A decision-analytic Markov model was used to estimate the cost-effectiveness of risk-stratified care using a genomic assay. Lower-risk patients were given longer screening intervals, high-risk patients were immediately treated with surgery, and controls had standard care (biannual follow-up). The mean cost of assay-guided management was $8,123 less than the cost of standard care, largely because of reductions in resource use among people who did not develop cancer.
Keywords: Cost-effectiveness, Decision modeling, Mouth neoplasms, Precancerous conditions, Genetic testing
Abstract
Background.
Management of low-grade oral dysplasias (LGDs) is complicated, as only a small percentage of lesions will progress to invasive disease. The current standard of care requires patients to undergo regular monitoring of their lesions, with intervention occurring as a response to meaningful clinical changes. Recent improvements in molecular technologies and understanding of the biology of LGDs may allow clinicians to manage lesions based on their genome-guided risk.
Methods.
We used a decision-analytic Markov model to estimate the cost-effectiveness of risk-stratified care using a genomic assay. In the experimental arm, patients with LGDs were managed according to their risk profile using the assay, with low- and intermediate-risk patients given longer screening intervals and high-risk patients immediately treated with surgery. Patients in the comparator arm had standard care (biannual follow-up appointments at an oral cancer clinic). Incremental costs and outcomes in life-years gained (LYG) and quality-adjusted life-years (QALY) were calculated based on the results in each arm.
Results.
The mean cost of assay-guided management was $8,123 (95% confidence interval [CI] $2,973 to $23,062 in 2013 Canadian dollars) less than the cost of standard care. This difference was driven largely by reductions in resource use among people who did not develop cancer. Mean incremental effectiveness was 0.18 LYG (95% CI 0.08 to 0.39) or 0.64 QALY (95% CI 0.46 to 0.89). Sensitivity analysis suggests that these findings are robust to both expected and extreme variation in all parameter values.
Conclusion.
Use of the assay-guided management strategy costs less and is more effective than standard management of LGDs.
Implications for Practice:
The findings of this study strongly suggest that the use of a risk-stratification method such as a genomic assay can result in improved quality-adjusted survival outcomes for patients with low-grade oral dysplasia (LGD). The use of such an assay in this study provides “precision medicine,” allowing for a change in follow-up frequency or early intervention as compared with current standard care. As genomic technologies become more common in cancer care, it is hoped that such an assay, once validated, will become part of a new model for the standard management of LGDs in similar health systems.
Introduction
Cancers of the oral cavity have an age-standardized incidence rate of 9% in Canada [1], with similar rates experienced in other countries with industrialized economies such as the U.S. [2] and the U.K. [3]. Despite their relatively low incidence (compared with malignancies of the colon or breast), oral cancers have high case mortality, with an overall 5-year survival rate of 60%–63% [4]. Early detection has a meaningful impact on survivability: locally controlled oral cancers have 5-year survival rates of 75%–93%; cancers that spread to other tissue sites have 20%–52% 5-year survival rates [5]. Given that more than 40% of oral cancers are diagnosed at late stages with either regional or distant disease, the argument for early detection is strong: early detection increases the proportion of early-stage, curable cancers [6].
Early detection of lesions in the oral cavity is usually performed by a community dentist [7, 8]. Suspicious lesions are referred for diagnostic biopsy, where they may be identified as low-grade dysplasia (LGD) (mild or moderate dysplasia), which is monitored on an ongoing basis, or high-grade dysplasia, which is referred for treatment (usually surgery) [8]. The majority of LGDs will not develop into cancer, and the incorporation of better risk identification techniques into routine oral health management is recommended [7].
A recent prospective study showed that a specific molecular panel of biomarkers using loss of heterozygosity (LOH) is the most significant predictor of progression of LGD to invasive cancer, superseding clinical and pathologic features [9]. The samples were collected prospectively from a population-based cohort over a 10-year period (1997–2007). The results obtained from the LOH test were performed on an ongoing basis without knowing the outcome. Using this biomarker test, patients presenting with an LGD can be stratified into high-, intermediate-, or low-risk groups that correspond to the likelihood of developing cancer. Theoretically, patients in the low- or intermediate-risk categories may receive less frequent follow-up monitoring without appreciably increasing their risk of developing cancer, which would change their pattern of health care system resource use from the current standard of care.
