Following our recent literature review, Construction and use of body weight measures from administrative data in a large national health system: A system review (In Review), in which 492 published documents were examined and 39 subsequently analyzed, we provide a more in-depth analysis of 33(133) weight cleaning algorithms (7(1,5,10,18,28,31,32) of 39 did not have an appropriate algorithm for inclusion). We have then chosen 12 algorithms representing the diversity of methods from the literature, while eliminating redundancies:

  • Janney 2016(20)
  • Littman 2012(24)
  • Maciejewski 2016(25)
  • Breland 2017(7)
  • Maguen 2013(26)
  • Goodrich 2016(14)
  • Chan 2017(9)
  • Jackson 2015(19)
  • Buta 2018(8)
  • Kazerooni 2016(22)
  • Noel 2012(27)
  • Rosenberger 2011(30)

Weight samples come from the US Dept. of Veteran’s Affairs Corporate Data Warehouse and the algorithms to deal with these data are specific to the source. However, some could be used regardless of the source as long as the data structure is similar1.

NOTE: algorithms here written in R rely on functions from these libraries c(“dplyr”, “magrittr”, “data.table”, “lazyeval”)


1. Adams CE, Gabriele JM, Baillie LE, et al. Tobacco use and substance use disorders as predictors of postoperative weight loss 2 years after bariatric surgery. The Journal of Behavioral Health Services & Research [electronic article]. 2012;39(4):462–471. ( (Accessed December 6, 2019)

5. Bounthavong M, Tran JN, Golshan S, et al. Retrospective cohort study evaluating exenatide twice daily and long-acting insulin analogs in a veterans health administration population with type 2 diabetes. Diabetes & Metabolism [electronic article]. 2014;40(4):284–291. ( (Accessed December 6, 2019)

7. Breland JY, Phibbs CS, Hoggatt KJ, et al. The obesity epidemic in the veterans health administration: Prevalence among key populations of women and men veterans. Journal of General Internal Medicine [electronic article]. 2017;32(S1):11–17. ( (Accessed December 6, 2019)

8. Buta E, Masheb R, Gueorguieva R, et al. Posttraumatic stress disorder diagnosis and gender are associated with accelerated weight gain trajectories in veterans during the post-deployment period. Eating Behaviors [electronic article]. 2018;29:8–13. ( (Accessed December 6, 2019)

9. Chan SH, Raffa SD. Examining the dose–response relationship in the veterans health administration’s MOVE!® weight management program: A nationwide observational study. Journal of General Internal Medicine [electronic article]. 2017;32(S1):18–23. ( (Accessed December 6, 2019)

10. Copeland LA, Pugh MJ, Hicks PB, et al. Use of obesity-related care by psychiatric patients. 2012;63(3):7.

14. Goodrich DE, Klingaman EA, Verchinina L, et al. Sex differences in weight loss among veterans with serious mental illness: Observational study of a national weight management program. Women’s Health Issues [electronic article]. 2016;26(4):410–419. ( (Accessed December 6, 2019)

18. Ikossi DG, Maldonado JR, Hernandez-Boussard T, et al. Post-traumatic stress disorder (PTSD) is not a contraindication to gastric bypass in veterans with morbid obesity. Surgical Endoscopy [electronic article]. 2010;24(8):1892–1897. ( (Accessed December 6, 2019)

19. Jackson SL, Long Q, Rhee MK, et al. Weight loss and incidence of diabetes with the veterans health administration MOVE! Lifestyle change programme: An observational study. The Lancet Diabetes & Endocrinology [electronic article]. 2015;3(3):173–180. ( (Accessed December 9, 2019)

20. Janney CA, Kilbourne AM, Germain A, et al. The influence of sleep disordered breathing on weight loss in a national weight management program. Sleep [electronic article]. 2016;39(1):59–65. ( (Accessed December 6, 2019)

22. Kazerooni R, Lim J. Topiramate-associated weight loss in a veteran population. Military Medicine [electronic article]. 2016;181(3):283–286. ( (Accessed December 6, 2019)

24. Littman AJ, Damschroder LJ, Verchinina L, et al. National evaluation of obesity screening and treatment among veterans with and without mental health disorders. General Hospital Psychiatry [electronic article]. 2015;37(1):7–13. ( (Accessed December 6, 2019)

25. Maciejewski ML, Arterburn DE, Van Scoyoc L, et al. Bariatric surgery and long-term durability of weight loss. JAMA Surgery [electronic article]. 2016;151(11):1046. ( (Accessed December 6, 2019)

26. Maguen S, Madden E, Cohen B, et al. The relationship between body mass index and mental health among iraq and afghanistan veterans. Journal of General Internal Medicine [electronic article]. 2013;28(S2):563–570. ( (Accessed December 6, 2019)

27. Noël PH, Wang C-P, Bollinger MJ, et al. Intensity and duration of obesity-related counseling: Association with 5-year BMI trends among obese primary care patients. Obesity [electronic article]. 2012;20(4):773–782. ( (Accessed December 6, 2019)

28. Pandey N, Ashfaq SN, Dauterive EW, et al. Military sexual trauma and obesity among women veterans. Journal of Women’s Health [electronic article]. 2018;27(3):305–310. ( (Accessed December 6, 2019)

30. Rosenberger PH, Ning Y, Brandt C, et al. BMI trajectory groups in veterans of the iraq and afghanistan wars. Preventive Medicine [electronic article]. 2011;53(3):149–154. ( (Accessed December 6, 2019)

31. Rutledge T, Braden AL, Woods G, et al. Five-year changes in psychiatric treatment status and weight-related comorbidities following bariatric surgery in a veteran population. Obesity Surgery [electronic article]. 2012;22(11):1734–1741. ( (Accessed December 6, 2019)

32. Shi L, Zhao Y, Szymanski K, et al. Impact of thiazolidinedione safety warnings on medication use patterns and glycemic control among veterans with diabetes mellitus. Journal of Diabetes and its Complications [electronic article]. 2011;25(3):143–150. ( (Accessed December 6, 2019)

33. Xiao DY, Luo S, O’Brian K, et al. Weight change trends and overall survival in united states veterans with follicular lymphoma treated with chemotherapy. Leukemia & Lymphoma [electronic article]. 2017;58(4):851–858. ( (Accessed December 6, 2019)

  1. To be honest, most of these algorithms could be used for “cleaning” any continuous set data