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CANCER EPIDEMIOLOGY, BIOMARKERS & PREVENTION | RESEARCH ARTICLE
Impact of Tobacco Control Policies on Smoking-Related
Cancer Incidence in Germany 2020 to 2050—A
Simulation Study
Thomas Gredner
1,2
, Tobias Niedermaier
1
, Hermann Brenner
1,3,4
, and Ute Mons
1,5
ABSTRACT
◥
Background: Germany is known for its weak tobacco control.
We aimed to provide projections of potentially avoidable
cancer cases under different tobacco control policy intervention
scenarios.
Methods: To estimate numbers and proportions of potentially
avoidable cancer cases under different policy intervention sce-
narios (cigarette price increases, comprehensive marketing ban,
and plain packaging), we calculated cancer site–specificpotential
impact fractions by age, sex, and year of study period (2020–
2050), considering latency periods between reduction in smok-
ing prevalence and manifestation in declining cancer excess
risks. To obtain estimates of future incident case numbers,
we assumed a continuation of recent smoking trends, and
combined German cancer registry data with forecasted popula-
tion sizes, published effect sizes, and national daily smoking
prevalence data.
Results: Over a 30-year horizon, an estimated 13.3% (men 14.0%
and women 12.2%) of smoking-related cancer cases could be pre-
vented if a combination of differenttobacco control policies were to
be implemented in Germany, with repeated price increases being the
most effective single policy (men 8.5% and women 7.3%). Extensive
sensitivity analyses indicated that the model is fairly robust.
Conclusions: Our results suggest that the expected cancer inci-
dence in Germany could be considerably reduced by implementing
tobacco control policies as part of a primary cancer prevention
strategy.
Impact: Our straightforward modeling framework enables a
comparison of the impact of different health policy measures. To
further accelerate the currently observed tentative trend of declining
smoking prevalence in Germany and thereby curtail smoking-
related cancer incidence, there is a great need to urgently intensify
efforts in tobacco control.
Introduction
Cancer is a major public health burden in Germany, accounting for
about 490,000 new cases and 230,000 deaths each year (1). Projections
of the numbers of future cancer cases suggest that this burden will
continue to increase mainly due to population aging (2). Despite
reductions in smoking prevalence over the past decades, smoking
remains the most important preventable cancer risk factor in Ger-
many, which is, according to the current state of knowledge, causally
associated with at least 12 different types of cancer (3, 4). Given the still
quite high rates of smoking (men: 26.4% and women: 18.6%; ref. 5) and
the fact that one of five cancers are estimated to be attributable to
smoking in Germany (6), a large proportion of cancer cases could be
prevented through determined tobacco control efforts. However,
despite the considerable smoking attributable disease burden,
Germany continues to be ranked among the most inactive countries
in Europe when it comes to implementing evidence-based tobacco
control policies. According to the most recent edition of the tobacco
control scale, Germany brings up the rear in tobacco control activity in
Europe (7).
To assess the potential of tobacco control policies in reducing the
smoking-associated cancer burden, we set out to provide projections of
potentially avoidable cancer cases under different tobacco control
policy intervention scenarios in Germany over a 30-year horizon.
In previous studies, such predictions have often been based on the
Prevent macro-simulation model (8), which calculates the effect of
changes in risk factor prevalence on cancer burden. However, the
Prevent software entails some disadvantages that naturally come along
with a menu-driven interface, such as limited flexibility and transpar-
ency with regards to model calculations. For the purpose of our study,
we thus developed a similar, yet more flexible and transparent model-
ing strategy to simulate the impact of tobacco control policy inter-
ventions on future cancer incidence based on the “potential impact
fraction”(PIF) and incorporating time effects [lag (LAG) and latency
times (LAT)] as well as demographic changes in the population.
Material and Methods
For the reference scenario under status quo policies, we estimated
the future numbers of site-specific cancer cases for 5-year age and sex
groups for the German population aged 15 years and above by
combining the following sources of data.
Trend in daily smoking prevalence
National data on the age- and sex-specific prevalence of daily
smoking were obtained from the Microcensus 2017 of the Federal
Statistical Office of Germany. The Microcensus is a representative
1
Division of Clinical Epidemiology and Aging Research, German Cancer Research
Center (DKFZ), Heidelberg, Germany.
2
Medical Faculty Heidelberg, University of
Heidelberg, Heidelberg, Germany.
3
Division of Preventive Oncology, German
Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT),
Heidelberg, Germany.
4
German Cancer Consortium (DKTK), German Cancer
Research Center (DKFZ), Heidelberg, Germany.
