Estimated Public Health Gains From Smokers in Germany Switching to Reduced-Risk Alternatives: Results From Population Health Impact Modelling by Socioeconomic Group

This is the second of two related papers on estimating the population health impact in Germany of introducing two types of reduced-risk products (RRP) – the heat-not-burn product (HnB) and the e-cigarette (ECig) – under various assumptions about the rate of product uptake. The paper also compares these estimates with those derived by assuming that the whole population ceased smoking cigarettes immediately. The first paper (1), henceforward referred to as our “companion paper”, took no account of the possibility that product use (here cigarettes, ECigs or HnBs), transitions between products, and mortality might vary by socioeconomic group (SEG), a possibility allowed for in the current paper.

In Germany, smoking prevalence is higher in lower SEGs, as expressed by the level of education and income (2), and socioeconomic differences based on occupational classification have increased recently (3). Smoking is believed to contribute to health-related inequalities between SEGs, for example as regards quality of life, morbidity, and mortality (4,5,6,7). Lower success rates in quitting smoking in lower SEGs contribute to these differences (8,9,10,11). Targeted smoking cessation interventions could help reduce social inequalities, while untargeted interventions are likely to have increased inequalities (12).

While smokers would best quit smoking, switching to RRPs is an alternative that may reduce their disease risk, and the one we investigate here. There are numerous examples of how switching to RRPs can work, including the use of snus in Sweden (13), ECigs in the UK (14), and HnBs in Japan (15) as well as the modelling shown in our companion paper on the use of ECigs and HnBs in Germany. Recent studies have also suggested that RRPs like ECigs can aid smoking cessation (14, 16, 17) and they have become the most popular cessation aid in Germany (18) and the UK (19). It has recently been proposed that the effect of ECigs on cessation might narrow the health inequalities from smoking (20).

Our main objective is to estimate the population health impact, as measured by the drop in deaths (DD) and the years of life saved (YLS), of introducing RRPs (ECigs and HnBs) in Germany on two different SEGs. We investigate various assumptions about the rate of uptake and compare the estimates of DD and YLS with those derived assuming that all smokers in Germany quit immediately.

As in our companion paper, we use a “hindcasting” approach in which individuals start in 1995, with a nationally representative distribution of cigarette smoking, and are then followed up until 2015. This approach avoids uncertainty about the future, and needing to take into account the effect on future mortality rates of factors such as medical progress and infectious disease epidemics.

By comparing scenarios where RRPs are or are not introduced, this approach generates estimates of the DD and YLS associated with RRP introduction for the four main diseases related to cigarette smoking - lung cancer (LC), chronic obstructive pulmonary disease (COPD), ischaemic heart disease (IHD), and stroke. Compared to our companion paper, our present estimates are derived separately for two SEGs.

Such estimates are useful in themselves as they could inform policy decisions by enabling cost-benefit considerations for tobacco control and educational programs targeted at the most vulnerable SEGs to increase smoking cessation or, if needed, switching to less harmful products.

The full results of the analyses are available in Additional File S6.

Figure 1 compares the never, current, and former smoking prevalence estimates for Germany by sex and SEG for age groups 40–44 and 60–64 years as simulated in the Null Scenario (broken lines) with those derived as described in the Methods section (solid lines). The fit for these (and other) age groups is generally quite good, confirming the validity of our approach. Distributions for other age groups are shown in Additional File S6.

Figure 1

Figure 2 presents simulated estimates of product usage in the Conversion Scenario by sex, age (40–44 and 60–64 years), year (1995, 2000, 2005, 2010, and 2015), and SEG. In 1995, the estimates for current, never, and former smoking are, as expected, identical to those in the Null Scenario shown in Figure 1. The main difference between Figures 1 and 2 is in the distribution of current product users, who all smoke cigarettes in the Null Scenario but are split into four groups in the Conversion Scenario.

Figure 2

In both SEGs A and B, the prevalence of current exclusive cigarette smoking declines steadily over the period, while the prevalences of current exclusive HnB use and current exclusive ECig use increase. The sharper increase in the first 10 years reflects the higher assumed uptake of ECigs. More details for scenarios 6 and 7 are shown in Additional File S6. For all Alternative Scenarios, Additional File S7 summarizes, by SEG, the current product use distribution in 2005, overall current product use in 2010, and drop in the percentage of current product use in 2010.

As expected (from the assumptions described earlier), there were no current product users at all in scenario 1, and the total proportion of current product users in scenarios 3, 6, and 7 were essentially the same, the variation between these scenarios being only in the distribution of the four current product use groups.

