Cost-Effectiveness of a Multicancer Early Detection Test in the US

ABSTRACT

Objectives: Multicancer early detection (MCED) testing could result in earlier cancer diagnosis, thereby improving survival and reducing treatment costs. This study evaluated the cost-effectiveness of MCED testing plus usual care (UC) screening while accounting for the impact of clinical uncertainty and population heterogeneity for an MCED test with broad coverage of solid cancer incidence.

Study Design: Cost-effectiveness analysis of MCED testing plus UC vs UC alone in an adult population in the US.

Methods: A hybrid cohort-level model compared annual MCED testing plus UC with UC alone in detecting cancer among individuals aged 50 to 79 years over a lifetime horizon from a US payer perspective. Sensitivity and scenario analyses were conducted to explore the impact of key clinical uncertainties and population heterogeneity in valuing MCED, including differential survival by cell-free DNA detectability status, cancer progression rate, and how the test is likely to be implemented in clinical practice.

Results: Among 100,000 individuals, MCED testing plus UC shifted 7200 cancers to earlier stages at diagnosis compared with UC alone, resulting in an additional 0.14 quality-adjusted life-years (QALYs) and $5241 treatment cost savings per person screened and an incremental cost-effectiveness ratio (ICER) of $66,048/QALY gained at $949 test price. Among analyses of clinical uncertainties, differential survival had the greatest impact on cost-effectiveness. In probabilistic sensitivity analyses, MCED testing plus UC was cost-effective in all analyses with a maximum ICER of $91,092/QALY.

Conclusions: Under a range of likely clinical scenarios, MCED testing was estimated to be cost-effective, improving survival and reducing treatment costs.

Am J Manag Care. 2024;30(12):In Press

Takeaway Points

Early cancer diagnosis may improve survival and reduce treatment costs. We evaluated the cost-effectiveness of multicancer early detection (MCED) testing in addition to usual care (UC) screening in an adult population in the US. MCED testing reduced treatment costs through earlier diagnosis, yielding an incremental cost-effectiveness ratio of $66,048 per quality-adjusted life-year gained.

• MCED testing plus UC diagnosed more early-stage cancers than UC alone, leading to increased survival and decreased cancer treatment costs.
• Adjusting for clinical uncertainties, including cancer progression rate and population heterogeneity, showed minimal impact on the cost-effectiveness of MCED testing plus UC.

Cancer is a major public health issue worldwide and is the second leading cause of death in the US.1 In 2023, nearly 610,000 cancer deaths are projected in the US, partly due to late detection. Cancer is also a substantial economic burden in the US, with national costs for cancer care projected to exceed $245 billion by 2030.2 Evidence suggests that early detection and treatment can reduce the mortality and economic burden of cancer because patients who receive an early diagnosis are more likely to respond to treatment and have a greater survival probability.3-6 Cancers detected at a later stage require more invasive and time-consuming care and, therefore, are usually more expensive.7 Most cancers, including those with low prevalence, lack recommended screening procedures, resulting in possible late detection with poor prognosis, limited therapeutic options, and higher economic burden.

Emerging blood-based multicancer early detection (MCED) tests for the early detection of cancer may offer a new approach to complement traditional cancer screening programs.8-10 Galleri, a blood-based MCED test, detects a shared cancer signal from more than 50 cancer types based on circulating cell-free DNA (cfDNA) methylation patterns. Several studies have been conducted to develop, train, and validate the targeted methylation assay of Galleri and assess its clinical implementation, feasibility, and safety (eg, NCT02889978 and NCT04241796)11-13; others are evaluating the test’s potential as a screening tool (eg, NCT05155605).14 Post hoc analyses of these studies suggested additional prognostic value, with DNA shedding a marker of tumor aggressiveness and the cancers missed by MCED relatively indolent.11,15,16 The ongoing randomized, controlled, 140,000-patient NHS-Galleri trial (NCT05611632) also aims to evaluate the clinical utility of this test in an asymptomatic population.17

The objective of this study was to assess the cost-effectiveness (CE) of MCED testing in addition to usual care (UC) screening in the general population from a US payer perspective and to explore the impact of key clinical uncertainties in valuing MCED, including test characteristics, differential survival by cfDNA detectability status, cancer progression rate, and how the test is likely to be implemented in clinical practice.

