Machine Learning-Augmented Propensity Score-Adjusted Multilevel Mixed Effects Panel Analysis of Hands-On Cooking and Nutrition Education versus Traditional Curriculum for Medical Students as Preventive Cardiology: Multisite Cohort Study of 3,248 Trainees over 5 Years

• Main Authors: Dominique J. Monlezun, Lyn Dart, Anne Vanbeber, Peggy Smith-Barbaro, Vanessa Costilla, Charlotte Samuel, Carol A. Terregino, Emine Ercikan Abali, Beth Dollinger, Nicole Baumgartner, Nicholas Kramer, Alex Seelochan, Sabira Taher, Mark Deutchman, Meredith Evans, Robert B. Ellis, Sonia Oyola, Geeta Maker-Clark, Tomi Dreibelbis, Isadore Budnick, David Tran, Nicole DeValle, Rachel Shepard, Erika Chow, Christine Petrin, Alexander Razavi, Casey McGowan, Austin Grant, Mackenzie Bird, Connor Carry, Glynis McGowan, Colleen McCullough, Casey M. Berman, Kerri Dotson, Tianhua Niu, Leah Sarris, Timothy S. Harlan, on behalf of the CHOP Co-investigators
• Format: Article
• Language: English
• Published: Hindawi Limited 2018-01-01
• Series: BioMed Research International
• Online Access: http://dx.doi.org/10.1155/2018/5051289

Background. Cardiovascular disease (CVD) annually claims more lives and costs more dollars than any other disease globally amid widening health disparities, despite the known significant reductions in this burden by low cost dietary changes. The world’s first medical school-based teaching kitchen therefore launched CHOP-Medical Students as the largest known multisite cohort study of hands-on cooking and nutrition education versus traditional curriculum for medical students. Methods. This analysis provides a novel integration of artificial intelligence-based machine learning (ML) with causal inference statistics. 43 ML automated algorithms were tested, with the top performer compared to triply robust propensity score-adjusted multilevel mixed effects regression panel analysis of longitudinal data. Inverse-variance weighted fixed effects meta-analysis pooled the individual estimates for competencies. Results. 3,248 unique medical trainees met study criteria from 20 medical schools nationally from August 1, 2012, to June 26, 2017, generating 4,026 completed validated surveys. ML analysis produced similar results to the causal inference statistics based on root mean squared error and accuracy. Hands-on cooking and nutrition education compared to traditional medical school curriculum significantly improved student competencies (OR 2.14, 95% CI 2.00–2.28, p<0.001) and MedDiet adherence (OR 1.40, 95% CI 1.07–1.84, p=0.015), while reducing trainees’ soft drink consumption (OR 0.56, 95% CI 0.37–0.85, p=0.007). Overall improved competencies were demonstrated from the initial study site through the scale-up of the intervention to 10 sites nationally (p<0.001). Discussion. This study provides the first machine learning-augmented causal inference analysis of a multisite cohort showing hands-on cooking and nutrition education for medical trainees improves their competencies counseling patients on nutrition, while improving students’ own diets. This study suggests that the public health and medical sectors can unite population health management and precision medicine for a sustainable model of next-generation health systems providing effective, equitable, accessible care beginning with reversing the CVD epidemic.