Suchergebnisse

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.

IntroductionCardiovascular events are largely preventable though access to timely quality primary health care and use of guideline-recommended medication. However, around half of Australians with cardiovascular disease (CVD) are not receiving best practice treatment. This study aims to identify factors associated with under treatment, using National Health Survey (NHS) linked for the first time to administrative health data.
Objectives and ApproachParticipants with self-reported CVD in the NHS 2014-15 were included in the study, with data linked to Medicare (MBS) and pharmaceutical (PBS) data by the Australian Bureau of Statistics through the Multi-Agency Data Integration Project (MADIP). Use of primary and specialist ambulatory care and blood pressure- and lipid-lowering medications and their relation to sociodemographic and health characteristics were quantified using logistic and Poisson regression analyses.
Results1100 participants with self-reported CVD were available for analysis, with linkage rates for NHS data to a Person Linkage Spine of 95%. We will present our findings from adjusted regression models (incomplete at time of abstract submission).
Conclusion / ImplicationsThe nationally representative linked data developed under this project provides a unique opportunity to quantify and identify points to improve access to best practice CVD care, with the ultimate aim of preventing secondary CVD events in the population. Findings will also inform optimal use of MADIP data by the research community in order to answer questions of national importance and provide robust evidence to drive improvement in health and health care.

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.