1 Volume 11 Supplement VALUE IN HEALTH The Association of Body Mass Index with Health-Related Quality of Life: An Exploratory Study in a Multiethnic Asian Population Hwee-Lin Wee, PhD, 1 Yin-Bun Cheung, PhD, 2 Wai-Chiong Loke, FCFP (Singapore), 3 Chee-Beng Tan, FCFP (Singapore), 3 Mun-Hong Chow, FCFP (Singapore), 3 Shu-Chuen Li, PhD, 4,5 Kok-Yong Fong, FRCP (Edin), 1,6 David Feeny, PhD, 7,8 David Machin, PhD, 9,10 Nan Luo, PhD, 11 Julian Thumboo, FRCP (Edin) 1,6 1 Department of Rheumatology and Immunology, Singapore General Hospital, Singapore; 2 MRC Tropical Epidemiology Group, London School of Hygiene and Tropical Medicine, London, UK; 3 SingHealth Polyclinics, Singapore; 4 Department of Pharmacy, National University of Singapore, Singapore; 5 Discipline of Pharmacy & Experimental Pharmacology, School of Biomedical Sciences, University of Newcastle, Callaghan, NSW, Australia; 6 Department of Medicine,Yong Loo Lin School of Medicine, National University of Singapore, Singapore; 7 Kaiser Permanente North-west Center for Health Research, Portland, OR, USA; 8 Health Utilities Incorporated, Dundas, ON, Canada; 9 National Cancer Center Singapore, Singapore; 10 Clinical Trials & Epidemiology Research Unit, Singapore; 11 Centre for Health Services Research,Yong Loo Lin School of Medicine, National University of Singapore, Singapore ABSTRACT Objectives: To evaluate the association between body mass index (BMI) and health-related quality of life (HRQoL) in a multiethnic Asian population in Singapore, and to explore if the World Health Organization (WHO) recommendation of alternative BMI cutoffs for Asians could be further strengthened by evidence of higher risk of impaired HRQoL using these criteria. Methods: Consenting English, Chinese, Malay and Tamilspeaking primary care patients (age 21 years) were interviewed using English/their respective mother tongue versions of the EQ-5D/EQ-VAS, Health Utilities Index (HUI2 & HUI3) and the SF-6D. We first evaluated the relationship between BMI and HRQoL (overall and individual attributes for each instrument) using multiple linear/logistic regression (where appropriate) to adjust for factors known to affect HRQoL. We next reorganized BMI into five categories (reflecting the differences in cutoffs between International/ Asian classifications) and evaluated if median HRQoL scores were significantly different across these categories. Results: Among 411 participants [response rate: 87%; median age: 51 years; obese: 19% (International); 33% (Asian)], after adjusting for sociodemographic and other factors, a tendency for underweight and obese subjects to report lower overall HRQoL scores was observed for most instruments. At the individual attribute level, obese subjects reported significantly lower HUI2 pain scores (regression coefficient: , P = 0.029) and greater odds of reporting problems for SF-6D role-limitations (odds ratio: 2.9, P = 0.005). Median overall HRQoL scores were not significantly different across the five BMI categories. Conclusion: Consistent with available studies, obese subjects reported worse HRQoL than normal-weight subjects. That underweight subjects also reported worse HRQoL is interesting and requires confirmation. HRQoL was similar in Asians using either WHO criteria. Keywords: Asia, body mass index, obesity, primary, healthcare, quality of life, Singapore. Introduction The global prevalence of obesity has been increasing and is fast approaching epidemic proportions in many countries [1 4]. Obesity is associated with a significant disease burden and costs and is therefore an important public health concern. For example, obesity is an important independent risk factor for cardiovascular disease [5,6]. The total direct and indirect annual costs Address correspondence to: Julian Thumboo, Department of Rheumatology and Immunology, Singapore General Hospital, Outram Road, Singapore julian /j x of obesity in the 15 member states of the European Union amounted to 32.8 trillion euros in 2002 . In the United States, in 1995, the total cost attributable to obesity amounted to $99.2 billion, with approximately $51.64 billion being direct medical costs . To the best of our knowledge, similar data are not available for Asia. There is an increasing recognition of the association between high or low body mass index (BMI) and healthrelated quality of life (HRQoL) [8,9]. HRQoL refers to the overall impact of a medical condition on the physical, mental, and social well-being of an individual . In a cross-sectional study involving 5,817 people aged 14 to 61 from the general population, being overweight 2008, International Society for Pharmacoeconomics and Outcomes Research (ISPOR) /08/S105 S105 S114 S105
2 S106 was associated with poorer functional status, considerable pain, worry, and restricted activity . In another cross-sectional study involving 5633 people aged 16 to 64 years from the general population, obesity was similarly found to be associated with worse HRQoL as measured by the SF-36, and this association was reportedly modified by age and sex . Besides crosssectional studies, other studies have evaluated the impact of weight loss interventions [13,14] on HRQoL with mixed results. In a meta-analysis of 34 randomized controlled trials with behavioral, surgical or pharmacological interventions, nine showed improvements in HRQoL measured using generic instruments . Nevertheless, overall quality of these trials was judged to be poor. Mixed results were also obtained from trials evaluating the impact of bariatric surgery on HRQoL . A review of the impact of LAP-BAND placement reported all aspects of HRQoL improved substantially, especially physical disability, and post weight-loss HRQoL measures approximated those of the general population . Although several studies have shown that obese subjects are more likely than nonobese subjects to suffer from poorer physical health [18,19] and in some studies, poorer mental health [20,21], the relationship between obesity and HRQoL among Asians is poorly understood. To the best of our knowledge, there was only one such published study to date , which studied only one ethnic group (Chinese) and showed that excess weight was associated with worse physical health, but not mental health. Given that Asia is a culturally diverse region, a better understanding of the relationship between obesity and HRQoL in a multiethnic Asian population would be important. Additionally, few studies (in the East or West) have evaluated the association between being underweight and HRQoL. Using three measures of HRQoL (the EQ-5D, EQ-VAS, and SF-6D) , Sach et al. found that underweight subjects reported significantly poorer HRQoL compared to normal-weight subjects as measured by the SF-6D. This result was not confirmed by the scores from the EQ-5D and EQ-VAS. Huang et al.  reported that compared to normal-weight subjects, underweight subjects had similar or slightly lower scores on the physical health subscales of the SF-36 and clearly lower scores on the mental health subscales of the SF-36 domains, although the differences were not clinically important (i.e., less than 5 points on a 100-point scale). Nevertheless, these results were not adjusted for the presence of medical conditions which themselves might lead to being underweight and/or might directly affect HRQoL. Given that being underweight could be associated with higher morbidity [24 27] and mortality [28 30], and poorer health-seeking behavior , it is thus important to study the association between being underweight and HRQoL, with adjustment for other potential confounders. Wee et al. In 2004, the World Health Organization (WHO) revised its recommendation