Melbourne School of Population and Global Health - Research Publications
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ItemSocio-economic pathways to diet: modelling the association between socio-economic position and food purchasing behaviour( 2006)Objectives: To examine the association between education level and food purchasing behaviour and the contribution of dietary knowledge to this relationship; and the association between household income and purchasing behaviour and the contribution made by subjective perceptions about the cost of healthy food. Design and setting: The study was conducted in Brisbane City (Australia) in 2000. The sample was selected using a stratified two-stage cluster design. Data were collected by face-to-face interview from residents of private dwellings (n ¼ 1003), and the response rate was 66.4%. Dietary knowledge was measured using a 20-item index that assessed general knowledge about food, nutrition, health and their interrelationships. Food-cost concern was measured using a three-item scale derived from principal components analysis (a ¼ 0.647). Food purchasing was measured using a 16-itemindex that reflected a household’s purchase of grocery items that were consistent (or otherwise) with dietary guideline recommendations. Associations among the variables were analysed using linear regression with adjustment for age and sex. Results: Significant associations were found between education, household income and food purchasing behaviour. Food shoppers with low levels of education, and those residing in low-income households, were least likely to purchase foods that were comparatively high in fibre and low in fat, salt and sugar. Socio-economic differences in dietary knowledge represented part of the pathway through which educational attainment exerts an influence on diet; and food purchasing differences by household income were related to diet in part via food-cost concern. Conclusions: Our findings suggest that socio-economic differences in food purchasing behaviour may contribute to the relationship between socio-economic position and food and nutrient intakes, and, by extension, to socio-economic health inequalities for diet-related disease. Further, socio-economic differences in dietary knowledge and concerns about the cost of healthy food play an important role in these relationships and hence should form the focus of future health promotion efforts directed at reducing health inequalities and encouraging the general population to improve their diets.
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ItemArea variation in mortality in Tasmania (Australia): the contributions of socioeconomic disadvantage, social capital and geographic remoteness(Elsevier, 2006)This study investigated the association between socioeconomic disadvantage, social capital, geographic remoteness and mortality in the Australian state of Tasmania. The analysis is based on death rates among persons aged 25-74 years in 41 statistical local areas (SLA) for the period 1998-2000. Multilevel binomial regression indicated that death rates were significantly higher in disadvantaged areas. There was little support for an association between social capital and mortality, thereby contesting the often held notion that social capital is universally important for explaining variations in population health. Similarly, we found little evidence of a link between geographic remoteness and mortality, which contrasts with that found in other Australian states; this probably reflects the small size of Tasmania, and limited variation in the degree of remoteness amongst its SLA.
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ItemWeight and place: a multilevel cross-sectional survey of area-level social disadvantage and overweight/obesity in Australia( 2005)OBJECTIVE: To estimate variation between small areas in adult body mass index (BMI), and assess the importance of area level socioeconomic disadvantage in predicting BMI. METHODS: We identified all census collector districts (CCDs) in the 20 innermost Local Government Areas in metropolitan Melbourne, Australia, and ranked them by the percentage of low income households (o$400/week). In all, 50 CCDs were randomly selected from the least, middle and most disadvantaged septiles of the ranked list and 4913 residents (61.4% participation rate) completed one of two surveys. Multilevel linear regression was used to estimate area level variance in BMI and the importance of area level socioeconomic disadvantage in predicting BMI. RESULTS: There were significant variations in BMI between CCDs for women, even after adjustment for individual and area SES (P¼0.012); significant area variation was not found for men. Living in the most versus least disadvantaged areas was associated with an average difference in BMI of 1.08 kg/m2 (95% CI: 0.48–1.68 kg/m2) for women, and of 0.93 kg/m2 (95% CI: 0.32–1.55 kg/m2) for men. Living in the mid versus least disadvantaged areas were associated with an average difference in BMI of 0.67 kg/m2 (95% CI: 0.09–1.26 kg/m2) for women, and 0.43 kg/m2 for men (95% CI: 0.16–1.01). CONCLUSION: These findings suggest that area disadvantage is an important predictor of adult BMI, and support the need to focus on improving local environments to reduce socioeconomic inequalities in overweight and obesity.