1 – How mortality risk varies with duration and intensity

5The seminal study on smoking’s effect on health by Doll and Peto (1978) followed a cohort of British doctors for 20 years and modelled the effect of duration (proxied by age) and intensity (number of cigarettes per day) on the incidence of lung cancer, showing a strong duration effect and a somewhat smaller intensity effect. The authors concluded that epidemiological data confirm the biology of cancer developing cumulatively and in discrete stages. Their additional finding that smoking duration is a key factor in lung cancer incidence has been reproduced in other populations (Flanders et al., 2003; Rachet et al., 2004; Zhang et al., 2005) after controlling for potential confounders (such as body mass index, physical exercise, education) and using mortality rather than incidence as the outcome variable. Flanders et al. (2003) used CPS-II data to show that the risk of dying from lung cancer increases by a factor of 9 to 14 for males and 4 to 8 for females when smoking duration increases from 20 to 30 years.

6Epidemiological studies also demonstrate that quitting smoking at younger ages promptly reduces the risk of dying from lung cancer. Zhang et al. (2005) used a large population of middle-aged Canadian women to show that the hazard ratio of developing lung cancer decreases with earlier cessation when controlling for smoking duration and intensity. For instance, quitting before age 30 reduces the risk to the same level as never-smokers. Knoke et al. (2008) used CPS-I to show that cessation had a strong effect on lung cancer mortality, reducing the risk by half after 5 years if the smoker quits at 40 (after 10 years if they quit later) and by 90% after 15–20 years. Their study controls for the ‘quitting ill’ effect, in which some smokers quit after learning they have respiratory disease. Not controlling for this effect would overstate the relative risk of quitters.

7The evidence is therefore clear that lung cancer mortality is influenced by the duration of smoking and abstinence, and to a lesser extent by smoking intensity. Less is known about other smoking-related diseases, such as other cancers, chronic obstructive pulmonary disease (COPD), or cardiovascular diseases. In a meta-analysis of smoking’s effect on colorectal cancer, Liang et al. (2009) found a strong effect of smoking duration on both incidence and mortality, but they did not report on any cessation effect. Based on a meta-analysis, Forey et al. (2011) observed a higher incidence of COPD, chronic bronchitis, and emphysema among current smokers, with a strong effect of smoking intensity but no clear duration effect. Streppel et al. (2007) reported their results from a longitudinal study on smoking’s association with mortality (all-cause and cause-specific), which was conducted on men living in the Netherlands, born between 1900 and 1920, and followed-up from 1960 to 2000 (Zutphen Cohort Study; Keys, 1970). Controlling for competing risks, they find that 1 more year of smoking increases the hazard of all-cause mortality by 1.2%, and one more cigarette smoked per day increases it by 1.1%. Duration is the only driver of mortality for cardiovascular diseases, COPD, and lung cancer, since smoking until 40 and quitting at that age means a loss of 1.9 years of life compared to a never-smoker, but it implies a gain of 1.3 years compared to a smoker who quits at 50, and of 1.8 years over a smoker who quits at 60. The evidence on the effects of cessation for the US population is summarized in the 1990 report by the US Surgeon General, who found that after 15 years of cessation, all-cause mortality of ex-smokers relative to never-smokers is 1.0 for former light smokers (less than 20 cigarettes per day) and between 1.1 and 1.4 for former heavy smokers (more than 20 cigarettes per day) (USDHHS, 1990). [3] This review of the empirical literature establishes the role that duration and intensity play in lung cancer mortality and COPD. Overall, these two causes account for around 60% of smoking-related deaths and provide the best evidence for a causal link between smoking and mortality (PAFs of 0.82 and 0.79, respectively) (Godtfredsen et al., 2008; USDHHS, 2014; Ribassin-Majed and Hill, 2015; Bonaldi et al., 2019). Furthermore, they are proven to be highly sensitive to duration. This confirms that prevalence is not a sufficient statistic for risk exposure and legitimates the choice of a duration-based approach.

8It is therefore worth exploring the use of risk according to durations and intensity, in combination with data on duration and intensity to estimate and simulate the effects of changes in prevalence on SAM. Our new method is calibrated to mimic the actual number of deaths observed in reference epidemiological sources, which then allows us to run simulations based on changes in initiation and cessation behaviours.

