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Effectiveness of and overdiagnosis from mammography screening in the Netherlands: population based study

Abstract Objective To analyse stage specific incidence of breast cancer in the Netherlands where women have been invited to biennial mammography screening since 1989 (ages 50-69) and 1997 (ages 70-75), and to assess changes in breast cancer mortality and quantified overdiagnosis. Design Population based study. Setting Mammography screening programme, the Netherlands. Participants Dutch women of all ages, 1989 to 2012. Main outcome measures Stage specific age adjusted incidence of breast cancer from 1989 to 2012. The extra numbers of in situ and stage 1 breast tumours associated with screening were estimated by comparing rates in women aged 50-74 with those in age groups not invited to screening. Overdiagnosis was estimated after subtraction of the lead time cancers. Breast cancer mortality reductions during 2010-12 and overdiagnosis during 2009-11 were computed without (scenario 1) and with (scenario 2) a cohort effect on mortality secular trends. Results The incidence of stage 2-4 breast cancers in women aged 50 or more was 168 per 100 000 in 1989 and 166 per 100 000 in 2012. Screening would be associated with a 5% mortality reduction in scenario 1 and with no influence on mortality in scenario 2. In both scenarios, improved treatments would be associated with 28% reductions in mortality. Overdiagnosis has steadily increased over time with the extension of screening to women aged 70-75 and with the introduction of digital mammography. After deduction of clinical lead time cancers, 32% of cancers found in women invited to screening in 2010-12 and 52% of screen detected cancers would be overdiagnosed. Conclusions The Dutch mammography screening programme seems to have little impact on the burden of advanced breast cancers, which suggests a marginal effect on breast cancer mortality. About half of screen detected breast cancers would represent overdiagnosis.


Mammography screening effectiveness and overdiagnosis in the Netherlands
Philippe Autier, Magali Boniol, Alice Koechlin, Cécile Pizot, Mathieu Boniol Age-period-modelling of breast cancer mortality rates in the Netherlands and the method used for projecting breast cancer mortality rates after 1994.
Table S1 -Expected and observed in situ and stage 1 breast cancer incidence rates in the Netherlands, with estimation of extra cancers when expected changes in rates follow changes of rates in women less than 50 years of age (APC = 1.61% ) Table S2 -Estimations of the extra in situ and stage 1 breast cancers in the Netherlands.  Age-period-modelling of breast cancer mortality rates in the Netherlands and the method used for projecting breast cancer mortality rates after 1994.

Methods
Analysis of time trends of breast cancer mortality rates in the Netherlands show that there was a break in mortality trends from 1994 onwards, after an uninterrupted period of moderate but steady increase from 1970 to 1994 (see article). An age-period-cohort (APC) model was applied on breast cancer mortality data spanning from 1950 to 2012, with a focus on the period 1950 to 1994 for the sake of exploring the main determinants of breast cancer mortality trends before the downward inflexion of 1994 1 . The determinants examined by APC models are the age at death, the (birth) cohort (i.e., long-term changes in genetic make-up, or changes in exposure to environmental and lifestyle factors in successive generations), and the period (i.e., changes due to the introduction of new classification of diseases or a new early-detection or diagnostic methods or of effective therapies). APC models cannot inform on the simultaneous effects of the three determinants. Hence, APC models are constructed so that each model can inform on the simultaneous effects of at least two determinants, generally, age plus cohort, or age plus period. To this end, APC models are "penalized" on the period or on the cohort effect, which means that the average period or cohort effect is constrained to be null with a null slope for the entire period of observation. In the model penalized on the period, the reference cohort was set to 1927 because it was the median birth cohort of subjects who died from breast cancer in the period 1950 to 1994. Results from APC models included age-specific effects and cohorts effects reported as relative risk with the reference cohort set to 1927 (RR = 1.00). In the model penalized on the cohort, the reference period was set to the midyear of analysed data, i.e., 1972. Results from APC models included age-specific effects and period effects reported as relative risk with a reference period set to 1970 (RR = 1.00).
The results obtained were then projected for the years 1995 to 2013 with a log-linear projection of the most recent cohort effect found in the period 1950 to 1994, with keeping stable the influence of age and without period effect. These projected mortality rates thus represent an estimate of secular trends, had trends until 2012 not been interrupted by period effects that started around 1994. Then, age-standardised mortality rates were computed from the projected data for women aged 50 and more.
Age-period-cohort models were fitted to mortality data using R 3.0.0. More precisely, the function apc.fit from the Epi 2.7 package 2 was used.

Interpretation of birth and of period effects
The summary results of the APC modelling for mortality data from 1950 to 1994 are displayed in Figures S1 and S2. The age-specific breast cancer mortality rates are referred on the left vertical axis, and the risk of breast cancer death relative to the reference years (i.e., the rate ratio) for the cohort or the period effects are referred on the right vertical axis. When the period effect is penalised, that is set to zero for the entire period ( Figure S1), significant cohort effects are apparent, with steadily greater risk of breast cancer death for the successive generations of women born from 1880 to 1923, followed by steadily decreasing risk after 1923. When the cohort effect is penalised ( Figure S2), no period effect is apparent after 1970, meaning that until 1994, screening and or therapies had no influence of breast cancer mortality trends. Of note, even though the model is penalized on the cohort, a pronounced cohort effect is observed.
The Figure S3 displays observed and projected mortality trends after 1994. The projection after 1994 (the dashed line with triangles) represents the estimation of trends if the period effect starting around 1994 (which led to marked mortality declines) had not occurred. The projected mortality rates in 2012 is about 5% lower than the rate obtained from the linear extrapolation of trends observed in 1967-95. Rates are per 100,000 women per year * Equal to (mean difference between observed and expected rates ) divided by the mean observed rates † Results used for estimation of overdiagnosis in Table 3.   Figure S4 -Trends in age-adjusted stage-specific breast cancer incidence in women of all age in the Netherlands (1989-2012) Figure S5 -Trends in age-adjusted stage-specific breast cancer incidence in women less than 50 years of age in the Netherlands