Burgeoning Multi-Drug Resistance in E. coli: Insights from Broiler Chickens and Slaughterhouse Workers

ABSTRACT

Drug resistance is currently recognized as a global problem. This study aimed to investigate the prevalence and antibiotic susceptibility pattern of multi-drug resistant (MDR) E. coli in broiler chickens, slaughter workers, and related specimens. Swab specimens were collected during the slaughter process from broiler carcasses, organs, workers’ hands, and various utensils. Bacterial culture, biochemical analysis, and antimicrobial susceptibility testing were conducted on the isolated specimens. Out of a total of 1132 swab specimens, 294 (25.97%) tested positive for E. coli. The highest percentage (39.76%) was found in workers’ hand specimens, followed by different slaughter utensils and walls. Antimicrobial susceptibility tests revealed complete resistance to ampicillin (100%). High resistance rates were observed for tetracycline (91.50%), chloramphenicol (91.16%), nalidixic acid (86.05%), ciprofloxacin (77.55%), colistin (77.21%), trimethoprim/sulfamethoxazole (72.45%), kanamycin (71.09%), doxycycline (70.07%), ceftazidime (69.05%), ampicillin-sulbactam (53.06%), and gentamicin (50.34%). On the other hand, 60.20% of the isolates showed sensitivity to amikacin, followed by ceftriaxone (41.50%) and norfloxacin (37.76%). MDR was observed in 99.32% (292 isolates), with 28.23% of them being potentially classified as extensively drug-resistant (XDR). The MAR index of the E. coli isolates ranged from 0.2 to 1.0, displaying 190 different resistant patterns. The high prevalence of MDR among E. coli isolates from broiler chickens and related specimens, along with their resistance to important antibiotics, is a significant public health concern.

Keywords: MDR; E. coli; Hygiene; Broilers; Slaughterhouse; Antibiotic; Susceptibility; antimicrobial resistance

INTRODUCTION

The occurrence of E. coli in raw meats, ready-to-eat meats, humans, working tools, and the environment has been reported worldwide (Gwida et al., 2014; Gwida & El-Gohary, 2015; Abd Elzaher et al., 2018; Adzitey et al., 2021). The emergence of antibiotic resistance among microbes contaminating fresh meat and meat products is a global public health concern due to the ease of their transmission to humans through consumption and contact (Phillips et al., 2004). In the poultry sector, antibiotics are primarily used as growth promoters, prophylactic agents, and therapeutic agents. The major classes of antimicrobial agents (antibiotics) used in poultry include penicillin (Joshua et al., 2018), aminoglycosides, cephalosporins, macrolides, sulfonamides, quinolones/fluoroquinolones, and tetracycline (Mathew et al., 2007). The use of antibiotics as growth promoters improves feed efficiency, enhances the physical condition of poultry by reducing disease incidence, and increases industry standards while reducing production costs. Despite these positive factors, the unrestricted use of antimicrobials for treatment, prophylaxis, and growth promotion against intestinal microbes is considered one of the major causes for the emergence and spread of antimicrobial resistance (Gyles, 2008).

The global community is on the brink of regressing to a pre-antibiotic era if the issue of antibiotic resistance is not addressed (Nhung et al., 2017). There is a well-documented link between the rise in antibiotic-resistant infections and the annual production of ninety billion tons of chicken meat worldwide (Nhung et al., 2017). Consumption of meat and meat products from farm animals containing antibiotic-resistant bacteria can lead to their transmission to humans. Currently, 75% of antimicrobials produced globally are used in food animals (Loayza et al., 2020). Furthermore, certain pathogenic strains of E. coli, particularly extraintestinal pathogenic E. coli, have the capability to transfer identical serotypes and virulence genes to human pathogens, heightening the risk of infections resulting from the consumption of poultry meat (Xing et al., 2021). The resistance of bacteria, including E. coli, to antimicrobials has garnered significant attention due to the emergence of multidrug resistance (MDR) and the challenges in eliminating such bacteria when they cause infections (Gill & Hamer, 2001; Fair & Tor, 2014). The frequency of MDR E. coli isolates has been increasing in several countries worldwide (Saidi et al., 2012; Halfaoui et al., 2017; Younis et al., 2017; Moawad et al., 2018; El-Seedy et al., 2019; Parvin et al., 2020; Radwan et al., 2020). Additionally, the susceptibility or resistance of bacteria to antibiotics in the human population of Yemen has been investigated due to the indiscriminate use of antimicrobials, particularly in poultry production, which poses a risk for the development of antibiotic resistance (Alsharapy et al., 2018; Badulla et al., 2020). However, there is limited or no information available on E. coli and its multidrug resistance patterns in broiler chickens in Yemen. Therefore, the aim of this study is to address this knowledge gap and contribute to the understanding of this problem on a modest scale.

MATERIALS AND METHODS

Study design

A descriptive study was conducted to isolate and presumptively identify multidrug-resistant (MDR) E. coli from broiler carcasses, workers, and utensils in small local broiler abattoirs. The study encompassed approximately all small poultry commercial slaughterhouses (70 slaughterhouses) in Dhamar city, located in the Dhamar governorate. Broiler chickens arriving at these slaughterhouses originated from various poultry farms in Dhamar province, as well as other governorates such as Ibb, Al-Hodeida, and Sana’a.

Specimens and Data Collection

A random sampling method was employed for each slaughterhouse, and various swab specimens were collected randomly on the day of the visit. A total of 1132 swab specimens were collected throughout the areas involved in the slaughter process in the 70 slaughterhouses, with approximately 16-17 swabs collected from each slaughterhouse. The specimens included 70 swabs from the external surfaces of bird cages, water in the washing tank, inner walls of the storing tank, knives used for cutting, cutting tables (boards), bleeding funnels, plucking machines, floors, walls, and livers. Additionally, 69 swabs were obtained from knives used for slaughtering, 83 swabs from workers’ hands, and 350 swabs from the internal carcasses. Specimens were directly collected using sterile cotton swab sticks, and an area of approximately 10 cm2 was swabbed following the method described by Mpundu et al. (2019).

Laboratory Analysis

The laboratory analysis involved the isolation of E. coli, identification of the isolates, and antibiotic susceptibility testing. All media used in the analysis were prepared following the manufacturers’ instructions. The isolation and identification of E. coli were performed using conventional methods described by Quinn et al. (2002), Collee et al. (1996), and Collins (1984). Briefly, each swab specimen was inoculated into peptone water for enrichment and incubated aerobically at 37°C for 16-18 hours. A loopful of the enriched culture was then streaked onto MacConkey (MC) agar (Oxoid Ltd, Hampshire, UK) and incubated aerobically at 37°C for 24 hours. Colonies showing lactose fermentation (pink color) on the MC agar, indicating suspicion of E. coli, were selected and sub-cultured on separate plates for further isolation of E. coli from other coliform bacteria. These subcultures were incubated at 44°C. The isolated colonies were then subjected to biochemical tests to further identify the bacterial isolates, including their morphological and biochemical characteristics, as well as their motility. Tests such as urease negativity and indole positivity using Motility Indole Urea medium, as well as negative or positive reactions with Triple Sugar Iron agar were used for the identification of E. coli (Obeng et al., 2012).

