Estimated Public Health Gains From German Smokers Switching to Reduced-Risk Alternatives: Results From Population Health Impact Modelling

The basic method used for estimating the impact of introducing an RRP into a country is as described earlier (30), and involves two components, the Prevalence (P-) component and the Epidemiologic (E-) component.

The P-component starts in a specified year with individuals of a given sex and age range with a defined cigarette smoking distribution. This group is then followed over discrete time intervals under a “Null Scenario” and various “Alternative Scenarios”, by using different sets of transition probabilities (TPs). In the Null Scenario, RRPs are never introduced, and each individual's cigarette smoking status (never, current, or former) is updated yearly. In each Alternative Scenario, RRPs are introduced during follow-up, and the TPs allow for switching between six groups (never user, current exclusive cigarette smoker, current exclusive HnB user, current exclusive ECig user, current multiple product user, and former product user). “Never users” have never used cigarettes or either of the two RRPs considered. “Current multiple product users” currently use two or three of the products considered, while “former product users” have previously used at least one product but currently do not use any. At the end of the P-component, each individual has a complete history of product use over the follow-up period under each scenario. The modelling ignores products other than cigarettes, ECigs, and HnBs.

The E-component then uses the product histories to estimate, for each individual, the relative risk (RR), compared to never users, of LC, COPD, IHD, and stroke for each follow-up year and scenario. The estimation involves an extension of the negative exponential model (NEM), allowing for multiple changes in use, fully described elsewhere (27). The NEM requires estimates of the RR for continued smoking for each of the four diseases, the quitting half-life (H) – i.e., the time from quitting when the excess relative risk (RR!1) declines to half of that for continuing smokers – and also estimates of the effective doses for current exclusive HnB use, exclusive ECig use, and multiple product use relative to that for current cigarette smoking (taken as one unit). The decline of the excess relative risk by time since quitting cigarette smoking is well described using a NEM for LC (31), COPD (32), IHD (33), and stroke (34).

The estimation of the RR for an individual does not specifically take into account the amount smoked, but the effective dose for multiple product users may be set to reflect a reduced cigarette consumption. A discussion of how the effective dose may be quantified for an RRP is given elsewhere (30).

For each scenario, the average RR for each disease for individuals of a given sex and age group is then calculated for each follow-up year, from which the proportions of SRD can be derived. These are then converted to numbers using national mortality estimates by sex, age group, and year. The differences in estimated numbers and proportions between the scenarios then quantify the effect of RRP introduction.

For a given scenario, YLL is estimated using the method of GARDNER and SANBORN (35). YLS is then calculated from the difference in YLL between the Alternative and Null Scenarios.

Each of the individuals in each scenario is followed up over the whole period considered, with no removals for death. While, the estimates of DD and YLS assume that the size of the populations of risk remains the same during follow-up, with no correction for differential survival, a correction can be made if required (30).

The methodology can also compare the Null Scenario with Alternative Scenarios where RRPs are not introduced but where different sets of TPs for cigarette smoking are used. The modelling starts with a population aged 10–79 years, individuals dropping out of the calculations as they reach 80 years of age. This is partly because cause of death certification is unreliable at an older age and partly as our estimates of population health impact also include YLS, which is unaffected by deaths above the age of 74 years.

Each simulation involved follow-up of 100,000 individuals in 1-year intervals from 1995, with the product use status of each individual estimated at each year of follow-up until the year 2015 (or age 79, if that came earlier). For each situation described, separate simulations were conducted for each sex.

As previously described (27), each individual in a simulation is allocated at the start of the P-component to a year of age, then to a cigarette smoking group (never, current, or former), and, then, for former smokers, to an age of quitting, based on random numbers and the relevant distributions for Germany.

The sex-specific age distributions used for Germany for 1995 are as published by the United Nations (36).

Sex- and age-specific distributions of current and former smoking prevalence for Germany for individual years from 1995 to 2015 were derived by combining data from three sources: International Smoking Statistics (37), which provides results by 5-year age groups from 1980–2015 for current smoking; a report by FOREY and LEE (2012) (38), which provides results by 15-year age groups from 1980–2005 for former smoking; and the German Socioeconomic Panel (39), which provides data for 2002 and 2012 for current and former smoking.

The sex- and age-specific distribution of time quit for former smokers used for the baseline population in 1995 was taken, in the absence of alternative data, from estimates for 2002 derived from the German Socioeconomic Panel (39). Because this source only provided data for age groups 20–24 and above, data for younger age groups were taken from US estimates for 2006 (27).

Additional File S1 gives further details on the derivation of the data on current and former smoking prevalence and time quit. It also includes tables summarizing the age-specific distribution of the population and the data on smoking habits used to assign the initial status of each member of the simulated population in 1995.

