Misclassification of Neonatal Abstinence Syndrome Surveillance Estimates: Is Considering the Positive Predictive Value Enough?. Issue 2 (March 2022)

• Record Type: Journal Article
• Authors: Labgold, Katie; Collin, Lindsay J.; Howards, Penelope P.
• Is Part Of: Epidemiology. Volume 33:Issue 2(2022)
• Publication Date: 2022-03
• Subjects: Hospital discharge data -- Misclassification -- Neonatal abstinence syndrome -- Quantitative bias analysis -- Surveillance; Epidemiology -- Periodicals; Epidemiology -- Environmental aspects -- Periodicals; Epidemiology -- Periodicals; 614.405
• DOI: 10.1097/EDE.0000000000001441
• Languages: English
• ISSNs: 1044-3983

Abstract

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 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. Results: 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%. Conclusions: 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. Abstract : Supplemental Digital Content is available in the text.