Assessment model

The model used in the present HTA was a Markov model adapted from a model developed in the UK. In each simulation, a woman was modelled from age 20 to 83 in six-monthly cycles. At the end of each cycle, the woman in the model was in one of 10 discrete states. In as far as possible, the health states were chosen so as to enable accurate modelling of surveillance. By having multiple states that effectively recorded the length of time a women with invasive cancer goes undetected, it was possible to incorporate the potential benefits of early detection.

Although the preferred modelling approach, a natural history model was not used, primarily because of the difficulties in acquiring the large amount of data that would be required to define the parameters in the model. Information on disease

progression and sojourn time is limited, particularly for women under 50 years of age

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at elevated risk. In the absence of such data, it was pragmatic to develop a model that maximised the use of local data and data relevant to the target population. As the model does not estimate disease progression for individuals, the stage at diagnosis is sampled from an estimated distribution based on the number of years since disease onset.

The CEA model used a cohort of 1,000 women to estimate summary information such as quality-adjusted life years (QALYs) per person and cost per person for each of the risk subgroups. For a number of the subgroups, the cohort is larger than the known population. The purpose of using this cohort size is that it was sufficiently large to estimate the potential quality of life and mortality associated with different strategies without posing a significant computational burden.

The cost-utility analysis follows a cohort of women from age 20. In reality, many of the women at elevated risk are not identified until they are in their later 20s or in their 30s. As reflected in the budget impact assessment, not all eligible women will avail of surveillance and some may miss surveillance rounds for a variety of reasons, such as pregnancy. The cost-utility analysis attempts to compare each surveillance strategy on an equal footing and, by including women from the age of 20, it

incorporates the possibility of modelling surveillance strategies that start from age 20.

As mentioned in the previous section, risk is assumed to be homogeneous within a risk subgroup – that is, for a given age, all women in the risk subgroup have the same risk of developing breast cancer. While this is a convenience from a modelling perspective, it does mean that the results are based on the average risk within a risk subgroup. Hence although a strategy may be, on average, not cost-effective, it may be for some in a particular risk group, and vice versa. This is an unavoidable

difficulty in modelling interventions for target populations with heterogeneous risk profiles. A scenario analysis of varying risk showed that cost-effectiveness improves with increasing risk. However, surveillance based on exact risk levels supposes that precise estimate of individual risk is feasible, which may not be the case in practice.

The model includes both invasive breast cancer and ductal carcinoma in situ (DCIS).

Some DCIS will develop into invasive cancers while others could go undetected in the absence of surveillance and not cause morbidity or mortality. A detected DCIS will always be removed although there is a possibility that it will never develop further.

As such, a surveillance or screening programme will result in the detection and treatment of cancers that do not contribute to mortality. The cost of no surveillance may therefore be lower than for a surveillance programme due in part to fewer cancers treated.

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In the model it was assumed that the rate of disease progression is such that even in the absence of any surveillance programme, cancers will be symptom-detected after two years. When women are symptom-detected, the model assumed a greater probability of later stage at diagnosis and consequently lower survival rates. The target population are likely to be well informed of their elevated risk and are likely to be quite diligent in breast self-examination and regular check-ups, so it is possible that early detection is more likely. However, this must be offset by the possibility of a faster disease progression than might be observed in an average risk population.

The budget impact assessment estimated the costs and certain resource implications over five years for each surveillance strategy. The BIA was based on the size of the known population, and hence the accuracy is a function of the accuracy of the population estimates. In many cases, the known cohort is relatively small and thus the budget impact is subject to a great deal of variability.

Two comparators were used in the study: no surveillance and the existing ad hoc system of surveillance. Both were necessary as the latter represents the current standard of care, while the former provides a universal reference standard. The frequency and intensity of ad hoc surveillance is not known with much accuracy. It is likely that, at the time of this HTA, the 2006 guidelines developed by NICE regarding MRI surveillance are being applied to women with identified high penetrance genetic mutations. It is possible that digital mammography surveillance for women with identified high penetrance genetic mutations starts at 35 or even 30, compared to the 2006 NICE recommendation of starting at 40. However, for high familial risk in the absence of an identified genetic mutation or for moderate risk, the surveillance patterns were estimated from family risk clinic data. It is likely that surveillance patterns vary greatly across the country and the data used in the study may not be nationally representative. For some of the risk subgroups, it would appear that strategies may be cost-effective compared to ad hoc surveillance, but not when compared to no surveillance. Indeed, in the case of the moderate risk group it appears that ad hoc surveillance is less effective than no surveillance. This

interpretation should be viewed with some caution. It must again be stressed that the cancer risk is heterogeneous within any risk subgroup. It is possible that those currently receiving surveillance that appears to be inappropriate for their risk subgroup, may have a higher risk than is typical for their risk subgroup. The family risk clinic data did not provide a level of detail that identified estimates of risk at an individual level, only at the level of the risk subgroup.

The model incorporates an estimate of increased risk due to radiation exposure from digital mammography based on the likely radiation dose. The additional risk

generated by radiation exposure results in cases of breast cancer that would not have occurred in the absence of a surveillance programme. The increase in risk is

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cumulative, such that a higher frequency of mammograms will lead to a higher risk increase than a lower frequency of mammograms. The impact of the increased risk is therefore a function of both baseline risk and surveillance frequency and so varies for each risk subgroup and surveillance strategy. There is therefore a trade-off between timely detection possible with higher frequency mammography surveillance and more induced cancers. There is evidence to suggest that early radiation exposure in BRCA mutation carriers through mammography may increase breast cancer risk over and above what has been observed in other cohorts.⁽¹¹²⁾ As MRI is not associated with radiation exposure, it does not induce cases of breast cancer.