HEALTH TECHNOLOGY ASSESSMENT IN PRACTICE: EVALUATION OF THE COSTS AND BENEFITS OF SCREENING FOR BREAST CANCER IN THE CZECH REPUBLIC

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1 HTA in Real-Time HEALTH TECHNOLOGY ASSESSMENT IN PRACTICE: EVALUATION OF THE COSTS AND BENEFITS OF SCREENING FOR BREAST CANCER IN THE CZECH REPUBLIC SVOBODNÍK A. 1, KLIMEŠ D. 1, BARTOŇKOVÁ H. 2, DANEŠ J. 3, SKOVAJSOVÁ M. 4, KREJČOVÁ L. 5 1 CENTER OF BIOSTATISTICS AND ANALYSES, FACULTY OF MEDICINE, MASARYK UNIVERSITY, BRNO, CZECH REPUBLIC 2 MASARYK MEMORIAL CANCER INSTITUTE, BRNO, CZECH REPUBLIC 3 DEPARTMENT OF RADIOLOGY, FIRST FACULTY OF MEDICINE, CHARLES UNIVERSITY IN PRAGUE, CZECH REPUBLIC 4 DTC PRAGUE, MAMMACENTRUM, CZECH REPUBLIC 5 MINISTRY OF HEALTH, CZECH REPUBLIC Introduction As the efficiency of mammography screening on reduction of breast cancer mortality was confirmed in several clinical trials (Shapiro 1966, Alexander 1994, Andersson 1988, Tabar 1985, Frisell 1986, Bjurstam 1997, Nyström 1993, Miller 1992), screening has been adopted in most European countries in the past decades. As screening is enormously demanding project from both financial and organizational perspective, exact evaluation of its costs and benefits should be conducted. In the Czech Republic, national-wide screening has started in September In this article we present the methods of screening costs and benefits evaluation that will be applied in the Czech Republic, with the aim of rational control and application of the project respecting the HTA principles. Mammography screening in Czech Republic and management of data Breast cancer screening is presently organized in 17 countries over Europe. As significant differences between particular projects exist, we provide comparison of selected projects' characteristics in Europe and in the Czech Republic (Table 1). It is obvious that mortality form breast cancer is moderate in the Czech Republic, age group of women invited to screening is rather wider, frequency of examinations standard and the usage of mammography as the main diagnostic technique is also on standard level. What is important and has to be taken into account when data from first round of official screening will be analyzed, is the obvious existence of opportunistic screening before the official screening has started. Many previous analyses of the costs and benefits of mammography screening showed that reliable estimates are impossible when valid and comprehensive data are unavailable or even missing. Taking into account this fact, system of data management (data collection and data 77

2 Oncology as Educational Model for Data Management in Health Care Assessment analysis) was set up prospectively in the Czech Republic and with substantial effort. As a result, following policies were adopted to ensure high quality of data gathered: 1/ Obligatory structure of data collected by particular screening centers was defined 2/ User friendly software tool for screening data management was developed and offered to screening centers 3/ Independent data center responsible for central data analysis and quality control was set up 4/ Regular monitoring of data management process at all screening centers is guaranteed The simplified structure of data collected by all screening centers is presented in Table 2. Part of this data is mandatory for every screening center, and collection of other data is optional (breast cancer risk factors). For management of screening data, special software tool named MaSc was developed, which is presently used by about 70% of screening centers (remaining centers have incorporated obligatory data model into their own hospital information systems). The software tool MaSc consists of the following five separate modules: 1/ Database module (used for data collection, data editing and searching) 2/ Printouts module (enables print out of examination results etc.) 3/ Data audit module (contains simple calculator for computation of screening efficacy parameters: e.g. false and true positive results, interval cancers and positive predictive value of mammography etc.) 4/ Statistics and Graphs module (contains tables and graphs of selected parameters: summarization of all cancers detected, numbers of women in age categories etc.) 5/ HIC module (module for health insurance companies) The responsibility for independent central statistical analyses of screening data lies on Center of Biostatistics and Analyses (CBA). This academic institution has sufficient experiences in medical informatics, especially in the field of clinical trials and mulitcentric evidence-based registries and in the area of epidemiological data analyses. CBA could also take advantage of the close collaboration with other important institutions like Czech Statistical Office, operating with fundamental demographic data. To ensure high quality of data collected within the project of mammography screening, regular monitoring and auditing of all screening centers is performed every year and non-complying centers are conditionally closed. Evaluation of screening efficiency Principal evaluation of screening efficiency should be based on valid assessment of its impact on breast cancer and total mortality (Friedman 1986), incidence, stage-distribution and survival (van der Maas 1989). As comparison of survival data of cases diagnosed in screening and outside the program could be substantially biased in favor of screening due to lead-time bias, length bias, selection bias and overdiagnosis bias (IPSTEG 1999, Boer 1994, Habbema 1983, Walter 1987, Swartz 1980, Pelikan 1993), decrease in mortality (overall and site specific) is presently mostly used to estimate the real effect of mass screening in population. Expected decrease in mortality from breast cancer and other causes will be analyzed in the Czech Republic using the data of Czech National Cancer Registry (NOR). NOR is a database containing information about almost all cancer cases diagnosed in the Czech Republic from 1977 (foregoing data are also available but the quality is questionable). NOR contains also information about the distribution of clinical stages at diagnosis, basic information about 78