The objective of this study is to examine cost-effectiveness by evaluating the incremental costs and outcomes experienced by a hypothetical cohort of patients with LGDs whose care is managed according to the results of an assay that identifies their risk of developing cancer based on their LOH profile, compared with current standard of practice.
Methods
Cost-effectiveness analysis was performed using a cohort-based Markov modeling approach. Incremental cost-effectiveness ratios (ICERs) were calculated based on the costs and outcomes of the model. Probabilistic sensitivity analysis (PSA) was performed to investigate the impact of uncertainty in model parameters on the cost-effectiveness of the change in management.
Decision Modeling
A cohort-based decision analytic Markov model was constructed in the R language (R Foundation for Statistical Computing, Vienna, Austria, https://www.r-project.org/foundation). The model simulated a hypothetical cohort of people 20–80 years of age diagnosed with an LGD in the province of British Columbia (BC), Canada. Nearly all cancers in BC are managed within the auspices of the BC Cancer Agency (BCCA), a provincial entity responsible for cancer care and research. Cancer treatment is provided by oncologists, pathologists, and other cancer care professionals within the BCCA. Oral precancerous lesion care across BC is primarily conducted in Vancouver at Vancouver General Hospital or the BCCA Oral Oncology Clinic [10].
The model designed for this exercise had two arms (Fig. 1): an assay-informed arm, in which the schedule of follow-up and management for a person with an LGD was informed by the results of the molecular test, and an assay-naive arm, in which people with LGDs received care according to the current standard of practice. Because the follow-up period for the LGD progression data were limited to 5 years, our model assumes that the risk of developing cancer from an LGD remains constant over a person’s lifetime.
Figure 1.
Structure of the decision model.
Abbreviations: HR, high-risk; IR, intermediate-risk; LGD, low-grade oral dysplasia; LR, low-risk.
The model was composed of six health states: low-, intermediate-, and high-risk LGDs; locally controlled oral cancer, representing an invasive oral cancer that has undergone successful treatment and does not show signs of progression; persistent/metastatic disease, representing cancers that are refractory to curative treatment; and remission, representing a locally controlled cancer that has shown no signs of disease return for at least 5 years.
Follow-up in the assay-informed arm was scheduled according to a person’s risk group: low, intermediate, or high. People in the low-risk LGD group returned for a reappraisal of their lesion (including assay and biopsy) every 5 years. People in the intermediate-risk LGD group were assessed (with assay and biopsy) every 2 years. People in the high-risk LGD group were treated with surgery immediately, as though they had a high-grade precancerous lesion (HGL) (severe dysplasia or carcinoma in situ) and transitioned into the locally controlled oral cancer group. It was possible in the model to be diagnosed with cancer during any follow-up appointment, and the cancer could be HGL or squamous cell carcinoma (SCC).
People in the assay-naive arm returned for a follow-up appraisal of the lesion every 6 months, regardless of risk group. Cancer (either HGL or SCC) could be diagnosed at any follow-up appointment.
People with detected HGL were managed surgically. HGLs removed with positive surgical margins (i.e., evidence that cancerous cells exist within a margin of apparently healthy tissue drawn around the lesion) required a second surgery. Patients with successfully treated HGL transitioned into the locally controlled oral cancer group and were followed up every 6 months for 5 years, after which they transitioned into the remission group and were discharged from the health care system (i.e., no more follow-up visits). Patients whose HGL was not controlled by treatment transitioned into the persistent/metastatic disease group.
People with detected SCC were managed with either surgery or external-beam radiation therapy (XRT). After treatment, patients were followed up to detect recurrence of their disease. Patients experiencing recurrence transitioned into the persistent/metastatic disease group and were treated with chemotherapy and palliative care. Patients who lived 5 years beyond the initial diagnosis with no recurrence transitioned into the remission group.