5
Cancer Prevention Unit,
German Cancer Research Center (DKFZ), Heidelberg, Germany.
Note: Supplementary data for this article are available at Cancer Epidemiology,
Biomarkers & Prevention Online (http://cebp.aacrjournals.org/).
Corresponding Author: Ute Mons, German Cancer Research Center, Heidelberg
69120, Germany. Phone: 49-6221-42-3007; Fax: 49-6221-42-3020; E-mail:
u.mons@dkfz.de
Cancer Epidemiol Biomarkers Prev 2020;XX:XX–XX
doi: 10.1158/1055-9965.EPI-19-1301
2020 American Association for Cancer Research.
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annual survey of one percent of German households (5). Figure 1
shows the sex-specific prevalence of daily smoking in 5-year age
groups. To project future smoking prevalence until 2050, we assumed
a continuation of recent smoking trends (2005–2015) and applied
published sex-specific annualized rates of change in smoking preva-
lence (9) to the baseline smoking prevalence of the year 2017 and
stepwise to each following year until the end of the study period.
Accordingly, we expected the smoking prevalence to be reduced by
0.6% in men and 0.5% in women, respectively, for each year of the
study period.
Forecasted population
Population forecasts by 5-year age groups and sex for the years
2020–2050 for Germany were obtained from the Federal Statistical
Office of Germany, assuming a constant trend regarding birth rates
and life expectancy, and a low net migration (10). Starting with a
German population of 81.4 million in 2020, the projected population is
steadily aging, and at the same time decreasing to 71.9 million by 2050,
reflecting the expected demographic changes in Germany.
Cancer data and RR estimates
Most recent national cancer incidence data, which were those for the
year 2016, were drawn from the German Centre for Cancer Registry
Data (ZfKD; ref. 11). We estimated the number of site-specific cancer
cases for each year of the study period (2020–2050), stratified accord-
ing to age- and sex-group, by multiplying these rates with the
corresponding age- and sex-specific population forecasts for Germany.
We used incidence data for all cancers determined as causally related
with smoking, based on the evaluations of carcinogenicity of the
International Agency for Research on Cancer (IARC; ref. 12) and
health authorities in the United States (3). The cancer site–specificRR
estimates for current smokers compared with never smokers were
taken from the U.S. Surgeon General reports (3, 13–15). If reported, the
age-specific RRs were used. All cancer sites considered in this study
with corresponding ICD-10 codes and age- and sex-specific risk
estimates are presented in Table 1.
Impact of tobacco control policies
For the policy intervention scenarios, we considered evidence-based
tobacco control policies that have been shown to be effective in terms
of reducing the prevalence of smoking. As such, we focused on those
tobacco control policies that are embedded in the World Health
Organization (WHO) Framework Convention on Tobacco Con-
trol (16), but which are currently not fully implemented in Germany
(see also the most recent WHO report on the global tobacco epidemic;
ref. 17, for more details on the current state of tobacco control in
Germany). By screening the literature, we identified the effect sizes of
the corresponding interventions on the prevalence of smoking from
pertinent reviews or meta-analyses.
Cigarette price regulation
The effect of changes in cigarette price on consumption can be
measured by price elasticities. A recent study estimated cigarette price
elasticity as 0.503 [95% confidence interval (CI), 0.291 to 0.715]
for high-income countries in Europe (18). Accordingly, an increase by
10% in cigarette prices would be expected to reduce cigarette con-
sumption by 5.03%. On the basis of findings of studies examining the
effect of price increases on both smoking prevalence and intensity, it is
assumed that smoking prevalence is reduced by about half those
rates (19, 20). For our model, we therefore assumed that for each
10% increase in price, a relative reduction of 2.5% in the prevalence of
daily smoking occurs.
Comprehensive marketing ban
The empirical evidence shows that a comprehensive advertising
ban applied to all media can substantially decrease tobacco con-
sumption. In this context, a study (21) of 22 high-income countries
concluded that while partial advertisement bans have little or no
Figure 1.
Prevalence of daily smoking among men and women in 5-year age groups in the German population from the Microcensus 2017.
Gredner et al.
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effect, a comprehensive marketing ban could reduce tobacco con-
sumption by up to 7.4%. The Germany SimSmoke model (22)
assumed that with a comprehensive marketing ban in Germany,
smoking prevalence is expected to be reduced by 5%. For our model,
we likewise assumed that a comprehensive marketing ban would
lead to a reduction in the smoking prevalence by 5% in the first year
of implementation.