2 – Exposure as a function of duration (flows) rather than prevalence (stock)

9The standard method has a considerable practical advantage in that it uses highly reliable data (number of deaths by cause and smoking prevalence in a given year). Its main weakness, however, is that it relies on the strong assumption that prevalence accurately represents exposure to risk (and can replace duration and intensity).

10Our alternative method therefore draws on the well-established ideas in the literature that the risk of dying from smoking depends on intensity and on the duration of both smoking and cessation, and that prevalence does not adequately describe duration and intensity. For these reasons, changes in prevalence can alter relative risks, and it is impossible to use the standard method to run scenarios for the effects of these changes on SAM. To illustrate, let us assume that smoking prevalence falls in the 35–54 age group due to an increase in that cohort’s proportion of smokers who quit at age 45. If the relative risk of dying is driven mostly by current smokers older than 45, the relative risk of the age category would decline. However, the standard method will keep the relative risk of the age category unchanged and only account for a decline in prevalence.

11This weakness has already been identified and acknowledged by the proponents of the standard method. According to the US Surgeon General report for 2004, ‘The burden of disease attributable to smoking is driven by those with long-term previous exposures, so unless smoking cessation among current smokers increases quite rapidly, SAM is not expected to decline substantially for many years’ (USDHHS, 2004). And, in the report for 2014, ‘Unless smoking behaviour (including cessation) is stable over time, cross-sectional SAM estimates do not accurately reflect the risks of past cohorts of smokers’ (USDHHS, 2014). This weakness is also the main reason why estimates of SAM outside the United States do not combine relative risks with in-country observations of prevalence but instead use a simulated prevalence based on a given country’s age-related smoking behaviours. In other words, the simulated prevalence is chosen to be compatible with observed mortality from lung cancer (Ribassin-Majed and Hill, 2015), which is in following with the indirect method, also known as the smoking-impact ratio or Peto–Lopez method (Peto et al., 1992; Peto et al., 1994; Ezzati and Lopez, 2003). This estimated prevalence [4] is then used to calculate SAM for all other causes of death. A more refined but conceptually similar way to use lung cancer mortality as a proxy for smoking history is to regress all-cause mortality rates on lung cancer mortality (and other factors), specifically by using the lung cancer mortality difference between smokers and non-smokers to indicate the damage from smoking and arrive at a point estimate of SAM (Preston et al., 2010; Rostron, 2010; Rostron and Wilmoth, 2011; Janssen et al., 2013).

12Using lung cancer mortality to reflect the extent of the smoking epidemic effectively produces a point estimate of SAM for one country under the assumption that most lung cancer deaths are due to smoking, as substantiated by a PAF of 0.82 (USDHHS, 2014). However, this approach cannot generate predictions or what-if scenarios to assist public policies. A better, albeit more demanding, approach combines mortality risks according to durations of smoking and abstinence and intensity (estimated using CPS-I data) with observed duration and intensity in the population of interest. Such a combination would allow researchers and policymakers to discuss and assess the effectiveness and costs of various interventions for reducing SAM.

13Our alternative method works as follows. For a given year, we know the proportions of current smokers, never-smokers, and ex-smokers in a given age–sex category. Additionally, we know the following about current and ex-smokers: their average smoking duration, average duration since quitting, and average smoking intensity. All these data are estimated from reconstructed smoking histories based on pseudo-cohorts in France, as explained in Section II.1. We then use the literature’s reported relative risks by age, sex, smoking duration, cessation duration, and intensity (Flanders et al., 2003; Knoke et al., 2008) to calculate for each age–sex category the probability of dying from smoking.

1 – Reconstructing smoking histories in France

Constructing life course prevalence rates for various pseudo-cohorts

15Unlike prevalence, duration distribution data are not published regularly and must be extracted from general population surveys. Thus, we use answers on current smoking status in a series of repeated cross-sections to generate duration distributions for several birth cohorts. [6] We discuss in our conclusion why we chose a pseudo-cohort method rather than use retrospective individual data on initiation and cessation. To construct our pseudo-cohorts, we pool eight cross-sectional surveys on health-related behaviours (1977 to 2010), conducted by the French national institute for health education and promotion (INPES, see Beck et al., 2007) and representative of the French population described in Table 1. These were called ‘Enquêtes CFES’ from 1977 to 1990 and have been known as ‘Baromètre Santé’ since 1992. These sources were preferred over other health-related surveys with larger samples (especially Enquête Santé, INSEE), mostly because the questionnaire and definition of smoking status have been consistent over the years.

