Decision analysis, economic evaluation, and newborn screening: challenges and opportunities

Abstract

The number of conditions included in newborn screening panels has increased rapidly in the United States during the past decade, and many more conditions are under consideration for addition to state panels. The rare nature of candidate conditions for newborn screening makes their evaluation challenging. The scarcity of data on the costs of screening, follow-up, treatment, and long-term disability must be addressed to improve the evaluation process for nominated conditions. Decision analyses and economic evaluations can help inform policy decisions for newborn screening programs by providing a systematic approach to synthesizing available evidence and providing projected estimates of long-term clinical and economic outcomes when long-term data are not available. In this review, we outline the types of data required for the development of decision analysis and cost-effectiveness models for newborn screening programs and discuss the challenges faced when applying these methods in the arena of newborn screening to help inform policy decisions.Genet Med advance online publication 5 April 2012.

Figure 1. Simplified schematic of simulation model to project clinical and economic outcomes for PKU using one of many possible screening algorithms

¹Or “positive screen”; ²based on additional follow-up, newborns with initial presumed diagnoses are eventually categorized as either “false positive” or “true positive”; ³children without PKU follow natural history of general population; ⁴diagnosed through clinical identification. PKU, phenylketonuria.