Estimating the Induction Period of Pleural

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Volume 45, Number 10, October 2003

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Estimating the Induction Period of Pleural Mesothelioma From Aggregate Data on Asbestos Consumption Markku Nurminen, DPH, PhD Antti Karjalainen, MD Ken Takahashi, MD

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This study aimed to estimate the induction period from causal action of asbestos exposure to the manifestation of mesothelioma. We included the 9 countries for which we could find published aggregate data on the use of raw asbestos for a relevant time period. We extracted the annual numbers of cases of pleural cancer among men from the World Health Organization mortality database for those years using the International Classification of Diseases, 9th revision, classification. For the Scandinavian countries, we used published national cancer incidence data. In autoregressive Poisson regression modeling, we invoked different time lags of the mean annual use of asbestos to specify which time span produced the best correlation between the 2 time series. The ecologic analysis suggested that the most probable estimate for the mean induction period (use versus morbidity at society level) is approximately 25 years. (J Occup Environ Med. 2003;45:1107–1115)

From the Department of Epidemiology and Biostatistics (Dr Nurminen, Dr Karjalainen), Finnish Institute of Occupational Health, Helsinki, Finland; and the Department of Environmental Epidemiology (Dr Takahashi), University of Occupational and Environmental Health, Kitakyushu, Japan. Address correspondence to: Markku Nurminen, Department of Epidemiology and Biostatistics, Finnish Institute of Occupational Health, Topeliuksenkatu 41 a A, FIN-00,250 Helsinki, Finland; E-mail: [email protected]. Copyright © by American College of Occupational and Environmental Medicine DOI: 10.1097/01.jom.0000091682.95314.01

he future occurrence of mesothelioma resulting from past exposure, predominantly occupational, to asbestos is of great interest to health authorities, workers, and society at large. Hence, estimating accurately the induction period1 from the initiation of the causal action of a risk factor to the manifestation of the disease is of importance. Disease, in its initial stage, will not necessarily be apparent. The time span for which the illness remains inapparent has been termed the latent period,1 although others have used this term for the time delay between exposure to a disease-causing agent and the appearance of manifestations of the disease.2 In practice, the period between etiologic action and illness detection can rarely be separated empirically into induction and latent periods. It is also difficult to exactly assess the onset of exposure and the onset of disease on an individual level. Thus, Rothman1 has referred to their sum, that is, the time between causal action and illness detection, as the empiric (or apparent) induction period. This is the epidemiologic concept that we are, at an ecologic level, concerned with in this article. Data from the Australian Mesothelioma Register,3 the most complete mesothelioma registry extant, suggests that mesothelioma incidence matches asbestos consumption with an induction period of approximately 35 years (range, 20 –50 years).4,5 The asbestos consumption was high in many countries in the 1960s and 1970s. In Australia, for example, the

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peak use was around 1975.3 Because of the long induction period of mesothelioma, a worldwide mesothelioma epidemic is expected in the coming decades.6,7 In Britain, for example, the peak incidence for male mesothelioma is estimated to occur around 2020, and the incidence will probably tail off rapidly thereafter.8 In contrast, the worst-affected American male cohort was exposed earlier, so that the peak will probably have been reached before the year 2000.9 In fact, the latest Surveillance, Epidemiology and End Results (SEER) data confirm that mesothelioma incidence in North America seem to be decreasing.10 The consensus is that the peak incidence of mesothelioma in Canada and the United States should now be passing. In Australia, the maximum incidence was estimated to occur in 2010.3 In France, mortality burden of mesothelioma was predicted to occur around 2020 and was found to be intermediate between the estimates of the United Kingdom and the United States, but there was still high uncertainty about the time of occurrence of the peak incidence between 2020 and 2060.11 As a result of the enforced strict exposure limits in the manufacture of asbestos-containing products and the introduced legislation to ban the use of asbestos, the trend in mesothelioma incidence is predicted to decline eventually, say within the next 20 years.12 However, the effects of asbestos exposure in the maintenance, removal, and demolition work of older buildings during the 1980s and 1990s, although not yet apparent, could prove considerable.6 Some countries have not banned the use of asbestos completely. For example, Japan just adopted a ban policy, but until recently has imported a significant amount of chrysotile asbestos and also produces many asbestoscontaining products under the controlled-use policy.13 Estimation of the induction period has commonly been based on the period of time between first exposure

