A case-control study to estimate the impact on breast cancer death of the breast screening programme in Wales

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1 07 Fielder cpp 5/11/04 12:04 PM Page ORIGINAL PAPER A case-control study to estimate the impact on breast cancer death of the breast screening programme in Wales HM Fielder, J Warwick, D Brook, K Gower-Thomas, J Cuzick, I Monypenny, SW Duffy J Med Screen 2004;11: See end of article for authors affiliations Correspondence to: Dr J Warwick, Cancer Research UK Centre for Epidemiology, Mathematics and Statistics, Wolfson Institute of Preventive Medicine, Charterhouse Square, London, EC1M 6BQ, UK; jane.warwick@cancer.org. uk Accepted for publication 23 August Objectives: The aim of this study was to estimate the effect of service screening, as provided by the NHS breast screening programme, on breast cancer mortality in Wales. Furthermore, we wished to ascertain whether a reduction in breast cancer mortality consistent with that observed in the randomised screening trials was being achieved. Setting: The NHS Breast Screening Programme in Wales, managed by Breast Test Wales, with headquarters in Cardiff. Methods: A case-control study design with 1:2 matching. The cases were deaths from breast cancer in women aged years at diagnosis who were diagnosed after the instigation of screening in 1991 and who died after The controls were women who had not died of breast cancer or any other condition during the study period. One was from the same GP practice and the other from a different GP practice within the same district, matched by year of birth. Results: Based on 419 cases, the odds ratio for risk of death from breast cancer for women who have attended at least one routine screen compared to those never screened was 0.62 (95% confidence interval [CI] , p=0.001). After excluding cases diagnosed prior to 1995 and adjusting for self-selection bias, the estimated mortality reduction was 25% (odds ratio=0.75, 95% CI , p=0.09). Conclusion: The Breast Test Wales screening programme is achieving a reduction in breast cancer mortality of 25% in women attending for screening, which is consistent with the results of the randomized controlled trials of mammographic screening. INTRODUCTION The randomised trials of mammographic screening for breast cancer have demonstrated a reduction in breast cancer mortality of 20 30% with invitation to screening. 1 3 The magnitude of the reduction achieved in practice depends upon a number of factors, including the age of the target population, the screening interval used, attendance rates and the quality of the screening service being provided. Close monitoring of a breast screening programme is therefore required to ascertain whether the desired mortality reduction (as observed in the research setting) is being (or is likely to be) attained. The practicalities of delivering screening on a large scale, such as poor attendance, poor positioning at X-ray, problems with film processing and inaccurate reading, threaten to undermine a programme s success. For this reason the NHS Breast Screening Programme was set up with specific quality control targets against which its performance is measured annually using strict criteria, on both a nationwide and a regional basis. Key targets include the attendance rate (uptake), the cancer detection rate, and the detection rate of small invasive disease ( 10 mm). 4 The National Health Service Breast Screening Programme in Wales is administered by Breast Test Wales, based in Cardiff. This service, established in 1989, invites all female residents aged years who are registered with a general practitioner to attend for breast screening once every three years. Eligible women are identified from the health authority registers and around 250,000 women are invited for screening at each round. The quality assurance policy is that mammograms are double read. The initial aim of the screening programme in Wales was to reduce mortality from breast cancer in the region by 25%. 12 It took time to get the appropriate administrative resources in place, so screening did not get fully underway until With over a decade of screening experience, it now seems pertinent to ascertain whether this initial target has been met. Owing to the often considerable time-lag between the detection of breast cancer and death from the disease, and the large numbers of unaffected individuals to be followed up in case they develop the disease, it is not practical in this case to assess the reduction in breast cancer mortality attributable to screening via a prospective study. A retrospective design should therefore be considered, one possibility being the case-control study. Such studies have been successfully used to evaluate cervical screening programmes in the past. 5,6 They are attractive because at the individual level they directly relate the intervention of interest (screening) to the clinical endpoint, they require a relatively small number of cases and controls to be identified, and they allow the results to be obtained fairly rapidly. The design has weaknesses though, as it is particularly prone to bias. 7 Some problems, such as time of diagnosis bias, ascertainment bias, determination of cause of death and ensuring that only those who had the opportunity to attend screening are included in the study, can be minimized or avoided through careful study design. Others, such as selfselection bias, are unavoidable and must be dealt with by adjusting the statistical analysis.