Table 5 presents the estimated DDs at ages 30–79 years over the whole follow-up period in each scenario. They are shown by disease, for the four diseases combined, and by SEG (A, B, and A+B). The results are expressed both as numbers and percentages of all the smoking-related deaths from the four causes studied.

The DDs in the Conversion Scenario are also shown by sex, disease, and SEG over the whole follow-up period in Figure 3. The pattern of results in each SEG is similar to that described in our companion paper, the largest DDs in both sexes being in scenarios 1 and 3, with smaller DDs in scenarios 6 and 7, where the switch to RRPs is gradual. In both sexes, larger DDs are seen for IHD (particularly in men) and LC than for COPD or stroke, and the percentages of DDs in smoking-related deaths are the highest for IHD and stroke, which have shorter half-lives than LC and COPD.

Figure 3

In all the scenarios, DDs are substantially larger in SEG B than A, with SEG B being older, having more individuals, and a higher prevalence of product use. Thus, the combined DDs for both sexes in scenarios 1, 3, 6, and 7 are, respectively, 72,725, 60,081, 24,010, and 25,648 for SEG A and 147,684, 122,343, 50,385, and 52,652 for SEG B, the DD for B always being about twice that for A. Although the DDs are higher for B than A, those expressed as a proportion of the DDs in scenario 1 are similar. Thus, the DDs in scenarios 3, 6, and 7 for the sexes combined represent, as a percentage of those in scenario 1, respectively, 82.6%, 33.0%, and 35.3% in A and 82.8%, 34.1%, and 35.7% in B. The conclusion that DDs are markedly higher in B than A but the ratios of DDs by scenario are similar in A and B also applies to the results for individual sexes and diseases. As the proportions by SEG vary substantially by age (see Table 1), the ratio of DDs for B to A also does. Thus, for scenario 6, and for all four diseases combined, DDs in B, compared to A, were 12% lower at age 40–44 (1266 vs. 1433), 34% higher at age 50–54 (5025 vs. 3758), 177% higher at age 60–64 (9355 vs. 3376) and 190% higher at age 70–74 (10117 vs. 3492). A clear tendency for the ratio to rise with age was similarly evident in both sexes, all four diseases, and each scenario (see Additional File 6).

The total DD in SEGs A and B combined can be seen as an SEG-adjusted total, which can be compared with the unadjusted estimates in Table 4 in our companion paper. They are quite similar. Thus, the unadjusted DDs of 56,263 among men and 18,132 among women for the diseases combined in scenario 6 becomes 55,928 among men and 18,098 among women when adjusted. Similarly, the unadjusted and adjusted estimates are quite close when considering DDs by sex, disease, and scenario.

Table 6 and Figure 4 summarize the results for YLS by age 75 years by SEG for the four scenarios over the whole follow-up period. As in the companion paper, the relative values for the different scenarios are very similar to those of the DDs seen in Table 5. While the population health impact estimates are greater for SEG B than for SEG A, the difference is less marked for YLS than for DD, as individuals in B are more likely to be older and therefore have fewer expected years of life. Thus, for the sexes combined, the YLS in B exceed those in A by factors of 1.55, 1.55, 1.59, and 1.54, respectively, for scenarios 1, 3, 6, and 7 (i.e., less than the factor of slightly over 2 seen for the difference in DDs between A and B). The ratios of YLS between the scenarios are, however, very similar in A and B and quite similar to those noted above for DDs.

Figure 4

Thus, for the sexes combined, the YLS values for scenarios 3, 6, and 7, expressed as percentages of those in scenario 1, are, respectively, 81.3%, 31.1%, and 33.8% in A and 81.0%, 31.9%, and 33.4% in B.

As in the companion paper, accounting for the increase in population size associated with the reduced mortality in the Alternative Scenarios relative to that in the Null Scenario made little difference to the estimated DDs. This is shown in the detailed results in Additional File S6.

In scenario 6, the estimated DD for both sexes and all four diseases combined was 24,010 in SEG A and 50,385 in SEG B, an excess of 26,375 in B (see Table 5). In the additional analysis, which assumed no variation in the prevalence of smoking by SEG, the estimates were 24,998 in A and 49,022 in B, an excess of 24,024 in B that was 91.1% of that for the original analysis. Corresponding percentages were 93.6% for men and 85.4% for women. Based on YLS, totals over sex and disease (in thousands) in our original analysis (see Table 6) were 369 in A and 587 in B, an excess of 218. In the additional analysis, these estimates were 395 in SEG A and 587 in SEG B, a difference of 163, which is 74.8% of that seen originally. For both DD and YLS, these results demonstrate that most of the excess mortality seen in SEG B was due to the age differences among the SEG groups, though differences in smoking habits also contributed. To be clear, the smoking-related diseases, we consider here, originate from smoking, not from age differences, which is why public health measures have focused on lowering smoking prevalence to curb the health consequences of smoking cigarettes.