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to asbestos (usually the year of first employment in a risk-involved occupation) and the first manifestation of mesothelioma.4,14 –16 For example, in the Australian study,4 the delay was from the year of first exposure to the year of presumptive diagnosis, that is, clinical diagnosis before final histologic confirmation (J. Leigh, personal communication–June 18, 2002). The generally accepted model for the estimation of incident cases of pleural mesothelioma involves asbestos type, dose, and a power function of time since first exposure.17–19 For example, Berry20 used such a model to predict future incidence of mesothelioma from the Wittenoom crocidolite mine in Western Australia. However, as a result of uncertainty about the functional form of the relation between mesothelioma rate and time, and insufficient data to estimate the elimination rate of crocidolite from the lungs, Berry proposed a wide range of projections. Rogers14 used an alternative approach that assumed an induction distribution in the Wittenoom cases similar to that in the occupational histories observed in the Australian mesothelioma statistics. Both approaches yielded compatible estimates. Projections can also be based on a demographic age and birth cohort model for mesothelioma mortality.6 Because mesothelioma is a rapidly progressing fatal illness, analysis of future trends in incidence rates can be estimated reliably from past cohort mortality data. The time interval between the clinical appearance of the mesothelioma and the terminal course is short, with an estimated median survival time of approximately 1 year.16 Moving back in time from the mesothelioma incidence by the estimated induction period must conceivably locate the point in time somewhere between the occurrence of first exposure to asbestos and the first manifestation of the progression of the malignancy (the time of actual onset of the pathogenic process remains unknown). This retrospective approach provides

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a better temporal referent for the timing of the etiologically relevant experience than reference to a time period since first exposure.21 This viewpoint is useful for characterizing protracted exposures. We can think of a protracted exposure as a sequence of short-term exposures experienced annually by cohorts defined by calendar year. The total effect of the protracted exposure on the disease risk is then the sum of the cohorts’ annual exposure intensities. Langholtz et al.22 provided a general statistical framework for conceptualizing and modeling the effect of induction period on disease risk in epidemiologic cohort studies conducted at an individual level. We describe the aggregate exposure and disease incidence datasets and the statistical model for disease risk, present the results of the model fitting to the data, and discuss methodologic issues and uncertainties involved in the estimation of the induction period for mesothelioma incidence.

Data and Methods Our approach to the estimation of the interval of time between the causal action of asbestos exposure and the incidence of mesothelioma uses aggregate data on the consumption of asbestos from 9 countries.8,23–30 We included the countries for which we could identify both published data on the use of raw asbestos for a relevant time period and data on annual numbers of cases of pleural cancer among men (International Classification of Diseases, 9th revision [ICD-9] code 163) from the World Health Organization (WHO) mortality database for those years using ICD-9 classification.31 (The Australian3 asbestos consumption data were excluded because the statistics were grouped for decades and not for quinquennials.) For Denmark,32 Finland,33 and Sweden,28 we used published national cancer incidence data that were available for a considerably longer period than the ICD-9-based mortality data. Both the

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data on the use of asbestos and the data on pleural cancer incidence were grouped into 5-year periods and all the years given refer to the starting year of these periods. The last incidence rate for Denmark was deleted as a statistical “outlier” because it was based on an incomplete 5-year period. Data on the male population were extracted from the WHO mortality database. In the statistical analysis, we applied a Poisson regression model34 for the logarithm of the mean annual number of incident cases of pleural cancer. The primary explanatory variate was the total annual consumption of asbestos. The size of a country’s male population 15 years of age and over (the denominator of the incidence rate) was used as an “offset” (ie, a regression term with a constant coefficient of 1 for each observation). The “intercept” parameter in the regression equation is interpreted as the mean value of the logarithm of the disease incidence at the reference level (at which the coefficients of the calendar year and time-lagged variate equal zero). The method invoked different induction periods (from 20 –50 years) to explore which time span provides the best correlation between the 2 time series. As a result of the changes in industrial hygiene standards, in the type of asbestos products, and in the proportion of amphiboles, the same amount of asbestos used could correspond to a different risk of mesothelioma for different time periods. Therefore, the calendar year was also included in the model. However, calendar year and induction period variates were correlated. Thus, we considered also models in which induction period was the single predictor variate. Information on smoking was not included in the model because mesothelioma risk is not influenced by tobacco smoking.35 We computed Chi-squared deviance statistics for assessing the goodness of the model-fitted values with the observed number of cases. When a model is consistent with the data,

