An Assessment of Public Preferences for Newborn Screening Using Best–Worst Scaling

Abstract

To identify and quantify public preferences for attributes of newborn screening conditions. We conducted an online national survey of the public (n = 502) to evaluate preferences for attributes of candidate newborn screening conditions. Respondents were presented with hypothetical condition profiles that were defined using 10 attributes with 2-6 levels per attribute. Participants indicated whether they would recommend screening for a condition and which condition attributes were most and least important when making this decision (best-worst scaling). Difference scores were calculated and stratified by condition recommendation (recommend or not recommend for screening). Regression analyses were used to evaluate the effect of attributes on choice to screen or not screen. The number of babies diagnosed was important to those who would recommend newborn screening for a profile, and age at which the treatment would start was important to those who would not recommend newborn screening. Cost was considered to be a key attribute, and treatment effectiveness and impact of making the diagnosis through newborn screening were of low importance for both groups. Public preferences identified through survey methods that provide an adequate baseline understanding of newborn screening can be used to inform newborn screening decisions.