{"paper_id":"4dcd0e28-95b6-4218-acdc-6ca0f939da71","body_text":"Large-animal farmers’ knowledge, attitudes, and practices regarding antibiotic usage in dairy and beef fattening farms in Bangladesh’s milk pocket areas | 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 Large-animal farmers’ knowledge, attitudes, and practices regarding antibiotic usage in dairy and beef fattening farms in Bangladesh’s milk pocket areas Fardina Sultana Sumi, Kazi Rafiq, A K M Anisur Rahman, Muhammad Tofazzal Hossain, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7061032/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Feb, 2026 Read the published version in Tropical Animal Health and Production → Version 1 posted 4 You are reading this latest preprint version Abstract In Bangladesh, the excessive use of antibiotics on cattle farms has led to antibiotic residues in animal products, contributing to antimicrobial resistance (AMR). This study aimed to assess the large animal farmers’ knowledge, attitudes, and practices regarding antibiotic usage in dairy and beef fattening farms in Bangladesh’s milk pocket areas. Data was collected through a pre-tested questionnaire, administered via face-to-face interviews and farmed record observations. A multivariable logistic regression analysis was conducted to evaluate the association between farmers’ KAP and demographic variables such as age, education, training, and farm type. The study identified significant gaps in farmers' KAP regarding antibiotic use and AMR. While 98.3% had heard of antibiotics, only 55% understood withdrawal periods, 58.3% recognized antibiotic residues, and 35.8% knew residues could transfer through milk or meat. AMR awareness was low, with 64.2% denying its link to antibiotic dosage and 52.9% unaware of its public health risks. Misconceptions were prevalent, as 65% believed antibiotics were ineffective for most diseases, and an equal proportion stopped treatment once clinical signs subsided. Antibiotic misuse was widespread—68.7% used them without prescriptions, 69.2% ignored dosage guidelines, and 55.4% failed to complete treatment courses. Moreover, only 20% maintained antibiotic records, and 31.1% had received AMR-related training. The findings indicate that young farmers with education up to SSC and training had significantly better knowledge (OR: 11.70, 95% CI: 2.54–54.04). Farmers with education up to HSC were more likely to have a positive attitude (OR: 25.28, 95% CI: 3.67-174.76) and engaged in better farming practices (OR: 24.81, 95% CI: 4.45-138.25). Dairy farmers exhibited significantly lower knowledge (OR: 0.17, 95% CI: 0.03–0.95), attitude (OR: 0.67, 95% CI: 0.05–9.86), and practice (OR: 0.61, 95% CI: 0.07–4.92) compared to beef fattening farmers. In contrast, mixed farming practitioners demonstrated superior knowledge (OR: 14.73, 95% CI: 2.85–76.36), attitude (OR: 29.30, 95% CI: 2.19–39.07), and practice (OR: 8.00, 95% CI: 1.00-64.07). This study highlights critical gaps in farmers' KAP regarding antibiotic use and AMR, emphasizing the urgent need for targeted interventions. Despite high antibiotic awareness, widespread misconceptions and improper practices contribute to AMR risks. Education and training significantly improve KAP, underscoring the need for enhanced farmer education and stricter regulatory measures. Tailored training programs and policies should prioritize high-risk groups, such as dairy farmers, to promote responsible antibiotic use and mitigate AMR threats. Antimicrobial Resistance Antibiotic misuse Education Training Regulatory measures Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Antibiotics are a class of antimicrobial substances produced naturally or synthetically. They can kill or inhibit the growth of microorganisms (1). In livestock, antibiotics are used to cure diseases, reduce morbidity and mortality, increase productivity, and ensure the safety and quality of food consumed by humans (2). Antibiotic residues refer to the active amounts of antibiotics or their metabolites that remain in bodily cells, tissues, and organs after administration(3). The emergence of antibiotic-resistant germs dates to the 20th century, as early as the 1950s (4) (5). Antibiotic resistance is a significant global health issue, responsible for approximately 0.7 million deaths annually, with projections to rise to 10 million by 2050 (6). Certain antibiotics, such as oxytetracycline, furazolidone, and sulfamethazine, have been associated with negative side effects (7). In Hong Kong, preschoolers have been found to have antibiotics used in veterinary medicine in their food, drinking water, and urine (8). In some industrialized countries, the preventative use of antibiotics in animal reproduction has been banned (9). Denmark has been a leader in this area, implementing a comprehensive monitoring program for antibiotic use in both human and animal breeding in 1995 (10). The FDA reported that about 80% of all antimicrobials in the agricultural sector are used for food animals (11). In Bangladesh, the most used antibiotics in livestock include macrolides, quinolones, β-lactams, polypeptides, amphenicols, tetracyclines, aminoglycosides, and sulfonamides (12). Residues often result from the use of unlicensed antibiotics, extra-label dosages, irrational use without adherence to withdrawal periods, and contamination of animal feed with excrement from treated animals (13). In many cases, irrational antibiotics are prescribed without considering clinical test results. Farmers, especially in severe infectious situations, often lack the awareness to complete recommended antibiotic dosages (14). Antimicrobial resistance (AMR) is a multi-sectoral issue that requires coordinated responses from human, animal, and environmental sectors, adopting a holistic strategy like One Health. The Bangladeshi government launched the “National One Health Strategy” in 2012 to reduce infection spread from animals and the environment to humans (15). In 2016, with support from development partners and personnel from the environment, animal health, and human health ministries, a “One Health Secretariat” was established at the IEDCR of Bangladesh (16). To address the indiscriminate use of antibiotics and steroid hormones in milk pocket areas, it is crucial to first understand farmers’ knowledge, attitudes, and practices (KAP) regarding these substances. A KAP survey was conducted in milk pocket regions to know the farmers knowledge, attitude, practice on antibiotic and steroid hormone residues in meat, milk, and cattle ready feed, and to correlate these residues with AMR pathogens. 2. Materials and Methods 2.1 Survey Region: The survey region included the milk pocket areas of Bangladesh. These areas are crucial for distributing milk from one location to another, which poses a potential risk of spreading resistance microorganisms through milk and meat. With this in mind, we conducted a study in milk pocket areas like Sathia and Bera upazilas of Pubna, Ullapara and Shahjadpur upazilas of Sirajgong Zila, and Sherpur and Shariakandi upazilas of Bogura (Fig. 1 ). 2.2 Survey Design and Target Populations: A cross-sectional study was conducted from November 2023 to February 2024 to investigate farmers’ knowledge, attitudes, and practices regarding antimicrobial residues and antimicrobial resistance in the milk pocket area. Data collection involved administering a pre-tested questionnaire through face-to-face interviews and observing farm records. The sample size was determined using Solvin’s formula (17), as follows: $$\\:n=\\frac{N}{1+N{e}^{2}}$$ Where: n = sample size, N = population size (600), e = acceptable margin of error (5% = 0.05) These assumptions led to a sample size of 222, and we ultimately surveyed 240 farmers. 2.3 Questionnaire Development: The questionnaire consists of five sections: a. General information, b. Management practices on livestock farms, c. Farmer’s knowledge about antibiotics, antibiotic residues, and AMR, d. Farmer’s attitude about antibiotics, antibiotic residues, and AMR, e. Farmer’s practices about antibiotics, antibiotic residues, and AMR. In the personal information section, we gathered details such as name, age, sex, address, geographical coordinates of the farm, type of farm, herd size, herd composition, shed number, education, and farm name. Section two includes 15 questions, with six being multi-optional (questions 5, 6, 7, 8, 10, 12) and the remaining nine offering ‘Yes’ or ‘No’ options (questions 1, 2, 3, 4, 9, 11, 13, 14, 15). Section three concentrates on the farmer’s knowledge about antibiotics, antibiotic residues, and AMR, containing 16 questions about antibiotics and four about hormones. Section four comprises 12 questions about the farmer’s attitude, with one related to hormones. It includes five negative questions (questions 1, 2, 6, 7, 8) and seven positive questions (questions 3, 4, 5, 9, 10, 11, 12). Section five examines the farmer’s practices concerning antibiotics and hormones, with 14 questions about antibiotics and three about hormones. The questionnaire was first created in English and then translated into Bangla, the national language. A pilot study with 25 participants was conducted prior to the main data collection to identify any gaps in the questionnaire. 2.4 Statistical Analysis: The data was primarily stored in Excel version 2018 and transferred to SPSS version 22 for further analysis. Descriptive statistics, such as frequency and proportion, were performed using a contingency table. The univariable association between demographic characteristics and KAP regarding antibiotic and steroid hormone usage was assessed by the chi-square test. Any explanatory variable associated with KAP status with a p-value of ≤ 0.20 was selected for multiple logistic regression analysis. Multicollinearity among explanatory variables was checked by the variation inflation factor (VIF) with a cut-off of ≤ 5. A backward elimination method of model selection strategy was used to select the final model. The impact of confounding and interaction was assessed using previously described methods (18). 2.5 Ethical consideration: We clearly communicated the main goal of our study to the target population. We ensured strict confidentiality for their personal and private information. Throughout the survey design, we respected religious sensitivity. Participants were fully informed that the survey was non-harmful and entirely voluntary. They were free to ask questions and received detailed answers about the study. Informed consent was obtained from every participant. The ethical considerations were approved by AWEEC/BAU/2022(75) on November 14, 2022. 3. Results 3.1. Demographic characteristics of the respondents Table 1 Demographic and Farm Characteristics of Farmers Characteristic Category Count Column N % Age of farmer Young 52 21.7% Adult 188 78.3% Sex Male 145 60.4% Female 95 39.6% Educational status of farmers Uneducated 39 16.3% Up to JSC 132 55.0% Up to HSC 50 20.8% Graduate or above 19 7.9% Farm type Dairy 115 47.9% Beef fattening 82 34.2% Mixed 43 17.9% Size of herd Large 68 28.3% Medium 52 21.7% Small 120 50.0% The demographic characteristics of farmers are presented in Table 1 . In the study areas we covered for this survey among 240 farmers from different districts, only 21.07% (52) of the farmers were young, while 78.3% (188) were adults. Among the participants, 39.6% (95) were female, and 60.4% (145) were male. Regarding educational status, we created four categories: Uneducated, Up to JSC, Up to HSC, and Graduate or above. The lowest percentage of farmers were in the Graduate or above category, which was 7.9% (19). The highest number of farmers belonged to the education level Up to JSC, at 55.0% (132). Here, 20.8% (50) were educated up to HSC, and 16.3% (39) of the farmers were uneducated. If you focused on the farm type, most farmers were dairy practitioners, about 47.9% (115). Additionally, 34.2% (82) of the farmers were from beef fattening farming, and only 17.9% were mixed farming farmers. Half of the farmers raised small-scale herds, where 21.7% were medium and 28.3% were large-scale farmers. 