the deviance statistic will stochastically be expected to equal the degrees of freedom (ie, the number of observations minus the number of estimated regression coefficients); there were 3 (or 2) parameters in all the models. Overdispersion did not affect the Poisson model because the distribution was scaled. The scale parameter was estimated by the square root of the deviance divided by the degrees of freedom. We considered a correlation structure that assumes each time series to be an autoregressive process36,37 covering the years 1900 through 1995 for asbestos use and the years 1950 through 1995 for incidence. In the first-order autoregressive covariance structure, the correlation between 2 measurements decreases exponentially as the length of time between the measurements increases. The generalized linear modeling procedure, GENMOD, with generalized estimating equations was applied for the analysis of the longitudinal data in SAS.38 Confidence limits for the predicted number of cancer cases were obtained using the variance and covariance estimates of the regression coefficients as described by Greenland.39 A graphical inspection of the datasets was used to discover any indication of a downward trend in the incidence rates reflecting past reduction of exposure to asbestos. To display the data, we produced Trellis Graphics40 in S-PLUS.41

Results Figure 1 depicts a panel of graphs that shows that the peak consumption of asbestos (5-year average) took place in Finland in the quinquennial starting in 1960; in Great Britain and Sweden in 1965; in Denmark, France, Germany, Japan, and New Zealand in 1970; and in Italy in 1980. With the exception of Denmark, Germany, and Sweden,43 the incidence rate of pleural cancer was still rising linearly in 1990 through 1995. For the induction periods of 20, 25, 30, 35, 40, 45, and 50 years, the

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deviance statistics (the smaller the better), with the number of degrees of freedom in parentheses, were 67, (56), 68 (53), 70 (49), 77 (44), 86 (39), 96 (33), and 91 (24), respectively. It is evident that the observed values deviated considerably from the expected ones. In all the regressions, the effect of the calendar year was highly significant. The regression coefficient was positive for the induction periods of 25, 30, and 35 years, indicating excess risk, and significantly so only for the 25-year induction period (P ⫽ 0.013). In this modeling, however, the numbers of observations were not equal as a result of missing values (ie, for some time-lagged exposure data, there were no matching incidence figures). To achieve comparability, we used the same number of observations (ie, 24 degrees of freedom) in the model fittings. We chose the years of cancer incidence that had no missing values for any of the induction period (time-lagged) variates considered. The corresponding analysis yielded similar results as the previously mentioned analysis. Again, the induction periods of 25, 30, and 35 years gave the best model fits to the data, and the risk parameter was statistically significant only for the 25-year induction period (P ⫽ 0.049). Because the calendar year and induction period variates were correlated, we also estimated models in which time-lagged asbestos use was the single predictor variate. The ranking of the induction period according to their predictive significance was again the same as in the modeling that included both the calendar year and asbestos use variates. More importantly, the induction periods of 25, 30, and 35 years were the best predictors; P values were 0.006, 0.059, and 0.044, respectively. We note that despite the correlation between calendar year and the timelagged variates, the model-based (na¨ıve) and empiric (robust) covariance estimates of their coefficients were

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Fig. 1. Mean annual incidence of mesothelioma cases in men (per million man-years) in relation to the annual use of asbestos (kilogram per adult man). Data for the 5-year periods obtained from nine countries. Symbols: f use of asbestos; Œ incidence of mesothelioma.

similar, indicating an adequate model for the covariance structure. Therefore, the analysis would seem to suggest that the most likely estimate of the induction period is approximately 25 years. However, there was a rather large variation around the fitted models, for example, as shown in the 2-dimensional scatterplot (without the calendar year dimension; Fig. 2). The estimated regression function is almost linear, and the confidence bands for the predicted mean incidence of pleural cancer are log-symmetric and valid only within the range of the observations. For example, for the annual asbestos use of 4.2 (overall mean level in these data), the model prediction was 13.1 per million manyears (95%) for pleural cancer, with the model-based 95% confidence interval of 8.8 –19.7 per million manyears. In Figure 3, a smooth regression surface was fitted to the predicted incidence rates according to asbestos use (lagged 25 years) and calendar year. The graph depicts a linear in-

crease in incidence with increasing use and passage of time with the slope of increase becoming steeper along both axes. There is no indication that the incidence rate would have leveled off during the study period.

