Exploratory Spatial Mapping of the Occurrence of Antimicrobial Resistance in E. coli in the Community

Modeling spatial evolution of multi-drug resistance under drug environmental gradients

Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria based on a drug-concentration rescaling approach. We show how the resistance to drugs in space, and the consequent adaptation of growth rate, is governed by a Price equation with diffusion, integrating features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Although in many evolution models, per capita growth rate is a natural surrogate for fitness, in spatially-extended, potentially heterogeneous habitats, fitness is an emergent property that potentially reflects additional complexities, from boundary conditions to the specific spatial variation of growth rates. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical metric for characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem, λ1. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits to the relative advantage of each mutant across the environment. Our approach allows one to predict the precise outcomes of selection among mutants over space, ultimately from comparing their λ1 values, which encode a critical interplay between growth functions, movement traits, habitat size and boundary conditions. Such mathematical understanding opens new avenues for multi-drug therapeutic optimization.

Modeling spatial evolution of multi-drug resistance under drug environmental gradients

Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria, based on a rescaling approach (Gjini and Wood, 2021). We show how the resistance to drugs in space, and the consequent adaptation of growth rate is governed by a Price equation with diffusion. The covariance terms in this equation integrate features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits, to the relative advantage of each mutant across the environment. Such a mathematical understanding allows to predict the precise outcomes of selection over space, ultimately from the fundamental balance between growth and movement traits, and their diversity in a population.

Spatial clusters of extended-spectrum beta-lactamase-producing Escherichia coli causing community-onset bacteriuria due to repeat infections: cluster analysis from a large urban medical center, San Francisco, 2014-2020

BACKGROUND

Urinary tract infections caused by extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli (ESBL-E. coli) may occur as outbreaks due to common-source exposures. Yet, it is currently unknown if they cluster geographically as would be expected as part of an outbreak.

METHODS

We collected electronic health record data on all patients living in San Francisco with culture-documented community-onset E. coli bacteriuria in a safety-net public healthcare system from January 2014 to March 2020 (diagnosed < 48 h after hospital admission or in outpatient clinical settings without a hospitalization in the past 90 days). We assessed the presence of spatial clusters of (1) ESBL-E. coli bacteriuria episodes, and (2) individuals with any ESBL-E. coli bacteriuria episode, with Global and Local Moran's I. We evaluated differences in prevalence of bacteriuria recurrence by ESBL-production by Poisson regression.

RESULTS

Out of 4,304 unique individuals, we identified spatial clusters of ESBL-E. coli bacteriuria episodes (n = 461) compared to non-ESBL-E. coli bacteriuria episodes (n = 5477; Global Moran's p < 0.001). Spatial clusters of individuals with any bacteriuria caused by ESBL-E. coli were not identified (p = 0.43). Bacteriuria recurrence was more likely to occur with ESBL-E. coli (odds ratio [OR] 2.78, 95% confidence interval [95% CI] 2.10, 3.66, p < 0.001), particularly after an initial ESBL-E. coli bacteriuria episode (OR 2.27, 95% CI 1.82, 2.83, p < 0.001).

CONCLUSION

We found spatial clusters of ESBL-E. coli bacteriuria episodes. However, this was partly explained by clustering within individuals more than between individuals, as having an ESBL-E. coli bacteriuria was associated with recurrence with ESBL-E. coli. These findings may help better tailor clinical treatment of patients with recurrent urinary tract infections after an initial episode caused by ESBL-E. coli.

