Burden of antibiotic prescription, associated factors, and compliance with the Uganda Clinical Guidelines among outpatients at Mulago National Referral Hospital, Uganda. A cross-sectional study

Burden of antibiotic prescription, associated factors, and compliance with the Uganda Clinical Guidelines among outpatients at Mulago National Referral Hospital, Uganda. 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A cross-sectional study Namakula Edith, Enock Suubi Segawa, Kateregga James, Keren Ebong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4840000/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The burden of antibiotic prescription in Uganda ranges between 12–79%, and compliance with the Uganda treatment guidelines (UCG) is still low; at 30%. There is limited information about antibiotic prescription levels and their appropriateness in public health facilities. This study, therefore, aimed to determine the prevalence of antibiotic prescription, compliance with the Uganda treatment guidelines; and factors associated with antibiotic prescription among outpatients at Mulago National Referral Hospital, Uganda. Methods We employed a cross-sectional design, and collected quantitative data at Mulago National Referral Hospital, among 2480 outpatients. We used a data abstraction tool to collect data from systematically sampled patient prescriptions. Ethical approval was obtained from the Mulago Hospital Research and Ethics Committee, and permission was sought from the Uganda National Council of Science and Technology (Reference: HS3440ES). Data were entered into Epidata software, and analysed in STATA, using Modified Poisson regression. Results The median age of 2480 participants was 62 years (IQR: 56–68), and 60.6% (1501/2479) were 50 and older. The prevalence of antibiotic prescription among outpatients was 60.4% (1479/2480). The compliance with the UCG was 57.5% (861/1479). The factors associated with antibiotic prescription were; prescription from the directorate of surgery (aPR: 0.995; 95%CI:0.919, 0.993), bacterial infection diagnosis (aPR: 8.083; 95%CI: 6.833, 9.560), prescription of three or more drugs (aPR: 1.133, 95%CI: 1.093, 1.175), patient age of 6 to 17 years (aPR:1.052; 95%CI: 0.991, 1.118), and gender (aPR: 1.012; 95%CI:0.979, 1.046), Conclusion Antibiotic prescription prevalence was high while compliance to the UCG was moderate. All prescribers should present their authentic signatures to the pharmacy department to strengthen therapeutic intervention. Constant availability of laboratory reagents in the hospital; and refresher training in rational prescription writing are needed. Sensitization of the public about disease preventive measures should be strengthened. The current UCG 2023 copies should be available to all prescribers, and antibiotic prescriptions among inpatients should be investigated. antibiotics antibiotic prescription antibiotic prescription prevalence burden of antibiotics prescription Mulago National Referral Hospital Background Irrational antibiotic use can lead to antimicrobial-resistant infections [ 1 – 4 ]. An estimated 4.95 million deaths associated with bacterial antimicrobial resistance (AMR) occurred globally in 2019. The deaths attributable to AMR were highest in western Sub-Saharan Africa, with 27.3 deaths per 100,000; followed by other regions in Sub-Saharan Africa, and lowest in Australasia at 6.5 deaths per 100,000 [ 1 ]. In Uganda, there were 7,100 deaths attributable to; and 30,700 deaths associated with AMR [ 5 ]. AMR hurts global health security and healthcare [ 2 ]. AMR is accelerated by misuse of antimicrobial agents, self-medication, and unrestricted access to medicines; that result in the flourishment of drug-resistant organisms [ 2 ]. To curb the growing problem, WHO recommended a strategy to combat AMR; and the prudent use of antimicrobials in 2015[ 6 ]. As a result, Uganda came up with an AMR National Action Plan. The Ugandan AMR action plan aims to improve awareness; and optimize access to effective antimicrobial medicines in human and animal health by ensuring controlled access, effective antimicrobial stewardship, and appropriate use [ 2 ]. However, the burden of antibiotic prescription is still high, at 79% in Uganda [ 3 ], and compliance with the Uganda treatment guidelines (UCG) is still low; at 30% [ 4 ]; which indicates a high degree of inappropriate antibiotic use. The recommended antibiotic prescription for the Access category in the WHO AWaRe classification for a facility is at least 60%[ 7 ]. The underlying principles of the AWaRe classification include; maximizing clinical effectiveness, minimizing toxicity, minimizing unnecessary costs to patients and the health care system, reducing emergency and spread of antibiotic resistance, simplicity, and alignment with existing WHO guidelines [ 7 ]. The factors that are associated with antibiotic prescription include; type of diagnosis, availability of antibiotics [ 8 ], female gender, age, and individuals prescribed at least three drugs [ 9 ]. Irrational antibiotic prescription and use can lead to antimicrobial resistance which increases morbidity and mortality because of limited treatment options for the patients with such resistance. Therefore, there is a need for appropriate antibiotic use to avoid escalation of this resistance. Resistance can lead to sepsis which can also lead to reduced human productivity because of long hospital stays. This can reduce the gross domestic product of the country, and affect the attainment of universal health coverage by 2030. This study, therefore, aimed to determine the prevalence of antibiotic prescription, compliance to the UCG, and factors associated with antibiotic prescription among outpatients at Mulago National Referral Hospital, Uganda. Methods Study setting The study was carried out in the directorate of surgery, paediatrics, and internal medicine at Mulago National Referral Hospital. The hospital has a bed capacity of 1600, with several directorates namely; surgical services, pediatrics and