Addressing the challenges of intellectual disability identification for health policy and research in Australia

This article discusses the important issue of the need for a stable definition of intellectual disability in order to allow comparisons by place and over time such as in the monitoring of this population’s health needs and utilization. The aim of the new Australian National Centre for Intellectual Disability Health, established in 2023, is to ensure that all Australian children and adults with intellectual disability receive high-quality healthcare that meets their needs. Monitoring changes in this regard requires accurate identification of the number of people with intellectual disability within a population which itself is inherently dependent on how intellectual disability is defined. We have used a definition which is relatively easy to operationalize through different sources i.e. a full-scale IQ of less than 70, a condition known to be consistent with intellectual disability or documentation of intellectual disability in medical records; through education a level of intellectual disability defined as either mild/moderate or greater, and through the National Disability Insurance Scheme an International Classification of Diseases (ICD-10-CM) diagnostic code associated with intellectual disability. In contrast the definition required by Inclusion Australia “a lifelong condition that affects a person’s intellectual skills and their behavior in different situations” is much more difficult to operationalize. We discuss this challenge within the context of historical changes and the range of sources possibly able to provide this information today. We present two case studies from different Australian states and conclude with some suggestions for a multi-source approach using data linkage.

Introduction

Nearly a quarter of a century ago, the critical need for a stable definition of intellectual disability was recognized as essential to enable comparable research across time and place (1). The complexity of defining ‘intellectual disability’ in the face of diverse classification systems and data sources creates a significant challenge for research and policy (2). We acknowledge that prevalence estimates vary across studies due to definitional differences (3), particularly regarding the inclusion in some datasets of borderline intellectual disability functioning (IQ 70-84) which can double the prevalence (4, 5). This challenge persists today and is fundamental to the work and activities of the National Centre of Excellence in Intellectual Disability Health (NCEIDH), establishing the contemporary relevance of the problem. The NCEIDH was established in 2023 with the vision of ensuring that all Australian children and adults with intellectual disability receive high-quality healthcare that meets their needs (6). For NCEIDH to achieve its mission, we must establish a consistent approach to the taxonomy of identifying and defining intellectual disability across our work, particularly given that prevalence estimates vary widely depending on the classification systems used (3, 7). This requires us to be explicit about which definition we employ, the limitations of our data in its alignment with that definition, and to monitor changes in classification systems that might affect our ability to track health outcomes over time.

One purpose of this paper is to argue that for population-level health research and policy monitoring, reliance on any single administrative data source is likely insufficient, and that a validated, multi-source approach may address some of the limitations of single source administrative data. To make this case, the paper is structured as follows. First, we unpack the core challenges in defining and classifying intellectual disability. Second, we present two case studies using state- based data to identify people with intellectual disability. Finally, we discuss the path forward, advocating for a national, collaborative data linkage strategy specifically for intellectual disability. Drawing on recent developments in linked data infrastructure in Australia we highlight the need for a consistent approach to identifying intellectual disability, aligning with a clearly defined definition and outlining the crucial role the NCEIDH can play in leading this work.

The challenge of defining and classifying intellectual disability

The difficulty in achieving consistent identification stems from both conceptual and practical complexities, as the very construct of ‘intellectual disability’ is multifaceted and has evolved significantly over time. Schalock’s (2011) framework of five critical questions provides a constructive lens through which to understand this evolution (8). The first question, regarding naming, is reflected in the shift from terms like “mental retardation” to “intellectual disability”, though international variations such as “learning disability” in the UK persist (9). The second and third questions, on explaining and defining the construct, highlight the crucial shift from a purely medical or deficit model to a functional, biopsychosocial one. This modern approach, championed by organizations like the American Association on Intellectual and Developmental Disabilities (AAIDD) and articulated in the WHO’s International Classification of Functioning, Disability and Health (ICF), moves beyond pathology to focus on the interaction between a person’s health condition and their environment (10–12). It also distinguishes between an operational definition, necessary for research, and a constitutive definition, which considers a person’s broader life context. The fourth question, on classification, has similarly evolved to prioritize adaptive functioning and support needs over IQ scores alone. Finally, all these elements culminate in the fifth question, concerning public policy, as how we name, define, and classify intellectual disability directly shapes how we understand the health experiences of people with intellectual disability, inform policy decision making, and monitor progress. This conceptual richness, however, is likely to clash with the practical reality of administrative datasets. For a condition with a population prevalence as low as 1-2% we need population datasets with a large sample size. However, such datasets are not designed to capture the nuances of conceptual richness, creating a central challenge which this paper addresses.

