Prescription Patterns and Knowledge of Rational Antimicrobial Use among Primary Healthcare Workers in a Humanitarian Setting in Nigeria

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Musa, Manir Jega This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7583809/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Feb, 2026 Read the published version in Discover Public Health → Version 1 posted 12 You are reading this latest preprint version Abstract Background: Antimicrobial resistance (AMR) is a major global health threat, particularly in low- and middle-income countries such as Nigeria, where inappropriate antibiotic use is common. This study assessed antimicrobial prescription patterns and knowledge of AMR among primary healthcare workers in humanitarian settings. Methods: A cross-sectional survey was conducted among primary healthcare prescribers using a structured digital questionnaire (Kobo/ODK). Data were analysed with SPSS v29. Knowledge of AMR was assessed through 10 binary questions and categorized as poor (0–50%), moderate (51–70%), or good (71–100%). Results: A total of 112 respondents participated, including 94(83.9%) females, with a mean age of 36.6 years (SD = 9.7). Participants comprised 50(44.6%) CHEWs, 34 (30.4%) nurses/midwives, 15 (13.4%) CHOs, and 13 (11.6%) JCHEWs. Only 36(32.1%) had AMR-related training in the past year. While 109(97.3%) correctly defined AMR and 102(91.1%) acknowledged its link to mortality, gaps remained. Thirty-four (30.4%) believed antibiotics treat viral infections, 19(17.0%) prescribed antibiotics for influenza, and 79(70.5%) co-prescribed them for malaria. Overall, 39 (34.8%) had poor knowledge, 39(34.8%) moderate, and 34(30.4%) good. Knowledge differed significantly by age (p = .017) and cadre (p = .018), with nurses/midwives and CHOs scoring higher. Commonly prescribed antibiotics were amoxicillin (12%), metronidazole (11%), ciprofloxacin (10%), and gentamicin (9%). Conclusion: Despite high awareness, irrational prescribing and knowledge gaps persist. Targeted training, revised standing orders, and strengthened antimicrobial stewardship at the PHC level are urgently needed to curb AMR in Nigeria. Antimicrobial resistance primary healthcare healthcare workers Figures Figure 1 Figure 2 INTRODUCTION The emergence and spread of antimicrobial-resistant pathogens have become a major global public health concern. It is a major cause of prolonged hospitalization and increased mortality [ 1 ]. Globally, it is estimated that over 5 million deaths are due to antimicrobial-resistant pathogens with the highest record from sub-Saharan Africa [ 2 ]. Although antimicrobial resistance (AMR) is a natural process that happens over time through genetic changes in pathogens, it is accelerated by human activities, mainly the misuse and overuse of antimicrobials to treat, prevent, or control infections in humans, animals, and plants [ 2 – 4 ]. Lack of awareness and knowledge of rational antimicrobial use and antimicrobial resistance, among other things, have been suggested as being part of the problem [ 1 ]. In Nigeria, like in many other low- and middle-income countries (LIMCs), overuse and misuse of antibiotics have been observed, primarily due to factors such as inadequate regulatory controls, poor diagnostic capacity, and insufficient knowledge among healthcare professionals, especially at the primary health care level [ 3 ]. Due to the shortage of doctors and nurses, Nigeria has adopted a task-shifting strategy that grants prescription authority to Community Health Officers (CHOs), Community Health Extension Workers (CHEWs), and Junior Community Health Extension Workers (JCHEWs), guided by standing orders [ 5 ]. This healthcare workforce currently makes up the largest human resource for health and dominates the primary health care (PHC) system, which serves Nigeria's large population. There is a call for global action against AMR [ 6 ]. The World Health Organization (WHO) is raising awareness among health workers as part of its efforts to reduce the prevalence of AMR. A key priority of WHO is identifying the drivers of appropriate and inappropriate antibiotic prescribing, use, and consumption [ 7 ]. Despite recognizing AMR as a global challenge, there is limited evidence about the knowledge level of antimicrobial resistance and how primary healthcare providers prescribe and dispense antimicrobial agents in Nigeria. The study aimed to identify the most frequently prescribed antimicrobials, assess prescribers' awareness of AMR, and compare knowledge across different prescriber cadres. Identifying knowledge gaps and prevailing prescription patterns will guide the development and implementation of targeted strategies to combat AMR with a focus on the lowest level of prescribers in a resource-limited setting. MATERIALS AND METHODS Study Design A cross-sectional survey design was employed to assess the depth of knowledge among primary healthcare workers regarding rational antibiotic use, antimicrobial resistance, and antibiotic prescription patterns in refugee host communities within Nigeria. The study was conducted using a structured questionnaire developed based on existing literature and validated tools used in previous similar studies. Study Location The study was carried out in communities hosting refugees in Akwa Ibom, Benue, Cross River, Lagos, Oyo, and Taraba states. This study was part of a baseline assessment for facilities providing services to both host nationals and over 80,000 Cameroonian refugees living in the communities as part of the recommendation for quality of care in humanitarian settings [ 8 , 9 ]. Study Population The study included Primary health care workers in UNHCR/NRCS-supported health facilities who provide consultation services and write prescriptions for patients. Primary healthcare workers who do not prescribe for patients, and primary healthcare providers who decline to join the study were excluded. Sample Size Determination The population size of the PHC staff who prescribe for patients was 152. Assuming an expected good knowledge level of 50% with 5% acceptable margin of error and a 95% confidence interval, a minimum sample size of 109 respondents was obtained using Epi Info StatCal sample size calculator for population survey or descriptive study. Sampling Method A purposive sampling method was employed in the study, selecting health workers who voluntarily agreed to participate. All health workers who gave consent were included. This non-probability sampling approach was appropriate given the study's goal of including all health workers at UNHCR/NRCS-supported facilities. Data Collection and Analysis A self-administered questionnaire containing information on socio-demographics and antimicrobial prescription frequency. A set of 10 binary questions, which assessed knowledge of rational antimicrobial use and antimicrobial resistance, was included. The questionnaire was pre-tested on 15 primary healthcare providers within Ogoja who do not render healthcare services in supported health facilities to examine the respondents' level of understanding of the questions and options to avoid potential ambiguities. Feedback