Comments on a recent case-control study of malignant mesothelioma of the pericardium and the tunica vaginalis testis.

As the first case-control study of malignant mesothelioma of the pericardium and the tunica vaginalis testis (mTVT), the paper by Marinaccio et al (1) is potentially an important epidemiologic contribution. A careful review of the paper, however, raises a number of methodological issues. Any case-control study can be viewed as being nested within a conceptual cohort, with controls being sampled from the at-risk cohort as cases arise over time. This view of case-control studies leads to the concept of incidence-density sampling of controls (eg, 2, 3). For Marinaccio et al (1) this would mean that, as cases were registered over the study period, each would be matched to an individual control or set of controls of the same gender, age, and region of the country (since asbestos exposure varies by time and region [4]). For example, if a case were 50 years old in 1995, then any matched control should be close to age 50 in 1995 and of the same gender and from the same region as the case. Matching for age in this fashion automatically results in matching for year of birth, which is essential in this context because birth-cohort effects are determinants of asbestos exposure and mesothelioma incidence (eg, 5-8). If Marinaccio et al (1) used this scheme for age-matching, one would expect to see similar distributions of cases (table 1) and controls (table S3 in the supplemental material) by period of birth. Among males, however, the distributions of mesothelioma cases (whether pericardial or mTVT) and controls by period of birth are clearly different (P<0.001). Among females, the distributions of cases of pericardial mesothelioma and controls by birth year are less dissimilar (P≈0.05). Thus, the female cases of pericardial mesothelioma are better matched to controls on year of birth than are male cases of either mTVT or pericardial mesothelioma. We note also that the distributions of male and female controls by year of birth are distinctly different (P<0.002), whereas the birth-year distributions of cases of mesothelioma by site and gender are not (P≈0.8). In the Marinaccio et al (1) sensitivity analysis restricted to subjects born before 1950, the distributions of cases and controls by period of birth remain significantly different. Therefore, based on the reported evidence, cases and controls were not matched on birth cohort, thereby possibly biasing the results. Similarly, bias may result from the lack of matching on geographic region; while cases were registered from across Italy, controls were selected from only six regions. Although a sensitivity analysis restricted cases and controls to those from only the six regions, a comparison of tables S1 and S3 indicates that the regional distribution of controls is different from that of person-time observed; that is, the controls do not appear to be representative of the underlying population at risk by region. The second major issue of concern has to do with ascertainment of asbestos exposure. Information on exposure for the cases was presumably obtained at the time of registration. The two sets of controls, obtained from previously unpublished case-control studies, were interviewed during 2014-2015 and 2014-2016; that is, many years after the exposure for most cases was ascertained (1993-2015). Few other details of the control groups are provided, except that participation by one set of controls was <50%, raising additional concerns about selection bias. For details on the second set of controls, Marinaccio et al (1) reference a paper by Brandi et al (9). On review of that paper, however, we found no description of the control group, only references to three earlier papers. Marinaccio et al (1) present analyses only with both sets of controls combined; to evaluate potential sources of bias from the use of different sets of controls, they should also report results using each set of controls separately. The authors also did not detail their methods of exposure classification. For example, what does probable or possible exposure mean? The authors should at least present separate analyses of definite occupational exposure. Eighty cases of mTVT were registered, but only 68 were included in the analyses. Information on the 12 omitted cases (eg, age, year of birth, and region) would be helpful. Marinaccio et al (1) did not provide clear information on what occupations and/or industries they considered as exposed to asbestos. In an earlier study, Marinaccio et al (10) remarked on the absence of pericardial mesothelioma and mTVT in industries with the highest exposures to asbestos, saying, "[t]he absence of exposures in the shipbuilding, railway and asbestos-cement industries … for all the 67 pericardial and testicular cases is noteworthy but not easy to interpret." By contrast, Marinaccio et al (1) stated, "[t]he economic sectors more frequently associated with asbestos exposure were construction, steel mills, metal-working industry, textile industry and agriculture." The possibility of exposure in the "agriculture economic sector" was not mentioned