Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2018 (2018), Article ID 5051289, 10 pages
https://doi.org/10.1155/2018/5051289
Research Article

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

1The Goldring Center for Culinary Medicine, Tulane University School of Medicine, 300 N. Broad St., Suite 102, New Orleans, LA 70119, USA
2Tulane University School of Public Health & Tropical Medicine, New Orleans, LA, USA
3Texas Christian University, Fort Worth, TX, USA
4Texas College of Osteopathic Medicine, Fort Worth, TX, USA
5University of Texas School of Medicine in San Antonio, San Antonio, TX, USA
6Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
7Lake Erie College of Osteopathic Medicine, Arnot Ogden Medical Center, Erie, PA, USA
8Meharry Medical College, Nashville, TN, USA
9University of Illinois-Chicago College of Medicine, Chicago, IL, USA
10University of Colorado-Denver School of Medicine, Denver, CO, USA
11Western University of Health Sciences College of Osteopathic Medicine of the Pacific-Northwest, Lebanon, OR, USA
12University of Chicago Pritzker School of Medicine, Chicago, IL, USA
13Pennsylvania State University College of Medicine, Hershey, PA, USA

Correspondence should be addressed to Dominique J. Monlezun; ude.enalut@uzelnomd

Received 15 December 2017; Accepted 28 February 2018; Published 15 April 2018

Academic Editor: Abdelaziz M. Thabet

Copyright © 2018 Dominique J. Monlezun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. I. I. Abubakar, T. Tillmann, and A. Banerjee, Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global, Lancet, 2015.
  2. D. Lloyd-Jones, R. J. Adams, T. M. Brown et al., “Heart disease and stroke statistics--2010 update: a report from the American Heart Association,” Circulation, vol. 121, no. 7, pp. e46–e215, 2010. View at Google Scholar
  3. RTI International: “Cardiovascular disease: A costly burden for America. Projections through 2035.” American Heart Association , 2017.
  4. P. A. Heidenreich, J. G. Trogdon, O. A. Khavjou et al., “Forecasting the future of cardiovascular disease in the United States: a policy statement from the American Heart Association,” Circulation, vol. 123, no. 8, pp. 933–944, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. A. V. Mattioli, P. Palmiero, O. Manfrini et al., “Mediterranean diet impact on cardiovascular diseases,” Journal of Cardiovascular Medicine, vol. 18, no. 12, pp. 925–935, 2017. View at Publisher · View at Google Scholar
  6. L. R. Pool, H. Ning, D. M. Lloyd‐Jones, and N. B. Allen, “Trends in Racial/Ethnic Disparities in Cardiovascular Health Among US Adults From 1999–2012,” Journal of the American Heart Association, vol. 6, no. 9, p. e006027, 2017. View at Publisher · View at Google Scholar
  7. Centers for Medicare and Medicaid Services, National health expenditure fact sheet, 2017.
  8. T. Laveist, D. Gaskin, and P. Richard, “Estimating the economic burden of racial health inequalities in the United States,” International Journal of Health Services, vol. 41, no. 2, pp. 231–238, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. R. Kahn, R. M. Robertson, R. Smith, and D. Eddy, “The impact of prevention on reducing the burden of cardiovascular disease,” Diabetes Care, vol. 31, no. 8, pp. 1686–1696, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. T. B. Huedo-Medina, M. Garcia, J. D. Bihuniak, A. Kenny, and J. Kerstetter, “Methodologic quality of meta-analyses and systematic reviews on the Mediterranean diet and cardiovascular disease outcomes: A review,” American Journal of Clinical Nutrition, vol. 103, no. 3, pp. 841–850, 2016. View at Publisher · View at Google Scholar · View at Scopus
