INTRODUCTION
⌅Antimicrobial resistance (AMR) is a complex problem that best illustrates the One Health approach and it is widely perceived as a threat to human, animal, and environmental health (11. Velazquez-Meza ME, Galarde-López M, Carrillo-Quiróz B, Alpuche-Aranda CM. Antimicrobial resistance: One Health approach. Vet World. 2022;15:743-9. Available from: http://dx.doi.org/10.14202/vetworld.2022.743-749 ). The spread of resistance from animal sources negatively affects human health, directly by the spread of the resistant bacteria typically through food or indirectly by the spread of resistance genes from animal bacteria to human bacteria (22. Assoumy MA, Bedekelabou AP, Teko-Agbo A, Ossebi W, Akoda K, Nimbona F, et al. Antibiotic resistance of Escherichia coli and Salmonella spp. strains isolated from healthy poultry farms in the districts of Abidjan and Agnibilékrou (Côte d’Ivoire). Vet World. 2021;14(4):1020-7. Available from: http://dx.doi.org/10.14202/vetworld.2021.1020-1027 ).
To address related threats to AMR, various efforts and initiatives are coordinated through alliances between international and national organisations including a global action plan to tackle AMR (33. FAO. The FAO Action Plan on Antimicrobial Supporting innovation and resilience in food and agriculture sectors [Internet]. Rome, Italia; 2021 [cited 2023 Jun 19]. Available from: https://doi.org/10.4060/cb5545en ). A recent review recognizes the collective need for limiting the emergence and spread of resistant pathogens and highlights the importance of an integrated and holistic multisectoral One Health approach to face AMR (44. WHO, FAO, OIE, UNEP. Strategic Framework for collaboration on antimicrobial resistance - together for One Health. Geneva: World Health Organization, Food and Agriculture Organization of the United Nations and World Organization for Animal Health; 2022. Licence: CC BY-NC-SA 3.0 IGO. [cited 2023 Jun 19] Available from: https://www.who.int/publications/i/item/9789240045408 ).
The World Health Organization report on global tricycle protocol (55. WHO. WHO integrated global surveillance on ESBL-producing E. coli using a “One Health” approach: implementation and opportunities [Internet]. Geneva, Switzerland: World Health Organization; 2021. Available from: https://www.who.int/publications/i/item/9789240021402 ) provides some specific and valuable approaches for integrated multisectoral surveillance to detect and estimate the prevalence of Escherichia coli as a key sentinel or indicator organism of AMR. Furthermore, E. coli strains are zoonotic pathogens, causing disease in animals and humans, respectively. Poultry meat is the food from animal origin most closely linked to human extraintestinal pathogenic E. coli (ExPEC) (66. Ramos S, Silva V, de Lurdes Enes Dapkevicius M, Caniça M, Tejedor-Junco MT, Igrejas G, et al. Escherichia coli as Commensal and Pathogenic Bacteria among Food-Producing Animals: Health Implications of Extended Spectrum β-Lactamase (ESBL) Production. Anim an Open Access J from MDPI [Internet]. 2020 Dec 1 [cited 2022 May 9];10(12):1-15. Available from: https://doi.org/10.3390/ANI10122239 ), requiring the proper use of antibiotics for its control based on susceptibility testing.
