https://doi.org/10.4081/gh.2023.1168
Spatial analysis of antimicrobial resistance in the environment. A systematic review
Submitted: 14 November 2022
Accepted: 20 March 2023
Published: 25 May 2023
Accepted: 20 March 2023
Abstract Views: 1643
PDF: 529
Supplementary Materials: 120
HTML: 39
Supplementary Materials: 120
HTML: 39
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
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.
Agga GE, Cook KL, Netthisinghe AMP, Gilfillen RA, Woosley PB, Sistani KR, 2019, Persistence of antibiotic resistance genes in beef cattle backgrounding environment over two years after cessation of operation. PloS one 14:e0212510.
DOI: https://doi.org/10.1371/journal.pone.0212510
Angelo Lellis Moreira M, dos Santos M, Francisco Simoes Gomes C, 2020, promethee123: PROMETHEE I, II, and III Methods. R package version 0.1.0.
Arya BK, Robert D, Das Bhattacharya S, Mukhopadhyay J, 2013, A framework for web based geographical information systems for country wide antimicrobial resistance monitoring. Health Policy Technol 2:85–93.
DOI: https://doi.org/10.1016/j.hlpt.2013.03.005
Baquero F, Martínez J-L, Cantón R, 2008, Antibiotics and antibiotic resistance in water environments. Curr Opin Biotechnol 19:260–265.
DOI: https://doi.org/10.1016/j.copbio.2008.05.006
Behzadian M, Kazemzadeh RB, Albadvi A and Aghdasi M, 2010, PROMETHEE: A comprehensive literature review on methodologies and applications. Eur J Oper Res 2001:198–215.
DOI: https://doi.org/10.1016/j.ejor.2009.01.021
Bueno I, Beaudoin A, Arnold WA, Kim T, Frankson LE, LaPara TM, Kanankege K, Wammer KH, Singer RS, 2021, Quantifying and predicting antimicrobials and antimicrobial resistance genes in waterbodies through a holistic approach: a study in Minnesota, United States. Sci Rep 11:1–15.
DOI: https://doi.org/10.1038/s41598-021-98300-5
Bueno I, Rodríguez A, Beaudoin A, Arnold WA, Wammer KH, de la Torre A, Singer RS, 2022, Identifying the spatiotemporal vulnerability of soils to antimicrobial contamination through land application of animal manure in Minnesota, United States. Sci Tot Environ 832:155050.
DOI: https://doi.org/10.1016/j.scitotenv.2022.155050
Bueno I, Williams-Nguyen J, Hwang H, Sargeant JM, Nault AJ, Singer RS, 2017, Impact of point sources on antibiotic resistance genes in the natural environment: a systematic review of the evidence. Anim Health Res Rev 18:112–7.
DOI: https://doi.org/10.1017/S146625231700007X
Bueno I, Williams-Nguyen J, Hwang H, Sargeant JM, Nault AJ, Singer RS, 2018, Systematic Review: Impact of point sources on antibiotic-resistant bacteria in the natural environment. Zoonoses Public Health 65:e162–e184.
DOI: https://doi.org/10.1111/zph.12426
Chique C, Cullinan J, Hooban B and Morris D, 2019, Mapping and Analysing Potential Sources and Transmission Routes of Antimicrobial Resistant Organisms in the Environment using Geographic Information Systems—An Exploratory Study. Antibiotics (Basel) 2019;8:16.
DOI: https://doi.org/10.3390/antibiotics8010016
Czekalski N, Diez EG, Buergmann H, 2014, Wastewater as a point source of antibiotic-resistance genes in the sediment of a freshwater lake. ISME J 8:1381–90.
DOI: https://doi.org/10.1038/ismej.2014.8
Davies J, Davies D, 2010, Origins and evolution of antibiotic resistance. MMBR 74: 417–433.
DOI: https://doi.org/10.1128/MMBR.00016-10
de la Torre A, Iglesias I, Carballo M, Ramirez P, Jesus Munoz M, 2012, An approach for mapping the vulnerability of European Union soils to antibiotic contamination. Sci Tot Environ 414:672–679.
DOI: https://doi.org/10.1016/j.scitotenv.2011.10.032
Digital Scholar, 2022, Zotero. Digital Scholar: Vienna, VA.
