a cost utility analysis of mammography screening in australia

Sot. Sci. Med. Vol. 34, No. 9. pp. 993-1004, 1992 0277-9536192 f5.W + 0.00 Printed in Great Britain. All rights reserved Copyright % 1992 Pergamon Press Ltd

A COST UTILITY ANALYSIS OF MAMMOGRAPHY SCREENING IN AUSTRALIA

JANE HALL’, KAREN GEPARD’, GLENN SALKELD’ and JEFF RICHARDSON’

‘Centre for Health Economics Research and Evaluation, University of Sydney at the Department of Community Medicine, Westmead Hospital, Westmead, NSW 2145, Australia, ‘Western Sector Public Health Unit, Western Sector Area Health Service, Cumberland Hospital, Westmead, NSW 2145, Australia and ‘Centre for Health Program Evaluation, Monash University, Clayton, Victoria 3168,

Australia

Abstract-Cost utility analysis is the preferred method of analysis when quality of life instead is an important outcome of the project being appraised. However, there are several methodological issues to be resolved in implementing cost utility analysis, including whether to use generalised measures or direct disease specific outcome assessment, the choice of measurement technique, and the combination of different health states. Screening for breast cancer meets this criterion as mammographic screening has been shown to reduce mortality; and it is said that earlier treatment frequently results in less radical surgery so that women are offered the additional benefit of improved quality of life.

Australia, like many other countries, has been debating whether to introduce a national mammographic screening programme. This paper presents the results of a cost utility analysis of breast cancer screening using an approach to measuring outcome, Healthy Year Equivalents, developed within this study to resolve these problems.

Descriptions of breast cancer quality of life were developed from surveys of women with breast cancer, health professionals and the published literature. The time trade off technique was then used to derive values for breast cancer treatment outcomes in a survey of women in Sydney, Australia. Respondents included women with breast cancer and women who had not had breast cancer. Testing of(i) the effect of prognosis on the value attached to a health scenario; and (ii) whether the value attached to a health scenario remains constant over time has been reported.

The estimate of the net costs of screening are reported. The costs of breast cancer screening include the screening programme itself, the further investigations and the subsequent treatment of breast cancer cases. Breast cancer is treated in the absence of screening, many commentators claim earlier treatment is costly but there is little evidence. Therefore we have investigated current patterns of breast cancer treatment, current use of investigations for women presenting with symptoms and current use of covert mammography screening.

The results are extrapolated to obtain estimates of the costs and outcomes presented as cost per healthy year equivalent. This analysis produces important information for the Australian policy debate over mammography. It also contributes to the development of cost utility analysis and the approach developed here can be applied more generally.

Ke.v words-economic evaluation, health economics, breast cancer screening, mammography screening

1. INTRODLKTION

1. I. Background

This project was undertaken in the context of an evaluation of mammographic screening for the early detection and treatment of breast cancer in Australia. Breast cancer is common; it will afflict 1 in 15 Australian women in their lifetime and is the most common cause of cancer death in women [l]. Mam- mographic screening for its early detection and treatment has been shown in controlled trials to reduce mortality from this disease [2-4] although whether such results can be achieved outside tightly controlled conditions is disputed [S]. The establishment of breast cancer screening programmes in several countries, particularly the United Kingdom following the publi- cation of the Forrest Report [6], led to strong press- ures in Australia for the establishment of a national programme. A national breast cancer screening pro-

gramme has been estimated to cost between $60 and SlOOm annually in Australia [7]. Clearly economic appraisal of such an investment is important.

There are two aims of this paper. First, to report on the development of the Healthy Year Equivalent as an outcome measure, both in theory and applied within the context of mammography screening. Sec- ond, by recognising the importance of economic appraisal, the net costs of mammography screening are reported in order to provide an estimate of cost per healthy year equivalent.

The paper is divided into 4 main sections. In Section 1, some of the issues associated with measuring health related quality of life are discussed both in general and in the context of breast cancer. Section 2 is concerned with the methods and results of applying HYEs to breast cancer. Section 3 reports briefly on the methods and results of the additional components in this cost-utility analysis of

993

994 Ja3-r HALL et al.

mammography screening: life years and costs. A discussion is offered in Section 4.

1.2. Assessing health related quality of life

While there is general agreement that quality of life should be incorporated in the assessment of the benefits of health programmes. there is disagreement about how ill health states should be evaluated. One approach is to use a multi-attribute scale of health state utilities. Any health state is disaggregated into its component dimensions, such as physical function, mental state. emotional well-being, and scores are assigned to each dimension; each score is then assigned a pre-determined weight and incorporated into an overall scale. Such measures include the Rosser-Williams scale [8,9], the Kaplan Quality of Well-Being index [IO] and Torrance’s MAU scale [I I]. The Kaplan Quality of Well-Being index has not been widely applied in the context of cost utility analysis; nor has the Torrance MAU scale although it was applied in a study of neonatal intensive care [12]. The Rosser-Williams scale has been used in a number of cost utility studies in the U.K., including the Forrest Report on Breast Cancer Screening [6] and the Buxton study of breast cancer treatment

outcomes [l3]. This latter study also included the Torrance MAU scale.