People in the persistent/metastatic disease group were managed with palliative care until they die of oral cancer (transitioning to the “death from cancer” health state). People in all groups could die of causes unrelated to cancer.
The model had a lifetime horizon (i.e., the model continued until all simulated people died of cancer or another cause) [11]. The cycle length of the model was 6 months. The cycle tree method was used for half-cycle correction [12].
Probabilities
Risk stratification into high-, intermediate-, and low-risk groups was estimated based on results from the Oral Cancer Prediction Longitudinal Study [9]. The associated risk of developing cancer for these risk groups was taken from the same study [9]. The probability of moving into another risk category (e.g., from low- to intermediate-risk) was assumed to be zero (0%) for this exercise; this assumption was examined in sensitivity analyses.
We used BC Cancer Registry data to identify a retrospective cohort of 148 patients who had developed oral cancer from a monitored LGD in BC from February 2004 to November 2011. Data from this cohort were used to estimate the probability of developing HGL or SCC. The probabilities of requiring a second surgery for HGL and of local control following treatment for HGL were also estimated from this dataset.
We used data from a second retrospective cohort of 864 patients diagnosed and treated for SCC in BC from January 2000 to September 2009 to estimate the proportion of SCCs treated primarily with surgery versus XRT. The proportion of SCC surgeries requiring neck dissection was estimated from preliminary (blinded) data from the pan-Canadian Optically-guided Oral Lesions Surgical (COOLS) trial [13]. Risk of SCC recurrence [14] or death from persistent/metastatic oral cancer [15] and relative risk of death according to age [16] were estimated from published studies. Age-specific death rates published by Statistics Canada were used to estimate the probability of dying from causes other than cancer from all health states [17].
Costs
The cost of the assay was assumed to be $500 (all costs expressed in 2013 Canadian dollars [$CAD]). The cost of medical appointments, biopsy, surgical resection, and neck dissection were taken to be the medically insured cost of a doctor’s visit as established in the provincial Medical Services Plan (MSP) fee schedule [18].
Additionally, the cost associated with a patient’s out-of-pocket expenses such as travel and accommodations were included, estimated based on preliminary (blinded) results of 383 patients from the COOLS trial [13]. Participants in the study were issued a questionnaire about distance traveled, method of travel, and any other expenses incurred as a result of their visit to the OCC. A fixed cost of $0.50 per kilometer was applied to trips taken by car based on reimbursement values used by the BC Provincial Health Services Authority.
The cost of XRT was estimated by applying a fixed per-fraction cost of $325.50 to a schedule of 25–30 fractions per person. The per-fraction cost is based on budgetary numbers from the Vancouver Cancer Centre. The cost of chemotherapy was based on a health economic study conducted by Hannouf et al. [19], which used a costing model that synthesized data from hospital drug formularies and the Ontario Case Costing Initiative. The cost of resources used in follow-up surveillance for locally controlled cancers were based on the MSP fee schedule.
The cost of the first 12 months and all subsequent months of persistent/metastatic disease was taken from a hospital-based cohort study conducted by Speight et al. in the U.K. (Canadian estimates were not available in the literature) [20]. Costs, originally published in 2006 U.K. pounds, were first converted to $CAD based on the midyear currency exchange rate then inflated according to the consumer price index to 2013 $CAD. Costs after cancer remission were assumed to be zero.
Health State Utilities
Health state utilities were applied to each state in the model. Utilities reflect a person’s preference for a health state, anchored between 1.0 (perfect health) and 0.0 (equivalent to death). Estimates for each health state were taken from a study conducted by Downer et al., based on a standard gamble exercise conducted in a convenience sample of 100 members of the general public in the U.K. [21]. Health utility experienced by people in remission was assumed to be 1.0. A summary of all values used in the model is provided in Table 1.
Table 1.