Spatial analysis of antimicrobial resistance in the environment. A systematic review

Antimicrobial resistance (AMR) is a global major health concern. Spatial analysis is considered an invaluable method in health studies. Therefore, we explored the usage of spatial analysis in Geographic Information Systems (GIS) in studies on AMR in the environment. This systematic review is based on database searches, a content analysis, ranking of the included studies according to the preference ranking organization method for enrichment evaluations (PROMETHEE) and estimation of data points per km2. Initial database searches resulted in 524 records after removal of duplicates. After the last stage of full text screening, 13 greatly heterogeneous articles with diverse study origins, methods and design remained. In the majority of studies, the data density was considerably less than one sampling site per km2 but exceeded 1,000 sites per km2 in one study. The results of the content analysis and ranking showed a variation between studies that primarily used spatial analysis and those that used spatial analysis as a sec ondary method. We identified two distinct groups of GIS methods. The first was focused on sample collection and laboratory testing, with GIS as supporting method. The second group used overlay analysis as the primary method to combine datasets in a map. In one case, both methods were combined. The low number of articles that met our inclusion criteria highlights a research gap. Based on the findings of this study we encourage application of GIS to its full potential in studies of AMR in the environment.

Spatial clusters of extended-spectrum beta-lactamase-producing Escherichia coli causing community-onset bacteriuria due to repeat infections: cluster analysis from a large urban medical center, San Francisco, 2014-2020

Background

Urinary tract infections caused by extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli (ESBL-E. coli) may occur as outbreaks due to common-source exposures. Yet, it is currently unknown if they cluster geographically as would be expected as part of an outbreak.

Methods

We collected electronic health record data on all patients living in San Francisco with culture-documented community-onset E. coli bacteriuria in a safety-net public healthcare system from January 2014 to March 2020 (diagnosed < 48 hours after hospital admission or in outpatient clinical settings without a hospitalization in the past 90 days). We assessed the presence of spatial clusters of (1) ESBL-E. coli bacteriuria episodes, and (2) individuals with any ESBL-E. coli bacteriuria episode, with Global and Local Moran's I. We evaluated differences in prevalence of bacteriuria recurrence by ESBL-production by Poisson regression.

Results

Out of 4,304 unique individuals, we identified spatial clusters of ESBL-E. coli bacteriuria episodes (n = 461) compared to non-ESBL-E. coli bacteriuria episodes (n = 5477; Global Moran's p < 0.001). Spatial clusters of individuals with any bacteriuria caused by ESBL-E. coli were not identified (p = 0.43). Bacteriuria recurrence was more likely to occur with ESBL-E. coli (odds ratio [OR] 2.78, 95% confidence interval [95% CI] 2.10, 3.66, p < 0.001), particularly after an initial ESBL-E. coli bacteriuria episode (OR 2.27, 95% CI 1.82, 2.83, p < 0.001).

Conclusion

We found spatial clusters of ESBL-E. coli bacteriuria episodes. However, this was partly explained by clustering within individuals more than between individuals, as having an ESBL-E. coli bacteriuria was associated with recurrence with ESBL-E. coli.

A geospatial approach to identify patterns of antibiotic susceptibility at a neighborhood level in Wisconsin, United States

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.

Geographic distribution of the major clone of extended-spectrum beta-lactamase-producing Escherichia coli infection in a pediatric community in southern Taiwan

BACKGROUND

The geographic distribution of the major clone of sequence type 131 (ST131) in extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli (E. coli) infections is not known. We analyzed the clinical features, resistance mechanisms, and geographic distribution of ESBL-producing E. coli clones in 120 children.

METHODS

We studied the 120 ESBL-producing E. coli strains from children younger than 18 years. A VITEK 2 automated system was used to determine bacterial identification and ESBL production. Sequence type was determined by multi-locus sequence typing (MLST). The genetic relationship of the ESBL-producing strains was studied using pulsed-field gel electrophoresis (PFGE). Phylogenetic group and blaCTX-M group was performed using polymerase chain reaction (PCR). Multiplex PCR for detecting the common group 9 variant, CTX-M-14, and group 1 variant, CTX-M-15, was also performed. The addresses of the 120 children were collected, and plotted on the Taiwan map.

RESULTS

The groups in the center of Kaohsiung City lived mainly in urban areas with a population density of over 10,000 people per square kilometer, and the majority of the Kaohsiung groups on the outskirts of the city center lived in suburban areas with a population density of under 6000 people per square kilometer. There was no statistically significant difference between the city center and outskirt groups in terms of clinical presentation, laboratory, and imaging data. However, more ST131 clones, major pulsotype groups, and phylogenetic group B2 strains were found in the center of Kaohsiung than on the outskirts.