Current information about the geographical distribution of resistance is limited, laboratory capacity may be underdeveloped and the challenges of conducting comprehensive population-based surveillance are high (77. Iskandar K, Molinier L, Hallit S, Sartelli M, Hardcastle TC, Haque M, et al. Surveillance of antimicrobial resistance in low- and middle-income countries: a scattered picture. Antimicrob Resist Infect Control. 2021;10(1):1-19. Available from: https://doi.org/10.1186/s13756-021-00931-w ). The demand for such data highlights the need for surveillance research and methods, given AMR surveillance is the core component of disease management and the foundation for a better understanding of the cause and spread of AMR (88. Schnall J, Rajkhowa A, Ikuta K, Rao P, Moore CE. Surveillance and monitoring of antimicrobial resistance: limitations and lessons from the GRAM project. BMC Med 2019 171 [Internet]. 2019 Sep 20 [cited 2021 Jul 22];17(1):1-3. Available from: https://doi.org/10.1186/S12916-019-1412-8 ). Calls for the monitoring, surveillance and prediction of AMR have prompted the design and implementation of surveillance systems at all geographic levels (99. Safdari R, Ghazi Saeedi M, Masoumi-Asl H, Rezaei-Hachesu P, Mirnia K, Mohammadzadeh N, et al. National Minimum Data Set for Antimicrobial Resistance Management: Toward Global Surveillance System. Iran J Med Sci [Internet]. 2018 Sep 1 [cited 2023 Jun 19];43(5):494. Available from: PMID: 30214102; PMCID: PMC6123552.).
Some studies in Cuba address the environment, human patients, farm animals, and slaughterhouses as major components of the AMR problem (1010. Expósito B, Bermellón S, Lescaille G, Delgado R, Aliaga C. Resistencia antimicrobiana de la Escherichia coli en pacientes con infección del tracto urinario. Rev Inf Científica [Internet]. 2019;98(6):755-64. Available from: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S1028-99332019000600755&lng=es. -1212. Baez M, Espinosa I, Collaud A, Miranda I, Montano D de las N, Feria AL, et al. Genetic Features of Extended-Spectrum β-Lactamase-Producing Escherichia coli from Poultry in Mayabeque Province, Cuba. Antibiotics [Internet]. 2021 Jan 22 [cited 2021 Feb 22];10(2):107. Available from: https://doi.org/10.3390/antibiotics10020107 ). However, the identification of priority areas of intervention through trend analysis or AMR spatial distribution remains to be assessed. Consequently, there are knowledge gaps concerning the magnitude of AMR and intervention strategies in case of infections. Such information provides a meaningful basis for a more rational approach to the prescribing and use of antimicrobials, in agreement with regulations to prevent the development of AMR.
Retrospective studies can capitalise on AMR historical data aiming to identify trends, even spatially explicit. These approaches, besides identifying AMR spatial distribution, can anticipate opportunities for therapeutic success based on accumulated knowledge and categorise classes of antimicrobials or molecules whose use should also be reconsidered.
Kernel Density Estimation and Kernel smoothed relative risk surfaces to explore the spatial distribution of different events have been widely used (1313. Ruckthongsook W, Tiwari C, Oppong JR, Natesan P. Evaluation of threshold selection methods for adaptive kernel density estimation in disease mapping. Int J Health Geogr. 2018 May 8;17(1):1-13. Available from: https://doi.org/10.1186/s12942-018-0129-9 ,1414. Elson R, Davies TM, Jenkins C, Vivancos R, O’Brien SJ, Lake IR. Application of kernel smoothing to estimate the spatio-temporal variation in risk of STEC O157 in England. Spat Spatiotemporal Epidemiol. 2020 Feb 1;32(2). Available from: https://doi.org/10.1016/j.sste.2019.100305 ). On this basis, Spatial-temporal models for the magnitude of AMR in food-producing animals could improve surveillance by targeting those areas and regions at risk of higher AMR and help to anticipate when and where it may become a problem of greater concern.
This study, therefore, aimed to characterise the spatial pattern of antimicrobial resistance of extra-intestinal clinical E. coli isolated from commercial poultry in western provinces of Cuba.
METHODS
⌅Study area
⌅The study area included three western provinces (Artemisa, La Habana and Mayabeque), where high-density of commercial poultry farms are present. This area is about 8476,37 km² encompassing a Researching and Diagnosis Laboratory for avian diseases. The laboratory (LIDA, in Spanish) includes bacteriological diagnosis and AMR testing facilities (Figure 1).
