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Mortality from Tobacco in Developed Countries: Are Indirect Estimates Reliable?
REGULATORY TOXICOLOGY AND PHARMACOLOGY ARTICLE NO.
24, 60–68 (1996)
0064
Mortality from Tobacco in Developed Countries: Are Indirect Estimates Reliable? PETER N. LEE P. N. Lee Statistics and Computing Ltd., 17 Cedar Road, Sutton, Surrey SM2 5DA, United Kingdom Received February 20, 1996
ety (ACS) Cancer Prevention Study II (CPS II). Surprisingly they made no attempt to use available data on the prevalence of smoking or other risk factors in the countries concerned. This paper clarifies the assumptions underlying their methodology, many of which were not made clear by the authors, and points out a number of important limitations.
The ICRF and WHO recently published estimates of smoking-attributed deaths in almost 50 developed countries. These estimates are derived without using any available data on the prevalence of smoking or of other risk factors in the countries concerned. This paper describes and discusses the various assumptions on which the derivation depends and shows that they are unlikely to hold in practice. ICRF/WHO attempt to control for confounding by introducing a modification to the standard attributable risk formula which they admit is ‘‘crude and arbitrary’’ but state is ‘‘conservative.’’ In fact, this modified formula carries no guarantee of conservatism at all and may overestimate deaths even where there is no confounding. The validity of the ICRF/WHO estimates of smoking-attributed deaths is further undermined by the lack of correlation seen in males between the smoking prevalence rates they estimate indirectly and smoking prevalence rates actually published. The ICRF/WHO estimates of smoking-attributed deaths are shown to have no scientific basis and to be unreliable. q 1996 Academic Press, Inc.
METHODS
The methodology used by ICRF/WHO was obtained from the authors and is described in detail in the Appendix because it was not given fully in their report and differs slightly from that described in Peto et al. (1992). As part of this methodology, lung cancer mortality rates are used to obtain sex-, age-, year-, and country-specific estimates of a parameter F, which can be termed ‘‘the effective proportion of smokers.’’ For 21 of the developed countries (essentially all those considered in the ICRF/WHO report except countries in the former Communist bloc) sex- and age-specific data on smoking habits were extracted for 5-year periods up to and including 1981–1985 from Nicolaides-Bouman et al. (1993). These data included estimates of the prevalence of manufactured cigarette smokers, of all cigarette smokers, and of smokers of any product, and of daily consumption per adult of manufactured cigarettes and of all cigarettes. Using the ICRF/WHO methodology and national lung cancer mortality rate data, sex- and age (35–69, 70/)-specific estimates of the ‘‘effective proportion of smokers’’ were calculated for the same 21 countries for the eight 5-year periods 1951–1955, 1956–1960, . . . , 1986–1990, and correlated with the various smoking indices (with and without a 20-year lag period) for the corresponding sex, age, and period. Sex-specific mortality rates for 1990 for age 35–69 for lung cancer, all vascular disease, and chronic obstructive pulmonary disease (COPD) were extracted from the ICRF/WHO report for all 44 individual developed countries considered and were used to estimate correlations between the diseases and to quantify the
INTRODUCTION
The Imperial Cancer Research Fund (ICRF) and the World Health Organization (WHO) recently published a report (Peto et al., 1994) giving detailed estimates of smoking-attributed deaths by sex, age, year, and cause for almost 50 developed countries, which we refer to subsequently as the ICRF/WHO report. For all causes and all developed countries combined ICRF/WHO estimated that a total of 1.8 million smoking-attributed deaths had occurred in 1990. Extrapolating from this they predicted that worldwide the number of deaths per year attributed to tobacco would exceed 10 million in about the 2020s. Their report was based on methodology similar to that described previously (Peto et al., 1992) in which smoking-attributed deaths were estimated indirectly using only the overall mortality rates observed in the target country and the smoking-specific mortality rates observed in the American Cancer Soci60
0273-2300/96 $18.00 Copyright q 1996 by Academic Press, Inc. All rights of reproduction in any form reserved.
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extent of variation by country in rates and in ratios of rates. RESULTS
Lung Cancer Deaths Attributed to Smoking—Basic Formulae Used by ICRF/WHO If a population consists of a proportion of current cigarette smokers, F, with lung cancer rate C, and a proportion of lifelong nonsmokers, 1 0 F, with lung cancer rate A, the rate for the total population, L, will be given by L Å FC / (1 0 F)A. The proportion of smokers can be estimated from the rates by F Å (L 0 A)/(C 0 A). The excess rate associated with smoking, G, as a proportion of the total rate, can be estimated by G Å (L 0 A)/L. In the ICRF/WHO methodology, these formulae are applied to age- and sex-specific data, taking L to be the lung cancer rate observed in the target country, and using rates observed in ACS CPS II to provide estimates of C and A. G is taken to be the proportion of deaths attributed to smoking (subject to the condition that it is taken as 0 if L õ A and as 1 if L ú C). Lung Cancer Deaths Attributed to Smoking— Assumptions behind the Formulae Three main assumptions underlie these calculations. First, it is assumed that diagnostic standards of lung cancer are comparable in the United States and all the target countries. Because numerous autopsy studies over the past 50 years consistently indicate that clinical diagnosis is subject to both high false-positive rates (often exceeding 20%) and high false-negative rates (often exceeding 30%) (Lee, 1994), and because marked differences exist between countries in the frequency with which autopsies are carried out (e.g., almost half of all deaths in Hungary and barely any in Japan) (WHO, 1994), this assumption seems unlikely to be valid. The second assumption is that all of the excess risk of lung cancer seen in smokers is due to their smoking. Because many factors other than smoking can be considered definite or probable causes of lung cancer (including exposure to asbestos, radon, diesel exhaust, and various other chemicals; working in various occupations; cooking with smoky coal; a family history of lung cancer; a personal history of lung disease; and dietary factors such as high fat and low fruit and vegetable consumption), and there is good evidence that for most disease risk factors smokers tend to be more exposed than nonsmokers (Matanoski et al., 1995; Thornton et al., 1994), there seem to be good grounds for believing some of the excess risk is not actually due to smoking. A third, and particularly crucial, assumption is that the lifelong nonsmoker lung cancer rate for all countries
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and all time periods is well estimated by the rate observed in CPS II. While nonsmoker rates were in fact similar in the first (1959–1965) and second (1984– 1988) ACS Cancer Prevention Studies (USDHHS, 1989), and in the first (1951–1971) and second (1972– 1991) periods of follow-up in the British doctors’ study (Doll et al., 1994), there are a number of arguments why this assumption is probably invalid: (i) Some of the lung cancer risk factors noted above substantially modify risk in nonsmokers. Recent studies have, for example, demonstrated a five- or sixfold variation in risk in nonsmokers in relation to vegetable consumption (Candelora et al., 1992) and in relation to fat consumption (Alavanja et al., 1993). It seems very likely that there would be variation over time and country in the extent of exposure to lung cancer risk factors, and that this would lead to variation in risk among nonsmokers; (ii) The CPS study populations are unrepresentative of the U.S. population, being predominantly white and of above-average socioeconomic status (USDHHS, 1989). They are less exposed than average to industrial hazards and pollution and have a better than average diet. They are still less representative of the populations of the target countries; (iii) There are well documented populations where lung cancer risk is high but the smoking rate is very low, e.g., ethnic Chinese women in various countries (Koo et al., 1985); (iv) There are countries (e.g., Portugal, Spain, and France) where, over the period 1951–1990, lung cancer rates in smoking and nonsmoking women combined have generally been lower than CPS II nonsmoker rates. Unless smoking protects against lung cancer in these countries, this implies that nonsmoker rates in these countries are lower than those seen in CPS II. It would scarcely be surprising if, in a number of other countries, nonsmoker rates were higher than in CPS II; and (v) There are reports claiming to have demonstrated a rise in nonsmoker lung cancer risk over time in Italy (Forastie`re et al., 1993), Japan (Mori and Sakai, 1984), and the United States (Swartz, 1992). Relationship between Actual and Indirectly Estimated Smoking Habits For 21 developed countries, for the 5-year periods from 1951–1955 to 1986–1990, and for the age groups 35–69 and 70/ separately, ICRF/WHO effective proportions of smokers, F, indirectly estimated via lung cancer rates, were correlated with a variety of indices of smoking habits, current or lagged by 20 years, directly estimated from published sources (Nicolaides-Bouman et al., 1993). For females, correlations were virtually always positive and usually statistically significant. For males, on the other hand, the correlations were
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TABLE 1 Rank Correlation between ICRF/WHO ‘‘Effective Proportion of Smokers’’ in 1981–85 (F) and Various Directly Estimated Smoking Indices, Unlagged and Lagged 20 Years in Men and Women Aged 35–69 and 70/ Males
Females
Rank correlations Directly estimated smoking index Unlagged % Smokers of cigarettes % Smokers of any product Manufactured cigarettes per day Lagged 20 years % Smokers of cigarettes % Smokers of any product Manufactured cigarettes per day
Rank correlations
35–69
70/
Countries considered
35–69
70/
Countries considered
0.10 0.17 00.22
00.14 0.17 00.38
18 19 16
0.92*** 0.79*** 0.90***
0.92*** 0.78** 0.61*
18 19 16
00.07 0.01 0.20
0.06 0.37 0.15
12 14 10
0.79* 0.81** 0.81*
0.72* 0.67* 0.86*
10 13 8
* P õ 0.05; **P õ 0.01; ***P õ 0.001 for rank correlation with F.