Table 1

Surveys used to reconstruct prevalence for various cohorts in France

Year conducted Population selection Sample size Interview mode Age range Number of age groups Tobacco measurement 2010 Random sampling (randomly generating phone numbers) 27,493 Telephone (including mobile-only) 15–85 14 Distinction between regular and occasional smokers 2005 29,113 12–75 12 2000 13,064 Telephone 1995 1,975 18–75 1992 2,099 Telephone + self-administered 1986 Quota method 2,000 15–75 6 No distinction between regular and occasional smokers 1981 1,018 Self-administered 18–75 5 1977 1,010

Surveys used to reconstruct prevalence for various cohorts in France

16We were unable to access the raw data for the older waves of the survey (1977, 1981, and to a lesser extent 1986) and had to use published tables for these waves, which resulted in some inconsistencies across waves in smoking status definitions, smoking prevalence measures, and age categories. [7] Questions on smoking behaviours are similar across all waves of the Baromètre Santé, except for answer categories, in which some but not all waves distinguish between regular and occasional smokers. We could not use that information accurately and pooled all smokers into a single category for all waves. The main issue with consistency concerns the sample age range (minimum changing from 12 to 18 and maximum from 75 to 85) and age categories. Consistent age categories were reconstructed mostly by interpolation. Since the results published for the survey’s first waves are not weighted, we use unweighted data for all surveys to estimate smoking prevalence rates. For the surveys providing raw data, we compared prevalence rates based on weighted and unweighted data and concluded that there were no meaningful differences; i.e. within a given age–sex category, smokers have the same response rate as non-smokers. These data allow us to create a consistent collection of smoking prevalence rates by sex and age group over a span of 35 years (from 1975 to 2010) and to recreate age profiles of prevalence for 5-year birth cohorts, starting with the cohort born in 1936–1940. Because the surveys we pool are not conducted 1 year apart but rather every 5 years, we use the median age of intervals (e.g. 22.5 years for the age category 20–24) to interpolate our set of discrete measures (survey years and age categories) into continuous duration values.

2 – Estimating mortality risk according to duration and intensity

23Estimates of current smokers’ lung cancer mortality risks according to smoking duration and intensity are derived from Flanders et al. (2003), who express the absolute risk of lung cancer as an exponential function of duration (D) and intensity (I) with parameters (α, β and γ) that vary by sex (s) and age category (x):

24

25These are complemented by estimates of lung cancer mortality risks according to cessation duration provided by Knoke et al. (2008), who estimate a decrease (denoted as f) in excess risk of lung cancer for ex-smokers compared to continuing smokers as a function of age at cessation (A_c) and the time since cessation (T_c): [9]

26

27Because the probability in Equation 2 is relative to that of an individual with the same characteristics (age and sex) who would have smoked and never quit, we use the probabilities generated by Equation 1 as the baseline and apply the estimated decrease parameter (f) to that number.

28Both estimates apply to people aged only 40–79, and the result we generate is thus a lung cancer SAM restricted to the population aged 40–79. This is a reasonable approximation, since almost no lung cancer deaths occur before age 40, and only a few thousand occur among those aged 80 and above.

29Having first calculated current smokers’ probability of dying with Equation 1, and then applying Equation 2 to their mortality risk to establish the probability of dying for ex-smokers of the same age and sex, we then combine each probability with the proportion of current and ex-smokers in that age–sex category. This yields an average probability of dying for that age–sex category. Next, we multiply that probability by the population in that age–sex category to generate a number of deaths attributable to smoking. This method allows one to simulate what-if scenarios by altering the smoking histories of cohorts (e.g. changing the smoking or cessation durations) to measure the effects of such changes on mortality.

