Regional variations in medical trainee diet and nutrition counseling competencies: Machine learning-augmented propensity score analysis of a prospective multi-site cohort study
Anish Patnaik, Justin Tran, John W McWhorter, Helen Burks, Alexandra Ngo, Tu Dan Nguyen, Avni Mody, Laura Moore, Deanna M Hoelscher, Amber Dyer, Leah Sarris, Timothy Harlan, C Mark Chassay, Dominique Monlezun, Anish Patnaik, Justin Tran, John W McWhorter, Helen Burks, Alexandra Ngo, Tu Dan Nguyen, Avni Mody, Laura Moore, Deanna M Hoelscher, Amber Dyer, Leah Sarris, Timothy Harlan, C Mark Chassay, Dominique Monlezun
Abstract
Background: Medical professionals and students are inadequately trained to respond to rising global obesity and nutrition-related chronic disease epidemics, primarily focusing on cardiovascular disease. Yet, there are no multi-site studies testing evidence-based nutrition education for medical students in preventive cardiology, let alone establishing student dietary and competency patterns.
Methods: Cooking for Health Optimization with Patients (CHOP; NIH NCT03443635) was the first multi-national cohort study using hands-on cooking and nutrition education as preventive cardiology, monitoring and improving student diets and competencies in patient nutrition education. Propensity-score adjusted multivariable regression was augmented by 43 supervised machine learning algorithms to assess students outcomes from UT Health versus the remaining study sites.
Results: 3,248 medical trainees from 20 medical centers and colleges met study criteria from 1 August 2012 to 31 December 2017 with 60 (1.49%) being from UTHealth. Compared to the other study sites, trainees from UTHealth were more likely to consume vegetables daily (OR 1.82, 95%CI 1.04-3.17, p=0.035), strongly agree that nutrition assessment should be routine clinical practice (OR 2.43, 95%CI 1.45-4.05, p=0.001), and that providers can improve patients' health with nutrition education (OR 1.73, 95%CI 1.03-2.91, p=0.038). UTHealth trainees were more likely to have mastered 12 of the 25 competency topics, with the top three being moderate alcohol intake (OR 1.74, 95%CI 0.97-3.11, p=0.062), dietary fats (OR 1.26, 95%CI 0.57-2.80, p=0.568), and calories (OR 1.26, 95%CI 0.70-2.28, p=0.446).
Conclusion: This machine learning-augmented causal inference analysis provides the first results that compare medical students nationally in their diets and competencies in nutrition education, highlighting the results from UTHealth. Additional studies are required to determine which factors in the hands-on cooking and nutrition curriculum for UTHealth and other sites produce optimal student - and, eventually, preventive cardiology - outcomes when they educate patients in those classes.
Keywords: Machine learning; medical education; medical student; nutrition; public health.
Conflict of interest statement
Conflict of InterestThere are no conflicts of interest to report.
© International Association of Medical Science Educators 2020.
Source: PubMed