[Universal healthcare in a mixed public-private system: the Australian experience].

Australian governments and health service providers play a continual role in ensuring efficient healthcare budget use, which also concerns the meeting of goals within a universal health system, and ensuring compliance with relevant legislation and regulations. As is the case in most developed countries, there is substantial pressure on healthcare budgets, and this in turn places pressure on the capacity of governments to fund universal healthcare. The mixed public-private system in Australia has some imperfections including the fragmentation of care and limitations to fund use oversight, however overall, the system serves Australians relatively well. This article explores the Australian approach to a mixed public-private system and considers some of the complexities which arise as it to support universal healthcare.

Social exclusion, deprivation and child health: a spatial analysis of ambulatory care sensitive conditions in children aged 0-4 years in Victoria, Australia.

Recent Australian policy initiatives regarding primary health care focus on planning services around community needs and delivering these at the local area. As in many other countries, there has also been a growing concern over social inequities in health outcomes. The aims of the analysis presented here were firstly to describe small area variations in hospital admissions for ambulatory care sensitive conditions (ACSC) among children aged 0-4 years between 2003 and 2009 in the state of Victoria, Australia, and secondly to explore the relationship of ACSC hospitalisations with socio-economic disadvantage using a comparative analysis of the Child Social Exclusion (CSE) index and the Composite Score of Deprivation (CSD). This is a cross sectional secondary data analysis, with data sourced from 2003 to 2009 ACSC data from the Victorian State Government Department of Health; the Australian Standard Geographical Classification of remoteness; the Australian 2006 Census of Population and Housing; and AMPCo General Practitioner data from 2010. The relationship between the indexes and child health outcomes was examined through bivariate analysis and visually through a series of maps. The results show there is significant variation in the geographical distribution of the relationship between ACSCs and socio-economic disadvantage, with both indexes capturing important social gradients in child health conditions. However, measures of access, such as geographical accessibility and workforce supply, detect additional small area variation in child health outcomes. This research has important implications for future primary health care policy and planning of services, as these findings confirm that not all areas are the same in terms of health outcomes, and there may be benefit in tailoring mechanisms for identifying areas of need depending on the outcome intended to be affected.

Is the self-reported private health insurance status in the National Health Survey representative of private health insurance coverage in Australia?

OBJECTIVE: This study determines whether the distribution of self-reported private health insurance (PHI) status in the 2004/05 National Health Survey (NHS) is representative of PHI coverage in Australia. METHODS: Weighted estimates from the NHS 2004/05 are compared with PHI status reported for 2004/05 by the Private Health Insurance Administration Council (PHIAC, the independent regulator of the private health insurance industry). PHI status was imputed to children in the NHS based on PHI status of the adult in the household. The two data sources were deemed to be different if the PHIAC results were not within the 95% CI range for the NHS estimate. RESULTS: PHI status reported in the NHS and PHIAC are generally comparable except for some categories such as hospital cover of males aged 5-9 years and females aged 85 years and older where the NHS estimates are below PHIAC numbers; and males aged 25-29, 35-39, and 50-54 years where the NHS estimates are higher. CONCLUSIONS: The findings suggest that while the NHS 2004/05 estimates may accurately represent coverage in Australia particularly when examined at an aggregated level, there is some variation in the NHS estimates when examined by sex and age group. IMPLICATIONS: Researchers need to be aware of the potential for sampling and reporting bias to contribute to some misrepresentation of PHI status when using the NHS to generalise to the Australian population. Exploring corrective measures will ensure that the NHS continues to be a valuable data resource for health researchers in Australia.

Hospital admissions in the National Health Survey and hospital separations in the National Hospital Morbidity Dataset: what is the difference?

