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Evaluation of Population Screening for Breast Cancer in Belgium Unit of Cancer Screening Scientific Institute of Public Health Brussels Federal Cell for Evaluation of Cancer Screening Programmes _____________________________________________________________________

Trend of breast cancer mortality in Belgium

Marc ARBYN1

Francis CAPET1

Marisa ABARCA1

1 Scientific Institute of Public Health J. Wytsmanstreet, 14

B-1050 Brussels

IPH / EPI – REPORTS Nr. 2002- 026 Dépotnum: D/2002/2505/47


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Evaluation of Population Screening for Breast Cancer in Belgium Unit Cancer Screening Scientific Institute of Public Health Brussels Federal Cell for Evaluation of Cancer Screening Programmes _____________________________________________________________________

Trend of breast cancer mortality in Belgium

Marc ARBYN1

Francis CAPET1

Marisa ABARCA1

1 Scientific Institute of Public Health J. Wytsmanstreet, 14

B-1050 Brussels

IPH / EPI – REPORTS Nr. 2002- 026 Dépotnum: D/2002/2505/47


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1. Table of contents 1. TABLE OF CONTENTS..............................................................................................................3

2. ABSTRACT...................................................................................................................................4 2.1. BACKGROUND ...............................................................................................................................4 2.2. MATERIAL AND METHODS..............................................................................................................4 2.3. RESULTS........................................................................................................................................4 2.4. DISCUSSION ...................................................................................................................................5

3. INTRODUCTION.........................................................................................................................6

4. MATERIAL AND METHODS....................................................................................................8 4.1. SOURCE OF DATA...........................................................................................................................8

4.1.1. Belgian data......................................................................................................................8 4.1.2. European data ..................................................................................................................8 4.1.3. World data ........................................................................................................................8

4.2. TREND ANALYSES..........................................................................................................................9 4.2.1. Graphical and tabular presentation of trends ..................................................................9 4.2.2. Break points in trends: joinpoint regression ..................................................................10 4.2.3. Loglinear Poisson models...............................................................................................10

4.3. SPATIAL VARIATION OF BREAST CANCER .....................................................................................11 4.3.1. Variation of breast cancer mortality by arrondissement ................................................11 4.3.2. World maps.....................................................................................................................12

5. RESULTS ....................................................................................................................................13 5.1. BREAST CANCER MORTALITY IN BELGIUM (1954-1996) ..............................................................13

5.1.1. Current burden of breast cancer in Belgium ..................................................................13 5.1.2. Description of the trends ................................................................................................15 5.1.3. ACP models using Poisson regression ...........................................................................26

1.1.1.1 Model selection................................................................................................................... 26 1.1.1.2 Effect estimates ................................................................................................................... 26 1.1.1.3 Predicted mortality rates ..................................................................................................... 29

5.2. MORTALITY FROM BREAST CANCER AT THE LEVEL OF THE REGIONS (1969-1996) ......................31 5.2.1. Description of the trends ................................................................................................31 5.2.2. ACP models using Poisson regression ...........................................................................33

1.1.1.4 Model selection................................................................................................................... 33 1.1.1.5 Predicted mortality rates ..................................................................................................... 35

5.3. VARIATION OF BREAST CANCER MORTALITY BY PROVINCE..........................................................37 5.4. VARIATION OF BREAST CANCER MORTALITY BY DISTRICT ...........................................................41 5.5. BREAST CANCER MORTALITY IN THE EUROPEAN UNION..............................................................52

5.5.1. Trend of breast cancer mortality in neighbouring countries ..........................................52 5.5.2. Mortality from and incidence of breast cancer in the European Union (1995) .............56

5.6. BURDEN OF BREAST CANCER IN THE WORLD (EXTRAPOLATION FOR THE YEAR 2000) ..................57 6. DISCUSSION ..............................................................................................................................58

6.1. QUALITY OF THE DATA ................................................................................................................58 6.2. RISK FACTORS FOR BREAST CANCER............................................................................................58 6.3. INCIDENCE OF BREAST CANCER IN BELGIUM................................................................................61 6.4. SURVIVAL OF BREAST CANCER PATIENTS.....................................................................................63 6.5. SCREENING EFFECTS ....................................................................................................................65 6.6. GENERAL COMMENTS ..................................................................................................................66

Limitations of ACP models ...........................................................................................................67


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7. ACKNOWLEDGEMENTS........................................................................................................68

8. REFERENCES............................................................................................................................69

9. ABBREVIATIONS .....................................................................................................................75 2. Abstract 2.1. Background In 2001, a nation-wide screening programme for breast cancer was set up for all women in Belgium belonging to the age groups 50-69. They are offered a free mammographic examination every two years, which has to be performed in certified screening units. Double interpretation by two independent radiologists will be required. The campaign will be organised by the Communities and the Common Community Commission in charge of the Capital Region of Brussels. The process will be managed and monitored by 11 screening centres. The overall evaluation of the programme at national level is confined to the Scientific Institute of Public Health, assisted by a working group of epidemiologists from all three Communities, the National Cancer Registry and the health insurance agencies. The first objective of mammographic is screening is the reduction of mortality from breast cancer. An analysis of the mortality trends is therefore an essential component, necessary to assess the future impact of the campaign. 2.2. Material and methods Data concerning the number of deaths from breast cancer, and the size of the female population, aggregated by 5-year age groups and individual calendar years were received from the National Institute of Statistics for the period 1954-96. The following mortality indicators were computed: absolute number of deaths, crude and age-specific rates and directly and indirectly standardised mortality rates and ratios. The change in mortality, over time period and by birth cohort, was explored using standard procedures and more modern statistical modelling techniques, such as age-period-cohort analysis based on log-linear Poisson regression and join point regression. Time tends were studied at 4 different geographical levels: Belgium, the 3 Regions, the 11 provinces and the 43 arrondissemens. Belgian trends were being compared with those, observed in neighbouring countries that were downloaded from the WHO mortality database. 2.3. Results The annual number of women that died from breast cancer increased with 75% over the four decades: from 1405 in the period 1954-59 to 2454 in the period 2 1990-96. The


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age-standardised rate, using the European reference population, increased from 25-27 per 100 000 in the fifties to 35-36 per 100 000 in the mid-eighties. Since then, the adjusted rate remained stable. Currently, 3.0% of women died of breast cancer before the age of 75 years. The change in mortality was limited in the age groups younger than 50 years. In postmenopausal, mortality was rising until 1986, there after it remained stable. An obvious increase was noted for women born between 1900 and 1925. The age-adjusted mortality in the Flemish Region was systematically higher than in the Walloon region (a difference of about 10/100 000 women-years). This difference tended to disappear after 1990. Mortality was always highest in the province of West-Flanders. The Belgian age-adjusted mortality trend was intermediate between France and Germany that showed lower rates and The Netherlands and England & Wales that showed higher mortality. The mortality rate did not further increase in the 1970, while Belgian mortality was still increasing. A spectacular drop was noted in England and Wales after 1990. 2.4. Discussion With almost 6 000 new cases and about 2 400 deaths per year, Belgium (female population of 5.2 million) ranks among the most affected countries in the World. Although changes over time are limited in Belgium, they have important impact on the total number of deaths, since the neoplasia occurs so frequently and because of the ageing of the population. Understanding changes in factors that determine trends is import to explain passed trends and to predict future evolutions. The increased cohort effect observed in women belonging to the birth cohorts C1900-C1920 is probably due to decreased fertility observed in the women before the second world war. The recent fall in mortality in certain western countries is most often explained as the consequence of earlier diagnosis mediated by increased breast awareness and improved treatment. The impact of well-conducted screening programme is expected to become observable only 5-7 years after starting campaigns. Meanwhile it is essential to set up data collection systems allowing monitoring of process parameters concerning participation of the target population and the quality of all technical procedures. Only when the quality at all steps can be assured a reduction in mortality from breast cancer up to 30% can be expected.


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3. Introduction Breast cancer is the most incident cancer in women worldwide. Especially in industrialised countries incidence of and mortality from breast cancer is high. With almost 6 000 new cases and about 2 500 deaths per year, Belgium (female population of 5.2 million) ranks among the most affected countries in the World. Breast and cervical cancer are the two malignancies for which evidence is available that organised mass screening is the best public health policy to reduce incidence of advanced disease and mortality [Advisory Committee on Cancer Prevention, 2000]. The efficacy and efficiency of screening can be maximised by optimising the participation of the target population, assuring the quality of the screen test and the compliance of screen positive women with further diagnostic and/or therapeutic procedures [IARC 2002]. The European Commission recommends all member states to set up mammographic screening programmes targeting women aged 50-69 years, at two- to three-year intervals. Within the framework of Europe Against Cancer guidelines were worked out, containing recommendations concerning all technical aspects of setting up population screening [European Commission, 2001]. In several parts of Belgium, experience in organisation of breast cancer screening was established through pilot projects in the 1990s. These projects received support from the Community governments and Europe Against Cancer [Renard, 2000; Flemish Advisory Board for Cancer Prevention, 1997]. By these experiences it became clear that structural administrative and financial measures and collaboration with national health authorities was required for a straightforward implementation of organised screening. It might be appropriate to remind that, in Belgium, organisation of preventive health care is a responsibility attributed to the Communities, while reimbursement of health care depends on the Federal authoritya. The 25th of October 2000, an agreement was reached between the Federal Ministers, responsible for social affairs and public health, the Community Ministers and the authority from the Common Community Commission responsible for Brussels concerning the principles of a nation-wide breast cancer screening programme, based on the European guidelines. The programme offers free mammographic screening for women between 50 and 69 years old at an interval of 2 years. Mammographies must be performed in certified radiological units and double interpretation by two independent radiologists is required. Minister Vandenbroucke provided a budget of 6 million EUR in 2000 and, since 2001, 11,250,000 EUR yearly for the financing of mammographies and remuneration of radiologists. The Communities are responsible for the organisation of the programme.

a For instance: reimbursement for a consultation of a general practitioner or gynaecologist, a mammography, a subsequent diagnostic or therapeutic intervention is regulated by the National Health Insurance Institute (RIZIV/INAMI), which depends on the Federal authority.


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The 25th of November 2000, the involved authorities signed an annex to the agreement. This annex stipulates the roles of involved partners. The responsibility of the intermediate co-ordination of the programme (invitation of women, the second reading and data-collection) is confined to Screening Centres. Eleven Screening Centres were identified: one in each of the 5 provinces in the Walloon Region; 5 in the Flemish Region (the 4 Flemish universities and 1 Centre in Bruges) and finally 1 Screening Centre for Brussels. Women can be invited by two possible procedures: (1) reference by her GP or gynaecologist and (2) by direct invitation sent by the Screening Centre women. The second procedure will be restricted to women for which the Screening Centre does not have information concerning recent screening. The national population bank used for the social security service will provide the databases containing the administrative data of women from the target population consisting of about 1.1 million women. Registration of data will be based using the unique social security identification number. Links with the cancer registry will allow to identify interval cancers and to delete women with history of cancer from invitation lists. Results from screening and subsequent follow-up will only be recorded after obtaining written informed consent requested at the first mammography. The Scientific Institute of Public Health was indicated as responsible for the over all-evaluation at the national level. The agreements were published in 'Het Staatsblad/Le Moniteur' the 31st of May 2001. The programme started officially the 15th of June 2001. At the same time new codes for 'organised mammographic screening (1st and 2nd reading) distinct from the existing 'diagnostic mammography' were created by the National Health Insurance Institute. This report containing the trend study of mortality from breast cancer in Belgium is a second achievement within framework of national evaluation. The first was the development of a consensus document on objectives and activities. A third output will be the assessment of the screening status for breast cancer screening from the National Health Interviews conducted in 1997 and 2001. The first purpose of mammographic screening is the reduction of mortality from breast cancer. According to evidence from historical trials, a drop of 20 to 30% in cause-specific mortality with respect to the situation without screening can be expected 5 to 7 years after introduction of a programme. This trend study will document changes in background mortality. To what extent changes in risk factors, previous opportunistic screening, earlier diagnosis and improved treatment have influenced incidence and mortality trends will be discussed. Quantitative assessment of the extent of these effects will require further statistical research and modelling. We will study the changes over calendar period and birth cohort in total number of deaths, crude, age-specific, age-adjusted mortality rates or ratios. We will consider 4 geographic levels: the country, the Region, the Provinces and the Arrondissements. The Belgian mortality data will be compared with those of other European countries. This work should be considered as preliminary. It will be submitted for discussion at the meeting of the National Steering Committee for Breast Cancer Screening on the 21st of January 2003. We are grateful to receive comments from the project partners.


