Assumptions of value: Exploring normative aspects of newborn genomic sequencing in decision analytic models

ELSIcon2024 • Paper • June 12, 2024 

Authors:

Speaker: Madison R. Hickingbotham, MS - Harvard University

Researchers worldwide are exploring population-based newborn genomic sequencing programs. However, data about the benefits, harms, and costs of such programs is lacking, and traditional clinical trials to generate this evidence may be infeasible due to the rarity of high-impact variants, the prohibitively long timeframe over which outcomes need to be evaluated, and ethical concerns. Decision analytic modeling provides a systematic approach that circumvents those challenges by projecting the consequences of interventions under conditions of uncertainty. Results of model-based analyses can provide critical information on population-wide consequences to inform implementation decisions. Yet, all model-based analyses rely on assumptions made by the researchers who build these models. We examine normative considerations that shape model-based analyses in the context of population-based newborn genomic sequencing. Specifically, we address: 1) ways in which selection of conditions to model impacts public health policy, especially regarding the selection of conditions that disproportionately impact different population groups; 2) value-relevant assumptions that underlie the conceptualization of the model structure, such as family composition, uptake of testing, access and adherence to follow-up recommendations after a genetic variant is identified, and outcome measures generated; and 3) sources and availability of data used to derive model parameter estimates, particularly given inherent limitations such as variant frequencies from genomic databases for minoritized groups. We propose ways in which each of these points can and should be addressed and how decision makers should consider results from decision models alongside complementary social science research on ethical and social implications in program design.

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