Plain packaging
Plain packaging is a demand-reduction measure that has been
shown to be effective in reducing smoking prevalence indepen-
dently from health warning labels, which are usually printed
prominently on plain tobacco packs. In Australia, the introduction
of plain packaging was followed by a significant decline in smoking
prevalence: a study (23) evaluating the effectiveness of the tobacco
plain packaging measure found a reduction in smoking prevalence
of 3.7% (95% CI, 1.1–6.2) in the first year after implementation
followed by additional annual declines of 1.7% (95% CI, 1.3–2.2).
For our model, we applied these effect sizes to the German
population and assumed the implementation of plain packaging
would result in a 3.7% decline in smoking prevalence within
the first year and an annual 1.7% decrease over the following
4 years, as the effects of plain packaging are expected to wear out
after a few years.
Overall, we modeled five different intervention scenarios that were
set to start in 2020. An overview of the different investigated scenarios
and corresponding effect on smoking prevalence is shown in Table 2.
When calculating the combined effect of intervention scenarios, we
expected the different tobacco control policies to influence smoking
prevalence independently.
Change in cancer risk
The time that elapses between the removal of a cancer risk factor and
the full manifestation of the resulting decline in cancer excess risk is
modeled using the concept of LAT and LAG (8, 24). LAT is the time the
cancer risk remains constant until changes in exposure to the cancer
risk factor start being reflected in cancer risk. LAG is the time taken for
the risk among previously exposed persons to reduce to the level of
unexposed persons.
The likelihood of developing cancer as well as the decline of
the excess risk after smoking cessation depends on a variety of
smoking-related factors, such as the intensity and duration of smoking
over the life course, as well as the age at smoking cessation. Although it
is difficult to determine a universally valid LAT irrespective of these
factors for all cancer sites, we defined, in analogy with previous
modeling studies (8, 25) and based on existing evidence (26), the LAT
to be 5 years and the LAG to be 15 years assuming a log linear decline in
cancer risk.
Statistical analysis
Similar to the mathematical calculations in Prevent, we
used a simulation modeling based on the epidemiologic measures
“trend impact fraction”(TIF)andPIF(8).TheTIFandPIFderive
a proportional change in cancer risk from a change in risk
factor exposure due to an autonomous trend or an intervention,
respectively, and the RR of the association of that risk factor with
cancer.
To obtain the number of cancer cases in the reference scenario
taking into account the autonomous development of smoking
prevalence, the TIF was calculated for each age, sex, year of
study period, and smoking-related cancer site using the following
equation (27):
TIFi¼Pn
c¼1pcRRcPn
c¼1p
cRRc
Pn
c¼1pcRRc
where p
c
is the proportion of the age-, sex-, and period-specific
population in risk factor category c;RR
c
is the corresponding time-
dependent and cancer site–specific RR for that category; and p
c
is the altered proportion in category ctaking into account the
autonomous trend. The TIF is applied to the corresponding
number of predicted cancer cases, derived by multiplying the most
recent cancer incidence rates with the forecasted population sizes,
to estimate the future number of cancer cases in the reference
scenario.
Subsequently, the number of cancer cases prevented by a specific
intervention was calculated using the analogous equation for the PIF:
PIFi¼Pn
c¼1pcRRcPn
c¼1p
cRRc
Pn
c¼1pcRRc
where p
c
is now the altered smoking prevalence in risk factor
category cdue to the intervention. In analogy to the TIF estimates,
Table 1. RRs for smokers versus never smokers for smoking-related cancers, by sex and age group.
Cancer site (ICD-10) <54 years 55–64 years 65–74 years ≥74 years Reference(s)
Men
Lip, pharynx, and oral cavity (C00–C14) 10.9 10.9 10.9 10.9 Parkin and colleagues, 2010 (15);
U.S. Department of Health and Human Services,
2004 (13)
Esophagus (C15) 6.8 6.8 6.8 6.8
Larynx (C32) 14.6 14.6 14.6 14.6
Trachea, bronchus, and lung (C33–C34) 14.33 19.03 28.29 22.51 Thun and colleagues, 2013 (14); U.S. Department
of Health and Human Services, 2014 (3)Other smoking-related cancers
a
1.74 1.86 2.35 2.18
Women
Lip, pharynx, and oral cavity (C00–C14) 5.1 5.1 5.1 5.1 Parkin and colleagues, 2010 (15);
U.S. Department of Health and Human Services,
2004 (13)
Esophagus (C15) 7.8 7.8 7.8 7.8
Larynx (C32) 13.0 13.0 13.0 13.0
Trachea, bronchus, and lung (C33–C34) 13.30 18.95 23.65 23.08 Thun and colleagues, 2013 (14); U.S. Department
of Health and Human Services, 2014 (3)Other smoking-related cancers
a
1.28 2.08 2.06 1.93
a
Other cancers include cancers of the stomach (C16), colon and rectum (C18–C20), liver (C22), pancreas (C25), cervix uteri (only among women; C53), kidney and
renal pelvis (C64–C65), bladder (C67), and acute myeloid leukemia (C92).
