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1. SUMMARY Introduction Evidence is limited on how the onset of health conditions affects tobacco/nicotine use. We report analyses from adults in the U.S. PATH study relating changes in established cigarette and/or e-cigarette use between Waves 1 and 4 to onset of conditions in Waves 2 or 3. Methods Nine conditions had sufficient onsets for analysis. The main analyses adjusted for sex and age. Additional analyses excluded other tobacco/nicotine product users, controlled for more variables, restricted changes in one product to never users of the other, or restricted attention to changes occurring after onset of the condition. Results In the main analyses, onset of each condition predicted significantly increased overall e-cigarette initiation rates, with highest odds ratios (OR) for any major respiratory condition (2.86, 95% confidence interval (CI) 2.10–3.90) and cancer (2.51, 1.52–4.14). Each onset also predicted higher cigarette smoking initiation rates, but ORs were lower than for e-cigarette initiation, and only significant for any cardiovascular condition (1.65, 1.31–2.08), high blood pressure (1.34, 1.13–1.58), and any major smoking-related condition (1.35, 1.12–1.62). For quitting smoking only six conditions showed increased ORs, significant only for high cholesterol (1.29, 1.02–1.62). The additional analyses generally confirmed these patterns. Relationships with re-initiating cigarettes or becoming dual users of cigarettes and e-cigarettes were inconsistent. Onset of conditions also tended to reduce amount smoked by smokers. Conclusions While PATH is nationally representative, its sample size does not always allow precise estimates. There is concern about accuracy of statements by some older participants claiming to be Wave 1 never smokers but Wave 4 ever smokers. Nevertheless the previously unreported association between onset of the health conditions and e-cigarette initiation (which mainly occurs in Wave 1 cigarette smokers) appears real. [Contrib. Tob. Nicotine Res. 32 (2023) 1–10]

Summary Background Smoking is associated with cancer and cardio-respiratory mortality. Reducing smoking prevalence will lead to fewer deaths and more life-years. Here, we estimate the impact of hypothetical introduction of reduced-risk products (heat-not-burn products and e-cigarettes) in Germany from 1995 to 2015 on mortality from lung cancer, chronic obstructive pulmonary disease, ischaemic heart disease, and stroke in men and women aged 30–79 years. Methods We used a previously described population health impact model, with individuals with a defined baseline cigarette smoking distribution followed under a “Null Scenario”, with reduced-risk products never introduced, and various “Alternative Scenarios” where they are. Transition probabilities allow product use to change annually, with the individual product histories allowing estimation of risks, relative to never users, which are then used to estimate reductions in deaths and life-years lost for each Alternative Scenario. Results In the Null Scenario, we estimated 852,000 deaths from cigarette smoking (42,600 per year), with 8.61 million life-years lost. Had everyone ceased smoking in 1995, and with no use of reduced-risk products, these numbers would reduce by 217,000 and 2.88 million. Compared to the Null Scenario, the estimated reductions would be 159,000 and 2.06 million with an immediate complete switch to heat-not-burn products and 179,000 and 2.34 million with 50% of smokers immediately switching to heat-not-burn products and 50% to e-cigarettes. In four Scenarios with a more gradual switch, the estimated decreases were 39,800–81,000 deaths and 0.50–1.05 million life-years, representing 17.5%–37.5% of the effect of immediate cessation in 1995. These estimates assume that switching to heat-not-burn products and e-cigarettes involves risk decreases of 80% and 95% of those from quitting, respectively. The reductions in mortality would be greater with more diseases and a wider age range considered or with a longer follow-up period, as the decreases increased markedly with time. Various limitations are discussed, none affecting the conclusion that introducing these new products into Germany in 1995 could have substantially reduced deaths and life-years lost. Conclusions Deaths from cigarette smoking could be substantially reduced not only by cessation but additionally by switching to reduced-risk products. Respective public health campaigns might increase such switching.

Summary Background We previously estimated the impact of introducing heat-not-burn products and e-cigarettes in Germany on smoking-related disease mortality in men and women aged 30–79 years between 1995 and 2015. Here, we estimate the impact by socioeconomic group. Methods Individuals with a defined baseline cigarette smoking distribution were followed under a “Null Scenario” (no reduced-risk products) and “Alternative Scenarios” (reduced-risk products introduced). Transition probabilities allowed estimation of annual product use changes, with individual product histories used to estimate reductions in deaths and life-years lost. Here, however, individuals were classified into two socioeconomic groups defined by income and education, with allowance for variation by group in initial smoking prevalence and the probability of changing product use, or of changing socioeconomic group. Results With no allowance for socioeconomic group, deaths would have reduced by 217,000 (from 852,000 for continued smoking) had everyone immediately ceased smoking in 1995 and by 40,000 to 179,000 had one or two types of reduced-risk products – the heat-not-burn product and the e-cigarette – been adopted by smokers to varying extents. With such allowance, we estimate substantial drops in each socioeconomic group. Where all cigarette smokers switched immediately, half of them to heat-not-burn products, half to e-cigarettes, the estimated drops in deaths were 60,000 in group A (higher socioeconomic group) and 122,000 in group B (lower), about 82% of the drops associated with immediate cessation (73,000 in A and 148,000 in B). With more gradual conversion, the drops were 26,648 in A and 53,000 in B, about 35% of those from cessation. The drops in deaths and life-years saved were about 2 and 1.5 times higher in group B, respectively, associated with its greater numbers, older age, and higher smoking prevalence. The estimated reductions would increase upon considering more diseases, a wider age range, or longer follow-up. Methodological limitations would not affect the conclusion that introducing these products in 1995 in Germany could have substantially reduced deaths and life-years lost in both groups, more so in B. Conclusions Although cessation is optimal for reducing mortality, switching to reduced-risk products also provides substantial health gains. A public health approach encouraging lower socioeconomic group smokers to switch to reduced-risk products could diminish smoking-related health inequalities relative to continued smoking.

