Elevated Bladder Cancer in Northern New England: The Role of Drinking Water and Arsenic.

BACKGROUND: Bladder cancer mortality rates have been elevated in northern New England for at least five decades. Incidence rates in Maine, New Hampshire, and Vermont are about 20% higher than the United States overall. We explored reasons for this excess, focusing on arsenic in drinking water from private wells, which are particularly prevalent in the region. METHODS: In a population-based case-control study in these three states, 1213 bladder cancer case patients and 1418 control subjects provided information on suspected risk factors. Log transformed arsenic concentrations were estimated by linear regression based on measurements in water samples from current and past homes. All statistical tests were two-sided. RESULTS: Bladder cancer risk increased with increasing water intake (Ptrend = .003). This trend was statistically significant among participants with a history of private well use (Ptrend = .01). Among private well users, this trend was apparent if well water was derived exclusively from shallow dug wells (which are vulnerable to contamination from manmade sources, Ptrend = .002) but not if well water was supplied only by deeper drilled wells (Ptrend = .48). If dug wells were used pre-1960, when arsenical pesticides were widely used in the region, heavier water consumers (>2.2 L/day) had double the risk of light users (<1.1 L/day, Ptrend = .01). Among all participants, cumulative arsenic exposure from all water sources, lagged 40 years, yielded a positive risk gradient (Ptrend = .004); among the highest-exposed participants (97.5th percentile), risk was twice that of the lowest-exposure quartile (odds ratio = 2.24, 95% confidence interval = 1.29 to 3.89). CONCLUSIONS: Our findings support an association between low-to-moderate levels of arsenic in drinking water and bladder cancer risk in New England. In addition, historical consumption of water from private wells, particularly dug wells in an era when arsenical pesticides were widely used, was associated with increased bladder cancer risk and may have contributed to the New England excess.

Computer-based coding of free-text job descriptions to efficiently identify occupations in epidemiological studies.

BACKGROUND: Mapping job titles to standardised occupation classification (SOC) codes is an important step in identifying occupational risk factors in epidemiological studies. Because manual coding is time-consuming and has moderate reliability, we developed an algorithm called SOCcer (Standardized Occupation Coding for Computer-assisted Epidemiologic Research) to assign SOC-2010 codes based on free-text job description components. METHODS: Job title and task-based classifiers were developed by comparing job descriptions to multiple sources linking job and task descriptions to SOC codes. An industry-based classifier was developed based on the SOC prevalence within an industry. These classifiers were used in a logistic model trained using 14 983 jobs with expert-assigned SOC codes to obtain empirical weights for an algorithm that scored each SOC/job description. We assigned the highest scoring SOC code to each job. SOCcer was validated in 2 occupational data sources by comparing SOC codes obtained from SOCcer to expert assigned SOC codes and lead exposure estimates obtained by linking SOC codes to a job-exposure matrix. RESULTS: For 11 991 case-control study jobs, SOCcer-assigned codes agreed with 44.5% and 76.3% of manually assigned codes at the 6-digit and 2-digit level, respectively. Agreement increased with the score, providing a mechanism to identify assignments needing review. Good agreement was observed between lead estimates based on SOCcer and manual SOC assignments (κ 0.6-0.8). Poorer performance was observed for inspection job descriptions, which included abbreviations and worksite-specific terminology. CONCLUSIONS: Although some manual coding will remain necessary, using SOCcer may improve the efficiency of incorporating occupation into large-scale epidemiological studies.

Combining Decision Rules from Classification Tree Models and Expert Assessment to Estimate Occupational Exposure to Diesel Exhaust for a Case-Control Study.