from the pre-test was used to rephrase some questions for clarity. The questionnaire was input on the Kobo/ODK tool and sent to respondents through their emails and WhatsApp. The data was submitted directly by respondents through the Kobo application. Data from the Kobo toolbox was exported to Microsoft Excel for cleaning before importing into SPSS (SPSS Ver 29, IBM., Ca, USA) for analysis. The 10 knowledge of Antimicrobial Resistance (AMR) questions were awarded 10 points each. Subsequently, the scores were transformed into ordinal categorization scores as poor knowledge (score 0–50), Intermediate/Moderate level of knowledge (score 51–70), and High/Good level of knowledge (71–100). Data is presented as frequency and percentages. ANOVA was employed to evaluate associations of knowledge as a dependent variable with gender, age, cadre, and years of experience as independent variables. Value of p < 0.05 was considered statistically significant. Ethical Considerations The study was reviewed and approved by the Cross River State Health Research Ethics Committee and granted ethics number CRSMOH/HRP/REC/2024/488. Informed consent was obtained from all participants, and the study complied with national ethical guidelines and the declaration of Helsinki. RESULT A total of 112(73.7%) of the target health workers, made up of 94(83.9%) females and 18 (16.1%) males, submitted responses. The mean age of responders was 36.6 years (SD = 9.7), with the age group 30–39(33.9%) being most preponderant. The responders were made up of 50(44.6%) Community Health Extension Workers (CHEWs), 34(30.4%) Nurse/Midwives, 15(13.4%) Community Health Officers (CHO), and 13 (11.6%) Junior Community Health Extension Workers (JCHEWs). A total of 92 (82.1%) had less than 5 years of experience, 15 (13.4%) had 5–9 years, and 5 (4.5%) had more than 9 years of experience. On average, 47 (42.0%) of consultations are to 51–100 patients per month, while 11 (9.8%) see more than 200 patients per month. Considering the average antibiotics prescription rates, 4(3.6) admitted to prescribing antibiotics to > 75 patients per month, 25(22.3) prescribed for 51–75 patients while 39(34.8) and 44(39.3) prescribed for 26–50 and ≤ 25 respectively. Only 36(32.1) reported having any onsite or offsite training on AMR in the last year. Table 1 Sociodemographic characteristics of the study participants (n = 112) Variable No (%) Gender Female 94(83.9) Male 18(16.1) Age group 20–29 34(30.4) 30–39 38(33.9) 40–49 23(20.5) 50–59 17(15.2) Mean (SD) 36.59(9.7) Cadre Nurse/Midwife 34(30.4) CHO 15(13.4) CHEW 50(44.6) JCHEW 13(11.6) Years of experience 9 years 5(4.5) On average, how many patients do you attend to in a month? ≤ 50 37(33.0) 51–100 47(42.0) 101–150 12(10.7) 151–200 5(4.5) > 200 11(9.8) On average, how many of your patients receive an antibiotic per month? ≤ 25 44(39.3) 26–50 39(34.8) 51–75 25(22.3) > 75 4(3.6) On-site or off-site training in last one year? No 76(67.9) Yes 36(32.1) Knowledge of rational antimicrobial use and antimicrobial resistance As shown in Table 2 , a total of 109(97.3%) appropriately defined AMR, 102(91.1%) believed it is a significant cause of mortality, and 101(90.2%) agreed that antimicrobial misuse is a major cause of the problem. However, 34(30.4%) could not differentiate between antibacterial agents and antivirals as they believed antibacterial agents could also treat viruses, and 19(17.0%) regularly prescribed antibacterial agents for the common cold or suspected viral flu, and 79(70.5%) co-prescribed an antibacterial agent in patients with suspected or confirmed malaria. A total of 44(39.3%) believed it is more useful to combine antimicrobials, and 69(61.6%) preferred to prescribe intravenous and intramuscular injectable antibiotics because they believe they work faster than oral antibiotics. A total of 89(79.5%) believed patients could stop their antibiotics when their symptoms disappear, and 61(54.5%) did not find the available standing orders useful. Table 2 Knowledge of Antimicrobial resistance (AMR) Variables Frequency (%) Antimicrobial resistance (AMR) is when germs or microorganisms like bacteria, viruses, fungi and parasites stop responding to medicines that kill or stop them from growing. No 3(2.7) Yes 109(97.3) Do you think antimicrobial resistance contributes significantly to the death of patients in Nigeria? No 10(8.9) Yes 102(91.1) Do you think too much use of antimicrobial agents/medications is causing antimicrobial resistance? No 11(9.8) Yes 101(90.2) Antibacterial agents are medications that can treat both bacterial and viral infections No 78(69.6) Yes 34(30.4) I can use Augmentin or Erythromycin to treat a cold or viral flu No 93(83.0) Yes 19(17.0) I routinely prescribe Amoxil or any other antibacterial agents for patients who are suffering from malaria. No 33(29.5) Yes 79(70.5) I think it is more effective to combine two or more antibacterial agents to treat a patient who has upper respiratory tract infection. No 68(60.7) Yes 44(39.3) I prefer to prescribe injectable (IV and IM) antibiotics because they work faster than oral antibiotics (tablets) in every of my patients. No 43(38.4) Yes 69(61.6) Patients do not need to complete their antibiotics; they can stop it when their symptoms have improved. No 23(20.5) Yes 89(79.5) Standing orders and Wall charts are not very helpful in guiding my antimicrobial prescription. No 51(45.5) Yes 61(54.5) Table 3 Classification of the level of knowledge on AMR Categories Frequency (%) Poor level of knowledge (0–50) 39(34.8) Moderate level of knowledge (51–70) 39(34.8) Good level of knowledge (71–100) 34(30.4) Based on the antimicrobial resistance knowledge categorization, 34(30.4%) of the respondents demonstrated good knowledge, while 39(34.8%) demonstrated poor and moderate/intermediate knowledge, respectively (Table 3 ). The mean population score for knowledge of AMR was 63.1(SD = 21.5). Analysis of Variance (ANOVA) (Table 4 ) showed a significant difference in knowledge between age groups, with participants aged 30–39 (Mean = 68.68, SD = 22.6) and 50–59 (Mean = 68.24, SD = 15.9) having significantly higher knowledge scores compared to the age groups 20–29 (Mean = 53.82, SD = 21.3) and 40–49 (Mean = 63.91, SD = 19.9), F(3, 108) = 3.53, p = .017. They was no significant difference in knowledge between males (Mean = 58.33, SD = 20.1) and females (Mean = 64.04, SD = 21.7), F(1, 110) = 1.07, p = .304 (Table 4 ). There was also a significant difference in knowledge scores across cadres (Fig. 1 ). Nurses/midwives (Mean = 69.4, SD = 24.5) and Community Health Officers (CHO) (Mean = 70, SD = 20.7) had a higher knowledge compared to Community Health Extension Workers (CHEW) (Mean = 60, SD = 18.6) and Junior Community Health Extension Workers (JCHEW) (Mean = 50.77, SD = 18.0), F(3, 108) = 3.49, p = .018. Table 4 Analysis of variance (ANOVA) of the knowledge of AMR on Independent Variables Variable Mean (SD) F df1 df 2 p-value Gender Female 64.04(± 21.7) 1.07 1 110 0.304 Male 58.33(± 20.1) Age group 20–29 53.82(± 21.3) 3.53 3 108 0.017 30–39 68.68(± 22.6) 40–49 63.91(± 19.9) 50–59 68.24(± 15.9) Cadre Nurse/Midwife 69.4(± 24.5) 3.49 3 108 0.018 CHO 