Using models to make policy: an inflection point?

How can polygenic inheritance be used in population screening for common diseases?

Advances in genomics have near-term impact on diagnosis and management of monogenic disorders. For common complex diseases, the use of genomic information from multiple loci (polygenic model) is generally not useful for diagnosis and individual prediction. In principle, the polygenic model could be used along with other risk factors in stratified population screening to target interventions. For example, compared to age-based criterion for breast, colorectal, and prostate cancer screening, adding polygenic risk and family history holds promise for more efficient screening with earlier start and/or increased frequency of screening for segments of the population at higher absolute risk than an established screening threshold; and later start and/or decreased frequency of screening for segments of the population at lower risks. This approach, while promising, faces formidable challenges for building its evidence base and for its implementation in practice. Currently, it is unclear whether or not polygenic risk can contribute enough discrimination to make stratified screening worthwhile. Empirical data are lacking on population-based age-specific absolute risks combining genetic and non-genetic factors, on impact of polygenic risk genes on disease natural history, as well as information on comparative balance of benefits and harms of stratified interventions. Implementation challenges include difficulties in integration of this information in the current health-care system in the United States, the setting of appropriate risk thresholds, and ethical, legal, and social issues. In an era of direct-to-consumer availability of personal genomic information, the public health and health-care systems need to prepare for an evidence-based integration of this information into population screening.

Improving the Accuracy and Comparability of Model-Based Economic Evaluations of Health Technologies for Reimbursement Decisions

Increasingly, decision analytic models are used within economic evaluations of health technologies (e.g., pharmaceuticals) submitted to national reimbursement bodies in countries like Australia and UK, where such models play a fundamental role in informing public funding decisions. Concerns regarding the accuracy of model outputs and hence the credibility of national reimbursement decisions are frequently raised. We propose a framework for developing reference models for specific diseases to inform economic evaluations of health technologies and their appraisal. The structure of a reference model reflects the natural history of the condition under study and defines the clinical events to be represented, the relationships between the events, and the effect of patient characteristics on the probability and timing of events. We contend that the use of reference models will improve the accuracy and comparability of public funding decisions. This can lead to the more efficient allocation of public funds.