for BMI cutoffs for Asians  (see Methods) based on evidence that compared to Caucasians, the risks of Type 2 diabetes and cardiovascular diseases among Asians is already substantial even at BMI lower than the existing WHO cutoffs for overweight ( 25 kg/m 2 ). The evidence base for this recommendation could be further strengthened if there are other areas of health (e.g., HRQoL) in which risk of impairment is greater for Asians compared to Caucasians of comparable BMI. Aims Thus, the aims of this exploratory study were 1) to evaluate the association between BMI and HRQoL (at both overall and individual attribute levels) in a multiethnic Asian population in Singapore, and 2) to explore if HRQoL impairment is greater at BMI lower than the existing WHO cutoffs in this same population. Hypotheses Based on the literature, we hypothesized that 1) compared to normal-weight subjects, underweight, preobese and obese subjects would experience decrements in HRQoL, and 2) decrements in HRQoL would be larger for obese compared to pre-obese and smaller for underweight compared to obese subjects. To provide further evidence to support the revised BMI cutoffs, we would expect to observe a gradation in HRQoL across the five BMI categories defined by the Asian and International BMI classifications, with successively lower HRQoL being observed in successively higher BMI categories. Methods Subjects and Study Design Chinese, Malay and Indian patients were recruited as part of an Institutional Review Board approved study of HRQoL performed at the SingHealth Geylang Polyclinic, a public health-care institution facility with 15 doctors responsible for delivering primary care. Inclusion criteria were aged 21 and above, ability to comprehend one of the four survey languages (English, Chinese, Malay, and Tamil) and absence of cognitive impairment as assessed by the recruiters. Consenting subjects were interviewed by interviewers of the same ethnicity using a questionnaire containing the Singapore English, Chinese, Malay, and Tamil versions of the EQ-5D/EQ-VAS, HUI2, HUI3 and SF-6D. Validity of several of these instruments in this study sample was previously reported [33 36], while validity for others is currently being reported (Wee et al. unpublished data, 2006). Scores for the EQ-5D, HUI2, HUI3, and SF-6D are all on the conventional
3 Body Mass Index and Health-Related Quality of Life among Asians S107 scale in which dead = 0.00 and perfect health = Sociodemographic and other factors known to influence HRQoL were also assessed. Physician-reported acute and chronic medical conditions were obtained using a standardized, pretested form. Instruments EuroQoL 5-Dimensions (EQ-5D). The EQ-5D is a generic, preference-based instrument comprising a health classification system with five dimensions (mobility, self-care, usual activities, pain, and anxiety/ depression), each with three response levels (no problem, some problems, and severe problems) and a visual analog scale (EQ-VAS). The health classification system describes a total of 243 health states, each of which is assigned a utility weight, range to 1, using a utility scoring function derived from the UK general population using the time trade-off method . We performed similar analyses using the US scoring function  but did not present the results here as they were very similar to those using the UK scoring function. Respondents classified and rated their health on the day of the survey. A difference of 0.07 or more in EQ-5D utility scores has been reported to be clinically important  while a difference of 5 or more in EQ-VAS scores has been proposed to be clinically important . Health Utilities Index (HUI2 and HUI3). The HUI2 and HUI3 consist of two independent but complementary systems, which together describe almost 1 million unique health states. Both instruments include a generic comprehensive health status classification system and a generic HRQoL utility scoring system, using a utility scoring function derived from a representative sample of the Canadian general population using the standard gamble (SG) and visual analog scale (VAS) methods . The scores for HUI2 range from (worst health state) to 1 (perfect health) and scores for HUI3 range from to 1. The questionnaire used in this study was the self-assessment, selfcompleted version of the HUI 15Q (4-week recall period) that includes items sufficient to classify respondents in both the HUI2 and HUI3 systems [1,2]. A difference of 0.03 or more for overall HUI2 and HUI3 scores is clearly clinically important [41 43]. SF-6D. The SF-6D is a six-dimensional health classification system comprising physical functioning, social functioning, role-limitations, vitality, pain and mental functioning, with four to six levels per dimension, thus defining a total of 18,000 health states. The scores for SF-6D range from 0.29 to 1 and were obtained using a utility scoring function derived from a representative sample of the UK general population using the SG technique . A difference of 0.04 or more in SF-6D scores is considered clinically important . Family functioning measure (FFM). The FFM is a three-item scale (five-level response options, poor to excellent) previously validated in Singapore , assessing the quality of interactions among family members , with higher scores (range, 0 100) reflecting better family functioning. High FFM scores have previously been found to be associated with better HRQoL . Body mass index. We calculated subjects BMI (kg/m 2 ) by dividing their weight (in kilograms) by the square of their height (in meters). Weight and height for all subjects were measured using the same instruments. We evaluated the relationship between BMI and HRQoL by treating BMI as a categorical variable according to 1) WHO International BMI classification, and 2) WHO revised cutoffs for Asians. The International WHO BMI classifications are: 1) <18.5 kg/m 2 : underweight; 2) 18.5 to <25 kg/m 2 : normal-weight; 3) 25 to <30 kg/m 2 : pre-obese and 4) 30 kg/m 2 : obese. The WHO Asian BMI classifications are: 1) <18.5 kg/m 2 : underweight; 2) 18.5 to <23 kg/m 2 : normal-weight; 3) 23 to <27.5 kg/m 2 : pre-obese and 4) 27.5 kg/m 2 : obese. Using the new Asian classifications, subjects with BMI between 23 to <25 kg/m 2 would be reclassified as pre-obese and subjects with BMI between 27.5 to <30 kg/m 2 would be reclassified as obese. Hence, we additionally reorganized BMI (kg/m 2 ) into five categories reflecting the differences in cutoffs between International and Asian classifications: 18.5 to <23, 23 to <25, 25 to <27.5, 27.5 to <30, 30, for the purpose of comparison between International and Asian cutoffs. As the definition for underweight (<18.5 kg/m 2 ) was not different in the revised BMI definition, this category was not included in the reorganization. Statistical Analyses We compared differences in subject characteristics by ethnicity using chi-square tests for categorical variables and Kruskal Wallis test for continuous variables. To evaluate the influence of BMI on overall HRQoL scores, we performed multiple linear regression (MLR) analyses with overall HRQoL scores as dependent variables in separate models, while adjusting for other covariates that may potentially influence HRQoL including sociodemographic (i.e., age, sex, ethnicity, and years of education) and other factors including marital status (yes/no), smoking status (yes/no), work status (yes/no), presence of chronic medical conditions, and FFM scores. To allow for comparisons with other published studies, we used BMI categories defined according to WHO International classification (i.e., non-asian classification) in the MLR analyses.
