Isolation and characterization of multidrug-resistant Escherichia coli in pigs, farm workers and effluents in Calabar, southern Nigeria

Isolation and characterization of multidrug-resistant Escherichia coli in pigs, farm workers and effluents in Calabar, southern Nigeria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Isolation and characterization of multidrug-resistant Escherichia coli in pigs, farm workers and effluents in Calabar, southern Nigeria Aderigbigbe Adegunwa, Samuel Akpan, Uduak Akpabio, Ayodeji Adegunwa, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7880879/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Multidrug-resistant (MDR) Escherichia coli ( E. coli ) poses a serious threat to animal and human health, especially in low- and middle-income countries (LMICs), where antibiotic use in livestock is poorly regulated. Pigs act as reservoirs for resistant bacteria that can spread to humans through direct contact, contaminated pork, or the farm environment. This study aimed to determine the prevalence and resistance patterns of MDR E. coli in pigs, pig handlers, and farm effluents and to assess farmers’ awareness of antimicrobial resistance (AMR) in Calabar, southern Nigeria. A cross-sectional study design was used to collect 210 samples across 17 farms. These samples included pig faecal (n = 152), human stool (n = 30), and effluent (n = 28) samples. E. coli was isolated via standard microbiological methods and confirmed via biochemical tests. Antibiotic susceptibility testing followed the Kirby–Bauer disk diffusion method, with MDR defined as resistance to three or more antibiotics. A semi-structured questionnaire was used to assess farmers’ knowledge and practices regarding antimicrobial use and AMR. The prevalence of E. coli was 63.8% in pigs, 40.0% in humans, and 35.7% in effluents. All the isolates were resistant (100%) to cephalosporins, polymyxins, and penicillins, whereas the rates of fluoroquinolone resistance were 79.8%, 75.0%, and 90.0% in the pig, human, and effluent isolates, respectively. The multiple antibiotic resistance index (MARI) exceeded 0.2 across all the isolates. Awareness of AMR was poor, with 93% of respondents showing poor understanding of withdrawal periods. These findings indicate pigs and pig farm environments as a high-risk platform for MDR E. coli spread, underscoring the need for intensified antibiotic stewardship, farmer education, and AMR surveillance in livestock production systems in Nigeria and other LMICs. Antimicrobial resistance Multidrug resistance Escherichia coli Pigs Humans Effluent Figures Figure 1 Introduction Antimicrobial resistance (AMR) has emerged as one of the most formidable global public health threats of the 21st century, undermining decades of medical and veterinary progress [ 1 ]. The World Health Organization (WHO) warns that without urgent action, society risks entering a postantibiotic era in which common infections become untreatable and routine medical procedures are increasingly life-threatening [ 2 ]. Among the microorganisms of greatest concern, Escherichia coli ( E. coli ) plays a central role as both a commensal and a pathogenic bacterium capable of acquiring and transmitting resistance genes across human, animal, and environmental interfaces [ 3 ]. This characteristic makes E. coli an important indicator of antimicrobial use and resistance patterns within communities and a sentinel organism in One Health surveillance systems [ 4 – 5 ]. The intensification of livestock production, particularly in low- and middle-income countries (LMICs), has been accompanied by widespread and often unregulated use of antibiotics for prophylaxis, meta-phylaxis, and growth promotion [ 6 ]. In food-producing animals such as pigs, frequent exposure to antimicrobial agents creates selective pressure for the emergence of multidrug-resistant (MDR) bacterial populations [ 7 ]. These resistant strains can be disseminated via multiple pathways, such as direct animal‒human contact, consumption of contaminated meat, and environmental contamination through farm effluents or manure used as fertilizer [ 8 , 9 ]. Such interlinked routes of transmission emphasize the necessity of the one-health framework, which recognizes that the health of humans, animals, and the environment is interconnected and must be addressed collectively to combat AMR effectively [ 10 ]. E. coli is a gram-negative, rod-shaped bacterium of the family Enterobacteriaceae that is widely distributed in the intestinal tracts of humans and animals [ 11 ]. Although most strains are harmless, pathogenic variants such as enterotoxigenic E. coli (ETEC), enterohemorrhagic E. coli (EHEC), and Shiga toxin-producing E. coli (STEC) cause severe diarrheal and systemic diseases in both humans and animals [ 12 ]. In pigs, E. coli is a leading cause of neonatal and postweaning diarrhea and is responsible for considerable economic losses, poor growth performance, and high piglet mortality rates [ 13 , 14 ]. The animal intestine provides an ideal environment for gene exchange between commensal and pathogenic E. coli , enabling the acquisition and spread of resistance determinants [ 14 ]. Consequently, pigs have been identified as significant reservoirs of antimicrobial-resistant bacteria and resistance genes that can be transferred to humans via direct contact, pork consumption, or environmental exposure [ 15 , 16 ]. Several studies have reported high prevalence rates of MDR E. coli in pigs and pig production environments globally. Resistance to multiple antibiotic classes, including β-lactams, tetracyclines, fluoroquinolones, and polymyxins, has been reported in Asian countries such as Thailand and China [ 14 , 15 ]. Similar findings have been reported in Europe, where resistance to critically important antimicrobials, such as third-generation cephalosporins and colistin, has been detected in both commensal and pathogenic E. coli from pigs [ 17 ]. In Africa, studies from Nigeria, Ghana, and Kenya reported the occurrence of extended-spectrum β-lactamase (ESBL)-producing E. coli and plasmid-mediated colistin resistance genes ( mcr-1 , mcr-3 ) in swine populations and farm workers, highlighting the zoonotic potential of these resistant strains [ 18 – 20 ]. In Nigeria, irrational antibiotic use in livestock remains a challenge, driven by easy over-the-counter access (OTC), limited veterinary supervision, and low awareness of withdrawal periods and AMR risks [ 21 ]. Previous studies have identified E. coli with high resistance to tetracyclines, β-lactams, and fluoroquinolones in poultry, cattle, and swine [ 16 , 22 – 24 ]. Despite these alarming reports, surveillance data are fragmented, and information from the pig production chain remains scarce. To date, little is known about the prevalence of MDR E. coli in pigs, handlers, and farm effluents within the southern-southern Nigerian subregion. This study therefore bridges this knowledge gap by determining the prevalence and resistance patterns of multidrug-resistant E. coli isolates from pigs, pig handlers, and farm effluents in Calabar, Cross River State, Nigeria. This study also evaluated farmers’ awareness of antimicrobial resistance, thereby providing empirical evidence to support health-based interventions for improved antibiotic stewardship, surveillance, and food safety within the pig production system in Nigeria. Results Farm Characteristics From a total of 17 farms were visited, 10 (58.8%) were in urban areas, with 14 (82.4%) having less than 5 years in operation. All the sampled farms (100%) raised pigs mainly for pork production, and 8 (47.1%) of the farmers kept other animals on the pig farm, with fish and poultry (n = 5 and 62.5%, respectively) being the animals mostly kept with pigs. The principal source of water for most farms was boreholes (15 (88.2%) (Table 1 ). Table 1 Farm characteristics Variable Frequency (%) Farm operation (Years) 1–5 14 (82.4) > 5 3 (17.6) Farm location Semiurban 7 (41.2) Urban 10 (58.8) Keep other animals on the farm Yes 8 (47.1) No 9 (52.9) Other animals kept on the farm Fish 5 (62.5) Poultry 5 (62.5) Rabbit 1 (12.5) Goat 2 (25.0) Other 1 (12.5) Major source of water Borehole 15 (88.2) Stream 1 (5.9) Well 1 (5.9) Veterinarian/animal health professional in charge of farm Yes 15 (88.2) No 2 (11.8) Sociodemographic characteristics of the human participants We sampled 30 humans, 25 (83.3%) of whom were males. The mean age of the respondents was 36.2 ± 10.3 years. The majority (20, 66.7%) of the respondents were between 30 and 49 years old, with 25 (83.3%) working as farm attendants. Fifteen (50.0%) of the respondents had only primary education, followed by 11 (36.7%) with secondary education, and sixteen (53.3%) of the participants were single (Table 2 ). Table 2 Sociodemography of the respondents Variable Frequency (%) Sex Female 5 (16.7) Male 25 (83.3) Age (Years; Mean age 36.2 ± 10.3) < 20 1 (3.3) 20–29 6 (20.0) 30–39 11 (36.7) 40–49 9 (30.0) ≥ 50 3 (10.0) Marital status Married 14 (46.7) Single 16 (53.3) Level of education No formal education 1 (3.5) Primary 15 (50.0) Secondary 11 (36.7) Tertiary 3 (10.0) Role of respondent Farm attendant/worker 25 (83.3) Farm owner 4 (13.3) Animal health worker 1 (3.3) Antimicrobial Use and Related Factors in Sampled Farms Fifteen out of 18 (88.2%) farms reported using antibiotics on a regular basis, with 10 (66.8%) using antibiotics at least once a month and 5 (33.3%) using antibiotics two or more times a month. The majority (12, 80.0%) used antimicrobials for preventive purposes. A similar number of antibiotics (12, 80.0%) were used to treat diseases, whereas 2 (13.3%) used antibiotics as growth promoters. Most (14, 93.3%) of the participants had never heard about the withdrawal period for antibiotics, and only one (6.7%) respondent said he knew about withdrawal and observed the recommended withdrawal periods before slaughter. Fifteen (88.2%) of the farms had a veterinarian/animal health specialist who visited the farm on call. Most (94.1%) of the farms used or sold pig dung as manure for vegetable farming. Only 2 (11.8%) of the farms had functional foot dips (Table 3 ). Table 3 Antimicrobial use and related factors in the sampled farms Variable Frequency (%) Use antibiotics Yes 15 (88.2) No 2 (11.8) Frequency of antibiotics use At least once a month 10 (66.8) At least twice a month 2 (13.3) Every 2 weeks 1 (6.8) More than twice a month 2 (13.3) Reason for antibiotics use Growth promotion 2 (13.3) For prophylaxes 12 (80.0) Treatment 12 (80.0) Heard of withdrawal period Yes 1 (6.7) No 14 (93.3) Observe withdrawal period Yes 1 (100.0) No 0 (0.0) Consultation by specialist Veterinarian/Trained animal health worker Yes 15 (88.2) No 2 (11.8) Handling of sick animals Call another farm owner to treat the sick animal 2 (100.0) Sell off sick animal 2 (100.0) Slaughter and sell 2 (100.0) Treat by myself 1 (50.0) Have a functional foot dip Yes 2 (11.8) No 15 (88.8) Sell/use pig manure for crop farming Yes 16 (94.1) No 1 (5.9) Distribution of Samples and Prevalence of E. coli A total of 210 samples were collected from 17 farms over the study period. The samples included 152 (72.4%) pig, 30 (14.3%) human and 28 (13.3%) effluent samples. The prevalence of E. coli in the collected samples was as follows: pig, 63.8% (97/152); human, 40.0% (12/30); and effluent, 35.7% (10/28). The prevalence rates from the 17 farms were 57.8% (pigs), 34.6% (humans) and 39.2% (effluent) (Table 4 ). Table 4 Distribution of samples collected and prevalence of E. coli Farm Pigs Farm workers Effluents Samples No. of positives (%) Samples No. of positives (%) Samples No. of positives (%) A 30 24 (80.0) 8 3 (37.5) 3 1 (33.3) B 12 8 (66.7) 1 0 (0.0) 3 0 (0.0) C 10 7 (70.0) 2 2 (100.0) 2 1 (50.0) D 2 1 (50.0) 1 0 (0.0) 1 1 (100.0) E 2 1 (50.0) 1 0 (0.0) 1 0 (0.0) F 2 1 (50.0) 2 0 (0.0) 1 1 (100.0) G 2 1 (50.0) 1 0 (0.0) 1 0 (0.0) H 4 2 (50.0) 1 0 (0.0) 1 1 (100.0) I 4 2 (50.0) 1 1 (100.0) 1 0 (0.0) J 4 3 (75.0) 2 2 (100.0) 1 0 (0.0) K 12 8 (66.7) 2 0 (0.0) 2 0 (0.0) L 4 1 (25.0) 1 0 (0.0) 1 0 (0.0) M 26 15 (57.7) 2 2 (100.0) 3 1 (33.3) N 7 4 (57.1) 2 1 (50.0) 2 1 (50.0) O 12 6 (50.0) 1 1 (100.0) 1 1 (100.0) P 10 9 (90.0) 1 0 (0.0) 2 1 (50.0) Q 9 4 (44.4) 1 0 (0.0) 2 1 (50.0) Total 152 97 (63.8) 30 12 (40.0) 28 10 (35.7) Antimicrobial susceptibility patterns The antimicrobial