Exposure-response estimate for lung cancer and asbestosis in a predominantly chrysotile-exposed Chinese factory cohort.

BACKGROUND: Cumulative fiber exposures, predominantly chrysotile, were estimated in a Chinese asbestos worker cohort and exposure-response relationships with lung cancer mortality and cumulative incidence of asbestosis were determined. METHODS: Individual time-dependent cumulative exposures were estimated for 577 asbestos workers, followed prospectively for 37 years. Occupational history and smoking data were obtained from company records and personal interviews; vital status and causes of death were ascertained from death registries and hospital records. Hazard ratios were generated for disease outcomes, with adjustments for smoking and age. RESULTS: Median cumulative fiber exposure for the cohort was 132.6 fiber-years/ml (IQR 89.3-548.4). Exposure-response relationships were demonstrated for both disease outcomes, with nearly sixfold and threefold increased risks seen at the highest exposure level for lung cancer deaths and asbestosis, respectively. CONCLUSION: Evidence using quantitative exposure estimates was provided for increased risks of lung cancer mortality and development of asbestosis in a predominantly chrysotile-exposed cohort.

Size- and type-specific exposure assessment of an asbestos products factory in China.

This study describes fibre size and type-specific airborne asbestos exposures in an asbestos product factory. Forty-four membrane filter samples were analysed by scanning electron microscopy to determine the size distribution of asbestos fibres, by workshop. Fibre frequencies of bivariate (length by width) categories were calculated and differences between workshops were tested by analysis of variance. Data were recorded for 13,435 chrysotile and 1075 tremolite fibres. The proportions between size metrics traditionally measured and potentially biologically important size metrics were found to vary in this study from proportions reported in other cohort studies. One, common size distribution was generated for each asbestos type over the entire factory because statistically significant differences in frequency between workshops were not detected. This study provides new information on asbestos fibre size and type distributions in an asbestos factory. The extent to which biologically relevant fibre size indices were captured or overlooked between studies can potentially reconcile currently unexplained differences in asbestos-related disease (ARD) risk between cohorts. The fibre distributions presented here, when combined with similar data from other sites, will contribute to the development of quantitative models for predicting risk and our understanding of the effects of fibre characteristics in the development of ARD.

Causal versus spurious spatial exposure-response associations in health risk analysis.

Many recent health risk assessments have noted that adverse health outcomes are significantly statistically associated with proximity to suspected sources of health hazard, such as manufacturing plants or point sources of air pollution. Using geographic proximity to sources as surrogates for exposure to (possibly unknown) releases, spatial ecological studies have identified potential adverse health effects based on significant regression coefficients between risk rates and distances from sources in multivariate statistical risk models. Although this procedure has been fruitful in identifying exposure-response associations, it is not always clear whether the resulting regression coefficients have valid causal interpretations. Spurious spatial regression and other threats to valid causal inference may undermine practical efforts to causally link health effects to geographic sources, even when there are clear statistical associations between them. This paper demonstrates the methodological problems by examining statistical associations and regression coefficients between spatially distributed exposure and response variables in a realistic data set for California. We find that distance from "nonsense" sources (such as arbitrary points or lines) are highly statistically significant predictors of cause-specific risks, such as traffic fatalities and incidence of Kaposi's sarcoma. However, the signs of such associations typically depend on the distance scale chosen. This is consistent with theoretical analyses showing that random spatial trends (which tend to fluctuate in sign), rather than true causal relations, can create statistically significant regression coefficients: spatial location itself becomes a confounder for spatially distributed exposure and response variables. Hence, extreme caution and careful application of spatial statistical methods are warranted before interpreting proximity-based exposure-response relations as evidence of a possible or probable causal relation.

A cautionary tale: the characteristics of two-dimensional distributions and their effects on epidemiological studies employing an ecological design.

