For over 50 years, newborn screening programs across the United States have implemented laboratory screening and follow-up programs to detect and report infants at high risk for rare diseases. As we look towards the future, current testing challenges will likely become more pronounced with the anticipated addition of new conditions to the Recommended Uniform Screening Panel (RUSP), increasing sophistication of testing platforms and methodologies, and greater complexity of biomarker profiles.

Building the data analytic capacity of newborn screening programs will help support the analysis and interpretation of patient data, providing tools and resources to create efficiencies in time-intensive program activities.

APHL and the Newborn Screening and Molecular Biology Branch of the Centers for Disease Control and Prevention (CDC) are exploring solutions aimed at improving the interpretation of laboratory tests by expanding data analytic capacity in the following ways:

• Increasing state newborn screening programs’ capacity to evaluate and interpret laboratory test data by providing Newborn Screening Bioinformatics Fellows
• Creating a Newborn Screening Data Analytic Workgroup focused on sharing and harmonizing best practices and solutions
• Enhancing data-driven decision making in the newborn screening community by designing and developing data science resources to address newborn screening-specific data challenges

In March 2019, APHL and CDC hosted a national meeting in Atlanta, GA to broaden their efforts, engage state newborn screening programs in a collective data analytics initiative, and discuss progress toward enhanced disease detection utilizing improved data analytics resources and technologies specific to newborn screening.

The meeting provided a forum for participants to discuss the needs around biochemical and molecular screening methodologies and their related data analytics requirements, as well as the value of data to improving health outcomes.

This national dialogue will help guide CDC development of an in-house data analytics resource that will improve the interpretation of biochemical and molecular test results.

This activity was supported by Cooperative Agreement #NU60OE000103-04 funded by the Centers for Disease Control and Prevention. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of CDC or the Department of Health and Human Services.

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