Summary of inputs used in the health state transition model
Cost-Effectiveness Analysis
The difference in total years of life between the two arms was defined as the incremental effectiveness (ΔE) in life-years gained (LYG). Incremental effectiveness was also expressed in quality-adjusted life-years (QALY), the number of years spent in each health state multiplied by the utility associated with that health state.
The sum of all costs experienced by people in the model was calculated for both arms in a similar way. Incremental cost (ΔC) was defined as the difference between the sum of costs between the two arms. Costs and outcomes (LYG, QALY) were discounted annually at a rate of 5% to reflect time preference [22].
ICERs were calculated as the ratio of incremental costs to incremental effectiveness (ΔC/ΔE), expressed as cost (in $CAD) per LYG and per QALY. ICERs are typically compared with a threshold value (λ) that represents decision-makers’ willingness to pay for an additional LYG or QALY. If the ICER is less than λ, the associated intervention or program is considered to be cost-effective.
Probabilistic Sensitivity Analysis
Probabilistic sensitivity analysis was performed using Monte Carlo simulation. A total of 10,000 iterations were drawn from the input distributions (Table 1) to generate a range of ICERs. The ICERs were plotted on the cost-effectiveness plane [23]. The cost-effectiveness plane is divided into four quadrants, representing positive/negative incremental cost (on the y axis) and positive/negative effectiveness (on the x axis). ICERs associated with new technologies are commonly found in the northeast quadrant (i.e., costs more and is more effective, compared with current practice).
Because the value of λ varies across decision-making contexts, it is often useful to consider the proportion of PSA-sampled ICERs that lie below the threshold (i.e., percentage of ICERs that are cost-effective) at various levels of willingness to pay. This is done through the use of cost-effectiveness acceptability curves [24]. These curves illustrate the value of λ at which a certain percentage (such as 50% or 95%) of ICERs lie, suggesting the level of willingness to pay that an intervention is, for example, 95% likely to be cost-effective.
Univariate Sensitivity and Threshold Analysis
In addition to PSA, it is often useful to investigate the extent to which the results of the model change based on changes to the value of a single input. This kind of “what-if analysis” is commonly referred to as univariate sensitivity analysis.
Threshold analysis is a form of univariate sensitivity analysis in which individual model inputs are adjusted to determine the value at which the recommendations drawn from the model would change. An n% threshold represents the point at which n% of ICERs are at the decision point (λ in most cases). Thresholds of 50% and 95% were calculated for all model inputs and the discount rate.
Results
Cost
The mean per-person cost of oral precancer and cancer management was $7,150 (95% confidence interval [CI] $5,195 to $11,882) in the assay-informed arm, compared with $15,272 (95% CI $8,540 to $34,030) in the assay-naive arm. Costs for both cohorts were primarily generated in the asymptomatic phase, by people who did not develop cancer: 60% (SD 12.5%) in the assay-informed arm and 64% (SD 18.1%) in the assay-naive arm. A summary of the proportion of total cost represented by each state can be seen in Figure 2.
Figure 2.
Proportion of total costs accrued in each model health state.
Abbreviations: HGL, high-grade precancerous lesion; LGD, low-grade oral dysplasia; SCC, squamous cell carcinoma.
Effectiveness
People managed according to the assay-informed protocol experienced an average of 17.1 LYG (95% CI 14.2–19.2), compared with 16.9 LYG (95% CI 14.1–19.0) in the assay-naive arm. The assay-informed arm had an average of 15.9 QALY per person (95% CI 9.7–18.9) compared with 15.2 QALY (95% CI 9.03–18.3) in the assay-naive arm.
Cost-Effectiveness
The mean per-person cost of oral precancer and cancer management was $7,150 (95% CI $5,195 to $11,882) in the assay-informed arm, compared with $15,272 (95% CI $8,540 to $34,030) in the assay-naive arm. Mean survival in the assay-informed arm was 17.1 life-years (95% CI 14.2–19.2) compared with 16.9 life-years (95% CI 14.1–19.0) in the assay-naive arm. Quality-adjusted survival in the assay-informed arm was 15.9 QALY per person (95% CI 9.7–18.9) compared with 15.2 QALY (95% CI 9.0–18.3) in the assay-naive arm.