3.2 Farmers’ Knowledge about antibiotic use and antimicrobial resistance We asked 15 questions about antibiotic resistance (AMR) and its effects on farmers. Although 98.3% had heard of ‘antibiotics,’ only 100% knew the names of banned antibiotics. Awareness of the withdrawal period was 55%, and 58.3% understood antibiotic residues. However, only 35.8% knew if these residues could be transmitted through milk or meat. Additionally, 32.1% connected AR to AMR. While 40% were familiar with ‘AMR,’ 64.2% negatively responded to whether there was a link between AMR and antibiotic dosage. A significant 80% agreed that antibiotics should be used for disease prevention. Surprisingly, 65% were unaware that antibiotics are not used in beef fattening. Furthermore, 52.9% believed AMR posed no public health risk. Most respondents were not knowledgeable about whether antibiotics target bacterial or viral diseases, with only 31.7% thought that they work on bacterial diseases. On a positive note, 81.7% were aware of steroids, but only 45% considered steroid hormones to have adverse effects. Lastly, 58.3% expressed concern that beef could be hazardous for human consumption when hormones and steroids are used for fattening. Overall, farmers’ responses varied, and their understanding of AMR was lacking (Fig. 2 ). The assessment of knowledge is conducted through a series of questions 1. Are you familiar with the term “antibiotic”? (Yes/No) 2. Do you know the names of prohibited antibiotics? (Yes/No) 3. Do you have any knowledge of the withdrawal period? (Yes/No) 4. Do you have any understanding of antibiotic residue? (Yes/No) 5. Can antibiotic residues be passed through milk or meat? (Yes/No) 6. Is there a relationship between antibiotic resistance (AR) and antibiotic-resistant microorganisms (AMR)? (Yes/No) 7. Are you familiar with the term “AMR”? (Yes/No) 8. Is there a relationship between AMR and the dosage of antibiotics? (Yes/No) 9. Should antibiotics be used for disease prevention? (Yes/No) 10. Should antibiotics be used for beef fattening? (Yes/No)11. Do AR pose any public health risks? (Yes/No) 12. What is the function of antibodies? a) They are used in bacterial diseases. b) They are used in viral diseases. c) They are used in all diseases. d) They are used to maintain animal health. 13. Are you familiar with steroid hormones? (Yes/No) 14. Are there any adverse effects of steroid hormones? (Yes/No) 15. Is beef hazardous for human consumption when hormones and steroids are used for beef fattening? (Yes/No) 3.2. Farmers’ attitudes toward antibiotic use and antimicrobial resistance Twelve questions were asked to farmers regarding their attitudes towards antibiotics. Sixty-five percent of farmers responded that antibiotics are ineffective in treating most diseases. Sixty-eight and eighty-eight percent of farmers answered negatively when asked if they believed antibiotics could treat viral infections. We also inquired about the relationship between withdrawal periods and antibiotic residues. Eighty percent of farmers responded negatively to this question. Only 21% and 70% of farmers believed there was a relationship between antibiotic residues and antibiotic-resistant microorganisms (AMR). Thirty-seven percent of farmers responded positively that antibiotics should be prescribed. The most concerning issues that sixty-five percent of farmers responded affirmatively to when asked if they believed antibiotic treatment should be discontinued once symptoms have subsided. Sixteen and 37% of farmers believed that there was a relationship between hormones and steroids and AMR (Fig. 3 ). The questions used to evaluate farmers’ attitudes towards antibiotic use and antimicrobial resistance are as follows: 1. Do you believe that antibiotics can treat most diseases? (Yes/No), 2. Do you think antibiotics can treat viral infections? (Yes/No), 3. Do you think there is a relationship between the withdrawal period and antibiotic residues? (Yes/No), 4. Do you think there is a relationship between antibiotic residues and antimicrobial resistance (AMR)? (Yes/No), 5. Do you think antibiotics should be used with a prescription? (Yes/No), 6. Do you think antibiotic treatment should be stopped when symptoms have disappeared? (Yes/No), 7. Do you believe that antibiotics should always be available on the farm? (Yes/No), 8. Do you think they should be used for treating similar symptoms? (Yes/No), 9. Do you think the withdrawal period should be followed before selling milk and meat? (Yes/No), 10. Do you think every farmer should receive training on antibiotic use? (Yes/No), 11. Do you think antibiotic residue in milk and meat is hazardous to humans? (Yes/No), 12. Do you think there is a relationship between hormonal and steroidal residues and AMR? (Yes/No) 3.3. Farmers’ practices concerning antibiotic use and antimicrobial resistance We asked 10 questions to assess farmers’ practices regarding antibiotic use and antimicrobial resistance. We found that approximately 68.70% of the farmers used antibiotics without a doctor’s prescription. Regarding the completion of antibiotic courses for treating sick animals, 55.40% of the farmers responded negatively. When asked about the timing of antibiotic use, only 35% of the farmers indicated they used antibiotics when necessary. For the question about checking the expiry date of antibiotics before use, 36.7% of the farmers confirmed they did so. Only 26.7% of the farmers destroyed expired antibiotics, while 56.70% reused them. Additionally, 69.20% of the farmers did not use antibiotics according to the recommended dose. Just 20% of the farmers kept records of their antibiotic usage. When inquired about receiving training on related topics, 31.1% of the farmers answered affirmatively. Finally, 76.70% of the farmers reported using antibiotics on their farm within the last 6 months (Fig. 4 ). The questions asked to evaluate farmers’ practices regarding antibiotic use and antimicrobial resistance are as follows: 1. Do you use antibiotics as prescribed by a doctor? a) Only as prescribed by a doctor. b) Without a doctor’s prescription. c) Based on the pharmacist’s advice. d) According to the farmer’s experience. e) Based on the advice of the village quack. 2. Do you finish the antibiotic course to treat a sick animal? (Yes/No) 3. How often do you administer antibiotics to an animal? a) Every month. b) Every two months. c) Every six months. d) As needed. 4. Do you check the expiry date of antibiotics before use? (Yes/No) 5. If an antibiotic is expired, do you destroy it? (Yes/No) 6. Do you reuse leftover antibiotics? (Yes/No) 7. Do you use the recommended dose of antibiotics? (Yes/No) 8. Do you maintain a record of antibiotic usage? (Yes/No) 9. Have you received training on the following topics? a) Antibiotic use in animals. b) Antibiotic resistance. c) Antibiotic residue in food. d) General information about antibiotics. 10. Have you used antibiotics on your farm in the last 6 months? (Yes/No). 3.4. Common antibiotics used in farming practices The most used antibiotic was oxytetracycline. More than half of the farmers (53.8%) used oxytetracycline on their farms. The second choice of antibiotic in the study population was ciprofloxacin (32.9%). Other antibiotics used on their farms included doxycycline (5.8%), amoxicillin (4.6%), gentamycin (1.7%), and sulfa drugs (1.3%) (Fig. 5A) 3.5. Common steroids used in farming practice The common uses of steroids in farming were evident, with 42.9% of farmers using dexamethasone and 39.2% using prednisolone as steroids in their farm practices. Meanwhile, 17.9% of farmers were unaware of steroids (Fig. 5B). 3.7. Univariable analysis results Table 2 The relationship between farmers’ demographic characteristics and their knowledge about antibiotic use and AMR, as analyzed in univariable terms. Characteristics Category Knowledge Attitude Practices Good Count (%) Poor Count (%) p-Value Positive Count (%) Negative Count (%) p-Value Good Count (%) Poor Count (%) p-Value Sex .005 .015 .059 Male 75 (72.1) 74 (54.4) 57 (73.1) 92 (56.8) 57 (70.4) 92 (57.9) Female 29 (27.9) 62 (45.6) 21 (26.9) 70 (43.2) 24 (29.6) 67 (42.1) Age < .001 .023 .014 Young 11 (10.6) 37 (27.2) 9 (11.5) 39 (24.1) 9 (11.1) 39 (24.5) Adult 93 (89.4) 99 (72.8) 69 (88.5) 123 (75.9) 72 (88.9) 120 (75.5) Education < .001 < .001 < .001 Illiterate 11 (10.6) 28 (20.6) 5 (6.4) 34 (21) 9 (11.1) 30 (18.9) Up to SSC 34 (32.7) 94 (72.1) 20 (25.6) 112 (69.1) 18 (22.2) 114 (71.7) Up to HSC 44 (43.3) 6 (4.4) 37 (47.4) 13 (8) 38 (46.9) 12 (7.5) Graduate or above 15 (14.4) 4 (2.9) 16 (20.5) 3 (1.9) 16 (19.8) 3 (1.9) Training < .001 < .001 < .001 Yes 84 (80.8) 68 (50) 68 (87.2) 84 (51.9) 73 (90.1) 79 (49.7) No 20 (19.2) 68 (50) 10 (12.8) 78 (48.1) 8 (9.9) 80 (50.3) Farm Type < .001 < .001 < .001 Dairy 93 (89.4) 14 (10.3) 73 (93.6) 34 (21) 69 (85.2) 38 (23.9) Fattening 8 (7.7) 111 (81.6) 73 (93.6) 34 (21) 69 (85.2) 38 (23.9) Mixed 3 (2.9) 11 (8.1) 4 (5.1) 115 (71) 10 (12.3) 109 (68.6) 1 (1.3) 13 (8) 2 (2.5) 12 (7.5) The univariable analysis reveals the relation of independent variables on knowledge, attitude and practice that has significant effects on the antimicrobials residues (AMRs) and antimicrobials resistance (AMR). The study shows that farmers age, sex, educations, farm types and training on AMRs and AMR have significant effect on KAP. Knowledge about antibiotic use and AMR In terms of the knowledge of farmers, dairy-practicing adult male farmers with an education level up to HSC and those who had training possessed better knowledge than others. 89.4% of dairy-practicing farmers (p-Value < 0.001), 72.1% of male farmers (p-Value 0.005), 89.4% of adult farmers (p-Value 0.001), and 43.3% of farmers educated up to HSC (p-Value < 0.001) had better knowledge than other categories. Most importantly, 80.8% of farmers (p-Value < 0.001) had better knowledge who had training on AMRs and AMR. Attitude about antibiotic use and AMR The study implied that 88.5% of adults (p-Value 0.023) and 56.8% of males (p-Value 0.015) had a positive attitude. Furthermore, education levels up to HSC, dairy practicing, and those farmers who received training had 47.7%, 93.6%, and 87.2% positive attitudes, respectively, then other groups of farmers with significant p-Values < 0.001. Practices regarding antibiotic use and AMR The relationships of demographic characteristics of farmers with practices showed that 88.9% of adults had good practices, which was statistically significant with a p-value of 0.014. However, although 70.4% of male farmers had good practices, this was weakly significant as the p-value was 0.059. On the other hand, 90.1% of trained farmers had good practices, with a p-value less than 0.001. Surprisingly, 49.7% of farmers who had training on AMRs and AMR had poor practices. The education level up to HSC had significant good practices, with 46.9% of farmers in this group, but the number of farmers in this group was only 38 out of 240. Additionally, 85.2% of dairy-practicing farmers also had good practices on AMRs and AMR. 3.8. Multivariable analysis results The analysis (Table 3 ) reveals that the significant association of Age, Sex, Education and Training with the dependent variables such as knowledge and attitude. Young farmers have significantly 4.871 (OR = 4.871, CI = 1.306–18.171, p = 0.018 ) times more knowledge and 3.544 (OR = 3.544, CI = 1.032–12.170, p = 0.0.044 ) times practice (OR = 2.128, CI = 0.564–8.029, p = 0.265 ) than adult’s farmers. Specifically, higher education shows better association with knowledge and practice. Education up to SSC indicates height knowledge (OR = 11.704, CI = 2.535–54.036, p = < 0.001 ) than uneducated farmers. On the other hand, education up to HSC showed 24.813 times good practice (OR = 24.813, CI = 4.454–138.250, p = < 0.001 ) than uneducated farmers. Moreover, Farmers those have training on AMRs And AMR have 5.155 times good knowledge (OR = 5.155, CI = 1.737–15.297, p = < 0.003 ) and 11.454 times good practice (OR = 11.454, CI = 3.861–33.982, p = < 0.001 ) in compare with non-trained farmers. The analysis further revealed that the mixed farming farmers have significant association with knowledge and practice in comparison with beef fattening farmers. Mixed farming practice has 14.729 times better knowledge (OR = 11.454, CI = 2.849–76.356, p = < 0.001 ) and 8.008 times better practice (OR = 8.008, CI = 1.018–62.973, p = < 0.001 ) than other farming types. Table 3 Results of the final multivariable logistic regression analysis identifying factors related to respondents’ knowledge and practices regarding antimicrobial use (AMU) and antimicrobial resistance (AMR) Variable Category Knowledge Practice Odds Ratio Odds Ratio 95% Confidence Interval 95% Confidence Interval P-value P-value Age Adult 1.000 1.000 Young 4.87(1.31–18.17) 3.54(1.03–12.17) 0.018 0.044 Education Uneducated 1.000 1.000 Graduate or above 1.08 (0.34–3.44) 3.34 (1.10-10.13) Up to HSC 2.83 (0.48–16.44) 24.81 (4.45-138.25) Up to SSC 11.70 (2.53–54.03) 9.75 (3.46–27.43) 0.013 < 0.001 Training Not received 1.000 1.000 Received 5.15 (1.74–15.29) 11.45 (3.86–33.98) 0.003 < 0.001 Farm type Beef fattening 1.000 1.000 Dairy 0.17(0.03–0.95) 0.61(0.07–4.92) Mixed 14.72 (2.84–76.35) 8.01(1.01–62.97) < 0.001 < 0.001 Table 4 Results of the final multivariable logistic regression analysis identifying factors related to respondents’ attitude regarding antimicrobial use (AMU) and antimicrobial resistance (AMR) Variable Category Attitude Odds Ratio (Exp. B) 95% C.I (Lower - Higher) P-value Education Uneducated 1.000 Graduate or above 0.902(0.244–3.339) Up to HSC 25.278(3.656-174.756) Up to SSC 5.463(1.905–15.667) < 0.001 Training Not received 1.000 Received 4.967(1.712–14.411) 0.003 Farm type Beef fattening 1.000 Dairy 0.666(0.045–9.857) Mixed 29.303(2.185-393.065) < 0.001 The demographic variables of farmers, such as education, training, and farm type, are significantly linked to the dependent variable of practice (Table 4 ). This analysis shows that education up to HSC results in a 25.278 times higher attitude (C.I. = 3.656-174.756, P-value < 0.001) compared to uneducated farmers. Similarly, education up to SSC leads to a 5.46 times higher attitude (C.I. = 1.905–15.667, P-value < 0.001). Farmers who received training on AMU & AMR exhibit a better attitude (OR = 4.967, C.I. = 1.712–14.411, P-value = 0.003). Lastly, it was observed that farmers practicing mixed farming systems have a 29.303 times more positive attitude than those practicing dairy farming. 