  11. E. Koloverou, K. Esposito, D. Giugliano, and D. Panagiotakos, “The effect of Mediterranean diet on the development of type 2 diabetes mellitus: a meta-analysis of 10 prospective studies and 136,846 participants,” Metabolism - Clinical and Experimental, vol. 63, no. 7, pp. 903–911, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. O. Ajala, P. English, and J. Pinkney, “Systematic review and meta-analysis of different dietary approaches to the management of type 2 diabetes1-3,” American Journal of Clinical Nutrition, vol. 97, no. 3, pp. 505–516, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. F. Sofi, F. Cesari, R. Abbate, G. F. Gensini, and A. Casini, “Adherence to Mediterranean diet and health status: meta-analysis,” British Medical Journal, vol. 337, article a1344, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. A. Trichopoulou, T. Costacou, C. Bamia, and D. Trichopoulos, “Adherence to a Mediterranean diet and survival in a Greek population,” The New England Journal of Medicine, vol. 348, no. 26, pp. 2599–2608, 2003. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Estruch, E. Ros, J. Salas-Salvadó et al., “Primary prevention of cardiovascular disease with a Mediterranean diet,” The New England Journal of Medicine, vol. 368, no. 14, pp. 1279–1290, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Fitó, M. Guxens, D. Corella et al., “Effect of a traditional Mediterranean diet on lipoprotein oxidation: A randomized controlled trial,” JAMA Internal Medicine, vol. 167, no. 11, pp. 1195–1203, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. A. W. Smith, L. A. Borowski, B. Liu et al., “U.S. primary care physicians' diet-, physical activity—, and weight-related care of adult patients,” American Journal of Preventive Medicine, vol. 41, no. 1, pp. 33–42, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. D. J. Frantz, S. A. McClave, R. T. Hurt, K. Miller, and R. G. Martindale, “Cross-sectional study of U.S. interns' perceptions of clinical nutrition education,” Journal of Parenteral and Enteral Nutrition, 2015. View at Publisher · View at Google Scholar
  19. S. A. Wartman and C. D. Combs, “Medical Education Must Move from the Information Age to the Age of Artificial Intelligence,” Academic Medicine: Journal of the Association of American Medical Colleges, p. 1, 2018. View at Publisher · View at Google Scholar
  20. IBM., Preparing for the future of artificial intelligence: IBM response to the White House Office of Science and Technology’s Policy Request for information, IBM, 2016.
  21. J. H. Chen and S. M. Asch, “Machine learning and prediction in medicine-beyond the peak of inflated expectations,” The New England Journal of Medicine, vol. 376, no. 26, pp. 2507–2509, 2017. View at Publisher · View at Google Scholar · View at Scopus
  22. Z. Obermeyer and E. J. Emanuel, “Predicting the future-big data, machine learning, and clinical medicine,” The New England Journal of Medicine, vol. 375, no. 13, pp. 1216–1219, 2016. View at Publisher · View at Google Scholar · View at Scopus
  23. M. J. Khoury and S. Galea, “Will precision medicine improve population health?” Journal of the American Medical Association, vol. 316, no. 13, pp. 1357-1358, 2016. View at Publisher · View at Google Scholar · View at Scopus
  24. P. J. Wallace, N. D. Shah, T. Dennen, P. A. Bleicher, and W. H. Crown, “Optum labs: Building a novel node in the learning health care system,” Health Affairs, vol. 33, no. 7, pp. 1187–1194, 2014. View at Publisher · View at Google Scholar · View at Scopus
  25. L. Lessard, W. Michalowski, M. Fung-Kee-Fung, L. Jones, and A. Grudniewicz, “Architectural frameworks: Defining the structures for implementing learning health systems,” Implementation Science, vol. 12, no. 1, article no. 78, 2017. View at Publisher · View at Google Scholar · View at Scopus
  26. K. O. Lewis, G. R. Frank, R. Nagel et al., “Pediatric trainees' engagement in the online nutrition curriculum: preliminary results,” BMC Medical Education, vol. 14, no. 1, article 190, 2014. View at Publisher · View at Google Scholar · View at Scopus
  27. S. Ray, R. Udumyan, M. Rajput-Ray et al., “Evaluation of a novel nutrition education intervention for medical students from across England,” BMJ Open, vol. 2, Article ID e000417, 2012. View at Publisher · View at Google Scholar · View at Scopus
  28. A. Afaghi, A. A. H. A. Mohamadi, A. Ziaee, and R. Sarchami, “Effect of an integrated case-based nutrition curriculum on medical education at Qazvin University of Medical Sciences, Iran,” Global Journal of Health Science, vol. 4, no. 1, pp. 112–117, 2012. View at Google Scholar · View at Scopus