The sex- and age-specific TPs used in the P-component for developing the histories of cigarette smoking for the Null Scenario were derived as described in Additional File S2 and are shown in Table 1. To test the validity of the TPs, the prevalences predicted by using them were compared with the estimates for Germany derived for years up to 2015 as described in Additional File S1.

Seven different Alternative Scenarios were tested and are summarized in Table 2; Scenarios 1 to 3 are termed “Extreme Scenarios” and Scenarios 4 to 7 “Pragmatic Scenarios”. Together with the Null Scenario, which reflects the actual observed smoking prevalence in Germany in 1995–2015, the Extreme Scenarios calculate theoretical maximum effects of immediate cessation or immediate switch to RRPs. The Pragmatic Scenarios were designed to reflect a range of more gradual potential uptake rates of HnBs and ECigs, loosely based on early market data for Germany and international RRP uptake dynamics by 2019, with Scenario 4 (the “Conservative Scenario”) being a more pessimistic one, Scenario 5 (the “Dynamic Scenario”) an intermediate one and Scenarios 6 (the “Conversion Scenario”) and 7 (the “Full Conversion Scenario”) more optimistic ones. Exclusive RRP users are defined as the estimated number of Legal Age (over 18 years old) users that used the RRP for 100% of their daily nicotine product consumption over the past 7 days.

No RRP is introduced in Alternative Scenario 1. For the other six Alternative Scenarios, the effective doses are assumed to be 0.2 for exclusive HnB use and 0.05 for exclusive ECig use, in contrast to an effective dose of 1 for exclusive cigarette smoking. The value for HnBs was based on biomarkers and clinical findings (40), and for ECigs on a published expert opinion (41). For multiple product use, the effective dose is assumed to be the mean of the three effective doses (i.e., 0.42).

The TPs used in the P-component for developing usage histories in the Alternative Scenario are presented in Additional File S3. Note that, for each Alternative Scenario, the sum of the initiation TPs (for a given sex, age, and follow-up period) was constrained to equal the corresponding initiation TP for the Null Scenario. The same constraint was applied to the re-initiation TPs. Each cessation TP in the Alternative Scenario was also constrained to equal the cessation TP in the Null Scenario. These constraints were applied so that the various Alternative Scenarios considered only the effect of the RRPs introduced on the distribution of current effect sizes, without any effect on overall initiation, cessation, or re-initiation rates.

For each disease, the estimates of RR for continued cigarette smoking and of H were derived from published meta-analyses. The estimates and sources are given in Table 3. The sex- and age-specific data on national population size for Germany for 1995 to 2015 are as published by the United Nations Department of Economic and Social Affairs Population Division (36).

The data on numbers of deaths in Germany from LC, COPD, IHD, and stroke come from the WHO (42). The data on population size and numbers of deaths for Germany for 1995 to 2015 are presented in Additional File S4, which gives fuller details on sources and disease definitions.

The method of estimating the number of deaths and increase in death rates associated with tobacco is as described earlier (27). Unless indicated, results are presented without adjustment for changes in population size associated with each Alternative Scenario.

Figure 1 compares never, current, and former smoking prevalence estimates for Germany by sex for years up to 2015 and for age groups 30–34, 50–54, and 70–74 years as simulated in the Null Scenario (broken lines) with those derived as described in the Methods section (solid lines). The fit is generally very good, though there is some tendency for the Null Scenario current smoking estimates to be lower than the derived estimates at age 70–74 years.

Figure 1

Figure 2 presents the simulated estimates of product usage in the Conversion Scenario by sex, age (30–34, 50–54, and 70–74 years), and year (1995, 2000, 2005, 2010, and 2015). In 1995, the estimates for current, never and former product use are identical, as expected, to those for cigarette smoking in the Null Scenario shown in Fig 1. The proportions of never and former product users in Figure 1 and Figure 2 remain very similar over the whole time period. While in the Null Scenario, the current product users all smoke cigarettes, in the Conversion Scenario, they fall into four groups. Over the first 15 years, there is a large decline in exclusive cigarette smoking and a corresponding increase in the other three current product use categories. This pattern flattens out between 2010 and 2015, with some decline in some of the groups. Further details for the Conversion Scenario as well as other Pragmatic Scenarios are shown in Additional File S5.

Figure 2

Additional File S6 summarizes the current product use distributions in 2005 for all the scenarios. With regard to the distributions in 2005, after 10 years follow-up, there were, as expected, no current product users at all in Scenario 1, with all HnB users in Scenario 2 and half HnB and half ECig users in Scenario 3.