child care, nursing services, medical services, diagnostics and therapeutics services, private patients’ services, and administration and support services. The diagnostic services include Radiology, Clinical Laboratory, Pathology, and Nuclear Medicine. The hospital cares for 2500 outpatients monthly (hospital monthly reports). The study was carried out in the directorates of surgery, paediatrics and child health, and internal medicine. These are the directorates where most of the outpatient prescriptions originate from within Mulago Hospital. Study design and population This was a cross-sectional study; which collected only quantitative data. We used data abstraction tools to obtain information from 2480 prescriptions written, among outpatients from the directorates of surgery, paediatrics and child care, and internal medicine. These had consulted a clinician within Mulago Hospital from February to March 2024; and had been prescribed treatment. Patient prescriptions without patient number, diagnosis, clinic, age, sex, and those for medicine refills were excluded. Sample size and sampling procedure We used the Kish-Leslie formula for a single proportion to calculate a sample size of 2480, with a precision of 5%, a proportion of antibiotic prescription of 45% [ 10 ], Z α/2 - the standard normal value corresponding to a 95% level of confidence (1.96), design effect of 2, and 10% missing data. For factors associated, we used the formula for two proportions, with a Z β -standard normal value corresponding to an 80% power of 0.84. The proportion of female participants, q 1 was 0.57. The proportion of female participants prescribed antibiotics, P 1 was 0.81. The proportion of male patients prescribed antibiotics, P 2 was 0.73 [ 3 ], and the design effect of 2. Patients were systematically sampled; using a sampling interval of two, as they presented prescriptions to the hospital outpatient pharmacies. The selected prescriptions were checked for eligibility (Patient number, age, sex, clinic, diagnosis, treatment, and name of prescriber). Information was abstracted from eligible prescriptions using a data abstraction tool. Data abstracted from prescriptions included; patient number, clinic, age, sex, weight (for paediatrics), pharmacy unit, clinical presentation, laboratory investigations requested, laboratory investigations performed, names of laboratory investigations performed, diagnosis, treatment, and name of prescriber. The prescriptions were then forwarded to pharmacists/pharmacy technicians for filling and dispensing. The researchers checked the filled tools for completion and then kept them for data entry. Outcome measurement Antibiotic prescription in the study was when a prescriber wrote treatment for a patient, that included any medicine that acts against bacterial infections. This was measured by calculating the proportion of prescriptions with antibiotics as one of the medicines. Compliance was the appropriate use of antibiotics so that their selection, dose, and duration are according to the UCG, 2023; and are suitable for their clinical needs. This was measured by calculating the proportion of antibiotic prescriptions with the right antibiotic for the diagnosis, right dose, and right duration of treatment, as per the UCG, 2023. Data management and analysis We summarized continuous variables as median and interquartile ranges; and categorical variables into frequencies and percentages. A 95% confidence interval was calculated for antibiotic prescription and compliance. Bivariable analysis was carried out for all variables using modified Poisson regression with robust standard errors. A cut-off of 0.2 was used to get variables for multivariable analysis. Prior knowledge from published literature was used to include other variables associated with antibiotic prescription but had a p-value greater than 0.2 in the data. Variables included in multivariable analysis included; age in years, gender, directorate, laboratory investigations, diagnosis, and number of drugs. Multivariable modified Poisson regression was employed at a 5% level of significance, and an assessment of interaction was done using the likelihood ratio test. Confounding was assessed using a change in prevalence ratio > 10%, to establish if there were any variables confounding others in the final model. The factors independently associated with antibiotic prescription were established. Results Baseline characteristics Table 1 Sociodemographic characteristics of 2480 participants at Mulago Hospital Variable Median (IQR) Frequency Percentage Age (years)* 0–5 6–17 18–35 36–49 ≥ 50 62 (30–68) 116 191 400 268 1501 4.7 7.7 16.2 10.8 60.6 Gender Female Male 1468 1012 59.2 40.8 Pharmacy Central Medical OPD Ward 15 944 877 659 38.0 35.4 26.5 Directorate Internal medicine Paediatrics Surgery 868 740 872 35.0 29.8 35.2 Clinic** Mac adult*** Mopd# Skin Mac paed Paed chest Paed TB Sopd## Dental Mental health 610 162 96 688 29 22 752 102 18 24.5 6.5 3.9 27.8 1.2 0.9 30.3 4.1 0.7 *-n was 2476 **-n was 2479 ***-Mac- Mulago Assessment Center #Mopd-Medical outpatient department ##Sopd-Surgical outpatient department The median age of 2480 participants was 62 years (IQR: 56–68), and 60.6% (1501/2479) were 50 years and above. The majority of the participants were female (59.2%, 1468/2480). A large number of prescriptions (35.2%, 872/2480) were from the directorate of surgery, with most of them (30.3%, 752/2479) being prescribed from surgical outpatient clinics. In the directorate of internal medicine, most of the prescriptions (24.7%, 610/2479) were from the Mac adult clinic, while 27.8% (688/2479) were from the Mac paediatric clinic in the paediatrics directorate ( Table 1 ). Clinical characteristics of study participants Table 2 Clinical characteristics for 2480 outpatients at Mulago Hospital Variable Frequency Percentage Diagnosis Respiratory tract infection Urinary tract infection Pelvic inflammatory disease Peptic ulcer disease Tooth extraction/RCT* Conjunctivitis Others ** 414 118 26 90 73 73 1686 16.7 4.8 1.1 