Objectives: To examine factors associated with declaration of disability by medical students and doctors, and the association of declared disability with academic performance., Design: Observational study using record-linked data collected between 2002 and 2018., Setting: UK Medical Education Database is a repository of data relating to training of medical students and doctors. Disability and other data are record-linked., Participants: All students starting at a UK medical school between 2002 and 2018 (n=135 930)., Main Outcome Measures: Declared disability was categorised by the Higher Education Statistics Authority. Outcomes related to undergraduate academic performance included scores in the educational performance measure (EPM), prescribing safety assessment and situational judgement test. Performance in postgraduate examinations was studied, as well as prior attainment in school examinations and aptitude tests., Results: Specific learning disability (SLD) was the most commonly declared disability (3.5% compared with the next most commonly declared disability at 1.0% of n=129 345 all cases in the study), and during the period covered by the data, SLD declarations increased from 1.4% (n=6440 for students starting in 2002) to 4.6% (n=8625 for students starting in 2018). In a logistic regression, the following factors predicted recording of SLD on entry to medical school ((exp(B)±95% CI), p<0.0001 unless otherwise stated): attendance at a fee-paying school (2.306±0.178), graduate status (1.806±0.205), participation of local areas quintile (1.089±0.030), age (1.034±0.012). First year medical students were less likely to declare SLD if they were from a non-white ethnic background (Asian/Asian British 0.324±0.034, black/black British 0.571±0.102, mixed 0.731±0.108, other ethnic groups 0.566±0.120), female (0.913±0.059; p=0.007) or from a low index of multiple deprivation quintile (0.963±0.029); p=0.017. In univariate analysis with Bonferroni corrections applied for multiple tests, no significant difference was observed in the recording of SLD according to socioeconomic class (χ 2 =5.637, p=1), whether or not a student's parents had a higher education (χ 2 =0.140, p=1), or whether or not a student had received a United Kingdom Clinical Aptitude Test (UKCAT) bursary (χ 2 =7.661, p=0.068). Students who declared SLD at some point in medical school (n=4830) had lower EPM normalised deviate values (-0.390) than those who did not (-0.119) (F=189.872, p<0.001). Those for whom SLD was recorded were as likely to complete the course successfully as those who did not declare disability (93.0% successful completion by those for whom SLD declared from year 1 (n=2480), 92.2% by those for whom SLD declared after year 1 (n=2350), 91.6% by those for whom SD not declared at any point (n=85 180)) (χ 2 =6.905, p=0.032). Of 3580 first year students who declared SLD, 43.1% had not sat the UKCAT Special Educational Needs aptitude test (which gives extra time for those with special educational needs), while 28% of 2400 registrants for whom SLD was recorded as medical students did not declare it at General Medical Council registration., Conclusions: Substantial increases in declaration of SLD may reflect changes in the social and legal environment during the period of the study. Those who declare SLD are just as likely to gain a primary medical qualification as those who do not. For some individuals, disability declaration appears to depend on context, based on differences in numbers declaring SLD before, during and after medical school., Competing Interests: Competing interests: All authors have completed the Unified Competing Interest form (available on request from the corresponding author) and declare their competing interests as per below. Otherwise, there is: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years, no other relationships or activities that could appear to have influenced the submitted work. Michael Murphy: no competing interest declared. Jon Dowell: UKMED Advisory Board member and chair of the UKMED research group. Daniel Smith is employed by the GMC as data analyst working on the UKMED project., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)