in Marinaccio et al (10) and appears not to have been considered in previous epidemiologic studies in Italy. In general, epidemiologic studies indicate that farmers and agricultural workers are not at increased risk of developing mesothelioma (eg, 11-17). The fact that few, if any, cases of mTVT and pericardial mesothelioma occurred in industries traditionally associated with high asbestos exposure raises the possibility that the results of Marinaccio et al (1) are attributable to deficiencies in study design, very possibly bias in the selection of controls, and deficiencies in exposure assessment and classification as described above, leading to a spurious association of occupational exposure with mTVT and male pericardial mesothelioma. Conflict of interest This research has received no outside funding. All authors are employees of Exponent, Inc., an international scientific and engineering consulting company. All authors have worked as both consulting and testifying experts in litigation matters related to asbestos exposure and asbestos-related disease. References 1. Marinaccio A, Consonni D, Mensi C, Mirabelli D, Migliore E, Magnani C et al.; ReNaM Working Group. Association between asbestos exposure and pericardial and tunica vaginalis testis malignant mesothelioma: a case-control study and epidemiological remarks. Scand J Work Environ Health. 2020;46(6):609-617. https://doi.org/10.5271/sjweh.3895. 2. Rothman KJ, Greenland S, Lash TL. Modern Epidemiology. 2008; Philadelphia: Wolters Kluwer/Lippincott Williams & Wilkins. 3. Richardson DB. An incidence density sampling program for nested case-control analyses. Occup Environ Med 2004 Dec;61(12):e59. https://doi.org/10.1136/oem.2004.014472. 4. Marinaccio A, Binazzi A, Marzio DD, Scarselli A, Verardo M, Mirabelli D et al.; ReNaM Working Group. Pleural malignant mesothelioma epidemic: incidence, modalities of asbestos exposure and occupations involved from the Italian National Register. Int J Cancer 2012 May;130(9):2146-54. https://doi.org/10.1002/ijc.26229. 5. La Vecchia C, Decarli A, Peto J, Levi F, Tomei F, Negri E. An age, period and cohort analysis of pleural cancer mortality in Europe. Eur J Cancer Prev 2000 Jun;9(3):179-84. https://doi.org/10.1097/00008469-200006000-00005. 6. Price B, Ware A. Mesothelioma trends in the United States: an update based on Surveillance, Epidemiology, and End Results Program data for 1973 through 2003. Am J Epidemiol 2004 Jan;159(2):107-12. https://doi.org/10.1093/aje/kwh025. 7. Moolgavkar SH, Meza R, Turim J. Pleural and peritoneal mesotheliomas in SEER: age effects and temporal trends, 1973-2005. Cancer Causes Control 2009 Aug;20(6):935-44. https://doi.org/10.1007/s10552-009-9328-9. 8. Moolgavkar SH, Chang ET, Mezei G, Mowat FS. Chapter 3. Epidemiology of mesothelioma. In Testa JR. Asbestos and mesothelioma; 2017. pp. 43-72. Cham, Switzerland: Springer International Publishing. 9. Brandi G, Di Girolamo S, Farioli A, de Rosa F, Curti S, Pinna AD et al. Asbestos: a hidden player behind the cholangiocarcinoma increase? Findings from a case-control analysis. Cancer Causes Control 2013 May;24(5):911-8. https://doi.org/10.1007/s10552-013-0167-3. 10. Marinaccio A, Binazzi A, Di Marzio D, Scarselli A, Verardo M, Mirabelli D et al. Incidence of extrapleural malignant mesothelioma and asbestos exposure, from the Italian national register. Occup Environ Med 2010 Nov;67(11):760-5. https://doi.org/10.1136/oem.2009.051466. 11. Teschke K, Morgan MS, Checkoway H, Franklin G, Spinelli JJ, van Belle G et al. Mesothelioma surveillance to locate sources of exposure to asbestos. Can J Public Health 1997 May-Jun;88(3):163-8. https://doi.org/10.1007/BF03403881. 12. Bouchardy C, Schüler G, Minder C, Hotz P, Bousquet A, Levi F et al. Cancer risk by occupation and socioeconomic group among men--a study by the Association of Swiss Cancer Registries. Scand J Work Environ Health 2002;28(1 Suppl 1):1-88. 13. Hemminki K, Li X. Time trends and occupational risk factors for pleural mesothelioma in Sweden. J Occup Environ Med 2003a Apr;45(4):456-61. https://doi.org/10.1097/01.jom.0000058341.05741.7e. 14. Hemminki K, Li X. Time trends and occupational risk factors for peritoneal mesothelioma in Sweden. J Occup Environ Med 2003b Apr;45(4):451-5. https://doi.org/10.1097/01.jom.0000052960.59271.d4. 15. Pukkala E, Martinsen JI, Lynge E, Gunnarsdottir HK, Sparén P, Tryggvadottir L et al. Occupation and cancer - follow-up of 15 million people in five Nordic countries. Acta Oncol 2009;48(5):646-790. https://doi.org/10.1080/02841860902913546. 16. Rolland P, Gramond C, Berron H, Ducamp S, Imbernon E, Goldberg M et al. Mesotheliome pleural: Professions et secteurs d'activite a risque chez les hommes [Pleural mesothelioma: Professions and occupational areas at risk among humans]. 2005; Institut de VeilleSanitaire, Departement Sante Travai, Saint-Maurice, France. 17. Rolland P, Gramond C, Lacourt A, Astoul P, Chamming's S, Ducamp S et al. PNSM Study Group. Occupations and industries in France at high risk for pleural mesothelioma: A population-based case-control study (1998-2002). Am J Ind Med 2010 Dec;53(12):1207-19. https://doi.org/10.1002/ajim.20895.