METHODS

Model Structure

A hybrid cohort-level model was developed to compare annual MCED testing plus UC screening with UC alone for cancer detection over a lifetime horizon from a US payer perspective, as previously presented.18 This model has 2 components: a state transition estimating the annual number of cancers diagnosed and a decision tree assessing the clinical and economic impacts of cancer diagnoses. Patients received annual MCED testing through age 79 years. The model estimated cancer diagnoses in the UC arm based on age-specific and stage-specific incidence rates for each cancer type. MCED testing allowed for earlier detection and diagnosis of cancer compared with UC alone, resulting in stage and time shifts in cancer diagnosis. These shifts were estimated from a previously published model, taking into account MCED testing frequency, sensitivity, and cancer progression speed (ie, dwell time) by cancer type/stage and assuming all patients with positive test results received diagnostic workups.18,19 Test performance was informed by a prior case-control study, with a single false-positive (FP) rate of 0.5% and sensitivity varying by cancer type/stage (eg, for lung cancer, sensitivities of 22%, 80%, 91%, and 95% were used for stages I to IV, respectively; for pancreatic cancer, sensitivities of 61%, 61%, 86%, and 96% were used).12 Age- and stage-specific cancer incidence rates sourced from the Surveillance, Epidemiology, and End Results (SEER) program20,21 informed diagnosis in both arms. UC was defined as observed adherence to recommendations for lung, breast, cervical, colorectal, and prostate cancer workups.18 Further details are in the eAppendix (available at ajmc.com). Cancers accounting for more than 80% of the annual cancer incidence in the US were aggregated into 19 solid cancer types: lung, colon and rectum, pancreas, liver, hormone receptor–negative breast, hormone receptor–positive breast, esophagus, head and neck, stomach, ovary, kidney, prostate, lymphoma, anus, uterus, bladder, cervix, urothelial, and an aggregated other cancer group (sarcoma, melanoma, thyroid, and gallbladder). Outcomes were calculated pre- and post diagnosis. During each cycle, remaining survival, treatment costs, and quality-adjusted life-years (QALYs) were estimated for those receiving a diagnosis based on age at diagnosis and diagnosed cancer type/stage. Stage-specific treatment costs were based on analyses of SEER-Medicare data for the Medicare perspective and further adjusted for the commercial perspective using a cost multiplier22,23 to ensure comprehensive and consistent costs across cancer types/stages. Utilities were estimated from prior studies using stage-specific multipliers, aiming to capture cancer progression’s consequences.24-33 These costs and utilities were assigned at diagnosis, accruing for up to 5 years post diagnosis; thereafter patients returned to general population utility. US life tables for individuals with no cancer informed mortality,34 and stage-based overall survival rates from SEER20 were used to estimate the mean survival of individuals post cancer diagnosis by age group and cancer type/stage (see eAppendix).

A 2-population model for survival evaluated the impact of differential survival depending on cfDNA detectability status (cfDNA positive [cfDNA+; ie, shedding] or cfDNA negative [cfDNA–]). The incidences of detectable and undetectable cancers were assigned proportionally to each group based on published test sensitivity by stage such that total incidence and test sensitivity in the overall population were preserved. Similarly, the net survivals were adjusted in both groups such that the combined survival matched that observed in the overall population. Cancer management was assumed to be determined by cancer type/stage at diagnosis regardless of detectability. The model also considered the potential for overdiagnosis, assuming that 5% of individuals who died from other causes had undiagnosed cancer.35 The model further considered incorrect cancer signal origin (CSO) and FP test results from MCED testing and their corresponding workup costs and disutility. For individuals with missing cancer stage information in SEER, the cancer incidence by stage was estimated by assuming the staging distribution was the same as that in individuals with reported cancer stage information (see eAppendix). Health and cost outcomes were discounted at 3% annually. Treatment, UC screening, and workup costs were inflated to 2023 US$ using the US Bureau of Economic Analysis price index for personal consumer expenditures for health care (see eAppendix).

