Risk Prediction of Metachronous Colorectal Cancer from Molecular Features of Adenomas: A Nested Case-Control Study.

Current morphologic features defining advanced adenomas (size ≥10 mm, high-grade dysplasia or ≥25% villous component) cannot optimally distinguish individuals at high risk or low risk of metachronous colorectal cancer (me-CRC), which may result in suboptimal surveillance. Certain DNA copy-number alterations (CNAs) are associated with adenoma-to-carcinoma progression. We aimed to evaluate whether these molecular features can better predict an individual's risk of me-CRC than the morphologic advanced adenoma features.In this nested case-control study, 529 individuals with a single adenoma at first colonoscopy were selected from a Norwegian adenoma cohort. DNA copy-number profiles were determined, by low-coverage whole-genome sequencing. Prevalence of CNAs in advanced and non-advanced adenomas and its association (OR) with me-CRC was assessed. For the latter, cases (with me-CRC) were matched to controls (without me-CRC) on follow-up, age and sex.CNAs associated with adenoma-to-carcinoma progression were observed in 85/267 (32%) of advanced adenomas and in 27/262 (10%) of non-advanced adenomas. me-CRC was statistically significantly associated, also after adjustment for other variables, with age at baseline [OR, 1.14; 95% confidence interval CI), 1.03-1.26; P = 0.012], advanced adenomas (OR, 2.46; 95% CI, 1.50-4.01; P < 0.001) and with the presence of ≥3 DNA copy-number losses (OR, 1.90; 95% CI. 1.02-3.54; P = 0.043).Molecularly-defined high-risk adenomas were associated with me-CRC, but the association of advanced adenoma with me-CRC was stronger. SIGNIFICANCE: Identifying new biomarkers may improve prediction of me-CRC for individuals with adenomas and optimize surveillance intervals to reduce risk of colorectal cancer and reduce oversurveillance of patients with low risk of colorectal cancer. Use of DNA CNAs alone does not improve prediction of me-CRC. Further research to improve risk classification is required.

Diversity in the pathway from medical student to specialist in the Netherlands: a retrospective cohort study.

Background: Medical specialist workforces are not representative of the society they serve, partially due to loss of diversity in the path from student to specialist. We investigated which demographic characteristics of bachelor students of medicine (BSM) are associated with becoming a physician and (particular type of) medical specialist; and whether this suggests 'cloning' (reproduction of sameness) of the existing workforce. Methods: We used a retrospective cohort design, based on Statistics Netherlands data of all first-year BSM in 2002-2004 in The Netherlands (N = 4503). We used logistic regression to analyze the impact of sex, migration background, urbanity of residence, parental income and assets categories, and having healthcare professional parents, on being registered as physician or medical specialist in 2021. We compared our results to the national pool of physicians (N = 76,845) and medical specialists (N = 49,956) to identify cloning patterns based on Essed's cultural cloning theory. Findings: Female students had higher odds of becoming a physician (OR 1.87 [1.53-2.28], p < 0.001). Physicians with a migration background other than Turkish, Moroccan, Surinamese, Dutch Caribbean or Indonesian (TMSDI) had lower odds of becoming a specialist (OR 0.55 [0.43-0.71], p < 0.001). This was not significant for TMSDI physicians (OR 0.74 [0.54-1.03], p = 0.073). We found a cloning pattern with regard to sex and migration background. Nationwide, physicians with a Turkish or Moroccan migration background, and female physicians with other migration backgrounds, are least likely to be a medical specialist. Interpretation: In light of equity in healthcare systems, we recommend that every recruitment body increases the representativeness of their particular specialist workforce. Funding: ODISSEI.

Inequality of opportunity in selection procedures limits diversity in higher education: An intersectional study of Dutch selective higher education programs.

