Jamal-Allial Aziza, Sponholtz Todd, Vojjala Shiva K, Paullin Mark, Papazian Anahit, Eshete Biruk, Mahmoudpour Seyed Hamidreza, Verpillat Patrice, Beachler Daniel C
Safety & Epidemiology, Carelon Research, Newton, MA, USA.
Safety & Epidemiology, Carelon Research, Wilmington, DE, USA.
Pragmat Obs Res. 2025 Feb 7;16:19-25. doi: 10.2147/POR.S498221. eCollection 2025.
The National Death Index (NDI) is the gold standard for mortality data in the United States (US) but has a time lag and can be operationally intensive. This validation study assesses the accuracy of various mortality data sources with the NDI.
This validation study is a secondary analysis of an advanced cancer cohort in the US between January 2010 and December 2018, with an established NDI linkage. Mortality data sources, inpatient discharge, disenrollment, death master file (DMF), Center for Medicare and Medicaid Services (CMS), Utilization management data (U.M.), and online obituary data were compared to NDI.
Among 40,692 patients, 25,761 (63.3%) had a death date using NDI; the composite algorithm had a sensitivity of 88.9% (95% CI = 88.5%, 89.3%), specificity was 89.1% (95% CI = 88.6%, 89.6%). At the same time, positive predictive value (PPV) was 93.4% (95% CI = 93.1%, 93.7%), negative predictive value (NPV) was 82.3% (95% CI = 81.7%, 82.9%), and when comparing each individual source, each had a high PPV but limited sensitivity.
The composite algorithm was demonstrated to be a sensitive and precise measure of mortality, while individual database sources were accurate but had limited sensitivity.
美国国家死亡指数（NDI）是美国死亡率数据的金标准，但存在时间滞后且操作可能较为繁琐。本验证研究评估了各种死亡率数据源与NDI的准确性。
本验证研究是对2010年1月至2018年12月期间美国一个晚期癌症队列的二次分析，已建立NDI链接。将死亡率数据源、住院出院、退保、死亡主文件（DMF）、医疗保险和医疗补助服务中心（CMS）、利用管理数据（U.M.）以及在线讣告数据与NDI进行比较。
在40,692名患者中，25,761名（63.3%）使用NDI确定了死亡日期；综合算法的灵敏度为88.9%（95%置信区间 = 88.5%，89.3%），特异度为89.1%（95%置信区间 = 88.6%，89.6%）。同时，阳性预测值（PPV）为93.4%（95%置信区间 = 93.1%，93.7%），阴性预测值（NPV）为82.3%（95%置信区间 = 81.7%，82.9%），并且在比较每个单独的数据源时，每个数据源的PPV都很高，但灵敏度有限。
综合算法被证明是一种敏感且精确的死亡率测量方法，而单个数据库来源准确但灵敏度有限。
