Suchergebnisse

IntroductionThe enrolment data for Medicare, the Australian universal health insurance provider, covers almost the entire population. Medicare data are commonly used for data linkage, usually to access national medical and pharmaceutical data. However, the enrolment data also enable the identification of geographical cohorts for studies analysing exposure to environmental hazards.
Objectives and ApproachOne example of this was the ACT Asbestos Health Study examining the health risks associated with living in houses insulated with loose-fill asbestos in the Australian Capital Territory. The Medicare Enrolment File contains the personal details and addresses of all people enrolled since 1984, including all updates to these details. We linked these data to a register of ~1100 affected properties, with subsequent linkage to the national death index and the Australian Cancer Database. We estimated Standardized Incidence Ratios (SIR) for selected cancers in people living in these houses to obtain a measure of exposure to environmental risk within the population.
ResultsAfter intensive cleaning and standardisation, nearly all (99.8%) of the affected addresses were linked. There were over one million people who had at least one ACT address between 1983 and 2013, and 2% of these had lived at an affected address and classified as exposed. The adjusted incidence of mesothelioma in exposed males was 2·5 times that of unexposed males (SIR 2·54, 95% CI 1·02–5·24), and there were some statistically significant results. The study population, number of deaths and cancers of interest were validated against the ACT census and registry figures. There were some limitations in coverage due to the period of available data, the frequency of address updates, and records with postal rather than residential addresses, but these were tested by sensitivity analyses.
Conclusion/ImplicationsThe study demonstrates the power of data linkage to (a) obtain a measure of exposure to an environmental risk within a population, and (b) obtain outcomes for the resulting case and control cohorts. This method could be applied in other risk studies where exposure is based on geography.

IntroductionDescribing out-of-pocket (OOP) healthcare costs in relation to ability to pay requires multiple linked data sources not previously available. Current estimates of the progressivity of OOP healthcare costs in Australia are based on self-report surveys. Using newly linked Census to administrative income and medical claims data, we aimed to quantify, for the first time, the progressivity of OOP costs for government-subsidised out-of-hospital healthcare in Australia.
Objectives and ApproachWe used Australian Census 2011 linked to Personal Income Tax (PIT), Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) data compiled through the Multi-Agency Data Integration Project (MADIP). Personal disposable income was estimated using a combination of PIT data and Census self-reported income, and aggregated across the household to estimate equivalised household income. We estimated annual MBS (out-of-hospital only) and PBS OOP costs as a proportion of equivalised household income, and assessed progressivity by reporting this for each income decile and computing a Kakwani Index.
ResultsWe will present findings on progressivity overall, and separately by age, sex and location (incomplete at time of abstract submission).
Conclusion / ImplicationsOur study will present one measure regarding the equity of healthcare costs, and help to identify vulnerable or at-risk groups. These findings may inform policy changes on equity in the financing of healthcare. Newly linked data from the MADIP can be used to relate healthcare costs to ability to pay.

Background The health risks associated with living in houses insulated with asbestos are unknown. Loose-fill asbestos was used to insulate some houses in the Australian Capital Territory (ACT). We compared the incidence of mesothelioma and other cancers in residents of the ACT who did and did not live in these houses. Methods Our cohort study included all ACT residents identified using Medicare enrolment data. These data were linked to addresses of affected residential properties in the ACT to ascertain exposure. We followed up residents by linking data to the Australian Cancer Database and National Death Index. Outcomes were diagnosis of mesothelioma and selected other cancers. Effects were estimated for males and females separately using standardised incidence ratios (SIRs), adjusting for age and calendar time of diagnosis. Findings Between Nov 1, 1983, and Dec 31, 2013, 1 035 578 ACT residents were identified from the Medicare database. Of these, 17 248 (2%) had lived in an affected property, including seven (2%) of 285 people diagnosed with mesothelioma. The adjusted incidence of mesothelioma in males who had lived at an affected property was 2·5 times that of unexposed males (SIR 2·54, 95% CI 1·02–5·24). No mesotheliomas were reported among females who had lived at an affected property. Among individuals who had lived at an affected property, there was an elevated incidence of colorectal cancer in women (SIR 1·73, 95% CI 1·29–2·26) and prostate cancer in men (1·29, 1·07–1·54); colorectal cancer was increased, although not significantly, in males (SIR 1·32, 95% CI 0·99–1·72), with no significant increase in the other cancers studied. Interpretation Residential asbestos insulation is likely to be unsafe. Our findings have important health, social, financial, and legal implications for governments and communities in which asbestos has been used to insulate houses. ; This work was funded by the ACT Government.