4 S108 Table 1 Characteristics of subjects, mean body mass index and health-related quality of life (HRQoL) scores by ethnicity Wee et al. N (%) unless otherwise stated Ethnicity All (n = 411) Chinese (n = 164) Malays (n = 127) Indians (n = 120) P-values Median age (IQR) (years) 51.0 (38.0, 61.0) 51.0 (39.0, 61.0) 52.0 (39.0, 64.0) 49.0 (37.0, 63.0) 0.66 Female 216 (52.6) 78 (47.6) 67 (52.8) 71 (59.2) 0.16 Median education (IQR) (years) 8.0 (3.0, 10.0) 10.0 (3.0, 12.0) 8.0 (3.0, 10.0) 8.0 (3.0, 10.0) Married 288 (70.1) 109 (66.5) 88 (69.3) 91 (75.8) 0.23 Working 247 (60.1) 102 (62.2) 73 (57.5) 72 (60.0) 0.72 Smoking cigarettes 77 (18.7) 29 (17.7) 28 (22.1) 20 (16.7) 0.50 Presence of chronic medical conditions* 259 (63.0) 109 (66.5) 86 (67.7) 64 (53.3) Median family functioning scores (IQR) 66.7 (50.0, 75.0) 58.3 (41.7, 75.0) 62.5 (50.0, 75.0) 75.0 (50.0, 83.3) Body mass index (International classification) (kg/m 2 ) <0.001 Underweight (<18.5) 17 (4.1) 14 (8.5) 2 (1.6) 1 (0.8) Normal weight (18.5 to <25) 166 (40.4) 92 (56.1) 41 (32.3) 33 (27.5) Pre-obese (2.5 to <30) 150 (36.5) 49 (29.9) 44 (34.7) 57 (47.5) Obese ( 30) 78 (19.0) 9 (5.5) 40 (31.5) 29 (24.2) Body mass index (Asian classification) (kg/m 2 ) <0.001 Underweight (<18.5) 17 (4.1) 14 (8.5) 2 (1.6) 1 (0.8) Normal weight (18.5 to <27.5) 95 (23.1) 56 (34.2) 24 (18.9) 15 (12.5) Pre-obese (23 to <27.5) 163 (39.7) 69 (42.1) 45 (35.4) 49 (40.8) Obese ( 27.5) 136 (33.1) 25 (15.2) 56 (44.1) 55 (45.8) Median HRQoL scores (IQR) EQ-5D (possible score range: to 1) 0.80 (0.73, 1) 0.80 (0.73, 1) 0.80 (0.73, 1) 0.80 (0.73, 0.97) 0.18 EQ-VAS (possible score range: 0 to 100) 70.0 (60.0, 80.0) 70.0 (50.0, 80.0) 70.0 (60.0, 80.0) 70.0 (60.0, 80.0) 0.20 HUI2 (possible score range: to 1) 0.88 (0.80, 0.95) 0.87 (0.80, 0.92) 0.90 (0.80, 0.95) 0.88 (0.80, 0.95) HUI3 (possible score range: to 1) 0.85 (0.68, 0.92) 0.83 (0.67, 0.91) 0.88 (0.79, 0.95) 0.82 (0.62, 0.93) SF-6D (possible score range: 0.29 to 1) 0.89 (0.79, 0.95) 0.87 (0.76, 0.94) 0.94 (0.81, 1) 0.88 (0.76, 0.94) <0.001 *List of physician-reported chronic medical conditions include hypertension, diabetes mellitus, ischemic heart disease, cerebrovascular accident, hyperlipidaemia, osteoarthritis, other type of arthritis, rheumatism and asthma. IQR, interquartile range. To evaluate the influence of BMI on individual attributes of HRQoL, we performed MLR analyses with HUI2 and HUI3 individual single-attribute utility scores as dependent variables in separate models, while adjusting for the potential influence of sociodemographic and other covariates (as specified above). As individual attribute scores are not available for EQ-5D and SF-6D, we collapsed responses to EQ-5D and SF-6D items into two levels with or without problems, and performed multiple logistic regression (LOGIT) with these dichotomous responses as dependent variables in separate models. This approach of collapsing responses has also been used in other studies [49,50]. We similarly adjusted for the potential influence of sociodemographic factors and other covariates. To evaluate if HRQoL impairment is greater at BMI lower than the existing WHO cutoffs, we compared, using Kruskal Wallis tests, median HRQoL scores across the five predefined BMI categories that contrast the International and Asian cutoffs as aforementioned. Statistical significance was defined at P < We did not adjust for multiple comparisons as based on the approach taken by several respected researchers that the presumption (of universal null hypothesis) underlying the theory of adjustment for multiple comparisons does not hold [51 53]. Results Subjects Of 660 subjects approached, 574 participated (response rate: 87%), 108 did not complete the study, another 55 subjects were excluded from this analysis because 23 had missing sociodemographic or clinical information, 29 had missing HRQoL data, and three had BMI above 50.0 kg/m 2 (identified as outliers by visual inspection of scatter plots). Thus, a total of 411 subjects (40% Chinese, 31% Malays and 29% Indians) provided complete data for analysis. The percentage of obese subjects was 19% by the WHO International classification and 33% by the WHO Asian classification. Details of subject characteristics and HRQoL scores are summarized in Table 1. As compared with other ethnic groups, Chinese subjects reported significantly more years of education and lower FFM scores while Malay subjects reported a significantly higher prevalence of chronic medical conditions, and higher HUI3 and SF-6D utility scores. Influence of BMI on Overall HRQoL Scores (Table 2) With or without adjustment for sociodemographic factors and other covariates, compared to normalweight subjects, a trend of underweight and obese subjects reporting lower overall HRQoL scores (i.e., negative regression coefficients) was observed on most instruments, with some of these differences being statistically significant but not clinically important. Compared to normal-weight subjects, pre-obese subjects generally reported better (albeit marginally) overall HRQoL scores on all instruments except the SF-6D. Using the SF-6D, pre-obese subjects reported worse (albeit marginally) overall HRQoL scores compared to normal-weight subjects.