4. Discussion The KAP survey findings from this study reveal misconceptions and a lack of knowledge about antibiotic residues and resistance among farmers. Farmers in the milk-producing regions of Bangladesh supply milk throughout the country. However, their level of knowledge is not satisfactory, posing a significant threat to the global issue of antibiotic resistance. Training and education are crucial factors, as trained and educated farmers tend to have better KAP. Our research shows that 43.3% of farmers are informed about antimicrobial use (AMU) and antimicrobial resistance (AMR), a rate higher than that reported by (19) but lower than (20) in Algeria. Although 98% of farmers recognize antibiotics, only 31.7% understand their purpose, which is to treat bacterial diseases, aligning with findings by (21). Furthermore, 41.70% and 40% of farmers grasp the concepts of antibiotic residues and resistance, respectively, which are lower than the knowledge levels reported by (22) in Bangladesh. Another survey on mastitis control using antibiotics indicated that 55.4% of participating farmers were unaware of antibiotic residues (23). Despite widespread awareness of antibiotics, a significant knowledge gap persists due to inadequate awareness and resources about the connection between antibiotics and antimicrobial resistance. Our study also found that 35.80% of farmers are aware of the relationship between AMR and AMRs, a rate lower than the previous study in Ethiopia(24). Additionally, only 35.80% of farmers know that antibiotic residues can be present in milk and meat, and 35.8% are aware that antibiotic dosage is linked to AMR, findings like those of (24). Furthermore, 65% of farmers acknowledge that antibiotics are used for beef fattening, and 47.10% are aware that AMR poses public health risks. Misconceptions about antibiotics significantly contribute to AMR, and this is reflected in various studies. For instance, 57.7% of dairy farm owners and workers in Addis Ababa, Ethiopia, have good knowledge (25), while 38.8% of veterinarians and para-veterinarians in Bhutan also demonstrate good knowledge on antibiotic use and AMR (26). However, a KAP survey among veterinary students highlighted poor knowledge among non-medical students (27), and another survey on poultry feed and drugs showed that most respondents have insufficient knowledge, less positive attitudes, and inappropriate practices regarding AMU and AMR (28). In this study, only 32.8% of farmers have a positive attitude toward AMR and AMRs, which is lower than in previous studies (29–31). Our findings indicate that 65% of farmers believe antibiotics can treat most diseases, which is lower than a previous study in Bangladesh (19) but higher than one in Turkey (32). It also reveals that 31.30% of farmers are aware that antibiotics should be used on a veterinarian’s prescription, whereas a study reported more than 50% (33), and another study among farmers in the Mymensingh region of Bangladesh showed only 37.7% used antimicrobials on a veterinarian’s recommendation (19). A significant number of farmers (65%) stopped using antibiotics when clinical signs disappeared, which is higher than another study (24). 65% of farmers believe antibiotics should be stopped when signs disappear, higher than a previous study (19) but lower than a Turkish study (32). Another study showed that farmers change their own doses without consulting a veterinarian (34). More than half of the study population (65%) always kept antibiotics on their farm, which is lower than a previous study (34). Furthermore, 57.25% of farmers think that every farmer should receive training on antibiotic use, indicating a lack of knowledge about antibiotic use. Therefore, policymakers should focus on this to reduce AMR. The current study shows that less than half of the farmers involved (33.8%) follow proper practices regarding antibiotic use and resistance, which is lower than a finding in Ethiopia (34). Additionally, only 31.30% of farmers use antibiotics as prescribed by a veterinarian, a finding lower than that reported in a study conducted in Bangladesh (19) and several other studies (35,36). In Bangladesh, the shortage of qualified veterinary services and personnel often forces farm owners to depend on unqualified, illegal practitioners, or quacks for treating livestock. Sometimes, owners or neighboring farmers diagnose diseases themselves, resulting in the indiscriminate and excessive use of antibiotics (37). The current study indicates that 44.60% of farmers complete their antibiotic course. In contrast, a study in Ghana found that 65% of farmers do not complete their antibiotic regimen (38). Additionally, less than one-third (26.70%) of farmers practice proper disposal of leftover antibiotics, which is lower than previous findings (31). Additionally, only 20% of farmers maintain records of antibiotic usage. In contrast, as anticipated due to government policies and regulations, a higher percentage of farmers in the USA were found to keep such records (39). Demographic factors such as age, sex, education, and training significantly impact farmers’ knowledge, attitudes, and practices (KAP). A study by(40) also notes significant differences in KAP based on these factors. In Bangladesh, the most used antibiotics in farm practices are oxytetracycline (53.8%) and the hormone dexamethasone (42.9%), whereas in the UK, penicillin is the most used (41). A major reason for farmers’ poor knowledge of antibiotic use and resistance is their lack of education or inadequate education (29,42). Government-employed veterinarians in Bangladesh showed a 2.59 times more favorable attitude towards antibiotic misuse (AMU) and antibiotic resistance (AMR) (43). The government should improve its policies to raise awareness about AMR and AMU through training, seminars, and enhanced education. The study has several limitations, including a small study population with data collected from a limited number of farmers in each upazila. Additionally, only two upazilas from a single district were studied, which does not represent the entire milk pocket area. To our knowledge, this is the first study on AMR and AMU in the milk pocket areas of Bangladesh. 5. Conclusion The survey on the KAP regarding AMR in livestock at the milk pocket area in Bangladesh, conducted in Bogura, Pabna, and Sirajganj zila under the Rajshahi division, highlighted that large animal farmers have inadequate knowledge, negative attitudes, and poor practices. Socio-demographic factors such as sex, age, education, farm type, and training on antibiotic use and antimicrobial resistance significantly influence the KAP. The lack of manpower and facilities in the livestock sector prevents farmers from receiving proper guidance on antibiotics and AMR. To combat this globally threatening issue, the government should regulate the misuse, overuse, and abuse of antibiotics and ban their marketing as over-the-counter drugs. Effective implementation and monitoring of policies could lead to positive outcomes in reducing AMR. Declarations Author Contributions K.R. (Kazi Rafiq) and F.S.S. (Fardina Sultana Sumi) made equal contributions to this work. F.S.S. was responsible for drafting the manuscript; K.R. and A.K.M.A.R. (A K M Anisur Rahman) were involved in designing and overseeing the research; K.R., A.K.M.A.R., and M.T.H. (Muhammad Tofazzal Hossain) undertook the revision and finalization of the manuscript draft. F.S.S., A.B.Z. (Anan Binte Zaman) and S.M.I. (Shah Md. Iqbal) collected and entered data; M.R.H. (Md. Rakib Hasan) and F.S.S. conducted the statistical analysis and prepared the figures, maps and tables. All authors have reviewed and consented to the published version of the manuscript. Acknowledgement The authors would like to express their deepest gratitude to LDDP (Livestock and Dairy Development Project) Research and Innovation Sub-project (project ID: RP-D-03-22) under DLS, Dhaka to Prof. Dr. Kazi Rafiqul Islam, Department of Pharmacology of Bangladesh Agricultural University and the Bangladesh Agricultural University Research System (BAURES) in Mymensingh for their invaluable support in project management and monitoring. Furthermore, the authors would like to express their gratitude to the district livestock officers, upazila livestock officers, and veterinary surgeons for their unwavering support in the study regions. Lastly, we would like to thank all the respondents, particularly the large-animal farmers in the study area, for their generous cooperation and for sharing their valuable time and insights during the interviews. Competing Interest No competing interests exist. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7061032\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":488819480,\"identity\":\"11e59157-0b98-4fb2-b95d-d726eeb1d3b8\",\"order_by\":0,\"name\":\"Fardina Sultana Sumi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Bangladesh Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Fardina\",\"middleName\":\"Sultana\",\"lastName\":\"Sumi\",\"suffix\":\"\"},{\"id\":488819481,\"identity\":\"9e194370-d81f-4286-a55e-abcebeae9afc\",\"order_by\":1,\"name\":\"Kazi Rafiq\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Bangladesh Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Kazi\",\"middleName\":\"\",\"lastName\":\"Rafiq\",\"suffix\":\"\"},{\"id\":488819482,\"identity\":\"3e4a2a53-eb27-4ff0-bfee-90a5461f09a2\",\"order_by\":2,\"name\":\"A K M Anisur Rahman\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYHACNhjD8DGETmBgRgji12JsTLIWM2mitPDPPvzs0Y2aOwy67c3bqgtqDjPws+cYMBeU4dYicS7N3Djn2DMGszPHym7POHaYQbLnjQHzjHN4nHUG6J4ctsMMZjdyzG7zABkGN4C28Lbh1iF/hv2bdM4/oJb7b8yKeYAMe0JaDM7wmEnntoFs4TEDqgTaIkFAi+EZnjLp3D6gljNpxdIz+9J5JM48KziMzy9yZ9i3Sed8A2o5fnjj54Jv1nL87ckbH+MLMRiob4AyeEDEAcIaRsEoGAWjYBTgAwAbAE4DjK/RlgAAAABJRU5ErkJggg==\",\"orcid\":\"https://orcid.org/0000-0001-9660-4949\",\"institution\":\"Bangladesh Agricultural University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"A\",\"middleName\":\"K M Anisur\",\"lastName\":\"Rahman\",\"suffix\":\"\"},{\"id\":488819483,\"identity\":\"fdfdd8b4-cc69-4024-b660-26578f8b96e4\",\"order_by\":3,\"name\":\"Muhammad Tofazzal Hossain\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Bangladesh Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Muhammad\",\"middleName\":\"Tofazzal\",\"lastName\":\"Hossain\",\"suffix\":\"\"},{\"id\":488819484,\"identity\":\"f4601e31-d17e-42a8-add7-21de9cab5ce8\",\"order_by\":4,\"name\":\"Purba Islam\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Bangladesh Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Purba\",\"middleName\":\"\",\"lastName\":\"Islam\",\"suffix\":\"\"},{\"id\":488819485,\"identity\":\"f605356c-c534-464a-adb6-c4ae84bfbd47\",\"order_by\":5,\"name\":\"Md. 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Iqbal\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Bangladesh Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Shah\",\"middleName\":\"Md.\",\"lastName\":\"I\",\"suffix\":\"Md.