Impact of Tobacco Control on Cancer Incidence in Germany
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the PIF is age-, sex-, and cancer site–specific and is calculated for each
year of study period. By applying the cancer site–specific PIFs for each
hypothetical intervention scenario to the future number of cancers
under the reference scenario, we then estimated the number of future
cancer cases that would be expected under the corresponding scenario
for each age group, sex, cancer site, and period.
To calculate the combined incidence for all smoking-related cancer
sites that would be expected under a hypothetical intervention scenario
(Inc
all
), the cancer site–specific TIFs and PIFs were cumulated using
the following formula:
Incall ¼X
n
i¼1
Inc0;i1TIFi
ðÞ1PIFi
ðÞ
where Inc
0,i
is the predicted cancer site–specific baseline incidence and
TIFiand PIFiis the corresponding cancer site–specific TIF or PIF,
respectively.
With the reference scenario reflecting autonomous trends and the
intervention scenario reflecting the potential impact of the interven-
tion, the difference in cancer incidence between both scenarios can be
attributed to the intervention.
Sensitivity analyses
To deal with uncertainty in the modeling assumptions, sensitivity
analyses were conducted modeling a linear decrease in cancer risk and
using different periods of LAG and LAT (see Supplementary Data S2
for more details). In addition, sensitivity analyses were run based on
the lower limit of the corresponding 95% CIs, and the upper limit,
respectively, of the effect estimate of each tobacco policy intervention.
For the scenario of a comprehensive marketing ban, we used alter-
native effect estimates of 2.5% and 7.5%, respectively, as no CI limits
were reported.
All analyses were performed using the statistical software R version
3.5.2 (28).
Data availability
All data used in this research are publicly available (3, 5, 10, 11, 13)
and these, as well as the analysis script, can be obtained upon
reasonable request from the corresponding author.
Results
Prevalence of daily smoking in Germany for the years 2020–2050 for
the reference scenario assuming a continuation of recent smoking
trends, and under different policy intervention scenarios are shown
in Fig. 2. Compared with the baseline smoking prevalence in 2017
(men 22.3% and women 15.3%), we projected the proportion of daily
smokers in the German population in the reference scenario to decline
to 14.8% in men and 10.2% in women by 2050 assuming a continuation
of the previous decreasing trend in smoking prevalence. In the scenario
of all tobacco control policies combined, in contrast, the smoking
prevalence was projected to decline to 9.7% in men and 6.7% in
women.
The estimated number of preventable cancer cases in Germany for
all smoking-related cancers under the reference scenario and each
policy intervention scenario is shown in Fig. 3. Over a 30-year period,
an estimated 14.0% of smoking-related cancer cases in men and 12.2%
in women could be prevented, if a combination of the observed tobacco
Table 2. Investigated tobacco control policies, description, and effect on smoking prevalence.
Sensitivity analysis
Tobacco control
policy Description
Assumed effect on
smoking prevalence
(base case) Effect worst case Effect best case Reference(s)
Single cigarette price
increase
10% price increase in cigarettes
in 2020 (e.g., through tax
increase)
2.5% reduction in
2020
1.5% reduction in 2020 3.6% reduction in 2020 Yeh and
colleagues,
2017 (18); IARC,
2012 (19)
Repeated cigarette
price increases
Annual 10% price increase in
cigarettes for 10 years
(2020–2029; e.g., through
tax increases)
Annual 2.5%
reduction from
2020 to 2029
Annual 1.5% reduction
from 2020 to 2029
Annual 3.6% reduction
from 2020 to 2029
Yeh and
colleagues,
2017 (18); IARC,
2012 (19)
Comprehensive
marketing ban
Introduction of a complete
advertising ban in 2020
applied to all kind of media
5% reduction in 2020 2.5% reduction in 2020
(by assumption)
7.5% reduction in 2020
(by assumption)
Levy and
colleagues, 2012
(22); Saffer and
colleagues,
2000 (21)
Plain packaging Implementation of plain
packaging in 2020
3.7% reduction in
2020; 1.7%
reduction from 2021
to 2024
1.1% reduction in 2020;
1.3% reduction from
2021 to 2024
6.2% reduction in 2020;
2.2% reduction from
2021 to 2024
Diethelm and
colleagues,
2015 (23)
All combined A combination of tobacco
control policies: repeated
cigarette price increase,
comprehensive marketing
ban, and plain packaging
11.2% reduction in
2020; 4.2%
reduction from 2021
to 2024; 2.5%
reduction from
2025 to 2029
5.1% reduction in 2020;
2.8% reduction from
2021 to 2024; 1.5%
reduction from 2025
to 2029
17.3% reduction in 2020;
5.8% reduction from
2021 to 2024; 3.6%
reduction from 2025
to 2029
Gredner et al.
