Health system spending on disease and injury in Australia 2022–23

The main source of information for this web report is the AIHW’s Disease Expenditure database. It contains estimates of spending by Australian Burden of Disease Study condition, age group, and sex for:

• hospital services
  • public hospital admitted patients
  • public hospital emergency departments
  • public hospital outpatient services
  • private hospital admitted patients
• primary health care
  • general practitioner services
  • allied health services
  • benefit paid pharmaceuticals
  • dental services
• referred medical services
  • specialist services
  • medical imaging
  • pathology

The methods used for estimating disease spending is a mixture of ‘top-down’ and ‘bottom-up’ approaches. A ‘top-down’ approach is where total spending across the health system is estimated and then allocated to the relevant conditions based on the available service use data.

Although this approach produces consistency, good coverage and totals that add up to known expenditure, it is not as comprehensive for any specific disease as a detailed ‘bottom-up’ analysis, which would include the actual costs incurred for that disease. A lack of amenable data sources means that a more granular ‘bottom-up’ analysis is not possible.

Estimates in the AIHW Disease Expenditure database have been derived by combining information from the following data sources:

• National Hospital Morbidity Database (NHMD)
• National Non-admitted Patient Emergency Department Care Database (NNAPEDC)
• National Non-admitted Patient Databases (aggregate, NAPAGG, and unit record, NAPUR)
• IHACPAs National Weighted Activity Unit (NWAU) calculators and the National Efficient Price (NEP)
• Private Hospital Data Bureau (PHDB) collection
• Bettering the Evaluation and Care of Health (BEACH) survey
• Medicare Benefits Schedule (MBS)
• Pharmaceutical Benefits Scheme (PBS)
• Health Expenditure Database.

It is not technically appropriate or feasible to allocate all spending on health goods and services by disease. For example, neither administration expenditure nor capital expenditure can be meaningfully attributed to any particular condition due to their nature.

This study includes payments from all sources of funds, such as the Australian and state and territory governments, Private Health Insurance, and out of pocket payments by patients.

Some components of recurrent spending are allocated differently between the AIHW Health Expenditure database, and the Disease Expenditure database. This approach was taken to reflect patterns of healthcare use for particular conditions, which is the focus of disease expenditure analysis, rather than health funding arrangements. Spending estimates in hospitals in the Disease Expenditure database are slightly higher than in the Health Expenditure database. This is discussed further in the accompanying methodology report.

Expenditure information is added to hospital activity data for every admitted patient record in the NHMD, all emergency department presentations in the NNAPEDC, and all service events in the National Non-admitted Patient Databases. Data sets have been constructed for all private hospital admitted patient separations. Aggregated data sets by sex, age group, state/territory and SA3 geographical area, including patient co-payments, have been created for MBS services by provider specialty and subgroup, and pharmaceuticals by Anatomical Therapeutic Classification (ATC). All of the data sets include expenditure estimates for each ABDS condition.

Changes to methodology compared to the 2020–21 study

The scope of expenditure and methods used in this disease expenditure study are similar to those used in the 2020–21 report (AIHW 2023b) however there are changes that have been made that make comparison of data between the 2020–21 report and this report to be done with caution. The key changes that have been made in the 2022–23 study compared with the 2020–21 study were changes to the methods used for estimating costs of services in public hospitals, allocating costs to specific conditions, and the list of conditions that are included in the study.

The methodology changes are outlined briefly below. For further details on the methods used, refer to accompanying methods report, Health system spending on disease and injury in Australia: Overview of analysis and methodology 2022–23 available in the Related material section.

Costing public hospital services

Previous iterations of the disease expenditure analysis used cost data from the Independent Hospital and Aged Care Pricing Authority’s (IHACPA) National Hospital Cost Data Collection (NHCDC) to estimate costs for public hospital admissions, emergency department presentations, and non-admitted services. In the 2022–23 study, the costing method was updated to use the IHACPAs National Weighted Activity Unit (NWAU) calculators and the National Efficient Price (NEP), to estimate the relative resource intensity and cost for each service based on service level information such as diagnosis, comorbidities, clinical complexity, length of stay, procedures, and outpatient clinic type. This allows more timely reporting of disease expenditure estimates.

Allocating costs to conditions

In previous disease expenditure reports, cost redistributions included most comorbidities as in scope. The 2022–23 study updates this to exclude conditions that are related to the principal diagnosis, symptoms of the conditions, not cost relevant for the service, ‘other’ residual conditions, or adverse effects of the medical treatment received.

Diagnoses excluded from the redistribution are listed in Table 5.1 in the methods report. Spending on cancer was impacted by this change as all comorbidities are now excluded from the costs for patient separations, that is: all costs for patients with cancer as a principal diagnosis is assumed to be related to the cancer. Previously, some of the cost was assumed to be due to the comorbidities. This change in method has increased spending on cancer to what was published in earlier reports. This change in method was made to align with methods used by the United States and other countries.

List of conditions

The list of conditions that are included in the study was updated to report further details for certain types of conditions that were previously included in the ‘other’ categories. This includes:

• medical treatments for certain risk factors (hypertension, hyperlipidemia, obesity, and tobacco interventions)
• long outcomes of chronic conditions (renal failure, heart failure, and septicaemia)
• certain types of ‘well care’ (well person, well dental, pregnancy and postpartum care, family planning, counselling services, social services and donor).

Identification of COVID–19 cases

Analysis for private and public hospitals:

The 12th version of the International Classification of Diseases (ICD) codes, including U07.1, U07.2, U07.4, U07.5, and U07.7, were used in the diagnostic field of both public and private admitted hospitals to identify COVID–19 cases.

Analysis for ED cases:

The analysis included both confirmed COVID–19 cases using the designated codes and ruled-out cases (U06.0) in the emergency department.

Introduction of Tier 2 clinic classes:

For NAP, four Tier 2 clinic classes (10.21, 20.57, 40.63, 30.09) were established to capture and track the diagnosis, treatment, and COVID–19 vaccination activities in outpatient clinics.

MBS mapping for keyword search:

A mapping file was created for MBS areas to search for specific keywords in the item descriptions. The keywords used in the search were 'COVID–19', 'SARS–COV–2', and 'COVID'. This process enabled the identification of 66 MBS items that could be linked to COVID–19 based on the presence of these keywords in the item descriptions.

The AIHW continually seeks to improve the methods used to produce these estimates. Estimates for disease expenditure are subject to revision. Hence the most recently published results are not directly comparable with previously published data.