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4. Material and methods 4.1. Source of data 4.1.1. Belgian data Data on deaths from breast cancer, from all causes in women and on the composition of the female population in Belgium between 1954 and 1996 were obtained from the National Institute of Statistics (NIS). Data were aggregated over 1-year calendar years and 5-year age groups. Deaths from breast cancer were coded as 170 for the period 1954-1968 (ICD VI and VII) and as 174 for the period 1969-1996 (ICD VIII and IX). For the period 1954-1968 data were provided at the national level only. From 1969 data are available at the level of 'Arrondissements' or Districts. The level of 'Provinces' and 'Regions' can be reconstituted by aggregation from the district level. Data were also requested from the NIS at the municipality level, but these are not delivered yet. Nevertheless, mortality data at the municipality level were obtained previously from the Administration of Health Care of the Flemish Community for the period 1990-98 [Arbyn, 2001]. 4.1.2. European data We downloaded files from the World Health Organisation (WHO) Mortality Database (http://www.who.int/whosis/mort), selected data concerning breast cancer from Euro-pean countries and recoded causes of death into the codes of the 9th ICD edition. For mortality and population data of Germany, we obtained series from 1952 to 1990 for the former W-Germany (Bundes Republik Deutschland), from 1973 to 1990 for the former E-Germany (Deutsche Democratische Republik) and from 1990 to 1997 from the united Germany. We computed mortality for the whole of Germany from 1973 to 1989 by adding the number of deaths and population at risk from E- and W-Germany. Aggregated data on incidence and mortality of breast cancer in the European Union (EU) were extracted from the EUCAN CDROM edited by the International Agency for Research on Cancer (IARC) [Ferlay, 1999]. It must be remarked that Belgian incidence rates were computed by applying M/I ratios from countries with reliable data on Belgian mortality figures. Survival of breast cancer cases was derived from the Eurocare-1 and -2 databases [1995; 1999] containing information from several European cancer registries, pooled by the IARC. Experts of the IARC assume that survival data from Dutch and German registries might be representative for the Belgian situation [Ferlay, 1999]. 4.1.3. World data Just for reasons of comparison we include an overview map of the estimated age standardised incidence of and mortality from of all countries in the year 2000, using the


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standard world population of reference. This map is derived from the Globocan programme developed by the IARC [Ferlay, 2001]. Further exploration of data at world level is beyond the focus of this study. 4.2. Trend analyses 4.2.1. Graphical and tabular presentation of trends Number of deaths, mortality rates We present the absolute number of deaths from breast cancer, the crude, standardised and cumulative cause-specific mortality rates by calendar year a,b,c. The European reference population is used for direct standardisation [Waterhouse, 1976]. In the assessment of the burden of breast cancer mortality we calculated the potential years of life lost (PYLL) between the age of one year and life expectancy d,e. We computed the theoretic gain in life expectancy if breast could be eliminated completely as cause of death. Proportional mortality is defined as the proportion of all female deaths that were caused by breast cancer. Age specific rates are aggregated by five-year periods. Cohort-specific mortality rates are studied as well. Cohorts include women who are born in the same period, hence, are ageing together and are exposed to risks isolated in time. They constitute the diagonals in a table with period- and age-specific rates respectively in rows and columns [Kupper, 1985]. Since age groups and periods all span five years, the corresponding birth-cohorts are necessarily ten years large. Successive cohorts overlap partly and are usually indicated by their central year [Kupper, 1985; Moolgavkar, 1998; Arbyn, 2002]. For instance: women between 55 and 59 years old in the period 1975-79 will belong in the period 1985-89 to the age-group 65-69 and were between 35 and 39 years old in the period 1955-59. They all belong to the 1920 cohort (C1920), which means that they were born between 1915 and 1924 (see Lexis-diagram in Figure 1). Cohort effects The cohort-effect represents the relative risk of a certain cohort to die from breast cancer in comparison with the mean mortality rate of all generations together. This cohort-effect is calculated by an indirect standardisation method [Beral, 1974; Osmond,

a For definitions, methods of calculation of parameters and their 95% confidence intervals we refer to Jensen [1991]. b For the computation of 95% CIs around the SMR (standardised mortality ratios), we used the method proposed by Clayton [1995]: SMRlow/up=SMR:/*error factor. Error factor=exp(1.96*sqrt(1/O), where O is the number of observed deaths. c Cumulative mortality (CM) was computed as the complement of cumulated survival using the formula: CM=1-�(1-e-ai*�T), where ai is the age-specific mortality rate over �T (=5 year age interval) [Kleinbaum, 1982]. d PYLL: potential years of life lost. For each person dying from a given cause of death, the potential life years lost is the difference between a target age (in this case the life-expectancy) and the age at death [Romeder, 1977]. The PYLL is defined as the sum of these differences divided by the number of all individuals dying from the considered disease multiplied by 100 000. e Life tables were constructed using the method described by Chiang [1984].


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1982]. It consists of the ratio of the number of observed deaths in a given cohort k over the number of expected deaths, if the average age-specific mortality rates are applied to the respective age segments of the population in cohort k.

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Figure 1. A cohort can be presented as an oblique parallelogram in a Lexis-diagram, where calendar time and age constitute respectively the abscise the ordinate. Women in the age-category between 55 and 59 years in the period 1975-79 were in the period 1955-59 between 35 and 39 years old en will be between 1985 and 1990 in the group of 65-69 years. They belong to the cohort, born between 1915 and 1924, indicated as cohort C1920.

4.2.2. Break points in trends: join point regression Mortality trends over long periods are rarely linear. In order to test for changes in slopes we used the join point regression model, developed recently by statisticians at the National Cancer Institute [Kim, 2000]. We used therefore the Join point software [2002]. This method allows the identification of periods with a distinct slope that can be separated by a number of breakpoints or "join points". Modelling follows the principle of minimisation of the weighted sum of squared errors and the choice of the number of join points is based on permutation tests [Lerman, 1980; Kim, 2000]. 4.2.3. Loglinear Poisson models In order to disentangle the separate effects of age, period and cohort, we applied log-linear Poisson regression as described by Clayton [1987a & b]. We consider 13 age


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groups (indexed with a), 8 periods (indexed p) and 20 cohorts (indexed k). The three indices are related by

k=13-a+p (Equation 1)

Poisson variation is assumed for the expected number of deaths. For a thorough discussion of ACP-modelling, we refer to an earlier publication concerning cervical cancer mortality trend in Belgium [Arbyn, 2002]. Shortly: the logarithm of the mortality rate (Map) in a period p and age group a can be considered as an additive combination of an age- (�a), period (�p) and/or cohort-effects (�k). See equation 2,

Ln (Map) = � + �a + �p + �k (Equation 2)

where �� represents the basis log-rate for the reference levels (when a, p or k=0). The antilogs of the effects �a, �p or �k are to be interpreted as the adjusted rate ratios with respect to the reference categories for a, p or k. Age is added as first term to the model. Before adding period or cohort effect, a linear drift is added. This drift cannot be attributed to cohort or period effects, because of the inter-relation between age, period and cohort (Equation 1) [Clayton, 1987a & b]. The addition of terms is assessed by the chance in residual deviance for the considered loss in degrees of freedom, which follows approximately a �2 distribution. By rescaling the deviance criterion we can allow for extra-Poisson variation in case of an unsatisfactory goodness of fit for the final model containing all appropriate terms [Breslow, 1983; Arbyn, 2002]. 4.3. Spatial variation of breast cancer Besides time trends, we will explore spatial variation at the level of the 3 Regions, 11 Provinces and 43 districts or arrondissements. For small area variation at the level of municipalities, we refer to previous work [Arbyn, 2001]a. Formal ACP-modelling is limited to the level of the Regions. For lower geographical levels we just present period trends. 4.3.1. Variation of breast cancer mortality by arrondissement We produced maps showing the geographic variation of the ASMR. We used a 7 colour relative scale ranging from green to red following the template used by Smans [1992]. The cut-offs were defined at percentiles p5, p15, p30, p70, p85 and p95 for all district-period combinations (43x5); the corresponding absolute ASMRs are detailed in the legend. The ASMR cut-off values were hold constant to facilitate comparison between maps.

a We are currently developing age-cohort/period-space models applicable to small area-data using Bayesian Markov Chain Monte Carlo simulation procedures. These methods will be tested on breast cancer mortality trends at municipality level.


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We produced also maps indicating the districts with significant linear changes over time using an age-drift*district model, according to the method recommended by Pickle [1987]. 4.3.2. World maps An absolute scale was used for categorisation of countries, where class limits are assigned after taking into account the overall range of values: the five classes are computed by taking the difference between the maximum and the minimum values, then by dividing this by 5. The intervals delimiting classes are therefore equal [Ferlay, 2001].


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5. Results 5.1. Breast cancer mortality in Belgium (1954-1996) 5.1.1. Current burden of breast cancer in Belgium Currently, cancer is the second most important group of death causes after cardiovascular disease. In 1996, 21 423 women died from cardiovascular diseases and 11 643 from cancer. Breast cancer constitutes the main cause of death in the cancer group with 2494 deaths. More than one fifth of women dying from cancer had breast cancer. Table 1 and Table 2 show respectively the mortality rates and the adjusted potential life years lost due to the 20 most common causes of death by cancer. In Figure 2 these parameters are displayed graphically.

Figure 2. Crude mortality rate and number of potential life-years lost between the age of 1 year to life-expectancy from breast cancer by 100 000 women-years (Belgium, 1996).

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Table 1. Mortality from the 20 most frequent cancers, ranked by crude mortality rate and proportion of all deaths caused by specific malignant tumours (Belgium, 1996). Source: SPMA, 2002.

Rank Topographic origin of cancer ICD-9 mortality rate (deaths/100 000

women-years)

% of all deaths from malignant

tumours1 Breast 174 48.04 21.4% 2 Colon-rectum 153-154 32.00 14.3% 3 Larynx-trachea-bronchus-lung-pleura 161-163 19.99 8.9% 4 Ovary and annexes 183 13.66 6.1% 5 Cancer not specified 199 12.73 5.7% 6 Pancreas 157 12.21 5.4% 7 Lymphomas and myelomas 200-203 10.25 4.6% 8 Stomach 151 9.65 4.3% 9 Leukemia 204-208 8.28 3.7% 10 Brain-central nervous system 191-192 6.80 3.0% 11 Kidney 189 5.30 2.4% 12 Bladder and urethra 188 5.14 2.3% 13 Liver 155 4.78 2.1% 14 Uterus nos 179 4.06 1.8% 15 Cervix uteri 180 3.43 1.5% 16 Gallbladder and extra hepatic ducts 156 3.31 1.5% 17 Corpus uteri 182 3.16 1.4% 18 Oesophagus 150 3.10 1.4% 19 Mouth and pharynx, lips, etc… 140-149 2.74 1.2% 20 Melanoma skin 172 2.27 1.0% 1-20 210.90 94.0%

All malignant tumours 140-208 224.28 100.0%

Table 2. Mortality from the 20 most frequent cancers, ranked by PYLL (potential life-years lost) due to breast cancer between the age of 1 year through life-expectancy (Belgium, 1996). Source: SPMA, 2002.