\"},{\"id\":488819487,\"identity\":\"83a86506-5e27-4abd-8ef9-125bb3fd741c\",\"order_by\":7,\"name\":\"Anan Binte Zaman\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Bangladesh Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Anan\",\"middleName\":\"Binte\",\"lastName\":\"Zaman\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-07-07 04:03:49\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-7061032/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7061032/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1007/s11250-026-04944-8\",\"type\":\"published\",\"date\":\"2026-02-20T15:59:30+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":87478906,\"identity\":\"fa9f40df-3cfc-4738-8fda-1af85a9f0862\",\"added_by\":\"auto\",\"created_at\":\"2025-07-24 09:34:25\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":508279,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eStudy areas shown in Bangladesh map\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7061032/v1/372149b01cf87c85b8a5a6ba.png\"},{\"id\":87478484,\"identity\":\"b3e07695-77c3-4723-af0c-85517294d1d4\",\"added_by\":\"auto\",\"created_at\":\"2025-07-24 09:26:25\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":45332,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFarmers’ Knowledge about antibiotic use and antimicrobial resistance\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7061032/v1/470657b34ff2298a01eb4715.png\"},{\"id\":87478904,\"identity\":\"bdde588b-5051-4e5c-935c-b1adc18bfd80\",\"added_by\":\"auto\",\"created_at\":\"2025-07-24 09:34:25\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":57416,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFarmers’ attitudes toward antibiotic use and antimicrobial resistance\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7061032/v1/f4391a48478195aa70bdafc0.png\"},{\"id\":87478485,\"identity\":\"fdba5ba8-419b-4d2a-9348-8fc991648a12\",\"added_by\":\"auto\",\"created_at\":\"2025-07-24 09:26:25\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":65057,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFarmers’ practices concerning antibiotic use and antimicrobial resistance\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7061032/v1/bad70798a9fc25b121f105a1.png\"},{\"id\":87478907,\"identity\":\"0b1970df-4b2d-416e-8ea5-2f0df95aa72b\",\"added_by\":\"auto\",\"created_at\":\"2025-07-24 09:34:25\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":112812,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSee image above for figure legend.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7061032/v1/9c1c992e65484ff5383aaaec.png\"},{\"id\":103251536,\"identity\":\"62055fd9-a7cf-4590-87f1-39e77c7244bc\",\"added_by\":\"auto\",\"created_at\":\"2026-02-23 16:10:36\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1927858,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7061032/v1/1068c042-22e7-45fa-88cf-f8d45f39c712.pdf\"}],\"financialInterests\":\"\",\"formattedTitle\":\"Large-animal farmers’ knowledge, attitudes, and practices regarding antibiotic usage in dairy and beef fattening farms in Bangladesh’s milk pocket areas\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eAntibiotics are a class of antimicrobial substances produced naturally or synthetically. They can kill or inhibit the growth of microorganisms (1). In livestock, antibiotics are used to cure diseases, reduce morbidity and mortality, increase productivity, and ensure the safety and quality of food consumed by humans (2). Antibiotic residues refer to the active amounts of antibiotics or their metabolites that remain in bodily cells, tissues, and organs after administration(3). The emergence of antibiotic-resistant germs dates to the 20th century, as early as the 1950s (4) (5). Antibiotic resistance is a significant global health issue, responsible for approximately 0.7\\u0026nbsp;million deaths annually, with projections to rise to 10\\u0026nbsp;million by 2050 (6). Certain antibiotics, such as oxytetracycline, furazolidone, and sulfamethazine, have been associated with negative side effects (7). In Hong Kong, preschoolers have been found to have antibiotics used in veterinary medicine in their food, drinking water, and urine (8). In some industrialized countries, the preventative use of antibiotics in animal reproduction has been banned (9). Denmark has been a leader in this area, implementing a comprehensive monitoring program for antibiotic use in both human and animal breeding in 1995 (10). The FDA reported that about 80% of all antimicrobials in the agricultural sector are used for food animals (11).\\u003c/p\\u003e\\u003cp\\u003eIn Bangladesh, the most used antibiotics in livestock include macrolides, quinolones, β-lactams, polypeptides, amphenicols, tetracyclines, aminoglycosides, and sulfonamides (12). Residues often result from the use of unlicensed antibiotics, extra-label dosages, irrational use without adherence to withdrawal periods, and contamination of animal feed with excrement from treated animals (13). In many cases, irrational antibiotics are prescribed without considering clinical test results. Farmers, especially in severe infectious situations, often lack the awareness to complete recommended antibiotic dosages (14). Antimicrobial resistance (AMR) is a multi-sectoral issue that requires coordinated responses from human, animal, and environmental sectors, adopting a holistic strategy like One Health. The Bangladeshi government launched the \\u0026ldquo;National One Health Strategy\\u0026rdquo; in 2012 to reduce infection spread from animals and the environment to humans (15). In 2016, with support from development partners and personnel from the environment, animal health, and human health ministries, a \\u0026ldquo;One Health Secretariat\\u0026rdquo; was established at the IEDCR of Bangladesh (16).\\u003c/p\\u003e\\u003cp\\u003eTo address the indiscriminate use of antibiotics and steroid hormones in milk pocket areas, it is crucial to first understand farmers\\u0026rsquo; knowledge, attitudes, and practices (KAP) regarding these substances. A KAP survey was conducted in milk pocket regions to know the farmers knowledge, attitude, practice on antibiotic and steroid hormone residues in meat, milk, and cattle ready feed, and to correlate these residues with AMR pathogens.\\u003c/p\\u003e\"},{\"header\":\"2. Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.1 Survey Region:\\u003c/h2\\u003e\\u003cp\\u003eThe survey region included the milk pocket areas of Bangladesh. These areas are crucial for distributing milk from one location to another, which poses a potential risk of spreading resistance microorganisms through milk and meat. With this in mind, we conducted a study in milk pocket areas like Sathia and Bera upazilas of Pubna, Ullapara and Shahjadpur upazilas of Sirajgong Zila, and Sherpur and Shariakandi upazilas of Bogura (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.2 Survey Design and Target Populations:\\u003c/h2\\u003e\\u003cp\\u003eA cross-sectional study was conducted from November 2023 to February 2024 to investigate farmers\\u0026rsquo; knowledge, attitudes, and practices regarding antimicrobial residues and antimicrobial resistance in the milk pocket area. Data collection involved administering a pre-tested questionnaire through face-to-face interviews and observing farm records. The sample size was determined using Solvin\\u0026rsquo;s formula (17), as follows:\\u003cdiv id=\\\"Equa\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equa\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:n=\\\\frac{N}{1+N{e}^{2}}$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eWhere: n\\u0026thinsp;=\\u0026thinsp;sample size, N\\u0026thinsp;=\\u0026thinsp;population size (600), e\\u0026thinsp;=\\u0026thinsp;acceptable margin of error (5% = 0.05)\\u003c/p\\u003e\\u003cp\\u003eThese assumptions led to a sample size of 222, and we ultimately surveyed 240 farmers.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.3 Questionnaire Development:\\u003c/h2\\u003e\\u003cp\\u003eThe questionnaire consists of five sections: a. General information, b. Management practices on livestock farms, c. Farmer\\u0026rsquo;s knowledge about antibiotics, antibiotic residues, and AMR, d. Farmer\\u0026rsquo;s attitude about antibiotics, antibiotic residues, and AMR, e. Farmer\\u0026rsquo;s practices about antibiotics, antibiotic residues, and AMR. In the personal information section, we gathered details such as name, age, sex, address, geographical coordinates of the farm, type of farm, herd size, herd composition, shed number, education, and farm name. Section two includes 15 questions, with six being multi-optional (questions 5, 6, 7, 8, 10, 12) and the remaining nine offering \\u0026lsquo;Yes\\u0026rsquo; or \\u0026lsquo;No\\u0026rsquo; options (questions 1, 2, 3, 4, 9, 11, 13, 14, 15). Section three concentrates on the farmer\\u0026rsquo;s knowledge about antibiotics, antibiotic residues, and AMR, containing 16 questions about antibiotics and four about hormones. Section four comprises 12 questions about the farmer\\u0026rsquo;s attitude, with one related to hormones. It includes five negative questions (questions 1, 2, 6, 7, 8) and seven positive questions (questions 3, 4, 5, 9, 10, 11, 12). Section five examines the farmer\\u0026rsquo;s practices concerning antibiotics and hormones, with 14 questions about antibiotics and three about hormones. The questionnaire was first created in English and then translated into Bangla, the national language. A pilot study with 25 participants was conducted prior to the main data collection to identify any gaps in the questionnaire.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.4 Statistical Analysis:\\u003c/h2\\u003e\\u003cp\\u003eThe data was primarily stored in Excel version 2018 and transferred to SPSS version 22 for further analysis. Descriptive statistics, such as frequency and proportion, were performed using a contingency table. The univariable association between demographic characteristics and KAP regarding antibiotic and steroid hormone usage was assessed by the chi-square test. Any explanatory variable associated with KAP status with a p-value of \\u0026le;\\u0026thinsp;0.20 was selected for multiple logistic regression analysis. Multicollinearity among explanatory variables was checked by the variation inflation factor (VIF) with a cut-off of \\u0026le;\\u0026thinsp;5. A backward elimination method of model selection strategy was used to select the final model. The impact of confounding and interaction was assessed using previously described methods (18).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.5 Ethical consideration:\\u003c/h2\\u003e\\u003cp\\u003eWe clearly communicated the main goal of our study to the target population. We ensured strict confidentiality for their personal and private information. Throughout the survey design, we respected religious sensitivity. Participants were fully informed that the survey was non-harmful and entirely voluntary. They were free to ask questions and received detailed answers about the study. Informed consent was obtained from every participant. The ethical considerations were approved by AWEEC/BAU/2022(75) on November 14, 2022.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.1. Demographic characteristics of the respondents\\u003c/h2\\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\\u003eDemographic and Farm Characteristics of Farmers\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" 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char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e16.3%\\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\\u003cp\\u003eUp to JSC\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e132\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e55.0%\\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\\u003cp\\u003eUp to HSC\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e50\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e20.8%\\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\\u003cp\\u003eGraduate or above\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e19\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e7.9%\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFarm type\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eDairy\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e115\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e47.9%\\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\\u003cp\\u003eBeef fattening\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e82\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e34.2%\\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\\u003cp\\u003eMixed\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e43\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e17.9%\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eSize of herd\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eLarge\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e68\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e28.3%\\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\\u003cp\\u003eMedium\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e52\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e21.7%\\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\\u003cp\\u003eSmall\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e120\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e50.0%\\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\\u003eThe demographic characteristics of farmers are presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. In the study areas we covered for this survey among 240 farmers from different districts, only 21.07% (52) of the farmers were young, while 78.3% (188) were adults. Among the participants, 39.6% (95) were female, and 60.4% (145) were male. Regarding educational status, we created four categories: Uneducated, Up to JSC, Up to HSC, and Graduate or above. The lowest percentage of farmers were in the Graduate or above category, which was 7.9% (19). The highest number of farmers belonged to the education level Up to JSC, at 55.0% (132). Here, 20.8% (50) were educated up to HSC, and 16.3% (39) of the farmers were uneducated. If you focused on the farm type, most farmers were dairy practitioners, about 47.9% (115). Additionally, 34.2% (82) of the farmers were from beef fattening farming, and only 17.9% were mixed farming farmers. Half of the farmers raised small-scale herds, where 21.7% were medium and 28.3% were large-scale farmers.