never statistically significant and there was no consistent evidence of any positive relationship. Illustrative results for 1981–1985 are shown in Table 1. For males the variation in F was much larger than the variation in the directly estimated smoking indices. Thus, for example, at age 35–69, F varied from a high of 82.9% in Belgium to a low of 16.6% in Japan. In contrast, the estimated percentages of smokers of any product 20 years earlier in the 14 countries for which data were available ranged from a high of 78.3% in The Netherlands to a low of only 54.3% in Sweden. Lack of age-specific data for enough countries limits the extent to which correlations can be made using a longer lag period than 20 years. However, too short a lag period seems unlikely to be the explanation, bearing in mind, for example, the low lung cancer rates in Japan, a country which has had a very high proportion of male smokers for many years (e.g., 83% in 1956). The poor correlation between directly and indirectly estimated smoking habits in males may well have arisen because of differences between countries in diagnostic standards of lung cancer and in the extent of exposure to other risk factors. In any event, the poor correlation suggests the indirect estimates may be invalid. Even for females, the correlation is far from perfect. For both age groups, many indirect estimates were zero, especially at the beginning of the 40-year period considered. France, notably, had an estimated zero prevalence at age 35–69 for every period except the last (1986–1990), although direct estimates of smoking prevalence in French women have typically been about 20–30% over the whole period. Deaths from Other Diseases Attributable to Smoking—The ICRF/WHO Modified Attributable Risk Formula If a population consists of a proportion F of smokers and 1 0 F of nonsmokers, and if smoking multiplies
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risk of a disease by R, the overall risk of the population (relative to that of a population consisting wholly of nonsmokers) is given by 1 / F(R 0 1). Writing E Å F(R 0 1), the proportion of deaths associated with smoking can therefore be estimated by B1 Å E/(1 / E). When considering mortality for those six disease groups (other than lung cancer) where smoking is assumed to contribute to risk, ICRF/WHO do not use B1 to estimate the proportion of deaths attributed to smoking. Instead they use the alternative formula B2 Å E/(2 / E). This modification is presented without any formal theoretical justification and is admitted to be ‘‘crude and arbitrary.’’ As shown in Table 2, where B1 and B2 are compared for various values of F and R, B2 is always less than B1 . However, although it is substantially lower (by a factor approaching 2) when the proportion of smokers is low and the relative risk is not much greater than 1, the modification has much less effect as E increases. ICRF/WHO state that their modification is made in order ‘‘to ensure that the hazards of tobacco are not exaggerated.’’ However, although it is true that B2 is less than B1 , so that use of B2 reduces any exaggeration, it will be demonstrated clearly below (in sections that consider in turn the various assumptions behind use of B2) that in fact use of B2 does not carry any guarantee of conservatism at all. Deaths from Other Diseases Associated with Smoking—Assumptions Underlying the Modified Formula Because B2 depends on F, and because F depends on the validity of the three assumptions referred to above when discussing lung cancer deaths attributable to smoking, use of B2 (or indeed B1) will of course also depend on the same three assumptions. However, there are a number of important additional assumptions underlying the use of B2 to which attention should be drawn.
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TABLE 2 Comparison of Estimated Proportions of Deaths Associated with Smoking (B1) and Attributed to Smoking by ICRF/WHO (B2) for Various Values of the Proportion of Smokers (F) and the Relative Risk Associated with Smoking (R) Proportion of deaths Proportion of smokers, F
Relative risk, R
E Å F (R 0 1)
Smoking associated, B1 Å E/(1 / E)
Smoking attributed, B2 Å E/(2 / E)
0.10
1.5 2 5 10 20
0.05 0.10 0.40 0.90 1.90
0.048 0.091 0.286 0.474 0.655
0.024 0.048 0.167 0.310 0.487
0.25
1.5 2 5 10 20
0.125 0.25 1.00 2.25 4.75
0.111 0.200 0.500 0.692 0.826
0.059 0.111 0.333 0.529 0.704
0.50
1.5 2 5 10 20
0.25 0.50 2.00 4.50 9.50
0.200 0.333 0.667 0.818 0.905
0.111 0.200 0.500 0.692 0.826
0.75
1.5 2 5 10 20
0.375 0.75 3.00 6.75 14.25
0.273 0.429 0.750 0.871 0.934
0.158 0.273 0.600 0.771 0.877
One additional assumption is that diagnostic standards for diseases other than lung cancer are comparable in the United States and the target countries. It is clear that applying a relative risk estimate derived for deaths diagnosed according to U.S. criteria to deaths occurring in another country diagnosed according to different criteria may cause over- or underestimation of smoking-attributed deaths. A second additional assumption is that use of the modified formula B2 will compensate for the fact that the CPS II relative risk estimates are not adjusted for potential confounding by other risk factors. The CPS II relative risk estimates were only adjusted for age, although smokers are known to have higher exposure to most risk factors (Thornton et al., 1994) and although other risk factors clearly play an important role in the etiology of many of these diseases. Thus confounding by alcohol consumption, strongly associated both with smoking and with risk of upper aerodigestive tract cancer, has not been formally accounted for. Nor has the higher sexual activity of smokers, which may fully explain their increased risk of cervical cancer (Phillips and Davey Smith, 1994), been considered. Instead reliance has been put on a totally arbitrary conservatism factor, with no supportive evidence presented that it is conservative. A third additional assumption is that any effects of other factors on risk of the diseases in question act
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multiplicatively with the presumed effects of smoking. The authors recognize that, for diseases other than lung cancer, nonsmoker rates are likely to vary substantially from country to country, due to differences in the frequency of exposure to risk factors other than smoking. For this reason they do not attempt to use CPS II nonsmoker rates for these diseases as a reference against which to compare rates in the target countries. Instead, they rely on CPS II estimates of relative risk, arguing that this is valid provided smoking and other risk factors act multiplicatively. There are two objections to this argument. First, even if there were a true underlying multiplicative relationship, this would only apply to relative risk estimates that are fully adjusted for confounding, and not to the relative risk estimates actually used from CPS II that are adjusted only for age. Second, it seems remarkably bold to assume that all such underlying relationships would be multiplicative. It would seem quite plausible that some risk factors would act additively, increasing risk to the same extent in smokers and non-smokers. If risks are additive, both B1 and B2 may overestimate deaths due to smoking. This is illustrated in the hypothetical example in Table 3, in which risks are assumed to be additive and in which frequency of exposure to a risk factor other than smoking is greater in the target country than in CPS II. In this example, the frequency of smoking is assumed to be the same in both populations and
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TABLE 3 Possible Failure of Formula B2 to Be Conservative If Risks Are Additive (Based on Hypothetical Data) Smokers Risk factor Attribute Relative risk assumed (additive model) Frequency assumed in target population Risk in target population (relative to base)a Relative risk in target population smokers Excess risk in target population smokers
Yes
No
12
2
0.4
Nonsmokers Risk factor Total
Yes 11
0.1
0.5 10 1.111 0.111
No
Total
1 (base)
0.4
0.1
0.5 9 1
0.1
0.4
0.5 3 1
[ True proportion of deaths attributed to smokingb Å (0.5 1 0.111)/(1 / (0.5 1 0.111)) Å 0.053 Frequency assumed in CPS II Risk in CPS II (relative to base)a Relative risk in CPS II smokers Excess risk in CPS II smokers
0.1
0.4
0.5 4 1.333 0.333
[ B1 Å (0.5 1 0.333)/(1 / (0.5 1 0.333)) Å 0.143 B2 Å (0.5 1 0.333)/(2 / (0.5 1 0.33)) Å 0.077 ú 0.053 [ B2 is not conservative a b
Average of two risks weighted on smoker frequency; e.g., ((0.4 1 12) / (0.1 1 2))/0.5 Å 10. Given the relative risks and assuming no confounding by other factors.