Incremental cost between the two model arms was −$8,123 (95% CI −$23,062 to −$2,973). Incremental effectiveness was 0.18 LYG (95% CI 0.08–0.39) or 0.64 QALY (95% CI 0.46–0.89). Use of the assay dominated (i.e., cost less and was more effective than) standard care in this model. Cost-effectiveness results are summarized in Table 2.
Table 2.
Costs and effectiveness results
Incremental costs and effectiveness were plotted on the cost-effectiveness plane (Fig. 3). ICERs tended to fall in the southeast quadrant (less costly, more effective), with a few in the northeast quadrant (more costly, more effective).
Figure 3.
Cost-effectiveness plane. (A): Cost per LYG. (B): Cost per QALY. Abbreviations: LYG, life-years gained; QALY, quality-adjusted life-years.
Univariate Sensitivity and Threshold Analysis
No threshold value was found for the proportion of the population within each risk group. The cost of the assay, precancerous screening appointments, biopsies, and postcancer follow-up were influential in the size and direction of the ICER, but only at levels that are unlikely to be seen in health care systems like Canada’s (Table 3). The impact of reductions in parameter uncertainty was estimated using the expected value of partial perfect information [25]; however, we found no parameters with meaningful values (i.e., there was no model parameter for which a reduction in uncertainty changed the decision recommendation for use of the LOH assay).
Table 3.
Results of threshold analysis for λ = $0/LYG
Discussion
This is the first economic evaluation of oral precancer care in the published literature outside the context of a screening program, and the first to consider molecular subtypes in estimating the development of oral cancer. Our findings suggest that a genomic assay with the capability of determining the risk for people with precancerous oral lesions will progress to develop oral cancer is cost-effective if it allows for different schedules of patient follow-up. We constructed a Markov decision model to estimate the costs and effectiveness of such an assay, in which low- and intermediate-risk patients were seen on a schedule elongated from standard care. Patients identified as high risk had their lesions resected immediately to reduce the incidence of oral cancer. Under this scenario, overall costs to the health care system were lower and average patient survival was higher (i.e., use of the assay dominated standard care).
The cost-effectiveness findings were primarily driven by two factors. First, the reduction in cost was largely because of the reduced number of clinic visits among people who did not develop cancer. Second, people who were at high risk were treated immediately, with a very high predicted cure rate. As a result, the rate of cancer morbidity and mortality (with associated costs) was lower in the assay-informed arm.
Our model showed that, by using the assay and the adjusted schedule, the rate of oral cancer decreased by an average of 51.1%, and people who were high risk had a decreased mortality rate of 12.7%. People who were high risk (most likely to go on to develop cancer) represented only 2% of the total cost in the standard-care arm, whereas those who were low risk were responsible for 38%. Under the assay-informed scenario, that proportion of total cost dropped to 25%. This suggests that a population who would not be expected to have appreciably different survival could be expected to have dramatically lower health care costs.
There is additional value to patients as well. Patients in the COOLS trial reported out-of-pocket expenditures totaling an average of $68 per visit to attend oral cancer clinic appointments. Travel, accommodations, and other related costs are not paid by provincial health insurance, leaving it up to patients to cover these expenses themselves. This can be particularly difficult to bear for patients with low incomes and is likely to be much higher among patients who live a great distance from the clinic. In BC, these patients are predominantly people with lower incomes as well, making the change in practice a question of accessibility as well as clinical benefit. The scenario modeled in this exercise resulted in an average of 46 fewer precancer clinic visits per person, saving the average patient $3,200 over a lifetime.
This cost-effectiveness exercise is part of an ongoing program of research conducted by scientists, clinicians, and other researchers at the BCCA. Further evaluations of the clinical efficacy of LOH as a risk-screening tool, the costs of oral cancer treatment, and the nonsurvival impact of LGD management strategies are under way. As new technologies in oral cancer control become available to transition into clinical practice, we hope to provide an evidence base to guide decision making.
