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Background The association of childhood leukemia with traffic pollution was considered in a number of studies from 1989 onwards, with results not entirely consistent and little information regarding subtypes. Aim of the study We used the data of the Italian SETIL case-control on childhood leukemia to explore the risk by leukemia subtypes associated to exposure to vehicular traffic. Methods We included in the analyses 648 cases of childhood leukemia (565 Acute lymphoblastic–ALL and 80 Acute non lymphoblastic-AnLL) and 980 controls. Information on traffic exposure was collected from questionnaire interviews and from the geocoding of house addresses, for all periods of life of the children. Results We observed an increase in risk for AnLL, and at a lower extent for ALL, with indicators of exposure to traffic pollutants. In particular, the risk was associated to the report of closeness of the house to traffic lights and to the passage of trucks (OR: 1.76; 95% CI 1.03–3.01 for ALL and 6.35; 95% CI 2.59–15.6 for AnLL). The association was shown also in the analyses limited to AML and in the stratified analyses and in respect to the house in different period of life. Conclusions Results from the SETIL study provide some support to the association of traffic related exposure and risk for AnLL, but at a lesser extent for ALL. Our conclusion highlights the need for leukemia type specific analyses in future studies. Results support the need of controlling exposure from traffic pollution, even if knowledge is not complete.

PURPOSE: Tobacco smoke could cause childhood acute lymphoblastic leukemia (ALL) through at least three pathways: (1) prenatal parental smoking; (2) fetal exposure through maternal smoking during pregnancy; and (3) childhood exposure to secondhand smoke (SHS). We tested these hypotheses in a large population-based case-control study (SETIL) primarily designed to evaluate the role of electromagnetic fields in childhood hematopoietic malignancies. METHODS: From 1998 to 2003, we enrolled 602 incident cases of ALL from 14 Italian Regions, and 918 controls were individually matched by birthdate, sex, and area of residence. Cases (n = 557) and controls (n = 855) with complete information were analyzed; odds ratios (OR) and 95 % confidence intervals (95 % CI) were estimated with logistic regression models conditioned on matching variables and adjusted by birth order, birthweight, duration of breastfeeding, parental age at delivery, education, and occupational exposure to benzene. RESULTS: No evidence associating paternal smoking in the conception period or maternal smoking during the pregnancy with ALL was found. An association of ALL with maternal exposure to SHS during pregnancy (adjusted OR for mothers exposed more than 4 h/day = 2.18, 95 % CI 1.39-3.42) was observed, but recall bias cannot be excluded. Exposure of the children to SHS was associated with ALL only in unadjusted analysis (unadjusted OR for highly exposed children = 1.64; 95 % CI 1.10-2.45). CONCLUSIONS: This study does not support the hypothesis that parental active smoking is associated with ALL. We found very weak evidence of increased risk of ALL for children exposed to SHS. Maternal exposure to SHS was associated with ALL, but recall bias is likely to inflate our estimates.

Background Aetiology of childhood leukaemia and childhood neoplasm is poorly understood. Information on the prevalence of risk factors in the childhood population is limited. SETIL is a population based case–control study on childhood leukaemia, conducted with two companion studies on non-Hodgkin Lymphoma (NHL) and neuroblastoma. The study relies on questionnaire interviews and 50 Hz magnetic field (ELF-MF) indoor measurements. This paper discusses the SETIL study design and includes descriptive information. Methods The study was carried out in 14 Italian regions (78.3% of Italian population aged 0–10). It included leukaemia, NHL and neuroblastoma cases incident in 0–10 year olds in 1998–2001, registered by the Italian Association of Paediatric Haematology and Oncology (AIEOP) (accrual over 95% of estimated incidence). Two controls for each leukaemia case were randomly sampled from the Local Health Authorities rolls, matched by gender, birthdate and residence. The same controls were used in NHL and neuroblastoma studies. Parents were interviewed at home on: physical agents (ELF-MF and ionizing radiation), chemicals (smoking, solvents, traffic, insecticides), occupation, medical and personal history of children and parents, infectious diseases, immunizations and associated factors. Occupational exposure was collected using job specific modules. ELF-MF was measured in the main rooms (spot measurement) and close to child’s bed (48 hours measurement). Results The study included: 683 leukaemia cases (87% ALL, 13% AnLL), 97 NHL, 155 neuroblastomas, and 1044 controls. ELF-MF long term measurements were obtained for 61.1% of controls and 81.6% of leukaemia cases; 8.8% of controls were exposed at over 0.1 microTesla (μT), 3.5% and 2.1% at respectively over 0.2 and 0.3 μT. 25% of controls’ fathers had smoked over 10 cigarettes/day during the year of conception, varying according to education and region. Maternal smoking was less common (71.4% did not smoke in pregnancy). Maternal passive smoking during pregnancy was reported by 31.2% of controls; the child’s passive smoking for 28.6%. Occupational exposure to solvents was estimated in 18.3% of controls’ fathers and 7.7% of mothers. Contact with public was more frequent among mothers (36.1%) than fathers (23.4%). Conclusions SETIL represents a data source on exposure of Italian children to a broad array of potential carcinogenic factors. Electronic supplementary material The online version of this article (doi:10.1186/s13052-014-0103-5) contains supplementary material, which is available to authorized users.