70(± 20.7) CHEW 60(± 18.6) JCHEW 50.77(18.0) Antibacterial agents prescription pattern. Within the last month before the survey, the most commonly prescribed antibiotics as shown on Fig. 2 , were Amoxicillin (12%), Metronidazole (11%), Ciprofloxacin (10%), Gentamicin (9%), Erythromycin (8%), Ampicillin-cloxacillin (8%), Crystal penicillin (8%), and Ampicillin (8%). Less commonly prescribed antibiotics include Clotrimazole (8%), Ceftriaxone (7%), Levofloxacin (4%), Cefuroxime (3%), Amoxycillin-Clavulanate (3%), Cefixime (2%), and Ofloxacin (1%). Discussion The findings from this study reveal considerable gaps in knowledge and practices related to antimicrobial prescription among primary healthcare workers in refugee host communities across Nigeria. While awareness of antimicrobial resistance (AMR) appears high, with 97.3% correctly defining AMR and 91.1% acknowledging its contribution to mortality, deeper issues persist regarding rational use. A key area of concern is the widespread prescription of antibiotics for conditions that do not warrant such interventions. For instance, 17.0% of respondents admitted to prescribing antibiotics for viral infections such as the common cold or flu, and over 70.5% reported co-prescribing antibacterial agents in suspected or confirmed cases of malaria. These findings reflect a misunderstanding of antimicrobial targets and suggest a tendency to overprescribe as a precautionary or habitual measure. This pattern was also observed in similar studies conducted in Nigeria and other low-resource settings [ 3 , 10 ]. Additionally, a significant proportion of healthcare workers (61.6%) believed that injectable antibiotics are more effective than oral formulations, indicating misconceptions about routes of administration. This belief, if left unaddressed, can lead to unnecessary injections, increase healthcare costs, and elevate the risk of injection-related complications. Alarmingly, 79.5% of respondents believed patients could stop taking antibiotics once their symptoms improved, contrary to established guidelines that emphasize completing prescribed courses to prevent resistance. This finding aligns with reports from a previous study and points to a critical need for enhanced training and supervision of primary healthcare workers [ 11 ]. Knowledge scores varied significantly across cadres and age groups. Nurses/midwives and Community Health Officers (CHOs) demonstrated higher knowledge levels than Community Health Extension Workers (CHEWs) and Junior CHEWs (JCHEWs). This disparity likely reflects differences in formal education and clinical exposure. The younger age group (20–29 years) also exhibited significantly lower knowledge scores than their older counterparts, possibly due to limited professional experience or insufficient post-qualification training. The fact that only 32.1% of respondents reported receiving AMR-related training in the past year further underscores the need for regular, structured, and mandatory capacity-building programs. Moreover, the finding that over half (54.5%) of respondents did not find the standing orders helpful in guiding antimicrobial prescriptions suggests that these tools may require updates, simplification, or contextual adaptation to enhance usability. The prescription patterns observed, particularly the high use of broad-spectrum antibiotics like amoxicillin and ciprofloxacin, reinforce concerns over inappropriate antibiotic choices. While these antibiotics are essential for treating bacterial infections, their overuse contributes to the development of multidrug-resistant organisms, posing a threat to future treatment options. Conclusions and recommendations Overall, this study provides valuable insight into antimicrobial prescribing behaviors at the primary healthcare level in a humanitarian setting in Nigeria. It identifies specific gaps in knowledge and practices that must be addressed through targeted interventions, including mandatory AMR training for all PHC prescribers, regular review and dissemination of updated standing orders, enhanced supervision and mentoring systems, particularly for junior cadres, and public health education to promote rational drug use among patients. Addressing these gaps is crucial to curbing AMR in Nigeria and safeguarding the effectiveness of existing antibiotics, especially in humanitarian settings with limited resources, as has been advanced [ 12 ]. Declarations Conflict of Interest None declared. Funding No funding received for this study. Author Contribution EAO: Concept, design, data analysis, manuscript review, manuscript approval. OKO: literature search, data collection, manuscript preparationAAM: Data collection, statistical analysis, manuscript review.MJ: Manuscript review, manuscript approval. Data Availability All data generated or analysed during this study are included in this published article and its supplementary information file. References Llor C, Bjerrum L. Antimicrobial resistance: risk associated with antibiotic overuse and initiatives to reduce the problem. Therapeutic Adv drug Saf. 2014;5(6):229–41. WHO. (2023a). Antimicrobial resistance https://www.who.int/news-room/fact-sheets/detail/antimicrobial-resistance Chukwu EE, Oladele DA, Enwuru CA, Gogwan PL, Abuh D, Audu RA, Ogunsola FT. Antimicrobial resistance awareness and antibiotic prescribing behavior among healthcare workers in Nigeria: a national survey. BMC Infect Dis. 2021;21(1):1–12. Kamuhabwa AA, Silumbe R. (2013). Knowledge among drug dispensers and antimalarial drug prescribing practices in public health facilities in Dar es Salaam. Drug Healthc Patient Saf, 181–9. NPHCDA. (2012). Minimum Standards for Primary Healthcare in Nigeria . Retrieved from https://ngfrepository.org.ng:8443/jspui/handle/123456789/3153 Laxminarayan R, Duse A, Wattal C, Zaidi AK, Wertheim HF, Sumpradit N, Vlieghe E, Hara GL, Gould IM, Goossens H. Antibiotic resistance-the need for global solutions. Lancet Infect Dis. 2013;13(12):1057–98. WHO. (2023b). Global research agenda for antimicrobial resistance in human health: Policy brief https://www.who.int/publications/m/item/global-research-agenda-for-antimicrobial-resistance-in-human-health UNHCR. (2023). UNHCR Operational Data Portal: Refugees in Nigeria Operational data portal. https://data.unhcr.org/en/country/nga WHO. (2020). Quality of care in fragile, conflict-affected and vulnerable settings: taking action. https://www.who.int/publications/i/item/9789240015203 Ogoina D, Iliyasu G, Kwaghe V, Otu A, Akase IE, Adekanmbi O, Mahmood D, Iroezindu M, Aliyu S, Oyeyemi AS. Predictors of antibiotic prescriptions: a knowledge, attitude and practice survey among physicians in tertiary hospitals in Nigeria. Antimicrob Resist Infect Control. 2021;10:1–17. Manga MM, Mohammed Y, Suleiman S, Fowotade A, Yunusa-Kaltungo Z, Usman MA, Abulfathi AA, Saddiq MI. Antibiotic prescribing habits among primary healthcare workers in Northern Nigeria: a concern for patient safety in the era of global antimicrobial resistance. PAMJ - One Health. 