Rank Topographic origin of cancer ICD-9 PYLL per 100 000 females

1 Breast 174 841.52 Colon-rectum 153-154 346.23 Larynx-trachea-bronchus-lung-pleura 161-163 328.14 Ovary and annexes 183 198.65 Brain-central nervous system 191-192 154.16 Pancreas 157 144.47 Cancer not specified 199 140.18 Lymphomas and myelomas 200-203 130.69 Leukemia 204-208 118.810 Stomach 151 100.411 Cervix uteri 180 68.812 Kidney 189 63.613 Liver 155 53.214 Uterus nos 179 52.415 Bladder + urethra 188 46.516 Melanoma skin 172 45.317 Corpus uteri 182 38.918 Oesophagus 150 34.619 Gallbladder and extra hepatic ducts 156 33.320 Mouth and pharynx, lips, etc… 140-149 27.0


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The burden of deaths due to breast cancer is substantially higher than for the next most important cancer. For instance colo-rectal cancer and lung-larynx cancer are responsible for respectively 13% and 9% of all female cancer deaths. Breast cancer causes the loss of 842 potential life years per 100 000 females between one year and life expectancy. This is more than double of the PYLL for the second cause of cancer mortality, which is colo-rectal cancer (PYLL of 346). Life expectancy at birth among females should increase from 80.50 to 81.23 years if breast cancer was eliminated completely as a cause of death. This means an increase in life expectancy of 0.73 years. The probability of dying from breast cancer before the age of 75 (or cumulative mortality) was 2.8 %. The cumulative mortality up to a given age group is plotted in Figure 3.

Figure 3. Cumulative mortality from breast cancer up to a given age group (Belgium, 1996). Age groups in the abcis are indicated by the first year of the 5-year interval. The last age group, indicated by 85 is an open category and concerns women aging 85 years or more.

5.1.2. Description of the trends The evolution of the absolute number of deaths, due to breast cancer in women by year in Belgium, is shown in Figure 4. The average yearly number of deaths from breast cancer increased steadily from 1405 in the fifties to 2454 in the nineties. The proportion of all female deaths attributed to breast cancer varied between 2.5 % in 1955 and 4.8% in 1995 (see Figure 5). The crude cause-specific mortality rate rose from less than 30 to almost 50 per 100 000 women-years. This corresponds with a relative yearly increase of 1.013 (CI: 1.012-1.013). Over the whole study period (43 years, from 1954 to 1996) the relative

Cumulative mortality up to a given age group from breast cancer

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increase was 1.72 (1.68-1.76). The increasing slope was not uniform over time. It was maximal in the period 1970-89. The slope became insignificant in the nineties. Table 3. Relative yearly increase and 95% confidence interval of the crude mortality rate, by period (breast cancer, by Belgium, 1954-96).

Period Relative

increase (%) 95% CI 1954-59 100.4 99.2 101.7 1960-69 100.8 100.3 101.3 1970-79 101.1 100.6 1016 1980-89 101.1 100.7 101.6 1990-96 100.7 99.9 101.4 1954-96 101.3 101.2 101.3

Figure 4. Number of deaths from breast cancer by year (Belgium, 1954-1996).

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Figure 5 Proportional mortality: proportion of all female deaths attributed to breast cancer (Belgium, 1954-1996) (proportion in red bold line, 95% confidence intervals in blue dotted line).

Figure 6. Trend of the crude and standardized mortality rate (number of deaths from breast cancer / 100 000 women-years) (Belgium, 1954-96; European reference population used for standardization).

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The increase in the crude rate was partly due to the ageing of the population at risk. This can be derived from the fact that the slope of the age standardised mortality rate was less steep than that of the crude rate (Figure 6). The standardised rate increased from 25-27/100 000 in the fifties to 35-36/100 000 in the beginning of the eighties. Since then, the adjusted rate remained stable. Table 4. Relative change and 95% confidence intervals of the standardized mortality rate, by period (breast cancer, Belgium, 1954-96).

Period Relative change 95% CI

1954-59 0.9480 0.9872 1.0122 1960-69 1.0055 1.0012 1.0099 1970-79 1.0063 1.0038 1.0088 1980-89 1.0044 1.0021 1.0067 1990-96 1.0020 0.9983 1.0058 1954-96 1.0071 1.0067 1.0074

Figure 7. Standardized mortality ratio (SMR), using the average mortality rate in 1955-59 as reference rate (breast cancer, Belgium, 1954-96).

The same trend is presented in Figure 7, using the standardised mortality ratio (SMR). After adjustment for age, the average mortality rate in the nineties is 31 to 34% higher than in the period 1955-59.

0

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1954 1959 1964 1969 1974 1979 1984 1989 1994

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19

The evolution of cumulative mortality is an alternative way to present age-standardised trends. The trend of cumulative mortality up to the age of 74 years is shown in Figure 8. The same shape of curve as for the directly or indirectly standardised mortality is obtained. The cumulative mortality increased from 2.0-2.4% in the fifties to 2.9-3.0% at the second half of the eighties. The curve becomes horizontal hereafter.

Figure 8. Cumulative mortality from breast cancer until the age of 74 (Belgium, 1954-96). Analysis of the change in slope of the period trend We analysed the change in age standardised mortality rate by calendar period also using the join point regression method. A breakpoint could be identified in 1986. Before that year, the fitted standardised rate increased with 0.98% per year. After 1986, the slope was not statistically different from a horizontal line (Table 5). Raw and fitted rates are plotted in Figure 9. Table 5. Slope and annual percent of change of fitted breast cancer mortality in the periods 1954-85 and 1987-1996.

Period Slope (=exp(b)) Annual percent change estimate 95% CI

1954-85 1.0098 0.98 (0.86; 1.10)1987-96 0.9958 -0.42 (-1.03: 0.19)

Cumulative mortality up to the age of 74

Cum

ulativ

e m

orta

lity (%

)

Year

1955 1960 1965 1970 1975 1980 1985 1990 1995

0

.5

1

1.5

2

2.5

3


20

Figure 9. Observed (points) and fitted age-standardised rate (broken line) of mortality from breast cancer (deaths/ 100 000 women-years, European reference population, Belgium 1954-96). One breaking point can be identified in 1986. Age specific mortality rates The levelling-off of the age-standardised rate is essentially caused by the halt in the increase of mortality in the age groups 50-74 (Table 6, Figure 10 and Figure 11). The variation in younger groups is limited. The mortality rate in older groups (75+) steadily increases. Table 6. Mortality rates (deaths/100 000 women-years) arranged by 5-year age groups (columns) and 5-year periods (rows). Age groups and periods are indicated by the first year. Ten-year cohorts follow the diagonals in the age x period table and are indicated by the mid-year.

Age group (a) 20 25 30 35 40 45 50 55 60 65 70 75 80

Period (p) 1 2 3 4 5 6 7 8 9 10 11 12 13 Cohort (k)1955 1 0.2 1.7 5.4 11.7 25.4 39.8 49.7 59.7 68.4 85.5 94.2 119.9 143.1 1870 11960 2 0.3 1.3 5.4 14.0 23.9 42.4 54.8 60.9 71.6 88.6 102.2 126.2 158.5 1875 21965 3 0.2 2.2 5.8 13.8 24.8 43.6 60.3 68.3 68.3 84.2 101.0 129.2 159.5 1880 31970 4 0.3 1.6 6.3 12.7 27.4 43.5 58.6 78.3 85.8 90.5 106.7 129.8 168.4 1885 41975 5 0.2 1.7 5.7 15.2 28.9 45.0 66.1 79.3 86.2 94.6 109.9 140.3 165.8 1890 51980 6 0.1 1.3 6.1 13.8 29.3 47.3 66.5 85.7 100.1 108.0 130.0 132.1 174.1 1895 61985 7 0.2 0.9 5.1 12.6 29.9 47.4 68.5 87.5 100.1 115.6 127.9 150.1 182.2 1900 71990 8 0.2 1.1 4.6 12.3 27.4 43.9 62.4 81.6 96.8 111.2 125.8 155.5 192.0 1905 8

Cohort 1970 1965 1960 1955 1950 1945 1940 1935 1930 1925 1920 1915 1910 (k) 21 20 19 18 17 16 15 14 13 12 11 10 9

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1954 1959 1964 1969 1974 1979 1984 1989 1994

Year

ASM

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eath

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ObservedFitted


21

Figure 10. Age-specific mortality rates by period (breast cancer, Belgium, 1954-1996).

Figure 11. Same type of information as in Figure 10 but with X-axis and legend variables switched. Periods of the first half of each decade 1960-64, 1970-74 etc. are omitted for reason of graphical clearness.

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1965-69

1975-79

1985-89

1995-96


22

Change in mortality at pre- and postmenopausal age Different risk factors are associated with respectively pre- and post-menopausal breast cancer [Swerdlow, 2001]. Therefore, we pooled mortality data separately for women ageing less of more than fifty years. Join point regression allowed to identify one breakpoint for premenopausal in the year 1982 and one for postmenopausal breast cancer in 1988 (Figure 12). The slopes of the SMRa before and after this join points are presented in Table 7. The fitted SMRs increased with respectively 0.7 and 1.0% by year in respectively pre- and postmenopausal ages until the breakpoint. Thereafter, a decrease of 1.1% by year could be notified in pre-menopausal age groups, while no significant time trend could be observed in post-menopausal age groups. Table 7. Slope and annual percent of change of fitted SMRs for breast cancer at pre- and post-menopausal ages. By join point regression breakpoints in respectively 1982 and 1988 were identified.

Period Slope Annual percent change estimate 95% CI Premenopausal (<50 years)

1954-81 1.0071 0.71 (0.39; 1.03)1983-96 0.9895 -1.05 (-1.88; -0.21)

Postmenopausal (>=50 years)

1954-87 1.0100 1.00 (0.87; 1.12)1989-96 0.9980 -0.20 (-1.14; 0.74)

Figure 12. Observed (points) and fitted SMRs (lines) for breast cancer at respectively pre- and postmenopausal age groups (Belgium, 1954-96).

a The SMRs were calculated as before, using the age-specific rate of 1955-59 for the whole of Belgium as reference.

0

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1954 1959 1964 1969 1974 1979 1984 1989 1994

Year

SMR

Premenopausal (obs)Premenopausal (fit)Postmenopausal (obs)Postmenopausal (fit)


23

Change in age-specific rates by cohort The evolution of age-specific mortality by cohort is shown in Figure 13. In the age groups between 50 and 79 years a nearly horizontal trend is observed until cohort 1900 (C1900). Around C1905 an increase is noticed. The slopes become less prominent in further cohorts and even decrease in certain age groups after C1920. In the oldest age group (80-84 years), the trend rises continuously, while little variation is observed in the younger groups (< 45 years).

Figure 13. Age-specific mortality rate (in 5-year age bands) by birth cohort (breast cancer, Belgium, 1954-96). Standardised cohort mortality ratio (SCMR) The influence of birth cohort is summarised by the standardised cohort mortality ratio (SCMR) (see Figure 14). The range of variation is rather limited: less than 20% under and 10 % above the reference. The cohort effect remains stable under the mean level until C1900. From C1905 an increase can be noticed which diminishes in later cohorts. A plateau is reached between C1920 and C1940. After C1940 the cohort effect decreases. After C1960 the trend seems to rise again. Nevertheless, no firm conclusions can be drawn from the most recent cohorts since the estimate is very imprecise (confidence intervals are very wide).

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1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970

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Figure 14. Standardised cohort mortality ratio and 95% confidence intervals (Belgium, 1955-1994). By join point regression on the SCMR, three slopes could be distinguished, separated by two breakpoints at C1905 and C1920 (see Figure 15). Slope 1 from C1870 through C1900 did not differ from a horizontal line.

Figure 15. Raw or observed SCMR (points) and fitted SCMR obtained by join point regression.

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1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970

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1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970

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25

We fitted also a join point model to the standardised cohort mortality ratios computed separately for deaths at premenopausal age (<50 year) and deaths at postmenopausal age (>=50 and <85 years) (Figure 16). For the SCMR at postmenopausal age, 2 break points could be identified at C1900 and C1920. Between these two birth cohort a significant increasing slope could be noticed: 1.2% (CI: 0.6-1.9%). The slope was not significant beyond these breakpoints. For the SCMR at premenopausal age, only 1 breakpoint was identified at C1940. Before C1940, the SCMR increased significantly by 0.7% (CI: 0.4; 0.9%) by birth-year. After C1940, a significant decrease was observed: -1.0% (CI: -1.6; -0.4%).

Figure 16. Raw and fitted SCMR obtained by join point regression concerning the change in risk of mortality from breast cancer by birth cohort determined at pre- and postmenopausal age categories.

