Association Between Body Mass Index and Health-Related Quality of Life Among an Australian Sample

doi:10.1016/j.clinthera.2011.08.009

Clinical Therapeutics

Volume 33, Issue 10, October 2011, Pages 1466-1474

https://doi.org/10.1016/j.clinthera.2011.08.009 Get rights and content

Abstract

Objective

This study investigated the association between body mass index (BMI) and changes in BMI over time with health-related quality-of-life data among a general and representative sample of the Australian population.

Methods

The sample consisted of respondents between the ages of 18 and 79 who completed the Household, Income and Labour Dynamics in Australia (HILDA) Survey in 2007 and 2009. These respondents completed the SF-36 questionnaire and provided data on their height, weight, medical conditions, and sociodemographic characteristics. SF-36 questionnaire responses were converted into health state utility values using the SF-6D algorithm. Regression analysis was used to examine the relationship between BMI and utility, controlling for a range of obesity-related medical conditions and sociodemographic characteristics.

Results

Obese men (BMI value ≥30) had, on average, a lower utility score (–0.0190, P < 0.001) than men within an “acceptable” BMI range (BMI 18.5 to <25). Obese women (BMI value ≥30) also had, on average, a lower utility score (–0.0338, P < 0.001) than women within an acceptable BMI range (18.5 to <25). Although BMI was not associated longitudinally with utility, there was a statistically significant negative longitudinal relationship between arthritis (–0.0153, P < 0.01) and depression/anxiety disorders (–0.0358, P < 0.001) and utility.

Conclusions

Cross-sectional results suggest that BMI is negatively associated with utility and that further investigation of the longitudinal relationship between BMI and utility is warranted.

Introduction

Obesity has become a worldwide epidemic. Based on measured height and weight data from the National Health and Nutrition Examination Survey, it is estimated that nearly one third of the US population is obese.¹ In Australia, by way of comparison, it is estimated that one fourth of the population is obese based on measured height and weight data from the 2007–2008 National Health Survey.² The degree to which a person is classified as being overweight is usually defined by body mass index (BMI), which is calculated as weight in kilograms divided by height in meters squared. The World Health Organization (WHO) defines an “acceptable” weight as a BMI value between 18.5 and <25, overweight as a BMI value between 25 and <30, and obese as a value ≥30.³

The medical literature has demonstrated clearly that being overweight or obese is a risk factor for many serious medical conditions, including type 2 diabetes, hypertension, coronary heart disease (CHD), elevated cholesterol levels, depression, musculoskeletal disorders, gallbladder disease, and several cancers.4, 5, 6 Moreover, evidence from the Framingham Heart Study has indicated this increased risk of diseases can also result in relatively large decrements in life expectancy.⁷ Although many of these medical conditions associated with obesity increase the risk of mortality, they also affect an individual's health-related quality of life (HRQoL).8, 9, 10, 11, 12, 13, 14

In measuring HRQoL, health outcome researchers have generally used utility-based methods to obtain individual preferences through techniques such as the standard gamble and the time trade-off technique to combine the quality and quantity of life into a single measure such as quality-adjusted life years (QALYs).

A range of previous studies have explored the relationship between obesity and generic HRQoL, such as those using the SF-36 survey.15, 16, 17, 18, 19 A consistent finding reported across these studies is the negative impact that obesity (as measured by BMI) exerts on HRQoL. Although these studies provide useful insights into the association between obesity and HRQoL, they do not provide an overall estimate of the impact of BMI on a preference-based measure of health outcomes such as QALYs.²⁰

In recent years, however, the preference-based measurement of health outcomes has been assisted by the development of several algorithms that transform item responses from generic health questionnaires such as the SF-36 into health state utility values.21, 22, 23, 24, 25 For example, the SF-6D,²⁰ which is a summary preference-based measure of health derived from the SF-36 questionnaire, has been used in a number of studies to examine the association between BMI and utility.8, 9, 10, 12

The purpose of this paper is to contribute to this growing body of literature by estimating for a representative general sample of the Australian population (1) the cross-sectional association between BMI and utility for individuals participating in the Household, Income and Labour Dynamics in Australia (HILDA) Survey; and (2) the longitudinal relationship between BMI and utility for respondents who participated in both the 2007 and 2009 HILDA waves.

In addition to providing recent estimates for Australia, we also correct for the presence of measurement error in self-reported height and weight, using a recently developed Australian algorithm, and provide—to our knowledge—the first initial analysis of the longitudinal relationship between BMI and utility using Australian data.

