A cross-sectional and longitudinal study of neighbourhood disadvantage and cardiovascular disease and the mediating role of physical activity.

We investigate the prospective association between neighbourhood-level disadvantage and cardiovascular disease (CVD) among mid-to-older aged adults and whether physical activity (PA) mediates this association. The data come from the HABITAT project, a multilevel longitudinal investigation of health and wellbeing in Brisbane. The participants were 11,035 residents of 200 neighbourhoods in 2007, with follow-up data collected in 2009, 2011, 2013 and 2016. Multilevel binomial regression was used for the cross-sectional analysis and mixed-effect parametric survival models were used for the longitudinal analysis. Models were adjusted for age, sex, education, occupation, and household income. Those with pre-existing CVD at baseline were excluded from the longitudinal analyses. The mediated effect of PA on CVD was examined using multilevel generalized structural equation modelling. There was a total of 20,064 person-year observations across the five time-points clustered at three levels. Results indicated that the incidence of CVD was significantly higher in the most disadvantaged neighbourhoods (OR 1.50; HR 1.29) compared with the least disadvantaged. Mediation analysis results revealed that 11.5% of the effect of neighbourhood disadvantage on CVD occurs indirectly through PA in the most disadvantaged neighbourhoods while the corresponding figure is 5.2% in the more advantaged areas. Key findings showed that neighbourhood disadvantage is associated with the incidence of CVD, and PA is a significant mediator of this relationship. Future research should investigate which specific social and built environment features promote or inhibit PA in disadvantaged areas as the basis for policy initiatives to address inequities in CVD.

Spatial and socioeconomic inequities in liveability in Australia's 21 largest cities: Does city size matter?

Spatial and area-level socioeconomic variation in urban liveability (access to social infrastructure, public transport, open space, healthy food choices, local employment, street connectivity, dwelling density, and housing affordability) was examined and mapped across 39,967 residential statistical areas in Australia's metropolitan (n = 7) and largest regional cities (n = 14). Urban liveability varied spatially, with inner-city areas more liveable than outer suburbs. Disadvantaged areas in larger metropolitan cities were less liveable than advantaged areas, but this pattern was reversed in smaller cities. Local data could inform policies to redress inequities, including those designed to avoid disadvantage being suburbanised as cities grow and gentrify.

Supporting pandemic disease preparedness: Development of a composite index of area vulnerability.

Although pandemics are rare, planning and preparation for responding to them plays a crucial role in preventing their spread. The management and control of pandemics such as COVID-19 relies heavily on a country's health capacity. Measuring vulnerability to pandemics in geographical areas could potentially delay a pandemic's exponential growth and reduce the number of cases, which would alleviate the disease impact on communities and the health care sector. The aim of this study is to generate an area-level COVID-19 Pandemic Vulnerability Index (CPVI) and to assess its correlation with COVID-19 cases. Data were collected for Local Government Areas (LGAs) across Australia from different sources including Australia Bureau of Statistics, Australian Institute of Health and Welfare, and General Transit Feed Specification. Based on recent official reports about the COVID-19 outbreak, 18 factors were identified as influencing vulnerability to the disease within LGAs. Using factor analysis, four latent factors were identified and named as sociodemographic, medical conditions, transportation, and land use. Predicted factor scores were summed to generate a CPVI for each LGA. The CPVI was evaluated by correlating with confirmed cases of COVID-19 standardised by adult population in New South Wales and Victoria, the two Australian states with the highest numbers of confirmed cases. There was a statistically significant correlation between the CPVI and COVID-19 in New South Wales (r = 0.49) and Victoria (r = 0.48). LGAs scoring higher on the CPVI also had a higher absolute number of cases. The CPVI could be used by policymakers to identify at-risk areas and to develop preparedness and response plans to help mitigate the spread of COVID-19 and future pandemics.