This is the first study to estimate the population health impact of introducing RRPs in Germany by SEG. Depending on the scenario, the health gains would have reached 34–83% of that from immediate smoking cessation, regardless of the SEG. For each of scenarios 3, 6, and 7, the gains in the lower SEG population (B) compared with those in the higher SEG population (A) would have been more than twice as high for DD and more than 1.5 times as high for YLS. Depending on the scenario, these excess gains in B than in A corresponded to a greater DD by 27,004 to 62,262 and a greater YLS by 215,000 to 526,000.

In our companion paper, which took no account of SEG, we estimated that, in scenarios 1 (Complete cessation), 3 (Complete switch to RRPs–50% HnBs, 50% ECigs), 6 (Conversion), and 7 (Full Conversion), the DDs for the sexes combined and all four diseases combined would have been, respectively, 216,650, 179,470, 75,597, and 81,293. According to the results in Tables 5 and 6 in this paper, which did take into account the SEG, the corresponding estimates were similar (220,409, 182,424, 74,395, and 78,300). Our results suggest that taking SEG into account little affected the overall estimated DDs, and that substantial DDs were seen in both SEG groups, A and B, with the drops about twice as high in B as in A.

The estimates of population health impact in both SEGs are likely to be pessimistic for the four main reasons discussed in our companion paper: deaths were only counted for four diseases; deaths above age 79 years were not considered; only a 20-year follow-up period was considered; and no account was taken of the possibility that cigarette smokers taking up HnBs and ECigs might be more likely to quit cigarettes completely than those who do not take up these products. We also note that the RRs we have used for current and former smoking (based on published meta-analyses) are lower than those used by others (based on specific studies) when estimating deaths attributable to smoking in Germany (26). Using higher RR estimates would have increased our DD estimates. However, our estimates might be optimistic if the rates of uptake of HnBs and ECigs are lower than we have assumed, or if cigarette smokers taking up HnBs and ECigs, compared to those who do not, are less likely to quit smoking or tend to increase their cigarette consumption. There is little evidence, however, that the latter is the case.

While scientific consensus accumulates that RRPs like ECigs and HnBs represent less risk than cigarettes (27, 28), there has been debate on whether smoking-related health inequalities between SEGs could be reduced by smokers switching to RRPs (29). Some studies found more advantaged smokers to be more likely to use ECigs in the UK (30) or HnBs in Germany (31), while others recently observed that ECigs might help disadvantaged smokers to quit (20). ECigs might have contributed to some of the highest UK smoking cessation rates so far, with parity across SEGs (32). This suggests that ECigs worked as quitting aids for low SEG smokers previously not reached by conventional methods, and can be explained by the high acceptability of RRPs as substitutes for cigarettes, ECigs being the most popular quitting aids in the UK (19) and Germany (18, 33). It is notable that conventional pharmacotherapies for smoking cessation are more commonly used in Germany by smokers with higher incomes (18, 33), thus possibly increasing health inequalities. The question whether this is linked to the affordability of pharmacotherapies or to factors related to education has not been conclusively answered. While a recent study found higher income of German smokers to be associated with more frequent use of pharmacotherapies, neither income nor education affected quit success (33). On the other hand, use of ECigs, the most popular quitting aid, was not associated with income or education, underlining that RRPs could be a promising addition to public health strategies aimed at providing equal chances for smokers from different SEGs to exit out of cigarettes (33).

Data from several countries show that lower social grades and level of education are significantly linked to inaccurate harm perception of ECigs (27, 34). Currently, 61% of German smokers falsely perceive ECigs as equally harmful or more harmful than cigarettes, with only 5% correctly perceiving them as much less harmful (35). Given ECigs are likelier to be used for smoking cessation if perceived as less harmful than cigarettes (36), misperceptions might well be discouraging many smokers from trying RRPs. Improving perception seems particularly important for disadvantaged populations of smokers, such as those in lower SEGs, who could benefit more from RRPs as a harm reduction tool.

Uptake of RRPs by smokers is also affected by factors such as taxation (37), affordability (38), moral concerns around addiction (39), and consumer choice regarding ECig flavors (40). To reduce smoking-related health inequalities, an integrated strategy has been proposed which combines targeted cessation programs, tobacco control measures, and educational media campaigns, all applied within wider attempts to address inequalities in health (10). Clear communication of relative risks by targeted public health educational campaigns could help realize the potential of RRPs for harm reduction among lower SEGs.