3 HTA in Real-Time treatment and patients' follow-up (cause of death). The database could be also used to analyze potential increase in the incidence of early breast cancer stages diagnosed in screening and supposed change in the distribution of clinical stages. As screening has indispensable impact on the distribution of stages at diagnosis of breast cancer, it could also affect the costs of treatment (differences between treatment of stage I and stage III of breast cancer could be substantial). Data for these kinds of analyses will be abstracted from selected reference centers providing complex health care for cancer patients. Evaluation of screening costs To estimate the total costs of screening in reliable way, we should enumerate all relevant sources of expanses and avoid omission of any important one. Costs of screening could be divided into following categories: 1/ Costs of preventive examinations: staff time (processing and reading of mammograms etc.) consumable items (films and chemicals) overheads (e.g. heating and lighting) capital (buildings and equipment) (Hohnston 1999, van der Maas 1989). 2/ Costs of program organization 3/ Costs of data management 4/ Social costs (women travel and time expanses) 5/ Costs of treatment (overdiagnosis) Costs of preventive examinations will be estimated at selected reference centers, which will be chosen as representative for the whole country (respecting differences between private and state screening centers). Expanses associated with organization of the program (e.g. training and monitoring of centers) will be assessed by the KOMD organization (Scientific Committee for Mammary Diagnostic), which is responsible for organizational and scientific aspects of the project. Costs of data management (central data collection, validation and statistical processing) will be calculated by the independent data center, Center of Biostatistics and Analyses. The most challenging issue will be estimation of the social costs and costs of treatment due to detection of cancers that would never manifest clinically in the absence of screening (problem of overdiagnosis). These data will be abstracted from the information originating from the early phase of screening. Is screening for breast cancer justifiable in Czech Republic? Finding answer to this question will be the main ground for sophisticated and detailed analyses of screening costs and benefits. Generally, there are two main approaches to assess the direct and indirect impact of mass screening on epidemiological characteristics of disease, and these are prospective and retrospective methods. The most frequently used retrospective methods are randomized clinical trials and epidemiological studies (cohort and case-control analyses) with all its advantages and disadvantages. The prospective methods are mostly represented by simulation analyses. A huge amount of models used for analyses of screening efficiency were published (Eddy 1982, Prorok 1986, Alexander 1989, Baker 1991), the most sophisticated and 79

