Estimating the Potential Population Health Impact of Authorizing … · 2020-01-27 · increased...
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Estimating the Potential Population Health Impact of Authorizing the Marketing of E-cigarettes in the US 1 2 3 Research, Development & Sciences, Consumer & Marketplace Insights, Regulatory Affairs Altria Client Services LLC, 601 East Jackson Street, Richmond, VA 23219, USA SRNT 24th Annual Meeting, February 21 - 24, 2018, Baltimore, MD, USA 1 1 2 1 2 3 1 Raheema Muhammad-Kah , Thaddeus Hannel , Lai Wei , Ryan Black , Thomas Bryan , Maria Gogova , Yezdi B.Pithawalla This poster may be accessed at www.altria.com/ALCS-Science No conflict of interest Computational models can be used to assess the likely impact of introducing a new tobacco product on the U.S. population. We have developed and validated an Agent-Based model using best modeling practices, as recommended by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making. Our model quantitatively integrates an estimate of the relative risk of e-cigarettes (compared to conventional cigarette) and a range of potential changes in behavioral patterns to assess different scenarios to estimate the likely impact of introducing a new tobacco product on the population as a whole. The results are presented as estimated changes in tobacco use prevalence and all-cause mortality by assessing the difference in number of survivors comparing a Base Case (the current market, where cigarettes are the predominately used tobacco product) and a Modified Case (a new market where cigarettes and e-cigarettes are both available). Statistical models combined with likely excess relative risks (ERRs) were used to determine survival probabilities of current and former e-cigarette users. Nationally representative transition probabilities were obtained for the Base Case. The Modified Case transitions probabilities were estimated from a case study based on Wave 1 and Wave 2 data from the Population Assessment of Tobacco and Health (PATH) study which was designed to assess tobacco use transitions of non-users and established tobacco users. Employing these transition probabilities and an ERR value of 0.05 for e-cigarette use, we demonstrate a net benefit to the population through a reduction in mortality of ~ 600,000 lives , along with a reduction in cigarette smoking prevalence over a follow-up period of 60 years. Sensitivity analysis, varying the risk of e-cigarettes, a key input parameter, showed a decrease in the reduction in mortality as the ERR of e-cigarettes increased (i.e. ERRs (0.025 to 0.4) resulted in ~650,000 to 270,000 lives respectively). Population models are tools that can be used to predict the public health impact of changes in use of tobacco products with varying level of inherent risk on the population as a whole. Abstract Ÿ Computational models can be used to predict the likely impact of introducing a new tobacco product on the U.S. population. Ÿ We have developed and validated a population model using best modeling practices to assess the overall population effects following a market authorization of an E-cigarette. Ÿ In our model, agents form a hypothetical population in which they transit between different tobacco use states using defined transition probabilities. The set of attributes associated with each agent (e.g., age, gender, current use status, and tobacco use history) determines the path an agent may consider. Statistical mortality models combined with excess relative risks are used to determine the survival probabilities of an agent at each time step. Introduction Methods Ÿ Mortality: - Tobacco-related mortality was estimated from a Kaiser Permanente Medical Care Program Cohort study and adjusted for U.S. mortality rates in the year 2000 using data from the Human Mortality Database (HMDB). - Excess relative risk (ERR) of E-cigarettes use compared to smoking was 0.05 ( i.e. a 95% reduction in risk compared to mortality risk of cigarettes) as determined by Nutt et al. Ÿ Base Case Transitions: Reflects the 2000 US population in terms of age, gender, and smoking status and age & gender specific cigarette transition probabilities (i.e. Initiation & Cessation) (NHIS & CISNET). Ÿ Modified Case Transitions: Transitions were estimated from a case