Generalised measures have been criticised for being insensitive to important aspects of quality of life [14]; but this argument has two issues. One is that the scale may not encompass aspects of quality of life that are important, i.e. it lacks comprehensiveness. Thus, in a study of elderly people Donaldson et al. [I51 argued that the Rosser distress dimension did not capture important aspects of psychological and emotional well-being, at least in this group of subjects. The other issue is that the scores themselves may be insensitive to anything other than major levels of disability and distress, i.e. the values attached to levels of well-being are not valid. The Rosser scale does not value health outcomes lower than 90% of full health until physical disability is rated as ‘confined to chair or wheelchair’ or distress is rated as ‘severe’; then the value drops dramatically, even to negative values (i.e. states worse than death).

As an alternative to the use of generalised measures, health state descriptions can be developed that are specific to the disease or condition under consideration and used to derive weights or values for the disease specific years. This approach has been more frequently adopted by Torrance and his colleagues. The standard gamble has been promoted as the gold standard for measurement because it is based on the von Neumann-Morgernstern axioms of utility theory [16]. However, there is evidence of internal inconsistencies within the standard gamble which led Loomes and MacKenzie to ask “whether we can obtain any reliable estimates of individuals’ von Neumann-Morgernstern utility functions.” Further- more, there are difficulties with the practical appli-

cation of the standard gamble. It presents a complex task for survey respondents. Beyond these issues there is the question of whether von Neumann- Morgernstern utility is the appropriate measure of what is required to estimate quality adjusted life years (QALYs). Mooney and Olsen [ 171 concluded that “in many situations the TTO poses the right QALY question”, a position argued by Richardson [I81 elsewhere. The TTO technique requires the respondent to trade off life years in a poor quality health state for good health. Torrance claims the TTO give results consistent with the SG.

With either, the generalised multi-attribute scale or the direct disease specific valuation, the estimation of QALYs has usually involved the following steps. Weights are derived for a series of specified health states. The appropriate weight is then applied to each year spent in that health state; and discounted according to the conventional rules. The assumption underlying this method of estimation is that there is a constant proportional trade-off of quality and quantity of life; that is that the loss of I year from a 5 year lifespan is, subject to conventional discounting, equivalent to the loss of IO years from a 50 year lifespan. Therefore the value attached to any health state (undiscounted) is independent of the time spent in it. There is, however, empirical evidence to suggest that this may not be the case. The constant proportional trade off assumption also requires that health states are valued independently of the time in an individual’s life stage at which they occur. Loomes and MacKenzie [14] argue for a complex pattern where an individual’s values are influenced by life stages; for example adults responsible for the care and support of young children would trade less life years than people who have retired giving a higher value for the same health state.

The application of conventional discounting requires the assumption that the consumption of life is subject to the same constant rate of time preference as commodities. Usually it is argued that a positive rate of time preference is appropriate due to decreas- ing utility in a poor health state. Lipscomb [19] observed this by varying the onset and duration of a temporary poor health state. However, Llewellyn- Thomas [20] et al. observed a non-monotonic time preference rate and Mooney and Olsen [l7] describe the possibility of negative time preference due to individual’s ability to come to terms with poor health. Thus rejection of a constant rate of time preference has been claimed, further undermining the constant proportional trade off assumption.

For health outcomes which involve time spent in a series of health states, the years spent in each state are valued independently, discounted conventionally and then aggregated. This implies that the value of any health state is independent of the health state which follows it. Yet, as Loomes and MacKenzie [ 141 argue, values may be affected by the individual’s perception of how the present state will affect their future state.

A cost utility analysis of mammography screening 995

Thus individuals may place a higher value on a health state which is part of a treatment as a result of which they expect to recover than the same health state if it is a step in a degenerative process with no hopes of recovery.

For these reasons Mehrez and Gafni [21] have suggested an alternative approach in which utility is estimated directly for a specified time in a variety of health states. In the Mehrez and Gafni foimulation this is derived from a two stage standard gamble and the resultant outcome measure is termed Healthy Year Equivalents (HYEs). The argument for a holistic measure of all the health states to be experienced does not imply the need for standard gamble and the issue of the appropriate measurement instrument can be separated from the issue of holistic measurement.

Whichever instrument is used to measure utility, the standard gamble, time trade-off or rating scale, the most common practice has been to calibrate the scale by placing the health state ‘full health’ or ‘good health’ at the top of the scale. Another criticism of the conventional methodology is that it is unclear whether the anchor point of good health has the same meaning to different individuals. Individuals may hold different concepts of what ‘good health’ means over a remaining life span and at different ages. Nor is it clear that a year of good health holds the same value across individuals. Using Mooney and Olsen’s phrase, QALYs are at best quasi-utilitarian in the sense that they give each individual’s life year an equal weight and do not attempt aggregate pure or unweighted utilities.

Two further problems arise which must be recog- nised in cost utility analysis. First, evidence from Kahneman and Tversky [22] and McNeil [23] suggests the existence of framing effects; that is the resultant values are effected by the framing of the choices. This presents a practical problem of devising a means for presenting information, that, as far as possible, does not introduce bias. A second issue is whose utilities should be elicited. There are practical and theoretical reasons to be taken into account in deciding whether to assess utilities as estimated by patients with the disease or by potential patients or by members of the general community [ll]. Other studies have reported that patients systematically give higher ratings to their quality of life than do non- patients [24].

The methodology described in the sections below evolved from an attempt to resolve some of these problems with the current techniques.

