Methodologies used to estimate tobacco-attributable mortality: a review

  • Mónica Pérez-Ríos1, 2, 3Email author and

    Affiliated with

    • Agustín Montes2, 3

      Affiliated with

      BMC Public Health20088:22

      DOI: 10.1186/1471-2458-8-22

      Received: 05 March 2007

      Accepted: 22 January 2008

      Published: 22 January 2008



      One of the most important measures for ascertaining the impact of tobacco on a population is the estimation of the mortality attributable to its use. To measure this, a number of indirect methods of quantification are available, yet there is no consensus as to which furnishes the best information. This study sought to provide a critical overview of the different methods of attribution of mortality due to tobacco consumption.


      A search was made in the Medline database until March 2005 in order to obtain papers that addressed the methodology employed for attributing mortality to tobacco use.


      Of the total of 7 methods obtained, the most widely used were the prevalence methods, followed by the approach proposed by Peto et al, with the remainder being used in a minority of studies.


      Different methodologies are used to estimate tobacco attributable mortality, but their methodological foundations are quite similar in all. Mainly, they are based on the calculation of proportional attributable fractions. All methods show limitations of one type or another, sometimes common to all methods and sometimes specific.


      Since the association between tobacco and mortality was first discovered [1, 2], the task of attributing a given number deaths to smoking has been and continues to be a controversial process, beset by limitations and questioned from different quarters, including the powerful tobacco industry. With the appearance of the successive revisions of the International Classification of Diseases (ICD), there has been considerable progress in the process of categorizing mortality, but little in methods for attributing mortality to risk factors such as tobacco. Obtaining reliable estimates of the impact of tobacco on mortality would facilitate to have a clearer picture of the problem caused by smoking and would be of help in the planning of health policy.

      The task of quantifying smoking-attributable mortality has been performed mainly through indirect methods. This review sought to list and to describe the different methods of estimating mortality attributed to tobacco use, to indicate the principal methodological differences existing among them, and to identify the possible sources of variability in the results.


      In order to obtain papers that addressed the methodology employed for attributing mortality to tobacco use, a search was made in the Medline database until March 2005, using the terms, mortality, attribut,* method * and tobacco or smok *. The search was completed with a manual review of the bibliographic references cited by the papers retrieved and of other publications, such as the monographs published by the Centers for Disease Control and Prevention (CDC). The main inclusion criteria was the use of an epidemiological method to estimate attributable mortality. Papers describing mortality, such as cohort follow-up or mortality studies were excluded unless an epidemiological analysis had been used. Animal studies and communications presented at congresses were also excluded from the search.

      The estimation of attributable mortality is also applied to other risk factors in addition to tobacco, such as alcohol consumption or obesity. In order to avoid the exclusion of valid methodologies the search was repeated without restricting it to tobacco or smoke.


      The search yielded a total of 372 papers. Of these, 74 were finally included, as the rest did not apply mortality attribution methods. Some papers included more than one method. The unrestricted search, without the terms tobacco or smoke, did not furnish any new alternative methodology.

      Revision of the 74 papers enabled us to identified 2 types of mortality attribution procedures for the specific case of tobacco. The first one is based on individual analysis of deaths to ascertain if tobacco use had any role in mortality. Only three studies applied this procedure [35]. The second is based on the application of indirect methods and constitutes the most commonly used methodology for attributing mortality. The total number of papers that employed this indirect methodology was 73, with 61 of these being yielded by the automatic and 12 by the manual search.

      Seven indirect methods for estimating tobacco-attributable mortality were identified. The applied methodology in these 7 methods can be classified under four categories: Prevalence-based analysis (Prevalence-based analysis in cohort studies, prevalence-based analysis in case-control studies and the basic method), Peto and colleagues' method, methodologies based on the calculation of excess mortality (Garfinkel's and Roger's method) and predictive models (Prevent). The methods differ in terms of calculation processes, information requirement, data sources and assumptions required for their application. A summary of these methods is showed in Table 1. The main characteristics of the different indirect methods are described below.
      Table 1

      Methods used to estimate tobacco attributable mortality


      Data employed

      Data source

      Method applied to estimate mortality due to:



      Estimations calculated

      Prevalence-based analysis in cohort studyes/SAMMEC (n = 52)


      National Statistics

      Tobacco consumption, exposure to environmental tobacco smoke (ETS), obesity, alcohol intake,...

      - Does not take latency into account.

      - Worldwide use.

      Attributable mortality for all causes.


      Relative Risks: Smokers, non-smokers and former smokers

      Cohort study


      - Application of risks other than CPS.


      Method proposed by Peto and colleagues (n = 6)

      Relative Risks: Smokers and non-smokers

      CPS II

      Tobacco consumption.

      - Assumes constant worldwide lung cancer mortality rates among never smokers.

      - Worldwide use.

      Attributable mortality for all causes.


      Lung cancer death rates: Global (non smokers + smokers + former smokers), non-smokers and smokers.

      National Statistics/CPS II


      - Assumes the same latency for all death causes related to tobacco.

      - Mortality estimation in absence of smoking prevalence.


      - Does not take into account former smokers.

      - Takes latency into account for lung cancer.


      Basic method (n = 1)

      Lung cancer death rates

      National Statistics

      Tobacco consumption.

      - Partial view of attributable mortality (only used to estimate mortality by lung cancer).

      - Takes into account induction time.

      Lung cancer death rate attributable and not attributable to active smoking.



      National statistics/Estimated


      - Use of constants.

      - Estimates smoking-adjusted RR in different time periods.


      Lung cancer relative risk



      - High need of information.


      Packs of cigarettes smoked

      National statistics/Estimated


      - Rate ratios for former smokers.


      Age of starting/giving up tobacco consumption

      National statistics/Estimated


      - Assumes constant worldwide lung cancer mortality rates among never smokers.



      Previous studies


      Prevent method (n = 2)

      Composition of the population

      National Statistics

      Tobacco consumption and general scenarios of effective health promotion.

      - High need of information.

      -Takes into account the multiplicity of cause or effect.

      Attributable mortality for all causes.


      Mortality (population) and birth (women) rates

      National Statistics


      - Proportional decrease in risk reduction related to time.


      Latency and delay

      Previous studies


      - To measure the results of intervention policies.


      Time-Tendency of tobacco consumption.

