{"paper_id":"0e5580ff-e4cf-488e-aa0d-6d7538efa374","body_text":"Antibiotic Use Among University Students in Malaria Therapy and Its Implications for Antimicrobial Resistance in Nigeria: A Quantitative Cross-sectional Study | 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 Antibiotic Use Among University Students in Malaria Therapy and Its Implications for Antimicrobial Resistance in Nigeria: A Quantitative Cross-sectional Study Victor Ekoche Ali, Sunday Nguher Uketeh, Abdulbasit Hamza, Ikechukwu Obiajulu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7439669/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Oct, 2025 Read the published version in Discover Public Health → Version 1 posted 10 You are reading this latest preprint version Abstract Background Antimicrobial resistance (AMR) is a global health crisis, driven partly by inappropriate antibiotic use. In Nigeria, malaria remains highly prevalent and often mismanaged with antibiotics, particularly in presumed malaria-typhoid co-infections. This study examined patterns of antibiotic use in malaria treatment among university students, highlighting implications for AMR. Methods A cross-sectional survey was conducted among undergraduates purposively selected from 12 universities across Nigeria’s six geopolitical zones. Data were collected via validated online questionnaires (February–March 2025) and analysed using descriptive statistics, chi-square tests, logistic regression, and Spearman correlation (SPSS v26). Results Of 646 respondents, > 97% demonstrated general antibiotic knowledge, yet 27.6% misidentified chloroquine as an antibiotic. While 94.6% correctly recognised antibiotics for bacterial infections, about one-fifth believed they were effective against fungal, parasitic, or viral diseases. Despite 84.7% AMR awareness, 49.1% reported using antibiotics for malaria treatment. Misuse was highest in the Northeast (62.3%), Northwest (63.7%), and South-South (32.9%). In the Northeast, key drivers included prior experience (35.4%), pharmacist advice (29.9%), and peer influence (28.0%), while only 6.7% followed physician prescriptions. Misuse correlated with the belief that antibiotics treat all illnesses (rₛ = 0.329, p < 0.001). Nearly half (49.5%) accessed antibiotics without prescriptions. Conclusions High AMR awareness contrasts with persistent misuse of antibiotics for malaria, reflecting misconceptions, regional disparities, and weak regulation. Targeted education, stricter antibiotic controls, and improved diagnostics are urgently needed to curb AMR in Nigeria. Antibiotic misuse antimicrobial resistance malaria Nigeria Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Malaria continues to pose a major public health threat, especially across Africa, causing substantial morbidity, mortality, and economic burden. A considerable rise in the number of malaria cases in the African region was reported post-COVID-19 pandemic era, increasing from 218 million cases in 2019 to 233 million in 2022( 1 ). This region bears a disproportionate share of the global malaria burden, with the WHO estimating that in 2022, 94% and 95% of all malaria cases and deaths, respectively, occurred in the African continent( 2 ). Within the African continent, the sub-Saharan region accounts for almost half of the cases worldwide, with Mozambique, Uganda, and the Democratic Republic of Congo making up 4.2%, 5.1% and 12.3% of the total estimate, respectively. Similarly, Tanzania, Niger, and the Democratic Republic of Congo accounted for 4.4%, 5.6% and 11.6% of global malaria deaths, respectively( 2 ). In Nigeria, the situation is particularly severe, with the country accounting for approximately 27% of all malaria cases and 31.1% of mortality attributed to malaria globally as of 2022( 2 ). Plasmodium falciparum ( P. falciparum ) is the predominant malaria parasite species in Sub-Saharan Africa, accounting for most infections( 3 ). Compared to other species, P. falciparum is associated with severe disease and death, especially among vulnerable populations such as children under five years of age and pregnant women( 4 ). The high transmission rates, particularly in Sub-Saharan Africa, are attributed to favourable climatic conditions for mosquito vectors, primarily Anopheles gambiae , and limited access to effective prevention and treatment measures( 5 ). Despite notable progress in malaria control over the past two decades, the disease continues to exact a heavy toll in this region ( 3 ). According to Shi et al. (2023), children under five years of age are particularly vulnerable, accounting for 67% of all malaria deaths worldwide, while malaria in pregnancy has also been linked with adverse outcomes such as maternal anaemia, low birth weight, and increased infant mortality( 6 ). The economic burden of malaria in Sub-Saharan Africa is substantial. Direct costs, such as expenses for prevention, diagnosis, and treatment, and indirect costs comprising lost productivity due to illness and premature death, exert a huge burden on an already disadvantaged population( 7 ). Among children, the disease impedes economic development by affecting school attendance, while in adults, the effects include a reduction in workforce productivity and discouraging foreign investment and tourism( 8 ). Furthermore, it is estimated that malaria costs African economies billions of dollars annually in lost GDP( 7 – 9 ). Unfortunately, many affected populations in this region have inadequate access to prompt, accurate diagnosis and effective treatment, particularly in rural and remote areas, where it is further compounded by socio-economic factors such as poverty, poor housing conditions and limited education that contribute to increased malaria risk and hamper prevention efforts ( 10 ). The standard treatment protocol for malaria includes artemisinin-based combination therapies (ACTs), while more serious cases are managed using injectable artesunate or artemether( 11 ). ACTs have become the most important medication for malaria treatment worldwide, particularly for uncomplicated P. falciparum malaria. The WHO recommended ACTs as the first-line treatment due to their high efficacy, rapid action, and ability to slow the development of drug resistance( 12 ). Artemether-lumefantrine (AL) and Artesunate-amodiaquine (ASAQ) are two of the most widely used ACTs in Sub-Saharan Africa. Both combinations have proven highly effective in treating uncomplicated P. falciparum malaria and are recommended by the WHO as first-line treatments( 12 ). These ACTs have played a crucial role in reducing malaria morbidity and mortality across the region. Their widespread adoption has been facilitated by their efficacy, relatively good safety profiles, and availability as fixed-dose combinations, which improve treatment adherence and reduce the risk of using monotherapies( 13 ). Although antibiotics are not part of recommended malaria treatment, they are sometimes prescribed—often inappropriately—when bacterial infections are assumed to coexist or when malaria is wrongly diagnosed ( 14 , 15 ). In Nigeria, there is a common belief in ‘malaria-typhoid co-infections’, which, combined with limited access to proper diagnostics, has led to the frequent misuse of antibiotics ( 16 , 17 ). This misuse of antibiotics in the empirical treatment of ‘malaria-typhoid co-infections’ adds to the already serious problem of antimicrobial resistance (AMR), a challenge that has gained global attention. Antimicrobial resistance (AMR) represents a significant global health issue that jeopardises the effectiveness of antibiotics, the advancements in modern medicine, and the health of populations across the globe ( 18 ). In 2019, AMR was linked to more than 1.27 million fatalities globally, surpassing the cumulative mortality rates associated with malaria and AIDS( 19 ). West sub-Saharan Africa bears the greatest burden of AMR, with a mortality rate of 27.3 in every 100,000 deaths attributable to AMR( 20 ). According to the WHO and other recent studies, sub-Saharan Africa currently experiences the highest death rates linked to drug-resistant infections ( 19 , 21 ). In Nigeria, the situation is worsened by factors such as self-medication, over-the-counter access to antibiotics without prescriptions, and a general lack of reliable diagnostic services( 22 ). In addition, the supervision of prescriptions remains inadequate, resulting in the routine sales of prescription medications, including antimicrobials and antimalarials, over the counter in pharmacies and by vendors of patent proprietary medicines ( 18 ). Enhancing public awareness and understanding of antimicrobial resistance (AMR) among healthcare providers, policymakers, agricultural professionals, and the public remains a cornerstone of both global and national strategies to combat AMR and curb its spread( 23 , 24 ). In addition, it is necessary to increase investments in laboratory infrastructure and manpower training in Nigeria to ensure appropriate diagnostic testing and widespread antibiotics susceptibility testing in hospitals, as these will decrease empirical treatment and the misuse of antibiotics ( 18 ). Therefore, this study investigated how antibiotics are used in the context of malaria treatment among Nigerian undergraduate university students and pharmacists. It examined the spread of this practice, the driving factors, and the associated public health risks. By evaluating the prescribing habits, patient perceptions, and knowledge of antibiotic stewardship, the research hopes to support more focused strategies to reduce misuse and help tackle AMR. METHODS Study Design and Sites This descriptive cross-sectional study employed a structured questionnaire to obtain data from undergraduate students enrolled in Nigerian universities. To ensure geographical representation, universities were purposively selected across Nigeria’s six geopolitical zones. Two universities were chosen from each zone, yielding a total of 12 participating institutions: North East : Abubakar Tafawa Balewa University and University of Maiduguri North West : Ahmadu Bello University and Federal University Dutsin-Ma North Central : University of Abuja and University of Jos South East : University of Nigeria and Enugu State University of Technology South South : University of Uyo and University of Calabar South West : University of Ibadan and Oduduwa University Within these institutions, the questionnaire was distributed online via WhatsApp and other social media platforms. Convenience sampling was used, as participation was voluntary. Study Participants and Eligibility Criteria Undergraduate students were eligible to participate if they: Were enrolled in any of the 12 selected universities, Could read and write in English, and Provided informed consent to participate. Students who did not meet all these criteria were excluded. Sample Size Determination The minimum sample size was calculated using the single proportion formula n = Z2P(1 − P)/d2n = Z^2P(1-P)/d^2n = Z2P(1 − P)/d2. At a 95% confidence level, Z = 1.96; expected prevalence (P) was set at 50% to maximise variability; and margin of error (d) was fixed at 0.05. This gave a minimum sample size of 384 respondents. To account for potential non-response, 10% was added, yielding a final target sample size of 422 undergraduate students. Data Collection and Representation While convenience sampling was employed due to the online distribution method, efforts were made to reduce bias by sharing the questionnaire across multiple student groups and platforms in each university. This approach aimed to capture a wide range of respondents across different faculties and levels of study, ensuring diversity while acknowledging the limitations of self-selection. The final sample included all students who responded during the data collection window, providing sufficient data for descriptive and statistical analysis of the study objectives. Study Instrument The study instrument consisted of a pre-tested and validated self-administered online questionnaire ( Supplementary file 1 ), which was adapted from the World Health Organization's 2015 'Antibiotic Resistance: Multi-Country Public Awareness Survey' ( 25 ) and modified to address antibiotic use in malaria therapy among Nigerian university students. Additional questions were incorporated to reflect the local context and study objectives. The modified questionnaire was pre-tested using 20 pharmacy students randomly selected from the University of Nigeria (UNN) Enugu Campus, and the feedback comments were used to fine-tune, adapt, and modify the structure of the questionnaire. The questionnaire consisted of five sections (A–E): Section A : Collected demographic data, including gender, age group, and geopolitical zone. Section B : Assessed respondents' knowledge of antibiotics, including recognition of antibiotics, their uses, and awareness of antimicrobial resistance (AMR). Section C : Evaluated respondents' attitudes toward antibiotic use Section D : Examined respondents' practices related to malaria therapy, including malaria treatment history and use of antibiotics in malaria treatment. Section E : Assessed awareness and