Supporting the support systems: Integrating assistive technology access into aging policy frameworks

Overview   

Individuals who can obtain and effectively use assistive products, such as wheelchairs, hearing aids, and accessible software, are understood to have reliable access to assistive technology (AT). Such access is increasingly recognised as critical support in the context of global population ageing, where the prevalence of functional difficulties is rising and the demand for supportive solutions and services is expanding. In response, AT outcomes such as need, use, and unmet need are more often included in routine data collection systems, including censuses and household surveys. Similarly, dedicated surveys, such as the WHO Rapid Assistive Technology Assessment (rATA), have been successfully administered in dozens of countries, expanding the portfolio of available AT data.   

Despite these advances, significant gaps remain in how these data are translated into forward-looking policy insights. Existing data sources are often single-wave and/or limited in their ability to capture how access varies across population groups and over time. Yet understanding how AT access differs by key demographic characteristics, such as age, gender, and socioeconomic status, is essential for identifying populations at higher risk of unmet need and for designing equitable interventions. Moving beyond static descriptions to anticipate how these disparities may evolve requires approaches that integrate population structure, demographic change, and projected outcomes. 

Demographic forecasting methods offer a practical framework for addressing these challenges. By incorporating age structure, mortality patterns, and population projections, these approaches enable the estimation of how AT access and unmet need may change over time. When combined with disaggregated survey data, they also support intersectional analysis, allowing policymakers to examine how overlapping characteristics shape access to AT across the life course. In doing so, these methods help bridge the gap between available data and the evidence required for anticipatory, equity-oriented policy planning. 

To these aims, this article presents how AT access data, increasingly captured through WHO survey tools and routine national data collection, can be analysed to produce reliable forecasts. Specifically, this article describes how these data can be used in a variety of demographic forecasting methods, including multistate life tables (MSLTs), both where multiple waves of data are available and through an adaptation that enables the use of single-wave data (Sullivan’s method for MSLTs). While the latter relies on stronger assumptions, it provides a practical alternative in settings where repeated data collection is not yet feasible. 

This article begins by describing the role of forecasting in the AT evidence sector. It then outlines five demographic forecasting methods, characterised by their data requirements and analytical outputs. Sullivan life tables are described in greater detail, with particular attention to their applicability in data-constrained contexts. The article then considers the types of insights these approaches generate and their relevance for policy and innovation in the AT sector, before discussing key limitations and concluding.