Adobe PDF - MCT-06-0767--Suppl_Materials.pdf from 2-Methoxy antimycin reveals a unique mechanism for Bcl-xL inhibition

Overexpression of Bcl-xL in multiple cancers correlates with resistance to chemotherapy and radiation therapy, and provides a rationale for development of small-molecule Bcl-xL inhibitors. Based on knockout studies, nonneoplastic cells also require Bcl-xL survival functions, particularly when challenged with cytotoxic agents. We analyze the selective cytotoxicity of one Bcl-xL inhibitor, 2-methoxy antimycin A, toward cells with excess exogenous Bcl-xL in isogenic cell line pairs. This selectivity, characteristic of a gain-of-function mechanism, is not shared by other known Bcl-xL inhibitors, including BH3I-2, HA14-1, ABT-737, gossypol, or the stapled BH3 helical peptide SAHB-BID. We show that Bcl-xL overexpression induces a shift in energy metabolism from oxidative phosphorylation to glycolysis. Treatment with 2-methoxy antimycin A acutely reverses the metabolic effects of Bcl-xL, causing mitochondrial hyperpolarization and a progressive increase in mitochondrial NAD(P)H. We identify an additional small-molecule Bcl-xL inhibitor, NSC 310343, establishing a class of Bcl-xL inhibitors with gain-of-function activity. In contrast to other Bcl-xL inhibitors, combining gain-of-function Bcl-xL inhibitors with a standard inducer of apoptosis, staurosporine, enhances selective cytotoxicity toward Bcl-xL–overexpressing cells. These results provide an example of the intersection of bioenergetic metabolism and Bcl-xL functions and suggest a metabolic basis for the gain-of-function mechanism of Bcl-xL inhibitors. [Mol Cancer Ther 2007;6(7):2073–80]

BackgroundFor smokers not intending to quit, switching to a reduced-risk nicotine product should be healthier than continuing smoking. We estimate the health impact, over the period 2000-2050, of introducing into the US the nicotine pouch ZYN. ZYN’s toxicant profile and method of use is like that for Swedish snus, a product with known health effects much less than smoking. MethodsOur modelling approach is similar to others developed for estimating potential effects of new tobacco products. It starts with a simulated cohort of 100,000 individuals in the year 2000 subdivided by age, sex, and smoking status (including years since quitting). They are followed annually accounting for births, net immigrations, deaths and product use changes, with follow-up carried out in the Base Case (ZYN not introduced) and Modified Case (ZYN introduced). Using informed assumptions about initiation, quitting and switching rates, distributions of the population over time are then constructed for each Case, which are used to estimate product-related mortality based on assumptions about the relative risk according to product use. ResultsWhereas in both Base and Modified Cases, the prevalence of any current product use is predicted to decline from about 22% to 10% during follow-up, in the Modified Case about 25% of current users use ZYN by 2050, about a quarter being dual users and the rest ZYN-only users. Over the 50 years, deaths at ages 35-84 from product use among the 100,000 are estimated as 249 less in the Modified than the Base Case, equivalent to about 700,000 less deaths in the whole US. Sensitivity analyses varying individual parameter values confirm the benefits of switching to ZYN, which increase as either the switching rate to ZYN increases or the initiation rate of ZYN relative to smoking increases. Even assuming the reduction in excess mortality risk using ZYN use is 20% of that from smoking rather than the 3.5% assumed in the main analyses, the reduction in product-related deaths would still be 213, or about 600,000 in the US. ConclusionsAlthough such model-based estimates are subject to uncertainties, the results suggest that introducing ZYN could substantially reduce smoking-attributable deaths.

Background For smokers not intending to quit, switching to a reduced-risk nicotine product should be healthier than continuing smoking. We estimate the health impact, over the period 2000–2050, had the nicotine pouch ZYN hypothetically been introduced into the US in 2000. ZYN’s toxicant profile and method of use is like that for Swedish snus, a product with known health effects much less than smoking. Methods Our modelling approach is similar to others developed for estimating potential effects of new tobacco products. It starts with a simulated cohort of 100,000 individuals in the year 2000 subdivided by age, sex, and smoking status (including years since quitting). They are followed annually accounting for births, net immigrations, deaths and product use changes, with follow-up carried out in the Base Case (ZYN not introduced) and Modified Case (ZYN introduced). Using informed assumptions about initiation, quitting and switching rates, distributions of the population over time are then constructed for each Case, and used to estimate product mortality based on assumptions about the relative risk according to product use. Results Whereas in both Base and Modified Cases, the prevalence of any current product use is predicted to decline from about 22% to 10% during follow-up, in the Modified Case about 25% of current users use ZYN by 2050, about a quarter being dual users and the rest ZYN-only users. Over the 50 years, deaths at ages 35–84 from product use among the 100,000 are estimated as 249 less in the Modified than the Base Case, equivalent to about 700,000 less in the whole US. Sensitivity analyses varying individual parameter values confirm the benefits of switching to ZYN, which increase as either the switching rate to ZYN increases or the initiation rate of ZYN relative to smoking increases. Even assuming the reduction in excess mortality risk using ZYN use is 20% of that from smoking rather than the 3.5% assumed in the main analyses, the reduction in product-related deaths would still be 213, or about 600,000 in the US. Conclusions Although such model-based estimates involve uncertainties, the results suggest that introducing ZYN could substantially reduce product-related deaths.

Background Recent estimates indicated substantially replacing cigarettes by e-cigarettes would, during 2016–2100, reduce US deaths and life-years lost (millions) by 6.6 and 86.7 (Optimistic Scenario) and 1.6 and 20.8 (Pessimistic). To provide additional insight we use alternative modelling based on a shorter period (1991–2040), four main smoking-associated diseases, deaths aged 30–79 years, and a full product history. We consider variations in: assumed effective dose of e-cigarettes versus cigarettes (F); their relative quitting rate (Q); proportions smoking after 10 years (X); and initiation rate (I) of vaping, relative to smoking. Methods We set F = 0.05, X = 5%, Q = 1.0 and I = 1.0 (Main Scenario) and F = 0.4, X = 10%, Q = 0.5 and I = 1.5 (Pessimistic Scenario). Sensitivity Analyses varied Main Scenario parameters singly; F from 0 to 0.4, X 0.01% to 15%, and Q and I 0.5 to 1.5. To allow comparison with prior work, individuals cannot be dual users, re-initiate, or switch except from cigarettes to e-cigarettes. Results Main Scenario reductions were 2.52 and 26.23 million deaths and life-years lost; Pessimistic Scenario reductions were 0.76 and 8.31 million. These were less than previously, due to the more limited age-range and follow-up, and restriction to four diseases. Reductions in deaths (millions) varied most for X, from 3.22 (X = 0.01%) to 1.31 (X = 15%), and F, 2.74 (F = 0) to 1.35 (F = 0.4). Varying Q or I had little effect. Conclusions Substantial reductions in deaths and life-years lost were observed even under pessimistic assumptions. Estimates varied most for X and F. These findings supplement literature indicating e-cigarettes can importantly impact health challenges from smoking.

In our latest update of the evidence on smoking bans and heart disease we summarize 59 studies. We take account of the underlying trends in incidence rates as far as possible by using control data in eight studies, and by adjustment based on observed trends in cases pre- and post-ban in 40 studies, being unable to make an adjustment in the remaining 11 studies. Overall, based on 62 independent estimates from the 59 studies, we estimate that bans reduce incidence by 5.0% (95% CI 3.2-6.8%), though this estimate reduces to 2.9% (0.01-5.6%) when we exclude regional estimates where national estimates are available, and studies where trend adjustment is not possible. For 25 of the studies, quadratic rather than linear adjustment is possible, but this hardly affects the overall estimates. Ban effects are somewhat greater when the pre-ban period studied is relatively short, and in smaller studies. We compare our findings with those in other recent reviews, one of which totally ignored underlying trends and results from control populations. We discuss reasons why we believe there is likely to be a true small effect of smoking bans, and weaknesses in the data which preclude reaching any very confident conclusion.

