Mapping amyotrophic lateral sclerosis lake risk factors across northern New England

Abstract

BackgroundAmyotrophic lateral sclerosis (ALS) is a progressive, fatal neurodegenerative disease with a lifetime risk of developing as 1 in 700. Despite many recent discoveries about the genetics of ALS, the etiology of sporadic ALS remains largely unknown with gene-environment interaction suspected as a driver. Water quality and the toxin beta methyl-amino-alanine produced by cyanobacteria are suspected environmental triggers. Our objective was to develop an eco-epidemiological modeling approach to characterize the spatial relationships between areas of higher than expected ALS incidence and lake water quality risk factors derived from satellite remote sensing as a surrogate marker of exposure.MethodsOur eco-epidemiological modeling approach began with implementing a spatial clustering analysis that was informed by local indicators of spatial autocorrelation to identify locations of normalized excess ALS counts at the census tract level across northern New England. Next, water quality data for all lakes over 6 hectares (n = 4,453) were generated using Landsat TM band ratio regression techniques calibrated with in situ lake sampling. Derived lake water quality risk maps included chlorophyll-a (Chl-a), Secchi depth (SD), and total nitrogen (TN). Finally, a spatially-aware logistic regression modeling approach was executed characterizing relationships between the derived lake water quality metrics and ALS hot spots.ResultsSeveral distinct ALS hot spots were identified across the region. Remotely sensed lake water quality indicators were successfully derived; adjusted R2 values ranged between 0.62-0.88 for these indicators based on out-of-sample validation. Map products derived from these indicators represent the first wall-to-wall metrics of lake water quality across the region. Logistic regression modeling of ALS case membership in localized hot spots across the region, i.e., census tracts with higher than expected ALS counts, showed the following: increasing average SD within a radius of 30 km corresponds with a decrease in the odds of belonging to an ALS hot spot by 59%; increasing average TN within a radius of 30 km and average Chl-a concentration within a radius of 10 km correspond with increased odds of belonging to an ALS hot spot by 167% and 4%, respectively.ConclusionsThe strengths of satellite remote sensing information can help overcome traditional field limitations and spatiotemporal data gaps to provide the public health community valuable exposure data. Geographic scale needs to be diligently considered when evaluating relationships among ecological processes, risk factors, and human health outcomes. Broadly, we found that poorer lake water quality was significantly associated with increased odds of belonging to an ALS cluster in the region. These findings support the hypothesis that sporadic ALS (sALS) can, in part, be triggered by environmental water-quality indicators and lake conditions that promote harmful algal blooms.

Access to parks and physical activity: An eight country comparison

“…To maximize variance in neighborhood walkability (a construct that indicates how conducive for utilitarian walking a neighborhood is, with components including residential density and mixed land use; Frank et al, 2010) and socioeconomic status (SES), IPEN study procedures involved identifying similar neighborhoods across cities stratified as follows: higher walkable/higher SES, higher walkable/lower SES, lower walkable/higher SES, and lower walkable/lower SES. Using GIS, neighborhood walkability index scores (Adams et al, 2014; Frank et al, 2010) were created for small geographic areas (neighborhoods) in each city. The neighborhoods were delineated based on the locally available “administrative units” that were more or less equivalent to US Census block groups, with between 500 and 3000 inhabitants.…”

“…IPEN project teams in participating countries used ArcGIS software to geocode participant residences, create 1-kilometer (km) and 500-meter street-network buffers around the home address for each participant, and create park access measures (Adams et al, 2014). Street-network distances provide a better representation of the experienced distance than Euclidian distances.…”

The development of a participatory assessment technique for infrastructure: Neighborhood-level monitoring towards sustainable infrastructure systems

“…For example, in São José dos Campos Airport in Brazil, a PPGIS tool was used as a method along with public hearings to involve the surrounding community in identifying the area impacted by aircraft noise during development of land-use and occupancy codes for noise mitigation from the local airport (Santos, Arantes Gomes, & Antonio dos Santos, 2017). GIS technology was also used by HealthStreet, a community-engagement program, to identify cancer clusters (Ruktanonchai, Pindolia, Striley, Odedina, & Cottler, 2014). Similarly, the Central Corridor Friendly Streets (CCFS) was developed to improve streets in urban areas (Christiansen, 2015).…”

The Epidemiology of Amyotrophic Lateral Sclerosis in New Hampshire, USA, 2004-2007

“…Higher-than-expected ALS incidence was previously identified in localized areas of N.H. [25,26], but it is difficult to interpret this finding without an understanding of the state-wide incidence and mortality of the disease. We report the standardized incidence and mortality rates for MND in the state of N.H., and the values we have derived are similar to those reported in other industrialized nations.…”

“…We published data previously on ALS incidence and geographic variation within the north-eastern USA, including a town in New Hampshire (N.H.) with 25 times the expected incidence rate of ALS [26,27]. This study aims to describe the incidence and mortality of ALS in N.H. over 4 years.…”