In recent years, many spatial epidemiological studies that use proximity of subjects to putative sources as a surrogate for exposure have been published and are increasingly cited as evidence of environmental problems requiring public health interventions. In these studies, the simple finding of a significant, positive association between proximity and disease incidence has been interpreted as evidence of causality. However, numerous authors have pointed out limitations to such interpretations. This, the first of two companion studies, examines the effects of analyzing (real and simulated) spatial data using logistic regression. Simulation is also employed to explore the statistical power of such analyses to detect true effects, quantify the probabilities of Type I and Type II errors, and to evaluate a proposed mechanism that explains the observed effects. Results indicate that, even when the odds ratios of cases and controls are regressed against random or nonsense sources, significant, positive associations are observed at frequencies substantially greater than chance. These frequencies increase when targets are highly non-uniformly distributed such that, for example, false-positive associations are more likely than not when odds ratios are regressed against the actual distribution of ultramafic rocks in California. The coefficients of true, causal associations are substantially attenuated under realistic conditions so that, absent corroborating analyses, there is no non-arbitrary means of distinguishing causal from spurious or real but non-causal associations. Factors affecting where people choose to live act as powerful confounders, creating spurious or real but non-causal associations between exposure and response variables (as well as between other pairs of variables). Consequently, future epidemiological studies that use proximity as a surrogate for exposure should be required to include adequate negative control analyses and/or other kinds of corroborating analyses before they are accepted for publication.

Overreliance on a single study: there is no real evidence that applying quality criteria to exposure in asbestos epidemiology affects the estimated risk.

A critical need exists for reliable risk management policies and practices that can effectively mitigate asbestos-related health threats, and such policies and practices need to be based on sound science that adequately distinguishes hazardous situations from those that are not. Toward that end, the disparate means by which study quality has been addressed in recent meta-analyses used to establish potency factors (K ( L ) and K ( M ) values) for asbestos cancer risks were compared by conducting additional sensitivity analyses. Results suggest that, other than placing undue emphasis on the influence of the K ( L ) and K ( M ) values reported from a single study, there appears to be little to no evidence of a systematic effect of study quality on K ( L ) or K ( M ) values; none of the findings warrant excluding studies from current or future meta-analyses. Thus, we argue that it is better to include as much of the available data as possible in these analyses while formally addressing uncertainty as part of the analysis itself, rather than sequentially excluding studies based on one type of limitation or another. Throwing out data without clearly proving some type of bias is never a good idea because it will limit both the power to test various hypotheses and the confidence that can be placed in any findings that are derived from the resulting, truncated data set. We also believe that it is better to identify the factors that contribute to variation between studies included in a meta-analysis and, by adjusting for such factors as part of a model, showing that the disparate values from individual studies can be reconciled. If such factors are biologically reasonable (based on other evidence) and, if such a model can be shown to fit the data from all studies in the meta-analysis, the model is likely to be predictive of the parameters being evaluated and can then be applied to new (unstudied) environments.

More on the dynamics of dust generation: the effects of mixing and sanding chrysotile, calcium carbonate, and other components on the characteristics of joint-compound dusts.

An ongoing research effort designed to reconstruct the character of historical exposures associated with use of chrysotile-containing joint compounds naturally raised questions concerning how the character (e.g. particle size distributions) of dusts generated from use of recreated materials compares to dusts from similar materials manufactured historically. This also provided an opportunity to further explore the relative degree that the characteristics of dusts generated from a bulk material are mediated by the properties of the bulk material versus the mechanical processes applied to the bulk material by which the dust is generated. In the current study, the characteristics of dusts generated from a recreated ready mix and recreated dry mix were compared to each other, to dusts from a historical dry mix, and to dusts from the commercial chrysotile fiber (JM 7RF3) used in the recreated materials. The effect of sanding on the character of dusts generated from these materials was also explored. Dusts from the dry materials studied were generated and captured for analysis in a dust generator-elutriator. The recreated and historical joint compounds were also prepared, applied to drywall, and sanded inside sealed bags so that the particles produced from sanding could be introduced into the elutriator and captured for analysis. Comparisons of fiber size distributions in dusts from these materials suggest that dust from commercial fiber is different from dusts generated from the joint compounds, which are mixtures, and the differences persist whether the materials are sanded or not. Differences were also observed between sanded recreated ready mix and either the recreated dry mix or a historical dry mix, again whether sanded or not. In all cases, however, such differences disappeared when variances obtained from surrogate data were used to better represent the 'irreducible variation' of these materials. Even using the smaller study-specific variances, no differences were observed between the recreated dry mix and the historical dry mix, indicating that chrysotile-containing joint compounds can be recreated using historical formulations such that the characteristics of the modern material reasonably mimic those of a corresponding historical material. Similarly, no significant differences were observed between dusts from sanded and unsanded versions of similar materials, suggesting (as in previous studies) that the characteristics of asbestos-containing dusts are mediated primarily by the properties of the bulk material from which they are derived.

