The majority of U.K. doctors consider themselves to be left-wing and liberal-minded, and they believe Brexit bodes poorly for the National Health Service (NHS), according to research published online on 30 July in the Journal of Epidemiology and Community Health.
In light of upcoming shifts in healthcare due to political decisions including Brexit, first author Dr. Kate Mandeville and colleagues from various U.K. institutions conducted a survey on the political beliefs and voting behaviors of doctors in the U.K. They sent their survey out to a wide range of physician specialty associations and associated social media groups and also directly to individual doctors through the online professional network Doctors.net.uk.
Among the 1,172 doctors who responded, nearly two-thirds described their political views as left-wing or liberal, with a score of 4 on a scale of 0 (extremely left wing) to 10 (extremely right wing). Surgeons and doctors in higher income brackets tended to register a right-wing score.
In addition, 79.4% of the doctors claimed to have voted for the U.K. to remain part of the European Union (EU) in the June 2016 referendum, with 98.6% advocating that EU nationals working in the NHS should be allowed to stay in the U.K. post-Brexit. Most respondents also agreed with the ideas of charging patients who are not eligible for NHS treatment for nonurgent care (70.6%) and reserving a part of national spending for the NHS (87.1%).
Regardless of income or specialty, nearly all doctors thought Brexit would be "very bad" for the NHS, giving the ruling an average rating of 2 on a scale of 0 (worst outcome) to 10 (best).
![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&dpr=2&fit=crop&h=167&q=70&w=250)
