Brexit is likely to have far-reaching adverse effects on healthcare in the U.K. and the National Health Service (NHS) and result in threats to the NHS' workforce and its finances, according to a health policy review published 26 September in the Lancet.
A group of researchers from several U.K. institutions asserts that Brexit also will have negative effects on the licensing of medical products and the nation's role in public health and scientific research.
"As this analysis shows, leaving the EU will have wide-ranging impacts on health and the National Health Service," said lead author Nick Fahy, a researcher and consultant in health policy and systems at the University of Oxford. "These must be addressed now if the consequences of Brexit are not to be borne by the sick and the vulnerable."
The authors outlined three likely scenarios as a result of Brexit:
- A "soft Brexit," which creates access to a single market but restricts free movement of people
- A "hard Brexit," which crafts a free-trade agreement between the U.K. and the European Union
- A "failed Brexit," which reverts the U.K. to World Trade Organization rules
While the soft Brexit would have a more minimal effect on health, all three potential options have serious deficiencies, the authors wrote.
One risk is the loss of NHS funding, which could occur through the loss of European funds and from Brexit's impact on the U.K. economy. Funding shortfalls also could affect individuals living abroad and people traveling to the EU for work or study.
The EU's direct funding accounts for 17% of research contracts held by U.K. universities and an estimated 16% of academics in the U.K. are from other parts of the EU.










![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)