study based on Wave 1 and Wave 2 data from the Population Assessment of Tobacco and Health (PATH) study which was designed to assess tobacco use transitions of non-users and established tobacco users (i.e. cigarette & E-cigarette users). Ÿ The population is updated on a yearly basis over a 60 year time frame through birth and immigration (US Census). Inputs Ÿ Dual use (cigarette & E-cigarette use) were assigned the same mortality risk of exclusive cigarette use Ÿ Transition probabilities for relapse behaviors are set to zero in the Base case, since cessation rates reflect successful smoking cessation for at least two years (CISNET) Assumptions Ÿ Modified Case Scenario: the effect of all the most-likely changes in tobacco use patterns occur simultaneously (Table 1) Ÿ Sensitivity Analyses: evaluating the effects of varying input parameters on the net population Analyses Table 1: Input Parameters, Source and Assumptions in the Modified Case Scenario Parameter Transition Source Assumptions Proportion of never tobacco user who initiate cigarette smoking NT à CS PATH Analysis Proportion of never tobacco user who initiate Ecig use NTà ECIG PATH Analysis Proportion of cigarette smokers who cessate (quit smoking) on an annual basis CS à FS CISNET Represents long term cessation with no relapse Proportion of cigarette smokers who initiate Ecig use and quit smoking on an annual basis CS à ECIG-FS PATH Analysis Proportion of cigarette users who transition to dual product use on an annual basis CS à DU PATH Analysis Proportion of Ecig users who cessate (quit Ecig use) on an annual basis ECIGà FECIG CISNET Long-term e-cigarette cessation would be similar to cigarette cessation Proportion of Ecig users who initiate cigarette smoking and quit Ecig use on an annual basis ECIGà CS-FECIG PATH Analysis Proportion of Ecig users who transition to dual product use on an annual basis ECIG à DU PATH Analysis Proportion of former cigarette smokers who initiate Ecig use on an annual basis FSà ECIG-FS PATH Analysis Proportion of Dual users who quit smoking cigarettes and remain Ecig users DUà ECIG-FS CISNET Cessation of cigarettes when dual using occurs at the same rate as exclusive cigarette cessation Proportion of Dual users who quit Ecig use and remain cigarette smokers DU à CS-FECIG CISNET Cessation of Ecig when dual using occurs at the same rate as exclusive cigarette cessation Proportion of Ecig users former cigarette smokers who quit all tobacco use. ECIG-FS à FDU CISNET Cessation of the Ecig with former use of cigarettes occurs at the same rate as exclusive cigarette cessation Proportion of cigarette smokers former Ecig users who quit all tobacco use. CS-FECIG àFDU CISNET Cessation of cigarettes with former use of the Ecig occurs at the same rate as exclusive cigarette cessation E-cigarette ERR is set to 5% , E-cigarette are introduced into the model in 2009, Long term cessation with no relapse- reflect successful smoking cessation for at least two years Difference in tobacco use prevalence & all-cause mortality: Ÿ Base Case (status quo; the current market, where cigarettes are the predominately used tobacco product) & Ÿ Modified Case (where cigarettes and E-cigarettes are both available on the market) Outputs Figure 1: Transition Probabilities Value Under the Modified Case Scenario FDU CS- FECIG ECIG- FS CS ECIG NT FS DU FECIG CS à DU 12-17 15.5% 18-24 6.35% 25-44 5.43% 45-65 2.39% CS à ECIG-FS 12-17 2.49%* 18-24 1.65% 25-44 1.58% 45-64 0.58% ECIG à CS-FECIG 12-17 21.1% 18-24 12.7%* ECIG à DU 12-17 6.44% 18-24 8.70%* FS à ECIG-FS 18-44 0.98% NT à CS 12-17 15.6% 18-24 10.7% 25-30 0.7% NT à ECIG 12-17 1.46% 18-24 1.49% Permanent Cessation Age M F 12 -17 1.0% 0.8% 18 - 24 3.0% 3.9% 25 - 44 3.4% 3.0% 45 - 64 3.5% 3.9% 65 + 7.6% 6.5% Permanent Cessation Permanent Cessation Permanent Cessation Permanent Cessation Permanent Cessation Permanent Cessation NT = Never-Use of Tobacco CS = Current Cigarette Smoker FS = Former Cigarette Smoker ECIG= E-cigarette User FECIG = Former E-cigarette User CS-FECIG = Current Cigarette Smoker Former E-cigarette User DU = Dual User FDU = Former Dual User ECIG-FS = E-cigarette User Former Smoker Statistical Suppression Rule *Estimates are reported as statistical unreliable if the estimates have coefficient of variation greater than