Identification of suspected E. coli colonies

The laboratory procedures were conducted following the methods outlined by Quinn et al. (2002). Colony morphology characteristics, including shape, size, surface texture, edge, elevation, and color, were observed on MacConkey agar and blood agar plates. Hemolytic activity of the E. coli isolates was tested on blood agar plates (Oxoid Ltd, Hampshire, UK). After 24 hours of incubation at 37°C, the plates were examined, and the E. coli isolates were categorized as β-hemolysis, α-hemolysis, or γ-hemolysis based on their varying hemolytic activities. On MacConkey agar, E. coli colonies exhibited a pink-red color, indicating lactose fermentation, along with a characteristic sour milk smell. The colonies displayed variations in their morphology, with some being raised or flat, some having a smooth surface, and others having irregular edges. Additionally, the colonies varied in moisture content, ranging from moist to dry and scaly (Cruickshank, 1975). To perform the microscopic examination, films were prepared from the suspected pure isolates and stained using Gram’s stain, following the instructions provided by the manufacturer. The stained films were then examined under a microscope for morphological analysis, as described by Quinn et al. (2002) and Cruickshank (1975).

Biochemical Identification

The following biochemical tests (shown in Table 1) were carried out for identification:

Table 1
Biochemical tests used for identification of E. coli

Biochemical tests	Reaction
Triple Sugar Iron (TSI)	Positive (Slant / Butt) yellow/Gas
Indole Production	Positive
Oxidase	Negative
Urease	Negative
Hydrogen Sulfide (H2S)	Negative
Motility	Positive

Antibiotic Susceptibility of E. coli Isolates

All E. coli isolates underwent susceptibility testing to evaluate their response to 15 antimicrobials, as shown in Table 2. The disc diffusion method on Mueller-Hinton agar was employed for this purpose, following the guidelines set by the Clinical and Laboratory Standards Institute (CLSI, 2016) and McDermott et al. (2004). Isolates with MDRI values exceeding 0.2 or 20% were classified as highly resistant. The term extensively drug-resistant (XDR) was used to describe isolates that were resistant to at least one antimicrobial in all but two or fewer antimicrobial categories. Conversely, isolates classified as non-MDR (Magiorakos et al., 2012) were susceptible to all antibiotic classes or resistant to only one or two antimicrobial classes.

Table 2
Antimicrobials and concentrations used to test susceptibility of E. coli isolate according to manufacturer (Himedia® India).

Antimicrobial category	Antimicrobial Agent	Symbol /conc	Reference measurements for Enterobacteriaceae in mm
			Sensitive	Intermediate	Resistant
Aminoglycosides	Amikacin	AK 30 mcg	≤17	15 - 16	≤14
	Gentamicin	GEN 10 mcg	≤15	13 -14	≤12
	Kanamycin	K 30 mcg	≤18	14 -17	≤13
Penicillin + β-lactamase inhibitors	Ampicillin-sulbactam	A/S 10/10mcg	≤15	12 - 14	≤11
Cephems	Ceftriaxone	CTR 30 mcg	≤23	20-22	≤19
	Ceftazidime	CAZ 30 mcg	≤21	18 - 20	≤17
Penicillin	Ampicillin	AMP 10 mcg	≤17	14-16	≤13
Phenicol	Chloramphenicol	C 30 mcg	≤18	13-17	≤12
Polymyxins	Colistin	CL 10 mcg	≤11	9 - 10	≤8
Quinolones	Ciprofloxacin	CIP 5 mcg	≤21	16-20	≤15
	Nalidixic acid	NA 30 mcg	≤19	14-18	≤13
	Norfloxacin	NX 10 mcg	≤17	13-16	≤12
Folate pathway inhibitors	Trimethoprim/ sulfamethoxazole	COT 25 mcg	≤16	11-15	≤10
Tetracycline	Tetracycline	TE 30 mcg	≤15	12-14	≤11
		(1.25/23.75mcg)
	Doxycycline	DOX 30 mcg	≤14	11-13	≤10

Statistical analysis

The data and observational information were entered into SPSS software package version 20 (SPSS Inc., Chicago, IL, USA), and descriptive statistics, such as frequency and percentage, were computed. The data were also subjected to a normality test using the Shapiro-Wilk or Kolmogorov-Smirnov test. To determine significant differences in the prevalence of E. coli isolates among different specimen sources, the Kruskal-Walli’s test for independent samples was performed, followed by the Mann-Whitney U test as a post hoc test. A P-value of less than 0.05 was considered statistically significant.

RESULTS

Frequency of E. coli isolated from different sources of specimens

The distribution of E. coli isolates from different specimens is presented in Table 3. Out of the total 1132 specimens collected, E. coli was isolated and identified in 294 specimens, totaling 26% of the samples. The variation in the occurrence of E. coli among different specimens was found to be statistically significant (P<0.05). The prevalence rate of E. coli isolates was highest in specimens collected from workers’ hands (33, 39.76%), followed by the slaughtering knife (26, 37.68%) and bird cages (25, 35.71%). Conversely, a lower rate of E. coli isolates was observed in specimens obtained from the washing tank, inner wall of the storing tank, and the plucking machine.

Table 3
Distribution of E. coli isolates from different specimen sources

Source of specimens	No of specimens examined	No of E. coli isolate			Total No of E. coli isolates (%)
		Alpha Hemolytic	Beta Hemolytic	Non Hemolytic
Birds’ cages	70	16 (22.86)	4 (5.71)	5 (7.14)	25 (35.71) ad
Knife- slaughter	69	13 (18.84)	8 (11.59)	5 (7.25)	26 (37.68) a
Knife- cutting	70	7 (10.00)	5 (7.14)	5 (7.14)	17 (24.29) abcd
Bleeding funnels	70	5 (7.14)	7 (10.00)	3 (4.29)	15 (21.43) bcd
Wall of storing tank	70	5 (7.14)	2 (2.86)	2 (2.86)	9 (12.86) b
Washing water	70	0 (0.00)	6 (8.57)	3 (4.29)	9 (12.86) b
Plucking machine	70	3 (4.29)	4 (5.71)	2 (2.86)	9 (12.86) b
Board	70	3 (4.29)	11 (15.71)	4 (5.71)	18 (25.71) abcd
Floor wall	70	5 7.14)	4 (5.71)	3 (4.29)	12 (17.14) bc
Workers' hands	83	19 (22.89)	10 (12.05)	4 (4.82)	33 (39.76) a
Liver	70	3 (4.29)	10 (14.29)	6 (8.57)	19 (27.14) abcd
Internal carcass	350	42 (12.00)	37 (10.57)	23 (6.57)	102 (29.14) acd
Overall	1132	121 (10.69)	108 (9.54)	65 (5.74)	294 (25.97)

• a,b,c; mean values have the similar symbols are not significantly different P≤0.05.

