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Chronic exposure to arsenic-contaminated drinking water is detrimental to human health. We develop an autologistic regression model to evaluate if the geology, pH, and well depth can improve our ability to predict the presence of arsenic at and above detectable levels (≥ 5 µg/L) found in private wells. We use arsenic samples measured in private well water across Gaston County, North Carolina, from 2011 to 2017. We use kriging to map the probability of arsenic at detectable levels across Gaston County. Arsenic at detectable levels was reported at 78 private wells. The median pH for samples containing detectable levels of arsenic was 7.3 and for samples with arsenic

Critical to identifying the risk of environmentally driven disease is an understanding of the cumulative impact of environmental conditions on human health. Here we describe the methodology used to develop an environmental burden index (EBI). The EBI is calculated at U.S. census tract level, a finer scale than many similar national-level tools. EBI scores are also stratified by tract land cover type as per the National Land Cover Database (NLCD), controlling for urbanicity. The EBI was developed over the course of four stages: 1) literature review to identify potential indicators, 2) data source acquisition and indicator variable construction, 3) index creation, and 4) stratification by land cover type. For each potential indicator, data sources were assessed for completeness, update frequency, and availability. These indicators were: (1) particulate matter (PM2.5), (2) ozone, (3) Superfund National Priority List (NPL) locations, (4) Toxics Release Inventory (TRI) facilities, (5) Treatment, Storage, and Disposal (TSD) facilities, (6) recreational parks, (7) railways, (8) highways, (9) airports, and (10) impaired water sources. Indicators were statistically normalized and checked for collinearity. For each indicator, we computed and summed percentile ranking scores to create an overall ranking for each tract. Tracts having the same plurality of land cover type form a 'peer' group. We re-ranked the tracts into percentiles within each peer group for each indicator. The percentile scores were combined for each tract to obtain a stratified EBI. A higher score reveals a tract with increased environmental burden relative to other tracts of the same peer group. We compared our results to those of related indices, finding good convergent validity between the overall EBI and CalEnviroScreen 4.0. The EBI has many potential applications for research and use as a tool to develop public health interventions at a granular scale.

Public water systems must be tested frequently for coliform bacteria to determine whether other pathogens may be present, yet no testing or disinfection is required for private wells. In this paper, we identify whether well age, type of well, well depth, parcel size, and soil ratings for a leachfield can predict the probability of detecting coliform bacteria in private wells using a multivariate logistic regression model. Samples from 1163 wells were analyzed for the presence of coliform bacteria between October 2017 and October 2019 across Gaston County, North Carolina, USA. The maximum well age was 30 years, and bored wells (median age = 24 years) were older than drilled wells (median age = 19 years). Bored wells were shallower (mean depth = 18 m) compared to drilled wells (mean depth = 79 m). We found coliform bacteria in 329 samples, including 290 of 1091 drilled wells and 39 of 72 bored wells. The model results showed bored wells were 4.76 times more likely to contain bacteria compared to drilled wells. We found that the likelihood of coliform bacteria significantly increased with well age, suggesting that those constructed before well standards were enforced in 1989 may be at a higher risk. We found no significant association between poorly rated soils for a leachfield, well depth, parcel size and the likelihood of having coliform in wells. These findings can be leveraged to determine areas of concern to encourage well users to take action to reduce their risk of drinking possible pathogens in well water.

Background Travel time as a proxy for spatial accessibility in health care location-allocation studies can be particularly relevant when spatially concentrated population have a repeating pattern of infections, or a shorter return interval for services. This travel time based accessibility study focuses on a population identified as having repeat sexually transmitted infections. This population has been shown to be strongly associated with low socioeconomic status, which often corresponds to substantive reliance on public transit. Public health intervention studies barely consider transit when quantifying accessibility. Moreover, common frameworks used in these studies mostly focus on efficient allocation of services and usually disregard equity issues. Methods This study proposes a Pareto optimality approach to recommend locations that are multimodal accessible and allow equitable and efficient access to services. A simulated at-risk population, drawn from known core areas of repeat sexually transmitted infections in Kalamazoo County, Michigan, is examined. A raster-based drive time and a transit time model were developed using ArcGIS. Bi-objective optimization models were then developed to balance efficiency (by minimizing average travel time of the cohort) and equity (by minimizing variations of travel time from all simulated households). Results This raster-based cost optimization approach successfully identifies potential locations that are efficiently and equitably accessible using different transportation modes. A range of alternative solutions (Pareto frontiers) spatially and statistically enable decision makers to select from multiple locations and assess acceptable limits to travel for a given population. In our example, choosing a location, T2 over T1 means reducing the average travel time by 2.19 minutes by conceding only 11.94% increase in standard deviation of travel time. Additionally, T2 reduces the average travel time by 8.91 minutes and standard deviation by 3.7 minutes when comparing to existing facility location. In general, the spatial area bounded by the minimum average and minimum standard deviation location for a frontier line would represent the constraints to intervention location. The area then becomes a usable metric to measure transit versus private vehicle accessibility. Conclusions Improving spatial access to health facilities is important in reducing the prevalence of disease. Therefore, multimodal accessibility should be emphasized in future intervention placement research. The analysis of individual's travel time from distinct households to facility locations helps to address the inherent mismatch between current statistical methods that require detection of significant densities and the reality of individuals located on a street network or constrained by a particular transportation modality.