Data sources
⌅An analysis, derived from retrospective data on the susceptibility profile of extra-intestinal E. coli isolates from clinical cases associated with avian colibacillosis, was conducted. Data collection was carried out at the Poultry Research and Diagnosis Laboratory (LIDA) "Jesús Menéndez". Data correspond to isolates (n = 287) originating from 68 commercial poultry farms for four years (January-2014 to December-2017). Data were manually entered into a worksheet using Microsoft Office Excel 2016 (1515. Microsoft Corporation. Microsoft Excel TM. Redmond, Washington: Microsoft Corporation; 2016.).
Bacteria isolation/identification and antimicrobial susceptibility determination
⌅Isolated samples were cultured in Tryptone Soya Broth and incubated aerobically overnight at 37°C. Subsequently, isolates were also streaked on MacConkey Agar. After 24 hours, plates were observed for examination of similar characteristic with E. coli (pink colony in MacConkey; Gram-negative coccobacilli). Samples were obtained from heart, kidney and liver of poultry suffering from septicaemia in the past 24 hours.
Antimicrobial susceptibility testing was performed by diffusion agar following the instruction of Clinical Laboratory Standard Institute guidelines (16). E. coli strains were evaluated against four veterinary important antibiotics from Oxoid disks (Oxoid TM, Basingstoke, United Kingdom): enrofloxacin (ENR 5μg), norfloxacin (NOR 10μg), tetracycline (TE 30μg), and oxytetracycline (T 30μg). E. coli ATCC 25922 was used as a reference strain for the quality control of antibiotic sensitivity testing. Results were interpreted using CLSI clinical breakpoint for Enterobacteriaceae (1616. CLSI. Performance Standards for Antimicrobial Susceptibility Testing 27 th ed. CLSI supplement M100. Wayne, PA: Clinical and Laboratory Standards Institute [Internet]. 2017. Available from: https://file.qums.ac.ir/repository/mmrc/clsi2017.pdf ), as reference guide to classify in 3 categories as Susceptible (S), Intermediate (I) or Resistant (R).
Exploratory descriptive analysis
⌅Resistance frequency was calculated as the proportion of isolates resistant to the antimicrobials researched in relation to the number of isolates tested. A linear regression analysis was made to determine the tendency of resistance in the study period. Time (years) was considered as an independent variable and antibiotic resistance per year as a dependent variable. The formula used was , where is a dependent or outcome variable; is an independent variable or so called predictor; and model parameters (1717. Thrusfield M, Christley R, Brown H, Diggle PJ, French N, Howe K, et al. Veterinary Epidemiology [Internet]. Fourth Edi. Hoboken N, editor. Wiley; 2018. Available from: https://www.wiley.com/en-us/Veterinary+Epidemiology%2C+4th+Edition-p-9781118280287 ). Determination coefficient R2 was used as a reference for measuring the goodness of fit of the models.
A descriptive analysis was performed to check the quality of the data, filter and describe the variables, and extract their general statistics (minimum value, maximum value, mean, median, 95% confidence interval, standard deviations, and standard error).
Kernel Estimation Spatial Analysis
⌅For spatial data representation, the Universal Transverse Mercator (UTM) coordinate system and Datum EPSG:4267- NAD27 were used as references. Data geo-processing was performed using the latitude and longitude (decimal degrees) of each poultry farm. Resistance of the Tetracycline and Fluoroquinolone classes were added in the context of point processes of analysis (events), based on the geographical coordinates representing the exact location of poultry farms with the presence of Escherichia coli resistance.
A non-parametric Smoothing Method by Kernel Density Estimation (KDE) was carried out to analyse regions with high and low resistance to antibiotic classes. Therefore, Sheather & Jones plug-in (SJDP) method was selected to calculate the bandwidth. According to those calculations, the spatial bandwidth was set to 2km and an output cell size of 1km2.
Kernel Estimation of spatial relative risk
⌅To study the relative abundance of AMR cases with respect to the at-risk population dispersion over a well-defined geographical region, the Kernel smoothing spatial relative risk surface method defined in Equation 4 was chosen.