Modeled Scenarios

The base-case scenario evaluated the CE of an MCED-based screening program for 100,000 adults from a commercial perspective. The MCED test, priced at $949, was initiated for individuals aged 50 years and conducted annually until they were aged 79 years, with a 90% annual adherence rate. Additional scenarios were tested to study the impact of clinical uncertainties and population heterogeneity on outcomes by accounting for differential survival by cfDNA detectability status and faster cancer progression. The cfDNA+ cancers were modeled as having 1.5 times (HR, 1.5) the stage-specific risk of cancer death compared with cfDNA– cancers of the same cancer type.16 A scenario considering fast dwell times (ie, on average, dwell times were cut in half compared with the base)18 that increased the probability of interval cancers and a scenario with an HR of 3 also were explored.15

Additionally, multicohort scenarios were conducted to model MCED use in an age-distributed population in commercial and Medicare payer settings. The MCED screening population was broken into 3 separate 5-year age intervals for commercial (aged 50-54, 55-59, and 60-64 years) and Medicare (aged 65-69, 70-74, and 75-79 years) payers and each cohort was run separately and then aggregated according to US population distribution by age group.

Sensitivity Analyses

In a probabilistic sensitivity analysis (PSA), parameter inputs, including MCED test sensitivity, disutility associated with cancer diagnosis, and costs for cancer treatment, UC screening, and incorrect CSO and FP workups, were randomly sampled from distributions over 1000 iterations. The PSA bootstrapped estimates of sensitivity based on sampled draws from the case-control study. In a deterministic sensitivity analysis (DSA), one parameter was adjusted at a time to determine its impact on key results (Table 1).

RESULTS

Modeled Scenarios

In the base-case analysis, the addition of annual MCED testing to UC resulted in the shift of 7200 cancers to earlier stages at diagnosis compared with UC alone. These shifts led to a mean undiscounted increase of 4.85 LYs and 3.83 QALYs per patient with a shifted cancer, with ranges of 1.93 to 8.24 and 1.61 to 7.48 across modeled cancer types, respectively. Additionally, these shifts resulted in cancer treatment cost savings of $152,270 per patient with a shifted cancer, with the amount varying based on cancer type and the stage shifted. When aggregated over all the program participants and discounted, MCED reduced cancer treatment costs by $5241 and increased the LYs and QALYs by 0.18 and 0.14 per person, respectively (see results, including total cancers diagnosed by stage, in Table 2). However, the overall per-person discounted cost of MCED was $9163 higher than UC alone, mainly due to $14,036 additional MCED screening costs per person, which resulted in an incremental cost-effectiveness ratio (ICER) of $66,048 per QALY gained. The ICER is sensitive to test cost, and a 25% reduction in the test price would result in an ICER of $40,615. See eAppendix for detailed discounted and undiscounted base-case results.

Utilizing HRs of 1.5 or 3 in scenarios that account for differential survival based on cfDNA detectability status yielded reduced overall incremental QALYs compared with the base case where no difference in survival was considered, resulting in gains of 0.12 or 0.10 QALYs vs 0.14. These findings remained relatively consistent even when fast dwell times were considered, resulting in QALY gains of 0.11 or 0.09 vs 0.13. Assuming that cancer management is the same regardless of detectability, incremental costs slightly increased compared with base case (see eAppendix). These results led to higher ICERs of $77,781/$86,506 and $106,962/$115,995 per QALY gained for base/fast dwell times at HRs of 1.5 and 3, respectively. When the MCED test price was reduced by 25%, the ICERs decreased to $49,029/$54,566 and $69,937/$75,346 per QALY gained, respectively.