or equal to 30 but less than 50. Estimates are suppressed if the estimates are based on a sample size of less than 50, or coefficient of variation >=50. Permanent Cessation= Long term cessation without relapse (i.e. reflects successful smoking cessation for at least two years) Results Conclusions Ÿ Our model suggests overall net benefit of introducing E-cigarette into the US market, as indicated by likely reduction in smoking prevalence and a decreased mortality (632,000 deaths prevented) in the US population compared to the status quo. Ÿ The sensitivity analysis indicates that relatively large changes to the ERR of an E-cigarette and cigarette smokers switching to E-cigarette predict a net benefit to the population under our defined scenario. Ÿ As more real-world data on smoking/E-cigarette transition, initiation and cessation rates become available, we will further refine our assumptions to confirm a net impact of E-cigarette on the population as a whole. References Ÿ PATH Study Public Use Files: User Guide. www.icpsr.umich.edu/icpsrweb/NAHDAP/studies/36498. Accessed September 5, 2017. Ÿ Anderson, C. M., Burns, D. M., Dodd, K. W., & Feuer, E. J. (2012). Chapter 2: Birth-cohort-specific estimates of smoking behaviors for the U.S. population. Risk Anal, 32 Suppl 1, S14-24. doi:10.1111/j.1539-6924.2011.01703.x Ÿ Friedman G., Tekawa I.S., Sadler M. and Sidney S. (1997). Smoking and Mortality: the Kaiser Permanente Experience. In Changes in Cigarette-Related Disease Risks and Their Implication for Prevention and Control. Shopland D.R., Burns D.M., Garfinkel L. and Samet J., eds. US Department of Health and Human Services, Public Health Service, National Institutes of Health, National Cancer Institute. pp. 477-499. Ÿ Nutt, D. J., Phillips, L. D., Balfour, D., Curran, H. V., Dockrell, M., Foulds, J., . . . Sweanor, D. (2014). Estimating the harms of nicotine- containing products using the MCDA approach. Eur Addict Res, 20(5), 218-225. doi:10.1159/000360220 Figure 2: Impact of Introducing E-cigarettes on All-cause Mortality between the Base case and Modified case under the defined scenario 2000 2010 2020 2030 2040 2050 2060 Year -8 -6 -4 -2 0 2 Cumulative Difference in Mortality Ages 35-85 [Modified - Base] (x100K) NET BENEFIT 632k fewer deaths predicted by 2060 RISK BENEFIT Figure 3: Impact of Introducing E-cigarettes on Product Prevalence under the defined scenario Table 2 : Product Prevalence Product Use 2060 Prevalence Base: Cigarettes 13.9% Modified: Cigarettes* 7.2% Modified: E- cigarettes* 5.2% Modified: Dual Use 3.1% *Exclusive cigarette and E-cigarette prevalence. 2000 2010 2020 2030 2040 2050 2060 Year 0 0.05 0.1 0.15 0.2 0.25 Adult Prevalence Under the defined scenario a net benefit to the population can be observed even for an ERR of 40% for E-cigarettes. RISK BENEFIT Line Color ERR Total Reduced Mortality 40% 277,000 30% 369,000 20% 475,000 10% 584,000 5.0% 632,000 2.5% 611,000 2000 2010 2020 2030 2040 2050 2060 Year -8 -6 -4 -2 0 2 Cumulative Difference in Mortality Ages 35-85 [Modified - Base] (x100K) ERR = 40% ERR = 30% ERR = 20% ERR = 10% ERR = 5% ERR = 2.5% Figure 4: Cumulative Difference in All-cause Mortality between the Base case and Modified case under the defined scenario varying the ERR value Table 3: Impact of varying the Excess Relative Risk (ERR) of E-cigarettes 2000 2010 2020 2030 2040 2050 2060 Year -10 -8 -6 -4 -2 0 2 Cumulative Difference in Mortality Ages 35-85 [Modified - Base] (x100K) Modified Scenario Switching Reduced by 50% Switching Increased by 50% Line Color Varying Switching Rate Total Reduced Mortality Increase by 50% 862,000 Modified Case 632,000 Increased by 50% 371,000 Figure 5: Cumulative Difference in All-cause Mortality between the Base case and Modified case under the defined scenario varying the Cigarette to E-cigarette transition rate Base Case Former Cigarette Smoker Never Tobacco User Cigarette Smoker Modified Case Base Case Assessment of the Impact on a Hypothetical Population Estimated change in outputs, as a result of E-Cigarette introduction Dual User E-Cigarette User Former E-Cigarette User Former Cigarette Smoker Never Tobacco User Cigarette Smoker Modified Case Former Dual User
Transcript of Estimating the Potential Population Health Impact of Authorizing … · 2020-01-27 · increased...