1.3. Assessing health related quaIity of life in women with breast cancer

By referring to the experience learnt from an earlier pilot study conducted by three of the authors (J.H., G.S. and J.R.) [25] we are able to justify the approach used in the current study. Particularly, why a holistic, or scenario, approach is more realistic for outcome

SSM 34%-E

measurement of breast cancer, why the TTO technique has been preferred, what variables should be tested, how the anchor state, full health, should be described and whose utilities should be measured.

As breast cancer is a condition that can seriously affect the quality of a woman’s life, it is important that the outcome measure chosen for the evaluation encompass both quality of life and survival. The outcomes of breast cancer treatment were identified from a review of the literature [26-301. Further information was gathered from health professionals involved in the treatment of breast cancer and more importantly, from a series of qualitative interviews with women who had undergone treatment [31]. A number of aspects of health related quality of life that were considered important by women themselves were identified. As well as the physical symptoms of tenderness, discomfort, stiffness and swelling in the treated area, women reported anxiety about the diagnosis of cancer, fear of its recurrence, fear of death, concern with their physical appearance, difficulties with dressing and exposure to the sun (Cameron and Gerard, 1989). These outcomes were compared with the Rosser scale and Torrance’s MAU scale but neither were capable of providing an accurate description of these health states. For example, level III of the Rosser scale is ‘severe social disability and/or slight physical impairment’ and level IV is ‘physical ability severely limited’. We therefore decided to use the disease specific approach to assessing QALY weights as this is the only available method for describing the relevant health states and therefore for eliciting valid utility weights.

The investigation of the outcomes of breast cancer treatment also revealed that the outcome is rarely one health state that continues until death. Rather, women reported a series of changes in their health states, particularly aspects of mental and emotional wellbeing, over time. The outcome of treatment, should be seen as a scenario which involves a number of different health states. As the conventional approach to the estimation of QALYs is to derive weights for each state independently of the time spent in that state and the state which follows it, we tested its validity in an earlier pilot study. The pilot study found that independence of duration and prognosis were not valid assumptions.

For the pilot study a convenience sample of 63 women aged 40-73 years was recruited. Respondents were interviewed in person; the interviewers were all female, all with previous interviewing experience and received additional training in the use of the interview schedule. Three breast cancer treatment outcomes were described, all following the same treatment. These were constructed so as to give an unambiguous ranking from best to worse. The three states were then combined to give a scenario which described progression from one health state to another ending in death. The methods and results are described in detail elsewhere [ 171.

996 JASE HALL el al.

The data allowed for testing of the hypothesis that the value of the combined scenario could be derived from the independently assessed values of the three health states. The results indicate that the utility associated with a complex scenario may not be accurately calculated by a weighted average of the utilities of the constituent states and the assumption of a reasonable time preference, It appears that the prognosis has a significant effect upon the assessment of preceding health states and therefore the holistic approach must be adopted to obtain valid results.

The pilot study included a comparison of the

different measurement approaches, the standard gamble, time trade off and category rating. The standard gamble was found to be the most difficult technique for respondents to understand. In addition. there are serious theoretical difficulties underpinning the standard gamble. We interpreted this to mean the TTO technique was the most appropriate method of eliciting utility values.

The results from the pilot study suggested that

prognosis could affect the valuation of a health state. The most important prognostic variable is whether a woman dies of breast cancer. Breast cancer screening does not prevent cancer, it is a means of early detection and treatment. Although breast cancer screening trials have reported reductions in mortality over a follow up period of 5-18 years [2,4] it cannot be established from the currently available evidence whether screening prevents deaths from breast cancer or postpones them. Therefore it was important to ascertain the extent to which cause of death affected health state utilities.

In the light of these considerations we decided to

test the following in the current study:

(i) the effect of prognosis, specifically in terms of breast cancer death;

(ii) the effect of duration in a health state; (iii) the effect of age on health outcome utilities;

and (iv) the effect of the respondent having breast

cancer.

Excellent health is an exceptional state. As people age they typically experience more minor and major illness; however, as health experience alters so do expectations. Consequently, in our study health states were contrasted with a standardised scenario which defined ‘healthy years’ as reflecting the experience of a rising level of minor symptoms with age. Thus the ‘healthy year’ for which healthy year equivalents were being sought was specified in the same detail as the other health outcomes.

As in any study, a choice had to be made about whose utilities to measure. We chose to obtain utilities from patients and potential patients. It could be argued that men’s values, as members of the community paying for a mammography screening programme, are also relevant. Despite this we elicited

only women’s values as we believe that there would be widespread support for the view that the assessment of benefits of a woman’s health programme should be based on women’s values. The women surveyed included both women who had breast cancer and women who had not suffered the disease, as we were seeking to explore differences in values between these two groups.

We attempted to minimise any bias introduced by framing effects by standardizing the descriptions of health states, the questions asked and rigorous training and monitoring of the interviewers. All health state descriptions were described as breast cancer as this would normally be known by patients and would affect the quality of their life. The effect of framing and labeling is being explored in a further study.

We use the terminology suggested by Mehrez and Gafni, that of the Healthy Year Equivalent (HYE). to refer to our outcome measure.

2. HEALTHY YEAR EQCIYALENTS

2. I. Methods

The descriptions of health states were derived from the qualitative analyses [26-301. These were combined with type of surgery (removal of the breast or breast preservation) to give 6 combinations of type of surgery (radical or conservative) physical health (good or poor) and mental health (good or poor). Although 8 combinations are possible, the number of outcomes was restricted to 6 so as to reduce the length of the interview; good mental health was not combined with poor physical well-being as the qualitative interviews had indicated by combining it with one of two possible prognoses that this was not as important. Each of the 6 outcomes was converted into two scenarios: death from unspecified causes, or death from advanced cancer.

