Proxy Responses in Research on COVID-19 Among People With Intellectual and Developmental Disabilities.

Samuel et al. (2024) looked at COVID-19 survey answers from people with intellectual and developmental disabilities. They compared answers given by the person themselves to answers given by a family member or staff proxy.

The team used the National Core Indicators COVID-19 survey. They checked if the same person would get different scores just because someone else answered for them.

Proxy answers did not match self-answers. The differences were big enough to change what the data seem to say.

These gaps stayed even after the researchers controlled for age, disability level, and other basic facts. The mode—self or proxy—mattered on its own.

Spriggs et al. (2015) and Schmidt et al. (2010) saw the same pattern in quality-of-life surveys. Proxies consistently gave lower scores than adults with ID gave themselves. Sarah’s team shows the gap also shows up in pandemic health questions.

Emerson et al. (2013) warned that proxy data can mislead. This new paper gives real numbers behind that warning. It extends the caution from QoL studies to COVID-19 data.

Gur et al. (2023) found life satisfaction dropped during COVID-19. If many of their data points came from proxies, the drop could look bigger than it really was. Sarah’s findings help you read those results with caution.

When you read survey reports, check who answered. If proxies spoke for most participants, expect lower scores on stress, health, and satisfaction items. Before you act on state or agency survey data, ask for the response-mode breakdown. When you run your own social-validity checks, try to collect both self and proxy answers, then note any gap in your report. This simple step keeps plans and policies grounded in real client views, not proxy shadow scores.