Section snippets

Data Description and Sample

Data for this study were taken from HILDA, a longitudinal survey that commenced in 2001 with a national probability sample of Australian households. We exploit data available in the 2007 and 2009 waves of the HILDA Survey, which contain detailed sociodemographic information on participants who completed the SF-36 health survey and questions relating to their height and weight and the presence of medical conditions. For a detailed discussion of the survey and sampling methods see Wooden and

Results

Table I reports the summary statistics for the 8230 men and 8258 women in the sample. Among men in the sample, 75% are overweight or obese, whereas among women 64% are overweight or obese. The mean value for the SF-6D was 0.78 for men and 0.76 for women. For men, the medical conditions with the highest reported prevalence were hypertension (16%) and arthritis (14%). For women, the medical conditions with the highest reported prevalence were arthritis (21%), hypertension (19%), and

Discussion

The results of this study indicate that utility scores are responsive to an individual's BMI. The pooled cross-sectional regression analyses indicate that BMI and the vast majority of obesity-related medical conditions have a negative impact on utility after controlling for age, education, income, smoking status, and period effect. Obese men and women (BMI ≥30) had, on average, lower utility scores than men and women within an acceptable BMI range (–0.0190 and –0.0338, respectively). These

Conclusions

Cross-sectional analysis provides evidence of a statistically negative association between BMI and utility. Longitudinal analysis, however, suggests that only a few variables—namely, the presence of arthritis and depression/anxiety—were statistically significant in predicting change in utility. Our preferred interpretation of why we did not observe a longitudinal relationship between BMI and utility is that the impact of BMI on HRQoL is likely to take several years to manifest and is thus not

Acknowledgments

This article uses unit record data from the HILDA Survey. The HILDA Project was initiated and is funded by the Australian Government Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA) and is managed by the Melbourne Institute of Applied Economic and Social Research (MIAESR). The findings and views reported in this article, however, are those of the authors and should not be attributed to either FaHCSIA or the MIAESR. Without implication, we would also like to

References (31)

G. Bray
Pathophysiology of obesity
Am J Clin Nutr
(1992)
G. Colditz
Economic costs of obesity
Am J Clin Nutr
(1992)
F.X. Pi-Sunyer
A review of long-term studies evaluating the efficacy of weight loss in ameliorating disorders associated with obesity
Clin Ther
(1996)
S.L. Dennett et al.
The impact of body weight on patient utilities with or without type 2 diabetes: a review of the medical literature
Value Health
(2008)
J. Brazier et al.
The estimation of a preference-based measure of health from the SF-36
J Health Econ
(2002)
A.J. Hayes et al.
Estimating equations to correct self-reported height and weight: implications for prevalence of overweight and obesity in Australia
Aust N Z J Public Health
(2008)
M.L. Baskin et al.
Prevalence of obesity in the United States
Obes Rev
(2005)
National health survey: summary of results, 2007–2008 (reissue)
(2008)
J.C. Seidell et al.
Assessing obesity: classification and epidemiology
Br Med Bull
(1997)
A. Peeters et al.
Obesity in adulthood and its consequences for life expectancy: a life-table analysis
Ann Intern Med
(2003)

R.T. Ackermann et al.

Changes in health state utilities with changes in body mass in the diabetes prevention program

Obesity

(2009)

S. Dixon et al.

Utility values for obesity and preliminary analysis of the Health Outcomes Data Repository

Expert Rev Pharmacoecon Outcomes Res

(2004)

H. Jia et al.

The impact of obesity on health-related quality-of-life in the general adult US population

J Public Health (Oxf)

(2005)

M.A. Kortt et al.

Estimating utility values for health states of overweight and obese individuals using the SF-36

Qual Life Res

(2005)

T.H. Sach 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

(2007)

Cited by (30)