30The next step is to calibrate the SAM observed in France. While Flanders et al. (2003) estimated a series of absolute mortality risks for current smokers, their true focus was on the relative effects of smoking duration and intensity (parameters β and γ) rather than baseline risk (parameter α). The reason for this is that baseline risk cannot credibly be extrapolated from their sample, while duration and intensity can, given that CPS-II comprises a self-selected sample of smokers less likely to die; it is nevertheless highly reliable for estimating the effects of duration and intensity on the relative risk of dying (Thun et al., 2000). Thus, we take the estimates for β_s,x and γ_s,x for each s, x from Flanders et al. (2003) and combine them with a calibrated value of α_s,x, denoted as α^c_s,x, which calibrates the number of deaths generated by the formula to the lung cancer SAM observed in France for that age–sex category in 2010. We used mortality figures published at the time by the Institut National de Veille Sanitaire (currently called Santé publique France), which used a demographic model to predict lung cancer mortality by age and sex for recent years from French population data for the year 2000 (INVS, 2005). We start with the number of projected lung cancer deaths among people aged 40–79 for the years 2005–2009, divide that number by 5 to get an annual number of deaths, and then apply a PAF of 0.82 [10] (USDHHS, 2014) to obtain only lung cancer SAM. Denoting the lung cancer SAM at age x and for sex s as SAM_s,x, α^c_s,x we calculate as follows:

31

3 – Projection and simulations

32Using our method to generate lung cancer SAM over a period of 50 years (2010–2060), we apply hypotheses of unchanged smoking behaviours or of future changes in prevalence by age for various cohorts, specifically those resulting from either initiation or cessation shocks.

33This calculation involves the following three steps. First, we prolong the trends by projecting age-specific smoking prevalence rates to 2060, separately by sex and birth cohort. This projection is based on recursive smoothing in which, for a given birth cohort c, prevalence for age group a (observed in year N = c + a) results from smoking prevalence in the age group a − 1 for the same birth cohort c, which is affected by a reproduced evolution of smoking behaviour between age categories a − 1 and a in the three preceding birth cohorts c − 1, c − 2, and c − 3. [11] It is recursive in that each age category and cohort builds on preceding cohorts (years of observation), by which the projection unfolds recursively.

34Second, we apply these simulated prevalence rates of smokers and quitters to age distribution projections for the French population (Blanpain and Chardon, 2010), [12] which allows us to generate numbers of smokers by smoking and cessation duration in a given year N by adding all cohorts or, equivalently, age categories observed in that year.

35Finally, we replicate the calculation steps for SAM, by which we ultimately generate what-if scenarios that reproduce shocks to prevalence by age for a given cohort as a result of changes in either initiation or cessation behaviours. This allows us to predict future SAM evolution as a response to these shocks.

36The online supplementary material provides detailed information on the following methodologies: a step-by-step description of the overall SAM calculation (Supplementary Material D); calculations for smoking prevalence projections (Supplementary Material E); and the what-if scenarios (Supplementary Material F).

1 – Smoking histories

37The age profiles of male smoking prevalence in France show a recent decline in total prevalence. It seems to be more the result of initiation among recent cohorts declining than cessation rates increasing among older ones (Table 2). Half of male smokers will have quit before age 54, at least for the three cohorts for which we have data past age 50.

38In each row of Table 2, we can follow the prevalence of smoking of a given cohort and calculate crude estimates of ‘quit rates’. Dividing the smoking prevalence of a given age group by the prevalence observed for the same cohort at its peak (either at ages 20–24 or 25–29) yields the proportion of smokers at peak consumption age who still smoke later in life. The complement to 1 of that proportion is the percentage of smokers at peak consumption age who will have quit at each later age for a given cohort.