understanding of antimicrobial resistance (AMR) and its link to antibiotic misuse. Data Collection The survey questionnaire was administered via Google Forms and distributed through WhatsApp class groups and other social media platforms across the selected universities. The online questionnaire provided a summary of study information, screening questions to verify eligibility, and participant consent to participate in the study. Participation was voluntary, and submissions were anonymous. Participants were also informed that they can withdraw at any time before submitting the questionnaire, as submitted responses cannot be identified or removed due to the anonymity of the questionnaire design. Data collection was conducted over one month, from February 22nd to March 31st, 2025. Ethical Considerations Ethical approval for the study was obtained from the Research Ethics Committee of the Enugu State Ministry of Health ( Approval number : MH/MSD/REC21/747) before the commencement of the study. No identifiable data was collected, and all information was securely handled and used exclusively for research purposes. Informed consent was obtained from all participants which included the publication of data collated in anonymised form. The study complied with all ethical regulations outlined in the approval letter and the Helsinki declaration for human research. Data Analysis Data collected were exported in Microsoft Excel spreadsheet (Microsoft Office 2016) for analysis. IBM SPSS version 26 was employed to further code, clean, and analyse the data. The data was summarised using descriptive statistics, which included frequencies, percentages, means, and standard deviations. The chi-square test was used to assess associations between categorical socio-demographic variables (such as age group, gender, and geopolitical zone) and awareness of antibiotics. Binary logistic regression was conducted to identify predictors of antibiotic resistance knowledge. Additionally, Spearman correlation analysis was performed to examine the relationship between beliefs and practices regarding the use of antibiotics in the treatment of malaria. A p-value of < 0.05 was considered statistically significant. RESULTS A minimum sample size of 422 was estimated, but a total of 646 undergraduate students participated in the study, giving a response rate above the calculated requirement. Although responses were obtained from all six geopolitical zones, the distribution was not balanced across regions. Socio-demographic characteristics of participants The socio-demographic characteristics of 646 study participants, stratified by their awareness of antibiotics, are presented in Table 1 . Most participants were aged 18–24 years (71.7%), with high antibiotic awareness across all age groups: 98.7% in both the 18–24 and 25–30 age groups, and 90.9% among those above 30 years of age. A statistically significant difference was observed (p = 0.014), though awareness remained high across all groups. In terms of gender, awareness was high among both males (97.8%) and females (99%), but the difference was not statistically significant ( p = 0.176), indicating that gender did not significantly influence antibiotic awareness (Table 1 ) . Antibiotic awareness was high across all geopolitical zones, with the lowest awareness observed in the Northwest (93%) and the highest in the Southeast and South-south (100%). A statistically significant difference was found between geographical zones (p = 0.001), though the overall variation in awareness levels remains small (Table 1 ) . Table 1 – SOCIO-DEMOGRAPHIC CHARACTERISTICS OF STUDY PARTICIPANTS (N = 646) BY ANTIBIOTICS AWARENESS Characteristics Frequency (%) Antibiotic awareness (%) p-values Age group 0.014 - 18–24 463 (71.7) 457 (98.7) - 25–30 161 (24.9) 159 (98.7) - Above 30 22 (3.4) 20 (90.9) Gender 0.176 - Male 315 (48.8) 308 (97.8) - Female 331 (51.2) 328 (99) Geopolitical zone 0.001 - North Central 145 (22.4) 144 (99) - North East 69 (10.7) 68 (99) - North West 102 (15.8) 95 (93) - South East 165 (25.5) 165 (100) - South South 85 (13.2) 85 (100) - South West 80 (12.4) 79 (98.8) Knowledge, perception, and attitudes towards antibiotics Figure 1 and Fig. 2 illustrate key insights into Nigerian undergraduates' knowledge and perception regarding antibiotics, including common misconceptions. While most participants correctly identified Amoxicillin (87.16%) and Ciprofloxacin (73.1%) as antibiotics, a considerable number of participants misclassified drugs like Chloroquine (27.6%), Paracetamol (16.4%), and Ibuprofen (14.4%) as antibiotics, indicating notable gaps in understanding. Similarly, although the majority recognized that antibiotics treat bacterial infections (94.6%), many respondents mistakenly believed they are effective against fungal (20.4%), parasitic (17%), or viral infections (20.6%) infections as shown in Fig. 2 . Table 2 presents data on attitudes and access-related behaviours. Attitudes varied regarding the belief that antibiotics are always needed when sick, with 33.6% disagreeing and 21.4% agreeing. Furthermore, access to antibiotics was not always prescription-based (49.5%) and 35.2% of participants agreed that antibiotics are always needed when sick, suggesting opportunities for misuse. Table 2 – STUDY PARTICIPANTS’ KNOWLEDGE, PERCEPTION, AND ATTITUDE TOWARDS ANTIBIOTICS Where do you usually get your antibiotics from? - Pharmacist with prescription 484 (74.9) - Pharmacist without prescription 207 (32.0) - Leftovers from previous prescriptions 57 (8.8) - Friends/Family 56 (8.7) Are antibiotics always needed when you are sick? - Agree 138 (21.4) - Disagree 217 (33.6) - Neutral 96 (14.9) - Strongly Agree 89 (13.8) - Strongly Disagree 106 (16.4) Knowledge, perception and attitude of antibiotics use in malaria treatment Table 3 explores Nigerian undergraduates' knowledge, perceptions, and attitudes toward using antibiotics for malaria treatment. Over half of the participants (57.1%) reported treating malaria with a doctor’s prescription, while 35.2% relied on over-the-counter (OTC) self-medication, and 7% used herbal remedies. Notably, 43.7% incorrectly believed antibiotics work against malaria, while 38.1% correctly stated they do not, and 18.3% were unsure. This misconception is reflected in the finding that 49.1% admitted to using antibiotics for malaria treatment, highlighting a concerning misuse of antibiotics for a condition they cannot treat. Table 3 – STUDY PARTICIPANTS' (NIGERIAN UNDERGRADUATES) KNOWLEDGE, PERCEPTION, AND ATTITUDE OF ANTIBIOTICS TOWARDS MALARIA TREATMENT Questions Frequency (%) Last Malaria treatment - Within the past month 197 (30.5) - 3 months ago 212 (32.8) - 4–6 months ago 104 (16.1) - More than 6 months ago 126 (19.5) - Never 7 (1.1) How did you treat the malaria? - Chemist 1 (0.2) - Pharmacist 4 (0.6) - Herbal remedies 50 (7.7) - Doctors’ prescription 406 (62.9) - OTC self-medication 250 (38.7) - Treated by a Nurse 1 (0.2) Do antibiotics work against malaria? - Yes 282 (43.7) - No 246 (38.1) - Not sure 118 (18.3) Have you ever used antibiotics to treat malaria? - Yes 317 (49.1) - No 329 (50.9) Distribution of the use of antibiotics in malaria therapy among study participants across geographical zones Figure 3 highlights the use of antibiotics for treating malaria among the participants across different geographic regions. Nearly half of the respondents (49.1%) reported using antibiotics for malaria treatment, despite the fact that antibiotics are not recommended for malaria management. The highest rates of antibiotic use were observed in the North East (62.3%) and North West (63.7%) regions, while the lowest was in the South-South (32.9%). Conversely, 50.9% of respondents reported not using antibiotics for malaria. The highest proportion of those who refrained from antibiotic use was in the South-South (67.1%) and North Central (60.0%) regions (Fig. 3 ) . Factors affecting the use of antibiotics in malaria therapy among participants from various geopolitical zones Figure 4 highlights the factors influencing Nigerian undergraduates' decision to use antibiotics for treating malaria. The most common reason cited was previous experience (35.4%), followed closely by advice from pharmacists (29.9%) and recommendations from friends or family (28.0%). Only a small proportion (6.7%) relied on a doctor's prescription. Variation across regions was noted in the decision-making patterns. The North East (37.7%) and South West (37.5%) had the highest reliance on previous experience, while the North Central (31.7%) and North West (29.4%) showed a strong influence from friends and family. Pharmacist advice played a significant role across all regions, ranging from 28.3–36.2% (Fig. 4 ) . Awareness and Knowledge of antimicrobial resistance among the participants Table 4 highlights the participants’ knowledge and understanding of antimicrobial resistance (AMR). Majority of the students (84.7%) reported being aware of AMR and its consequences, indicating a relatively high level of knowledge about this critical public health issue. Furthermore, 83.4% correctly recognized that the misuse of antibiotics contributes to AMR, whereas. a notable minority (15.3%) were unaware of AMR, and 16.6% did not believe that antibiotic misuse contributes to resistance. Table 4 – ANTIMICROBIAL RESISTANCE KNOWLEDGE AMONG UNDERGRADUATES IN NIGERIA. Questions Frequency (%) Are you aware of antimicrobial resistance (AMR) and its consequences? - Yes 547 (84.7) - No 99 (15.3) Can the misuse of antibiotics contribute to AMR? - Yes 539 (83.4) - No 107 (16.6 No statistically significant associations were found between AMR awareness and gender, age group, or geopolitical zone (Table 5 ). Logistic regression analysis similarly indicated no significant predictors of AMR awareness. However, Spearman correlations revealed significant associations between antibiotic use for malaria and two beliefs: that antibiotics are always needed when sick (ρ = 0.329, p < 0.001), and that stopping antibiotics when feeling better is acceptable (ρ = 0.087, p = 0.026). Other factors showed no significant relationships (Table 5 & Supplementary file 2 ). Table 5 – SUMMARY OF STATISTICAL ANALYSES ON AMR AWARENESS AND ANTIBIOTIC USE FACTORS Analysis Type Variable / Comparison Test Statistic p -value Effect Size / Exp(B) Significance Interpretation Chi-Square Gender χ² (1, N = 646) = 0.077 0.781 — Not significant Age Group χ² (2, N = 646) = 2.072 0.355 — Not significant Geopolitical Zone χ² (5, N = 646) = 5.822 0.324 — Not significant Logistic Regression Male vs Female Wald = 0.095 0.758 Exp(B) = 0.931 Not significant Age 18–24 vs 31+ Wald = 1.742 0.187 Exp(B) = 3.939 Not significant Age 25–30 vs 31+ Wald = 0.010 0.922 Exp(B) = 0.974 Not significant Geopolitical Zones vs Southeast (all groups) Wald = 0.200–0.932 > 0.3 Exp(B) = various Not significant Spearman Correlation Belief : Antibiotics always needed when sick ρ = 0.329 < 0.001 — Moderate positive, statistically significant Belief : OK to stop when feeling better ρ = 0.087 0.026 — Weak positive, statistically significant Complete full course ρ = 0.026 0.509 — Not significant AMR awareness ρ = -0.041 0.299 — Not significant Belief: misuse causes AMR ρ = -0.008 0.844 — Not significant DISCUSSION Use of antibiotics in malaria therapy To our knowledge, the current study is the first to investigate antibiotic misuse specifically for malaria treatment among Nigerian university undergraduates. Our findings indicate that malaria remains a significant health challenge, with 79.4% of participants reporting treatment within the past six months and 63.3% within the last three months. This high frequency of malaria episodes often leads to self-medication and reliance on easily accessible drugs, including antibiotics ( 26 – 28 ). Despite their ineffectiveness against malaria, nearly half (49.1%) of participants reported using antibiotics for treatment. This misuse is primarily driven by prescriptions from doctors (6.7%), advice from friends and family (28.0%), pharmacists (29.9%), and personal experience (35.4%). These findings suggest that both healthcare professionals and social networks perpetuate misconceptions about antibiotic misuse in malaria treatment. The high reliance on OTC self-medication and the widespread misuse of antibiotics underscores the need for targeted education and awareness campaigns to address these gaps and promote appropriate malaria treatment practices Our study findings show that 43.7% of participants believed antibiotics work against malaria, 38.1% correctly stated they do not, and 18.3% were unsure. The misconception that antibiotics are effective for malaria treatment persists as a significant public health challenge in the tropical regions where malaria is endemic. While the WHO recommends empirical antibiotic use in children with severe malaria due to potential bacterial co-infections ( 29 , 30 ) this guideline applies primarily to hospitalized patients ( 31 ) and does not explain the widespread belief that antibiotics treat malaria. Our study participants are mostly young adults (71.7% aged 18–24 years; 24.9% aged 25–30 years), underscoring that this misconception extends