Population Genomic Screening for Three Common Hereditary Conditions : A Cost-Effectiveness Analysis

Abstract

Background: The cost-effectiveness of screening the U.S. population for Centers for Disease Control and Prevention (CDC) Tier 1 genomic conditions is unknown.

Objective: To estimate the cost-effectiveness of simultaneous genomic screening for Lynch syndrome (LS), hereditary breast and ovarian cancer syndrome (HBOC), and familial hypercholesterolemia (FH).

Design: Decision analytic Markov model.

Data sources: Published literature.

Target population: Separate age-based cohorts (ages 20 to 60 years at time of screening) of racially and ethnically representative U.S. adults.

Time horizon: Lifetime.

Perspective: U.S. health care payer.

Intervention: Population genomic screening using clinical sequencing with a restricted panel of high-evidence genes, cascade testing of first-degree relatives, and recommended preventive interventions for identified probands.

Outcome measures: Incident breast, ovarian, and colorectal cancer cases; incident cardiovascular events; quality-adjusted survival; and costs.

Results of base-case analysis: Screening 100 000 unselected 30-year-olds resulted in 101 (95% uncertainty interval [UI], 77 to 127) fewer overall cancer cases and 15 (95% UI, 4 to 28) fewer cardiovascular events and an increase of 495 quality-adjusted life-years (QALYs) (95% UI, 401 to 757) at an incremental cost of $33.9 million (95% UI, $27.0 million to $41.1 million). The incremental cost-effectiveness ratio was $68 600 per QALY gained (95% UI, $41 800 to $88 900).

Results of sensitivity analysis: Screening 30-, 40-, and 50-year-old cohorts was cost-effective in 99%, 88%, and 19% of probabilistic simulations, respectively, at a $100 000-per-QALY threshold. The test costs at which screening 30-, 40-, and 50-year-olds reached the $100 000-per-QALY threshold were $413, $290, and $166, respectively. Variant prevalence and adherence to preventive interventions were also highly influential parameters.

Limitations: Population averages for model inputs, which were derived predominantly from European populations, vary across ancestries and health care environments.

Conclusion: Population genomic screening with a restricted panel of high-evidence genes associated with 3 CDC Tier 1 conditions is likely to be cost-effective in U.S. adults younger than 40 years if the testing cost is relatively low and probands have access to preventive interventions.

Primary funding source: National Human Genome Research Institute.

Figure 1:. Conceptual Diagram of Decision Analytic Model Structure Showing Identification of a Variant, Receipt of Recommended Interventions, and Clinical Events

For each age from 20 to 60 (age n), a representative cohort of U.S. individuals enter the decision tree on the left and can receive either population genomic screening or usual care. In the screening arm, patients with a variant (probands) who have not already been affected (e.g., been diagnosed with breast or colon cancer or experienced a CVD event) receive an intervention(s) to reduce their risk, although a proportion of patients are modeled as non-adherent. In both arms, a proportion of probands will have already known their variant status (previous diagnosis) due to genetic work-up indicated by family history and/or prior clinical presentation. Use of confirmatory testing for probands was assumed to eliminate false positives. A small proportion of probands are false negatives and do not receive preventative interventions. Of note, in both arms, most individuals do not have a variant and/or prior clinical presentation; we assume these individuals will still be screened and incur screening costs but not receive additional health benefits. Individuals from the decision tree (left) enter the long-term (Markov) model on the right with or without knowledge of their variant and/or previous disease history. Probands who know their variant status may or may not adhere to recommended interventions, while those who do not know their status are never offered the recommendations. Probands who undertake recommended interventions are at reduced risk for disease incidence and/or severity compared to probands who do not undertake recommended interventions. All individuals (probands and non-probands) who develop disease enter a disease-specific post-event health state, and all individuals eventually die due to disease or background mortality. Some structural differences among the three disease sub-models are not represented in this high-level conceptual diagram. Please refer to Appendix sections 2-4 for additional details on each sub-model’s unique structure and parameterization.

Figure 2:. Contributions of Familial Hyperlipidemia, Hereditary Breast and Ovarian Cancer, and Lynch Syndrome to Incremental Cost, QALYs, and Cost-Effectiveness of Screening versus No Screening, by Age at Time of Screening and per 100,000 Screened Individuals

Color-coded bars represent each model component’s individual contribution to incremental cost (top graph) and incremental QALYs (middle graph) per 100,000 screened individuals. Results of the probabilistic sensitivity analysis (bottom graph) show the deterministic cost per QALY gained (i.e., the incremental cost-effectiveness ratio [ICER]) and 95% uncertainty interval for each age-based cohort. M = million; K = thousand; FH = familial hypercholesterolemia; HBOC = hereditary breast and ovarian cancer; LS - Lynch syndrome; QALY = quality-adjusted life-year; UI = uncertainty interval.