Airborne lead and polychlorinated biphenyls (PCBs) are associated with amyotrophic lateral sclerosis (ALS) risk in the U.S.

Amyotrophic lateral sclerosis (ALS) risk is linked to environmental exposures. The National Emissions Inventory (NEI) database compiles mandatory reports of levels of airborne contaminants from a variety of stationary and mobile pollution sources across the U.S. The objective of this study was to identify airborne contaminants that may be associated with ALS etiology for future study. We geospatially estimated exposure to airborne contaminants as risk factors for ALS in a nationwide large de-identified medical claims database, the SYMPHONY Integrated Dataverse®. We extracted zip3 of residence at diagnosis of ~26,000 nationally distributed ALS patients and n = 3 non-ALS controls matched per case for age and sex. We individually aggregated the median levels of each of 268 airborne contaminants recorded in the NEI database for 2008 to estimate local residential exposure. We randomly broke the dataset into two independent groups to form independent discovery and validation cohorts. Contaminants associated with increased ALS risk in both the discovery and validation studies included airborne lead (false discovery rate (FDR) = 0.00077), and polychlorinated biphenyls (PCBs), such as heptachlorobiphenyl (FDR = 3.60E-05). Small aircraft were the largest source of airborne lead, while the PCB emissions came from certain power plants burning biomass, and from industrial boilers. Associations with residential history of lead exposure were confirmed in two additional cohorts (10 year top quartile in New Hampshire/Vermont OR 2.46 95% CI 1.46-2.80, and in Ohio OR 1.60 95% CI 1.28-1.98). The results of our geospatial analysis support neurotoxic airborne metals and PCBs as risk factors for ALS.

ALS risk factors: Industrial airborne chemical releases.

Most amyotrophic lateral sclerosis (ALS) cases are sporadic (∼90%) and environmental exposures are implicated in their etiology. Large industrial facilities are permitted the airborne release of certain chemicals with hazardous properties and report the amounts to the US Environmental Protection Agency (EPA) as part of its Toxics Release Inventory (TRI) monitoring program. The objective of this project was to identify industrial chemicals released into the air that may be associated with ALS etiology. We geospatially estimated residential exposure to contaminants using a de-identified medical claims database, the SYMPHONY Integrated Dataverse®, with ∼26,000 nationally distributed ALS patients, and non-ALS controls matched for age and gender. We mapped TRI data on industrial releases of 523 airborne contaminants to estimate local residential exposure and used a dynamic categorization algorithm to solve the problem of zero-inflation in the dataset. In an independent validation study, we used residential histories to estimate exposure in each year prior to diagnosis. Air releases with positive associations in both the SYMPHONY analysis and the spatio-temporal validation study included styrene (false discovery rate (FDR) 5.4e-5), chromium (FDR 2.4e-4), nickel (FDR 1.6e-3), and dichloromethane (FDR 4.8e-4). Using a large de-identified healthcare claims dataset, we identified geospatial environmental contaminants associated with ALS. The analytic pipeline used may be applied to other diseases and identify novel targets for exposure mitigation. Our results support the future evaluation of these environmental chemicals as potential etiologic contributors to sporadic ALS risk.

Pesticides applied to crops and amyotrophic lateral sclerosis risk in the U.S.

BACKGROUND: Environmental exposures are implicated in the etiology of amyotrophic lateral sclerosis (ALS). Application of insecticides, herbicides, and fungicides with neurotoxic properties to crops is permitted in the U.S., however reporting of the quantities is government mandated. OBJECTIVE: To identify pesticides that may be associated with ALS etiology for future study. METHODS: We geospatially estimated exposure to crop-applied pesticides as risk factors for ALS in a large de-identified medical claims database, the SYMPHONY Integrated Dataverse®. We extracted residence at diagnosis of ∼26,000 nationally distributed ALS patients, and matched non-ALS controls. We mapped county-level U.S. Geological Survey data on applications of 423 pesticides to estimate local residential exposure. We randomly broke the SYMPHONY dataset into two groups to form independent discovery and validation cohorts, then confirmed top hits using residential history information from a study of NH, VT, and OH. RESULTS: Pesticides with the largest positive statistically significant associations in both the discovery and the validation studies and evidence of neurotoxicity in the literature were the herbicides 2,4-D (OR 1.25 95 % CI 1.17-1.34) and glyphosate (OR 1.29 95 %CI 1.19-1.39), and the insecticides carbaryl (OR 1.32 95 %CI 1.23-1.42) and chlorpyrifos (OR 1.25 95 %CI 1.17-1.33). SIGNIFICANCE: Our geospatial analysis results support potential neurotoxic pesticide exposures as risk factors for sporadic ALS. Focused studies to assess these identified potential relationships are warranted.

Estimation of Environmental Exposure: Interpolation, Kernel Density Estimation, or Snapshotting.

In environmental health researches and practices, spatial analysis became an important approach to estimation of environmental exposure of human subjects under concern. A typical situation in this kind of application is that the data of pollution are available only at certain locations, and thus inference is needed to convert a limited number of values at discrete locations into a continuous surface. This paper intends to clarify the distinction among three methods that can be used to achieve this conversion, namely interpolation, kernel density estimation (KDE), and snapshotting. Due to the apparent similarity of the three, they may cause confusions that lead to misuses. We compare and contrast the three methods, in terms of nature of the input data, mathematical process of the inference, and essential meaning of the output. For each method we suggest appropriate applications within the context of estimation of environmental exposure.