Strengths of our study include the use of nationally representative data and the hindcasting approach, which helps avoid problems in accounting for the unknown future effects of other factors on future death rates. Our methodology also allows the population health impact of RRP introduction to be estimated under various assumptions and by SEG.

There are, however, some potential limitations to be considered in interpreting our estimates. Issues regarding our failure to consider other sources of nicotine or environmental tobacco smoke, the possible limitations in our negative exponential model, the choice of effective doses, limiting our attention to deaths at ages 30–79 years, and the choice of uptake rates of HnBs and ECigs have been discussed in our companion paper and are not considered further here. Also failure to account for the reduced mortality in the Alternative Scenarios compared to that in the Null Scenario was shown to have very little effect on the results.

Some issues specifically relating to the estimation of population health impact by SEG merit consideration. One is that the age- and sex-specific prevalence data for current and former smoking in Germany by SEG was only available for two years (2002 and 2012), so that annual data had to be estimated by a combination of interpolation/extrapolation and smoothing (see Additional File S1). More detailed source data might have led to some revision of our estimates, but it seems unlikely this would have made much difference, given that adjustment for SEG had little effect on the unadjusted estimates.

Similar considerations apply to the derivation of TPs by SEG in the Null Scenario, which (see Additional File S2) are calculated using the distribution of smoking habits in the same birth cohort five years apart. Errors in the distributions would have led to errors in the estimated TPs, and inspection of the TPs in Table 3 shows that though the general patterns by SEG and age look plausible, there are a few exceptions. For example for SEG A period 1–5 years, the estimated initiation rates in men rise between ages 25–29 and 30–34 years, while at age 15–19, but not at other ages, quitting and re-initiation rates were much higher in women than men. Given that the changes in the distribution of smoking habits over time generated by the model using the TPs matched quite well observed distributions in Germany, it seems unlikely that any further attempt to improve estimation of these TPs would have materially affected our conclusions.

Another issue is that data on transfer between SEG groups A and B were not available for Germany, and the data used here (see Additional File S4) were derived partly from the current smoking prevalence rates in the USA coupled with assumptions about the age-specific level of transfer from A to B. In fact, weaknesses in these data seem unlikely to be very relevant as re-running the analyses for Alternative Scenario 6 by disallowing the possibility of transfer little affected the overall estimates and only slightly reduced the estimated DDs shown in Table 5, changing 19,007 to 18,738 [A men], 37,256 to 37,193 [B men], 5,003 to 4,962 [A women], and 13,129 to 12,999 [B women]. (Detailed results not shown.)

A potential criticism relates to how we defined the SEGs. As noted earlier, we used an established method (22) based on a combination of net annual income and mean years of education. As our population health model is not restricted to a single method for deriving SEGs and accommodates alternative approaches for doing so, it provides an excellent basis for future research on this topic.

Overall, our results clearly demonstrate that increasing uptake of HnBs and ECigs would reduce the adverse population health impact of cigarette smoking in both SEG groups A and B.

Our population health impact model showed that introducing RRPs (ECigs and HnBs) into the tobacco market in Germany in 1995 would have resulted in substantial reductions in mortality from the four main smoking-related diseases over the following 20 years in both SEGs studied here. Depending on the scenario chosen, these gains would have amounted to 31–82% of those achieved by immediate smoking cessation, the optimal scenario. Considering that only 19% of German smokers even attempt cessation each year and few succeed (18), tobacco harm reduction by fully switching to RRPs represents a public health opportunity currently unexploited in Germany.

While our model predicts similar percentage reductions in mortality in both SEGs, the total predicted reductions were greater in the lower SEG, B, by about 2-fold for DDs and about 1.5-fold for YLS, because of individuals in B being somewhat more numerous, of a higher average age (as the proportion of the population in B increases markedly with age), and more commonly current smokers (evident especially among men and at younger ages). These greater reductions in mortality in B correspond to an extra 27,000 to 62,000 DDs and 215,000 to 526,000 YLS, depending on whether scenarios 3, 6 or 7 are considered.

Our results suggest that inclusive access to RRPs, i.e., access to acceptable, affordable products as well as comprehensible relative risk information about them, could diminish smoking-related health inequalities between SEGs. A public health approach encouraging low SEG smokers who would otherwise continue to smoke to switch to RRPs, and complemented by risk-based regulation, could synergize with tobacco control measures targeted at lower SEGs to further reduce such inequalities.