The definition of intellectual disability remains complex, with variations in terminology and criteria, many of which are difficult to operationalize, used by clinical, service and advocacy bodies in Australia today. For example, the Australian Commission on Safety and Quality in Health Care uses the DSM-5 definition of intellectual disability as “a neurodevelopmental disorder that begins in childhood and is characterized by intellectual difficulties as well as difficulties in conceptual, social and practical areas of living” (13). On the other hand, Inclusion Australia describes intellectual disability as: “a lifelong condition that affects a person’s intellectual skills and their behavior in different situations” (14). This conceptual diversity creates a fragmented Australian data landscape, where intellectual disability is captured inconsistently across key datasets.

This inconsistency is evident across different data types. National surveys like the Survey of Disability, Ageing and Carers (SDAC) (15) and Household, Income and Labor Dynamics in Australia (HILDA) Survey (16) rely on self- or proxy-reports of “difficult learning or understanding things”, which are hard to align with clinical criteria. Administrative data also has limitations, Centrelink records, for instance, depend on the use of specific payment types and medical codes to determine eligibility (17), while the National Disability Insurance Scheme (NDIS) (18) ‘secondary disability’ field was not previously available to researchers. Clinical data sources are also incomplete (19). Hospital records may provide a diagnostic code, especially where there is a clear medical aetiology but reveal little about a person’s daily functioning. Unfortunately, health claims data (Medicare Benefit Schedule (MBS) and Pharmaceutical Benefit Scheme (PBS)) lack a specific identifier for intellectual disability reducing their applicability to research. A detailed summary of these datasets and their limitations is provided in Table 1.

Table 1

Table 1. Summary of information on intellectual disability captured in administrative and relevant survey data.

Given this heterogeneity, a clear and consistent definition is required for research purposes. Therefore, this study adopts the definition used by the Western Australian Intellectual Disability (IDEA) Database (20): a recorded IQ score of less than 70 accompanied by evidence of deficits in adaptive functioning. This approach is not proposed as a universal standard, but as a transparent and operationalizable definition which is suitable for analysis within administrative datasets– in line with the criteria put forward by Schalock (8).

While the administrative datasets presented in Table 1 are individually incomplete, they can potentially be connected through data linkage to build a more holistic picture. Data linkage is the methodological process of connecting records that correspond to the same unique entity across disparate data sources, thereby creating a unified dataset for enhanced statistical analysis. In Australia, this process is often facilitated by a national ‘population spine’, which integrates information from multiple sources such as Medicare, tax, and social security (Centrelink) records to cover almost every resident (21). However, while linkage connects the data, it does not erase the fundamental inconsistencies in how intellectual disability was originally defined and recorded in each dataset. Addressing this challenge should be a key goal of the National Disability Data Asset (NDDA) initiative (22). The NDDA is a new integrated data asset designed to improve outcomes for Australians with disability by bringing together de-identified linked data from sources including government welfare payments and services, the National Disability Insurance Scheme (NDIS), and health services. Datasets are linked at an individual level via a deterministic linkage to a person linkage spine that aims to cover all residents in Australia. Although the NDDA aims to improve disability information, robustly identifying people with an intellectual disability remains a complex issue (2). People with intellectual disability have unique support needs compared to those with other disabilities (23). Therefore accurately differentiating them in national datasets is essential. This requires a sophisticated multi-source approach to defining intellectual disability, a necessity the following case studies will illustrate in practice.