Counting rules for estimating concentrations of long asbestos fibers.

Mounting evidence that long asbestos fibers (e.g. >20 or even 40 µm) pose the greatest cancer risk underscores the need for accurate measurement of concentrations of such fibers. These fiber lengths are of the same order of magnitude as the size of openings in the grids (typically ≈90 µm per side) used to analyze asbestos samples by transmission electron microscopy. This means that a substantial proportion of long fibers will cross the edge of a grid opening (GO) and therefore not be completely visible. Counting rules generally deal with such fibers by assigning a length equal to twice the visible length. Using both theoretical and simulation methods, we show that this doubling rule introduces bias into estimates of fiber concentrations and the amount of bias increases with fiber length. We investigate an alternative counting rule that counts only fibers that lie completely within a GO and weights those fibers by the reciprocal of the probability that a fiber of that length lies totally within a GO. This approach does not have the bias inherent in the doubling rule and is essentially unbiased if the stopping rule specifies a fixed number of GOs to be scanned. However, a stopping rule based on successively scanning GOs until a fixed number of fibers have been counted will introduce bias into any counting method, although this bias may typically not be large enough to be of practical concern. We recommend use of the weighted approach as a supplement to use of the doubling rule when estimating concentrations of long fibers, irrespective of the stopping rule employed.

Chamber for testing asbestos-containing products: validation and testing of a re-created chrysotile-containing joint compound.

Joint compound products containing chrysotile asbestos were commonly used for building construction from the late 1940s through the mid-1970s. Few relevant data exist to support reconstructing historical worker exposures to fibers generated by working with this material. Therefore, we re-created 1960s-era chrysotile-containing joint compound (JCC) and compared its characteristics to a current-day asbestos-free joint compound (JCN). Validation studies showed that a bench-scale chamber with controlled flow dynamics, designed to quantify particulate emissions from joint compound products, provided precise and reliable measurements of generated airborne dust mass, chrysotile fiber concentrations, and corresponding activity-specific emission rates. Subsequent chamber studies characterized fibers counted by phase contrast microscopy (PCM) per mass of respirable dusts and total suspended particulate dusts (total dusts), generated during JCC sanding or sweeping, as well as corresponding dust emission rates for JCC and JCN, and the ratio of total to respirable dust mass for JCN. From these data we estimated factors, F(CH-rd) and F(CH-td) (in units of f cm(-3) per mg m(-3)), by which respirable JCN dust mass concentrations collected during construction use can be converted to corresponding airborne PCM fiber concentrations generated by sanding or sweeping JCC. For sanding, median values (95% confidence limits) of F(CH-rd) and F(CH-td) were estimated to be 0.044 (0.039-0.050) and 0.212 (0.115-0.390) f cm(-3) per mg m(-3), respectively. The F(CH-td) to F(CH-rd) ratio indicates that approximately five times as many airborne PCM fibers are anticipated per unit air volume sampled when JCC dust is collected on cassettes (as done historically), than when respirable JCC dust is collected on cyclones. As the sizes of individual fibers collected appear to be primarily respirable, this difference may be a sampling artifact and suggests caution in interpreting historical fiber concentration measures made using cassettes during work with JCC-like materials. F(CH-rd) can be used with published and newly generated field measurements of respirable dust mass concentrations associated with the use of JCN or equivalent JCN materials to better characterize historical worker exposures to PCM fibers from use of JCC or equivalent JCCs. The experimental process described also can be used to develop conversion factors for other combinations of modern-day asbestos-free and historical chrysotile-containing products.

