The paper describes a method of small area estimation which uses a reweighting algorithm to reweight survey data to a number of known totals (benchmarks) for small areas. The method has so far been used to estimate small area poverty rates and housing stress. The method gives poverty rates for small areas that are similar to those available from the 2006 Australian census, when the same definition of poverty was used. Various methods of validating the poverty rates have been used, including aggregating the poverty rates to a larger area and comparing them with official Australian Bureau of Statistics estimates from a survey, and applying the spatial microsimulation to larger areas and comparing with official Australian Bureau of Statistics survey results. Both these tests show that the estimates are comparable and fairly robust for most states in Australia.

Socio-environmental factors, including the neighbourhoods in which children live and grow, are key determinants of children's developmental outcomes. Thus, it is important to examine and consider the relationships between these factors and the multiple contexts that influence children. Drawing on a broad disciplinary range of existing research, we aimed to develop a conceptual model of neighbourhood effects influencing early childhood development. The neighbourhood effects literature was reviewed with a specific focus on existing models and frameworks. This review was then further expanded through consultation with our cross-disciplinary research collaboration (Kids in Communities Study Collaboration). From this a theoretical model specific to early childhood development was developed. The hypothesised model comprised five interconnected domains: physical, social, service, socioeconomic , and governance. A small trial of indicator measurement was conducted and findings were used to make a series of recommendations regarding measures or indicators which might provide useful and effective for neighbourhood effects research. The proposed model provides a useful and novel conceptual framework for classifying neighbourhood effects research. By synthesising disparate but related areas of research, the resultant five domains provide a useful approach to understanding and measuring child development in the context of community and environment, therefore advancing knowledge in this area. Expanding the current neighbourhood effects paradigm to accommodate broader constructs appears critical in considering the multiple environments that may act as key determinants of children's wellbeing and psychosocial outcomes.

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Objectives: To estimate (1) productive life years (PLYs) lost because of chronic conditions in Australians aged 45–64 years from 2010 to 2030, and (2) the impact of this loss on gross domestic product (GDP) over the same period. Design, setting and participants: A microsimulation model, Health&WealthMOD2030, was used to project lost PLYs caused by chronic conditions from 2010 to 2030. The base population consisted of respondents aged 45–64 years to the Australian Bureau of Statistics Survey of Disability, Ageing and Carers 2003 and 2009. The national impact of lost PLYs was assessed with Treasury's GDP equation. Main outcome measures: Lost PLYs due to chronic disease at 2010, 2015, 2020, 2025 and 2030 (ie, whole life years lost because of chronic disease); the national impact of lost PLYs at the same time points (GDP loss caused by PLYs); the effects of population growth, labour force trends and chronic disease trends on lost PLYs and GDP at each time point. Results: Using Health&WealthMOD2030, we estimated a loss of 347 000 PLYs in 2010; this was projected to increase to 459 000 in 2030 (32.28% increase over 20 years). The leading chronic conditions associated with premature exits from the labour force were back problems, arthritis and mental and behavioural problems. The percentage increase in the number of PLYs lost by those aged 45–64 years was greater than that of population growth for this age group (32.28% v 27.80%). The strongest driver of the increase in lost PLYs was population growth (accounting for 89.18% of the increase), followed by chronic condition trends (8.28%). Conclusion: Our study estimates an increase of 112 000 lost PLYs caused by chronic illness in older workers in Australia between 2010 and 2030, with the most rapid growth projected to occur in men aged 55–59 years and in women aged 60–64 years. The national impact of this lost labour force participation on GDP was estimated to be $37.79 billion in 2010, increasing to $63.73 billion in 2030.

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