3.6 2.9 2.9 68.0 Diagnosis type Bacterial Non-bacterial 1417 1063 57.1 42.9 Laboratory investigations requested Yes No 285 2195 11.5 88.5 Laboratory investigations requested per directorate Internal medicine Paediatrics Surgery 69 161 55 24.2 56.5 19.3 Laboratory investigations performed Yes No 239 46 83.9 16.1 Antibiotic prescribed (n = 1479) Cefixime Levofloxacin Azithromycin Amoxicillin Amoxicillin/flucloxacillin Amoxicillin/clavulanic acid Ciprofloxacin Others # Metronidazole*** 321 196 188 124 180 150 94 226 313 21.7 13.3 12.7 8.4 12.2 10.0 6.4 15.3 21.2 AWaRe antibiotic Classification Access Watch Reserve 590 889 0 40.0 60.0 0.0 * RCT - root canal treatment ** Other diagnoses included; bacteremia, septiceamia, skin sepsis, dog bite, gastritis, hypertension, epilepsy, diabetes mellitus, and otitis media externa. ***Metronidazole was prescribed with other antibiotics like cefixime, amoxicillin, levofloxacin, amoxicillin/flucloxacillin, ampicillin/cloxacillin, and cefuroxime. # Other antibiotics included; cefuroxime, cefpodoxime, cephalexin, clarithromycin, ampicillin/cloxacillin, cloxacillin, ofloxacin, doxycycline, neomycin, gentamycin, chloramphenicol, tetracycline, erythromycin, nitrofurantoin, moxifloxacin, and erythromycin. The most common diagnosis was respiratory tract infection (16.7%, 414/2480); while 57.1% (1417/2480) were diagnosed with bacterial infections, and cefixime was the most prescribed antibiotic (21.7%, 321/1479). Furthermore; most of the prescribed antibiotics were in the WHO Watch category (60%,889/1479) ( Table 2 ). Prevalence of antibiotic prescription Table 3 Prevalence of antibiotic prescription among 2480 participants in Mulago Hospital Variable Prevalence (n) 95% Confidence interval Lower bound Upper bound Antibiotic prescription Yes No 60.4 (1497) 39.6 (983) 58.4 62.3 37.7 41.6 Directorate antibiotic prescription Internal Medicine Paediatrics Surgery 57.6 (529) 69.5 (540) 54.5 (428) 54.4 60.8 66.2 72.6 51.0 58.0 Among the 2480 participants enrolled in the study, 60.4% (n = 1497; 95%CI: 58.4, 62.3) were prescribed at least one antibiotic from outpatient clinics. From the study, 69.5% (540/777; 95%CI: 66.2, 72.6) of the participants in the directorate of paediatrics were prescribed at least one antibiotic ( Table 3 ). Compliance of antibiotic prescriptions with the Uganda Clinical Guidelines Table 4 Compliance with UCG among 1479 outpatient antibiotic prescriptions at Mulago Hospital Variable Prevalence (n) 95% Confidence interval Lower bound Upper bound Compliance with UCG Yes No 57.5 (861) 42.5 (636) 55.0 60.0 40.0 45.0 Compliance with UCG per directorate Internal medicine Paediatrics Surgery 58.6 (310) 47.4 (256) 68.9 (295) 54.3 62.7 43.2 51.6 64.4 78.1 Among the 1479 participants, 57.5% (n = 861, 95%CI: 55–60) of the antibiotic prescriptions were written in compliance with the UCG 2023, while only 47.4% (256/540) of the antibiotic prescriptions in the directorate of paediatrics were appropriately written ( Table 4 ). Appropriateness of antibiotic prescriptions Table 5 Appropriateness of antibiotic prescription among 1479 outpatients at Mulago Hospital Variable Prevalence (n) 95% Confidence Interval Lower bound Upper bound Right antibiotic Yes No 87.5 (1310) 12.5 (187) 85.8 89.1 10.8 14.2 Right antibiotic per directorate Internal medicine Paediatrics Surgery 82.0 (434) 88.5 (478) 93.0 (398) 78.5 85.1 85.5 90.9 90.1 95.1 Right dose Yes No 61.2 (916) 38.8 (581) 58.7 63.7 36.3 41.3 Right dose per directorate Internal medicine Paediatrics Surgery 65.0 (34.4) 48.1 (260) 72.9 (312) 60.8 68.9 43.9 52.4 68.5 76.9 Right duration Yes No 79.6 (1191) 20.4 (306) 77.5 81.6 18.4 22.8 Right duration per directorate Internal medicine Paediatrics Surgery 70.7 (374) 83.7 (452) 85.3 (365) 66.7 74.4 80.3 86.6 81.6 88.3 From the study, 12.5% (187/1417) of the participants were not prescribed the right antibiotic medicine, 38.8% (581/1417) were not prescribed the right dose, and 20.4% (306/1417) were not prescribed the right duration of the medicine ( Table 5 ). Factors associated with antibiotic prescription among outpatients at Mulago Hospital Uganda Table 6 Multivariable analysis for factors associated with antibiotic prescription among outpatients Variable Antibiotic prescription Yes, n (%) No, n (%) Crude Prevalence ratio, cPR (95% CI) Adjusted prevalence ratio aPR (95% CI) Age in years 0–5 6–17 18–35 36–49 ≥ 50 86 (5.8) 140 (9.4) 262 (12.5) 174 (11.6) 834 (55.7) 30 (3.1) 51 (5.2) 138 (14.1) 94 (9.6) 667 (68.1) 0.582 (0.425 0.796) 0.601 (0.472 0.765) 0.776 (0.671 0.899) 0.789 (0.664 0.988) 1 1.034 (0.962 1.110) 1.052 (0.991 1.118) 1.042 (0.995 1.090) 1.031 (0.986 1.079) 1 Gender Male Female 641 (42.8) 856 (57.2) 371 (37.7) 612 (62.3) 0.879 (0.795 0.973) 1 1.012 (0.979 1.046) 1 Directorate Internal medicine Paediatrics Surgery 529 (35.3) 540 (36.1) 428 (28.6) 389 (39.6) 237 (24.1) 357 (36.3) 1 0.720 (0.632 0.820) 1.017 (0.964 1.120) 1 0.962 (0.918 1.009) 0.955 (0.919 0.993) Type of diagnosis Bacterial Non-bacterial 1373 (91.7) 124 (8.3) 44 (4.5) 939 (95.5) 8.306 (7.038 9.803) 1 8.083 (6.833 9.560) 1 Number of drugs 1–3 ≥ 4 801 (53.5) 696 (46.5) 750 (76.3) 233 (23.7) 1 0.512 (0.459 0.586) 1 1.133 (1.093 1.175 ) Multivariable analysis showed that directorate of surgery (aPR: 0.955; 95%CI:0.919, 0.993), bacterial diagnosis (aPR: 8.083; 95%CI:6.833, 9.560), and number of drugs (aPR: 1.133; 95%CI: 1.093, 1.1750) were significantly associated with antibiotic prescription. The analysis showed that gender; and age confounded the directorate from which the medicine was prescribed. ( Table 6 ) . Discussion Prevalence of antibiotic prescription Approximately 61 out of 100 outpatients at Mulago Hospital during the study period were prescribed at least one antibiotic medicine. From this study, 57 out of 100 patients were diagnosed with bacterial infections; hence the high prescription of antibiotic medicines. However, 8.3% of the patients were prescribed antibiotic medicines yet they did not have any bacterial infection, which led to wastage. This wastage