Understanding Cognitive Decline in Armenia’s Aging Population: A Nationwide Screening Study
Bibliographic record
Abstract
BACKGROUND: Amid developing health infrastructure, the Republic of Armenia's older adult population faces increasing concerns for cognitive impairment (CI) and subsequent dementia. The landlocked, post-Soviet country presents a unique opportunity to study a population that has been genetically isolated with a history of stressors like generational trauma and a high burden of vascular disease. The objective of this study is to contextualize the biological and environmental risk factors associated with CI in the severely understudied Armenian population. METHOD: This is a population-based, secondary analysis. Early detection cognitive screenings were conducted across 8 provinces by Alzheimer's Care Armenia from 2022-2023. The sample consisted of 2,598 older adults (55 years and older). The Montreal Cognitive Assessment (MoCA) was administered to assess CI. CI was dichotomized into no CI or any level of CI. Age was categorized into ten-year intervals. Health behaviors and conditions were self-reported. Descriptive statistics and multivariable logistic regression analysis were performed to understand population-level trends. RESULT: CI was observed in 37.1% of total participants: aged 55-64 (35.1%), 65-74 years (41.2%), and 75+ (23.7%). Individuals aged 65-74 and 75+ had increasingly higher odds of CI [AOR=2.00(1.64, 2.43); 5.92(4.37, 8.07), respectively] compared to younger individuals aged 55-64. Males had higher odds of CI [AOR=1.36(1.04, 1.78)]. Urban residence served as a protective factor for developing CI [AOR=0.72(0.59, 0.88)]. Obese BMI level was also protective [AOR=0.70(0.55, 0.90)]. Hearing loss was significantly associated with CI [AOR=1.52(1.25, 1.86)]. These results were significant (p <0.05). CONCLUSION: CI presents an emerging problem in developing countries, like Armenia, and requires engagement with the Ministry of Health and local stakeholders to provide culturally tailored preventive care. Establishing a national brain health registry in Armenia is vital to guide research efforts, design tailored interventions, structure public health planning, and contribute to global brain health knowledge. Future research should explore the contribution of individual risk factors to CI in the Armenian population, such as a potential genetic predisposition to hearing loss.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".