2 07 Fielder cpp 5/11/04 12:04 PM Page 195 Case-control study of breast screening in Wales 195 MATERIALS AND METHODS The aim of this study was to estimate the effect of service screening on breast cancer mortality. Routine information regarding women invited to screening was obtained from the Breast Test Wales database. Its data sources include the Patient Episode Database for Wales, pathology results and extracts from patients medical records. Breast Test Wales also maintains a register of all new cases of breast cancer diagnosed in the population, which is regularly updated with information from the Welsh Cancer Intelligence and Surveillance Unit. The date of diagnosis is the date when diagnosis was confirmed, taken from the relevant pathology report. Deaths are attributed to breast cancer if the disease is listed as either a primary or secondary cause of death upon the death certificate. We used a case-control study design where the cases were deaths from breast cancer in women aged years at diagnosis and who were diagnosed after the instigation of screening in Cases were selected from the Breast Test Wales database by working through the list of deaths, most recent first, until the required number had been identified. Two controls were matched by year of birth to each case, one from the same GP practice and the other from a different GP practice within the same district. This was partly to provide loose matching of cases and controls by socioeconomic factors, but also to avoid the potential confounding of screening effect with GP practice that was a problem in equivalent studies of the cervical screening programme. If more than one potential control of each kind was identified for a particular case, the one with the nearest date of birth to that of the case was used. It is unlikely, but not impossible, for the controls to have breast cancer themselves, as such a diagnosis would not render them ineligible so long as they were alive at the time the case was diagnosed. With a study size of 500 cases and 1000 matched controls, the study has 90% power to detect a 30% difference in mortality. The methods for avoiding or correcting for six potential biases are explained below: Ascertainment bias Cases and controls can only be selected if they are known to the data source. Cases can be identified as a breast cancer death reported through the standard death registration procedure. Controls can be identified through being on the database of women eligible for breast screening. The cases and controls are therefore selected from slightly differing populations, which could again lead to a bias in favour of screening. This can be avoided by excluding cases that were not known to the screening programme before diagnosis. A variant of ascertainment bias Screening identifies cases of breast cancer at a much earlier stage of the disease than might otherwise be possible. Once diagnosed with the disease, it is likely that breast cancer will find its way onto an individual s death certificate as a secondary cause, even if the death is attributable to another cause. 9 It is possible that some of the screen-detected cases would never have come to light in the absence of screening. This introduces a bias against screening which, although unlikely to be large, does raise issues of interpretation with regard to time since last screen and case-control status, and similarly time since last screen and disease severity in screen-detected cancers. This bias is likely to be minimal in this study because the majority of deaths occurred in those aged less than 75 years. Opportunity for screening After diagnosis with breast cancer, further mammography during treatment and follow-up is not counted, as it constitutes clinical management rather than screening. The controls, however, continue to be invited to routine screening. We gave the controls a pseudo-diagnosis date (the date of diagnosis of the matched case) and considered only that part of their screening history that took place before the pseudo-diagnosis, to avoid a bias in favour of screening. Time of diagnosis bias This arises because long-term survival with breast cancer is not unusual, so that many of the deaths occurring within the study period would have been diagnosed in the pre-screening era. This confers a bias in favour of screening, which we avoided by excluding those cases diagnosed prior to the inception of the screening programme. Self-selection bias The women who consent to being screened tend to have higher incidence of breast cancer due to higher socioeconomic status but much lower case fatality rates if they do develop the disease. The net effect is that they are less likely to die of breast cancer regardless of screening. 8 This also confers a bias in favour of screening. It cannot easily be obviated by design, but it can be adjusted for in analysis by using estimates of this bias from the trials. As part of a secondary analysis correcting for bias, we use the method of Duffy et al. 8 to obtain the effect of screening in potential attendees. This is valid if the self-selection bias in the casecontrol study population is similar to that in the populations used in the trials. If not, adjustment for socio-economic status is desirable, since the latter is a very powerful predictor of attendance at screening. 6 Screen-detected cases Inclusion in the screening history of the screen at which a screen-detected case was diagnosed confers a bias against screening, but exclusion of the diagnostic screen confers bias in favour. 