susceptibility patterns of E. coli isolates obtained from pigs, humans, and effluents revealed widespread resistance to multiple classes of antibiotics. In the aminoglycoside class, 36 (37.1%) of the 97 pig isolates, 5 (41.7%) of the 12 human isolates, and 7 (70.0%) of the 10 effluent isolates presented resistance to gentamicin, resulting in an overall resistance rate of 40.3%. Resistance to fluoroquinolones was high, with 95 (79.8%) of all the isolates resistant to ciprofloxacin, including 9 (90%) of the environmental isolates and 9 (75%) of the human isolates. Similarly, a high level of resistance was observed to the cephamycin antibiotic cefoxitin, where 99 (83.2%) of all the isolates were resistant, comprising 80 (82.5%) pig isolates and 11 (91.7%) human isolates. Resistance to tetracycline was universal among human and environmental isolates (100%) and prevalent in pig isolates (82.5%). Furthermore, all 119 (100%) E. coli isolates demonstrated complete resistance to cephalosporins (ceftazidime and cefotaxime), polymyxin (colistin), and penicillin (ampicillin), indicating multidrug resistance across all sources. However, a moderate degree of susceptibility was observed for azithromycin (75%), chloramphenicol (66.7%), and gentamicin (58.3%) among the human isolates (Table 5 ). Table 5 Antimicrobial susceptibility patterns of isolates from pigs, farm workers and effluents S/N Class Antibiotic RBP Pig (n = 97) Farm workers (n = 12) Effluent (n = 10) Susceptible (%) Resistant (%) Susceptible (%) Resistant (%) Susceptible (%) Resistant (%) 1 Aminoglycosides Gentamycin (5 µg) < 15 61 (62.9) 36 (37.1) 7 (58.3) 5 (41.7) 3 (30.0) 7 (70.0) 2 Fluoroquinolones Ciprofloxacin (30 ug) < 21 20 (20.6) 77 (79.4) 3 (25.0) 9 (75.0) 1 (10.0) 9 (90.0) 3 Cephamycins Cefoxitin (30 µg) < 14 17 (17.5) 80 (82.5) 1 (8.3) 11 (91.7) 2 (20.0) 8 (80.0) 4 Tetracyclines Tetracycline (30 µg) < 11 17 (17.5) 80 (82.5) 0 (0.0) 12 (100.0) 0 (0.0) 10 (100.0) 5 Nitrofurans Nitrofurantoin (300 µg) < 14 31 (32.9) 66 (68.0) 2 (16.7) 10 (83.3) 1 (10.0) 9 (90.0) 6 Cephalosporins Ceftazidime (30 µg) < 17 0 (0.0) 97 (100.0) 0 (0.0) 12 (100.0) 0 (0.0) 10 (100.0) Cefotaxime (30 µg) < 22 0 (0.0) 97 (100.0) 0 (0.0) 12 (100.0) 0 (0.0) 10 (100.0) 7 Polymixins Colistin (10 µg) 0 (0.0) 97 (100.0) 0 (0.0) 12 (100.0) 0 (0.0) 10 (100.0) 8 Phenicols Chloramphenicol (30 µg) < 12 62 (63.9) 35 (36.1) 8 (66.7) 4 (33.3) 6 (60.0) 40 (40.0) 9 Carbapenems Imipenem (10 µg) < 19 29 (29.9) 68 (70.1) 5 (41.7) 7 (58.3) 7 (70.0) 3 (30.0) 10 Penicillins Ampicillin (10 µg) < 13 0 (0.0) 97 (100.0) 0 (0.0) 12 (100.0) 0 (0.0) 10 (100.0) 11 Macrolides Azythromycin < 12 70 (72.2) 27 (27.8) 8 (75.0) 4 (25.0) 8 (80) 2 (20) Antibiotic Patterns and Multiple Antibiotic Resistance (MAR) Indices of E. coli Isolates The MDR pattern of the E. coli isolates revealed that 9 (9.3%) of the pig isolates were resistant to 7 antibiotic combinations from 6 classes. At least one isolate from each sample (human, pig, or effluent) was resistant to a combination of ceftazidime, colistin, cefotaxime or ampicillin. None (0.0%) of the isolates were pansusceptible to all classes of antibiotics used in this study. The multiple antibiotic resistance index for all the isolates from pigs, humans and the environment was between 0.5 and 0.9 (Table 6 ). Table 6 Antibiogram patterns and multiple antibiotic resistance (MAR) indices of E. coli isolates Pattern No. of resistant isolates No. of antibiotic classes resistant MARI Pigs (n = 97) Farm workers (n = 12) Effluents (n = 10) Pansusceptible 0 0 0 0 0 0 FoxTetCefAmpTaxCol 6 5 0.5 4 0 0 CipTetCefAmpTaxCol 6 5 0.5 0 1 0 CipFoxNitCefAmpTaxCol 7 6 0.6 4 0 0 CipFoxNitCefAmpTaxCol 7 6 0.6 9 0 0 TetFoxCefNemAmpTaxCol 7 6 0.6 1 1 0 FoxTetNitCefAmpTaxCol 7 6 0.6 0 1 0 CipFoxTetNitCefAmpTaxCol 8 7 0.7 6 1 2 GenCipTetNitCefAmpTaxCol 8 7 0.7 1 0 1 CipTetNitCefChlAmpTaxCol 8 7 0.7 2 1 0 CipFoxTetNitCefNemAmpTaxCol 9 8 0.8 2 1 0 CipFoxTetNitCefChlAmpTaxCol 9 8 0.8 6 0 1 GenCipFoxTetNitCefAmpTaxCol 9 8 0.8 3 0 2 CipFoxNitCefChlAztAmpTaxCol 9 8 0.8 1 1 0 CipFoxTetNitCefChlNemAmpTaxCol 10 9 0.8 2 1 0 GenCipFoxTetNitCefAztAmpTaxCol 10 9 0.8 1 1 0 CipFoxTetNitCefChlNemAmpTaxCol 10 9 0.8 0 1 0 GenCipFoxtetNitCefChlNemAmpTaxCol 11 10 0.9 1 1 1 GenCipFoxTetNitCefNemAztAmpTaxCol 11 10 0.9 0 1 0 GenCipFoxTetNitCefChlNemAztAmpTaxCol 12 11 0.9 4 0 0 Gen: Gentamycin; Amp: ampicillin; Tet: Tetracycline; Cip: Ciprofloxacin; Azt: Azithromycin; Fox: Cefoxitin; Nit: Nitrofuran; Cef: ceftazidime; Chl: Chloramphenicol; Nem: Imipenem; Col: Colistin; Tax: Cefotaxime; Pansusceptible, susceptible, susceptible to all tested antimicrobials. MARI: Multiple antibiotic resistance index Risk Factors for MDR E. coli in Human Subjects Thirteen (43.3%) participants used dedicated clothes for farming activities. Those without dedicated farm clothing were approximately 4 times more likely to have multidrug-resistant E. coli than those with dedicated farm clothing were (OR: 3.8, CI: 0.8–18.6, p > 0.05). The majority (25, 83.3%) of the participants did not use personal protective equipment (PPE) when cleaning the farm or when handling pigs or even pork. This group was 3 times more likely to have a multidrug-resistant E. coli isolate than were those who used PPE (OR: 3.1, CI: 0.3–32.3, p > 0.05). Those who were involved in processing pork were 2 times more likely to have multidrug-resistant E. coli than those who were not (OR: 2.2, CI: 0.2–24.1, p > 0.05). We also found that those who washed their hands regularly before eating were 2 times less likely to have MDR E. coli than those who did not wash their hands (OR: 2.3; CI: 0.5–11.5, p > 0.05). Overall, 93% of the respondents showed poor awareness, with only 6.7% able to correctly identify antibiotic withdrawal requirements or recognize E. coli as a zoonotic pathogen (Table 7 ). Table 7 Risk factors for MDR E. coli in farm workers Variable Presence of MDR E. coli (N = 30) Odd Ratio (95% Confidence interval) p value Yes No Use dedicated farm cloths No 9 (52.9%) 8 (47.1%) 3.8 (0.8–18.6) >0.05 Yes 3 (23.1%) 10 (76.9%) Involved with pork processing Yes 11 (42.3%) 15 (57.7%) 2.2 (0.2–24.1) >0.05 No 1 (25.0%) 3 (75.0%) Use PPE 11 (44.0%) 14 (56.0%) 3.1 (0.3–32.3) >0.05 Yes 1 (20.0%) 4 (80.0%) HAND HYGIENE PRACTICES Wash hands before handling pork No 10 (47.6%) 11 (52.4%) 3.2 (0.5–19.1) >0.05 Yes 2 (22.2%) 7 (77.8%) Wash hands after handling Pork No 4 (50.0%) 4 (50.0%) 1.8 (0.3-9.0) >0.05 Yes 8 (36.4%) 14 (63.6%) Wash hands before eating No 5 (62.5%) 3 (37.5%) 2.3 (0.5–11.5) >0.05 Yes 7 (31.8%) 15 (68.2%) Wash hands after eating No 0 (0.0%) 4 (100%) undefined undefined Yes 12 (46.2%) 14 (53.8%) Wash hands after using the toilet No 0 (0.0%) 1 (100.0%) undefined undefined Yes 12 (41.2%) 17 (56.6%) Awareness of pathogenic E. coli No 12 (42.9%) 16 (57.1%) undefined undefined Yes 0 (0.0%) 2 (100.0%) Awareness of other zoonoses No 11 (40.7%) 16 (59.3%) 1.4 (0.1–17.1) >0.05 Yes 1 (33.3%) 2 (66.7%) Discussion The findings of this study revealed a high prevalence of E. coli among pigs, pig handlers, and farm effluents in Calabar, Cross River State, Nigeria. This result highlights the extensive circulation of E. coli within the pig production ecosystem, reflecting the ubiquitous presence of this bacterium and its potential for cross-species transmission. Similar to previous studies conducted in Nigeria [ 32 – 34 ] and other low- and middle-income countries [ 14 , 16 ], the detection of E. coli across animal, human, and environmental samples reinforces the interconnectedness of the animal‒human‒environmental interface, which is a key premise of the One Health paradigm in antimicrobial resistance (AMR) control. The greater prevalence of E. coli in pigs than in humans and effluents suggests that pigs serve as the principal reservoir from which resistant strains may spread to handlers and the environment. This finding is consistent with previous findings [ 35 , 36 ] that reported swine as major reservoirs for multidrug-resistant E. coli in Nigerian pig farms. The relatively lower prevalence in human handlers may reflect differences in exposure intensity, hygiene practices, or host susceptibility [ 37 ]. However, the detection of E. coli in effluent samples indicates environmental contamination and potential dissemination of resistant bacteria beyond farm boundaries, contributing to wider ecological exposure. The widespread use of pig dung as manure reported in this study further highlights the risk of environmental dissemination and transmission through soil and crops, as previously highlighted [ 38 ]. This study revealed alarming resistance patterns, with all the isolates showing 100% resistance to the cephalosporin, polymyxin, and penicillin classes considered critically important for both human and veterinary medicine. The high resistance to fluoroquinolones and tetracycline mirrors global and regional trends where these antibiotics are widely misused in livestock [ 34 , 38 , 39 ]. Resistance to colistin, a last-resort antibiotic in human medicine, is particularly concerning and suggests possible circulation of plasmid-mediated mcr genes, as previously reported in Nigerian livestock farms [ 40 ]. The complete resistance to cephalosporins and penicillins points toward extensive dissemination of extended-spectrum β-lactamases (ESBLs) or AmpC β-lactamases among the isolates [ 41 ], further emphasizing the urgency of molecular surveillance. The observed multidrug resistance (MDR) in 100% of the isolates and the high multiple antibiotic resistance index (MARI) values indicate continuous and high-risk exposure of these bacterial populations to antimicrobial agents. MARI values exceeding 0.2 are indicative of environments where antibiotics are frequently and indiscriminately used, reflecting high selective pressure for antimicrobial resistance. The findings of this study therefore align with previous reports documenting widespread misuse of antibiotics in Nigerian animal production systems, largely driven by over-the-counter availability, weak regulatory enforcement, and limited veterinary oversight [ 34 , 39 ]. This pattern reinforces the critical need for stricter antibiotic stewardship and regulatory enforcement within livestock systems. Antibiotic use on the sampled farms was both extensive and poorly regulated. Most farmers administer antimicrobials for prophylactic and therapeutic purposes, with minimal understanding of withdrawal periods or antimicrobial resistance implications. This aligns with the findings of Daodu [ 42 ], who reported that a lack of knowledge and weak veterinary extension services significantly contributed to AMR emergence in Nigerian livestock systems. The low awareness of antimicrobial resistance (AMR) among pig handlers, where over 90% had never heard of Escherichia coli or understood its public health relevance, illustrates a substantial educational gap. Similar findings have been reported among livestock farmers in Nigeria and other sub-Saharan African countries, indicating that AMR literacy remains a systemic challenge across the region [ 42 – 44 ]. Effective awareness and training programs targeting farmers, butchers, and animal health workers are therefore essential to mitigate the misuse of antibiotics and reduce occupational exposure risks. The detection of MDR E. coli in humans working directly with pigs highlights the zoonotic potential of resistant bacterial strains in the pig production environment. Human colonization may occur via direct contact with animals, contaminated feed, aerosols, or effluents. This aligns with the findings of Strasheim [ 45 ] in South Africa, who demonstrated shared sequence types and clonality between E. coli isolates from pigs and their handlers. The presence of MDR E. coli in effluents further underscores environmental contamination as a critical conduit for resistance dissemination. Effluents can contaminate surface and groundwater, thereby facilitating the spread of resistance genes to other bacterial populations, including environmental and human commensals [ 46 ]. Although most risk factors in this study were not statistically significant, the findings suggest that occupational exposure is a key determinant of MDR E. coli carriage. The participants without dedicated farm clothing or personal protective equipment presented greater odds of carriage; this finding is consistent