Lifetime exposure to arsenic in drinking water and bladder cancer: a population-based case-control study in Michigan, USA.

OBJECTIVE: Arsenic in drinking water has been linked with the risk of urinary bladder cancer, but the dose-response relationships for arsenic exposures below 100 microg/L remain equivocal. We conducted a population-based case-control study in southeastern Michigan, USA, where approximately 230,000 people were exposed to arsenic concentrations between 10 and 100 microg/L. METHODS: This study included 411 bladder cancer cases diagnosed between 2000 and 2004, and 566 controls recruited during the same period. Individual lifetime exposure profiles were reconstructed, and residential water source histories, water consumption practices, and water arsenic measurements or modeled estimates were determined at all residences. Arsenic exposure was estimated for 99% of participants' person-years. RESULTS: Overall, an increase in bladder cancer risk was not found for time-weighted average lifetime arsenic exposure >10 microg/L when compared with a reference group exposed to <1 microg/L (odds ratio (OR) = 1.10; 95% confidence interval (CI): 0.65, 1.86). Among ever-smokers, risks from arsenic exposure >10 microg/L were similarly not elevated when compared to the reference group (OR = 0.94; 95% CI: 0.50, 1.78). CONCLUSIONS: We did not find persuasive evidence of an association between low-level arsenic exposure and bladder cancer. Selecting the appropriate exposure metric needs to be thoughtfully considered when investigating risk from low-level arsenic exposure.

Breast and prostate cancer survival in Michigan: can geographic analyses assist in understanding racial disparities?

BACKGROUND: Racial disparities in survival from breast and prostate cancer are well established; however, the roles of societal/socioeconomic factors and innate/genetic factors in explaining the disparities remain unclear. One approach for evaluating the relative importance of societal and innate factors is to quantify how the magnitude of racial disparities changes according to the geographic scales at which data are aggregated. Disappearance of racial disparities for some levels of aggregation would suggest that modifiable factors not inherent at the individual level are responsible for the disparities. METHODS: The Michigan Cancer Surveillance Program compiled a dataset from 1985 to 2002 that included 124,218 breast cancer cases and 120,615 prostate cancer cases with 5-year survival rates of 78% and 75%, respectively. Absolute and relative differences in survival rates for whites and blacks were quantified across different geographic scales using statistics that adjusted for population size to account for the small numbers problem common with minority populations. RESULTS: Whites experienced significantly higher survival rates for prostate and breast cancer compared with blacks throughout much of southern Michigan in analyses conducted using federal House legislative districts; however, in smaller geographic units (state House legislative districts and community-defined neighborhoods), disparities diminished and virtually disappeared. CONCLUSIONS: The current results suggest that modifiable societal factors are responsible for apparent racial disparities in breast and prostate cancer survival observed at larger geographic scales. This research presents a novel strategy for taking advantage of inconsistencies across geographic scales to evaluate the relative importance of innate and societal-level factors in explaining racial disparities in cancer survival.

Influence of groundwater recharge and well characteristics on dissolved arsenic concentrations in southeastern Michigan groundwater.

Arsenic concentrations exceeding 10 microg/l, the United States maximum contaminant level and the World Health Organization guideline value, are frequently reported in groundwater from bedrock and unconsolidated aquifers of southeastern Michigan. Although arsenic-bearing minerals (including arsenian pyrite and oxide/hydroxide phases) have been identified in Marshall Sandstone bedrock of the Mississippian aquifer system and in tills of the unconsolidated aquifer system, mechanisms responsible for arsenic mobilization and subsequent transport in groundwater are equivocal. Recent evidence has begun to suggest that groundwater recharge and characteristics of well construction may affect arsenic mobilization and transport. Therefore, we investigated the relationship between dissolved arsenic concentrations, reported groundwater recharge rates, well construction characteristics, and geology in unconsolidated and bedrock aquifers. Results of multiple linear regression analyses indicate that arsenic contamination is more prevalent in bedrock wells that are cased in proximity to the bedrock-unconsolidated interface; no other factors were associated with arsenic contamination in water drawn from bedrock or unconsolidated aquifers. Conditions appropriate for arsenic mobilization may be found along the bedrock-unconsolidated interface, including changes in reduction/oxidation potential and enhanced biogeochemical activity because of differences between geologic strata. These results are valuable for understanding arsenic mobilization and guiding well construction practices in southeastern Michigan, and may also provide insights for other regions faced with groundwater arsenic contamination.

Using satellite derived land cover information for a multi-temporal study of self-reported recall of proximity to farmland.

