7 Discussion
7.3 Data
The accuracy of model outputs is a function of the model inputs and how they are combined. A large number of parameters were derived from a variety of sources mixing both local and international data. In many cases, the data were not specific to the elevated risk groups that defined the target population. A univariate sensitivity analysis was used to establish which parameters contributed most to uncertainty in the model results.
The diagnostic test accuracy is clearly a major factor in assessing a screening or surveillance programme. Two imaging techniques were being considered in this study: digital mammography and MRI. It was important to gather data on test accuracy relevant to the target population. However, few studies have assessed test accuracy in women at elevated risk of developing breast cancer, and only a small subset considered women less than 50 years of age. While it is assumed that the test accuracy of MRI is largely unaffected by breast density, that is not the case for
mammography. The evidence around the test accuracy of digital mammography in the target population is very limited and not entirely consistent. A conservative estimate was used in the model for the diagnostic test accuracy of digital
mammography. The accuracy of film mammography was used as a proxy, as some studies indicate that this may be an appropriate assumption in women less than 50 years old. A number of the studies in the systematic review and meta-analysis did include digital mammography or a mix of digital and film mammography. From these studies it was apparent that the estimated diagnostic test accuracy of digital
mammography was similar to that of film mammography in the target
population.(63;70) A scenario analysis estimated the impact of increasing the test sensitivity of digital mammography from the 38% used in the main model to 50%.
While this reduced the ICER of digital mammography surveillance strategies relative to a ‘no surveillance’ option, it did not result in any of the digital mammography surveillance strategies moving onto the cost-effectiveness frontier. The evidence of diagnostic accuracy used in the study is also based on annual screening, which is
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unlikely to be generalisable to other surveillance frequencies, such as biannually. For biennial strategies, we adjusted the test sensitivity for both digital mammography and MRI based on observed difference inferred from US data. These data suggest a modest increase in test sensitivity of approximately 5% when the test frequency is every 18 months and over.
As discussed previously, outcomes stemmed from stage at diagnosis. Data from the National Cancer Registry Ireland were used to estimate typical stage at diagnosis for women under 50 in the general population who are screen-detected and symptom-detected. To what extent this applies to women at elevated risk is debateable, although women at elevated risk are likely to contribute disproportionally to breast cancer in women less than 50 years. The data provide sampling weights that are used to determine stage at diagnosis, rather than providing an average which would not, in any case, be appropriate for a discrete distribution. Screen-detected cases will on average have a lower stage at diagnosis than symptom-detected cases. However, the use of sampling means that a screen-detected case could be diagnosed at stage IV while a symptom-detected case could be diagnosed at stage I. This gives rise to the occasional inconsistency where a screen-detected case has a poorer outcome than an equivalent symptom-detected case. The model for each strategy was run with 5,000 simulations and checked for stability of outcome estimates. The use of a large number of simulations as well as use of differences of medians rather median of differences guards against the risk of inconsistencies impacting on decision making.
Treatment was estimated as a function of stage at diagnosis. The proportion of patients receiving each form of treatment was obtained from a number of sources, including the National Cancer Registry Ireland, local hospital databases and the advice of the EAG. The data on treatment did not impact on the calculation of health outcomes, but did directly contribute the estimate of costs. A scenario analysis showed that the calculated ICERs compared to ‘no surveillance’ were largely
unaffected by using different assumptions about proportions receiving each form of treatment. However, the situation highlighted the difference between data
apparently representative of routine practice and opinion on what should be standard practice.
Survival rates were obtained from the National Cancer Registry and are based on the total population of women aged less than 50 years diagnosed with breast cancer.
The survival data used in the study was specific to five-year survival. In the model it was assumed that any women surviving to five years would have normal life
expectancy thereafter. This is a simplification as it can be anticipated that, for
example, 10-year survival will be lower than for the general population. The survival data are based on 10-year age bands by stage at diagnosis. In some cases, survival
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improves substantially from one age group to the next. This has the potential to cause inconsistencies in the model as survival is computed based on age at
diagnosis. For example, a stage II cancer diagnosed at age 29 has a probability of mortality of 0.162, compared to 0.086 for the same cancer diagnosed at age 30. In rare cases, deferred diagnosis may paradoxically lead to an improved outcome, although on average deferred diagnosis should lead to a later stage at diagnosis.
