Health State Profiles and Service Utilization in Community-Living Elderly
Bibliographic record
Abstract
BACKGROUND: We know that health status in older people is heterogeneous and that many need complex care. What is now required is a comprehensive description of this heterogeneity and the estimation of its effects on patterns of service utilization. OBJECTIVE: This study examines the possibility of classifying older people according to their complex health conditions and whether the way in which they consume services differs based on these classes. METHODS: We used latent class analysis to model heterogeneity and classify community living elderly into homogenous health state categories (ie, health profiles). The number of health profiles present in the sample was revealed using 17 health indicators collected at baseline in the demonstration project of SIPA (French acronym for System of Integrated Care for the frail elderly), a system of integrated care for frail older people (n = 1164). These profiles were then used in 2-part econometric models to study access and costs of several measures of services using data collected prospectively over the 22-months of the SIPA trial. RESULTS: We identified 4 substantially meaningful health profiles (prevalence: 23%, 11%, 36%, 30%) characterized by differences along the physical, cognitive, and disability dimensions of health. Subsequent econometric modeling showed a differential effect of health profiles on use and costs along the continuum of health and social services. CONCLUSIONS: For older people with complex care needs, classification into homogeneous health subgroups unmasks differences in utilization patterns that can be used by decision makers in their attempt to improve the trajectory of care and adjust the distribution of resources to the needs of older people.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".