10 Including a large number of screen-detected cases in the study will therefore lead to underestimation of benefit. In the first few years of any screening programme the number of prevalence screens, and consequently screendetected cancers, is considerably higher than one would expect later on in the programme. To avoid this conservative bias, without giving rise to the converse anticonservative bias, we have excluded all cancers diagnosed early in the programme (see Results, below). This reduces the dependence on the prevalence screen, and therefore the conservative influence of screen-detected cases, without incurring the anticonservative bias of excluding diagnostic screens from the screening history altogether. In addition, this strategy has the advantage of estimating the benefit of the programme free of setup effects (i.e. in steady state ). With regard to the above selection/exclusion criteria, due consideration must be given to the practicalities of administering a population-based screening programme. At any particular point in time, a proportion of the women on the database will be awaiting their first invitation to screening. The easiest way to ensure that all cases were known to Breast Test Wales prior to diagnosis and that cases and Journal of Medical Screening 2004 Volume 11 Number 4

3 07 Fielder cpp 5/11/04 12:04 PM Page Fielder, Warwick, Brook, et al. controls were comparable with respect to screening opportunity was to include only those who had at least one invitation to screening prior to the date of diagnosis or pseudo-diagnosis in the final analysis. The analysis was carried out using conditional logistic regression, 10 which takes account of the matching and provides odds ratios estimates of the effect of breast screening on breast cancer mortality. Secondary analyses to adjust for various biases were also performed as described above. RESULTS Primary analysis A total of 500 cases were identified and checked for compliance with the eligibility criteria. Matching was attempted for all eligible cases but was not always possible because the existence of suitable controls is not guaranteed. The final dataset therefore consisted of 419 cases: 298 cases were matched to two controls and 121 cases were matched to one control. The average age at diagnosis was 60 years (range years) and the average age at death was 63 years (range years). Table 1 shows age at diagnosis (or pseudo diagnosis) for cases and controls. Year of diagnosis ranged from Year of death ranged from To maximize power, the primary analysis was carried out using the pooled controls. Secondary analyses, using the same GP practice or different GP practice controls only, were carried out and found to give very similar results to the pooled data. The results of these sub-analyses are therefore not presented. The estimated odds ratios for risk of death from breast cancer by screening history, from the conditional logistic regression model, are shown in Table 2. The odds ratio for women who have attended at least one routine screen compared to those never screened was 0.62 (95% confidence interval [CI] ). Analysis by number of routine screens attended shows the magnitude of the reduction in risk to be approximately 35% during the first two screening rounds, increasing to roughly 60% for more than two Table 1 Age at diagnosis (or pseudo diagnosis) of cases and controls Age (years) Controls (n) Cases (n) Total Total rounds. With the exception of cases diagnosed within six months of their last routine screen, as one would expect, the risk of death from breast cancer increases with time since last routine screen. Bias-corrected analysis As stated above, in a case-control study such as this, inclusion in the screening history of the screen at which the tumour was detected (for those cases which were screen detected) introduces a bias against screening. 10 In this case the potential bias is substantial because this study relates to the first 10 years of the programme and therefore includes a considerable amount of prevalence screening. Consequently there are more screen-detected cases than we would expect given the study entry criteria. Figure 1 shows the proportion of prevalence screen cases by year of diagnosis. Prevalence screen cases dominate the earlier years of the study ( ). Thus we exclude cases diagnosed during This gives an estimate of the long-term screening effect as an odds ratio of 0.49 (95% CI ). To correct for self-selection bias we used Duffy et al s (2002) second estimator, which gives the bias adjusted effect of actually being screened in the potential attendees. The attendance rate in Wales was 77% 12 and the increased risk of dying for non-attendees was estimated, from the combined results from five randomised mammographic screening trials, to be 36%. 8 After adjustment, the odds ratio is 0.75 (95% CI ). Table 3 shows the uncorrected and bias-corrected estimates (excluding tumours diagnosed prior to 1995) for ever-screened, number of screens attended and time since last screen. To evaluate the influence of including the screen at which the cancer was detected (for screen detected cases only) in the screening history the analysis was repeated after adding 18 months to the pseudo-diagnosis dates of the controls for % of prevalence cases Year of diagnosis Figure 1 Percentage of cases diagnosed at prevalence screen, by year of diagnosis Table 2 Odds ratios for risk of death from breast cancer by screening history Level Number of cases/controls Odds ratio (unadjusted) Ever attended routine screening Never 144/ Ever 275/ (0.47, 0.82) Number of routine screens attended None 144/ / (0.48, 0.88) 2 83/ (0.43, 0.96) 3+ 23/ (0.19, 0.72) Time since last screen Never screened 144/ <6 months 72/ (0.92, 2.70) 6 months 1 year 23/ (0.22, 0.85) 1 2 years 60/ (0.25, 0.68) 2 4 years 71/ (0.39, 0.89) 4+ years 49/ (0.36, 0.93)

