Trends and Factors Associated with Obesity Prevalence in Rural Australian Adults-Comparative Analysis of the Crossroads Studies in Victoria over 15 Years.
Stephanie HannahKingsley Emwinyore AghoMilan K PiyaKristen M GlenisterLisa BourkeUchechukwu Levi OsuagwuDavid SimmonsPublished in: Nutrients (2022)
This study examined the changes in the prevalence of obesity and associated lifestyle factors using data from repeated cross-sectional, self-reported surveys (Crossroads I: 2001-2003 and Crossroads II: 2016-2018, studies) and clinic anthropometric measurements collected from regional and rural towns in the Goulburn Valley, Victoria. Given that past community studies have only focused categorically on dietary intake, or assessed caloric energy intake, we examined the difference in broad dietary practices at two different times. Clinical assessments from randomly selected household participants aged ≥18 years were analyzed. Differences in obesity prevalence were calculated for each individual variable. Logistic regression was used to determine the odds ratios (95% confidence intervals (CI)) with and without adjustment for key lifestyle factors. There were 5258 participants in Crossroads I and 2649 in Crossroads II surveys. Obesity prevalence increased from 28.2% to 30.8% over 15 years, more among those who ate fried food, but decreased significantly among rural dwellers (31.7: 27.0, 36.8% versus 25.1: 22.9, 27.5%) and those who had adequate fruit intake (28.5: 25.0, 32.3% to 23.9: 21.8, 26.2%). Obesity was associated with older age (≥35 years), use of fat-based spreads for bread (adjusted odds ratio, aOR:1.26: 1.07, 1.48) and physical inactivity. The increase in obesity prevalence especially in the rural towns, was associated with unhealthy dietary behaviour which persisted over 15 years. Understanding and addressing the upstream determinants of dietary intake and choices would assist in the development of future health promotion Programs.
Keyphrases
- metabolic syndrome
- weight loss
- insulin resistance
- weight gain
- type diabetes
- high fat diet induced
- risk factors
- cross sectional
- south africa
- physical activity
- adipose tissue
- healthcare
- mental health
- cardiovascular disease
- machine learning
- skeletal muscle
- fatty acid
- artificial intelligence
- electronic health record
- risk assessment
- case control
- big data
- deep learning
- human health