ESRI, 2021, ArcGIS Pro (Version 2.8.2). Esri: Redlands, CA.
Galvin S, Cormican M, Murphy AW, Hanahoe B, Hennessy R, Bergin N, Vellinga A, 2013, Exploratory Spatial Mapping of the Occurrence of Antimicrobial Resistance in E. coli in the Community. Antibiotics 2:328–338.
DOI: https://doi.org/10.3390/antibiotics2030328
Ginn O, Nichols D, Rocha-Melogno L, Bivins A, Berendes D, Soria F, Andrade M, Deshusses MA, Bergin M, Brown J, 2021, Antimicrobial resistance genes are enriched in aerosols near impacted urban surface waters in La Paz, Bolivia. Environ Res 194:110730.
DOI: https://doi.org/10.1016/j.envres.2021.110730
Goulas A, Belhadi D, Descamps A, Andremont A, Benoit P, Courtois S, Dagot C, Grall N, Makowski D, Nazaret S, Nélieu S, Patureau D, Petit F, Roose-Amsaleg C, Vittecoq M, Livoreil B, Laouénan C, 2020, How effective are strategies to control the dissemination of antibiotic resistance in the environment? A systematic review. Environ Evid 9:1–32.
DOI: https://doi.org/10.1186/s13750-020-0187-x
Gould K, 2016, Antibiotics: from prehistory to the present day. J Antimicrob Chemother 71:572–575.
DOI: https://doi.org/10.1093/jac/dkv484
Haddaway N, Macura B, Whaley P, Pullin A, 2018, ROSES Flow Diagram for Systematic Reviews. Version 1.0. figshare.
Harring N, Jagers SC and Löfgren Å, 2021, COVID-19: Large-scale collective action, government intervention, and the importance of trust. World Dev 138:105236.
DOI: https://doi.org/10.1016/j.worlddev.2020.105236
Holmes AH, Moore LSP, Sundsfjord A, Steinbakk M, Regmi S, Karkey A, Guerin PJ and Piddock LJV, 2016, Understanding the mechanisms and drivers of antimicrobial resistance. Lancet 387:176–187.
DOI: https://doi.org/10.1016/S0140-6736(15)00473-0
Hu A, Wang H, Li J, Mulla S, Qiu Q, Tang L, Rashid A, Wu Y, Sun Q and Yu C-P, 2020, Homogeneous selection drives antibiotic resistome in two adjacent sub- watersheds, China. J Hazard Mater 398:122820.
DOI: https://doi.org/10.1016/j.jhazmat.2020.122820
Kelsey RH, Scott GI, Porter DE, Thompson B, Webster L, 2003, Using Multiple Antibiotic Resistance and land use characteristics to determine sources of fecal coliform bacterial pollution. Environ Monit Assess 81:337–348.
DOI: https://doi.org/10.1007/978-94-017-0299-7_28
Kucukdogan A, Guven B and Balcioglu I, 2015, Mapping the Environmental Risk of Antibiotic Contamination by Using Multi-Criteria Decision Analysis. Clean 43:1316–1326.
DOI: https://doi.org/10.1002/clen.201400757
Kümmerer K, 2009, Antibiotics in the aquatic environment – A review – Part I. Chemosphere 75:417–34.
DOI: https://doi.org/10.1016/j.chemosphere.2008.11.086
Luz CF, van Niekerk JM, Keizer J, Beerlage-de Jong N, Braakman-Jansen LMA, Stein A, Sinha B, van Gemert-Pijnen JEWC, Glasner C, 2022, Mapping twenty years of antimicrobial resistance research trends. Artif Intell Med 123:102216.
DOI: https://doi.org/10.1016/j.artmed.2021.102216
Martinez JL, 2009, The role of natural environments in the evolution of resistance traits in pathogenic bacteria. Proc R Soc B 276:2521–2530.
DOI: https://doi.org/10.1098/rspb.2009.0320
Miller EA, Ponder JB, Willette M, Johnson TJ, VanderWaal KL, 2020, Merging Metagenomics and Spatial Epidemiology To Understand the Distribution of Antimicrobial Resistance Genes from Enterobacteriaceae in Wild Owls. Appl Env Microbiol 86:e00571-20.