2021;5. https://doi.org/10.11604/pamj-oh.2021.5.19.30847 . Truppa C, Alonso B, Clezy K, Deglise C, Dromer C, Garelli S, Jimenez C, Kanapathipillai R, Khalife M, Repetto E. Antimicrobial stewardship in primary health care programs in humanitarian settings: the time to act is now. Antimicrob Resist Infect Control. 2023;12(1):89. Additional Declarations No competing interests reported. Supplementary Files AMRawarenessDatasetandOutputResult2024.zip Cite Share Download PDF Status: Published Journal Publication published 22 Feb, 2026 Read the published version in Discover Public Health → Version 1 posted Editorial decision: Revision requested 15 Oct, 2025 Reviews received at journal 01 Oct, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviews received at journal 23 Sep, 2025 Reviewers agreed at journal 23 Sep, 2025 Reviews received at journal 23 Sep, 2025 Reviewers agreed at journal 22 Sep, 2025 Reviewers invited by journal 22 Sep, 2025 Editor assigned by journal 22 Sep, 2025 Editor invited by journal 15 Sep, 2025 Submission checks completed at journal 15 Sep, 2025 First submitted to journal 13 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":16730,"visible":true,"origin":"","legend":"\u003cp\u003eBox plot comparing knowledge of antimicrobial resistance among the PHC workers cadre\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7583809/v1/4ba69ea5140ae260112b1890.jpg"},{"id":93007395,"identity":"838b8d18-2213-4f8e-babf-210a9f55b754","added_by":"auto","created_at":"2025-10-08 06:53:26","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":151028,"visible":true,"origin":"","legend":"\u003cp\u003ePrescription patterns of antibacterial prescribed in the last month among the primary healthcare practitioners\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7583809/v1/2ddc5e4719af0197f467ab2c.jpg"},{"id":103251020,"identity":"bc058a9f-731a-433e-93e9-c8e2df5dbac1","added_by":"auto","created_at":"2026-02-23 16:01:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1043960,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7583809/v1/42e7341b-d60a-4e25-b06c-6b3d82f03a86.pdf"},{"id":93006108,"identity":"1b562f42-3ddd-4ff5-a0ad-df3f440b8a29","added_by":"auto","created_at":"2025-10-08 06:45:26","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":220710,"visible":true,"origin":"","legend":"","description":"","filename":"AMRawarenessDatasetandOutputResult2024.zip","url":"https://assets-eu.researchsquare.com/files/rs-7583809/v1/84b5269a93ec8942242b6cad.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prescription Patterns and Knowledge of Rational Antimicrobial Use among Primary Healthcare Workers in a Humanitarian Setting in Nigeria","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe emergence and spread of antimicrobial-resistant pathogens have become a major global public health concern. It is a major cause of prolonged hospitalization and increased mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Globally, it is estimated that over 5\u0026nbsp;million deaths are due to antimicrobial-resistant pathogens with the highest record from sub-Saharan Africa [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although antimicrobial resistance (AMR) is a natural process that happens over time through genetic changes in pathogens, it is accelerated by human activities, mainly the misuse and overuse of antimicrobials to treat, prevent, or control infections in humans, animals, and plants [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLack of awareness and knowledge of rational antimicrobial use and antimicrobial resistance, among other things, have been suggested as being part of the problem [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In Nigeria, like in many other low- and middle-income countries (LIMCs), overuse and misuse of antibiotics have been observed, primarily due to factors such as inadequate regulatory controls, poor diagnostic capacity, and insufficient knowledge among healthcare professionals, especially at the primary health care level [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDue to the shortage of doctors and nurses, Nigeria has adopted a task-shifting strategy that grants prescription authority to Community Health Officers (CHOs), Community Health Extension Workers (CHEWs), and Junior Community Health Extension Workers (JCHEWs), guided by standing orders [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This healthcare workforce currently makes up the largest human resource for health and dominates the primary health care (PHC) system, which serves Nigeria's large population.\u003c/p\u003e\u003cp\u003eThere is a call for global action against AMR [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The World Health Organization (WHO) is raising awareness among health workers as part of its efforts to reduce the prevalence of AMR. A key priority of WHO is identifying the drivers of appropriate and inappropriate antibiotic prescribing, use, and consumption [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Despite recognizing AMR as a global challenge, there is limited evidence about the knowledge level of antimicrobial resistance and how primary healthcare providers prescribe and dispense antimicrobial agents in Nigeria. The study aimed to identify the most frequently prescribed antimicrobials, assess prescribers' awareness of AMR, and compare knowledge across different prescriber cadres. Identifying knowledge gaps and prevailing prescription patterns will guide the development and implementation of targeted strategies to combat AMR with a focus on the lowest level of prescribers in a resource-limited setting.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design\u003c/h2\u003e\u003cp\u003eA cross-sectional survey design was employed to assess the depth of knowledge among primary healthcare workers regarding rational antibiotic use, antimicrobial resistance, and antibiotic prescription patterns in refugee host communities within Nigeria. The study was conducted using a structured questionnaire developed based on existing literature and validated tools used in previous similar studies.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Location\u003c/h3\u003e\n\u003cp\u003eThe study was carried out in communities hosting refugees in Akwa Ibom, Benue, Cross River, Lagos, Oyo, and Taraba states. This study was part of a baseline assessment for facilities providing services to both host nationals and over 80,000 Cameroonian refugees living in the communities as part of the recommendation for quality of care in humanitarian settings [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eThe study included Primary health care workers in UNHCR/NRCS-supported health facilities who provide consultation services and write prescriptions for patients. Primary healthcare workers who do not prescribe for patients, and primary healthcare providers who decline to join the study were excluded.\u003c/p\u003e\n\u003ch3\u003eSample Size Determination\u003c/h3\u003e\n\u003cp\u003eThe population size of the PHC staff who prescribe for patients was 152. Assuming an expected good knowledge level of 50% with 5% acceptable margin of error and a 95% confidence interval, a minimum sample size of 109 respondents was obtained using Epi Info StatCal sample size calculator for population survey or descriptive study.