Prevalence and influencing factors of dementia in the elderly iranian population: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The global rise in dementia prevalence, a major neurocognitive disorder, poses a significant challenge to aging populations. This study aimed to determine the prevalence of dementia and its associated risk factors among community-dwelling elderly in selected provinces of Iran. METHODS: A cross-sectional study was conducted from October 2022 to March 2024, enrolling 58,228 individuals aged 60-75 years from two provinces (Qazvin and Yazd) selected via multi-stage, stratified random sampling. A multi-stage diagnostic protocol was employed, using validated Persian versions of the Geriatric Depression Scale (GDS), Abbreviated Mental Test Score (AMTS), Mini-Mental State Examination (MMSE), Functional Assessment Staging (FAST), and Montreal Cognitive Assessment (MoCA) with standardized cut-offs. Multivariable logistic regression was used to identify factors associated with dementia. RESULTS: After exclusions, the final analytical sample was 56,456 participants. The overall prevalence of dementia was 10.1%. Prevalence was significantly higher in women (13.2%) than in men (7.0%). In the adjusted model, female gender (aOR: 2.04, 95% CI: 1.92-2.15), older age (70-79 years vs. 60-64 years, aOR: 1.70, 95% CI: 1.59-1.82), widowhood (aOR: 1.50, 95% CI: 1.05-2.16), and lower educational attainment (illiterate vs. academic, aOR: 6.55, 95% CI: 5.43-7.91) were significantly associated with higher odds of dementia. CONCLUSIONS: This large-scale study reveals a substantial burden of dementia in the studied Iranian provinces. The identified risk factors, including female sex, advanced age, widowhood, and low education, highlight vulnerable subgroups and underscore the urgent need for targeted public health strategies focused on early detection, risk reduction, and support systems for the elderly in Iran.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".