      Personal interviews


      Relative risks

      CPS II


      Prevalence-based analysis in case-control studyes (n = 4)

      Mortality observed

      National Statistics

      Tobacco consumption and exposure to ETS.

      - Case-control study design.

      - Specific risk dates.

      Attributable mortality for all causes.


      Exposure prevalence: case or controls

      Case-control study


      - Recall bias.


      Odds Ratios

      Case-control study


      Garfinkel's method (n = 2)

      Mortality observed

      National Statistics

      Tobacco consumption and alcohol intake.

      - Partial view of the attributable mortality (only used to estimate cancer mortality).

      - Necessary dates are few.

      Cancer deaths attributable to smoking.


      Cancer mortality rates in non smokers.

      American Cancer Society


      - Assumes constant worldwide cancer mortality rates among never smokers.

      - Does not use risks or prevalence.


      Rogers' method (n = 1)

      Mortality observed (all causes)

      National Statistics

      Tobacco consumption.

      - Availability of mortality registries.

      - Risks calculated ad hoc.

      Attributable mortality for all causes.


      Prevalence (7 categories)



      - Has a population representative survey about health-risks.

      - The population division is more reliable.


      Odds Ratios

      Discrete-time hazard models


      - Assumption: smoking status remains steady since the survey about health-risks.


      a) Prevalence-based analysis

      Prevalence-based analysis or prevalence-risks models are based on the different distributions of the risk of dying from various tobacco-related diseases in relation to the prevalence of tobacco consumption in the population.

      To apply these methods it is necessary to know the prevalence of smoking in the study population, the total number of deaths due to diseases causally related to tobacco use, and a measure that summarizes the increased risk of dying due to these causes among smokers and ex-smokers.

      We can distinguish 3 methods due mainly to data source:

      - Prevalence-based analysis in cohort studies

      This method is the most widely employed in the literature [4, 651].

      Attributable deaths are calculated for each cause of mortality using the following formula:

      AM = OM * PAF;

      where AM is the mortality attributed to tobacco, OM the observed mortality, and PAF the population attributable fraction.

      To calculate PAF, different methods exist [52, 53], though the most widely used is based on the formula proposed by Levin [54] which divides the population into various categories according to tobacco use (non-smokers, ex-smokers and smokers):

      PAF = ((p0 + p1RR1 + p2RR2)-1)/(p0 + p1RR1 + p2RR2);

      where p 0, p 1 and p 2 represent the prevalence of non-smokers, smokers and ex-smokers, respectively. RR1 and RR2 refer to the risk of dying for any cause of smokers and ex-smokers respectively compared to a baseline population of non-smokers.

      Data are drawn from registries in the case of observed mortality and from surveys in the case of smoking prevalence. The relative risks (RRs) employed in the calculations are extracted mainly from the prospective cohort study conducted by the American Cancer Society, i.e., the Cancer Prevention Study II (CPS II) with follow-up at 4 [55] and 6 [56] years.

      A modification of this method was proposed in the 1992 Surgeon General's report "Smoking and Health in the Americas" [57]. The authors created an index for measuring the smoking maturity in a population, based on a comparison of lung cancer rates. This index is multiplied by the disease-specific PAF to obtain an adjusted disease-specific PAF for a country.

      The CDC's SAMMEC (Smoking-Attributable Mortality, Morbidity, and Economic Cost) computer software application [58] uses this methodology. SAMMEC is a software package commonly used in the United States to estimate attributable mortality due to smoking, years of potential life lost and indirect mortality costs. SAMMEC computes PAF automatically after the user includes prevalence of tobacco consumption. Furthermore, the user must supply the number of deaths by 5-years age groups from 35 or older, for each smoking-related diagnosis. Estimations from SAMMEC can include attributed deaths to fires and secondhand smoke. The Simsmoke model, a model that predicts the effect of policies on smoking rates and deaths attributable to smoking, uses this computer application to estimate deaths attributable to smoking.

      Apart from being employed for calculating mortality due to tobacco use, this method has also been used for estimating mortality associated with exposure to environmental tobacco smoke [5961], alcohol intake [8, 18, 21, 24, 29, 6265], illicit drugs [18, 21], obesity [66, 67], oral contraceptive use [68], hypertension status [69], cardiovascular processes [70], and diabetes status [71].

      Prevalence-based analysis in case-control studies

      Employing a similar calculation procedure to the previous method, this one emerged as a consequence of the objections raised by certain researchers about using RRs to estimate smoking attributable mortality from other countries [72]. This method has been used to estimate mortality attributable to tobacco use [7375] in China when the epidemic was still in the initial phase.

      To apply this method, it is necessary to know the total deaths for all causes among subjects aged 35 years or more for a given period of time. By interviewing survivors, information is collected retrospectively on smoking habits of deceased subjects 15 years before their death. Based on a case-control study risks are estimated.

      Once these risks obtained, the population attributable fraction (PAF) can then be calculated, applying the formula:

      PAF = P*(1-(1/RR));

      where P is the proportion of deaths occurring among smokers and RR the relative risk calculated as OR after completion of a case-control study.

      When the PAF has been calculated, deaths attributed to tobacco use (AM) in the study population can be estimated as follows:

      AM = OM * PAF

      Basic model

      The Basic model [76] was originally applied in the setting of occupational cohort studies, to assess confounding generated by tobacco use.

      This model has been employed in only one study [76] to estimate non-tobacco-attributable lung cancer mortality rates. Unlike the previous methods, different processes are specified here for calculating the RRs of lung cancer in smokers and ex-smokers versus non-smokers. From a paper previously published [77] authors adapted two functions to compute rate ratios. Both of them take into account duration and intensity of smoking.

      Lung cancer rate not attributable to smoking (Io) can be calculated as follows: ; where I is the overall lung cancer mortality rate.

      b) Method proposed by Peto et al

      Although this method could be defined as a prevalence-risk model, particularities in its calculation procedure and assumptions would classify it separately.

      Peto et al. [78, 79] established a method for estimating tobacco-related mortality in which the need for data, especially for lung cancer estimates, is less demanding than in any of the other procedures reviewed. These authors postulate that lung cancer mortality is an indicator of the maturity of the smoking epidemic in a population, and thus, that tobacco-attributable mortality can be estimated by lung cancer mortality. This model may estimate mortality independently of the prevalence of smoking in the study population.