Where : is the study window, and the case and control data respectively, are two distinct samples of planar points assumed to originate from (unknown, possibly equivalent) density functions (cases) and (control), and the bandwidth kernel estimates of the case and control densities and . The ratio is an estimate of the relative risk function , more commonly expressed on the (natural) scale as (1818. Davies TM, Marshall JC, Hazelton ML. Tutorial on kernel estimation of continuous spatial and spatiotemporal relative risk. Stat Med. 2017;37(7):1-31. Available from: https://doi.org/10.1002/sim.7577 ).
A case-control data set was created for resistance to Tetracycline and Fluoroquinolone classes. AMR cases confirmed by LIDA were selected. As a control, the poultry farms studied without resistance signals but representative of the at-risk poultry population in the geographic area studied, were also selected. For all spatial risk surfaces, symmetric adaptive risk function estimates were calculated, using the pooled case/control data set to compute variable bandwidth factors (1919. Davies TM, Jones K, Hazelton ML. Symmetric adaptive smoothing regimens for estimation of the spatial relative risk function. Comput Stat Data Anal. 2016 Sep 1;101:12-28. Available from: https://doi.org/10.1016/j.csda.2016.02.008 ). All estimates were edge-corrected to account for Kernel weight lost over the boundary of the study region and results were reported as log-relative risk surfaces for symmetry around the ‘null’ log risk value of zero. Finally, the corresponding asymptotic p-value was estimated for each surface (1818. Davies TM, Marshall JC, Hazelton ML. Tutorial on kernel estimation of continuous spatial and spatiotemporal relative risk. Stat Med. 2017;37(7):1-31. Available from: https://doi.org/10.1002/sim.7577 ), and tolerance contours were superimposed at the 5 % significance level to delineate areas of significantly higher risk.
Descriptive statistical analysis and graphical visualisation were performed using the contributed package ggplot2 v3.3.3 (2020. Wickham H. ggplot2 - Elegant Graphics for Data Analysis (2nd Edition). J Stat Softw. 2017;77(April):3-5.), bandwidth calculation was carried out using the package EnvStats v2.4.0 (2121. Millard SP. Getting Started. In: EnvStats [Internet]. New York, NY: Springer New York; 2013 [cited 2021 May 12]. p. 1-24. Available from: http://link.springer.com/10.1007/978-1-4614-8456-1_1 ) and Kernel Estimation of spatial relative risk surface using the package Sparr v2.2.15 (1818. Davies TM, Marshall JC, Hazelton ML. Tutorial on kernel estimation of continuous spatial and spatiotemporal relative risk. Stat Med. 2017;37(7):1-31. Available from: https://doi.org/10.1002/sim.7577 ) in the R environment v4.0.4 (2222. R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria; 2017. Available from: https://www.r-project.org/ ). Spatial analysis, data preparation and mapping were performed using ArcGIS v10.4 (2323. ESRI. ArcGIS E. Release 10.4. Redlands, CA: ESRI [Internet]. 2015. Available from: https://www.esri.com/en-us/arcgis/about-arcgis/overview ).
RESULTS
⌅Exploratory descriptive analysis
⌅Two hundred eighty-seven extra-intestinal clinical E. coli isolates originating from 68 commercial poultry farms were assessed during the study period (2014-2017). The overall frequency of resistance in E. coli showed a wide variation (0-93%) against the antimicrobial tested, although an incremental trend of resistance was observed. The highest levels of resistance were found to Oxytetracycline (93 %), Enrofloxacin (90 %), Tetracycline (70 %), and Norfloxacin (61 %), in the last year (2017). Only low levels of resistance were observed between 2014 and 2015 (Figure 2).
