A Role of Socioeconomic Status in Cognitive Impairment Among Older Adults in Macau: A Decomposition Approach
Bibliographic record
Abstract
BACKGROUND: To explore the prevalence and inequality of cognitive impairment among older adults in Macau and the contributions of influencing factors to inequality in cognitive impairment. METHODS: The Montreal Cognitive Assessment (MoCA) was used to measure the prevalence of cognitive impairment. Socioeconomic status scores were used to describe economic level of the subjects. The concentration index was applied to measure the inequality of cognitive impairment associated with socioeconomic status (SES) and decomposed into its influencing factors. RESULTS: The prevalence of cognitive impairment was 44.35% in Macau. The concentration index of cognitive impairment among the elderly was -0.165 (95% CI: -0.232 to -0.098), indicating that the prevalence of cognitive impairment was concentrated in households with poor SES. Older age, poor SES, long or short sleep duration, other marital status, poor appetite, no tea/coffee drinking, no religious belief, and depression contributed positively to the inequality of cognitive impairment. Among these factors, SES made the largest contribution to inequality, accounting for 76.74%. CONCLUSION: In Macau, the prevalence of cognitive impairment among people aged 65 years and older is concentrated among those who are in poor economic conditions. Reducing the gap between the rich and the poor at the source, increasing educational level of low SES population and improving their access to healthcare services will help to improve the inequity of cognitive impairment.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".