beyond paediatric cases. The high rate of antibiotic misuse in our study may be attributed to a combination of diagnostic uncertainty, overlapping symptoms with other febrile illnesses, limited access to reliable laboratory tests, and widespread self-medication practices. These factors are well-documented contributors to inappropriate antibiotic use in Nigeria ( 18 , 32 ). This current study also shows that the highest rates of antibiotic use in malaria treatment were observed in the North East (62.3%) and North West (63.7%) regions, while the lowest was in the South-South region (32.9%). These regional differences may be driven by broader healthcare inequities, including disparities in healthcare access, diagnostic availability, and public health awareness ( 33 ). In northern regions, limited access to healthcare facilities often leads to presumptive treatment of febrile illnesses with antibiotics, a pattern that may influence students' health-seeking behaviours ( 33 ). In contrast, the southern regions benefit from better healthcare infrastructure and stronger public health interventions, which may contribute to lower misuse rates among students ( 34 ). In previous studies conducted in Northern Nigeria, 21.3% of respondents considered malaria a condition requiring antibiotics, while 14.5% of undergraduates admitted to self-medicating with antibiotics for treatment ( 35 , 36 ). Similarly, in urban centres like Lagos and Abuja, 55.3% of adults incorrectly attributed malaria to bacterial causes, and 48.0% believed antibiotics were necessary for treatment ( 37 ). This issue of antibiotic misuse in malaria treatment is not confined to Nigeria. For instance, 42% of malaria patients received unnecessary antibiotic prescriptions in Uganda ( 38 ), while antibiotics were frequently used for non-bacterial infections, including malaria in India ( 39 ). In Japan, similar patterns of antibiotic misuse have been documented ( 40 ). Knowledge, awareness, and attitudes towards antibiotic use High levels of antibiotic awareness among Nigerian undergraduates, as observed in this study, reflect trends reported in similar populations both within and outside Nigeria. Previous research involving pharmacy students, non-healthcare students, and university residents has consistently reported moderate to high awareness, though often alongside significant misconceptions about proper use and indications ( 23 , 35 , 36 ). In this study, the near-universal recognition of commonly used antibiotics such as Amoxicillin (87.6%) and Ciprofloxacin (73.1%) is encouraging. However, the misidentification of non-antibiotics like Paracetamol (16.4%), ibuprofen (14.4%), and Chloroquine (27.6%) echoes findings from other studies, indicating persistent gaps in basic knowledge of antibiotics ( 23 , 35 ). Although the majority of participants in our study recognized that antibiotics treat bacterial infections (94.6%), many respondents mistakenly believed they are effective against fungal (20.4%), parasitic (17%), or viral infections (20.6%). A consistent concern across multiple studies is the belief that antibiotics are general-purpose remedies, used even when not medically indicated. This includes common use for conditions such as malaria, colds, and flu — illnesses not typically caused by bacteria ( 23 , 34 – 36 ). Such misuse is often rooted in limited pharmacological understanding and a failure to appreciate antibiotic specificity, despite high self-reported awareness. Even among final-year pharmacy students in northern Nigeria, less than half felt confident that their knowledge of antibiotic use and antimicrobial resistance (AMR) was sufficient for their future roles ( 35 ). These findings suggest that awareness, while necessary, does not guarantee accurate application. The issue of informal and unregulated antibiotic access further complicates responsible use. Our study reinforces previous reports that antibiotics are frequently obtained without prescriptions, with students relying on unlicensed outlets or advice from non-professionals ( 36 ). In Zambia, 76.7% of university students admitted to self-medicating with antibiotics without proper guidance( 23 ). These patterns reflect broader regulatory gaps and ingrained individual behaviours that treat antibiotics as routine medication. Attitudes captured in this study — such as the belief that antibiotics are always needed when sick — mirror earlier findings from both healthcare and non-healthcare contexts. For instance, Nigerian healthcare workers have acknowledged their role in AMR but still prescribe antibiotics unnecessarily for viral infections like sore throats and measles, often \"to be on the safe side.\"( 34 ). Likewise, many pharmacy students have expressed a desire for further training, reflecting self-perceived inadequacies despite exposure to relevant content ( 35 ). This dissonance between knowledge and attitude underscores the complexity of antibiotic misuse, particularly in malaria treatment, where misconceptions remain prevalent. Knowledge and Awareness of Antimicrobial Resistance (AMR) In this study, 84.7% of participants reported awareness of AMR and its consequences, while 83.4% correctly identified that misuse of antibiotics contributes to resistance. Despite these figures, the continued use of antibiotics for treating malaria illustrates a recurring pattern: awareness does not consistently translate into responsible behaviour ( 41 ). This aligns with existing literature where awareness levels, though high, coexist with poor antibiotic practices ( 23 , 35 ). Similar trends have been observed among healthcare professionals, who, despite recognising AMR as a global threat, frequently engage in inappropriate prescribing ( 34 , 42 ). Among student populations, both medical and non-medical, awareness of AMR often fails to lead to rational antibiotics use, revealing persistent knowledge-practice gaps ( 43 – 45 ). While educational interventions have improved awareness to some extent, misconceptions remain, limiting their effectiveness ( 45 ). System-level constraints further complicate AMR management. Inadequate laboratory facilities, high costs of testing, and limited access to rapid diagnostics encourage empirical antibiotic use ( 18 , 34 ). For example, medical laboratory scientists in Nigeria often face obstacles in performing susceptibility testing, reducing the ability to make evidence-based prescribing decisions ( 18 ). These infrastructural limitations partly explain why even informed individuals may engage in antibiotic misuse. In addition, public misunderstanding also plays a role. Many remain unaware that antibiotics do not treat viral infections and that improper use worsens resistance ( 23 , 32 , 36 ). The widespread availability of antibiotics through informal providers — including patent medicine vendors and unregulated sellers — perpetuates this problem ( 32 , 36 ), highlighting regulatory and public health communication deficits. Taken together, the findings from this study and broader literature reflect a consistent pattern: high awareness of antibiotics and AMR does not automatically translate into proper use. Misconceptions about malaria treatment, unregulated access, and structural barriers all contribute to this disconnect, emphasising that knowledge alone is insufficient to ensure rational antibiotic use. This study has some limitations. The selection of participants from universities with active AMR clubs may limit generalizability, as it excludes non-student populations, rural communities, younger adolescents, and older adults who might have different antibiotic use behaviours. However, the persistence of inappropriate antibiotic use even among this presumably informed group underscores that awareness alone does not guarantee proper practice. Despite these limitations, the study highlights critical gaps between knowledge and behaviour, emphasizing the urgent need for targeted, context-specific interventions. Conclusions This study presents information on antibiotic misuse specifically for malaria treatment among Nigerian university undergraduates across various Universities and geographical zones. Our findings indicate that malaria remains a significant health challenge, and the misuse of antibiotics in malaria treatment is prevalent among the surveyed students. There were high levels of awareness among the participants, but the misidentification of non-antibiotics highlights persistent gaps in basic knowledge of antibiotics. Also, majority of the participants had high awareness of AMR and its consequences and correctly identified that misuse of antibiotics contributes to resistance. However, the continued use of antibiotics by nearly half of the students for treating malaria illustrates that awareness does not consistently translate into responsible behaviour. Diagnostic challenges, financial constraints, and lack of regulatory mechanisms are known to exacerbate the misuse of antibiotics in malaria therapy. Therefore, there is a need for increased investments in laboratory infrastructure, targeted education and awareness campaigns to address these gaps, and promote appropriate malaria treatment practices. Declarations Author contributions VEA : Conceptualisation, Formal analysis, Project administration, Writing—review & editing. USN, AH, IO, AEO, HYA : Data curation (investigation). AH, OSU : Writing—original draft, Writing—review & editing. MOA : Conceptualisation, Writing—review & editing. JO, UE : Methodology, Writing—review & editing. Funding This research did not receive any funding from any organisation or funding body. Transparency declarations None to declare. Data availability All data generated or analysed during this study are included in this published article [and its supplementary information files]. Ethics approval Ethical approval for the study was obtained from the Research Ethics Committee of the Enugu State Ministry of Health ( Approval number: MH/MSD/REC21/747). Clinical trial number: not applicable. Informed consent and consent for publication Informed consent was obtained from all participants, which included the publication of data collated in anonymised form. The study complied with all ethical regulations outlined in the approval letter and the Helsinki declaration for human research. 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Risk Manag Healthc Policy. 2023;16:1187–201. Degarege A, Fennie K, Degarege D, Chennupati S, Madhivanan P. Improving socioeconomic status may reduce the burden of malaria in sub Saharan Africa: A systematic review and meta-analysis. Carvalho LH, editor. PLOS ONE. 2019;14(1):e0211205. El-Houderi A, Constantin J, Castelnuovo E, Sauboin C. Economic and Resource Use Associated With Management of Malaria in Children Aged < 5 Years in Sub-Saharan Africa: A Systematic Literature Review. MDM Policy Pract. 2019;4(2):238146831989398. Laia Cirera C, Sacoor, Meremikwu M, Ranaivo L, Manun’Ebo MF, Dachi A, et al. The economic costs of malaria in pregnancy: evidence from four sub-Saharan countries. Gates Open Res. 2023;7:47–47. Rouamba T, Samadoulougou S, Compaoré CS, Tinto H, Gaudart J, Kirakoya-Samadoulougou F. How to Estimate Optimal Malaria Readiness Indicators at Health-District Level: Findings from the Burkina Faso Service Availability and Readiness Assessment (SARA) Data. Int J Environ Res Public Health. 2020;17(11):3923. www.who.int [Internet]. 2022. Updated WHO recommendations for malaria chemoprevention among children and pregnant women. Available from: https://www.who.int/news/item/03-06-2022-Updated-WHO-recommendations-for-malaria-chemoprevention-among-children-and-pregnant-women Lyu HN, Ma N, Meng Y, Zhang X, Wong YK, Xu C, et al. Study towards improving artemisinin-based combination therapies. Nat Prod Rep. 2021;38(7):1243–50. Li J, Haragakiza J, Docile, Fisher D, Khrystyna Pronyuk ZL. Current Status of Malaria Control and Elimination in Africa: Epidemiology, Diagnosis, Treatment, Progress and Challenges. J Epidemiol Glob Health. 2024;14:561–79. Kotepui M, Kotepui KU, Milanez GDJ, Masangkay FR. Prevalence of and risk factors for severe malaria caused by Plasmodium and dengue virus co-infection: a systematic review and meta-analysis. Infect Dis Poverty. 2020;9(1). Hooft AM, Ndenga B, Mutuku F, Otuka V, Ronga C, Chebii PK, et al. High Frequency of Antibiotic Prescription in Children With Undifferentiated Febrile Illness in Kenya. Clin Infect Dis. 2020;73(7):e2399–406. Odikamnoro OO, Ikeh IM, Okoh FN, Ebiriekwe SC, Nnadozie IA, Nkwuda JO, INCIDENCE OF MALARIA/TYPHOID CO-INFECTION AMONG ADULT POPULATION IN UNWANA COMMUNITY, et al. AFIKPO NORTH LOCAL GOVERNMENT AREA, EBONYI STATE, SOUTHEASTERN NIGERIA. Afr J Infect Dis. 2017;12(1):33–8. Akinola O, Dartmouth Digital C. 2024 [cited 2024 Jan 1]. Dispelling the Malaria-Typhoid Co-infection Myth in Nigeria. A Literature Review. Available from: https://digitalcommons.dartmouth.edu/dlstaffpubs/44/ Huang S, Eze UA. Awareness and Knowledge of Antimicrobial Resistance, Antimicrobial Stewardship and Barriers to Implementing Antimicrobial Susceptibility Testing among Medical Laboratory Scientists in Nigeria: A Cross-Sectional Study. Antibiot Basel Switz. 