Most isolates were highly resistant to Tetracycline (41.36 %) and Fluoroquinolone (39.83 %) classes, with smaller variations (Figure 2). Correspondingly, a distribution of resistance to Tetracycline (0 %, 43.60 %, 62.68 %) and Fluoroquinolone (10.38 %, 35.32 %, 65.62 %) classes, were observed in quartiles (Table 1).
| Antibiotic classes | Descriptive Statistics | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Min. Value | Max. Value | 1rst Quartile | 2nd Quartile (median) | 3rd Quartile | Mean | CI (95%) | Standard deviations | Standard error | |
| Tetracyclines | 0 | 93.33 | 0 | 43.60 | 62.68 | 41.36 | 26.3471- 52.3803 | 36.8164 | 13.0168 |
| Fluoroquinolones | 0 | 90.48 | 10.38 | 35.32 | 65.52 | 39.83 | 27.4792- 51.9082 | 34.1661 | 12.0795 |
*CI (95%) Confidence Interval
Kernel Estimation Spatial Analysis
⌅The AMR of Tetracycline and Fluoroquinolone classes was spatially distributed throughout the geographical region studied, although the density distribution indicated that resistance to Tetracyclines was more widespread in the West region, while to Fluoroquinolones, it was more widespread in the East and West regions of the study area (Figure 3). During the period 2014-2017, a higher density (high values) of AMR was mostly located in the municipalities of Artemisa province. Nevertheless, areas with medium densities occurred in all the provinces with variable extensions.
A higher density of E. coli resistance to the Tetracycline class was identified in six municipalities (Alquízar, Artemisa, Bauta, Caimito, Güira de Melena, and San Antonio de los Baños) in Artemisa province, and in two municipalities (Boyeros and La Lisa) in Havana province.
On the other hand, the highest density of E. coli resistance to the Fluoroquinolone class was located in five municipalities (Alquízar, Artemisa, Bauta, Caimito, and Güira de Melena) of Artemisa, and in two municipalities (Boyeros and La Lisa) of Havana province, respectively. It should be noted that the high density of Kernel was also evident in several municipalities of Mayabeque: Bejucal, Batabanó, Madruga, Quivicán, and San Nicolás.
Kernel Estimation of spatial relative risk
⌅The relative risk surfaces for E. coli resistance to Tetracycline and Fluoroquinolone classes are presented in Figure 4. Two main areas (in the South and East of the study area) were observed to be at higher risk with respect to the underlying risk population. Lower risk areas were limited to the North.
For Tetracyclines, the greatest risk was observed in the South/South-East of Mayabeque province. Areas of lower risk were confined to the South-West, the North and a rural area in the North-East.
Compared to Tetracyclines, the highest risk surfaces for Fluoroquinolones were restricted to the extreme North and Northeast. Lower risk surfaces were more uniform throughout the study area, specifically in the West, South and a small portion in the Southeast.
DISCUSSION
⌅The current research provides the first insight about AMR trends and spatial risk in Cuba, specifically for AMR of extra-intestinal clinical E. coli from commercial poultry farms in western provinces. The followed approach offers alternatives for refining antibiotherapy interventions based on evidence from accumulated susceptibility data. The results evidenced the need to use other therapeutic options for the control of avian colibacillosis in the farms where isolates with high resistance frequency to Tetracyclines and Fluoroquinolones were detected. This information is useful for overcoming the impossibility of defining antibiotherapy in near real-time based on susceptibility testing.
Existing studies have shown the limitations faced in the surveillance of AMR or have elucidated the difficulty in determining the antimicrobial susceptibility profile on a case by case before deciding on treatment in developing countries (77. Iskandar K, Molinier L, Hallit S, Sartelli M, Hardcastle TC, Haque M, et al. Surveillance of antimicrobial resistance in low- and middle-income countries: a scattered picture. Antimicrob Resist Infect Control. 2021;10(1):1-19. Available from: https://doi.org/10.1186/s13756-021-00931-w ). As such, the approach based on AMR trends and spatial risk may represent a relevant solution, particularly in the cases of intensive commercial poultry, swine, or cattle, in which large populations at high rearing densities favour the rapidity of infectious processes and require timely control intervention.
