Trends in Minority Representation Among Independent Plastic Surgery Match Applicants: An Analysis of 1000 Applicants Over an 11-Year Period

BACKGROUND: Improving diversity within plastic and reconstructive surgery (PRS) trainees is a crucial step to reduce inequities at the provider level. Trends in minority representation among independent program match applicants are understudied. We analyzed gender, racial, and ethnic demographic trends among independent PRS match applicants. METHODS: With the approval of the American Council of Educators in Plastic Surgery, the San Francisco Match provided data for the independent PRS match from 2013 to 2023. Trends in the independent PRS match were reviewed, and a Cochran- Armitage test was conducted to evaluate the significance of match trends in minority applicants (Female, Black, Asian, Other Race, Hispanic ethnicity) over time. RESULTS: A total of 1000 applicants participated in the independent plastic surgery match during the study period, of whom 735 matched. A 31% decrease in the number of independent PRS programs was observed. The match rate decreased from 86% to 60%. Statistical analysis by race (White, Black, Asian, Other) and match outcomes revealed significant differences in racial distributions between applicants and matched participants in 2014 (p = 0.002) and 2018 (p = 0.042). The proportion of female applicants and Hispanic applicants correlated yearly to the number of matched females and Hispanics, respectively (p > 0.05). Cochran- Armitage tests showed a significant increase in match trends among female participants over time (p = 0.004). CONCLUSIONS: We show a significant increase in female representation in the independent PRS match in the last decade. However, representation of racial and ethnic minorities has shown minimal change over the years. Ongoing efforts are needed to identify barriers and reduce inequities. ( J Surg Ed 82:103388. (c) 2024 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.)