with the findings of similar studies [ 47 , 48 ], which reported that butchers and slaughterhouse workers had significantly higher MDR E. coli prevalence, especially when they were engaged in waste collection, were eating at work, or were in settings lacking sanitary facilities. The limited use of PPE and the absence of basic biosecurity measures such as footbaths indicate systemic weaknesses in farm hygiene and occupational health practices. These gaps not only facilitate the transmission of resistant pathogens between animals and humans but also increase the likelihood of community-level dissemination. The resistance profile observed in this study, which was characterized by high-level MDR and widespread resistance to critically important antimicrobials, aligns with global concerns about antimicrobial misuse in food animals. The World Organization for Animal Health (WOAH) has repeatedly emphasized the need to curtail antibiotic use in livestock, especially those classes vital for human health [ 49 , 50 ]. From a public health standpoint, the circulation of MDR E. coli across pigs, humans, and the environment in Calabar represents a potential reservoir for horizontal gene transfer to other pathogens, thereby increasing the risk of untreatable infections. Without urgent interventions such as farm-level antibiotic stewardship, waste treatment, and hygiene improvement, these resistance determinants could spread beyond the livestock sector into clinical and community settings. Conclusion This study demonstrated the widespread occurrence of multidrug-resistant E. coli across pigs, humans, and environmental samples in Calabar, Nigeria. The high MARI values, extensive resistance to multiple antibiotic classes, including last-resort drugs such as colistin, and low awareness of AMR among farmers highlight the urgent need for coordinated one-health interventions. Strengthening antibiotic stewardship, promoting biosecurity and hygiene practices, and enhancing AMR surveillance in the livestock sector are crucial steps toward mitigating the spread of antimicrobial resistance in Nigeria and similar LMIC settings. Future studies should integrate molecular genotyping and longitudinal surveillance to elucidate transmission dynamics, particularly between animals and handlers. Moreover, environmental sampling should be expanded to include soil, water, and vegetable samples to better understand the ecological dissemination of resistance. Materials and methods Study Area This study was conducted in Calabar, the capital of Cross River State, which comprises two local government areas: Calabar South and Calabar Municipality (Fig. 1 ). The city is located at latitude 4° 57' 32 N and longitude 8° 19' 37E, with a land mass of approximately 406 km², a population of 657,000 according to the 2006 census, and a population growth rate of 4.12% [ 25 ]. It hosts a growing pig industry dominated by small- and medium-scale farms that rely heavily on antibiotics for disease prevention and growth promotion [ 25 ]. Pork is widely consumed in cities, with untreated pig waste often disposed of in surrounding environments, creating multiple pathways for resistant bacteria to spread among animals, humans, and ecosystems [ 25 ]. Study Design, Sampling Population and Sizes The study was a cross-sectional study design, and a nonprobabilistic convenience sampling approach was used, where farms were selected on the basis of accessibility and willingness to participate. Within each participating farm, pigs were sampled purposively to include apparently healthy animals across different pens, whereas human samples were collected from willing farm attendants. Effluent samples were obtained from discharge points directly connected to pig waste outlets. A list of registered pig farms in the Calabar metropolis was obtained from the Cross River State Department of Veterinary Services. The farms were stratified into small- and large-scale farms, with farms with fewer than 500 pigs considered small scale and those with more than 500 pigs considered large scale. Verbal consent was obtained from all the human study participants, as witnessed by the lead author. All criteria for the inclusion of farm attendants were those who had spent up to six months on the farms. Farm owners or attendants with long-standing health issues or comorbidities were exempted from the study. Additionally, sick pigs receiving antimicrobial therapy were excluded. The sample size was determined according to the formula described by Thrusfield [ 27 ]: N = Z 2 PQ/D 2 . N = sample size, Z = appropriate value for the standard normal deviation for the desired confidence level = (1.96), P = anticipated prevalence rate, Q = 1 - P, D = desired absolute precision (5%). A prevalence of 8.6% [ 28 ] was used for this study: $$\:N=\frac{{1.96}^{2}\:\times\:\:0.86\:(1-0.86)}{0.0025}\:\:=\:\:126$$ The sample size for humans was determined by the number of pig farm attendants who were willing to participate in the study. Data and Sample Collection Questionnaire Administration Enumerators who were fluent in the local language were trained to administer questionnaires to human participants. The data collection tool was a semistructured interviewer-administered electronic questionnaire designed via the KoboToolbox (Harvard Humanitarian Initiative, Cambridge, MA, USA) and mounted on the Open Data Kit (ODK) mobile application (ODK, Seattle, WA, USA). The questionnaire had two distinct sections: the first section comprised questions pertaining to the sociodemographic characteristics of the respondents, whereas the second section comprised questions on potential exposure factors/variables for multidrug-resistant E. coli. , such as the use of antibiotics for growth promotion on farms, farming experience, and the use of personal protective equipment (PPE) on farms. The outcome variable was the presence or absence of multidrug-resistant E. coli on the farm. Fecal samples Fecal samples were collected from selected pigs and willing human participants via sterile sample bottles containing Amies transport medium. Fecal samples from pens with multiple pigs were pooled. The samples were stored at 4°C in Giostyle containing ice packs and transported to the Microbiology Laboratory, University of California, for analysis. Environmental Samples A total of 28 effluent samples were collected, with at least one effluent sample collected from each farm environment, from channels connecting to wastes from the farms. Microbiological analysis Isolation of E. coli from Faecal Samples The faecal samples were cultured in lactose broth enrichment medium and incubated at 37°C for 24 h in an incubator. After 24 h of incubation, the enriched cultures were plated via direct streaking on prepared MacConkey agar poured into sterile Petri dishes. The plates were incubated in an inverted position at 37°C for 24 h. The 24 h MacConkey culture plate was viewed and observed for possible isolation of presumptive E. coli colonies. The pinkish colonies observed were inferred to be presumptive E. coli colonies, which were further inoculated on Eosin Methylene Blue (EMB) agar and incubated at 37°C for 24 h for preconfirmation. Colonies showing a green metallic sheen on EMB after 24 h of incubation were preconfirmed as E. coli and further subjected to IMViC tests and carbohydrate utilization tests for further confirmation. Isolation of E. coli from Environmental Samples A tenfold serial dilution of each of the samples was carried out using buffered peptone water. Appropriate dilutions were made by mixing 9 mL of peptone water into ten test tubes in different test tube racks. MacConkey agar was prepared according to the manufacturer’s instructions, and both the peptone water blank in the test tubes and the media were sterilized by autoclaving at 150 psi at 121°C for 15 min. After the samples were serially diluted, 0.1 mL of each dilution was spread in triplicate on a MacConkey agar plate and incubated at 37°C for 24 h. Pinkish colonies observed on the MacConkey agar plates were inferred as presumptive E. coli isolates, which were further inoculated on prepared EMB agar for precirmation if the colonies were metallic. Purification and Stock Culturing Distinct preconfirmed E . coli isolates were subcultured repeatedly on nutrient agar plates and incubated at 37°C for 24 h. Pure isolates were cultured on nutrient agar slants in Mac Cartney bottles for further biochemical analysis. Characterization and identification of preconfirmed isolates Preconfirmed Escherichia coli isolates were characterized via standard biochemical tests, including the IMViC series and carbohydrate utilization tests [ 29 ], and were identified according to the criteria described in Bergey’s Manual of Determinative Bacteriology [ 30 ]. Antibiotic susceptibility tests Disc diffusion (Kirby-Bauer), which conforms to the recommended standard of the Clinical and Laboratory Standards Institute (CLSI [ 31 ]), was employed, and the results were interpreted using the same standards and with the use of commonly prescribed and important antibiotics in Nigeria. Eleven classes of antibiotics (Oxoid UK) were used, namely, tetracyclines (Tetracycline-30 µg), penicillin (Ampicillin-10 µg), phenicols (Chloramphenicol-30 µg), carbapenem (Imipenem-10 µg), cephalosporins (Cefotaxime-30 µg and Ceftazidime-30 µg), aminoglycosides (Gentamicin-5 µg), nitrofurans (Nitrofurantoin-300 µg), polymyxins (Colistin-30 µg), fluoroquinolones (Ciprofloxacin-30 µg) and cefoxitin-30 µg). Statistical analysis The data collected at ODK were exported into Microsoft Excel 2019 software. The data were cleaned, coded and stored. Data analysis was performed via EpiInfo® (version 7.2.2). The questionnaire data were coded, and the results obtained from the laboratory analysis were treated as dichotomous variables for bivariate analysis. Data are presented as frequencies, proportions, and percentages. Bivariate analysis was carried out to explore the associations among each of the potential risk factors, including the use of personal protective equipment (PPE), hand hygiene practices (before eating, after handling pork, and after using the toilet), use of dedicated farm clothing, involvement in pork processing, awareness of E. coli and other zoonoses, antimicrobial use practices and the outcome variable (presence or absence of multidrug-resistant E. coli infection in human participants). The level of significance was set at p < 0.05. Declarations Clinical Trial Numbers Not Applicable Ethical Approval The research received ethical clearance from the University of Calabar Research and Ethics Committee and the Cross River State Ministry of Health with the approval number CRSMOH/RP/REC/2021/221. Conflict of interest None to be declared Consent to participate Written informed consent was obtained from the respondents. Funding None to be declared Author Contribution Aderigbigbe Adegunwa: Conceptualization, investigation, methodology, resources. Samuel Akpan: Data curation, methodology, writing – original draft preparation, and validation. Uduak Akpabio: Validation, writing – review & editing. Ayodeji Adegunwa: Software, Writing – review & editing. Mathias Besong: Validation, Writing – review & editing. Ini Bassey: Investigation, writing- review & editing. Samuel Owoicho: Writing – review & editing. Oluwawemimo Adebowale: Validation, writing- review & editing. Acknowledgement The authors wish to acknowledge the support of laboratory management and staff of the Department of Microbiology, University of Calabar, for their support during laboratory analysis. We also acknowledge Koli Adegunwa, Emmanuel Adegunwa, and Mabel Aworh Ajumobi for their support and meaningful contributions to the success of this research. Data Availability The raw data supporting the results of this study are available upon reasonable request from the corresponding author. References Ye Z, Li M, Jing Y, Liu K, Wu Y, Peng Z. What are the drivers triggering antimicrobial resistance emergence and spread? Outlook from a One Health perspective. Antibiotics. 2025;14(6):543. Olagunju OJ, Egbo B, Olayinka O, Majolagbe OG, Osanyinlusi OO, Titilade A, Atoyebi OF, Ojo IO, Dawha SD. Poorly regulated antibiotic use in Nigeria: a critical public health concern and its impact on medical practice. Cureus. 2025;17(6). 