Estimating the Potential Population Health Impact of Authorizing the Marketing of E-cigarettes in the US
1 2 3 Research, Development & Sciences, Consumer & Marketplace Insights, Regulatory AffairsAltria Client Services LLC, 601 East Jackson Street, Richmond, VA 23219, USA
SRNT 24th Annual Meeting, February 21 - 24, 2018, Baltimore, MD, USA
1 1 2 1 2 3 1Raheema Muhammad-Kah , Thaddeus Hannel , Lai Wei , Ryan Black , Thomas Bryan , Maria Gogova , Yezdi B.Pithawalla
This poster may be accessed at www.altria.com/ALCS-Science
No conflict of interest
Computational models can be used to assess the likely impact of introducing a new tobacco product on the U.S. population. We have developed and validated an Agent-Based model using best modeling practices, as recommended by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making. Our model quantitatively integrates an estimate of the relative risk of e-cigarettes (compared to conventional cigarette) and a range of potential changes in behavioral patterns to assess different scenarios to estimate the likely impact of introducing a new tobacco product on the population as a whole. The results are presented as estimated changes in tobacco use prevalence and all-cause mortality by assessing the difference in number of survivors comparing a Base Case (the current market, where cigarettes are the predominately used tobacco product) and a Modified Case (a new market where cigarettes and e-cigarettes are both available). Statistical models combined with likely excess relative risks (ERRs) were used to determine survival probabilities of current and former e-cigarette users. Nationally representative transition probabilities were obtained for the Base Case. The Modified Case transitions probabilities were estimated from a case study based on Wave 1 and Wave 2 data from the Population Assessment of Tobacco and Health (PATH) study which was designed to assess tobacco use transitions of non-users and established tobacco users. Employing these transition probabilities and an ERR value of 0.05 for e-cigarette use, we demonstrate a net benefit to the population through a reduction in mortality of ~ 600,000 lives , along with a reduction in cigarette smoking prevalence over a follow-up period of 60 years. Sensitivity analysis, varying the risk of e-cigarettes, a key input parameter, showed a decrease in the reduction in mortality as the ERR of e-cigarettes increased (i.e. ERRs (0.025 to 0.4) resulted in ~650,000 to 270,000 lives respectively). Population models are tools that can be used to predict the public health impact of changes in use of tobacco products with varying level of inherent risk on the population as a whole.
Abstract
Ÿ Computational models can be used to predict the likely impact of introducing a new tobacco product on the U.S. population.
Ÿ We have developed and validated a population model using best modeling practices to assess the overall population effects following a market authorization of an E-cigarette.
Ÿ In our model, agents form a hypothetical population in which they transit between different tobacco use states using defined transition probabilities. The set of attributes associated with each agent (e.g., age, gender, current use status, and tobacco use history) determines the path an agent may consider. Statistical mortality models combined with excess relative risks are used to determine the survival probabilities of an agent at each time step.
Introduction
Methods
Ÿ Mortality: - Tobacco-related mortality was estimated from a Kaiser Permanente Medical Care Program Cohort study and
adjusted for U.S. mortality rates in the year 2000 using data from the Human Mortality Database (HMDB).- Excess relative risk (ERR) of E-cigarettes use compared to smoking was 0.05 ( i.e. a 95% reduction in risk
compared to mortality risk of cigarettes) as determined by Nutt et al.Ÿ Base Case Transitions: Reflects the 2000 US population in terms of age, gender, and smoking status and age &
gender specific cigarette transition probabilities (i.e. Initiation & Cessation) (NHIS & CISNET).Ÿ Modified Case Transitions: Transitions were estimated from a case study based on Wave 1 and Wave 2 data from
the Population Assessment of Tobacco and Health (PATH) study which was designed to assess tobacco use transitions of non-users and established tobacco users (i.e. cigarette & E-cigarette users).
Ÿ The population is updated on a yearly basis over a 60 year time frame through birth and immigration (US Census).