The effect of how MCED testing may be implemented in clinical practice was further examined through multicohort analyses. The analyses produced ICERs of $61,347 and $92,658 per QALY gained for commercial and Medicare payer settings, respectively, showing varying age trends (Figure 1). In the commercial setting, older age groups showed greater CE, primarily due to increased cancer incidence rates. Conversely, in the Medicare payer setting, younger age groups showed greater CE, reflecting lower overall survival rates in older age groups that limit potential benefits from MCED testing. Additionally, the lower cost of cancer treatment in the Medicare population led to reduced expected savings from early cancer detection, resulting in relatively higher ICERs in Medicare age groups compared with those in the commercial payer setting.

Sensitivity Analyses

PSA demonstrated that MCED testing plus UC is cost-effective compared with UC alone (Figure 2) after accounting for potential test performance uncertainties and variations in the cost and utility parameters. A CE plane indicates a wider variation in incremental QALYs than incremental costs, largely because of the uncertainty around MCED testing sensitivity, which affects survival. The narrow cost-effectiveness acceptability curve for MCED testing suggests high confidence that parameter uncertainty does not have a substantial impact on estimated ICERs, with a maximum ICER of $91,092/QALY.

In the DSA, parameters were varied to determine individual factors with the greatest impact on results. Inputs related to health discount rate, incidence, MCED sensitivity, MCED screening age, treatment costs, and dwell time were most influential, whereas those related to cost discount rate, overdiagnosis rates, adherence, FP rate, disutility, and workup costs were least influential. When accounting for cancers with missing stage information using an alternative approach, higher incidence was considered in later stages, which increased cancers detected and highlighted the survival benefit of earlier diagnosis with MCED testing, decreasing the ICER compared with base case (Figure 3). Detailed results of sensitivity analyses are presented in the eAppendix.

Validation Results

Key model outcomes were validated against published data and results from modeling studies for the US Preventive Services Task Force (USPSTF).36,37 Specifically, estimates of cancer incidence, stage distribution, and benefit per cancer shifted were validated. The model estimated a cumulative lifetime cancer incidence of 39% across modeled cancers, consistent with the lifetime risk of invasive cancers in the US of 42% for men and 37% for women.38 Importantly, survival gains caused by shifting cancers to earlier stages were consistent with results from CISNET modeling of lung cancer and colorectal cancer.36,37 This model estimated 3.87 and 6.73 LY gained (LYG) per stage-shifted lung cancer and colorectal cancer, respectively. Although the screening methods and settings considered differ, the benefit per lung cancer shifted is consistent with the mean estimate of 4.94 LYG per lung cancer detected across 4 models analyzing the USPSTF-recommended low-dose CT screening regimen. Similarly, the benefit estimated herein per colorectal cancer shifted (6.73 LYG) is within the range of 6.6 to 10 LYG per avoided cancer reported across 3 models analyzing 7 recommended colorectal cancer screening regimens. Full details of these comparisons and additional validations are provided in the eAppendix.

DISCUSSION

Key Findings and Considerations

This study evaluated the CE of MCED testing plus UC screening in the general population, with test performance as estimated in a case-control study and under various model assumptions, and further explored the impact of clinical uncertainty and population heterogeneity on model outcomes. The model indicated notable clinical and economic benefits for patients whose cancer stage was shifted by MCED testing.