CONCLUSION

ESBL-producing E. coli clones may be more challenging to treat clinically. Most infections were community-acquired, and there appeared to be major pulsotype clones, mainly in urban areas. This reinforces the necessity of environmental surveillance and sanitary procedures for ESBL-producing E. coli.

Geographic mapping of Enterobacteriaceae with extended-spectrum β-lactamase (ESBL) phenotype in Pereira, Colombia

Background

Antimicrobial resistance is an ecological and multicausal problem. Infections caused by extended-spectrum β-lactamase producing Enterobacteriaceae (ESBL-E) can be acquired and transmitted in the community. Data on community-associated ESBL-E infections/colonizations in Colombia are scarce. Georeferencing tools can be used to study the dynamics of antimicrobial resistance at the community level.

Methods

We conducted a study of geographic mapping using modern tools based on geographic information systems (GIS). Two study centers from the city of Pereira, Colombia were involved. The records of patients who had ESBL-producing Enterobacteriaceae were reviewed. Antimicrobial susceptibility testing and phenotypic detection of ESBL was done according to CLSI standards.

Results

A population of 415 patients with community-acquired infections/colonizations and 77 hospital discharges were obtained. Geographic distribution was established and heat maps were created. Several hotspots were evidenced in some geographical areas of the south-west and north-east of the city. Many of the affected areas were near tertiary hospitals, rivers, and poultry industry areas.

Conclusions

There are foci of antimicrobial resistance at the community level. This was demonstrated in the case of antimicrobial resistance caused by ESBL in a city in Colombia. Causality with tertiary hospitals in the city, some rivers and the poultry industry is proposed as an explanation of the evidenced phenomenon. Geographic mapping tools are useful for monitoring antimicrobial resistance in the community.

Spatial relationships between small-holder farms coupled with livestock management practices are correlated with the distribution of antibiotic resistant bacteria in northern Tanzania

We examined the spatial distribution of antibiotic-resistant coliform bacteria amongst livestock from three distinct cultural groups, where group-level differences in practices (e.g., antibiotic use) may influence the magnitude of antibiotic resistance, while livestock interactions (e.g., mixing herds, shared markets) between these locations may reduce heterogeneity in the distribution of antibiotic resistant bacteria. Data was collected as part of a larger study of antibiotic-resistance in northern Tanzania. Simple regression and generalized linear regression were used to assess livestock management and care practices in relation to the prevalence of multidrug-resistant (MDR) coliform bacteria. Simple and multivariable logistic regression were then used to identify how different management practices affected the odds of households being found within MDR "hotspots." Households that had a higher median neighbourhood value within a 3000 m radius showed a significant positive correlation with livestock MDR prevalence (β = 4.33, 95% CI: 2.41-6.32). Households were more likely to be found within hotspots if they had taken measures to avoid disease (Adjusted Odds Ratio (AOR) 1.53, CI: 1.08-2.18), and if they reported traveling less than a day to reach the market (AOR 2.66, CI: 1.18-6.01). Hotspot membership was less likely when a greater number of livestock were kept at home (AOR 0.81, CI: 0.69-0.95), if livestock were vaccinated (AOR 0.32, CI: 0.21-0.51), or if distance to nearest village was greater (AOR 0.46, CI: 0.36-0.59). The probability of MDR increases when herds are mixed, consistent with evidence for passive transmission of resistant bacteria between animals. Reduced MDR with vaccination is consistent with many studies showing reduced antibiotic use with less disease burden. The neighbourhood effect has implications for design of intervention studies.