Table 2

Prevalence (in %) of smoking by sex and age of birth cohorts

Males Birth cohort Age 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 1986–1990 31 51 1981–1985 38 54 58 1976–1980 33 52 52 53 1971–1975 36 55 50 46 47 1966–1970 37 59 53 54 45 44 1961–1965 45 59 62 53 49 39 39 1956–1960 40 71 65 53 54 43 39 33 1951–1955 64 61 55 48 39 35 32 27 1946–1950 59 55 49 41 46 30 26 19 1941–1945 53 50 42 46 39 30 21 18 1936–1940 54 45 47 45 32 21 13 8 1931–1935 49 46 50 34 19 18 11 1926–1930 50 48 38 26 20 20 Females Birth cohort Age 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 1986–1990 31 45 1981–1985 46 43 44 1976–1980 41 45 39 39 1971–1975 42 59 44 37 37 1966–1970 38 53 44 42 39 40 1961–1965 36 56 45 35 43 35 34 1956–1960 34 53 50 42 39 38 29 31 1951–1955 50 52 46 41 35 30 23 21 1946–1950 40 40 46 29 24 18 12 14 1941–1945 30 30 32 14 19 17 8 8 1936–1940 32 30 15 10 18 11 8 6 1931–1935 32 21 16 8 8 9 6 1926–1930 23 19 12 9 12 5

Prevalence (in %) of smoking by sex and age of birth cohorts

Note: Grey cells required linear interpolations from other cohorts.

39Quit rates for the 40–44 and 45–49 age groups are of particular interest, since epidemiological evidence proves that not quitting at this pivotal age of 40–45 years old significantly increases the likelihood of lung cancer. For males, quit rates at these ages have essentially increased over generations. The quit rate at ages 40–44 increased from 31% for the 1946–1950 cohort to nearly 40% for the next three cohorts. A similar pattern is observed for the quit rate at ages 45–49, which rose from 13% for the oldest cohort to 45% for the 1956–1960 cohort, before falling slightly for that of 1961–1965. Among females, the quit rate at ages 40–44 has remained stable around 30% for those born between 1946 and 1960, but it has increased to 38% for the most recent cohort (1966–1970). At ages 45–49, this rate has remained constant around 40% for the most recent cohorts. These numerical results are detailed in Supplementary Material G.

40Although these quit rates are inching in the right direction, they are still too low as more than half of ever-smokers continue to smoke at ages at which the risk of lung cancer increases exponentially.

41Based on similar raw data for our pseudo-cohorts, Figure 1 illustrates typical smoking duration patterns separately for males and females. The numbers of smokers for each smoking duration are expressed as a percentage of ever-smokers, where 100% represents the maximum stock of smokers observed before age 30. Cessation starts earlier for females than for males, while half the stock of ever-smokers has quit after 37 years of smoking for males but only after around 25 years for females.

2 – Smoking-attributable mortality in 2010

42The simulation under our central values for intensity and age of initiation produces a lung cancer SAM of 20,439 in 2010: 16,362 males versus 4,077 females; 12,261 current smokers versus 8,178 ex-smokers. This is very close to published SAM rates for France, as expected, since our calibration parameters are INVS-projected values (2005). After using a method developed by Ribassin-Majed and Hill (2015) to adjust for some deaths of unknown cause, Bonaldi et al. (2016) estimated between 19,920 and 22,395 strictly lung cancer deaths between the ages of 30 and 80 for 2013. These estimates are similar to ours, although they are for 2010 and between ages 40 and 80. The detailed results of SAM by age, sex, and smoking status in 2010 can be found in Table 3.

43Alternative assumptions for smoothing and interpolating prevalence produce lung cancer SAM values in the range of 19,904 to 20,799, representing a variation of −2.6% to +1.8% in our reference scenario. This suggests that our estimate is not highly sensitive to our assumptions for imputing missing or imprecise data. The results are more sensitive to the parameters for average age of initiation and smoking intensity. Varying intensity from 10 to 20 cigarettes per day yields a SAM of 16,509 to 23,886, i.e. a variation of −19% to +17% relative to the reference scenario. Using differential intensity by age and gender as reported in a recent survey (Enquête Santé et Protection Sociale, 2010; Dourgnon et al., 2012) does not lead to significant changes in observed lung cancer deaths, which are 20,041 versus 20,439 in the reference scenario. Conversely, varying the age of initiation between 13.5 and 21.5 years produces a SAM of 24,732 to 16,657, which represents a variation of +21% to −19% from the reference scenario. [13]

Figure 1

Typical smoking duration patterns among smokers

Typical smoking duration patterns among smokers

Interpretation: Reading from left to right, the second dash indicates that among male ever-smokers, 90% have smoked for at least 12.5 years (i.e. until age 30); that proportion falls to 80% for those who have smoked at least 17.5 years (i.e. until age 35).