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In: Antimicrobial Strategies in the Food System: Updates, Opportunities, Challenges. Cham: Springer Nature Switzerland; 2025. p. 331–364. Abbreviations Gen: Gentamycin; Amp: ampicillin; Tet: Tetracycline; Cip: Ciprofloxacin; Azt: Azithromycin; Fox: Cefoxitin; Nit: Nitrofuran; Cef: ceftazidime; Chl: Chloramphenicol; Nem: Imipenem; Col: Colistin; Tax: Cefotaxime; Pansusceptible, susceptible, susceptible to all tested antimicrobials. MARI: Multiple antibiotic resistance index Additional Declarations No competing interests reported. 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20:36:11","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":199611,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7880879/v1/53e03e4c46e1a53ff7e8bfe9.html"},{"id":93969454,"identity":"886032b1-236e-42ec-9df3-665af6528666","added_by":"auto","created_at":"2025-10-20 20:36:11","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35261,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Cross River State showing the study locations (map created via QGIS 2.18.1)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7880879/v1/6e728f04d9630fc1589e4e95.jpeg"},{"id":93970048,"identity":"e0e6293d-8d9d-48ca-831f-3b4eb519d22b","added_by":"auto","created_at":"2025-10-20 20:52:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1906720,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7880879/v1/e2cacf36-0afe-4385-b515-2dc659f9e7c6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Isolation and characterization of multidrug-resistant Escherichia coli in pigs, farm workers and effluents in Calabar, southern Nigeria","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAntimicrobial resistance (AMR) has emerged as one of the most formidable global public health threats of the 21st century, undermining decades of medical and veterinary progress [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The World Health Organization (WHO) warns that without urgent action, society risks entering a postantibiotic era in which common infections become untreatable and routine medical procedures are increasingly life-threatening [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Among the microorganisms of greatest concern, \u003cem\u003eEscherichia coli\u003c/em\u003e (\u003cem\u003eE. coli\u003c/em\u003e) plays a central role as both a commensal and a pathogenic bacterium capable of acquiring and transmitting resistance genes across human, animal, and environmental interfaces [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This characteristic makes \u003cem\u003eE. coli\u003c/em\u003e an important indicator of antimicrobial use and resistance patterns within communities and a sentinel organism in One Health surveillance systems [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The intensification of livestock production, particularly in low- and middle-income countries (LMICs), has been accompanied by widespread and often unregulated use of antibiotics for prophylaxis, meta-phylaxis, and growth promotion [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In food-producing animals such as pigs, frequent exposure to antimicrobial agents creates selective pressure for the emergence of multidrug-resistant (MDR) bacterial populations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These resistant strains can be disseminated via multiple pathways, such as direct animal‒human contact, consumption of contaminated meat, and environmental contamination through farm effluents or manure used as fertilizer [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Such interlinked routes of transmission emphasize the necessity of the one-health framework, which recognizes that the health of humans, animals, and the environment is interconnected and must be addressed collectively to combat AMR effectively [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e is a gram-negative, rod-shaped bacterium of the family \u003cem\u003eEnterobacteriaceae\u003c/em\u003e that is widely distributed in the intestinal tracts of humans and animals [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Although most strains are harmless, pathogenic variants such as enterotoxigenic \u003cem\u003eE. coli\u003c/em\u003e (ETEC), enterohemorrhagic \u003cem\u003eE. coli\u003c/em\u003e (EHEC), and Shiga toxin-producing \u003cem\u003eE. coli\u003c/em\u003e (STEC) cause severe diarrheal and systemic diseases in both humans and animals [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In pigs, \u003cem\u003eE. coli\u003c/em\u003e is a leading cause of neonatal and postweaning diarrhea and is responsible for considerable economic losses, poor growth performance, and high piglet mortality rates [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The animal intestine provides an ideal environment for gene exchange between commensal and pathogenic \u003cem\u003eE. coli\u003c/em\u003e, enabling the acquisition and spread of resistance determinants [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Consequently, pigs have been identified as significant reservoirs of antimicrobial-resistant bacteria and resistance genes that can be transferred to humans via direct contact, pork consumption, or environmental exposure [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral studies have reported high prevalence rates of MDR \u003cem\u003eE. coli\u003c/em\u003e in pigs and pig production environments globally. Resistance to multiple antibiotic classes, including β-lactams, tetracyclines, fluoroquinolones, and polymyxins, has been reported in Asian countries such as Thailand and China [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Similar findings have been reported in Europe, where resistance to critically important antimicrobials, such as third-generation cephalosporins and colistin, has been detected in both commensal and pathogenic \u003cem\u003eE. coli\u003c/em\u003e from pigs [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In Africa, studies from Nigeria, Ghana, and Kenya reported the occurrence of extended-spectrum β-lactamase (ESBL)-producing \u003cem\u003eE. coli\u003c/em\u003e and plasmid-mediated colistin resistance genes (\u003cem\u003emcr-1\u003c/em\u003e, \u003cem\u003emcr-3\u003c/em\u003e) in swine populations and farm workers, highlighting the zoonotic potential of these resistant strains [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Nigeria, irrational antibiotic use in livestock remains a challenge, driven by easy over-the-counter access (OTC), limited veterinary supervision, and low awareness of withdrawal periods and AMR risks [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Previous studies have identified \u003cem\u003eE. coli\u003c/em\u003e with high resistance to tetracyclines, β-lactams, and fluoroquinolones in poultry, cattle, and swine [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Despite these alarming reports, surveillance data are fragmented, and information from the pig production chain remains scarce. To date, little is known about the prevalence of MDR \u003cem\u003eE. coli\u003c/em\u003e in pigs, handlers, and farm effluents within the southern-southern Nigerian subregion. This study therefore bridges this knowledge gap by determining the prevalence and resistance patterns of multidrug-resistant \u003cem\u003eE. coli\u003c/em\u003e isolates from pigs, pig handlers, and farm effluents in Calabar, Cross River State, Nigeria. This study also evaluated farmers\u0026rsquo; awareness of antimicrobial resistance, thereby providing empirical evidence to support health-based interventions for improved antibiotic stewardship, surveillance, and food safety within the pig production system in Nigeria.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eFarm Characteristics\u003c/h2\u003e\u003cp\u003eFrom a total of 17 farms were visited, 10 (58.8%) were in urban areas, with 14 (82.4%) having less than 5 years in operation. All the sampled farms (100%) raised pigs mainly for pork production, and 8 (47.1%) of the farmers kept other animals on the pig farm, with fish and poultry (n\u0026thinsp;=\u0026thinsp;5 and 62.5%, respectively) being the animals mostly kept with pigs. The principal source of water for most farms was boreholes (15 (88.2%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFarm characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarm operation (Years)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14 (82.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3 (17.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFarm location\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSemiurban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7 (41.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10 (58.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eKeep other animals on the farm\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8 (47.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9 (52.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOther animals kept on the farm\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFish\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5 (62.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoultry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5 (62.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRabbit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (12.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (25.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (12.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMajor source of water\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBorehole\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15 (88.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStream\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (5.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (5.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVeterinarian/animal health professional in charge of farm\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15 (88.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (11.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSociodemographic characteristics of the human participants\u003c/h3\u003e\n\u003cp\u003eWe sampled 30 humans, 25 (83.3%) of whom were males. The mean age of the respondents was 36.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3 years. The majority (20, 66.7%) of the respondents were between 30 and 49 years old, with 25 (83.3%) working as farm attendants. Fifteen (50.0%) of the respondents had only primary education, followed by 11 (36.7%) with secondary education, and sixteen (53.3%) of the participants were single (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSociodemography of the respondents\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5 (16.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25 (83.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (Years; Mean age 36.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (3.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6 (20.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11 (36.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9 (30.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3 (10.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14 (46.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16 (53.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLevel of education\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo formal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (3.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15 (50.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11 (36.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3 (10.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRole of respondent\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarm attendant/worker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25 (83.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarm owner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4 (13.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnimal health worker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (3.