Measuring mixed cellulose ester (MCE) filter mass under variable humidity conditions.

Mixed cellulose ester (MCE) filters, used routinely to collect dust samples from air for fiber analysis, are the only filter type that can be prepared for both phased contrast microscopy and transmission electron microscopy analyses. However, whenever fiber counts require collecting dust masses <100 µg on a single filter under variable relative humidity (RH) conditions, historically noted effects of humidity on MCE filter mass can hinder accurate estimates of dust mass, measured as loaded minus unloaded filter mass (M). In this study, a baseline set of hundreds of paired measures of change in RH versus M over different time intervals were obtained over a 5-day period for replicate series of 40 unloaded 37-mm MCE filters under varying RH conditions at a nearly constant temperature. Similar baseline data were obtained for 25-mm MCE filters. Linear regressions fit to these data allow improved estimates of dust mass loaded onto MCE filters from measures of M and RH made before and after loading occurs. Using established theory, these relationships were generalized to address temperature variation as well, and examples of numerical applications are provided.

Apples to apples: the origin and magnitude of differences in asbestos cancer risk estimates derived using varying protocols.

Given that new protocols for assessing asbestos-related cancer risk have recently been published, questions arise concerning how they compare to the "IRIS" protocol currently used by regulators. The newest protocols incorporate findings from 20 additional years of literature. Thus, differences between the IRIS and newer Berman and Crump protocols are examined to evaluate whether these protocols can be reconciled. Risks estimated by applying these protocols to real exposure data from both laboratory and field studies are also compared to assess the relative health protectiveness of each protocol. The reliability of risks estimated using the two protocols are compared by evaluating the degree with which each potentially reproduces the known epidemiology study risks. Results indicate that the IRIS and Berman and Crump protocols can be reconciled; while environment-specific variation within fiber type is apparently due primarily to size effects (not addressed by IRIS), the 10-fold (average) difference between amphibole asbestos risks estimated using each protocol is attributable to an arbitrary selection of the lowest of available mesothelioma potency factors in the IRIS protocol. Thus, the IRIS protocol may substantially underestimate risk when exposure is primarily to amphibole asbestos. Moreover, while the Berman and Crump protocol is more reliable than the IRIS protocol overall (especially for predicting amphibole risk), evidence is presented suggesting a new fiber-size-related adjustment to the Berman and Crump protocol may ultimately succeed in reconciling the entire epidemiology database. However, additional data need to be developed before the performance of the adjusted protocol can be fully validated.

Comparing milled fiber, Quebec ore, and textile factory dust: has another piece of the asbestos puzzle fallen into place?

