How fair is Medicare? The income-related distribution of Medicare benefits with special focus on chronic care items

While Australia’s system of financing primary health care has been in operation for almost 30 years,1 there have been surprisingly few attempts to understand the degree to which health care provided under Medicare is related to socioeconomic factors such as income. While some analyses have examined equity of access to certain aspects of the system, such as visits to general practitioners,2-4 what has previously been unavailable is information on the distribution of Medicare funding. This requires Medicare records to be linked at a patient level to information on socioeconomic status, such as levels of household income. For such an analysis to be meaningful, it is necessary to control for differences in morbidity across income groups, since chronic disease is likely to be concentrated in lower income households,5 and this will require a higher level of health expenditure.

In recent years, the government has introduced a number of items under the Medicare Benefits Schedule (MBS) to mitigate the burden of disease associated with chronic illness. Such items include those under the Chronic Disease Management (CDM) program, the Chronic Disease Dental Scheme (CDDS), and various allied health services. The CDM program aims to improve the coordination of care for people with chronic conditions and complex care needs. The CDM Medicare items provide funding to GPs to manage the health care of eligible patients by preparing, coordinating, reviewing and/or contributing to GP Management Plans (GPMPs) and Team Care Arrangements (TCAs). Once a GPMP and a TCA are in place, GPs can refer patients to a range of providers, such as occupational therapists, speech pathologists and podiatrists, for a set number of Medicare-rebatable services.6 Similarly, the CDDS allows eligible patients (those with chronic conditions and complex care needs and a GPMP and TCA in place) up to $4250 worth of Medicare-funded dental services over 2 consecutive years.7

In this study, we use patient-level data from the Australian Hypertension and Absolute Risk Study (Aus HEART)8 that have been linked with Medicare administrative records over a 1-year period to assess the level of equity in the distribution of benefits overall and under the schemes listed earlier. An important feature of this study is that it combines information obtained from the patient on self-reported health status and socioeconomic and demographic characteristics with clinical information provided in the course of a GP examination. This allows us to examine annual Medicare expenditure across income groups after controlling for differences in need resulting from age, sex and reported health status, as well as a range of clinically assessed conditions.

A total of 322 GPs provided data for the AusHEART study, which has been described previously.8 The Aus HEART researchers selected a stratified random sample of 534 GPs nationwide from the 1416 who expressed interest in participating in the study. Each GP was asked to provide clinical information on 15–20 consecutively presenting, consenting patients aged 55 years and over, irrespective of their reason for the consultation. All patients were given an option to consent to a second component of the study, namely linkage with Medicare administrative data. To account for oversampling in rural and remote practices, weights were generated according to age, sex and remoteness to ensure the sample used in all analyses was representative of the Australian population aged 55 years and over.9 The study was approved by the Royal Australian College of General Practitioners National Research and Evaluation Ethics Committee.

Of the 5293 patients who consented to participate in the AusHEART study, 3538 (67%) consented to data linkage with Medicare information. Of these patients, 676 (19%) did not provide income data, and so the analysis was undertaken on the remaining subset of 2862.

Clinical and socioeconomic details of the study sample are summarised in Box 1, along with comparative statistics of the entire AusHEART sample. The results of t tests and z tests showed that no variables differed significantly between the two samples (Box 1, last two columns) at the 5% level of significance.

Box 2 shows the proportion of people with chronic conditions and those reporting below-average self-assessed health across the income groups. It is evident that there is a higher concentration of people reporting below-average health (ie, “fair” or “poor”) among lower income quartiles (trend test P < 0.001). About 37% of people in the lowest income quartile reported below-average health, compared with 15% in the highest income quartile. Trend tests found an income-related gradient for cardiovascular disease (P < 0.001), diabetes (P < 0.001) and kidney disease (P = 0.002), but not for cancer (P = 0.23).

Box 3 displays the need-adjusted concentration curves, and the unadjusted and need-adjusted indices for benefits accruing from the schemes. The overall concentration index for unadjusted out-of-hospital Medicare expenditure is − 0.069 (95% CI, − 0.069 to 0.068), compared with − 0.008 (95% CI, − 0.009 to 0.008) for need-adjusted expenditure. A further analysis showed that total unadjusted Medicare expenditure in the lowest income quartile was $394.65 higher per year (95% CI, $237.15 to $552.20) than that in the highest quartile. After accounting for need, the difference was reduced to $22.90 (95% CI, − $118.45 to $164.30), which is less than 2% of average annual benefits per capita, suggesting that overall need-adjusted expenditure is relatively evenly distributed across income groups. The need-adjusted indices for CDM and allied health items are slightly larger in magnitude, indicating that expenditures accruing from these services are slightly pro-poor. In contrast, the size of the concentration index for need-adjusted dental benefits is much larger at − 0.205 (95% CI, − 0.208 to − 0.201), and the concentration curve is much further from the line of equality, suggesting that dental benefits favour lower income households, as the two lowest income groups received more than two and a half times the amount of funding as the highest income group.

Factors that were associated with the use of these schemes were determined using multivariate analyses (Box 4). Across all three categories, the propensity to use was significantly higher for people with one or more comorbid conditions, those reporting below-average self-assessed health, and for those in the second income quartile. People located in regional and remote areas were 69% less likely to use the CDDS.