0.0

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1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970

Birth cohort

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Premenopausal (raw)Premenopausal (fit)Postmenopausal (raw)Postmenopausal (fit)


26

5.1.3. ACP models using Poisson regression

1.1.1.1 Model selection The decrease in deviance for progressively more complex models is shown in Table 8. The addition of age yields an enormous jump in deviance. Linear drift and non-linear cohort further improves the model, but a complete age-cohort-period model is needed to describe data adequately.

1.1.1.2 Effect estimates The age effects derived from the complete age-cohort-period model are displayed in Figure 17. The constant � was included in the computation, so that the age-effects take the format of age-specific mortality rates. We plotted two sets of age-effects: (1) when a linear period drift is considered, and (2) using a linear cohort drift. Mortality rises exponentially with age, until the age group of 55-59, where a clear deceleration in the increase is noticed. After the age of 65 mortality increases exponentially again. The different estimated time-effects (linear period or drift, non-linear cohort and period) are plotted in Figure 18. The cohort effects were computed as a mortality risk relative to that of the 1930 cohort. The period effects are referred to the first period (1955-59). Numerous sets of estimated C-, P-, and drift effects can be considered, all predicting the same fitted mortality rates. They differ by allocation of the drift into a cohort and/or a period component. Due to identifiability problems, intrinsically linked to ACP-modelling, we cannot express any rational preference for a particular solution. In Figure 18, we separated out the drift as a pure linear period effect. This yielded a C-effect that looks different from the standardised cohort mortality ratio plotted in Figure 14. Nevertheless, certain identifiable common contrasts, such as accelerations or decelerations (changes in slope) can be noticed in both plots. For instance, around C1905 and around C1960, a convex curvature (indicating a sudden temporary acceleration) is observed. It should be remarked that the cohort-specific ratios were computed relative to the overall mortality in Figure 14, while the estimated cohort-effect in Figure 18 is relative to the 1930 birth cohort. In the period effect, we observe a deceleration around the 1980-84. This phenomenon matches the observation in the age-standardised curves in Figure 6 and Figure 7, where a stabilisation in the trend is observed since the beginning of the eighties.


27

Table 8. Deviance, degrees of freedom and significance of progressively more complex APC-models. The contribution of an extra term is evaluated by the change in deviance (col 6) with the �2 corresponding with the number of lost df (7). The corresponding p-value (col 8) should be < 0.05. The adequacy of the model is assessed by comparing the residual deviance (3) with the � 2 for the resting df (4). The corresponding p-value is presented in column 9. Akaike's information criterion (AIC) is used for comparison of non-nested models.

1 2 3 4 5 6 7 8 9 10

N° Model Deviance df Comparison

with model � dev. � df p for

�dev/� df p for

fitted vs. observed

AIC

0 Null 67131.2 103 653.151 Age 898.5 91 0 66232.8 12 0.00000 0.00000 16.532 Age-drift 294.9 90 1 603.5 1 0.00000 0.00000 10.743a Age-Age*drift 194.9 78 2 100.0 12 0.00000 0.00000 10.013b Age-Period 252.1 84 2 42.9 6 0.00000 0.00000 10.453c Age-Cohort 115.3 72 2 179.6 18 0.00000 0.00090 9.364 Age-Cohort-Period 78.7 66 3c 36.7 6 0.00000 0.13666 9.13


28

Figure 17. Estimated age effects derived from a full age-cohort-period model, considering a linear period drift (full line) or a linear cohort drift (interrupted line). Age effects are computed by inclusion of the constant ��so that they take the format of overall age-specific mortality rates.

Figure 18. Estimated linear drift and non-linear cohort and period effects derived from the full ACP-model. Breast cancer mortality, Belgium (1955-94).

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1.1.1.3 Predicted mortality rates The predicted age-specific mortality rates computed from the ACP estimated effects described above are plotted as a function of period in Figure 19. The observed rates are plotted with symbols, while the predicted rates are plotted as line curves.

Figure 19. Observed and fitted age-specific mortality rates from breast cancer as a function of period. Observed rates are indicated with symbols, the fitted rates with lines. Intervening age groups are omitted for reason of graphical clearness. Legend for the age groups: 20-24 (dark blue); 30-34 (green); 40-44 (brown); 50-54 (red); 60-64 (purple); 70-74 (blue); 80-84 (yellow). The fitted rates are derived from a complete age-cohort-period model. The same fitted rates, but now plotted against birth cohort, yield the picture shown in Figure 20. The fitted rates in Figure 19-Figure 20 can be compared with the graphs in Figure 10-Figure 13. Fitted rates yield a certain smoothing facilitating interpretation. The abrupt increase in slope between 1965-69 in age group 60-64 is reflected in age groups 70-74 in 1975-79 and in age group 80-84 in 1985-90. This clearly looks a cohort effect. Acceleration in the rate around C1905 and a deceleration around C1920 can be easily discerned as noticed earlier. The recent acceleration in mortality for cohorts born after 1960 can hardly be seen on the linear Y-axis. The estimate of its effect is very imprecise; its impact on global mortality is unimportant and might just be based on random Poisson error.

Breast cancer mortality, Belgium, 1955-94observed (points) and fitted from ACP model (lines)

deat

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Figure 20. Fitted age-specific rates of mortality from breast cancer as a function of birth cohort, computed from a complete age-cohort-period model (Belgium, 1955-94).

.

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31

5.2. Mortality from breast cancer at the level of the Regions (1969-1996) 5.2.1. Description of the trends We restrict the presentation of regional trends to the directly standardised mortality rate, the age specific rates and the standard cohort mortality ratio. The standardised mortality rate almost always was lower in the Walloon than in the Flemish Region. The Brussels rate did not differ significantly from the 2 other regions.

Figure 21. Age adjusted rate of mortality from breast cancer by region (standardized using the European reference population), Belgium, 1969-1996.

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BrusselsFlemish RegionWalloon Region


32

Figure 22. Age-specific mortality rates as a function the calendar year in the three Belgian regions (breast cancer, Belgium, 1969-96).

Walloon Region

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Figure 23. Standardised cohort mortality ratio by region (breast cancer, Belgium, 1969-96). The risk of mortality in the Flemish Region was higher than in the Walloon Region for all cohorts at the exception of the 3 youngest ones. 5.2.2. ACP models using Poisson regression

1.1.1.4 Model selection Region Brussels For the region of Brussels a model containing an age*drift interaction provides a satisfactory fit. Among all considered models at step 3 it yields the lowest AIC (see Table 9). This means that the fitted age-specific log mortality rates can be plotted as straight lines with different slopes (see Figure 24). Table 9. Deviance analysis of ACP models (Breast cancer mortality, Region Brussels, 1969-96). addition

of termsfitted vs

observed

N° Model Deviance df Comparison with model

� dev. � df p for �dev/� df

p for dev/df

AIC

0 Null 5920.7 65 0.00000 96.63 1 Age 96.2 52 0 5824.4 13 0.00000 0.00019 7.40 2 Age-drift 79.2 51 1 17.1 1 0.00004 0.00698 7.16 3a Age-Age*drift 39.17 39 2 40.0 12 0.00007 0.46227 6.92 3b Age-Period 73.44 48 2 5.7 3 0.12658 0.01051 7.17 3c Age-Cohort 38.34 36 2 40.8 15 0.00034 0.36399 7.00 4 Age-Cohort-Period 32.65 33 3c 5.7 3 0.12806 0.48433 7.00

0

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1885 1895 1905 1915 1925 1935 1945 1955 1965

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SCM

R

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34

Flemish Region A complete age-cohort-period model is needed to describe the Flemish trend adequately (see Table 10). This means that age-specific time trends vary irregularly with period and cohort (see Figure 24). Table 10. Deviance analysis of ACP models (Breast cancer mortality, Flemish Region, 1969-96). addition

of termsfitted vs

observed N° Model Deviance df Comparison


�dev/� dfp for

dev/df

AIC 0 Null 27147.7 65 0.00000 424.83 1 Age 161.5 52 0 26986.2 13 0.00000 0.00000 10.03 2 Age-drift 127.0 51 1 34.6 1 0.00000 0.00000 9.53 3a Age-Age*drift 96.9 39 2 30.0 12 0.00277 0.00000 9.43 3b Age-Period 91.9 48 2 35.1 3 0.00000 0.00014 9.08 3c Age-Cohort 64.9 36 2 62.1 15 0.00000 0.00223 9.03 4 Age-Cohort-Period 32.8 33 3c 32.0 3 0.00000 0.47604 8.63

Walloon Region The Walloon trend can be described adequately with a simple age-drift model (Table 11). This means that the age-specific log-mortality rates follow a straight-line pattern with one common slope (Figure 25). Table 11. Deviance analysis of ACP models (breast cancer mortality, Walloon Region, 1969-96). addition

of termsfitted vs

observed

N° Model Deviance df Comparison with model

� dev. � df p for �dev/� df

p for dev/df

AIC

0 Null 14690.5 65 0.00000 232.54 1 Age 106.8 52 0 14583.7 13 0.00000 0.00001 8.55 2 Age-drift 58.3 51 1 48.4 1 0.00000 0.22367 7.83 3a Age-Age*drift 39.3 39 2 19.0 12 0.08791 0.45581 7.91 3b Age-Period 55.7 48 2 2.6 3 0.45901 0.20636 7.88 3c Age-Cohort 35.8 36 2 22.5 15 0.09512 0.47662 7.95 4 Age-Cohort-Period 33.5 33 3c 2.3 3 0.50538 0.44338 8.00

Belgium and the 3 regions A complete common ACP model does not provide a satisfactory fit, when ACP Poisson regression is applied to the whole of Belgium over the period 1969-1994, Only when over-dispersion is allowed for an acceptable goodness of fit is obtained. Addition of region into the model yields a better prediction of observed data, which still can be improved by considering a period*region interaction.


35

Table 12. Deviance analysis of ACP models (Breast cancer mortality, all Belgian Regions, 1969-96). addition

of termsfitted vs

observed N° Model Deviance df Comparison


�dev/� dfp for

dev/df AIC

without region effect 0 Null 47869.7 194 0.00000 251.88 1 Age 514.0 182 0 47355.8 12 0.00000 0.00000 9.16 2 Age-drift 416.4 181 1 97.6 1 0.00000 0.00000 8.67 3a Age-Age*drift 359.2 169 2 57.2 12 0.00000 0.00000 8.50 3b Age-Period 386.7 178 2 29.7 3 0.00000 0.00000 8.54 3c Age-Cohort 324.5 166 2 91.8 15 0.00000 0.00000 8.35 4 Age-Cohort-Period 300.7 163 3c 23.9 3 0.00003 0.00000 8.26 with over-dispersion 163.4 163 0.47629 with region effect 0.a Null 47758.9 192 0 110.8 2 0.00000 0.00000 251.88 1.a Age 395.6 180 1 118.4 2 0.00000 0.00000 8.57 2.a Age-drift 300.5 179 2 115.9 2 0.00000 0.00000 8.09 3a.a Age-Age*drift 243.3 167 3a 115.9 2 0.00000 0.00011 7.92 3b.a Age-Period 271.1 176 3b 115.6 2 0.00000 0.00001 7.97 3c.a Age-Cohort 208.4 164 3c 116.2 2 0.00000 0.01089 7.77 4.a Age-Cohort-Period 184.6 161 4 116.1 2 0.00000 0.09836 7.68 with region-specific interactions 2a.b Age-drift*Region 296.1 177 2.a 4.3 2 0.11382 0.00000 8.09 3b.b Age-Period*Region 253.0 168 3b.a 18.0 8 0.02099 0.00002 7.96 3c.b Age-Cohort*Region 168.9 132 3c.a 39.5 32.0 0.17019 0.01669 7.90 4.b1 Age-Cohort*Region-Period 145.0 129 4.a 39.6 32.0 0.16749 0.15922 7.81 4.b2 Age-Period*Region-Cohort 167.5 153 4.a 17.0 8.0 0.02959 0.19967 7.68

1.1.1.5 Predicted mortality rates The predicted age-specific mortality rates computed with the models selected above are plotted separately for the 3 regions (see Figure 24-Figure 25). The observed rates are plotted with symbols, while the predicted rates are plotted as line curves.