Results of a meta-analysis indicate that the variation in potency factors observed across published epidemiology studies can be substantially reconciled (especially for mesothelioma) by considering the effects of fiber size and mineral type, but that better characterization of historical exposures is needed before improved exposure metrics potentially capable of fully reconciling the disparate potency factors can be evaluated. Therefore, an approach for better characterizing historical exposures, the Modified Elutriator Method (MEM), was evaluated to determine the degree that dusts elutriated using this method adequately mimic dusts generated by processing in a factory. To evaluate this approach, elutriated dusts from Grade 3 milled fiber (the predominant feedstock used at a South Carolina [SC] textile factory) were compared to factory dust collected at the same facility. Elutriated dusts from chrysotile ore were also compared to dusts collected in Quebec mines and mills. Results indicate that despite the substantial variation within each sample set, elutriated dusts from Grade 3 fiber compare favorably to textile dusts and elutriated ore dusts compare to dusts from mines and mills. Given this performance, the MEM was also applied to address the disparity in lung cancer mortality per unit of exposure observed, respectively, among chrysotile miners/millers in Quebec and SC textile workers. Thus, dusts generated by elutriation of stockpiled chrysotile ore (representing mine exposures) and Grade 3 milled fiber (representing textile exposures) were compared. Results indicate that dusts from each sample differ from one another. Despite such variation, however, the dusts are distinct and fibers in Grade 3 dusts are significantly longer than fibers in ore dusts. Moreover, phase-contrast microscopy (PCM) structures in Grade 3 dusts are 100% asbestos and counts of PCM-sized structures are identical, whether viewed by PCM or transmission electron microscope (TEM). In contrast, a third of PCM structures in ore dusts are not asbestos and only a third that are counted by PCM are also counted by TEM. These distinctions also mirror the characteristics of the bulk materials themselves. Perhaps most important, when the differences in size distributions and PCM/TEM distinctions in these dusts are combined, the combined difference is sufficient to completely explain the difference in exposure/response observed between the textile worker and miner/miller cohorts. Importantly, however, evidence that such an explanation is valid can only be derived from a meta-analysis (risk assessment) covering a diverse range of epidemiology study environments, which is beyond the scope of the current study. The above findings suggest that elutriator-generated dusts mimic factory dusts with sufficient reliability to support comparisons between historical exposures experienced by the various cohorts studied by epidemiologists. A simulation was also conducted to evaluate the relative degree that the characteristics of dust are driven by the properties of the bulk material processed versus the nature of the mechanical forces applied. That results indicate it is the properties of bulk materials reinforces the theoretical basis justifying use of the elutriator to reconstruct historical exposures. Thus, the elutriator may be a valuable tool for reconstructing historical exposures suitable for supporting continued refinements of the risk models being developed to predict asbestos-related cancer risk.

A meta-analysis of asbestos-related cancer risk that addresses fiber size and mineral type.