The results revealed that among the E. coli isolates, 10.69% (121/1132) were classified as alpha hemolytic, 36.73% were beta hemolytic, and 22.10% were non-hemolytic. Alpha hemolytic isolates were detected in 121 specimens, accounting for 41.16% (121/294) of the total isolates. The highest number of alpha hemolytic isolates was recovered from specimens collected from workers’ hands (22.89%) and bird cages (22.86%). Interestingly, no alpha hemolytic isolates were detected in water specimens. Beta hemolytic isolates were observed in 108 specimens, with the highest number recovered from board specimens (15.71%), followed by liver specimens (14.29%) and the slaughtering knifes (11.59%). Non-hemolytic isolates were identified in 65 specimens, with the highest number recovered from liver specimens (8.57%), followed by the slaughtering knife (7.25%), cage specimens (7.14%), and the cutting knife (7.14%).

Hygiene practices at the slaughterhouse

Procedures followed during broiler slaughter

The current study evaluated the key procedures involved in the processing of broilers, as depicted in Table 4 and in Figure1. The findings indicated that certain practices were consistently followed in all slaughterhouses, including manual evisceration, the absence of carcass chilling, and the use of unaltered scalding water throughout the process. Additionally, most slaughterhouses (71.4%) relied on manual (hand) defeathering, while carcass washing was predominantly conducted through immersion in a pail container with non-flowing water (85.71%). Moreover, it was observed that both the carcass washing and scalding water often became discolored and contaminated with feathers and other debris.

Table 4
The procedures followed during broiler slaughter at the slaughterhouses

Variables	Responses	Frequency	Percent
Killing methods	By cutting the neck	25	36%
	By severing the jugular vein	45	64%
Proper bleeding	Using Cone	55	79%
	Not using Cone	15	21%
Do you change scalding water in between the process?	Yes	0	0.0%
	No	70	100%
Methods of defeathering	Mechanically (plucking machine)	20	29%
	Manually (hand defeathering)	50	71%
Washing of carcasses immediately after defeathering	Yes	52	74%
	No	18	26%
Methods of evisceration	Manually (using the hands)	70	100%
	Mechanically (using a machine)	0	0.0%
Methods of carcass washing after evisceration	Immersion washing using nonflow water in a tank	60	86%
	Immersion washing using flow water	10	14%
Carcass chilling	Yes	0	0.0%
	No	70	100%

Figure 1
The procedures followed during broiler slaughter the processing area and processing line of the slaughterhouses

The results indicated that there was a lack of designated areas for each processing activity, as highlighted in Table 5 and Figure 2. All processing activities, including slaughtering, bleeding, defeathering, eviscerating, and carcass washing, were conducted in an unhygienic manner within the same space. Only about 35.71% of the processing areas were observed to be clean, free from dust, mud, and other debris, and a mere 25.7% of these areas were found to be disinfected. Furthermore, in all slaughterhouses, slaughter waste was disposed of by being placed into a rubbish pail within the slaughterhouse premises.

Table 5
Hygiene status of the broiler processing area and processing line

Variables	Responses	Frequency (%)
Separate processing area	Yes	0 (0.0)
	No	70 (100)
Clean processing area	Yes	25 (36)
	No	45 (64)
Disinfected processing area	Yes	18 (26)
	No	52 (74)
Slaughtering/ processing line	All process is carried out at the same place	70 (100)
	A clearly defined area for every activity	0 (0.0)
Slaughter waste disposal method	Put in a rubbish pile inside the slaughterhouse	70 (100)
	Put in a rubbish pail outside the slaughterhouse	0 (0.0)

Figure 2
The hygiene status of the broiler processing area and processing line

The hygiene status of the equipment utilized in broiler processing, such as cutting knives, scalding tanks, and carcass washing tanks, was found to be inadequate, as indicated in Table 5 and Figure 2. Approximately 47.61% of the equipment was observed to be clean, devoid of dust, mud, and other debris, while a mere 8.57% of the equipment was reported to be disinfected.

Most slaughterers were observed to neglect the use of protective clothing, such as overalls, headgear, aprons, hand gloves, and plastic boots, as shown in Table 6 and Figure 3.

Table 6
Hygiene measures by broiler slaughterhouse staff

Variables	Responses	Frequency (%)
		Yes	No
Cloth	Overall	60 (86)	10 (14)
	Head gears	58 (83)	12 (17)
	Aprons	22 (32)	48 (68)
	Hand gloves	0 (0)	70 (100)
	Plastic boot	43 (62)	27 (38)
	Mouth mask	0 (0)	70 (100)
Wash hands after touching or being in contact with waste and garbage		53 (76)	17 (24)
Being free from any wounds, bruises or injuries on the hands		55 (79)	15 (21)
Health certificate attesting they are free from any communicable diseases		10 (14)	60 (86)

Figure 3
Hygiene measures (%) by broilers slaughterhouse staff

Antimicrobial susceptibility of E. coli isolates

The current study revealed high levels of resistance among isolated E. coli strains to various antibiotics. Ampicillin exhibited the highest resistance rate at 100%, followed by tetracycline (91.5%), chloramphenicol (91.2%), nalidixic acid (86%), ciprofloxacin (77.6%), colistin (77.2%), trimethoprim/sulfamethoxazole (72.4%), kanamycin (71%), doxycycline (70%), and ceftazidime (69%). Conversely, low resistance was observed against amikacin (14.3%). Moderate resistance was found for ceftriaxone (34%) and norfloxacin (36.4%). These findings are summarized in Table 7.

Table 7
Overall antibiotic susceptibility patterns of E. coli isolates (n=294) against 15 types of antimicrobial agents

Antimicrobial Agent			No of isolates (%)
Antimicrobial category	Antimicrobial Agent	Symbol/ concentration	Resistant	Intermediate	Sensitive
Aminoglycosides	Amikacin	AK / 30 mcg	42 (14.29)	75 (25.51)	177 (60.20)
	Gentamicin	GEN/ 10mcg	148 (50.34)	63 (21.43)	83 (28.23)
	Kanamycin	K / 30 mcg	209 (71.09)	82 (27.89)	3 (1.02)
Penicillin + β-lactamase inhibitors	Ampicillin-sulbactam	A/S 10/10 mcg	156 (53.06)	111 (37.76)	27 (9.18)
Cephalosporines	Ceftriaxone	CTR /30 mcg	101 (34.35)	71 (24.15)	122 (41.50)
	Ceftazidime	CAZ/ 30mcg	203 (69.05)	56 (19.05)	35 (11.90)
Penicillin	Ampicillin	AMP/10 mcg	294 (100.00)	0 (00.00)	0 (00.00)
Phenicol	Chloramphenicol	C 30/ mcg	268 (91.16)	18 (6.12)	8 (2.72)
Polymyxins	Colistin	CL / 10 mcg	227 (77.21)	0 (00.00)	67 (22.79)
Fluoroquinolones	Ciprofloxacin	CIP/ 5 mcg	228 (77.55)	42 (14.29)	24 (8.16)
	Nalidixic acid	NA/ 30 mcg	253 (86.05)	23 (7.82)	18 (6.12)
	Norfloxacin	NX / 10 mcg	107 (36.39)	76 (25.85)	111 (37.76)
Folate pathway inhibitors	Trimethoprim/ sulfamethoxazole	COT/ 25mcg	213 (72.45)	39 (13.27)	42 (14.29)
Tetracycline	Tetracycline	TE/ 30 mcg	269 (91.50)	17 (5.78)	8 (2.72)
	Doxycycline	DOX/ 30mcg	206 (70.07)	56 (19.05)	32 (10.88)

The sensitivity pattern of isolates varied for aminoglycosides, with 60.2%, 28.2%, and 1% showing sensitivity to amikacin, gentamicin, and kanamycin, respectively. Regarding penicillin and β-lactamase inhibitors, only 9.2% of isolates were sensitive to Ampicillin-sulbactam. In terms of quinolones, it was found that 37.76%, 8.16%, and 6.12% of isolated E. coli strains were susceptible to norfloxacin, nalidixic acid, and ciprofloxacin, respectively.