OBJECTIVES: To efficiently and reproducibly assess occupational diesel exhaust exposure in a Spanish case-control study, we examined the utility of applying decision rules that had been extracted from expert estimates and questionnaire response patterns using classification tree (CT) models from a similar US study. METHODS: First, previously extracted CT decision rules were used to obtain initial ordinal (0-3) estimates of the probability, intensity, and frequency of occupational exposure to diesel exhaust for the 10 182 jobs reported in a Spanish case-control study of bladder cancer. Second, two experts reviewed the CT estimates for 350 jobs randomly selected from strata based on each CT rule's agreement with the expert ratings in the original study [agreement rate, from 0 (no agreement) to 1 (perfect agreement)]. Their agreement with each other and with the CT estimates was calculated using weighted kappa (κ w) and guided our choice of jobs for subsequent expert review. Third, an expert review comprised all jobs with lower confidence (low-to-moderate agreement rates or discordant assignments, n = 931) and a subset of jobs with a moderate to high CT probability rating and with moderately high agreement rates (n = 511). Logistic regression was used to examine the likelihood that an expert provided a different estimate than the CT estimate based on the CT rule agreement rates, the CT ordinal rating, and the availability of a module with diesel-related questions. RESULTS: Agreement between estimates made by two experts and between estimates made by each of the experts and the CT estimates was very high for jobs with estimates that were determined by rules with high CT agreement rates (κ w: 0.81-0.90). For jobs with estimates based on rules with lower agreement rates, moderate agreement was observed between the two experts (κ w: 0.42-0.67) and poor-to-moderate agreement was observed between the experts and the CT estimates (κ w: 0.09-0.57). In total, the expert review of 1442 jobs changed 156 probability estimates, 128 intensity estimates, and 614 frequency estimates. The expert was more likely to provide a different estimate when the CT rule agreement rate was <0.8, when the CT ordinal ratings were low to moderate, or when a module with diesel questions was available. CONCLUSIONS: Our reliability assessment provided important insight into where to prioritize additional expert review; as a result, only 14% of the jobs underwent expert review, substantially reducing the exposure assessment burden. Overall, we found that we could efficiently, reproducibly, and reliably apply CT decision rules from one study to assess exposure in another study.

Modification of Occupational Exposures on Bladder Cancer Risk by Common Genetic Polymorphisms.

Few studies have demonstrated gene/environment interactions in cancer research. Using data on high-risk occupations for 2258 case patients and 2410 control patients from two bladder cancer studies, we observed that three of 16 known or candidate bladder cancer susceptibility variants displayed statistically significant and consistent evidence of additive interactions; specifically, the GSTM1 deletion polymorphism (P interaction ≤ .001), rs11892031 (UGT1A, P interaction = .01), and rs798766 (TMEM129-TACC3-FGFR3, P interaction = .03). There was limited evidence for multiplicative interactions. When we examined detailed data on a prevalent occupational exposure associated with increased bladder cancer risk, straight metalworking fluids, we also observed statistically significant additive interaction for rs798766 (TMEM129-TACC3-FGFR3, P interaction = .02), with the interaction more apparent in patients with tumors positive for FGFR3 expression. All statistical tests were two-sided. The interaction we observed for rs798766 (TMEM129-TACC3-FGFR3) with specific exposure to straight metalworking fluids illustrates the value of integrating germline genetic variation, environmental exposures, and tumor marker data to provide insight into the mechanisms of bladder carcinogenesis.

Using hierarchical cluster models to systematically identify groups of jobs with similar occupational questionnaire response patterns to assist rule-based expert exposure assessment in population-based studies.

