Technical notes

Disease expenditure database

The main source of information for this 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 admitted patient, emergency department, and outpatient hospital services, out-of-hospital medical services, and prescription pharmaceuticals.

The methods used for estimating disease spending is a mixture of ‘top-down’ and ‘bottom-up’ approaches, 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 Disease Expenditure Database have been derived by combining information from the:

  • National Hospital Morbidity Database (NHMD)
  • National Public Hospitals Establishments Database (NPHED)
  • National Non-admitted Patient Emergency Department Care Database (NNAPEDC)
  • National Non-admitted Patient Databases (aggregate, NAPAGG, and unit record, NAPUR)
  • National Hospital Costs Data Collection (NHCDC)
  • 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. For the purposes of this report, $136 billion, or 73% of recurrent spending, was attributed to specific diseases and injuries. This expenditure 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 expenditure are allocated differently between the health expenditure Australia database, and the disease expenditure study. This approach was taken to reflect patterns of healthcare use for particular conditions, which is the focus of this body of work, rather than health funding arrangements. Spending estimates in hospitals are slightly higher than in the Health Expenditure Database, while spending on referred medical services are lower. Further details of methods used are described in the Disease Expenditure 2018–19 Study: Overview of analysis and methodology report.

Australian Burden of Disease Study

Burden of disease quantifies the gap between a population’s actual health, and an ideal level of health in the given year – that is, every individual living in full health for his or her ideal or potential life span – and includes both fatal and non-fatal components. Risk factor analysis allows death and health loss to be attributed to specific underlying (or linked) risk factors. 

The Australian Burden of Disease Database contains aggregate burden of disease metrics from the Australian Burden of Disease Study. This includes Years of life lost (YLL), Years lived with disability (YLD) and Disability-adjusted life years (DALY) for over 200 diseases and injuries. It also includes estimates of attributable burden (DALY) for around 30 risk factors.

The ABDS 2018 uses and adapts the methods of global studies to produce estimates that are more relevant to the Australian health policy context. The chosen reference period (2018) reflects the data availability from key data sources (such as the National Health Survey, deaths data, hospital admissions data and various disease registers) at the time of analysis.

Results from the study provide an important resource for health policy formulation, health service planning and population health monitoring. The results provide a foundation for further assessments.

Full details on the various methods, data sources and standard inputs used in the ABDS 2018 are available in Australian Burden of Disease Study 2018: methods and supplementary material.

Risk factor modelling

A risk factor is any determinant that causes (or increases the likelihood of) one or more diseases or injuries. As well as providing estimates of fatal and non-fatal burden, burden 
 of disease methodology allows death and health loss to be attributed to specific underlying (or linked) risk factors. Quantification of the impact of risk factors assists evidence-based decisions about where to direct efforts to prevent disease and injury and to improve population health.

The basic steps of estimating risk factor attributable burden are:

  1. select risk factors
  2. identify linked diseases based on convincing or probable evidence in the literature that the risk factor has a causal association with increased prevalence or mortality
  3. define the exposure to the risk factor that is not associated with increased risk of disease (the theoretical minimum risk exposure distribution, or TMRED, or counterfactual)
  4. estimate the population attributable fractions (PAFs) by either a direct method or the comparative risk assessment method:
    1. if PAFs appropriate to the disease and population in question are available from a comprehensive data source (such as a disease register), they are estimated directly from this data source (named a direct PAF in this report) and do not require steps 5, 6 and 7
    2. if not, PAFs are created using the comparative risk assessment method, which involves steps 5, 6 and 7
  5. define the amount of increased risk (relative risk) of morbidity or mortality for the linked disease due to exposure to the risk factor
  6. estimate exposure to each risk factor in the population
  7. use these inputs to calculate the PAF. The PAF has a value between 0 and 1, where 0 means there was no burden attributable to the risk factor and 1 means that all the burden for the linked disease was attributable to the risk factor.

The burden attributable to each risk factor is calculated by applying the PAFs for each linked disease to the relevant year of life lost and year lived with disease.

For further information, refer to the Australian Burden of Disease Study: methods and supplementary material 2018.