Indigenous Peoples and the Australian census: value, trust, and participation

No abstract.

Estimating the RMSE of Small Area Estimates without the Tears

Small area estimation (SAE) methods can provide information that conventional direct survey estimation methods cannot. The use of small area estimates based on linear and generalized linear mixed models is still very limited, possibly because of the perceived complexity of estimating the root mean square errors (RMSEs) of the estimates. This paper outlines a study used to determine the conditions under which the estimated RMSEs, produced as part of statistical output (‘plug-in’ estimates of RMSEs) could be considered appropriate for a practical application of SAE methods where one of the main requirements was to use SAS software. We first show that the estimated RMSEs created using an EBLUP model in SAS and those obtained using a parametric bootstrap are similar to the published estimated RMSEs for the corn data in the seminal paper by Battese, Harter and Fuller. We then compare plug-in estimates of RMSEs from SAS procedures used to create EBLUP and EBP estimators against estimates of RMSEs obtained from a parametric bootstrap. For this comparison we created estimates of current smoking in males for 153 local government areas (LGAs) using data from the NSW Population Health Survey in Australia. Demographic variables from the survey data were included as covariates, with LGA-level population proportions, obtained mainly from the Australian Census used for prediction. For the EBLUP, the estimated plug-in estimates of RMSEs can be used, provided the sample size for the small area is more than seven. For the EBP, the plug-in estimates of RMSEs are suitable for all in-sample areas; out-of-sample areas need to use estimated RMSEs that use the parametric bootstrap.

Utilisation of Dental Services of Older People in Australia: An Economic Explanatory Model Based on Cost and Geographic Location

The increased percentage of older people retaining their natural dentition was associated with a burden of poor oral health and increased service demands. This study analyses the dental service utilisation of the ageing population in Australia and develops a modelled cost design that estimates the dental expenditure required to cover dental services for the aged population. Using the Australian Census of Population and Housing, ageing population and socioeconomic data were mapped to geographic boundaries and integrated with dental service provision data to estimate a model for the utilisation of dental services. The estimated financial cost of dental services was calculated based on the mean fees as per the Australian Dental Association’s Dental Fees Survey. The utilisation of the services varied considerably across the states and also by type of service, with limited numbers using periodontic services. However, there was an increase in cost for replacement and restorative services (5020 million AUD), most evident in the socioeconomic deprivation areas. In addition, the average dental services utilisation cost increased noticeably in the lower socioeconomic deciles of all regions outside major cities. The geographic maldistribution of older people significantly affects the utilisation of dental services, especially among disadvantaged communities. A predicted cost model of 6385 million AUD would cover the oral health needs of older Australians.

Migrant residential concentrations and socio-economic disadvantage in two Australian gateway cities

This article explores the relationship of residential concentrations of non-Anglophone migrants with socio-economic disadvantage at the suburb (SA2) level. We look at two main Australian gateway cities, Sydney and Melbourne. We use the ‘person-counts’ of the latest available (2016) Australian Census data, matching them with the socio-economic data provided by the Australian Bureau of Statistics ‘socio-economic indexes for areas’ (SEIFA). Our analysis shows that despite decades of careful filtering of migrants for skills and language, socio-economic disadvantage in migrant concentrations persists in the main gateway cities, being more pronounced in Melbourne than in Sydney. The article employs an original quantitative analysis in order to advance the understanding of relationship between ethnicity, socio-economic position and residential location. We seek to contribute to the ongoing scholarly and policy debate about migrant concentration areas in large immigrant-receiving cities.

1539Australia’s worsening mental health – what’s next?

Background The population prevalence of mental illness over time, and by sociodemographic subgroups, are important benchmark data. Examining reliable population level data can highlight groups with greater mental-illness related symptom burden and inform policy and strategy. Methods Secondary analysis of Australian National Health Surveys (n = 78,204) from 2001-02 to 2017-18. Trends in the prevalence of very high scores on the Kessler-10 (K10), a measure of psychological distress capturing symptoms of affective and anxiety disorders, were examined by time, age, gender, and socioeconomic status. Data were standardised to the 2001 Australian census population on the strata of sex and age. Results In 2017-18, the rate of probable mental illness was estimated at 5.1%, a 1.5% increase (representing an additional 367,000 Australians) since 2007. In 2017-18, the subgroups with the highest rates were women aged 18-24 (8.01%, 95% CI = 5.9%-10.2%), and the poorest fifth of Australians (8.02%, 95% CI = 7.0%-9.0%). Women aged 55-64 demonstrated the greatest increase in rates (2001: 3.5%, 95% CI = 2.5%-4.6%; 2017: 7.2%, 95% CI = 5.9%-8.5%; z = 4.10, p ≤ 0.001). Conclusions Despite efforts to improve population mental health, rates of probable mental illness in Australia have increased since 2007. Findings will be discussed in conjunction to extant social and health policies, and potential gaps in the delivery of gold-standard mental health care. Key messages The rate of probable mental illness in Australia seem to be increasing, especially in women aged 55-64, and those from low-SES backgrounds.

