Alessandra T Andreacchi1, Lauren E Griffith1, G Emmanuel Guindon1,2, Alexandra Mayhew1, Carol Bassim1, Marie Pigeyre3,4, Saverio Stranges5,6,7, Laura N Anderson8,9,10. 1. Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada. 2. Centre for Health Economics and Policy Analysis, McMaster University, Hamilton, Ontario, Canada. 3. Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada. 4. Department of Medicine, McMaster University, Hamilton, Ontario, Canada. 5. Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada. 6. Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg. 7. Department of Family Medicine, Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada. 8. Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada. LN.Anderson@mcmaster.ca. 9. Centre for Health Economics and Policy Analysis, McMaster University, Hamilton, Ontario, Canada. LN.Anderson@mcmaster.ca. 10. Division of Child Health Evaluative Sciences (CHES), Sick Kids Research Institute, Toronto, Ontario, Canada. LN.Anderson@mcmaster.ca.
Abstract
BACKGROUND/ OBJECTIVES: 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. SUBJECTS/ METHODS: 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. RESULTS: 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. CONCLUSIONS: All measures of adiposity were positively associated with increased HCU although obesity may not be a strong predictor of HCU in older adults.
BACKGROUND/ OBJECTIVES: 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. SUBJECTS/ METHODS: 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. RESULTS: 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. CONCLUSIONS: All measures of adiposity were positively associated with increased HCU although obesity may not be a strong predictor of HCU in older adults.
Authors: José A Luchsinger; Wei-nch Lee; Olveen Carrasquillo; Daniel Rabinowitz; Steven Shea Journal: J Am Geriatr Soc Date: 2003-11 Impact factor: 5.562
Authors: Luz M León-Muñoz; Pilar Guallar-Castillón; Esther López García; José R Banegas; Juan L Gutiérrez-Fisac; Fernando Rodríguez-Artalejo Journal: Obes Res Date: 2005-08
Authors: Yeshambel T Nigatu; Ute Bültmann; Robert A Schoevers; Brenda W J H Penninx; Sijmen A Reijneveld Journal: Eur J Public Health Date: 2017-12-01 Impact factor: 3.367
Authors: Mariel M Finucane; Gretchen A Stevens; Melanie J Cowan; Goodarz Danaei; John K Lin; Christopher J Paciorek; Gitanjali M Singh; Hialy R Gutierrez; Yuan Lu; Adil N Bahalim; Farshad Farzadfar; Leanne M Riley; Majid Ezzati Journal: Lancet Date: 2011-02-03 Impact factor: 79.321
Authors: Natascia Rinaldo; Stefania Toselli; Emanuela Gualdi-Russo; Meriem Khyatti; Amina Gihbid; Luciana Zaccagni Journal: Int J Environ Res Public Health Date: 2022-06-02 Impact factor: 4.614