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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.

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.

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.