Australia has a gambling problem. In the 2014-2015 financial year, expenditure on gambling across Australia exceeded $22.7 billion. Electronic Gaming (Pokie) Machines (EGMs) accounted for over half of this expenditure at $11.5 billion [1]. Excessive gambling is associated with harm, however, given difficulties in measuring and quantifying harm, gambling expenditure has been used as a proxy [2]. Expenditure is not uniform across the population and previous studies have shown the association between socio-economic deprivation and gambling participation in geographical areas of Australia [3,4] and internationally [4]. We investigate the association between socio-economic status and gambling expenditure by using SEIFA at different spatial resolution, and considering both rural and urban populations.
All EGM gambling venues in Victoria were geocoded and ABS socio-economic data extracted at the SA1 level through to the postcode level. Strong associations were present at finer spatial resolutions and generally disappeared when examined at postcode level. The correlation coefficients for total number of EGMs and total expenditure at SA1 were 0.88 and 0.87 respectively.

The emotions people experience on a daily basis have important repercussions for their behaviors, health, well-being, and even their reactions and behaviors towards other people. It is well known that emotions change as a result of engagement in different behaviors, such as physical activity, and of being exposed to different, social and physical, environments. This project aims to understand physical activity, behavior and emotions in reference to the user’s location. This study will use an observational real-time ‘people as sensors’ approach for data collection. A mobile app called: “Map my Emotions” has been developed and used to assess participants' emotions geo-tagged to their location at that specific time. This was also matched to physical activity levels assessed via GeneActiv accelerometers. Over a 1-week period, participants aged 18 and over (n=100) will be asked to log their emotions on the app three times daily, responding to brief questions regarding context and perceptions of their setting. A tangible outcome will be the production of heat maps, concurrently mapping users’ emotions, location, and physical activity. These maps can eventually be used to inform physical activity practice and policy.

Background: Three smoking cessation medicines (SCM) are subsidised: nicotine patches, bupropion and varenicline. Smoking rates vary by area and socioeconomic status. We explored geographic, demographic and health indicators associated with SCM use. Method: We used census and Pharmaceutical Benefits Schedule data for 2012. We examined primary health care districts and tested SCM use (starter or continuation packs per smoker) by regions and correlations with social and health indicators. Results: SCM use per smoker (except patches) increased with increasing remoteness (p<0.01). SCM use (except patches) was positively correlated with proportion of population that were; Indigenous (in all areas), health care card holders (in metropolitan areas), and in poor health (in regional areas). Varenicline use was positively correlated (patches were negative) with those who delaying purchase of medicines due to financial hardship. Varenicline and bupropion use was positively correlated with premature mortality, circulatory system diseases (p<0.01), and risky alcohol use but negatively correlated with high psychological distress, GP services and hospital admissions. Conclusion: SCM use varies across areas in Australia, with mixed correlations with socioeconomic and health indicators. We need better ways to encourage use of SCM among smokers residing in remote areas as this may increase their success in quitting smoking.

Diet is one area of ongoing interest, as we seek to understand who eats what, when, where and why; these choices influence the distribution of public health outcomes including diet-related ill health. This research aims to answer some aspects of these questions, focusing on the diet choices of adolescents age 10-19 years living in England. Consumption of fruit, vegetables, sugar-sweetened beverages (SSB) and water are modelled using small-area estimation methods in English nieghbourhoods to identify areas where reported portions are significantly different from recommended and average levels of consumption. The four aspects of diet are modelled from respondents in the National Diet and Nutrition Survey using pooled data from 2008-12. The results identify areas which may be prioritised for targeted interventions to support reduced consumption of SSB or an increase in adolescents consuming recommended portions of fruit, vegetables and water. Less healthy choices are also important to understand, as more than 30% of adolescents are overweight or obese. The outcomes will be valuable at a time when the sugar tax is planned to begin in 2018 to assess the local impacts a change in cost may have on SSB consumption, with the intention of reducing reported levels.