We performed an analysis of All cancer and Lung cancer mortality in relation to estimated absorbed dose of dioxin (2,3,7,8-tetrachlorodibenzo-p-dioxin, TCDD) in the cohort of chemical workers at 12 US plants assembled by the US National Institute for Occupational Safety and Health (NIOSH) (n=5172). Estimates of cumulative exposure to TCDD were based on a minimal physiologic toxicokinetic model (MPTK) that accounts for inter- and intra-individual variations in body mass index (BMI) over time. Population-level parameters related to liver elimination and background (input or concentration) of TCDD were estimated from separate data with repeated measures of serum TCDD (US Air Force Health Study). An occupational TCDD input parameter was estimated based on one-point-in-time TCDD data available for a subset (n=253) of the NIOSH cohort. Model-based time-dependent cumulative dose estimates (area under the curve (AUC) of the lipid-adjusted serum TCDD concentration over time) were obtained for members of the full cohort with recorded body height and weight (n=4049), as this information is required by the MPTK model to compute dose. Missing-value problems arose in the estimation of the occupational input parameter (n=42) and in TCDD-dose calculation in the full cohort (n=886) and they were handled with multiple imputation methods. Risk-regression analyses were based on Cox log-linear models including age at entry, year of entry and duration of employment as categorical covariates in addition to the logarithm of cumulative TCDD dose in ppt-years. Risk sets were stratified on birth cohort. Estimates of the unlagged exposure coefficient in these models were 0.1249 [95% confidence interval (CI) 0.0144, 0.2354] for All cancer and 0.2158 (95% CI 0.02376, 0.4078) for lung cancer. A 10-year lag produced an increase in the estimate for all cancer (0.1539, 95% CI 0.0387, 0.2691), whereas, the estimate for lung cancer was not affected much (0.2125, 95% CI 0.0138, 0.4112). At a dose level of 100 times the background the estimates obtained with a 10-year lag translate into a relative risk of 2.03 (95% CI 1.19–3.45) for all cancer and of 2.66 (95% CI 1.07–6.64) for lung cancer. Higher estimates of the exposure coefficients were obtained after imputation of missing values. This increase in risk seemed due to the inclusion of short-term workers, who may exhibit a higher mortality for reasons other than dioxin exposure.

Background A retrospective cohort mortality study evaluated ischemic heart disease (IHD) among workers in the “rubber chemicals” manufacturing department of a Western New York plant. A previous study at the plant found elevated chest pain and angina among workers in this department. Methods Mortality experience of workers employed from 1946–1988 was followed through December 31, 1994. Mortality was compared to U.S. population rates and to local Niagara county rates by using the NIOSH life table analysis system. Poisson regression was used to examine patterns of IHD within the cohort. Results The standardized mortality ratio (SMR) for IHD among workers in the rubber chemicals department was 1.51 (U.S. rates) and 1.19 (Niagara county rates). Increased mortality from IHD in the rubber chemicals department was most pronounced at younger ages (

