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Epidemiology and Population Health

Body mass index, waist circumference, waist-to-hip ratio, and body fat in relation to health care use in the Canadian Longitudinal Study on Aging



Obesity is associated with increased health care use (HCU), but it is unclear whether this is consistent across all measures of adiposity. The objectives were to compare obesity defined by body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), and percent body fat (%BF), and to estimate their associations with HCU.


Baseline data from 30,092 participants aged 45–85 years from the Canadian Longitudinal Study on Aging were included. Measures of adiposity were recorded by trained staff and obesity was defined as BMI ≥ 30.0 kg/m2 for all participants and WC ≥ 88 cm and ≥102 cm, WHR ≥ 0.85 and ≥0.90, and %BF > 35% and >25% (measured using dual energy x-ray absorptiometry) for females and males, respectively. Self-reported HCU in the past 12 months was collected for any contact with a general practitioner, specialist, emergency department, and hospitalization. Pearson correlation coefficients (r) compared each measure to %BF-defined obesity, the reference standard. Relative risks (RR) and risk differences (RD) adjusted for age, sex, education, income, urban/rural, marital status, smoking status, and alcohol use were calculated, and results were age- and sex-stratified.


Obesity prevalence varied by measure: BMI (29%), WC (42%), WHR (62%), and %BF (73%). BMI and WC were highly correlated with %BF (r ≥ 0.70), while WHR demonstrated a weaker relationship with %BF, with differences by sex (r = 0.29 and r = 0.46 in females and males, respectively). There were significantly increased RR and RD for all measures and health care services, for example, WC-defined obesity was associated with an increased risk of hospitalization (RR: 1.40, 95% CI: 1.28–1.54 and RD per 100: 2.6, 95% CI:1.9–3.3). Age-stratified results revealed that older adult groups with obesity demonstrated weak or no associations with HCU.


All measures of adiposity were positively associated with increased HCU although obesity may not be a strong predictor of HCU in older adults.

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Fig. 1


  1. Finucane MM, Stevens GA, Cowan M, Danaei G, Lin JK, Paciorek CJ, et al. National, regional, and global trends in body mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9.1 million participants. Lancet. 2011;377:557–67.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Dixon JB. The effect of obesity on health outcomes. Mol Cell Endocrinol. 2010;316:104–8.

    Article  CAS  PubMed  Google Scholar 

  3. WHO. Obesity: preventing and managing the global epidemic: report of a WHO consultation [Internet]. Report No.: 984. WHO; 2000. p. 1–253.

  4. Health Canada. Canadian guidelines for body weight classification in adults [Internet]. Ottawa: Health Canada; 2003. Report No.: H49-179/2003E.

  5. Lau DCW, Douketis JD, Morrison KM, Hramiak IM, Sharma AM, Ur E, et al. 2006 Canadian clinical practice guidelines on the management and prevention of obesity in adults and children. Can Med Assoc J. 2007;176:S1–13.

    Article  Google Scholar 

  6. Zamboni M, Mazzali G, Zoico E, Harris TB, Meigs JB, Di Francesco V, et al. Health consequences of obesity in the elderly: a review of four unresolved questions. Int J Obes. 2005;29:1011–29.

    Article  CAS  Google Scholar 

  7. World Health Organization. Waist circumference and waist-hip ratio: report of a WHO expert consultation. Geneva: World Health Organization; 2011.

  8. Ahn S, Smith ML, Dickerson JB, Ory MG. Health and health care utilization among obese and diabetic baby boomers and older adults. Am J Health Promot. 2012;27:123–32.

    Article  PubMed  Google Scholar 

  9. Andreyeva T, Sturm R, Ringel JS. Moderate and severe obesity have large differences in health care costs. Obes Res. 2004;12:1936–43.

    Article  PubMed  Google Scholar 

  10. Bertakis KD, Azari R. Obesity and the use of health care services. Obes Res. 2005;13:372–9.

    Article  PubMed  Google Scholar 

  11. Chen Y, Jiang Y, Mao Y. Hospital admissions associated with body mass index in Canadian adults. International J Obes. 2007;31:962–7.

