Charging of overseas visitors in England and universal health coverage: a cross-sectional analysis of NHS trusts
Bibliographic record
Abstract
BACKGROUND: In 2017, new regulations in England introduced upfront charging for non-urgent care within the National Health Service (NHS). Individuals from outside the European Economic Area who have not paid the immigration surcharge are chargeable for NHS care at 150% of cost. METHODS: A freedom of information (FOI) request was sent to 135 acute non-specialist NHS trusts in England to create a database of overseas visitors charges. This was analysed using multiple linear regression to explore the relationship between sex, age, nationality, ethnicity, urgency and the cost of healthcare. RESULTS: Of 135 acute non-specialist trusts in England 64 replied, providing a data set of 13 484 patients. Women were found to be invoiced higher amounts than men (P = 0.002). Patients were more likely to be women (63 versus 37% men), and within this group, almost half of patients were of reproductive age, with 47.9% (3165) aged 16-40 years old. Only seven trusts supplied data on urgency, and within these trusts the urgency of treatment was significantly related to cost, with the most urgent (immediately necessary) treatment costing the most (P < 0.001). CONCLUSION: This research reflects that that migrant women, and particularly undocumented women, are disproportionately impacted by the NHS charging policies in England.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".