Exposure misclassification is a major concern in epidemiologic studies. The potential for misclassification becomes even more problematic when participants are asked to recall historical information. Yet, historical information is important in cancer studies, where latency is long and causative exposures may have occurred years or even decades prior to diagnosis. Even though self-reported proximity to farmland is a commonly used exposure measure, the accuracy of recall is seldom, if ever validated. Geographic Information Systems (GISs) and land cover information derived from satellite imagery can allow researchers to assess the accuracy of this exposure measure, and to quantify the extent and importance of exposure misclassification. As part of a bladder cancer case-control study in Michigan, participants were asked whether they lived on a farm, or within a distance of 1/4, 1/4-1, 1-5, or >5 miles from farmland for each residence over their lifespan. Responses from 531 participants over two time periods--1978 and 2001--were investigated. Self reported proximity to farmland was compared to a "gold standard" derived from Michigan land cover files for the same time periods. Logistic regression and other statistical measures including sensitivity, specificity, and percentage matching were evaluated. In comparing self-reported and land cover-derived proximity to farmland, cases exhibited better agreement than controls in 2001 (adjusted OR=1.74; 95% CI=1.01, 2.99) and worse agreement in 1978, although not significantly (adjusted OR=0.74; 95% CI=0.47, 1.16). When comparing 2001 with 1978, both cases and controls showed better agreement in 2001, but only cases showed a significant difference (adjusted OR=2.36; 95% CI=1.33, 4.18). These differences in agreement may be influenced by differences in educational attainment between cases and controls, although adjustment for education did not diminish the association. Gender, age, number of years at residence, and geocoding accuracy did not influence agreement between the proximity approaches. This study suggests that proximity measures taken from satellite-derived land cover imagery may be useful for assessing proximity to farmland, and it raises some concerns about the use of self-reported proximity to farmland in exposure assessments.

Validity of spatial models of arsenic concentrations in private well water.

OBJECTIVE: Arsenic is a pervasive contaminant in underground aquifers worldwide, yet documentation of health effects associated with low-to-moderate concentrations (<100microg/L) has been stymied by uncertainties in assessing long-term exposure. A critical component of assessing exposure to arsenic in drinking water is the development of models for predicting arsenic concentrations in private well water in the past; however, these models are seldom validated. The objective of this paper is to validate alternative spatial models of arsenic concentrations in private well water in southeastern Michigan. METHODS: From 1993 to 2002, the Michigan Department of Environmental Quality analyzed arsenic concentrations in water from 6050 private wells. This dataset was used to develop several spatial models of arsenic concentrations in well water: proxy wells based on nearest-neighbor relationships, averages across geographic regions, and geostatistically derived estimates based on spatial correlation and geologic factors. Output from these models was validated using arsenic concentrations measured in 371 private wells from 2003 to 2006. RESULTS: The geostatisical model and nearest-neighbor approach outperformed the models based on geographic averages. The geostatistical model produced the highest degree of correlation using continuous data (Pearson's r=0.61; Spearman's rank rho=0.46) while the nearest-neighbor approach produced the strongest correlation (kappa(weighted)=0.58) using an a priori categorization of arsenic concentrations (<5, 5-9.99, 10-19.99, > or = 20microg/L). When the maximum contaminant level was used as a cut-off in a two-category classification (<10, > or =10microg/L), the nearest-neighbor approach and geostatistical model had similar values for sensitivity (0.62-0.63), specificity (0.80), negative predictive value (0.85), positive predictive value (0.53), and percent agreement (75%). DISCUSSION: This validation study reveals that geostatistical modeling and nearest-neighbor approaches are effective spatial models for predicting arsenic concentrations in private well water. Further validation analyses in other regions are necessary to indicate how widely these findings may be generalized.

Toenails as a biomarker of inorganic arsenic intake from drinking water and foods.

Toenails were used recently in epidemiological and environmental health studies as a means of assessing exposure to arsenic from drinking water. While positive correlations between toenail and drinking-water arsenic concentrations were reported in the literature, a significant percentage of the variation in toenail arsenic concentration remains unexplained by drinking-water concentration alone. Here, the influence of water consumption at home and work, food intake, and drinking-water concentration on toenail arsenic concentration was investigated using data from a case-control study being conducted in 11 counties of Michigan. The results from 440 controls are presented. Log-transformed drinking-water arsenic concentration at home was a significant predictor (p < .05) of toenail arsenic concentration (R2 = .32). When arsenic intake from consumption of tap water and beverages made from tap water (microg/L arsenic x L/d = microg/d) was used as a predictor variable, the correlation was markedly increased for individuals with >1 microg/L arsenic (R2 = .48). Increased intake of seafood and intake of arsenic from water at work were independently and significantly associated with increased toenail arsenic concentration. However, when added to intake at home, work drinking-water exposure and food intake had little influence on the overall correlation. These results suggest that arsenic exposure from drinking-water consumption is an important determinant of toenail arsenic concentration, and therefore should be considered when validating and applying toenails as a biomarker of arsenic exposure.

Individual lifetime exposure to inorganic arsenic using a space-time information system.