    Article  CAS  Google Scholar 

  12. Lengerke T, von, Happich M, Reitmeir P, John J, Kora Study Group. Utilization of out- and inpatient health services by obese adults: a population-based study in the Augsburg Region, Germany. Gesundheitswesen. 2005;67:150–7.

    Article  Google Scholar 

  13. León‐Muñoz LM, Guallar‐Castillón P, García EL, Banegas JR, Gutiérrez‐Fisac JL, Rodríguez‐Artalejo F. Relationship of BMI, waist circumference, and weight change with use of health services by older adults. Obes Res. 2005;13:1398–404.

    Article  PubMed  Google Scholar 

  14. Luchsinger JA, Lee W, Carrasquillo O, Rabinowitz D, Shea S. Body mass index and hospitalization in the elderly. J Am Geriatr Soc. 2003;51:1615–20.

    Article  PubMed  Google Scholar 

  15. Musich S, MacLeod S, Bhattarai GR, Wang SS, Hawkins K, Bottone FG, et al. The impact of obesity on health care utilization and expenditures in a medicare supplement population. Gerontol Geriatr Med. 2016;2:1–9.

    Google Scholar 

  16. Nigatu YT, Bültmann U, Schoevers RA, Penninx BWJH, Reijneveld SA. Does obesity along with major depression or anxiety lead to higher use of health care and costs? A 6-year follow-up study. Eur J Public Health. 2017;27:965–71.

    Article  PubMed  Google Scholar 

  17. Quesenberry CP, Caan B, Jacobson A. Obesity, health services use, and health care costs among members of a health maintenance organization. Arch Intern Med. 1998;158:466–72.

    Article  PubMed  Google Scholar 

  18. Raebel MA, Malone DC, Conner DA, Xu S, Porter JA, Lanty FA. Health services use and health care costs of obese and nonobese individuals. Arch Intern Med. 2004;164:2135–40.

    Article  PubMed  Google Scholar 

  19. Suehs BT, Kamble P, Huang J, Hammer M, Bouchard J, Costantino ME, et al. Association of obesity with healthcare utilization and costs in a Medicare population. Curr Med Res Opin. 2017;33:2173–80.

    Article  PubMed  Google Scholar 

  20. Trakas K, Lawrence K, Bs M, Shear NH. Utilization of health care resources by obese Canadians. CMAJ. 1999;160:6.

    Google Scholar 

  21. Twells LK, Knight J, Alaghehbandan R. The relationship among body mass index, subjective reporting of chronic disease, and the use of health care services in Newfoundland and Labrador, Canada. Popul Health Manag. 2010;13:47–53.

    Article  PubMed  Google Scholar 

  22. Twells LK, Bridger T, Knight JC, Alaghehbandan R, Barrett B. Obesity predicts primary health care visits: a cohort study. Popul Health Manag. 2012;15:29–36.

    Article  PubMed  Google Scholar 

  23. Vals K, Kiivet R-A, Leinsalu M. Alcohol consumption, smoking and overweight as a burden for health care services utilization: a cross-sectional study in Estonia. BMC Public Health. 2013;13:772.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Wildenschild C, Kjøller M, Sabroe S, Erlandsen M, Heitmann BL. Change in the prevalence of obesity and use of health care in Denmark: an observational study. Clin Epidemiol. 2011;3:31–41.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Wolfenstetter SB, Menn P, Holle R, Mielck A, Meisinger C, von Lengerke T. Body weight changes and outpatient medical care utilisation: results of the MONICA/KORA cohorts S3/F3 and S4/F4. Psychosoc Med. 2012;9:1–14.

    Google Scholar 

  26. Wong JJ, Hood KK, Breland JY. Correlates of health care use among White and minority men and women with diabetes: an NHANES study. Diabetes Res Clin Pract. 2019;150:122–8.

    Article  PubMed  Google Scholar 

  27. Gorber SC, Tremblay M, Moher D, Gorber B. A comparison of direct vs. self-report measures for assessing height, weight and body mass index: a systematic review. Obes Rev. 2007;8:307–26.