2023;12(5):815. Antimicrobial Resistance Collaborators. Global Burden of Bacterial Antimicrobial Resistance in 2019: A Systematic Analysis. Lancet. 2022;399(10325):629–55. Kariuki S, Kering K, Wairimu C, Onsare R, Mbae C. Antimicrobial Resistance Rates and Surveillance in Sub-Saharan Africa: Where Are We Now? Infect Drug Resist. 2022;15:3589–609. Ayukekbong JA, Ntemgwa M, Atabe AN. The threat of antimicrobial resistance in developing countries: causes and control strategies. Antimicrob Resist Infect Control [Internet]. 2017;6(1). Available from: https://aricjournal.biomedcentral.com/articles/ 10.1186/s13756-017-0208-x Iheanacho CO, Eze UIH. Antimicrobial resistance in Nigeria: challenges and charting the way forward. Eur J Hosp Pharm. 2022;29(2):119–119. Mudenda S, Chisha P, Chabalenge B, Daka V, Mfune RL, Kasanga M, et al. Antimicrobial stewardship: knowledge, attitudes and practices regarding antimicrobial use and resistance among non-healthcare students at the University of Zambia. JAC-Antimicrob Resist. 2023;5(6):dlad116. Imade E. Ben Jesuorsemwen Enagbonma, Brenda Osayi Isichei-Ukah, Etinosa Igbinosa. Curbing the menace of antimicrobial resistance in Nigeria: exploring social action approaches. Niger Health J. 2024;24(2):1178–88. World Health Organization. Antibiotic resistance: multi-country public awareness survey [Internet]. World Health Organization. 2015. (apps.who.int). Available from: https://apps.who.int/iris/handle/10665/194460 Chimezie RO. Malaria Hyperendemicity: The Burden and Obstacles to Eradication in Nigeria. J Biosci Med. 2020;08(11):165–78. Bassi PU, Osakwe AI, Builders M, Ettebong E, Kola G, Binga B, et al. Prevalence and determinants of self-medication practices among Nigerians. Afr J Health Sci. 2021;34(5):634–49. Venkatesan P. The 2023 WHO World malaria report. Lancet Microbe. 2024;5(3). Tuan NHPPQ, Hoang NT, Thi T, Van Ly H et al. Concomitant Bacteremia in Adults With Severe Falciparum Malaria. Clin Infect Dis [Internet]. 2020 [cited 2023 Jan 1]; Available from: https://academic.oup.com/cid/article/71/9/e465/5763096 Ashley EA, Poespoprodjo JR. Treatment and prevention of malaria in children. Lancet Child Adolesc Health. 2020;4(10):775–89. Baraka V, Nhama A, Aíde P, Quique Bassat DA, Samwel, Gesase et al. Prescription patterns and compliance with World Health Organization recommendations for the management of uncomplicated and severe malaria: A prospective, real-world study in sub-Saharan Africa. Malar J. 2023;22(1). Fuller WL, Aboderin AO, Yahaya A, Adeyemo AT, Gahimbare L, Kapona O, et al. Gaps in the implementation of national core elements for sustainable antimicrobial use in the WHO-African region. Front Antibiot. 2022;1:1047565. Atobatele S, Omeje O, Ayodeji O, Oisagbai F, Sampson S. Situational Analysis of Access to Essential Healthcare Services in Nigeria: Implication for Trans-Sectorial Policy Considerations in Addressing Health Inequities. Health (N Y). 2022;14(05):553–75. Chukwu EE, Oladele DA, Enwuru CA, Gogwan PL, Abuh D, Audu RA, et al. Antimicrobial resistance awareness and antibiotic prescribing behavior among healthcare workers in Nigeria: a national survey. BMC Infect Dis. 2021;21(1):22. Abdu-Aguye SN, Barde KG, Yusuf H, Basira Kankia Lawal, Shehu A, Mohammed E. Investigating Knowledge of Antibiotics, Antimicrobial Resistance and Antimicrobial Stewardship Concepts Among Final Year Undergraduate Pharmacy Students in Northern Nigeria. Integr Pharm Res Pract. 2022;11:187–95. Ajibola O, Omisakin OA, Eze AA, Omoleke SA. Self-Medication with Antibiotics, Attitude and Knowledge of Antibiotic Resistance among Community Residents and Undergraduate Students in Northwest Nigeria. Dis Basel Switz. 2018;6(2):32. Abdulmuminu Isah AB, Aina K, Ben-Umeh CA, Onyekwum, Egbuemike CC et al. Chiemekam Samuel Ezechukwu,. Assessment of public knowledge and attitude toward antibiotics use and resistance: a community pharmacy-based survey. J Pharm Policy Pract. 2023;16(1). Means AR, Weaver MR, Burnett SM, Mbonye MK, Naikoba S, McClelland RS. Correlates of Inappropriate Prescribing of Antibiotics to Patients with Malaria in Uganda. Diemert DJ, editor. PLoS ONE. 2014;9(2):e90179. Landstedt K, Sharma A, Johansson F, Stålsby Lundborg C, Sharma M. Antibiotic prescriptions for inpatients having non-bacterial diagnosis at medicine departments of two private sector hospitals in Madhya Pradesh, India: a cross-sectional study. BMJ Open. 2017;7(4):e012974. Kimura Y, Fukuda H, Hayakawa K, Ide S, Ota M, Saito S, et al. Longitudinal trends of and factors associated with inappropriate antibiotic prescribing for non-bacterial acute respiratory tract infection in Japan: A retrospective claims database study, 2012–2017. PLoS ONE. 2019;14(10):e0223835–0223835. Rana S, Karuna N, Kaur P, Narad, Walia K, Saeed S, Chandra A et al. Knowledge, attitudes, and practices of antimicrobial resistance awareness among healthcare workers in India: a systematic review. Front Public Health. 2024;12. Adesina OO, Onyebuchi CE, Adesina OA. ECronicon. 2024 [cited 2025 Jan 1]. A Platform for Scientific Journals and Research Publications. Available from: https://ecronicon.net/eccmc/effect-of-typhoid-fever-misdiagnosis-on-febrile-patients-in-ogun-state-nigeria Akande-Sholabi W, Ajamu AT. Antimicrobial stewardship: Assessment of knowledge, awareness of antimicrobial resistance, and appropriate antibiotic use among healthcare students in a Nigerian University. BMC Med Educ 2021 Sept 10;21(1):488. Scaioli G, Gualano MR, Gili R, Masucci S, Bert F, Siliquini R. Antibiotic Use: A Cross-Sectional Survey Assessing the Knowledge, Attitudes and Practices amongst Students of a School of Medicine in Italy. Manzoli L, editor. PLOS ONE. 2015;10(4):e0122476. Edidiong Orok F, Ikpe, Williams T, Inimuvie E. Impact of educational intervention on knowledge of antimicrobial resistance and antibiotic use patterns among healthcare students: a pre- and post-intervention study. BMC Med Educ. 2025;25(1). Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile1Researchquestionnaire.pdf Supplementaryfile2AdditionalAnalysis.pdf Cite Share Download PDF Status: Published Journal Publication published 27 Oct, 2025 Read the published version in Discover Public Health → Version 1 posted Editorial decision: Revision requested 29 Sep, 2025 Reviews received at journal 25 Sep, 2025 Reviews received at journal 18 Sep, 2025 Reviewers agreed at journal 14 Sep, 2025 Reviewers agreed at journal 09 Sep, 2025 Reviewers invited by journal 03 Sep, 2025 Editor invited by journal 01 Sep, 2025 Editor assigned by journal 30 Aug, 2025 Submission checks completed at journal 30 Aug, 2025 First submitted to journal 23 Aug, 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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A considerable rise in the number of malaria cases in the African region was reported post-COVID-19 pandemic era, increasing from 218\\u0026nbsp;million cases in 2019 to 233\\u0026nbsp;million in 2022(\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e). This region bears a disproportionate share of the global malaria burden, with the WHO estimating that in 2022, 94% and 95% of all malaria cases and deaths, respectively, occurred in the African continent(\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e). Within the African continent, the sub-Saharan region accounts for almost half of the cases worldwide, with Mozambique, Uganda, and the Democratic Republic of Congo making up 4.2%, 5.1% and 12.3% of the total estimate, respectively. Similarly, Tanzania, Niger, and the Democratic Republic of Congo accounted for 4.4%, 5.6% and 11.6% of global malaria deaths, respectively(\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e). In Nigeria, the situation is particularly severe, with the country accounting for approximately 27% of all malaria cases and 31.1% of mortality attributed to malaria globally as of 2022(\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003ePlasmodium falciparum\\u003c/em\\u003e (\\u003cem\\u003eP. falciparum\\u003c/em\\u003e) is the predominant malaria parasite species in Sub-Saharan Africa, accounting for most infections(\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). Compared to other species, \\u003cem\\u003eP. falciparum\\u003c/em\\u003e is associated with severe disease and death, especially among vulnerable populations such as children under five years of age and pregnant women(\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e). The high transmission rates, particularly in Sub-Saharan Africa, are attributed to favourable climatic conditions for mosquito vectors, primarily \\u003cem\\u003eAnopheles gambiae\\u003c/em\\u003e, and limited access to effective prevention and treatment measures(\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e). Despite notable progress in malaria control over the past two decades, the disease continues to exact a heavy toll in this region (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). According to Shi et al. (2023), children under five years of age are particularly vulnerable, accounting for 67% of all malaria deaths worldwide, while malaria in pregnancy has also been linked with adverse outcomes such as maternal anaemia, low birth weight, and increased infant mortality(\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe economic burden of malaria in Sub-Saharan Africa is substantial. Direct costs, such as expenses for prevention, diagnosis, and treatment, and indirect costs comprising lost productivity due to illness and premature death, exert a huge burden on an already disadvantaged population(\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e). Among children, the disease impedes economic development by affecting school attendance, while in adults, the effects include a reduction in workforce productivity and discouraging foreign investment and tourism(\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e). Furthermore, it is estimated that malaria costs African economies billions of dollars annually in lost GDP(\\u003cspan additionalcitationids=\\\"CR8\\\" citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e). Unfortunately, many affected populations in this region have inadequate access to prompt, accurate diagnosis and effective treatment, particularly in rural and remote areas, where it is further compounded by socio-economic factors such as poverty, poor housing conditions and limited education that contribute to increased malaria risk and hamper prevention efforts (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe standard treatment protocol for malaria includes artemisinin-based combination therapies (ACTs), while more serious cases are managed using injectable artesunate or artemether(\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e). ACTs have become the most important medication for malaria treatment worldwide, particularly for uncomplicated \\u003cem\\u003eP. falciparum\\u003c/em\\u003e malaria. The WHO recommended ACTs as the first-line treatment due to their high efficacy, rapid action, and ability to slow the development of drug resistance(\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). Artemether-lumefantrine (AL) and Artesunate-amodiaquine (ASAQ) are two of the most widely used ACTs in Sub-Saharan Africa. Both combinations have proven highly effective in treating uncomplicated P. falciparum malaria and are recommended by the WHO as first-line treatments(\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). These ACTs have played a crucial role in reducing malaria morbidity and mortality across the region. Their widespread adoption has been facilitated by their efficacy, relatively good safety profiles, and availability as fixed-dose combinations, which improve treatment adherence and reduce the risk of using monotherapies(\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eAlthough antibiotics are not part of recommended malaria treatment, they are sometimes prescribed\\u0026mdash;often inappropriately\\u0026mdash;when bacterial infections are assumed to coexist or when malaria is wrongly diagnosed (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e). In Nigeria, there is a common belief in \\u0026lsquo;malaria-typhoid co-infections\\u0026rsquo;, which, combined with limited access to proper diagnostics, has led to the frequent misuse of antibiotics (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e). This misuse of antibiotics in the empirical treatment of \\u0026lsquo;malaria-typhoid co-infections\\u0026rsquo; adds to the already serious problem of antimicrobial resistance (AMR), a challenge that has gained global attention. Antimicrobial resistance (AMR) represents a significant global health issue that jeopardises the effectiveness of antibiotics, the advancements in modern medicine, and the health of populations across the globe (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e). In 2019, AMR was linked to more than 1.27\\u0026nbsp;million fatalities globally, surpassing the cumulative mortality rates associated with malaria and AIDS(\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e). West sub-Saharan Africa bears the greatest burden of AMR, with a mortality rate of 27.3 in every 100,000 deaths attributable to AMR(\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e). According to the WHO and other recent studies, sub-Saharan Africa currently experiences the highest death rates linked to drug-resistant infections (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eIn Nigeria, the situation is worsened by factors such as self-medication, over-the-counter access to antibiotics without prescriptions, and a general lack of reliable diagnostic services(\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e). In addition, the supervision of prescriptions remains inadequate, resulting in the routine sales of prescription medications, including antimicrobials and antimalarials, over the counter in pharmacies and by vendors of patent proprietary medicines (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e). Enhancing public awareness and understanding of antimicrobial resistance (AMR) among healthcare providers, policymakers, agricultural professionals, and the public remains a cornerstone of both global and national strategies to combat AMR and curb its spread(\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e). In addition, it is necessary to increase investments in laboratory infrastructure and manpower training in Nigeria to ensure appropriate diagnostic testing and widespread antibiotics susceptibility testing in hospitals, as these will decrease empirical treatment and the misuse of antibiotics (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eTherefore, this study investigated how antibiotics are used in the context of malaria treatment among Nigerian undergraduate university students and pharmacists. It examined the spread of this practice, the driving factors, and the associated public health risks. By evaluating the prescribing habits, patient perceptions, and knowledge of antibiotic stewardship, the research hopes to support more focused strategies to reduce misuse and help tackle AMR.