Advances in technology have led to the availability of genetic testing for a wide range of conditions for healthy or high-risk newborns. It is expected that the funds spent on genetic testing in the U.S. will reach $25 billion by 2021. With the numerous uses of genomic information, understanding the clinical value and long-term impact of genomic technologies on morbidity, mortality, quality of life, and diagnosis and treatment costs is essential. Conducting genomic sequencing in the newborn period of life has compelling logic, as it may provide insights for an active illness that a baby has, or early warning for future illnesses in childhood or adulthood. While providing genomic sequencing and interpretation for all newborns may be unrealistic at the present time, rapid advances in genomic technologies and informatics may make this feasible. Regardless of the cost of sequencing newborns, what is as yet unclear is how beneficial and valuable such population-based testing might be. A randomized clinical trial to study and provide timely estimates of the lifetime health impact and cost of population-based newborn genomic sequencing is infeasible given the sample size and time horizon needed. Thus, in this proposed study, we aim to develop a detailed mathematical model to simulate the natural history, clinical outcomes, and cost-effectiveness of integrating various genomic sequencing strategies into clinical care in the U.S. The model will provide an important link between scientific developments in genomics and the policy implications of using this information, both in clinical and economic terms. We will create a flexible model that will allow updating with the most current evidence in genomic medicine as it evolves. Thus, as new genomic technologies and screening tests are developed, we can quickly assess their clinical utility and economic value. This study will leverage the direct sequencing experiences of the NIH-funded BabySeq Project, a first-of-its-kind randomized controlled trial designed to examine how best to use genomics in clinical pediatric medicine by integrating genomic sequencing into the care of healthy and high-risk newborns. We have assembled an interdisciplinary team of experts in simulation modeling, health economics, genomics, pediatrics, predictive modeling, and health systems research. We propose a highly innovative application of modeling methods to genomic technologies and will develop a novel analytic framework, with the goal of synthesizing available clinical and epidemiological data into a unified modeling effort. The goal is to project clinical and economic outcomes associated with alternative strategies to assess the potential value of genomic technologies for newborn screening. This study will provide a durable platform for integration of genomic information into clinical care and health policy over the next decades.
With many genetic tests becoming available for clinical use, including genomic sequencing all newborns at birth to predict and prevent future diseases, precision medicine holds promise to improve patient care by using genomic information to predict future diseases. Our research goal is to develop and apply a detailed simulation we are calling the Precision Medicine Policy and Treatment (PreEMPT) Model, a sophisticated computer model capable of simulating short- and long-term clinical benefits and estimating the cost-effectiveness of integrating different genome screening strategies into clinical care for healthy or high-risk newborns for a wide variety of heritable conditions. With this model, we will synthesize the best available clinical, epidemiologic, and economic data on genetic variants present at birth for a wide variety of genetically-driven childhood conditions to project health outcomes, and provide a dynamic tool to evaluate evolving knowledge in the area of genomics and precision medicine in the United States over the next decade.