5 Body Mass Index and Health-Related Quality of Life among Asians S109 Table 2 Results of five linear regression models relating five health related-quality of life scores with body mass index (BMI) categories (with normal-weight as reference group), with and without adjustment for covariates EQ-5D utility scores P-value EQ-VAS P-value Regression coefficients, 95% confidence interval HUI2 utility scores P-value HUI3 utility scores P-value SF-6D utility scores P-value Not adjusting for covariates Underweight (n = 17) (-0.17, 0.004) (-17, 2.1) 0.012* (-0.085, 0.047) (-0.018, 0.046) (-0.076, 0.042) Normal weight (n = 166) Pre-obese (n = 150) (-0.038, 0.038) Obese (n = 78) (-0.10, -0.01) Adjusting for age, sex, ethnicity, education and other covariates Underweight (n = 17) (-0.18, ) (-2.6, 3.9) 0.015* -2.2 (-6.2, 1.7) 0.026* -11 (-18, -3.4) (-0.022, 0.037) (-0.047, 0.024) 0.005* (-0.010, 0.032) (-0.026, 0.074) (-0.064, 0.059) (-0.21, 0.013) (-0.032, 0.020) (-0.050, 0.014) (-0.082, 0.032) Normal weight (n = 166) Pre-obese (n = 150) (-0.019, 0.055) Obese (n = 78) (-0.082, 0.011) (-3.0, 3.6) (-7.8, 0.5) (-0.018, 0.040) (-0.050, 0.024) (-0.020, 0.077) (-0.072, 0.050) (-0.026, 0.025) (-0.059, 0.004) *P < 0.05, showing subjects in this category had health-related quality of life scores significantly different from subjects with normal weight. Based on WHO International classification: 1) <18.5 kg/m 2 : underweight; 2) 18.5 to <25 kg/m 2 : normal-weight; 3) 25 to <30 kg/m 2 : pre-obese and 4) 30 kg/m 2 : obese. Reference category: normal-weight subjects. Other covariates include marital status (yes/no), smoking status (yes/no), work status (yes/no), presence of chronic medical conditions (yes/no) and Family Functioning Measures scores (continuous variable, score range 0 100). HUI, Health Utilities Index.
6 S110 Wee et al. Table 3 Results of three linear regression models relating three single attribute utility scores with body mass index (BMI) categories (with normal weight as reference group), with and without adjustment for covariates Regression coefficients, 95% confidence interval HUI2 pain P-value HUI3 dexterity P-value HUI3 pain P-value Not adjusting for covariates Underweight (n = 17) (-0.070, 0.040) (-0.030, 0.036) (-0.16, ) <0.001* Normal weight (n = 166) Pre-obese (n = 150) (-0.037, 0.011) (-0.013, 0.017) (-0.032, 0.018) 0.66 Obese (n = 78) (-0.074, 0.015) 0.004* (-0.036, ) 0.04* (-0.053, 0.009) 0.23 Adjusting for age, sex, ethnicity, education and other covariates Underweight (n = 17) (-0.083, 0.030) (-0.036, 0.034) (-0.16, 0.041) 0.001* Normal weight (n = 166) Pre-obese (n = 150) (-0.033, 0.018) (-0.012, 0.019) (-0.032, 0.020) 0.63 Obese (n = 78) (-0.066, ) 0.029* (-0.034, 0.004) (-0.054, 0.012) 0.23 *P < 0.05, showing subjects in this category had single attribute utility scores significantly different from subjects with normal weight. Based on WHO International classification: 1) <18.5 kg/m 2 : underweight; 2) 18.5 to <25 kg/m 2 : normal weight; 3) 25 to <30 kg/m 2 : pre-obese; and 4) 30 kg/m 2 : obese. Reference category: normal weight subjects. Other covariates include marital status (yes/no), smoking status (yes/no), work status (yes/no), presence of chronic medical conditions (yes/no) and Family Functioning Measures scores (continuous variable, score range 0 100). HUI, Health Utilities Index. Without adjustment for sociodemographic factors and other covariates, compared to normal weight subjects, obese subjects reported significantly lower EQ-5D utility scores (regression coefficient: , P = 0.015). Nevertheless, with adjustment for sociodemographic factors and other covariates, the impact of obesity on EQ-5D utility scores (-0.036, P = 0.13) was no longer statistically significant. Without adjustment for sociodemographic factors and other covariates, compared to normal weight subjects, underweight subjects reported lower EQ-VAS scores (regression coefficient: -9.4, P = 0.012), which were both statistically significant and clinically important. With adjustment for sociodemographic factors and other covariates, underweight subjects reported lower EQ-5D utility (-0.096, P = 0.026) and EQ-VAS scores (-10.8, P = 0.005), the magnitude of which was both statistically significant and clinically important, as well as lower HUI2 (-0.034, P = 0.32) and HUI3 (-0.097, P = 0.085) utility scores; the differences were clinically important but did not reach statistical significance. Interestingly, among all BMI categories, underweight rather than obese subjects reported the largest decrements in EQ-5D utility, EQ-VAS, HUI2 and HUI3 utility scores while obese subjects reported the largest decrements in SF-6D utility scores. As coexisting chronic medical condition was a potential confounder in this situation, we evaluated the prevalence of any chronic medical conditions across BMI categories and found that they were not significantly different. The prevalence of chronic medical conditions in the various BMI categories were 82% (underweight), 57% (normal), 64% (pre-obese) and 69% (obese), respectively (P = 0.091). Prevalence of specific chronic medical conditions (e.g., diabetes, hypertension, etc.) was also similar across BMI categories (results not shown). Influence of BMI on Individual HRQoL Attributes (Tables 3 and 4) Without adjustment for sociodemographic factors and other covariates, the influence of BMI on individual HRQoL attributes was statistically significant for HUI2 pain, HUI3 dexterity and pain, EQ-5D mobility and usual activities, as well as SF-6D role limitations and pain attributes (Tables 3 and 4, results shown only for those attributes reaching statistical significance). With adjustment for sociodemographic factors and other covariates, compared to normalweight subjects, underweight subjects reported significantly lower HUI3 pain scores (regression coefficient: -0.10, P = 0.001, Table 3) and greater odds of reporting problems on EQ-5D usual activities (odds ratio: 8.3, P = 0.011, Table 4). With adjustment for sociodemographic factors and other covariates, compared to normal-weight subjects, obese subjects reported significantly lower HUI2 pain scores (regression coefficient: , P = 0.029, Table 3) and greater odds of reporting problems on SF-6D role limitations (odds ratio: 2.9, P = 0.005, Table 4). Overall HRQoL Scores Across Five BMI Categories Median overall HRQoL scores were not significantly different across the five BMI categories (Table 5), suggesting that unlike risk of Type 2 diabetes and cardiovascular diseases, use of the different BMI cutoffs would have no impact as far as HRQoL is concerned.