Background Smoking is associated with cancer and cardiorespiratory disease mortality. Reducing smoking prevalence reduces deaths and life-years lost. Here, we estimate the impact of introducing heat-not-burn products and e-cigarettes in Germany from 1995 to 2015 on mortality from lung cancer, chronic obstructive pulmonary disease, ischaemic heart disease, and stroke in men and women aged 30–79 years. Methods We used the previously described population health impact model. Modelling starts with individuals of a given sex and age range with a defined cigarette smoking distribution. They are then followed under a “Null Scenario”, where reduced-risk products are never introduced, and one of seven “Alternative Scenarios”, where they are. Transition probabilities allow tobacco product use to change annually, and the individual product histories then allow estimation of risks, relative to never users, for each year and Scenario, which are then used to estimate reductions in deaths and life-years lost for each Alternative Scenario. Results In the Null Scenario, we estimated 852,357 deaths from cigarette smoking, with 8.61 million life-years lost. Had everyone quit in 1995, with no further use of the three products, these numbers would reduce by 216,650 and 2.88 million. The reductions would be 159,278 and 2.06 million with an immediate complete switch from cigarettes to heat-not-burn products, and 179,470 and 2.34 million with an immediate switch to 50% heat-not-burn products and 50% e-cigarettes. In four Scenarios with a more gradual switch, estimated decreases were 39,818–81,293 deaths and 0.50–1.05 million life-years, representing 17.5%-37.5% of the effect of cessation. These estimates assume switching to heat-not-burn products and e-cigarettes involves risk decreases of 80% and 95% of those from quitting. The decreases would have increased, had more diseases and a wider age range been considered, and also with a longer follow-up period, the decreases increasing markedly with time. Conclusions Various limitations are discussed, none affecting our conclusion that introducing these new products into Germany in 1995 could have substantially reduced mortality. Our results imply deaths from cigarette smoking could be substantially reduced, both by cessation and switching to reduced-risk products. Risk-proportionate public health campaigns and regulation might increase such switching.

Background Previously, we estimated the impact of introducing heat-not-burn products and e-cigarettes in Germany between 1995 and 2015 on mortality from the four main smoking-related diseases in men and women aged 30–79 years. Deaths would have reduced by 216,650 (from 852,357 for continued smoking) had everyone quit smoking in 1995 and by 39,818 − 179,470 had one or both of heat-not burn products and e-cigarettes been introduced and adopted to varying extents. Here, we report substantial mortality reductions separately in two socioeconomic groups (A = higher, B = lower) defined by income and education. Methods Modelling is essentially as before, with individuals of a given sex, age range and cigarette smoking distribution followed over time under a “Null Scenario” (reduced-risk products never introduced) and various “Alternative Scenarios” (reduced-risk products introduced), the individual product histories generated then allowing estimation of reductions in mortality for each Alternative Scenario compared to the Null Scenario. Here, however, individuals are subdivided into two socioeconomic groups with transition probabilities between product use groups varying by group as well as by sex, age and length of follow-up. The possibility of transitioning between socioeconomic groups is also allowed for. Results Where all cigarette smokers switch immediately, half to each new product, the drops in deaths were estimated as 60,081 in group A and 122,343 in group B, about 82% of the drops associated with immediate cessation (72,725 A, 147,684 B). Where the conversion was more gradual, the drops were about 35% of those from cessation (25,648 A, 52,652 B). Drops were about two times higher in B, because of their greater numbers, older age, and higher frequency of current smokers. Years of life saved were also higher (about 1.5 times) in B than A. Conclusions Methodological limitations would not have affected our conclusion that introducing these products in 1995 in Germany could have substantially reduced mortality in each group, with greater gains in B. While cessation is the best choice to reduce mortality, switching to reduced-risk products also provides substantial health gains. Encouraging lower socioeconomic group smokers who would otherwise continue smoking to fully switch to reduced-risk products could diminish smoking-related health inequalities.

Background: Considerable interest exists in whether e-cigarette use (“vaping”) by youths increases the risk of initiating cigarette smoking. Based on Waves 1 and 2 of the Population Assessment of Tobacco and Health study we reported that adjustment for propensity for vaping using Wave 1 variables explained about 80% of the unadjusted relationship. This analysis may be over-adjusted had vaping at Wave 1 affected some variables recorded then. Here we present analyses using Waves 1 to 3 to avoid this possibility. Methods: Our main analysis M1 concerned those who had never smoked by Wave 2 and never vaped by Wave 1. Wave 2 vaping was linked to smoking initiation by Wave 3, adjusting for Wave 1 predictors. Sensitivity analyses excluded other tobacco product users at Wave 1, included other tobacco product use as an additional predictor, or were based on propensity for ever smoking or ever any tobacco use, rather than ever vaping. Other analyses adjusted for propensity as derived originally, or ignored Wave 1 data. Other analyses used grouped age (only available originally) or exact age (available now) as a confounder variable, attempted residual confounding adjustment by modifying values of predictor variables using data later recorded, or considered interactions with age. Results: In M1, propensity adjustment removed about 50% of the excess odds ratio (i.e. OR–1), the unadjusted OR, 5.60 (95% CI 4.52-6.93) becoming 3.37 (2.65-4.28), 3.11 (2.47-3.92) or 3.27 (2.57-4.16) depending whether adjustment was for propensity as a continuous variable, as quintiles, or for the 16 variables making up the propensity score. Many factors studied hardly affected the results, including using grouped or exact age, consideration of other tobacco products, including interactions, or using predictors of smoking or tobacco use rather than vaping. The clearest conclusion was that analyses avoiding over-adjustment only explained about 50% of the excess OR whereas analyses subject to over-adjustment explained about 80%. Conclusions: Although much of the unadjusted gateway effect results from uncontrolled confounding, our current analysis provides stronger evidence of a causal effect of vaping than did our earlier analysis. However, some doubts remain about the completeness of confounder adjustment.