  29. P. Barss, M. Grivna, F. Al-Maskari, and G. Kershaw, “Strengthening public health medicine training for medical students: development and evaluation of a lifestyle curriculum,” Medical Teacher, vol. 30, no. 9-10, pp. e196–e218, 2008. View at Publisher · View at Google Scholar · View at Scopus
  30. S. Schlair, K. Hanley, C. Gillespie et al., “How medical students’ behaviors and attitudes affect the impact of a brief curriculum on nutrition counseling,” Journal of Nutrition Education and Behavior, vol. 44, no. 6, pp. 653–657, 2012. View at Publisher · View at Google Scholar · View at Scopus
  31. M. B. Conroy, H. K. Delichatsios, J. P. Hafler, and N. A. Rigotti, “Impact of a preventive medicine and nutrition curriculum for medical students,” American Journal of Preventive Medicine, vol. 27, no. 1, pp. 77–80, 2004. View at Publisher · View at Google Scholar · View at Scopus
  32. M. Kohlmeier, W. J. McConathy, K. Cooksey Lindell, and S. H. Zeisel, “Adapting the contents of computer-based instruction based on knowledge tests maintains effectiveness of nutrition education,” American Journal of Clinical Nutrition, vol. 77, no. 4, pp. 1025S–1027S, 2003. View at Publisher · View at Google Scholar
  33. P. Lebensohn, B. Kligler, S. Dodds et al., “Integrative medicine in residency education: developing competency through online curriculum training,” Journal of Graduate Medical Education, vol. 4, no. 1, pp. 76–82, 2012. View at Publisher · View at Google Scholar
  34. D. J. Monlezun, B. Leong, E. Joo, A. G. Birkhead, L. Sarris, and T. S. Harlan, “Novel longitudinal and propensity score matched analysis of hands-on cooking and nutrition education versus traditional clinical education among 627 medical students,” Advances in Preventive Medicine, vol. 2015, pp. 1–8, 2015. View at Publisher · View at Google Scholar
  35. Liaison Committee on Medical Education: “Functions and structure of a medical school: Accreditation and the Liaison Committee on Medical Education: Standards for accreditation of medical education programs leading to the M.D. degree.” Association of American Medical Colleges , 2016.
  36. K. M. Adams, M. Kohlmeier, and S. H. Zeisel, “Nutrition education in U.S. medical schools: latest update of a national survey,” Academic Medicine: Journal of the Association of American Medical Colleges, vol. 85, no. 9, pp. 1537–1542, 2010. View at Publisher · View at Google Scholar · View at Scopus
  37. W. C. McGaghie, S. B. Issenberg, E. R. Cohen, J. H. Barsuk, and D. B. Wayne, “Does simulation-based medical education with deliberate practice yield better results than traditional clinical education? A meta-analytic comparative review of the evidence,” Academic Medicine: Journal of the Association of American Medical Colleges, vol. 86, no. 6, pp. 706–711, 2011. View at Publisher · View at Google Scholar · View at Scopus
  38. A. Garcia-Arce, F. Rico, and J. L. Zayas-Castro, “Comparison of Machine Learning Algorithms for the Prediction of Preventable Hospital Readmissions,” Journal for Healthcare Quality, p. 1, 2018. View at Publisher · View at Google Scholar
  39. M. J. Funk, D. Westreich, C. Wiesen, T. Stürmer, M. A. Brookhart, and M. Davidian, “Doubly robust estimation of causal effects,” American Journal of Epidemiology, vol. 173, no. 7, pp. 761–767, 2011. View at Publisher · View at Google Scholar · View at Scopus
  40. A. Yazdani and E. Boerwinkle, “Causal Inference in the Age of Decision Medicine,” Journal of Data Mining in Genomics & Proteomics, vol. 06, no. 01, 2015. View at Publisher · View at Google Scholar
  41. M. Shardell and L. Ferrucci, “Joint mixed-effects models for causal inference with longitudinal data,” Statistics in Medicine, vol. 37, no. 5, pp. 829–846, 2018. View at Publisher · View at Google Scholar
  42. F. I. Gunasekara, K. Richardson, K. Carter, and T. Blakely, “Fixed effects analysis of repeated measures data,” International Journal of Epidemiology, vol. 43, no. 1, pp. 264–269, 2014. View at Publisher · View at Google Scholar · View at Scopus
