Chatterjee Paula, Macneal Eliza, Roberts Eric T
Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia.
Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.
JAMA Health Forum. 2025 Jul 3;6(7):e251923. doi: 10.1001/jamahealthforum.2025.1923.
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.
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.
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.
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).
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.
医疗保健机构越来越多地使用Z编码来衡量社会风险。Z编码所涵盖的社会风险类型包括与就业、住房、教育或其他心理社会状况相关的问题。先前的研究发现Z编码的总体使用率较低，但测量可能在其他方面存在偏差，这对风险调整和资源分配具有影响。
描述不同临床复杂程度和历史医疗保健利用率水平的住院医疗保险受益人的Z编码测量情况，并研究这些模式对死亡率预测的影响。
设计、设置和参与者：这项回顾性队列研究纳入了2022年住院的医疗保险受益人。数据于2024年5月至2025年6月进行分析。
住院记录中任何诊断字段出现Z编码（Z55至Z65编码）的情况；按临床风险（埃利克斯豪泽合并症指数风险评分和预测的30天死亡风险）和历史利用率水平（前一年住院次数）分类的受益人中Z编码记录的差异；以及在控制医院固定效应的情况下，Z编码记录与观察到的30天死亡率之间的关联。
在2022年7069611名住院的医疗保险受益人中，3816420名（54.0%）为女性，6093932名（86.1%）年龄在65岁及以上。共有148592名（2.1%）在索引医院索赔中有至少1个Z编码。埃利克斯豪泽合并症指数临床风险评分较低的受益人在医院内的Z编码患病率较高（2.8%对1.5%），前一年至少住院2次的患者中Z编码患病率（2.6%）高于前一年住院0次（1.8%）或1次（2.1%）的患者。尽管已知社会风险与死亡率上升在人群层面存在关联，但Z编码患病率在预测30天死亡风险最低的受益人中最高（4.4%），在死亡风险最高的受益人中最低（1.6%）。相应地，在未对人口统计学特征和临床风险等患者层面协变量进行调整的医院内分析中，Z编码的存在与观察到的30天死亡率较低的可能性相关（5.1%对4.2%；差异为-0.9个百分点；95%CI为-1.0至-0.8）。
这项队列研究发现，Z编码的使用模式可能低估了临床复杂患者中的社会风险，导致记录的社会风险与死亡率之间出现虚假的负相关。包括为人群和公共卫生监测收集的数据在内的替代社会经济指标，可能比Z编码提供更可靠的社会风险衡量标准。