Morbid obesity, multiple long-term conditions, and health-related quality of life among Australian adults: Estimates from three waves of a longitudinal household survey
2022, Preventive Medicine Reports
Citation Excerpt :
These findings are consistent with the existing literature, which has reported a strong correlation between severe obesity and reduced HRQoL in Spain (Busutil et al., 2017), the United States (Fontaine and Barofsky, 2001; Andenæs et al., 2012), Brazil (Tamura et al., 2017), and Norway (Duval et al., 2006), particularly in relation to the physical and physiological components of HRQoL (Pimenta et al., 2015; Hopman et al., 2007; Swinburn and Wood, 2013). However, these findings do contradict those of an earlier Australian study that found no support for a negative association between BMI and quality of life over time using earlier waves of the HILDA dataset (Kortt and Dollery, 2011). One possible explanation for this difference is that the so-called obesity epidemic was a relatively new phenomenon at the time of the earlier paper.
This study aims to investigate the impact of morbid obesity and multiple long-term conditions (MLTCs) on health-related quality of life (HRQoL). Data for this study were sourced from three waves (waves 9, 13 and 17) of the Household, Income and Labour Dynamics in Australia (HILDA) survey. The paper analyses 37,887 person-year observations from 19,387 individuals during the period 2009–2017. The longitudinal random-effects Tobit model was fitted to examine the association between morbid obesity, MLTCs and HRQoL. This study found that morbid obesity and MLTCs were both negatively associated with HRQoL as measured through physical component summary (PCS), mental component summary (MCS), and the short-form six-dimension utility index (SF-6D) of the 36-item Short-Form Health Survey (SF-36). Morbidly obese scored lower points on the PCS (β = −5.05, 95% CI: −5.73, −4.37), MCS (β = −1.03, 95% CI: −1.84, −0.23), and in the SF-6D utility index (β = −0.045, 95% CI: −0.054, −0.036) compared to their healthy weight counterparts. Similar findings were observed for individuals with MLTCs, with lower scores for the PCS (β = −4.79, 95% CI: −5.20, −4.38), MCS (β = −4.95, 95% CI: −5.43, −4.48), and SF-6D utility (β = −0.071, 95% CI: −0.076, −0.066). Additionally, multiplicative interaction between morbid obesity and MLTCs was observed to modestly exacerbated the negative effect of morbid obesity on PCS scores (β = −1.69, 95% CI: −2.74, −0.64). The interaction effect, on the other hand, significantly lessen the unfavourable effect of morbid obesity on the MCS score (β = 1.34, 95% CI: 0.10, 2.58). The findings of this study will be useful for future cost-effectiveness analyses and measuring the burden of diseases since it provides information on the disutility associated with morbid obesity and MLTCs.
Association of sleep duration and sleep quality with the physical, social, and emotional functioning among Australian adults
2018, Sleep Health
Citation Excerpt :
Covariates were all self-reported and comprised key sociodemographic factors, health behaviors, obesity, pain, physical and mental illnesses. These variables were identified a priori due to their established associations with sleep and functioning in these data and elsewhere.22–26 For the descriptive analyses, age was classified into two groups: 15 to 44 years, and 45 years or more, as poor sleep quality tends to increase particularly in midlife23,27 alongside declines in health and functioning.26
We aimed to evaluate the interaction of two key determinants of sleep health, quantity and quality, with physical, emotional, and social functioning, in the general population.
Nationally-representative Australian cross-sectional study.
General population.
14,571 people aged 15 or older in Household, Income and Labor Dynamics in Australia (HILDA) in 2013.
The associations of sleep quality (good/poor) in combination with mid-range (6–8 hours), short (<6) or long (>8) sleep duration with functioning, determined from the SF-36, were evaluated using logistic regression adjusting for sociodemographic, relationships, health behaviors, obesity, pain, and mental and physical illness confounders.
After adjusting for gender, and age, poor sleep quality in combination with short, mid-range and long sleep was associated with worse physical, emotional and social functioning. Pain and comorbid illness explained much of these associations, while attenuation from other covariates was minor. The associations of poor sleep quality with worse functioning remained after full adjustment regardless of sleep duration, while among people with good quality sleep, only those with long sleep duration reported poorer functioning.
Poor sleep quality has robust associations with worse functioning regardless of total duration in the general population. There appears to be a substantial number of functional short sleepers with good quality sleep.
Economic analysis of the link between diet quality and health: Evidence from Kosovo
2017, Economics and Human Biology
We analyse the link between diet diversity, (which is a proxy of diet quality) and health outcomes measured by body-mass index (BMI) in a representative sample of Kosovar adults using household expenditure micro-data. Building on a household model of health production we devise a two-stage empirical strategy to estimate the determinants of diet diversity and its effect on BMI. Economic factors and demographic characteristics play an important role in the choice of balanced diets. Results from the BMI analysis support the hypothesis that diet diversity is associated with optimal BMI. One standard deviation increase in diet diversity leads to 2.3% increase in BMI of the underweight individuals and to 1.5% reduction in BMI of the obese individuals. The findings have important implications for food security policies aiming at enhancing the public health in Kosovo.
The associations between overweight, weight change and health related quality of life: Longitudinal data from the Stockholm Public Health Cohort 2002-2010
2015, Preventive Medicine
Citation Excerpt :
Overweight and obesity can be considered as one of the leading preventable causes of death (Neovius et al., 2009). Preceding cross-sectional studies from both the general population and disease specific populations have consistently shown that people with obesity and overweight report lower health related quality of life (HRQoL) compared to those with normal weight (de Zwaan et al., 2009; Dey et al., 2013; Hassan et al., 2003; Hopman et al., 2007; Huang et al., 2006; Jia and Lubetkin, 2005; Kearns et al., 2013; Kortt and Dollery, 2011; Larsson et al., 2002; Renzaho et al., 2010; Soltoft et al., 2009). While there are a few observational studies on the association between weight change and HRQoL based on selected populations (e.g. with underlying diseases, high risk profile or derived from an earlier intervention study) (Cameron et al., 2012; Leon-Munoz et al., 2005; Muller-Nordhorn et al., 2014; Seppälä et al., 2014; Verkleij et al., 2013), longitudinal analyses on the influence of weight change on HRQoL in the general population remain scarce and results are somewhat inconsistent, partly due to varying study populations, HRQoL instruments or statistical modeling (Laxy et al., 2014; Milder et al., 2014).
Cross-sectional studies have shown that people with obesity and overweight report lower health related quality of life (HRQoL). With a lack of longitudinal studies, this study aims to assess the association between eight-year weight change and HRQoL measured by the EQ-5D instrument and to investigate whether the association differs with regard to baseline body mass index (BMI).
A population-based survey was conducted among a random sample of 31,182 individuals from Stockholm County aged 18–84 years in 2002 and reassessed in 2010 and supplemented by record linkage with regional and national registers. Multivariate Poisson regression and linear regression were conducted with adjustments for socio-demographic and health-related variables and baseline BMI category as effect modifier for the association between weight change and HRQoL.
Individuals with overweight and obesity respectively have 0.014 and 0.039 lower EQ-5D indexes compared to those being normal weight. Over the eight-year follow-up, 17.6% gained moderately (≥ 5% body weight) and 13.9% gained heavily (≥ 10% body weight) in weight. In the fully adjusted analysis, heavy weight gain was associated with a significantly lower overall EQ-5D index and an increased risk of reporting impairment in all but one EQ-5D dimensions irrespective of baseline BMI category. Weight reduction had no significant preventive effect.
Next to obesity status itself, weight gain leads to impairment in HRQoL irrespective of BMI category at baseline while eight year weight loss seems not to have the reversed effect on HRQoL, emphasizing the importance of primary prevention of weight gain.
Impact of elevated BMI and types of comorbid conditions on health-related quality of life in a nationally representative US sample
2021, Public Health Nutrition
The association between BMI and self-reported health among a Brazilian sample: a cross-sectional study
2024, Quality of Life Research