The effect of time on utility weights was tested as follows: respondents were asked, ceteris paribus, to provide a further assessment for the 6 scenarios if their life expectancy were equal to 50 and 10% of their initially defined life span but the health state remained unchanged. In sum each woman provided responses to 24 health scenarios. This is shown diagrammatically in Table 1.

Healthy year equivalents were derived using the time trade off technique. The time trade off questions were administered in a personal interview. Women were individually interviewed by trained female interviewers, generally in their own homes.

Table I Scenario I

Scenario 6

Full life expectancy

50% life expectancy 10% life expectancy

Full life expectancy

50% life expectancy 10% life expectancy

Death unspecified Death breast cancer Death unspecified Death unspecified

Death unspecified Death breast cancer Death unspecified Death unspecified

A cost utility analysis of mammography screening

Table 2. Characteristics of study women

Breast Non-breast cancer cancer Total n=60 n=44 n = 104

Average age 54.9 yr 58. I yr 56.3 yr Age range 41-70 yr 40-70 yr 40-70 yr Married 40 (67%) 30 (68%) 70 (67%) Children 52 (87%) 37 (84%) 89 (86%) Grandchildren 22 (37%) 24 (55%) 46 (44%) Current health-good or excellent 45 (75%) 30 (68%) 75 (72%)

(self-assessed) * Chronic health problem I6 (27%) 30 (68%) 44 (43%)

(self-assessed) Life threatening illness 60 (100%) 20 (45%) 80 (77%)

(self-assessed)

997

The respondent’s life expectancy was estimated from Australian life tables and placed in the closest of 3 categories-30,20 and 10 yr. The respondent was presented with a noncancer treatment health outcome first, as a ‘warm-up’ exercise. The respondent was asked first to choose between her expected lifespan in full health or in the chronic health state. This provided a check on whether the respondent had under- stood the choice; this device is used by Torrance and his colleagues (Ill. Once the preference for full health had been stated, the interviewer offered choice between a short period in good health and then proceeded ‘ping ponging’ between longer (but de- creasing) and shorter (but increasing) periods in good health until the respondent’s preference changed or the respondent was indifferent between the two op- tions. Visual aids were used-these consisted of a series of charts each showing two bars representing the respondent’s expected lifespan and the alternative shorter period.

The respondents were then presented with the six cancer scenarios and asked to rank them from best to worst. The scenarios were assessed in the order shown in Table 1. The average time for an interview was 1 hr.

There were 104 respondents to the survey, all aged 40 yr and over as screening mammography is usually targeted at this age group. The characteristics of the women are summarised in Table 2. Forty-four women were selected as a community sample. These women were recruited from general practitioner sur- geries and women’s clubs within the local area. The remaining 60 women had breast cancer. They were women who had participated in a clinical trial (the Ludwig Cancer Trials Centre based at Royal Prince Alfred Hospital, Sydney), who were not currently

Table 3. HYE determinants: summary of ANOVA analyses

Determinant Level of significance

Scenario’ <O.ool Prognosis* <O.OOl Duration’--100% vs 10% 0.916

100% “I 50% 0.322 50% “S 10% 0.244

Age** <O.OOl Cancer status** <O.OOl

l 2-way ANOVA taking account of differences between women. **l-way ANOVA.

hospitalized or suffering recurrent disease. Breast cancer had been diagnosed between I and 10 years previously. These women had therefore survived and it is likely that they had better quality of life than would be observed in a random sample of breast cancer patients.

The interviewers were female with previous interviewing experience. All were trained in the use of this interview schedule by the survey manager who, her- self, undertook some of the interviewing. Quality control of the interviewing was implemented by selective tape recording of interviews and detailed reporting from interviewers to the survey manager.

2.2. Results

The effect of scenario and prognosis was tested by 2-way analysis of variance, controlling for systematic differences between women. The effect of age and cancer status was tested by one way analysis of variance. The results are summarised in Table 3. Utility varies significantly across the different scenarios. Cause of death (indicated by prognosis in the table) is important with scenarios ending in breast cancer death rated as significantly lower than those ending in death from other causes. The differences in the utility weights attached to health scenarios did not differ if life expectancy was shortened. There were significant differences in the utilities across age groups and between breast cancer and non-cancer respondents (indicated by cancer status in Table 3).

Not all the scenarios are significantly different from all the others as shown by the confidence intervals around the mean values (Table 4). The six scenarios appear to fall into two homogeneous groups. Two scenarios can be categorised as good health. The remaining four can be categorised as poor health. The

Table 4. Mean HYEs weights and confidence intervals

Scenario Mean 95% CI n

I 0.80 0.75 0.85 104 2 0.77 0.71 0.83 104 3 0.27 0.24 0.33 104 4 0.3 I 0.25 0.33 104 5 0.33 0.27 0.39 104 6 0.31 0.25 0.37 104

Scheffe’s homogenotu groups method Av I and 2 (Good) 0.79 0.75 0.83 208 Av 3, 4, 5 and 6 (Poor) 0.30 0.27 0.34 416

998 JANE HALL et al.

confidence intervals indicate that these two outcomes differ significantly from good health, and from each other. Therefore, we have averaged the utility weights of the two good health states to give a value of 0.79 for good health outcomes; and similarly averaged the utility weights for the four poor health states to give a value of 0.30 for poor health outcomes.