This paper deals with sources of uncertainty in the use of a minimal physiological toxicokinetic model to obtain dose estimates for a dose-response analysis of cancer in an occupational cohort. Toxicokinetic models make it possible to construct exposure parameters that are more closely related to the individual dose than traditional measures of exposures to toxic agents. However, the process introduces a wide array of sources of uncertainty. Selecting a model structure to describe the kinetics of a toxic agent implies necessarily making simplifications and assumptions that influence the range of applicability of the model. Once a model has been selected, the value of certain model parameters (constants) must be assigned, for example, from anthropometric data. The question then arises of how sensitive the model predictions are to variations in the values of these constants. Other model parameters, typically those describing the kinetics of the agent, are next estimated from actual data. There may be limitations in the data concerning, for example, sparseness (too few observations per subject) or missing values. The methods used for parameter estimation carry their own set of assumptions that need to be appropriate to the situation at hand. In summary, the dioxin example is used to characterize the sources of uncertainty at different levels, such as model structure, methods and data used for parameter estimation, estimation of occupational exposure, and imputation of missing values in exposure indices derived from the kinetic model.

In this study we investigated estimation of occupational exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) based on a minimal physiologic toxicokinetic model in humans. Our purpose was to obtain a mathematical tool for dose-response studies based on human data. We first simplified an existing model of TCDD kinetics in humans and estimated its parameters (i.e., liver elimination and background input of TCDD) using repeated measures of serum dioxin taken in Vietnam veterans (Ranch Hand data and data from an unexposed reference group). We carried out computer simulation and estimation of the model parameters both under a nonlinear weighted least-squares model (naive pooled data approach) and under a nonlinear mixed-effects model. The best parameter estimates were obtained with log-transformed data under a mixed-effects model: liver elimination parameter kf = 0.022 days-1 (95% confidence interval [CI] = 0.020, 0.024), and background input rate input = 0.1251 pg/kg/day (95% CI = 0.071, 0.179). The dioxin kinetic model and its estimated parameters were then used to provide dose estimates for a cohort of workers with exposure to TCDD at chemical plants in the United States. First, the model was used to estimate the rate of occupational intake of TCDD in a subset of the cohort consisting of 253 subjects for whom one measure of serum TCDD was available. A model of change in body-mass index over time was also identified for this subsample. The occupational exposure rate was estimated by linear regression using the above values of kinetic parameters and assuming an initial condition for serum TCDD of 7 ppt, i.e., the average level found in unexposed workers. The estimate of the occupational exposure parameter was 232.7 pg/kg/day (95% CI 192, 273). This value can be applied to the full cohort to obtain for each cohort member the time course of serum dioxin concentration from which exposure indices can be derived. Sensitivity coefficients to model parameters (background input, kf, occupational exposure, and the assumed TCDD concentration at hire) allow for a convenient recalculation of the serum TCDD curve and of the derived exposure indices for different assumed values of the model parameters. Images Figure 1 Figure 5

Epithelial-cell micronuclei (MN) are potentially useful markers of occupational exposure to genotoxicants. With intermittent exposures, cells sampled either before or after a specific time interval, reflecting the time it takes for damaged cells to become available at the epithelial surface, are unlikely to be exposure-related. It may then be important to conduct an exposure-window analysis, with the goal of identifying the relevant exposures.We re-analysed individual exposure data from a previous study (Suruda et al. 1993) of MN formation in 22 male mortuary science students exposed to formaldehyde during a 90-day embalming class. We conducted an exposurewindow analysis and compared the results with those obtained with 90-day cumulative exposure. The window widths varied between 7 and 25 days, in 1 day increments, assuming a constant 7-day cell-cycle. We assessed the fit (likelihood-ratio test) of a linear regression model, regressing the change in buccal MN prevalence on formaldehyde exposure, using both asymptotic and non-asymptotic methods. Exposures defined from 7-15 to 7-18 days before specimen collection provided a slightly better fit than the 90-day cumulative exposure, with a doubling of the regression coefficient for the exposure effect (for the 7-16-days window LR = 5.32, p = 0.032, coefficient = 0.088 MN per 1000 cells per ppm-hr; 95% CI = 0.014, 0.16; for the 90-day cumulative exposure LR = 4.44, p = 0.048, coefficient = 0.045 MN per 1000 cells per ppm-hr, 95% CI = 0.0038, 0.086). Although hampered by the small number of subjects, these results reinforce the potential importance of exposure timing.