\\u003c/p\\u003e\"},{\"header\":\"METHODS\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStudy Design and Sites\\u003c/h2\\u003e\\u003cp\\u003eThis descriptive cross-sectional study employed a structured questionnaire to obtain data from undergraduate students enrolled in Nigerian universities. To ensure geographical representation, universities were purposively selected across Nigeria\\u0026rsquo;s six geopolitical zones. Two universities were chosen from each zone, yielding a total of 12 participating institutions:\\u003c/p\\u003e\\u003cp\\u003e\\u003cul\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eNorth East\\u003c/b\\u003e: Abubakar Tafawa Balewa University and University of Maiduguri\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eNorth West\\u003c/b\\u003e: Ahmadu Bello University and Federal University Dutsin-Ma\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eNorth Central\\u003c/b\\u003e: University of Abuja and University of Jos\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eSouth East\\u003c/b\\u003e: University of Nigeria and Enugu State University of Technology\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eSouth South\\u003c/b\\u003e: University of Uyo and University of Calabar\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eSouth West\\u003c/b\\u003e: University of Ibadan and Oduduwa University\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/ul\\u003e\\u003c/p\\u003e\\u003cp\\u003eWithin these institutions, the questionnaire was distributed online via WhatsApp and other social media platforms. Convenience sampling was used, as participation was voluntary.\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eStudy Participants and Eligibility Criteria\\u003c/h3\\u003e\\n\\u003cp\\u003eUndergraduate students were eligible to participate if they:\\u003c/p\\u003e\\u003cp\\u003e\\u003cul\\u003e\\u003cli\\u003e\\u003cp\\u003eWere enrolled in any of the 12 selected universities,\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eCould read and write in English, and\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eProvided informed consent to participate.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/ul\\u003e\\u003c/p\\u003e\\u003cp\\u003eStudents who did not meet all these criteria were excluded.\\u003c/p\\u003e\\n\\u003ch3\\u003eSample Size Determination\\u003c/h3\\u003e\\n\\u003cp\\u003eThe minimum sample size was calculated using the single proportion formula n\\u0026thinsp;=\\u0026thinsp;Z2P(1\\u0026thinsp;\\u0026minus;\\u0026thinsp;P)/d2n\\u0026thinsp;=\\u0026thinsp;Z^2P(1-P)/d^2n\\u0026thinsp;=\\u0026thinsp;Z2P(1\\u0026thinsp;\\u0026minus;\\u0026thinsp;P)/d2. At a 95% confidence level, Z\\u0026thinsp;=\\u0026thinsp;1.96; expected prevalence (P) was set at 50% to maximise variability; and margin of error (d) was fixed at 0.05. This gave a minimum sample size of 384 respondents. To account for potential non-response, 10% was added, yielding a final target sample size of 422 undergraduate students.\\u003c/p\\u003e\\n\\u003ch3\\u003eData Collection and Representation\\u003c/h3\\u003e\\n\\u003cp\\u003eWhile convenience sampling was employed due to the online distribution method, efforts were made to reduce bias by sharing the questionnaire across multiple student groups and platforms in each university. This approach aimed to capture a wide range of respondents across different faculties and levels of study, ensuring diversity while acknowledging the limitations of self-selection. The final sample included all students who responded during the data collection window, providing sufficient data for descriptive and statistical analysis of the study objectives.\\u003c/p\\u003e\\n\\u003ch3\\u003eStudy Instrument\\u003c/h3\\u003e\\n\\u003cp\\u003eThe study instrument consisted of a pre-tested and validated self-administered online questionnaire (\\u003cb\\u003eSupplementary file 1\\u003c/b\\u003e), which was adapted from the World Health Organization's 2015 'Antibiotic Resistance: Multi-Country Public Awareness Survey' (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e) and modified to address antibiotic use in malaria therapy among Nigerian university students. Additional questions were incorporated to reflect the local context and study objectives. The modified questionnaire was pre-tested using 20 pharmacy students randomly selected from the University of Nigeria (UNN) Enugu Campus, and the feedback comments were used to fine-tune, adapt, and modify the structure of the questionnaire.\\u003c/p\\u003e\\u003cp\\u003eThe questionnaire consisted of five sections (A\\u0026ndash;E):\\u003c/p\\u003e\\u003cp\\u003e\\u003cul\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eSection A\\u003c/b\\u003e: Collected demographic data, including gender, age group, and geopolitical zone.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eSection B\\u003c/b\\u003e: Assessed respondents' knowledge of antibiotics, including recognition of antibiotics, their uses, and awareness of antimicrobial resistance (AMR).\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eSection C\\u003c/b\\u003e: Evaluated respondents' attitudes toward antibiotic use\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eSection D\\u003c/b\\u003e: Examined respondents' practices related to malaria therapy, including malaria treatment history and use of antibiotics in malaria treatment.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eSection E\\u003c/b\\u003e: Assessed awareness and understanding of antimicrobial resistance (AMR) and its link to antibiotic misuse.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/ul\\u003e\\u003c/p\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eData Collection\\u003c/h2\\u003e\\u003cp\\u003eThe survey questionnaire was administered via Google Forms and distributed through WhatsApp class groups and other social media platforms across the selected universities. The online questionnaire provided a summary of study information, screening questions to verify eligibility, and participant consent to participate in the study. Participation was voluntary, and submissions were anonymous. Participants were also informed that they can withdraw at any time before submitting the questionnaire, as submitted responses cannot be identified or removed due to the anonymity of the questionnaire design. Data collection was conducted over one month, from February 22nd to March 31st, 2025.\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eEthical Considerations\\u003c/h3\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical approval\\u003c/strong\\u003e\\u003cp\\u003efor the study was obtained from the Research Ethics Committee of the Enugu State Ministry of Health (\\u003cb\\u003eApproval number\\u003c/b\\u003e: MH/MSD/REC21/747) before the commencement of the study. No identifiable data was collected, and all information was securely handled and used exclusively for research purposes. Informed consent was obtained from all participants which included the publication of data collated in anonymised form. The study complied with all ethical regulations outlined in the approval letter and the Helsinki declaration for human research.\\u003c/p\\u003e\\u003c/p\\u003e\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eData Analysis\\u003c/h2\\u003e\\u003cp\\u003eData collected were exported in Microsoft Excel spreadsheet (Microsoft Office 2016) for analysis. IBM SPSS version 26 was employed to further code, clean, and analyse the data. The data was summarised using descriptive statistics, which included frequencies, percentages, means, and standard deviations.\\u003c/p\\u003e\\u003cp\\u003eThe chi-square test was used to assess associations between categorical socio-demographic variables (such as age group, gender, and geopolitical zone) and awareness of antibiotics. Binary logistic regression was conducted to identify predictors of antibiotic resistance knowledge. Additionally, Spearman correlation analysis was performed to examine the relationship between beliefs and practices regarding the use of antibiotics in the treatment of malaria. A p-value of \\u0026lt;\\u0026thinsp;0.05 was considered statistically significant.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"RESULTS\",\"content\":\"\\u003cp\\u003eA minimum sample size of 422 was estimated, but a total of 646 undergraduate students participated in the study, giving a response rate above the calculated requirement. Although responses were obtained from all six geopolitical zones, the distribution was not balanced across regions.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eSocio-demographic characteristics of participants\\u003c/h2\\u003e\\u003cp\\u003eThe socio-demographic characteristics of 646 study participants, stratified by their awareness of antibiotics, are presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. Most participants were aged 18\\u0026ndash;24 years (71.7%), with high antibiotic awareness across all age groups: 98.7% in both the 18\\u0026ndash;24 and 25\\u0026ndash;30 age groups, and 90.9% among those above 30 years of age. A statistically significant difference was observed (p\\u0026thinsp;=\\u0026thinsp;0.014), though awareness remained high across all groups. In terms of gender, awareness was high among both males (97.8%) and females (99%), but the difference was not statistically significant \\u003cem\\u003e(\\u003c/em\\u003ep\\u0026thinsp;=\\u0026thinsp;0.176), indicating that gender did not significantly influence antibiotic awareness (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e\\u003cb\\u003e)\\u003c/b\\u003e.\\u003c/p\\u003e\\u003cp\\u003eAntibiotic awareness was high across all geopolitical zones, with the lowest awareness observed in the Northwest (93%) and the highest in the Southeast and South-south (100%). A statistically significant difference was found between geographical zones (p\\u0026thinsp;=\\u0026thinsp;0.001), though the overall variation in awareness levels remains small (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e\\u003cb\\u003e)\\u003c/b\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003e\\u0026ndash; SOCIO-DEMOGRAPHIC CHARACTERISTICS OF STUDY PARTICIPANTS (N\\u0026thinsp;=\\u0026thinsp;646) BY ANTIBIOTICS AWARENESS\\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=\\\"char\\\" char=\\\".\\\" 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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\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\\u003eFrequency (%)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eAntibiotic awareness (%)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003ep-values\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAge group\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.014\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; 18\\u0026ndash;24\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e463 (71.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e457 (98.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; 25\\u0026ndash;30\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e161 (24.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e159 (98.