      To apply this method, one needs to know the age- and sex-specific lung cancer mortality rates in the target country (CLC) and also in never-smokers of the same population (NLC), the relative risks for all diseases and disorders causally related to tobacco, except lung cancer; and the cause-specific lung cancer mortality rates in smokers (S*LC) and never-smokers (N*LC), taken from a cohort study. Peto et al used data drawn from the CPS II.

      The calculation of the estimated tobacco-attributable mortality has two well-defined procedures: one to estimate attributed lung cancer mortality, and the other to estimate mortality attributable to all the remaining diseases with an established causal relationship [55, 56].

      The sex- and age-specific proportions of lung cancer deaths attributable to tobacco are obtained through the following formula:

      (CLC - N*LC)/CLC

      For the remainder of the diseases causally associated with tobacco use, the calculation process is different. The first step is to estimate thesummarized smoking prevalence or smoking impact ratio (SIR), which summarizes the history of tobacco use in the population by age and sex. SIR was defined as population lung-cancer mortality in excess of never-smokers, relative to excess lung-cancer mortality for a known reference group of smokers, adjusted for differences in never-smoker lung-cancer mortality rates across populations [80]. Smokers in the study population are converted into equivalent of smokers in the reference population. The formula used for its calculation is:

      This formula is used in all populations where lung cancer mortality rates among non-smokers are unknown. Where these data are available one needs to normalize the formula [80].

      The second step of this process consists of computing the population etiological fraction (PEF) on the basis of the previously calculated summarized prevalence (SIR) and the relative risks of dying due to the respective causes (RR), by age group and sex, as per the CPS II.PEF = SIR(RR - 1)/(1 + (SIR(RR - 1)).

      To ensure that the resulting PEF was not exaggerated by excessively high RRs, Peto et al. adjusted the formula proposed by Levin [54] by replacing the 1 in the denominator by a 2.

      Once the RRs from the CPS II had been re-analyzed and their robustness confirmed, the earlier reduction was viewed as excessive, and a reduction of 30% applied instead [81]. In countries like China, where country-specific risks are available, the reduction applied is lower.

      The last step in this procedure would involve applying the following formula: AM = OM * PEF, in order to obtain the estimation of attributed mortality, AM, in accordance with the PEF previously calculated and the observed mortality, OM.

      This method has only been applied to estimation of tobacco-attributable mortality [11, 78, 79, 8183].

      c) Excess mortality methods

      Garfinkel's method

      Cancer deaths due to smoking are calculated as the difference between observed and expected deaths in a population. To apply this method, age- and sex-specific cancer mortality rates are needed, and age- and sex-specific cancer mortality rates for non-smokers are computed on the basis of the CPS study [84]. The expected deaths are related to the number of deaths that would occur if the whole population was formed by non smokers. To calculate the expected number of deaths, the follow-up over 12 years of the never smokers enrolled at the CPS I study was employed and death rates for cancer were computed. These rates were applied to the estimated number of person-years of exposure for non-smokers to obtain the expected number of deaths for each cancer. The attributable fractions calculated in this way were similar to those yielded by the CPS [85]. Garfinkel's method was applied to estimate cancer mortality attributable to tobacco use [8587].

      Rogers' method

      The method proposed by Rogers et al. [88] combines prevalence and mortality risk rates in order to offer more precise estimates of smoking attributable mortality. This calculation procedure attempts to avoid some problems related to previous methods as 1) the use of risks derived from selected populations, 2) the absence of adjustment for confounding factors or 3) the classification of the smoking status in crude categories without attending to the number of cigarettes smoked by former and current smokers. At first, age-specific smoking prevalence and mortality risks were estimated. The authors define 7 population groups distinguished by reference to the amount of cigarettes smoked (p) and classifies them by sex and age-group: non-smokers, light smokers, moderate and heavy smokers, light ex-smokers, and moderate and heavy ex-smokers. To determine the risk of death due to cigarette smoking, Roger et al. matched data of a health survey to mortality data. Discrete time hazard models were employed to compute the risks.

      The next step is to determine how many people exist in each smoking status (n):

      n = p *Pop, being Pop the age-specific population in the area studied.

      The last step is to estimate the excess risk of death (R) of each smoking status relative to never smokers:

      mx,c - mx,n, where mx,c is the age-specific central death rate for each smoking status and mx,n is the age-specific central death rate relative to never smokers.

      Finally the excess number of deaths is calculated as follows:

      ED = ∑n*(mx,c - mx,n), in the different ages-groups considered.

      This method has been used once to estimate tobacco attributable mortality [88].

      d) Predictive models

      These models are represented essentially by one model: the Prevent model [83].

      The Prevent simulation model [83] was developed in 1988 in The Netherlands and is regarded as being a multifactorial generalization of the etiologic fraction. It has been used basically to predict mortality due to various causes, including tobacco [89]. The methodology used allows, among other factors, for a temporal dimension to be considered and takes into account the possibility of a risk factor to associate with more than one disease and a disease to associate with more than one risk factor. The process of calculation is tedious and needs knowledge of multiple data, such as birth- and mortality-rate series or the likelihood of dying at different ages for each sex [90]. The calculation procedure was described in detail in a phD dissertation [90] and is summarized elsewhere [83, 91]. Due its scarce use, the calculation procedure is not described in this paper. However it is important to introduce two epidemiological effect measures that this method uses: the "potential impact fraction" and the "trend impact fraction". Both are indicators of the reduction in the incidence of a disease in the population studied, the former reflects changes in the evolution of a disease after an intervention and the latter is referred to autonomous or natural trends.


      This paper constitutes, to our knowledge the first methodological review of procedures for estimating smoking-attributable mortality. In the context of decision-making it is essential to know, albeit approximately, the impact that a given risk factor has on the mortality of a population. Estimation of tobacco-related mortality is not confined to one procedure alone, inasmuch as any of the different methods outlined above can be used for the purpose.

      Despite the fact that different methodologies have been found, the foundations of more of them are the same and only few differences arise in the calculation procedures (Table 2). Data availability has been taken into account when choosing a method and also methodology limitations and assumptions have to be considered. Some of them are described below.
      Table 2

      Methodologies' modifications taking into account prevalence-based analysis in cohort studies as base method


      Peto's et al method

      Prevalence-based analysis in case-control studies

      Basic method

      Variation respect prevalence-based analysis in cohort studies

      Problem: Smoking prevalence is a poor proxy for cumulative hazards of smoking.

      Solution: Defining SIR (Smoking impact ratio or Synthetic prevalence) authors avoid prevalence limitations.