3. COST UTILITY ANALiSIS

3.1. Surciral analysis

It is necessary to estimate the number of life years gained as the result of the intervention. This requires the comparison of life expectancies of women offered breast cancer screening and women without screening services. Survival curves were derived for breast cancer and non breast cancer deaths from life tables for New South Wales women. The best available evidence of the impact of screening is that it will result in a reduction of 30% of breast cancer deaths in the 45 plus age group commencing 5 years after the start of a screening programme and continuing until IO years after the last screen [24]. Screening is assumed to commence at age 45 and continue until age 69. No reduction in mortality for women screened after the age of 69 years is assumed. As mortality from breast cancer is decreased, the number of deaths from other causes will increase (the rate of death from

other causes remains constant). This procedure provides an estimate of the breast

cancer deaths averted under a screening programme and the additional deaths from other causes. It predicts that there are 22,549 life years gained as a result of screening (Table 5).

3.2. Quality of life

From the previous results four separate health scenarios emerged-these are good health with non-

cancer, good health with cancer death, poor health with cancer death, poor health with non-cancer death. The HYE weights for these vary with age and range from 0.18 to 0.85 (Table 6).

The number of life years in each health scenario was estimated as follows. First, the probability of breast cancer death was assumed to be equal for both the good and poor health groups. Poor health encom- passes poor mental health with or without poor physical health and as mental health is unrelated to breast cancer death, the assumption appears reasonable.

Table 5. Discounted life years gained from screening (projected for 1989-2029, discount factor = 5%)

Aac cohort

Life years gained from BC deaths

averted

Life years lost from

extra deaths

45-54 yr 11,411 1620 55-64 yr 10.928 2175 65-69 yr 4926 920 Total 21,264 4715 Life years gained from screening 22,549

Table 6. HYE adjustment Factors

Scenarios

Age group

45-54 yr 55-63 yr 6-9 yr

Breast cancer

Good P00r

0.58 0.26 0.59 0.30 0.32 0.18

Non-breast cancer

Good PCXX

0.85 0.29 0.77 0.35 0.64 0.24

Mammography will reduce the total number of breast cancer deaths over the period considered but some of these will be deaths postponed rather than death prevented. A number of women who gain life years will still eventually die of breast cancer. The proportion is unknown but was estimated conservatively on the following basis. Currently 25% of women with breast cancer die from the disease within 5 years [35]; we have assumed that 70% of these women or 18% of the total will die of breast cancer within the 30 year period.

Studies of psychological adjustment after breast cancer suggest that 20-30% of women have ongoing psychological problems, mostly related to fear of cancer recurrence [22]. We have assumed that 25% of women diagnosed with cancer will experience poor mental health. The remaining 75% experience good physical and mental health.

Discounting is implicit in the conversion of years of ill health into a smaller number of years in full or normal health by the TTO method. However, in most instances the health scenario itself commences in the future; a value discounted to some point of time in the future must be further discounted to yield a present value. For this reason future HYEs are discounted from the time of cancer incidence to present value at a rate of 5%.

From these assumptions the discounted HYEs for each age group gained from screening can be calculated. Table 7 shows there is a net gain of 9913 HYEs.

3.3. Costs of screening

A population based mammographic screening programme involves the establishment of population recruitment strategies, dedicated facilities for screening, the training of specialized personnel and the follow up investigations of positive results. The costs of the pilot programme in Sydney have been reported elsewhere [7]. The programme used a mobile van which offered screening at various locations through- out the area. Recruitment strategies included local advertising campaigns, letter box distribution of

Table 7. Discounted HYEs gained by screening

HYEs gained from BC deaths HYEs lost from

Age cohort averted extra deaths

45-54 yr 5706 1150 55-64 yr 5656 1446 65-69 yr I404 497 Total 12,766 3093

HYEs gained from screening 9913


leaflets, and invitations issued through general practitioners. Women attended voluntarily and no medical referral was necessary. The screening films were read by two radiologists independently at the hospital from which the programme was administered. Diag- nostic services for further investigation were also available at this hospital.

The costs of the screening programme were found to be $127.51 per woman screened. The attribution of costs to recruitment, screening and further investigation is shown in Table 8.

These results were extrapolated to obtain the cost of a New South Wales statewide screening programme as follows. All women aged between 45 and 69 formed the cohort eligible for screening. Screening is available two yearly for these women until they reach 70 yr of age. Response to screening is estimated at 70% of the target population. Future costs are discounted at 5%. Table 9 shows the present value costs of screening, assessment and excision biopsies these total $23896253 1.

Cancer detection rates are based on the experience of the Sydney programme and are 7.1/1000 for the first or prevalent screen with detection falling to 4.0/1000 in subsequent or incident screening rounds [36]. Cancer incidence is assumed to be the same in the absence of screening, i.e. screening is not assumed to increase the rate of cancer detected.

In the absence of a screening programme, asymp- tomatic women would not be encouraged to attend for mammography. However, women with suspected breast cancer would be investigated and such investigation will probably include mammography. The proportion of all women in the cohort requiring investigation would be less under the non-screening situation but some of these women have a diagnosis of cancer ruled out. The extent of investigations in the absence of screening is not known and probably varies from country to country. However, the level of investigation cannot be assessed by considering women with a breast cancer diagnosis only.