Table 3

SAM due to lung cancer in 2010, re-evaluated by age, sex, and smoking status

Age Males Females Overall Current smokers Ex-smokers Total Current smokers Ex-smokers Total Current smokers Ex-smokers Total 40–44 264 28 291 153 10 162 416 38 454 45–49 746 99 845 327 21 348 1,073 120 1,193 50–54 1,318 370 1,688 475 78 553 1,792 448 2,240 55–59 1,984 591 2,575 442 144 586 2,426 735 3,161 60–64 1,652 1,053 2,704 372 153 525 2,023 1,206 3,229 65–69 1,600 965 2,565 294 216 510 1,894 1,182 3,075 70–74 999 1,864 2,863 374 253 627 1,374 2,117 3,491 75–79 887 1,944 2,831 377 389 765 1,263 2,333 3,596 Total 9,448 6,914 16,362 2,813 1,264 4,077 12,261 8,178 20,439

SAM due to lung cancer in 2010, re-evaluated by age, sex, and smoking status

Note: Numbers rounded to nearest integer.

3 – Projection to 2060 and what-if scenarios: SAM evolution if initiation and cessation were to change

44If prevalence rates are projected according to the observed trend (projection scenario), lung cancer SAM will increase rapidly up to a maximum value of 29,100 deaths around 2035. This average increase of +7% every 5 years reflects the fact that the generations of heavy smokers born during the 1950s and 1960s will be at maximum risk of lung cancer within the next 20 years, after which lung cancer SAM will plateau at around 30,000 deaths (see Figure 2). From 2040 onward, the projections strongly assume that prevalence will stabilize for future cohorts, as the prevalence rates are predicted recursively using the averaged rates of the three preceding cohorts.

Figure 2

Projected SAM due to lung cancer from 2010 to 2060 (reference scenario)

Projected SAM due to lung cancer from 2010 to 2060 (reference scenario)

45We now illustrate how the method can be used by simulating shocks to prevalence rates by increasing cessation (raising quit rates by 25%, 50%, 100%, and 200%) or by markedly decreasing initiation among teenagers (reducing initiation rates by 25%, 50%, 80%, and 100%), with these differential effects on mortality depending on whether the shock originates in initiation or cessation. Such shocks result from policies that alter behaviours, such as increasing taxation to reduce initiation, stringently controlling sales to youth, banning smoking in public spaces, and promoting health campaigns. Increased quit rates usually result from subsidizing cessation therapies and, to a lesser extent, by imposing higher taxes on cigarettes and bans on smoking in public spaces. The literature is quite clear that all these methods are somewhat effective in reducing initiation and/or increasing cessation (Levy et al., 2004).

46One may legitimately expect that duration and intensity also affect mortality due to causes other than lung cancer, and it is therefore essential to bear in mind that our scenarios and conclusions apply only to lung cancer mortality, which is expressed in numbers of deaths per year between 2010 and 2060. Our extreme initiation scenario is absolute (nobody starts smoking), whereas our extreme cessation scenario is relative (some smokers never quit).

47Figure 3 shows that the effect on mortality from reduced initiation would be null until 2035. Were one to extrapolate, the total number of lives saved (prevented SAM) over our simulation’s 50-year period varies between 10,000 and 41,000, with the former representing a 25% reduction in initiation and the latter an extreme scenario of no initiation at all (−100% initiation) in future cohorts.

Figure 3

SAM due to lung cancer from 2010 to 2060 if smoking initiation declines among youth, reference scenario vs. various reduced initiation rates

SAM due to lung cancer from 2010 to 2060 if smoking initiation declines among youth, reference scenario vs. various reduced initiation rates

48In contrast, the effect on SAM from increased cessation rates would begin as early as 2015, and total lives saved in this scenario would vary between 28,000 (increased quit rates of 25%) and 144,000 (tripling quit rates) over the next 50 years (Figure 4).