We use Population Health Impact Modelling to assess effects on tobacco prevalence and mortality of introducing a Reduced Risk Tobacco Product (RRP). Simulated samples start in 1990 with a US-representative smoking prevalence. Individual tobacco histories are updated annually until 2010 using estimated probabilities of switching between never/current/former smoking where the RRP is not introduced, with current users subdivided into cigarette/RRP/dual users where it is. RRP-related mortality reductions from lung cancer, IHD, stroke and COPD are derived from the histories and the assumed relative risks of the RRP. A basic analysis assumes a hypothetical RRP reduces effective dose 80% in users and 40% in dual users, with an uptake rate generating ∼10% RRP and ∼6% dual users among current users after 10 years. Sensitivity study changes in tobacco prevalence and mortality from varying effective doses, current smoking risks, quitting half-lives and rates of initiation, switching, re-initiation and cessation. They also study extreme situations (e.g. everyone using RRP), and investigate assumptions which might eliminate the RRP-related mortality reduction. The mortality reduction is proportional to the dose reduction, increasing rapidly with time of follow-up. Plausible increases in re-initiation or dual users' consumption, or decreased quitting by smokers would not eliminate the drop.

Background We have developed an approach for modelling the health impact of introducing new smoke-free tobacco products. We wished to compare its estimates with those of alternative approaches, when applied to snus, used in Sweden for many years. Methods Modelling was restricted to men aged 30–79 years for 1980–2009 and to four smoking-related diseases. Mortality data were extracted for Sweden and other European countries. Published data provided Swedish prevalence estimates for combinations of never/former/current smoking and snus use, and smoking prevalence estimates for other European countries. Approach 1 compares mortality in Sweden and in other countries with a smoking prevalence similar to Sweden’s prevalence of combined smoking/snus use. Approaches 2 and 3 compare mortality in Sweden with hypothetical mortality had snus users smoked. Approach 3 uses our health impact model, individuals starting with the tobacco prevalence of Sweden in 1980. Tobacco histories during 30-year follow-up were then estimated using transition probabilities, with risk derived using a negative exponential model. Approach 2 uses annual tobacco prevalence estimates coupled with estimates of relative risk of current and former smokers regardless of history. The main applications of Approaches 2 and 3 assume that only smoking affects mortality, though sensitivity analyses using Approach 3 allow for risk to vary in snus users and dual users. Results Using Approach 2, estimated mortality increases in Sweden in 1980–2009 had snus not been introduced were: lung cancer 8786; COPD 1781; IHD 10,409; stroke 1720. The main Approach 3 estimates were similar (7931, 1969; 12,501; 1901). They decreased as risk in snus users and dual users increased. Approach 1 estimates differed wildly (77,762, 32,538; 77,438; 76,946), remaining very different following correction for differences between Sweden and the comparison countries in non-smoking-related disease mortality. Conclusions Approach 1 is unreliable, accounting inadequately for non-tobacco factors affecting mortality. Approaches 2 and 3 provide reasonably similar approximate estimates of the mortality increase had snus not been available, but have differing advantages and disadvantages. Only Approach 3 considers tobacco history, but develops histories using tobacco transition probabilities, which is possibly less reliable than using estimated tobacco prevalences at each follow-up year.

Recently, Carroll and colleagues ([1][1]) concluded that filter ventilation was not associated with biomarkers of exposure (BOE) or biomarkers of potential harm (BOPH). When one assesses exposure and harm from different cigarette types, one usually consider results not only in terms of harm to the

Background: Interest exists in whether youth e-cigarette use (“vaping”) increases risk of initiating cigarette smoking. Using Waves 1 and 2 of the US PATH study we previously reported adjustment for vaping propensity using Wave 1 variables explained about 80% of the unadjusted relationship. Here data from Waves 1 to 3 are used to avoid over-adjustment if Wave 1 vaping affected variables recorded then. Methods: Main analyses M1 and M2 concerned Wave 2 never smokers who never vaped by Wave 1, linking Wave 2 vaping to Wave 3 smoking initiation, adjusting for predictors of vaping based on Wave 1 data using differing propensity indices. M3 was similar but derived the index from Wave 2 data. Sensitivity analyses excluded Wave 1 other tobacco product users, included other product use as another predictor, or considered propensity for smoking or any tobacco use, not vaping. Alternative analyses used exact age (not previously available) as a confounder not grouped age, attempted residual confounding adjustment by modifying predictor values using data recorded later, or considered interactions with age. Results: In M1, adjustment removed about half the excess OR (i.e. OR–1), the unadjusted OR, 5.60 (95% CI 4.52-6.93), becoming 3.37 (2.65-4.28), 3.11 (2.47-3.92) or 3.27 (2.57-4.16), depending whether adjustment was for propensity as a continuous variable, as quintiles, or the variables making up the propensity score. Many factors had little effect: using grouped or exact age; considering other products; including interactions; or using predictors of smoking or tobacco use rather than vaping. The clearest conclusion was that analyses avoiding over-adjustment explained about half the excess OR, whereas analyses subject to over-adjustment explained about 80%. Conclusions: Although much of the unadjusted gateway effect results from confounding, we provide stronger evidence than previously of some causal effect of vaping, though doubts still remain about the completeness of adjustment.

Background: Modelling disease risk from e-cigarette use requires knowing how much e-cigarette uptake affects a smoker’s cigarette consumption. From Waves 1-3 of the US PATH study and other evidence, we sought answers to three questions: (Q1) Does consumption differ between dual users and exclusive smokers? (Q2) Does initiating e-cigarettes affect cigarette consumption? (Q3) Is baseline consumption related to later initiation of e-cigarettes? Methods: Consumption data from PATH were corrected for clear recording errors, with e-cigarette use classified as established or every day, and adjustment made for demographics, use of alcohol, drugs and other tobacco products, and age of starting smoking. Searches identified other studies published since 2008, with Q1 answered from cross-sectional studies, and Q2 and Q3 also from prospective studies. Results: (Q1) In PATH, consumption in current every day e-cigarette users was 2-3 cigarettes per day lower in dual users, with no difference seen in established users. From 31 published studies, consumption was no lower in dual users for current or ever e-cigarette users. (Q2) In PATH study consistent decreases in consumption occurred in those becoming every day dual users with increases seen in those reverting to exclusive smoking. Thirteen published studies consistently showed reduced consumption in those becoming dual users. (Q3) Three studies consistently showed greater consumption in smokers subsequently initiating e-cigarettes, but no significant difference was seen for PATH. Conclusions: Assessment is complicated by the few every day e-cigarette users in PATH, and between-study variability in quantifying e-cigarette use and presentation of results. As taking up e-cigarettes is associated with reducing consumption, finding no clear cross-sectional difference between dual users and exclusive smokers suggests smokers taking up e-cigarettes had higher consumption initially, limited evidence supporting this. Given the much lower disease risk of e-cigarettes vs. cigarettes, smokers becoming dual users should somewhat reduce their disease risk.

