Literature DB >> 34457749

Regional variations in medical trainee diet and nutrition counseling competencies: Machine learning-augmented propensity score analysis of a prospective multi-site cohort study.

Anish Patnaik1, Justin Tran1, John W McWhorter2,3, Helen Burks1, Alexandra Ngo1, Tu Dan Nguyen1, Avni Mody1, Laura Moore2,3, Deanna M Hoelscher2,3, Amber Dyer4, Leah Sarris4, Timothy Harlan4, C Mark Chassay1, Dominique Monlezun1,4.   

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. © International Association of Medical Science Educators 2020.

Entities:  

Keywords:  Machine learning; medical education; medical student; nutrition; public health

Year:  2020        PMID: 34457749      PMCID: PMC8368255          DOI: 10.1007/s40670-020-00973-6

Source DB:  PubMed          Journal:  Med Sci Educ        ISSN: 2156-8650


  31 in total

1.  What's the relative risk? A method to directly estimate risk ratios in cohort studies of common outcomes.

Authors:  Anthony S Robbins; Susan Y Chao; Vincent P Fonseca
Journal:  Ann Epidemiol       Date:  2002-10       Impact factor: 3.797

2.  Matching methods for causal inference: A review and a look forward.

Authors:  Elizabeth A Stuart
Journal:  Stat Sci       Date:  2010-02-01       Impact factor: 2.901

3.  Strengthening public health medicine training for medical students: development and evaluation of a lifestyle curriculum.

Authors:  Peter Barss; Michal Grivna; Fatma Al-Maskari; Geraldine Kershaw
Journal:  Med Teach       Date:  2008       Impact factor: 3.650

4.  Heart disease and stroke statistics--2010 update: a report from the American Heart Association.

Authors:  Donald Lloyd-Jones; Robert J Adams; Todd M Brown; Mercedes Carnethon; Shifan Dai; Giovanni De Simone; T Bruce Ferguson; Earl Ford; Karen Furie; Cathleen Gillespie; Alan Go; Kurt Greenlund; Nancy Haase; Susan Hailpern; P Michael Ho; Virginia Howard; Brett Kissela; Steven Kittner; Daniel Lackland; Lynda Lisabeth; Ariane Marelli; Mary M McDermott; James Meigs; Dariush Mozaffarian; Michael Mussolino; Graham Nichol; Véronique L Roger; Wayne Rosamond; Ralph Sacco; Paul Sorlie; Véronique L Roger; Randall Stafford; Thomas Thom; Sylvia Wasserthiel-Smoller; Nathan D Wong; Judith Wylie-Rosett
Journal:  Circulation       Date:  2009-12-17       Impact factor: 29.690

5.  The rare-disease assumption revisited. A critique of "estimators of relative risk for case-control studies".

Authors:  S Greenland; D C Thomas; H Morgenstern
Journal:  Am J Epidemiol       Date:  1986-12       Impact factor: 4.897

6.  Comparison of Machine Learning Algorithms for the Prediction of Preventable Hospital Readmissions.

Authors:  Andres Garcia-Arce; Florentino Rico; José L Zayas-Castro
Journal:  J Healthc Qual       Date:  2018 May/Jun       Impact factor: 1.095

Review 7.  The effect of Mediterranean diet on the development of type 2 diabetes mellitus: a meta-analysis of 10 prospective studies and 136,846 participants.

Authors:  Efi Koloverou; Katherine Esposito; Dario Giugliano; Demosthenes Panagiotakos
Journal:  Metabolism       Date:  2014-04-24       Impact factor: 8.694

8.  Effect of a traditional Mediterranean diet on lipoprotein oxidation: a randomized controlled trial.

Authors:  Montserrat Fitó; Mònica Guxens; Dolores Corella; Guillermo Sáez; Ramón Estruch; Rafael de la Torre; Francesc Francés; Carmen Cabezas; María Del Carmen López-Sabater; Jaume Marrugat; Ana García-Arellano; Fernando Arós; Valentina Ruiz-Gutierrez; Emilio Ros; Jordi Salas-Salvadó; Miquel Fiol; Rosa Solá; María-Isabel Covas
Journal:  Arch Intern Med       Date:  2007-06-11

9.  Causal Inference in the Age of Decision Medicine.

Authors:  A Yazdani; E Boerwinkle
Journal:  J Data Mining Genomics Proteomics       Date:  2015-01

10.  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.

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
Journal:  Biomed Res Int       Date:  2018-04-15       Impact factor: 3.411

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  1 in total

Review 1.  Exploring culinary medicine as a promising method of nutritional education in medical school: a scoping review.

Authors:  Jacqueline Tan; Levi Atamanchuk; Tanish Rao; Kenichi Sato; Jennifer Crowley; Lauren Ball
Journal:  BMC Med Educ       Date:  2022-06-07       Impact factor: 3.263

  1 in total

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