Applicants to medical school in the UK apply through the Universities and Colleges Admissions System (UCAS) and most UK medical schools also require applicants to take one of two specific aptitude tests. Over the period examined, 25 (78 %) medical schools required applicants to sit the United Kingdom Clinical Aptitude test (UKCAT). Linked UCAS and UKCAT data were used to examine the SES of applicants to, and those with an accepted offer from 22 UKCAT Consortium Schools.
Data from three admission cycles were examined (2009–2010 to 2011–2012). An admission cycle crosses calendar years since an applicant sits the UKCAT and makes their UCAS application in one year for medical school entry in the next calendar year at the earliest. Widening participation in the UK is primarily framed in terms of the family and social-economic background of applicants. From this perspective, it is relatively difficult to account for graduate entrants, whose current individual SES based on any measure may or may not reflect their family background. This analysis is therefore of UK domiciled applicants aged 19 or under at the time of application, who applied to at least one of 22 UKCAT Consortium medical schools (three graduate-entry schools were excluded). For all included applicants, data are available on whether the individual received an offer from each of the UKCAT medical schools they applied to, and whether they firmly accepted that offer.
Measures of SES
Three measures of SES were defined. First, the Index of Multiple Deprivation (IMD) was calculated for the postcode of residence recorded in the UCAS application. IMD is a weighted score of a number of indicators of SES and ranks small areas in order of deprivation. IMD is not an individual measure of SES, since an affluent individual may live in a deprived area and vice versa. Each UK country has its own IMD measure, and although they are similar, there is variation in the indicators used, the weighting assigned to each indicator, the frequency of updating, and the size of the geography being measured (from ~750 residents in Scotland to ~2000 in Northern Ireland) . IMD scores are therefore not strictly comparable across countries and the Office of National Statistics recommends not using IMD as a UK wide measure , although IMD is often treated as such. For each country, small areas defined by postcode were ranked in ascending order of affluence, and categorised into centiles (equally sized hundredths) and deciles (equally sized tenths, where centile/decile 1 represents the least affluent group).
Second, data from the UCAS application were used to define the school type an applicant attended. Like IMD, school type is a proxy rather than an individual measure of SES, but because it is routinely collected, it is also commonly used to examine participation. The state schooling systems in the four UK countries differ. In England, following the Direct Grant Grammar Schools Regulations of 1975 many grammar schools were closed, but some English counties retain significant numbers of state-funded selective grammar schools, which have a higher percentage of students from more affluent backgrounds . Selective schools are those which require potential students to sit an entrance examination with selection and admission to the school based on the individual’s performance. Scotland and Wales no longer have a selective grammar system. Northern Ireland has attempted to move away from selective secondary schooling and no longer uses the ‘transfer test’ to determine secondary school entry at age 11 years, although most secondary schools continue to set their own entrance exam. Given the differences in interpretation of state school type between countries, and the public focus on independent vs. state schools when considering widening participation, we defined school type as independent (fee-paying) school, grammar school (selective entrance exam, state funded), or a non-selective state funded school (comprehensive, sixth form college, or further education college). In the UK as a whole, only 6.5 % of children are educated in the independent sector .
Third, for each candidate we calculated parental National Statistics Socio-Economic Classification (NS-SEC) based on applicant responses to the self-report questions used by the Office of National Statistics (ONS)  undertaken during the UKCAT registration process. Applicants were allocated to one of five NS-SEC groups based on the highest NS-SEC of either parent. NS-SEC differs from the previous Registrar General Classification of occupation. Firstly, it is not strictly linear. NS-SEC 1 (higher managerial, administrative and professional occupations) and NS-SEC 2 (intermediate occupations) can be considered higher SES and NS-SEC 4 (lower supervisory and technical occupations) and NS-SEC 5 (semi-routine and routine occupations) lower SES, but NS-SEC 3 (small employers and own account workers) is a distinct occupational group which is not ordered in the same way. Secondly when considering the most affluent group in both classification systems, NS-SEC 1 is a broader, numerically larger group than the previous social class 1, including both traditional professional occupations like medicine and law and modern professional occupations like nursing and teaching, as well as senior management. The proportion of the UK population in the five categories in 2011 was NS-SEC 1 41.4 %, NS-SEC 2 12.7 %, NS-SEC 3 9.4 %, NS-SEC 4 6.9 % and NS-SEC 5 25.2 % . Unlike IMD and school type, NS-SEC is an individual measure of SES, but completion of the NS-SEC questions during registration is voluntary so this information is not available for all applicants with approximately 10 % of data missing for school-leaver applicants in the period examined.
For each country and for the UK as a whole, we calculated the percentage of applicants from each decile of IMD score (equal tenths of postcodes ranked in ascending order of affluence), each school type and each NS-SEC category. For IMD and NS-SEC, we then estimated an ‘application ratio’ which is the ratio of the proportion of applicants in each subcategory to the expected proportion of the population in that subcategory (akin to the standardised admission ratio proposed by Seyan et al) . This process was repeated for the percentage of applicants who received an accepted offer, with a ‘selection ratio’ estimated which is the ratio of the proportion of applicants with an accepted offer in each subcategory to the proportion of all applicants in that subcategory. To further examine the distribution of applicant by SES, we calculated the percentage of applicants and the percentages of applicants with an accepted offer for 100 equally sized groups of postcodes ranked in ascending order of affluence by IMD, and examined how school type and NS-SEC were distributed across the range of IMD. Finally, we defined a single binary measure of participation by applicants from less affluent families as an applicant having parents in NS-SEC 4 or 5 (lower supervisory and technical, and semi-routine and routine occupations), and examined how the percentage of applicants and accepted offers made to this group varied between the 22 medical schools. Data were analysed in a web-based safe haven managed by the Health Informatics Centre, University of Dundee, under data governance rules established by the UKCAT Consortium. The study was reviewed and approved by the University of St Andrews Teaching and Research Ethics Committee. Analysis was carried out in IBM SPSS v11.