The association between obesity, health service use, and work productivity in Australia: a cross-sectional quantile regression analysis.

The burden of disease attributable to obesity is rapidly increasing and becoming a public health challenge globally. Using a nationally representative sample in Australia, this study aims to examine the association of obesity with healthcare service use and work productivity across outcome distributions. We used Household, Income and Labour Dynamics Australia (HILDA) Wave 17 (2017-2018), including 11,211 participants aged between 20 and 65 years. Two-part models using multivariable logistic regressions and quantile regressions were employed to understand variations in the association between obesity levels and the outcomes. The prevalence of overweight and obesity was 35.0% and 27.6%, respectively. After adjusting for socio-demographic factors, low socioeconomic status was associated with a higher probability of overweight and obesity (Obese III: OR = 3.79; 95% CI 2.53-5.68) while high education group was associated with a lower likelihood of being high level of obesity (Obese III OR = 0.42, 95% CI 0.29-0.59). Higher levels of obesity were associated with higher probability of health service use (GP visit Obese, III: OR = 1.42 95% CI 1.04-1.93,) and work productivity loss (number of paid sick leave days, Obese III: OR = 2.40 95% CI 1.94-2.96), compared with normal weight. The impacts of obesity on health service use and work productivity were larger for those with higher percentiles compared to lower percentiles. Overweight and obesity are associated with greater healthcare utilisation, and loss in work productivity in Australia. Australia's healthcare system should prioritise interventions to prevent overweight and obesity to reduce the cost on individuals and improve labour market outcomes.

Out-Of-Pocket Expenditure Associated with Physical Inactivity, Excessive Weight, and Obesity in China: Quantile Regression Approach.

INTRODUCTION: Previous studies exploring associations of physical inactivity, obesity, and out-of-pocket expenditure (OOPE) mainly used traditional linear regression, and little is known about the effect of both physical inactivity and obesity on OOPE across the percentile distribution. This study aims to assess the effects of physical inactivity and obesity on OOPE in China using a quantile regression approach. METHODS: Study participants included 10,687 respondents aged 45 years and older from the recent wave of the China Health and Retirement Longitudinal Study in 2015. Linear regression and quantile regression models were used to examine the association of physical activity, body weight with annual OOPE. RESULTS: Overall, the proportion of overweight and obesity was 33.2% and 5.8%, respectively. The proportion of individuals performing high-level, moderate-level, and low-level physical activity was 55.2%, 12.7%, and 32.1%, respectively. The effects of low-level physical activity on annual OOPE were small at the bottom quantiles but more pronounced at higher quantiles. Respondents with low-level activity had an increased annual OOPE of 26.9 USD, 150.3 USD, and 1,534.4 USD, at the 10th, 50th, and 90th percentiles, respectively, compared with those with high-level activity. The effects of overweight and obesity on OOPE were also small at the bottom quantiles but more pronounced at higher quantiles. CONCLUSION: Interventions that improve the lifestyles and unhealthy behaviors among people with obesity and physical inactivity are likely to yield substantial financial gains for the individual and health systems in China.