OBJECTIVES: Rule-based expert exposure assessment based on questionnaire response patterns in population-based studies improves the transparency of the decisions. The number of unique response patterns, however, can be nearly equal to the number of jobs. An expert may reduce the number of patterns that need assessment using expert opinion, but each expert may identify different patterns of responses that identify an exposure scenario. Here, hierarchical clustering methods are proposed as a systematic data reduction step to reproducibly identify similar questionnaire response patterns prior to obtaining expert estimates. As a proof-of-concept, we used hierarchical clustering methods to identify groups of jobs (clusters) with similar responses to diesel exhaust-related questions and then evaluated whether the jobs within a cluster had similar (previously assessed) estimates of occupational diesel exhaust exposure. METHODS: Using the New England Bladder Cancer Study as a case study, we applied hierarchical cluster models to the diesel-related variables extracted from the occupational history and job- and industry-specific questionnaires (modules). Cluster models were separately developed for two subsets: (i) 5395 jobs with ≥1 variable extracted from the occupational history indicating a potential diesel exposure scenario, but without a module with diesel-related questions; and (ii) 5929 jobs with both occupational history and module responses to diesel-relevant questions. For each subset, we varied the numbers of clusters extracted from the cluster tree developed for each model from 100 to 1000 groups of jobs. Using previously made estimates of the probability (ordinal), intensity (µg m(-3) respirable elemental carbon), and frequency (hours per week) of occupational exposure to diesel exhaust, we examined the similarity of the exposure estimates for jobs within the same cluster in two ways. First, the clusters' homogeneity (defined as >75% with the same estimate) was examined compared to a dichotomized probability estimate (<5 versus ≥5%; <50 versus ≥50%). Second, for the ordinal probability metric and continuous intensity and frequency metrics, we calculated the intraclass correlation coefficients (ICCs) between each job's estimate and the mean estimate for all jobs within the cluster. RESULTS: Within-cluster homogeneity increased when more clusters were used. For example, ≥80% of the clusters were homogeneous when 500 clusters were used. Similarly, ICCs were generally above 0.7 when ≥200 clusters were used, indicating minimal within-cluster variability. The most within-cluster variability was observed for the frequency metric (ICCs from 0.4 to 0.8). We estimated that using an expert to assign exposure at the cluster-level assignment and then to review each job in non-homogeneous clusters would require ~2000 decisions per expert, in contrast to evaluating 4255 unique questionnaire patterns or 14983 individual jobs. CONCLUSIONS: This proof-of-concept shows that using cluster models as a data reduction step to identify jobs with similar response patterns prior to obtaining expert ratings has the potential to aid rule-based assessment by systematically reducing the number of exposure decisions needed. While promising, additional research is needed to quantify the actual reduction in exposure decisions and the resulting homogeneity of exposure estimates within clusters for an exposure assessment effort that obtains cluster-level expert assessments as part of the assessment process.

Estimation of the probability of exposure to machining fluids in a population-based case-control study.

We describe an approach for estimating the probability that study subjects were exposed to metalworking fluids (MWFs) in a population-based case-control study of bladder cancer. Study subject reports on the frequency of machining and use of specific MWFs (straight, soluble, and synthetic/semi-synthetic) were used to estimate exposure probability when available. Those reports also were used to develop estimates for job groups, which were then applied to jobs without MWF reports. Estimates using both cases and controls and controls only were developed. The prevalence of machining varied substantially across job groups (0.1->0.9%), with the greatest percentage of jobs that machined being reported by machinists and tool and die workers. Reports of straight and soluble MWF use were fairly consistent across job groups (generally 50-70%). Synthetic MWF use was lower (13-45%). There was little difference in reports by cases and controls vs. controls only. Approximately, 1% of the entire study population was assessed as definitely exposed to straight or soluble fluids in contrast to 0.2% definitely exposed to synthetic/semi-synthetics. A comparison between the reported use of the MWFs and U.S. production levels found high correlations (r generally >0.7). Overall, the method described here is likely to have provided a systematic and reliable ranking that better reflects the variability of exposure to three types of MWFs than approaches applied in the past. [Supplementary materials are available for this article. Go to the publisher's online edition of Journal of Occupational and Environmental Hygiene for the following free supplemental resources: a list of keywords in the occupational histories that were used to link study subjects to the metalworking fluids (MWFs) modules; recommendations from the literature on selection of MWFs based on type of machining operation, the metal being machined and decade; popular additives to MWFs; the number and proportion of controls who reported various MWF responses by job group; the number and proportion of controls assigned to the MWF types by job group and exposure category; and the distribution of cases and controls assigned various levels of probability by MWF type.].

Developing estimates of frequency and intensity of exposure to three types of metalworking fluids in a population-based case-control study of bladder cancer.