Limitations
The clinical management of oral cancer is more complex than we could feasibly represent here. Although we allowed for the possibility of two surgeries for HGL, our model does not treat local recurrence differently from regional or distant recurrence. Locally recurring oral cancers may be successfully treated, allowing the possibility of long-term remission. In our model, all recurrences are regional and terminal, which likely overestimates the risk and costs of treating cancer. However, our threshold analysis suggests that even if the risk of recurrence were one-tenth of the model estimate, the decision recommendation drawn from this cost-effectiveness exercise would not be affected. Models necessarily exchange speed for complexity, but we believe that our model takes the necessary complexity into account for the purpose of this scientific question.
The base case of this model also assumes that people will remain in the same risk group over time; however, it is possible that people with LGDs will develop lesions that are more suspicious for cancer over time, and will move from low risk to intermediate or high risk. As the value of the parameter “p_uprisk,” which represents this possibility, increases, both ΔC and ΔE become more favorable to use of the assay. Because the model treats high-risk LGDs as HGLs, as risk increases so too does the number of people who have successful resection and move to 5-year remission. If HGLs were less treatable, this relationship would change.
Similarly, our model assumes that the risk of progression to cancer from an LGD remains constant over a person’s lifetime. It is conceivable that risk increases over the lifespan, but our follow-up data were limited to 5 years of observation. Allowing the risk of an LGD progressing to cancer to increase by 5% per cycle over the model run did not meaningfully affect the cost-effectiveness results.
This exercise assumes that the assay is 100% accurate in determining LOH; however, it is entirely likely that such a test would have some misclassification rate (false negatives, false positives). A person misclassified as high risk, for example, would receive a surgical intervention from which they are not likely to derive any medical benefit and may experience serious adverse outcomes. Similarly, misclassifying someone as low risk would increase their likelihood of developing invasive disease. Our model did not explicitly factor in the accuracy of the assay, but our threshold analysis suggests that the rate of misclassification would have to be extraordinarily high to change the cost-effectiveness findings.
Finally, the structure of the health care system in BC (in which cancer is treated by one central agency) may not be reflective of clinical practice in other jurisdictions. We believe that the survival outcomes found in our exercise are less likely to vary between jurisdictions, especially given the robustness of these findings to uncertainty around clinical progression and treatment outcomes. While we cannot be certain that use of the LOH assay would reduce costs and be more effective under all circumstances, the robustness of our findings under sensitivity analysis suggests that the cost-effectiveness of this type of change in management is likely not specific to BC.
Conclusion
Our model suggests that using a genomic assay to risk-stratify the management of LGDs is less costly and more effective than the current standard of practice. The reduction in clinic visits for the majority of precancerous patients who will never go on to develop cancer will reduce health care expenditures, and the early identification and treatment of high-risk lesions will result in improved patient outcomes.
Acknowledgments
We acknowledge the contributions of the COOLS Trial Study Group (principal investigators C. Poh, S. Durham, and M. Rosin). This project received approval from the University of British Columbia Research Ethics Board (H13-01158). This research was supported by funding from Genome British Columbia (SOF5-020). The COOLS trial is supported by the Terry Fox Research Institute (2009-24). The Canadian Centre for Applied Research in Cancer Control is funded by the Canadian Cancer Society, Grant 2015-703549.
Author Contributions
Conception/Design: Ian Cromwell, Stuart J. Peacock, Catherine F. Poh
Provision of study material or patients: Ian Cromwell, Catherine F. Poh
Collection and/or assembly of data: Ian Cromwell
Data analysis and interpretation: Ian Cromwell, Dean A. Regier, Catherine F. Poh
Manuscript writing: Ian Cromwell, Dean A. Regier, Stuart J. Peacock, Catherine F. Poh
Final approval of manuscript: Ian Cromwell, Dean A. Regier, Stuart J. Peacock, Catherine F. Poh
Disclosures
The authors indicated no financial relationships.
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