Example one: the utility of establishing a single dataset for identification of intellectual disability using data from three different sources

To illustrate the practical challenges of identification, we examined Western Australia’s deidentified, population-based intellectual disability database, the Western Australian (WA) Intellectual Disability Exploring Answers (IDEA) database (20). The IDEA Database is a comprehensive, longitudinal data resource currently containing records of individuals with intellectual disability ascertained between 1983 and 2020. It was established through the ongoing linkage of several administrative datasets principally the WA Disability Services Commission (DSC), now the Department of Communities, and the WA Department of Education. More recently, data from the National Disability Insurance Scheme (NDIS) has also been integrated. For context, the NDIS is Australia’s national program for providing individualized disability support to people with a permanent and significant disability (6), Eligibility through DSC required a full-scale IQ of less than 70, a condition known to be consistent with intellectual disability (such as Down syndrome) or documentation of intellectual disability in medical records; through Education a level of intellectual disability defined as either mild/moderate, severe or greater and through NDIS an International Classification of Diseases (ICD-10-CM) diagnostic code associated with intellectual disability (F70-F73, F79) or a condition known to be consistent with intellectual disability (20). Within the NDIS data specifically, individuals can be assigned by the National Disability Insurance Agency (NDA) into disability groups based on “primary disability group” or ICD-based variables. The primary disability groups we used to identify intellectual disability included “Intellectual Disability,” “Developmental delay,” “Global developmental delay,” or “Down syndrome.” Identification using ICD-based variables was based on the following codes: F70 (Mild intellectual disability), F71 (Moderate intellectual disability), F72 (Severe intellectual disability), F73 (Profound intellectual disability), F79 (Unspecified intellectual disability), F84.2 (Rett syndrome), F84.90 (Developmental delay), F84.91 (Global developmental delay), Q87.1 (Cornelia de Lange syndrome), Q87.8 (Coffin-Lowry syndrome), Q90 (Down syndrome), Q91 (Edwards syndrome and Patau syndrome), Q93.4 (Cri du Chat syndrome), Q93.5 (Angelman syndrome), and Q99.2 (Fragile X syndrome). As participants can have up to six secondary disabilities recorded, the presence of intellectual disability in any of these fields was also considered sufficient for positive identification. Data from any or all ascertainment sources was used to determine eligibility. The ethical oversight of the IDEA Database is managed under HREC 2014/24 - Intellectual Disability Exploring Answers (IDEA) Database: Infrastructure Linkage.

We assessed the sensitivity of the WA NDIS intellectual disability indicators, using the IDEA database as the gold standard. Sensitivity referred to the proportion of individuals in the IDEA reference database who were also identified in the NDIS data. To maximize identification, we created derived indicators combining primary disability groups, primary ICD-coded diagnoses, and secondary disability codes. The specific terms and diagnostic codes used for this identification are detailed in the notes for Table 2.

Table 2

Table 2. Sensitivity of different NDIS variable combinations in identifying intellectual disability in Western Australia.

The analysis revealed significant variation in the NDIS dataset utility based on co-occurring conditions. As expected, the sensitivity for identifying individuals with intellectual disability from all categories was highest when all diagnostic information was utilized. Overall sensitivity of identifying individuals with intellectual disability was 55.1%. However, our findings reveal a striking disparity; while the NDIS demonstrated very high sensitivity (99.3%) for identifying individuals with intellectual disability alone, sensitivity dropped dramatically to 23.2% for individuals with co-occurring intellectual disability and autism spectrum disorder. This 76.1 percentage point difference (95% confidence interval 75.2,76.9; z-test P<0.001) highlighted a critical gap in the ability of the NDIS to capture the full spectrum of intellectual disability, particularly affecting those with complex diagnostic profiles.

