Potential Biases in Estimating Absolute and Relative Case-Fatality Risks during Outbreaks.
Potential Biases in Estimating Absolute and Relative Case-Fatality Risks during Outbreaks.
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DOI：
10.1371/journal.pntd.0003846
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发表时间：
2015
影响因子：
3.8
通讯作者：
Hernán MA
中科院分区：
文献类型：
--
作者：
Lipsitch M;Donnelly CA;Fraser C;Blake IM;Cori A;Dorigatti I;Ferguson NM;Garske T;Mills HL;Riley S;Van Kerkhove MD;Hernán MA
估计病死率风险（CFR），即一个人因感染而死亡的概率，是新出现的传染病的流行病学调查中的一个高度优先事项，有时在已知传染病的新暴发中也是如此。可用于估计总体病死率的数据通常是在具有挑战性的情况下为其他目的（例如监测）收集的。我们描述了可能影响总体病死率估计的两种形式的偏倚——对严重病例的优先确定和报告延迟的偏倚——并回顾了在过去的流行病中提出和实施的解决方案。同样令人感兴趣的是对特定干预措施（例如，住院或在特定医院住院）对生存的因果影响的估计，这可以作为两个或多个组的相对CFR来估计。当将观察数据用于此目的时，可能会出现另外三个偏倚来源：混淆、生存偏倚和因优先纳入住院和/或死亡患者的监测数据集而进行的选择。我们说明了这些偏差，并对在观察数据集中接受不同干预措施的患者之间差异CFR的因果解释提出了警告。再次，我们讨论了减少这些偏差的方法，特别是通过在症状出现之前确定的较小但更系统定义的队列中估计结果，例如通过前向接触者追踪确定的队列。最后，我们讨论了这些偏差可能影响病例中死亡风险因素的非因果解释的情况。
Estimating the case-fatality risk (CFR)—the probability that a person dies from an infection given that they are a case—is a high priority in epidemiologic investigation of newly emerging infectious diseases and sometimes in new outbreaks of known infectious diseases. The data available to estimate the overall CFR are often gathered for other purposes (e.g., surveillance) in challenging circumstances. We describe two forms of bias that may affect the estimation of the overall CFR—preferential ascertainment of severe cases and bias from reporting delays—and review solutions that have been proposed and implemented in past epidemics. Also of interest is the estimation of the causal impact of specific interventions (e.g., hospitalization, or hospitalization at a particular hospital) on survival, which can be estimated as a relative CFR for two or more groups. When observational data are used for this purpose, three more sources of bias may arise: confounding, survivorship bias, and selection due to preferential inclusion in surveillance datasets of those who are hospitalized and/or die. We illustrate these biases and caution against causal interpretation of differential CFR among those receiving different interventions in observational datasets. Again, we discuss ways to reduce these biases, particularly by estimating outcomes in smaller but more systematically defined cohorts ascertained before the onset of symptoms, such as those identified by forward contact tracing. Finally, we discuss the circumstances in which these biases may affect non-causal interpretation of risk factors for death among cases.