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eAntimicrobial Use and Related Factors in Sampled Farms\u003c/h3\u003e\n\u003cp\u003eFifteen out of 18 (88.2%) farms reported using antibiotics on a regular basis, with 10 (66.8%) using antibiotics at least once a month and 5 (33.3%) using antibiotics two or more times a month. The majority (12, 80.0%) used antimicrobials for preventive purposes. A similar number of antibiotics (12, 80.0%) were used to treat diseases, whereas 2 (13.3%) used antibiotics as growth promoters. Most (14, 93.3%) of the participants had never heard about the withdrawal period for antibiotics, and only one (6.7%) respondent said he knew about withdrawal and observed the recommended withdrawal periods before slaughter. Fifteen (88.2%) of the farms had a veterinarian/animal health specialist who visited the farm on call. Most (94.1%) of the farms used or sold pig dung as manure for vegetable farming. Only 2 (11.8%) of the farms had functional foot dips (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAntimicrobial use and related factors in the sampled farms\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUse antibiotics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15 (88.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (11.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFrequency of antibiotics use\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt least once a month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10 (66.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt least twice a month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (13.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEvery 2 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (6.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMore than twice a month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (13.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReason for antibiotics use\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrowth promotion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (13.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFor prophylaxes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12 (80.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12 (80.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHeard of withdrawal period\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (6.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14 (93.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eObserve withdrawal period\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eConsultation by specialist Veterinarian/Trained animal health worker\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15 (88.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (11.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHandling of sick animals\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCall another farm owner to treat the sick animal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSell off sick animal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlaughter and sell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreat by myself\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (50.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHave a functional foot dip\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (11.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15 (88.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSell/use pig manure for crop farming\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16 (94.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (5.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDistribution of Samples and Prevalence of\u003c/b\u003e \u003cb\u003eE. coli\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 210 samples were collected from 17 farms over the study period. The samples included 152 (72.4%) pig, 30 (14.3%) human and 28 (13.3%) effluent samples. The prevalence of \u003cem\u003eE. coli\u003c/em\u003e in the collected samples was as follows: pig, 63.8% (97/152); human, 40.0% (12/30); and effluent, 35.7% (10/28). The prevalence rates from the 17 farms were 57.8% (pigs), 34.6% (humans) and 39.2% (effluent) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of samples collected and prevalence of \u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFarm\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003ePigs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eFarm workers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eEffluents\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSamples\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo. of positives (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSamples\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo. of positives (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSamples\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo. of positives (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24 (80.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3 (37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8 (66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7 (70.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (50.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (75.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8 (66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15 (57.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (57.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (50.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9 (90.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (50.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (44.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (50.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e152\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e97 (63.8)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e30\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e12 (40.0)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e28\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e10 (35.7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eAntimicrobial susceptibility patterns\u003c/h3\u003e\n\u003cp\u003eThe antimicrobial susceptibility patterns of \u003cem\u003eE. coli\u003c/em\u003e isolates obtained from pigs, humans, and effluents revealed widespread resistance to multiple classes of antibiotics. In the aminoglycoside class, 36 (37.1%) of the 97 pig isolates, 5 (41.7%) of the 12 human isolates, and 7 (70.0%) of the 10 effluent isolates presented resistance to gentamicin, resulting in an overall resistance rate of 40.3%. Resistance to fluoroquinolones was high, with 95 (79.8%) of all the isolates resistant to ciprofloxacin, including 9 (90%) of the environmental isolates and 9 (75%) of the human isolates. Similarly, a high level of resistance was observed to the cephamycin antibiotic cefoxitin, where 99 (83.2%) of all the isolates were resistant, comprising 80 (82.5%) pig isolates and 11 (91.7%) human isolates. Resistance to tetracycline was universal among human and environmental isolates (100%) and prevalent in pig isolates (82.5%). Furthermore, all 119 (100%) \u003cem\u003eE. coli\u003c/em\u003e isolates demonstrated complete resistance to cephalosporins (ceftazidime and cefotaxime), polymyxin (colistin), and penicillin (ampicillin), indicating multidrug resistance across all sources. However, a moderate degree of susceptibility was observed for azithromycin (75%), chloramphenicol (66.7%), and gentamicin (58.3%) among the human isolates (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAntimicrobial susceptibility patterns of isolates from pigs, farm workers and effluents\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eS/N\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eClass\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAntibiotic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRBP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003ePig (n\u0026thinsp;=\u0026thinsp;97)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eFarm workers (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eEffluent (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSusceptible (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eResistant (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSusceptible (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eResistant (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSusceptible (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eResistant (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAminoglycosides\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGentamycin (5 \u0026micro;g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e61 (62.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e36 (37.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7 (58.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5 (41.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3 (30.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7 (70.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFluoroquinolones\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCiprofloxacin (30 ug)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20 (20.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e77 (79.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9 (75.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9 (90.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCephamycins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCefoxitin (30 \u0026micro;g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17 (17.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e80 (82.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e11 (91.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8 (80.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTetracyclines\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTetracycline (30 \u0026micro;g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17 (17.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e80 (82.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e12 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e10 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNitrofurans\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNitrofurantoin (300 \u0026micro;g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e31 (32.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e66 (68.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e10 (83.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9 (90.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCephalosporins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCeftazidime (30 \u0026micro;g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e97 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e12 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e10 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCefotaxime (30 \u0026micro;g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e97 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e12 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e10 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePolymixins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eColistin (10 \u0026micro;g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e97 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e12 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e10 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhenicols\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChloramphenicol (30 \u0026micro;g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e62 (63.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e35 (36.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8 (66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6 (60.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e40 (40.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCarbapenems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eImipenem (10 \u0026micro;g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29 (29.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e68 (70.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5 (41.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7 (58.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7 (70.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3 (30.