Beyond autism comorbidity, other factors also influenced identification (Table 3). For example, individuals with mild or moderate levels of intellectual disability, males, younger cohorts, those ascertained solely through the public education department and those who were born outside WA were less likely to be identified in the NDIS dataset. These patterns demonstrate that relying on a single data source such as the NDIS can create a biased and incomplete evidence base. If the true population is not captured in a dataset, there are major implications for research and the development of policies and evaluation of their impacts and equities.

Table 3

Table 3. Characteristics of Western Australian individuals with intellectual disability (with or without ASD), by NDIS identification status.

Example two: The multi-source approach in New South Wales

This illustrates a multi-source, bottom-up approach used in NSW to identify people with intellectual disability from existing administrative data (24, 25). The method is detailed in a cohort profile of over 100,000 individuals who accessed disability and health services in NSW between 2001 and 2018. In contrast to the WA case study which builds from core disability data. the NSW model combined multiple, individual health and disability records within a linked data infrastructure, creating a rich longitudinal resource (25).

Individuals were included in the NSW intellectual disability cohort if any of their records contained a diagnosis or service related to intellectual disability. The datasets used for identification were extensive, including the Disability Services Minimum Data Set (DS-MDS); health services records from the Admitted Patient Data Collection (APDC) and the Emergency Department Data Collection (EDDC) and Mental Health Ambulatory Data Collection (MH-AMB); data from targeted specialist support services in public schools; and records from the NSW Ombudsman, the NSW Public Guardian, and State-wide Disability Services for offenders in custody (25). While standard diagnostic criteria are generally applied, some variation existed across these sources, potentially including a small number of individuals with borderline intellectual functioning. This reflects real-world service delivery patterns and strengthens the case for multi-source validation.

The linkage performed by the NSW Centre for Health Record Linkage (CHeReL) (26) and the AIHW Data Linkage Unit (27), creates an invaluable resource for examining health outcomes in this population. While this model provides a robust pathway for ascertainment, validation against methods is crucial. For instance, the cross-sectional prevalence of intellectual disability identified by the NSW cohort was 1.1% (24) which is lower than the birth prevalence of 1.7% estimated from the WA IDEA Database for a similar time period (28). The NSW method may be more efficient at identifying adults who interact with services, whereas the WA method, with its inclusion of education records, is stronger at capturing childhood prevalence. The lower prevalence in the NSW adult cohort may be partially explained by the “transition cliff” (2) – poorer ascertainment in adulthood – and the fact that birth prevalence does not account for mortality over the life course.

The path forward: a collaborative, multi-source solution

The case studies from WA and NSW (20, 25) confirm a fundamental principle: integrating multiple data sources is an ideal way to build a complete picture of a population. This approach moves beyond a siloed service-centric view to a more holistic one – a strategy already proven effective at a state level (25).

Australia is now well-positioned to apply this principle nationally through its major data linkage infrastructure. This includes initiatives such as the National Health Data Hub (NHDH) (29), which aims to streamline access to linked health data, the disability-focused National Disability Data Asset (NDDA) (22), as well as the Person-Level Integrated Data Asset (PLIDA) platform (30). Together, these initiatives aim to integrate health, disability, and social service data to address complex questions. In relation to intellectual disability, these national linked data assets will be transformative for generating evidence to better understand the health of people with intellectual disability. Intellectual disability indicators created from multiple sources of data have the potential to identify a large population of Australians with intellectual disability as has been done to generate an identifier for Indigenous status (31). However, the indicators in the first instance may not be representative and ongoing efforts will be needed to improve the quality of data, address known data gaps (underrepresented populations) through inclusion of additional datasets, and generation of statistical methods to ensure representativeness. For example, the inclusion of primary care data such as GP records (32), which are not currently linked to any of the existing assets, are likely to improve the representativeness of the intellectual disability indicators.

