Surveillance data plays a crucial role in identifying health disparities, informing policy, and guiding the allocation of resources. Among the greatest challenges to understanding and addressing health disparities and inequities among this population is the difficulty in identifying persons with IDD at a population level using existing data sources and approaches.
The Affordable Care Act required all Federally funded population health surveys to include a standard set of questions—the American Community Survey Disability Questions (ACS-6)—assessing functional limitations in the general population2. The ACS-6, like the Washington Group Short Set (WG-SS) that is also in wide use globally, represents significant progress in standardizing disability data collection. However, both question sets have been criticized for not distinguishing between developmental and adult-onset disabilities, as well as for under-representing individuals with intellectual disabilities.
Importantly, the ACS-6 and WG-SS questions were not designed to identify all individuals with disabilities but to describe functional limitations within populations. For example, the WG-SS cognition limitation question is used to define intellectual disabilities in epidemiologic studies, but it lacks the detail needed to differentiate intellectual disabilities from other cognitive disabilities that could be related to neurological disorders like Alzheimer’s disease or stroke. Since many population health surveys do not adequately capture those with intellectual disabilities, external data sources that include intellectual disability diagnoses or self-identification alongside these question sets are necessary to identify functional limitations in this population.