      Problem: RR extrapolation to different populations than the original is inconsistent.

      Solution: Designing a case-control study OR could be assessed.

      Problem: RR extrapolation to different populations than the original is inconsistent.

      Solution: RR can be estimated applying a calculation procedure.

      Calculation procedure

      where CLC, NLC, S*LC, N*LC are age-sex specific lung cancer mortality rates for smokers and never smokers in the study and in the reference population (*).


      p1 is the prevalence between the cases

      a1 is exposed cases

      b1 is exposed controls

      a0 is non exposed cases

      b0 is non exposed controls

      Packs-function in smokers

      RRs = 1 + ac((t - 5))-t0)

      Multistage-function in smokers

      RRs = 1 + [(t - 5)4.5 + ac(1 + 2ac)((t - 5)-t0)4.5 + 2ac((t - 5)4.5-t0 4.5)]/(t - 5)4.5

      Where a is a constant, c is the number of packs of cigarettes smoked per year, t is the current age and t 0 is age at start of smoking.

      In former smokers t1 replaces (t - 5) and t1 is age at stop smoking.

      The first limitation affecting intercomparison of methods and studies stems from the absence of a universal definition of the categorization of tobacco use. The publications analyzed furnish different definitions of "smoker", "non-smoker" and "ex-smoker" [6, 9295], something that inevitably determines the result of the estimation [96].

      To view smokers as a single entity could lead to a distorted mortality estimate, since failure to take account of the number of cigarettes smoked, age at initiation, years of smoking and other variables that could modify risks values can occur. It would thus be interesting to explore tobacco use in the studied populations [88]. A correct classification of ex-smokers is very important for estimating and predicting mortality attributable to tobacco use. To avoid overestimation of attributed mortality, Anthonisen [97] proposed that account must be taken of the decrease in risk that takes place at 15 years after quitting the habit. But this decrease is also determined by the subject's age at cessation [98, 99], the duration of smoking [100] and the cause studied. The fact that this information was not expressly gathered in the majority of surveys means that mortality among ex-smokers may be overestimated. This problem is solved, at least in part, by ex-smokers reclassifying themselves as non-smokers after the elapse of a long time without smoking [101].

      The second limitation, present mainly in the proportional method, resides in their reliance on current smoking prevalences to reflect mortality occasioned by tobacco use in previous years. Knowing current smoking prevalence could be a great help when it comes to predicting future mortality, but not present[102]: indeed, knowing the prevalence of tobacco use in any given year could help predict lung cancer mortality in 20 years' time [103]. As yet, this problem has no easy solution, due to the absence of historical series of smoking prevalence in most countries. Moreover, even if such series were to exist, lack of knowledge of the latency and induction times for each of the tobacco-related causes of death would constitute another problem. The use of current prevalence may overestimate or underestimate the attributable mortality. In countries where the prevalence is decreasing, as U.S.A. or some European nations, the use of current prevalence is conservative in the proportional attribution method. The opposite occurs in countries where prevalence is increasing. Given the unavailability or inaccuracy of prevalence data, and emphasizing that current prevalence is a poor proxy for cumulative hazards of smoking, the knowledge of the period of time from tobacco consumption until mortality related to this use it is necessary.

      Ascertaining the induction period might be feasible if only one specific component cause was active in triggering the disease. However, if one allows for the presence of more than one component cause, then each may have its own induction time; furthermore, the action of effect modifiers could alter the induction period [104]. It would therefore seem that ascertainment of the induction period is complicated; nevertheless, ascertaining the latency period is no easy matter either, since it varies according to the diagnostic methods. What should be clear, however, is that an induction time is needed for tobacco to cause harm, and it is for this reason that the age ranges between 30–35 years are considered the time to begin measuring the effects of exposure. Measuring such effects without taking into account an induction time could lead to overestimated mortality results. On the other hand, some authors [88] feel that ignoring mortality under the age of 35 years may give rise to underestimates of mortality figures, due to the existence of individuals who started smoking at early ages.

      Peto et al. avoided the problem entailed in prevalence-dependent methods of attribution. For the application of their estimation procedure, lack of knowledge of the tobacco consumption or latency and induction periods are no a limitation. But this method has not been exempt from criticism [25, 105110] directed, mainly, at the calculation of summarized prevalence. Some of these critics were supported by the tobacco industry, which tried to undermine the studies focused on estimations of mortality attributable to tobacco consumption. Peto and colleagues defined synthetic prevalence as an indicator that summarizes a population's smoking history, and calculate it by assuming CPS II data on lung cancer mortality rates among smokers and non-smokers to be valid. The use of these 2 sets of data gave rise to numerous criticisms that highlighted the low population representativeness of the CPS II [25, 107, 111, 112]. Most of the population included in this cohort study was middle class, which may result in lung cancer mortality in non-smokers being underestimated [88] leading, in turn, to an overestimation of lung cancer mortality attributable to tobacco use and, by extension, to an overestimation of the summarized prevalence [25]. To justify their validity and universality, these data were compared with those yielded by the study that targeted British physicians [93]; despite the fact that the results obtained were similar, no conclusion could be drawn, since the representativeness of this latter study was also limited. The only thing that could be said was that the lung cancer mortality rate among non-smokers had not varied over the years[111]. Nonetheless, in countries where the use of coal is widespread, lung cancer mortality among non-smokers is higher, and thus the data, rather than being drawn from the CPS II, have been drawn from a local study [72].

      The third limitation centers on the absence of world-wide risk indicators that would reflect the degree of association between tobacco and smoking related-causes of mortality. The most widely used effect measure is RR, and a sensitivity analysis has shown that changes in its value lead to a greater impact on the estimation of mortality than do changes in prevalence [102]. Although drawn from different sources, the RRs used in the various studies mainly came from the CPS II [55, 56]. Applying these risks to populations other than that of the USA aroused criticism because, inter alia, of their only being adjusted for age and sex, and because of the difficulty inherent in assimilating identical tobacco consumption and genetic variability patterns, or the same influence of confounding factors or effect modifiers. A solution to these problems was sought through a re-analysis of the data [9, 113115], and the RRs were shown robust. Notwithstanding this, the criticisms continued unabated [116].

      The risks obtained from the CPS II are plausible in the light of current knowledge [25] and have been extrapolated [117] to different EU countries, in absence of other high quality indicators. Nevertheless, other authors have chosen to apply RRs which are drawn from studies with less robust designs or possibly inconsistent with present knowledge.