DOI: https://doi.org/10.1128/AEM.00571-20
Morgan RL, Whaley P, Thayer KA, Schünemann HJ, 2018, Identifying the PECO: A framework for formulating good questions to explore the association of environmental and other exposures with health outcomes. Env Intern 121:1027–31.
DOI: https://doi.org/10.1016/j.envint.2018.07.015
Nicolaou KC, Rigol S, 2018, A brief history of antibiotics and select advances in their synthesis. J Antibiot 71:153–184.
DOI: https://doi.org/10.1038/ja.2017.62
R Core Team, 2021, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, Austria.
Rousham E, Unicomb L, Wood P, Smith M, Asaduzzaman M, Islam MA, 2018, Spatial and temporal variation in the community prevalence of antibiotic resistance in Bangladesh: An integrated surveillance study protocol. BMJ Open 8: e023158.
DOI: https://doi.org/10.1136/bmjopen-2018-023158
RStudio Team, 2020, RStudio: Integrated Development Environment for R. RStudio, PBC: Boston, MA.
Sacristán I, Esperón F, Acuña F, Aguilar E, García S, López MJ, Cevidanes A, Neves E, Cabello J, Hidalgo-Hermoso E, Poulin E, Millán J, Napolitano C, 2020, Antibiotic resistance genes as landscape anthropization indicators: Using a wild felid as sentinel in Chile. Sci Tot Environ 703.
DOI: https://doi.org/10.1016/j.scitotenv.2019.134900
Servais P, Passerat J, 2009, Antimicrobial resistance of fecal bacteria in waters of the Seine river watershed (France). Sci Tot Environ 408:365–372.
DOI: https://doi.org/10.1016/j.scitotenv.2009.09.042
Singer RS, Ward MP and Maldonado G, 2006, Can landscape ecology untangle the complexity of antibiotic resistance? Nat Rev Microbiol 4:943–952.
DOI: https://doi.org/10.1038/nrmicro1553
Xiang Q, Chen Q-L, Zhu D, An X-L, Yang X-R, Su J-Q, Qiao M, Zhu Y-G, 2018, Spatial and temporal distribution of antibiotic resistomes in a peri-urban area is associated significantly with anthropogenic activities. Env Pollute 235525–533.
DOI: https://doi.org/10.1016/j.envpol.2017.12.119
Yang Y, Liu G, Ye C and Liu W, 2019, Bacterial community and climate change implication affected the diversity and abundance of antibiotic resistance genes in wetlands on the Qinghai-Tibetan Plateau. J Hazard Mater 361:283–293.
DOI: https://doi.org/10.1016/j.jhazmat.2018.09.002
Yi X, Lin C, Ong EJL, Wang M and Zhou Z, 2019, Occurrence and distribution of trace levels of antibiotics in surface waters and soils driven by non-point source pollution and anthropogenic pressure. Chemosphere 216:213–223.
DOI: https://doi.org/10.1016/j.chemosphere.2018.10.087
Yopasa-Arenas A, Fostier AH, 2018, Exposure of Brazilian soil and groundwater to pollution by coccidiostats and antimicrobial agents used as growth promoters. Sci Tot Environ 644:112–121.
DOI: https://doi.org/10.1016/j.scitotenv.2018.06.338
Young MA, Macreadie PI, Duncan C, Carnell PE, Nicholson E, Serrano O, Duarte CM, Shiell G, Baldock J, Ierodiaconou D, 2018, Optimal soil carbon sampling designs to achieve cost-effectiveness: a case study in blue carbon ecosystems. Biol Lett 14:20180416.
DOI: https://doi.org/10.1098/rsbl.2018.0416
Zhao F, Chen L, Yen H, Li G, Sun L, Yang L, 2020, An innovative modeling approach of linking land use patterns with soil antibiotic contamination in peri-urban areas. Environ Int 134:105327.
DOI: https://doi.org/10.1016/j.envint.2019.105327
How to Cite
Spets, P., Ebert, K., & Dinnétz, P. (2023). Spatial analysis of antimicrobial resistance in the environment. A systematic review. Geospatial Health, 18(1). https://doi.org/10.4081/gh.2023.1168
Copyright (c) 2023 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