Selection for higher education (HE) programs may hinder equal opportunities for applicants and thereby reduce student diversity and representativeness. However, variables which could play a role in inequality of opportunity are often studied separately from each other. Therefore, this retrospective cohort study conducts an innovative intersectional analysis of the inequality of opportunity in admissions to selective HE programs. Using a combination of multivariable logistic regression analyses and descriptive statistics, we aimed to investigate 1) the representativeness of student populations of selective HE programs, as compared to both the applicant pool and the demographics of the age cohort; 2) the demographic background variables which are associated with an applicant's odds of admission; and 3) the intersectional acceptance rates of applicants with all, some or none of the background characteristics positively associated with odds of admission. The study focused on all selective HE programs (n = 96) in The Netherlands in 2019 and 2020, using Studielink applicant data (N = 85,839) and Statistics Netherlands microdata of ten background characteristics. The results show that student diversity in selective HE programs is limited, partly due to the widespread inequality of opportunity in the selection procedures, and partly due to self-selection. Out of all ten variables, migration background was most often (negatively) associated with the odds of receiving an offer of admission. The intersectional analyses provide detailed insight into how (dis)advantage has different effects for different groups. We therefore recommend the implementation of equitable admissions procedures which take intersectionality into account.

A progressive three-state model to estimate time to cancer: a likelihood-based approach.

BACKGROUND: To optimize colorectal cancer (CRC) screening and surveillance, information regarding the time-dependent risk of advanced adenomas (AA) to develop into CRC is crucial. However, since AA are removed after diagnosis, the time from AA to CRC cannot be observed in an ethically acceptable manner. We propose a statistical method to indirectly infer this time in a progressive three-state disease model using surveillance data. METHODS: Sixteen models were specified, with and without covariates. Parameters of the parametric time-to-event distributions from the adenoma-free state (AF) to AA and from AA to CRC were estimated simultaneously, by maximizing the likelihood function. Model performance was assessed via simulation. The methodology was applied to a random sample of 878 individuals from a Norwegian adenoma cohort. RESULTS: Estimates of the parameters of the time distributions are consistent and the 95% confidence intervals (CIs) have good coverage. For the Norwegian sample (AF: 78%, AA: 20%, CRC: 2%), a Weibull model for both transition times was selected as the final model based on information criteria. The mean time among those who have made the transition to CRC since AA onset within 50 years was estimated to be 4.80 years (95% CI: 0; 7.61). The 5-year and 10-year cumulative incidence of CRC from AA was 13.8% (95% CI: 7.8%;23.8%) and 15.4% (95% CI: 8.2%;34.0%), respectively. CONCLUSIONS: The time-dependent risk from AA to CRC is crucial to explain differences in the outcomes of microsimulation models used for the optimization of CRC prevention. Our method allows for improving models by the inclusion of data-driven time distributions.

Selection for health professions education leads to increased inequality of opportunity and decreased student diversity in The Netherlands, but lottery is no solution: A retrospective multi-cohort study.

BACKGROUND: Concerns exist about the role of selection in the lack of diversity in health professions education (HPE). In The Netherlands, the gradual transition from weighted lottery to selection allowed for investigating the variables associated with HPE admission, and whether the representativeness of HPE students has changed. METHOD: We designed a retrospective multi-cohort study using Statistics Netherlands microdata of all 16-year-olds on 1 October 2008, 2012, and 2015 (age cohorts, N > 600,000) and investigated whether they were eligible students for HPE programs (n > 62,000), had applied (n > 14,000), and were HPE students at age 19 (n > 7500). We used multivariable logistic regression to investigate which background variables were associated with becoming an HPE student. RESULTS: HPE students with ≥1 healthcare professional (HP) parent, ≥1 top-10% income/wealth parent, and women are overrepresented compared to all age cohorts. During hybrid lottery/selection (cohort-2008), applicants with ≥1 top-10% wealth parent and women had higher odds of admission. During 100% selection (cohort-2015) this remained the case. Additionally, applicants with ≥1 HP parent had higher odds, those with a migration background had lower odds. CONCLUSIONS: Odds of admission are increasingly influenced by applicants' backgrounds. Targeted recruitment and equitable admissions procedures are required to increase matriculation of underrepresented students.