Smoking, air pollution, and lung cancer risk in the Nurses' Health Study cohort: time-dependent confounding and effect modification.

The proportional hazards (PH) model is commonly used in epidemiology despite the stringent assumption of proportionality of hazards over time. We previously showed, using detailed simulation data, that the impact of a modest risk factor cannot be estimated reliably using the PH model in the presence of confounding by a strong, time-dependent risk factor. Here, we examine the same and related issues using a real dataset. Among 97,303 women in the prospective Nurses' Health Study cohort from 1994 through 2010, we used PH regression to investigate how effect estimates for cigarette smoking are affected by increasingly detailed specification of time-dependent exposure characteristics. We also examined how effect estimates for fine particulate matter (PM2.5), a modest risk factor, are affected by finer control for time-dependent confounding by smoking. The objective of this analysis is not to present a credible estimate of the impact of PM2.5 on lung cancer risk, but to show that estimates based on the PH model are inherently unreliable. The best-fitting model for cigarette smoking and lung cancer included pack-years, duration, time since cessation, and an age-by-pack-years interaction, indicating that the hazard ratio (HR) for pack-years was significantly modified by age. In the fully adjusted best-fitting model for smoking including pack-years, the HR per 10-µg/m3 increase in PM2.5 was 1.06 (95% confidence interval (CI) = 0.90, 1.25); the HR for PM2.5 in the full cohort ranged between 1.02 and 1.10 in models with other smoking adjustments, indicating a residual confounding effect of smoking. The HR for PM2.5 was statistically significant only among former smokers when adjusting for smoking pack-years (HR = 1.35, 95% CI = 1.00, 1.82 in the best-fitting smoking model), but not in models adjusting for smoking duration and average packs (pack-years divided by duration). The association between cumulative smoking and lung cancer is modified by age, and improved model fit is obtained by including multiple time-varying components of smoking history. The association with PM2.5 is residually confounded by smoking and modified by smoking status. These findings underscore limitations of the PH model and emphasize the advantages of directly estimating hazard functions to characterize time-varying exposure and risk. The hazard function, not the relative hazard, is the fundamental measure of risk in a population. As a consequence, the use of time-dependent PH models does not address crucial issues introduced by temporal factors in epidemiological data.

A 37-year Update on Mortality Patterns in an Expanded Cohort of Vermont Talc Miners and Millers.

OBJECTIVES: The aim of this study was to update a cohort of Vermont talc workers to include 37 additional years of follow-up time. METHODS: Standardized mortality ratios (SMR) and 95% confidence intervals (CIs) were calculated for 70+ causes of death. US population mortality rates were used as reference. RESULTS: All-cause mortality was 30% higher than the US population (SMR 133.4, 95% CI, 119.7 to 148.3). Significant elevations occurred in nonmalignant respiratory disease (NMRD) (SMR 273.0, 95% CI, 210.2 to 348.6) and other nonmalignant respiratory disease (ONMRD) (SMR 413.1, 95% CI, 287.7 to 574.5). ONMRD was elevated across all length of employment categories and a test for linear trend was significant (P = 0.007). CONCLUSIONS: This study provides further evidence that excess deaths among Vermont talc workers are due largely to excess mortality from NMRD; there is no evidence of increased risk of respiratory cancer.

Reanalysis of Diesel Engine Exhaust and Lung Cancer Mortality in the Diesel Exhaust in Miners Study Cohort Using Alternative Exposure Estimates and Radon Adjustment.