4 07 Fielder cpp 5/11/04 12:04 PM Page 197 Case-control study of breast screening in Wales 197 Table 3 Odds ratios for risk of death from breast cancer by screening history, using only tumours diagnosed in , with and without adjustment for self-selection bias Level Number of cases/controls Odds ratio (unadjusted) Odds ratio (adjusted) Ever attended routine screening Never 126/ Ever 218/ (0.36, 0.66) 0.75 (0.49, 1.14) Number of routine screens attended None 126/ / (0.33, 0.67) 0.72 (0.45, 1.14) 2 81/ (0.38, 0.87) 0.87 (0.53, 1.43) 3+ 22/ (0.17, 0.65) 0.50 (0.24, 1.04) Time since last screen Never screened 126/ <6 months 41/ (0.50, 1.83) 1.46 (0.71, 2.99) 6 months 1 year 13/ (0.06, 0.44) 0.24 (0.09, 0.68) 1 2 years 52/ (0.20, 0.60) 0.53 (0.28, 1.00) 2 4 years 63/ (0.34, 0.83) 0.81 (0.47, 1.38) 4+ years 49/ (0.35, 0.91) 0.85 (0.49, 1.49) the screen-detected cases. This means that the related controls have a further 18 months of screening history included in the analysis, the amount by which the diagnosis of the case was brought forward by screen detection, thus balancing the opportunity for screening bias mentioned in the Methods section. The adjustment had virtually no effect on the odds ratio or confidence intervals for ever attending routine screening, even when all pre-1994 cases were included. DISCUSSION The interpretation of observational studies of primary or secondary prevention activities is never easy. We were compelled to make numerous design and analysis adjustments to account for potential biases. Our uncorrected estimate of the breast cancer mortality reduction associated with screening was 38% (odds ratio 0.62, 95% CI , p=0.001). After correcting for various anticonservative and conservative biases, the estimated mortality reduction was 25% (odds ratio 0.75, 95% CI , p=0.09). The latter did not reach statistical significance because the numbers used in the external estimate of the relative risk in non-compliers (needed for the correction) were relatively small, resulting in large standard errors. The correction for self-selection is liable to be conservative in terms of the odds ratio estimate as well as the 95% CI. This is because it is based on the relative mortality of the non-compliers in the Two-county, Malmo, Stockholm, Canada and Gothenburg randomised trials of screening. In these trials, the non-attendees were a small and probably more extreme subgroup than the non-attendees in the Breast Test Wales screening programme. Relative mortality figures from the Edinburgh and New York trials were not available, but it is known that the cases in non-attendees in these trials had survival rates close to those of the cases in the control groups. 13,14 It is therefore likely that the mortality reduction from screening lies somewhere between the uncorrected 38% and the bias-corrected 25%. This is consistent with the randomised trial results and suggests that the Breast Test Wales programme is achieving a similar quality to that of the trials. An alternative to the self-selection correction would be to adjust in the logistic regression for a measure of socioeconomic status. 6 Adjustment of the Townsend index, 15 based on measures of deprivation in the area of residence, made no difference to the odds ratios. We also found a decreasing risk of breast cancer death with increasing number of screens attended and a generally increasing risk of breast cancer death with increasing time since last screen. The exception to this was an increased risk associated with a screen in the six months prior to diagnosis. These are essentially the screen-detected cases and show an extreme form of the screen-detected cancers bias mentioned in the Methods and Results sections above. Exclusion of these would give a very low odds ratio (0.03) for this category. Our uncorrected odds ratio of 0.62 indicates a smaller effect than observed in other case-control studies. 16,17 This may be partly due to the era in which the other case-control studies were conducted the 1970s and 1980s when typical clinical cases presented at a more advanced stage than now, so that a higher fatality rate would have occurred in the unscreened women. It is also possible that the other case-control studies may have been less influenced by the prevalence screen and therefore had a smaller conservative bias from screen-detected cancers. Improved treatment, particularly the widespread use of tamoxifen, may also explain the reduced difference. In conclusion, this study suggests that the Breast Test Wales screening programme is achieving a 25% reduction in breast cancer mortality in women attending for screening. This is consistent with the results of the randomised controlled trials of mammographic screening. ACKNOWLEDGEMENTS We thank Cerilan Rogers, Jane Evans and the Welsh Cancer Intelligence and Surveillance Unit for their cooperation. The European Commission Public Health Programme, within the European Breast Cancer Network, provided partial funding support Authors affiliations Hilary M Fielder, Director, Breast Test Wales, Cardiff, UK Jane Warwick, Research Fellow, Cancer Research UK Centre for Epidemiology, Mathematics and Statistics, Wolfson Institute of Preventive Medicine, Queen Mary College, University of London, UK Diane Brook, Head of Information, Breast Test Wales, Cardiff, UK Kate Gower-Thomas, Consultant Radiologist, Breast Test Wales, Cardiff, UK Jack Cuzick, John Snow Professor of Epidemiology, Cancer Research UK Centre for Epidemiology, Mathematics and Statistics, Wolfson Institute of Preventive Medicine, Queen Mary College, University of London, UK Ian Monypenny, Consultant Surgeon, Department of Surgery, Llandough Hospital, Cardiff, UK Stephen W Duffy, Professor of Cancer Screening, Cancer Research UK Centre for Epidemiology, Mathematics and Statistics, Wolfson Institute of Preventive Medicine, Queen Mary College, University of London, UK REFERENCES 1 Nystrom L, Rutqvist LE, Wall S, et al. 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