Discussion Our approach to the estimation of the induction period of asbestosrelated mesothelioma was to model for the autoregressive aggregate data with time-dependent variates representing the assumed time delays in cancer incidence. Frequently, when epidemiologists make calculations of an induction period, they focus on the point estimate of the mean induction period they have obtained from their analysis. This gives the impression that the data have given one answer regarding the length of the induction period. Clearly, data are typically consistent with an array of values, and the point estimate is merely the value that is most likely. We would be much better off to think of an interval estimate as rep-

resenting the values of the induction period that are consistent with the empiric evidence. The estimated possible range of 25 to 35 years is not a confidence interval, in the sense that 30 years would be the best estimate of the induction period, and that 25 years and 35 years would be equally probable lower and upper limit estimates. Rather, it is the lower limit of the time range, 25 years, that we rank as the best estimate. This estimate of the induction period is 10 years shorter than, for example, the Australian4,5 register-based estimate of the “mean latency” of 35 years. This discrepancy is probably the result of the different conceptualizations of induction period (time since causal action versus first exposure to mesothelioma incidence) and the type of data (aggregate statistics versus individual information) used in these studies. An induction period is of theoretical interest for contemplating the causal mechanisms that produce illness in an individual, and it is of practical value for compensation

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Fig. 2. Occurrence (incidence/mortality) of pleural mesothelioma among men over 14 years of age (cases per million man-years) in relation to the use of asbestos (kilogram per adult man) with a 25-year induction period. Aggregate data combined from 9 countries. Regression equation (without calendar year term): number of cases ⫽ exp (2.07 ⫹ 0.12 ⫻ [asbestos use, lagged 25 years]). Symbols: ‚ Denmark; 䡺 Finland; 〫 France; E Germany; ƒ Great Britain; F Italy; Œ Japan; f New Zealand; ⽧ Sweden; —fitted regression function; 䡠䡠䡠䡠䡠䡠 upper and – 䡠 – 䡠 – lower 95% confidence limits for the predicted mean incidence.

schemes. It has been remarked,42 “Although it is not possible to reduce the induction period proper by earlier detection of disease, it may be possible to observe intermediate stages of a causal mechanism.” The recent interest in biomarkers such as DNA copy number changes in malignant mesothelioma is an example of attempting to focus on causes more proximal to the disease occurrence,44 Such biomarkers could reflect the effect of earlier-acting agents on the organism.45 In litigation cases, a minimum induction period is used in the decision to accept or reject compensation claims, because the concept constitutes a prerequisite for the attribution of cause as a result of a particular exposure to an agent such as asbestos. This is important because it has been argued on mathematical grounds that the probability of causation is not estimable from epidemiologic data without resorting

to strong assumptions about the biologic mechanisms by which exposure affects cancer risk.46 The time difference between the occurrence of the peak exposure to asbestos and the peak incidence of mesothelioma could be taken as an approximate estimate of the induction period. In most countries, exposure to asbestos has reached its peak, whereas mesothelioma incidence is still rising. In such circumstances, the prediction of the latter is imprecise. For example, in France, the peak was predicted to occur between 2020 and 2060.11 Another study23 presented a more accurate estimation of mesothelioma’s peak mortality in France using a risk function model that links mortality to past exposure to asbestos. According to this study, peak mortality is expected around the year 2030 based on both optimistic and pessimistic scenarios of future exposure to asbestos. The differ-

ence between the 2 scenarios shows up only after 2020. Knowing that in France asbestos was banned in 1997 adds credibility to the approximately 25-year induction period estimated in the present study. Note that using our modeling approach, it was not necessary for the peak incidence to have occurred. We only needed to have matching time-lagged exposure and incidence time series for a sufficiently long period of time. The induction period could well be modified by the type of asbestos or the intensity of exposure. For example, data from the German Mesothelioma Register indicate that the heavier the exposure to asbestos, the shorter the induction period. 16 Therefore, the ecologic correlation between the total use of raw asbestos and the incidence of mesothelioma would also fit varying induction periods depending on how the total use was distributed in terms of asbestos

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Fig. 3. A smooth regression (spline function) surface based on a 3-parameter model depicting the predicted mean incidence of pleural cancer (cases per million man-years) for a grid of values of asbestos consumption (kilogram per adult man) and calendar year.

type or intensity of exposure. A study conducted in Italy15 dealt with induction period dependent on the type and intensity of exposure; the study supports the German finding that heavy exposure results in a shorter induction period. Induction periods ranged from 14 to 72 years (mean, 49 years) and differed significantly from one occupational group to another. These data suggest that intensity of exposure is a relevant, but not the only, factor in determining the duration of induction time. One should note also that in previous studies, the induction periods have been calculated from the start of employment in the risk occupation and not from the intensity-weighted time of exposure. One cannot be sure that exposure always started on the first day of employment or that the heaviest exposure occurred at the beginning of the employment. One could also postulate a level of exposure below which there would be no risk of mesothelioma, even in longterm exposure. However, the available data do not allow the identification of such a “threshold” level. Therefore, in the absence of defini-

