The global threat of antimicrobial resistance (AMR) varies regionally. This study explores whether geospatial analysis and data visualization methods detect both clinically and statistically significant variations in antibiotic susceptibility rates at a neighborhood level. This observational multicenter geospatial study collected 10 years of patient-level antibiotic susceptibility data and patient addresses from three regionally distinct Wisconsin health systems (UW Health, Fort HealthCare, Marshfield Clinic Health System [MCHS]). We included the initial Escherichia coli isolate per patient per year per sample source with a patient address in Wisconsin (N = 100,176). Isolates from U.S. Census Block Groups with less than 30 isolates were excluded (n = 13,709), resulting in 86,467 E. coli isolates. The primary study outcomes were the results of Moran’s I spatial autocorrelation analyses to quantify antibiotic susceptibility as spatially dispersed, randomly distributed, or clustered by a range of − 1 to + 1, and the detection of statistically significant local hot (high susceptibility) and cold spots (low susceptibility) for variations in antibiotic susceptibility by U.S. Census Block Group. UW Health isolates collected represented greater isolate geographic density (n = 36,279 E. coli, 389 = blocks, 2009–2018), compared to Fort HealthCare (n = 5110 isolates, 48 = blocks, 2012–2018) and MCHS (45,078 isolates, 480 blocks, 2009–2018). Choropleth maps enabled a spatial AMR data visualization. A positive spatially-clustered pattern was identified from the UW Health data for ciprofloxacin (Moran’s I = 0.096, p = 0.005) and trimethoprim/sulfamethoxazole susceptibility (Moran’s I = 0.180, p < 0.001). Fort HealthCare and MCHS distributions were likely random. At the local level, we identified hot and cold spots at all three health systems (90%, 95%, and 99% CIs). AMR spatial clustering was observed in urban areas but not rural areas. Unique identification of AMR hot spots at the Block Group level provides a foundation for future analyses and hypotheses. Clinically meaningful differences in AMR could inform clinical decision support tools and warrants further investigation for informing therapy options.
Background The global threat of antimicrobial resistance (AMR) varies regionally. Regional differences may be related to socio-economic factors such as the Area Deprivation Index (ADI) score. Our hypothesis is that AMR spatial distribution is not random. Methods Patient level antibiotic susceptibility data was collected from three regionally distinct Wisconsin health systems (UW Health, Fort HealthCare, Marshfield Clinic Health System [MCHS]). Patient addresses were geocoded to coordinates and joined with US Census Block Groups. For each culture source, we included the initial E. coli isolate per patient per year with a patient address in Wisconsin. Percent susceptibility was calculated by block group. Spatial autocorrelation was determined by Global Moran’s I, which quantifies the attribute being analyzed as spatially dispersed, randomly distributed, or clustered by a range of −1 to +1. Linear regression correlated ADI to susceptibility. Hot spot analysis identified blocks with statistically significant higher and lower susceptibility (Figure 1). Figure 1. Geographic example of hot spot analysis and interpretation. Results The UW Health results included more urban areas, more block groups and greater isolate geographic density (n = 44,629 E. coli, 2009-2018), compared to Fort HealthCare (n = 6,065 isolates, 2012-2018) and MCHS (50,405 isolates, 2009-2018). A positive spatially clustered pattern was identified from the UW Health data for ciprofloxacin (Moran’s I = 0.096, p = 0.005) and trimethoprim/sulfamethoxazole (TMP/SMX) susceptibility (Moran’s I = 0.180, p < 0.001; Figures 2-3). Fort HealthCare and MCHS distribution was likely random for TMP/SMX and ciprofloxacin by Moran’s I. Linear regression of ADI (scale 1-10, least to most disadvantaged) and susceptibility did not find significance, but susceptibility was lower in more disadvantaged block groups. At the local level, we identified hot and cold spots with 90%, 95%, and 99% confidence, with more hot spots in rural regions. Figure 2. Results from Moran’s Index analysis identifying geographically clustered ciprofloxacin susceptibility results. Figure 3. Results from Moran’s Index analysis identifying geographically clustered sulfamethoxazole/trimethoprim susceptibility results. Conclusion Overall, Moran’s I analysis is more able to identify a clustered pattern in urban versus rural areas. Yet, the local hot spot results indicate that variations in antibiotic susceptibility may be more common in rural areas. The results are limited to data from patients with access to the health systems included. Disclosures Warren Rose, PharmD, MPH, Merck (Grant/Research Support)Paratek (Grant/Research Support, Advisor or Review Panel member)