36

Figure 24. Observed and fitted age-specific mortality rates from breast cancer. Observed rates are indicated with symbols, the fitted rates with lines. Intervening age groups are omitted for reason of graphical clearness. Legend for the age groups: 20-24 (dark blue); 30-34 (green); 40-44 (brown); 50-54 (red); 60-64 (purple); 70-74 (blue); 80-84 (yellow). The fitted rates for the Region of Brussels (above) are derived from an age*drift model, those for the Flemish Region (below) are derived from a complete age-cohort-period model.

Breast cancer mortality, Brussels, 1970-94observed (points) and fitted from ACP model (lines)

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Breast cancer mortality, Flemish Region, 1970-94observed (points) and fitted from ACP model (lines)

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Figure 25. Observed and fitted age-specific mortality rates from breast cancer. Observed rates are indicated with symbols, the fitted rates with lines. Intervening age groups are omitted for reason of graphical clearness. Legend for the age groups: 20-24 (dark blue); 30-34 (green); 40-44 (brown); 50-54 (red); 60-64 (purple); 70-74 (blue); 80-84 (yellow). The fitted rates for the Walloon Region are derived from an age+drift model.

5.3. Variation of breast cancer mortality by province The variation of the directly age-standardised mortality rate over time period between 1970 and 1994 is shown in Figure 27 for each separate province. We plotted the ASMR for all provinces together in one graph as well (Figure 26). In Table 13, provinces are ranked by decreasing ASMR for all the periods. In all periods, age-adjusted mortality from breast cancer was highest in the province of West-Flanders. The rate was particularly elevated in the period of 1975-1984, when the ASMR was even significantly higher than for province ranking at the 2nd place. East-Flanders, Antwerp, Flemish-Brabant and Brussels are usually observed in the top 5 of most affected provinces. In Limburg, Liege and Luxembourg, the risk of mortality was lower than in other provinces. Since 1985, the trend in West-Flanders was decreasing. The linear increase assessed by an age-period*province model often was highest in the provinces where the mortality from breast cancer was initially low: Liege, Brabant wallon, Namur, Luxembourg. Limburg is exceptional in this aspect: the mortality remained stable at a rather low level. The ratio highest/lowest rate varied from 1.36 in 1970-74 to respectively 1.43, 1.43, 1.33 and 1.28 in the next four periods.

Breast cancer mortality, Walloon Region, 1970-94observed (points) and fitted from ACP model (lines)

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200


38

Table 13. Ranking of provinces by decreasing ASMR, by 5-year period. Period Province ASMR 95% CI Period Province ASMR 95% CI low high low high 1970 West-Vlaanderen 36.65 34.36 38.95 1985 West-Vlaanderen 42.05 39.76 44.34 Brussel 36.00 33.98 38.02 Brussel 41.51 39.07 43.96 Oost-Vlaanderen 34.76 32.80 36.72 Vlaams-Brabant 40.25 38.01 42.48 Antwerpen 33.84 32.02 35.66 Oost-Vlaanderen 38.87 36.87 40.86 Hainaut 33.49 31.64 35.34 Antwerpen 37.95 36.15 39.75 Vlaams-Brabant 32.75 30.38 35.13 Hainaut 35.98 34.04 37.92 Limburg 30.69 27.57 33.81 Namur 35.14 31.77 38.51 Namur 30.01 26.72 33.31 Brabant wallon 34.39 30.39 38.40 Luxembourg 29.55 25.11 33.98 Luxembourg 32.71 28.21 37.22 Brabant wallon 28.44 24.39 32.50 Limburg 31.84 29.16 34.52 Liège 27.04 25.16 28.91 Liège 31.63 29.58 33.671975 West-Vlaanderen 38.73 36.42 41.04 1990 West-Vlaanderen 40.02 37.84 42.19 Antwerpen 36.37 34.53 38.22 Vlaams-Brabant 37.94 35.72 40.17 Vlaams-Brabant 35.69 33.67 37.72 Brabant wallon 37.74 33.71 41.77 Oost-Vlaanderen 34.80 32.85 36.74 Brussel 37.25 35.03 39.48 Hainaut 33.82 31.95 35.69 Oost-Vlaanderen 36.69 34.80 38.59 Brussel 32.93 30.62 35.24 Antwerpen 36.46 34.73 38.18 Brabant wallon 32.11 27.98 36.23 Liège 35.89 33.76 38.03 Namur 31.95 28.58 35.32 Hainaut 34.30 32.44 36.16 Luxembourg 31.24 26.79 35.70 Namur 34.27 30.94 37.59 Limburg 31.23 28.23 34.22 Luxembourg 34.23 29.72 38.75 Liège 31.09 29.06 33.11 Limburg 31.18 28.70 33.671980 West-Vlaanderen 45.02 42.61 47.44 Oost-Vlaanderen 38.58 36.57 40.60 Antwerpen 37.46 35.63 39.29 Vlaams-Brabant 37.16 35.04 39.29 Brussel 36.86 34.51 39.21 Hainaut 36.61 34.68 38.55 Brabant wallon 35.78 31.51 40.05 Luxembourg 33.34 28.71 37.96 Limburg 32.60 29.72 35.48 Namur 32.18 28.91 35.46 Liège 31.41 29.38 33.44

Relative change 95% CI Province by 10 year low highAntwerpen 1.05 1.02 1.08W-Vlaanderen 1.04 1.01 1.08O-Vlaanderen 1.05 1.02 1.09Hainaut 1.03 1.00 1.07Liège 1.14 1.09 1.19Limburg 1.00 0.94 1.06Luxembourg 1.08 0.99 1.18Namur 1.10 1.03 1.18Brussel 1.08 1.04 1.12Vl-Brabant 1.11 1.06 1.15Brabant wallon 1.12 1.04 1.21


39

Figure 26. Variation of the age-adjusted mortality rate for breast cancer by 5-year calendar period for the 11 provinces

25

30

35

40

45

1970 1975 1980 1985 1990

Period

ASM

R (d

eath

s/10

0 00

0 w

omen

-yea

rs)

Antwerpen

Brussel

Brabant wallon

Hainaut

Liège

Limburg

Luxembourg

Namur

O-Vlaanderen

Vl-Brabant

W-Vlaanderen


40

Figure 27. Evolution of the age-standardized mortality rate by 5-year period and by province.

Mortality from breast cancer, Belgium (1970-94), by provinceAge adjusted rate, European ref pop, deaths/100 000 women-years

death

s/100

000 w

omen

-year

s

Antwerpen

20

30

40

50W-Vlaanderen O-Vlaanderen Hainaut

Liège

20

30

40

50Limburg Luxembourg Namur

1970 1975 1980 1985 1990Brussels

1970 1975 1980 1985 199020

30

40

50Vl-Brabant

1970 1975 1980 1985 1990

Brabant wallon

1970 1975 1980 1985 1990


41

5.4. Variation of breast cancer mortality by district The variation of the directly age-standardised mortality rate by district and by 5-year period between 1970 and 1994 is shown in Figure 29. The geographical variation of the ASMR is also mapped in Figure 30 and Figure 31. In Figure 32, arrondissements are ranked by decreasing ASMR in the periods 1970-74 and 1990-94. The significance and the direction of linear change over the 25-year period, 1970 through 1994 is shown in Figure 33. The matrix with Spearman's rank-correlation coefficients computed for the rank of the districtal ASMRs is shown in Table 14. The Spearman coefficient always was positive and significantly different from zero (p<0.05). This indicates that districts tend to maintain their ranking. The districts of Veurne, Ieper, Roeselare, Bruges, Kortijk, are in general in top of the list. The extent of inter-district variation is summarised using a Box-whisker plot (Figure 28). Veurne and Roeselare (in 1975-79) and Tielt (in 1980-84) showed extremely high ASMR values, outlying the whiskers in the BW-plot. Table 14. Matrix containing Spearman's rank-correlation coefficients for districtal ASMRs of different periods.

1970 1975 1980 1985 1990

1970 1.00 0.38 0.49 0.60 0.35 1975 - 1.00 0.61 0.45 0.45 1980 - - 1.00 0.57 0.51 1985 - - - 1.00 0.55 1990 - - - - 1.00

A significant positive linear trend over the 5 periods was observed for Antwerp, Mechelen, Vilvoorde, Brussels, Leuven, Bruges, Oostende, Mouscron, Liege and Namur (Figure 33). This was assessed by fitting a loglinear model including age group and linear period drift as parameters. Most districts show a plateau reached in the period 1980-89, followed by a decrease in the last 2 periods. An overall decreasing trend is observed in only a few areas: Soignies, Virton, and Charleroi. Nevertheless, this decline never was significant.


42

Figure 28. Box- and whisker plot, showing the inter-district variation of the ASMR over time.

Breast cancer mortality, Belgium, 1970-94ASMR distribution among districts by period

ASM

R

period

20

30

40

50

1970 1975

RoeselarVeurne

1980

Tielt

1985 1990 Total

TieltRoeselar


43

Figure 29. Evolution of the age-standardized mortality rate by 5-year period and by district.

Mortality from breast cancer, Belgium (1970-94), by arrondissementAge adjusted rate, European ref pop, deaths/100 000 women-years

deat

hs/1

00 0

00 w

omen

-yea

rs

period

Antwerpen

10

20

30

40

50

Mechelen Turnhout

Brussels

10

20

30

40

50

Vilvoorde Leuven

Bra-W/Nivelles

1970 1975 1980 1985199010

20

30

40

50

Brugge

1970 1975 1980 19851990

Diksmuide

1970 1975 1980 19851990


44

Figure 29 (continued). Evolution of the age-standardized mortality rate by 5-year period and by district.


deat

hs/1

00 0

00 w

omen

-yea

rs

period

Ieper

20

30

40

50

60

Kortrijk Oostende

Roeselare

20

30

40

50

60

Tielt Veurne

Aalst

1970 1975 1980 1985199020

30

40

50

60

Dendermonde

1970 1975 1980 19851990

Eeklo

1970 1975 1980 19851990


45



deat

hs/1

00 0

00 w

omen

-yea

rs

period

Gent

20

30

40

50

60

Oudenaarde St-Niklaas

Ath

20

30

40

50

60

Charleroi Mons

Mouscron

1970 1975 1980 1985199020

30

40

50

60

Soignies

1970 1975 1980 19851990

Thuin

1970 1975 1980 19851990


46



deat

hs/1

00 0

00 w

omen

-yea

rs

period

Tournai

20

30

40

50

Huy Liège

Verviers

20

30

40

50

Waremme Hasselt

Maaseik

1970 1975 1980 19851990

20

30

40

50

Tongeren

1970 1975 1980 19851990

Arlon

1970 1975 1980 19851990


47



deat

hs/1

00 0

00 w

omen

-yea

rs

period

Bastogne

0

20

40

60

Marche-en-Famenne Neufchateau

Virton

0

20

40

60

Dinant

1970 1975 1980 19851990

Namur

1970 1975 1980 19851990Phillipevi lle

1970 1975 1980 198519900

20

40

60


48

Figure 30. Variation of the age-standardized mortality rate (ASMR) from breast cancer over the 43 districts in 1970-74 (above) and 1980-84 (below).

Breast cancer mortality, 1970-74ASMR, by 100 000 women-years

<27 (7)>=27<30 (7)>=30<33 (10)>=33<38 (15)>=38<40 (2)>=40<44 (2)>=44 (0)


<27 (1)>=27<30 (5)>=30<33 (11)>=33<38 (20)>=38<40 (3)>=40<44 (1)>=44 (2)


49

Figure 31. Variation of the age-standardized mortality rate (ASMR) from breast cancer over the 43 districts in the period 1990-94.


<27 (1)>=27<30 (1)>=30<33 (8)>=33<38 (20)>=38<40 (6)>=40<44 (7)>=44 (0)


50

Figure 32. Age-standardized mortality rate by district, ranked by decreasing ASMR in the periods 1970-74 and 1990-94.