Again, the use of many model simulations protects against results being influenced by inconsistencies that may arise.
As a cost-utility analysis was used, health-related quality of life (HRQoL) data were used in the model. HRQoL was assumed to diminish with age. Values were also used specific to treatment by stage at diagnosis, false positives, and terminal cancer. The applicability of HRQoL data is always open to debate, and different studies may obtain quite different estimates of HRQoL for apparently the same condition and population. The values used in this study follow a clear and consistent gradient such that the disutility associated with false positives is smallest, followed by an increasing disutility associated with treatment for later stages. Finally, the quality of life is
lowest for those with terminal cancer.
The model included costs for the surveillance imaging, further testing of positive screens, and cancer treatment. There were difficulties in determining costs in all cases. Estimates were obtained from a variety of sources, often with differences that could be reconciled. For the cost of MRI, a micro-costing exercise was undertaken with UK data used to determine the likely range of costs. The cost of MRI in Ireland was estimated to be slightly higher than in the UK. Given the sensitivity of the model to the choice of screening cost, it was important to obtain accurate estimates. In the univariate sensitivity analysis, varying the cost of MRI by ±12% resulted in a ±4.5%
change in total cost. An equivalent variation in digital mammography cost resulted in a ±3.3% change in total cost.
For surgical costs, figures were originally obtained from a patient-level-costings exercise by a public acute hospital. However, the values were inconsistent with the DRG costings for day case and inpatient episodes for the relevant procedures. Given that casemix represents the funding mechanism in public acute hospitals in Ireland, it was deemed that this approach would generate costs in line with how much hospitals currently receive. The average cost of treatment ranged from €9,352 for a woman with DCIS to €24,212 for a woman with stage III cancer. The largest
contributor to treatment cost was chemotherapy, and uncertainty in the cost of chemotherapy was a major contributor to uncertainty in the total cost of surveillance.
The contribution of treatment costs to total cost depends on the strategy being modelled, although it is typically 60% of the total cost. Treatment contributes least to total cost in a high frequency, early start MRI surveillance strategy, where the
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imaging costs will be high. In a low frequency, late start digital mammography strategy, for example, treatment will contribute a greater proportion of total cost.
The uptake rate of surveillance was included in the budget impact assessment. The same estimates were used for all risk subgroups and based on digital mammography surveillance strategies. It is unlikely that uptake rates would be greatly affected by the choice between MRI and digital mammography, although rates may be affected by risk level. Women at higher risk may be more likely to avail of surveillance on the grounds that they will have a greater awareness of their risk and the benefits of early detection. Uptake rates may be impacted by surveillance frequency, as high
frequency surveillance may entail greater inconvenience in travel to and from
surveillance clinics. It is possible that there is a correlation between uptake rates and the rate of prophylactic surgery, in so far as the factor impacting on the decision to avail of surveillance may also influence the decision to avail of prophylactic surgery.
There was little evidence available on either uptake rates or prophylactic surgery, and no data on the possible correlations that may exist between the two options.
The study used international data relevant to the target population which should be representative, although regional variations may mean that an Irish population may behave differently.
7.4 Key messages
Although the definitions of the risk subgroups are clear, the population is poorly identified and understanding of disease pathology and progression in these cohorts is not fully defined.
The benefits of surveillance are based on assumptions of early detection leading to improved outcomes. Disease progression and pathology in women with high penetrance genetic mutations may be different from older women at average risk, which may impact on the benefits of surveillance.
Where possible, the model used in this study incorporated data specific to the target population. However, for some parameters the underlying data relate to an average risk population, which will potentially have affected the results.Health Information and Quality Authority