Quantitative estimates of the risk of lung cancer or mesothelioma in humans from asbestos exposure made by the U.S. Environmental Protection Agency (EPA) make use of estimates of potency factors based on phase-contrast microscopy (PCM) and obtained from cohorts exposed to asbestos in different occupational environments. These potency factors exhibit substantial variability. The most likely reasons for this variability appear to be differences among environments in fiber size and mineralogy not accounted for by PCM. In this article, the U.S. Environmental Protection Agency (EPA) models for asbestos-related lung cancer and mesothelioma are expanded to allow the potency of fibers to depend upon their mineralogical types and sizes. This is accomplished by positing exposure metrics composed of nonoverlapping fiber categories and assigning each category its own unique potency. These category-specific potencies are estimated in a meta-analysis that fits the expanded models to potencies for lung cancer (KL's) or mesothelioma (KM's) based on PCM that were calculated for multiple epidemiological studies in our previous paper (Berman and Crump, 2008). Epidemiological study-specific estimates of exposures to fibers in the different fiber size categories of an exposure metric are estimated using distributions for fiber size based on transmission electron microscopy (TEM) obtained from the literature and matched to the individual epidemiological studies. The fraction of total asbestos exposure in a given environment respectively represented by chrysotile and amphibole asbestos is also estimated from information in the literature for that environment. Adequate information was found to allow KL's from 15 epidemiological studies and KM's from 11 studies to be included in the meta-analysis. Since the range of exposure metrics that could be considered was severely restricted by limitations in the published TEM fiber size distributions, it was decided to focus attention on four exposure metrics distinguished by fiber width: "all widths," widths > 0.2 micro m, widths < 0.4 microm, and widths < 0.2 microm, each of which has historical relevance. Each such metric defined by width was composed of four categories of fibers: chrysotile or amphibole asbestos with lengths between 5 microm and 10 microm or longer than 10 microm. Using these metrics three parameters were estimated for lung cancer and, separately, for mesothelioma: KLA, the potency of longer (length > 10 microm) amphibole fibers; rpc, the potency of pure chrysotile (uncontaminated by amphibole) relative to amphibole asbestos; and rps, the potency of shorter fibers (5 microm < length < 10 microm) relative to longer fibers. For mesothelioma, the hypothesis that chrysotile and amphibole asbestos are equally potent (rpc = 1) was strongly rejected by every metric and the hypothesis that (pure) chrysotile is nonpotent for mesothelioma was not rejected by any metric. Best estimates for the relative potency of chrysotile ranged from zero to about 1/200th that of amphibole asbestos (depending on metric). For lung cancer, the hypothesis that chrysotile and amphibole asbestos are equally potent (rpc = 1) was rejected (p < or = .05) by the two metrics based on thin fibers (length < 0.4 microm and < 0.2 microm) but not by the metrics based on thicker fibers. The "all widths" and widths < 0.4 microm metrics provide the best fits to both the lung cancer and mesothelioma data over the other metrics evaluated, although the improvements are only marginal for lung cancer. That these two metrics provide equivalent (for mesothelioma) and nearly equivalent (for lung cancer) fits to the data suggests that the available data sets may not be sufficiently rich (in variation of exposure characteristics) to fully evaluate the effects of fiber width on potency. Compared to the metric with widths > 0.2 microm with both rps and rpc fixed at 1 (which is nominally equivalent to the traditional PCM metric), the "all widths" and widths < 0.4 microm metrics provide substantially better fits for both lung cancer and, especially, mesothelioma. Although the best estimates of the potency of shorter fibers (5 < length < 10 microm) is zero for the "all widths" and widths < 0.4 microm metrics (or a small fraction of that of longer fibers for the widths > 0.2 microm metric for mesothelioma), the hypothesis that these shorter fibers were nonpotent could not be rejected for any of these metrics. Expansion of these metrics to include a category for fibers with lengths < 5 microm did not find any consistent evidence for any potency of these shortest fibers for either lung cancer or mesothelioma. Despite the substantial improvements in fit over that provided by the traditional use of PCM, neither the "all widths" nor the widths < 0.4 microm metrics (or any of the other metrics evaluated) completely resolve the differences in potency factors estimated in different occupational studies. Unresolved in particular is the discrepancy in potency factors for lung cancer from Quebec chrysotile miners and workers at the Charleston, SC, textile mill, which mainly processed chrysotile from Quebec. A leading hypothesis for this discrepancy is limitations in the fiber size distributions available for this analysis. Dement et al. (2007) recently analyzed by TEM archived air samples from the South Carolina plant to determine a detailed distribution of fiber lengths up to lengths of 40 microm and greater. If similar data become available for Quebec, perhaps these two size distributions can be used to eliminate the discrepancy between these two studies.

Update of potency factors for asbestos-related lung cancer and mesothelioma.