to be unrelated to the frequency of the risk factor. Bias arises not because of confounding, but because of failure to take into account properly the joint effects of smoking and the risk factor. A fourth additional assumption is that the relative association of smoking with lung cancer and with any other disease does not depend on what is smoked and when. A major weakness of the ICRF/WHO estimation procedure for deaths from causes other than lung cancer lies in the failure to take into account differences between CPS II and the target countries in the distribution of various aspects of smoking, such as amount smoked, duration of smoking, inhalation, type of cigarette smoked, smoking of pipes and cigars, and frequency of past smoking. When estimating lung cancer deaths attributable to smoking such differences were not an issue. Provided that the three assumptions described in the section commenting on these estimates hold, any excess death rate over and above the CPS II nonsmoker rate can be considered due to smoking, regardless of what was smoked. When estimating deaths attributable to smoking from other diseases, however, bias will occur unless, for all subdivisions of the population by smoking habit, the set of ratios of excess risks for lung cancer and for other diseases is in a constant proportion. To illustrate this, consider the hypothetical example in Table 4. Here excess risks for former and current smokers for lung cancer are assumed to be in the ratio 8:16 Å 1:2. In one case (‘‘constancy’’), the excess risks assumed for the other disease, 4:8, are also kept in the ratio 1:2; however, in the other case (‘‘nonconstancy’’), the excess risks 0:8 are
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not kept in this ratio. In the first case, B1 is an accurate and B2 a conservative estimate of the proportion of deaths due to smoking. In the second case, B1 and B2 are both overestimates. What is happening in this example is that, among former smokers in the target population, an excess risk of lung cancer is being used to infer an excess risk of other diseases regardless of whether such an excess might actually exist or not. The inherent assumption that excess risks by smoking category are in a constant ratio regardless of the disease in question is not well supported by the epidemiological evidence. For example, although the risk of lung cancer is strongly related to lifetime duration of smoking and an excess risk is still evident in former smokers many years after they have given up smoking, the risk of ischemic heart disease and cerebrovascular disease is more strongly dependent on recent smoking, with some studies showing little evidence of increased risk a few years after giving up smoking (USDHHS, 1983). Also, tar reduction and the switch from plain to filter cigarettes has been consistently associated with a reduced risk of lung cancer but not with a reduced risk of ischemic heart disease (ISC, 1988; Lee and Garfinkel, 1981). It seems highly dubious to extrapolate indices of smoking (F) based on lung cancer risks to infer risks of other diseases in countries with smoking patterns very different from CPS II. Relationship between Mortality from Lung Cancer, Vascular Disease, and Chronic Obstructive Lung Disease Based on the sex-specific death rates for age 35 – 69 in 1990 presented in the ICRF/WHO report lung
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TABLE 4 Possible Failure of Conservative Formula If Detailed Smoking Habits Unaccounted for (Based on Hypothetical Data) Never smoked
Attribute
Assumed relative risks Lung cancer 1 1 Other disease (constancy)a Other disease (nonconstancy) 1 Assumed frequencies in population ACS CPS II 0.4 Target population 0.4 Proportion of deaths from other diseases attributed to smoking in target country Constancy 0.737 True proportion based on assumed datab 0.737 As estimated by formula B1 As estimated by formula B2 0.583 a b
Former smoker
Current smoker
9 5 1
17 9 9
0.2 0.5
0.4 0.1
Nonconstancy 0.444 0.737 0.583
Constancy implies current smoker and former smoker excess risks in same ratio for lung cancer and for other disease. Further assuming no relevant confounding by other factors.
cancer, vascular disease and COPD rates were compared in 44 developed countries. A strong rank correlation was seen between vascular disease and COPD rates in both sexes (males r Å 0.66, P õ 0.001; females r Å 0.67, P õ 0.001). However, no strong or consistent rank correlation was seen between lung cancer and either vascular disease (males r Å 0.37, P õ 0.05; females r Å 0.29, NS) or COPD (males r Å 0.28, NS, females r Å 0.09, NS). Table 5 shows lung cancer and vascular disease death rates and their ratios for 13 selected developed
countries, 5 from countries in Asia, formerly part of the USSR. Table 5 shows that the Asian countries have relatively low lung cancer rates but high vascular disease rates, so that their ratios are very different indeed from those seen in the other developed countries. A similar pattern is seen (results not shown in detail) when ratios of COPD and lung cancer are compared. Thus, in males, COPD/lung cancer ratios rise from a minimum of 0.06 in Greece, via 0.40 in the United Kingdom, to a maximum of 2.19 in Tajikistan, while in females ratios rise from 0.13 (again in Greece), via 0.58
TABLE 5 Comparison of Vascular Disease and Lung Cancer Mortality Rates at Age 35–69 in Selected Developed Countries Male
Female
Lung cancer
Vascular disease
Ratio
Lung cancer
Vascular disease
Ratio
Minimum Maximum
37.1 207.0
207.0 999.6
1.96 15.97
7.3 64.9
74.8 584.4
2.37 67.95
Australia Canada France Hungary Italy Japan U.S.A. UK
93.6 131.2 115.7 207.0 142.8 57.5 138.9 124.4
355.3 332.5 226.5 874.5 291.6 207.0 429.2 494.3
3.80 2.53 1.96 4.23 2.04 3.60 3.09 3.97
31.0 53.1 12.4 36.1 16.6 15.0 64.9 52.9
141.6 125.7 74.8 384.4 115.8 98.1 192.2 206.1
4.57 2.37 6.03 10.65 6.98 6.54 2.96 3.90
71.3 79.1 37.1 63.1 58.0
888.3 809.2 592.4 999.6 759.0
12.46 10.23 15.97 15.84 13.09
10.2 11.0 9.2 8.6 13.4
443.5 399.1 383.1 584.4 448.7
43.48 36.28 41.64 67.95 33.49
Azerbaijan Georgia Tajikistan Turkmenistan Uzbekistan
Note. Rates are per 100,000 per year. Minimum and maximum are for all 44 developed countries.
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PETER N. LEE
in the United Kingdom, to a maximum of 6.70 in Tajikistan. It is notable that in Azerbaijan, Tajikistan, and Turkmenistan no deaths at all were attributable to smoking in females because their lung cancer death rates were below those seen in CPS II. In males, however, despite the relatively low lung cancer rates in these countries, as many as respectively 23, 12, and 19% of deaths from vascular disease and 68, 48, and 64% of deaths from COPD were attributed to smoking. This high attribution of vascular disease and COPD deaths from smoking results from ICRF/WHO’s inherent assumption that effects of smoking and other risk factors multiply. This seems very unlikely to be appropriate here. It is much more plausible that the high rates of vascular disease and COPD seen in these countries result from a factor other than smoking which probably, given that its effect is clearly evident in females, acts independently of smoking. Loss of Life Due to Smoking The method by which ICRF/WHO calculate deaths due to smoking essentially involves comparison of the total number of deaths that actually did occur in the target population in the year in question with the number estimated to have occurred in that year if the whole population had the death rate of never-smokers. Even assuming that the method of estimation correctly takes into account the potential role of confounding factors, it should be realized that this is a rather artificial concept. After all, if the population had never smoked, the population at risk would not be that observed. Furthermore, even if one ignores this and assumes that in one given year, death rates magically reduced to those that would have occurred had no one ever smoked, it is clear that although there may be less deaths in that year and some subsequent years, years would eventually come where more deaths occurred than would have occurred without the lowering in death rates. We all die sometime, and the excess deaths summed over all years from the year of the reduction must sum to zero. A more appropriate method of quantifying loss of life due to smoking is by using life table methods. Such life table methods should take into account not only the fact that confounding variables may bias age-specific estimates of the smoker/nonsmoker relative risk, but also variability in susceptibility of the population at risk. Those who die young from smoking-associated diseases are unlikely to be representative, in terms of susceptibility to disease, of all subjects. They are likely, on average, not only to be more exposed to environmental factors unrelated to smoking but also to be more genetically prone to disease. The ICRF/WHO report includes estimates of years of life lost due to smoking, but these fail to take such possibilities into account, and may be considerable overestimates. It is
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perfectly possible (Haybittle, 1994) to construct life tables very similar to those reported for the CPS II study using alternative assumptions under which smokers’ genetic susceptibility varies, with smoking being assumed to take off considerably less years of life than calculated by ICRF/WHO. DISCUSSION AND CONCLUSIONS
There is a well established strong association between smoking and risk of death from lung cancer, upper aerodigestive cancer, and COPD which has not been explained by the confounding effect of other risk factors. If one assumes that no relevant confounding factor has been overlooked, it is inevitable that a substantial number of the 1.1 million deaths recorded as being from these diseases in 1990 in developed countries can be attributed to smoking. Adding in some deaths from other causes more weakly associated with smoking will increase the figure if some of the excess risk is directly attributable to smoking. The question arises, however, whether there is any validity to the estimated number of 1,814,627 deaths due to smoking in developed countries in 1990 or to the numerous other estimates which fill the 553-page ICRF/WHO report. The ICRF/WHO method of estimation is remarkable in completely ignoring data on the distribution of smoking habits or other risk factors in the target countries, relying wholly on age- and sexspecific estimates of death rates by cause, and calibrating these based on death rates for lung cancer and for the disease in question observed in lifelong nonsmokers and in current cigarette smokers in the ACS CPS II study. The ICRF/WHO method of estimation takes no account of the known inaccuracy of death certificate diagnosis nor of variation in diagnostic standards between countries. It also fails to take into account variation in risk by amount or duration of smoking or by other aspects of smoking such as frequency of past smoking or type of product smoked. The authors assume (despite substantial epidemiological evidence to the contrary) that the relative strengths of the associations of smoking with lung cancer and with the other diseases considered is constant regardless of what is smoked and when. Their method of estimation implausibly assumes that lung cancer rates in lifelong nonsmokers do not vary by country and by year, thus ignoring possible effects of other risk factors. Possibilities of confounding are also not properly taken into account by the fact that smoker/nonsmoker relative risks for diseases other than lung cancer are estimated from unadjusted CPS II data and assumed to apply to countries with a very different distribution of risk factor exposure than the CPS II population, a population which is not even representative of the United States.