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.2 Farmers\\u0026rsquo; Knowledge about antibiotic use and antimicrobial resistance\\u003c/h2\\u003e\\u003cp\\u003eWe asked 15 questions about antibiotic resistance (AMR) and its effects on farmers. Although 98.3% had heard of \\u0026lsquo;antibiotics,\\u0026rsquo; only 100% knew the names of banned antibiotics. Awareness of the withdrawal period was 55%, and 58.3% understood antibiotic residues. However, only 35.8% knew if these residues could be transmitted through milk or meat. Additionally, 32.1% connected AR to AMR. While 40% were familiar with \\u0026lsquo;AMR,\\u0026rsquo; 64.2% negatively responded to whether there was a link between AMR and antibiotic dosage. A significant 80% agreed that antibiotics should be used for disease prevention. Surprisingly, 65% were unaware that antibiotics are not used in beef fattening. Furthermore, 52.9% believed AMR posed no public health risk. Most respondents were not knowledgeable about whether antibiotics target bacterial or viral diseases, with only 31.7% thought that they work on bacterial diseases. On a positive note, 81.7% were aware of steroids, but only 45% considered steroid hormones to have adverse effects. Lastly, 58.3% expressed concern that beef could be hazardous for human consumption when hormones and steroids are used for fattening. Overall, farmers\\u0026rsquo; responses varied, and their understanding of AMR was lacking (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eThe assessment of knowledge is conducted through a series of questions\\u003c/strong\\u003e\\u003cp\\u003e1. Are you familiar with the term \\u0026ldquo;antibiotic\\u0026rdquo;? (Yes/No) 2. Do you know the names of prohibited antibiotics? (Yes/No) 3. Do you have any knowledge of the withdrawal period? (Yes/No) 4. Do you have any understanding of antibiotic residue? (Yes/No) 5. Can antibiotic residues be passed through milk or meat? (Yes/No) 6. Is there a relationship between antibiotic resistance (AR) and antibiotic-resistant microorganisms (AMR)? (Yes/No) 7. Are you familiar with the term \\u0026ldquo;AMR\\u0026rdquo;? (Yes/No) 8. Is there a relationship between AMR and the dosage of antibiotics? (Yes/No) 9. Should antibiotics be used for disease prevention? (Yes/No) 10. Should antibiotics be used for beef fattening? (Yes/No)11. Do AR pose any public health risks? (Yes/No) 12. What is the function of antibodies? a) They are used in bacterial diseases. b) They are used in viral diseases. c) They are used in all diseases. d) They are used to maintain animal health. 13. Are you familiar with steroid hormones? (Yes/No) 14. Are there any adverse effects of steroid hormones? (Yes/No) 15. Is beef hazardous for human consumption when hormones and steroids are used for beef fattening? (Yes/No)\\u003c/p\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.2. Farmers\\u0026rsquo; attitudes toward antibiotic use and antimicrobial resistance\\u003c/h2\\u003e\\u003cp\\u003eTwelve questions were asked to farmers regarding their attitudes towards antibiotics. Sixty-five percent of farmers responded that antibiotics are ineffective in treating most diseases. Sixty-eight and eighty-eight percent of farmers answered negatively when asked if they believed antibiotics could treat viral infections. We also inquired about the relationship between withdrawal periods and antibiotic residues. Eighty percent of farmers responded negatively to this question. Only 21% and 70% of farmers believed there was a relationship between antibiotic residues and antibiotic-resistant microorganisms (AMR). Thirty-seven percent of farmers responded positively that antibiotics should be prescribed. The most concerning issues that sixty-five percent of farmers responded affirmatively to when asked if they believed antibiotic treatment should be discontinued once symptoms have subsided. Sixteen and 37% of farmers believed that there was a relationship between hormones and steroids and AMR (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe questions used to evaluate farmers\\u0026rsquo; attitudes towards antibiotic use and antimicrobial resistance are as follows: 1. Do you believe that antibiotics can treat most diseases? (Yes/No), 2. Do you think antibiotics can treat viral infections? (Yes/No), 3. Do you think there is a relationship between the withdrawal period and antibiotic residues? (Yes/No), 4. Do you think there is a relationship between antibiotic residues and antimicrobial resistance (AMR)? (Yes/No), 5. Do you think antibiotics should be used with a prescription? (Yes/No), 6. Do you think antibiotic treatment should be stopped when symptoms have disappeared? (Yes/No), 7. Do you believe that antibiotics should always be available on the farm? (Yes/No), 8. Do you think they should be used for treating similar symptoms? (Yes/No), 9. Do you think the withdrawal period should be followed before selling milk and meat? (Yes/No), 10. Do you think every farmer should receive training on antibiotic use? (Yes/No), 11. Do you think antibiotic residue in milk and meat is hazardous to humans? (Yes/No), 12. Do you think there is a relationship between hormonal and steroidal residues and AMR? (Yes/No)\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.3. Farmers\\u0026rsquo; practices concerning antibiotic use and antimicrobial resistance\\u003c/h2\\u003e\\u003cp\\u003eWe asked 10 questions to assess farmers\\u0026rsquo; practices regarding antibiotic use and antimicrobial resistance. We found that approximately 68.70% of the farmers used antibiotics without a doctor\\u0026rsquo;s prescription. Regarding the completion of antibiotic courses for treating sick animals, 55.40% of the farmers responded negatively. When asked about the timing of antibiotic use, only 35% of the farmers indicated they used antibiotics when necessary. For the question about checking the expiry date of antibiotics before use, 36.7% of the farmers confirmed they did so. Only 26.7% of the farmers destroyed expired antibiotics, while 56.70% reused them. Additionally, 69.20% of the farmers did not use antibiotics according to the recommended dose. Just 20% of the farmers kept records of their antibiotic usage. When inquired about receiving training on related topics, 31.1% of the farmers answered affirmatively. Finally, 76.70% of the farmers reported using antibiotics on their farm within the last 6 months (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe questions asked to evaluate farmers\\u0026rsquo; practices regarding antibiotic use and antimicrobial resistance are as follows: 1. Do you use antibiotics as prescribed by a doctor? a) Only as prescribed by a doctor. b) Without a doctor\\u0026rsquo;s prescription. c) Based on the pharmacist\\u0026rsquo;s advice. d) According to the farmer\\u0026rsquo;s experience. e) Based on the advice of the village quack. 2. Do you finish the antibiotic course to treat a sick animal? (Yes/No) 3. How often do you administer antibiotics to an animal? a) Every month. b) Every two months. c) Every six months. d) As needed. 4. Do you check the expiry date of antibiotics before use? (Yes/No) 5. If an antibiotic is expired, do you destroy it? (Yes/No) 6. Do you reuse leftover antibiotics? (Yes/No) 7. Do you use the recommended dose of antibiotics? (Yes/No) 8. Do you maintain a record of antibiotic usage? (Yes/No) 9. Have you received training on the following topics? a) Antibiotic use in animals. b) Antibiotic resistance. c) Antibiotic residue in food. d) General information about antibiotics. 10. Have you used antibiotics on your farm in the last 6 months? (Yes/No).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.4. Common antibiotics used in farming practices\\u003c/h2\\u003e\\u003cp\\u003eThe most used antibiotic was oxytetracycline. More than half of the farmers (53.8%) used oxytetracycline on their farms. The second choice of antibiotic in the study population was ciprofloxacin (32.9%). Other antibiotics used on their farms included doxycycline (5.8%), amoxicillin (4.6%), gentamycin (1.7%), and sulfa drugs (1.3%) (Fig.\\u0026nbsp;5A)\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.5. Common steroids used in farming practice\\u003c/h2\\u003e\\u003cp\\u003eThe common uses of steroids in farming were evident, with 42.9% of farmers using dexamethasone and 39.2% using prednisolone as steroids in their farm practices. Meanwhile, 17.9% of farmers were unaware of steroids (Fig.\\u0026nbsp;5B).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.7. Univariable analysis results\\u003c/h2\\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\\u003eThe relationship between farmers\\u0026rsquo; demographic characteristics and their knowledge about antibiotic use and AMR, as analyzed in univariable terms.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"11\\\"\\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\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" 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\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCharacteristics\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCategory\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003eKnowledge\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c8\\\" namest=\\\"c6\\\"\\u003e\\u003cp\\u003eAttitude\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c11\\\" namest=\\\"c9\\\"\\u003e\\u003cp\\u003ePractices\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\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\\u003eGood\\u003c/p\\u003e\\u003cp\\u003eCount (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003ePoor\\u003c/p\\u003e\\u003cp\\u003eCount (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003ep-Value\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003ePositive\\u003c/p\\u003e\\u003cp\\u003eCount (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eNegative\\u003c/p\\u003e\\u003cp\\u003eCount (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003ep-Value\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003eGood\\u003c/p\\u003e\\u003cp\\u003eCount (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003ePoor\\u003c/p\\u003e\\u003cp\\u003eCount (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003ep-Value\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eSex\\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\\u003cp\\u003e.005\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e.015\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e.059\\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\\u003cp\\u003eMale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e75 (72.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e74 (54.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e57 (73.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e92 (56.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" 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colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003e\\u0026lt;\\u003c/em\\u003e\\u0026thinsp;.001\\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\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e84 (80.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e68 (50)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e68 (87.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e84 (51.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e73 (90.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e79 (49.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\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\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e20 (19.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e68 (50)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e10 (12.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e78 (48.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e8 (9.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e80 (50.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFarm Type\\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\\u003cp\\u003e\\u003cem\\u003e\\u0026lt;\\u003c/em\\u003e\\u0026thinsp;.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003e\\u0026lt;\\u003c/em\\u003e\\u0026thinsp;.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003e\\u0026lt;\\u003c/em\\u003e\\u0026thinsp;.001\\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\\u003cp\\u003eDairy\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e93 (89.