The most recent update of the U.S. Environmental Protection Agency (EPA) health assessment document for asbestos (Nicholson, 1986, referred to as "the EPA 1986 update") is now 20 years old. That document contains estimates of "potency factors" for asbestos in causing lung cancer (K(L)'s) and mesothelioma (K(M)'s) derived by fitting mathematical models to data from studies of occupational cohorts. The present paper provides a parallel analysis that incorporates data from studies published since the EPA 1986 update. The EPA lung cancer model assumes that the relative risk varies linearly with cumulative exposure lagged 10 years. This implies that the relative risk remains constant after 10 years from last exposure. The EPA mesothelioma model predicts that the mortality rate from mesothelioma increases linearly with the intensity of exposure and, for a given intensity, increases indefinitely after exposure ceases, approximately as the square of time since first exposure lagged 10 years. These assumptions were evaluated using raw data from cohorts where exposures were principally to chrysotile (South Carolina textile workers, Hein et al., 2007; mesothelioma only data from Quebec miners and millers, Liddell et al., 1997) and crocidolite (Wittenoom Gorge, Australia miners and millers, Berry et al., 2004) and using published data from a cohort exposed to amosite (Paterson, NJ, insulation manufacturers, Seidman et al., 1986). Although the linear EPA model generally provided a good description of exposure response for lung cancer, in some cases it did so only by estimating a large background risk relative to the comparison population. Some of these relative risks seem too large to be due to differences in smoking rates and are probably due at least in part to errors in exposure estimates. There was some equivocal evidence that the relative risk decreased with increasing time since last exposure in the Wittenoom cohort, but none either in the South Carolina cohort up to 50 years from last exposure or in the New Jersey cohort up to 35 years from last exposure. The mesothelioma model provided good descriptions of the observed patterns of mortality after exposure ends, with no evidence that risk increases with long times since last exposure at rates that vary from that predicted by the model (i.e., with the square of time). In particular, the model adequately described the mortality rate in Quebec chrysotile miners and millers up through >50 years from last exposure. There was statistically significant evidence in both the Wittenoom and Quebec cohorts that the exposure intensity-response is supralinear(1) rather than linear. The best-fitting models predicted that the mortality rate varies as [intensity](0.47) for Wittenoom and as [intensity](0.19) for Quebec and, in both cases, the exponent was significantly less than 1 (p< .0001). Using the EPA models, K(L)'s and K(M)'s were estimated from the three sets of raw data and also from published data covering a broader range of environments than those originally addressed in the EPA 1986 update. Uncertainty in these estimates was quantified using "uncertainty bounds" that reflect both statistical and nonstatistical uncertainties. Lung cancer potency factors (K(L)'s) were developed from 20 studies from 18 locations, compared to 13 locations covered in the EPA 1986 update. Mesothelioma potency factors (K(M)'s) were developed for 12 locations compared to four locations in the EPA 1986 update. Although the 4 locations used to calculate K(M) in the EPA 1986 update include one location with exposures to amosite and three with exposures to mixed fiber types, the 14 K(M)'s derived in the present analysis also include 6 locations in which exposures were predominantly to chrysotile and 1 where exposures were only to crocidolite. The K(M)'s showed evidence of a trend, with lowest K(M)'s obtained from cohorts exposed predominantly to chrysotile and highest K(M)'s from cohorts exposed only to amphibole asbestos, with K(M)'s from cohorts exposed to mixed fiber types being intermediate between the K(M)'s obtained from chrysotile and amphibole environments. Despite the considerable uncertainty in the K(M) estimates, the K(M) from the Quebec mines and mills was clearly smaller than those from several cohorts exposed to amphibole asbestos or a mixture of amphibole asbestos and chrysotile. For lung cancer, although there is some evidence of larger K(L)'s from amphibole asbestos exposure, there is a good deal of dispersion in the data, and one of the largest K(L)'s is from the South Carolina textile mill where exposures were almost exclusively to chrysotile. This K(L) is clearly inconsistent with the K(L) obtained from the cohort of Quebec chrysotile miners and millers. The K(L)'s and K(M)'s derived herein are defined in terms of concentrations of airborne fibers measured by phase-contrast microscopy (PCM), which only counts all structures longer than 5 microm, thicker than about 0.25 microm, and with an aspect ratio > or =3:1. Moreover, PCM does not distinguish between asbestos and nonasbestos particles. One possible reason for the discrepancies between the K(L)'s and K(M)'s from different studies is that the category of structures included in PCM counts does not correspond closely to biological activity. In the accompanying article (Berman and Crump, 2008) the K(L)'s and K(M)'s and related uncertainty bounds obtained in this article are paired with fiber size distributions from the literature obtained using transmission electron microscopy (TEM). The resulting database is used to define K(L)'s and K(M)'s that depend on both the size (e.g., length and width) and mineralogical type (e.g., chrysotile or crocidolite) of an asbestos structure. An analysis is conducted to determine how well different K(L) and K(M) definitions are able to reconcile the discrepancies observed herein among values obtained from different environments.