Duchenne muscular dystrophy (DMD) is the most common pediatric‐onset form of muscular dystrophy, occurring in 1 in 5,000 live male births. DMD is a multi‐system disease resulting in muscle weakness with progressive deterioration of skeletal, heart, and smooth muscle, and learning disabilities. Pathogenic/likely pathogenic (P/LP) variants in the DMD gene, which encodes dystrophin protein, cause dystrophinopathy. All males with a P/LP variant in the X‐linked DMD gene are expected to be affected. Two to 20% of female heterozygotes with a P/LP variant develop symptoms of dystrophinopathy ranging from mild muscle weakness to significant disability similar to Becker muscular dystrophy. Recently, with improvements in therapies and testing methodology, there is stronger evidence supporting newborn screening (NBS) for DMD for males and females because females may also develop symptoms. A consented pilot study to screen newborns for DMD was initiated in New York State (NYS) and conducted from 2019 to 2021. The identification of female carriers and the realization of the subsequent uncertainty of providers concerning follow‐up during the pilot led to the development of algorithms for screening and diagnosis of carrier females, including both NBS and cascade molecular testing of family members.

Advancements in therapies for Duchenne muscular dystrophy (DMD) have made diagnosis within the newborn period a high priority. We undertook a consortia approach to advance DMD newborn screening in the United States. This manuscript describes the formation of the Duchenne Newborn Screening Consortium, the development of the pilot protocols, data collection tools including parent surveys, and findings from the first year of a two-year pilot. The DMD pilot design is population-based recruitment of infants born in New York State. Data tools were developed to document the analytical and clinical validity of DMD NBS, capture parental attitudes, and collect longitudinal health information for diagnosed newborns. Data visualizations were updated monthly to inform the consortium on enrollment. After 12 months, 15,754 newborns were screened for DMD by the New York State Newborn Screening (NYS NBS) Program. One hundred and forty screened infants had borderline screening results, and sixteen infants were referred for molecular testing. Three male infants were diagnosed with dystrophinopathy. Data from the first year of a two-year NBS pilot for DMD demonstrate the feasibility of NBS for DMD. The consortia approach was found to be a useful model, and the Newborn Screening Translational Research Network’s data tools played a key role in describing the NBS pilot findings and engaging stakeholders.

The goal of newborn screening is to improve health outcomes by identifying and treating affected newborns. This manuscript provides an overview of a data tool to facilitate the longitudinal collection of health information on newborns diagnosed with a condition through NBS. The Newborn Screening Translational Research Network (NBSTRN) developed the Longitudinal Pediatric Data Resource (LPDR) to capture, store, analyze, visualize, and share genomic and phenotypic data over the lifespan of NBS identified newborns to facilitate understanding of genetic disease and to assess the impact of early identification and treatment. NBSTRN developed a consensus-based process using clinical care experts to create, maintain, and evolve question and answer sets organized into common data elements (CDEs). The LPDR contains 24,172 core and disease specific CDEs for 118 rare genetic diseases, and the CDEs are being made available through the NIH CDE Repository. The number of CDEs for each condition average of 2200 with a range from 69 to 7944. The LPDR is used by state NBS programs, clinical researchers, and community-based organizations. Case level, de-identified data sets are available for secondary research and data mining. The development of the LPDR for longitudinal data gathering, sharing, and analysis supports research and facilitates the translation of discoveries into clinical practice.

Each year, through population-based newborn screening (NBS), 1 in 294 newborns is identified with a condition leading to early treatment and, in some cases, life-saving interventions. Rapid advancements in genomic technologies to screen, diagnose, and treat newborns promise to significantly expand the number of diseases and individuals impacted by NBS. However, expansion of NBS occurs slowly in the United States (US) and almost always occurs condition by condition and state by state with the goal of screening for all conditions on a federally recommended uniform panel. The Newborn Screening Translational Research Network (NBSTRN) conducted the NBS Expansion Study to describe current practices, identify expansion challenges, outline areas for improvement in NBS, and suggest how models could be used to evaluate changes and improvements. The NBS Expansion Study included a workshop of experts, a survey of clinicians, an analysis of data from online repositories of state NBS programs, reports and publications of completed pilots, federal committee reports, and proceedings, and the development of models to address the study findings. This manuscript (Part One) reports on the design, execution, and results of the NBS Expansion Study. The Study found that the capacity to expand NBS is variable across the US and that nationwide adoption of a new condition averages 9.5 years. Four factors that delay and/or complicate NBS expansion were identified. A companion paper (Part Two) presents a use case for each of the four factors and highlights how modeling could address these challenges to NBS expansion.

Recent advancements in gene‐targeted therapies have highlighted the critical role data sharing plays in successful translational drug development for people with rare diseases. To scale these efforts, we need to systematize these sharing principles, creating opportunities for more rapid, efficient, and scalable drug discovery/testing including long‐term and transparent assessment of clinical safety and efficacy. A number of challenges will need to be addressed, including the logistical difficulties of studying rare diseases affecting individuals who may be scattered across the globe, scientific, technical, regulatory, and ethical complexities of data collection, and harmonization and integration across multiple platforms and contexts. The NCATS/NIH Gene‐Targeted Therapies: Early Diagnosis and Equitable Delivery meeting series held during June 2021 included data sharing models that address these issues and framed discussions of areas that require improvement. This article describes these discussions and provides a series of considerations for future data sharing.