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e14 (10.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e73 (93.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e34 (21)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e69 (85.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e38 (23.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\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\\u003eFattening\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e8 (7.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e111 (81.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e73 (93.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e34 (21)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e69 (85.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e38 (23.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\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\\u003eMixed\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3 (2.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e11 (8.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e4 (5.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e115 (71)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e10 (12.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e109 (68.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\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\\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\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1 (1.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e13 (8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e2 (2.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e12 (7.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe univariable analysis reveals the relation of independent variables on knowledge, attitude and practice that has significant effects on the antimicrobials residues (AMRs) and antimicrobials resistance (AMR). The study shows that farmers age, sex, educations, farm types and training on AMRs and AMR have significant effect on KAP.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eKnowledge about antibiotic use and AMR\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn terms of the knowledge of farmers, dairy-practicing adult male farmers with an education level up to HSC and those who had training possessed better knowledge than others. 89.4% of dairy-practicing farmers (p-Value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), 72.1% of male farmers (p-Value 0.005), 89.4% of adult farmers (p-Value 0.001), and 43.3% of farmers educated up to HSC (p-Value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) had better knowledge than other categories. Most importantly, 80.8% of farmers (p-Value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) had better knowledge who had training on AMRs and AMR.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eAttitude about antibiotic use and AMR\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe study implied that 88.5% of adults (p-Value 0.023) and 56.8% of males (p-Value 0.015) had a positive attitude. Furthermore, education levels up to HSC, dairy practicing, and those farmers who received training had 47.7%, 93.6%, and 87.2% positive attitudes, respectively, then other groups of farmers with significant p-Values\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003ePractices regarding antibiotic use and AMR\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe relationships of demographic characteristics of farmers with practices showed that 88.9% of adults had good practices, which was statistically significant with a p-value of 0.014. However, although 70.4% of male farmers had good practices, this was weakly significant as the p-value was 0.059. On the other hand, 90.1% of trained farmers had good practices, with a p-value less than 0.001. Surprisingly, 49.7% of farmers who had training on AMRs and AMR had poor practices. The education level up to HSC had significant good practices, with 46.9% of farmers in this group, but the number of farmers in this group was only 38 out of 240. Additionally, 85.2% of dairy-practicing farmers also had good practices on AMRs and AMR.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.8. Multivariable analysis results\\u003c/h2\\u003e\\u003cp\\u003eThe analysis (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e) reveals that the significant association of Age, Sex, Education and Training with the dependent variables such as knowledge and attitude. Young farmers have significantly 4.871 (OR\\u0026thinsp;=\\u0026thinsp;4.871, CI\\u0026thinsp;=\\u0026thinsp;1.306\\u0026ndash;18.171, \\u003cem\\u003ep\\u0026thinsp;=\\u0026thinsp;0.018\\u003c/em\\u003e) times more knowledge and 3.544 (OR\\u0026thinsp;=\\u0026thinsp;3.544, CI\\u0026thinsp;=\\u0026thinsp;1.032\\u0026ndash;12.170, \\u003cem\\u003ep\\u0026thinsp;=\\u0026thinsp;0.0.044\\u003c/em\\u003e) times practice (OR\\u0026thinsp;=\\u0026thinsp;2.128, CI\\u0026thinsp;=\\u0026thinsp;0.564\\u0026ndash;8.029, \\u003cem\\u003ep\\u0026thinsp;=\\u0026thinsp;0.265\\u003c/em\\u003e) than adult\\u0026rsquo;s farmers. Specifically, higher education shows better association with knowledge and practice. Education up to SSC indicates height knowledge (OR\\u0026thinsp;=\\u0026thinsp;11.704, CI\\u0026thinsp;=\\u0026thinsp;2.535\\u0026ndash;54.036, \\u003cem\\u003ep\\u0026thinsp;=\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001\\u003c/em\\u003e) than uneducated farmers. On the other hand, education up to HSC showed 24.813 times good practice (OR\\u0026thinsp;=\\u0026thinsp;24.813, CI\\u0026thinsp;=\\u0026thinsp;4.454\\u0026ndash;138.250, \\u003cem\\u003ep\\u0026thinsp;=\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001\\u003c/em\\u003e) than uneducated farmers. Moreover, Farmers those have training on AMRs And AMR have 5.155 times good knowledge (OR\\u0026thinsp;=\\u0026thinsp;5.155, CI\\u0026thinsp;=\\u0026thinsp;1.737\\u0026ndash;15.297, \\u003cem\\u003ep\\u0026thinsp;=\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.003\\u003c/em\\u003e) and 11.454 times good practice (OR\\u0026thinsp;=\\u0026thinsp;11.454, CI\\u0026thinsp;=\\u0026thinsp;3.861\\u0026ndash;33.982, \\u003cem\\u003ep\\u0026thinsp;=\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001\\u003c/em\\u003e) in compare with non-trained farmers. The analysis further revealed that the mixed farming farmers have significant association with knowledge and practice in comparison with beef fattening farmers. Mixed farming practice has 14.729 times better knowledge (OR\\u0026thinsp;=\\u0026thinsp;11.454, CI\\u0026thinsp;=\\u0026thinsp;2.849\\u0026ndash;76.356, \\u003cem\\u003ep\\u0026thinsp;=\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001\\u003c/em\\u003e) and 8.008 times better practice (OR\\u0026thinsp;=\\u0026thinsp;8.008, CI\\u0026thinsp;=\\u0026thinsp;1.018\\u0026ndash;62.973, \\u003cem\\u003ep\\u0026thinsp;=\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001\\u003c/em\\u003e) than other farming types.\\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\\u003eResults of the final multivariable logistic regression analysis identifying factors related to respondents\\u0026rsquo; knowledge and practices regarding antimicrobial use (AMU) and antimicrobial resistance (AMR)\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\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\\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\\u003eCategory\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eKnowledge\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003ePractice\\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\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eOdds Ratio\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eOdds Ratio\\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\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e95% Confidence Interval\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e95% Confidence Interval\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\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\\u003e\\u003cb\\u003eP-value\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eP-value\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAge\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eAdult\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.000\\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\\u003cp\\u003eYoung\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4.87(1.31\\u0026ndash;18.17)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.54(1.03\\u0026ndash;12.17)\\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\\u003e0.018\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.044\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEducation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eUneducated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.000\\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\\u003cp\\u003eGraduate or above\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.08 (0.34\\u0026ndash;3.44)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.34 (1.10-10.13)\\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\\u003cp\\u003eUp to HSC\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.83 (0.48\\u0026ndash;16.44)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e24.81 (4.45-138.25)\\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\\u003cp\\u003eUp to SSC\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e11.70 (2.53\\u0026ndash;54.03)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e9.75 (3.46\\u0026ndash;27.43)\\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\\u003e0.013\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003e\\u0026lt;\\u003c/em\\u003e\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTraining\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNot received\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.000\\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\\u003cp\\u003eReceived\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5.15 (1.74\\u0026ndash;15.29)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e11.45 (3.86\\u0026ndash;33.98)\\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\\u003e0.003\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003e\\u0026lt;\\u003c/em\\u003e\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFarm type\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eBeef fattening\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.000\\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\\u003cp\\u003eDairy\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.17(0.03\\u0026ndash;0.95)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.61(0.07\\u0026ndash;4.92)\\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\\u003cp\\u003eMixed\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e14.72 (2.84\\u0026ndash;76.35)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e8.01(1.01\\u0026ndash;62.97)\\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\\u003e\\u003cem\\u003e\\u0026lt;\\u003c/em\\u003e\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003e\\u0026lt;\\u003c/em\\u003e\\u0026thinsp;0.001\\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\\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\\u003eResults of the final multivariable logistic regression analysis identifying factors related to respondents\\u0026rsquo; attitude regarding antimicrobial use (AMU) and antimicrobial resistance (AMR)\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"3\\\"\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\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\\u003eCategory\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eAttitude\\u003c/p\\u003e\\u003cp\\u003eOdds Ratio (Exp. B)\\u003c/p\\u003e\\u003cp\\u003e95% C.I (Lower - Higher) P-value\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEducation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eUneducated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.000\\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\\u003cp\\u003eGraduate or above\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.902(0.244\\u0026ndash;3.339)\\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\\u003cp\\u003eUp to HSC\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e25.278(3.656-174.756)\\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\\u003cp\\u003eUp to SSC\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5.463(1.905\\u0026ndash;15.667)\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003e\\u0026lt;\\u003c/em\\u003e\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTraining\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNot received\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.000\\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\\u003cp\\u003eReceived\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4.967(1.712\\u0026ndash;14.411)\\u003c/p\\u003e\\u003cp\\u003e0.003\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFarm type\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eBeef fattening\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.000\\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\\u003cp\\u003eDairy\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.666(0.045\\u0026ndash;9.857)\\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\\u003cp\\u003eMixed\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e29.303(2.185-393.065)\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003e\\u0026lt;\\u003c/em\\u003e\\u0026thinsp;0.001\\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\\u003eThe demographic variables of farmers, such as education, training, and farm type, are significantly linked to the dependent variable of practice (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). This analysis shows that education up to HSC results in a 25.278 times higher attitude (C.I. = 3.656-174.756, P-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) compared to uneducated farmers. Similarly, education up to SSC leads to a 5.46 times higher attitude (C.I. = 1.905\\u0026ndash;15.667, P-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Farmers who received training on AMU \\u0026amp; AMR exhibit a better attitude (OR\\u0026thinsp;=\\u0026thinsp;4.967, C.I. = 1.712\\u0026ndash;14.411, P-value\\u0026thinsp;=\\u0026thinsp;0.003). Lastly, it was observed that farmers practicing mixed farming systems have a 29.303 times more positive attitude than those practicing dairy farming.