tive information, we deemed it appropriate to assume a nonthreshold model for the asbestos exposure, mesothelioma risk relation. The diagnosis of mesothelioma is often difficult, and misdiagnosis was especially common in the past. In the historical cohorts, as many as one half of the pleural mesothelioma deaths were originally attributed to other cancers.8 In the absence of a specific ICD-9 code for mesothelioma, deaths from pleural cancer have been used as a surrogate for mesothelioma mortality. We adopted the same approach. Britain and France are the only countries for which relevant data to estimate the ratio between pleural cancer and real mesothelioma mortality have been published.6 In Britain, the ratio of male mesothelioma to recorded pleural cancer is approximately 1.6:1, and in France a ratio of approximately 1:1 is obtained by including all possible cases and a ratio of 1:0.81 is obtained by including only certain and probable mesotheliomas. In our analysis, the incidence of mesothelioma was much lower in Japan than in the other countries (Fig. 1). In

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part, this discrepancy is certainly the result of the later timing of the peak use of asbestos. Yet, there is also some indication that the coding practice of pleural cancer could have been different in Japan. After the implementation of the ICD-10 coding, which has a specific code for mesothelioma, there have been approximately 450 annual deaths from mesothelioma among men in 1995 through 2000, as compared with an average of approximately 140 deaths from pleural cancer in 1990 through 1994. From 1994 on, the figure jumped from 196 pleural cancers to 356 mesotheliomas in 1995. We restricted our analysis to men 15 years of age and older, but we did not restrict our exposed population according to industry or occupation. We assumed that a given level of the use of raw asbestos in the society would in one way or another incur a certain risk of mesothelioma for the male adult population. Of course, the female and child populations would incur the risk as well, but the highest risk and the strongest correlation would certainly afflict the adult male population. We had access to data only available on the use of raw asbestos. Some countries could have imported significant amounts of asbestos in the form of asbestos products fabricated in other countries. Such practice would cause an exposure risk for the end-users of these products (eg, construction workers). In addition, we had no data available to estimate the contribution of building renovation, maintenance, and demolition, which will prolong the asbestos epidemic long after some 35 to 40 years have elapsed from the cessation of all manufacture and installation of asbestos products in a society. Ecologic studies have shown that the incidence of mesothelioma rises in parallel to the amount of asbestos in use, after approximately 30 years on the average.47,48 Our ecologic analysis also indicated that the earliest incidence of mesothelioma in a society correlates best with the soci-

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ety’s use of asbestos 25 years earlier, with a possible range of 25 to 35 years. A previous analysis showed that, with an induction period, there is a strong correlation between the overall consumption of asbestos and both the incidence and mortality from mesothelioma in 9 western nations and Japan.49 This analysis used only one point estimate per country, that is, the most recent incidence and the use of asbestos data 18 to 27 years earlier. On the other hand, an induction period of 38 years from first exposure was obtained based on information from the German Mesothelioma register.16 In a series of pleural mesotheliomas reported to the Finnish Cancer Registry, 9% of the cases were younger than 35 years of age at death. Thus, the induction period of their fatal disease, if workrelated, could not have been longer than 20 years.50 In this series, the age at death ranged from 3 to 93 years. These analyses show that a given level of asbestos use will invariably result in an increased incidence of mesothelioma, usually decades later, but that it can also occur among children. The exact correlation depends on various factors and is difficult to assess as a result of inaccuracies concerning both the use of asbestos data and the mesothelioma occurrence data. Ecologic studies have well-known limitations.51 However, we chose this method for the estimation of induction period instead of conventional cohort studies for 2 reasons. First, there are few large cohorts of workers exposed to asbestos with the necessary information on exposure– disease occurrence readily available in the literature. By contrast, most of the datasets that we used were based on nationwide statistics. Second, the estimation of an induction period using cohort data depends completely on the length of follow up, unless one has a follow up extending from the start of exposure to the expected lifetime. The longer the follow up, the longer will the induction period be (eg, a follow up of, at

