Association between body mass index and health-related quality of life among an Australian sample

Clin Ther. 2011 Oct;33(10):1466-74. doi: 10.1016/j.clinthera.2011.08.009. Epub 2011 Sep 15.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Algorithms
  • Australia / epidemiology
  • Body Mass Index*
  • Cross-Sectional Studies
  • Female
  • Health Status Indicators*
  • Humans
  • Male
  • Middle Aged
  • Obesity / epidemiology
  • Quality of Life*
  • Regression Analysis
  • Socioeconomic Factors
  • Surveys and Questionnaires
  • Young Adult