      A fourth limitation of the attribution methodology is the uncertainty present in the relationship between exposure, tobacco use, and different causes of death. While lung cancer was the first disease to be causally associated with tobacco use, many studies have observed more causal associations. The latest report of the Surgeon General [56] has added 2 further causes of mortality that had not been considered to date, i.e., stomach cancer and acute myeloid leukemia and excludes hypertension.

      Some methods have been compared by applying them in the same population. Published comparisons are the individual analysis and SAMMEC [3, 4], Peto and Prevent methods [83], Peto and proportional attribution method [11], and Garfinkel's and proportional attribution method [85]. The results obtained in all of these comparisons have proved to be similar estimations, thereby conferring validity on the respective methodologies. Observational epidemiology and, despite their limitations, the use of the above-described calculation procedures offer a good approximation of the impact of tobacco on the mortality of a population [4].


      Prior to conducting a study on estimation of tobacco-attributable mortality, it is essential to assess which method is best suited to the type and quality of the available information.

      When the mortality estimation objective is going to be the knowledge of tobacco impact on a population, it is important to take into account all the diseases related with consumption. For this reason, the applications of methodologies that involve all the causes of disease are important. These methodologies are: Prevalence-based analysis in cohorts and in case-control studies, Peto et al. and Roger's methodology. All of them supply accurate and reliable estimations of mortality attributed to tobacco consumption.

      The absence of a simulation study involving and comparing all calculations procedures do not allow us to recommend a method over other one.

      These types of methods furnish estimates that constitute valuable information and help forming a more accurate picture of the problem that smoking poses to world health.



      The study was undertaken with the aid of a grant from the Galician Directorate-General for Public Health (Dirección Xeral de Saúde Pública).

      We should like to thank Dr Alberto Malvar for his unstinting help throughout the course of drafting this manuscript, in the form of contributing ideas as well as reading the different versions produced. In addition, sincere thanks must go: to Prof Alberto Ruano-Ravina for his critical review of the manuscript and invaluable comments; and to Soly Santiago and Xurxo Hervada for their painstaking proof reading and suggestions.

      Thanks to professor Bahi Takkouche, Pedro Branas and Michael Lisman for their comments in order to improve the quality of the manuscript.

      Authors’ Affiliations

      Department of Epidemiology, Directorate-General for Public Health, Galician Regional Health Authority
      Department of Preventive Medicine and Public Health, University of Santiago de Compostela
      CIBER Epidemiología y Salud Pública (CIBERESP)