Many occupational studies analyze trends between cumulative exposure and mortality. The authors show that such trends are, in general, negatively confounded by employment status. Mortality rates for workers who leave work ("inactive" workers) are higher than for active workers because some workers leave because they are ill. The percentage of inactive relative to active person-time is higher in low categories of cumulative exposure, causing employment status to act as a negative confounder of exposure-response trends (the opposite occurs for time-since-hire). We illustrate these phenomena using 10 "negative" mortality studies, in which adjustment for employment status removes false trends. However, adjustment for employment status will lead to biased estimates when it acts as an intermediate variable between cumulative exposure and death, as occurs directly when exposure causes a disabling disease that, in turn, causes death or indirectly when exposure causes workers to leave work. The authors illustrate this problem using simulated follow-up data for leaving, disease incidence, and mortality. In the null case in which cumulative exposure affects neither disease incidence (or mortality) nor leaving rates, employment status indeed acts as a negative confounder of exposure-response trends, and traditional adjustment eliminates this confounding. However, when cumulative exposure affects disease incidence or rates of leaving, adjustment for employment status will not be adequate. Employment status falls under the general rubric of variables that are simultaneously confounders and intermediate variables.

In epidemiology, there is an inclination to consider more credible the larger estimates of exposure effect. For example, higher relative risks or rate ratios are often emphasized as a criterion for choosing among various hypothesized exposure-lag values. An alternative criterion for this choice might be based on a goodness-of-fit measure. We present examples, based on hypothetical data, in which an exposure-lag parameter is estimated by trial and error fitting: we compare the behavior of the likelihood-ratio goodness-of-fit statistic obtained over the assigned values of the parameter with that of the relative risk. We show that there can be inconsistencies between the highest-estimate and likelihood-based goodness-of-fit criteria. Concern about the validity of the highest-estimate criterion prompts us to recommend that this criterion not be used for the estimation of exposure-weighting parameters, which should preferably be based on a priori biological knowledge or on goodness-of-fit criteria.

OBJECTIVES. To update mortality risks for Navajo uranium miners, a retrospective cohort mortality study was conducted of 757 Navajos from the cohort of Colorado Plateau uranium miners. METHODS. Vital status was followed from 1960 to 1990. Standardized mortality ratios were estimated, with combined New Mexico and Arizona non-White mortality rates used for comparison. Cox regression models were used to evaluate exposure-response relationships. RESULTS. Elevated standardized mortality ratios were found for lung cancer (3.3), tuberculosis (2.6), and pneumoconioses and other respiratory diseases (2.6). Lowered ratios were found for heart disease (0.6), circulatory disease (0.4), and liver cirrhosis (0.5). The estimated relative risk for a 5-year duration of exposure vs none was 3.7 for lung cancer, 2.1 for pneumoconioses and other respiratory diseases, and 2.0 for tuberculosis. The relative risk for lung cancer was 6.9 for the midrange of cumulative exposure to radon progeny compared with the least exposed. CONCLUSIONS. Findings were consistent with those from previous studies. Twenty-three years after their last exposure to radon progeny, these light-smoking Navajo miners continue to face excess mortality risks from lung cancer and pneumoconioses and other respiratory diseases.

The one-step (Peto) method for obtaining pooled effect estimates can yield extremely biased results when applied to unbalanced data. Even for balanced studies, the one-step estimate may incorporate an unacceptable degree of bias. In place of the one-step estimate, we recommend use of ordinary Mantel-Haenszel, weighted least squares, or maximum likelihood estimates whenever the total number of events is adequate for such methods. If the total number of events is small, we recommend exact methods.

Imputation of missing values in a cancer mortality analysis in relation to estimated dose of dioxin for a cohort of chemical workers is considered. In particular, some subjects of the cohort have the body mass index (BMI) missing. This quantity is an essential ingredient for a toxicokinetic model that gives the estimated absorbed dose, which is then used for risk estimation in a proportional hazards model. Imputation of BMI allows to recover information and to use the entire cohort for risk estimation. Both conditional mean imputation and multiple imputation are used. The latter is a simulation-based approach to the analysis of missing data which takes into account the uncertainty of the imputation process using several imputations for each missing value. In the present context, the two imputation methods gave similar results, both correcting for bias (although with some questions) and leading to increased efficiency with respect to the complete-case analysis that simply discards the partially unobserved individuals.

