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Abstract.Cancer incidence and mortality rates are high in West Virginia compared to the rest of the United States of America. Previous research has suggested that exposure to activities of the coal mining industry may contribute to elevated cancer mortality, although exposure measures have been limited. This study tests alternative specifications of exposure to mining activity to determine whether a measure based on location of mines, processing plants, coal slurry impoundments and underground slurry injection sites relative to population levels is superior to a previously-reported measure of exposure based on tons mined at the county level, in the prediction of age-adjusted cancer mortality rates. To this end, we utilize two geographical information system (GIS) techniques -exploratory spatial data analysis and inverse distance mapping -to construct new statistical analyses. Total, respiratory and "other" age-adjusted cancer mortality rates in West Virginia were found to be more highly associated with the GIS-exposure measure than the tonnage measure, before and after statistical control for smoking rates. The superior performance of the GIS measure, based on where people in the state live relative to mining activity, suggests that activities of the industry contribute to cancer mortality. Further confirmation of observed phenomena is necessary with person-level studies, but the results add to the body of evidence that coal mining poses environmental risks to population health in West Virginia.

The association between pollution sources and mortality risk is not a phenomenon limited to metropolitan areas. Results carry policy implications regarding the need for effective environmental standards and monitoring. Further research is needed to better understand the types and distributions of pollution in rural areas, and the health consequences that result.

BackgroundThe study conducts statistical and spatial analyses to investigate amounts and types of permitted surface water pollution discharges in relation to population mortality rates for cancer and non-cancer causes nationwide and by urban-rural setting. Data from the Environmental Protection Agency's (EPA) Discharge Monitoring Report (DMR) were used to measure the location, type, and quantity of a selected set of 38 discharge chemicals for 10,395 facilities across the contiguous US. Exposures were refined by weighting amounts of chemical discharges by their estimated toxicity to human health, and by estimating the discharges that occur not only in a local county, but area-weighted discharges occurring upstream in the same watershed. Centers for Disease Control and Prevention (CDC) mortality files were used to measure age-adjusted population mortality rates for cancer, kidney disease, and total non-cancer causes. Analysis included multiple linear regressions to adjust for population health risk covariates. Spatial analyses were conducted by applying geographically weighted regression to examine the geographic relationships between releases and mortality.ResultsGreater non-carcinogenic chemical discharge quantities were associated with significantly higher non-cancer mortality rates, regardless of toxicity weighting or upstream discharge weighting. Cancer mortality was higher in association with carcinogenic discharges only after applying toxicity weights. Kidney disease mortality was related to higher non-carcinogenic discharges only when both applying toxicity weights and including upstream discharges. Effects for kidney mortality and total non-cancer mortality were stronger in rural areas than urban areas. Spatial results show correlations between non-carcinogenic discharges and cancer mortality for much of the contiguous United States, suggesting that chemicals not currently recognized as carcinogens may contribute to cancer mortality risk. The geographically weighted regression results suggest spatial variability in effects, and also indicate that some rural communities may be impacted by upstream urban discharges.ConclusionsThere is evidence that permitted surface water chemical discharges are related to population mortality. Toxicity weights and upstream discharges are important for understanding some mortality effects. Chemicals not currently recognized as carcinogens may nevertheless play a role in contributing to cancer mortality risk. Spatial models allow for the examination of geographic variability not captured through the regression models.

This paper proposes a method to improve landscape-pollution interaction regression models through the inclusion of a variable that describes the spatial distribution of a land type with respect to the pattern of runoff within a drainage catchment. The proposed index is used as an independent variable to enhance the strength, as quantified by R² values, of regression relationships between empirical observations of in-stream pollutant concentrations and land type by considering the spatial distribution of key land-type categories within the sample point's drainage area. We present an index that adds a new dimension of explanatory power when used in conjunction with a variable describing the proportion of the land type. We demonstrate the usefulness of this index by exploring the relationship between nitrate (− 3 NO) and land type within 40 drainage sub-catchments in the Ipswich River watershed, Massachusetts. Nutrient loads associated with non-point source pollution paths are related to land type within the upstream drainage catchments of sample sites. Past studies have focused on the quantity of particular land type within a sample point's drainage catchment. Quantifying the spatial distribution of key land-type categories in terms of location on a runoff surface can improve our understanding of the relationship between sampled − 3 NO concentrations and land type. Regressions that employ the proportion of residential and agricultural land type within catchments provide a fair fit (R² = 0.67). However, we find that a regression adding a variable that indicates the spatial distribution of residential land improves the overall relationship between instream − 3 NO measurements and associated land types (R² = 0.712). We test the sensitivity of the results with respect to variations in the surface definition in order to determine the conditions under which the spatial index variable is useful.

The results support the use of the TRI as a public reporting tool and a research tool, and demonstrate that greater amounts of air and water TRI releases are related to mortality outcomes for both rural and urban populations.

Recent evidence suggests a strong link between rurality and obesity. Our hypothesis for this study is that children living in more rural areas will have higher percentages of body mass index (BMI) above the median; higher levels of total cholesterol (TC), triglycerides (TRIG) and low-density lipoprotein cholesterol (LDL); lower levels of high-density lipoprotein cholesterol (HDL); and higher systolic (SBP) and diastolic blood pressure (DBP) levels after controlling for individual covariates. Coronary Artery Risk Detection in Appalachian Communities (CARDIAC) data was used; 65,531 5 th grade children with home zip codes. Our statistical approach uses a clustered ANCOVA nesting individual children’s fasting lipid profile, BMI and BP results within their home zip coded using the Rural-Urban Continuum Code (RUCC) and controlling for child age, gender, race, and parent education. RUCC, a 9-point classification system, was reduced to 5 categories for this analysis (Figure 1). Significant nested effects were seen for all variables after controlling for covariates, including BMI, HDL, SBP, DBP, TRIG, LDL, and TC (p < 0.001). However, post-hoc comparisons disputed our hypothesis that rural areas would have significantly higher risk factors than urban or metro areas. In particular, Mid-sized metro areas had significantly higher BMI percent above ideal than Large or Small metro areas [21.45 v. 16.57 (p=0.035) and 16.65 (p=0.008)]; no differences between Rural or Urban areas. Large metro and Rural had significantly higher HDLs than Small metro, Urban, and Mid-sized metro areas. Mid-sized metro areas had higher SBP and LDL than Rural or Small Metro areas. This data directly contradicts previous literature suggesting a straight-forward link between rurality and cardiovascular risk factors. In general, Mid-sized metro areas presented the worst outcomes. Future research should include longitudinal design and mechanisms between cardiovascular risk factors and rurality, which could not be addressed in this study.

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