1970-74

0 5 10 15 20 25 30 35 40 45

Neufchâteau

Disksmuide

Marche-en-Famenne

Liège

Eeklo

Waremmes

Maaseik

Arlon

Turnhout

Verviers

Huy

Nivelles

Namur

Bastoigne

Phillipeville

Mouscron

Charleroi

Hasselt

Dinant

Leuven

Oostende

Tielt

Tongeren

Mechelen

Vilvoorde

Thuin

Mons

Gent

Soignies

Aalst

Ath

Oudenaarde

Dendermonde

Antwerpen

Brussels

St-Niklaas

Tournai

Ieper

Kortrijk

Brugge

Virton

Roeselare

Veurne

ASMR (deaths/100 000 women-years)

1990-94

0 5 10 15 20 25 30 35 40 45

Virton

Charleroi

Maaseik

Arlon

Waremmes

Tongeren

Hasselt

Turnhout

Verviers

Soignies

Eeklo

Disksmuide

Ath

Namur

Bastoigne

Phillipeville

Leuven

Dinant

Aalst

Veurne

Thuin

St-Niklaas

Antwerpen

Mons

Dendermonde

Gent

Huy

Liège

Nivelles

Brussels

Tielt

Mouscron

Neufchâteau

Vilvoorde

Mechelen

Oostende

Roeselare

Oudenaarde

Brugge

Ieper

Marche-en-Famenne

Tournai

Kortrijk

ASMR (deaths/100 000 women-years)


51

Figure 33. Presence of a linear period trend between 1970 and 1994, by district.


52

5.5. Breast cancer mortality in the European Union 5.5.1. Trend of breast cancer mortality in neighbouring countries We restrict the comparison of Belgian trends to those from countries adjacent to Belgium: France, Luxembourg, The Netherlands, Germany and England & Wales. In Figure 34, we show the age-standardised mortality rate. The rates remained significantly lower in France (rate difference of 6-11/105) and in Germany (difference 4-7/105). The rates were significantly higher in the Netherlands and in England & Wales in the left part of Figure 34, but this difference disappears near the end of the seventies for The Netherlands and near 1995 for England & Wales. The mortality rates in Luxembourg were very unstable due to the small population size.

Figure 34. Evolution of age standardized rates for mortality from breast cancer in Belgium and neighboring countries (standardized using the European reference population, 1950-1999; for Germany in the period 1973-1989: ASR was computed from the sum of the data of East- and West Germany).

We observe an overall increase followed by a stabilisation and, finally, a discrete or substantial decrease. A maximum was reached early in the Netherlands in 1967 and 1973. In the other countries peak rates were observed in the eighties. A dramatic decrease occurred in recent years only in England & Wales. In all other countries the decrease was modest.

15

20

25

30

35

40

45

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

Year

ASR

(dea

ths

/ 100

000

wom

en-y

ears

)

BelgiumEngland & WalesFranceGermanyGermany (East)Germany (West)LuxembourgThe Netherlands


53

The standardised cohort mortality ratio was calculated using the average age-specific mortality rate for Belgium as reference, allowing international comparison. The shape of the cohort effect trend was rather similar for all countries. It was stable for cohorts born before the end the 19th century, at the exception of The Netherlands. Around C1900-C1905 an obvious increase was noticeable. This increase became less important for later cohorts and a plateau was reached that extended to C1920 for the Netherlands and England & Wales or until C1935-1940 for Germany, France and Belgium. From then the cohort-effect dropped. The recent decrease seems to stop after C1960 in Belgium and Luxembourg, but this is only a very imprecise impression.

Figure 35. Trend in standardized cohort mortality ratio for breast cancer in Belgium and neighboring countries. Standardization is based on the mean age-specific mortality risk in the period 1955-1994.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970

Birth cohort

SCM

R

BelgiumEngland & WalesFranceGermanyGermany (East)Germany (West)LuxembourgThe Netherlands


54

Figure 36. Age-specific mortality from breast cancer in England & Wales, France and the Netherlands.

The Netherlands

0

50

100

150

200

250

300

1955 1960 1965 1970 1975 1980 1985 1990

5 year-period

Dea

ths

/ 100

000

wom

en-y

ears

20-2930-3940-4950-5960-6970-7980+

England & Wales

0

50

100

150

200

250

300

1955 1960 1965 1970 1975 1980 1985 1990 1995

5 year-period

Dea

ths

/ 100

000

wom

en-y

ears

20-2930-3940-4950-5960-6970-7980+

France

0

50

100

150

200

250

300

1955 1960 1965 1970 1975 1980 1985 1990

5 year-period

Dea

ths

/ 100

000

wom

en-y

ears

20-2930-3940-4950-5960-6970-7980+


55

In Figure 36, we have plotted age-specific mortality rates over 10-year age-bands for England & Wales, France and The Netherlands. They can be compared with the Belgian observed or fitted trends respectively in Figure 10 and Figure 19. Age-group 20-29 The mortality rates remained low (< 1/105) in all countries. Age group 30-39 Rates remained systematically lower in France (5-6/105) in comparison with Belgium. Rates in the Netherlands and England & Wales were similar to those in Belgium (between 9 and 11/105). The trend was horizontal for all countries. Age group 40-49 The mortality rate in Belgium raised from 35/105 to 38/105 in the eighties, then dropped steadily to 35/105 in the period 1990-94. In France, the mortality rate remained substantially lower, but followed a similar pattern: 20/105 in 1950-54, with a maximum in 1980-84 and a slow decrease thereafter, 26/105 in the period 1990-94. The mortality rate was higher in the Netherlands before 1975-79, thereafter it was lower than or similar to the Belgian rates. English rates were higher than the Belgian ones until 1980-84, then they became similar. Age group 50-59 In Belgium, the rates rose continuously from 55/105 in the fifties to 78/105 in 1980-84; then it dropped discretely and became 76/105 in 1990-94. The French rates followed a similar pattern but remained substantially below the Belgian trend (rate differences ranging from 13-20/105). The mortality rates were 4-14/105 higher in England & Wales, in 1990-94 they became similar to the rate observed in Belgium. In the Netherlands the rates changed parallel to the Belgian curve (between 8-12/105 higher) until 1965; after that period the Dutch curve approximated the Belgian curve. Age group 60-69 The Belgian rate increased from 76/105 1955-59 to 106/105 in 1985-89; then it remained stable. In France, the rate increased steadily from 51 to 82/105. In England & Wales a peak was reached in 1985-89 (119/105), then it dropped substantially: 94/105 in 1995-99. In The Netherlands, mortality raised with a milder slope than in Belgium, since 1980 rates became similar. Age group 70-79 In Belgium, the rate increased continuously from 105/105 in the fifties to 138/105 in 1985, thereafter a rather horizontal trend was observed. In France the slope was increasing regularly over all periods: from 78/105 in the first period to 105/1055 in the last period. The English trend followed the Belgian trend almost parallelly but was between 11 and 20/105 higher than in Belgium. In The Netherlands, the rate was substantially higher (about


56

30/105) until 1970, but the discrepancy became less since 1975 (rate difference of about 10/105). Age group 85 years and older The mortality rate in Belgium increased from 159/105 to 225-227/105 over the last 2 periods. The rates in France increased as well, but the slope was less dramatic than in Belgium. In England & Wales, rates were higher than in Belgium, in the left and right part of the graph. In the central period they were quite similar. The rates were highest in The Netherlands; but the pattern there was parallel to the Belgian curve (rate difference 44-62/105). 5.5.2. Mortality from and incidence of breast cancer in the European Union (1995) Figure 37 shows the age standardised mortality and incidence for all member states of the European Union, ranked by increasing mortality, for the year 1995 [Ferlay, 1999]. The European reference population was used for standardisation [Waterhouse, 1976]. Belgium ranks respectively on the fifth and sixth place of countries with the highest breast cancer mortality and incidence rates.

Figure 37. Age standardized rate of incidence of and mortality from breast cancer in the European member states in 1995, using the European standard population as reference. (Adapted from Ferlay, 1999).

0 20 40 60 80 100 120 140

Greece

Spain

Sweden

Portugal

Finland

France

Italy

European Union

Austria

Germany

Luxembourg

Belgium

United Kingdom

The Netherlands

Ireland

Denmark

Age standardised rate (per 100 000 women)

Incidence

Mortality


57

5.6. Burden of breast cancer in the world (extrapolation for the year 2000) The world standardised incidence and mortality rate for Belgium is estimated to be respectively 82.2 and 26.4 per 100 000 women-years. High incidence rates are noticed for the NorthWest of Europe, US, Canada, Argentina, Uruguay and Oceania with WSR above 56.9/100 000.

Figure 38. Age standardized incidence of breast cancer in all countries of the world estimated for the year 2000, using the world standard population as reference [Ferlay, 2001].

Highest mortality rates are observed in northwestern Europe, New Zealand and Uruguay (WSR above 22.8/ 100 000).

Figure 39. Age standardized rate of mortality from breast cancer in all countries of the world estimated for the year 2000, using the world standard population as reference [Ferlay, 2001].

Belgium, together with the other countries of N-Western Europe, appears to have the highest burden of breast cancer in the whole world.


58

6. Discussion Breast cancer is the most frequent cause of cancer death in Belgium. Although changes over time are limited, they have important impact on the total number of deaths, since the neoplasia occurs so frequently. We want to discuss now factors that have driven those changes. We can distinguish data artefacts, changes in exposure to risk and protective factors and effects of screening, early diagnosis and treatment. It is extremely difficult to disentangle all influences without knowledge of trends in incidence, staging at diagnosis and survival. 6.1. Quality of the data We expect that no major classification errors or changes in accuracy of death cause certification (such as described for cervical cancer [Arbyn, 2002]) have influenced the Belgian breast cancer mortality trends [IARC, 2002]. For age groups older than 75 years, we must take into account improvements in certification of death causes. Nevertheless, this hypothesis will be tested in further studies. For instance, we plan to assess the impact of the centralisation of codification of death causes from the provinces to the communities in the period 1987 to 1993. 6.2. Risk factors for breast cancer Available evidence indicates a major hormonal influence on the occurrence and development of breast cancer (see Table 15). Known risk factors can be considered as the result of the cumulative exposure to estrogens and perhaps progesterone [Henderson, 1988]. Temporarily we restrict the discussion to the listing of risk factors. Comments and key references for the risk factors are presented at the bottom of Table 15 and subsequent pages. In later work, we will collect Belgian data concerning the evolution of exposure to risk factors that might have contributed to changes in incidence of and mortality from breast cancer or that might influence future trends.


59

Table 15. Factors associated with the development of breast cancer (adapted from Henderson, 1996)

Risk factors associated with increased exposure to estrogen/progesterone Younger age at menarchea

Older age at menopauseb

Postmenopausal obesityc

Nulliparity and first pregnancy at older aged

Hormonal contraceptione

Hormone replacement therapyf

Protective factors associated with decreased exposure to estrogen/progesterone Early first pregnancy

Lactationg

Anti-oestrogen prevention/treatmenth Nutritional risk factors (possibly linked to hormonal effects) Low age at menarchei

Tall adult height High birthweightj

High fat consumptionk, body weight High alcohol consumptionl

Protective nutritional factors Physical activitym

Diet rich in fiber contentn

Phyto-estrogens (for instance soya)o Cigarette smokingp

Benign breast diseaser

Genetic risk factors Family history of breast/ovarium cancer

Alterations, mutations in tumour suppressor genes: BRCA1, BRCA2, p53s

A. Age at menarche: approximately 20% decrease in breast cancer risk results from each year that menarche is delayed. Over the last century age at menarche has progressively decreased, in Western world due to improved nutrition and growth [Henderson, 1996]. B. Age at menopause: women who experience menopause before the age of 45 have only half of the risk of those whose menopause occurs after 55 [Trichopolous, 1972]. C. Postmenopausal obesity: For women under the age of 50 there is no association between body weight and breast cancer risk; after the age of 60, a gain of 10 kg in weight yields an approximately 80% increase in breast cancer risk [De Waard, 1977]. After menopause, the main source of estrogen comes from conversion of adrenal androgen that essentially occurs in adipose tissue [Siiteri, 1981]. D. Risk is increased in childless women and women who give birth late in life and, to a lesser extent, women who have few children. E. Hormonal contraception and therapy: Current and recent users appear to have a modestly increased risk [CGHFBC, 1996]. Postmenopausal hormone replacement therapy yields an increase in risk as well [CGHFBC, 1997]. Treatment with diethylstilboestrol in pregnancy augments the risk of breast cancer. Tamoxifen treatment of breast cancer protects against cancer in the contra lateral breast [EBCTCG, 1998].


