Search

Research into the link between food environments and health is scarce. Research in this field has progressed, and new comprehensive methods (i.e., incorporating all food retail outlets) for classifying food retail environments have been developed and are yet to be examined alongside measures of obesity. In this study, we examine the association and temporal trends between the food environment and BMI of a repeated cross-sectional sample of the adult population between 2008 and 2016. Methods: Food retail data for 264 postal areas of Greater Melbourne was collected for the years 2008, 2012, 2014, and 2016, and a container-based approach was used to estimate accessibility to supermarkets, healthy and unhealthy outlets. Data on BMI for postal areas was obtained from the Victorian Population Health Survey (n = 47,245). We estimated the association between the food environment and BMI using linear mixed models. Results indicated that BMI increased as accessibility to healthy outlets decreased by up to −0.69 kg/m2 (95%CI: −0.95, −0.44). BMI was lower with high and moderate access to supermarkets compared to low access by −0.33 kg/m2 (−0.63, −0.04) and −0.32 kg/m2 (−0.56, −0.07), and with high access to unhealthy outlets compared to low access (−0.38 kg/m2: −0.64, −0.12) and moderate access (−0.54 kg/m2: −0.78, −0.30). Conclusion: Our results show that increasing access and availability to a diverse range of food outlets, particularly healthy food outlets, should be an important consideration for efforts to support good health. This research provides evidence that Australia needs to follow suit with other countries that have adopted policies giving local governments the power to encourage healthier food environments. [ABSTRACT FROM AUTHOR]

Prior to the 2020 outbreak of COVID-19, 70% of Australians' food purchases were from supermarkets. Rural communities experience challenges accessing healthy food, which drives health inequalities. This study explores the impact of COVID-19 on food supply and purchasing behaviour in a rural supermarket. Group model building workshops explored food supply experiences during COVID-19 in a rural Australian community with one supermarket. We asked three supermarket retailers "What are the current drivers of food supply into this supermarket environment?" and, separately, 33 customers: "What are the current drivers of purchases in this supermarket environment?" Causal loop diagrams were co-created with participants in real time with themes drawn afterwards from coded transcripts. Retailers' experience of COVID-19 included 'empty shelves' attributed to media and government messaging, product unavailability, and community fear. Customers reported fear of contracting COVID-19, unavailability of food, and government restrictions resulting in cooking more meals at home, as influences on purchasing behaviour. Supermarket management and customers demonstrated adaptability and resilience to normalise demand and combat reduced supply. [ABSTRACT FROM AUTHOR]

Obesity prevalence is inequitably distributed across geographic areas. Food environments may contribute to health disparities, yet little is known about how food environments are evolving over time and how this may influence dietary intake and weight. This study aimed to analyse intra-city variation in density and healthiness of food outlets between 2008 and 2016 in Melbourne, Australia. Food outlet data were classified by location, type and healthiness. Local government areas (LGAs) were classified into four groups representing distance from the central business district. Residential population estimates for each LGA were used to calculate the density of food outlets per 10,000 residents. Linear mixed models were fitted to estimate the mean density and ratio of 'healthy' to 'unhealthy' food outlets and food outlet 'types' by LGA group over time. The number of food outlets increased at a faster rate than the residential population, driven by an increasing density of both 'unhealthy' and 'healthy' outlets. Across all years, ratios of 'unhealthy' to 'healthy' outlets were highest in LGAs located in designated Growth Areas. Melbourne's metropolitan food environment is saturated by 'unhealthy' and 'less healthy' food outlets, relative to 'healthy' ones. Melbourne's urban growth areas had the least healthy food environments.

(1) Background: Childhood overweight and obesity is a significant and preventable problem worldwide. School environments have been suggested to be plausible targets for interventions seeking to improve the quality of children's dietary intake. The objective of this study was to determine the extent to which the current characteristics of the school food environment were associated with primary school students' dietary intake and Body Mass Index (BMI) z scores in a representative sample in regional Victoria. (2) Methods: This study included 53 schools, comprising a sample of 3,496 students in year levels two (aged 7-8 years), four (9-10 years) and six (11-12 years). Year four and six students completed dietary questionnaires. Principals from each school completed a survey on school food environment characteristics. Mixed-effects logistic regression was used to assess the relationship between students' dietary intake and school food environment scores, controlling for confounders such as socio-economic status, school size and sex. Food environment scores were also analysed against the odds of being healthy weight (defined as normal BMI z score). (3) Results: Mixed associations were found for the relationship between students' dietary intake and food environment scores. Meeting the guidelines for vegetable intake was not associated with food environment scores, but students were more likely (OR: 1.68 95% CI 1.26, 2.24) to meet the guidelines if they attended a large school (>300 enrolments) and were female (OR: 1.28 95% CI: 1.02, 1.59). Healthy weight was not associated with school food environment scores, but being a healthy weight was significantly associated with less disadvantage (OR: 1.24 95% CI 1.05, 1.45). Conclusion: In this study, the measured characteristics of school food environments did not have strong associations with dietary intakes or BMI among students.

