A Markov Decision Process Approach to Estimate the Risk of Obesity Related Cancers

  • Emine YaylaliEmail author
  • Umut Karamustafa
Conference paper
Part of the Lecture Notes in Management and Industrial Engineering book series (LNMIE)


Around 13% of the world’s adult population was obese in 2016 and the prevalence of obesity increased at a significant rate in the last decade (Deitel in Obes Surg 13:329–330, 2003). One of the health consequences of obesity is an increased cancer risk. In this study, we model obesity levels based on BMI, cancer, and death using a Markov decision process model in order to observe the effect of obesity on cancer and mortality risks. The objective of the model is total discounted quality adjusted life years and we simulate an individual’s lifetime from 20 to 70 years by sex. Actions available to the decision makers are no intervention and bariatric surgery. Bariatric surgery is one of the effective clinical prevention methods of obesity and it is particularly recommended for morbidly obese patients. However, it is also associated with increased mortality risk. Our model aims to observe this complex dynamic between obesity, cancer and mortality risks and bariatric surgery. We parametrize the model using randomized clinical trials and published literature and obtain the optimal policy by sex. Our results suggest that obese patients for all obesity levels should undergo bariatric surgery to improve their health outcomes and to decrease cancer risk. This study has the potential to provide guidance to the obese individuals when considering bariatric surgery and it could be further enhanced by the addition of other health outcomes of obesity to the model.


Medical decision making Markov decision process Stochastic modeling Cancer Obesity 