The Diesel Exhaust in Miners Study (DEMS) (United States, 1947-1997) reported positive associations between diesel engine exhaust exposure, estimated as respirable elemental carbon (REC), and lung cancer mortality. This reanalysis of the DEMS cohort used an alternative estimate of REC exposure incorporating historical data on diesel equipment, engine horsepower, ventilation rates, and declines in particulate matter emissions per horsepower. Associations with cumulative REC and average REC intensity using the alternative REC estimate and other exposure estimates were generally attenuated compared with original DEMS REC estimates. Most findings were statistically nonsignificant; control for radon exposure substantially weakened associations with the original and alternative REC estimates. No association with original or alternative REC estimates was detected among miners who worked exclusively underground. Positive associations were detected among limestone workers, whereas no association with REC or radon was found among workers in the other 7 mines. The differences in results based on alternative exposure estimates, control for radon, and stratification by worker location or mine type highlight areas of uncertainty in the DEMS data.

An Assessment of the Cox Proportional Hazards Regression Model for Epidemiologic Studies.

The basic assumptions of the Cox proportional hazards regression model are rarely questioned. This study addresses whether hazard ratio, i.e., relative risk (RR), estimates using the Cox model are biased when these assumptions are violated. We investigated also the dependence of RR estimates on temporal exposure characteristics, and how inadequate control for a strong, time-dependent confounder affects RRs for a modest, correlated risk factor. In a realistic cohort of 500,000 adults constructed using the National Cancer Institute Smoking History Generator, we used the Cox model with increasing control of smoking to examine the impact on RRs for smoking and a correlated covariate X. The smoking-associated RR was strongly modified by age. Pack-years of smoking did not sufficiently control for its effects; simultaneous control for effect modification by age and time-dependent cumulative exposure, exposure duration, and time since cessation improved model fit. Even then, residual confounding was evident in RR estimates for covariate X, for which spurious RRs ranged from 0.980 to 1.017 per unit increase. Use of the Cox model to control for a time-dependent strong risk factor yields unreliable RR estimates unless detailed, time-varying information is incorporated in analyses. Notwithstanding, residual confounding may bias estimated RRs for a modest risk factor.

Epidemiology of mesothelioma of the pericardium and tunica vaginalis testis.

PURPOSE: Malignant mesothelioma most commonly arises in the pleura and peritoneum but also occurs rarely at other anatomical sites with mesothelial tissue, namely, the pericardium and tunica vaginalis testis (TVT). This review provides a better understanding of the epidemiology of mesothelioma of these extrapleural sites. METHODS: We conducted a systematic review of the epidemiologic and clinical literature on pericardial mesothelioma and mesothelioma of the TVT. We also analyzed U.S. Surveillance, Epidemiology, and End Results cancer registry data to describe incidence patterns of these malignancies. RESULTS: An etiologic role of asbestos exposure has been hypothesized for pericardial and TVT mesotheliomas, but no analytical case-control epidemiologic studies exist to test this relationship. A substantial proportion of cases with these malignancies report no known asbestos exposure. In large occupational cohorts with heavy asbestos exposures, no cases of pericardial or TVT mesothelioma have been reported. Trends in the incidence of these malignancies do not match those of pleural mesothelioma, which correspond to historical trends of commercial asbestos use. A male preponderance of pericardial mesothelioma is not evident. CONCLUSIONS: In the absence of analytic epidemiologic studies, the etiologic role of environmental risk factors for mesothelioma of the pericardium and TVT remains elusive.

Reanalysis of the DEMS nested case-control study of lung cancer and diesel exhaust: suitability for quantitative risk assessment.

The International Agency for Research on Cancer (IARC) in 2012 upgraded its hazard characterization of diesel engine exhaust (DEE) to "carcinogenic to humans." The Diesel Exhaust in Miners Study (DEMS) cohort and nested case-control studies of lung cancer mortality in eight U.S. nonmetal mines were influential in IARC's determination. We conducted a reanalysis of the DEMS case-control data to evaluate its suitability for quantitative risk assessment (QRA). Our reanalysis used conditional logistic regression and adjusted for cigarette smoking in a manner similar to the original DEMS analysis. However, we included additional estimates of DEE exposure and adjustment for radon exposure. In addition to applying three DEE exposure estimates developed by DEMS, we applied six alternative estimates. Without adjusting for radon, our results were similar to those in the original DEMS analysis: all but one of the nine DEE exposure estimates showed evidence of an association between DEE exposure and lung cancer mortality, with trend slopes differing only by about a factor of two. When exposure to radon was adjusted, the evidence for a DEE effect was greatly diminished, but was still present in some analyses that utilized the three original DEMS DEE exposure estimates. A DEE effect was not observed when the six alternative DEE exposure estimates were utilized and radon was adjusted. No consistent evidence of a DEE effect was found among miners who worked only underground. This article highlights some issues that should be addressed in any use of the DEMS data in developing a QRA for DEE.