This collaborative approach is already being actioned. The NCEIDH, as part of its work program, is providing scientific leadership by developing and validating a national algorithm to identify people with intellectual disability using these linked assets. This work is crucial for creating a more accurate national research and policy tool, and the future validation of this algorithm against state-based registers, such as the WA IDEA Database, also remains valuable if future linkage opportunities permit. To realize the full potential of our national data, a coordinated effort is needed. Data custodians, researchers, clinicians and government agencies must continue to work together to support data linkage, improve the accessibility of key data fields for research and ensure findings are fed back to improve services (not only health but also intersecting systems such as early intervention, rehabilitation, social care, and education) for all Australians with intellectual disability.

Conclusion

The challenge of identifying individuals with intellectual disability consistently and reliably across Australia persists, nearly a quarter of a century after its critical importance was first highlighted (1). This manuscript underscores that without robust national data, the ability to monitor health outcomes, plan services, and develop informed policy is significantly hampered. Our findings, particularly the limitations of single-source data, demonstrate an urgent need for the refined, multi-source approaches outlined. Ultimately, the goal is to foster a data landscape that provides a much clearer, more accurate understanding of the prevalence, needs, and health outcomes of Australians with intellectual disability. Such an evidence base is fundamental for the NCEIDH and all stakeholders to effectively fulfil their missions, ensuring that policies and services lead to tangible improvements in health equity and quality of life.

Data availability statement

The datasets presented in this article are not readily available because of ethical restriction. Requests to access the datasets should be directed to HL, aGVsZW4ubGVvbmFyZEB0aGVraWRzLm9yZy5hdQ==.

Ethics statement

The studies involving humans were approved by HREC 2014/24 - Intellectual Disability Exploring Answers (IDEA) Database: Infrastructure Linkage. The studies were conducted in accordance with the local legislation and institutional requirements. The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants’ legal guardians/next of kin because we used deidentified whole of population data where written informed consent is not feasible.

Author contributions

HL: Conceptualization, Data curation, Investigation, Methodology, Project administration, Resources, Validation, Writing – original draft, Writing – review & editing. KW: Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing, Data curation. PL: Data curation, Investigation, Methodology, Resources, Validation, Visualization, Writing – review & editing. MK: Data curation, Investigation, Methodology, Resources, Validation, Visualization, Writing – review & editing. ZA: Data curation, Investigation, Methodology, Resources, Validation, Writing – review & editing. JB: Data curation, Investigation, Methodology, Resources, Validation, Writing – original draft, Writing – review & editing. JT: Conceptualization, Investigation, Methodology, Validation, Writing – review & editing. JD: Investigation, Methodology, Validation, Writing – review & editing. MO: Investigation, Methodology, Resources, Writing – review & editing. AK: Conceptualization, Investigation, Resources, Writing – review & editing. PS: Conceptualization, Data curation, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. The article is an activity of the National Centre of Excellence in Intellectual Disability Health, an initiative funded by the Australian Government Department of Health, Disability and Ageing (4-IOT7SF3). JD is supported by a Fellowship from the Stan Perron Charitable Foundation. JT is supported by an NHMRC Investigator Grant, GNT 2009771. ZA is supported by an ARC Early Career Industry Fellowship IE230100561.

Acknowledgments

We thank the WA Department of Communities (previously Disability Services Commission), the Education Department of Western Australia and the National Disability Insurance Agency for their assistance with data collection for the IDEA database. We thank the Data Linkage Branch of the WA Department of Health for linking this data.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Generative AI statement

The author(s) declare that Generative AI was used in the creation of this manuscript. The authors used the AI language model, Gemini, to assist with drafting this manuscript. Assistance was limited to generating suggestions in response to queries, improving clarity, grammar, and conciseness. The authors reviewed and revised all generated content and take full responsibility for the accuracy and integrity of the final work, including the independent verification of all references.

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