Long-term care spending and hospital use among the older population in England
Bibliographic record
Abstract
This paper examines the impact of changes in public long-term care spending on the use of public hospitals among the older population in England. Mean per-person long-term care spending fell by 31% between 2009/10 and 2017/18, but cuts varied considerably geographically. We instrument public long-term care spending with predicted spending based on historical national funding shares and national spending trends. We find that reductions in public long-term care spending led to substantial increases in the number of emergency department (ED) visits made by patients aged 65 and above, explaining between a quarter and a half of the growth in ED use among this population over this period, and to an increase in the share of patients revisiting the ED within seven days. However, there was no impact on wider use of inpatient or outpatient services (which are more expensive to provide), and consequently little impact on overall hospital costs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".