60

F. Hormone replacement therapy (HRT): current use of estrogen is associated with increased breast cancer risk (RR: 1.21-1.40). The risk increases with long duration of use [Nelson, 2002 Beral, 2002; Writing Group WHII, 2002]. Data concerning association between HRT and mortality from breast cancer are conflicting [Nelson, 2002]. G. Lactation: the risk of breast cancer risk decreases with increasing cumulated months of lactation [Ross, 1994]. H. Anti-oestrogen prevention/treatment: anti-oestrogens such as Tamoxifen, Raloxifen, aromatase-inhibitors protect against contra lateral breast cancer and increases survival of patients who have undergone surgery [Veronesi, 1998; Bonneterre, 2001; Gruber, 2002]. I Low age at menarche and height: both risk factors might be a nutritional indicator for growth in childhood [Swerdlow, 2001]. J. Certain studies show an association between high birth weight and breast cancer [Michels, 1996; De Stavola, 2000]. K. Evidence concerning high fat consumption as risk factor for breast cancer comes essentially from ecological studies [Armstrong, 1975; Gray, 1979]. L. Alcohol: the relative risk for breast cancer increases by 7.1% (CI: 5.5-8.7%) for each additional 10g per day intake of alcohol, according to a recent meta-analysis [Collaborative Group HFBC, 2002]. The RR was similar in ever-smokers and never-smokers. The relation between smoking and breast cancer was substantially confounded by the alcohol effect and the authors of the meta-analysis concluded that smoking is not related to breast cancer. It is not clear if smoking in relation to start before or after first full term pregnancy was sufficiently controlled for [Band, 2002]. M. Postmenopausal obesity is a risk factor while vigorous physical exercise reduces risk for pre-menopausal breast cancer [Bernstein, 1994]. N. Diet rich in fibres should have a protective effect against breast cancer, by modifying the metabolism of estrogens. Fiber intake correlates negatively with plasma percentage free estradiol [Adlercreutz, 1987]. O. Phyto-estrogens: In women, phyto-oestrogen-rich foods, such as soy, a source of isoflavones, are slightly protective against breast cancer, if at all, but is more likely to be beneficial if initiated before puberty or during adolescence. These findings are supported by conclusions of studies of immigrants and other epidemiological studies [Adlercreutz, 2002]. P. Cigarette smoking. Discrepant results were reported in the past. A recent case-control study conducted in Canada allowed to distinguish a carcinogenic effect in pre-menopausal women having started smoking before full term pregnancy and a protective effect in post-menopausal women who have started to smoke after a first full term pregnancy. Smoking might reduce breast cancer through an anti-estrogenic effect on fully differentiated glandular cells; while undifferentiated glandular breast tissue should be particularly sensitive to the carcinogenic substances in cigarette smoke [Band, 2002]. In another recent study, it was found that risk of breast cancer increased with number of years and intensity of smoking, essentially in women who have smoked more than 40 years and 20 cigarettes a day or more [Terry, 2002]. It is not clear if alcohol consumption was sufficiently controlled for in these studies [Collaborative Group HFBC, 2002]. Q. Certain benign conditions are associated with increased risk for later malignancy of the breast: for instance, the relative risk of atypical ductal hyperplasia is 2.4-4.0; and for atypical lobular hyperplasia it is 4 to 5 [Dupont, 1985;1993; Marschall, 1997; Page, 1991]. R. Mutations in the tumour suppressor genes BRCA1 and BRCA2 predispose to breast and ovarian cancers. BRCA-deficient cells may gain uncontrolled proliferation of breast and ovarian epithelium, mediated by estrogen after the beginning of puberty [Welcsh; 2001]. Women at high risk for the development of breast cancer have several options open to them including increased cancer surveillance, prophylactic mastectomy


61

and/or oophorectomy, and chemo prevention [Blanchard, 2000]. Mutations in BRCA-1 and BRCA-2 are present in only a small portion (5-10%) of all breast cancers [Nicoletto, 2001]. 6.3. Incidence of breast cancer in Belgium No reliable long-term cancer incidence figures are available for Belgium or parts of Belgium. This is illustrated by the standardised incidence rate computed from data published by the National Cancer Register and plotted in Figure 40. This curve clearly does not correspond with reality and just reflects the intensity of declaration of cancer cases. Notification of new cases of cancers in Belgium started in 1943. The quality of registration suffered seriously from under declaration in the first decades. Only in the 1990s, registered incidence reached levels only 10-15/100 000 women-years lower than IARC estimations for Belgium. These estimations are based on extrapolations from Belgian mortality data using European mortality/incidence ratios derived from countries with registration systems of acceptable high quality [Ferlay, 1998].

Figure 40. Age standardized incidence of breast cancer in Belgium between 1943 and 1996, computed from data published by the National Cancer Registry, using the European reference populationa.

In the Flemish Region substantial improvements in cancer registration have been achieved over last years. For the period 1997-99 the standardised rate was 124/100 000 women-years according to the Flemish Cancer Register [Vlaams Kankerregistratienetwerk, 2002]. a In 1982 no cancer cases were registered, in 1981 and 1983 a substantially lower number of cases were declared in comparison with the period before and after. Therefore we estimated incidence by simple linear extrapolation for the period 1981-84.

Incidence of breast cancer, Belgium (source: NCR)Age standardised rate & 95% CI, Eur. ref. population, 1943-96

New

case

s/10

0 00

0 w

omen

-yea

rs

Year1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

0

25

50

75

100


62

We do not further focus on incidence statistics. We plan to estimate incidence trends from mortality for the whole period that death statistics are available. We will use polynomial regression models to estimate M/I ratios from countries with reliable registers that can be considered sufficiently representative for the Belgian situation [Jensen, 1990].


63

6.4. Survival of breast cancer patients Mortality trends are determined by changes in incidence and/or survival. Survival rates have increased over the last decades because of improved treatment, down staging by screening and increased breast-awareness [IARC, 2002]. No cancer survival data are available for Belgium. The IARC has published survival data from several cancer registries from Europe. We present here information from the Eurocare studies [1995 & 1999]. We mention observed and relative survival figures of breast cancer cases diagnosed in the period 1985-89. The relative survival is defined as the ratio of the observed survival rate in a given group of cancer patients / survival rate observed in the general population.

Table 16. One, two and five-year observed (obs) and relative (rel) survival (in %) of breast cancer cases diagnosed between 1985-89, by age group, derived from European cancer registries [Eurocare 2, 1999]

Age group Survival 15-44 45-54 55-64 65-74 75-99 All (years) obs rel obs rel obs rel obs rel obs rel obs rel 1 96 94 95 96 93 94 90 92 80 87 91 93 3 84 84 83 84 79 81 76 81 59 75 76 81 5 74 74 74 75 70 73 54 73 44 68 65 73 Table 17. Age standardized relative survival (in %) of breast cancer cases diagnosed between 1985-89 during 1 and 5 year, by country, derived from European cancer registries [Eurocare 2, 1999].

1-year survival 5-year survival Country (%) 95% CI (%) 95% CI Low High Low High Austria 86.5 83.5 89.7 63.2 58.7 68.2 Denmark 91.9 91.4 92.4 70.6 69.7 71.6 England 89.5 89.2 89.7 66.7 66.3 67.2 Estonia 86.8 85.0 88.7 59.5 56.6 62.5 Finland 95.0 94.5 95.6 78.4 77.4 79.5 France 95.6 95.0 96.3 80.3 78.9 81.8 Germany 92.6 91.4 93.7 71.7 69.5 74.1 Iceland 95.2 92.8 97.6 79.2 74.4 84.4 Italy 94.9 94.4 95.3 76.7 75.7 77.7 The Netherlands 94.2 93.0 95.4 74.4 71.9 77.0 Poland 85.8 84.0 87.6 58.5 55.9 61.3 Scotland 88.9 88.3 89.6 65.0 64.0 66.0 Slovakia 84.8 83.7 85.9 58.3 56.5 60.2 Slovenia 89.6 88.3 91.0 64.2 62.0 66.5 Spain 93.8 92.9 94.6 70.4 68.8 72.1 Sweden 95.7 95.0 96.5 80.6 79.0 82.2 Switzerland 96.6 95.6 97.6 79.6 77.4 82.0 Europe, 1985-89 92.7 92.4 93.0 72.5 71.9 73.1


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Estimated Belgian survival data presented in the EUCAN database were based on a simple average of survival data obtained from German and Dutch cancer registries. Accepting this procedure, we can estimate the 1-, 2- and 3-year age-standardised relative survival for Belgian breast cancer cases diagnosed in 1985-89 as being 93%, 83% and 73%. Figure 41 displays the time trend of the 5-year survival of women with breast cancer diagnosis in the Netherlands, Germany and in the European pool. The values and 95% CIs are displayed in Table 18. The country composition of the EUROCARE database was used for age-standardisation. A significant increase in survival can be observed for the Netherlands and the European pool. Over the 12-year period, the 5-year survival increased from 66.5% (CI: 62.8-70.5%) to 75.6% (CI: 72.3-78.6%) in the Netherlands; and from 64.6% (CI: 63.3-65.8%) to 73.5% (CI: 72.4-74.6%) in the European pool.

Figure 41. Trend of age-adjusted relative survival during 5 years for cases of breast cancer diagnosed between 1978 and 1989, registered in Dutch, German and a series of European cancer registries [Eurocare 2, 1999].

Table 18. Evolution of the age-adjusted relative 5-year survival (in %) from breast cancer and 95% CIs [Eurocare 2, 1999].

1978-80 1981-83 1984-86 1987-89

The Netherlands 66.7 (62.8 -70.5) 74.8 (71.0 -78.3) 72.7 (69.1 -76.0) 75.6 (72.3 -78.6)

Germany 68.5 (65.0 -71.7) 67.6 (64.5 -70.6) 72.0 (69.0 -74.7) 69.8 (66.4 -73.0)

European pool 64.6 (63.3 -65.8) 68.3 (67.1 -69.4) 71.4 (70.3 -72.4) 73.5 (72.4 -74.6)

50

55

60

65

70

75

80

78-80 81-83 84-86 87-89

Period

Age

adj r

el s

urvi

val (

%)

The NetherlandsGermanyEuropean pool


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6.5. Screening effects Evidence on efficacy of breast cancer screening by mammography has been addressed in several population trials conducted in the USA, Canada, Sweden, UK and Finland. The IARC Working Group on the Evaluation of Cancer-Preventive Strategies recently pooled data from randomised trials comparing mortality from breast cancer in women invited for mammographic screening versus control groups [IARC, 2002]. Pooling of results from Swedish women between 40 and 49 years old at recruitment yielded a relative mortality of 0.81 (CI: 0.65-1.01). Certain studies (Ostergötland-study, Stockholm study) showed a relative mortality risk higher than 1, but this never was significant. The pooled relative mortality risk, calculated over all Swedish and Finish studies, observed among women between 50 and 69 years, was significantly lower than unity: 0.75 (CI: 0.67-0.85). In all individual studies the RR was lower than 1. The reduction in mortality depends further on the extent of follow-up time, the number of subsequent screening examinations, the screening interval and the number of mammographic views. In a recent Cochrane review of RCTs, Gotzsche and Moller [2000] excluded all studies with imbalances in age-group composition of the experimental arms and retained only one Canadian and one Swedish (the Malmö trial) study that showed no benefit. They concluded that mammography screening does not reduce mortality from breast cancer and, moreover, screening should cause an increase in over-all mortality. Most experts have addressed the arguments of Gotzsche [Miller, Duffy, Moss, Nyström, Hayes, Law, Cates, de Koning, 2000] but nevertheless confidence of public, authorities and health professionals in mammographic has been eroded seriously. The effect of screening on mortality is expected to be observable only 5 to 8 years after the start of a screening programme [Tabar, 1997]. The observed effect will further be retarded when women with breast cancer diagnosed before the start of a programme are not excluded and when the quality of screening is lower than that in RCTs [Blanks, 2000]. Pre-existent opportunistic screening further dilute the impact of organised population based screening [IARC, 2002]. One of the main determinants will be the participation rate of target population at first and subsequent screenings [European Commission, 2001]. Data on mammographic screening coverage in Belgium, defined as the proportion of women having had a mammography over the last two years, are only available from surveys organised since the end the 1990s [SIPH, 1997 & 2002; Arbyn, 1997]. Historic information is available concerning the total number of reimbursement claims for mammography from the RIZIV/INAMI (the National Health Insurance Institute). It must be noted that the number plotted in Figure 42 concerns the number of radiological examinations (one per breast). This number must be divided by two, in order to obtain the approximate number of women concerned. The total number of mammographies cannot be simply translated in a useful coverage indicator. No information on age distribution is available, neither for the indication: opportunistic screening or clinical reason. The number of mammographic examinations increased from less than 200 000 in the period 1983-85 to


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more than 1,160,000 in 2001a. Since the chance that women underwent multiple examinations by year is limited and since this proportion probably was constant over time, a substantial increase in "opportunistic screening coverage" can be presumed.