Background ‘One Health’ recognizes the interconnectivity of humans with their production and companion animals, and the environment. Emergence and transmission of antimicrobial resistance (AMR) within and between these compartments is a recognized global threat that requires further understanding to design interventions protecting both human and animal health. In this study we identified resistance gene targets and clonotypes of Escherichia coli recovered from human, canine and bovine hosts and applied non-linear dimensionality reduction and visualization techniques to identify genetic relationships that may otherwise be unobservable within the data. Methods Non-duplicative E. coli isolates (N=3398; see Figure captions) were collected from humans, canines, bovines from the Midwest USA. We identified beta-lactamase gene targets for third-generation cephem multidrug resistant isolates and performed clonotype analysis on each. Uniform Manifold Approximation (UMAP) was used to create a two-dimensional “map” of the high dimensional space of the genetic results to identify similarities between both infecting and colonizing isolates, and between susceptible and resistant isolates in humans and animals in the study region (see Figure captions). Results The resulting “map” highlights similarities in: 1) genetic patterns of AMR among animals and humans, and 2) links between isolates that are infecting and colonizing in humans and canines (Figures 1-2). Our results suggest that there is strong genetic overlap linking human and animal patterns of AMR. UMAP also identified genetic segments that are unique to humans, distinct outliers, and suggest limited exchange among the neighboring counties (Figure 3). Figure 1. Distribution of infection and surveillance isolates shows distinct clusters and distribution within host species in the UMAP space. Each panel of the figure shows the same UMAP space with the labeled species in color and the other points in grey as a reference. The UMAP space is a non-linear two-dimensional representation of the genetic information contained in the clonotype analysis. UMAP is a dimensionality reduction technique similar to principal component analysis (PCA), except that it uses a non-linear combination of the underlying dimensions, which highlights the local structure and grouping of the cases. For more details see: Diaz-Papkovich, A., Anderson-Trocmé, L., & Gravel, S. (2021). A review of UMAP in population genetics. Journal of Human Genetics, 66(1), 85–91. Infection isolates: no bovine isolates tested, canine n=190, human n=115. Surveillance isolates: bovine n=175, canine n=747, human n=2171. Figure 2. Distribution of resistant and susceptible isolates shows the resistant cases are distributed in small clusters surrounding a large cluster of predominantly susceptible cases. This figure plots the same cases on the same UMAP space as Figure 1. The only difference is the color that distinguishes between resistant and susceptible cases. Resistant isolates: bovine n=91, canine n=300; human n=238. Susceptible isolates: bovine n=84. canine=637, human n=2048. Figure 3. The proportion of cases from each cluster in four adjoining counties varies considerably. The dark bars show the proportion of cases falling into each cluster for each county. The light bars provide a reference point for interpreting the dark bars by showing the proportion of cases falling into each cluster across all four counties. When the dark bars exceed the light bars it indicates that the proportion of cases in that cluster exceeds that of the neighboring counties, such as Cluster 2 for Taylor county and Cluster 3 for Marathon county. All counties shown include a population of at least 20,000. These stipulations are in compliance with federal (HIPAA) guidelines. Conclusion The results support that UMAP is a valuable tool for visualizing genetic AMR links across species. Human-animal transmission is likely for disparate and common clonotypes. Disclosures All Authors: No reported disclosures
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