most, 35 years from the start of exposure means that one can never have a longer induction period than 35 years). Thus, cohort data are right-censored, which limitation should be dealt with in the analysis. As pointed out by Breslow and Day,52,p.178 “. . . grouped data analyses are generally the method of choice for cohort analysis.” In effect, we used aggregated cohort data on mesothelioma that comprised all the cases that occurred during a given calendar year in a given country as a result of prolonged exposure to asbestos. These cases had a natural course from the beginning of the causal action to the manifestation of the illness with the only limitation being the existence of matching incidence and time-lagged exposure data; the longest induction period that we considered was 50 years. Characterizing the evolution of the increased risk with timing of exposure is important for understanding the health implications to exposed individuals. Our estimated statistical model for mesothelioma incidence rate was a monotonically increasing function of asbestos use, rather close to a linear one (Fig. 2). Interpretation of a linear model would be that an increase in the use of asbestos would linearly increase the relevant exposure occasions for mesothelioma in a society. Interestingly, the intercept of our model equaled the incidence rate of 7.9 (95% confidence limits, 4.0, 15.5) million man-years. This level is much higher than the background in the general population incidence rate, which is expected to be unrelated to asbestos. This “natural” level is probably lower than the often cited 1 to 2 per million man-years.53 Even if the biologic dose-response mechanism would have a threshold, or would be nonlinear, the existence of the background cancers shows that the threshold is already exceeded by a background exposure. Yet, there was relatively wide variation in the incidence rates in the vicinity of near zero values of the use of asbestos 25 years earlier. One setback of our

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analysis is that we did not have incidence data that would correspond to the use of asbestos during the early decades of the 1900s. The data points of low use represent the use of asbestos around the 1930s and 1940s. It is probable that in those decades, the exposure was heavier and the use of amphiboles more common than later on. Consequently, a given amount of asbestos used around these data points would correspond to a higher risk of mesothelioma than around the data points representing the use of asbestos in the 1950s or 1960s. It is also likely that the data on the use of asbestos were more unreliable for the early years, especially for the years around World War II. Given these uncertainties, the interpretation of the intercept point of our model requires caution. A recent article from Australia reported an apparent consumption (ie, production ⫹ import ⫺ export) of 485,400 tonnes of raw asbestos in 1960 through 1969, that is, approximately 11 kg per adult man annually (total over 10 years).3 Because the Australian data on the consumption of asbestos were not included in the model estimation, they were used for predicting pleural cancer incidence. Applied to the size of the Australian male population over 14 years of age in 1965, the estimated regression equation of Figure 2 would predict approximately 209 annual cases of pleural cancer in men 30 years later. It is reasonable to assume that the ratio between male mesothelioma and coded pleural cancer in Australia would be close to 1.6:1 like in Britain.6 If this assumption holds, it follows that the 209 pleural cancers would suggest 334 mesotheliomas. In fact, the Australian Mesothelioma Register, the most complete mesothelioma register extant, received approximately 300 annual notifications of male mesothelioma in 1990 through 1999.3 Of course, the point estimate is not as accurate as it would appear, but it probably is reasonably unbiased and, at the very

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least, of the correct order of magnitude. Finally, we remind that the use of asbestos in Australia in the 1960s represents a level for which we had very few observations in our dataset. The statistical assessment of the health effects of asbestos exposure is complicated by the fact that there is a certain time relation between exposure and its manifestation in terms of a change in morbidity or mortality. Thus, with exposure that is not narrowly bounded in time, the induction period for the outcome ranges from the minimum induction period to the maximum induction period. In the case of pleural mesothelioma, this time interval extends over several decades. The consensus of a group of international experts was that a minimum of 10 years from the first exposure is required to attribute the mesothelioma to asbestos exposure. 35 (Incidentally, we would rather define the minimum induction period as the shortest time sufficient for the effect of exposure to concretize since the inception of the causal process.) The Helsinki criterion for a minimum induction period has met widespread acceptance complying with current compensation, insurance, and litigation in several countries,54 for example, in New South Wales, Australia. On the other hand, it might not be possible to demarcate a maximum induction period after which the risk of developing mesothelioma disappears. This is because the asbestos fibers that cause the disease have a slow clearance rate (7% per year among crocidolite workers20) and might not be eliminated completely from the lungs in a lifetime. The ecologic analysis of this study covered the time span that is relevant in relation to the etiologic experience, and the best fitting model yielded the estimate of approximately 25 years for the induction period of pleural mesothelioma. Acknowledgments The authors thank Jim Leigh and Antti Tossavainen for their helpful comments on the manuscript, Pertti Mutanen for his consultative advice in the data processing, and

Pleural Mesothelioma Terttu Kaustia for the English language revision.

