What can be done to improve the evidence?


A useful framework for improving data is presented in Figure DATA.1. It involves making improvements in 3 key areas:

  • maximising the use of existing data sources
  • improving the quality and comparability of data across data sources
  • adding to data sources, including by developing new data sources in priority areas and through data linkage.

Figure DATA.1: Priority themes to improve the evidence base for people with disability

Figure DATA.1: Priority themes to improve the evidence base for people with disability
Diagram showing 3 priority themes supporting the evidence base. The themes are: 1) Improve the quality and comparability of data sources, 2) Maximise existing sources, and 3) Add to data sources to address priority gaps.

Source: Adapted from Diagram 8 in ABS 2013.

Key to this is that data gaps or issues do not prohibit reporting on what is available. Instead data limitations are acknowledged and data agencies work together to continually improve data availability and quality.

Maximise the use of existing data sources

Bringing together information from multiple data sources helps support a person-centred, whole-of-system view of the experiences of people with disability in Australia within a coherent reporting framework. This provides a more comprehensive picture than is possible by relying on any one data source.

Examples of national reporting and associated frameworks that draw on multiple sources to understand the experiences of people with disability are:

  • this report
  • National Disability Strategy reporting (DSS 2019)
  • the Report on Government Services (SCRGSP 2020)
  • the disability and wellbeing monitoring framework and indicators developed by the Centre of Research Excellence in Disability and Health (Fortune et al. 2020).

Such national reports complement the large body of research on the experiences of people with disability in Australia and reporting at state and territory levels. However, it is through the sharing of existing data sources, particularly for data linkage, that much greater gains in understanding will become possible.

Improve the quality and comparability of data sources

Many data collections exist across the different agencies and sectors that collect information about people with disability, including by the:

  • AIHW
  • Australian Bureau of Statistics (ABS)
  • Department of Social Services (DSS)
  • National Disability Insurance Agency (NDIA).

Despite this, gaps exist, as do some inconsistencies in defining disability within different sources of data often reflecting the differing roles for the respective data collections and agencies.

Some options that could improve the quality and comparability of existing data sources include:

  • gaining agreement to adopt more consistent definitions across data collections, where possible
  • adding a disability flag in mainstream data collections—an agreed set of questions to identify people with disability and the level of their disability.

These options come with issues to consider, including privacy, the role of service providers and cost. Given these issues, there is a growing view that data sharing and linkage, combined with accommodating different definitions of disability and adopting more consistent definitions and disability flags where sensible, may be the most practical way forward.

Adopting more consistent definitions across sources where possible

Disability is generally defined in a dataset based on the purpose and type of data collected. This means that definitions differ between population surveys and across administrative data collections.

Variations in definition and scope can be managed, at least in part, by careful analysis and reporting. However, strategies to improve the consistency of definition and coverage between sources of data should also be considered. Classification frameworks, such as the World Health Organization’s International Classification of Functioning, Disability and Health (ICF), are useful in this process. Such frameworks help to understand differences in definition between data sources and can be used to improve consistency.

Adding a disability flag in mainstream data sources

The inclusion of a flag in data sources enables key interest groups, such as people with disability, to be identified. This can reduce the need to develop new data collections.

An example of a flag related to the identification of people with disability within mainstream data collections is the AIHW’s standardised disability flag. This flag is derived from a standard set of questions assessing a person’s level of functioning and need for support in everyday activities. These questions are based on the ICF, and are broadly consistent with the Short Disability Module questions the ABS uses in a number of its surveys. Versions of the flag have been implemented in the AIHW’s Specialist Homelessness Services Collection, the National Social Housing Survey, and National Prisoner Health Data Collection, and are being implemented within other AIHW collections.

The AIHW is also developing a flag for use in data collections to indicate if a person is receiving National Disability Insurance Scheme (NDIS) support. This flag could be used to look at the use of mainstream and other services by NDIS participants. If used together with the standardised disability flag, it could potentially also be used to look at whether there are differences in the use of mainstream services between NDIS participants and other people with disability.

A wider implementation of such flags, coupled with regular supply of these data for national collation and reporting, would improve the ability to report more comprehensively on people with disability. For example, the addition to, or improvement of, disability flags in existing national child protection, out-of-home care and youth justice data collections would improve visibility of children with disability in these systems.

Add to data sources to address priority gap areas

Data gaps can be addressed by:

  • enhancing or adding data items to existing data collections
  • enabling data sharing and linkage of data
  • creating new data collections or data assets to fill priority gaps.