Figure 42. Number of "diagnostic" mammographies by year, reimbursed by the National Health Insurance Institute. We will complete this topic with information on current attendance to screening assessed in the last Health Interview Survey [SIPH, 2002] and assessed from data to be received from health insurance agencies. 6.6. General comments Burden of breast cancer Mortality from breast cancer is the commonest cause among all cancer deaths in Belgian women. In certain western countries, such as the United States, Canada and Scotland where prevalence of smoking among females has been high since decades, lung cancer mortality exceeds that of mortality from breast cancer [Coleman, 1993; Ferlay, 2001]. In Belgium, the number of deaths from breast cancer increased with more than 75%, between the 1950s and 1990s. This increase can be attributed largely to the absolute increase of the population size and further by ageing.

a In 2001 a new code for organised mammographic screening (examination of both breasts) was introduced. About 32 500 organised screenings were reported to RIZIV/INAMI. This figure was multiplied by two and added to the number of "diagnostic" mammographies.

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001

Year

Num

ber /

yea

r


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Possible cohort effect In older age groups (50-75 years old), mortality raised following the general pattern of the age-standardised rate: increase until the mid-1980s and stabilisation or a discrete decline thereafter. The rising trend in the left zone of the period trends can be ascribed partly to diminish fertility in the cohorts C1900 and C1920. A substantial increase in fertility, observed after the Second World War, can have stopped this effect. The increasing trend was lower for women who died before the age of 50 and the change in trend occurred earlier (around 1982). Earlier diagnosis through increased breast awareness and improved treatment is probably the best explanation for this phenomenon. Possible treatment effects Remarkable is the absence of a significant recent decline in breast cancer mortality observed in several other western countries, in particular in the UK. The recent decline observed over the 1990s in several western countries, which was spectacular in the UK, is most often explained as the consequence of earlier diagnosis, better primary cancer treatment and systematic Tamoxifen administration in breast cancer patients. The impact of screening is probably limited since the decline was observed too early after the introduction of screening campaigns and also because the decline was also noted in younger age groups not included in the target population [Sverdlow, 2001]. The absence of a recent decline in Belgium merits particular attention. Limitations of ACP models First order parameters of trends, such as the magnitude or the direction of slopes, cannot be estimated unequivocally from age-cohort-period models. In spite of the importance of determining period and cohort effects as indicators for efficacy of interventions or changes in exposure to risk factors, identification of patterns is not directly possible. This identifiability problem has led to reluctance among researches about ACP modelling of trends. Nevertheless changes in linear trends can be estimated. Several identifiable contrasts are described in the statistical literature, such as the contrasts describing local departures from linearity (for instance the second differences [Clayton, 1987b; Arbyn, 2002] or long term changes (for instance Tarone contrasts) [Tarone, 1996]. ACP models can become helpful tools for programme evaluation, since they can be extended with screening and treatment parameters. ACP models can also be applied for trend forecasts. Plans for the future This trend analysis is just one step in the evaluation of the recently started breast cancer screening campaign. The work will be completed with following activities: - Continuous update when more recent data become availablea - Estimation of incidence trend from external and representative M/I ratio data, adapted

as accurately as possible to the local situation - Constitution of a mega basis feeded from 3 main sources:

- (1) data from organised screening provided by the Communities

a End 2002 we have received breast cancer mortality data for the whole of Belgium of 1997 and for the Flemish Region for 1999 and 2002.


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- (2) the National Cancer Registry, providing data of all diagnosed cases of breast cancer and cytological and histological data from patients with breast pathology

- (3) administrative data on screening, diagnostic and therapeutic interventions on the breast from health assurance agencies.

- Analysis of data from the Health Interview Survey in Belgium, - Completion of evaluation tables as recommend in the European guidelines for

mammographic screening at Belgian level. - Estimation of the potential impact on mortality of monitored process parameters

concerning participation of the target population and the quality of screening and follow-up.

- Analysis of costs of current breast cancer screening (organised and opportunistic, intra- and extra target population), diagnosis and treatment for breast cancer.

- Cost-effectiveness analysis.

7. Acknowledgements We acknowledge Mr. A. Doneux and Mrs. M. De Backer of the National Institute of Statistics for providing data concerning deaths from breast cancer and population data and Dr. M. Haelterman for providing data on breast cancer incidence. We are grateful to Ms. Catherine Vân Phan and Ms. Jessica Timmermans for compilation of data files.


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Dupont WD, Parl FF, Hartman WH, Brinton LA, Winfield AC, Worrel JA, Schuyler PA, Plummer WD. Breast cancer risk associated with proliferative breast disease and atypical hyperplasia. Cancer 1993; 71: 1258-1265. EBCTCG (Early Breast Cancer Trialists' Collaborative Group). Tamoxifen for early breast cancer: an overview of the randomised trials. Lancet 1998; 351: 1451-1467. EUROCARE. Survival of cancer patients in Europe. The Eurocare Study. Ed.: F. Berrino, M. Sant, A. Verdecchia, R. Capocaccia, T. Hakulinen, J. Estève; IARC-HO-European Commission. IARC Scientific Publications N° 132, Lyon, 1995. EUROCARE 2. Survival of Cancer Patients in Europe: the EUROCARE-2 Study. Eds. Berrino F, Capocaccia R, Estève J, Gatta G, Hakulinen T, Micheli A, Sant A, Verdecchia A. IARC Scientific Publication No. 151, Lyon, 1999; pp 1-572. European Commission. European guidelines for quality assurance in mammographic screening. Eds. Perry N, Broeders M, de Wolf C, Törnberg S, Schouten J. Office for Official Publications of the European Community, 3rd ed, Luxembourg, 2001, pp 1-366. Ferlay J, Bray F, Sankila R, Parkin DM. EUCAN: Cancer Incidence, Mortality and Prevalence in the European Union 1995, version 2.0. IARC CancerBase No. 4. Lyon, IARCPress, 1999. Ferlay J, Bray F, Pisani P, Parkin DM. GLOBOCAN 2000: Cancer Incidence, Mortality and Prevalence Worldwide, Version 1.0. IARC CancerBase No. 5. Lyon, IARCPress, 2001. Flemish Advisory Board for Cancer Prevention/ Sub commission for Breast Cancer Prevention. Multi-centre study breast cancer screening in Flanders (in Dutch). Eds: Weyler J, Schrijvers J, Vandermeeren I, Vuylsteke de Laps L. Ministry of the Flemish Community & Administration for Health Care, Brussels 1997. Gotzsche PC, Olsen O. Is screening for breast cancer with mammography justifiable? Lancet 2000; 355: 129-134. Gray GE, Pike M, Henderson BE. Breast cancer incidence and mortality in different countries in relation to known risk factors and dietary practices. Brit J Cancer 1979; 39: 1-7. Gruber CJ, Tschugguel W, Schneeberger C, Huber JC. Production and action of estrogens. NEJM 2002; 346: 340-352. Henderson BE, Ross RK, Bernstein L. Estrogens as a cause of human cancer: The Richard and Hinda Rosenthal Foundation Award Lecture. Cancer Res 1988; 48: 246-253. Henderson BE, Pike MC, Bernstein L, Ross RK. Chapter 47: Breast Cancer. In: Cancer Epidemiology and Prevention. Eds: Schottenfield D, Fraumeni JF. Oxford University Press, New York, 1996, pp 1022-1039.


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IARC. IARC Handbooks on Cancer Prevention. Vol 7: Breast Cancer Screening. Eds. Vaino H, Bianchini F. IARC, Lyon, 2002, pp 1-229. Jenssen OM, Estève J, Moller H, Renard H. Cancer in the European Community and its member states. Eur J Cancer 1990; 26: 1167-256. Jenssen OM, Parkin DM, MacLennan R, Muir CS, Skeet RG. Cancer Registration: principles and methods. IARC, Scientific Publications, N° 95; Lyon, 1991. pp 132-158. Join point Regression Programme. Version 2.6. Software produced by the Statistical Reseach and Application Branch, National Cancer Institute, Silver Spring, US. March 2002. Software is available at http://srab.cancer.gov/ Kim HJ, Fay MP, Feuer EJ, Midthune DN. Permutation tests for join point regression with applications to cancer rates. Stat Med 2000; 19: 335-351. Kleinbaum DG, Kupper LL, Morgenstern H. Epidemiologic Research: Principles and Quantitative Methods. Van Nostrand Reinhold, New York, 1982, pp 1-529. Kupper LL, Janis JM, Kamous A, Greenberg BG. Statistical age-period cohort analysis: a review and critique. J Chron Dis 1985; 38:811-30. Lerman PM. Fitting Segmented Regression Models by Grid Search. Appl Stat 1980; 29: 77-84. Marshall LM, Hunter DJ, Connlly JL, Schnitt SJ, Byrne C, London SJ, Colditz GA. Risk of breast cancer associated with atypical hyperplasia of lobular and ductal types. Cancer Epidemiol Biomarkers Prev 1997; 6: 297-301. Michels KB, Trichopoulos D, Robins JM, Rosner BA, Manson JE, Hunter DJ, Hankinson SE, Speizer FE, Willet WC. Birthweight as a riskfactor for breast cancer. Lancet 1996, 348: 1542-1546. Miller AB et al, Duffy SW et al, Moss S et al, Nyström L, Hayes C e tal , Law M et al, Cates C et al, Screening mammography re-evaluated. Lancet 2000; 355: 747-750. Moolgavkar SH, Lee JAH, Stevens RG. Analysis of vital statistics data. In: Rothman KJ, Greenland S, eds. Modern Epidemiology. Philadelphia: Lippincott-Raven Publishers, 1998, 481-97. Nelson HD, Humphreu LL, Nygren P, Teutsch SM, Allan JD. Postmenopausal hormone replacement therapy. Scientific Review. JAMA 2002; 288: 872-881. Nicoletto MO, Donach M, De Nicolo A, Artioli G, Banna G, Monfardini S. BRCA-1 and BRCA-2 mutations as prognostic factors in clinical practice and genetic counselling. Cancer Treat Rev 2001; 27: 295-304.


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9. Abbreviations ACP model: age-cohort-period model A-effect: age-effect AIC: Akaike's Information Criterion ASMR: age-standardised mortality rate ASR: age-standardised rate C-effect: cohort effect CI: 95% confidence interval CM: cumulative mortality EU: European Union IARC: International Agency for Research on Cancer ICD: International Classification of Diseases NCR: National Cancer Register NIS: Nationaal Instituut voor Statistieken NOS: not otherwise specified P-effect: period effect PYLL: potential life years lost RR: relative risk SCMR: standardised cohort mortality ratio SIPH: Scientific Institute of Public Health SMR: standardised mortality ratio WHO: World Health Organisation WIV: Wetenschappelijk Instituut Volksgezondheid WSR: world standardised rate (based on direct standardisation using the world standard

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