Diesel engine exhaust and lung cancer mortality: time-related factors in exposure and risk.

To develop a quantitative exposure-response relationship between concentrations and durations of inhaled diesel engine exhaust (DEE) and increases in lung cancer risks, we examined the role of temporal factors in modifying the estimated effects of exposure to DEE on lung cancer mortality and characterized risk by mine type in the Diesel Exhaust in Miners Study (DEMS) cohort, which followed 12,315 workers through December 1997. We analyzed the data using parametric functions based on concepts of multistage carcinogenesis to directly estimate the hazard functions associated with estimated exposure to a surrogate marker of DEE, respirable elemental carbon (REC). The REC-associated risk of lung cancer mortality in DEMS is driven by increased risk in only one of four mine types (limestone), with statistically significant heterogeneity by mine type and no significant exposure-response relationship after removal of the limestone mine workers. Temporal factors, such as duration of exposure, play an important role in determining the risk of lung cancer mortality following exposure to REC, and the relative risk declines after exposure to REC stops. There is evidence of effect modification of risk by attained age. The modifying impact of temporal factors and effect modification by age should be addressed in any quantitative risk assessment (QRA) of DEE. Until there is a better understanding of why the risk appears to be confined to a single mine type, data from DEMS cannot reliably be used for QRA.

A review and critique of U.S. EPA's risk assessments for asbestos.

U.S. Environmental Protection Agency (EPA) recently conducted a risk assessment for exposure to Libby amphibole asbestos that is precedent-setting for two reasons. First, the Agency has not previously conducted a risk assessment for a specific type of asbestos fiber. Second, the risk assessment includes not only an inhalation unit risk (IUR) for the cancer endpoints, but also a reference concentration (RfC) for nonmalignant disease. In this paper, we review the procedures used by the Agency for both cancer and nonmalignant disease and discuss the strengths and limitations of these procedures. The estimate of the RfC uses the benchmark dose method applied to pleural plaques in a small subcohort of vermiculite workers in Marysville, Ohio. We show that these data are too sparse to inform the exposure-response relationship in the low-exposure region critical for estimation of an RfC, and that different models with very different exposure-response shapes fit the data equally well. Furthermore, pleural plaques do not represent a disease condition and do not appear to meet the EPA's definition of an adverse condition. The estimation of the IUR for cancer is based on a subcohort of Libby miners, discarding the vast majority of lung cancers and mesotheliomas in the entire cohort and ignoring important time-related factors in exposure and risk, including effect modification by age. We propose that an IUR based on an endpoint that combines lung cancer, mesothelioma, and nonmalignant respiratory disease (NMRD) in this cohort would protect against both malignant and nonmalignant disease. However, the IUR should be based on the entire cohort of Libby miners, and the analysis should properly account for temporal factors. We illustrate our discussion with our own independent analyses of the data used by the Agency.

Validity of geographically modeled environmental exposure estimates.

Geographic modeling is increasingly being used to estimate long-term environmental exposures in epidemiologic studies of chronic disease outcomes. However, without validation against measured environmental concentrations, personal exposure levels, or biologic doses, these models cannot be assumed a priori to be accurate. This article discusses three examples of epidemiologic associations involving exposures estimated using geographic modeling, and identifies important issues that affect geographically modeled exposure assessment in these areas. In air pollution epidemiology, geographic models of fine particulate matter levels have frequently been validated against measured environmental levels, but comparisons between ambient and personal exposure levels have shown only moderate correlations. Estimating exposure to magnetic fields by using geographically modeled distances is problematic because the error is larger at short distances, where field levels can vary substantially. Geographic models of environmental exposure to pesticides, including paraquat, have seldom been validated against environmental or personal levels, and validation studies have yielded inconsistent and typically modest results. In general, the exposure misclassification resulting from geographic models of environmental exposures can be differential and can result in bias away from the null even if non-differential. Therefore, geographic exposure models must be rigorously constructed and validated if they are to be relied upon to produce credible scientific results to inform epidemiologic research. To our knowledge, such models have not yet successfully predicted an association between an environmental exposure and a chronic disease outcome that has eventually been established as causal, and may not be capable of doing so in the absence of thorough validation.