OBJECTIVES: A space-time information system (STIS) based method is introduced for calculating individual-level estimates of inorganic arsenic exposure over the adult life-course. STIS enables visualization and analysis of space-time data, overcoming some of the constraints inherent to spatial-only Geographic Information System software. The power of this new methodology is demonstrated using data from southeastern Michigan where 8% of the population is exposed to arsenic >10 microg/l (the World Health Organization guideline) in home drinking water. METHODS: Participants (N=440) are members of a control group in a population-based bladder cancer case-control study in southeastern Michigan and were recruited by phone using random digit dialing. Water samples were collected and analyzed for arsenic at current residence and participants were required to answer questions concerning lifetime mobility history and dietary habits. Inorganic arsenic concentrations were estimated at past residences and workplaces, and in select foods. Fluid and food consumption data were integrated with mobility histories and arsenic concentrations to calculate continuous estimates of inorganic arsenic intake over the adult life-course. RESULTS: Estimates of continuous arsenic exposure are displayed, making use of both participant age and calendar year as measures of time. Results illustrate considerable temporal variability in individual-level exposure, with 26% of the participants experiencing a change in drinking water arsenic concentration of at least +/-10 microg/l over their adult lives. The average cumulative intake over the adult life-course ranges from 2.53 x 10(4)-1.30 x 10(5) microg, depending on the selected exposure metric. CONCLUSIONS: The STIS-based exposure assessment method allows for flexible inclusion of different parameters or alternative formulations of those parameters, thus enabling the calculation of different exposure metrics. This flexibility is particularly useful when additional exposure routes are considered, input datasets are updated, or when a scientific consensus does not exist regarding the proper formulation of the exposure metric. These results demonstrate the potential of STIS as a useful tool for calculating continuous estimates of adult lifetime exposure to arsenic or other environmental contaminants for application in exposure and risk assessment.

Case-control geographic clustering for residential histories accounting for risk factors and covariates.

BACKGROUND: Methods for analyzing space-time variation in risk in case-control studies typically ignore residential mobility. We develop an approach for analyzing case-control data for mobile individuals and apply it to study bladder cancer in 11 counties in southeastern Michigan. At this time data collection is incomplete and no inferences should be drawn - we analyze these data to demonstrate the novel methods. Global, local and focused clustering of residential histories for 219 cases and 437 controls is quantified using time-dependent nearest neighbor relationships. Business address histories for 268 industries that release known or suspected bladder cancer carcinogens are analyzed. A logistic model accounting for smoking, gender, age, race and education specifies the probability of being a case, and is incorporated into the cluster randomization procedures. Sensitivity of clustering to definition of the proximity metric is assessed for 1 to 75 k nearest neighbors. RESULTS: Global clustering is partly explained by the covariates but remains statistically significant at 12 of the 14 levels of k considered. After accounting for the covariates 26 Local clusters are found in Lapeer, Ingham, Oakland and Jackson counties, with the clusters in Ingham and Oakland counties appearing in 1950 and persisting to the present. Statistically significant focused clusters are found about the business address histories of 22 industries located in Oakland (19 clusters), Ingham (2) and Jackson (1) counties. Clusters in central and southeastern Oakland County appear in the 1930's and persist to the present day. CONCLUSION: These methods provide a systematic approach for evaluating a series of increasingly realistic alternative hypotheses regarding the sources of excess risk. So long as selection of cases and controls is population-based and not geographically biased, these tools can provide insights into geographic risk factors that were not specifically assessed in the case-control study design.

Visualization and exploratory analysis of epidemiologic data using a novel space time information system.

BACKGROUND: Recent years have seen an expansion in the use of Geographic Information Systems (GIS) in environmental health research. In this field GIS can be used to detect disease clustering, to analyze access to hospital emergency care, to predict environmental outbreaks, and to estimate exposure to toxic compounds. Despite these advances the inability of GIS to properly handle temporal information is increasingly recognised as a significant constraint. The effective representation and visualization of both spatial and temporal dimensions therefore is expected to significantly enhance our ability to undertake environmental health research using time-referenced geospatial data. Especially for diseases with long latency periods (such as cancer) the ability to represent, quantify and model individual exposure through time is a critical component of risk estimation. In response to this need a STIS - a Space Time Information System has been developed to visualize and analyze objects simultaneously through space and time. RESULTS: In this paper we present a "first use" of a STIS in a case-control study of the relationship between arsenic exposure and bladder cancer in south eastern Michigan. Individual arsenic exposure is reconstructed by incorporating spatiotemporal data including residential mobility and drinking water habits. The unique contribution of the STIS is its ability to visualize and analyze residential histories over different temporal scales. Participant information is viewed and statistically analyzed using dynamic views in which values of an attribute change through time. These views include tables, graphs (such as histograms and scatterplots), and maps. In addition, these views can be linked and synchronized for complex data exploration using cartographic brushing, statistical brushing, and animation. CONCLUSION: The STIS provides new and powerful ways to visualize and analyze how individual exposure and associated environmental variables change through time. We expect to see innovative space-time methods being utilized in future environmental health research now that the successful "first use" of a STIS in exposure reconstruction has been accomplished.