Arsenic Exposure, Bladder Cancer, and You: The Geostatistical Story You've Got to Read

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  • Author: Lillian Pierson, P.E.
  • Date: 26 Aug 2014
  • Copyright: Image appears courtesy of iStock Photo

Since the dawn of time there’s been the haves and the have nots. Their stories precede them. While the moral and ethical viewpoints about economic disparity clash and rage, quietly in the backdrop, scientists are uncovering deeper truths about the extent to which we’re all affected.

thumbnail image: Arsenic Exposure, Bladder Cancer, and You: The Geostatistical Story You've Got to Read

Last year, researchers at the University of Exeter in the UK published a study that revealed a significant correlation between socio-economic status, body chemistry, and environmental exposure to harmful toxins. What they found is that the body of the average poor person has higher concentrations of lead, cadmium, and plastics, thought to be due to poor diet and a greater tendency for cigarette smoking among the poor. In contrast, rich people tend to have higher body chemistry concentrations of mercury, arsenic, and benzophenone-3, thought due to their greater consumption of shellfish and seafood, and the prevalent use of sunscreen among the middle-upper class. The study also confirmed what we’ve all known for quite some time, that chronic long-term exposure to chemical toxins usually causes adverse health effects.

But how, exactly, is our health affected? This question is central to the work of spatio-temporal epidemiologist, Dr Pierre Goovaerts. Dr Goovaerts is an established and long-respected leader in the fields of geostatistics and soil science, but he’s recently made a debut in the spatio-temporal epidemiology arena as well. Behind this recent career transition Goovaerts explains, “After 15 years devoted to the application of geostatistics to the characterization of contaminated sites, it seemed logical to wonder about the impact of this contamination on human health. After all, concern about human health should be one of the main drivers guiding the characterization and remediation of these sites.”

Goovaerts’ expertise in geostatistics coupled with his recent explorations into epidemiology have landed him square in the middle of an emerging field called “medical geology”. Medical geology studies the relationship between the geo-environmental characteristics of a location and the health of the humans and animals that live there.

As part of his recent work in the field, Goovaerts has been immersed in studying how spatial disease patterns and mortality are correlated to environmental exposure and socio-demographics. Of particular significance, Goovaerts has found spatial correlations between incidence of bladder, prostate, and breast cancer and long-term exposures to arsenic.

Of particular significance, Goovaerts has found spatial correlations between incidence of bladder, prostate, and breast cancer and long-term exposures to arsenic.

Much of Goovaerts’ success can be attributed to his innovative approach, where he’s used his expertise in advanced geostatistics to find correlations in spatial epidemiology. Throughout these studies, he’s found three geostatistical methods to be particularly useful in modeling local arsenic concentrations and in-situ cancer risks. These methods include semivariogram analysis, Poisson kriging, and stochastic simulation.

Goovaerts has been coupling semivariogram analysis with GIS technology to model dissimilarities between groundwater arsenic concentrations as a function of the separation distance between sampling points. Here, he uses semivariogram analysis and least squares regression model-fitting to uncover anisotropic patterns in spatial structures. Anisotropy indicates direction-dependent variability in groundwater concentrations of arsenic. Goovaerts uses this information to infer how arsenic concentrations are affected by human activities and natural environmental variables like geology, weather patterns, topography, and land cover.

Kriging is an advanced geostatistical algorithm that produces an estimated surface from attribute values of spatial data. Several types of kriging are generally performed in order to investigate before selecting the best estimation method for the data at hand. Popular types of kriging include ordinary kriging, universal kriging, block (Poisson) kriging, indicator kriging, and disjunctive kriging. Dr. Goovaerts favors Poisson kriging because it provides greater flexibility in spatial risk modeling and because it produces less smoothing than alternative methods. In spatial risk modeling it’s important to minimize smoothing in order to reduce the chances of overlooking small areas of high risk. He uses the method to overcome the “small number problem” when spatially modeling cancer risk in sparsely populated (rural) areas.

In geostatistics, it’s very important to account for error propagation by evaluating uncertainty associated with attribute values at unsampled locations. Either local uncertainty or spatial uncertainty should be evaluated; sometimes both are evaluated. To evaluate local uncertainty for one particular unsampled location, probability mapping is a commonly used method to map areas with a high likelihood of exceeding critical values. To evaluate the uncertainty of several unsampled locations in a joint assessment, however, spatial uncertainty should be evaluated and addressed. To address any issues of spatial uncertainty, Dr Goovaerts uses Monte Carlo methods and stochastic simulation “to generate alternate models of the spatial distribution of attribute (…) values that reproduce features of the data (e.g., histogram, semivariogram).” These types of uncertainty evaluations provide a quantitative description for error propagation caused by uncertainty in arsenic concentration data that propagates through the models and causes an increase in the uncertainty estimates of associated cancer risks.

In geostatistics, it’s very important to account for error propagation by evaluating uncertainty associated with attribute values at unsampled locations. To evaluate local uncertainty for one particular unsampled location, probability mapping is a commonly used method.

In one of his more recent studies, Goovaerts discovered larger incidence rates of prostate cancer in townships that have higher arsenic concentrations in the groundwater and smaller population densities. This most likely occurs in rural townships where people are consuming arsenic-rich well water. More research still needs to be conducted to screen out the potential impact that contextual and individual-level covariates (e.g., poverty level, health screening access, smoking habits or water consumption habits) may have on study findings. Medical geology is still in its infancy, but by incorporating advanced geostatistical methods to solve epidemiological problems associated with environmental exposure and disease risk, researchers like Dr Pierre Goovaerts are making significant advances in the field.

Sources

Goovaerts, P. (2011) Geostatistics: a common link between Medical Geography, Mathematical Geology, and Medical Geology. IV Conferência Internacional de Geologia Médica, Bari, Italy.

Associations between socioeconomic status and environmental toxicant concentrations in adults in the USA: NHANES 2001–2010 Author: Jessica Tyrrell, David Melzer, William Henley, Tamara S. Galloway, Nicholas J. Osborne Publication: Environment International Publisher: Elsevier Date: September 2013 http://dx.doi.org/10.1016/j.envint.2013.06.017.

Goovaerts, P. (2014) The Role of Geostatistics in Medical Geology. [Invited talk] [European Geosciences Union, General Assembly 2014, Vienna, 27 April – 02 May, 2014] In: European Geosciences Union, Geophysical Research Abstracts, Vol. 16, EGU 2014-5431.

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