    Article  PubMed  Google Scholar 

  28. Akhtar-Danesh N, Dehghan M, Merchant AT, Rainey JA. Validity of self-reported height and weight for measuring prevalence of obesity. Open Med. 2008;2:e83–8.

    PubMed  PubMed Central  Google Scholar 

  29. Elgar FJ, Stewart JM. Validity of self-report screening for overweight and obesity. Can J Public Health. 2008;99:423–7.

    Article  PubMed  PubMed Central  Google Scholar 

  30. World Health Organization. Ageing and health. 2020.

  31. Raina P, Wolfson C, Kirkland S, Griffith LE, Balion C, Cossette B. et al. Cohort profile: The Canadian Longitudinal Study on Aging (CLSA). Int J Epidemiol. 2019;48:1752–3j.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Canadian longitudinal study on aging. Standard operating procedure (SOP): standing height and weight measurement. 2016.

  33. Canadian longitudinal study on aging. Standard operating procedure (SOP): hip and waist circumferences. 2016.

  34. Canadian longitudinal study on aging. Standard operating procedure (SOP): bone mineral density by dual-energy X-ray absorption (DXA)—whole body scan. 2014.

  35. Ho-Pham LT, Campbell LV, Nguyen TV. More on body fat cutoff points. Mayo Clin Proc. 2011;86:584.

    Article  PubMed  PubMed Central  Google Scholar 

  36. World Health Organization. Physical status: the use of and interpretation of anthropometry, Report of a WHO Expert Committee. World Health Organization. 1995.

  37. Batsis JA, Mackenzie TA, Bartels SJ, Sahakyan KR, Somers VK, Lopez-Jimenez F. Diagnostic accuracy of body mass index to identify obesity in older adults: NHANES 1999–2004. Int J Obes. 2016;40:761–7.

    Article  CAS  Google Scholar 

  38. Elagizi A, Kachur S, Lavie CJ, Carbone S, Pandey A, Ortega FB, et al. An overview and update on obesity and the obesity paradox in cardiovascular diseases. Progr Cardiovasc Dis. 2018;61:142–50.

    Article  Google Scholar 

  39. Dickey RA, Bartuska D, Bray GW, Callaway CW, Davidson ET, Feld S. AACE/ACE position statement on the prevention, diagnosis, and treatment of obesity (1998 revision). Endocr Pract. 1998;4:297–350.

    Google Scholar 

  40. Canadian longitudinal study on aging. Maintaining contact questionnaire (tracing and comprehensive). 2015.

  41. Andersen RM. Revisiting the behavioral model and access to medical care: does it matter? J Health Soc Behav. 1995;36:1–10.

    Article  CAS  Google Scholar 

  42. Canadian longitudinal study on aging. Derived variable—alcohol use (ALC) (tracking and comprehensive assessments). 2017.

  43. Andresen EM, Malmgren JA, Carter WB, Patrick DL. Screening for depression in well older adults: evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale). Am J Prev Med. 1994;10:77–84.

    Article  CAS  PubMed  Google Scholar 

  44. Tjepkema M. Measured obesity adult obesity in Canada: measured height and weight. 2005. p. 32.

  45. Naimi AI, Whitcomb BW. Estimating risk ratios and risk differences using regression. Am J Epidemiol. 2020;189:508–10.

    Article  PubMed  Google Scholar 

  46. Banack HR, Wactawski-Wende J, Hovey KM, Stokes A. Is BMI a valid measure of obesity in post-menopausal women?. Menopause.2018;25:307–13.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Padwal R, Leslie WD, Lix LM, Majumdar SR. Relationship among body fat percentage, body mass index, and all-cause mortality: a cohort study. Ann Intern Med. 2016;164:532.

    Article  PubMed  Google Scholar 

  48. Blew RM, Sardinha LB, Milliken LA, Teixeira PJ, Going SB, Ferreira DL, et al. Assessing the validity of body mass index standards in early postmenopausal women. Obes Res. 2002;10:799–808.

    Article  PubMed  Google Scholar 

  49. Silva BR, Mialich MS, Hoffman DJ, Jordão AA. BMI, BMIfat, BAI or BAIFels—which is the best adiposity index for the detection of excess weight?. Nutr Hosp. 2017;34:389–95.