7 Body Mass Index and Health-Related Quality of Life among Asians S111 Table 4 Results of four logistic regression models relating four dimensions of health with body mass index (BMI) categories (with normal weight as reference group), with and without adjustment for covariates Odds ratio for reporting problems, 95% confidence interval EQ-5D mobility P-value EQ-5D usual activities P-value SF-6D role limitations P-value SF-6D pain P-value Not adjusting for covariates Underweight (n = 17) 0.8 (0.2, 3.7) (0.6, 9.1) (0.5, 5.2) (0.3, 2.3) 0.73 Normal weight (n = 166) Pre-obese (n = 150) 1.5 (0.8, 2.7) (0.7, 3.1) (0.6, 2.0) (1.1, 2.7) 0.027* Obese (n = 78) 2.5 (1.3, 4.7) 0.006* 2.6 (1.2, 5.7) 0.018* 2.3 (1.2, 4.3) 0.01* 1.3 (0.8, 2.3) 0.31 Adjusting for age, sex, ethnicity, education and other covariates Underweight (n = 17) 2.1 (0.4, 12.0) (1.6, 41.9) 0.011* 2.2 (0.6, 7.9) (0.3, 2.7) 0.86 Normal weight (n = 166) Pre-obese (n = 150) 1.1 (0.5, 2.2) (0.5, 2.5) (0.6, 2.1) (0.9, 2.3) 0.18 Obese (n = 78) 2.0 (0.9, 4.5) (0.7, 4.4) (1.4, 6.1) 0.005* 1.1 (0.6, 2.1) 0.73 *P < 0.05, showing that the odds of subjects in this category reporting problems on any health dimensions were significantly different from subjects with normal weight. Based on World Health Organization International classification: i) <18.5 kg/m 2 : underweight; ii) 18.5 to <25 kg/m 2 : normal weight; iii) 25 to <30 kg/m 2 : pre-obese and iv) 30 kg/m 2 : obese. Reference category: normal weight subjects. Other covariates include marital status (yes/no), smoking status (yes/no), work status (yes/no), presence of chronic medical conditions (yes/no) and Family Functioning Measures scores (continuous variable, score range 0 100). Discussion In this exploratory study, the first of its kind in a multiethnic Asian population and one of only two published Asian studies, we evaluated the association between BMI and HRQoL. As hypothesized, the magnitude of decrements in overall HRQoL scores was larger for obese compared to pre-obese subjects. Nevertheless, contrary to our a priori hypothesis, compared to normal weight subjects, pre-obese subjects reported marginally better overall HRQoL scores on five of six HRQoL instruments. Furthermore, we were surprised to find that underweight, rather than obese subjects, reported the largest decrements in overall HRQoL scores for four of five instruments. We regard these findings as interesting but preliminary given the small number of underweight subjects in this study. In addition, it is perhaps noteworthy to find that median HRQoL scores across five BMI categories (defined to reflect the different cutoffs between International and Asian BMI classifications) were not significantly different, thus suggesting that the use of these different BMI cutoffs would have no impact as far as HRQoL is concerned. If this result is confirmed in additional studies, it might imply that the same WHO International classification may be used in both Asian and Caucasian studies evaluating the association between BMI and HRQoL, thus enhancing the comparability of findings from such studies. Our observation that underweight subjects reported worst HRQoL compared to pre-obese and obese subjects differs from published studies [22,23] deserves comment. First, the discrepancies in our findings were unlikely to be explained by choice of instrument as identical instruments (EQ-5D, EQ-VAS, and the SF-6D) were used by Sach et al. . Second, the discrepancies were unlikely to be attributed to poor construct validity of these instruments as the validity of these instruments in this study population have been previously or are currently being reported (see Methods). Third, error in BMI measurement is unlikely to explain the discrepancies as we used actual measurements of weight and height, rather than figures based on self-report [22,23]. In fact, among all identified population-based studies evaluating the relationship between BMI and HRQoL [18 23], ours was the only study to use actual measurements of weight and height. Discrepancies in actual and self-reported Table 5 Comparison of median health-related quality of life (HRQoL) scores across body mass index (BMI) categories Median overall HRQoL scores (IQR) BMI (kg/m 2 ) 18.5 to <23 23 to <25 25 to < to <30 30 P-value EQ-5D 0.85 (0.73, 1) 0.80 (0.73, 1) 0.80 (0.73, 1) 0.80 (0.73, 1) 0.80 (0.73, 1) 0.34 EQ-VAS 70.0 (60.0, 80.0) 70.0 (60.0, 80.0) 72.5 (60.0, 80.0) 70.0 (60.0, 80.0) 70.0 (60.0, 80.0) 0.47 HUI (0.80, 0.92) 0.87 (0.80, 0.95) 0.88 (0.80, 0.95) 0.88 (0.80, 0.95) 0.90 (0.78, 0.95) 0.93 HUI (0.69, 0.92) 0.82 (0.69, 0.92) 0.88 (0.71, 0.93) 0.85 (0.64, 0.95) 0.85 (0.66, 0.93) 0.70 SF-6D 0.88 (0.80, 0.95) 0.88 (0.78, 0.95) 0.89 (0.79, 0.95) 0.89 (0.80, 0.95) 0.88 (0.74, 0.95) 0.98 IQR, interquartile range; HUI, Health Utilities Index.
