Technical notes

For information about the MBS items and population data used, see Health checks and Follow-up services overview.

Counting services and patients

This report presents data using 2 different counting units:

  • services – that is, the number of health checks (or follow‑ups, as applicable) provided in the specified period
  • patients – that is the number of people who received 1 or more health checks (or follow‑ups, as applicable) in the specified period.

In any given period (for example, 12 months), the number of patients may be smaller than the number of services provided. This occurs when patients have received more than 1 service in that period.

In this report, most figures and explanatory text relate to the number of patients (rather than services). Proportions of the population have been calculated using the number of patients only.

Patient information in the MBS data set is attached to each service. Thus, when analysing data for patients, there can be more than 1 service from which age and location can be derived (location is detailed later in the notes). In this report, different tactics were used for different analyses:

  • For counts of patients who received a health check or follow-up: where patients had more than 1 service in a financial year, age was calculated from the date of the first service for odd-numbered personal identifier numbers (PINs) and from the last service for even-numbered PINs in that financial year.
    • This tactic was used to reduce bias in the derivation of age, and was used to select from multiple patient postcodes as well. Upward bias on age is introduced when age is calculated at the date of the last health check in a financial year for patients with more than 1 service, because birthdays are likely to have passed by the time of the last service. A PIN’s final digit is effectively random, so this tactic splits the patient records into 2 groups, with upward bias on half and downward bias on the other half. Age could otherwise have been calculated at the 31st of December to reduce bias, but then a separate tactic would need to be used for infants born after the midpoint and for managing multiple postcodes.
  • For numbers of health checks over 5-years: where patients had more than 1 health check over the period, age was calculated from the date of the last health check in the reference period.
    • This tactic was used to better align with the population structure at the end of the reference period.
  • For time between health checks: where patients had more than 1 health check in a financial year, age was calculated from the date of the last health check in the financial year.
    • This tactic was used to capture the time between the most recent 2 consecutive health checks on record.

Note: Since patients are assigned to only one age group in a given year, it is safe to combine data from multiple age groups if required. Similarly, combining data from multiple regions is generally safe, however rounding errors may compound to give slightly inaccurate sums.

Dates and reference periods

The MBS data set includes information on the date the service was provided, as well as the date that the claim was processed by Medicare. These dates can differ due to a time lag between when a service is provided and when the claim for that service is processed by Medicare.

Most data in this report relate to services provided between 1 July 2011 and 30 June 2022, which were processed on or before 30 April 2023 (except for monthly data by telehealth status, processed on or before 30 June 2023, and the analysis of ‘time between health checks’, which covers activity back to 1999). Data are reported by date of service as this more accurately reflects when the service was provided. Due to lags between date of service and date of processing, there will be a small proportion of services provided during the reference period that are not captured in these data. For example, if a service was provided on 29 June 2022, but not processed until after the cut-off date, it will not be included in the data.

Data in this report are generally presented for financial years (1 July to 30 June). These are written with the second year abbreviated – for example, 2021–‍22 refers to the period from 1 July 2021 to 30 June 2022.

Geographic information

Geographic correspondences (sometimes referred to as concordances or mapping files) can be used where the location information in an original data is not available at the geographic level required for analysis and reporting. Geographic correspondences are a mathematical method for reassigning data from one geographic classification (for example, a postcode) to a new geographic classification (for example, remoteness area).

Geographic correspondences enable MBS data summated by postcode to be reported at various other geographic levels. However, there are various limitations associated with the use of postcode data for this purpose. Key issues include that:

  • postcodes do not fit neatly into the boundaries of geographic areas typically used for statistical reporting
  • defining geographic boundaries for postcodes is an imprecise process – postcodes can also change over time
  • people may not keep their postcode information up-to-date with Medicare
  • postcodes linked to patient records may belong to PO boxes or another mailing address, making correspondence to geographic areas potentially less accurate.

Due to these issues, various decisions need to be made about how best to allocate the postcode data to geographic regions. There will be some degree of inaccuracy in the resultant estimates, which will affect data in certain areas more than others – see Box 1 later on this page.