Inputs
Ÿ Dual use (cigarette & E-cigarette use) were assigned the same mortality risk of exclusive cigarette use Ÿ Transition probabilities for relapse behaviors are set to zero in the Base case, since cessation rates reflect
successful smoking cessation for at least two years (CISNET)
Assumptions
Ÿ Modified Case Scenario: the effect of all the most-likely changes in tobacco use patterns occur simultaneously (Table 1)
Ÿ Sensitivity Analyses: evaluating the effects of varying input parameters on the net population
Analyses
Table 1: Input Parameters, Source and Assumptions in the Modified Case Scenario
Parameter Transition Source Assumptions
Proportion of never tobacco user who initiate cigarette smoking NT à CS PATH Analysis
Proportion of never tobacco user who initiate Ecig use NTà ECIG PATH Analysis
Proportion of cigarette smokers who cessate (quit smoking) on an annual basis
CS à FS CISNET Represents long term cessation with no relapse
Proportion of cigarette smokers who initiate Ecig use and quit smoking on an annual basis
CS à ECIG-FS PATH Analysis
Proportion of cigarette users who transition to dual product use on an annual basis
CS à DU PATH Analysis
Proportion of Ecig users who cessate (quit Ecig use) on an annual basis ECIGà FECIG CISNET Long-term e-cigarette cessation would be similar to cigarette cessation
Proportion of Ecig users who initiate cigarette smoking and quit Ecig use on an annual basis
ECIGà CS-FECIG PATH Analysis
Proportion of Ecig users who transition to dual product use on an annual basis
ECIG à DU PATH Analysis
Proportion of former cigarette smokers who initiate Ecig use on an annual basis
FSà ECIG-FS PATH Analysis
Proportion of Dual users who quit smoking cigarettes and remain Ecig users DUà ECIG-FS CISNET Cessation of cigarettes when dual using occurs at the same rate as exclusive cigarette cessation
Proportion of Dual users who quit Ecig use and remain cigarette smokers DU à CS-FECIG CISNET Cessation of Ecig when dual using occurs at the same rate as exclusive cigarette cessation
Proportion of Ecig users former cigarette smokers who quit all tobacco use. ECIG-FS à FDU CISNET Cessation of the Ecig with former use of cigarettes occurs at the same rate as exclusive cigarette cessation
Proportion of cigarette smokers former Ecig users who quit all tobacco use. CS-FECIG àFDU CISNET Cessation of cigarettes with former use of the Ecigoccurs at the same rate as exclusive cigarette cessation
E-cigarette ERR is set to 5% , E-cigarette are introduced into the model in 2009, Long term cessation with no relapse- reflect successful smoking cessation for at least two years
Difference in tobacco use prevalence & all-cause mortality: Ÿ Base Case (status quo; the current market, where cigarettes are the predominately used tobacco product) &Ÿ Modified Case (where cigarettes and E-cigarettes are both available on the market)
Outputs
Figure 1: Transition Probabilities Value Under the Modified Case Scenario
FDUCS-
FECIG
ECIG-
FS
CS ECIGNT
FS DU FECIG
CS à DU12-17 15.5%18-24 6.35%25-44 5.43%45-65 2.39%
CS à ECIG-FS
12-17 2.49%*18-24 1.65%25-44 1.58%45-64 0.58%
ECIG à CS-FECIG
12-17 21.1%
18-24 12.7%*
ECIG à DU12-17 6.44%
18-24 8.70%*
FS à ECIG-FS
18-44 0.98%
NT à CS
12-17 15.6%
18-24 10.7%
25-30 0.7%
NT à ECIG
12-17 1.46%
18-24 1.49%
Permanent Cessation
Age M F
12 -17 1.0% 0.8%
18 - 24 3.0% 3.9%
25 - 44 3.4% 3.0%
45 - 64 3.5% 3.9%
65 + 7.6% 6.5%Permanent Cessation
Permanent Cessation
Permanent Cessation
Permanent Cessation
Permanent Cessation
Permanent Cessation
NT = Never-Use of TobaccoCS = Current Cigarette SmokerFS = Former Cigarette Smoker
ECIG= E-cigarette UserFECIG = Former E-cigarette UserCS-FECIG = Current Cigarette Smoker Former E-cigarette User
DU = Dual UserFDU = Former Dual UserECIG-FS = E-cigarette User Former Smoker
Statistical Suppression Rule*Estimates are reported as statistical unreliable if the estimates have coefficient of variation greater than or equal to 30 but less than 50.Estimates are suppressed if the estimates are based on a sample size of less than 50, or coefficient of variation >=50.
Permanent Cessation= Long term cessation without relapse (i.e. reflects successful smoking cessation for at least two years)
Results
ConclusionsŸ Our model suggests overall net benefit of introducing E-cigarette into the US market, as indicated by likely reduction in
smoking prevalence and a decreased mortality (632,000 deaths prevented) in the US population compared to the status quo.
Ÿ The sensitivity analysis indicates that relatively large changes to the ERR of an E-cigarette and cigarette smokers switching to E-cigarette predict a net benefit to the population under our defined scenario.
