Claims data analysis of medical specialist utilization among nursing home residents and community-dwelling older people
Bibliographic record
Abstract
Abstract Background Most older people, and especially those in need of long-term care, suffer from one or more chronic diseases. Consequently, older people have an increased need of medical care, including specialist care. There is little evidence as yet whether older people with greater medical care needs obtain adequate medical care because existing studies do not sufficiently control for differences in morbidity. In this study we investigate whether differences in medical specialist utilization exist between older people with and without assessed long-term care need in line with Book XI of the German Social Code, while at the same time controlling for individual differences in morbidity. Methods We used data from the 11 German AOK Statutory Health and Long-term Care Insurance funds of 100,000 members aged 60 years or over. Zero-inflated Poisson regression analyses were applied to investigate whether the need for long-term care and the long-term care setting are associated with the probability and number of specialist visits. We controlled for age, gender, morbidity and mortality, residential density, and general practitioner (GP) utilization. Results Older people in need of long-term care are more likely to have no specialist visit than people without the need for long-term care. This applies to nearly all medical specialties and for both care settings. Yet, despite these differences in utilization probability the number of specialist medical care visits between older people with and without the need for long-term care is similar. Conclusion Older people in need of long-term care might face access barriers to specialist care. Once a contact is established, however, utilization does not differ considerably between those who need long-term care and those who don’t; this indicates the importance of securing an initial contact.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".