Table 4.

Description of algorithm validation.

Algorithm name	First author, year	Data sources used for validation	Definition of disability used for validation	Validation results
				Overall accuracy	Sensitivity, specificity, PPV, NPV	Other
Access Risk Classification System	Ben-Shalom, 2016 (29)	Medicare Current Beneficiary Survey (panel survey of nationally representative sample of Medicare beneficiaries)	Self-reported disability on the basis of difficulties with activities of daily living (ADLs) or instrumental activities of daily living (IADLs)	Compared to 44.0% of individuals who self-reported ≥1 limitations to ADLs, and 70.3% who reported ≥1 limitations to ADLs or IADLs, 70.5% of individuals were rated as being ARCS levels C/D	Sensitivity = 54%, specificity = 67% (for ARCS and 5 other disability algorithms together)	After adjusting for sex, age, and dual eligibility, ARCS level C/D was a significant predictor of having ≥1 self-reported limitations to ADLs (OR=1.41, p<0.05)
	Palsbo, 2008 (16)	Survey mailed to a stratified random sample of Inland Empire Health Plan members entitled to Medicaid because of disability	Self-reported disability on the basis of a list of health problems and difficulties with activities of daily living	26% of individuals were correctly classified for level A, 10% for level B, 19% for level C, and 70% for level D	Level D: PPV = 59%, NPV = 57% Levels C/D: PPV = 72%, NPV = 46%, sensitivity 88%, specificity 30% Levels B/C/D: PPV = 89%, NPV = 29%	Levels A, B, and C tended to over-identify disability, while level D tended to under-identify disability
Khoury et al. algorithm	Khoury, 2013 (26)	Disability epidemiologist and physician reviewed the diagnostic and procedural codes for face validity	n/a	n/a	n/a	Reviewers were ‘highly consistent’ in their assessments of the codes