\u003c/p\u003e\n\u003ch3\u003eSampling Method\u003c/h3\u003e\n\u003cp\u003eA purposive sampling method was employed in the study, selecting health workers who voluntarily agreed to participate. All health workers who gave consent were included. This non-probability sampling approach was appropriate given the study's goal of including all health workers at UNHCR/NRCS-supported facilities.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eData Collection and Analysis\u003c/h2\u003e\u003cp\u003eA self-administered questionnaire containing information on socio-demographics and antimicrobial prescription frequency. A set of 10 binary questions, which assessed knowledge of rational antimicrobial use and antimicrobial resistance, was included. The questionnaire was pre-tested on 15 primary healthcare providers within Ogoja who do not render healthcare services in supported health facilities to examine the respondents' level of understanding of the questions and options to avoid potential ambiguities. Feedback from the pre-test was used to rephrase some questions for clarity. The questionnaire was input on the Kobo/ODK tool and sent to respondents through their emails and WhatsApp.\u003c/p\u003e\u003cp\u003eThe data was submitted directly by respondents through the Kobo application. Data from the Kobo toolbox was exported to Microsoft Excel for cleaning before importing into SPSS (SPSS Ver 29, IBM., Ca, USA) for analysis. The 10 knowledge of Antimicrobial Resistance (AMR) questions were awarded 10 points each. Subsequently, the scores were transformed into ordinal categorization scores as poor knowledge (score 0\u0026ndash;50), Intermediate/Moderate level of knowledge (score 51\u0026ndash;70), and High/Good level of knowledge (71\u0026ndash;100). Data is presented as frequency and percentages. ANOVA was employed to evaluate associations of knowledge as a dependent variable with gender, age, cadre, and years of experience as independent variables. Value of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003eThe study was reviewed and approved by the Cross River State Health Research Ethics Committee and granted ethics number CRSMOH/HRP/REC/2024/488. Informed consent was obtained from all participants, and the study complied with national ethical guidelines and the declaration of Helsinki.\u003c/p\u003e"},{"header":"RESULT","content":"\u003cp\u003eA total of 112(73.7%) of the target health workers, made up of 94(83.9%) females and 18 (16.1%) males, submitted responses. The mean age of responders was 36.6 years (SD\u0026thinsp;=\u0026thinsp;9.7), with the age group 30\u0026ndash;39(33.9%) being most preponderant. The responders were made up of 50(44.6%) Community Health Extension Workers (CHEWs), 34(30.4%) Nurse/Midwives, 15(13.4%) Community Health Officers (CHO), and 13 (11.6%) Junior Community Health Extension Workers (JCHEWs). A total of 92 (82.1%) had less than 5 years of experience, 15 (13.4%) had 5\u0026ndash;9 years, and 5 (4.5%) had more than 9 years of experience. On average, 47 (42.0%) of consultations are to 51\u0026ndash;100 patients per month, while 11 (9.8%) see more than 200 patients per month. Considering the average antibiotics prescription rates, 4(3.6) admitted to prescribing antibiotics to \u0026gt;\u0026thinsp;75 patients per month, 25(22.3) prescribed for 51\u0026ndash;75 patients while 39(34.8) and 44(39.3) prescribed for 26\u0026ndash;50 and \u0026le;\u0026thinsp;25 respectively. Only 36(32.1) reported having any onsite or offsite training on AMR in the last year.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSociodemographic characteristics of the study participants (n\u0026thinsp;=\u0026thinsp;112)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eVariable\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGender\u003c/em\u003e\u003c/p\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94(83.9)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18(16.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAge group\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34(30.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38(33.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23(20.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50\u0026ndash;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17(15.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMean (SD)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e36.59(9.7)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCadre\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNurse/Midwife\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34(30.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15(13.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHEW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50(44.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJCHEW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13(11.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYears of experience\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;5 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e92(82.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u0026ndash;9 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15(13.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;9 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5(4.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOn average, how many patients do you attend to in a month?\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37(33.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e51\u0026ndash;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47(42.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e101\u0026ndash;150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12(10.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e151\u0026ndash;200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5(4.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11(9.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOn average, how many of your patients receive an antibiotic per month?\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44(39.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e26\u0026ndash;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39(34.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e51\u0026ndash;75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25(22.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4(3.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOn-site or off-site training in last one year?\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e76(67.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36(32.