of antibiotic medicines can lead to other patients who require these medicines missing treatment because of faster antibiotic stockouts. On the other hand, 4.9% of the patients who were not prescribed antibiotic medicines were diagnosed with bacterial infections. This can lead to increased morbidity and mortality among the patients. However, a global point prevalence survey of 17 hospitals across Ghana, Uganda, Zambia, and Tanzania; about antimicrobial use reported an overall prevalence of antibiotic prescription of 50%, with Uganda’s rate standing at 45% [ 10 ]. The difference might be due to the changes in prescribing patterns over the years, and improved availability of antibiotics in Mulago Hospital. According to the WHO AWaRe antibiotic classification, Access antibiotics were prescribed to 40% of the patients versus the WHO-recommended country target of at least 60% [ 7 ]. This low prescription of Access antibiotics implies that the Watch category is prescribed mostly to patients. This could have been due to either, Access antibiotics becoming more resistant to bacterial infections, or the hospital availed more of the Watch compared to Access antibiotics. This can increase the cost of treatment to the hospital, and have more financial implications for the patients in case the prescribed medicines are out of stock. This may cause medicine non-adherence, hence the development of antibacterial resistance. This is consistent with a point prevalence survey to assess antibiotic use in 13 hospitals in Uganda which reported that the “Watch” antibiotics were used for 44% of prescriptions [ 4 ]. Only 11.2% of the patients who were prescribed antibiotic medicine had laboratory investigations requested before diagnosis. This led to the prescription of antibiotics in the absence of bacterial infections, which might lead to toxicity, and increase unnecessary costs to the patient in case of medicine stockouts. There is a possibility that sometimes laboratory reagents are stocked out, and when patients are sent for investigations, they are bounced back which reduces laboratory investigation requests. This coupled with the high patient load at the laboratory which increases patient waiting time, deters requests for laboratory investigations; hence the prescription of antibiotics based on the clinical presentation of the patient. This increases the prescription of antibiotics in the absence of confirmed bacterial infections, hence leading to antibiotics stockouts in the hospital. This can result in patients who genuinely require antibiotics not accessing them from the hospital. The majority of the patients cannot afford to buy these antibiotics from pharmacies outside the hospital, so they either buy half a dose or none at all. This can increase antibiotic resistance, morbidity, and mortality. Results are consistent with studies [ 3 ], and ([ 11 ] which reported that the prescription of Access antibiotics was below the WHO recommended level. Compliance with the Uganda Clinical Guidelines Results of the study show that 58 out of 100 outpatient antibiotic prescriptions were written as per the UCG 2023. These patient prescriptions had the right antibiotic prescribed for the diagnosis, in the right dose, and with the right duration of treatment. The majority of the outpatients (87.5%) were prescribed the right antibiotic for the diagnosis, with the WHO Watch category prescribed up to a level of 60%. However; 39 out of 100 were not prescribed the right dose, especially in paediatrics. This is possibly due to the weight-dose calculation for paediatric patients. Mulago Hospital is a teaching health facility with many medical students, spanning from years three to five, from intern doctors to senior house officers. There is a possibility that some of these prescriptions originated from these students who are still being perfected in prescription writing; hence this high level of incorrect dose among the prescriptions. Out of all the patients prescribed antibiotics, 20% of the prescriptions did not have the right treatment duration. This is possibly due to a lack of reference UCG for the prescribers. The UCG has just been reviewed, new copies have not yet been disseminated to the public hospitals, and even the old ones are limited; so, they cannot easily be accessed by the prescribers for reference. Therefore, doctors will have to rely on their knowledge, and experience to prescribe treatment for the patients. This can increase antibiotic resistance, and transmission of resistant bacterial strains; hence increasing the burden of bacterial infections. Most patients had shorter than the recommended duration for treatment, especially those diagnosed with peptic ulcer disease, and cystitis. Some respiratory tract infections had treatment for only three days. This breeds antibacterial resistance, increased morbidity, and less human productivity, reducing the country’s gross domestic product. The results are consistent with [ 12 ] who reported a UCG compliance of 30%. Factors associated with antibiotic prescription Directorate The prevalence of antibiotic prescription was 3.8% lower in the directorate of paediatric patients; and 4.5% lower in the directorate of surgery patients than in those in the directorate of internal medicine. Most of the prescriptions in internal medicine (849/918) are written based on clinical presentation without laboratory investigations. This leads to a high antibiotic prescription. The prescribers in the directorate of internal medicine have different qualifications including clinical officers, and intern doctors, especially Mac adult. These clinical officers have worked in this department for more than 20 years without any further knowledge improvement, and no continuous medical education about rational prescription writing. They end up prescribing antibiotics even when there is no need, hence the high antibiotic prescription. Type of diagnosis The odds of antibiotic prescription were 8 times higher in patients with bacterial diagnosis than in patients without