Australian national data are available on the number of mammograms for which medical (insurance) benefits are paid. Benefits are attracted only for diagnostic mammography and not for screening. Even so the data reveal a 4-fold increase in the number of mammograms for which benefits were paid over the period 1984-1988 without any evidence of a concomitant increase in the incidence of breast cancer. In 1984 just over 13 mammograms per 1000

Table 8. Unit costs of mammography screening (1988/89 prices)

cost per screen S WI

Recruitment’ 22.03 17.3 Screen taking’ 63.07 49.5 Diagnostic investigations’ 24.90 19.5 Excision biopsy 17.51 13.7

Total 127.51 100.0

‘Ref. [7].

Table 9. Present value cost of mammography screening compared with investigation costs without screening (1988/89 prices, discount factor = 5%. 2

yearly screening, 70% attendance, 1989-2019)

S

Screening progromme

Screening and assessment Excision biopsies Total

No screening programme Assessment Excision biopsies Total

Net cost

228.127.706 10,834,825

23&%2,531

40.535,840 7.351,469

47,ggvQ9

191,075,222

women aged 50-64 were claimed; in 1988 the figure was over 47 mammograms. At least some, if not all, of this increase is likely to represent ‘hidden’ (covert) screening. However, during the period mammography may have gained wider acceptance as a diagnostic tool so that all women with suspected breast cancer would receive a mammogram. We have assumed that 30 women per 1000 will be investigated for breast cancer each year.

The costs of investigations in the absence of a screening programme (but which may include some hidden screening) are estimated from a survey of women referred to the diagnostic breast clinic associated with the Sydney programme which showed that all women received a bilateral mammogram and 6% received subsequent investigations. This gives an estimated average cost of $150 per woman investigated and the total present value cost of assessment and biopsy of $47,887,309 (Table 9) in the absence of a statewide screening service. The net cost of assessment under screening is therefore $191,075,222.

3.4. Costs of treatment

There are no commonly accepted protocols in Australia for breast cancer treatment. Most women are treated by surgeons who perform less than four mastectomies each year, suggesting little special- isation and possibly wide variations in practice. It has been suggested that the preference or training of the medical practitioner rather than the type or stage of cancer is the major determinant of the type of treatment [37].

Wide variations make it difficult to estimate treatment costs. To simplify the situation, primary treatment for breast cancer was assumed to fall into 8 types, as shown in Fig. 1. The probabilities indicated in parentheses show what is expected under conditions of(i) established screening and (ii) no screening. The proportion of women receiving each treatment type currently (i.e. without screening) was estimated from New South Wales and Victorian treatment data [37]. This represents approximately two thirds of the Australian population and therefore the majority of breast cancer cases. Under screening, the assumption was made that breast preserving surgery would be increased form 30% to 35% of all


- Cancers detected

DCis 0.15

Mastectomy Breast 0.30 Mastectomy 0.53 Treatment for 0.02 preservation 0.28 _ 1 \Ked disease E

Radiotherapy 0.13 No further 0.17 Chemo- 0.1.~ Radio- 0.09 No further 0.28 treatment 0.16 therapy 0.20 therapy 0.11 treatment 0.35

IY - - -

External 0.08 No further 0.05

boost 0.07 treatment 0.05 - -

Fig. I. Primary treatment protocols (treatment probabilities). Figures show probabilities with steady state screening and without screening (underlined).

surgery. The proportion of women receiving palliative treatment as primary treatment is assumed to fall from 3% to 2%. A small proportion of cases do not receive surgery as the disease is too far advanced by the time it is detected. These cases receive palliative primary treatment. Non-invasive breast cancer (ductal carcinoma in situ) is treated with mastectomy surgery and invasive breast cancer by one of the following treatments: breast preservation; breast preservation with radiotherapy (with or without external boost, i.e. irridium implants); mastectomy; mastectomy with radiotherapy; and mastectomy with chemotherapy.

The costs of the components of treatment were determined using conventional methods [ 111. Actual resource use was documented for medical visits,

Table IO. Unit costs of primary breast cancer

treatment protocols (1988189 prices)

Protocol

DCis:’

Mastectomy

Invasive breast cancer:

Mastectomy

Mastectomy + chemotherapy

Mastectomy + radiotherapy

Breast preservation

Breast preservation + radiotherapy

Breast preservation + iridium implant

Palliative primary treatment

‘Ductal carcinoma in siru.

S

3890

3890

5496

5380

1900

3390

4564

4600

drugs, diagnostic tests and procedures. Work study methods were used to estimate the costs of radiotherapy. The cbsts of chemotherapy were based on recommended protocols. Disaggregated bed-day costs were derived for nursing care, ‘hotel’ costs, and overheads. The resultant average costs of each of the primary treatment types, including non-surgical treatment for advanced disease range from under $2000 to over $5000 per case (Table 10). Future costs were discounted by 5%. The present value costs are shown in Table 11.

In some women cancer will recur or metastasise and thus require further treatment in the future. The costs of the hospital component only of the treatment of advanced disease were estimated. Treatment patterns for advanced disease were based on a sample of 50 breast cancer patients who were included in the same clinical trial referred to previously. The average cost of the hospital component of treatment for advanced disease $12,848 (Table 11).

All breast cancer deaths were preceded by treatment for advanced disease. Treatment was assumed to be given in the year of death. A discount rate of 5% was used. The net saving on care of advanced disease under screening is $20,033,477 (Table 11).