One objective of these analyses was to estimate the potential impact of important uncertainties in the biology of preclinical and cfDNA-shedding cancers—the rate at which preclinical tumors progress and the clinical difference between cfDNA-shedding and nonshedding cancers. When accounting for worse differential survival based on cfDNA detectability status, the survival benefit decreased and the incremental costs increased, underscoring the significance of these clinical characteristics. However, the ICER remained below willingness-to-pay thresholds of $150,000/QALY at the base-case MCED test price and below $100,000/QALY when test price was reduced by 25% even when combining variations in survival and progression rates.39

Another objective herein was to explore the applicability of MCED test performance from case-control studies to real-world use in sensitivity analyses. Sensitivity analyses of test performance demonstrated a low impact from parameter uncertainty on model results, with all these analyses resulting in ICERs at or below $100,000/QALY. As demonstrated previously18 and herein, the number of cancers detected by MCED testing had the largest effect on model results. For example, when reducing the number of cancers detected by lowering incidence or MCED test sensitivity or increasing tumor progression rates (ie, to fast dwell time), the model yielded increased ICERs compared with base case.

Collectively, these analyses suggest that MCED testing with broad coverage of cancer types is likely to be cost-effective in a general population, even in the case of lower performance or less favorable biology than assumed.

These findings can be contrasted with those of a recent CE study conducted on a hypothetical MCED test in Ontario, Canada, which found a low likelihood of CE.40 The modeling methods and assumptions of these analyses differ in several ways, but most notably the present analysis and Lewis et al consider different MCED tests. Most importantly, this study considers an MCED test that can detect cancer types representing a significant majority of cancer incidence, whereas Lewis et al consider the impact of detecting a smaller subset of cancer types. Despite the limited clinical benefit of early detection for some low-incidence cancers, their cumulative impact meaningfully contributes to the overall value of MCED testing, as does the potential detection of interval and other cancers potentially missed by current screening.18 With the range of MCED tests in development growing, comparison of the potential of different approaches and tests will become increasingly important.

This study provides insights into the potential health economic impact of adding MCED testing to currently recommended screening, contingent on access to screening and subsequent care. Addressing equitable adoption and reimbursement challenges will be important in realizing these potential benefits. Future research should focus on overcoming these policy challenges, integrating MCED into clinical practice, and assessing its long-term impact on cancer detection, patient outcomes, and health care expenditures.

As payers evaluate coverage of new interventions, a key question is whether the clinical benefits delivered to the population support the incremental costs. This study is effective for hypothesis testing and hypothesis generating because it (1) estimates clinical benefits and financial impacts based on existing data, (2) projects how these outcomes might vary as new evidence emerges, and (3) identifies key input variables on which future research should focus. Moreover, as stakeholders consider how to better manage the potentially broad population for which MCED may be cost-effective in the presence of evolving evidence, early decision-making—including how future investments in research are determined—can be supported via modeling of how clinical and economic outcomes might vary for subsets of individuals within the population. For example, this study found opposing trends in CE by age in the commercial and Medicare populations, suggesting initial coverage in these populations could incorporate minimum and maximum age limits, respectively. Future research should evaluate additional subpopulations to support decision makers in considering coverage with alternate limits.

Key Model Assumptions and Limitations

This study applied some assumptions and limitations, and, where possible, conservative assumptions were made and the impact was tested in sensitivity analyses. This model was based on a previously published model18 with modifications; thus, most assumptions associated with that model apply herein. Notably, the model assumes that historical survival in the general population can be adjusted for MCED-detectable cancers and that a change in stage at diagnosis is associated with changes in survival. Analyses of the proportion of deaths caused by a diagnosed cancer41 and of the role of surgical resection following cancer diagnosis support the prognostic role of stage at diagnosis.42 The impact of this assumption was validated against previous modeling work underlying current USPSTF screening recommendations where possible. This assumption was also tested in scenario analyses, and even with combined changes in survival and progression rate, the CE of MCED testing was consistent with common CE thresholds. Additionally, the model represents the benefit of MCED exclusively through stage shift, but within-stage changes in detection may also be prognostically relevant and confer mortality benefits.43

