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
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
- reporting against the Australia’s Disability Strategy 2021–31 Outcomes Framework
- the Report on Government Services (SCRGSP 2021)
- 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:
- Australian Bureau of Statistics (ABS)
- Department of Social Services (DSS)
- National Disability Insurance Agency (NDIA)
- NDIS Quality and Safeguards Commission.
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 data set 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 whether 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 the diversity and intersectionality in the disability population. For example, key data gaps exist for people with disability who:
- are also Aboriginal and/or Torres Strait Islander
- live in rural and remote Australia
- live in care settings
- are also LGBTIQ+ 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.