[1] During the last decade one has witnessed an increasing interest in assessing health risks caused by exposure to contaminants present in the soil, air, and water. A key component of any exposure study is a reliable model for the space-time distribution of pollutants. This paper compares the performances of multi-Gaussian and indicator kriging for modeling probabilistically the spatial distribution of arsenic concentrations in groundwater of southeast Michigan, accounting for arsenic data collected at private residential wells and the hydrogeochemistry of the area. The arsenic data set, which was provided by the Michigan Department of Environmental Quality (MDEQ), includes measurements collected between 1993 and 2002 at 8212 different wells. Factorial kriging was used to filter the short-range spatial variability in arsenic concentration, leading to a significant increase (17-65%) in the proportion of variance explained by secondary information, such as type of unconsolidated deposits and proximity to Marshall Sandstone subcrop. Cross validation of well data shows that accounting for this regional background does not improve the local prediction of arsenic, which reveals the presence of unexplained sources of variability and the importance of modeling the uncertainty attached to these predictions. Slightly more precise models of uncertainty were obtained using indicator kriging. Well data collected in 2004 were compared to the prediction model and best results were found for soft indicator kriging which has a mean absolute error of 5.6 mg/L. Although this error is large with respect to the USEPA standard of 10 mg/L, it is smaller than the average difference (12.53 mg/L) between data collected at the same well and day, as reported in the MDEQ data set. Thus the uncertainty attached to the sampled values themselves, which arises from laboratory errors and lack of information regarding the sample origin, contributes to the poor accuracy of the geostatistical predictions in southeast Michigan.Citation: Goovaerts, P., G. AvRuskin, J. Meliker, M. Slotnick, G. Jacquez, and J. Nriagu (2005), Geostatistical modeling of the spatial variability of arsenic in groundwater of southeast Michigan, Water Resour. Res., 41, W07013,

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 µg/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 µg/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 µg/L when compared with a reference group exposed to <1 µg/L (odds ratio (OR) = 1.10; 95% confidence interval (CI): 0.65, 1.86). Among ever-smokers, risks from arsenic exposure >10 µg/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.

Background: This paper introduces a new approach for evaluating clustering in case-control data that accounts for residential histories. Although many statistics have been proposed for assessing local, focused and global clustering in health outcomes, few, if any, exist for evaluating clusters when individuals are mobile.

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.

A key problem facing epidemiologists who wish to account for residential mobility in their analyses is the cost and difficulty of obtaining residential histories. Commercial residential history data of acceptable accuracy, cost, and coverage would be of great value. The present research evaluated the accuracy of residential histories from LexisNexis, Inc. The authors chose LexisNexis because the Michigan Cancer Registry has considered using their data, they have excellent procedures for privacy protection, and they make available residential histories at 25 cents per person. Only first and last name and address at last-known residence are required to access the residential history. The authors compared lifetime residential histories collected through the use of written surveys in a case-control study of bladder cancer in Michigan to the 3 residential addresses routinely available in the address history from LexisNexis. The LexisNexis address matches, as a whole, accounted for 71.5% of participants' lifetime addresses. These results provided a level of accuracy that indicates routine use of residential histories from commercial vendors is feasible. More detailed residential histories are available at a higher cost but were not analyzed in this study. Although higher accuracy is desirable, LexisNexis data are a vast improvement over the assumption of immobile individuals currently used in many spatial and spatiotemporal studies.