Referral rate and false-positive rates in a hearing screening program among high-risk newborns

Kruthika Thangavelu1, Kyriakos Martakis2,3, Silke Feldmann4

  • 1Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Marburg, Philipps-Universität Marburg, Baldingerstrasse, 35043, Marburg, Germany. Kruthika.thangavelu@uk-gm.de.

Insights

False-positive results in newborn hearing screening are linked to prematurity and low birth weight. Higher infant age at screening also increases false-positives, impacting program effectiveness.

Area of Science:

  • Neonatal care
  • Audiology
  • Public health

Background:

  • Newborn hearing screening is crucial for early detection of hearing loss.
  • Improving the accuracy and cost-effectiveness of screening programs is essential.
  • Understanding factors influencing false-positive results can optimize screening protocols.

Purpose of the Study:

  • To determine referral and false-positive rates in a high-risk newborn hearing screening program.
  • To identify factors associated with false-positive results in auditory brainstem response (ABR) screening.

Main Methods:

  • Retrospective cohort study of 4512 newborns undergoing a two-stage AABR screening protocol.
  • Analysis of referral rates, false-positive rates, and potential risk factors.
  • Statistical analysis to assess associations between infant characteristics and false-positivity.

Main Results:

  • The overall referral rate was 3.8%, with a false-positive rate of 2.9%.
  • Higher birth weight and gestational age were associated with lower odds of false-positive results.
  • Increased chronological age at screening correlated with higher odds of false-positive results.

Conclusions:

  • Prematurity and low birth weight are significant contributors to false-positive hearing screening results in high-risk infants.
  • Chronological age at the time of screening is a key factor influencing false-positive rates.
  • These findings can inform strategies to enhance the efficiency of newborn hearing screening.
Abstract

Related Concept Videos

Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
534
Errors occurring during blood pressure monitoring01:25

Errors occurring during blood pressure monitoring

Blood pressure monitoring is a crucial clinical procedure in diagnosing and managing various cardiovascular conditions. Despite its significance, the accuracy of blood pressure measurements can be compromised by multiple factors, potentially leading to either falsely high or low readings. These inaccuracies are critical as they can significantly impact patient care. So, it is vital to understand these challenges deeply and adopt strategic approaches to minimize errors.
Several factors...
804
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
237
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
289