For this report, postcodes were re-assigned to 7 different geographies (based on the 2016 Australian Statistical Geography Standard) – Statistical Areas Level 4 (SA4s), Indigenous Regions (IREGs), Primary Health Networks (PHNs), Remoteness Areas, Greater Capital City Statistical Areas (GCCSAs), states and territories, and clusters of Statistical Areas Level 3 (SA3s). Where postcodes fell across the boundaries of multiple areas (for example, multiple SA4s), data were apportioned based on the population distribution of Indigenous Australians, according to AIHW analysis of ABS population estimates at 30 June 2016. Records with invalid postcode information could not be assigned to sub-national areas.

For patients who had more than one health check in a given reference period, the same selection process was followed as described in the Counting services and people section earlier.

For certain geographic levels (PHN, IREG and SA4), some of the Tableau figures allow areas to be filtered according to remoteness categories (Major cities, Inner and Outer regional, Remote and Very remote). These categories were assigned for each area based on the proportion of the Aboriginal and/or Torres Strait Islander Census count in 2016, across the 3 remoteness categories.

Box 1: Limitations of using postcode data to derive health check and follow‑up use by geographic area

There are various limitations associated with the use of postcode data for analysing the use of health checks and follow‑ups in sub-national regions.

A key issue is that postcodes do not fit neatly into the boundaries of geographic areas typically used for statistical reporting. For example, a single postcode can fall across multiple PHN boundaries. In such cases, the data for a single postcode need to be split across multiple areas – this requires decisions around how to divide the data across multiple areas that are normally made based on what is known about the population distribution within the area covered by the postcode. This method relies on the assumption that uptake of health checks does not vary within postcodes, which will result in some inaccuracy.

Another key issue is that some patients provide postcode details belonging to a PO box address. Patients who use PO box addresses may not necessarily live close to the post office where the PO box is located. When performing the analysis, decisions needed to be made about how to allocate data for non-residential areas.

These issues and analysis decisions are likely to have a greater impact on some areas more so than others. Within the geographic areas presented in this report, the areas most likely to be impacted are:

  • the following SA4s: Adelaide – Central and Hills (SA), Brisbane – West (Qld), Darwin (NT), Melbourne – Inner (Vic), Perth – Inner (WA), Sydney – Baulkham Hills and Hawkesbury (NSW), Sydney – City and Inner South (NSW)
  • the following IREGs: Alice Springs (NT), Apatula (NT), Darwin (NT), Jabiru – Tiwi (NT), Katherine (NT), Nhulunbuy (NT), Tennant Creek (NT)
  • Remote and Very remote areas in the analysis by remoteness.

Measuring time between health checks

To report the time interval between patients’ consecutive First Nations health checks (based on date of service), 2 slightly different methods were used to convert the days to months:

For ranges of months (for example, ‘Less than 12 months’, ’12–‍14 months’): the number of fully elapsed calendar months were calculated – where a calendar month has fully elapsed when the day's date returns to or surpasses the same-numbered day in consecutive months.

For example, a patient who received a health check on both 1 January 2021 and 1 January 2022 saw 12 calendar months elapse between health checks, whilst a patient who received a health check on both 1 January 2021 and 31 December 2021 saw only 11 calendar months elapse between health checks.

For mean and median time intervals: days were converted to months based on the average number of days per month {days ÷ (365÷12)}. This allowed for higher precision and accuracy compared with calculating means and medians from the number of fully elapsed months. Estimates were rounded downward to 0.1 of a month.

Comparability with other reports

As described in the Dates and reference periods section, the data in this report are based on the date of service (rather than date of processing), as this more accurately reflects when the service was provided. Data in this report may differ to those published elsewhere based on date of processing, including previous editions of this report. It may also differ to data published elsewhere based on date of service, where the date of processing cut-off is different. In certain cases, data with the same processing cut-off date may also differ slightly if patients’ sex or date of birth are changed on the live patient dataset linked to MBS records. Age and location were also determined in a slightly different way to some other reports (see Counting services and people and Geographic information, presented earlier).

In addition, this report primarily uses population estimates and projections, based on the 2016 Census, when calculating proportions. The proportions presented here may differ to those released in future updates of this report (or in other reports) when revised estimates based on the 2021 Census are used instead.

Temporary MBS items for people living in residential aged care facilities (RACF) are also excluded from this report.