Determinants of choice of usual source of care among older people with cardiovascular diseases in China: evidence from the Study on Global Ageing and Adult Health
Bibliographic record
Abstract
BACKGROUND: Cardiovascular diseases (CVD) are emerging as the leading contributor to death globally. The usual source of care (USC) has been proven to generate significant benefits for the elderly with CVD. Understanding the choice of USC would generate important knowledge to guide the ongoing primary care-based integrated health system building in China. This study aimed to analyze the individual-level determinants of USC choices among the Chinese elderly with CVD and to generate two exemplary patient profiles: one who is most likely to choose a public hospital as the USC, the other one who is most likely to choose a public primary care facility as the USC. METHODS: This study was a secondary analysis using data from the World Health Organization's Study on Global AGEing and Adult Health (SAGE) Wave 1 in China. 3,309 individuals aged 50 years old and over living with CVD were included in our final analysis. Multivariable logistic regression was built to analyze the determinants of USC choice. Nomogram was used to predict the probability of patients' choice of USC. RESULTS: Most of the elderly suffering from CVD had a preference for public hospitals as their USC compared with primary care facilities. The elderly with CVD aged 50 years old, being illiterate, residing in rural areas, within the poorest income quintile, having functional deficiencies in instrumental activities of daily living and suffering one chronic condition were found to be more likely to choose primary care facilities as their USC with the probability of 0.85. Among those choosing primary care facilities as their USC, older CVD patients with the following characteristics had the highest probability of choosing public primary care facilities as their USC, with the probability of 0.77: aged 95 years old, being married, residing in urban areas, being in the richest income quintile, being insured, having a high school or above level of education, and being able to manage activities living. CONCLUSIONS: Whilst public primary care facilities are the optimal USC for the elderly with CVD in China, most of them preferred to receive health care in public hospitals. This study suggests that the choice of USC for the elderly living with CVD was determined by different individual characteristics. It provides evidence regarding the choice of USC among older Chinese patients living with CVD.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".