Using Decision Analysis to Support Newborn Screening Policy Decisions: A Case Study for Pompe Disease

Abstract

Background: Newborn screening is a public health program to identify conditions associated with significant morbidity or mortality that benefit from early intervention. Policy decisions about which conditions to include in newborn screening are complex because data regarding epidemiology and outcomes of early identification are often incomplete. Objectives: To describe expected outcomes of Pompe disease newborn screening and how a decision analysis informed recommendations by a federal advisory committee. Methods: We developed a decision tree to compare Pompe disease newborn screening with clinical identification of Pompe disease in the absence of screening. Cases of Pompe disease were classified into three types: classic infantile-onset disease with cardiomyopathy, nonclassic infantile-onset disease, and late-onset disease. Screening results and 36-month health outcomes were projected for classic and nonclassic infantile-onset cases. Input parameters were based on published and unpublished data supplemented by expert opinion. Results: We estimated that screening 4 million babies born each year in the United States would detect 40 cases (range: 13–56) of infantile-onset Pompe disease compared with 36 cases (range: 13–56) detected clinically without screening. Newborn screening would also identify 94 cases of late-onset Pompe disease that might not become symptomatic for decades. By 36 months, newborn screening would avert 13 deaths (range: 8–19) and decrease the number of individuals requiring mechanical ventilation by 26 (range: 20–28). Conclusions: Pompe disease is a rare condition, but early identification can improve health outcomes. Decision analytic modeling provided a quantitative data synthesis that informed the recommendation of Pompe disease newborn screening.