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; Above 30\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e22 (3.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e20 (90.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGender\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.176\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; Male\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e315 (48.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e308 (97.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; Female\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e331 (51.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e328 (99)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGeopolitical zone\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; North Central\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e145 (22.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e144 (99)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; North East\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e69 (10.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e68 (99)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; North West\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e102 (15.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e95 (93)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; South East\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e165 (25.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e165 (100)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; South South\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e85 (13.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e85 (100)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; South West\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e80 (12.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e79 (98.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e\\u003c/h2\\u003e\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003eKnowledge, perception, and attitudes towards antibiotics\\u003c/h2\\u003e\\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e illustrate key insights into Nigerian undergraduates' knowledge and perception regarding antibiotics, including common misconceptions. While most participants correctly identified Amoxicillin (87.16%) and Ciprofloxacin (73.1%) as antibiotics, a considerable number of participants misclassified drugs like Chloroquine (27.6%), Paracetamol (16.4%), and Ibuprofen (14.4%) as antibiotics, indicating notable gaps in understanding. Similarly, although the majority recognized that antibiotics treat bacterial infections (94.6%), many respondents mistakenly believed they are effective against fungal (20.4%), parasitic (17%), or viral infections (20.6%) infections as shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e presents data on attitudes and access-related behaviours. Attitudes varied regarding the belief that antibiotics are always needed when sick, with 33.6% disagreeing and 21.4% agreeing. Furthermore, access to antibiotics was not always prescription-based (49.5%) and 35.2% of participants agreed that antibiotics are always needed when sick, suggesting opportunities for misuse.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003e\\u0026ndash; STUDY PARTICIPANTS\\u0026rsquo; KNOWLEDGE, PERCEPTION, AND ATTITUDE TOWARDS ANTIBIOTICS\\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\\u003eWhere do you usually get your antibiotics from?\\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\\u003e- \\u0026nbsp; Pharmacist with prescription\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e484 (74.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; Pharmacist without prescription\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e207 (32.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; Leftovers from previous prescriptions\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e57 (8.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; Friends/Family\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e56 (8.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAre antibiotics always needed when you are sick?\\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- \\u0026nbsp; Agree\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e138 (21.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; Disagree\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e217 (33.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; Neutral\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e96 (14.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; Strongly Agree\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e89 (13.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; Strongly Disagree\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e106 (16.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e\\u003c/h2\\u003e\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003eKnowledge, perception and attitude of antibiotics use in malaria treatment\\u003c/h2\\u003e\\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e explores Nigerian undergraduates' knowledge, perceptions, and attitudes toward using antibiotics for malaria treatment. Over half of the participants (57.1%) reported treating malaria with a doctor\\u0026rsquo;s prescription, while 35.2% relied on over-the-counter (OTC) self-medication, and 7% used herbal remedies. Notably, 43.7% incorrectly believed antibiotics work against malaria, while 38.1% correctly stated they do not, and 18.3% were unsure. This misconception is reflected in the finding that 49.1% admitted to using antibiotics for malaria treatment, highlighting a concerning misuse of antibiotics for a condition they cannot treat.\\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\\u003e\\u0026ndash; STUDY PARTICIPANTS' (NIGERIAN UNDERGRADUATES) KNOWLEDGE, PERCEPTION, AND ATTITUDE OF ANTIBIOTICS TOWARDS MALARIA TREATMENT\\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\\u003eQuestions\\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\\u003eLast Malaria treatment\\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- \\u0026nbsp; Within the past month\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e197 (30.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; 3 months ago\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e212 (32.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; 4\\u0026ndash;6 months ago\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e104 (16.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; More than 6 months ago\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e126 (19.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; Never\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e7 (1.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHow did you treat the malaria?\\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- \\u0026nbsp; Chemist\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1 (0.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; Pharmacist\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4 (0.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; Herbal remedies\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e50 (7.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; Doctors\\u0026rsquo; prescription\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e406 (62.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; OTC self-medication\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e250 (38.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- Treated by a Nurse\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1 (0.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDo antibiotics work against malaria?\\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- \\u0026nbsp; Yes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e282 (43.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; No\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e246 (38.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; Not sure\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e118 (18.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHave you ever used antibiotics to treat malaria?\\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- \\u0026nbsp; Yes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e317 (49.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; No\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e329 (50.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e\\u003c/h2\\u003e\\u003cdiv id=\\\"Sec18\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003eDistribution of the use of antibiotics in malaria therapy among study participants across geographical zones\\u003c/h2\\u003e\\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e highlights the use of antibiotics for treating malaria among the participants across different geographic regions. Nearly half of the respondents (49.1%) reported using antibiotics for malaria treatment, despite the fact that antibiotics are not recommended for malaria management. The highest rates of antibiotic use were observed in the North East (62.3%) and North West (63.7%) regions, while the lowest was in the South-South (32.9%).\\u003c/p\\u003e\\u003cp\\u003eConversely, 50.9% of respondents reported not using antibiotics for malaria. The highest proportion of those who refrained from antibiotic use was in the South-South (67.1%) and North Central (60.0%) regions (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e\\u003cb\\u003e)\\u003c/b\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e\\u003c/h2\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eFactors affecting the use of antibiotics in malaria therapy among participants from various geopolitical zones\\u003c/h2\\u003e\\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e highlights the factors influencing Nigerian undergraduates' decision to use antibiotics for treating malaria. The most common reason cited was previous experience (35.4%), followed closely by advice from pharmacists (29.9%) and recommendations from friends or family (28.0%). Only a small proportion (6.7%) relied on a doctor's prescription.\\u003c/p\\u003e\\u003cp\\u003eVariation across regions was noted in the decision-making patterns. The North East (37.7%) and South West (37.5%) had the highest reliance on previous experience, while the North Central (31.7%) and North West (29.4%) showed a strong influence from friends and family. Pharmacist advice played a significant role across all regions, ranging from 28.3\\u0026ndash;36.2% (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e\\u003cb\\u003e)\\u003c/b\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eAwareness and Knowledge of antimicrobial resistance among the participants\\u003c/h2\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"BlockQuote\\\"\\u003e\\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e highlights the participants\\u0026rsquo; knowledge and understanding of antimicrobial resistance (AMR). Majority of the students (84.7%) reported being aware of AMR and its consequences, indicating a relatively high level of knowledge about this critical public health issue. Furthermore, 83.4% correctly recognized that the misuse of antibiotics contributes to AMR, whereas. a notable minority (15.3%) were unaware of AMR, and 16.6% did not believe that antibiotic misuse contributes to resistance.\\u003c/p\\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\\u003e\\u0026ndash; ANTIMICROBIAL RESISTANCE KNOWLEDGE AMONG UNDERGRADUATES IN NIGERIA.\\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\\u003eQuestions\\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\\u003eAre you aware of antimicrobial resistance (AMR) and its consequences?\\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- \\u0026nbsp; Yes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e547 (84.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; No\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e99 (15.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCan the misuse of antibiotics contribute to AMR?\\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- \\u0026nbsp; Yes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e539 (83.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e- \\u0026nbsp; No\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e107 (16.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e\\u003c/h2\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"BlockQuote\\\"\\u003e\\u003cp\\u003eNo statistically significant associations were found between AMR awareness and gender, age group, or geopolitical zone (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). Logistic regression analysis similarly indicated no significant predictors of AMR awareness. However, Spearman correlations revealed significant associations between antibiotic use for malaria and two beliefs: that antibiotics are always needed when sick (ρ\\u0026thinsp;=\\u0026thinsp;0.329, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), and that stopping antibiotics when feeling better is acceptable (ρ\\u0026thinsp;=\\u0026thinsp;0.087, p\\u0026thinsp;=\\u0026thinsp;0.026). Other factors showed no significant relationships (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e \\u003cb\\u003e\\u0026amp; Supplementary file 2\\u003c/b\\u003e).