References 1. Rothman KJ. Induction and latent periods. Am J Epidemiol. 1981;114:253–259. 2. Last JM, ed. A Dictionary of Epidemiology, 4th ed. Oxford: Oxford University Press; 2000. 3. Leigh J, Davidson P, Hendrie Berry D. Malignant mesothelioma in Australia, 1945–2000. Am J Ind Med. 2002;41: 188 –201. 4. Leigh J. Presentation at the Fifth Meeting of the International Mesothelioma Interest Group. IMIG Newsletter Number 5, April 2001. Available at: http://www. imig.org/ (accessed May 30, 2002). 5. Rogers A. Asbestos exposure in various Australian industries—predictions of future disease incidence from Wittenoom. In: National Occupational Health and Safety Commission Research Report 1991-92. Canberra, Australia: Australian Government Publishing Service; 1991: 54 –56. 6. Peto J, Decarli A, La Vecchia C, et al. The European mesothelioma epidemic. Br J Cancer. 1999;79:666 – 672. 7. Bianchi C, Brollo A, Ramani L, et al. World mesothelioma epidemic approaching fast. Ind Health. 2001;39:161–167. 8. Peto J, Hodgson JT, Mathews FE, et al. Continuing increase in mesothelioma mortality in Britain. Lancet. 1995;345: 535–539. 9. Price B. Analyses of current trends in US mesothelioma incidence. Am J Epidemiol. 1997;145:211–218. 10. Greenberg AK, Lee TC, Ross WN. The North American experience with mesothelioma incidence. In: Robinson BWS, Chahinian AP, eds. Mesothelioma. London: Martin Dunitz Ltd; 2002:1–27. 11. Ilg AG, Bignon J, Valleron AJ. Estimation of the past and future burden of mortality from mesothelioma in France. Occup Environ Med. 1998;55:760 –765. 12. Rogers A. Predicting the future incidence of occupationally-related mesotheliomas. In: National Occupational Health and Safety Commission Research Report 1985–90. Canberra, Australia: Australian Government Publishing Service; 1991: 60 – 61. 13. Nakano T. Mesothelioma in Japan. In: Robinson BWS, Chahinian AP, eds. Mesothelioma. London: Martin Dunitz Ltd; 2002:351–358. 14. Rogers A. Predictions of mesothelioma in former Wittenoom asbestos workers [Letter]. Br J Ind Med. 1992;49:451– 452.

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15. Bianchi C, Giarelli L, Grandi G, et al. Latency periods in asbestos-related mesothelioma of the pleura. Eur J Cancer Prev. 1997;6:162–166. 16. Neumann V, Gu¨ nther S, Mu¨ ller KM, et al. Malignant mesothelioma—German mesothelioma register 1987–1999. Int Arch Occup Environ Health. 2001;74: 383–395. 17. Peto J, Seidman H, Selikoff IJ. Mesothelioma mortality in asbestos workers: implications for models of carcinogenesis and risk assessment. Br J Cancer. 1982; 45:124 –135. 18. Boffetta P. Health effects of asbestos exposure in humans: a quantitative assessment. La Medicina del Lavoro. 1998; 89:471– 480. 19. Albin M, Magnani C, Krstev S, et al. Asbestos and cancer: an overview of current trends in Europe. Environ Health Perspect. 1999;107:289. 20. Berry G. Prediction of mesothelioma, lung cancer, and asbestosis in former Wittenoom asbestos workers. Br J Ind Med. 1991;48:793– 802. 21. Miettinen OS. Etiologic research. Needed revisions of concepts and principles. Scand J Work Environ Health. 1999;25: 484 – 490. 22. Langholtz B, Thomas D, Xiang A, et al. Latency analysis in epidemiologic studies of occupational exposures: application to the Colorado plateau uranium miners cohort. Am J Ind Med. 1999;35: 246 –256. 23. Banaei A, Auvert B, Goldberg M, et al. Future trends in mortality of French men from mesothelioma. Occup Environ Med. 2000;57:488 – 494. 24. Hirvonen A, Mattson K, Karjalainen A, et al. SV40-like sequences are not necessary for the development of asbestosinduced mesothelioma. Mol Carcinog. 1999;26:93–99. 25. Kjaergaard J, Andersson M. Incidence rates of malignant mesothelioma in Denmark and predicted future number of cases among men. Scand J Work Environ Health. 2000;26:112–117. 26. Gaffuri E, Maranelli G. Prospettive di comparsa del mesotelioma pleurico in Italia. Med Lav. 1991;82:155–159. 27. Woitowitz HJ, Kraus T. Screening of asbestos-exposed workers in Germany. In: Tossavainen A, Lehtinen S, Huuskonen M, et al., eds. New Advances in Radiology and Screening of AsbestosRelated Diseases. Helsinki: People and Work 36, Finnish Institute of Occupational Health; 2000. 28. Ja¨ rvholm B, Englund E, Albin M. Pleural mesothelioma in Sweden: an analysis of the incidence according to the use of

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30.

31. 32.

33.

34.

35.

36.

37.

38.

39.