8 S112 weight and height have been previously reported . Fourth, cultural differences were also unlikely to fully explain the discrepancies in findings as a study among Chinese in Taiwan  also found that higher BMI was associated with worse physical (but not mental) HRQoL measured using the SF-36. Having ruled out several possible sources of errors, we shall attempt to provide some plausible explanations for the observed discrepancies, although these need to be confirmed in future studies. First, BMI may not be an appropriate marker for obesity even though it is most easily measured . In view of this, further studies are required to study the association between obesity and HRQoL using other markers of obesity such as waist-to-hip ratio or percent body fat. Second, another possible explanation is that the effect size of the influence of BMI on HRQoL is generally small. In the only published study that directly reported the effect sizes of the influence of BMI on SF-36 subscales and summary scales , the effect size did not exceed 0.5 (Cohen s definition of medium effect size)  for any of the subscales or summary scales, with the largest effect size of 0.34 being reported for obese subjects on the physical functioning subscale without adjusting for any covariates. Third, it should be noted that BMI is a risk factor of poor health. Being obese increases the probability of developing chronic conditions such as type 2 diabetes and cardiovascular disease. Thus, the absence of a stronger relationship between current HRQoL and current BMI in a cross-sectional analysis may be because of the effects of BMI on health status and therefore HRQoL have not yet occurred. It will be important to explore the relationship between BMI and HRQoL in longitudinal studies. Limitations We recognize several limitations of this study. First, our subjects were recruited from a primary care setting and our findings may therefore not be generalizable to the general population. Nevertheless, our results are interesting in suggesting that the association of BMI with HRQoL among Asians could be different from that among Caucasians and provides a basis for future research. Second, as the number of underweight subjects is small (n = 17), and confidence interval is wide for some results, we advise caution in interpreting the findings. Further studies with disproportionate sampling to include a larger number of underweight subjects may be useful to confirm our findings. Third, we have computed health utility scores using population preference weights from the UK and Canada and these may not fully reflect local preferences. Nevertheless, this is a pragmatic compromise as a Singapore utility function was not available at the time of this study. Fourth, the cross-sectional nature of this study does not allow us to make any inferences about the causal associations between BMI and HRQoL. Future prospective longitudinal studies are required to better understand the causal association, if any, between BMI and HRQoL. Finally, although obesity-specific HRQoL instruments could be more sensitive than the generic instruments that we had used in our study, the reliability and validity of such instruments have not been previously evaluated in this multiethnic Asian population and are thus not available for use in our study. Conclusions In conclusion, in this multiethnic Asian population, underweight subjects unexpectedly reported poorer HRQoL than normal weight, pre-obese, and obese subjects on five of six HRQoL instruments. If further studies confirm that the use of the different BMI cutoffs would have no impact as far as HRQoL is concerned, then the WHO International classification may be used in both Asian and Caucasian studies evaluating the association between BMI and HRQoL, thus improving the comparability of findings from such studies. The authors wish to thank Dr. Tai Ee Shyong for his valuable comments on an earlier draft of this manuscript and Ms. Coralie Ang, Ms. Gladys Yap, Ms. Malini and other staff of SingHealth Polyclinics (Geylang), Republic of Singapore, for their assistance with the logistics of this study. Source of financial support: This study was funded by programme grant 03/1/27/18/226 from the Biomedical Research Council of Singapore. Conflict of Interest: It should be noted that David Feeny has a proprietary interest in Health Utilities Incorporated, Dundas, Ontario, Canada. HUInc. distributes copyrighted Health Utilities Index (HUI) materials and provides methodological advice on the use of HUI. References Wee et al. 1 Dal Grande E, Gill T, Taylor AW, et al. Obesity in South Australian adults prevalence, projections and generational assessment over 13 years. Aust N Z J Public Health 2005;29: Fry J, Finley W. The prevalence and costs of obesity in the EU. Proc Nutr Soc 2005;64: Wang HS, Zhai F, Popkin BM. Trends in the distribution of body mass index among Chinese adults, aged years ( ). Int J Obes (Lond) 2007;31: Ogden CL, Carroll MD, Curtin LR, in the United States, JAMA 2006;295: Zhou B, Wu Y, Yang J, et al. Overweight is an independent risk factor for cardiovascular disease in Chinese populations. Obes Rev 2002;3:
9 Body Mass Index and Health-Related Quality of Life among Asians S113 6 Nicklas BJ, Cesari M, Penninx BW, et al. Abdominal obesity is an independent risk factor for chronic heart failure in older people. J Am Geriatr Soc 2006;54: Wolf AM, Colditz GA. Current estimates of the economic cost of obesity in the United States. Obes Res 1998;6: Katz DA, McHorney CA, Atkinson RL. Impact of obesity on health-related quality of life in patients with chronic illness. J Gen Intern Med 2000;15: Jensen GL. Obesity and functional decline: epidemiology and geriatric consequences. Clin Geriatr Med 2005;21: Guyatt GH, Feeny DH, Patrick DL. Measuring health-related quality of life. Ann Intern Med 1993; 118: Stewart AL, Brook RH. Effects of being overweight. Am J Public Health 1983;73: Larsson U, Karlsson J, Sullivan M. Impact of overweight and obesity on health-related quality of life a Swedish population study. Int J Obes Relat Metab Disord 2002;26: Torquati A, Lutfi RE, Richards WO. Predictors of early quality-of-life improvement after laparoscopic gastric bypass surgery. Am J Surg 2007;193: Karlsson J, Taft C, Ryden A, et al. Ten-year trends in health-related quality of