Racial Bias in Risk Adjustment Algorithms and Implications for Racial Health Disparities: Evidence from Dual-Eligible Medicare/Medicaid Long-term Care Patients in New York

项目概要/摘要 越来越多的证据表明数据算法中存在种族偏见。在医疗保健方面，种族 由于分类和编码、数据可用性或数据准确性方面的系统偏差，可能会出现偏差 不同种族群体之间存在差异。例如，算法使用医疗保健成本数据（而不是疾病数据）来 预测需要向我们当前系统服务不足的黑人患者分配太少的资源 并且产生的支出低于具有相同健康状况的白人患者。这个问题越来越 相关的，因为大多数美国公共健康保险计划都实行按人头管理的医疗系统， 受益人参加私人保险计划，政府每月向保险公司支付固定费用 每位登记者按人头付费。这些人均付款通常使用风险调整来计算 算法，其中根据年龄、性别和所选健康状况的信息来预测患者费用 来自过去登记者的数据。然而，种族通常被排除在这些算法之外，这增加了以下可能性： 风险调整管理式医疗可能会扩大患者护理和结果方面的种族差异。然而还有 关于风险调整管理式医疗系统对护理种族差异影响的实证证据很少 和健康结果，特别是在长期护理等高成本环境中。该项目将推进 通过研究风险调整管理长期护理 (MLTC) 对这些问题的因果影响 具有双重资格的医疗保险/医疗补助长期护理受益人在护理和结果方面的种族差异 纽约，使用 8 年有关医疗补助和医疗保险登记、索赔和评估的行政数据 记录。该项目将确定风险调整后的 MLTC 对一系列护理利用和健康的影响 结果，包括住院、急性后、疗养院和家庭护理、处方药使用和 死亡率，分别按患者种族/民族分类。利用逐县推出管理式医疗服务 根据要求，分析将使用双重差分模型来比较县内的变化 纽约患者在实施管理式医疗之前和之后的结果。我们将估计 按患者的种族/民族划分模型，测试 MLTC 效应的统计差异。项目 还将确定风险调整算法中受种族偏见影响最严重的亚组，通过 亚组分析，比较性别、年龄、慢性病存在情况和邮政编码级别的影响 收入中位数。该项目还将研究管理式医疗计划功能在推动种族歧视方面的作用 医疗保健利用和健康结果方面的差异。结果将有助于政策制定者、医疗保健 组织、提供者和患者了解风险调整算法中偏差的影响 患者健康状况，确定受影响最严重的患者亚组，并了解有效的计划 可以遏制或消除管理医疗环境中种族健康差异的功能。

PROJECT SUMMARY/ABSTRACT A growing body of evidence demonstrates the presence of racial bias in data algorithms. In healthcare, racial bias could arise due to systematic biases in classification and coding, data availability, or data accuracies that differ across racial groups. For instance, algorithms that use data on healthcare costs—rather than illness—to predict need tend to allocate too few resources to Black patients who are underserved by our current system and generate lower spending than white patients with the same health conditions. This issue is increasingly relevant because most U.S. public health insurance programs operate capitated managed care systems, in which beneficiaries enroll in private insurance plans, and the government pays insurers a fixed monthly capitation payment per enrollee. These per-capita payments are typically calculated using risk-adjustment algorithms, in which patient costs are predicted with information on age, gender, and selected health conditions from data on past enrollees. However, race is often excluded from these algorithms, raising the possibility that risk-adjusted managed care could widen racial disparities in care and outcomes among patients. Yet there is little empirical evidence on the impacts of risk-adjusted managed care systems on racial differences in care and health outcomes, especially in high-cost settings, such as long-term care. This project will advance knowledge on these issues by studying the causal effects of risk-adjusted managed long-term care (MLTC) on racial disparities in care and outcomes among dual-eligible Medicare/Medicaid long-term care beneficiaries in New York, using 8 years of administrative data on Medicaid and Medicare enrollment, claims, and assessment records. The project will identify the effects of risk-adjusted MLTC on a range of care utilization and health outcomes, including inpatient, post-acute, nursing home, and at-home care, prescription drug use, and mortality, separately by patient race/ethnicity. Leveraging the county-by-county rollout of managed care mandates, the analysis will use difference-in-differences models to compare within-county changes in outcomes of patients in New York from before to after managed care was implemented. We will estimate separate models by race/ethnicity of the patient, testing for statistical differences in MLTC effects. The project will also identify subgroups who are most severely affected by racial bias in risk-adjustment algorithms, through sub-group analyses that compare effects by gender, age, presence of chronic conditions, and zip code level median income. The project will additionally examine the role of managed care plan features in driving racial disparities in health care utilization and health outcomes. Results will help policymakers, healthcare organizations, providers, and patients to understand the implications of bias in risk-adjustment algorithms on patient health, identify subgroups of patients who are most severely impacted, and learn about effective plan features that could curb or eliminate racial health disparities in managed care settings.