Domestic violence in the urban fringe

Police call data for domestic violence incidents in the city of Brisbane were used to further explore the locational disadvantage thesis. It was hypothesised that the supposed additional burdens and stresses on disadvantaged families living in the outer suburbs may be reflected in significantly higher rates of reported domestic violence. Using an index of relative socioeconomic disadvantage and employing Analysis of Variance (ANOVA), this research shows that significantly higher rates of reported domestic violence occur in the inner suburbs relative to the middle or outer suburbs of Brisbane. This finding adds further doubt as to the magnitude of locational disadvantage impacts on outer suburban low income family households.

Socioeconomic disadvantage and changes in health risk behaviours in Australia: 1989-90 to 2001

Objective. Lower socioeconomic status (SES) is associated in industrialized countries with unhealthy lifestyle characteristics, such as smoking, physical inactivity and being overweight or obese. This paper examines changes over time in the association between SES and smoking status, physical activity and being overweight or obese in Australia. Methods. Data were taken from three successive national health surveys in Australia carried out in 1989-90 (n = 54 576), 1995 (n = 53 828) and 2001 (n = 26 863). Participants in these surveys were selected using a national probability sampling strategy, and aggregated data for geographical areas are used to determine the changing association between SES and lifestyle over time. Findings. Overall, men had less healthy lifestyles, In 2001 inverse SES trends for both men and women showed that those living in lower SES areas were more likely to smoke and to be sedentary and obese, There were some important socioeconomic changes over the period 1989-90 to 2001. The least socioeconomically disadvantaged areas had the largest decrease in the percentage of people smoking tobacco (24% decrease for men and 12% for women) and the largest decrease in the percentage of people reporting sedentary activity levels (25% decrease for men and 22% for women). While there has been a general increase in the percentage over time of those who are overweight or obese, there is a modest trend for being overweight to have increased (by about 16% only among females) among those living in areas of higher SES. Conclusion. Socioeconomic inequalities have been increasing for several key risk behaviours related to health; this suggests that T specific population-based prevention strategies intended to reduce health inequalities are needed.

Variation in mortality rates in Australia: correlation with Indigenous status, remoteness and socio-economic deprivation

Background The aim of this study was to study ecological correlations between age-adjusted all-cause mortality rates in Australian statistical divisions and (1) the proportion of residents that self-identify as Indigenous, (2) remoteness, and (3) socio-economic deprivation. Methods All-cause mortality rates for 57 statistical divisions were calculated and directly standardized to the 1997 Australian population in 5-year age groups using Australian Bureau of Statistics (ABS) data. The proportion of residents who self-identified as Indigenous was obtained from the 1996 Census. Remoteness was measured using ARIA (Accessibility and Remoteness Index for Australia) values. Socioeconomic deprivation was measured using SEIFA (Socio-Economic index for Australia) values from the ABS. Results Age-standardized all-cause mortality varies twofold from 5.7 to 11.3 per 1000 across Australian statistical divisions. Strongest correlation was between Indigenous status and mortality (r = 0.69, p < 0.001). correlation between remoteness and mortality was modest (r = 0.39, p = 0.002) as was correlation between socio-economic deprivation and mortality (r = -0.42, p = 0.001). Excluding the three divisions with the highest mortality, a multiple regression model using the logarithm of the adjusted mortality rate as the dependent variable showed that the partial correlation (and hence proportion of the variance explained) for Indigenous status was 0.03 (9 per cent; p = 0.03), for SEIFA score was -0.17 (3 per cent; p = 0.22); and for remoteness was -0.22 (5 per cent; p = 0.13). Collectively, the three variables studied explain 13 per cent of the variability in mortality. Conclusions Ecological correlation exists between all-cause mortality, Indigenous status, remoteness and disadvantage across Australia. The strongest correlation is with indigenous status, and correlation with all three characteristics is weak when the three statistical divisions with the highest mortality rates are excluded. intervention targeted at these three statistical divisions could reduce much of the variability in mortality in Australia.