We present a local influence analysis to assigned model quantities in the context of a dose-response analysis of cancer mortality in relation to estimated absorbed dose of dioxin. The risk estimation is performed using dioxin dose as a time-dependent explanatory variable in a proportional hazard model. The dioxin dose is computed using a toxicokinetic model, which depends on some factors, such as assigned constants and estimated parameters. We present a local influence analysis to assess the effects on final results of minor perturbations of toxicokinetic model factors. In the present context, there is no evidence of local influence in risk estimates. It is however possible to identify which factors are more influential.

Summary The paper describes how to use hierarchical models to assess the reliability of and agreement between two or more types of measurement device. The idea is illustrated by fitting a linear model with nested random effects to a set of data that was obtained from the calibration of two samples of extremely low frequency magnetic field meters. The paper focuses on the formulation of a suitable model that accounts for the various aspects of the calibration protocol and the subsequent interpretation of the parameter estimates. The approach is very flexible and can easily be tuned to the various needs arising in the measurement agreement framework. It can be seen as an extension of the common practice of using a one-way random-effects model to retrieve a measure of agreement.

Understanding the kinetics of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) concentrations in humans is an important step for TCDD cancer risk assessment. In this paper longitudinal series of serum TCDD concentration measurements on U.S. veterans of the Vietnam war, who were exposed to dioxin during herbicide-spraying operations, are studied. The overall aim is to use these data to infer the dynamics of TCDD concentrations in humans. This is done by identifying a kinetic model describing the dioxin time course at the individual level. The individual toxicokinetic model is then expanded into a population model within a Bayesian hierarchical framework which allows residual variations across subjects that cannot be explained by observed covariates. Other complications in the data, such as unknown exposure histories, are also resolved implicitly through the hierarchical model. Moreover, the choice of a Bayesian approach enables the accumulation of external source of information in the form of prior distributions. The model is subjected to various diagnostic checks and analyses of sensitivity to distributional assumptions showing a good fit in terms of both the population and the kinetic features.

To determine whether the use of video display terminals (VDTs) is associated with an increased risk of reduced birthweight (RBW) and preterm birth, a cohort o f telephone operators who used VDTs at work was compared to a cohort of non- VDT- use rs. Am ong 2,430 women interviewed, 713 eligible singleton live births were reported. Exposure was estim ated from company records and a representative sample of electromagnetic fields was m easured at the VDT workstations. For RBW (^2,800 g)t we found no excess risk associated with any VDT use during pregnancy (odds ratio [OR] = 0.9; 95% confidence interval [C l] — 0.5—1,7). For preterm birth 37 weeks), we similarly found no excess risk (OR ~ 0.7; 95% C I = 0.4-1.1). The risks estimated did not change substantially when hours working with VDTs were used as exposure variables. By contrast, increased risks were found fo r several known risk factors fo r LBW and preterm birth. We conclude that occupational VDT use does not increase the risk o f RBW and preterm birth. Am. J. Ind. Med. 32:681-688, 1997. © 1997 Wiley-Liss, lnc.f

To address earlier reports of excess cancer mortality associated with employment at a large transformer manufacturing plant, each plant operation was rated for seven exposures: Pyranol (a mixture of poly chlorinated biphenyls and trichlorobenzene), trichloroethylene, benzene, mixed solvents, asbestos, synthetic resins, and machining fluids. Site-specific cancer deaths among active or retired employees were cases; controls were selected from deaths (primarily cardiovascular deaths) presumed to be unassociated with any of the study exposures. Using job records, we then computed person-years of exposure for each subject. All subjects were white males. The only unequivocal association was that of resin systems with lung cancer (odds ratio = 2.2 at 16.6 years of exposure, P = 0.001, in a multiple logistic regression including asbestos, age, year of death, and year of hire). Certain other odds ratios appeared larger, but no other association was so robust and remained as distinct after considering the multiplicity of comparisons. Study power was very limited for most associations, and several biases may have affected our results. Nevertheless, further investigation of synthetic resin systems of the type used in the study plant appears warranted.

We appreciate the opportunity to respond to comments by Valentgas and colleagues regarding our report examining mortality among rubber chemical manufacturing workers [Prince et al., 2001]. Following are our responses to their comments, focusing on the authors' main points