Data-Related and Methodological Challenges in Assessing the Cost-Effectiveness of Traditional and Genomic Newborn Screening Programs.

As newborn screening (NBS) programs consider adding conditions, policymakers would benefit from evidence showing whether early screening offers greater economic and health benefits than standard clinical diagnosis and treatment. However, economic evaluations have not been consistently used to guide decisions on expansion of NBS programs globally. This narrative review discusses data-related and methodological challenges in conducting cost-effectiveness analyses (CEAs) of NBS programs including (1) difficulties in identifying appropriate comparators and related data, (2) gaps in understanding and limited data on health outcomes and costs of care among children with newborn-screened conditions who received effective treatment after clinical diagnosis without screening, (3) lack of data on short- and longer-term costs and outcomes for affected individuals identified through screening, (4) challenges in distinguishing the effects of screening from improvements in treatment over time, and (5) limited data on costs outside of the healthcare system, limiting the ability to conduct evaluations from the societal perspective. Advancements in genomic technologies, such as genomic sequencing and gene-targeted therapies, are expanding the number of conditions that are potential targets for NBS. Genomic NBS poses additional challenges for CEAs that stem from the ability to simultaneously identify hundreds or even thousands of conditions and limitations of knowledge of genotype-phenotype associations. The data-related challenges discussed herein may encourage cross-disciplinary collaboration to improve data collection. Addressing these methodological issues is imperative for generating reliable evidence on the costs and benefits of NBS program expansion, thereby supporting informed policymaking on adding new conditions to NBS programs.