Reduced Risk Products (RRPs) do not burn tobacco and produce lower levels of toxicants than in cigarette smoke. The long-term effects of using RRPs on health are difficult to assess in a pre-market setting and a modeling approach is required to quantify harm reduction. The Population Health Impact Model (Weitkunat et al., 2015) follows a hypothetical population of individuals over time, creating their tobacco use histories and, based on these, estimating relative and absolute risks of lung cancer, ischemic heart disease, stroke and chronic obstructive pulmonary disease. Linking the tobacco use to the risk profile allow us to assess how the relative and absolute risks of these diseases vary between individuals aged 20, 30, 40 or 50 at baseline who have never smoked or who initiated smoking at 19 years old and either continued to smoke, quit smoking, or switched to RRPs with varying degrees of harm reduction. The simulations suggest that, for smokers in their 20s-30s quitting, or switching to RRP primarily prevents the accrual of risk, while in their 40s-50s it reduces risk. Though tobacco prevention has been the primary approach to limit smoking-related diseases, RRPs can also substantially reduce risks in individuals who do not quit.

We present analyses relating cigarette type to lung cancer based on a case-control study in five European countries. The analyses involved 3561 cases and 2301 controls with diseases not associated with smoking. Subjects completed a detailed questionnaire, including a lifetime smoking history. Analyses included never smokers, and those who smoked for at least 80% of the “critical period” from 2 to 20 years before diagnosis, ignoring those who ever smoked pipes or cigars, or chewed tobacco. The main analysis compares risk in those who, in the critical period, smoked ultra-low tar (ULT) cigarettes (machine yield ≤3 mg tar/cigarette) for 8 + years, with those who only smoked full flavour (FF) cigarettes (≥10 mg tar/cigarette). After adjustment for sex, age, country, education, age of starting smoking, mean cigarette consumption and mean tar level 21–50 years before interview, the odds ratio (OR) was 0.73 (95% confidence interval (CI) 0.50–1.06). Other analyses showed a modest, not statistically significant, reduction in risk with tar reduction. Risk in ULT smokers for 8 + years was substantially higher than in never smokers (OR 16.27, 95% CI 10.14–26.09). The study was prematurely terminated due to cost overrun, limiting the power to detect an association. More evidence is needed, particularly on lifetime ULT smoking.

Meta-analyses often combine covariate-adjusted effect estimates (odds ratios or relative risks) and confidence intervals relating a specified endpoint to a given exposure. Standard techniques are available to do this where the exposure is a simple presence/absence variable, or can be expressed in defined units. However, where the definition of exposure is qualitative and may vary between studies, meta-analysis is less straightforward. We introduce a new “Uniform Scale” approach allowing expression of effect estimates in a consistent manner, comparing individuals with the most and least possible exposure. In 2008, we presented methodology and made available software to obtain estimates for specific pairwise comparisons of exposure, such as any versus none, where the source paper provides estimates for multiple exposure categories, expressed relative to a common reference group. This methodology takes account of the correlation between the effect estimates for the different levels. We have now extended our software, available in Excel, SAS and R, to obtain effect estimates per unit of exposure, whether the exposure is defined or is to be expressed in the “Uniform Scale”. Examples of its use are presented.

We update an earlier review of smoking bans and heart disease, restricting attention to admissions for acute myocardial infarction. Forty-five studies are considered. New features of our update include consideration of non-linear trends in the underlying rate, a modified trend adjustment method where there are multiple time periods post-ban, comparison of estimates based on changes in rates and numbers of cases, and comparison of effect estimates according to post-ban changes in smoking restrictiveness. Using a consistent approach to derive ban effect estimates, taking account of linear time trends and control data, the reduction in risk following a ban was estimated as 4.2% (95% confidence interval 1.8–6.5%). Excluding regional estimates where national estimates are available, and studies where trend adjustment was not possible, the estimate reduced to 2.6% (1.1–4.0%). Estimates were little affected by non-linear trend adjustment, where possible, or by basing estimates on changes in rates. Ban effect estimates tended to be greater in smaller studies, and studies with greater post-ban changes in smoking restrictiveness. Though the findings suggest a true effect of smoking bans, uncertainties remain, due to the weakness of much of the evidence, the small estimated effect, and various possibilities of bias.

We quantified the decline in COPD risk following quitting using the negative exponential model, as previously carried out for other smoking-related diseases. We identified 14 blocks of RRs (from 11 studies) comparing current smokers, former smokers (by time quit) and never smokers, some studies providing sex-specific blocks. Corresponding pseudo-numbers of cases and controls/at risk formed the data for model-fitting. We estimated the half-life (H, time since quit when the excess risk becomes half that for a continuing smoker) for each block, except for one where no decline with quitting was evident, and H was not estimable. For the remaining 13 blocks, goodness-of-fit to the model was generally adequate, the combined estimate of H being 13.32 (95% CI 11.86–14.96) years. There was no heterogeneity in H, overall or by various studied sources. Sensitivity analyses allowing for reverse causation or different assumed times for the final quitting period little affected the results. The model summarizes quitting data well. The estimate of 13.32years is substantially larger than recent estimates of 4.40years for ischaemic heart disease and 4.78years for stroke, and also larger than the 9.93years for lung cancer. Heterogeneity was unimportant for COPD, unlike for the other three diseases.

Background: A recent meta-analysis of nine cohort studies in youths reported that baseline ever e-cigarette use strongly predicted cigarette smoking initiation in the next 6-18 months, with an adjusted odds ratio (OR) of 3.62 (95% confidence interval 2.42-5.41). A recent e-cigarette review agreed there was substantial evidence for this “gateway effect”. As the number of confounders considered in the studies was limited we investigated whether the effect might have resulted from inadequate adjustment, using Waves 1 and 2 of the US PATH study. Methods: Our main analyses considered Wave 1 never cigarette smokers who, at Wave 2, had data on smoking initiation.We constructed a propensity score for ever e-cigarette use from Wave 1 variables, using this to predict ever cigarette smoking. Sensitivity analyses accounted for other tobacco product use, linked current e-cigarette use to subsequent current smoking, or used propensity scores for ever smoking or ever tobacco product use as predictors. We also considered predictors using data from both waves, attempting to reduce residual confounding from misclassified responses. Results: Adjustment for propensity dramatically reduced the unadjusted OR of 5.70 (4.33-7.50) to 2.48 (1.85-3.31), 2.47 (1.79-3.42) or 1.85 (1.35-2.53), whether adjustment was made as quintiles, as a continuous variable or for the individual variables. Additional adjustment for other tobacco products reduced this last OR to 1.59 (1.14-2.20). Sensitivity analyses confirmed adjustment removed most of the gateway effect. Control for residual confounding also reduced the association. Conclusions: We found that confounding is a major factor, explaining most of the observed gateway effect. However, our analyses are limited by small numbers of new smokers considered and the possibility of over-adjustment if taking up e-cigarettes affects some predictor variables. Further analyses are intended using Wave 3 data to try to minimize these problems, and clarify the extent of any true gateway effect.