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePenicillins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAmpicillin (10 \u0026micro;g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e97 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e12 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e10 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMacrolides\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAzythromycin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e70 (72.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e27 (27.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8 (75.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8 (80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2 (20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAntibiotic Patterns and Multiple Antibiotic Resistance (MAR) Indices of\u003c/b\u003e \u003cb\u003eE. coli\u003c/b\u003e \u003cb\u003eIsolates\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe MDR pattern of the \u003cem\u003eE. coli\u003c/em\u003e isolates revealed that 9 (9.3%) of the pig isolates were resistant to 7 antibiotic combinations from 6 classes. At least one isolate from each sample (human, pig, or effluent) was resistant to a combination of ceftazidime, colistin, cefotaxime or ampicillin. None (0.0%) of the isolates were pansusceptible to all classes of antibiotics used in this study. The multiple antibiotic resistance index for all the isolates from pigs, humans and the environment was between 0.5 and 0.9 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAntibiogram patterns and multiple antibiotic resistance (MAR) indices of \u003cem\u003eE. coli\u003c/em\u003e isolates\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePattern\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo. of resistant isolates\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo. of antibiotic classes resistant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMARI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePigs (n\u0026thinsp;=\u0026thinsp;97)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFarm workers (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEffluents (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePansusceptible\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFoxTetCefAmpTaxCol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCipTetCefAmpTaxCol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCipFoxNitCefAmpTaxCol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCipFoxNitCefAmpTaxCol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTetFoxCefNemAmpTaxCol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFoxTetNitCefAmpTaxCol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCipFoxTetNitCefAmpTaxCol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenCipTetNitCefAmpTaxCol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCipTetNitCefChlAmpTaxCol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCipFoxTetNitCefNemAmpTaxCol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCipFoxTetNitCefChlAmpTaxCol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenCipFoxTetNitCefAmpTaxCol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCipFoxNitCefChlAztAmpTaxCol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCipFoxTetNitCefChlNemAmpTaxCol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenCipFoxTetNitCefAztAmpTaxCol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCipFoxTetNitCefChlNemAmpTaxCol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenCipFoxtetNitCefChlNemAmpTaxCol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenCipFoxTetNitCefNemAztAmpTaxCol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenCipFoxTetNitCefChlNemAztAmpTaxCol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eGen: Gentamycin; Amp: ampicillin; Tet: Tetracycline; Cip: Ciprofloxacin; Azt: Azithromycin; Fox: Cefoxitin; Nit: Nitrofuran; Cef: ceftazidime; Chl: Chloramphenicol; Nem: Imipenem; Col: Colistin; Tax: Cefotaxime; Pansusceptible, susceptible, susceptible to all tested antimicrobials. MARI: Multiple antibiotic resistance index\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRisk Factors for MDR\u003c/b\u003e \u003cb\u003eE. coli\u003c/b\u003e \u003cb\u003ein Human Subjects\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThirteen (43.3%) participants used dedicated clothes for farming activities. Those without dedicated farm clothing were approximately 4 times more likely to have multidrug-resistant \u003cem\u003eE. coli\u003c/em\u003e than those with dedicated farm clothing were (OR: 3.8, CI: 0.8\u0026ndash;18.6, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The majority (25, 83.3%) of the participants did not use personal protective equipment (PPE) when cleaning the farm or when handling pigs or even pork. This group was 3 times more likely to have a multidrug-resistant \u003cem\u003eE. coli\u003c/em\u003e isolate than were those who used PPE (OR: 3.1, CI: 0.3\u0026ndash;32.3, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Those who were involved in processing pork were 2 times more likely to have multidrug-resistant \u003cem\u003eE. coli\u003c/em\u003e than those who were not (OR: 2.2, CI: 0.2\u0026ndash;24.1, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). We also found that those who washed their hands regularly before eating were 2 times less likely to have MDR \u003cem\u003eE. coli\u003c/em\u003e than those who did not wash their hands (OR: 2.3; CI: 0.5\u0026ndash;11.5, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Overall, 93% of the respondents showed poor awareness, with only 6.7% able to correctly identify antibiotic withdrawal requirements or recognize \u003cem\u003eE. coli\u003c/em\u003e as a zoonotic pathogen (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRisk factors for MDR \u003cem\u003eE. coli\u003c/em\u003e in farm workers\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003ePresence of MDR\u003c/p\u003e\u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e (N\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOdd Ratio (95% Confidence interval)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUse dedicated farm cloths\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (52.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (47.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3.8 (0.8\u0026ndash;18.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (23.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (76.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInvolved with pork processing\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (42.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (57.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2.2 (0.2\u0026ndash;24.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (25.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (75.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUse PPE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (44.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (56.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3.1 (0.3\u0026ndash;32.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (20.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (80.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHAND HYGIENE PRACTICES\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWash hands before handling pork\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (47.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (52.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3.2 (0.5\u0026ndash;19.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (22.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (77.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWash hands after handling Pork\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1.8 (0.3-9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (36.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (63.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWash hands before eating\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (62.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (37.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2.3 (0.5\u0026ndash;11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (31.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (68.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWash hands after eating\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eundefined\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eundefined\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (46.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (53.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWash hands after using the toilet\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (100.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eundefined\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eundefined\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (41.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (56.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAwareness of pathogenic\u003c/b\u003e \u003cb\u003eE. coli\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (42.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (57.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eundefined\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eundefined\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (100.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAwareness of other zoonoses\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (40.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (59.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1.4 (0.1\u0026ndash;17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (66.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of this study revealed a high prevalence of \u003cem\u003eE. coli\u003c/em\u003e among pigs, pig handlers, and farm effluents in Calabar, Cross River State, Nigeria. This result highlights the extensive circulation of \u003cem\u003eE. coli\u003c/em\u003e within the pig production ecosystem, reflecting the ubiquitous presence of this bacterium and its potential for cross-species transmission. Similar to previous studies conducted in Nigeria [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and other low- and middle-income countries [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], the detection of \u003cem\u003eE. coli\u003c/em\u003e across animal, human, and environmental samples reinforces the interconnectedness of the animal‒human‒environmental interface, which is a key premise of the One Health paradigm in antimicrobial resistance (AMR) control. The greater prevalence of \u003cem\u003eE. coli\u003c/em\u003e in pigs than in humans and effluents suggests that pigs serve as the principal reservoir from which resistant strains may spread to handlers and the environment. This finding is consistent with previous findings [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] that reported swine as major reservoirs for multidrug-resistant \u003cem\u003eE. coli\u003c/em\u003e in Nigerian pig farms. The relatively lower prevalence in human handlers may reflect differences in exposure intensity, hygiene practices, or host susceptibility [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, the detection of \u003cem\u003eE. coli\u003c/em\u003e in effluent samples indicates environmental contamination and potential dissemination of resistant bacteria beyond farm boundaries, contributing to wider ecological exposure. The widespread use of pig dung as manure reported in this study further highlights the risk of environmental dissemination and transmission through soil and crops, as previously highlighted [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study revealed alarming resistance patterns, with all the isolates showing 100% resistance to the cephalosporin, polymyxin, and penicillin classes considered critically important for both human and veterinary medicine. The high resistance to fluoroquinolones and tetracycline mirrors global and regional trends where these antibiotics are widely misused in livestock [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Resistance to colistin, a last-resort antibiotic in human medicine, is particularly concerning and suggests possible circulation of plasmid-mediated \u003cem\u003emcr\u003c/em\u003e genes, as previously reported in Nigerian livestock farms [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The complete resistance to cephalosporins and penicillins points toward extensive dissemination of extended-spectrum β-lactamases (ESBLs) or AmpC β-lactamases among the isolates [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], further emphasizing the urgency of molecular surveillance. The observed multidrug resistance (MDR) in 100% of the isolates and the high multiple antibiotic resistance index (MARI) values indicate continuous and high-risk exposure of these bacterial populations to antimicrobial agents. MARI values exceeding 0.2 are indicative of environments where antibiotics are frequently and indiscriminately used, reflecting high selective pressure for antimicrobial resistance. The findings of this study therefore align with previous reports documenting widespread misuse of antibiotics in Nigerian animal production systems, largely driven by over-the-counter availability, weak regulatory enforcement, and limited veterinary oversight [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This pattern reinforces the critical need for stricter antibiotic stewardship and regulatory enforcement within livestock systems.