      1. Wynder EL, Graham EA: Tobacco smoking as a possible etiologic factor in bronchiogenic carcinoma. JAMA 1950, 143: 329–336.
      2. Doll R, Hill AB: Smoking and carcinoma of the lung. BMJ 1950, ii: 739–748.View Article
      3. McAnulty JM, Hopkins DD, Grant-Worley JA, Baron RC, Fleming DW: A comparison of alternative systems for measuring smoking-attributable deaths in Oregon, USA. Tob Control 1994, 3: 115–119.View Article
      4. Thomas A, Hedberg K, Fleming D: Comparison of physician based reporting of tobacco attributable deaths and computer derived estimates of smoking attributable deaths, Oregon, 1989 to 1996. Tob Control 2001, 10: 161–164.PubMedView Article
      5. Zevallos JC, Huang P, Smoot M, Condon K, Alo C: Usefulness of tobacco check boxes on death certificates: Texas, 1987–1998. Am J Public Health 2004, 94: 1610–1613.PubMedView Article
      6. CDC: Annual smoking-attributable mortality, years of potential life lost, and economic cost United States, 1995–1999. MMWR 2002, 51: 300–303.
      7. Kuri-Morales P, Alegre-Díaz J, Mata-Miranda MP, Hernández-Avila M: Mortalidad atribuible al consumo de tabaco en México. Salud Publica Mex 2002, 44: 29–33.
      8. John U, Hanke M: Tobacco-and alcohol-attributable mortality and years of potential life lost in Germany. Eur J Public Health 2003, 13: 275–277.PubMedView Article
      9. Thun M, Apicella L, Henley S: Smoking vs other risk factors as the cause of smoking-attributable deaths. JAMA 2000, 284: 706–712.PubMedView Article
      10. Malarcher A, Schulman J, Epstein L, Thun M, Mowery P, Pierce B, Escobedo L, Giovino G: Methodological issues in estimating smoking-attributable mortality in the United States. Am J Epidemiol 2000, 152: 573–584.PubMedView Article
      11. Gorini G, Chellini E, Querci A, Seniori Costantini A: Impatto dell´abitudine al fumo in Italia nel 1998: decessi e anni potenziali di vita persi. Epidemiol Prev 2003, 27: 285–290.PubMed
      12. Makomaski E, Kaiserman M: Mortality attributable to tobacco use in Canada and its regions, 1998. Can J Public Health 2004, 95: 38–44.
      13. Wardman D, Khan NA: Smoking-attributable mortality among British Columbia´s first nations populations. Int J Circumpolar Health 2004, 63: 81–92.PubMed
      14. Rivara FP, Ebel BE, Garrison MM, Chiristkis DA, Wiehe SE, Levy DT: Prevention of smoking-related deaths in the United States. Am J Prev Med 2004, 27: 118–125.PubMedView Article
      15. Levy DT, Friend K: Examining the effects of tobacco treatment policies on smoking related deaths using the SimSmoke computer simulation model. Tob Control 2002, 11: 47–54.PubMedView Article
      16. Levy DT, Friend K, Holder H, Carmona M: Effect of policies directed at youth access to smoking: results from the SimSmoke computer simulation model. Tob Control 2001, 10: 108–116.PubMedView Article
      17. Levy DT, Friend K, Polishchuk E: Effect of clean indoor airs laws on smokers: the clean air module of the SimSmoke computer simulation model. Tob Control 2001, 10: 345–351.PubMedView Article
      18. Single E, Rehm J, Robson L, Truong MV: The relative risk and etiologic fractions of different causes of death and disease attributable to alcohol, tobacco and illicit drugs use in Canada. CMAJ 2000, 162: 1669–1675.PubMed
      19. Levy DT, Cummis D, Hyland A: A simulation of the effects of youth initiation policies on overall cigarette use. Am J Public Health 2000, 90: 1311–1314.PubMedView Article
      20. Makomaski E, Kaiserman M: Mortality attributable to tobacco use in Canada and its regions, 1994 and 1996. Chronic Dis Can 2000, 20: 111–117.
      21. Single E, Robson L, Rehm J, Xie X, Xi X: Morbidity and mortality attributable to alcohol, tobacco, and illicit drugs use in Canada. Am J Public Health 1999, 89: 385–390.PubMedView Article
      22. Stapleton M, Palmer C: Cigarette smoking in Kentucky: smoking-attributable mortality and years of potential life lost. J Ky Med Assoc 1998, 96: 451–455.PubMed
      23. Lam TH, He Y, Li LS, Li LS, He SF, Liang BQ: Mortality attributable to cigarette smoking in China. JAMA 1997, 278: 1505–1508.PubMedView Article
      24. Unwin CE, Gracey MS, Thomson NJ: The impact of tobacco smoking and alcohol consumption on aboriginal mortality in Western Australia, 1989–1991. Med J Aust 1995, 62: 475–478.
      25. Sterling TD, Rosenbaum WL, Weinkam JJ: Risk attribution and tobacco-related deaths. Am J Epidemiol 1993, 138: 128–139.PubMed
      26. Nelson D, Kirkendall R, Lawton R, Chrismon J, Merrit R, Arday D, Giovino G: Surveillance for smoking-attributable mortality and years of potential life lost, by state-United States, 1990. MMWR CDC Surveill Summ 1994, 43(1): 1–8.
      27. Dietz VJ, Novotny TE, Rigau-Perez J, Shultz JM: Mortalidad atribuible al tabaquismo, años de vida potencial perdidos y costos directos para la atención de salud en Puerto Rico, 1983. Bol of Sanit Panam 1991, 110: 378–389.
      28. Smoking attributable mortality and years of potential life lost-United States, 1984.JAMA 1987, 258: 2648–2657.
      29. Ouellet B, Romeder JM, Lance JM: Premature mortality attributable to smoking and hazardous drinking in Canada. Am J Epidemiol 1979, 109: 451–463.PubMed
      30. Holman CD, Shean RE: Premature adult mortality and short-stay hospitalization in Western Australia attributable to the smoking tobacco, 1979–1983. Med J Aust 1986, 145: 7–11.PubMed
      31. Liaw KM, Chen CJ: Mortality attributable to cigarette smoking in Taiwan: a 12-year follow-up study. Tob Control 1998, 7: 141–148.PubMedView Article
      32. Rothenbacher D, Brenner H, Arndt V, Fraisse E, Zschenderlein B, Fliedner TM: Smoking patterns and mortality attributable to smoking in a cohort of 3528 construction workers. Eur J Epidemiol 1996, 12: 335–340.PubMedView Article
      33. CDC: Cigarette smoking-attributable mortality and years of potential life lost United States, 1990. JAMA 1993, 270: 1408–1413.View Article
      34. Nelson D, Davis RM, Chrismon J, Giovino G: Pipe smoking in the United States, 1965–1991: Prevalence and attributable mortality. Prev Med 1996, 25: 91–99.PubMedView Article
      35. Holman J, Shean RE: Premature adult mortality and short-stay hospitalization in Western Australia attrubutable to the smoking of tobacco, 1979–1983. Med J Aust 1986, 145: 7–11.PubMed
      36. Wen CP, Tsai SP, Yen DY: The health impact of cigarette smoking in Taiwan. Asia Pac J Public Health 1994, 7: 206–213.PubMed
      37. Ellison LF, Mao Y, Gibbson L: Projected smoking-attributable mortality in Canada, 1991–2000. Chronic Dis Can 1995, 16: 1–8.
      38. Collishaw NE, Tostowaryk W, Wigle DT: Mortality attributable to tobacco use in Canada. Can J Public Health 1988, 166–169.