3.5. Results

The present value net cost of a screening programme is $162,125,470. The high cost of the

Table II. Present value cost of total primary treatment in screening and no screening programmes (1988189 m-ices)

Treatment woe

Screening No screening Net

(0) (Sj iSl

Breast preservation (including adjuvant therapy) 8,087,3 I8 9,437, I75 19349,857

Mastectomy (including adjuvant therapy) 30,107,993 36,902,845 6,794,852

Palliative primary treatment 705,084 I ,476.650 77 1,566

Treatment for recurrence, advanced disease 74,315,627 94.349.104 20.033.477

Total I13.216.022 142,165,774 283949.752


screening programme (S 19 1,075,222) is offset by savings in primary treatment ($8,916,275) and care of advanced disease ($20,033,477).

The net gain in life years saved is 22,549. The cost per life year saved is $7190. The net gain in HYEs is 99’13 giving a cost per HYE of $16,355.

The analysis is sensitive to (1) the level of investigation under no screening; (2) the discount rate and (3) the assumptions made on the epidemiology of breast cancer screening.

4. DISCUSSION

This study is the first major cost utility analysis to be conducted in Australia. Its funding as part of the national evaluation demonstrates the acceptance of the validity of this approach by policy makers and that investigation of methodological issues can be carried out within the context of an applied study. The results reinforce the importance of the methodological issues raised earlier.

4.1. Methodological issues in quality of life analysis

One of the important aims of this study has been to investigate four methodological issues associated with both general outcome measurement and particu- lar measurement of breast cancer treatment outcomes. These four issues were:

(i) whether prognosis, specifically death from cancer, affected the value attached to a health state;

(ii) whether the duration spent in the health state affects its value;

(iii) whether the age of a respondent affects the value;

(iv) whether having breast cancer affects the value.

The analysis shows that the type of surgical treatment a woman undergoes does not appear to make much difference to the utility of the outcome. This is an interesting finding as screening is promoted, at least in Australia, as offering a greater chance of breast preserving treatment. However, four health outcomes have been distinguished which have significantly different utility values. These are good health/non cancer death; good health/cancer death; poor health/non-cancer death; poor health/cancer death. Therefore, prognosis and whether death is eventually due to breast cancer or other causes does make an important difference to utility values.

Women’s assessment of the utility of health outcomes varies with age; but the relationship is not simple; quality is not simply valued more highly with increasing age. This lends support for Loomes and MacKenzie’s argument that there is a complex relationship between quality and life stage. This implies that different numbers of HYEs will accrue for the same quality-duration survival for women of different ages.

The importance of prognosis and age suggest that the holistic assessment of scenarios as adopted in this study is more appropriate than the conventional assessment of single health states and their subsequent combination.

The duration spent in a health scenario did not affect the utility weight attached to that scenario. This suggests a constant proportional trade off and thereby contradicts the usual assumption of a constant and positive rate of time preference. If conventional discounting applied a longer duration in a given health state would be associated with a larger ratio of poor quality years to HYEs since the poor quality years would be subject to greater discounting. This finding further complicates the selection of the appropriate rate for such studies but provides little guidance for the selection of the correct role for policy analysis.

The utility attached to health outcomes varied according to whether the woman had breast cancer; women who had experienced breast cancer attach significantly higher utilities to the same health states. This finding is supported by other studies.

The Forrest Report also considered the quality of life of breast cancer treatment outcomes, using the Rosser scale to suggest weights of 0.92-0.99 [6]. Buxton et al. reported similar values also using the Rosser scale; their reported mean values range from 0.91 to 0.99 [13]. This is in contrast to the weights derived in this study which range from 0.18 to 0.85. As noted earlier the Rosser scale is insensitive to breast cancer states and the contrasting results illus- trate the potential inadequacy of a multi-attribute scale.

Buxton et al. also used the TTO to assess health states. The results are comparable with those reported here; (Table 12). The present study reports slightly higher values than those obtained by the Buxton. This differences may be explained by the inclusion of both patients and women without cancer in our sample. The Buxton study sample comprised nurses, hospital doctors and general practitioners. Both studies show that the type of surgery (lumpectomy vs mastectomy) appears to have little effect on resultant values for health states. However, the psychological consequences of breast cancer and its treatment are important, and outweigh the physical aspects of health in this case. Both studies demon- strate that quality of life following breast cancer is an important consideration. Even a good outcome following a breast cancer diagnosis and treatment is valued significantly lower than good health and the

Table 12. Comparison of TTO values

BWS0n Health state et al.

Lumpectomy good physical, good mental 0.72 Mastectomy good physical. good mental 0.69 Lumpectomy poor physical, poor mental 0.27 Mastectomy poor physical, poor mental 0.23

This study

0.80 0.77 0.31 0.31

1002 JANE HALL et al

inclusion of this result has a significant effect upon the comparison of costs and benefits.

3.2. Polic) implications

The costs of the screening itself are based on

detailed studies of the Sydney programme. Costs reported from other pilot projects suggest that the Sydney costs are accurate indications of the costs across Australia [38].

The analysis is sensitive to the assumptions about the level of investigation in the absence of screening. It is difficult to predict the ‘non-screening scenario’ for the future but it is this that should form the ‘base case’ for comparison with the ‘project case’ of a

national screening programme. There are almost no data on current patterns of investigation for suspected breast cancer in Australia. We have used an estimate based on current levels of mammography usage but the future growth in this would depend on a number of factors.