OBJECTIVE: To compare the National Health Survey (NHS) derived estimates of hospital admissions with the number of hospital separations registered in the National Hospital Morbidity Dataset (NHMD). METHODS: Using the person weights in the NHS, the Expanded Confidential Unit Record File of the 2004-05 NHS was used to derive a population estimate of the number of hospital admissions in the 12 months preceding the conduct of the survey. These estimates, by age and sex categories and whether or not the admission involved an overnight stay, were compared with the number of hospital separations registered in the NHMD. RESULTS: The number of hospital admissions estimated from the NHS was approximately two thirds the number of hospital separations registered in the NHMD. The discrepancy between the two data sources was greater when hospital episodes did not involve an overnight stay in hospital. CONCLUSION: There are systematic differences between the number of admissions/separations derived by the NHS and the NHMD for reasons including the technical difference between a hospital admission and a separation, and the sampling frame and scope of the NHS. Researchers looking for individual level data on hospital utilisation must take note of the differences between NHS and the NHMD, and recognise that there are methods to simulate a representative population by enhancing an existing dataset with information from multiple data sources, thus providing researchers a cost-effective data resource.

Public policy and private health insurance: distributional impact on public and private hospital usage.

OBJECTIVE: To study the effectiveness of recent private health insurance (PHI) reforms, in particular the 30% rebate and Lifetime Health Cover, in terms of their stated aim of reducing the load on public hospitals. METHODS: Combines the use of two new projection models - "Private Health Insurance" (PHI) and "New South Wales Hospitals" that use public and private hospital inpatient data from 1996-97 to 1999-2000, and NSW population and private health insurance coverage statistics. RESULTS: With the PHI reforms 15% fewer individuals would use public hospitals in 2010 than without these reforms (around 18% fewer among the 40% most affluent Australians and 9% among the 40% least affluent). Lower public hospital usage would mainly be due to Lifetime Health Cover. CONCLUSION: If the PHI reforms remain in place, in 2010 a significant proportion of hospital use would be redirected away from the public sector and towards the private sector, with the shift being greatest among better-off Australians.

Hospital admissions by socio-economic status: does the 'inverse care law' apply to older Australians?

OBJECTIVE: To investigate whether the 'inverse care law' applies to New South Wales (NSW) hospital admissions--especially to older people with high socio-economic status (SES). DESIGN: Cross-sectional study analysing inequalities in public and private hospital admission rates by SES, defined in terms of age, sex and family income/size at the small geographic area level. SETTING: Admissions to NSW public and private hospitals in 1999-2000 (1.8 million admissions against a NSW population of 6.4 million). METHODOLOGY: Inequalities in hospitalisation rates were expressed as rate ratios across the most and least disadvantaged 20% of the NSW population. RESULTS: Public hospital admission rates for people aged 0-60 years were 24-35% higher for the most disadvantaged 20% of the NSW population than for the least disadvantaged 20%. For 70+ year-olds the direction of this difference was reversed--being 14% lower for the most disadvantaged 20% of the population (5% higher for public patients). For private hospitals this reversal prevailed for all age groups (23-49% lower). For all hospitals it was 16% and 27% lower for 60-69 and 70+ year-olds respectively, with higher admission rates for top SES 60+ year-olds most pronounced for renal dialysis, chemotherapy, colonoscopies and other diagnostic scopes, rehabilitation and follow-up, and cataract operations. CONCLUSION: While the 'inverse care law' did apply to 60+ year-olds, it did not apply either to younger NSW hospital users or to public patients in public hospitals. IMPLICATIONS: Awareness of these SES-level differentials should result in greater equality of access to hospital services, especially by older people.

Distributional impact of recent changes in private health insurance policies.

The impacts of changes to private health insurance (PHI) policies introduced since 1999 - in particular the 30% PHI rebate and the Lifetime Health Cover - have been much debated. We present historical analyses of the impacts in terms of the proportion of Australians having hospital insurance cover under different PHI policies, by age, gender and socioeconomic status, and project these to 2010 using a new Private Health Insurance coverage model. The combined effect of the 30% rebate and Lifetime Health Cover was to increase PHI membership from just over 30% in 1998 to just under 50% by the end of 2000, due mainly to more people taking out PHI cover from among the richest 20% of the population. Among the poorest 40% the impact was minimal. Model projections suggested that, had the new PHI policies not been introduced, then the proportion of Australians with PHI would have declined to around 20% by 2010, compared with 40% if the current arrangements remained in place. Also, analysis of 2001 survey data regarding choices to use a public or a private hospital indicated that higher income groups with or without PHI were the more likely to have used a private hospital than lower income groups. Among those with PHI, older people were more likely to have used a private hospital than younger ones.