The performance of MCED testing in real-world practice or population screening trials may differ from the MCED test performance used herein from a case-control study. Future prospective, randomized study data will be important to refine estimates of potential real-world performance. Variations in MCED test performance were tested in DSA and PSA; results indicated CE with MCED testing even when the MCED test sensitivity was decreased by 20%. Further, an age-distributed scenario was explored to test the likely implementation of MCED testing in clinical practice; results indicated that MCED testing was still cost-effective. Additionally, this study only compared those who enrolled in MCED testing with a counterfactual of the same group not receiving MCED testing, without consideration of uptake. In practice, uptake of new cancer screening tests can be gradual, which will have a direct effect on the population-level clinical impact, as seen in the uptake of low-dose CT for lung cancer screening in high-risk individuals.44

Further, the model used the US Bureau of Economic Analysis price index for personal consumer expenditures for health care to inflate cancer treatment costs. However, cancer-specific spending may have changed more rapidly because of growth in innovation and breakthrough new medicines.42

CONCLUSIONS

Despite uncertainty regarding MCED test performance, this analysis showed that the potential value of MCED testing is robust and the test remains cost-effective even under extreme scenarios of clinical uncertainty and population heterogeneity. MCED testing is associated with improved survival and reduced treatment costs as demonstrated in various scenarios, highlighting its potential survival benefit when used to detect cancers in a wider, real-world adult population.

Acknowledgments

The authors would like to acknowledge Ruth Sharf-Williams from Evidera for her contributions to drafting preliminary sections of the manuscript.

Author Affiliations: GRAIL, Inc. (ARK, AT), Menlo Park, CA; Evidera (AS, AC, WY, DZ), Bethesda, MD; Department of Internal Medicine and Center for Value-Based Insurance Design; University of Michigan (AMF), Ann Arbor, MI.

Source of Funding: GRAIL, Inc.

Author Disclosures: Dr Kansal is an employee of GRAIL, which is the manufacturer of a multicancer early detection test. Dr Tafazzoli is an employee of GRAIL. Ms Shaul, Mr Chavan, Mr Ye, and Ms Zou are employees of Evidera, a research and consulting firm for the biopharmaceutical industry. In their salaried positions, they work with a variety of Evidera’s clients and are precluded from receiving payment or honoraria directly from these organizations for services rendered. Evidera received funding from GRAIL for work on this project. Dr Fendrick reports serving as a consultant to AbbVie, CareFirst BlueCross BlueShield, Centivo, Community Oncology Alliance, EmblemHealth, Employee Benefit Research Institute, Exact Sciences, GRAIL, Health at Scale Technologies,* HealthCorum, Hopewell Fund, Hygieia, Johnson & Johnson, Medtronic, MedZed, Merck, Mother Goose Health,* Phathom Pharmaceuticals, Proton Intelligence, RA Capital Management, Sempre Health,* Silver Fern Healthcare,* Teladoc Health, US Department of Defense, Virginia Center for Health Innovation, Washington Health Benefit Exchange, Wellth,* Yale New Haven Health System, and Zansors* (asterisks indicate equity interest); research funding from Arnold Ventures, National Pharmaceutical Council, Patient-Centered Outcomes Research Institute, Pharmaceutical Research and Manufacturers of America, and Robert Wood Johnson Foundation; and outside positions as co–editor in chief of The American Journal of Managed Care, past member of the Medicare Evidence Development & Coverage Advisory Committee, and partner at VBID Health, LLC.

Authorship Information: Concept and design (ARK, AT, AS, AC, WY, AMF); acquisition of data (AT, AS, AC, WY); analysis and interpretation of data (ARK, AT, WY, DZ, AMF); drafting of the manuscript (ARK, AT, AS, WY, DZ, AMF); critical revision of the manuscript for important intellectual content (ARK, AT, AS, AMF); statistical analysis (ARK, AT, AS, AC, WY); provision of patients or study materials (ARK, AT); obtaining funding (ARK, AT); administrative, technical, or logistic support (AS, DZ); and supervision (ARK, AT).

Address Correspondence to: Anuraag R. Kansal, PhD, GRAIL, Inc., 1525 O’Brien Dr, Menlo Park, CA 94025. Email: akansal@grailbio.com.

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