Disability and Assistive Technology in Population-Based Data

This article aims to describe the data types and accompanying methods that are commonly-used to estimate disability prevalence in a population. These estimates are often commissioned by policymakers to scale supportive measures, and innovators to describe addressable markets to funders. 

Contributing to the publication, the GDI Hub led research into how disability prevalence is measured and its impact on policy and innovation. 

Disability data, from health records, surveys, and assistive technology, often oversimplifies complex experiences, risking misrepresentation, especially in low-resource settings. The research highlighted the need to distinguish between disability prevalence and identity, as well as the limitations of medical models versus functional assessments. It warns that poorly contextualised data and AI risk reinforce inequalities. Population-level assistive technology data offers a clearer way to assess support needs, especially for ageing populations.