  1. American Association for Cancer Research. (2017). Cancer progress report. Retrieved 01/02/2018, from
  2. Ayer, T., Alagoz, O., & Stout, N. K. (2012). OR forum—A POMDP approach to personalize mammography screening decisions. Operations Research, 60(5), 1019–1034.MathSciNetCrossRefGoogle Scholar
  3. Basen-Engquist, K., & Chang, M. (2011). Obesity and cancer risk: Recent review and evidence. Current Oncology Reports, 13(1), 71–76.CrossRefGoogle Scholar
  4. Buchwald, H., Avidor, Y., Braunwald, E, Jensen, M. D., Pories, W., et al. (2004). Bariatric surgery: A systematic review and meta-analysis. JAMA, 292(14), 1724–1737.CrossRefGoogle Scholar
  5. Buchwald, H., & Oien, D. M. (2013). Metabolic/bariatric surgery worldwide 2011. Obesity Surgery, 23(4), 427–436.CrossRefGoogle Scholar
  6. Cancer Research UK. (2018). Cancer survival statistics for all cancers combined. Retrieved 01/02/2018, from
  7. Deitel, M. (2003). Overweight and obesity worldwide now estimated to involve 1.7 billion people. Obesity Surgery, 13(3), 329–330.CrossRefGoogle Scholar
  8. Ekwaru, J. P., Ohinmaa, A., Tran, B. X., Setayeshgar, S., Johnson, J. A., & Veugelers, P. J. (2017). Cost-effectiveness of a school-based health promotion program in Canada: A life-course modeling approach. PLoS ONE, 12(5), e0177848.CrossRefGoogle Scholar
  9. Fildes, A., Charlton, J., Rudisill, C., Littlejohns, P., Prevost, A. T., Gulliford, M. C., et al. (2015). Probability of an obese person attaining normal body weight: Cohort study using electronic health records. American Journal of Public Health, 105(9), e54–e59.CrossRefGoogle Scholar
  10. Gold, M. R., Siegel, J. E., Russell, L. B., Weinstein, M. C. (1996). Cost-effectiveness in health and medicine. USA: Oxford University Press.Google Scholar
  11. Hammond, R. A. (2009). Peer reviewed: Complex systems modeling for obesity research. Preventing Chronic Disease, 6(3).Google Scholar
  12. Inge, T. H., Inge, T. H., Jenkins, T. M., Zeller, M., Dolan, L., Daniels, S. R., et al. (2010). Baseline BMI is a strong predictor of nadir BMI after adolescent gastric bypass. The Journal of Pediatrics, 156(1): 103–108. e101.CrossRefGoogle Scholar
  13. Leshno, M., Halpern, Z., & Arber, N. (2003). Cost-effectiveness of colorectal cancer screening in the average risk population. Health Care Management Science, 6(3), 165–174.CrossRefGoogle Scholar
  14. Livingston, E. H., & Ko, C. Y. (2002). Use of the health and activities limitation index as a measure of quality of life in obesity. Obesity, 10(8), 824–832.CrossRefGoogle Scholar
  15. Mayer-Davis, E. J., Lawrence, J. M., Dabelea, D., Divers, J., Isom, S., Dolan, L., et al. (2017). Incidence trends of type 1 and type 2 diabetes among youths, 2002–2012. New England Journal of Medicine, 376(15), 1419–1429.CrossRefGoogle Scholar
  16. Michaelson, R., Murphy, D. K., Gross, T. M., Whitcup, S. M., & LAP‐BAND® Lower BMI Study Group. (2013). LAP-BAND® for lower BMI: 2-year results from the multicenter pivotal study. Obesity, 21(6), 1148–1158.CrossRefGoogle Scholar
  17. National Center for Health Statistics. (2017). Health, United States, US Department of Health, Education, and Welfare, Public Health Service, Health Resources Administration, National Center for Health Statistics.Google Scholar
  18. Omalu, B. I., Ives, D. G., Buhari, A. M., Lindner, J. L., Schauer, P. R., Wecht, C. H., et al. (2007). Death rates and causes of death after bariatric surgery for Pennsylvania residents, 1995 to 2004. Archives of Surgery, 142(10), 923–928.CrossRefGoogle Scholar
  19. Patterson, E. J., Urbach, D. R., Swanström, L. L. (2003). A comparison of diet and exercise therapy versus laparoscopic Roux-en-Y gastric bypass surgery for morbid obesity: A decision analysis model. Journal of the American College of Surgeons, 196(3), 379–384.CrossRefGoogle Scholar
  20. Puterman, M. L. (2014). Markov decision processes: Discrete stochastic dynamic programming. New York: Wiley.Google Scholar
  21. Schauer, D. P., Arterburn, D. E., Livingston, E. H., Fischer, D., Eckman, M. H., et al. (2010). Decision modeling to estimate the impact of gastric bypass surgery on life expectancy for the treatment of morbid obesity. Archives of Surgery, 145(1), 57–62.CrossRefGoogle Scholar
  22. Schauer, D. P., Feigelson, H. S., Koebnick, C., Caan, B., Weinmann, S., Leonard, A. C., et al. (2017). Association between weight loss and the risk of cancer after bariatric surgery. Obesity, 25(S2).CrossRefGoogle Scholar
  23. Siebert, U., Alagoz, O., Bayoumi, A. M., Jahn, B., Owens, D. K., Cohen, D. J., et al. (2012). State-transition modeling: A report of the ISPOR-SMDM modeling good research practices task force-3. Value in Health, 15(6), 812–820.CrossRefGoogle Scholar
  24. Sonntag, D., Jarczok, M. N., & Ali, S. (2017). DC-obesity: A new model for estimating differential lifetime costs of overweight and obesity by socioeconomic status. Obesity, 25(9), 1603–1609.CrossRefGoogle Scholar
  25. Steimle, L. N., & Denton, B. T. (2017). Markov decision processes for screening and treatment of chronic diseases (pp. 189–222). Markov Decision Processes in Practice: Springer.zbMATHGoogle Scholar
  26. Stroud, A. M., Parker, D., & Croitoru, D. P. (2016). Timing of bariatric surgery for severely obese adolescents: A Markov decision-analysis. Journal of Pediatric Surgery, 51(5), 853–858.CrossRefGoogle Scholar
  27. Su, W., Huang, J., Chen, F., Iacobucci, W., Mocarski, M., Dall, T. M., et al. (2015). Modeling the clinical and economic implications of obesity using microsimulation. Journal of medical economics, 18(11), 886–897.CrossRefGoogle Scholar
  28. The Kaiser Family Foundation’s State Health Facts. (2017). Underlying cause of death 1999–2016. Retrieved 01/02/2018, from
  29. Turrentine, F. E., Hanks, J. B., Schirmer, B. D., & Stukenborg, G. J. (2012). The relationship between body mass index and 30-day mortality risk, by principal surgical procedure. Archives of Surgery, 147(3), 236–242.CrossRefGoogle Scholar
  30. US Cancer Statistics Working Group, United States cancer statistics: 1999–2014 incidence and mortality web-based report [Internet]. Atlanta, GA: Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute; 2015 [cited 2017 May 23].Google Scholar
  31. Vrettos, I., Kamposioras, K., Kontodimopoulos, N., Pappa, E., Georgiadou, E., Haritos, D., et al. (2012). Comparing health-related quality of life of cancer patients under chemotherapy and of their caregivers. The Scientific World Journal, 2012.Google Scholar
  32. Weinstein, M. C., Siegel, J. E., Gold, M. R., Kamlet, M. S., Russell, L. B., et al. (1996). Recommendations of the panel on cost-effectiveness in health and medicine. JAMA, 276(15), 1253–1258.CrossRefGoogle Scholar
  33. Wiseman, M. (2008). The second world cancer research fund/American Institute for Cancer Research expert report. Food, nutrition, physical activity, and the prevention of cancer: a global perspective: Nutrition Society and BAPEN medical symposium on ‘Nutrition support in cancer therapy’. Proceedings of the Nutrition Society, 67(3), 253–256.CrossRefGoogle Scholar
  34. World Health Organization. (2011). Physical status: The use and interpretation of anthropometry. Geneva; 1995. WHO Technical Report Series, 854: 2009–2006.Google Scholar
  35. World Health Organization (2018). Cancer fact sheet. Retrieved 01/02/2018, from
  36. Zhang, J., Denton, B. T., Balasubramanian, H., Shah, N. D., & Inman, B. A. (2012). Optimization of prostate biopsy referral decisions. Manufacturing & Service Operations Management, 14(4), 529–547.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Industrial Engineering DepartmentIstanbul Technical UniversityIstanbulTurkey

Personalised recommendations