An African American male enrolled in Medicaid has not been taking his blood pressure medication.

Medicaid risk adjustment models generally calculate risk using two major categories of inputs:demographicsanddiagnosis data. Demographic factors commonly includegenderand indicators tied toMedicaid status/eligibility(for example, eligibility category, dual status, disability-related eligibility, or other program qualifiers depending on the state/model). These demographic elements adjust expected cost and are foundational to the risk score even before considering diagnoses. By contrast,race is not a standard inputfor calculating Medicaid risk scores in typical risk adjustment methodologies, so options that include race are not supported. Likewise, “patient noncompliance” is primarily a clinical and quality-of-care issue and may affect treatment outcomes, but it isnot itself a standard risk-score driverunless it is documented as a reportable, supported diagnosis that the specific model recognizes (and most models don’t directly risk-adjust for nonadherence codes). Therefore, among the options given,Medicaid status and genderare the most clearly valid factors that impact the beneficiary’s risk score.