4 Oncology as Educational Model for Data Management in Health Care Assessment adapted to perform cost-effectiveness analyses is MISCAN program developed in Netherlands and applied in several other countries (Habbema et al. 1987). As no randomized clinical trial or meaningful epidemiological study designed to assess the effect of mammography screening on breast cancer mortality will be realized in the Czech Republic during the next years, advantages of simulation approach will be utilized. References Alexander FE.: Statistical analysis of population screening. Med Lab Sci 1989;46: Alexander FE, Anderson TJ, Brown HK et al.: The edinburgh randomised trial of breast cancer screening: results after 10 years of follow-up. Br J Cancer 1994;70: Andersson I, Aspegren K, Janzon L et al.: Mammographic screening and mortality from breast cancer: the Malmo mammographic screening trial. BMJ Oct 15;297(6654): Baker SG, Connor, RJ, Prorok PC.: Recent developments in cancer screening modeling. In: Miller AB, hamberlain, J, Day, NE, Hakama M, Prorok PC. Cancer Screening. Cambridge University Press, Cambridge, pp Boer R, Koning HJ, Oortmarssen GJ, et al. In Search for the best upper age limit for breast cancer screening. (submitted). Bjurstam N, Bjorneld L, Duffy SW et al.: The Gothenburg breast screening trial: first results on mortality, incidence, and mode of detection for women ages years at randomization. Cancer 1997;80: Eddy DM, Shwartz M: Mathematical Models in Screening. In: D. Schottenfeld and J.F. Fraumeni (eds.), Cancer Epidemiology and Prevention. Pp , WB Saunders, Philadelphia (1982). Friedman DR, Dubin N.: Case-control evaluation of breast cancer screening efficacy. Am J Epidemiol 1991;133: Frisell J, Glas U, Hellstrom L et al.: Randomized mammographic screening for breast cancer in Stockholm. Design, first round results and comparisons. Breast Cancer Res treat 1986;8: Habbema JDF, Lubbe JThN, van Oortmarssen GJ et al.: A simulation Approach to costeffectiveness and cot-benefit calculation of screening for early detection of disease. Europ. J. Oper. Res., 29, Habbema JDF, Oortmarssen GJ, Putten DJ: An analysis of survival differences between clinically and screen-detected cancer patients. Statistics in Medicine 1983; Johnston K, Brown J: Two view mammography at incident screens: cost effectiveness analysis of policy options. BMJ 1999; 319: Miller AB, Baines CJ, To T et al.: Canadian National Breast Screening Study I. Breast cancer detection and death rates among women aged 40 to 49 years. Can Med Assoc J. 1992;147: Nyström L, Rutqvist LE, Wall S et al.: Breast cancer screening with mammography: overview of Swedish randomised trials. Lancet Apr 17;341(8851): Pelikan S, Moskowitz M: Effects of lead time, length bias, and false-negative assurance on screening for breast cancer. Cancer 1993;71: Prorok PC: Mathematical models and natural history in cervical cancer screening. In: M. Hakama, AB Miller & NE Day, eds. Screening for Cancer of the Uterine Cervix. Lyon: Int Agency Res Cancer. 1986:

5 HTA in Real-Time Shapiro S, Strax P, Venet L.: Evaluation of periodic breast cancer screening with mammography. Methodology and early observations. JAMA 1966; 195: Shwartz M: Estimates of lead time and lenght bias in a breast cancer screening program. Cancer 1980;46: Tabar L, Fagerberg CJ, Gad A et al.: Reduction in mortality from breast cancer after mass screening with mammography. Randomised trial from the Breast Cancer Screening Working Group of the Swedish National Board of Health and Welfare. Lancet 1985;1: The International Prostate Screening Trial Evaluation group (IPSTEG): Rationale for randomised trials of prostate cancer screening. Eur J Cancer 1999; 35: Van der Maas PJ, de Koning HJ, van Ineveld BM et al.: The cost-effectiveness of breast cancer screening. Int. J. Cancer: 43, Walter SD, Stitt LW: Evaluating the survival of cancer cases detected by screening. Stat Med 1987;6: Table 1: Comparison of the main screening projects' characteristics in European countries and in the Czech Republic Country 1 Mortality from breast cancer (women) 2 Start of screening (year) Age category Interval between examinations 3 (years) Diagnostic methods Belgium Mammography Czech Republic Mammography Denmark Mammography Finland Mammography France Mammography Netherlands Mammography Ireland Mammography Iceland breast examination Italy Mammography Luxemburk breast examination Hungary breast examination Germany Mammography Portugal Mammography Greece breast examination, breast self-examination Spain Mammography Sweden Mammography Great Britain Mammography 1 Countries are presented in alphabetic order 2 Number of deaths/ 100,000 women ( ; age standardized on world population) 3 In some countries is interval different in age groups 4 Data from pilot projects 81

6 Oncology as Educational Model for Data Management in Health Care Assessment Table 2: Structure of data collected by screening centers in the Czech Republic 1 Group of parameters Parameters Information about screened women Examinations performed Pathological findings Biopsy and histological findings Person identification number 2 Date of birth Identification of the Health insurance Company Residence Type of examination (e.g. mammography, ultrasonography) Date of examination Result of examination (standard codebook is used) Breast density Axillary nodes status Double reading validation (mammography) TNM classification Laterality Localization in breast Size Date of examination Type of biopsy Method of localization Histological classification (standard codebook) Morphological and topographical code (MKN-O-2) Tumour grade MKN-10 classification ptnm classification 1 Minimal data structure is presented 2 Personal data are transferred in anonymous way from screening centers using the algorithm MD5 to encrypt personal identification number 82