The present study focused on a region with a high density of poultry farms near a laboratory with AMR testing facilities Such distinctiveness may offer a unique opportunity to understand AMR patterns and trends that may be representative and predominant among the poultry husbandry conditions. However, the evaluation of other areas in the country with high densities of food-producing animals where antimicrobial susceptibility testing may be insufficient is required.
The fact that the study encompassed a major and densely populated poultry region reinforces its significance to prevent AMR impact by giving a more appropriate vision of E. coli drug resistance patterns. The research highlights E. coli which is considered an extremely relevant and representative indicator of an antibiotic resistance issue of global clinical importance because it is part of the normal microbiota of humans and animals and it is also present in the environment (2424. Poirel L, Madec J-Y, Lupo A, Schink A-K, Kieffer N, Nordmann P, et al. Antimicrobial Resistance in Escherichia coli. In: Antimicrobial Resistance in Bacteria from Livestock and Companion Animals [Internet]. American Society of Microbiology; 2018 [cited 2021 Feb 22]. p. 289-316. Available from: https://doi.org/10.1128/microbiolspec.arba-0026-2017 ).
The spatial transmission of antimicrobial-resistant bacteria has been understudied in comparison to pathogens (2525. Hedman HD, Zhang L, Trueba G, Vinueza Rivera DL, Zurita Herrera RA, Barrazueta JJV, et al. Spatial Exposure of Agricultural Antimicrobial Resistance in Relation to Free-Ranging Domestic Chicken Movement Patterns among Agricultural Communities in Ecuador. Am J Trop Med Hyg [Internet]. 2020 Nov 4 [cited 2022 Mar 17];103(5):1803-9. Available from: https://doi.org/10.4269/AJTMH.20-0076 ). Additionally, there is no consensus on AMR surveillance methodology, particularly on systematic data collection in Cuba. These further limits several studies.
Various drivers are contributing factors to the occurrence of AMR in farms and the environment (2626. Ahmad I, Malak HA, Abulreesh HH. Environmental antimicrobial resistance and its drivers : a potential threat to public health. J Glob Antimicrob Resist [Internet]. 2021;27:101-11. Available from: https://doi.org/10.1016/j.jgar.2021.08.001 ). The identification of several patterns by KDE in Artemisa, La Habana and Mayabeque might suggest the implication of different AMR contributors such as antimicrobial usage, hygiene sanitation practices, horizontal transmission among animals, and feed strategies, as observed in other studies (2525. Hedman HD, Zhang L, Trueba G, Vinueza Rivera DL, Zurita Herrera RA, Barrazueta JJV, et al. Spatial Exposure of Agricultural Antimicrobial Resistance in Relation to Free-Ranging Domestic Chicken Movement Patterns among Agricultural Communities in Ecuador. Am J Trop Med Hyg [Internet]. 2020 Nov 4 [cited 2022 Mar 17];103(5):1803-9. Available from: https://doi.org/10.4269/AJTMH.20-0076 ,2727. Guo K, Zhao Y, Cui L, Cao Z, Zhang F. The Influencing Factors of Bacterial Resistance Related to Livestock Farm : Sources and Mechanisms. Front Anim Sci. 2021;2. Available from: https://doi.org/10.3389/fanim.2021.650347 ).
The increasing trend observed in resistance rates with broader spatial and temporal consideration is of concern, and closer monitoring of this problem is needed. A previous smaller-scale local and cross-sectional resistance study also revealed high rates of AMR (2828. Hernández R, Báez F, Zamora P, Espinosa I. Susceptibilidad antimicrobiana y formación de biopelícula en aislados de Escherichia coli procedentes de gallinas ponedoras. Revi Salud Anim. 2017;39(3):1-14. Available from: http://www.revistaccuba.cu/index.php/revacc/article/view/368 ), but lacked a geographically explicit consideration of AMR risk.