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Some of the criticisms expressed here have previously been made by others (Ashford, 1992; Davis and Hoel, 1992; Haybittle, 1994; Skrabanek, 1992; Sterling et al., 1992, 1993), when commenting on Peto et al. (1992). Notably Sterling et al. (1993) clearly demonstrated the bias due to use of the unrepresentative CPS population and failure to take into account confounding factors. Using data from the 1986 National Mortality Followback Survey (NMFS) and the 1987 National Health Interview Survey (NHIS) they computed causeand sex-specific smoker/nonsmoker relative risk estimates that were nationally representative for the United States. They found that excess deaths associated with smoking were reduced by 39% using the NMFS/NHIS data rather than the CPS II data and were further reduced by 26% by adjusting for confounding due to only two risk factors, income and alcohol consumption. If the observed risk of a population, relative to that of a population of lifelong nonsmokers, is 1 / E, the standard formula for estimating the proportion of deaths associated with smoking is B1 Å E/(1 / E). For diseases other than lung cancer, ICRF/WHO use an amended formula, B2 Å E/(2 / E), to estimate deaths attributed to smoking. This formula is not presented with any theoretical justification and is admitted to be ‘‘crude and arbitrary.’’ Its intent is to allow for the possibility of confounding by other risk factors, and it is stated to be ‘‘conservative,’’ implying that deaths due to smoking will not be exaggerated by its use. However, as has been clearly shown, there is no actual reason why it should be conservative. Even without postulating confounding by other risk factors, a number of situations have been demonstrated where use of the formula B2 may exaggerate the risk. And, of course, confounding is likely to be relevant for a number of smoking-associated diseases, in view of the greater exposure that smokers have to many lifestyle risk factors (Doll et al., 1994; Matanoski et al., 1995; Thornton et al., 1994). It is clear that the ICRF/WHO methodology is inadequate to estimate reliably deaths due to smoking in developed countries in recent years. It is self-evident that estimates extended to include all countries in the world and extrapolated 30 or 40 years into the future will be even less reliable. The overwhelming impression of the report is twofold. First, it is very unscientific, using methodology which is arbitrary and based on assumptions which are often not stated, and are unlikely to hold. Second, it has been produced as a tool for emphasizing the role of smoking as a cause of death with a mass of figures that can be easily and widely quoted. Rather than just ‘‘think of a number,’’ which would be totally or blatantly unacceptable, the authors have produced pseudo-scientific justification for numbers which are in fact little
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more reliable than guesses. It is a major concern when science is denigrated in this way. APPENDIX: CALCULATION OF SMOKING ATTRIBUTED DEATHS
Calculations are carried out separately for each sex and target country. Let i reference age groups: 1 Å 35–59, 2 Å 60–64, 3 Å 65–69, 4 Å 70–74, 5 Å 75–79. Let j reference 5-year age groups: 1 Å 35–39, 2 Å 40–44, . . . , 10 Å 80–84, 11 Å 85/. Let k reference causes of death: 1 Å lung cancer, 2 Å upper aerodigestive cancer, 3 Å other cancer, 4 Å COPD, 5 Å other respiratory disease, 6 Å vascular disease, 7 Å liver cirrhosis and other liver disease, 8 Å other medical causes, 9 Å all nonmedical causes. For lung cancer, let Ai be the smoothed ACS CPS II lifelong nonsmoker rate, let Si be the ACS CPS II current cigarette smoker/lifelong nonsmoker mortality ratio, and let Li be the rate in the target country. (NB for i Å 1, cumulative rates are used). Defining Ri Å Li/Ai , the ‘‘effective proportion of cigarette smokers’’ Fi is estimated by Fi Å (Ri 0 1)/(Si 0 1). If Fi õ 0, Fi is set equal to 0; if Fi ú 1, Fi is set equal to 1. For k Å 1 to 4, define Eik Å Fi(Mk 0 1), where Mk is the ACS CPS II mortality ratio for cause k for all ages. For k Å 5, 6, or 8, define Eik Å Fi(Vi 0 1), where Vi is the ACS CPS II mortality ratio for causes 5, 6, and 8 combined for age i. For k Å 7 or 9, define Eik Å 0. Now Bik , the proportion of deaths from smoking, is calculated by Bik Å Eik/(1 / Eik) for lung cancer (k Å 1) or by Bik Å Eik/(2 / Eik) for other causes (k Å 2 to 9). Numbers of deaths attributable to smoking are then calculated by multiplying total numbers of deaths in the 5-year age groups j by the appropriate value of Bik using B1k for j Å 1 to 5, B2k for j Å 6, B3k for j Å 7, B4k for j Å 8, and B5k for j Å 9 to 11. The ACS data used: 35–59
60–64
65–69
70–74
75–79
Males Females
144.196 133.737
15.863 14.712
21.849 20.264
29.388 27.256
38.727 35.918
Males Females
16.747 11.386
23.627 13.235
27.417 15.317
30.584 12.439
30.161 11.939
Males Females
3.047 2.693
2.308 2.679
2.092 2.520
2.001 2.001
1.541 1.444
Ai
Si
Vi
Mk Males Females
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Upper aero cancer
Other cancer
COPD
24.216 22.504
7.870 6.955
1.691 1.199
13.821 14.213
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ACKNOWLEDGMENTS I thank the World Health Organization for providing the mortality and population data, Dr. J. Boreham for providing fuller detail of the methods used by ICRF/WHO, Mrs. B. A. Forey and Dr. J. S. Fry for helpful comments, and Mrs. P. Wassell for typing the various drafts. Mrs. Forey also provided statistical assistance. Financial support was provided by British–American Tobacco, Imperial Tobacco, and Rothmans. All views expressed in this paper are my own.