The excess lung cancer risk from smoking declines with time quit, but the shape of the decline has never been precisely modelled, or meta-analyzed. From a database of studies of at least 100 cases, we extracted 106 blocks of RRs (from 85 studies) comparing current smokers, former smokers (by time quit) and never smokers. Corresponding pseudo-numbers of cases and controls (or at-risk) formed the data for fitting the negative exponential model. We estimated the half-life (H, time in years when the excess risk becomes half that for a continuing smoker) for each block, investigated model fit, and studied heterogeneity in H. We also conducted sensitivity analyses allowing for reverse causation, either ignoring short-term quitters (S1) or considering them smokers (S2). Model fit was poor ignoring reverse causation, but much improved for both sensitivity analyses. Estimates of H were similar for all three analyses. For the best-fitting analysis (S1), H was 9.93 (95% CI 9.31–10.60), but varied by sex (females 7.92, males 10.71), and age (

No previous review has formally modelled the decline in IHD risk following quitting smoking. From PubMed searches and other sources we identified 15 prospective and eight case-control studies that compared IHD risk in current smokers, never smokers, and quitters by time period of quit, some studies providing separate blocks of results by sex, age or amount smoked. For each of 41 independent blocks, we estimated, using the negative exponential model, the time, H, when the excess risk reduced to half that caused by smoking. Goodness-of-fit to the model was adequate for 35 blocks, others showing a non-monotonic pattern of decline following quitting, with a variable pattern of misfit. After omitting one block with a current smoker RR 1.0, the combined H estimate was 4.40 (95% CI 3.26–5.95) years. There was considerable heterogeneity, H being 10years for 12. H increased (p

Overexpression of Bcl-xL in multiple cancers correlates with resistance to chemotherapy and radiation therapy, and provides a rationale for development of small-molecule Bcl-xL inhibitors. Based on knockout studies, nonneoplastic cells also require Bcl-xL survival functions, particularly when challenged with cytotoxic agents. We analyze the selective cytotoxicity of one Bcl-xL inhibitor, 2-methoxy antimycin A, toward cells with excess exogenous Bcl-xL in isogenic cell line pairs. This selectivity, characteristic of a gain-of-function mechanism, is not shared by other known Bcl-xL inhibitors, including BH3I-2, HA14-1, ABT-737, gossypol, or the stapled BH3 helical peptide SAHB-BID. We show that Bcl-xL overexpression induces a shift in energy metabolism from oxidative phosphorylation to glycolysis. Treatment with 2-methoxy antimycin A acutely reverses the metabolic effects of Bcl-xL, causing mitochondrial hyperpolarization and a progressive increase in mitochondrial NAD(P)H. We identify an additional small-molecule Bcl-xL inhibitor, NSC 310343, establishing a class of Bcl-xL inhibitors with gain-of-function activity. In contrast to other Bcl-xL inhibitors, combining gain-of-function Bcl-xL inhibitors with a standard inducer of apoptosis, staurosporine, enhances selective cytotoxicity toward Bcl-xL–overexpressing cells. These results provide an example of the intersection of bioenergetic metabolism and Bcl-xL functions and suggest a metabolic basis for the gain-of-function mechanism of Bcl-xL inhibitors. [Mol Cancer Ther 2007;6(7):2073–80]

We examine in detail arguments put forward to support the claim that exposure to environmental tobacco smoke (ETS) increases the risk of lung cancer in non-smokers. Hackshaw et al. [Br Med J 1997;315:980-988] have esti mated that the risk increases by 23% (95% Cl 14-32%) per 10 cigarettes/day smoked by the husband. The esti mated increase essentially disappears if proper adjust ment is made for smoking misclassification bias, if cor rection is made for the joint effects of confounding by fruit, vegetables, dietary fat and education, if errors in published data in one study are corrected, and if results from all pertinent studies are included (and not just those which report risk by level of smoking by the husband). Taking account of all these factors and using un- weighted estimates of the association between ETS ex posure and the confounding variables (as one very large study reported results discrepant from those for numer ous smaller studies), the risk increase per 10 cigarettes/ day was found to be 2% (95% Cl -3 to +7.5%), based on data from 47 ETS/lung cancer studies. Using weighted estimates, the risk increase was 5.5% (95% Cl 0 to +11 %). Restricting attention to the 36 studies that had adjusted for age, the increase was reduced further to -2% (95% Cl -6 to+3%) using un-weighted estimates, or to + 1 % (95% Cl -4 to +6%) using weighted estimates. These estimates are not materially affected by bias due to the reference group (non-smokers married to non-smokers) having some ETS exposure from other sources. Other sources of potential upward and downward bias, not formally taken account of in the analysis, are discussed. Based on extrapolation from the known lung cancer risk in smok ers, Hackshaw et al. [Br Med J 1997;315:980-988] esti mate that environmental tobacco smoke exposure would be expected to increase the risk of lung cancer in non-smokers by 19%. Using more appropriate assump tions (for the relative exposure to smoke constituents of passive and active smokers, for the lung cancer risk in those who have ever smoked and for the dose-response model) leads to a much lower estimate of about 0.5%. Even this estimate is open to question as a threshold might exist for the effects of tobacco smoke constituents on lung cancer risk. Whether or not a true risk exists, it is clear that this is not demonstrated by the overall evi dence. The true increase in risk per 10 cigarettes/day smoked by the husband is very unlikely to be as large as 23%. It might be as much as 5%, but it could well be 1 % or less, or even zero.

There is highly significant (p < 0.001) heterogeneity between estimates from 47 studies of the lung cancer risk in lifelong non-smoking women associated with each 10 cigarettes per day smoked by the husband or with whether or not the husband smokes. Sources of this heterogeneity were investigated by relating risk esti mates to 15 different study characteristics using multiple logistic regression analysis. There was a marked tenden cy for risk estimates to be higher in the 11 studies that had not adjusted for age, and there is a strong case for removing such studies from meta-analyses as being of unacceptable quality. Studies which report dose-re sponse results for smoking by the husband also report higher risks, demonstrating that estimates of the risk per 10 cigarettes per day smoked by the husband based only on those studies will be biased upward. There is also evi dence that risk estimates tend to be lower in large stud ies, in studies published in the 1990s, in studies not requiring histological confirmation of all cases and in studies where the proportion of proxy respondents was The work was supported by the Tobacco Manufacturers Associa tion. The views expressed are those of the authors. no higher in cases than in controls, though these associa tions are not independent. Variation in risk by study char acteristics largely explains the apparently low relative risk reported in one large Chinese study, arguing against the view that it is an outlier and should be excluded from meta-analyses.

