The relationship between children’s mobility and obesity are affected by inequity factors, such as income, education, gender, race, religion and social capital. In Australia 25% of children are currently overweight or obese. Studies suggest that the overweight and obese are less active. This increases a child’s risk of poor physical health in both the short and long term. To effectively prevent and reduce childhood obesity it is essential to understand its relationship with children’s mobility behaviour. Recent studies link social and health inequity to the increase in childhood obesity. However, literatures are mixed in its findings in terms of magnitude and direction of equity impact on relationships between children mobility and obesity. This research aims to assess the influence of equity through a meta-analysis in respect to data source, spatial characteristics, temporal characteristics and analysis methods. This paper presents findings of relevant literature and outlines a research framework examining the role of socio-economic disadvantage and health equity in children’s mobility behaviour and its relationship with obesity. The research output will be an opportunity to magnify our understanding of individual children’s mobility behaviour, answer questions related to the surrounding environment, and promote new conversations on health inequity.

Building toilets and sustained use of toilets are critical for population health. Due to the ‘great distaste’ surrounding shit, however, there has been limited focus on sanitation and defecation as compared to safe drinking water and other hygiene behaviours. In this paper, I explore spaces where population health imperatives – i.e. reduction of faecal-oral disease transmission - intersect with complex factors that compel and constrain improved sanitation and elimination of open defecation. Drawing on evaluative research of water and sanitation interventions led by Red Cross societies in Nepal (2014) and the Philippines (2016/17), I discuss affective, proximate social pressures, biopolitical, technological, and environmental factors that influence toilet adoption in remote rural villages (Nepal) and school-based institutional settings (Philippines).

Specialist rehabilitation services (SRS) are an essential component of ongoing treatment for people with disability to optimize health and functioning. Due to geographical disparities, many of these individuals experience difficulties in accessing such services, resulting in secondary conditions. To ensure them have timely access to SRS, it is important to quantify their spatial accessibility (SA) to assist policy makers to optimally allocate the services. A widely used method to quantify SA is the two-step floating catchment area (2SFCA) method developed based on the context of primary care. Given the differences in the supply of and demand for services, it remains unclear whether the 2SFCA is applicable to SRS. This study evaluates the applicability of the 2SFCA method to measure SA using occupational therapy (OT) service, a frequently used SRS by people with disability, as the exemplar case study in the Greater Brisbane area. People with disability have complex needs, requiring a wide range of OT services. However, the 2SFCA does not differentiate the speciality expertise of OT providers. This method may overestimate the SA to OT, and alternatives would be required to better quantify SA to SRS. This study contributes to the development of SA models specific to SRS.

Studies have shown increased incidence of heart disease associated with increasing levels of socio-economic disadvantage. In Canberra there are small pockets of disadvantage surrounded by affluent housing. This may contribute to a scatter of higher incidence rates in very small areas in this population. We explored the effect of changing scale of the spatial units used in disease mapping, aiming to understand the impact for public health surveillance.
First, the Australian Bureau of Statistics Statistical Area 2 (SA2) was used as the spatial unit for analysis. Second, the smaller Statistical Area 1 (SA1) was used. Associations of age-and-sex-standardised rates for heart disease hospital admissions with disadvantage were estimated in Generalized Additive Models with penalised regression splines.
The relationships observed were different between the two types of spatial units. The SA1-level regression slope for rates against the disadvantage index varied in a linear dose-response fashion, while that found at SA2-level suggested a curvilinear form with no evidence that rates increased with increasing disadvantage beyond a threshold. From these results it can be concluded that scale of analysis does influence the understanding of geographical patterns of disadvantage and heart disease morbidity. Health surveillance should account for impacts of the scale of aggregation.