Association between the 2012 Health and Social Care Act and specialist visits and hospitalisations in England: A controlled interrupted time series analysis
Bibliographic record
Abstract
BACKGROUND: The 2012 Health and Social Care Act (HSCA) in England led to among the largest healthcare reforms in the history of the National Health Service (NHS). It gave control of £67 billion of the NHS budget for secondary care to general practitioner (GP) led Clinical Commissioning Groups (CCGs). An expected outcome was that patient care would shift away from expensive hospital and specialist settings, towards less expensive community-based models. However, there is little evidence for the effectiveness of this approach. In this study, we aimed to assess the association between the NHS reforms and hospital admissions and outpatient specialist visits. METHODS AND FINDINGS: We conducted a controlled interrupted time series analysis to examine rates of outpatient specialist visits and inpatient hospitalisations before and after the implementation of the HSCA. We used national routine hospital administrative data (Hospital Episode Statistics) on all NHS outpatient specialist visits and inpatient hospital admissions in England between 2007 and 2015 (with a mean of 26.8 million new outpatient visits and 14.9 million inpatient admissions per year). As a control series, we used equivalent data on hospital attendances in Scotland. Primary outcomes were: total, elective, and emergency hospitalisations, and total and GP-referred specialist visits. Both countries had stable trends in all outcomes at baseline. In England, after the policy, there was a 1.1% (95% CI 0.7%-1.5%; p < 0.001) increase in total specialist visits per quarter and a 1.6% increase in GP-referred specialist visits (95% CI 1.2%-2.0%; p < 0.001) per quarter, equivalent to 12.7% (647,000 over the 5,105,000 expected) and 19.1% (507,000 over the 2,658,000 expected) more visits per quarter by the end of 2015, respectively. In Scotland, there was no change in specialist visits. Neither country experienced a change in trends in hospitalisations: change in slope for total, elective, and emergency hospitalisations were -0.2% (95% CI -0.6%-0.2%; p = 0.257), -0.2% (95% CI -0.6%-0.1%; p = 0.235), and 0.0% (95% CI -0.5%-0.4%; p = 0.866) per quarter in England. We are unable to exclude confounding due to other events occurring around the time of the policy. However, we limited the likelihood of such confounding by including relevant control series, in which no changes were seen. CONCLUSIONS: Our findings suggest that giving control of healthcare budgets to GP-led CCGs was not associated with a reduction in overall hospitalisations and was associated with an increase in specialist visits.
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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 | 0.016 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".