In the Pragmatic Scenarios, the proportions of exclusive cigarette smokers decrease and those of exclusive HnB and ECig users increase from Scenarios 4 to 7. Relative to Scenario 5, the proportions of multiple product users decline in Scenarios 6 and 7. In 2010 (and also at other time points) the overall prevalence of current product users is essentially the same in each of Scenarios 2 to 7. This represents a drop of about 25% in men and 12% in women relative to the proportions in 1995.

As estimated by the E-component of PHIM, 852,000 deaths from LC, COPD, IHD, and stroke combined for both sexes, were attributed to cigarette smoking over the period of 20 years from 1995 to 2015, in the absence of any switching to HnBs or ECigs and with the patterns of prevalence of cigarette smoking as existing in Germany (our Null Scenario). 77.9% of these were in males, with the percentages by disease being 54.6% for LC, 26.4% for IHD, 13.4% for COPD and 5.7% for stroke.

In the Null Scenario, 8.61 million YLL were attributed to cigarette smoking. 76.2% of these were in males, with the percentages by disease being 51.6% for LC, 31.8% for IHD, 8.4% for COPD and 8.2% for stroke. The percentages by disease, compared to those given above for attributable deaths, reflect the higher proportion of deaths at younger ages for IHD and stroke than for LC and COPD.

We explored a wide range of scenarios assessing the possible effect of RRP introduction on the population health in Germany in 1995–2015.

Our Extreme Scenarios (1–3) estimated theoretical maximum effects. As expected, the greatest impact would have occurred had all cigarette smokers quit in 1995, with no further use of cigarettes, HnBs, or ECigs (Scenario 1), resulting in a DD of about 217,000 and 2.88 million YLS. This extreme scenario has been designed to demonstrate the maximum potential risk reduction for the German population, and it can be considered a point of reference for every other scenario investigated.

Substantial reductions would also have occurred had cigarette smoking in Germany been immediately replaced by either HnB use (Scenario 2; 159,000 DD and 2.06 million YLS) or equally by either HnB or ECig use (Scenario 3; 179,000 DD and 2.34 million YLS), with the greater numbers for Scenario 3 reflecting the assumed lower effective dose for ECigs (0.05) compared with HnBs (0.2). Four Pragmatic Scenarios revealed more plausible estimates by moving gradually a proportion of cigarette smokers to use HnBs and ECigs.

Table 4 presents the estimated DD values at age 30–79 years over the whole follow-up period for all seven scenarios. These are shown for each disease separately and combined. The results are expressed both as numbers and percentages of all SRD.

The DDs are lower in the Pragmatic Scenarios, because the transition from cigarettes to HnBs and ECigs is less rapid. As would be predicted from the patterns of uptake by scenario shown in Table 2, the greatest DDs are seen in the Full Conversion Scenario, where smokers switch gradually to the RRPs – they are about 40% of the DDs associated with Complete Cessation, where smokers quit smoking immediately in 1995.

The patterns of DDs for the individual diseases are similar to that for the four diseases combined. Among men, the largest absolute DDs are for IHD, with LC next, followed by stroke and COPD with lower and similar DDs. Among women, the DDs for LC are higher than those for IHD, reflecting the lower overall IHD rate among women. As a proportion of all SRDs, the DDs in both sexes are substantially higher for IHD and stroke than for LC and COPD, reflecting the shorter H values for IHD and stroke (i.e., the more rapid reduction in cardiovascular disease risk after smoking cessation or switching to RRPs).

The DDs in the Conversion Scenario are also shown by disease over the whole follow-up period in Figure 3.

Figure 3

As is shown for the Conversion Scenario (Figure 3), but as is also evident from Additional File S5 for the other scenarios, an increase in DDs is seen with time in both sexes. This is due partly to the time needed for take-up of HnBs and ECigs and partly to the time required for the resulting decline in risk. This trend suggests that the DDs would have been substantially greater had the follow-up period been extended.

Table 5 and Figure 4 summarize the results for the seven scenarios with regard to YLS by age 75 over the whole follow-up period. The relative values for the different scenarios are very similar to those for DD seen in Table 4. Indeed, on the basis of the results for the four diseases combined in Tables 4 and 5, the sex- and scenario-specific ratios of YLS to DD can be estimated to only vary between 12.5 and 13.4.

Figure 4

The analyses summarized above do not take into account the increase in population size associated with the reduced mortality in the Alternative Scenarios relative to that in the Null Scenario. As shown in the detailed results in Additional File S5, this had little effect on the estimated DD or YLS. For example, for the Conversion Scenario, where the overall unadjusted DDs were 57,655 (8.68% of all SRD) in men and 17,942 (9.53% of SRD) in women, the corresponding adjusted DDs were 57,026 (8.59%) and 17,892 (9.51%), respectively. Full results of the analyses are available in Additional File S5.