REFERENCES Alavanja, M. C. R., Brown, C. C., Swanson, C., and Brownson, R. C. (1993). Saturated fat intake and lung cancer risk among nonsmoking women in Missouri. J. Natl. Cancer Inst. 85, 1906–1916. Ashford, J. R. (1992). Deaths from tobacco [Letter]. Lancet 340, 121. Candelora, E. C., Stockwell, H. G., Armstrong, A. W., and Pinkham, P. A. (1992). Dietary intake and risk of lung cancer in women who never smoked. Nutr. Cancer 17, 263–270. Davis, D. L., and Hoel, D. G. (1992). Tobacco-associated deaths [Letter]. Lancet 340, 666. Doll, R., Peto, R., Wheatley, K., Gray, R., and Sutherland, I. (1994). Mortality in relation to smoking: 40 years’ observations on male British doctors. Br. Med. J. 309, 901–911. Forastie`re, F., Perucci, C. A., Arca, M., and Axelson, O. (1993). Indirect estimates of lung cancer death rates in Italy not attributable to active smoking. Epidemiology 4, 502–510. Haybittle, J. L. (1994). Should years lost always be equated with life expectancy? Int. J. Epidemiol. 23, 592–594. ISC (1988). Smoking and Health, 4th report, pp. 1–25. HMSO, London. Koo, L. C., Ho, J. H.-C., and Lee, N. (1985). An analysis of some risk factors for lung cancer in Hong Kong. Int. J. Cancer. 35, 149–155. Lee, P. N. (1994). Comparison of autopsy, clinical and death certificate diagnosis with particular reference to lung cancer: A review of the published data. APMIS 102, Suppl. 45. Lee, P. N., and Garfinkel, L. (1981). Mortality and type of cigarette smoked. J. Epidemiol. Community Health 35, 16–22. Matanoski, G., Kanchanaraksa, S., Lantry, D., and Chang, Y. (1995). Characteristics of nonsmoking women in NHANES 1 and
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NHANES 1 epidemiologic follow-up study with exposure to spouses who smoke. Am. J. Epidemiol. 142, 149–157. Mori, W., and Sakai, R. (1984). A study on chronologic change of the relationship between cigarette smoking and lung cancer based on autopsy diagnosis. Cancer 54, 1038–1042. Nicolaides-Bouman, A., Wald, N., Forey, B., and Lee, P. N. (1993). International Smoking Statistics: A Collection of Historical Data from 22 Economically Developed Countries. Oxford Univ. Press, Oxford. Peto, R., Lopez, A. D., Boreham, J., Thun, M., and Heath, C., Jr. (1992). Mortality from tobacco in developed countries: indirect estimation from national vital statistics. Lancet 339, 1268–1278. Peto, R., Lopez, A. D., Boreham, J., Thun, M., and Heath, C., Jr. (1994). Mortality from Smoking in Developed Countries 1950– 2000: Indirect Estimates from National Vital Statistics. Oxford Univ. Press, Oxford. Phillips, A. N., and Davey Smith, G. (1994). Cigarette smoking as a potential cause of cervical cancer: has confounding been controlled? Int. J. Epidemiol. 23, 42–49. Skrabanek, P. (1992). Smoking and statistical overkill. Lancet 340, 1208–1209. Sterling, T. D., Rosenbaum, W. L., and Weinkam, J. J. (1992). Tobacco-associated deaths [Letter]. Lancet 340, 666–668. Sterling, T. D., Rosenbaum, W. L., and Weinkham, J. J. (1993). Risk attribution and tobacco-related deaths. Am. J. Epidemiol. 138, 128–139. Swartz, J. B. (1992). Use of a multistage model to predict time trends in smoking induced lung cancer. J. Epidemiol. Community Health 46, 311–315. Thornton, A., Lee, P., and Fry, J. (1994). Differences between smokers, ex-smokers, passive smokers and non-smokers. J. Clin. Epidemiol. 47, 1143–1162. U.S. Department of Health and Human Services (1983). The Health Consequences of Smoking: Cardiovascular Disease, a Report of the Surgeon General. USDHHS, Public Health Service, Office on Smoking and Health, Rockville, Maryland. U.S. Department of Health and Human Services (1989). Reducing the health consequences of Smoking: 25 Years of Progress, a Report of the Surgeon General, DHHS Publication (CDC) 89-8411. USDHHS, Public Health Service, Centers for Disease Control, Office on Smoking and Health, Rockville, Maryland. World Health Organization (WHO) (1994). World Health Statistics Annual. WHO, Geneva.
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Mortality from Tobacco in Developed Countries: Are Indirect Estimates Reliable? PETER N. LEE P. N. Lee Statistics and Computing Ltd., 17 Cedar Road, Sutton, Surrey SM2 5DA, United Kingdom Received February 20, 1996
ety (ACS) Cancer Prevention Study II (CPS II). Surprisingly they made no attempt to use available data on the prevalence of smoking or other risk factors in the countries concerned. This paper clarifies the assumptions underlying their methodology, many of which were not made clear by the authors, and points out a number of important limitations.
The ICRF and WHO recently published estimates of smoking-attributed deaths in almost 50 developed countries. These estimates are derived without using any available data on the prevalence of smoking or of other risk factors in the countries concerned. This paper describes and discusses the various assumptions on which the derivation depends and shows that they are unlikely to hold in practice. ICRF/WHO attempt to control for confounding by introducing a modification to the standard attributable risk formula which they admit is ‘‘crude and arbitrary’’ but state is ‘‘conservative.’’ In fact, this modified formula carries no guarantee of conservatism at all and may overestimate deaths even where there is no confounding. The validity of the ICRF/WHO estimates of smoking-attributed deaths is further undermined by the lack of correlation seen in males between the smoking prevalence rates they estimate indirectly and smoking prevalence rates actually published. The ICRF/WHO estimates of smoking-attributed deaths are shown to have no scientific basis and to be unreliable. q 1996 Academic Press, Inc.
METHODS
The methodology used by ICRF/WHO was obtained from the authors and is described in detail in the Appendix because it was not given fully in their report and differs slightly from that described in Peto et al. (1992). As part of this methodology, lung cancer mortality rates are used to obtain sex-, age-, year-, and country-specific estimates of a parameter F, which can be termed ‘‘the effective proportion of smokers.’’ For 21 of the developed countries (essentially all those considered in the ICRF/WHO report except countries in the former Communist bloc) sex- and age-specific data on smoking habits were extracted for 5-year periods up to and including 1981–1985 from Nicolaides-Bouman et al. (1993). These data included estimates of the prevalence of manufactured cigarette smokers, of all cigarette smokers, and of smokers of any product, and of daily consumption per adult of manufactured cigarettes and of all cigarettes. Using the ICRF/WHO methodology and national lung cancer mortality rate data, sex- and age (35–69, 70/)-specific estimates of the ‘‘effective proportion of smokers’’ were calculated for the same 21 countries for the eight 5-year periods 1951–1955, 1956–1960, . . . , 1986–1990, and correlated with the various smoking indices (with and without a 20-year lag period) for the corresponding sex, age, and period. Sex-specific mortality rates for 1990 for age 35–69 for lung cancer, all vascular disease, and chronic obstructive pulmonary disease (COPD) were extracted from the ICRF/WHO report for all 44 individual developed countries considered and were used to estimate correlations between the diseases and to quantify the
INTRODUCTION
The Imperial Cancer Research Fund (ICRF) and the World Health Organization (WHO) recently published a report (Peto et al., 1994) giving detailed estimates of smoking-attributed deaths by sex, age, year, and cause for almost 50 developed countries, which we refer to subsequently as the ICRF/WHO report. For all causes and all developed countries combined ICRF/WHO estimated that a total of 1.8 million smoking-attributed deaths had occurred in 1990. Extrapolating from this they predicted that worldwide the number of deaths per year attributed to tobacco would exceed 10 million in about the 2020s. Their report was based on methodology similar to that described previously (Peto et al., 1992) in which smoking-attributed deaths were estimated indirectly using only the overall mortality rates observed in the target country and the smoking-specific mortality rates observed in the American Cancer Soci60
0273-2300/96 $18.00 Copyright q 1996 by Academic Press, Inc. All rights of reproduction in any form reserved.
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extent of variation by country in rates and in ratios of rates. RESULTS
Lung Cancer Deaths Attributed to Smoking—Basic Formulae Used by ICRF/WHO If a population consists of a proportion of current cigarette smokers, F, with lung cancer rate C, and a proportion of lifelong nonsmokers, 1 0 F, with lung cancer rate A, the rate for the total population, L, will be given by L Å FC / (1 0 F)A. The proportion of smokers can be estimated from the rates by F Å (L 0 A)/(C 0 A). The excess rate associated with smoking, G, as a proportion of the total rate, can be estimated by G Å (L 0 A)/L. In the ICRF/WHO methodology, these formulae are applied to age- and sex-specific data, taking L to be the lung cancer rate observed in the target country, and using rates observed in ACS CPS II to provide estimates of C and A. G is taken to be the proportion of deaths attributed to smoking (subject to the condition that it is taken as 0 if L õ A and as 1 if L ú C). Lung Cancer Deaths Attributed to Smoking— Assumptions behind the Formulae Three main assumptions underlie these calculations. First, it is assumed that diagnostic standards of lung cancer are comparable in the United States and all the target countries. Because numerous autopsy studies over the past 50 years consistently indicate that clinical diagnosis is subject to both high false-positive rates (often exceeding 20%) and high false-negative rates (often exceeding 30%) (Lee, 1994), and because marked differences exist between countries in the frequency with which autopsies are carried out (e.g., almost half of all deaths in Hungary and barely any in Japan) (WHO, 1994), this assumption seems unlikely to be valid. The second assumption is that all of the excess risk of lung cancer seen in smokers is due to their smoking. Because many factors other than smoking can be considered definite or probable causes of lung cancer (including exposure to asbestos, radon, diesel exhaust, and various other chemicals; working in various occupations; cooking with smoky coal; a family history of lung cancer; a personal history of lung disease; and dietary factors such as high fat and low fruit and vegetable consumption), and there is good evidence that for most disease risk factors smokers tend to be more exposed than nonsmokers (Matanoski et al., 1995; Thornton et al., 1994), there seem to be good grounds for believing some of the excess risk is not actually due to smoking. A third, and particularly crucial, assumption is that the lifelong nonsmoker lung cancer rate for all countries
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61