\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003e\\u0026ndash; SUMMARY OF STATISTICAL ANALYSES ON AMR AWARENESS AND ANTIBIOTIC USE FACTORS\\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=\\\"left\\\" 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=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAnalysis Type\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eVariable / Comparison\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTest Statistic\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003ep\\u003c/em\\u003e-value\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eEffect Size / Exp(B)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eSignificance Interpretation\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eChi-Square\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eGender\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eχ\\u0026sup2; (1, N\\u0026thinsp;=\\u0026thinsp;646)\\u0026thinsp;=\\u0026thinsp;0.077\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.781\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eNot significant\\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\\u003eAge Group\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eχ\\u0026sup2; (2, N\\u0026thinsp;=\\u0026thinsp;646)\\u0026thinsp;=\\u0026thinsp;2.072\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.355\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eNot significant\\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\\u003eGeopolitical Zone\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eχ\\u0026sup2; (5, N\\u0026thinsp;=\\u0026thinsp;646)\\u0026thinsp;=\\u0026thinsp;5.822\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.324\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eNot significant\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLogistic Regression\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMale vs Female\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eWald\\u0026thinsp;=\\u0026thinsp;0.095\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.758\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eExp(B)\\u0026thinsp;=\\u0026thinsp;0.931\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eNot significant\\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\\u003eAge 18\\u0026ndash;24 vs 31+\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eWald\\u0026thinsp;=\\u0026thinsp;1.742\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.187\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eExp(B)\\u0026thinsp;=\\u0026thinsp;3.939\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eNot significant\\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\\u003eAge 25\\u0026ndash;30 vs 31+\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eWald\\u0026thinsp;=\\u0026thinsp;0.010\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.922\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eExp(B)\\u0026thinsp;=\\u0026thinsp;0.974\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eNot significant\\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\\u003eGeopolitical Zones vs Southeast (all groups)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eWald\\u0026thinsp;=\\u0026thinsp;0.200\\u0026ndash;0.932\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026gt;\\u0026thinsp;0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eExp(B)\\u0026thinsp;=\\u0026thinsp;various\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eNot significant\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSpearman Correlation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eBelief\\u003c/b\\u003e: Antibiotics always needed when sick\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eρ\\u0026thinsp;=\\u0026thinsp;0.329\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eModerate positive, statistically significant\\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\\u003e\\u003cb\\u003eBelief\\u003c/b\\u003e: OK to stop when feeling better\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eρ\\u0026thinsp;=\\u0026thinsp;0.087\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.026\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eWeak positive, statistically significant\\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\\u003eComplete full course\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eρ\\u0026thinsp;=\\u0026thinsp;0.026\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.509\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eNot significant\\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\\u003eAMR awareness\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eρ = -0.041\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.299\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eNot significant\\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\\u003eBelief: misuse causes AMR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eρ = -0.008\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.844\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eNot significant\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"DISCUSSION\",\"content\":\"\\u003cdiv id=\\\"Sec24\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eUse of antibiotics in malaria therapy\\u003c/h2\\u003e\\u003cp\\u003eTo our knowledge, the current study is the first to investigate antibiotic misuse specifically for malaria treatment among Nigerian university undergraduates. Our findings indicate that malaria remains a significant health challenge, with 79.4% of participants reporting treatment within the past six months and 63.3% within the last three months. This high frequency of malaria episodes often leads to self-medication and reliance on easily accessible drugs, including antibiotics (\\u003cspan additionalcitationids=\\\"CR27\\\" citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e). Despite their ineffectiveness against malaria, nearly half (49.1%) of participants reported using antibiotics for treatment. This misuse is primarily driven by prescriptions from doctors (6.7%), advice from friends and family (28.0%), pharmacists (29.9%), and personal experience (35.4%). These findings suggest that both healthcare professionals and social networks perpetuate misconceptions about antibiotic misuse in malaria treatment. The high reliance on OTC self-medication and the widespread misuse of antibiotics underscores the need for targeted education and awareness campaigns to address these gaps and promote appropriate malaria treatment practices\\u003c/p\\u003e\\u003cp\\u003eOur study findings show that 43.7% of participants believed antibiotics work against malaria, 38.1% correctly stated they do not, and 18.3% were unsure. The misconception that antibiotics are effective for malaria treatment persists as a significant public health challenge in the tropical regions where malaria is endemic. While the WHO recommends empirical antibiotic use in children with severe malaria due to potential bacterial co-infections (\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e) this guideline applies primarily to hospitalized patients (\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e) and does not explain the widespread belief that antibiotics treat malaria. Our study participants are mostly young adults (71.7% aged 18\\u0026ndash;24 years; 24.9% aged 25\\u0026ndash;30 years), underscoring that this misconception extends beyond paediatric cases. The high rate of antibiotic misuse in our study may be attributed to a combination of diagnostic uncertainty, overlapping symptoms with other febrile illnesses, limited access to reliable laboratory tests, and widespread self-medication practices. These factors are well-documented contributors to inappropriate antibiotic use in Nigeria (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThis current study also shows that the highest rates of antibiotic use in malaria treatment were observed in the North East (62.3%) and North West (63.7%) regions, while the lowest was in the South-South region (32.9%). These regional differences may be driven by broader healthcare inequities, including disparities in healthcare access, diagnostic availability, and public health awareness (\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e). In northern regions, limited access to healthcare facilities often leads to presumptive treatment of febrile illnesses with antibiotics, a pattern that may influence students' health-seeking behaviours (\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e). In contrast, the southern regions benefit from better healthcare infrastructure and stronger public health interventions, which may contribute to lower misuse rates among students (\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e). In previous studies conducted in Northern Nigeria, 21.3% of respondents considered malaria a condition requiring antibiotics, while 14.5% of undergraduates admitted to self-medicating with antibiotics for treatment (\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e). Similarly, in urban centres like Lagos and Abuja, 55.3% of adults incorrectly attributed malaria to bacterial causes, and 48.0% believed antibiotics were necessary for treatment (\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e). This issue of antibiotic misuse in malaria treatment is not confined to Nigeria. For instance, 42% of malaria patients received unnecessary antibiotic prescriptions in Uganda (\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e), while antibiotics were frequently used for non-bacterial infections, including malaria in India (\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e). In Japan, similar patterns of antibiotic misuse have been documented (\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cdiv id=\\\"Sec25\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003eKnowledge, awareness, and attitudes towards antibiotic use\\u003c/h2\\u003e\\u003cp\\u003eHigh levels of antibiotic awareness among Nigerian undergraduates, as observed in this study, reflect trends reported in similar populations both within and outside Nigeria. Previous research involving pharmacy students, non-healthcare students, and university residents has consistently reported moderate to high awareness, though often alongside significant misconceptions about proper use and indications (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e). In this study, the near-universal recognition of commonly used antibiotics such as Amoxicillin (87.6%) and Ciprofloxacin (73.1%) is encouraging. However, the misidentification of non-antibiotics like Paracetamol (16.4%), ibuprofen (14.4%), and Chloroquine (27.6%) echoes findings from other studies, indicating persistent gaps in basic knowledge of antibiotics (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e). Although the majority of participants in our study recognized that antibiotics treat bacterial infections (94.6%), many respondents mistakenly believed they are effective against fungal (20.4%), parasitic (17%), or viral infections (20.6%).\\u003c/p\\u003e\\u003cp\\u003eA consistent concern across multiple studies is the belief that antibiotics are general-purpose remedies, used even when not medically indicated. This includes common use for conditions such as malaria, colds, and flu \\u0026mdash; illnesses not typically caused by bacteria (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR35\\\" citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e). Such misuse is often rooted in limited pharmacological understanding and a failure to appreciate antibiotic specificity, despite high self-reported awareness. Even among final-year pharmacy students in northern Nigeria, less than half felt confident that their knowledge of antibiotic use and antimicrobial resistance (AMR) was sufficient for their future roles (\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e). These findings suggest that awareness, while necessary, does not guarantee accurate application. The issue of informal and unregulated antibiotic access further complicates responsible use. Our study reinforces previous reports that antibiotics are frequently obtained without prescriptions, with students relying on unlicensed outlets or advice from non-professionals (\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e). In Zambia, 76.7% of university students admitted to self-medicating with antibiotics without proper guidance(\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e). These patterns reflect broader regulatory gaps and ingrained individual behaviours that treat antibiotics as routine medication.\\u003c/p\\u003e\\u003cp\\u003eAttitudes captured in this study \\u0026mdash; such as the belief that antibiotics are always needed when sick \\u0026mdash; mirror earlier findings from both healthcare and non-healthcare contexts. For instance, Nigerian healthcare workers have acknowledged their role in AMR but still prescribe antibiotics unnecessarily for viral infections like sore throats and measles, often \\\"to be on the safe side.