Tobacco control and the reduction in smoking-related premature deaths in the United States, 1964-2012.

IMPORTANCE: January 2014 marks the 50th anniversary of the first surgeon general's report on smoking and health. This seminal document inspired efforts by governments, nongovernmental organizations, and the private sector to reduce the toll of cigarette smoking through reduced initiation and increased cessation. OBJECTIVE: To model reductions in smoking-related mortality associated with implementation of tobacco control since 1964. DESIGN, SETTING, AND PARTICIPANTS: Smoking histories for individual birth cohorts that actually occurred and under likely scenarios had tobacco control never emerged were estimated. National mortality rates and mortality rate ratio estimates from analytical studies of the effect of smoking on mortality yielded death rates by smoking status. Actual smoking-related mortality from 1964 through 2012 was compared with estimated mortality under no tobacco control that included a likely scenario (primary counterfactual) and upper and lower bounds that would capture plausible alternatives. EXPOSURES: National Health Interview Surveys yielded cigarette smoking histories for the US adult population in 1964-2012. MAIN OUTCOMES AND MEASURES: Number of premature deaths avoided and years of life saved were primary outcomes. Change in life expectancy at age 40 years associated with change in cigarette smoking exposure constituted another measure of overall health outcomes. RESULTS: In 1964-2012, an estimated 17.7 million deaths were related to smoking, an estimated 8.0 million (credible range [CR], 7.4-8.3 million, for the lower and upper tobacco control counterfactuals, respectively) fewer premature smoking-related deaths than what would have occurred under the alternatives and thus associated with tobacco control (5.3 million [CR, 4.8-5.5 million] men and 2.7 million [CR, 2.5-2.7 million] women). This resulted in an estimated 157 million years (CR, 139-165 million) of life saved, a mean of 19.6 years for each beneficiary (111 million [CR, 97-117 million] for men, 46 million [CR, 42-48 million] for women). During this time, estimated life expectancy at age 40 years increased 7.8 years for men and 5.4 years for women, of which tobacco control is associated with 2.3 years (CR, 1.8-2.5) (30% [CR, 23%-32%]) of the increase for men and 1.6 years (CR, 1.4-1.7) (29% [CR, 25%-32%]) for women. CONCLUSIONS AND RELEVANCE: Tobacco control was estimated to be associated with avoidance of 8 million premature deaths and an estimated extended mean life span of 19 to 20 years. Although tobacco control represents an important public health achievement, efforts must continue to reduce the effect of smoking on the nation's death toll.

Cancer mortality and quantitative oil production in the Amazon region of Ecuador, 1990-2010.

PURPOSE: Controversy persists over whether cancer risk is increased in communities surrounding oil fields, especially in the Oriente region of Ecuador. This ecologic study uses quantitative exposure data, updated mortality data, and improved statistical methods to study the impact of oil exploration and production activities on cancer mortality rates in the Oriente. METHODS: Cancer mortality rates in the Oriente in 1990 through 2010 were compared between seven cantons with active oil exploration and production as of 1990 and thirteen cantons with little or no such activities. Poisson regression was used to estimate mortality rate ratios (RRs) adjusted for age and sex. In a two-stage analysis, canton-specific log-RRs were regressed against quantitative estimates of cumulative barrels of oil produced and well-years per canton, adjusting for canton-level demographic and socioeconomic factors. RESULTS: Overall and site-specific cancer mortality rates were comparable between oil-producing and non-oil-producing cantons. For overall cancer mortality in males and females combined, the RR comparing oil-producing to non-oil-producing cantons was 0.85 [95 % confidence interval (CI) 0.72-1.00]. For leukemia mortality, the corresponding RR was 0.80 (95 % CI 0.57-1.13). Results also revealed no excess of mortality from acute non-lymphocytic, myeloid, or childhood leukemia. Standardized mortality ratios were consistent with RRs. Canton-specific RRs showed no pattern in relation to oil production volume or well-years. CONCLUSIONS: Results from this first ecologic study to incorporate quantitative measures of oil exploration and production showed no association between the extent of these activities and cancer mortality, including from cancers associated with benzene exposure.