Does health service utilisation vary by remoteness? South Australian population data and the Accessibility and Remoteness Index of Australia

Objective: To compare rates of self-reported use of health services between rural, remote and urban South Australians. Methods: Secondary data analysis from a population-based survey to assess health and well-being, conducted in South Australia in 2000. In all, 2,454 adults were randomly selected and interviewed using the computer-assisted telephone interview (CATI) system. We analysed health service use by Accessibility and Remoteness Index of Australia (ARIA) category. Results: There was no statistically significant difference in the median number of uses of the four types of health services studied across ARIA categories. Significantly fewer residents of highly accessible areas reported never using primary care services (14.4% vs. 22.2% in very remote areas), and significantly more reported high use ( greater than or equal to6 visits, 29.3% vs. 21.5%). Fewer residents of remote areas reported never attending hospital (65.6% vs. 73.8% in highly accessible areas). Frequency of use of mental health services was not statistically significantly different across ARIA categories. Very remote residents were more likely to spend at least one night in a public hospital (15.8%) than were residents of other areas (e.g. 5.9% for highly accessible areas). Conclusion: The self-reported frequency of use of a range of health services in South Australia was broadly similar across ARIA categories. However, use of primary care services was higher among residents of highly accessible areas and public hospital use increased with increasing remoteness. There is no evidence for systematic rural disadvantage in terms of self-reported health service utilisation in this State.

Evaluation of short dietary questions from the 1995 National Nutrition Survey

Six of the short dietary questions used in the 1995 National Nutrition Survey (see box below) were evaluated for relative validity both directly and indirectly and for consistency, by documenting the differences in mean intakes of foods and nutrients as measured on the 24-hour recall, between groups with different responses to the short questions. 1. Including snacks, how many times do you usually have something to eat in a day including evenings? 2. How many days per week do you usually have something to eat for breakfast? 3. In the last 12 months, were there any times that you ran out of food and couldn’t afford to buy more? 4. What type of milk do you usually consume? 5. How many serves of vegetables do you usually eat each day? (a serve = 1/2 cup cooked vegetables or 1 cup of salad vegetables) 6. How many serves of fruit do you usually eat each day? (a serve = 1 medium piece or 2 small pieces of fruit or 1 cup of diced pieces) These comparisons were made for males and females overall and for population sub-groups of interest including: age, socio-economic disadvantage, region of residence, country of birth, and BMI category. Several limitations to this evaluation of the short questions, as discussed in the report, need to be kept in mind including: · The method for comparison available (24-hour recall) was not ideal (gold standard); as it measures yesterday’s intake. This limitation was overcome by examining only mean differences between groups of respondents, since mean intake for a group can provide a reasonable approximation for ‘usual’ intake. · The need to define and identify, post-hoc, from the 24-hour recall the number of eating occasions, and occasions identified by the respondents as breakfast. · Predetermined response categories for some of the questions effectively limited the number of categories available for evaluation. · Other foods and nutrients, not selected for this evaluation, may have an indirect relationship with the question, and might have shown stronger and more consistent responses. · The number of responses in some categories of the short questions eg for food security may have been too small to detect significant differences between population sub-groups. · No information was available to examine the validity of these questions for detecting differences over time (establishing trends) in food habits and indicators of selected nutrient intakes. By contrast, the strength of this evaluation was its very large sample size, (atypical of most validation studies of dietary assessment) and thus, the opportunity to investigate question performance in a range of broad population sub-groups compared with a well-conducted, quantified survey of intakes. The results of the evaluation are summarised below for each of the questions and specific recommendations for future testing, modifications and use provided for each question. The report concludes with some general recommendations for the further development and evaluation of short dietary questions.

The geography of reported domestic violence in Brisbane: a social justice perspective

Police call data and court data are used to map the incidence of reported domestic violence in Brisbane. These data are correlated with low family income, unemployment and a measure of multiple disadvantage (an Index of Relative Socio-Economic Disadvantage) for each Statistical Local Area (suburb) in Brisbane. Only the Index of Relative Socio-Economic Disadvantage is a statistically significant predictor of reported domestic violence. The finding of a significantly higher incidence of reported domestic violence among relatively worse-off families is investigated within a social justice context. A measure of multiple relative disadvantage is shown to better reflect the negative impacts of structural inequalities on families in explaining the reported occurrence of domestic violence.

A typology of community opportunity and vulnerability in metropolitan Australia

A multivariate model using hierarchical clustering and discriminant analysis is used to identify clusters of community opportunity and community vulnerability across Australia's mega metropolitan regions, Variables used in the model measure aspects of structural economic change, occupational change, human capital, income, unemployment, family/household disadvantage, and housing stress. A nine-cluster solution is used to categorise communities across metropolitan space. Significant between-city variations in the incidence of these clusters of opportunity and vulnerability are apparent, suggesting the emergence of marked differentiation between Australia's mega metropolitan regions in their adjustments to changing economic and social conditions. JEL classification: C49, R11, R12.