•


asbestos. Occup Environ Med. 1999;56: 110 –113. Kjellstro¨ m T, Smartt P. Increased mesothelioma incidence in New Zealand: the asbestos-cancer epidemic has started. N Z Med J. 2000;113:485– 490. Japan Customs, Ministry of Finance Japan. Trade Statistics. 2002. Available at: http://www.customs.go.jp/toukei/info/ index–e.htm. (accessed March 27, 2002). Available at: http://www.who.int/whosis/ Kjaergaard J, Andersson M. Incidence rates of malignant mesothelioma in Denmark and predicted future number of cases among men. Scand J Work Environ Health. 2000;26:112–117. Available at: http://www.cancerregistry.fi/eng/statistics.htm. (accessed March 27, 2002). Venables WN, Ripley BD. Modern Applied Statistics With S-PLUS, 3rd ed. New York: Springer; 2001. Finnish Institute of Occupational Health (FIOH). Asbestos, asbestosis, and cancer: the Helsinki criteria for diagnosis and attribution. Scand J Work Environ Health. 1997;23:311–316. Liang KY, Zeger SL. Longitudinal data analysis using generalised linear models. Biometrika. 1986;73:13–22. Diggle PJ, Liang K-Y, Zeger SL. Analysis of Longitudinal Data. Oxford: Clarendon Press; 1994. SAS Institute, Inc., SAS威 Procedures Guide, Version 8. Cary, NC: SAS Institute Inc; 1999;1643 pp. Greenland S. Introduction to regression modeling. In: Rothman KJ, Greenland S,

40.

41.

42.

43.

44.

45.

46.

eds. Modern Epidemiology, 2nd ed. Philadelphia: Lippincott-Raven Publishers; 1998. Becker RA, Cleveland WS, Shya M-J, et al. A Tour of Trellis Graphics. Murray Hill, NJ: AT&T Bell Laboratories, Seattle, WA: Statistical Science Division, MathSoft; 1995. Statistical Sciences, S-Plus for Windows User’s Manual, version 3.1. Seattle: Statistical Sciences, Inc; 1993. Rothman KJ, Greenland S. Causation and causal inference. In: Rothman KJ, Greenland S, eds. Modern Epidemiology, 2nd ed. Philadelphia, PA: Lippincott-Raven Publishers, 1998, p. 15. Hemminki K, Xinjun L. Mesothelioma incidence seems to have levelled off in Sweden. Int J Cancer. 2002;103:145– 146. Bjo¨ rkqvist A-M, Tammilehto L, Nordling S, et al. Comparison of DNA copy number changes in malignant mesothelioma, adenocarcinoma and large cell anaplastic carcinoma of the lung. Br J Cancer. 1998;77:260 –269. Henderson DW, Shilkin KB, Whitaker D. Early diagnosis of asbestos-related cancers. In: Finnish Institute of Occupational health (FIOH). Asbestos, Asbestosis, and Cancer. Proceedings of an International Expert Meeting. Helsinki: FIOH; 1997. People and Work Research Report 14, 80 – 84. Robins J, Greenland S. The probability of causation under a stochastic model for individual risk. Biometrics. 1989;45: 1125–1138.

1115 47. Andersson M, Olsen JH. Trend and distribution of mesothelioma in Denmark. Br J Cancer. 1985;51:699 –705. 48. Xu Z, Armstrong BK, Blundson BJ, et al. Trends in mortality from malignant mesothelioma of the pleura, and the production and use of asbestos in Australia. Med J Aust. 1985;143:185–187. 49. Takahashi K, Huuskonen MS, Tossavainen A, et al. Ecological relationship between mesothelioma incidence/ mortality and asbestos consumption in the Western countries and Japan. J Occup Health. 1999;51:8 –11. 50. Nurminen M. The epidemiologic relationship between pleural mesothelioma and asbestos exposure. Scand J Work Environ Health. 1975;1:128 –137. 51. Nurminen M. The ecologic method: linkage failures and bias corrections. In: Corvala´ n C, Nurminen M, Pastides H, eds. Linkage Methods for Environment and Health Analysis. Technical Guidelines. Geneva: WHO; 1997:21–72. 52. Breslow NE, Day NE. Statistical Methods in Cancer Research. Volume II—The Design and Analysis of Cohort Studies. Lyon: World Health Organization, International Agency for Research on Cancer; 1987. 53. Hillerdal G. Mesothelioma: cases associated with non-occupational and low dose exposures. Occup Environ Med. 1999;56: 505–513. 54. Tossavainen A. Asbestos, asbestosis, and cancer: the Helsinki criteria for diagnosis and attribution. Scand J Work Environ Health. 2000;23:211–216.