Hackshaw et al. estimated that adjustment for bias due to misclassification of smoking habits reduces the ob served relative risk of lung cancer in non-smoking wom en associated with smoking by the husband from 1.24 to 1.18. This bias estimate is little affected by using an alter native method for misclassification adjustment or by using updated data from 47 studies rather than data from the 37 studies used by Hackshaw et al. The bias is increased if strong evidence of much higher misclassifi cation rates in Asian women is taken into account and could then explain about half the observed association. Misclassification correction has not previously been at tempted for dose-response data. We describe a suggest ed approach and apply it to data relating risk to amount smoked by the husband. As shown in paper II of this series the unadjusted increase in risk per 10 cigarettes/ day smoked by the husband is 10% (95% Cl 5 to 15%), reducing to either 6% (95% Cl 1 to 11 %) or 9% (95% Cl 5 to 14%) after adjustment for confounding by fruit, vege table and fat consumption and education using respec tively unweighted or weighted means to combine evi dence on the ETS/confounder association from different studies. Further adjustment for plausible levels of mis classification similarly reduced these two estimates to, respectively, 2% (95% Cl -3 to 8%) or 5.5% (95% Cl 0 to 11 %). Difficulties in applying misclassification bias cor rections are discussed. Other sources of bias will be con sidered in later papers in this series.

Hackshaw et al. [BMJ 1997;315:980-988] estimated that, in non-smoking women, lung cancer risk rises 23% (95% Cl 14-32%) per 10 cigarettes per day smoked by the hus band and 11% (95% Cl 4-17%) per 10 years exposure. Although we reproduced these estimates approximately, use of later and more appropriate data gave lower val ues : 10% (95% Cl 5-15%) per 10 cigarettes per day and 7% (95% Cl 4-11%) per 10 years. Among the reasons Hackshaw et al. [1997] reported higher risks are failure to consider studies reporting results only as exposed/unex posed and substantial overweighting of results from one study. Extended analysis revealed that a linear model fits the data as well as the exponential model of Hackshaw et al. [1997] in the observed range, but yields more plausi ble risk estimates at higher doses. In this paper, we also introduce a method to convert dose-response data, re ported only as a set of correlated risk estimates and con fidence intervals, into a form amenable to dose-response summary analyses.

We attempted to quantify the decline in stroke risk following quitting using the negative exponential model, with methodology previously employed for IHD. We identified 22 blocks of RRs (from 13 studies) comparing current smokers, former smokers (by time quit) and never smokers. Corresponding pseudo-numbers of cases and controls/at risk formed the data for model-fitting. We tried to estimate the half-life (H, time since quit when the excess risk becomes half that for a continuing smoker) for each block. The method failed to converge or produced very variable estimates of H in nine blocks with a current smoker RR

A higher concentration of high density lipoprotein cholesterol (HDL-C) in ex-smokers than smokers has consistently been observed. Better evidence of quitting effects comes from within-subject changes. We extend an earlier meta-analysis to quantify the reduction, and investigate variation by time quit and other factors. We conducted Medline and Cochrane searches for studies measuring HDL-C in subjects while still smoking and later having quit. Using unweighted and inverse-variance weighted regression analysis, we related changes (in mmol/l) to intra-measurement period, and estimated time quit, and to study type, location and start year, age, sex, product smoked, validation of quitting, baseline HDL-C, baseline and change in weight/BMI, and any study constraints on diet or exercise. Forty-five studies were identified (17 Europe, 16 North America, 11 Asia, 1 Australia). Thirteen were observational, giving changes over at least 12 months, with most involving >1000 subjects. Others were smoking cessation trials, 12 randomized and 20 non-randomized. These were often small (18 of

John S Fry, Peter N Lee, Jan S HamlingPN Lee Statistics and Computing, Sutton, UKAbstract: In long-term carcinogenicity studies, the Peto test is the standard method for analyzing lesions that are not observable in life, taking into account differential between-group mortality. This method requires knowledge for each animal in each group of when the animal died, whether a lesion was seen postmortem, and the context of observation (ie, whether the lesion was fatal [considered to have contributed to its death] or whether it was incidental [seen in an animal dying for an unrelated reason]). The Peto test was not designed to take severity of the lesion into account. Age-adjusted analysis taking severity into account can be carried out using a stratified version of the Kruskal–Wallis one-way analysis of variance by ranks, but this does not take context of observation into account. Here, we describe an extended version of the stratified Kruskal–Wallis test that both allows analysis of lesions graded on severity and takes fatality of the lesion into account. This includes both a test for between-group heterogeneity and a test for dose-related trend, and provides a powerful method for analyzing such data. The Peto and Kruskal–Wallis tests may be considered special cases of this more general test. The test may be applied not only to nonneoplastic lesions (such as chronic progressive nephropathy), where pathologists often grade severity on a 5-point scale, but also to preneoplastic and neoplastic lesions, carrying out a single analysis of focal hyperplasia, benign tumor, and malignant tumor of a specific histological type, regarding these as successive stages of a progressive condition.Keywords: carcinogenicity tests, survival analysis, heterogeneity, trend, stratified analysis

Background Reduced FEV1 is known to predict increased lung cancer risk, but previous reviews are limited. To quantify this relationship more precisely, and study heterogeneity, we derived estimates of β for the relationship RR(diff) = exp(βdiff), where diff is the reduction in FEV1 expressed as a percentage of predicted (FEV1%P) and RR(diff) the associated relative risk. We used results reported directly as β, and as grouped levels of RR in terms of FEV1%P and of associated measures (e.g. FEV1/FVC). Methods Papers describing cohort studies involving at least three years follow-up which recorded FEV1 at baseline and presented results relating lung cancer to FEV1 or associated measures were sought from Medline and other sources. Data were recorded on study design and quality and, for each data block identified, on details of the results, including population characteristics, adjustment factors, lung function measure, and analysis type. Regression estimates were converted to β estimates where appropriate. For results reported by grouped levels, we used the NHANES III dataset to estimate mean FEV1%P values for each level, regardless of the measure used, then derived β using regression analysis which accounted for non-independence of the RR estimates. Goodness-of-fit was tested by comparing observed and predicted lung cancer cases for each level. Inverse-variance weighted meta-analysis allowed derivation of overall β estimates and testing for heterogeneity by factors including sex, age, location, timing, duration, study quality, smoking adjustment, measure of FEV1 reported, and inverse-variance weight of β. Results Thirty-three publications satisfying the inclusion/exclusion criteria were identified, seven being rejected as not allowing estimation of β. The remaining 26 described 22 distinct studies, from which 32 independent β estimates were derived. Goodness-of-fit was satisfactory, and exp(β), the RR increase per one unit FEV1%P decrease, was estimated as 1.019 (95%CI 1.016-1.021). The estimates were quite consistent (I2 =29.6%). Mean age was the only independent source of heterogeneity, exp(β) being higher for age Conclusions Although the source papers present results in various ways, complicating meta-analysis, they are very consistent. A decrease in FEV1%P of 10% is associated with a 20% (95%CI 17%-23%) increase in lung cancer risk.