This study provides evidence that Medicare funding is progressive in that a greater proportion of government subsidies for out-of-hospital Medicare items flow to lower income groups. Chronic care items, particularly dental plans which have been taken up to a greater degree by lower income groups, contribute to the progressivity of the overall scheme. Despite the progressive uptake of these chronic disease items, we also found that people located in regional/remote areas were significantly less likely to access the dental scheme compared with people located in cities. This suggests that limited availability of CDDS services in regional/remote locations is a major barrier to the scheme’s uptake.

Previous international comparisons of primary care use involving Australia have shown that GP access was also evenly distributed across income after adjusting for need based on self-reported health.2 A subsequent analysis also showed variation across types of providers, with GP use tending to be pro-poor and specialist use pro-rich.4 This study extends these findings in two respects. First, having access to administrative Medicare data allowed us to quantify average government expenditure across different income groups, which is something that cannot be obtained from population-level surveys. Second, the study involved a detailed clinical assessment that allows for a more refined degree of adjustment for medical need.

The merits of the CDDS have been the subject of ongoing debate,17 and recently the federal government announced the closure of the scheme from 1 December 2012.7 The Health Minister has questioned the merits of the scheme on the grounds that it is costly and may not be equitable.18 Alongside these views are those of the National Advisory Council on Dental Health, which sees an expansion of the CDDS as one of the ways to expand publicly financed dental care in Australia.19 While our study does not shed light on the merits of financing dental care through Medicare, the results do provide evidence that the CDDS in the period after its introduction was primarily used by people from lower income households; as the lower-half of the income distribution received more than two and a half times the amount of funding as the highest income group. In this regard it appears to be one of the more progressive elements of Medicare expenditure across income groups. If it is the objective of the government to better target dental services to low-income groups, then it would be important to show that new mechanisms of publicly financing dental care would be more progressive than the CDDS.

This study has several important limitations. First, our study captures equity only among older patients who have consulted a GP, and therefore, the likelihood of inclusion in the sample is related to Medicare use. While only around 15% of the adult population do not consult a doctor at least once per year,2 more frequent users are more likely to be included in this sample. To examine if our results were sensitive to the form of selection, we applied sample weights that were inversely proportional to observed number of GP visits and found little effect on the overall results. Second, we only have information on self-reported income as opposed to actual income, and this was grouped into categorical bands. Unless data linkage can be extended to income as reported in taxation records, which is the case in countries such as Sweden,20 this limitation is difficult to overcome. Third, the number of patients taking up the CDDS was small, and we could not control for differences in need relating directly to oral health. This could be amended by including relevant questions in future patient surveys, or clinical assessment of dental need. Finally, the study primarily examines use over a 1-year period. Examining a longer period will enhance our understanding of Medicare payments over the life cycle.

This study represents a starting point in understanding the distributional effect of Medicare and broader health care expenditure, such as the distribution of publicly subsidised pharmaceuticals through the PBS. While there are examples of studies quantifying health care costs using cross-jurisdictional data linkage in Australia,21 these generally do not involve linkage with clinical and patient-provided information. Further, population health surveys conducted by the Australian Bureau of Statistics22 do not currently involve linkage with administrative data such as Medicare use. Although surveys such as the Australian Longitudinal Study on Women’s Health23 and the 45 and Up Study24 link to administrative data, they do not involve clinical assessments. A unique feature of this study is that we have combined information from patients and clinicians with administrative information to measure the equity of Medicare funding in Australia. This highlights the need for linked administrative health data to be collected from a representative sample of the Australian population and for this to be linked to clinical and socioeconomic information at an individual level. Such data linkage could provide the necessary evidence to develop funding policies and monitor the implementation of programs to ensure that resources flow to Australians with the greatest need.

2 Distribution of reported health and chronic conditions across income groups*

* P for trend tests across income groups: below-average self-reported health, P < 0.001; established cardiovascular disease, P < 0.001; established diabetes, P < 0.001; established kidney disease, P = 0.002. † Income bands (equivalised weekly income): 1st quartile = $0 to $175.00; 2nd quartile = $208.15 to $333.00; 3rd quartile = $377.65 to $799.65; 4th quartile = $809.30 to $2000.00.

3 Concentration curves illustrating the distribution of benefits across income,* and summary concentration indices for benefits accruing from Medicare schemes

	Concentration indices		Expense per capita as a proportion of total out-of-hospital MBS	Expense per capita 2008–09 (95% CI)
	Unadjusted benefits (95% CI)	Need-adjusted benefits (95% CI)
CDM	− 0.134  (− 0.135 to − 0.133)	− 0.050  (− 0.051 to − 0.050)	2.8%	$38.30  ($34.25 to $42.40)
Allied health	− 0.172  (− 0.173 to − 0.170)	− 0.065  (− 0.066 to − 0.064)	1.6%	$21.95  ($18.60 to $25.30)
Dental	− 0.217  (− 0.220 to − 0.214)	− 0.205  (− 0.208 to − 0.201)	3.3%	$44.95  ($27.00 to $62.90)
Out-of-hospital balance	− 0.059  (− 0.060 to − 0.059)	− 0.001  (− 0.002 to − 0.001)	92.2%	$ 1244.00  ($1184.60 to $1303.35)
Out-of-hospital	− 0.069  (− 0.069 to − 0.068)	− 0.008  (− 0.009 to − 0.008)	100%	$1349.20  ($1284.75 to $1413.65)
CDM = chronic disease management. MBS = Medicare Benefits Schedule. * The concentration curves for CDM and allied health items were combined as the curves lie very close together.