bacterial diagnosis. Bacterial diagnoses should be prescribed antibiotics following the WHO AWaRe classification with reference to the UCG. In this study, most of the antibiotic prescriptions were from the Watch category which did not follow the WHO guidelines [ 7 ]. Some of the patients without bacterial diagnoses were prescribed antibiotics which can breed resistance. However, none of the patients were prescribed Reserve antibiotics. The prescription of antibiotic medicines in non-bacterial diagnoses shows how blindly prescriptions are written, specifically with no laboratory investigations. This implies that; either some prescriptions presented to the pharmacies do not originate from the hospital health care workers, or there is a knowledge gap amongst the prescribers which leads to antibiotic wastage, and faster stockouts. Number of drugs prescribed The prevalence of antibiotic prescription was 13.3% higher in patients who were prescribed four or more drugs than in those prescribed one to three drugs. Most of the patients prescribed more than three drugs had more than one diagnosis, and most probably one was bacterial, hence the higher antibiotic prescription than their counterparts. Sometimes antibiotics were indicated for diagnoses that were not bacterial; like malaria, hypertension, and diabetes mellitus which increased the antibiotic prescription. This irrational antibiotic use leads to wastage; and faster stockout of medicines which can lead to increased morbidity and mortality. This is consistent with studies [ 3 , 4 , 9 ]. Patient age The prevalence of antibiotic prescription was 3.4% higher among patients aged 0 to 5 years; 5.25% higher among patients who were aged 6 to 17 years; 4.2% higher in patients who were aged 18 to 35 years; and 3.1% higher in patients who were aged 36 to 49 years than in those aged 50 years and above. Most paediatric patients presented with respiratory tract infections, hence the high prescription of antibiotics compared to adults. Bacterial infections especially respiratory tract infections spread easily in paediatrics because of their low immunity, interactions at school, and social behavior, hence high antibiotic prescriptions. Urinary tract infections can also be high because of using the same toilets at school, hence the high prevalence of antibiotic prescriptions. The spread of bacterial infections is also rampant in 18 to 35 years because of workplace interactions, and the social lifestyles of the youth, hence high antibiotic prescription. Some youths don’t care so much about their health and thus, do not engage so much in disease prevention measures. They too, present mostly with bacterial infections which warrant an antibiotic prescription. At the ages of 36 years and above, people begin to be keener about their lifestyle, engage in more disease preventive measures, and hence can avoid some diseases like respiratory tract infections, and urinary tract infections. This leads to fewer antibiotic prescriptions. Our findings are consistent with studies done in the USA which showed that children below 18 years of age had more antibiotic prescriptions than older people [ 13 , 14 ]. However, the results are contrary to a study done in Uganda which reported that the age group of 18–59 years was associated with antibiotic prescription [ 9 ]. This might be due to the different facilities used in the two studies. Gender The prevalence of antibiotic prescription was 1.2% higher in male patients compared to their female counterparts. In Uganda, most male patients visit private facilities, and if they visit public facilities; they present late when they are too sick [ 15 , 16 ]. They also have poor health-seeking behavior compared to females. They therefore; miss several health education talks done routinely in health facilities about disease-preventive practices. Despite the introduction of sensitization programs to the public by the Ministry of Health through the media, males tend not to engage in them especially hand washing, and wearing masks, and hence end up getting more bacterial infections. This explains the higher antibiotic prescriptions in males compared to females. This is consistent with a study carried out at Mbarara Hospital [ 9 ]. However, our findings are contrary to a study done in the USA which showed that female patients had a higher overall rate of antibiotic visits than male patients [ 14 ]. Conclusion The prevalence of antibiotic prescriptions was high, while antibiotic appropriateness was moderate. The factors associated with antibiotic prescription were; patient age, gender, type of diagnosis, directorate from which the prescription was written, and number of medicines prescribed. All prescribers should avail their authentic signatures to the pharmacy department for faster therapeutic intervention. Constant availability of laboratory reagents in the hospital, and refresher training in rational prescription writing are needed. The current UCG 2023 copies should be availed to all prescribers, and antibiotic prescription among inpatients should be investigated. Declarations Ethical approval and consent to participate Ethical approval was obtained from the Mulago Hospital Research and Ethics Committee, and permission was obtained from the Uganda National Council of Science and Technology (Ref: HS3440ES). A waiver of informed consent was obtained from the ethics committee. Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. Authors information NA Funding Mulago National Referral Hospital provided funding for the study. Author Contribution NE designed and conceptualized the study, and performed data cleaning, data management, and preliminary analysis of the data. She also wrote the first draft of the paper. NE and ES contributed to the data analysis and report writing. All authors contributed to the interpretation of the findings. ES, KE, and KJ reviewed, revised, and contributed to writing the paper. All authors read and approved the final manuscript. NE, ES, KE, and KJ read and met the ICMJE criteria for authorship. Acknowledgments The authors extend their sincere thanks to the research participants, hospital staff, and the management of Mulago National Referral Hospital for funding, and supporting the implementation of the study. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. References Murray CJL, et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet. 2022;399(10325):629–55. MoH. Antimicrobial Resistance National Action Plan . 2018, Ministry of Health. p. 132. Kiguba R, Karamagi C, Bird SM. Extensive antibiotic prescription rate among hospitalized patients in Uganda: but with frequent missed-dose days. J Antimicrob Chemother. 2016;71(6):1697–706. Kiggundu R et al. Point Prevalence Survey of Antibiotic Use across 13 Hospitals in Uganda. Antibiot (Basel), 2022. 11(2). IHME. 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Evaluation of antibiotic prescription patterns using WHO AWaRe classification. EMHJ, 2024. 30 (2). Kizito M et al. Antibiotic Prevalence Study and Factors Influencing Prescription of WHO Watch Category Antibiotic Ceftriaxone in a Tertiary Care Private Not for Profit Hospital in Uganda. Antibiot (Basel), 2021. 10(10). Goldstein E, et al. Prescribing for different antibiotic classes across age groups in the Kaiser Permanente Northern California population in association with influenza incidence, 2010–2018. Epidemiol Infect. 2022;150:e180. Young EH et al. National Disparities in Antibiotic Prescribing by Race, Ethnicity, Age Group, and Sex in United States Ambulatory Care Visits, 2009 to 2016. Antibiot (Basel), 2022. 12(1). Okiring J, et al. Gender difference in the incidence of malaria diagnosed at public health facilities in Uganda. Malar J. 2022;21(1):22. Gertrude N, Lubega et al. Determinants of health seeking behaviour among men . in Luwero District. J Educ Res Behav Sci 2015. 4(2). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4840000","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":335130156,"identity":"13dedb6c-85f5-45b1-b32a-d4d82658d566","order_by":0,"name":"Namakula Edith","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYFCCxAZmEMXPzHwASEnIEKOlsRlESba3JYC08BChJYERrMXgzBkDEE1YCz97cvvjwh120Qw3cj6/ulFjwcPAfvjoBnxaJHseNjbPPJOc2zgjd5t1zjGgw3jS0m7g02JwA+gX3jbm3GaJ3G3GOWxALRI8Zni12EO01Oe2SeQ8M875R4QWAwmwlsO5PTxnmB8DNRLWInHmYeNs3rbjuTPY28yYc/skeNgI+YW/Pf3BZ9626tz9h5kff875VifHz374GF4tyIBNAkwSqxwEmD+QonoUjIJRMApGDgAAyc1K1V8HkeoAAAAASUVORK5CYII=","orcid":"","institution":"Mulago National Referral Hospital","correspondingAuthor":true,"prefix":"","firstName":"Namakula","middleName":"","lastName":"Edith","suffix":""},{"id":335130159,"identity":"a5659a17-8968-4e44-b7e9-b29fb0876553","order_by":1,"name":"Enock Suubi Segawa","email":"","orcid":"","institution":"Uganda Christian University","correspondingAuthor":false,"prefix":"","firstName":"Enock","middleName":"Suubi","lastName":"Segawa","suffix":""},{"id":335130162,"identity":"fa981f4c-40a0-46ca-b6f3-9edda2229943","order_by":2,"name":"Kateregga James","email":"","orcid":"","institution":"Soroti University","correspondingAuthor":false,"prefix":"","firstName":"Kateregga","middleName":"","lastName":"James","suffix":""},{"id":335130166,"identity":"9fed949c-0c11-4284-a05b-7c1c373335da","order_by":3,"name":"Keren Ebong","email":"","orcid":"","institution":"Mulago National Referral Hospital","correspondingAuthor":false,"prefix":"","firstName":"Keren","middleName":"","lastName":"Ebong","suffix":""}],"badges":[],"createdAt":"2024-08-01 07:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4840000/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4840000/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82546846,"identity":"739fde2d-9c98-4471-b249-fea699cccf74","added_by":"auto","created_at":"2025-05-12 18:31:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1749023,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4840000/v1/c45510f3-239c-414d-b353-2044fe903a5a.pdf"},{"id":63567704,"identity":"93b2a345-ac28-4b8d-9773-04b110bb91b5","added_by":"auto","created_at":"2024-08-29 16:31:24","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20189,"visible":true,"origin":"","legend":"","description":"","filename":"APCwaiverabxuse.docx","url":"https://assets-eu.researchsquare.com/files/rs-4840000/v1/e6aebdd0ad8780a76fb59056.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Burden of antibiotic prescription, associated factors, and compliance with the Uganda Clinical Guidelines among outpatients at Mulago National Referral Hospital, Uganda. A cross-sectional study","fulltext":[{"header":"Background","content":"\u003cp\u003eIrrational antibiotic use can lead to antimicrobial-resistant infections [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. An estimated 4.95\u0026nbsp;million deaths associated with bacterial antimicrobial resistance (AMR) occurred globally in 2019. The deaths attributable to AMR were highest in western Sub-Saharan Africa, with 27.3 deaths per 100,000; followed by other regions in Sub-Saharan Africa, and lowest in Australasia at 6.5 deaths per 100,000 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In Uganda, there were 7,100 deaths attributable to; and 30,700 deaths associated with AMR [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. AMR hurts global health security and healthcare [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. AMR is accelerated by misuse of antimicrobial agents, self-medication, and unrestricted access to medicines; that result in the flourishment of drug-resistant organisms [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo curb the growing problem, WHO recommended a strategy to combat AMR; and the prudent use of antimicrobials in 2015[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. As a result, Uganda came up with an AMR National Action Plan. The Ugandan AMR action plan aims to improve awareness; and optimize access to effective antimicrobial medicines in human and animal health by ensuring controlled access, effective antimicrobial stewardship, and appropriate use [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, the burden of antibiotic prescription is still high, at 79% in Uganda [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and compliance with the Uganda treatment guidelines (UCG) is still low; at 30% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; which indicates a high degree of inappropriate antibiotic use.