and all time periods is well estimated by the rate observed in CPS II. While nonsmoker rates were in fact similar in the first (1959–1965) and second (1984– 1988) ACS Cancer Prevention Studies (USDHHS, 1989), and in the first (1951–1971) and second (1972– 1991) periods of follow-up in the British doctors’ study (Doll et al., 1994), there are a number of arguments why this assumption is probably invalid: (i) Some of the lung cancer risk factors noted above substantially modify risk in nonsmokers. Recent studies have, for example, demonstrated a five- or sixfold variation in risk in nonsmokers in relation to vegetable consumption (Candelora et al., 1992) and in relation to fat consumption (Alavanja et al., 1993). It seems very likely that there would be variation over time and country in the extent of exposure to lung cancer risk factors, and that this would lead to variation in risk among nonsmokers; (ii) The CPS study populations are unrepresentative of the U.S. population, being predominantly white and of above-average socioeconomic status (USDHHS, 1989). They are less exposed than average to industrial hazards and pollution and have a better than average diet. They are still less representative of the populations of the target countries; (iii) There are well documented populations where lung cancer risk is high but the smoking rate is very low, e.g., ethnic Chinese women in various countries (Koo et al., 1985); (iv) There are countries (e.g., Portugal, Spain, and France) where, over the period 1951–1990, lung cancer rates in smoking and nonsmoking women combined have generally been lower than CPS II nonsmoker rates. Unless smoking protects against lung cancer in these countries, this implies that nonsmoker rates in these countries are lower than those seen in CPS II. It would scarcely be surprising if, in a number of other countries, nonsmoker rates were higher than in CPS II; and (v) There are reports claiming to have demonstrated a rise in nonsmoker lung cancer risk over time in Italy (Forastie`re et al., 1993), Japan (Mori and Sakai, 1984), and the United States (Swartz, 1992). Relationship between Actual and Indirectly Estimated Smoking Habits For 21 developed countries, for the 5-year periods from 1951–1955 to 1986–1990, and for the age groups 35–69 and 70/ separately, ICRF/WHO effective proportions of smokers, F, indirectly estimated via lung cancer rates, were correlated with a variety of indices of smoking habits, current or lagged by 20 years, directly estimated from published sources (Nicolaides-Bouman et al., 1993). For females, correlations were virtually always positive and usually statistically significant. For males, on the other hand, the correlations were
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TABLE 1 Rank Correlation between ICRF/WHO ‘‘Effective Proportion of Smokers’’ in 1981–85 (F) and Various Directly Estimated Smoking Indices, Unlagged and Lagged 20 Years in Men and Women Aged 35–69 and 70/ Males
Females
Rank correlations Directly estimated smoking index Unlagged % Smokers of cigarettes % Smokers of any product Manufactured cigarettes per day Lagged 20 years % Smokers of cigarettes % Smokers of any product Manufactured cigarettes per day
Rank correlations
35–69
70/
Countries considered
35–69
70/
Countries considered
0.10 0.17 00.22
00.14 0.17 00.38
18 19 16
0.92*** 0.79*** 0.90***
0.92*** 0.78** 0.61*
18 19 16
00.07 0.01 0.20
0.06 0.37 0.15
12 14 10
0.79* 0.81** 0.81*
0.72* 0.67* 0.86*
10 13 8
* P õ 0.05; **P õ 0.01; ***P õ 0.001 for rank correlation with F.
never statistically significant and there was no consistent evidence of any positive relationship. Illustrative results for 1981–1985 are shown in Table 1. For males the variation in F was much larger than the variation in the directly estimated smoking indices. Thus, for example, at age 35–69, F varied from a high of 82.9% in Belgium to a low of 16.6% in Japan. In contrast, the estimated percentages of smokers of any product 20 years earlier in the 14 countries for which data were available ranged from a high of 78.3% in The Netherlands to a low of only 54.3% in Sweden. Lack of age-specific data for enough countries limits the extent to which correlations can be made using a longer lag period than 20 years. However, too short a lag period seems unlikely to be the explanation, bearing in mind, for example, the low lung cancer rates in Japan, a country which has had a very high proportion of male smokers for many years (e.g., 83% in 1956). The poor correlation between directly and indirectly estimated smoking habits in males may well have arisen because of differences between countries in diagnostic standards of lung cancer and in the extent of exposure to other risk factors. In any event, the poor correlation suggests the indirect estimates may be invalid. Even for females, the correlation is far from perfect. For both age groups, many indirect estimates were zero, especially at the beginning of the 40-year period considered. France, notably, had an estimated zero prevalence at age 35–69 for every period except the last (1986–1990), although direct estimates of smoking prevalence in French women have typically been about 20–30% over the whole period. Deaths from Other Diseases Attributable to Smoking—The ICRF/WHO Modified Attributable Risk Formula If a population consists of a proportion F of smokers and 1 0 F of nonsmokers, and if smoking multiplies
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risk of a disease by R, the overall risk of the population (relative to that of a population consisting wholly of nonsmokers) is given by 1 / F(R 0 1). Writing E Å F(R 0 1), the proportion of deaths associated with smoking can therefore be estimated by B1 Å E/(1 / E). When considering mortality for those six disease groups (other than lung cancer) where smoking is assumed to contribute to risk, ICRF/WHO do not use B1 to estimate the proportion of deaths attributed to smoking. Instead they use the alternative formula B2 Å E/(2 / E). This modification is presented without any formal theoretical justification and is admitted to be ‘‘crude and arbitrary.’’ As shown in Table 2, where B1 and B2 are compared for various values of F and R, B2 is always less than B1 . However, although it is substantially lower (by a factor approaching 2) when the proportion of smokers is low and the relative risk is not much greater than 1, the modification has much less effect as E increases. ICRF/WHO state that their modification is made in order ‘‘to ensure that the hazards of tobacco are not exaggerated.’’ However, although it is true that B2 is less than B1 , so that use of B2 reduces any exaggeration, it will be demonstrated clearly below (in sections that consider in turn the various assumptions behind use of B2) that in fact use of B2 does not carry any guarantee of conservatism at all. Deaths from Other Diseases Associated with Smoking—Assumptions Underlying the Modified Formula Because B2 depends on F, and because F depends on the validity of the three assumptions referred to above when discussing lung cancer deaths attributable to smoking, use of B2 (or indeed B1) will of course also depend on the same three assumptions. However, there are a number of important additional assumptions underlying the use of B2 to which attention should be drawn.
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TABLE 2 Comparison of Estimated Proportions of Deaths Associated with Smoking (B1) and Attributed to Smoking by ICRF/WHO (B2) for Various Values of the Proportion of Smokers (F) and the Relative Risk Associated with Smoking (R) Proportion of deaths Proportion of smokers, F
Relative risk, R
E Å F (R 0 1)
Smoking associated, B1 Å E/(1 / E)
Smoking attributed, B2 Å E/(2 / E)
0.10
1.5 2 5 10 20
0.05 0.10 0.40 0.90 1.90
0.048 0.091 0.286 0.474 0.655
0.024 0.048 0.167 0.310 0.487
0.25
1.5 2 5 10 20
0.125 0.25 1.00 2.25 4.75
0.111 0.200 0.500 0.692 0.826
0.059 0.111 0.333 0.529 0.704
0.50
1.5 2 5 10 20
0.25 0.50 2.00 4.50 9.50
0.200 0.333 0.667 0.818 0.905
0.111 0.200 0.500 0.692 0.826
0.75
1.5 2 5 10 20
0.375 0.75 3.00 6.75 14.25
0.273 0.429 0.750 0.871 0.934
0.158 0.273 0.600 0.771 0.877
One additional assumption is that diagnostic standards for diseases other than lung cancer are comparable in the United States and the target countries. It is clear that applying a relative risk estimate derived for deaths diagnosed according to U.S. criteria to deaths occurring in another country diagnosed according to different criteria may cause over- or underestimation of smoking-attributed deaths. A second additional assumption is that use of the modified formula B2 will compensate for the fact that the CPS II relative risk estimates are not adjusted for potential confounding by other risk factors. The CPS II relative risk estimates were only adjusted for age, although smokers are known to have higher exposure to most risk factors (Thornton et al., 1994) and although other risk factors clearly play an important role in the etiology of many of these diseases. Thus confounding by alcohol consumption, strongly associated both with smoking and with risk of upper aerodigestive tract cancer, has not been formally accounted for. Nor has the higher sexual activity of smokers, which may fully explain their increased risk of cervical cancer (Phillips and Davey Smith, 1994), been considered. Instead reliance has been put on a totally arbitrary conservatism factor, with no supportive evidence presented that it is conservative. A third additional assumption is that any effects of other factors on risk of the diseases in question act
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multiplicatively with the presumed effects of smoking. The authors recognize that, for diseases other than lung cancer, nonsmoker rates are likely to vary substantially from country to country, due to differences in the frequency of exposure to risk factors other than smoking. For this reason they do not attempt to use CPS II nonsmoker rates for these diseases as a reference against which to compare rates in the target countries. Instead, they rely on CPS II estimates of relative risk, arguing that this is valid provided smoking and other risk factors act multiplicatively. There are two objections to this argument. First, even if there were a true underlying multiplicative relationship, this would only apply to relative risk estimates that are fully adjusted for confounding, and not to the relative risk estimates actually used from CPS II that are adjusted only for age. Second, it seems remarkably bold to assume that all such underlying relationships would be multiplicative. It would seem quite plausible that some risk factors would act additively, increasing risk to the same extent in smokers and non-smokers. If risks are additive, both B1 and B2 may overestimate deaths due to smoking. This is illustrated in the hypothetical example in Table 3, in which risks are assumed to be additive and in which frequency of exposure to a risk factor other than smoking is greater in the target country than in CPS II. In this example, the frequency of smoking is assumed to be the same in both populations and
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TABLE 3 Possible Failure of Formula B2 to Be Conservative If Risks Are Additive (Based on Hypothetical Data) Smokers Risk factor Attribute Relative risk assumed (additive model) Frequency assumed in target population Risk in target population (relative to base)a Relative risk in target population smokers Excess risk in target population smokers
Yes
No
12
2
0.4
Nonsmokers Risk factor Total
Yes 11
0.1
0.5 10 1.111 0.111
No
Total
1 (base)
0.4
0.1
0.5 9 1
0.1
0.4
0.5 3 1
[ True proportion of deaths attributed to smokingb Å (0.5 1 0.111)/(1 / (0.5 1 0.111)) Å 0.053 Frequency assumed in CPS II Risk in CPS II (relative to base)a Relative risk in CPS II smokers Excess risk in CPS II smokers
0.1
0.4
0.5 4 1.333 0.333
[ B1 Å (0.5 1 0.333)/(1 / (0.5 1 0.333)) Å 0.143 B2 Å (0.5 1 0.333)/(2 / (0.5 1 0.33)) Å 0.077 ú 0.053 [ B2 is not conservative a b
Average of two risks weighted on smoker frequency; e.g., ((0.4 1 12) / (0.1 1 2))/0.5 Å 10. Given the relative risks and assuming no confounding by other factors.