AMR is often inferred as dependent on antimicrobial usage (2929. Varga C, Guerin MT, Brash ML, Slavic D, Boerlin P, Susta L. Antimicrobial resistance in fecal Escherichia coli and Salmonella enterica isolates: a two-year prospective study of small poultry flocks in Ontario, Canada. BMC Vet Res [Internet]. 2019 Dec 21;15(1):1-10. Available from: https://doi.org/10.1186/s12917-019-2187-z ,3030. Bennani H, Mateus A, Mays N, Eastmure E, Stärk KDC, Häsler B. Overview of evidence of antimicrobial use and antimicrobial resistance in the food chain. Antibiotics [Internet]. 2020 Feb 1 [cited 2021 Feb 22];9(2):49. Available from: https://doi.org/10.3390/antibiotics9020049 ). Accordingly, Tetracyclines, recognized as the most widely used antimicrobial in veterinary medicine (3131. OIE. Anual report on antimicrobial agents intended for use in animals. Better undertsanding of the global situattion [Internet]. 2018. Available from: https://www.oie.int/fileadmin/Home/eng/Our_scientific_expertise/docs/pdf/AMR/A_Third_Annual_Report_AMR.pdf ), were the antibiotic class with the highest level of resistance, in the present study. The repeated exposure to therapeutic agents contributes to an increased selective pressure and, consequently, to a higher prevalence of antimicrobial resistance (3232. Tonoyan L, Fleming GTA, Friel R, O’Flaherty V. Continuous culture of Escherichia coli, under selective pressure by a novel antimicrobial complex, does not result in development of resistance. Sci Rep [Internet]. 2019 Dec 1 [cited 2023 Jun 19];9(1). Available from: https://doi.org/10.1038/S41598-019-38925-9 ,3333. Akram F, Imtiaz M, Haq I ul. Emergent crisis of antibiotic resistance: A silent pandemic threat to 21st century. Microb Pathog. 2023 Jan 1;174:105923. https://doi.org/10.1038/S41598-019-38925-9 ). Tetracyclines are a first-line antibiotic often used prior to determining the antibiotic resistance profile of a pathogen. As Tetracycline is a naturally derived compound, bacteria can be exposed to these agents in nature and outside of any human use for disease treatment, for livestock prophylaxis. In addition, Tetracycline resistance is plasmid-mediated, with a wide variety of genetic determinants (3434. Liu X, Li R, Chan EWC, Xia X, Chen S. Plasmid-mediated ciprofloxacin, carbapenem and colistin resistance of a foodborne Escherichia coli isolate. Food Control. 2022 Jul 1;137:108937. Available from: https://doi.org/10.1016/J.FOODCONT.2022.108937 ).
On the other hand, the use of antimicrobials as growth promoters has been forbidden in Cuba (3535. MINJUS. Gaceta Oficial No. 11 Ordinaria de 29 de enero de 2021. Decreto 20/2020 Contravenciones de la medicina veterinaria (GOC-2021-134-O11) [Internet]. Ministerio de Justicia. La Habanam Cuba: Ministerio de Justicia; 2021. Available from: https://www.gacetaoficial.gob.cu/es/gaceta-oficial-no-11-ordinaria-de-2021 ). Therefore, the use of sub-therapeutic doses of antibiotics would be of minor importance as a driver of resistance. Nevertheless, there is still a need for global progress in recording and reporting antimicrobial use data (AMU) for more effective management of the associated risk. In fact, the maintenance of general health and hygiene practices in food-producing animals is paramount for improving animal welfare and production and, consequently, reducing AMU. In this sense, high-density poultry areas would be the first line to verify and promote good hygiene and husbandry practices, as well as future studies on AMU. Particular consideration is needed in the control of vectors (e.g., flies, beetles and cockroaches), which can play a potential role in the transfer of pathogens and resistance genes from different environmental levels (2727. Guo K, Zhao Y, Cui L, Cao Z, Zhang F. The Influencing Factors of Bacterial Resistance Related to Livestock Farm : Sources and Mechanisms. Front Anim Sci. 2021;2. Available from: https://doi.org/10.3389/fanim.2021.650347 ).