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eThe KAP survey findings from this study reveal misconceptions and a lack of knowledge about antibiotic residues and resistance among farmers. Farmers in the milk-producing regions of Bangladesh supply milk throughout the country. However, their level of knowledge is not satisfactory, posing a significant threat to the global issue of antibiotic resistance. Training and education are crucial factors, as trained and educated farmers tend to have better KAP.\\u003c/p\\u003e\\u003cp\\u003eOur research shows that 43.3% of farmers are informed about antimicrobial use (AMU) and antimicrobial resistance (AMR), a rate higher than that reported by (19) but lower than (20) in Algeria. Although 98% of farmers recognize antibiotics, only 31.7% understand their purpose, which is to treat bacterial diseases, aligning with findings by (21). Furthermore, 41.70% and 40% of farmers grasp the concepts of antibiotic residues and resistance, respectively, which are lower than the knowledge levels reported by (22) in Bangladesh. Another survey on mastitis control using antibiotics indicated that 55.4% of participating farmers were unaware of antibiotic residues (23). Despite widespread awareness of antibiotics, a significant knowledge gap persists due to inadequate awareness and resources about the connection between antibiotics and antimicrobial resistance. Our study also found that 35.80% of farmers are aware of the relationship between AMR and AMRs, a rate lower than the previous study in Ethiopia(24). Additionally, only 35.80% of farmers know that antibiotic residues can be present in milk and meat, and 35.8% are aware that antibiotic dosage is linked to AMR, findings like those of (24). Furthermore, 65% of farmers acknowledge that antibiotics are used for beef fattening, and 47.10% are aware that AMR poses public health risks. Misconceptions about antibiotics significantly contribute to AMR, and this is reflected in various studies. For instance, 57.7% of dairy farm owners and workers in Addis Ababa, Ethiopia, have good knowledge (25), while 38.8% of veterinarians and para-veterinarians in Bhutan also demonstrate good knowledge on antibiotic use and AMR (26). However, a KAP survey among veterinary students highlighted poor knowledge among non-medical students (27), and another survey on poultry feed and drugs showed that most respondents have insufficient knowledge, less positive attitudes, and inappropriate practices regarding AMU and AMR (28).\\u003c/p\\u003e\\u003cp\\u003eIn this study, only 32.8% of farmers have a positive attitude toward AMR and AMRs, which is lower than in previous studies (29\\u0026ndash;31). Our findings indicate that 65% of farmers believe antibiotics can treat most diseases, which is lower than a previous study in Bangladesh (19) but higher than one in Turkey (32). It also reveals that 31.30% of farmers are aware that antibiotics should be used on a veterinarian\\u0026rsquo;s prescription, whereas a study reported more than 50% (33), and another study among farmers in the Mymensingh region of Bangladesh showed only 37.7% used antimicrobials on a veterinarian\\u0026rsquo;s recommendation (19). A significant number of farmers (65%) stopped using antibiotics when clinical signs disappeared, which is higher than another study (24). 65% of farmers believe antibiotics should be stopped when signs disappear, higher than a previous study (19) but lower than a Turkish study (32). Another study showed that farmers change their own doses without consulting a veterinarian (34). More than half of the study population (65%) always kept antibiotics on their farm, which is lower than a previous study (34). Furthermore, 57.25% of farmers think that every farmer should receive training on antibiotic use, indicating a lack of knowledge about antibiotic use. Therefore, policymakers should focus on this to reduce AMR.\\u003c/p\\u003e\\u003cp\\u003eThe current study shows that less than half of the farmers involved (33.8%) follow proper practices regarding antibiotic use and resistance, which is lower than a finding in Ethiopia (34). Additionally, only 31.30% of farmers use antibiotics as prescribed by a veterinarian, a finding lower than that reported in a study conducted in Bangladesh (19) and several other studies (35,36). In Bangladesh, the shortage of qualified veterinary services and personnel often forces farm owners to depend on unqualified, illegal practitioners, or quacks for treating livestock. Sometimes, owners or neighboring farmers diagnose diseases themselves, resulting in the indiscriminate and excessive use of antibiotics (37).\\u003c/p\\u003e\\u003cp\\u003eThe current study indicates that 44.60% of farmers complete their antibiotic course. In contrast, a study in Ghana found that 65% of farmers do not complete their antibiotic regimen (38). Additionally, less than one-third (26.70%) of farmers practice proper disposal of leftover antibiotics, which is lower than previous findings (31). Additionally, only 20% of farmers maintain records of antibiotic usage. In contrast, as anticipated due to government policies and regulations, a higher percentage of farmers in the USA were found to keep such records (39).\\u003c/p\\u003e\\u003cp\\u003eDemographic factors such as age, sex, education, and training significantly impact farmers\\u0026rsquo; knowledge, attitudes, and practices (KAP). A study by(40) also notes significant differences in KAP based on these factors. In Bangladesh, the most used antibiotics in farm practices are oxytetracycline (53.8%) and the hormone dexamethasone (42.9%), whereas in the UK, penicillin is the most used (41). A major reason for farmers\\u0026rsquo; poor knowledge of antibiotic use and resistance is their lack of education or inadequate education (29,42). Government-employed veterinarians in Bangladesh showed a 2.59 times more favorable attitude towards antibiotic misuse (AMU) and antibiotic resistance (AMR) (43). The government should improve its policies to raise awareness about AMR and AMU through training, seminars, and enhanced education.\\u003c/p\\u003e\\u003cp\\u003eThe study has several limitations, including a small study population with data collected from a limited number of farmers in each upazila. Additionally, only two upazilas from a single district were studied, which does not represent the entire milk pocket area. To our knowledge, this is the first study on AMR and AMU in the milk pocket areas of Bangladesh.\\u003c/p\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eThe survey on the KAP regarding AMR in livestock at the milk pocket area in Bangladesh, conducted in Bogura, Pabna, and Sirajganj zila under the Rajshahi division, highlighted that large animal farmers have inadequate knowledge, negative attitudes, and poor practices. Socio-demographic factors such as sex, age, education, farm type, and training on antibiotic use and antimicrobial resistance significantly influence the KAP. The lack of manpower and facilities in the livestock sector prevents farmers from receiving proper guidance on antibiotics and AMR. To combat this globally threatening issue, the government should regulate the misuse, overuse, and abuse of antibiotics and ban their marketing as over-the-counter drugs. Effective implementation and monitoring of policies could lead to positive outcomes in reducing AMR.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contributions\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eK.R. (Kazi Rafiq) and F.S.S. (Fardina Sultana Sumi) made equal contributions to this work. F.S.S. \\u0026nbsp;was responsible for drafting the manuscript; K.R. and A.K.M.A.R. (A K M Anisur Rahman) were involved in designing and overseeing the research; K.R., A.K.M.A.R., and M.T.H. (Muhammad Tofazzal Hossain) undertook the revision and finalization of the manuscript draft. F.S.S., A.B.Z. (Anan Binte Zaman) and S.M.I. (Shah Md. Iqbal) collected and entered data; M.R.H. (Md. Rakib Hasan) and F.S.S. conducted the statistical analysis and prepared the figures, maps and tables. All authors have reviewed and consented to the published version of the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors would like to express their deepest gratitude to LDDP (Livestock and Dairy Development Project) Research and Innovation Sub-project (project ID: RP-D-03-22) under DLS, Dhaka to Prof. Dr. Kazi Rafiqul Islam, Department of Pharmacology of Bangladesh Agricultural University and the Bangladesh Agricultural University Research System (BAURES) in Mymensingh for their invaluable support in project management and monitoring. Furthermore, the authors would like to express their gratitude to the district livestock officers, upazila livestock officers, and veterinary surgeons for their unwavering support in the study regions. Lastly, we would like to thank all the respondents, particularly the large-animal farmers in the study area, for their generous cooperation and for sharing their valuable time and insights during the interviews.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting Interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNo competing interests exist.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets generated and analyzed during the current study are included within the manuscript.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eSattar S, Hassan MM, Islam SKMA, Alam M, Faruk MS Al, Chowdhury S, et al. Antibiotic residues in broiler and layer meat in Chittagong district of Bangladesh. Vet World. 2014;7(9):738\\u0026ndash;43. \\u003c/li\\u003e\\n\\u003cli\\u003eAlmashhadany DA. Detection of antimicrobial residues among chicken meat by simple, reliable, and highly specific techniques. SVU-International Journal of Veterinary Sciences [Internet]. 2021 Mar 1 [cited 2025 Jun 5];4(1):1\\u0026ndash;9. Available from: https://svu.journals.ekb.eg/article_134535.html\\u003c/li\\u003e\\n\\u003cli\\u003eRahman MS, Hassan MM, Chowdhury S. Determination of antibiotic residues in milk and assessment of human health risk in Bangladesh. Heliyon [Internet]. 2021 Aug 1 [cited 2025 Jun 5];7(8):e07739. Available from: https://www.sciencedirect.com/science/article/pii/S2405844021018429\\u003c/li\\u003e\\n\\u003cli\\u003eUddin TM, Chakraborty AJ, Khusro A, Zidan BRM, Mitra S, Emran T Bin, et al. Antibiotic resistance in microbes: History, mechanisms, therapeutic strategies and future prospects. J Infect Public Health [Internet]. 2021 Dec 1 [cited 2025 Jun 5];14(12):1750\\u0026ndash;66. Available from: https://pubmed.ncbi.nlm.nih.gov/34756812/\\u003c/li\\u003e\\n\\u003cli\\u003eDodds DR. Antibiotic resistance: A current epilogue. Biochem Pharmacol [Internet]. 2017 Jun 15 [cited 2025 Jun 5];134:139\\u0026ndash;46. Available from: https://pubmed.ncbi.nlm.nih.gov/27956111/\\u003c/li\\u003e\\n\\u003cli\\u003eBonna AS, Pavel SR, Ferdous J, Khan SA, Ali M. Antibiotic resistance: An increasingly threatening but neglected public health challenge in Bangladesh. International Journal of Surgery Open [Internet]. 2022 Dec 1 [cited 2025 Jun 5];49:100581. Available from: https://www.sciencedirect.com/science/article/pii/S2405857222001449\\u003c/li\\u003e\\n\\u003cli\\u003eHiraku Y, Sekine A, Nabeshi H, Midorikawa K, Murata M, Kumagai Y, et al. Mechanism of carcinogenesis induced by a veterinary antimicrobial drug, nitrofurazone, via oxidative DNA damage and cell proliferation. Cancer Lett [Internet]. 