\u003c/p\u003e \u003cp\u003eThe recommended antibiotic prescription for the Access category in the WHO AWaRe classification for a facility is at least 60%[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The underlying principles of the AWaRe classification include; maximizing clinical effectiveness, minimizing toxicity, minimizing unnecessary costs to patients and the health care system, reducing emergency and spread of antibiotic resistance, simplicity, and alignment with existing WHO guidelines [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The factors that are associated with antibiotic prescription include; type of diagnosis, availability of antibiotics [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], female gender, age, and individuals prescribed at least three drugs [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Irrational antibiotic prescription and use can lead to antimicrobial resistance which increases morbidity and mortality because of limited treatment options for the patients with such resistance. Therefore, there is a need for appropriate antibiotic use to avoid escalation of this resistance.\u003c/p\u003e \u003cp\u003eResistance can lead to sepsis which can also lead to reduced human productivity because of long hospital stays. This can reduce the gross domestic product of the country, and affect the attainment of universal health coverage by 2030. This study, therefore, aimed to determine the prevalence of antibiotic prescription, compliance to the UCG, and factors associated with antibiotic prescription among outpatients at Mulago National Referral Hospital, Uganda.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy setting\u003c/h2\u003e \u003cp\u003eThe study was carried out in the directorate of surgery, paediatrics, and internal medicine at Mulago National Referral Hospital. The hospital has a bed capacity of 1600, with several directorates namely; surgical services, pediatrics and child care, nursing services, medical services, diagnostics and therapeutics services, private patients\u0026rsquo; services, and administration and support services. The diagnostic services include Radiology, Clinical Laboratory, Pathology, and Nuclear Medicine. The hospital cares for 2500 outpatients monthly (hospital monthly reports). The study was carried out in the directorates of surgery, paediatrics and child health, and internal medicine. These are the directorates where most of the outpatient prescriptions originate from within Mulago Hospital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eThis was a cross-sectional study; which collected only quantitative data. We used data abstraction tools to obtain information from 2480 prescriptions written, among outpatients from the directorates of surgery, paediatrics and child care, and internal medicine. These had consulted a clinician within Mulago Hospital from February to March 2024; and had been prescribed treatment. Patient prescriptions without patient number, diagnosis, clinic, age, sex, and those for medicine refills were excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSample size and sampling procedure\u003c/h2\u003e \u003cp\u003eWe used the Kish-Leslie formula for a single proportion to calculate a sample size of 2480, with a precision of 5%, a proportion of antibiotic prescription of 45% [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], Z\u003csub\u003eα/2\u003c/sub\u003e- the standard normal value corresponding to a 95% level of confidence (1.96), design effect of 2, and 10% missing data. For factors associated, we used the formula for two proportions, with a Z\u003csub\u003eβ\u003c/sub\u003e-standard normal value corresponding to an 80% power of 0.84. The proportion of female participants, q\u003csub\u003e1\u003c/sub\u003e was 0.57. The proportion of female participants prescribed antibiotics, P\u003csub\u003e1\u003c/sub\u003e was 0.81. The proportion of male patients prescribed antibiotics, P\u003csub\u003e2\u003c/sub\u003e was 0.73 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and the design effect of 2.\u003c/p\u003e \u003cp\u003ePatients were systematically sampled; using a sampling interval of two, as they presented prescriptions to the hospital outpatient pharmacies. The selected prescriptions were checked for eligibility (Patient number, age, sex, clinic, diagnosis, treatment, and name of prescriber). Information was abstracted from eligible prescriptions using a data abstraction tool. Data abstracted from prescriptions included; patient number, clinic, age, sex, weight (for paediatrics), pharmacy unit, clinical presentation, laboratory investigations requested, laboratory investigations performed, names of laboratory investigations performed, diagnosis, treatment, and name of prescriber. The prescriptions were then forwarded to pharmacists/pharmacy technicians for filling and dispensing. The researchers checked the filled tools for completion and then kept them for data entry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOutcome measurement\u003c/h2\u003e \u003cp\u003eAntibiotic prescription in the study was when a prescriber wrote treatment for a patient, that included any medicine that acts against bacterial infections. This was measured by calculating the proportion of prescriptions with antibiotics as one of the medicines.\u003c/p\u003e \u003cp\u003eCompliance was the appropriate use of antibiotics so that their selection, dose, and duration are according to the UCG, 2023; and are suitable for their clinical needs. This was measured by calculating the proportion of antibiotic prescriptions with the right antibiotic for the diagnosis, right dose, and right duration of treatment, as per the UCG, 2023.