Mapping and Analysing Potential Sources and Transmission Routes of Antimicrobial Resistant Organisms in the Environment using Geographic Information Systems-An Exploratory Study

Antimicrobial resistance (AMR) is one of the leading threats to human health worldwide. The identification of potential sources of antimicrobial resistant organisms (AROs) and their transmission routes in the environment is important for improving our understanding of AMR and to inform and improve policy and monitoring systems, as well as the identification of suitable sampling locations and potential intervention points. This exploratory study uses geographic information systems (GIS) to analyse the spatial distribution of likely ARO sources and transmission routes in four local authority areas (LAAs) in Ireland. A review of relevant spatial data in each LAA, grouped into themes, and categorised into sources and transmission routes, was undertaken. A range of GIS techniques was used to extract, organise, and collate the spatial data into final products in the form of thematic maps for visual and spatial analysis. The results highlight the location of 'clusters' at increased risk of harbouring AMR in each LAA. They also demonstrate the relevance of aquatic transmission routes for ARO mobility and risk of human exposure. The integration of a GIS approach with expert knowledge of AMR is shown to be a useful tool to gain insights into the spatial dimension of AMR and to guide sampling campaigns and intervention points.

Improving Antimicrobial Prescribing: A Multinomial Model Identifying Factors Associated With First- and Second-Line Prescribing

BACKGROUND/OBJECTIVES

Broad-spectrum, second-line antimicrobials may be prescribed when initial first-line options prove ineffective. This study compares prescribing practices and identifies potential influencing factors for first- and second-line antimicrobials in long-term care facilities.

DESIGN

Point prevalence survey of health care-associated infections and antimicrobial use in long-term care facilities (HALT), expanded by additional data collection.

SETTING

Long-term care facilities in Ireland.

PARTICIPANTS

Of long-term care facilities that participated in the HALT study 2016, additional data provided by 77 facilities with a record of 3677 residents.

MEASUREMENT

On the survey date, an institutional questionnaire was completed by each participating long-term care facility, and resident questionnaires were completed only for those residents who met a health care-associated infection surveillance definition and/or were prescribed a systemic antimicrobial. All participating long-term care facilities were contacted at a later time point to provide limited anonymized data (age, sex, urinary catheterization, and disorientation) on all current residents. These additional data were matched to the original data set, facilitating multilevel multinominal logistic regression (first-line/second-line/no antimicrobial).

RESULTS

Of 3677 residents in 77 long-term care facilities, 381 (10%) were prescribed systemic antimicrobials on the survey day. Of those, 46% were categorized as second-line choices, with substantial interfacility variation observed with regard to prescription of first- versus second-line antimicrobials. The odds of a second-line antimicrobial prescription for a resident doubled when comparing the highest with the lowest prescribing long-term care facilities (median odds ratio = 2.0, credibility interval = 1.5-2.9). Male residents were less often prescribed first-line antimicrobials [odds ratio (OR) = 0.6, 95% confidence interval (CI) = 0.4-0.9, P = .02]. Long-term care facilities that reported the provision of education on antimicrobial prescribing use significantly less second-line antimicrobials (OR = 0.2, 95% CI = 0.1-0.7, P = .02). Females and residents with a urinary catheter were more likely to receive first-line antimicrobials.

CONCLUSION/IMPLICATIONS

The use of second-line antimicrobials is common practice in long-term care facilities, but education and training on appropriate antimicrobial use has the potential to reduce second-line antimicrobial prescribing, improve patients' outcomes, and reduce antimicrobial resistance.

Spatial molecular epidemiology of carbapenem-resistant and New Delhi metallo beta-lactamase (blaNDM)-producing Escherichia coli in the piglets of organized farms in India

AIM

A cross-sectional study was conducted in 10 government-organized pig farms between 2014 and 2016 representing seven states of India to understand the epidemiology of carbapenem resistance in the Escherichia coli.