In addition, intra-country trade and animal mobility may play a more important role than differences in antimicrobial exposure in explaining geographic differences in antimicrobial resistance levels (3636. Argudín MA, Deplano A, Meghraoui A, Dodémont M, Heinrichs A, Denis O, et al. Bacteria from Animals as a Pool of Antimicrobial Resistance Genes. Antibiotics [Internet]. 2017 Jun 6 [cited 2021 Dec 29];6(2):12. Available from: https://doi.org/10.3390/ANTIBIOTICS6020012 ). In Cuba, the commercial poultry farming at the national level is based on provincial enterprises that used to be self-sufficient for poultry replacements, which has given a certain level of regionalization. However, animal batches within the egg production system, as the most predominant in Cuba (3737. ONEI. Anuario Estadístico de Cuba. Agricultura, Ganadería, Silvicultura y Pesca. Edición 2021 [Internet]. 2021 [cited 2021 Feb 22]. Available from: http://www.onei.gob.cu/sites/default/files/agropecuario_-2020_0.pdf ), usually change location two to three times from hatching to the final productive facilities within each province.
The feed composition and the poultry gut microbiome can lead to an increase in E. coli (3838. Bindari YR, Gerber PF. Centennial Review: Factors affecting the chicken gastrointestinal microbial composition and their association with gut health and productive performance. Poult Sci. 2022 Jan 1;101(1):101612. Available from: https://doi.org/10.1016/J.PSJ.2021.101612 ). However, the influence of this factor could not be considered in the present study. Such analyses could shape future research direction, given that E. coli is considered a major threat to the poultry industry and public health (3939. Ibrahim RA, Cryer TL, Lafi SQ, Basha EA, Good L, Tarazi YH. Identification of Escherichia coli from broiler chickens in Jordan, their antimicrobial resistance, gene characterization and the associated risk factors. BMC Vet Res [Internet]. 2019 May 22 [cited 2022 Mar 22];15(1):1-16. Available from: https://doi.org/10.1186/S12917-019-1901-1/TABLES/8 ) and it is also likely the most actively transmitted microbe between humans and poultry (4040. Muloi DM, Wee BA, McClean DMH, Ward MJ, Pankhurst L, Phan H, et al. Population genomics of Escherichia coli in livestock-keeping households across a rapidly developing urban landscape. Nat Microbiol 2022 74 [Internet]. 2022 Mar 14 [cited 2023 Jun 19];7(4):581-9. Available from: https://doi.org/10.1038/s41564-022-01079-y ).
Limitations and strengths
⌅Although the study was limited to one region, it included an important productive area totalling 152 poultry farms, with an animal population of over 5 109 542 individuals in 8476,37 km2. Through this study, the knowledge of AMR spatial patterns of E. coli from commercial poultry farms is enhanced. This research provides information on AMR risk areas to the most used antibiotic classes in poultry production areas in Cuba. Such territories must be prioritized in the intensification of AMR surveillance or containment measures in anticipation of the potential evolution of transmission events.
The study emphasises the need for antimicrobial stewardship interventions, to limit antimicrobial usage and preserve the effectiveness of these molecules wherever possible. More importantly, the research offers new insights for stakeholders to make evidence-based decisions and policies to prevent and control the occurrence and dissemination of AMR.
The possibility of analyzing more recent data was hampered by declining evidence and other limitations during COVID-19 pandemic, which broke out in 2020 and lasted more than three years. Nevertheless, the continuous four-year study period may reflect the current trend, although COVID-19 also involved a significant reduction in AMU, the influence of which on AMR profiles has yet to be assessed. This should be considered for further studies.
CONCLUSIONS
⌅This research revealed, for the first time, the trends and spatial risk for AMR of extra-intestinal clinical E. coli from commercial poultry farms in Cuba. These findings characterise a reference point for improving antibiotherapy intervention on commercial poultry farms throughout the western geographic region of Cuba. The results underline the need to reinforce the implementation of preventive measures that contribute to reduce the frequency of bacterial infection and act as the main driver of antibiotic use.