Background The rate of forced expiratory volume in 1 second (FEV1) decline ("beta") is a marker of chronic obstructive pulmonary disease risk. The reduction in beta after quitting smoking is an upper limit for the reduction achievable from switching to novel nicotine delivery products. We review available evidence to estimate this reduction and quantify the relationship of smoking to beta. Methods Studies were identified, in healthy individuals or patients with respiratory disease, that provided data on beta over at least 2 years of follow-up, separately for those who gave up smoking and other smoking groups. Publications to June 2010 were considered. Independent beta estimates were derived for four main smoking groups: never smokers, ex-smokers (before baseline), quitters (during follow-up) and continuing smokers. Unweighted and inverse variance-weighted regression analyses compared betas in the smoking groups, and in continuing smokers by amount smoked, and estimated whether beta or beta differences between smoking groups varied by age, sex and other factors. Results Forty-seven studies had relevant data, 28 for both sexes and 19 for males. Sixteen studies started before 1970. Mean follow-up was 11 years. On the basis of weighted analysis of 303 betas for the four smoking groups, never smokers had a beta 10.8 mL/yr (95% confidence interval (CI), 8.9 to 12.8) less than continuing smokers. Betas for ex-smokers were 12.4 mL/yr (95% CI, 10.1 to 14.7) less than for continuing smokers, and for quitters, 8.5 mL/yr (95% CI, 5.6 to 11.4) less. These betas were similar to that for never smokers. In continuing smokers, beta increased 0.33 mL/yr per cigarette/day. Beta differences between continuing smokers and those who gave up were greater in patients with respiratory disease or with reduced baseline lung function, but were not clearly related to age or sex. Conclusion The available data have numerous limitations, but clearly show that continuing smokers have a beta that is dose-related and over 10 mL/yr greater than in never smokers, ex-smokers or quitters. The greater decline in those with respiratory disease or reduced lung function is consistent with some smokers having a more rapid rate of FEV1 decline. These results help in designing studies comparing continuing smokers of conventional cigarettes and switchers to novel products.

We compared risk of lung cancer and chronic obstructive pulmonary disease (COPD) associated with flue-cured and blended cigarettes. Mortality and smoking data were collected for 1971-2000 by sex, age, and period for three countries with a mainly flue-cured market and four with a blended market. Epidemiological relative risk estimates for current and ex smoking were summarized. Smoking statistics and mortality were compared between flue-cured cigarette and blended cigarette countries. Unadjusted mortality rates were generally lower in blended cigarette countries early on, with the difference diminishing or reversing by the 1990s. Differences by cigarette type were rarely significant, due to variations, particularly for COPD, between countries within cigarette type. Current smoking prevalence was generally lower in blended cigarette countries in 1971-1975, with the difference reducing over time. Differences by type were never significant, with blended cigarette countries varying markedly. Ex-smoking increased over time and was lower for blended cigarette countries, generally not significantly. Consumption per smoker was somewhat lower for blended cigarette countries. Relative risk estimates for smoking, derived mainly from U.S. and UK studies, varied little by cigarette type. Conclusions based on estimated smoking-related excess mortality were similar to those based on unadjusted mortality rates. There was little indication of any difference between flue-cured and blended cigarettes on risk of lung cancer or COPD. Our approach could have detected differences of about 40% for male lung cancer, or twofold differences for females or for COPD, had they existed. Between-country differences in rates of two major diseases predominantly caused by smoking cannot materially be explained by whether the countries use flue-cured or blended cigarettes.

Trends in smoking associated respiratory diseases in England and Wales during 1941-85 have been studied, with careful attention to problems caused by changes in classification of cause of death. Three diseases were selected for analysis: lung cancer, emphysema, and chronic obstructive lung disease. During 1971-85 deaths that would previously have been certified under chronic bronchitis have increasingly tended to be classified under chronic airways obstruction. The definition of chronic obstructive lung disease that was used includes both terms to avoid the artificial decline caused by consideration of chronic bronchitis in isolation. Age specific rates for all three diseases show a pronounced cohort (period of birth) pattern, rates for men rising up to the rates for those born shortly after the turn of the century and then declining, and rates for women peaking in the cohort born 20-25 years later. For chronic obstructive lung disease, but not for lung cancer and emphysema, the cohort peak is superimposed on a sharply declining downward trend. In both sexes cohort patterns of cumulative cigarette consumption peak at a time broadly similar to those seen for the three diseases. Trends in cigarette consumption, however, cannot explain the underlying steeply declining rate of chronic obstructive lung disease. Nor can they fully explain the declining trends in lung cancer and emphysema rates in younger men and women.

BACKGROUND In 1993 we presented age and sex specific estimates of cumulative constant tar cigarette consumption (CCTCC) per adult for five year periods to 1986–90. These were derived from annual surveys conducted for the Tobacco Manufacturers’ Association (TMA) since 1946, extrapolated back to 1891 for men and to 1921 for women and corrected for the decline in average (machine smoked) tar levels. We now provide estimates for 1991–5. METHODS TMA surveys having ceased, 1991–5 estimates of manufactured cigarette consumption per adult (MCA) were derived from the General Household Survey (GHS) and corrected for the continuing decline in tar. These estimates were divided by 0.75 (men) and 0.80 (women), based on a comparison of GHS and TMA data for 1971–90, to allow accumulation with the TMA derived estimates prior to 1991. RESULTS For both sexes the GHS/TMA ratio of MCA varied little by age or five year period, justifying the use of the correction factors when adjusting GHS estimates for 1991–95. TMA estimates were higher than GHS estimates as only TMA sales-corrected their data for understatement of smoking and the surveys differed in questions on handrolled cigarette smoking. The 1991–95 data confirm the continuing decline in CCTCC at all ages in men. Women show a less steep decline for ages 30–64 and an increase for ages 65–84. CONCLUSION The GHS data can validly be used to update the CCTCC estimates. Some reservations about the use of CCTCC are discussed.

The extent to which current, ex- and passive smoking are associated with other risk factors, and the potential for confounding arising from these associations, was studied using a representative sample of 9003 British adults. The distribution of 33 lifestyle factors generally considered associated with adverse health were compared in current smokers, ex-smokers, never smokers living with a smoker ("passive smokers") and other never smokers. Of the 33 risk factors 27 showed a significantly higher prevalence in heavy smokers than in never smokers and only two showed a lower prevalence. For many risk factors, prevalence increased with amount smoked, decreased with time of smoking cessation and was increased in passive smokers. The possible magnitude of bias from confounding by the risk factors is estimated. It is concluded that confounding by multiple risk factors may be an important issue in smoking studies where weak associations are observed. This applies particularly to studies investigating the possible association of passive smoking with various health effects.