Enhance existing data sources to capture data about disability population subgroups

Existing data sources could be improved to better capture data about subgroups in the disability population, such as special or vulnerable groups. For example, key data gaps exist for people with disability who:

  • are also Aboriginal and Torres Strait Islander people
  • live in rural and remote Australia
  • live in care settings
  • are also LGBTQI+ people
  • are culturally and linguistically diverse
  • have suffered abuse
  • have suffered discrimination
  • are homeless.

Challenges exist in collecting data on population subgroups, including data quality and coverage. It can be difficult, for example, to obtain a large representative sample of some populations in national surveys and data become less reliable and robust as sample size decreases.

Examples of disability population subgroups for whom information is limited

Aboriginal and Torres Strait Islander people

Improving estimates of Aboriginal and Torres Strait Islander people living with disability is crucial as Indigenous Australians often have higher rates of disability and generally poorer outcomes than non-Indigenous Australians.

While data on Indigenous status are collected in national ABS surveys, data quality and reliability are compromised by identification of disability, sample size and/or geographical constraints (ABS 2019a). The Survey of Disability, Ageing and Carers (SDAC), for example, is the key source of disability prevalence data but it does not collect data from people living in very remote areas or from discrete Indigenous communities (ABS 2019b). As a result, information about Indigenous Australians living with disability who are aged or who are carers are instead usually sourced from the:

  • Australian Census
  • National Aboriginal and Torres Strait Islander Social Survey
  • National Aboriginal and Torres Strait Islander Health Survey.

However, these surveys do not as comprehensively identify disability compared with the more expansive set of questions used in the SDAC.

LGBTIQ+ people

The marginalisation of LGBTIQ+ people in general can make them vulnerable. Those who also have disability may be especially at risk. This community includes individuals who identify as lesbian, gay, bisexual, transgender, intersex, queer or otherwise diverse in gender, sex or sexuality.

LGBTIQ+ people can face harassment and discrimination based on their identity. There is very limited data about the intersection of LGBTIQ+ and disability in regular data collections, including in national surveys.

People who have suffered abuse

The evidence-base related to the abuse of, or by, people with disability (including domestic and sexual violence) needs to be improved, including by:

  • acknowledging that some people with disability face additional challenges in reporting abuse (for example, those who struggle to communicate because of the nature of their disability)
  • improving data on the prevalence and causes of violence, particularly in care settings
  • improving data on the safety and quality of services provided to people with disability.

While some data are available for this subgroup, these data have limitations. For example:

  • the ABS SDAC does not provide detail about the experience of violence against, or by, people with disability (ABS 2018a)
  • data on violence and safety is collected in national surveys, such as the ABS' Personal Safety Survey, but these do not identify disability as well as the SDAC, collect on disability at the time of the survey not the time of the abuse, are limited to people who live in private dwellings, and are conducted by personal interview and therefore preclude some people with communication difficulties (ABS 2018b)
  • limited mandatory reporting of some forms of abuse is available for some, but not all, settings (for example, reporting on suspected, alleged or witnessed assaults is required in residential aged care settings but not in other care settings, such as service provided at home).

People who have suffered discrimination

While the ABS SDAC collects data on discrimination against people with disability, it does not collect data on the experience of other forms of discrimination for people without disability. This means comparisons can be made only within the disability population and not between people with and without disability. Some information on this comparison is available from the ABS' General Social Survey, which uses the ABS' Short Disability Module to identify disability and includes questions on other forms of discrimination (such as age and sex). However, this module does not identify disability as well as the SDAC, and the resulting overestimate of disability means that the differences between those with and without disability are understated (ABS 2018a).

Limited data also exist on the direct effects of discrimination on people with disability. ABS SDAC data point to lower employment, lower income, lower social participation and poorer health outcomes for people with disability overall, and especially for those who have experienced discrimination because of their disability. However, these outcomes cannot be directly linked to an experience of discrimination.

People who are homeless

Population surveys with comprehensive measures of disability, such as the SDAC, do not include a measure of homelessness. Also, the ABS' Census of Population and Housing, which includes a measure of homelessness, does not capture disability as well as the SDAC and does not capture disability at all for people enumerated using the Special Short Form. This shortened version of the Census form is often used to gather information from rough sleepers (in 2016, 53% of rough sleepers were enumerated using the form) (ABS 2018c).