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control policy interventions were to be implemented in Germany.
Compared with a scenario assuming constant cancer incidence rates
and continuously decreasing smoking prevalence, these proportions
would correspond to a reduction of approximately 685,000 cancer
cases in men and 372,000 cases in women (Table 3). The most effective
single intervention was estimated to be annual 10% price increases in
cigarettes over 10 years, which may prevent about 8.5% of smoking-
related cancer cases in men and 7.3% in women. Implementation of
plain packaging was estimated to reduce the burden of incident cancer
cases by 4.4% in men and 3.8% in women and a comprehensive
marketing ban applied to all forms of tobacco advertisement was
estimated to prevent about 2.4% and 2.1% cancer cases in men and
women, respectively. If a single 10% price increase were to be enforced,
the resulting reduction in the burden of smoking-related cancers was
predicted to be 1.2% in men and 1.0% in women, which still translated
to approximately 90,000 cases (58,000 cases among men and
32,000 cases among women).
The cancers with the greatest proportion of potentially preventable
cancers were estimated to be lung cancer (20.5%) for both sexes
accounting for about 38.3% of all potentially preventable cases,
followed by cancer of the larynx (19.4%), the oral cavity (17.7%), and
the esophagus (17.4%). In addition, lung cancer has the highest
number of potentially avoidable cancers with approximately
405,000 cases for all policy interventions combined over the 30-year
period. Graphical representations of the estimated number of pre-
ventable cancer cases for each cancer site are shown in Supplementary
Data S1.1–S1.12.
The absolute number of potentially preventable cancer cases was
greater among men than women, with higher PIFs for all cancer sites.
When looking at the effect of different tobacco control policy inter-
ventions combined, PIFs ranged from 10.0% to 20.7% in men and from
7.0% to 20.2% in women.
Sensitivity analyses
Sensitivity analyses using the 95% confidence limits of effect
estimates on the impact of tobacco control policies indicated a
potential range of 8.0%–18.0% of preventable cancer cases for the
combined effect of all policy interventions (Supplementary Data S2.1)
indicating that the results are fairly robust within the applied effect
range.
As would be expected, the sensitivity analysis modeling a linear
decrease in cancer risk (Supplementary Data S2.2) yielded slightly
lower estimates of proportion and number of potentially preventable
cancer cases as the linear risk function implicates that the reduction of
the excess risk would occur at a slower rate.
On the contrary, the sensitivity analyses varying LATs and
LAGs indicated a potential range of 8.4%–16.3% of preventable
cancer cases (Supplementary Data S2.3). Despite the fact that the
proportion of potentially preventable cancers is essentially the
same in all scenarios after the full decline in excess cancer risk
occurred, the projections are particularly dependent on the
latency assumptions of the model. In the conservative scenario,
when using a LAT of 10 years and a LAG of 20 years, it would
take until the final year of the study period until the full impact
Figure 2.
Proportion of daily smokers in the
German population over a 30-year
period (2020–2050) under the refer-
ence scenario (trend) and different
tobacco control policy intervention
scenarios, stratified by sex.
Impact of Tobacco Control on Cancer Incidence in Germany
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Figure 3.
Total number of cancer cases (Aand B) and number of potentially preventable cancer cases (Can d D) for cancers causally linked to smoking under different tobacco
control policy scenarios over a 30-year period (2020–2050), stratified by sex.
Gredner et al.
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of the tobacco control policies would be reflected in cancer
incidence.
Discussion
In this study, we simulated the change in future smoking-related
cancer cases associated with a reduction in daily smoking prevalence
due to different tobacco control policies. Our results suggest that the
burden of smoking-related cancer cases could be considerably reduced
in Germany over a 30-year horizon even with small reductions in the
prevalence of daily smoking as a result of a single increase in cigarette
prices, for example, through a tax hike. If a combination of the
investigated evidence-based tobacco control policy interventions were
to be implemented in Germany in 2020, that is, plain packaging, a
comprehensive marketing ban, and repeated strong tax increases over
10 years, we estimated that about 14.0% of smoking-related cancer
cases in men and 12.2% in women could be prevented by 2050.