to be unrelated to the frequency of the risk factor. Bias arises not because of confounding, but because of failure to take into account properly the joint effects of smoking and the risk factor. A fourth additional assumption is that the relative association of smoking with lung cancer and with any other disease does not depend on what is smoked and when. A major weakness of the ICRF/WHO estimation procedure for deaths from causes other than lung cancer lies in the failure to take into account differences between CPS II and the target countries in the distribution of various aspects of smoking, such as amount smoked, duration of smoking, inhalation, type of cigarette smoked, smoking of pipes and cigars, and frequency of past smoking. When estimating lung cancer deaths attributable to smoking such differences were not an issue. Provided that the three assumptions described in the section commenting on these estimates hold, any excess death rate over and above the CPS II nonsmoker rate can be considered due to smoking, regardless of what was smoked. When estimating deaths attributable to smoking from other diseases, however, bias will occur unless, for all subdivisions of the population by smoking habit, the set of ratios of excess risks for lung cancer and for other diseases is in a constant proportion. To illustrate this, consider the hypothetical example in Table 4. Here excess risks for former and current smokers for lung cancer are assumed to be in the ratio 8:16 Å 1:2. In one case (‘‘constancy’’), the excess risks assumed for the other disease, 4:8, are also kept in the ratio 1:2; however, in the other case (‘‘nonconstancy’’), the excess risks 0:8 are
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not kept in this ratio. In the first case, B1 is an accurate and B2 a conservative estimate of the proportion of deaths due to smoking. In the second case, B1 and B2 are both overestimates. What is happening in this example is that, among former smokers in the target population, an excess risk of lung cancer is being used to infer an excess risk of other diseases regardless of whether such an excess might actually exist or not. The inherent assumption that excess risks by smoking category are in a constant ratio regardless of the disease in question is not well supported by the epidemiological evidence. For example, although the risk of lung cancer is strongly related to lifetime duration of smoking and an excess risk is still evident in former smokers many years after they have given up smoking, the risk of ischemic heart disease and cerebrovascular disease is more strongly dependent on recent smoking, with some studies showing little evidence of increased risk a few years after giving up smoking (USDHHS, 1983). Also, tar reduction and the switch from plain to filter cigarettes has been consistently associated with a reduced risk of lung cancer but not with a reduced risk of ischemic heart disease (ISC, 1988; Lee and Garfinkel, 1981). It seems highly dubious to extrapolate indices of smoking (F) based on lung cancer risks to infer risks of other diseases in countries with smoking patterns very different from CPS II. Relationship between Mortality from Lung Cancer, Vascular Disease, and Chronic Obstructive Lung Disease Based on the sex-specific death rates for age 35 – 69 in 1990 presented in the ICRF/WHO report lung
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TABLE 4 Possible Failure of Conservative Formula If Detailed Smoking Habits Unaccounted for (Based on Hypothetical Data) Never smoked
Attribute
Assumed relative risks Lung cancer 1 1 Other disease (constancy)a Other disease (nonconstancy) 1 Assumed frequencies in population ACS CPS II 0.4 Target population 0.4 Proportion of deaths from other diseases attributed to smoking in target country Constancy 0.737 True proportion based on assumed datab 0.737 As estimated by formula B1 As estimated by formula B2 0.583 a b
Former smoker
Current smoker
9 5 1
17 9 9
0.2 0.5
0.4 0.1
Nonconstancy 0.444 0.737 0.583
Constancy implies current smoker and former smoker excess risks in same ratio for lung cancer and for other disease. Further assuming no relevant confounding by other factors.
cancer, vascular disease and COPD rates were compared in 44 developed countries. A strong rank correlation was seen between vascular disease and COPD rates in both sexes (males r Å 0.66, P õ 0.001; females r Å 0.67, P õ 0.001). However, no strong or consistent rank correlation was seen between lung cancer and either vascular disease (males r Å 0.37, P õ 0.05; females r Å 0.29, NS) or COPD (males r Å 0.28, NS, females r Å 0.09, NS). Table 5 shows lung cancer and vascular disease death rates and their ratios for 13 selected developed
countries, 5 from countries in Asia, formerly part of the USSR. Table 5 shows that the Asian countries have relatively low lung cancer rates but high vascular disease rates, so that their ratios are very different indeed from those seen in the other developed countries. A similar pattern is seen (results not shown in detail) when ratios of COPD and lung cancer are compared. Thus, in males, COPD/lung cancer ratios rise from a minimum of 0.06 in Greece, via 0.40 in the United Kingdom, to a maximum of 2.19 in Tajikistan, while in females ratios rise from 0.13 (again in Greece), via 0.58
TABLE 5 Comparison of Vascular Disease and Lung Cancer Mortality Rates at Age 35–69 in Selected Developed Countries Male
Female
Lung cancer
Vascular disease
Ratio
Lung cancer
Vascular disease
Ratio
Minimum Maximum
37.1 207.0
207.0 999.6
1.96 15.97
7.3 64.9
74.8 584.4
2.37 67.95
Australia Canada France Hungary Italy Japan U.S.A. UK
93.6 131.2 115.7 207.0 142.8 57.5 138.9 124.4
355.3 332.5 226.5 874.5 291.6 207.0 429.2 494.3
3.80 2.53 1.96 4.23 2.04 3.60 3.09 3.97
31.0 53.1 12.4 36.1 16.6 15.0 64.9 52.9
141.6 125.7 74.8 384.4 115.8 98.1 192.2 206.1
4.57 2.37 6.03 10.65 6.98 6.54 2.96 3.90
71.3 79.1 37.1 63.1 58.0
888.3 809.2 592.4 999.6 759.0
12.46 10.23 15.97 15.84 13.09
10.2 11.0 9.2 8.6 13.4
443.5 399.1 383.1 584.4 448.7
43.48 36.28 41.64 67.95 33.49
Azerbaijan Georgia Tajikistan Turkmenistan Uzbekistan
Note. Rates are per 100,000 per year. Minimum and maximum are for all 44 developed countries.