Inequalities in life expectancy in Australia according to education level: a whole-of-population record linkage study

Background Life expectancy in Australia is amongst the highest globally, but national estimates mask within-country inequalities. To monitor socioeconomic inequalities in health, many high-income countries routinely report life expectancy by education level. However in Australia, education-related gaps in life expectancy are not routinely reported because, until recently, the data required to produce these estimates have not been available. Using newly linked, whole-of-population data, we estimated education-related inequalities in adult life expectancy in Australia. Methods Using data from 2016 Australian Census linked to 2016-17 Death Registrations, we estimated age-sex-education-specific mortality rates and used standard life table methodology to calculate life expectancy. For men and women separately, we estimated absolute (in years) and relative (ratios) differences in life expectancy at ages 25, 45, 65 and 85 years according to education level (measured in five categories, from university qualification [highest] to no formal qualifications [lowest]). Results Data came from 14,565,910 Australian residents aged 25 years and older. At each age, those with lower levels of education had lower life expectancies. For men, the gap (highest vs. lowest level of education) was 9.1 (95 %CI: 8.8, 9.4) years at age 25, 7.3 (7.1, 7.5) years at age 45, 4.9 (4.7, 5.1) years at age 65 and 1.9 (1.8, 2.1) years at age 85. For women, the gap was 5.5 (5.1, 5.9) years at age 25, 4.7 (4.4, 5.0) years at age 45, 3.3 (3.1, 3.5) years at 65 and 1.6 (1.4, 1.8) years at age 85. Relative differences (comparing highest education level with each of the other levels) were larger for men than women and increased with age, but overall, revealed a 10–25 % reduction in life expectancy for those with the lowest compared to the highest education level. Conclusions Education-related inequalities in life expectancy from age 25 years in Australia are substantial, particularly for men. Those with the lowest education level have a life expectancy equivalent to the national average 15–20 years ago. These vast gaps indicate large potential for further gains in life expectancy at the national level and continuing opportunities to improve health equity.

Counting gender and sexual identity in the Australian census

No abstract

Inequalities in life expectancy in Australia according to education level: A whole-of-population record linkage study

Objective: Life expectancy in Australia is amongst the highest globally, but national estimates mask within-country inequalities. We estimate education-related inequalities in adult life expectancy in Australia. Design and setting: We estimated age-sex-education specific mortality rates using data from 2016 Australian Census linked to 2016-17 Death Registrations and standard life table methodology to calculate life expectancy. Participants: 14,565,910 Australian residents aged 25 years and older. Main outcome measure: Absolute (in years) and relative (ratios) differences in life expectancy at ages 25, 45, 65 and 85 years by sex and education level (5 levels, from Bachelor degree or higher [highest] to no secondary or post-secondary qualification [lowest]). Results: At each age, those with lower education had shorter life expectancies. For men, the gap (highest vs lowest education) was 9.1 (95%CI: 8.8, 9.4) years at age 25, 7.3 (7.1, 7.5) years at age 45, 4.9 (4.7, 5.1) years at age 65 and 1.9 (1.8, 2.1) years at age 85. Absolute gaps were smaller for women compared with men but remained substantial: 5.5 (5.1, 5.9) years at age 25, 4.7 (4.4, 5.0) years at age 45, 3.3 (3.1, 3.5) years at 65 and 1.6 (1.4, 1.8) years at age 85. Relative differences were larger for men and increased with age. Conclusion: Education-related inequalities in life expectancy from age 25 years in Australia are substantial such that those with the lowest education have a life expectancy equivalent to the national average 15-20 years ago. These vast gaps indicate large potential for further population health gains.