Compared with a scenario assuming constant cancer incidence rates
and continuously decreasing smoking prevalence, this would translate
to a reduction of the future burden of incident cancers in Germany by
approximately 1,057,000 cancer cases.
Similar to other simulation studies (8, 25, 29), the modeling
approach used in our study is based on the PIF estimating counter-
factual-based effects of changing risk factor prevalence. In this meth-
odologic framework, the calculations are predicated on the assumption
that there is a causal relationship between exposure to a cancer risk
factor and the occurrence of cancer, and that the only difference
between the reference scenario and each intervention scenario is
because of the altered prevalence level.
In our modeling, the projected number of future incident cancer
cases in the reference scenario was on the basis of the predicted
population projections and most recent available cancer incidence
rates for Germany. The coverage of cancer incidence data is very
heterogeneous across the different German federal states; however,
since 2009, Germany has achieved nationwide coverage of population-
based cancer registration. Assuming constant incidence rates over the
study period, changes in numbers of cancers reflect the demographic
changes in the German population and the autonomous trend in
smoking prevalence. However, this simple prediction approach dis-
regards cancer site–specific trends in incidence rates beyond these
factors. For example, reflecting the differential progression of the
tobacco epidemic, an increase in lung cancer incidence rates is
expected for women, while a considerable decline is predicted
for men (2). Although large variation can also be seen by cancer type,
there is evidence that overall standardized cancer incidence rates in
Germany will increase by 5% over the next decade (2), which makes
our estimates of the number of preventable cancer cases likely to be
underestimated.
Generally, while the epidemiologic simulation framework devel-
oped and used for this study allows the comparison of the impact of
different health interventions on cancer burden by using only few
different data sources, it should not be considered a reliable prediction
Table 3. Estimated proportion and number of smoking-related cancer cases preventable by different tobacco control policies over a
30-year period (2020–2050) in the German population, stratified by sex.
Total (N) and relative (%) number of preventable cancer site–specific cases per scenario
Single price
increase þ10%
Repeated price
increase þ10% 10
Comprehensive
marketing ban
Plain
packaging All combined
a
Cancer site (ICD-10)
Expected
cancer
cases N%N%N%N%N%
Men
Lip, pharynx, and oral cavity (C00–C14) 307,978 4,840 1.6 34,304 11.1 9,622 3.1 17,922 5.8 56,837 18.5
Esophagus (C15) 187,335 2,749 1.5 19,487 10.4 5,465 2.9 10,185 5.4 32,289 17.2
Stomach (C16) 344,788 3,032 0.9 21,417 6.2 6,029 1.7 11,232 3.3 35,550 10.3
Colon and rectum (C18–C20) 1,194,097 10,565 0.9 74,508 6.2 21,005 1.8 39,118 3.3 123,749 10.4
Liver (C22) 226,333 1,994 0.9 14,020 6.2 3,964 1.8 7,376 3.3 23,314 10.3
Pancreas (C25) 338,935 3,003 0.9 21,164 6.2 5,971 1.8 11,117 3.3 35,161 10.4
Larynx (C32) 103,482 1,706 1.6 12,165 11.8 3,391 3.3 6,331 6.1 20,109 19.4
Trachea, bronchus, and lung (C33–C34) 1,264,094 22,090 1.7 159,123 12.6 43,916 3.5 82,297 6.5 262,041 20.7
Kidney and renal pelvis (C64–C65) 345,041 2,946 0.9 20,629 6.0 5,858 1.7 10,880 3.2 34,357 10.0
Bladder (C67) 477,229 4,305 0.9 30,533 6.4 8,559 1.8 15,973 3.3 50,603 10.6
Acute myeloid leukemia (C92) 111,621 968 0.9 6,839 6.1 1,925 1.7 3,587 3.2 11,352 10.2
Total 4,900,933 58,198 1.2 414,189 8.5 115,704 2.4 216,018 4.4 685,362 14.0
Women
Lip, pharynx, and oral cavity (C00–C14) 133,706 1,809 1.3 12,767 9.5 3,595 2.7 6,690 5.0 21,189 15.8
Esophagus (C15) 59,669 906 1.5 6,454 10.8 1,802 3.0 3,364 5.6 10,677 17.9
Stomach (C16) 208,253 1,628 0.8 11,498 5.5 3,237 1.6 6,031 2.9 19,087 9.2
Colon and rectum (C18–C20) 929,496 7,337 0.8 51,753 5.6 14,586 1.6 27,164 2.9 85,945 9.2
Liver (C22) 97,069 764 0.8 5,371 5.5 1,518 1.6 2,824 2.9 8,929 9.2
Pancreas (C25) 330,407 2,642 0.8 18,619 5.6 5,252 1.6 9,779 3.0 30,931 9.4
Larynx (C32) 15,842 255 1.6 1,806 11.4 506 3.2 943 6.0 2,991 18.9
Trachea, bronchus, and lung (C33–C34) 704,444 12,063 1.7 86,288 12.2 23,982 3.4 44,819 6.4 142,470 20.2