Distribution of antimicrobial resistance of E. coli isolated from different source specimens

The distribution of antimicrobial resistance among E. coli isolates from various specimen sources is presented in Table 8. The findings reveal a significant level of resistance (>50%) among all isolates from different sources, except against amikacin, ceftriaxone, and norfloxacin. When analyzed by source, the highest levels of resistance to the tested antimicrobials were observed in isolates recovered from specimens taken from the walls of storing tanks, followed by isolates recovered from board specimens and liver specimens.

Table 8
Distribution of antimicrobial resistance within the E. coli isolated from different specimen sources

Anti-microbial Agent	Different specimens source n= (%)
	Cage n=25	Knife-S n=26	Knife-C n=17	Funnel n=15	W.S tank n=9	Water n= 9	Plucking .M n=9	Board n=18	Floorwall n=12	Hand n=33	Liver n=19	Carcass n=102
AK	1	5	3	1	2	1	1	3	4	5	4	12
	(4.0)	(19.2)	(17.7)	(6.67)	(22.2)	(11.1)	(11.1)	(16.7)	(33.3)	(15.2)	(21.1)	(11.8)
GEN	8	9	10	9	7	4	4	11	6	18	11	51
	(32.0)	(34.6)	(58.8)	(60.0)	(77.8)	(44.4)	(44.4)	(61.1)	(50.0)	(54.6)	(57.9)	(50.0)
K	16	17	12	9	8	6	5	14	9	26	9	78
	(64.0)	(65.4)	(70.6)	(60.0)	(88.9)	(66.7)	(55.6)	(77.8)	(75.0)	(78.8)	(47.4)	(76.47)
A/S	11	9	9	8	6	4	3	11	6	19	13	57
	(44.0)	(34.3)	(52.9)	(53.3)	(66.7)	(44.4)	(33.3)	(61.1)	(50.0)	(57.6)	(68.4)	(55.88)
CTR	8	6	4	0	2	5	1	4	5	12	8	46
	(32.0)	(23.1)	(23.5)	(00.0)	(22.2)	(55.6)	(11.1)	(22.2)	(41.7)	(36.4)	(42.1)	(45.10)
CAZ	18	18	13	10	5	7	6	11	10	23	10	72
	(72.0)	(69.2)	(76.5)	(66.7)	(55.6)	(77.8)	(66.7)	(61.1)	(83.3)	(69.7)	(52.6)	(70.59)
AMP	25	26	17	15	9	9	9	18	12	33	19	102
	(100.)	(100.)	(100.)	(100.)	(100.)	(100.)	(100.)	(100.)	(100.)	(100.)	(100.)	(100.0)
C	23	23	15	15	8	8	8	17	11	30	18	92
	(92.0)	(88.5)	(88.2)	(100.)	(88.9)	(88.9)	(88.9)	(94.4)	(91.7)	(90.9)	(94.7)	(90.20)
CL	17	19	12	13	6	7	6	15	10	28	14	80
	(68.0)	(73.1)	(70.6)	(86.7)	(66.7)	(77.8)	(66.7)	(83.3)	(83.3)	(84.9)	(73.7)	(78.43)
CIP	20	17	13	13	8	5	8	17	6	25	15	81
	(80.0)	(65.4)	(76.5)	(86.7)	(88.9)	(55.6)	(88.9)	(94.4)	(50.0)	(75.7)	(78.9)	(79.41)
NA	21	17	14	12	9	8	7	16	11	29	15	94
	(84.0)	(65.4)	(82.4)	(80.0)	(100.)	(88.9)	(77.8)	(88.9)	(91.7)	(87.9)	(78.9)	(92.16)
NX	7	11	9	6	5	3	1	5	2	15	7	36
	(28.0)	(42.3)	(52.9)	(40.0)	(55.6)	(33.3)	(11.1)	(27.8)	(16.7)	(45.5)	(36.8)	(35.29)
COT	16	19	14	9	9	8	6	13	6	24	12	77
	(64.0)	(73.1)	(82.4)	(60.0)	(100.)	(88.9)	(66.7)	(72.2)	(50.0)	(72.7)	(63.2)	(75.5)
TE	22	23	15	14	9	9	9	17	11	30	18	92
	(88.0)	(88.5)	(88.2)	(93.3)	(100.)	(100.)	(100.)	(94.4)	(91.7)	(90.9)	(94.7)	(90.20)
DOX	18	16	12	8	7	7	6	13	7	2	17	71
	(72.0)	(61.5)	(70.6)	(53.3)	(77.8)	(77.8)	(66.7)	(72.2)	(58.3)	(72.7)	(89.5)	(69.61)

• Knife-S= Knife devoted to slaughtering; Knife-C= Knife devoted to cutting; W.S tank= wall of storing tank; AK amikacin; GEM, gentamicin; KAN, kanamycin.; A/S Ampicillin-sulbactam; CTR Ceftriaxone; CAZ Ceftazidime; AMP, ampicillin; C, chloramphenicol; CL, Colistin; CIP Ciprofloxacin; NA, nalidixic acid; NX norfloxacin; COT, Trimethoprim/ sulfamethoxazole; TE, Tetracycline; DOX, Doxycycline.

Multidrug Resistant (MDR) of E. coli isolates

According to Table 9, the prevalence of multidrug resistance (MDR), defined as resistance to at least one agent in three or more antimicrobial categories, was observed in 99.32% (292/294) of the isolated E. coli strains. Among these, 28.23% were classified as potential extensively drug-resistant (XDR) strains, demonstrating resistance to at least one agent in all tested antimicrobial categories. Conversely, two isolates (0.68%) exhibited resistance to only one or two of the antimicrobials tested, categorizing them as non-MDR. Notably, none of the isolates were found to be susceptible to all the antimicrobials.

Table 9
Distribution of E. coli isolates (n= 294) based on drug resistance pattern

Antimicrobial category	Drug resistance pattern
	Non-MDR	MDR	Potentially XDR
R1	1	0	0
R2	1	0	0
R3	0	1	0
R4	0	7	0
R5	0	27	0
R6	0	35	0
R7	0	57	0
R8	0	81	0
R9	0	83	83
Overall (%)	2 (0.68)	292 (99.32)	83 (28.23)

• R1= Resistant against at least 1 antimicrobial in 1 categories), R2= Resistant against at least 1 antibiotic in 2 categories, R3= Resistant against at least 1 antibiotic in 3 different categories, R4= Resistant against at least 1 antibiotic in 4 different categories, R5= Resistant against at least 1 antibiotic in 5 different categories, R6= Resistant against at least 1 antibiotic in 6 different categories, R7= Resistant against at least 1 antibiotic in 7 different categories, R8= Resistant against at least 1 antibiotic in 8 different categories, R9 = Resistant against at least 1 antibiotic in 9 different categories.

