The number of conditions included in newborn screening panels is increasing, but the merits and cost-effectiveness of these screenings have not been fully evaluated. This review describes the types of data and elements necessary for creating decision-analytic models and assessing the economics of newborn screening programs.
Decision analysis is a systematic approach to decision making under uncertain conditions. The models can be used to simulate research via randomized controlled trials, to predict beyond the clinical trial time limit, and to compare various protocols. This allows determination of an approach that could be the most-effective for a beneficial outcome. To create a decision-analytic decision model, the following aspects must be considered: defining possible alternative methods for identifying a specific disorder, screening technologies or protocols, timing of implementation, uncertainties, health outcomes, probability of these outcomes occurring, and values assigned to each outcome. This approach uses evidence from every available source, analyzes the evidence, and accounts for the strength of such evidence. For decisions regarding newborn screening policies, the evidence base is often sparse because of the rarity of many conditions. A decision-analytic model can provide estimates of long-term outcomes, identify parameters that will have the largest effect on results, and supplement evidence to assist in prioritizing research areas.
Once a decision-analytic model examines costs, it becomes an economic evaluation model. The 2 main types in health care are cost-effectiveness analysis and cost-benefit analysis. Cost-effectiveness analysis is more often used for evaluating health interventions. It is measured in dollar resources and looks at critical end points in health benefits. A special type of cost-effectiveness analysis is cost-utility analysis, which looks at a preference-based outcome, such as quality-adjusted life-years (QALYs) that will integrate morbidity and mortality effects into a single factor. For newborn screenings, cost-effectiveness is calculated by dividing net costs by net health benefits for a disorder or set of disorders compared with clinical identification of the disorder. Although many interventions are not cost saving, many can be cost-effective.
Data elements for screening include clinical outcomes, values, and costs. Screening algorithms must include sensitivity and specificity of the protocols to confirm accuracy of the testing relative to diagnosis. A high false-positive rate can increase costs and diminish effectiveness. As specificity increases, the number of false-positive results decreases, and cost-effectiveness improves. In the medical evaluation phase, each condition requires a protocol for follow-up evaluation. The services required for confirmation should be clearly delineated along with the probabilities of true positives at each stage. For individuals identified by screening, data on long-term outcomes are needed and must include information on differences in severity of cases identified by screening as opposed to discovery by clinical means. Accurate data on nonscreened cohorts are also required. An evaluation of a screening program should include treatments that were available to the screened and clinically identified cohorts, but these data are often difficult to obtain, even if they are available. To determine the effectiveness of treatment for identified individuals, estimates are necessary for short- and long-term clinical end points. Particularly, long-term outcomes are required to define the effectiveness of an intervention because, without lasting benefit, a screening program could be deemed too costly. Possible long-term outcomes and their costs and consequences must be considered in any cost-effectiveness analysis.
Costs of screening programs include those related to the screening, treatment, and short- and long-term care of the individual with the identified condition. Not only must the cost of the initial screening be considered, but also all costs associated with out-of-range test results, including medical evaluations and repeat testing, must be considered. Both direct and nondirect medical costs must be factored into any analysis. These can include hospitalizations, outpatient visits, drug treatments, procedures, diagnostic tests, medical equipment along with transportation costs, special education, and home modifications. Screening results and values for short- and long-term outcomes should be included using QALYs. Loss of quality of life connected to treatment regimens and requirements for dietary treatments or painful and difficult procedures should be considered in any evaluation. Treatment-related adverse events should be measured also using QALYs.
A decision-analytic model of screening strategies for newborns can assist policy makers by providing estimates of important outcomes, including anticipated numbers of infant and child deaths prevented, cases of permanent disability avoided, and changes in health care costs. Estimates of testing-related outcomes can be made. Each of these aspects can enhance the consideration of evidence for policy decisions regarding newborn screening.
Despite progress, challenges remain in conducting high-quality decision analyses and economic evaluations and include lack of data on long-term outcomes for newborn screening; difficulty in obtaining a full accounting of the costs of newborn screenings; identification of a comparator, which can affect the conclusions about cost-effectiveness of screening; methodologic aspects because of lack of standardization of optimal approaches; and precisely defining the scope of any analysis.
In the future, collection of long-term data on newborn screening could be extremely useful for evaluating conditions that have characteristics of currently screened conditions. Long-term data on health outcomes, costs of screening, follow-up, treatment, and disability must be obtained to improve the overall assessment process and to provide high-quality evaluations.
Child Health Evaluation and Research Unit, Division of General Pediatrics, University of Michigan, Ann Arbor, MI (L.A.P., B.A.T.); National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, GA (S.D.G.); Department of Pediatrics, Duke University, Durham, NC (A.R.K.); and Center for Child and Adolescent Health Policy, Massachusetts General Hospital for Children, Harvard Medical School, Boston, MA (J.M.P.).