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Page 10 of 14         Bros-Facer et al. Rare Dis Orphan Drugs J 2023;2:21  https://dx.doi.org/10.20517/rdodj.2023.26

               other data sources either by reviewing records locally or to qualitative and quantitative research with clinical
               teams and families.

               Data federation implies the possibility to combine data from multiple sources to facilitate sharing and
               pooling of data for analysis. Respondents were asked whether they had considered federating any data from
               their initiatives. Eight of 14 answered positively, with some arguing that sharing knowledge through a
               database would help with the rapid interpretation of variants. In addition, data federation would help assess
               the sensitivity and specificity of the NGS tests for NBS.


               Cost-effectiveness and health economics
               Twelve respondents will perform a micro-costing analysis of their NGS-based NBS test to understand the
               operational cost of the workflow. For one initiative in particular, the intent is to compare the operational
               costs of several NGS approaches, although the investigators have yet to secure funding for this part of the
               study. When asked whether they would be collecting data on economic utility and if they were planning
               long-term follow-up of individuals with identified etiological variants, only six respondents answered
               positively, indicating that they would be using the criteria described in Figure 5 to demonstrate the potential
               economic value of screening using NGS.


               The proposal assessing long-term economic impact is not one that appears to be fully mature for most
               respondents, with three having yet to define what type of data they will collect for that purpose. Five
               initiatives are planning to evaluate medical resource utilization through EHRs and one will also try to use
               health insurance claims to assess the long-term economic impact of the NGS-based NBS. Furthermore,
               seven initiatives will also attempt to capture cost data in conjunction with healthcare resource utilization
               data.


               For health economic analysis, it is important to describe a comparator group that will act as a control (e.g., a
               group of individuals that did not receive an early diagnosis through NBS). More than half of the initiatives
               have not included a comparator group within their initiatives. Among those who have, one initiative is
               comparing non-participating hospitals with participating hospitals to obtain matched controls by
               interrogating laboratory and clinical records. Others mentioned that historical cohorts will be used as
               controls for conditions with a well-known natural history.

               Vision for the future
               Apart from one respondent who sees NGS-based screening replacing biochemical screening in future
               national NBS programs, all others believe that genomic screening will be used and implemented in parallel
               to traditional NBS programs, at least until the sensitivity and specificity of NGS-based screening are
               comparable to those of biochemical screening for all conditions currently included in national NBS
               programs.

               All the initiatives included in this report are research-driven. Therefore, the impact within healthcare
               systems will only be tangible once adopted by decision makers and regulatory bodies. Most of the
               respondents believe that NGS-based screening will be adopted as a first-tier NBS test within the next 10 to
               15 years.


               DISCUSSION
               Increasing numbers of targeted therapies that drive precision medicine coupled with recent advances in
               genome sequencing technology, particularly reductions in turnaround time [16,30] , computational advances for
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