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eKnowledge of rational antimicrobial use and antimicrobial resistance\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, a total of 109(97.3%) appropriately defined AMR, 102(91.1%) believed it is a significant cause of mortality, and 101(90.2%) agreed that antimicrobial misuse is a major cause of the problem. However, 34(30.4%) could not differentiate between antibacterial agents and antivirals as they believed antibacterial agents could also treat viruses, and 19(17.0%) regularly prescribed antibacterial agents for the common cold or suspected viral flu, and 79(70.5%) co-prescribed an antibacterial agent in patients with suspected or confirmed malaria. A total of 44(39.3%) believed it is more useful to combine antimicrobials, and 69(61.6%) preferred to prescribe intravenous and intramuscular injectable antibiotics because they believe they work faster than oral antibiotics. A total of 89(79.5%) believed patients could stop their antibiotics when their symptoms disappear, and 61(54.5%) did not find the available standing orders useful.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eKnowledge of Antimicrobial resistance (AMR)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eFrequency (%)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAntimicrobial resistance (AMR) is when germs or microorganisms like bacteria, viruses, fungi and parasites stop responding to medicines that kill or stop them from growing.\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3(2.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e109(97.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDo you think antimicrobial resistance contributes significantly to the death of patients in Nigeria?\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10(8.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e102(91.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDo you think too much use of antimicrobial agents/medications is causing antimicrobial resistance?\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11(9.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e101(90.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAntibacterial agents are medications that can treat both bacterial and viral infections\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78(69.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34(30.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eI can use Augmentin or Erythromycin to treat a cold or viral flu\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e93(83.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19(17.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eI routinely prescribe Amoxil or any other antibacterial agents for patients who are suffering from malaria.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33(29.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79(70.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eI think it is more effective to combine two or more antibacterial agents to treat a patient who has upper respiratory tract infection.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68(60.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44(39.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eI prefer to prescribe injectable (IV and IM) antibiotics because they work faster than oral antibiotics (tablets) in every of my patients.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43(38.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69(61.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePatients do not need to complete their antibiotics; they can stop it when their symptoms have improved.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23(20.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89(79.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eStanding orders and Wall charts are not very helpful in guiding my antimicrobial prescription.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51(45.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61(54.5)\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=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClassification of the level of knowledge on AMR\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor level of knowledge (0\u0026ndash;50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39(34.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate level of knowledge (51\u0026ndash;70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39(34.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGood level of knowledge (71\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34(30.4)\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\u003eBased on the antimicrobial resistance knowledge categorization, 34(30.4%) of the respondents demonstrated good knowledge, while 39(34.8%) demonstrated poor and moderate/intermediate knowledge, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The mean population score for knowledge of AMR was 63.1(SD\u0026thinsp;=\u0026thinsp;21.5). Analysis of Variance (ANOVA) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) showed a significant difference in knowledge between age groups, with participants aged 30\u0026ndash;39 (Mean\u0026thinsp;=\u0026thinsp;68.68, SD\u0026thinsp;=\u0026thinsp;22.6) and 50\u0026ndash;59 (Mean\u0026thinsp;=\u0026thinsp;68.24, SD\u0026thinsp;=\u0026thinsp;15.9) having significantly higher knowledge scores compared to the age groups 20\u0026ndash;29 (Mean\u0026thinsp;=\u0026thinsp;53.82, SD\u0026thinsp;=\u0026thinsp;21.3) and 40\u0026ndash;49 (Mean\u0026thinsp;=\u0026thinsp;63.91, SD\u0026thinsp;=\u0026thinsp;19.9), F(3, 108)\u0026thinsp;=\u0026thinsp;3.53, p\u0026thinsp;=\u0026thinsp;.017. They was no significant difference in knowledge between males (Mean\u0026thinsp;=\u0026thinsp;58.33, SD\u0026thinsp;=\u0026thinsp;20.1) and females (Mean\u0026thinsp;=\u0026thinsp;64.04, SD\u0026thinsp;=\u0026thinsp;21.7), F(1, 110)\u0026thinsp;=\u0026thinsp;1.07, p\u0026thinsp;=\u0026thinsp;.304 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). There was also a significant difference in knowledge scores across cadres (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Nurses/midwives (Mean\u0026thinsp;=\u0026thinsp;69.4, SD\u0026thinsp;=\u0026thinsp;24.5) and Community Health Officers (CHO) (Mean\u0026thinsp;=\u0026thinsp;70, SD\u0026thinsp;=\u0026thinsp;20.7) had a higher knowledge compared to Community Health Extension Workers (CHEW) (Mean\u0026thinsp;=\u0026thinsp;60, SD\u0026thinsp;=\u0026thinsp;18.6) and Junior Community Health Extension Workers (JCHEW) (Mean\u0026thinsp;=\u0026thinsp;50.77, SD\u0026thinsp;=\u0026thinsp;18.0), F(3, 108)\u0026thinsp;=\u0026thinsp;3.49, p\u0026thinsp;=\u0026thinsp;.018.\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\u003eAnalysis of variance (ANOVA) of the knowledge of AMR on Independent Variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eVariable\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMean (SD)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003edf1\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003edf 2\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGender\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64.04(\u0026plusmn;\u0026thinsp;21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.304\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.33(\u0026plusmn;\u0026thinsp;20.1)\u003c/p\u003e\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAge group\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.82(\u0026plusmn;\u0026thinsp;21.