  43. E. A. Stuart, “Matching methods for causal inference: A review and a look forward,” Statistical Science, vol. 25, no. 1, pp. 1–21, 2010. View at Publisher · View at Google Scholar · View at Scopus
  44. B. Koch, D. M. Vock, and J. Wolfson, “Covariate selection with group lasso and doubly robust estimation of causal effects,” Biometrics, 2017. View at Publisher · View at Google Scholar · View at Scopus
  45. A. Rotnitzky, J. M. Robins, and D. . Scharfstein, “Semiparametric regression for repeated outcomes with nonignorable nonresponse,” Journal of the American Statistical Association, vol. 93, no. 444, pp. 1321–1339, 1998. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  46. D. B. Rubin and N. Thomas, “Combining propensity score matching with additional adjustments for prognostic covariates,” Journal of the American Statistical Association, vol. 95, no. 450, pp. 573–585, 2000. View at Publisher · View at Google Scholar · View at Scopus
  47. M. C. Elze, J. Gregson, U. Baber et al., “Comparison of Propensity Score Methods and Covariate Adjustment: Evaluation in 4 Cardiovascular Studies,” Journal of the American College of Cardiology, vol. 69, no. 3, pp. 345–357, 2017. View at Publisher · View at Google Scholar · View at Scopus
  48. G.M. Verbeke: “G., 2000. Linear Mixed Models for Longitudinal Data.” New YorkSpringer.
  49. R. Chu, L. Thabane, J. Ma, A. Holbrook, E. Pullenayegum, and P. J. Devereaux, “Comparing methods to estimate treatment effects on a continuous outcome in multicentre randomized controlled trials: A simulation study,” BMC Medical Research Methodology, vol. 11, article no. 21, 2011. View at Publisher · View at Google Scholar · View at Scopus
  50. J. L. Dieleman and T. Templin, “Random-effects, fixed-effects and the within-between specification for clustered data in observational health studies: a simulation study,” PLoS ONE, vol. 9, no. 10, Article ID e110257, 2014. View at Publisher · View at Google Scholar · View at Scopus
  51. J. D. Y. Kang and J. L. Schafer, “Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data,” Statistical Science, vol. 22, no. 4, pp. 523–539, 2007. View at Publisher · View at Google Scholar · View at Scopus
  52. J. P. T. Higgins, S. G. Thompson, J. J. Deeks, and D. G. Altman, “Measuring inconsistency in meta-analyses,” British Medical Journal, vol. 327, no. 7414, pp. 557–560, 2003. View at Publisher · View at Google Scholar · View at Scopus
  53. L. P. StataCorp, Stata multilevel mixed-effects Reference manual, StataCorp LP, College Station, TX, 2013.
  54. P. R. Rosenbaum and D. B. Rubin, “The central role of the propensity score in observational studies for causal effects,” Biometrika, vol. 70, no. 1, pp. 41–55, 1983. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  55. A. J. Streeter, N. X. Lin, L. Crathorne et al., “Adjusting for unmeasured confounding in nonrandomized longitudinal studies: a methodological review,” Journal of Clinical Epidemiology, vol. 87, pp. 23–34, 2017. View at Publisher · View at Google Scholar · View at Scopus
  56. A. S. Robbins, S. Y. Chao, and V. P. Fonseca, “What's the relative risk? A method to directly estimate risk ratios in cohort studies of common outcomes,” Annals of Epidemiology, vol. 12, no. 7, pp. 452–454, 2002. View at Publisher · View at Google Scholar · View at Scopus
  57. S. Greenland, D. C. Thomas, and H. Morgenstern, “The rare-disease assumption revisited: A CRITIQUE of "estimators OF relative RISK for case-control studies",” American Journal of Epidemiology, vol. 124, no. 6, pp. 869–876, 1986. View at Publisher · View at Google Scholar · View at Scopus
  58. D. J. Monlezun, E. Kasprowicz, and K. W. Tosh, “Medical school-based teaching kitchen improves HbA1c, blood pressure, and cholesterol for patients with type 2 diabetes: results from a novel randomized controlled trial,” Diabetes Research and Clinical Practice, vol. 109, no. 2, pp. 420–426, 2015. View at Publisher · View at Google Scholar
  59. R. Smith, ““Let food be thy medicine…”,” BMJ, vol. 328, no. 7433, p. 0, 2004. View at Publisher · View at Google Scholar · View at Scopus