METHODS AND RESULTS

In this study, fecal sample (n = 673) from non-diarrheic (n = 501) and diarrheic (n = 172) piglets were processed for isolation of carbapenem resistant E. coli. Of 673, E. coli isolate (n = 112) was genotyped for confirming the carbapenem resistance and associated virulence factors. Of the 112 isolates, 23 were phenotypically resistant to carbapenem and 8 were carrying the New Delhi metallo beta-lactamase (blaNDM) gene. The carbapenem-resistant isolates also produced extended spectrum beta-lactamases and were multidrug resistant. The PCR-based pathotyping revealed the presence of stx1, stx2, eae and hlyA genes. The enterobacterial repetitive intergenic consensus PCR dendrogram analysis of the isolates yielded three distinct clusters. The statistical analysis revealed no association between carriages of carbapenem-resistant E. coli in different breed of piglets however, location, sex, health status of piglets and age showed significant difference. The spatial analysis with SaTScan helped in identification of carbapenem-resistant clusters.

CONCLUSIONS

The presence of carbapenem resistant E. coli isolates with virulence genes in the piglet poses a potential public health risk through possible access and spread via the food chain and environment. Efflux pump may also play an important role in carbapenem resistance in piglet E. coli isolates. Furthermore, identification of risk factors in relation to spatial clusters will help in designing preventive strategies for reducing the risk of spread of carbapenem resistant bacteria.

SIGNIFICANCE AND IMPACT OF THE STUDY

1. Piglets harbor carbapenem resistant E. coli and have great public health significance. 2. Apart from carbapenemase, efflux pump is also important for carbapenem resistance. 3. This is the first report of blaNDM in the piglets from India.

Geographic information systems and multivariate analysis to evaluate fecal bacterial pollution in coastal waters of Andaman, India

Urbanization of coastal areas in recent years has driven us to consider a new approach for visually delineating sites that are contaminated with fecal bacteria (FB) in the coastal waters of the Andaman Islands in India. Geo-spatial analysis demarcated harbor, settlement, and freshwater/discharge influenced zones as hot spots for FB, while the open sea was demarcated as a cold spot. The land use types, such as developed and agriculture, with more anthropogenic activities increasing the FB counts while open sea showed the least FB. Box whisker plot indicated an increasing FB trend in the coastal waters during monsoon. Furthermore, principal component analysis revealed 67.35%, 78.62% and 70.43% of total variance at Port Blair, Rangat and Aerial bays, respectively. Strong factor loading was observed for depth (0.95), transparency (0.93), dissolved oxygen (0.93) and fecal streptococci (0.85). Distance proximity analysis revealed that fecal contaminations diluted significantly (P < 0.05) at the distance of 2.1 km toward the deeper or open sea water. This study demonstrates the effectiveness of an integrated approach in identifying the sources of fecal contamination and thus helping in better monitoring and management of coastal waters.

An outbreak of multi-drug resistant Escherichia coli urinary tract infection in an elderly population: a case-control study of risk factors

BACKGROUND

Prevention of infection due to multi-drug resistant organisms is particularly challenging because of the spread of resistant bacteria beyond hospitals into the community, including nursing homes. This study aimed to identify risk factors for the acquisition of a multidrug resistant (MDR) Escherichia coli in a local outbreak.

METHODS

Study participants were all aged over 65 years. Cases had the MDR E. coli isolated from a routine urine sample, and controls had a urine sample submitted to the laboratory in the same time period but the MDR E. coli was not isolated. Information from clinical records was used to identify risk factors both in the hospital and the community setting for acquisition of the MDR E. coli.

RESULTS

76 cases and 156 controls were identified and included in the study. In a multivariate analysis, risk factors statistically significantly associated with acquisition of the MDR E. coli were female gender (adjusted OR 3.2; 95 % confidence interval 1.5-6.9), level of care (high dependency OR 7.5; 2.2-25.7) compared with living independently), and in hospital prescription of antimicrobials to which the MDR E. coli was resistant (OR 5.6; 2.5-12.9).

CONCLUSIONS

The major risk factors for the acquisition of a MDR E. coli were found to be residence in a nursing home and in-hospital prescription of antimicrobials to which the MDR E. coli was resistant. This emphasises that prevention of transmission of MDROs within a community needs to involve both hospitals and also other healthcare organizations, in this case nursing homes.