Cervix uteri (C53) 129,655 779 0.6 5,403 4.2 1,548 1.2 2,864 2.2 9,027 7.0
Kidney and renal pelvis (C64–C65) 200,501 1,544 0.8 10,814 5.4 3,070 1.5 5,702 2.8 18,008 9.0
Bladder (C67) 156,478 1,250 0.8 8,838 5.6 2,485 1.6 4,632 3.0 14,664 9.4
Acute myeloid leukemia (C92) 89,095 668 0.7 4,703 5.3 1,328 1.5 2,471 2.8 7,816 8.8
Total 3,054,615 31,645 1.0 224,314 7.3 62,909 2.1 117,283 3.8 371,734 12.2
a
The “All combined”scenario comprises a combination of a repeated cigarette price increase, a comprehensive marketing ban, and plain packaging.
Impact of Tobacco Control on Cancer Incidence in Germany
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tool for future cancer incidence, as only a simple forward prediction
was applied. The purpose of our simulation model was not to provide
valid predictions of the future cancer incidence in Germany, but rather
to model the difference in proportions and numbers of preventable
cancer cases under different scenarios of changing smoking prevalence
when other factors remain unchanged. Given our long study period
and the limited availability of historic incidence data, the inclusion of
more detailed cancer incidence predictions would have required
further modeling assumptions and correspondingly would have intro-
duced further uncertainties (30, 31). However, because the PIF is not
sensitive to changes in cancer incidence rates, only the predictions of
absolute case numbers but not of the proportions of potentially
avoidable cancer cases would be affected by other assumptions regard-
ing future incidence rates.
To incorporate an autonomous trend in smoking prevalence, we
assumed recent trends in smoking prevalence (2005–2015) to continue
until the end of the study period without considering an attenuation
effect in prevalence decline, again potentially contributing to an
underestimation of the number of preventable cancer cases. On the
other hand, different external factors, such as a potential widespread
uptake of alternative nicotine products, could accelerate declining
trends in smoking prevalence and thus lead to an overestimation of the
number of preventable cancer cases.
To simulate the potential impact of tobacco control polices on
health outcomes, a lot of different statistical and computational
modeling methods have been used in previous international stud-
ies (32). For Germany, for example, the SimSmoke model has been
used to quantify the effect of tobacco control policies on future
smoking prevalence and smoking attributable deaths (22). However,
only few studies investigated the impact of changing smoking prev-
alence on future cancer incidence (8, 25, 33–36). A study (25) of Nordic
countries using the Prevent macro-simulation model to estimate the
future number of cancer cases under different counterfactual scenarios
found that proportion of smoking-related cancers preventable by a
combination of different country-specific interventions ranged
between 6.7% and 10.6%. Overall, our estimates of preventable cancer
cases are higher than those reported for the Nordic countries, but
because not exactly the same tobacco control policies were considered,
a direct comparison is difficult. Country-specific differences in the
prevalence of smoking as well as the use of more recent risk estimates in
our study could be further explanatory factors for differences in results.
However, the estimates for lung cancer are very similar indicating that
about 20% of lung cancer cases could be prevented by a combination of
country-specific tobacco control policies.
Limitations and strengths
Our results are based on several assumptions that inherently bring
along some limitations and result in a simplification of the complex
reality of cancer occurrence. For the selection of the hypothetical
intervention scenarios, we focused on tobacco control policies embed-
ded in the Framework Convention for Tobacco Control to investigate
the impact of scenarios that best match recommendations of already
existing cancer prevention programs.
Our simulations were restricted to interventions for which estimates
of the impact on the prevalence of smoking were available. Other
effective tobacco control policies such as the implementation of
comprehensive smoke-free legislation including all public places could
not be taken into account, because a partial implementation is already
in place in Germany.