It is also difficult to determine the accuracy of our estimates of treatment costs in the absence of treatment protocols. It is clear that treatment patterns have changed in Australia over the last 5 years with increasing acceptance of conservative surgery. Again, it is difficult to determine the ‘base case’ and the ‘project case’. The establishment of a national screening programme is likely to change treatment patterns as breast cancer treatment becomes more concen- trated in specialist centres. But there are many other factors in addition to screening which are likely to have an effect.

The estimate of life years saved is based on an assumption of a 30% reduction of breast cancer mortality in women offered screening. However, if women’s response to screening falls below the 70% compliance assumed, then benefits will also decrease. Experience in Australian pilot programmes to date

suggests that 6&70% response should be achievable [38] but no programme has yet achieved that. Experi- ence in other countries suggests that response to subsequent screening rounds will be lower but as yet there is little Australian data on this. If Australia’s experience also indicates a lower response to subsequent rounds, then the life years gained will be less than the number reported here.

A 30% reduction in total breast cancer mortality implies a 42% reduction in mortality in women who are actually screened; this is the reduction estimated by the Australian Institute of Health [38]. This assumption may be too optimistic as another meta- analysis suggests that a reduction of 30% in mortality among those actually screened [40] which would give a reduction of mortality in those offered screening of around 20%. This would also decrease the savings from the treatment of advanced disease and yield a cost per life year of approx $10,790 and a cost per HYE of $24,550.

The assumptions made about prognosis affect the number of HYEs gained. A scenario which ends in

death from cancer recurrence is considered worse than the same scenario ending in death from unspecified causes. Deaths from cancer can continue for up to 30 years after the initial diagnosis [33]. Yet there is little or no evidence of the extent to which the additional life years gained under screening represent cancer deaths postponed rather than cancer deaths prevented. As the best health state after cancer treatment is valued significantly less than good health this finding is perhaps a significant confir- mation of the adage ‘prevention is better than cure.’

We have assumed that whether the cancer was detected by screening or symptomatically does not affect one’s subsequent health state. But, as Roberts [41] points out, it may be that women whose cancer recurs after screening and treatment, or whose cancer becomes apparent symptomatically in spite of screening (i.e. interval cancers) have different psychological reactions. There is no evidence on this as yet.

The estimate of HYEs will also vary with whose values are used to determine utilities. Women with breast cancer rated their own health and valued the health scenarios more highly than other women who are potential patients. This finding is consistent with other studies [24]; although it confirms that different groups hold different values it does not help in deciding whose values are appropriate.

This analysis estimates HYEs for those women whose cancer is detected by screening (i.e. true positives). We have not estimated the quality of life impact on other women affected by screening particularly those who are false positives or negatives. There may be a positive value of information and reassur- ance for women with negative screening results; although small in value it affects a large number of women and so may increase the estimate of benefits. On the other hand, those women with false positive results may experience negative consequences on their quality of life as well as unnecessary investigation and, for some, unnecessary surgery. Broadening the analysis to include these other effects may alter the estimate of benefits; but the benefits derived from the detection and treatment of true positives should

be quantitatively the largest effect. The estimate of HYEs finally depends on the

validity of the epidemiological evidence on the effectiveness of screening. This is still equivocal. There is doubt about the validity of the conclusion that screening has reduced mortality and, whether such results can be achieved outside of clinical trials [5, 321. It has also been suggested that screening detects excess cancers, i.e. early cancers are found that would not have become clinically apparent in the women’s natural lifetime [3]. If screening does result in the detection and treatment of excess breast cancer then in this analysis the costs are under-estimated and the benefits are over-estimated in that they do not take into account the additional treatment of excess cases.

This analysis used a period of 30 years over which costs and benefits were estimated. This period was


selected as regular screening is recommended- for women over 25-30 years of their life and the benefits of screening do not become evident until at least 5 years after the first screen. However, new technology such as thermography, may make mammography obsolete sooner [38].

These uncertainties make it difficult to predict the actual costs and effects of a nationaLscreening programme with confidence. Overall, the estimates presented here are based on the best available epidemiological evidence, and, consequently, they represent the best estimate upon which to base a policy decision.

In the absence of other cost utility analyses and without an explicit statement of alternatives to the mammographic screening programme, it is not possible to conclude whether mammography represents the best value for the health dollar. Other studies are now underway which will enable such comparisons to be made. In the U.K., the Forrest Report found that an adjustment for the quality of life made little difference to the final results. This was probably due to the use of a utility scale that was insensitive to health states associated with breast cancer. By contrast, in the present study the cost of $7190 per life year saves is increased to $16,355 per HYE when quality of life is included.

This implies that both survival and quality of life outcomes should be assessed in any ongoing monitoring of the effectiveness of national screening for breast cancer.

Ackno~~ledgemenls-Sue Cameron of the Centre for Health Economics Research and Evaluation managed the data collection for the quality of life survey. David Newell of the NHMRC Clinical Trials Centre at the University of Sydney advised on the statistical analysis. Poppy Sindhusake assisted with the statistical analysis. Paul Glasziou, Depart- ment of Public Health and Wayne Smith, Department of Community Medicine, at the University of Sydney assisted with the survival analysis. We would also like to thank Martin Tattersall and other clinicians associated with the Central Sydney Breast X-Ray Programme. The research was supported by the Commonwealth Department of Commu- nity Services and Health under a women’s cancer screening programme evaluation grant.

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a cost utility analysis of mammography screening in australia

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