Measurement Bias in Documentation of Social Risk Among Medicare Beneficiaries.

Importance: Health care organizations are increasingly measuring social risk using Z codes. Types of social risk captured in Z codes include issues related to employment, housing, education, or other psychosocial circumstances. Prior work has found low use of Z codes overall, but measurement may be biased in other ways that have implications for risk adjustment and resource allocation.

Objective: To characterize Z code measurement among hospitalized Medicare beneficiaries across levels of clinical complexity and historical health care utilization and examine implications of these patterns for mortality prediction.

Design, setting, and participants: This retrospective cohort study included Medicare beneficiaries with an inpatient hospital admission in 2022. Data were analyzed from May 2024 to June 2025.

Main outcomes and measurements: Presence of Z codes (codes Z55 to Z65) in any diagnosis field for a hospital admission, variation in Z code documentation across beneficiaries categorized by clinical risk (Elixhauser Comorbidity Index risk scores and predicted 30-day mortality risk) and historical utilization levels (number of hospitalizations in the prior year), and the association between Z code documentation and observed 30-day mortality, controlling for hospital fixed effects.

Results: Among 7 069 611 hospitalized Medicare beneficiaries in 2022, 3 816 420 (54.0%) were female, and 6 093 932 (86.1%) were 65 years or older. A total of 148 592 (2.1%) had at least 1 Z code on the index hospital claim. Within-hospital Z code prevalence was higher for beneficiaries with lower Elixhauser Comorbidity Index clinical risk scores (2.8% vs 1.5%) and higher among patients with at least 2 hospitalizations in the prior year (2.6%) than patients with zero (1.8%) or 1 (2.1%) prior hospitalizations. Despite known population-level associations between social risk and increased mortality, Z code prevalence was highest among beneficiaries with the lowest predicted 30-day mortality risk (4.4%) and lowest among beneficiaries with the highest mortality risk (1.6%). Correspondingly, in within-hospital analyses that did not adjust for patient-level covariates such as demographic characteristics and clinical risk, the presence of a Z code was associated with a lower probability of observed 30-day mortality (5.1% vs 4.2%; difference, -0.9 percentage points; 95% CI, -1.0 to -0.8).

Conclusions and relevance: This cohort study found that Z code use patterns likely underrepresent social risk among clinically complex patients, resulting in a spurious negative association between documented social risk and mortality. Alternative socioeconomic indicators, including data collected for population and public health surveillance, may offer more reliable measures of social risk than Z codes.