View all citing articles on Scopus

View full text

Pharmaceutical economics & health policyOriginal researchAssociation Between Body Mass Index and Health-Related Quality of Life Among an Australian Sample

Abstract

Objective

Methods

Results

Conclusions

Introduction

Section snippets

Data Description and Sample

Results

Discussion

Conclusions

Acknowledgments

Am J Clin Nutr

Am J Clin Nutr

Clin Ther

Value Health

J Health Econ

Aust N Z J Public Health

Prevalence of obesity in the United States

Obes Rev

National health survey: summary of results, 2007–2008 (reissue)

Assessing obesity: classification and epidemiology

Br Med Bull

Obesity in adulthood and its consequences for life expectancy: a life-table analysis

Ann Intern Med

Changes in health state utilities with changes in body mass in the diabetes prevention program

Obesity

Utility values for obesity and preliminary analysis of the Health Outcomes Data Repository

Expert Rev Pharmacoecon Outcomes Res

The impact of obesity on health-related quality-of-life in the general adult US population

J Public Health (Oxf)

Estimating utility values for health states of overweight and obese individuals using the SF-36

Qual Life Res

The relationship between body mass index and health-related quality of life: comparing the EQ-5D, EuroQol VAS and SF-6D

Int J Obes

Pharmaceutical economics & health policy
Original research
Association Between Body Mass Index and Health-Related Quality of Life Among an Australian Sample