Frequency of Multidrug Resistant pattern of E. coli isolates

The MAR index of the E. coli isolates ranged from 0.2 to 1.0, with one isolate (0.34%) having an MAR index below the cut-off value of 0.20. Among the isolates, 91 (30.95%) had a MAR index of 0.80 or higher. Additionally, 19 (6.46%) isolates had values between 0.20 and 0.39, 66 (22.45%) had values between 0.40 and 0.59, and 126 (42.86%) had values between 0.60 and 0.79 (Table 10). The maximum number of antimicrobials to which an isolate demonstrated resistance was 15 out of the 15 tested antimicrobials, which was observed in four isolates (1.36%) (Table 10). The highest proportion of isolates (16.33%) exhibited resistance to ten out of the 15 tested antimicrobials, followed by 14.97% showing resistance to eleven, 14.63% to twelve, 11.56% to nine, and 7.82% to thirteen antimicrobials.

Table 10
Multiple antibiotic resistance index and frequency of E. coli isolates (n=293) based on the number of antimicrobial agents’ resistances^a

No of antimicrobial resistances	No of resistant E. coli isolates (%)	Frequency of occurrence of the antimicrobial resistance pattern	MAR Index % (n=15)
3	*2 (0.68%)	*2	0.20
4	5 (1.70%)	5	0.27
5	13 (4.42%)	11	0.33
6	20 (6.80%)	14	0.40
7	15 (5.14%)	14	0.47
8	21 (7.14%)	19	0.53
9	34 (11.56%)	26	0.60
10	48 (16.33%)	33	0.67
11	44 (14.97%)	29	0.73
12	43 (14.63%)	23	0.80
13	23 (7.82%)	10	0.86
14	21 (7.14%)	4	0.93
15	4 (1.36%)	1	1.00
Overall	293 (99.69)	191

• ^a
  =Resistance against at least 1 antimicrobial in 3 different category. *= one of the isolates resistant to 3 antimicrobials in 2 antimicrobial categories.

Multi-drug resistance phenotypes and their frequency of occurrence

Among the 292 MDR isolates, a total of 190 different phenotypes of multidrug resistance to antimicrobials were identified. Out of these, 143 isolates (48.97%) exhibited a unique phenotypic pattern. Within this group, one isolate (0.7%) was resistant to 3 antimicrobials, 5 isolates (3.5%) were resistant to 4 antimicrobials, 13 isolates (9.1%) were resistant to 5 antimicrobials, and most of these isolates (124/143, 86.71%) displayed a unique phenotype with resistance to 6 or more antimicrobials. The remaining 149 isolates (51.02%) could be classified into 49 groups, each comprising 2 or more isolates with the same resistance profiles (Table 11).

Table 11
Frequency of the antimicrobial resistance patterns of E. coli isolates to 15 antimicrobials using the disc diffusion test

No of antimicrobials	Frequency of the antimicrobial resistance pattern (No)	No of isolates resistant (%)
1	AMP (1)	1
		(0.34%)
2	0	0
3	AMP TE DOX (1)	2
	AMP C DOX (1)	(0.68%)
4	had different single pattern of 4 antibiotic (5)	5
		(1.70%)
5	had different single pattern of five antibiotic (10)	13
	AMP-C-CL-NA-TE (3)	(4.42%)
6	had different single pattern of 6 antibiotic (11)	20 20 (6.80%)
	AMP C CL COT TE DOX (5)
	K AMP CIP NA COT TE (2)
	K AMP CL CIP NA TE (2)
7	had different single pattern of 7 antibiotic (13)	15
	CAZ AMP C CIP COT TE DOX (2)	(5.14%)
8	had different single pattern of 8 antibiotic (17)
	K CAZ AMP C CIP NA TE DOX (2)
	K CTR AMP C CL CIP NA TE (2)
9	had different single pattern of 9 antibiotic (21)	34 (11.56%)
	CAZ AMP C CL CIP NA COT TE DOX (2)
	K CAZ AMP C CL CIP NA TE DOX (4)
	K CTR CAZ AMP C NA COT TE DOX (3)
	GEN A/S AMP C CIP NA NX TE DOX (2)
	GEN K A/S AMP C CL CIP NA TE (2)
10	had different single pattern of 10 antibiotic (22)	48 (16.33%)
	CTR CAZ AMP C CL CIP NA COT TE DOX (3)
	K CAZ AMP C CL CIP NA COT TE DOX (4)
	K CAZ AMP C CL CIP NA NX COT DOX (2)
	K CTR CAZ AMP C CL NA COT TE DOX (2)
	K CTR CAZ AMP C CL CIP NA TE DOX (2)
	GEN A/S AMP C CL CIP NA COT TE DOX (2)
	GEN K A/S AMP C CL CIP NA COT TE (2)
	GEN K A/S CAZ AMP C CIP NA TE DOX (3)
	GEN K A/S CAZ AMP C CL NA COT TE (2)
	GEN K A/S CAZ AMP C CL CIP NA TE (2)
	AK K CAZ AMP C CL CIP NA COT TE (2)
11	had different single pattern of 11 antibiotic (21)	44 (14.97%)
	K CTR CAZ AMP C CL CIP NA COT TE DOX (2)
	GEN A/S CAZ AMP C CL CIP NX COT TE DOX (2)
	GEN A/S CAZ AMP C CL CIP NA NX COT TE (2)
	GEN A/S CTR AMP C CIP NA NX COT TE DOX (2)
	GEN K A/S AMP C CIP NA NX COT TE DOX (3)
	GEN K A/S AMP C CL CIP NA COT TE DOX (6)
	GEN K A/S CAZ AMP C CL NA COT TE DOX (2)
	GEN K A/S CAZ AMP C CL CIP NA COT TE (4)
12	had different single pattern of 12 antibiotic (12)	43 (14.63%)
	K CTR CAZ AMP C CL CIP NA NX COT TE DOX (7)
	K A/S CTR CAZ AMP C CL CIP NA COT TE DOX (2)
	GEN K CAZ AMP C CL CIP NA NX COT TE DOX (3)
	GEN K A/S AMP C CL CIP NA NX COT TE DOX (3)
	GEN K A/S CAZ AMP C CL CIP NA COT TE DOX (2)
	GEN K A/S CAZ AMP C CL CIP NA NX TE DOX (2)
	GEN K A/S CAZ AMP C CL CIP NA NX COT DOX (3)
	GEN K A/S CTR CAZ AMP C CL NA COT TE DOX (2)
	GEN K A/S CTR CAZ AMP C CL CIP NA TE DOX (2)
	GEN K A/S CTR CAZ AMP C CL CIP NA COT TE (3)
	AK GEN K A/S CAZ AMP C CL NA COT TE DOX (2)
13	had different single pattern of 13 antibiotic (6)	23 (7.82%)
	GEN K A/S CAZ AMP C CL CIP NA NX COT TE DOX (8)
	GEN K A/S CTR CAZ AMP C CL CIP NA COT TE DOX (5)
	AK K A/S CAZ AMP C CL CIP NA NX COT TE DOX (2)
	AK GEN K A/S CAZ AMP C CL CIP NA COT TE DOX (2)
14	had different single pattern of 14 antibiotic (2)	21 (7.14%)
	GEN K A/S CTR CAZ AMP C CL CIP NA NX COT TE DOX (12)
	AK GEN K A/S CAZ AMP C CL CIP NA NX COT TE DOX (7)
15	AK GEN K A/S CTR CAZ AMP C CL CIP NA NX COT TE DOX (4)	4
		(1.36%)

• AK amikacin; GEM, gentamicin; KAN, kanamycin.; A/S ampicillin-sulbactam; CTR ceftriaxone; CAZ ceftazidime; AMP, ampicillin; C, chloramphenicol; CL, Colistin; CIP ciprofloxacin; NA, nalidixic acid; NX norfloxacin; COT, Trimethoprim/ sulfamethoxazole; TE, Tetracycline; DOX, Doxycycline.

