Improving Data Infrastructure for Person-Centered Outcomes Research on Intellectual and Developmental Disabilities.

Madjid and the team looked at 200+ papers about IDD research data. They wanted to see what stops us from tracking real-world outcomes for people with intellectual or developmental disabilities.

They found five big holes. No shared IDD definitions. Medicaid data locked away. Small samples. Missing race and income info. No way to link school, health, and social records.

The biggest gap is messy data. Every study uses different labels for IDD. Medicaid claims hide the details we need. Most samples are tiny and not diverse.

Because of this, we still can't answer basic questions. Which services help kids with IDD most? Do Black and Latino families get equal care? We just don't know.

Mae Simcoe et al. (2018) shows why this hurts. They found a large share of IPV and IDD studies were too small and used different measures. Madjid's call for shared definitions would fix this.

Safer-Lichtenstein et al. (2019) found the same problem in autism studies. Most RCTs enrolled mostly white, higher-IQ boys. Madjid's push for better demographic data would help here too.

McKenna et al. (2019) adds another layer. They found almost no solid evidence for teaching students with emotional disturbance in regular classes. Madjid's plan to link school and health data could finally give us that evidence.

You can't improve what you can't measure. When you write goals or pick interventions, you're flying blind if the data is junk. Push your agency to use clear IDD labels and track race, income, and setting. Ask if you can tap Medicaid data for outcome tracking. Better data means better choices for every client you serve.