      39. Collishaw NE, Leahy K: Mortality attributable to tobacco use in Canada, 1989. Chronic Dis Can 1991, 12: 46–49.
      40. King DR, Smith AH, Salter DM: Mortality attributable to smoking in New Zealand. N Z Med J 1983, 96: 195–199.PubMed
      41. Banegas JR, Diez L, Rodriguez-Artalejo F, González J, Graciani A, Villar F: Mortalidad atribuible al tabaquismo en España en 1998. Med Clin 2001, 109: 577–582.
      42. Rodríguez R, Bueno A, Pueyos A, Espigares M, Martínez MA, Gálvez R: Morbilidad, mortalidad y años potenciales de vida perdidos atribuibles al tabaco. Med Clin 1997, 108: 121–127.
      43. González GJ, Vega MG: Estudio de la mortalidad atribuible al tabaquismo en Jalisco, México. Rev Esp Salud Publica 1995, 69: 509–518.
      44. Jane M, Borrell C, Nebot M, Pasarin MI: Impacto del tabaquismo y del consumo excesivo de alcohol en la mortalidad de la población de la ciudad de Barcelona: 1983–1998. Gac Sanit 2003, 17: 108–115.PubMedView Article
      45. González J, Villar F, Banegas JR, Rodriguez-Artalejo F, Martín J: Tendencia de la mortalidad atribuible al tabaquismo en España, 1978–1992: 600000 muertes en 15 años. Med Clin 1997, 109: 577–582.
      46. González J, Rodriguez-Artalejo F, Martín J, Banegas JR, Villar F: Muertes atribuibles al consumo del tabaco en España. Med Clin 1989, 92: 15–18.
      47. Montes A, Pérez-Ríos M, Gestal JJ: Impacto del tabaquismo sobre la mortalidad. Adicciones 2004, 16: 75–82.
      48. Santos EF, Valero LF, Sáenz MC: Mortalidad atribuible al tabaco en Castilla y León. At Prim 2001, 27: 153–158.
      49. Criado JJ, Morant C, de Lucas A: Mortalidad atribuible al consumo de tabaco en los años 1987 y 1997 en Castilla la Mancha, España. Rev Esp Salud Publica 2002, 76: 27–36.
      50. Bello LM, Lorenzo P, Gil M, Saavedra P, Serra L: Evolución de la mortalidad atribuible al tabaco en las Islas Canarias (1975–1994). Rev Esp Salud Publica 2001, 75: 71–79.
      51. Banegas JR, Diez L, González J, Villar F, Rodriguez-Artalejo F: La mortalidad atribuible al tabaquismo empieza a descender en España. Med Clin 2005, 124(20): 769–771.View Article
      52. Llorca J, Fariñas-Alvarez J, Delgado-Rodriguez M: Fracción atribuible poblacional: cálculo e interpretación. Gac Sanit 2001, 15: 61–67.PubMed
      53. Rockhill B, Newman B, Weinberg C: Use and misuse of population attributable fractions. Am J Pub Health 1998, 88: 15–19.View Article
      54. Levin ML: The occurrence of lung cancer in man. Acta Un Intern Cancer 1953, 9: 531–541.
      55. CDC: US Department of Health and Human Services. Reducing the health consequences of smoking. 25 years of progress. A report of the Surgeon General. Centers for Disease Control (CDC) 1989, 8411.
      56. CDC: The Health Consequences of Smoking: A Report of the Surgeon General. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2004. 2004, 1–910.
      57. CDC: US Department of Health and Human Services.Smoking and Health in the Americas. Atlanta, Georgia: U.S. Department of Health and Human Services, Public Health and Human Services, Public Health Service, Centers for Disease Control, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health. 1992.
      58. Shultz JM, Novotny TE, Rice D: Quantifying the disease impact of cigarette smoking with SAMMEC II Software. Public Health Rep 1991, 106: 326–333.PubMed
      59. Nurminen M, Jaakkola MS: Mortality from occupational exposure to environmental tobacco smoke in Finland. J Occup Environ Med 2001, 43: 687–693.PubMedView Article
      60. Woodward A, Laugesen M: How many deaths are caused by second hand cigarette smoke? Tob Control 2001, 10: 383–388.PubMedView Article
      61. Jamrozik K: Estimate of deaths attributable to passive smoking among UK adults: database analysis. BMJ 2005, 330: 812–816.PubMedView Article
      62. Corrao G, Rubbiati L, Zambon A, Arico S: Alcohol-attributable and alcohol-preventable mortality in Italy. A balance in 1983 and 1996. Eur J Public Health 2002, 12: 214–223.PubMedView Article
      63. John U, Hanke M: Alcohol-attributable mortality in a high per capita consumption country-Germany. Alcohol Alcohol 2002, 37: 581–585.PubMed
      64. Makimoto K, Oda H, Higuchi S: Is heavy alcohol consumption an attributable risk factor for cancer-related deaths among japanese men? Alcohol Clin Exp Res 2000, 24: 382–385.PubMed
      65. Gorsky RD, Schwartz E, Dennis D: The mortality, morbidity, and economic cost of alcohol abuse in New Hampshire. Prev Med 1988, 17: 736–745.PubMedView Article
      66. Banegas JR, López-García E, Gutierrez-Fisac JL, Guallar-Castillon P, Rodriguez-Artalejo F: A simple estimate of mortality attributable to excess weight in the European Union. Eur J Clin Nutr 2003, 57: 201–208.PubMedView Article
      67. Flegal KM, Williamson DF, Pamuk ER, Rosenberg HM: Estimating deaths attributable to obesity in the United States. Am J Public Health 2004, 94: 1486–1489.PubMedView Article
      68. Schwingl PJ, Ory HW, Visness CM: Estimates of the risk of cardiovascular death attributable to low-dose oral contraceptives in the United States. Am J Obstet Gynecol 1999, 180: 241–249.PubMedView Article
      69. Banegas JR, Rodriguez-Artalejo F, Cruz JJ, Andrés B, Rey J: Mortalidad relacionada con la hipertensión y la presión arterial en España. Med Clin 1999, 112: 489–494.
      70. Banegas JR, Rodriguez-Artalejo F, Graciani A, Villar F, Herruzo R: Mortality attributable to cardiovascular risk factors in Spain. Eur J Clin Nutr 2003, 57 Suppl 1: S18–21.PubMedView Article
      71. Penman A: Excess mortality due to diabetes in Mississippi and the estimated extent of underreporting on death certificates. J Miss State Med Assoc 2003, 44: 319–325.PubMed
      72. Niu SR, Yang GH, Chen ZM, Wang JL, Wang GH, He XZ, Schoepff H, Boreham J, Pan HC, Peto R: Emerging tobacco hazards in China: 2. Early mortality results from a prospective study. BMJ 1998, 317: 1423–1424.PubMed
      73. Lam TH, Ho SY, Hedley AJ, Mak KH, Peto R: Mortality and smoking in Hong-Kong: case-control study of all adult deaths in 1998. BMJ 2004, 323: 361–362.View Article
      74. Gajalakshmi V, Peto R, Kanaka TS, Jha P: Smoking and mortality from tuberculosis and other diseases in India: retrospective study of 43000 adult male deaths and 35000 controls. Lancet 2003, 362: 507–515.PubMedView Article
      75. Liu BQ, Peto R, Chen ZM, Boreham J, Wu YP, Li JY, Campbell TC, Chen JS: Emerging tobacco hazards in China: 1. Retrospective proportional mortality study of one million deaths. BMJ 1998, 317: 1411–1422.PubMed
      76. Forastiere F, Perucci CA, Arca M, Axelson O: Indirect estimates of lung cancer death rates in Italy not attributable to active smoking. Epidemiology 1993, 4: 502–510.PubMedView Article