Ÿ As more real-world data on smoking/E-cigarette transition, initiation and cessation rates become available, we will further refine our assumptions to confirm a net impact of E-cigarette on the population as a whole.
ReferencesŸ PATH Study Public Use Files: User Guide. www.icpsr.umich.edu/icpsrweb/NAHDAP/studies/36498. Accessed September 5, 2017. Ÿ Anderson, C. M., Burns, D. M., Dodd, K. W., & Feuer, E. J. (2012). Chapter 2: Birth-cohort-specific estimates of smoking behaviors for
the U.S. population. Risk Anal, 32 Suppl 1, S14-24. doi:10.1111/j.1539-6924.2011.01703.xŸ Friedman G., Tekawa I.S., Sadler M. and Sidney S. (1997). Smoking and Mortality: the Kaiser Permanente Experience. In Changes in
Cigarette-Related Disease Risks and Their Implication for Prevention and Control. Shopland D.R., Burns D.M., Garfinkel L. and Samet J., eds. US Department of Health and Human Services, Public Health Service, National Institutes of Health, National Cancer Institute. pp. 477-499.
Ÿ Nutt, D. J., Phillips, L. D., Balfour, D., Curran, H. V., Dockrell, M., Foulds, J., . . . Sweanor, D. (2014). Estimating the harms of nicotine-containing products using the MCDA approach. Eur Addict Res, 20(5), 218-225. doi:10.1159/000360220
Figure 2: Impact of Introducing E-cigarettes on All-cause Mortality between the Base case and Modified case under the defined scenario
2000 2010 2020 2030 2040 2050 2060
Year
-8
-6
-4
-2
0
2
Cum
ula
tive
Diffe
ren
ce
in
Mo
rta
lity
Age
s
35
-85
[Mo
difie
d
-
Ba
se
]
(x1
00
K) 10
5
NET BENEFIT632k fewer deaths predicted by 2060
RISK
BENEFIT
Figure 3: Impact of Introducing E-cigarettes on Product Prevalence under the defined scenario
Table 2 : Product Prevalence
Product Use2060
Prevalence
Base: Cigarettes 13.9%
Modified: Cigarettes*
7.2%
Modified: E-cigarettes*
5.2%
Modified: Dual Use
3.1%
*Exclusive cigarette and E-cigarette
prevalence.
2000 2010 2020 2030 2040 2050 2060
Year
0
0.05
0.1
0.15
0.2
0.25
Ad
ult
Pre
va
lence
Under the defined scenario a net benefit to the population can be observed even for an ERR of 40% for E-cigarettes.
RISK
BENEFIT
Line Color ERRTotal Reduced
Mortality
40% 277,000
30% 369,000
20% 475,000
10% 584,000
5.0% 632,000
2.5% 611,000
2000 2010 2020 2030 2040 2050 2060
Year
-8
-6
-4
-2
0
2
Cum
ula
tive
Diffe
ren
ce
inM
ort
alit
y
Age
s3
5-8
5[M
od
ifie
d-B
ase
](x
10
0K
) 105
ERR = 40%
ERR = 30%
ERR = 20%
ERR = 10%
ERR = 5%
ERR = 2.5%
Figure 4: Cumulative Difference in All-cause Mortality between the Base case and Modified case under the defined scenario
varying the ERR valueTable 3: Impact of varying the Excess Relative
Risk (ERR) of E-cigarettes
2000 2010 2020 2030 2040 2050 2060
Year
-10
-8
-6
-4
-2
0
2
Cum
ula
tive
Diffe
ren
ce
inM
ort
alit
y
Age
s3
5-8
5[M
od
ifie
d-B
ase
](x
10
0K
) 105
Modified Scenario
Switching Reduced by 50%
Switching Increased by 50%
Line ColorVarying
Switching RateTotal Reduced
Mortality
Increase by 50% 862,000
Modified Case 632,000
Increased by 50% 371,000
Figure 5: Cumulative Difference in All-cause Mortality between the Base case and Modified case under the defined scenario
varying the Cigarette to E-cigarette transition rate
Base Case
Former Cigarette Smoker
Never Tobacco
User
Cigarette Smoker
Modified Case
BaseCase
Assessment of the Impact on a Hypothetical Population
Estimated change in outputs,
as a result of E-Cigarette introduction
Dual User
E-CigaretteUser
FormerE-Cigarette
User
Former Cigarette Smoker
Never Tobacco
User
Cigarette Smoker
Modified Case
Former Dual User