\u003c/p\u003e\u003cp\u003eAntibiotic use on the sampled farms was both extensive and poorly regulated. Most farmers administer antimicrobials for prophylactic and therapeutic purposes, with minimal understanding of withdrawal periods or antimicrobial resistance implications. This aligns with the findings of Daodu [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], who reported that a lack of knowledge and weak veterinary extension services significantly contributed to AMR emergence in Nigerian livestock systems. The low awareness of antimicrobial resistance (AMR) among pig handlers, where over 90% had never heard of \u003cem\u003eEscherichia coli\u003c/em\u003e or understood its public health relevance, illustrates a substantial educational gap. Similar findings have been reported among livestock farmers in Nigeria and other sub-Saharan African countries, indicating that AMR literacy remains a systemic challenge across the region [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Effective awareness and training programs targeting farmers, butchers, and animal health workers are therefore essential to mitigate the misuse of antibiotics and reduce occupational exposure risks. The detection of MDR \u003cem\u003eE. coli\u003c/em\u003e in humans working directly with pigs highlights the zoonotic potential of resistant bacterial strains in the pig production environment. Human colonization may occur via direct contact with animals, contaminated feed, aerosols, or effluents. This aligns with the findings of Strasheim [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] in South Africa, who demonstrated shared sequence types and clonality between \u003cem\u003eE. coli\u003c/em\u003e isolates from pigs and their handlers. The presence of MDR \u003cem\u003eE. coli\u003c/em\u003e in effluents further underscores environmental contamination as a critical conduit for resistance dissemination. Effluents can contaminate surface and groundwater, thereby facilitating the spread of resistance genes to other bacterial populations, including environmental and human commensals [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough most risk factors in this study were not statistically significant, the findings suggest that occupational exposure is a key determinant of MDR \u003cem\u003eE. coli\u003c/em\u003e carriage. The participants without dedicated farm clothing or personal protective equipment presented greater odds of carriage; this finding is consistent with the findings of similar studies [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], which reported that butchers and slaughterhouse workers had significantly higher MDR \u003cem\u003eE. coli\u003c/em\u003e prevalence, especially when they were engaged in waste collection, were eating at work, or were in settings lacking sanitary facilities. The limited use of PPE and the absence of basic biosecurity measures such as footbaths indicate systemic weaknesses in farm hygiene and occupational health practices. These gaps not only facilitate the transmission of resistant pathogens between animals and humans but also increase the likelihood of community-level dissemination. The resistance profile observed in this study, which was characterized by high-level MDR and widespread resistance to critically important antimicrobials, aligns with global concerns about antimicrobial misuse in food animals. The World Organization for Animal Health (WOAH) has repeatedly emphasized the need to curtail antibiotic use in livestock, especially those classes vital for human health [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. From a public health standpoint, the circulation of MDR \u003cem\u003eE. coli\u003c/em\u003e across pigs, humans, and the environment in Calabar represents a potential reservoir for horizontal gene transfer to other pathogens, thereby increasing the risk of untreatable infections. Without urgent interventions such as farm-level antibiotic stewardship, waste treatment, and hygiene improvement, these resistance determinants could spread beyond the livestock sector into clinical and community settings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrated the widespread occurrence of multidrug-resistant \u003cem\u003eE. coli\u003c/em\u003e across pigs, humans, and environmental samples in Calabar, Nigeria. The high MARI values, extensive resistance to multiple antibiotic classes, including last-resort drugs such as colistin, and low awareness of AMR among farmers highlight the urgent need for coordinated one-health interventions. Strengthening antibiotic stewardship, promoting biosecurity and hygiene practices, and enhancing AMR surveillance in the livestock sector are crucial steps toward mitigating the spread of antimicrobial resistance in Nigeria and similar LMIC settings. Future studies should integrate molecular genotyping and longitudinal surveillance to elucidate transmission dynamics, particularly between animals and handlers. Moreover, environmental sampling should be expanded to include soil, water, and vegetable samples to better understand the ecological dissemination of resistance.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eStudy Area\u003c/h2\u003e\u003cp\u003eThis study was conducted in Calabar, the capital of Cross River State, which comprises two local government areas: Calabar South and Calabar Municipality (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The city is located at latitude 4° 57' 32 N and longitude 8° 19' 37E, with a land mass of approximately 406 km², a population of 657,000 according to the 2006 census, and a population growth rate of 4.12% [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. It hosts a growing pig industry dominated by small- and medium-scale farms that rely heavily on antibiotics for disease prevention and growth promotion [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Pork is widely consumed in cities, with untreated pig waste often disposed of in surrounding environments, creating multiple pathways for resistant bacteria to spread among animals, humans, and ecosystems [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design, Sampling Population and Sizes\u003c/h2\u003e\u003cp\u003e The study was a cross-sectional study design, and a nonprobabilistic convenience sampling approach was used, where farms were selected on the basis of accessibility and willingness to participate. Within each participating farm, pigs were sampled purposively to include apparently healthy animals across different pens, whereas human samples were collected from willing farm attendants. Effluent samples were obtained from discharge points directly connected to pig waste outlets. A list of registered pig farms in the Calabar metropolis was obtained from the Cross River State Department of Veterinary Services. The farms were stratified into small- and large-scale farms, with farms with fewer than 500 pigs considered small scale and those with more than 500 pigs considered large scale. Verbal consent was obtained from all the human study participants, as witnessed by the lead author. All criteria for the inclusion of farm attendants were those who had spent up to six months on the farms. Farm owners or attendants with long-standing health issues or comorbidities were exempted from the study. Additionally, sick pigs receiving antimicrobial therapy were excluded.\u003c/p\u003e\u003cp\u003eThe sample size was determined according to the formula described by Thrusfield [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]:\u003c/p\u003e\u003cp\u003eN = Z\u003csup\u003e2\u003c/sup\u003ePQ/D\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eN = sample size, Z = appropriate value for the standard normal deviation for the desired confidence level = (1.96), P = anticipated prevalence rate, Q = 1 - P, D = desired absolute precision (5%).\u003c/p\u003e\u003cp\u003eA prevalence of 8.6% [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] was used for this study:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:N=\\frac{{1.96}^{2}\\:\\times\\:\\:0.86\\:(1-0.86)}{0.0025}\\:\\:=\\:\\:126$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe sample size for humans was determined by the number of pig farm attendants who were willing to participate in the study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eData and Sample Collection\u003c/h2\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003eQuestionnaire Administration\u003c/h2\u003e\u003cp\u003eEnumerators who were fluent in the local language were trained to administer questionnaires to human participants. The data collection tool was a semistructured interviewer-administered electronic questionnaire designed via the KoboToolbox (Harvard Humanitarian Initiative, Cambridge, MA, USA) and mounted on the Open Data Kit (ODK) mobile application (ODK, Seattle, WA, USA). The questionnaire had two distinct sections: the first section comprised questions pertaining to the sociodemographic characteristics of the respondents, whereas the second section comprised questions on potential exposure factors/variables for multidrug-resistant \u003cem\u003eE. coli.\u003c/em\u003e, such as the use of antibiotics for growth promotion on farms, farming experience, and the use of personal protective equipment (PPE) on farms. The outcome variable was the presence or absence of multidrug-resistant \u003cem\u003eE. coli\u003c/em\u003e on the farm.\u003c/p\u003e\u003cp\u003eFecal samples\u003c/p\u003e\u003cp\u003eFecal samples were collected from selected pigs and willing human participants via sterile sample bottles containing Amies transport medium. Fecal samples from pens with multiple pigs were pooled. The samples were stored at 4°C in Giostyle containing ice packs and transported to the Microbiology Laboratory, University of California, for analysis.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eEnvironmental Samples\u003c/h2\u003e\u003cp\u003eA total of 28 effluent samples were collected, with at least one effluent sample collected from each farm environment, from channels connecting to wastes from the farms.