BackgroundAqueous film forming foams (AFFF) containing per- and polyfluoroalkyl substances (PFAS) caused local environmental contamination in three Australian residential areas: Katherine in the Northern Territory (NT), Oakey in Queensland (Qld) and Williamtown in New South Wales (NSW). We examined whether children who lived in these areas had higher risks of developmental vulnerabilities than children who lived in comparison areas without known contamination.
MethodsAll children identified in the Medicare Enrolment File-a consumer directory for Australia's universal healthcare insurance scheme-who ever lived in exposure areas, and a sample of children who ever lived in selected comparison areas, were linked to the Australian Early Development Census (AEDC). The AEDC data were available from four cycles: 2009, 2012, 2015 and 2018. For each exposure area, we estimated relative risks (RRs) of developmental vulnerability on each of five AEDC domains and a summary measure, adjusting for sociodemographic characteristics and other potential confounders.
FindingsWe included 2,429 children from the NT, 2,592 from Qld and 510 from NSW. We observed lower risk of developmental vulnerability in the Communication skills and general knowledge domain in Katherine (RR = 0.74, 95% confidence interval (CI) 0.57 to 0.97), and higher risks of developmental vulnerability in the same domain (RR = 1.49, 95% CI 1.18 to 1.87) and in the Physical health and wellbeing domain in Oakey (RR = 1.31, 95% CI 1.06 to 1.61). Risks of developmental vulnerabilities on other domains were not different from those in the relevant comparison areas or were uncertain due to small numbers of events.
ConclusionThere was inadequate evidence for increased risks of developmental vulnerabilities in children who ever lived in three PFAS-affected areas in Australia.

IntroductionOfficial Australian estimates of socioeconomic inequalities in cause-specific mortality have been based on area-level socioeconomic measures. Using area-level measures is known to underestimate inequalities.
Objectives and ApproachUsing recently released census linked to mortality data, we estimate education-related inequalities in cause-specific mortality for Australia. We used 2016 Australian Census and Death Registration data (2016-17) linked via a Person Linkage Spine (linkage rates: 92% and 97%, respectively) from the Multi-Agency Data Integration Project (MADIP). Education, from the Census, was categorised as low (no secondary school graduation or other qualification), intermediate (secondary graduation with/without other non-tertiary qualifications) and high (tertiary qualification). Cause of death was coded according to the underlying cause of death using the ICD-10. We used negative binomial regression to estimate relative rates (RR) for cause-specific mortality at ages 25-84 years, in the 12-months following Census, comparing low vs high education, separately by sex and 20-year age group, adjusting for age.
Results80,317 deaths occurred among 13,856,202 people. For those aged 25-44 years, relative inequalities were large for causes related to injury and smaller for lesspreventable deaths (e.g. for men, suicide RR=5.6, 95%CI: 4.1-7.5 and brain cancer RR=1.3, 0.6-3.1). For those aged 45-64, inequalities were large for causes related to health behaviours and amenable to medical intervention, e.g. lung cancer (men RR= 6.4, 4.7-8.8) and ischaemic heart disease (women RR=5.0, 3.2-7.7), and were small for less preventable causes e.g. brain cancer (women RR=0.9, 0.6-1.3). Patterns among those aged 65-84years were similar to those aged 45-64 years.
Conclusion / ImplicationsIn Australia, inequalities in mortality are substantial. Our findings highlight the health burden from inequalities, opportunities for prevention and provide insights on targets to effectively reduce them.