2004 Nov 25 [cited 2025 Jun 5];215(2):141\\u0026ndash;50. 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Available from: https://www.sciencedirect.com/science/article/abs/pii/S0924857900001357\\u003c/li\\u003e\\n\\u003cli\\u003eHao H, Cheng G, Iqbal Z, Ai X, Hussain HI, Huang L, et al. Benefits and risks of antimicrobial use in food-producing animals. Front Microbiol [Internet]. 2014 Jun 12 [cited 2025 Jun 5];5(JUN):87623. Available from: www.frontiersin.org\\u003c/li\\u003e\\n\\u003cli\\u003eChowdhury R, Haque M, Islam K, Khaleduzzaman A. A Review On Antibiotics In An Animal Feed. Bangladesh Journal of Animal Science [Internet]. 2009 Jan 1 [cited 2025 Jun 5];38(1\\u0026ndash;2):22\\u0026ndash;32. Available from: https://www.banglajol.info/index.php/BJAS/article/view/9909\\u003c/li\\u003e\\n\\u003cli\\u003eChowdhury S, Hassan MM, Alam M, Sattar S, Bari MS, Saifuddin AKM, et al. Antibiotic residues in milk and eggs of commercial and local farms at Chittagong, Bangladesh. Vet World [Internet]. 2015 [cited 2025 Jun 5];8(4):467\\u0026ndash;71. Available from: https://pubmed.ncbi.nlm.nih.gov/27047116/\\u003c/li\\u003e\\n\\u003cli\\u003eBonna AS, Pavel SR, Ferdous J, Khan SA, Ali M. Antibiotic resistance: An increasingly threatening but neglected public health challenge in Bangladesh. International Journal of Surgery Open [Internet]. 2022 Dec 1 [cited 2025 Jun 5];49:100581. Available from: https://www.sciencedirect.com/science/article/pii/S2405857222001449\\u003c/li\\u003e\\n\\u003cli\\u003eHoque R, Ahmed SM, Naher N, Islam MA, Rousham EK, Islam BZ, et al. Tackling antimicrobial resistance in Bangladesh: A scoping review of policy and practice in human, animal and environment sectors. PLoS One [Internet]. 2020 Jan 1 [cited 2025 Jun 5];15(1):e0227947. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0227947\\u003c/li\\u003e\\n\\u003cli\\u003eDahal R, Upadhyay A, Ewald B. One Health in South Asia and its challenges in implementation from stakeholder perspective. 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Available from: https://www.nature.com/articles/s41598-021-00617-8\\u003c/li\\u003e\\n\\u003cli\\u003eKallu SA, Kebede N, Kassa T, Wubaye AM, Kainga H, Mekonnen H, et al. Knowledge, Attitudes, Practices, and Risk Perception of Antimicrobial Use and Antimicrobial Resistance Among Dairy Farm Owners/Workers in Addis Ababa, Ethiopia. Infect Drug Resist [Internet]. 2024 [cited 2025 Jun 5];17:1839\\u0026ndash;61. Available from: https://pubmed.ncbi.nlm.nih.gov/38745680/\\u003c/li\\u003e\\n\\u003cli\\u003eWangmoi K, Dorji T, Pokhrel N, Dorji T, Dorji J, Tenzin T. Knowledge, attitude, and practice on antibiotic use and antibiotic resistance among the veterinarians and para-veterinarians in Bhutan. PLoS One [Internet]. 2021 May 1 [cited 2025 Jun 5];16(5 May). Available from: https://pubmed.ncbi.nlm.nih.gov/33956905/\\u003c/li\\u003e\\n\\u003cli\\u003eChapot L, Sarker MS, Begum R, Hossain D, Akter R, Hasan MM, et al. Knowledge, attitudes and practices regarding antibiotic use and resistance among veterinary students in Bangladesh. Antibiotics [Internet]. 2021 Mar 1 [cited 2025 Jun 5];10(3). Available from: https://pubmed.ncbi.nlm.nih.gov/33809932/\\u003c/li\\u003e\\n\\u003cli\\u003eKalam MA, Alim MA, Shano S, Nayem MRK, Badsha MR, Al Mamun MA, et al. Knowledge, attitude, and practices on antimicrobial use and antimicrobial resistance among poultry drug and feed sellers in Bangladesh. Vet Sci [Internet]. 2021 Jun 1 [cited 2025 Jun 5];8(6). Available from: https://pubmed.ncbi.nlm.nih.gov/34203812/\\u003c/li\\u003e\\n\\u003cli\\u003eGemeda BA, Amenu K, Magnusson U, Dohoo I, Hallenberg GS, Alemayehu G, et al. Antimicrobial Use in Extensive Smallholder Livestock Farming Systems in Ethiopia: Knowledge, Attitudes, and Practices of Livestock Keepers. Front Vet Sci [Internet]. 2020 Feb 26 [cited 2025 Apr 8];7:503526. 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Animals 2019, Vol 9, Page 653 [Internet]. 2019 Sep 4 [cited 2025 Apr 8];9(9):653. Available from: https://www.mdpi.com/2076-2615/9/9/653/htm\\u003c/li\\u003e\\n\\u003cli\\u003eGebeyehu DT, Bekele D, Mulate B, Gugsa G, Tintagu T. Knowledge, attitude and practice of animal producers towards antimicrobial use and antimicrobial resistance in Oromia zone, north eastern Ethiopia. PLoS One [Internet]. 2021 May 1 [cited 2025 Jun 5];16(5):e0251596. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0251596\\u003c/li\\u003e\\n\\u003cli\\u003eGemeda BA, Amenu K, Magnusson U, Dohoo I, Hallenberg GS, Alemayehu G, et al. Antimicrobial Use in Extensive Smallholder Livestock Farming Systems in Ethiopia: Knowledge, Attitudes, and Practices of Livestock Keepers. Front Vet Sci [Internet]. 2020 Feb 26 [cited 2025 Jun 5];7. 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Household Animal and Human Medicine Use and Animal Husbandry Practices in Rural Bangladesh: Risk Factors for Emerging Zoonotic Disease and Antibiotic Resistance. Zoonoses Public Health [Internet]. 2015 Nov 1 [cited 2025 Jun 5];62(7):569\\u0026ndash;78. Available from: /doi/pdf/10.1111/zph.12186\\u003c/li\\u003e\\n\\u003cli\\u003eBoamah V, Agyare C, Odoi H, Dalsgaard A. Practices and factors influencing the use of antibiotics in selected poultry farms in Ghana. Article in Journal of Antimicrobial Agents [Internet]. 2016 [cited 2025 Apr 9];2(2). Available from: https://www.researchgate.net/profile/Christian-Agyare/publication/305272807_Antibiotic_Practices_and_Factors_Influencing_the_Use_of_Antibiotics_in_Selected_Poultry_Farms_in_Ghana/links/579f98ba08aece1c72156688/Antibiotic-Practices-and-Factors-Influencing-the-Use-of-Antibiotics-in-Selected-Poultry-Farms-in-Ghana.pdf\\u003c/li\\u003e\\n\\u003cli\\u003eGreen AL, Carpenter LR, Edmisson DE, Lane CD, Welborn MG, Hopkins FM, et al. Producer attitudes and practices related to antimicrobial use in beef cattle in Tennessee. J Am Vet Med Assoc [Internet]. 2010 Dec 1 [cited 2025 Jun 5];237(11):1292\\u0026ndash;8. Available from: https://avmajournals.avma.org/view/journals/javma/237/11/javma.237.11.1292.xml\\u003c/li\\u003e\\n\\u003cli\\u003eSawant AA, Sordillo LM, Jayarao BM. A survey on antibiotic usage in dairy herds in Pennsylvania. J Dairy Sci [Internet]. 2005 Aug 1 [cited 2025 Jun 5];88(8):2991\\u0026ndash;9. Available from: https://www.journalofdairyscience.org/action/showFullText?pii=S0022030205729799\\u003c/li\\u003e\\n\\u003cli\\u003eHigham LE, Deakin A, Tivey E, Porteus V, Ridgway S, Rayner AC. A survey of dairy cow farmers in the United Kingdom: knowledge, attitudes and practices surrounding antimicrobial use and resistance. Veterinary Record [Internet]. 2018 Dec 1 [cited 2025 Jun 5];183(24):746. Available from: https://pubmed.ncbi.nlm.nih.gov/30413678/\\u003c/li\\u003e\\n\\u003cli\\u003eAzim MR, Ifteakhar KMN, Rahman MM, Sakib QN. Public knowledge, attitudes, and practices (KAP) regarding antibiotics use and antimicrobial resistance (AMR) in Bangladesh. Heliyon. 2023 Oct 1;9(10). \\u003c/li\\u003e\\n\\u003cli\\u003eSarker MS, Nath SC, Ahmed I, Siddiky NA, Islam S, Kabir ME, et al. Knowledge, attitude and practice towards antibiotic use and resistance among the veterinarians in Bangladesh. PLoS One [Internet]. 2024 Aug 1 [cited 2025 Jun 6];19(8):e0308324. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0308324\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":true,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"tropical-animal-health-and-production\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"trop\",\"sideBox\":\"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)\",\"snPcode\":\"11250\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11250/3\",\"title\":\"Tropical Animal Health and Production\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Antimicrobial Resistance, Antibiotic misuse, Education, Training, Regulatory measures\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7061032/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7061032/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eIn Bangladesh, the excessive use of antibiotics on cattle farms has led to antibiotic residues in animal products, contributing to antimicrobial resistance (AMR). This study aimed to assess the large animal farmers\\u0026rsquo; knowledge, attitudes, and practices regarding antibiotic usage in dairy and beef fattening farms in Bangladesh\\u0026rsquo;s milk pocket areas. Data was collected through a pre-tested questionnaire, administered via face-to-face interviews and farmed record observations. A multivariable logistic regression analysis was conducted to evaluate the association between farmers\\u0026rsquo; KAP and demographic variables such as age, education, training, and farm type. The study identified significant gaps in farmers' KAP regarding antibiotic use and AMR. While 98.3% had heard of antibiotics, only 55% understood withdrawal periods, 58.3% recognized antibiotic residues, and 35.8% knew residues could transfer through milk or meat. AMR awareness was low, with 64.2% denying its link to antibiotic dosage and 52.9% unaware of its public health risks. Misconceptions were prevalent, as 65% believed antibiotics were ineffective for most diseases, and an equal proportion stopped treatment once clinical signs subsided. Antibiotic misuse was widespread\\u0026mdash;68.7% used them without prescriptions, 69.2% ignored dosage guidelines, and 55.4% failed to complete treatment courses. Moreover, only 20% maintained antibiotic records, and 31.1% had received AMR-related training. The findings indicate that young farmers with education up to SSC and training had significantly better knowledge (OR: 11.70, 95% CI: 2.54\\u0026ndash;54.04). Farmers with education up to HSC were more likely to have a positive attitude (OR: 25.28, 95% CI: 3.67-174.76) and engaged in better farming practices (OR: 24.81, 95% CI: 4.45-138.25). Dairy farmers exhibited significantly lower knowledge (OR: 0.17, 95% CI: 0.03\\u0026ndash;0.95), attitude (OR: 0.67, 95% CI: 0.05\\u0026ndash;9.86), and practice (OR: 0.61, 95% CI: 0.07\\u0026ndash;4.92) compared to beef fattening farmers. In contrast, mixed farming practitioners demonstrated superior knowledge (OR: 14.73, 95% CI: 2.85\\u0026ndash;76.36), attitude (OR: 29.30, 95% CI: 2.19\\u0026ndash;39.07), and practice (OR: 8.00, 95% CI: 1.00-64.07). This study highlights critical gaps in farmers' KAP regarding antibiotic use and AMR, emphasizing the urgent need for targeted interventions. Despite high antibiotic awareness, widespread misconceptions and improper practices contribute to AMR risks. Education and training significantly improve KAP, underscoring the need for enhanced farmer education and stricter regulatory measures. Tailored training programs and policies should prioritize high-risk groups, such as dairy farmers, to promote responsible antibiotic use and mitigate AMR threats.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Large-animal farmers’ knowledge, attitudes, and practices regarding antibiotic usage in dairy and beef fattening farms in Bangladesh’s milk pocket areas\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-07-24 09:26:20\",\"doi\":\"10.21203/rs.3.rs-7061032/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"reviewerAgreed\",\"content\":\"\",\"date\":\"2025-07-28T13:30:53+00:00\",\"index\":0,\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-07-21T22:52:46+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-07-10T18:07:05+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Tropical Animal Health and Production\",\"date\":\"2025-07-09T09:46:35+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"tropical-animal-health-and-production\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"trop\",\"sideBox\":\"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)\",\"snPcode\":\"11250\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11250/3\",\"title\":\"Tropical Animal Health and Production\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"9f1e66c1-9dac-4ad3-90ba-803dcdba7e3b\",\"owner\":[],\"postedDate\":\"July 24th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-02-23T16:07:56+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-7061032\",\"link\":\"https://doi.org/10.1007/s11250-026-04944-8\",\"journal\":{\"identity\":\"tropical-animal-health-and-production\",\"isVorOnly\":false,\"title\":\"Tropical Animal Health and Production\"},\"publishedOn\":\"2026-02-20 15:59:30\",\"publishedOnDateReadable\":\"February 20th, 2026\"},\"versionCreatedAt\":\"2025-07-24 09:26:20\",\"video\":\"\",\"vorDoi\":\"10.1007/s11250-026-04944-8\",\"vorDoiUrl\":\"https://doi.org/10.1007/s11250-026-04944-8\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7061032\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7061032\",\"identity\":\"rs-7061032\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}