      77. Whittemore AS: Effect of cigarette smoking in epidemiological studies of lung cancer. Stat Med 1988, 7: 223–238.PubMedView Article
      78. Peto R, Lopez A, Boreman J, Thun M, Heath C: Mortality from tobacco in developed countries: Indirect estimation from national vital statistics. Lancet 1992, 339: 1268–1278.PubMedView Article
      79. Peto R, Lopez A, Boreham J, Thun M, Heath C: Mortality from tobacco in developed countries 1950–2000: Indirect estimates from national vital statisics. Oxford, Oxford Univ. Press. 1994.
      80. Ezzati M, Lopez A: Measuring the accumulated hazards of smoking: global and regional estimates for 2000. Tob Control 2003, 12: 79–85.PubMedView Article
      81. Ezzati M, Lopez AD: Regional, disease specific patterns of smoking-attributable mortality in 2000. Tob Control 2004, 13: 388–395.PubMedView Article
      82. Peto R: Smoking and death: the past 40 years and the next 40. BMJ 1994, 309: 937–939.PubMed
      83. Bronnum-Hansen H, Juel K: Estimating mortality due to cigarette smoking: two methods, same result. Epidemiology 2000, 11: 422–426.PubMedView Article
      84. Garfinkel L: Cancer mortality in nonsmokers: prospective study by the American Cancer Society. J Natl Cancer Inst 1980, 65: 1169–1173.PubMed
      85. Gorsky RD, Schwartz E, Dennis D: The morbidity, mortality and economic cost of cigarette smoking in New Hampshire. J Community Health 1990, 15: 175–183.PubMedView Article
      86. Doll R, Peto R: The causes of cancer. Oxford, Oxford University Press 1981.
      87. Doll R, Peto R: The causes of cancer. Quantitative estimates of avoidable risks of cancer in the United States today. J Natl Cancer Inst 1981, 66: 1191–1308.PubMed
      88. Rogers RG, Hummer RA, Krueger PA, Pampel FC: Combining prevalence and mortality risk rates: the case of cigarette smoking. Research program on population processes University of Colorado, 2002, 30.
      89. Holowaty E, Chin S, Cori S, Garcia J, Luk R, Lyons C, Thériault ME: Tobacco or Health in Ontario. Ontario, Surveillance Unit and Prevention Unit Division of Preventive Oncology Cancer Care Ontario and Ontario Tobacco Research Unit. 2002.
      90. Gunning-Schepers LJ: The health benefits of prevention: a simulation approach. Health Policy (New York) 1989, 12: 1–256.
      91. Bronnum-Hansen H: How good is the Prevent model for estimating the health benefits of prevention? J Epidemiol Community Health 1999, 53: 300–305.PubMedView Article
      92. Rodríguez R, Pueyos A, Bueno A, Delgado M, Gálvez R: Proporción de la enfermedad atribuible al tabaco en la provincia de Granada. Med Clin 1994, 102: 571–574.
      93. Doll R, Peto R, Wheatley K, Gray R, Sutherland I: Mortality in relation to smoking: 40 years´ observations on male Brithish doctors. BMJ 1994, 309: 901–911.PubMed
      94. Doll R, Peto R, Boreham J, Sutherland I: Mortality in relation to smoking: 50 years´ observations on male British doctors. BMJ 2004, 328: 1519.PubMedView Article
      95. Ministerio de sanidad y Consumo. Encuesta Nacional de Salud 2001. Datos no publicados
      96. Pomerleau CS, Pomerleau OF, Snedecor SM, Mehringer AM: Defining a never-smoker: results from the nonsmokers survey. Addict Behav 2004, 29: 1149–1154.PubMedView Article
      97. Anthonisen NR, Skeans MA, Wise RA, Manfreda J, Kanner RE, Connett JE: The effects of a smoking cessation intervention on 14.5-year mortality. Ann Intern Med 2005, 142: 233–239.PubMed
      98. Jha P, Ranson MK, Nguyen SN, Yach D: Estimates of global and regional smoking prevalence in 1995, by age and sex. Am J Public Health 2002, 92: 1002–1006.PubMedView Article
      99. Ruano-Ravina A, Figueiras A, Montes-Martinez A, Barros-Dios JM: Dose-response relationship between tobacco and lung cancer: new findings. Eur J Cancer Prev 2003, 12: 257–263.PubMedView Article
      100. Flanders WD, Lally CA, Zhu B, Henley S, Thun M: Lung cancer mortality in relation to age, duration of smoking, and daily cigarette consumption: Results from Cancer Prevention Study II. Cancer Res 2003, 63: 6556–6562.PubMed
      101. Kemm J: A model to predict the results of changes in smoking behaviour on smoking prevalence. J Public Health Med 2003, 25: 318–324.PubMedView Article
      102. Tanuseputro P, Schultz S, Manuel D: Estimating smoking-attributable mortality (Letter). Can J Public Health 2004, 95: 132.PubMed
      103. Heloma A, Nurminen M, Reijula K, Rantanem J: Smoking prevalence, smoking-related lung diseases, and national tobacco control legislation. Chest 2004, 126: 1825–1831.PubMedView Article
      104. Rothman KJ, Greenland S: Clinical Epidemiology. Modern epidemiology Second Edition (Edited by: Lippincott-Raven). Philadelphia 1998, 519–529.
      105. Skrabanek P: Smoking and statistical overkill. Lancet 1992, 340: 1208–1209.PubMedView Article
      106. Lee D, Hoel D: Tobacco-associated deaths. Lancet 1992, 340: 666.View Article
      107. Sterling TD, Rosenbaum WL, Weinkam JJ: Tobacco-associated deaths (Letter). Lancet 1992, 340: 666–678.PubMedView Article
      108. Ashford J: Deaths from tobacco (Letter). Lancet 1992, 340: 121.PubMedView Article
      109. Lee D: Mortality from tobacco in developed countries: Are indirect estimates reliable? Regulatory Toxicol Pharmacol 1996, 24: 60–68.View Article
      110. Callum C: UK deaths from smoking (Letter). Lancet 1992, 339: 1484.PubMedView Article
      111. Rosenbaum WL, Sterling TD, Weinkam JJ: Use of multiple surveys to estimate mortality among never, current, and former smokers: changes over a 2 -year interval. Am J Public Health 1998, 88: 1664–1668.PubMedView Article
      112. Davis D: Trends in nonsmoking lung cancer. Epidemiology 1993, 4: 489–492.PubMedView Article
      113. Heinrich J: CDC´s Report On Smoking: Estimates of Selected Health Consequences of Cigarette Smoking Were Reasonable. Washington, United States General Accounting Office 2002, 19.
      114. Siegel M, Arday D, Merrit R, Giovino G: Re: "Risk attribution and tobacco-related deaths." (Letter). Am J Epidemiol 1994, 140: 1051.PubMed
      115. Thun M, Apicella L, Henley S: Estimating the numbers of smoking-related deaths (Letter). JAMA 2000, 284: 2319–2320.View Article
      116. Levy R: Estimating the numbers of smoking-related deaths (Letter). JAMA 2000, 284: 2319.PubMedView Article
      117. Harris R, Helfand M, Woolf S, Lohr K, Mulrow C, Teutsch S, Atkins D: Current methods of the U.S. preventive services task force: a review of the process. Am J Prev Med 2001, 20 Suppl 3: S21–35.View Article
      118. Pre-publication history

        1. The pre-publication history for this paper can be accessed here:http://​www.​biomedcentral.​com/​1471-2458/​8/​22/​prepub


      © Pérez-Ríos and Montes. 2008

      This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.