Figure 4

SAM due to lung cancer from 2010 to 2060 if smoking cessation increases after age 30, reference scenario vs. various increased cessation rates

SAM due to lung cancer from 2010 to 2060 if smoking cessation increases after age 30, reference scenario vs. various increased cessation rates

1 – Supporting adult cessation versus preventing teenage initiation

49To the best of our knowledge, ours is the first study to produce smoking prevalence age profiles and their associated smoking and abstinence durations for birth cohorts in France. We find that most of this country’s reductions in prevalence result from a drop in initiation and, to a lesser extent, from higher quit rates. The only comparable evidence comes from the United States, where a substantial reduction occurs in average smoking duration (Mannino et al., 2001), with a clear linear pattern in the decline in age at which a cohort’s smoking prevalence is at half the peak value (Pierce and Gilpin, 1996). Anderson et al. (2012) confirm the recent increase in cessation rates at each age for cohorts born after 1965 in the United States. Because a cross-sectional observation in France shows a clear educational gradient of age at cessation as the more-educated quit sooner (Legleye et al., 2011; Bricard and Jusot, 2012; Khlat et al., 2016), declining average age at cessation can reasonably be projected for future cohorts.

50Our method lends itself to running simulation-based estimates of lung cancer SAM so that analysts can produce what-if scenarios. These simulations and scenarios can help policymakers decide where to allocate economic resources for smoking reduction when assessing whether to prevent youth initiation or help smokers quit. While some often argue that both avenues should be pursued simultaneously (Myers, 1999), it remains true that one has to make a decision relative to the next unit of resource to be expended and, therefore, whether to fund cessation programmes or those targeting anti-initiation.

51Our projections of SAM to 2060 under various scenarios show that an effective strategy would be to increase cessation rates. Specifically, an intervention that doubles quit rates at all ages will prevent 5,000 to 14,000 lung cancer deaths each year, whereas preventing all teenagers from starting smoking would prevent 17,000 deaths in 2060 but almost none before 2040.

52This finding is certainly quite intuitive, being the result of different time horizons of policy tools (preventing teenage initiation vs. encouraging adult cessation), and our approach provides numerical evidence on the trade-off between these policies, which altogether quantifiably substantiates that financial support should favour adult cessation rather than teenage prevention programmes (Hill, 1999). Such policy recommendations would ultimately have to rely on a cost-effectiveness analysis that compares not only outcomes (as our method does) but also the costs of both approaches. While tools used to prevent initiation (higher taxes and controls on sales to minors) are widely considered inexpensive because they incur no direct cost to the public purse, they do generate indirect costs in the form of policing contraband and illegal sales to youth. Additionally, they are often associated with health promotion campaigns that incur direct costs. Admittedly, cessation therapies cost more, and a next step would be to conduct a full-fledged cost-effectiveness study of both approaches. Thus, we would like to restate that our current goal is merely to illustrate how our method can be used to run the type of analysis conducted here, which is not possible using the standard method.

2 – Methodological choices: limitations and potential improvements

53While our method’s principle is sound and adds to the standard method in that it allows analysts to conduct what-if scenarios that can help policymakers, something the standard method is not meant or able to do, we acknowledge that it depends crucially on how it is applied, which in turn hinges on assumptions and data availability. This new method assumes that duration and intensity—not mere prevalence—determine SAM and that, furthermore, independence exists between the main determinants of mortality, namely intensity, duration, and age at cessation. Specifically, our formulae assume that duration and intensity are independent and that smoking intensity remains constant for all birth cohorts and at all ages. Yet, it is plausible that heavier smokers tend to smoke longer and have a harder time quitting than lighter smokers. If so, we underestimate SAM and, more importantly, understate the effect of cessation therapies relative to decreases in initiation rates. Only panel data following smokers over a long period could provide estimates of the association between smoking duration and intensity at various ages, and this interdependence is policy-relevant information whose effect can be tested by running scenarios with our method. Considering all potential joint probabilities of dying would require a much more complex approach, similar to the abovementioned microsimulation models developed in Schultz et al. (2012) and Hazelton et al. (2012), whereas our method serves as a tool for running simple simulations with considerable flexibility—although the process may sacrifice some realism and possibly precision.

54This new method is also more data-demanding than the standard one. Our results depended on a series of choices and assumptions for complementing the available data, which we discuss below.