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.68(\u0026plusmn;\u0026thinsp;22.6)\u003c/p\u003e\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.91(\u0026plusmn;\u0026thinsp;19.9)\u003c/p\u003e\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50\u0026ndash;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.24(\u0026plusmn;\u0026thinsp;15.9)\u003c/p\u003e\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCadre\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNurse/Midwife\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69.4(\u0026plusmn;\u0026thinsp;24.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70(\u0026plusmn;\u0026thinsp;20.7)\u003c/p\u003e\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHEW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60(\u0026plusmn;\u0026thinsp;18.6)\u003c/p\u003e\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJCHEW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50.77(18.0)\u003c/p\u003e\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAntibacterial agents prescription pattern.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWithin the last month before the survey, the most commonly prescribed antibiotics as shown on Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, were Amoxicillin (12%), Metronidazole (11%), Ciprofloxacin (10%), Gentamicin (9%), Erythromycin (8%), Ampicillin-cloxacillin (8%), Crystal penicillin (8%), and Ampicillin (8%). Less commonly prescribed antibiotics include Clotrimazole (8%), Ceftriaxone (7%), Levofloxacin (4%), Cefuroxime (3%), Amoxycillin-Clavulanate (3%), Cefixime (2%), and Ofloxacin (1%).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings from this study reveal considerable gaps in knowledge and practices related to antimicrobial prescription among primary healthcare workers in refugee host communities across Nigeria. While awareness of antimicrobial resistance (AMR) appears high, with 97.3% correctly defining AMR and 91.1% acknowledging its contribution to mortality, deeper issues persist regarding rational use.\u003c/p\u003e\u003cp\u003eA key area of concern is the widespread prescription of antibiotics for conditions that do not warrant such interventions. For instance, 17.0% of respondents admitted to prescribing antibiotics for viral infections such as the common cold or flu, and over 70.5% reported co-prescribing antibacterial agents in suspected or confirmed cases of malaria. These findings reflect a misunderstanding of antimicrobial targets and suggest a tendency to overprescribe as a precautionary or habitual measure. This pattern was also observed in similar studies conducted in Nigeria and other low-resource settings [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdditionally, a significant proportion of healthcare workers (61.6%) believed that injectable antibiotics are more effective than oral formulations, indicating misconceptions about routes of administration. This belief, if left unaddressed, can lead to unnecessary injections, increase healthcare costs, and elevate the risk of injection-related complications.\u003c/p\u003e\u003cp\u003eAlarmingly, 79.5% of respondents believed patients could stop taking antibiotics once their symptoms improved, contrary to established guidelines that emphasize completing prescribed courses to prevent resistance. This finding aligns with reports from a previous study and points to a critical need for enhanced training and supervision of primary healthcare workers [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eKnowledge scores varied significantly across cadres and age groups. Nurses/midwives and Community Health Officers (CHOs) demonstrated higher knowledge levels than Community Health Extension Workers (CHEWs) and Junior CHEWs (JCHEWs). This disparity likely reflects differences in formal education and clinical exposure. The younger age group (20\u0026ndash;29 years) also exhibited significantly lower knowledge scores than their older counterparts, possibly due to limited professional experience or insufficient post-qualification training.\u003c/p\u003e\u003cp\u003eThe fact that only 32.1% of respondents reported receiving AMR-related training in the past year further underscores the need for regular, structured, and mandatory capacity-building programs. Moreover, the finding that over half (54.5%) of respondents did not find the standing orders helpful in guiding antimicrobial prescriptions suggests that these tools may require updates, simplification, or contextual adaptation to enhance usability.\u003c/p\u003e\u003cp\u003eThe prescription patterns observed, particularly the high use of broad-spectrum antibiotics like amoxicillin and ciprofloxacin, reinforce concerns over inappropriate antibiotic choices. While these antibiotics are essential for treating bacterial infections, their overuse contributes to the development of multidrug-resistant organisms, posing a threat to future treatment options.\u003c/p\u003e"},{"header":"Conclusions and recommendations","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003cp\u003eOverall, this study provides valuable insight into antimicrobial prescribing behaviors at the primary healthcare level in a humanitarian setting in Nigeria. It identifies specific gaps in knowledge and practices that must be addressed through targeted interventions, including mandatory AMR training for all PHC prescribers, regular review and dissemination of updated standing orders, enhanced supervision and mentoring systems, particularly for junior cadres, and public health education to promote rational drug use among patients. Addressing these gaps is crucial to curbing AMR in Nigeria and safeguarding the effectiveness of existing antibiotics, especially in humanitarian settings with limited resources, as has been advanced [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of Interest\u003c/h2\u003e\u003cp\u003eNone declared.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNo funding received for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eEAO: Concept, design, data analysis, manuscript review, manuscript approval. OKO: literature search, data collection, manuscript preparationAAM: Data collection, statistical analysis, manuscript review.MJ: Manuscript review, manuscript approval.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary information file.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLlor C, Bjerrum L. Antimicrobial resistance: risk associated with antibiotic overuse and initiatives to reduce the problem. Therapeutic Adv drug Saf. 2014;5(6):229\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWHO. (2023a). \u003cem\u003eAntimicrobial resistance\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/antimicrobial-resistance\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/antimicrobial-resistance\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChukwu EE, Oladele DA, Enwuru CA, Gogwan PL, Abuh D, Audu RA, Ogunsola FT. Antimicrobial resistance awareness and antibiotic prescribing behavior among healthcare workers in Nigeria: a national survey. BMC Infect Dis. 2021;21(1):1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKamuhabwa AA, Silumbe R. (2013). Knowledge among drug dispensers and antimalarial drug prescribing practices in public health facilities in Dar es Salaam. Drug Healthc Patient Saf, 181\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNPHCDA. (2012). \u003cem\u003eMinimum Standards for Primary Healthcare in Nigeria\u003c/em\u003e. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ngfrepository.org.ng:8443/jspui/handle/123456789/3153\u003c/span\u003e\u003cspan address=\"https://ngfrepository.org.ng:8443/jspui/handle/123456789/3153\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaxminarayan R, Duse A, Wattal C, Zaidi AK, Wertheim HF, Sumpradit N, Vlieghe E, Hara GL, Gould IM, Goossens H. Antibiotic resistance-the need for global solutions. Lancet Infect Dis. 2013;13(12):1057\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWHO. (2023b). \u003cem\u003eGlobal research agenda for antimicrobial resistance in human health: Policy brief\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/m/item/global-research-agenda-for-antimicrobial-resistance-in-human-health\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/m/item/global-research-agenda-for-antimicrobial-resistance-in-human-health\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUNHCR. (2023). \u003cem\u003eUNHCR Operational Data Portal: Refugees in Nigeria\u003c/em\u003e Operational data portal. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.unhcr.org/en/country/nga\u003c/span\u003e\u003cspan address=\"https://data.unhcr.org/en/country/nga\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWHO. (2020). Quality of care in fragile, conflict-affected and vulnerable settings: taking action. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/i/item/9789240015203\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/i/item/9789240015203\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOgoina D, Iliyasu G, Kwaghe V, Otu A, Akase IE, Adekanmbi O, Mahmood D, Iroezindu M, Aliyu S, Oyeyemi AS. Predictors of antibiotic prescriptions: a knowledge, attitude and practice survey among physicians in tertiary hospitals in Nigeria. Antimicrob Resist Infect Control. 2021;10:1\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eManga MM, Mohammed Y, Suleiman S, Fowotade A, Yunusa-Kaltungo Z, Usman MA, Abulfathi AA, Saddiq MI. Antibiotic prescribing habits among primary healthcare workers in Northern Nigeria: a concern for patient safety in the era of global antimicrobial resistance. PAMJ - One Health. 2021;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.11604/pamj-oh.2021.5.19.30847\u003c/span\u003e\u003cspan address=\"10.11604/pamj-oh.2021.5.19.30847\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTruppa C, Alonso B, Clezy K, Deglise C, Dromer C, Garelli S, Jimenez C, Kanapathipillai R, Khalife M, Repetto E. Antimicrobial stewardship in primary health care programs in humanitarian settings: the time to act is now. Antimicrob Resist Infect Control. 2023;12(1):89.\u003c/span\u003e\u003c/li\u003e\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Antimicrobial resistance, primary healthcare, healthcare workers","lastPublishedDoi":"10.21203/rs.3.rs-7583809/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7583809/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eAntimicrobial resistance (AMR) is a major global health threat, particularly in low- and middle-income countries such as Nigeria, where inappropriate antibiotic use is common. This study assessed antimicrobial prescription patterns and knowledge of AMR among primary healthcare workers in humanitarian settings.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eA cross-sectional survey was conducted among primary healthcare prescribers using a structured digital questionnaire (Kobo/ODK). Data were analysed with SPSS v29. Knowledge of AMR was assessed through 10 binary questions and categorized as poor (0\u0026ndash;50%), moderate (51\u0026ndash;70%), or good (71\u0026ndash;100%).\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eA total of 112 respondents participated, including 94(83.9%) females, with a mean age of 36.6 years (SD\u0026thinsp;=\u0026thinsp;9.7). Participants comprised 50(44.6%) CHEWs, 34 (30.4%) nurses/midwives, 15 (13.4%) CHOs, and 13 (11.6%) JCHEWs. Only 36(32.1%) had AMR-related training in the past year. While 109(97.3%) correctly defined AMR and 102(91.1%) acknowledged its link to mortality, gaps remained. Thirty-four (30.4%) believed antibiotics treat viral infections, 19(17.0%) prescribed antibiotics for influenza, and 79(70.5%) co-prescribed them for malaria. Overall, 39 (34.8%) had poor knowledge, 39(34.8%) moderate, and 34(30.4%) good. Knowledge differed significantly by age (p\u0026thinsp;=\u0026thinsp;.017) and cadre (p\u0026thinsp;=\u0026thinsp;.018), with nurses/midwives and CHOs scoring higher. Commonly prescribed antibiotics were amoxicillin (12%), metronidazole (11%), ciprofloxacin (10%), and gentamicin (9%).\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eDespite high awareness, irrational prescribing and knowledge gaps persist. Targeted training, revised standing orders, and strengthened antimicrobial stewardship at the PHC level are urgently needed to curb AMR in Nigeria.\u003c/p\u003e","manuscriptTitle":"Prescription Patterns and Knowledge of Rational Antimicrobial Use among Primary Healthcare Workers in a Humanitarian Setting in Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 06:45:21","doi":"10.21203/rs.3.rs-7583809/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-15T19:17:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-01T09:02:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251329426091059764679460877733661129184","date":"2025-09-24T20:24:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-23T21:32:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157190282390695009421227731706138232802","date":"2025-09-23T19:32:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-23T10:47:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289775147197702553409828033229003935450","date":"2025-09-22T19:30:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-22T17:54:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-22T17:52:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-15T16:50:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-15T16:43:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2025-09-13T15:15:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"507d5917-b270-4e25-a812-92ef14fd66ea","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:00:18+00:00","versionOfRecord":{"articleIdentity":"rs-7583809","link":"https://doi.org/10.1186/s12982-026-01603-z","journal":{"identity":"discover-public-health","isVorOnly":false,"title":"Discover Public Health"},"publishedOn":"2026-02-22 15:57:00","publishedOnDateReadable":"February 22nd, 2026"},"versionCreatedAt":"2025-10-08 06:45:21","video":"","vorDoi":"10.1186/s12982-026-01603-z","vorDoiUrl":"https://doi.org/10.1186/s12982-026-01603-z","workflowStages":[]},"version":"v1","identity":"rs-7583809","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7583809","identity":"rs-7583809","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}