life after surgical and conventional treatment for severe obesity: the SOS intervention study. Int J Obes (Lond) 2007;31: Maciejewski ML, Patrick DL, Williamson DF. A structured review of randomized controlled trials of weight loss showed little improvement in healthrelated quality of life. J Clin Epidemiol 2005;58: Ballantyne GH. Measuring outcomes following bariatric surgery: weight loss parameters, improvement in co-morbid conditions, change in quality of life and patient satisfaction. Obes Surg 2003;13: Dixon JB, O Brien PE. Changes in comorbidities and improvements in quality of life after LAP-BAND placement. Am J Surg 2002;184(Suppl.):S Doll HA, Petersen SE, Stewart-Brown SL. Obesity and physical and emotional well-being: associations between body mass index, chronic illness, and the physical and mental components of the SF-36 questionnaire. Obes Res 2000;8: Jia H, Lubetkin EI. The impact of obesity on healthrelated quality-of-life in the general adult US population. J Public Health (Oxf) 2005;27: Hassan MK, Joshi AV, Madhavan SS, Amonkar MM. Obesity and health-related quality of life: a crosssectional analysis of the US population. Int J Obes Relat Metab Disord 2003;27: Trakas K, Oh PI, Singh S, et al. The health status of obese individuals in Canada. Int J Obes Relat Metab Disord 2001;25: Huang IC, Frangakis C, Wu AW. The relationship of excess body weight and health-related quality of life: evidence from a population study in Taiwan. Int J Obes (Lond) 2006;30: Sach TH, Barton GR, Doherty M, et al. The relationship between body mass index and health-related quality of life: comparing the EQ-5D, EuroQol VAS and SF-6D. Int J Obes (Lond) 2007;31: Samanic C, Chow WH, Gridley G, et al. Relation of body mass index to cancer risk in 362,552 Swedish men. Cancer Causes Control 2006;17: Meacham LR, Gurney JG, Mertens AC, et al. Body mass index in long-term adult survivors of childhood cancer: a report of the Childhood Cancer Survivor Study. Cancer 2005;103: Reeves BC, Ascione R, Chamberlain MH, Angelini GD. Effect of body mass index on early outcomes in patients undergoing coronary artery bypass surgery. J Am Coll Cardiol 2003;42: Romero-Corral A, Montori VM, Somers VK, et al. Association of bodyweight with total mortality and with cardiovascular events in coronary artery disease: a systematic review of cohort studies. Lancet 2006; 368: Jee SH, Sull JW, Park J, et al. Body-mass index and mortality in Korean men and women. N Engl J Med 2006;355: Gu D, He J, Duan X, et al. Body weight and mortality among men and women in China. JAMA 2006;295: Navarro WH, Loberiza FR Jr, Bajorunaite R, et al. Effect of body mass index on mortality of patients with lymphoma undergoing autologous hematopoietic cell transplantation. Biol Blood Marrow Transplant 2006;12: Zhu K, Wu H, Jatoi I, et al. Body mass index and use of mammography screening in the United States. Prev Med 2006;42: WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363: Luo N, Seng BK, Thumboo J, et al. A Study of the Construct Validity of the Health Utilities Index Mark 3 (HUI3) in Patients with Schizophrenia. Qual Life Res 2006;15: Wang Q, Chen G. The health status of the Singaporean population as measured by a multi-attribute health status system. Singapore Med J 1999;40: Luo N, Chew LH, Fong KY, et al. Validity and reliability of the EQ-5D self-report questionnaire in Chinese-speaking patients with rheumatic diseases in Singapore. Ann Acad Med Singapore 2003;32: Luo N, Chew LH, Fong KY, et al. Validity and reliability of the EQ-5D self-report questionnaire in English-speaking Asian patients with rheumatic diseases in Singapore. Qual Life Res 2003;12: Dolan P. Modeling valuations for EuroQol health states. Med Care 1997;35: Shaw JW, Johnson JA, Coons SJ. US valuation of the EQ-5D health states: development and testing of the D1 valuation model. Med Care 2005;43: Brazier J, Roberts J, Tsuchiya A, Busschbach J. A comparison of the EQ-5D and SF-6D across seven patient groups. Health Econ 2004;13:
10 S Luo N, Chew LH, Fong KY, et al. Do English and Chinese EQ-5D versions demonstrate measurement equivalence? an exploratory study. Health Qual Life Outcomes 2003;1:7. 41 Horsman J, Furlong W, Feeny D, Torrance G. The Health Utilities Index (HUI(R)): concepts, measurement properties and applications. Health Qual Life Outcomes 2003;1: Feeny D, Furlong W, Barr RD, et al. A comprehensive multiattribute system for classifying the health status of survivors of childhood cancer. J Clin Oncol 1992; 10: Feeny D, Furlong W, Torrance GW, et al. Multiattribute and single-attribute utility functions for the health utilities index mark 3 system. Med Care 2002; 40: Brazier J, Roberts J, Deverill M. The estimation of a preference-based measure of health from the SF-36. J Health Econ 2002;21: Walters SJ, Brazier JE. Comparison of the minimally important difference for two health state utility measures: EQ-5D and SF-6D. Qual Life Res 2005;14: Thumboo J, Fong KY, Chan SP, et al. Validation of the medical outcomes study family and marital functioning measures in SLE patients in Singapore. Lupus 1999;8: Sherbourne CD, Kamberg CJ. Social functioning: family and marital functioning measures. In: Stewart Wee et al. AL, Ware JE Jr, eds. Measuring Functioning and Well-Being: The Medical Outcomes Study Approach. Durham, NC: Duke University Press, Thumboo J, Fong KY, Machin D, et al. Quality of life in an urban Asian population: the impact of ethnicity and socio-economic status. Soc Sci Med 2003;56: Roset M, Badia X, Mayo NE. Sample size calculations in studies using the EuroQol 5D. Qual Life Res 1999; 8: Pallant JF, Misajon R, Bennett E, Manderson L. Measuring the impact and distress of health problems from the individual s perspective: development of the Perceived Impact of Problem Profile (PIPP). Health Qual Life Outcomes 2006;4: Rothman KJ. No adjustments are needed for multiple comparisons. Epidemiology 1990;1: Feise RJ. Do multiple outcome measures require p-value adjustment? BMC Med Res Methodol 2002; 2:8. 53 Schulz KF, Grimes DA. Multiplicity in randomised trials I: endpoints and treatments. Lancet 2005;365: Palta M, Prineas RJ, Berman R, Hannan P. Comparison of self-reported and measured height and weight. Am J Epidemiol 1982;115: Cohen J. Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: L. Erlbaum Associates, 1988.