    Article  CAS  PubMed  Google Scholar 

  50. Chen Y-M, Ho SC, Lam SSH, Chan SSG. Validity of body mass index and waist circumference in the classification of obesity as compared to percent body fat in Chinese middle-aged women. Int J Obes. 2006;30:918–25.

    Article  Google Scholar 

  51. Flegal KM, Shepherd JA, Looker AC, Graubard BI, Borrud LG, Ogden CL, et al. Comparisons of percentage body fat, body mass index, waist circumference, and waist-stature ratio in adults123. Am J Clin Nutr. 2009;89:500–8.

    Article  CAS  PubMed  Google Scholar 

  52. Ehrampoush E, Arasteh P, Homayounfar R, Cheraghpour M, Alipour M, Naghizadeh MM, et al. New anthropometric indices or old ones: which is the better predictor of body fat? Diabetes Metab Syndr: Clin Res Rev. 2017;11:257–63.

    Article  Google Scholar 

  53. Ospina PA, Nydam DV, DiCiccio TJ. Technical note: the risk ratio, an alternative to the odds ratio for estimating the association between multiple risk factors and a dichotomous outcome. J Dairy Sci. 2012;95:2576–84.

    Article  CAS  PubMed  Google Scholar 

  54. Newbold B. Health status and health care of immigrants in Canada: a longitudinal analysis. J Health Serv Res Policy. 2005;10:77–83A.

    Article  PubMed  Google Scholar 

  55. Zarychanski R, Chen Y, Bernstein CN, Hébert PC. Frequency of colorectal cancer screening and the impact of family physicians on screening behaviour. CMAJ. 2007;177:593–7.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Short ME, Goetzel RZ, Pei X, Tabrizi MJ, Ozminkowski RJ, Gibson TB, et al. How accurate are self-reports? An analysis of self-reported healthcare utilization and absence when compared to administrative data. J Occup Environ Med. 2009;51:786–96.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Raina P, Torrance-Rynard V, Wong M, Woodward C. Agreement between self-reported and routinely collected health-care utilization data among seniors. Health Serv Res. 2002;37:751–74.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Bhandari A, Wagner T. Self-reported utilization of health care services: improving measurement and accuracy. Med Care Res Rev. 2006;63:217–35.

    Article  PubMed  Google Scholar 

  59. Stranges S, Dorn JM, Muti P, Freudenheim JL, Farinaro E, Russell M, et al. Body fat distribution, relative weight, and liver enzyme levels: a population-based study. Hepatology. 2004;39:754–63.

    Article  PubMed  Google Scholar 

  60. Woolcott OO, Bergman RN. Defining cutoffs to diagnose obesity using the relative fat mass (RFM): association with mortality in NHANES 1999–2014. Int J Obes. 2020;44:1301–10.

    Article  CAS  Google Scholar 

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The CLSA is led by Drs Parminder Raina, Christina Wolfson, and Susan Kirkland. Funding for the Canadian Longitudinal Study on Aging (CLSA) is provided by the Government of Canada through the Canadian Institutes of Health Research (CIHR) under grant reference LSA 94473 and the Canada Foundation for Innovation. This research has been conducted using the CLSA’s dataset Baseline Tracking version 3.4 and Baseline Comprehensive Version 4.0, under Application Number 19CA004.

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Correspondence to Laura N. Anderson.

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The authors declare that they have no conflict of interest. The opinions expressed in this manuscript are the author’s own and do not reflect the views of the Canadian Longitudinal Study on Aging.

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All participants provided written informed consent upon enrollment into the CLSA. Further, secondary data analysis for this specific project was approved by the Hamilton Integrated Research Ethics Board, Hamilton, Ontario (HiREB# 2019-7221-C).

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Andreacchi, A.T., Griffith, L.E., Guindon, G.E. et al. Body mass index, waist circumference, waist-to-hip ratio, and body fat in relation to health care use in the Canadian Longitudinal Study on Aging. Int J Obes 45, 666–676 (2021).

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