Misclassification of Neonatal Abstinence Syndrome (NAS) surveillance estimates: is considering the positive predictive value (PPV) enough?

• Type of Material: Article
• Authors: Catherine Labgold, Emory University; Lindsay Collin, Emory University; Penelope Howards, Emory University
• Language: English
• Date: 2022-03-01
• Publisher: Wolters Kluwer Health, Inc.
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2021 Wolters Kluwer Health, Inc. All rights reserved.
• Final Published Version (URL): https://doi.org/10.1097/EDE.0000000000001441
• Title of Journal or Parent Work: Epidemiology
• Volume: 33
• Issue: 2
• Start Page: 254
• End Page: 259
• Keywords: Quantitative Bias Analysis; Neonatal Abstinence Syndrome; Hospital Discharge Data; Surveillance; Misclassification
• Research Categories: Health Sciences, Epidemiology; Health Sciences, Public Health

Abstract

Background: 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. Methods: We used the 2016 New Jersey State Inpatient Databases, Healthcare Cost and Utilization Project. Surveillance data were adjusted for misclassification using quantitative bias analysis models to estimate the expected NAS prevalence under a range of PPV/NPV bias scenarios. Results: The 2016 observed NAS prevalence was 0.61%. The misclassification-adjusted prevalence estimates ranged from 0.31–0.91%. 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%. Conclusions: When we varied the NPV below 100%, our results suggested that the direction of bias (over-or underestimation) is always dependent on the PPV, and sometimes dependent on the NPV. However, NPV is always necessary to understand 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.