Background : Behavioral factors such as physical activity, sedentary behavior and diet have previously been found to be key modifiable determinants of childhood overweight and obesity, yet require further investigation to provide an understanding of their potential influence on sleep outcomes along with the sleep-obesity nexus. Methods : The study included 2253 students (ages 8.8-13.5) from two monitoring studies across regional Victoria. Students completed a self-report electronic questionnaire on demographic characteristics, health behaviors (including sleep, physical activity, screen time and diet) and well-being, and were invited to have anthropometric measurements (height and weight) taken. Regression models were used to assess the associations between sleep, behavioral factors and BMI z-scores. Results : Screen time (particularly in bed) and sugar-sweetened beverage (SSB) consumption were shown to increase the likelihood of having more than three sleep problems, while physical activity and other dietary factors were not. After controlling for these behaviors, significance remained for having two or more than three sleep problems and an increased odds of overweight/obesity. Conclusions : This study highlights how the usage of screen devices and SSB consumption behaviors might influence children's weight status via the sleep-obesity nexus.

Background: Tracking population trends in childhood obesity and identifying target areas for prevention requires accurate prevalence data. This study quantified the magnitude of non-participation bias for mean Body Mass Index-z scores and overweight/obesity prevalence associated with low (opt-in) compared to high (opt-out) participation consent methodologies. Methods: Data arose from all Local Government Areas (LGAs) participating in the Healthy Together Victoria Childhood Obesity Study, Australia. Primary schools were randomly selected in 2013 and 2014 and all Grades 4 and 6 students (aged approx. 9-12 years) were invited to participate via opt-in consent (2013) and opt-out consent (2014). For the opt-in wave N = 38 schools (recruitment rate (RR) 24.3%) and N = 856 students participated (RR 36.3%). For the opt-out wave N = 47 schools (RR 32%) and N = 2557 students participated (RR 86.4%)., Outcomes: differences between opt-in and opt-out sample estimates (bias) for mean BMI-z, prevalence of overweight/obesity and obesity (alone). Standardized bias (Std bias) estimates defined as bias/standard error are reported for BMI-z. Results : The results demonstrate strong evidence of non-participation bias for mean BMI-z overall (Std bias = -4.5, p < 0.0001) and for girls (Std bias = -5.4, p < 0.0001), but not for boys (Std bias = -1.1, p = 0.15). The opt-in strategy underestimated the overall population prevalence of overweight/obesity and obesity by -5.4 and -4.5 percentage points respectively ( p < 0.001 for both). Significant underestimation was seen in girls, but not for boys. Conclusions: Opt-in consent underestimated prevalence of childhood obesity, particularly in girls. Prevalence, monitoring and community intervention studies on childhood obesity should move to opt-out consent processes for better scientific outcomes., Competing Interests: Authors have no conflicts to disclose.

Background : Community-based initiatives show promise for preventing childhood obesity. They are characterized by community leaders and members working together to address complex local drivers of energy balance. Objectives : To present a protocol for a stepped wedge cluster randomized trial in ten communities in the Great South Coast Region of Victoria, Australia to test whether it is possible to: (1) strengthen community action for childhood obesity prevention, and (2) measure the impact of increased action on risk factors for childhood obesity. Methods: The WHO STOPS intervention involves a facilitated community engagement process that: creates an agreed systems map of childhood obesity causes for a community; identifies intervention opportunities through leveraging the dynamic aspects of the system; and, converts these understandings into community-built, systems-oriented action plans. Ten communities will be randomized (1:1) to intervention or control in year one and all communities will be included by year three. The primary outcome is childhood obesity prevalence among grade two (ages 7-8 y), grade four (9-10 y) and grade six (11-12 y) students measured using our established community-led monitoring system (69% school and 93% student participation rate in government and independent schools). An additional group of 13 external communities from other regions of Victoria with no specific interventions will provide an external comparison. These communities will also allow us to assess diffusion of the intervention to control communities during the first three years of the trial. Conclusion : This trial will test effectiveness, over a five-year period, of community-owned, -supported and -led strategies designed to address complex and dynamic causes of childhood obesity., Competing Interests: The authors declare no conflict of interest.