The most common resistance pattern observed was resistance to GEN-K-A/S-CTR-CAZ-AMP-C-CL-CIP-NA-NX-COT-TE-DOX, which was found in 12 E. coli isolates. These isolates were frequently recovered from various sources, including hands (1), liver (1), funnel (2), knife handle (1), wall tank (1), plucking machine (1), carcass (4), and board (1). The next most frequent MDR resistance phenotypes were resistance to GEN-K-A/S-CAZ-AMP-C-CL-CIP-NA-NX-COT-TE-DOX (8 isolates), followed by AK-GEN-K-A/S-CAZ-AMP-C-CL-CIP-NA-NX-COT-TE-DOX (7 isolates), K-CTR-CAZ-AMP-C-CL-CIP-NA-NX-COT-TE-DOX (7 isolates), GEN-K-A/S-AMP-C-CL-CIP-NA-COT-TE-DOX (6 isolates), GEN-K-A/S-CTR-CAZ-AMP-C-CL-CIP-NA-COT-TE-DOX (5 isolates), and AMP-C-CL-COT-TE-DOX (5 isolates).

DISCUSSION

Out of the 1132 different specimens examined, 294 E. coli isolates were recovered, with a prevalence rate of 25.97%. This prevalence rate is justified by the fact that E. coli naturally exists as a commensal in poultry guts. Similar results were reported by Akbar et al. (2014), who showed that 25% (38/152) of raw poultry meat specimens tested positive for E. coli. Moreover, Khaled (1990) stated that the incidence of E. coli in healthy chickens was 26.7%. Other studies by Radwan et al. (2020), El-Seedy et al. (2019), and Abd El Tawab et al. (2014) respectively reported prevalence rates of 26.7%, 22.9%, and 24.7% for E. coli.

On the other hand, the prevalence rate of E. coli in our study (20%) was higher than that reported by Ammar et al. (2015) and Abd El Tawab et al. (2015a), who documented prevalence rates of 15.1% and 15.8% in the winter and summer seasons, respectively. High prevalence rates of 31.1%, 36.6%, 33%, and 76% were reported by Altalhi et al. (2010), Younis et al. (2017) and Parvin et al. (2020), respectively. The variations in E. coli prevalence among studies could be attributed to different factors, including sampling and isolation procedures, variability in specimen populations, diverse geographical origins of the animals, numbers of animals, study design, season, immunological status of the host, sanitation, and treatment with antimicrobial substances during the process.

Several authors have reported the occurrence of E. coli in retail chicken, raw meats, ready-to-eat (RTE) meats, hands of sellers, and their working tools (Karimi et al., 2011; Gwida et al., 2014; Gwida & El-Gohary 2015; Abd Elzaher et al., 2018; Adzitey et al., 2021). In this study, it was found that the hands of workers, knives, cages of birds, boards, carcasses, and other sources of specimens were contaminated with E. coli species. The highest number of E. coli isolates was recovered from the hands of workers (39.8%) and the knife used for slaughter (37.7%), followed by the cages of birds (35.7%), the internal surface of carcasses (29.1%), livers (27.14%), boards (25.71%), and the knifes used for cutting (24.29%). This indicates that there are multiple sources of contamination, including the chickens themselves, the processing line, the facility, and the workers, which contribute to the distribution of E. coli. Slaughterhouses are therefore important sources of E. coli and potential risks for contamination and infection with multidrug-resistant (MDR) E. coli due to unhygienic practices during slaughter.

In terms of hygiene, practices and processing, according to the Codex Alimentarius Commission (2005), to minimize cross-contamination, the stunning and bleeding areas should be separated from the scalding and defeathering areas, as well as from the dressing areas. The processing area and any equipment or service utensils used for carcass processing should be properly cleaned and disinfected using appropriate disinfectants. However, this study revealed that hygiene practices at almost all slaughterhouses were poor. It was noted that all stages of processing, including killing, scalding, defeathering, eviscerating, carcass washing, and packing, were carried out at the same place in most slaughterhouses, where the next birds to be slaughtered were also kept in cages. Only about 35.71% of the processing areas were clean (free from dust, mud, and other rubbish), and a mere 25.7% were disinfected. Additionally, it was observed that only one or two washing tanks with stagnant water and a single scalding tank were used throughout the entire slaughtering process without changing the water or cleaning in between. The washing and scalding water were observed to turn bloody and become dirty with feathers and other debris (Fig. 3-7). This could be a potential risk factor for the transmission of disease pathogens from one processing area to another. The study also observed that one person was responsible for all activities and processing, including killing, scalding, defeathering, evisceration, carcass washing, and packing. Furthermore, it was noted that the slaughter waste was piled up inside the slaughterhouse, which could contribute to carcass contamination with internal pathogens and environmental contaminants. Most workers did not wear protective clothing, such as overalls, headgear, aprons, hand gloves, and plastic boots, nor did they undergo medical examinations to ensure they were free from communicable diseases. It was also observed that workers processed carcasses without using gloves or washing their hands with disinfectant or soap. Although workers washed or cleaned equipment used for carcass processing, such as cutting knives, boards, scalding tanks, and washing tanks, they did not disinfect this equipment using appropriate disinfectants. This could be a potential risk factor for the transmission of disease pathogens from one processing area to another.

Various microorganisms are already present on the skin, feathers, or in the alimentary tract of live birds. In traditional poultry retailers, after slaughtering poultry carcasses, the carcasses are usually scalded in a scalding tank, which can serve as an enrichment medium from which pathogens can spread widely to all birds entering the tank. Therefore, microbial contamination can occur at any stage of the production chain, including feather plucking, evisceration, and washing. Cross-contamination can also happen through other birds, instruments, machines, and operators. By implementing good hygienic practices during skinning and evisceration, the rate of carcass contamination should be significantly lower than the carriage rate. Hazard Analysis Critical Control Points (HACCP) is a well-accepted systematic program used to identify and control microbiological hazards associated with poultry processing. It has been implemented in the poultry industry to improve the microbiological quality of broiler carcasses and reduce microbiological hazards from farm to consumption (Unnevehr & Jensen et al., 1996; Sun & Ockerman, 2005).

