Optimizing Decision Making in Universal Newborn Hearing Screening: A Value of Information Perspective

Résumé fourni par la source

OBJECTIVES: The value of information framework assesses whether further research is warranted, and recent approximation methods have made the value of information analysis more feasible. There is ongoing uncertainty regarding the cost-effectiveness of Australia's universal newborn hearing screening (UNHS) program. This study aimed to quantify the value of additional research to guide future research priorities, including estimating the value by type of evidence and sample size needed to maximize the value to decision making. METHODS: A decision-analytic model, comprising an initial decision tree followed by a Markov model, with a 26-year time horizon, was developed to evaluate the cost-effectiveness of UNHS compared with no screening. Model parameters, including costs, probabilities, and outcomes, were derived from 2 Australian longitudinal studies, alongside relevant published sources. The expected value of perfect information, the expected value of partial information, and the expected value of sample information were estimated. RESULTS: The incremental cost-effectiveness ratio was $39 400/quality-adjusted life-year, with approximately 50% probability of being cost-effective at the $40 900/quality-adjusted life-year threshold. The expected value of perfect information for 2.5 million newborns over 10 years was $130.30 million, and the expected value of partial information revealed that the utility values were the primary source of uncertainty, particularly the association between utilities and diagnosis age. The expected value of sample information indicated that collecting utility data from 300 to 500 additional children could significantly reduce uncertainty and be worth between $106.41 million and $113.91 million. CONCLUSIONS: UNHS had an incremental cost-effectiveness ratio below the threshold, with some uncertainty in cost-effectiveness. Collecting additional utility data would reduce uncertainty and reduce decision uncertainty in Australia and elsewhere.

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