To quantify the change in smoking prevalence resulting from price
changes, for example, through tax increases, we used price elasticities.
However, we were not able to consider potential substitution effects
accompanying cigarette price increases by using cross-elasticities of
demand for other tobacco products. For the scenario of repeated
cigarette price increases, we assumed the price elasticity to be constant
over time, although there might be an attenuation effect in the
corresponding decrease in smoking prevalence.
When calculating the potential impact for a combination of tobacco
control policies, we assumed these policies to independently affect
smoking prevalence, because no information was available on the
magnitude of effect sizes for combinations of policies. Because both a
synergetic or attenuating effect could be possible, the estimated impact
for this scenario may be under- or overestimated, respectively.
Furthermore, we did not take into account the impact of tobacco
control policies on occasional smokers as well as indirect effects on
second-hand smoke exposure due to a decline in smoking, even though
second-hand smoke also contributes to the cancer burden in
Germany (37).
In our simulations, we focused on effects of tobacco control policies
on smoking prevalence in the adult populations, although there might
be even stronger effects on smoking prevalence among youths. In New
Zealand (38), a large decline in the proportion of youth ever and daily
smokers was observed after the introduction of an annual increase in
tobacco excise by at least 10% since 2010. Generally, we assumed the
selected effect estimates to influence the prevalence of daily smoking
homogenously across sex and age groups in the whole German
population. As smoking is highly addictive, tobacco control policies
such as marketing bans or plain packaging might affect long-term
smokers to a lesser extent. In contrast, younger smokers are more
responsive to price increases for tobacco products as well as more
susceptible to tobacco advertising (39, 40).
Finally, to take into account the delay in effects of tobacco control
policies on cancer incidence, we included in our model lag and
latency periods from the intervention to the complete decline in
cancer excess risk. In agreement with previous studies, we set these
time shifts at 20 years in total (8, 25). It is, however, important to
note that there is evidence for an elevated cancer risk even beyond
this period, in particular with regard to lung cancer (14, 26). In
extensive sensitivity analyses, we compared different simulation
scenarios by varying the magnitude of LAG and LAT, the effect
estimates, and using a linear decline in cancer risk to deal with
uncertainty in assumptions. They illustrate that, depending on the
true length of latency, the full effect of tobacco control policies
could take several years to emerge. However, our findings show the
considerable potential of tobacco control policies in reducing the
smoking-related cancer burden in any case and underline the need
for urgent efforts.
This is the first modeling study to provide estimates of the impact
of different tobacco control policies on future smoking-related
cancer incidence in Germany using nationally representative prev-
alence data on daily smoking, latest RR estimates from cohort
studies, as well as most recent population projections and cancer
registry data. Our straightforward modeling framework enables a
comparison of the impact of different health policy measures and
thereby contributes to a better understanding of the importance of
tobacco control for primary cancer prevention. Such data could well
be used to underpin advocacy efforts to strengthen tobacco control
in Germany and beyond.
Conclusions
Our results suggest that the expected cancer incidence in Germany
could be considerably reduced by implementing tobacco control
Gredner et al.
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policies as part of a primary cancer prevention strategy. To further
accelerate the currently observed tentative trend of declining smoking
prevalence in Germany, there is a great need to urgently intensify
efforts in tobacco control. This study illustrates that introducing
proven-to-be-effective measures such as plain packaging, a compre-
hensive marketing ban, and repeated annual tax increases in Germany
have the potential to avoid a tremendous amount of cancer cases over a
30-year horizon.
Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were disclosed.
Authors’Contributions
Conception and design: T. Gredner, H. Brenner, U. Mons
Development of methodology: T. Gredner, H. Brenner, U. Mons
Analysis and interpretation of data (e.g., statistical analysis, biostatistics,
computational analysis): T. Gredner, H. Brenner, U. Mons
Writing, review, and/or revision of the manuscript: T. Gredner, T. Niedermaier,
H. Brenner, U. Mons
Study supervision: H. Brenner, U. Mons
Acknowledgments
The study was funded by the German Cancer Aid (“Deutsche Krebshilfe”), grant
number 70112097.
The costs of publication of this article were defrayed in part by the payment of page
charges. This article must therefore be hereby marked advertisement in accordance
with 18 U.S.C. Section 1734 solely to indicate this fact.
Received October 18, 2019; revised January 22, 2020; accepted April 24, 2020;
published first May 23, 2020.
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Study A Simulation−−Cancer Incidence in Germany 2020 to 2050 Impact of Tobacco Control Policies on Smoking-Related
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