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eMicrobiological analysis\u003c/h2\u003e\u003cp\u003e\u003cb\u003eIsolation of\u003c/b\u003e \u003cb\u003eE. coli\u003c/b\u003e \u003cb\u003efrom Faecal Samples\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe faecal samples were cultured in lactose broth enrichment medium and incubated at 37°C for 24 h in an incubator. After 24 h of incubation, the enriched cultures were plated via direct streaking on prepared MacConkey agar poured into sterile Petri dishes. The plates were incubated in an inverted position at 37°C for 24 h. The 24 h MacConkey culture plate was viewed and observed for possible isolation of presumptive \u003cem\u003eE. coli\u003c/em\u003e colonies. The pinkish colonies observed were inferred to be presumptive \u003cem\u003eE. coli\u003c/em\u003e colonies, which were further inoculated on Eosin Methylene Blue (EMB) agar and incubated at 37°C for 24 h for preconfirmation. Colonies showing a green metallic sheen on EMB after 24 h of incubation were preconfirmed as \u003cem\u003eE. coli\u003c/em\u003e and further subjected to IMViC tests and carbohydrate utilization tests for further confirmation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIsolation of\u003c/b\u003e \u003cb\u003eE. coli\u003c/b\u003e \u003cb\u003efrom Environmental Samples\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA tenfold serial dilution of each of the samples was carried out using buffered peptone water. Appropriate dilutions were made by mixing 9 mL of peptone water into ten test tubes in different test tube racks. MacConkey agar was prepared according to the manufacturer’s instructions, and both the peptone water blank in the test tubes and the media were sterilized by autoclaving at 150 psi at 121°C for 15 min. After the samples were serially diluted, 0.1 mL of each dilution was spread in triplicate on a MacConkey agar plate and incubated at 37°C for 24 h. Pinkish colonies observed on the MacConkey agar plates were inferred as presumptive \u003cem\u003eE. coli\u003c/em\u003e isolates, which were further inoculated on prepared EMB agar for precirmation if the colonies were metallic.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003ePurification and Stock Culturing\u003c/h2\u003e\u003cp\u003eDistinct preconfirmed \u003cem\u003eE\u003c/em\u003e. \u003cem\u003ecoli\u003c/em\u003e isolates were subcultured repeatedly on nutrient agar plates and incubated at 37°C for 24 h. Pure isolates were cultured on nutrient agar slants in Mac Cartney bottles for further biochemical analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eCharacterization and identification of preconfirmed isolates\u003c/h2\u003e\u003cp\u003ePreconfirmed \u003cem\u003eEscherichia coli\u003c/em\u003e isolates were characterized via standard biochemical tests, including the IMViC series and carbohydrate utilization tests [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and were identified according to the criteria described in Bergey’s Manual of Determinative Bacteriology [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eAntibiotic\u003c/b\u003e susceptibility tests\u003c/p\u003e\u003cp\u003eDisc diffusion (Kirby-Bauer), which conforms to the recommended standard of the Clinical and Laboratory Standards Institute (CLSI [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]), was employed, and the results were interpreted using the same standards and with the use of commonly prescribed and important antibiotics in Nigeria. Eleven classes of antibiotics (Oxoid UK) were used, namely, tetracyclines (Tetracycline-30 µg), penicillin (Ampicillin-10 µg), phenicols (Chloramphenicol-30 µg), carbapenem (Imipenem-10 µg), cephalosporins (Cefotaxime-30 µg and Ceftazidime-30 µg), aminoglycosides (Gentamicin-5 µg), nitrofurans (Nitrofurantoin-300 µg), polymyxins (Colistin-30 µg), fluoroquinolones (Ciprofloxacin-30 µg) and cefoxitin-30 µg).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003eStatistical\u003c/b\u003e analysis\u003c/h2\u003e\u003cp\u003eThe data collected at ODK were exported into Microsoft Excel 2019 software. The data were cleaned, coded and stored. Data analysis was performed via EpiInfo® (version 7.2.2). The questionnaire data were coded, and the results obtained from the laboratory analysis were treated as dichotomous variables for bivariate analysis. Data are presented as frequencies, proportions, and percentages. Bivariate analysis was carried out to explore the associations among each of the potential risk factors, including the use of personal protective equipment (PPE), hand hygiene practices (before eating, after handling pork, and after using the toilet), use of dedicated farm clothing, involvement in pork processing, awareness of \u003cem\u003eE. coli\u003c/em\u003e and other zoonoses, antimicrobial use practices and the outcome variable (presence or absence of multidrug-resistant \u003cem\u003eE. coli\u003c/em\u003e infection in human participants). The level of significance was set at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eClinical Trial Numbers\u003c/h2\u003e\u003cp\u003eNot Applicable\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eEthical Approval\u003c/h2\u003e\u003cp\u003e The research received ethical clearance from the University of Calabar Research and Ethics Committee and the Cross River State Ministry of Health with the approval number CRSMOH/RP/REC/2021/221.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003cp\u003eNone to be declared\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003cp\u003e Written informed consent was obtained from the respondents.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNone to be declared\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAderigbigbe Adegunwa:\u0026nbsp;Conceptualization, investigation, methodology, resources. Samuel Akpan:\u0026nbsp;Data curation, methodology, writing \u0026ndash; original draft preparation, and validation. Uduak Akpabio:\u0026nbsp;Validation, writing \u0026ndash; review \u0026amp;amp; editing. Ayodeji Adegunwa:\u0026nbsp;Software, Writing \u0026ndash; review \u0026amp;amp; editing. Mathias Besong:\u0026nbsp;Validation, Writing \u0026ndash; review \u0026amp;amp; editing. Ini Bassey:\u0026nbsp;Investigation, writing- review \u0026amp;amp; editing. Samuel Owoicho:\u0026nbsp;Writing \u0026ndash; review \u0026amp;amp; editing. Oluwawemimo Adebowale: Validation, writing- review \u0026amp;amp; editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors wish to acknowledge the support of laboratory management and staff of the Department of Microbiology, University of Calabar, for their support during laboratory analysis. We also acknowledge Koli Adegunwa, Emmanuel Adegunwa, and Mabel Aworh Ajumobi for their support and meaningful contributions to the success of this research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe raw data supporting the results of this study are available upon reasonable request from the corresponding author.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYe Z, Li M, Jing Y, Liu K, Wu Y, Peng Z. What are the drivers triggering antimicrobial resistance emergence and spread? Outlook from a One Health perspective. \u003cem\u003eAntibiotics.\u003c/em\u003e 2025;14(6):543.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlagunju OJ, Egbo B, Olayinka O, Majolagbe OG, Osanyinlusi OO, Titilade A, Atoyebi OF, Ojo IO, Dawha SD. 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Antibiotics. 2022;11(5):667.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMohamed MY, Khalifa H, Elolimy A, Habib I. Antimicrobial Strategies in Animal Husbandry. In: Antimicrobial Strategies in the Food System: Updates, Opportunities, Challenges. Cham: Springer Nature Switzerland; 2025. p. 331\u0026ndash;364.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGen: Gentamycin; Amp: ampicillin; Tet: Tetracycline; Cip: Ciprofloxacin; Azt: Azithromycin; Fox: Cefoxitin; Nit: Nitrofuran; Cef: ceftazidime; Chl: Chloramphenicol; Nem: Imipenem; Col: Colistin; Tax: Cefotaxime; Pansusceptible, susceptible, susceptible to all tested antimicrobials. MARI: Multiple antibiotic resistance index\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"one-health-advances","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [One Health Advances](https://onehealthadv.biomedcentral.com/)","snPcode":"44280","submissionUrl":"https://submission.springernature.com/new-submission/44280/3","title":"One Health Advances","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Antimicrobial resistance, Multidrug resistance, Escherichia coli, Pigs, Humans, Effluent","lastPublishedDoi":"10.21203/rs.3.rs-7880879/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7880879/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMultidrug-resistant (MDR) \u003cem\u003eEscherichia coli\u003c/em\u003e (\u003cem\u003eE. coli\u003c/em\u003e) poses a serious threat to animal and human health, especially in low- and middle-income countries (LMICs), where antibiotic use in livestock is poorly regulated. Pigs act as reservoirs for resistant bacteria that can spread to humans through direct contact, contaminated pork, or the farm environment. This study aimed to determine the prevalence and resistance patterns of MDR \u003cem\u003eE. coli\u003c/em\u003e in pigs, pig handlers, and farm effluents and to assess farmers\u0026rsquo; awareness of antimicrobial resistance (AMR) in Calabar, southern Nigeria. A cross-sectional study design was used to collect 210 samples across 17 farms. These samples included pig faecal (n\u0026thinsp;=\u0026thinsp;152), human stool (n\u0026thinsp;=\u0026thinsp;30), and effluent (n\u0026thinsp;=\u0026thinsp;28) samples. \u003cem\u003eE. coli\u003c/em\u003e was isolated via standard microbiological methods and confirmed via biochemical tests. Antibiotic susceptibility testing followed the Kirby\u0026ndash;Bauer disk diffusion method, with MDR defined as resistance to three or more antibiotics. A semi-structured questionnaire was used to assess farmers\u0026rsquo; knowledge and practices regarding antimicrobial use and AMR. The prevalence of \u003cem\u003eE. coli\u003c/em\u003e was 63.8% in pigs, 40.0% in humans, and 35.7% in effluents. All the isolates were resistant (100%) to cephalosporins, polymyxins, and penicillins, whereas the rates of fluoroquinolone resistance were 79.8%, 75.0%, and 90.0% in the pig, human, and effluent isolates, respectively. The multiple antibiotic resistance index (MARI) exceeded 0.2 across all the isolates. Awareness of AMR was poor, with 93% of respondents showing poor understanding of withdrawal periods. These findings indicate pigs and pig farm environments as a high-risk platform for MDR \u003cem\u003eE. coli\u003c/em\u003e spread, underscoring the need for intensified antibiotic stewardship, farmer education, and AMR surveillance in livestock production systems in Nigeria and other LMICs.\u003c/p\u003e","manuscriptTitle":"Isolation and characterization of multidrug-resistant Escherichia coli in pigs, farm workers and effluents in Calabar, southern Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 20:36:06","doi":"10.21203/rs.3.rs-7880879/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-26T09:58:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T17:55:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T19:38:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109928822733336182374433093602596414456","date":"2026-04-04T06:02:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"206012108681322363485982589707928751732","date":"2026-04-02T06:22:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216294928166135929359332164343801394062","date":"2026-02-01T16:46:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218228423073875152955037201271008763222","date":"2026-01-08T18:00:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295864889893520203085210684553238556331","date":"2026-01-04T12:26:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227790034140370004265259747120408382611","date":"2025-11-04T07:38:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303291291964942279144372778447365478970","date":"2025-11-03T10:13:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-02T07:27:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-23T08:24:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-17T03:50:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"One Health Advances","date":"2025-10-16T20:41:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"one-health-advances","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [One Health Advances](https://onehealthadv.biomedcentral.com/)","snPcode":"44280","submissionUrl":"https://submission.springernature.com/new-submission/44280/3","title":"One Health Advances","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8a628717-2c57-469e-9f19-7be67955635a","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T06:55:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-20 20:36:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7880879","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7880879","identity":"rs-7880879","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}