Improvements could also be made to the AIHW’s Specialist Homeless Services Collection, which provides estimates about people who have sought assistance from a homelessness agency. This collection has included a version of the AIHW’s standardised disability flag since 2013–14, however, response rates, particularly in the early years, are an issue. Reporting relies on the assumption that clients whose disability status is not known have the same rate of disability as others.

Another key area in which existing data could be improved relates to the disability workforce. While some information is collected from National Disability Services member organisations and through the ABS' Labour Force Survey, there are opportunities to improve national information in this area.

Safely share and link data to better understand pathways and outcomes

Safely sharing data for statistical purposes, including for data linkage, could lead to major improvements in understanding the experience of people with disability in Australia.

What is data sharing?

Data sharing in this context refers to the sharing of data between 1 or more parties to better realise the economic and social benefits of increased data use, while maintaining public trust and confidence (PM&C 2018).

Many government agencies and organisations have arrangements in place to share and release non-sensitive data under existing frameworks and authorities. However, in some circumstances, pathways are not available to agencies wanting to share or release the data they hold (PM&C 2018; PC 2017a). This is improving over time, with the development of data-sharing agreements and processes within and between levels of government in Australia. For example, in 2020 the Office of the National Data Custodian released a draft Data Sharing Agreement Template designed to help government agencies produce agreements to confidently share data in a way that is safe, timely and transparent. The template is based on the National Data Commissioner’s Best Practice Guide to Applying the Data Sharing Principles (ONDC 2020).

Some data collected on people with disability are not widely available for use or sharing. These include, but are not limited to, data collected by non-government organisations but not collated for national analysis.

Improving the ability to access these data would assist in expanding the evidence base, particularly in understanding other services people with disability use.

What is data linkage?

Data linkage (also called data matching, data integration or record matching) combines information from multiple data sources while preserving privacy. This tells a much more powerful story than is possible from individual data sources in isolation. It can also improve understanding of a range of issues.

Some benefits of data sharing, however, cannot be realised without data linkage. At present, for example, it is difficult to understand how different specialist disability support systems interact, such as how the NDIS interacts with other specialist disability services. It is also difficult to understand how these specialist disability services interact with mainstream supports.

Examples of improving the evidence base through data linkage

Data linkage can be used in many ways to improve the evidence base about people with disability. Some examples using existing data include linking:

  • disability support services or payments data to national hospital data, the Medicare Benefits Schedule and the Pharmaceutical Benefits Scheme—to provide insights into how some people with disability interact with mainstream health services, and how these services complement specialist disability supports
  • disability support services data to aged care or mental health data—to help improve understanding of how these sectors interact
  • employment services data (including specialist disability employment services data) with income support payments data over time—to provide valuable information about the relationship between seeking employment and income support.

While data linkage is a powerful tool, challenges remain before its benefits can be fully realised. The lack of consistent linkage information across administrative systems in Australia, and complexities in data sharing and access arrangements, mean that linking data from various sources is often complex, time consuming and costly. There are also issues associated with working with linked data that add to the complexity, timeliness and cost, such as extensive data cleaning often being required prior to linkage (for example, as a result of different data ‘rules’ being applied to seemingly similar data items in different sources), and the careful work required to ensure protection of privacy.

The Commonwealth, New South Wales, Victorian, South Australian and Queensland governments are working together with the NDIA, the AIHW and the ABS to pilot test the development of a National Disability Data Asset. This will bring together data from a range of domains relevant to people with disabilities and their carers, such as health and wellbeing; learning and skills; justice, safety and rights; personal and community support; inclusion and accessibility; and economic security. The 18-month pilot phase is intended to demonstrate value for both government and community use, including platforms and information for people with disability, wider public reporting, and research. See the National Disability Data Asset for more information.

Fill gaps where limited or no data currently exist

New data collections may need to be developed.

One example is the collation of transport data, specifically data about the accessibility of transport and services for people with disability. Another is information about mainstream services of critical importance to some people with disabilities (for example, speech therapy and other allied health services).

Another example relates to specialist disability services provided outside the NDIS. While a large scheme, the NDIS will not provide all specialist disability supports to all people with disability. The AIHW’s Disability Services National Minimum Data Set (DS NMDS) filled part of this gap but, post 2018–19, the last year of collection under the DS NMDS, no national data will be available on services outside the NDIS, other than open employment services. Such data are vital for examining the interactions between the NDIS and other services (PC 2017b; PC 2019).