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Purpose Uncertainty is not always well captured, understood, or modeled properly, and can bias the robustness of complex relationships, such as the association between the environment and public health through exposure, estimates of geographic accessibility and cluster detection, to name a few. Methods We review current challenges and future opportunities as geospatial data and analyses are applied to the field of public health. We are particularly interested in the sources of uncertainty in geospatial data and how this uncertainty may propagate in spatial analysis. Results We present opportunities to reduce the magnitude and impact of uncertainty. Specifically, we focus on (1) the use of multiple reference data sources to reduce geocoding errors, (2) the validity of online geocoders and how confidentiality (e.g. HIPAA) may be breached, (3) use of multiple reference data sources to reduce geocoding errors, (4) the impact of geoimputation techniques on travel estimates, (5) residential mobility and how it affects accessibility metrics and clustering, and (6) modeling errors in the American Community Survey. Our paper discusses how to communicate spatial and spatiotemporal uncertainty, and high performance computing to conduct large amounts of simulations to ultimately increase statistical robustness for studies in public health. Conclusions Our paper contributes to recent efforts to fill in knowledge gaps at the intersection of spatial uncertainty and public health.

We compared severe acute respiratory syndrome coronavirus 2 seroprevalence estimated from commercial laboratory residual sera and a community household survey in metropolitan Atlanta during April and May 2020 and found these 2 estimates to be similar (4.94% vs 3.18%). Compared with more representative surveys, commercial sera can provide an approximate measure of seroprevalence.

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 Workers employed in the coal mining sector are at increased risk of respiratory diseases, including coal workers' pneumoconiosis (CWP). We investigated the prevalence of CWP and its association with sociodemographic factors among Medicare beneficiaries. Methods We used 5% Medicare Limited Data Set claims data from 2011 to 2014 to select Medicare beneficiaries with a diagnosis of ICD-9-CM 500 (CWP). We aggregated the data by county and limited our analysis to seven contiguous states: Illinois, Indiana, Kentucky, Ohio, Pennsylvania, Virginia, and West Virginia. We estimated county-level prevalence rates using total Medicare beneficiaries and miners as denominators and performed spatial hotspot analysis. We used negative binomial regression analysis to determine the association of county-wise sociodemographic factors with CWP. Results There was significant spatial clustering of CWP cases in Kentucky, Virginia, and West Virginia. Spatial clusters of 210 and 605 CWP cases representing an estimated 4200 to 12 100 cases of Medicare beneficiaries with CWP were identified in the three states. Counties with higher poverty levels had a significantly elevated rate of CWP (adjusted rate ratios [RR]: 1.15; 95% CI, 1.12-1.18). There was a small but significant association of CWP with the county-wise catchment area. Rurality was associated with a more than three-fold elevated rate of CWP in the unadjusted analysis (RR: 3.28, 95% CI, 2.22-4.84). However, the rate declined to 1.45 (95% CI, 1.04-2.01) after adjusting for other factors in the analysis. Conclusions We found evidence of significant spatial clustering of CWP among Medicare beneficiaries living in the seven states of the USA.

ObjectiveWe modelled individual vulnerability to STI using personal history of infection and neighbourhood characteristics.MethodsRetrospective chlamydia and gonorrhoea data of reported confirmed cases from Kalamazoo County, Michigan for 2012 through 2014 were analysed. Unique IDs were generated from the surveillance data in collaboration with local health officials to track the individual STI histories. We then examine the concept that individuals with similar STI histories form a ‘peer’ group. These peer group include: (1) individuals with a single chlamydia; (2) individuals with single gonorrhoea; (3) individuals with repeated cases of one type of STI and (4) individuals that were diagnosed with both infections during the study period. Using Kernel density estimation, we generated densities for each peer group and assigned the intensity of the infection to the location of the individual. Finally, the individual vulnerability was characterised through ordinary least square regression (OLS) using demographics and socioeconomic variables.ResultsIn an OLS regression adjusted for frequency of infection, individual vulnerability to STI was only consistently significant for race and neighbourhood-level socioeconomic status (SES) in all the models under consideration. In addition, we identified six areas in three townships in Kalamazoo County that could be considered for unique interventions based on overlap patterns among peer groups.ConclusionsThe results provide evidence that individual vulnerability to STI has some dependency on individual contextual (race) and exogenous factors at the neighbourhood level such as SES, regardless of that individual’s personal history of infection. We suggest place-based intervention strategies be adopted for planning STI interventions instead of current universal screening of at-risk populations.

We determine the impact of residential mobility in the prevalence and transmission dynamics of sexually transmitted infections. We illustrate our approach on reported chlamydia infections obtained from the Michigan Disease Surveillance System for Kalamazoo County, USA, from 2006 to 2014. We develop two scenarios, one with fixed residential addresses and one considering residential mobility. We then compare the resulting space-time clusters and relative risk (RR) of infection. The space-time scan statistics showed increased RR in an area with previously low risk of sexually transmitted infections. In addition, even though the spatial extent of the three clusters identified did not change significantly at the scale we conducted our analysis at, the temporal extent (duration) did exhibit significant changes and could be considered for unique interventions. The results indicate that residential mobility has some dependency on the prevalence and transmission dynamics of sexually transmitted infections to new areas. We suggest that strategies adopted to reduce the burden of sexually transmitted infections take into consideration the relatively high residential mobility of at-risk populations to reduce spreading the infections to new areas.

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.