Through the Looking Glass: A Data Lens on Health of People With Intellectual and Developmental Disabilities.

Greenlee et al. (2024) looked at how we track the health of people with intellectual and developmental disabilities.

They read many papers and wrote a story-style review.

The goal was to find what data we have and what data we still need.

The team found that most health records do not flag IDD in the same way.

Without one shared label, we cannot count who is sick, who gets care, or who dies early.

They say we need one clear IDD marker plus notes on race, income, and other life facts.

McQuaid et al. (2024) show why the gap matters. In a large COVID-19 study, people with ID had 1.4 times the hospital risk and 2.7 times the death risk after other illnesses were held equal.

That harm stayed hidden until the study team pulled private insurance files and coded IDD by hand.

Hu et al. (2012) saw the same pattern in kids. Poor Chinese families had up to three times the odds of having a child with ID, but only a one-time survey caught it.

Together these papers prove the review’s point: without steady, standard IDD flags we keep missing life-or-death patterns.

You cannot fix a disparity you cannot see. Ask your agency to add a simple IDD field to every intake form. Pick one code set—DSM-5, DC-LD, or state waiver flag—and stick with it. Over time your data can join a bigger pool that shows who needs help first.