\\\"(\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e). Likewise, many pharmacy students have expressed a desire for further training, reflecting self-perceived inadequacies despite exposure to relevant content (\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e). This dissonance between knowledge and attitude underscores the complexity of antibiotic misuse, particularly in malaria treatment, where misconceptions remain prevalent.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec26\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003eKnowledge and Awareness of Antimicrobial Resistance (AMR)\\u003c/h2\\u003e\\u003cp\\u003eIn this study, 84.7% of participants reported awareness of AMR and its consequences, while 83.4% correctly identified that misuse of antibiotics contributes to resistance. Despite these figures, the continued use of antibiotics for treating malaria illustrates a recurring pattern: awareness does not consistently translate into responsible behaviour (\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e). This aligns with existing literature where awareness levels, though high, coexist with poor antibiotic practices (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e). Similar trends have been observed among healthcare professionals, who, despite recognising AMR as a global threat, frequently engage in inappropriate prescribing (\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e). Among student populations, both medical and non-medical, awareness of AMR often fails to lead to rational antibiotics use, revealing persistent knowledge-practice gaps (\\u003cspan additionalcitationids=\\\"CR44\\\" citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e). While educational interventions have improved awareness to some extent, misconceptions remain, limiting their effectiveness (\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eSystem-level constraints further complicate AMR management. Inadequate laboratory facilities, high costs of testing, and limited access to rapid diagnostics encourage empirical antibiotic use (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e). For example, medical laboratory scientists in Nigeria often face obstacles in performing susceptibility testing, reducing the ability to make evidence-based prescribing decisions (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e). These infrastructural limitations partly explain why even informed individuals may engage in antibiotic misuse. In addition, public misunderstanding also plays a role. Many remain unaware that antibiotics do not treat viral infections and that improper use worsens resistance (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e). The widespread availability of antibiotics through informal providers \\u0026mdash; including patent medicine vendors and unregulated sellers \\u0026mdash; perpetuates this problem (\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e), highlighting regulatory and public health communication deficits.\\u003c/p\\u003e\\u003cp\\u003eTaken together, the findings from this study and broader literature reflect a consistent pattern: high awareness of antibiotics and AMR does not automatically translate into proper use. Misconceptions about malaria treatment, unregulated access, and structural barriers all contribute to this disconnect, emphasising that knowledge alone is insufficient to ensure rational antibiotic use.\\u003c/p\\u003e\\u003cp\\u003eThis study has some limitations. The selection of participants from universities with active AMR clubs may limit generalizability, as it excludes non-student populations, rural communities, younger adolescents, and older adults who might have different antibiotic use behaviours. However, the persistence of inappropriate antibiotic use even among this presumably informed group underscores that awareness alone does not guarantee proper practice. Despite these limitations, the study highlights critical gaps between knowledge and behaviour, emphasizing the urgent need for targeted, context-specific interventions.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eThis study presents information on antibiotic misuse specifically for malaria treatment among Nigerian university undergraduates across various Universities and geographical zones. Our findings indicate that malaria remains a significant health challenge, and the misuse of antibiotics in malaria treatment is prevalent among the surveyed students. There were high levels of awareness among the participants, but the misidentification of non-antibiotics highlights persistent gaps in basic knowledge of antibiotics. Also, majority of the participants had high awareness of AMR and its consequences and correctly identified that misuse of antibiotics contributes to resistance. However, the continued use of antibiotics by nearly half of the students for treating malaria illustrates that awareness does not consistently translate into responsible behaviour. Diagnostic challenges, financial constraints, and lack of regulatory mechanisms are known to exacerbate the misuse of antibiotics in malaria therapy. Therefore, there is a need for increased investments in laboratory infrastructure, targeted education and awareness campaigns to address these gaps, and promote appropriate malaria treatment practices.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAuthor contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eVEA\\u003c/strong\\u003e: Conceptualisation, Formal analysis, Project administration, Writing—review \\u0026amp; editing.\\u003cbr\\u003e\\u003cstrong\\u003eUSN, AH, IO, AEO, HYA\\u003c/strong\\u003e: Data curation (investigation).\\u003cbr\\u003e\\u003cstrong\\u003eAH, OSU\\u003c/strong\\u003e: Writing—original draft, Writing—review \\u0026amp; editing.\\u003cbr\\u003e\\u003cstrong\\u003eMOA\\u003c/strong\\u003e: Conceptualisation, Writing—review \\u0026amp; editing.\\u003cbr\\u003e\\u003cstrong\\u003eJO, UE\\u003c/strong\\u003e: Methodology, Writing—review \\u0026amp; editing.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research did not receive any funding from any organisation or funding body.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTransparency declarations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNone to declare.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll data generated or analysed during this study are included in this published article [and its supplementary information files].\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval \\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eEthical approval for the study was obtained from the Research Ethics Committee of the Enugu State Ministry of Health (\\u003cstrong\\u003eApproval number:\\u003c/strong\\u003e MH/MSD/REC21/747).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eClinical trial number:\\u003c/strong\\u003e not applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eInformed consent and consent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eInformed consent was obtained from all participants, which included the publication of data collated in anonymised form. The study complied with all ethical regulations outlined in the approval letter and the Helsinki declaration for human research.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eWorld Health Organization. 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Available from: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://ecronicon.net/eccmc/effect-of-typhoid-fever-misdiagnosis-on-febrile-patients-in-ogun-state-nigeria\\u003c/span\\u003e\\u003cspan address=\\\"https://ecronicon.net/eccmc/effect-of-typhoid-fever-misdiagnosis-on-febrile-patients-in-ogun-state-nigeria\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eAkande-Sholabi W, Ajamu AT. Antimicrobial stewardship: Assessment of knowledge, awareness of antimicrobial resistance, and appropriate antibiotic use among healthcare students in a Nigerian University. BMC Med Educ 2021 Sept 10;21(1):488.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eScaioli G, Gualano MR, Gili R, Masucci S, Bert F, Siliquini R. Antibiotic Use: A Cross-Sectional Survey Assessing the Knowledge, Attitudes and Practices amongst Students of a School of Medicine in Italy. Manzoli L, editor. PLOS ONE. 2015;10(4):e0122476.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eEdidiong Orok F, Ikpe, Williams T, Inimuvie E. Impact of educational intervention on knowledge of antimicrobial resistance and antibiotic use patterns among healthcare students: a pre- and post-intervention study. BMC Med Educ. 2025;25(1).\\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\":\"info@researchsquare.com\",\"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\":\"Antibiotic misuse, antimicrobial resistance, malaria, Nigeria\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7439669/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7439669/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 global health crisis, driven partly by inappropriate antibiotic use. In Nigeria, malaria remains highly prevalent and often mismanaged with antibiotics, particularly in presumed malaria-typhoid co-infections. This study examined patterns of antibiotic use in malaria treatment among university students, highlighting implications for AMR.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e\\u003cp\\u003eA cross-sectional survey was conducted among undergraduates purposively selected from 12 universities across Nigeria\\u0026rsquo;s six geopolitical zones. Data were collected via validated online questionnaires (February\\u0026ndash;March 2025) and analysed using descriptive statistics, chi-square tests, logistic regression, and Spearman correlation (SPSS v26).\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e\\u003cp\\u003eOf 646 respondents, \\u0026gt;\\u0026thinsp;97% demonstrated general antibiotic knowledge, yet 27.6% misidentified chloroquine as an antibiotic. While 94.6% correctly recognised antibiotics for bacterial infections, about one-fifth believed they were effective against fungal, parasitic, or viral diseases. Despite 84.7% AMR awareness, 49.1% reported using antibiotics for malaria treatment. Misuse was highest in the Northeast (62.3%), Northwest (63.7%), and South-South (32.9%). In the Northeast, key drivers included prior experience (35.4%), pharmacist advice (29.9%), and peer influence (28.0%), while only 6.7% followed physician prescriptions. Misuse correlated with the belief that antibiotics treat all illnesses (rₛ = 0.329, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Nearly half (49.5%) accessed antibiotics without prescriptions.\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e\\u003cp\\u003eHigh AMR awareness contrasts with persistent misuse of antibiotics for malaria, reflecting misconceptions, regional disparities, and weak regulation. Targeted education, stricter antibiotic controls, and improved diagnostics are urgently needed to curb AMR in Nigeria.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Antibiotic Use Among University Students in Malaria Therapy and Its Implications for Antimicrobial Resistance in Nigeria: A Quantitative Cross-sectional Study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-09-10 18:16:03\",\"doi\":\"10.21203/rs.3.rs-7439669/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-09-29T10:21:50+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-09-25T09:35:56+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-09-18T18:30:34+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"323213074757048447053398159047383461948\",\"date\":\"2025-09-14T17:15:17+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"292942383184965581124307824727439935008\",\"date\":\"2025-09-09T16:29:53+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-09-03T13:32:05+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2025-09-01T15:58:34+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-08-30T09:35:50+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-08-30T09:34:29+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Discover Public Health\",\"date\":\"2025-08-23T08:06:13+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"7c1d02dc-104e-4181-9ae1-7dd7e7b72b19\",\"owner\":[],\"postedDate\":\"September 10th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-11-03T15:59:30+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-7439669\",\"link\":\"https://doi.org/10.1186/s12982-025-01047-x\",\"journal\":{\"identity\":\"discover-public-health\",\"isVorOnly\":false,\"title\":\"Discover Public Health\"},\"publishedOn\":\"2025-10-27 15:56:59\",\"publishedOnDateReadable\":\"October 27th, 2025\"},\"versionCreatedAt\":\"2025-09-10 18:16:03\",\"video\":\"\",\"vorDoi\":\"10.1186/s12982-025-01047-x\",\"vorDoiUrl\":\"https://doi.org/10.1186/s12982-025-01047-x\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7439669\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7439669\",\"identity\":\"rs-7439669\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}