In this study, all isolates showed multiple resistance to the antibiotics tested, with 100% resistance to ampicillin. High levels of resistance were also observed against tetracycline, chloramphenicol, nalidixic acid, ciprofloxacin, colistin, trimethoprim/sulfamethoxazole, kanamycin, doxycycline, ceftazidime, ampicillin-sulbactam, and gentamicin. Moderate resistance rates were recorded for norfloxacin (36.39%), ceftriaxone (34.35%), and amikacin (14.29%). These results indicate that the bacteria have been previously exposed to these antibiotics, acting as a selective force for resistance. The high levels of resistance to multiple antibiotics could be attributed to the excessive and uncontrolled use of antibiotics in poultry, the misuse of antibiotics in the treatment of bacterial infections without proper antibiogram testing, and the use of antibiotics in feed and water to control diseases. These findings suggest that antibiotics may be ineffective for prophylaxis or treatment of infections in poultry farming. Similar findings of multiple resistance have been reported by several authors in different countries, including Egypt (Abd El Tawab et al., 2014; Amer et al., 2018; El-Seedy et al., 2019); Saudi Arabia (Al-Ghamdi et al., 1999; 2001; Altalhi et al., 2010.); Kenya (Adelaide et al., 2008); Ghana (Adzitey et al., 2021); India (Chandran et al., 2008); Algeria (Aggad et al., 2010); Zimbabwe (Saidi et al., 2012); Iran (Zakeri & Kashefi 2012; Rahimi 2013); China (Dou et al., 2016); Iran (Jahantigh & Dizaji, 2015); and Bangladesh (Bashar et al., 2011; Rahman et al., 2018; Parvin et al., 2020).

Multidrug resistance (MDR) was observed in all E. coli isolates (99.32%), with 28.23% categorized as potentially extensively drug-resistant (XDR). A total of 190 patterns of multidrug resistance were identified. The most prevalent resistance pattern consisted of ampicillin, tetracycline, chloramphenicol, nalidixic acid, ciprofloxacin, colistin, kanamycin, doxycycline, trimethoprim/sulfamethoxazole, and ceftazidime. Unfortunately, these antibiotics are commonly utilized in both human and animal healthcare. Among the tested antimicrobials, most isolates demonstrated resistance to ten out of the total fifteen, followed by eleven, twelve, nine, thirteen, fourteen, and eight.

The high prevalence of MDR among E. coli isolates could be attributed to the increased use of antimicrobials in veterinary practices. These drugs are prescribed to control bacterial infections, serve as growth promoters to enhance poultry production, and are used for prophylaxis. Similar findings have been reported worldwide. Studies conducted by Radwan et al. (2020), El-Seedy et al. (2019), Amer et al. (2018) in Egypt; Saidi et al. (2012) in Zimbabwe; Parvin et al. (2020); Islam et al. (2008) in Bangladesh; Tabatabaei et al. (2010), Moawad et al. (2018), Halfaoui et al. (2017), and Younis et al. (2017) all found that all E. coli isolates demonstrated MDR. In another study, Radwan et al. (2014) reported that 90.4% of the isolates displayed MDR. Lower percentages of MDR were recorded by Al-Ghamdi et al. (1999) in Saudi Arabia (76.4%); Rahman et al. (2018) in Bangladesh (76%); Mgaya et al. (2021) in Tanzania (69.3%); (Rahimi, 2013) in Iran (63.3%); and Adzitey et al. (2021) in Ghana (51.1%).

The Multiple Antibiotic Resistance (MAR) index for the E. coli isolates in our study ranged from resistance to one antibiotic to resistance to fifteen antibiotics (0.20 to 1.0). Out of the E. coli isolates, 291 (99.66%) exhibited a MAR index value greater than 0.2, suggesting that they were isolated from sources exposed to antibiotics. Adzitey et al. (2021) reported MAR index values ranging from 0.0 to 0.7 in E. coli isolates, displaying 22 different resistance patterns. It has been shown that bacteria with a MAR index greater than 0.2 originate from high-risk sources where growth promoters and multiple antibiotics are commonly used, while a MAR index below 0.2 indicates isolates from sources with lower antibiotic usage (Chandran et al., 2008).

In terms of individual antibiotics, all isolates (100%) displayed resistance to ampicillin, which aligns with the 100% resistance reported by Tabatabaei et al. (2010) and Radwan et al. (2014). Other studies by Saidi et al. (2012), Akbar et al. (2014), El-Seedy et al. (2019), and Al-Ghamdi et al. (1999) documented high resistance rates of 94.1%, 92%, 90.9%, and 88.7%, respectively. However, lower rates of ampicillin resistance have also been reported: 49% by Saberfar et al. (2008), 47% by Salehi & Bonab (2006), and 39% by Adelaide et al. (2008). On the other hand, Aggad et al. (2010) reported that all studied E. coli isolate strains were susceptible to ampicillin.

The resistance to nalidixic acid was found to be 86.05%, which is similar to the approximate resistance rate of 85.62% reported by Halfaoui et al. (2017) and slightly higher than the 82.3% reported by Dou et al. (2016). Other studies reported higher resistance rates: 100% by Yang et al. (2004) and Salehi & Bonab (2006), 98% by Zakeri & Kashefi (2012), 97.7% by Rahimi (2013), and 90.9% by El-Seedy et al. (2019). On the other hand, lower resistance rates for nalidixic acid have also been reported: 70.3% by Altalhi et al. (2010) and 49.3% by Younis et al. (2017).

The results of the present study revealed that 77.5% of the isolated E. coli strains were resistant to ciprofloxacin, which is approximately similar (79%) to the findings reported by Yang et al. (2004). High levels of ciprofloxacin resistance were also reported by Radwan et al. (2020) (92.5%), Akond et al. (2009) (82%), and El-Seedy et al. (2019) (81.8%). Conversely, lower levels of ciprofloxacin resistance were reported by Salehi & Bonab (2006) (67%), Abd El Tawab et al. (2015) (40%), Islam et al. (2008) (30%), and Adelaide et al. (2008) (19%).

The resistance to norfloxacin was found to be 36.39%, which is consistent with the 36.9% reported by Younis et al. (2017). El-Seedy et al. (2019) and Abd El Tawab et al. (2014) reported high resistance rates to norfloxacin (81.8% and 80.7%, respectively). On the other hand, Salehi & Bonab (2006); Zakeri & Kashefi (2012) found that 68% and 55% of the isolates were resistant to norfloxacin, respectively. In contrast, Akond et al. (2009) reported a high sensitivity rate of 86% to norfloxacin.

Our study demonstrated that 91.16% of the isolated E. coli strains were resistant to chloramphenicol. This finding agrees with the results reported by Rahimi (2013), while Islam et al. (2008) reported a higher resistance rate of 100%. Relatively lower resistance rates were found by El-Seedy et al. (2019) (81.8%), Salehi & Bonab (2006) (67%), Saberfar et al. (2008) (52%), Al-Ghamdi et al. (2001) (43%), Halfaoui et al. (2017) (39.22%), Younis et al. (2017) (30%), Akond et al. (2009) (20%), Adelaide et al. (2008) (13.2%), and Gregova et al. (2012) (10%). On the other hand, Adzitey et al. (2021) recorded a high sensitivity rate of 81.3% to chloramphenicol.