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PETER N. LEE
in the United Kingdom, to a maximum of 6.70 in Tajikistan. It is notable that in Azerbaijan, Tajikistan, and Turkmenistan no deaths at all were attributable to smoking in females because their lung cancer death rates were below those seen in CPS II. In males, however, despite the relatively low lung cancer rates in these countries, as many as respectively 23, 12, and 19% of deaths from vascular disease and 68, 48, and 64% of deaths from COPD were attributed to smoking. This high attribution of vascular disease and COPD deaths from smoking results from ICRF/WHO’s inherent assumption that effects of smoking and other risk factors multiply. This seems very unlikely to be appropriate here. It is much more plausible that the high rates of vascular disease and COPD seen in these countries result from a factor other than smoking which probably, given that its effect is clearly evident in females, acts independently of smoking. Loss of Life Due to Smoking The method by which ICRF/WHO calculate deaths due to smoking essentially involves comparison of the total number of deaths that actually did occur in the target population in the year in question with the number estimated to have occurred in that year if the whole population had the death rate of never-smokers. Even assuming that the method of estimation correctly takes into account the potential role of confounding factors, it should be realized that this is a rather artificial concept. After all, if the population had never smoked, the population at risk would not be that observed. Furthermore, even if one ignores this and assumes that in one given year, death rates magically reduced to those that would have occurred had no one ever smoked, it is clear that although there may be less deaths in that year and some subsequent years, years would eventually come where more deaths occurred than would have occurred without the lowering in death rates. We all die sometime, and the excess deaths summed over all years from the year of the reduction must sum to zero. A more appropriate method of quantifying loss of life due to smoking is by using life table methods. Such life table methods should take into account not only the fact that confounding variables may bias age-specific estimates of the smoker/nonsmoker relative risk, but also variability in susceptibility of the population at risk. Those who die young from smoking-associated diseases are unlikely to be representative, in terms of susceptibility to disease, of all subjects. They are likely, on average, not only to be more exposed to environmental factors unrelated to smoking but also to be more genetically prone to disease. The ICRF/WHO report includes estimates of years of life lost due to smoking, but these fail to take such possibilities into account, and may be considerable overestimates. It is
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perfectly possible (Haybittle, 1994) to construct life tables very similar to those reported for the CPS II study using alternative assumptions under which smokers’ genetic susceptibility varies, with smoking being assumed to take off considerably less years of life than calculated by ICRF/WHO. DISCUSSION AND CONCLUSIONS
There is a well established strong association between smoking and risk of death from lung cancer, upper aerodigestive cancer, and COPD which has not been explained by the confounding effect of other risk factors. If one assumes that no relevant confounding factor has been overlooked, it is inevitable that a substantial number of the 1.1 million deaths recorded as being from these diseases in 1990 in developed countries can be attributed to smoking. Adding in some deaths from other causes more weakly associated with smoking will increase the figure if some of the excess risk is directly attributable to smoking. The question arises, however, whether there is any validity to the estimated number of 1,814,627 deaths due to smoking in developed countries in 1990 or to the numerous other estimates which fill the 553-page ICRF/WHO report. The ICRF/WHO method of estimation is remarkable in completely ignoring data on the distribution of smoking habits or other risk factors in the target countries, relying wholly on age- and sexspecific estimates of death rates by cause, and calibrating these based on death rates for lung cancer and for the disease in question observed in lifelong nonsmokers and in current cigarette smokers in the ACS CPS II study. The ICRF/WHO method of estimation takes no account of the known inaccuracy of death certificate diagnosis nor of variation in diagnostic standards between countries. It also fails to take into account variation in risk by amount or duration of smoking or by other aspects of smoking such as frequency of past smoking or type of product smoked. The authors assume (despite substantial epidemiological evidence to the contrary) that the relative strengths of the associations of smoking with lung cancer and with the other diseases considered is constant regardless of what is smoked and when. Their method of estimation implausibly assumes that lung cancer rates in lifelong nonsmokers do not vary by country and by year, thus ignoring possible effects of other risk factors. Possibilities of confounding are also not properly taken into account by the fact that smoker/nonsmoker relative risks for diseases other than lung cancer are estimated from unadjusted CPS II data and assumed to apply to countries with a very different distribution of risk factor exposure than the CPS II population, a population which is not even representative of the United States.
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Some of the criticisms expressed here have previously been made by others (Ashford, 1992; Davis and Hoel, 1992; Haybittle, 1994; Skrabanek, 1992; Sterling et al., 1992, 1993), when commenting on Peto et al. (1992). Notably Sterling et al. (1993) clearly demonstrated the bias due to use of the unrepresentative CPS population and failure to take into account confounding factors. Using data from the 1986 National Mortality Followback Survey (NMFS) and the 1987 National Health Interview Survey (NHIS) they computed causeand sex-specific smoker/nonsmoker relative risk estimates that were nationally representative for the United States. They found that excess deaths associated with smoking were reduced by 39% using the NMFS/NHIS data rather than the CPS II data and were further reduced by 26% by adjusting for confounding due to only two risk factors, income and alcohol consumption. If the observed risk of a population, relative to that of a population of lifelong nonsmokers, is 1 / E, the standard formula for estimating the proportion of deaths associated with smoking is B1 Å E/(1 / E). For diseases other than lung cancer, ICRF/WHO use an amended formula, B2 Å E/(2 / E), to estimate deaths attributed to smoking. This formula is not presented with any theoretical justification and is admitted to be ‘‘crude and arbitrary.’’ Its intent is to allow for the possibility of confounding by other risk factors, and it is stated to be ‘‘conservative,’’ implying that deaths due to smoking will not be exaggerated by its use. However, as has been clearly shown, there is no actual reason why it should be conservative. Even without postulating confounding by other risk factors, a number of situations have been demonstrated where use of the formula B2 may exaggerate the risk. And, of course, confounding is likely to be relevant for a number of smoking-associated diseases, in view of the greater exposure that smokers have to many lifestyle risk factors (Doll et al., 1994; Matanoski et al., 1995; Thornton et al., 1994). It is clear that the ICRF/WHO methodology is inadequate to estimate reliably deaths due to smoking in developed countries in recent years. It is self-evident that estimates extended to include all countries in the world and extrapolated 30 or 40 years into the future will be even less reliable. The overwhelming impression of the report is twofold. First, it is very unscientific, using methodology which is arbitrary and based on assumptions which are often not stated, and are unlikely to hold. Second, it has been produced as a tool for emphasizing the role of smoking as a cause of death with a mass of figures that can be easily and widely quoted. Rather than just ‘‘think of a number,’’ which would be totally or blatantly unacceptable, the authors have produced pseudo-scientific justification for numbers which are in fact little
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more reliable than guesses. It is a major concern when science is denigrated in this way. APPENDIX: CALCULATION OF SMOKING ATTRIBUTED DEATHS
Calculations are carried out separately for each sex and target country. Let i reference age groups: 1 Å 35–59, 2 Å 60–64, 3 Å 65–69, 4 Å 70–74, 5 Å 75–79. Let j reference 5-year age groups: 1 Å 35–39, 2 Å 40–44, . . . , 10 Å 80–84, 11 Å 85/. Let k reference causes of death: 1 Å lung cancer, 2 Å upper aerodigestive cancer, 3 Å other cancer, 4 Å COPD, 5 Å other respiratory disease, 6 Å vascular disease, 7 Å liver cirrhosis and other liver disease, 8 Å other medical causes, 9 Å all nonmedical causes. For lung cancer, let Ai be the smoothed ACS CPS II lifelong nonsmoker rate, let Si be the ACS CPS II current cigarette smoker/lifelong nonsmoker mortality ratio, and let Li be the rate in the target country. (NB for i Å 1, cumulative rates are used). Defining Ri Å Li/Ai , the ‘‘effective proportion of cigarette smokers’’ Fi is estimated by Fi Å (Ri 0 1)/(Si 0 1). If Fi õ 0, Fi is set equal to 0; if Fi ú 1, Fi is set equal to 1. For k Å 1 to 4, define Eik Å Fi(Mk 0 1), where Mk is the ACS CPS II mortality ratio for cause k for all ages. For k Å 5, 6, or 8, define Eik Å Fi(Vi 0 1), where Vi is the ACS CPS II mortality ratio for causes 5, 6, and 8 combined for age i. For k Å 7 or 9, define Eik Å 0. Now Bik , the proportion of deaths from smoking, is calculated by Bik Å Eik/(1 / Eik) for lung cancer (k Å 1) or by Bik Å Eik/(2 / Eik) for other causes (k Å 2 to 9). Numbers of deaths attributable to smoking are then calculated by multiplying total numbers of deaths in the 5-year age groups j by the appropriate value of Bik using B1k for j Å 1 to 5, B2k for j Å 6, B3k for j Å 7, B4k for j Å 8, and B5k for j Å 9 to 11. The ACS data used: 35–59
60–64
65–69
70–74
75–79
Males Females
144.196 133.737
15.863 14.712
21.849 20.264
29.388 27.256
38.727 35.918
Males Females
16.747 11.386
23.627 13.235
27.417 15.317
30.584 12.439
30.161 11.939
Males Females
3.047 2.693
2.308 2.679
2.092 2.520
2.001 2.001
1.541 1.444
Ai
Si
Vi
Mk Males Females
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Lung cancer
Upper aero cancer
Other cancer
COPD
24.216 22.504
7.870 6.955
1.691 1.199
13.821 14.213
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ACKNOWLEDGMENTS I thank the World Health Organization for providing the mortality and population data, Dr. J. Boreham for providing fuller detail of the methods used by ICRF/WHO, Mrs. B. A. Forey and Dr. J. S. Fry for helpful comments, and Mrs. P. Wassell for typing the various drafts. Mrs. Forey also provided statistical assistance. Financial support was provided by British–American Tobacco, Imperial Tobacco, and Rothmans. All views expressed in this paper are my own.
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