase cross-sectional observational study was conducted during the SARS-CoV-2 pandemic. Five thousand two hundred and forty-five Latin American adults over 60 years of age were studied in 10 countries: Argentina, Bolivia, Chile, Colombia, Ecuador, Guatemala, Mexico, Peru, the Dominican Republic, and Venezuela. We used the telephone version of Montreal Cognitive Assessment, the “Alzheimer Disease 8” scale for functional and cognitive changes, and the abbreviated version of the Yesavage depression scale. We also asked for sociodemographic and lockdown data. All the evaluation was made by telephone. Cross-tabulations and χ2 tests were used to determine the variability of the prevalence of impairment by sociodemographic characteristics and binary logistic regression to assess the association between dementia and sociodemographic factors. Results: We observed that the prevalence of dementia in Latin America is 15.6%, varying depending on the country (Argentine = 7.83 and Bolivia = 28.5%). The variables most associated with dementia were race and age. It does not seem to be associated with the pandemic but with social and socio-health factors. Conclusion: The prevalence of dementia shows a significant increase in Latin America, attributable to a constellation of ethnic, demographic, and socioeconomic factors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".