Prevalence and associated factors of intrinsic capacity based on Andersen Model among older adults: a national cross-sectional study in China
Bibliographic record
Abstract
BACKGROUND: Extensive evidence indicates intrinsic capacity (IC) as a strong predictor for health outcomes. Identifying conditions associated with IC impairment provides an opportunity to intervene to slow down, stop or reverse the declines. However, current evidence on factors affecting IC remains limited and inconsistent. This study aimed to identify factors associated with IC and the degree of IC impairment in older adults, thereby assisting in the development of comprehensive intervention strategies to mitigate the IC impairment. METHODS: We analyzed data of 10,654 participants sourced from the fifth wave of the Chinese Longitudinal Healthy Longevity Survey (2020). Chi-square tests, and multinomial logistic regression analysis were employed to analyze the possible variables associated with IC impairment based on Andersen Model. RESULTS: 10,617 participants were included into this study, and 86.9% of them have impairment in at least one dimension of IC. We identified 18 factors based on Andersen Model and 14 of them were associated with IC impairment. In the predisposing characteristics dimension of Andersen Model, older adults particularly those aged ≥ 80 years exhibited higher risk of IC impairment than those aged 60–69 years. Individuals living in urban areas, educated, drinking less than once a month, using the internet, and having social participation were less likely to experience IC impairment. In the enabling resources dimension of Andersen Model, individuals with access to pension, medical insurance, work, and spousal emotional support exhibited lower risk of IC impairment. In the need factors dimension of Andersen Model, individuals who self-rated their health as poor, and those with pain and multimorbidity exhibited higher risk of IC impairment. Individuals sleeping 5–7 h per night demonstrated a lower risk of IC impairment relative to more than 7 h. CONCLUSION: More than half of the older adults exhibited varying degrees of IC impairment in China. Governments and community health systems should implement programs promoting rural urbanization, digital literacy, social participation, and equitable allocation of medical insurance and pension resources to mitigate IC impairment in older adults.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".