From the Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA.
Department of Population Health Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT.
Epidemiology. 2022 Mar 1;33(2):254-259. doi: 10.1097/EDE.0000000000001441.
Validation studies estimating the positive predictive value (PPV) of neonatal abstinence syndrome (NAS) have consistently suggested overreporting in hospital discharge records. However, few studies estimate the negative predictive value (NPV). Even slightly imperfect NPVs have the potential to bias estimated prevalences of rare outcomes like NAS. Given the challenges in estimating NPV, our objective was to evaluate whether the PPV was sufficient to understand the influence of NAS misclassification bias on conclusions of the NAS prevalence in surveillance research.
We used hospital discharge data from the 2016 New Jersey State Inpatient Databases, Healthcare Cost and Utilization Project. We adjusted surveillance data for misclassification using quantitative bias analysis models to estimate the expected NAS prevalence under a range of PPV and NPV bias scenarios.
The 2016 observed NAS prevalence was 0.61%. The misclassification-adjusted prevalence estimates ranged from 0.31% to 0.91%. When PPV was assumed to be ≥90%, the misclassification-adjusted prevalence was typically greater than the observed prevalence but the reverse was true for PPV ≤70%. Under PPV 80%, the misclassification-adjusted prevalence was less than the observed prevalence for NPV >99.9% but flipped for NPV <99.9%.
When we varied the NPV below 100%, our results suggested that the direction of bias (over or underestimation) was dependent on the PPV, and sometimes dependent on the NPV. However, NPV was important for understanding the magnitude of bias. This study serves as an example of how quantitative bias analysis methods can be applied in NAS surveillance to supplement existing validation data when NPV estimates are unavailable.
评估新生儿戒断综合征（NAS）阳性预测值（PPV）的验证研究表明，在医院出院记录中存在过度报告的情况。然而，很少有研究估计阴性预测值（NPV）。即使是稍微不完美的 NPV 也有可能对 NAS 等罕见结局的估计患病率产生偏差。鉴于估计 NPV 的挑战，我们的目标是评估 PPV 是否足以了解 NAS 分类偏倚对监测研究中 NAS 患病率结论的影响。
我们使用了 2016 年新泽西州住院患者数据库、医疗保健成本和利用项目中的医院出院数据。我们使用定量偏倚分析模型调整了监测数据，以在一系列 PPV 和 NPV 偏倚情景下估计预期的 NAS 患病率。
2016 年观察到的 NAS 患病率为 0.61%。偏倚调整后的患病率估计值在 0.31%至 0.91%之间。当假设 PPV≥90%时，偏倚调整后的患病率通常大于观察到的患病率，但当 PPV≤70%时则相反。在 PPV 为 80%的情况下，当 NPV>99.9%时，偏倚调整后的患病率低于观察到的患病率，但当 NPV<99.9%时则相反。
当我们改变 NPV 低于 100%时，我们的结果表明，偏倚的方向（高估或低估）取决于 PPV，有时也取决于 NPV。然而，NPV 对于理解偏倚的大小很重要。本研究为如何在 NAS 监测中应用定量偏倚分析方法提供了一个示例，当无法估计 NPV 时，可以补充现有验证数据。
