Association Between Neuroticism and Dementia on Healthcare Use: A Multi-Level Analysis Across 27 Countries from The Survey of Health, Ageing and Retirement in Europe (SHARE)
Bibliographic record
Abstract
BACKGROUND: People with high levels of neuroticism are greater users of health services. Similarly, people with dementia have a higher risk of hospitalization and medical visits. As a result, dementia and a high level of neuroticism increase healthcare use (HCU). However, how these joint factors impact the HCU at the population level is unknown. Similarly, no previous study has assessed the degree of generalization of such impacts, considering relevant variables including age, gender, socioeconomic, and country-level variability. OBJECTIVE: To examine how neuroticism and dementia interact in the HCU. METHODS: A cross-sectional study was performed on a sample of 76,561 people (2.4% with dementia) from 27 European countries and Israel. Data were analyzed with six steps multilevel non-binomial regression modeling, a statistical method that accounts for correlation in the data taken within the same participant. RESULTS: Both dementia (Incidence Rate Ratio (IRR): 1.537; α= 0.000) and neuroticism (IRR: 1.122; α= 0.000) increased the HCU. The effect of having dementia and the level of neuroticism increased the HCU: around 53.67% for the case of having dementia, and 12.05% for each increment in the level of neuroticism. Conversely, high levels of neuroticism in dementia decreased HCU (IRR: 0.962; α= 0.073). These results remained robust when controlling for age, gender, socioeconomic, and country-levels effects. CONCLUSION: Contrary to previous findings, neuroticism trait in people with dementia decreases the HCU across sociodemographic, socioeconomic, and country heterogeneity. These results, which take into account this personality trait among people with dementia, are relevant for the planning of health and social services.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".