BACKGROUND: A systematic, transparent, and data-driven approach was developed to estimate frequency and intensity of exposure to straight, soluble, and synthetic/semi-synthetic metalworking fluids (MWFs) within a case-control study of bladder cancer in New England. METHODS: We assessed frequency using individual-level information from job-specific questionnaires wherever possible, then derived and applied job group-level patterns to likely exposed jobs with less information. Intensity estimates were calculated using a statistical model developed from measurements and determinants extracted from the published literature. RESULTS: For jobs with probabilities of exposure≥0.5, median frequencies were 8-10 hr/week, depending on MWF type. Median intensities for these jobs were 2.5, 2.1, and 1.0 mg/m3 for soluble, straight, and synthetic/semi-synthetic MWFs, respectively. CONCLUSIONS: Compared to case-by-case assessment, these data-driven decision rules are transparent and reproducible and may result in less biased estimates. These rules can also aid future exposure assessments of MWFs in population-based studies.

Identifying gender differences in reported occupational information from three US population-based case-control studies.

OBJECTIVES: Growing evidence suggests that gender-blind assessment of exposure may introduce exposure misclassification, but few studies have characterised gender differences across occupations and industries. We pooled control responses to job-specific, industry-specific and exposure-specific questionnaires (modules) that asked detailed questions about work activities from three US population-based case-control studies to examine gender differences in work tasks and their frequencies. METHODS: We calculated the ratio of female-to-male controls that completed each module. For four job modules (assembly worker, machinist, health professional, janitor/cleaner) and for subgroups of jobs that completed those modules, we evaluated gender differences in task prevalence and frequency using χ(2) and Mann-Whitney U tests, respectively. RESULTS: The 1360 female and 2245 male controls reported 6033 and 12 083 jobs, respectively. Gender differences in female:male module completion ratios were observed for 39 of 45 modules completed by ≥20 controls. Gender differences in task prevalence varied in direction and magnitude. For example, female janitors were significantly more likely to polish furniture (79% vs 44%), while male janitors were more likely to strip floors (73% vs 50%). Women usually reported more time spent on tasks than men. For example, the median hours per week spent degreasing for production workers in product manufacturing industries was 6.3 for women and 3.0 for men. CONCLUSIONS: Observed gender differences may reflect actual differences in tasks performed or differences in recall, reporting or perception, all of which contribute to exposure misclassification and impact relative risk estimates. Our findings reinforce the need to capture subject-specific information on work tasks.

Improving the quality of industry and occupation data at a central cancer registry.

BACKGROUND: Central cancer registries are required to collect industry and occupation (I/O) information when available, but the data reported are often incomplete. METHODS: We audited the completeness of I/O data in the New Hampshire State Cancer Registry (NHSCR) database for diagnosis year 2005, and reviewed medical records for a convenience sample of 474 of these cases. We compared I/O data quality before and after a statewide registrar training session on occupationally related cancers. RESULTS: The original 2005 data contained both I/O data in 11.5% of cases, and lacked any I/O data in 74.5%. Corresponding figures for cases selected for audit were 15.2% and 77.2%, which improved to 54.2% and 11.8% after medical record review. After registrar training, 47% of reports contained both I/O data, and only 14.4% of cases lacked any I/O data. CONCLUSIONS: Statewide training to highlight the importance of I/O data is an effective method to improve I/O data quality.

Reliability of rapid reporting of cancers in New Hampshire.

OBJECTIVE: The New Hampshire State Cancer Registry (NHSCR) has a 2-phase reporting system. An abbreviated, "rapid" report of cancer diagnosis or treatment is due to the central registry within 45 days of diagnosis and a more detailed, definitive report is due within 180 days. Rapid reports are used for various research studies, but researchers who contact patients are warned that the rapid reports may contain inaccuracies. This study aimed to assess the reliability of rapid cancer reports. METHODS: For diagnosis years 2000-2004, we compared the rapid and definitive reports submitted to NHSCR. We calculated the sensitivity and positive predictive value of rapid reports; the reliability of key data items overall and for major sites; and the time between diagnosis and submission of the report. RESULTS: Rapid reports identified incident cancer cases with a sensitivity of 88.5%. The overall accuracy of key data items was high. The accuracy of primary sites identified by rapid reports was high generally but lower for ovarian and unknown primaries. A subset analysis showed that 47% of cancers were reported within 90 days of diagnosis. CONCLUSION: Rapid reports submitted to NHSCR are generally of high quality and present a useful opportunity for research investigations in New Hampshire.