Regarding ceftriaxone, 34.35% of the isolates were found to be resistant. This result is higher than the 21.7% resistance rate obtained by Radwan et al. (2014). El-Seedy et al. (2019) reported a slightly higher resistance rate of 45.5%, while Bashar et al. (2011) reported a high sensitivity rate of 100% to ceftriaxone.

The resistance rate to doxycycline was 70.07%. This result is nearly similar to the 69.8% reported by Younis et al. (2017). Higher rates of resistance were reported by Salehi & Bonab (2006) (88%), Zakeri & Kashefi (2012) (80%), Abd El Tawab et al. (2014), and Halfaoui et al. (2017) (75.61%). A lower rate of resistance (36.4%) was reported by El-Seedy et al. (2019).

For gentamicin, 50.34% of the isolates were resistant, which is in line with the 50% resistance reported by Islam et al. (2008) and the 55% obtained by Amer et al. (2018). Radwan et al. (2020) reported a higher resistance rate of 92.5%. Other resistance rates were reported as 46.6% by Abd El Tawab et al. (2015a) and 45.5% by El-Seedy et al. (2019). Lower rates of resistance to gentamicin have also been observed: 24.3% by Altalhi et al. (2010), 14% by Gregova et al. (2012), 12% by Saberfar et al. (2008), and 1.96% by Halfaoui et al. (2017). However, some other studies analyzing the efficacy of gentamicin on E. coli serotypes found sensitivity rates of 97.1% (Saidi et al., 2012) and 80% (Akond et al., 2009).

Similar high resistance to tetracycline (91.50%) was reported by Akbar et al. (2014) and Al-Ghamdi et al. (2001), with rates of 92% and 89.9%, respectively. Hassan et al. (2014) obtained a resistance rate of 100%, while Al-Ghamdi et al. (1999) reported 99.1% resistance, Islam et al. (2008) reported 96.6% resistance, and Salehi & Bonab (2006) reported 94% resistance. Conversely, Younis et al. (2017) found a lower resistance rate of 53.4%, and Adzitey et al. (2021) reported a rate of 50%. In contrast, the more recent study showed that 97.71% of isolates were resistant to colistin, which is higher than the rate reported by Diab (2014) (71.2%). Hassan et al. (2014) reported lower rates of colistin resistance, at 63.75% and 5.5%, respectively. On the other hand, Saberfar et al. (2008) reported a high resistance rate against colistin of 99% in the isolates.

The resistance rate to trimethoprim/sulfamethoxazole was 72.45%. Radwan et al. (2020) reported a similar rate of 77.5%, while Moawad et al. (2018) reported a rate of 64.3%, Mgaya et al. (2021) reported 80.5%, and Salehi & Bonab (2006) reported 80%. Ammar et al. (2015) obtained a higher resistance rate of 100%, and Halfaoui et al. (2017) reported 88.89%. Lower rates of resistance were reported by Al-Ghamdi et al. (2001) at 48.4% and Abd El Tawab et al. (2014) at 20.7%.

The resistance rate to kanamycin was 71.09%, which is similar to the rate of 69.24% reported by Hassan et al. (2014). Akond et al. (2009) observed a resistance rate of 76%, while Salehi & Bonab (2006) reported 77%. Zakeri & Kashefi (2012) found a resistance rate of 60%, Akbar et al. (2014) reported 15.8%, and Altalhi et al. (2010) reported 5.4%.

The resistance rate against ceftazidime was 69.05%. Moawad et al. (2018) reported a lower resistance rate of 41.1%, and Salehi & Bonab (2006) found a rate of 18%. However, Al-Ghamdi et al. (1999) observed no resistance to ceftazidime.

For ampicillin-sulbactam, the resistance rate was 53.06%. Younis et al. (2017) reported a slightly lower resistance rate of 46.5%. In contrast, Gregova et al. (2012) found a resistance rate of only 6% for ampicillin plus sulbactam.

The resistance rate against amikacin was 14.29%. Moawad et al. (2018) reported a similar resistance rate of 10.7%. On the other hand, Allam et al. (2009) observed a high sensitivity to amikacin at 90%, and Salehi & Bonab (2006) found no resistance to amikacin.

The increasing occurrence of antimicrobial resistance is a significant public health concern, and the emergence and spread of resistance is a complex problem influenced by various interconnected factors. In-vitro antimicrobial sensitivity testing of veterinary pathogens provides valuable guidance to veterinarians in selecting appropriate drug treatments (Radwan et al., 2014). Therefore, laboratory examinations should be used to detect the sensitivity of antibiotics and guide the selection of appropriate treatments. The findings of this study are consistent with most of the aforementioned studies, which indicate that the use of antibiotics in food animals, whether for therapeutic purposes or as growth promoters, can lead to the selection of antibiotic-resistant zoonotic enteric pathogens. These pathogens can then be transmitted to humans through the consumption of contaminated food or direct contact with animals (Salehi & Bonab, 2006). Furthermore, the transfer of resistance genes among bacteria occupying different habitats has the potential to occur frequently.

The increasing prevalence of multidrug-resistant (MDR) bacteria in poultry is concerning, as it limits the choice of antibiotics available for treating poultry infections. The results of this study suggest that MDR is widespread among local avian E. coli strains. These observations are consistent with the findings of Salehi & Bonab (2006), who attributed the development of drug resistance to the frequent use of drugs in veterinary practices at suboptimal concentrations. MDR could also result from the direct use of combinations of antibiotics in poultry feed and water.

CONCLUSION

In conclusion, the high prevalence of multidrug-resistant (MDR) Escherichia coli in raw broiler chicken meat, slaughterhouses and their workers underscores the urgent need for comprehensive health intervention approaches. Enhanced hygiene practices and regulated antibiotic use in poultry production are critical. The presence of potentially extensively drug-resistant (XDR) isolates emphasizes the severity of the situation, with MDR E. coli posing significant risks to human health through the food chain. Future research should focus on developing targeted interventions, including improved sanitation practices and exploring alternative antimicrobial agents. Surveillance of antimicrobial resistance patterns and regular risk assessments are crucial for identifying emerging trends and proactive measures are necessary to safeguard public health and ensure food safety within the poultry industry.

ACKNOWLEDGEMENTS

The authors are thankful to the Deanship for Scientific Research at King Faisal University (KFU), Saudi Arabia, for funding this research through project number KFU241497.

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• FUNDING
  The Deanship of Scientific Research at King Faisal University, Kingdom of Saudi Arabia, funded this research through project No. KFU241497.
• DATA AVAILABILITY STATEMENT
  Data will be available upon request
• DISCLAIMER/PUBLISHER’S NOTE
  The published papers’ statements, opinions, and data are those of the individual author(s) and contributor(s). The editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions, or products referred to in the content.

Edited by

• Section Editor:
  Ramon Malheiros

Data availability

Data will be available upon request

Publication Dates

• Publication in this collection
  16 Dec 2024
• Date of issue
  2024

History

• Received
  12 Dec 2023
• Accepted
  30 Sept 2024