JOEM Volume 51, Number 4, April 2009 411 FAST TRACK ARTICLE Health and Productivity as a Business Strategy: A Multiemployer Study Ronald Loeppke, MD, MPH Michael Taitel, PhD Vince Haufle, MPH Thomas Parry,
The changing social patterning of obesity: an analysis to inform practice and policy development Final report to the Policy Research Programme, Department of Health Version 2, 5 th November 2007 Martin
Volume 12 Number 4 2009 VALUE IN HEALTH Recommendations on Evidence Needed to Support Measurement Equivalence between Electronic and Paper-Based Patient-Reported Outcome (PRO) Measures: ISPOR epro Good
Getting the most out of proms Putting health outcomes at the heart of NHS decision-making Nancy J Devlin Director of Research, Office of Health Economics John Appleby Chief Economist, The King s Fund With
Evidence Suggesting That a Chronic Disease Self-Management Program Can Improve Health Status While Reducing Hospitalization: A Randomized Trial Author(s): Kate R. Lorig, David S. Sobel, Anita L. Stewart,
MedPage Tools Guide to Biostatistics Study Designs Here is a compilation of important epidemiologic and common biostatistical terms used in medical research. You can use it as a reference guide when reading
Evidence Report/Technology Assessment Number 87 Literacy and Health Outcomes Prepared for: Agency for Healthcare Research and Quality U.S. Department of Health and Human Services 540 Gaither Road Rockville,
HEALTH NEEDS AND CONSUMER VIEWS The Unmet Health Needs of America s Children Paul W. Newacheck, DrPH* ; Dana C. Hughes, DrPH* ; Yun-Yi Hung, PhD*; Sabrina Wong, PhD, RN*; and Jeffrey J. Stoddard, MD Abstract.
Volume 12 Supplement 1 2009 VALUE IN HEALTH QALYs: Some Challenges Erik Nord, PhD, 1 Norman Daniels, PhD, 2 Mark Kamlet, PhD 3 1 Norwegian Institute of Public Health, Oslo, Norway; 2 Department of Population
The NIHR Research Design Service for Yorkshire & the Humber Introduction to the Research Process Authors Antony Arthur Beverley Hancock This Resource Pack is one of a series produced by The NIHR RDS for
Engaging patients in their healthcare HOW IS THE UK DOING RELATIVE TO OTHER COUNTRIES? ANGELA COULTER PICKER INSTITUTE EUROPE APRIL 2006 Picker Institute Europe The Picker Institute works with patients,
Falls and Fear of Falling: Which Comes First? A Longitudinal Prediction Model Suggests Strategies for Primary and Secondary Prevention Susan M. Friedman, MD, MPH,* Beatriz Munoz, MS, Sheila K. West, PhD,
P a g e 0 Restricted Enhancing Recovery Rates in IAPT Services: Lessons from analysis of the Year One data. Alex Gyani 1, Roz Shafran 1, Richard Layard 2 & David M Clark 3 1 University of Reading, 2 London
High income improves evaluation of life but not emotional well-being Daniel Kahneman 1 and Angus Deaton Center for Health and Well-being, Princeton University, Princeton, NJ 08544 Contributed by Daniel
HEN synthesis report July 2012 For which strategies of suicide prevention is there evidence of effectiveness? Ann Scott Bing Guo Abstract Suicide is a serious global public health problem; it is associated
ANNEXES 1 WHO Library Cataloguing-in-Publication Data Atlas multiple sclerosis resources in the world 2008. 1.Multiple sclerosis - ethnology. 2.Multiple sclerosis - epidemiology. 3.Multiple sclerosis -
Choices in Methods for Economic Evaluation A METHODOLOGICAL GUIDE Choices in Methods for Economic Evaluation October 2012 Department of Economics and Public Health Assessment 1 Choices in Methods for Economic
Key Measurement Issues in Screening, Referral, and Follow-Up Care for Young Children s Social and Emotional Development April 2005 Prepared by Colleen Peck Reuland and Christina Bethell of the Child and
Evidence Report/Technology Assessment Number 153 Breastfeeding and Maternal and Infant Health Outcomes in Developed Countries Prepared for: Agency for Healthcare Research and Quality U.S. Department of
Blackwell Science, LtdOxford, UKOBRobesity reviews1467-78812004 The International Association for the Study of Obesity. 616785Review ArticleWeight maintenance K. Elfhag & S. Rössner obesity reviews Who
5():-7, GUEST EDITORIAL Twenty Statistical Errors Even YOU Can Find in Biomedical Research Articles Tom Lang Tom Lang Communications, Murphys, Ca, USA Critical reviewers of the biomedical literature have
Evidence Report/Technology Assessment Number 136 Value of the Periodic Health Evaluation Prepared for: Agency for Healthcare Research and Quality U.S. Department of Health and Human Services 540 Gaither
obesity reviews doi: 10.1111/obr.12040 Pro v Con Debate: Role of sugar sweetened beverages in obesity Resolved: there is sufficient scientific evidence that decreasing sugar-sweetened beverage consumption
School of Health Sciences, Jönköping University Assessment and tools for follow up of patients recovery after intensive care Eva Åkerman DISSERTATION SERIES NO. 30, 2012 JÖNKÖPING 2012 Eva Åkerman, 2012
Research Report Workplace Wellness Programs Study Final Report Soeren Mattke, Hangsheng Liu, John P. Caloyeras, Christina Y. Huang, Kristin R. Van Busum, Dmitry Khodyakov, Victoria Shier RAND Health Sponsored
The Efficacy of Distant Healing : A Systematic Review of Randomized Trials John A. Astin, PhD; Elaine Harkness, BSc; and Edzard Ernst, MD, PhD Purpose: To conduct a systematic review of the available data
What Works at Work? Darcy Hill, Daniel Lucy, Claire Tyers and Laura James What works at work? Review of evidence assessing the effectiveness of workplace interventions to prevent and manage common health
JOURNAL OF ADOLESCENT HEALTH 2000;26:213 225 ORIGINAL ARTICLE Smart Teens Don t Have Sex (or Kiss Much Either) CAROLYN TUCKER HALPERN, Ph.D., KARA JOYNER, Ph.D., J. RICHARD UDRY, Ph.D. AND CHIRAYATH SUCHINDRAN,