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData management and analysis\u003c/h2\u003e \u003cp\u003eWe summarized continuous variables as median and interquartile ranges; and categorical variables into frequencies and percentages. A 95% confidence interval was calculated for antibiotic prescription and compliance. Bivariable analysis was carried out for all variables using modified Poisson regression with robust standard errors. A cut-off of 0.2 was used to get variables for multivariable analysis. Prior knowledge from published literature was used to include other variables associated with antibiotic prescription but had a p-value greater than 0.2 in the data. Variables included in multivariable analysis included; age in years, gender, directorate, laboratory investigations, diagnosis, and number of drugs. Multivariable modified Poisson regression was employed at a 5% level of significance, and an assessment of interaction was done using the likelihood ratio test. Confounding was assessed using a change in prevalence ratio\u0026thinsp;\u0026gt;\u0026thinsp;10%, to establish if there were any variables confounding others in the final model. The factors independently associated with antibiotic prescription were established.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic characteristics of 2480 participants at Mulago Hospital\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)*\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003cp\u003e6\u0026ndash;17\u003c/p\u003e \u003cp\u003e18\u0026ndash;35\u003c/p\u003e \u003cp\u003e36\u0026ndash;49\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (30\u0026ndash;68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116\u003c/p\u003e \u003cp\u003e191\u003c/p\u003e \u003cp\u003e400\u003c/p\u003e \u003cp\u003e268\u003c/p\u003e \u003cp\u003e1501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003cp\u003e7.7\u003c/p\u003e \u003cp\u003e16.2\u003c/p\u003e \u003cp\u003e10.8\u003c/p\u003e \u003cp\u003e60.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1468\u003c/p\u003e \u003cp\u003e1012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.2\u003c/p\u003e \u003cp\u003e40.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePharmacy\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCentral\u003c/p\u003e \u003cp\u003eMedical OPD\u003c/p\u003e \u003cp\u003eWard 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e944\u003c/p\u003e \u003cp\u003e877\u003c/p\u003e \u003cp\u003e659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003cp\u003e35.4\u003c/p\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDirectorate\u003c/b\u003e\u003c/p\u003e \u003cp\u003eInternal medicine\u003c/p\u003e \u003cp\u003ePaediatrics\u003c/p\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e868\u003c/p\u003e \u003cp\u003e740\u003c/p\u003e \u003cp\u003e872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.0\u003c/p\u003e \u003cp\u003e29.8\u003c/p\u003e \u003cp\u003e35.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinic**\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMac adult***\u003c/p\u003e \u003cp\u003eMopd#\u003c/p\u003e \u003cp\u003eSkin\u003c/p\u003e \u003cp\u003eMac paed\u003c/p\u003e \u003cp\u003ePaed chest\u003c/p\u003e \u003cp\u003ePaed TB\u003c/p\u003e \u003cp\u003eSopd##\u003c/p\u003e \u003cp\u003eDental\u003c/p\u003e \u003cp\u003eMental health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e610\u003c/p\u003e \u003cp\u003e162\u003c/p\u003e \u003cp\u003e96\u003c/p\u003e \u003cp\u003e688\u003c/p\u003e \u003cp\u003e29\u003c/p\u003e \u003cp\u003e22\u003c/p\u003e \u003cp\u003e752\u003c/p\u003e \u003cp\u003e102\u003c/p\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003cp\u003e6.5\u003c/p\u003e \u003cp\u003e3.9\u003c/p\u003e \u003cp\u003e27.8\u003c/p\u003e \u003cp\u003e1.2\u003c/p\u003e \u003cp\u003e0.9\u003c/p\u003e \u003cp\u003e30.3\u003c/p\u003e \u003cp\u003e4.1\u003c/p\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*-n was 2476\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e**-n was 2479\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e***-Mac- Mulago Assessment Center\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e#Mopd-Medical outpatient department\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e##Sopd-Surgical outpatient department\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe median age of 2480 participants was 62 years (IQR: 56\u0026ndash;68), and 60.6% (1501/2479) were 50 years and above. The majority of the participants were female (59.2%, 1468/2480). A large number of prescriptions (35.2%, 872/2480) were from the directorate of surgery, with most of them (30.3%, 752/2479) being prescribed from surgical outpatient clinics. In the directorate of internal medicine, most of the prescriptions (24.7%, 610/2479) were from the Mac adult clinic, while 27.8% (688/2479) were from the Mac paediatric clinic in the paediatrics directorate \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics of study participants\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics for 2480 outpatients at Mulago Hospital\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003cp\u003eRespiratory tract infection\u003c/p\u003e \u003cp\u003eUrinary tract infection\u003c/p\u003e \u003cp\u003ePelvic inflammatory disease\u003c/p\u003e \u003cp\u003ePeptic ulcer disease\u003c/p\u003e \u003cp\u003eTooth extraction/RCT*\u003c/p\u003e \u003cp\u003eConjunctivitis\u003c/p\u003e \u003cp\u003eOthers **\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e414\u003c/p\u003e \u003cp\u003e118\u003c/p\u003e \u003cp\u003e26\u003c/p\u003e \u003cp\u003e90\u003c/p\u003e \u003cp\u003e73\u003c/p\u003e \u003cp\u003e73\u003c/p\u003e \u003cp\u003e1686\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003cp\u003e4.8\u003c/p\u003e \u003cp\u003e1.1\u003c/p\u003e \u003cp\u003e3.6\u003c/p\u003e \u003cp\u003e2.9\u003c/p\u003e \u003cp\u003e2.9\u003c/p\u003e \u003cp\u003e68.0\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosis type\u003c/p\u003e \u003cp\u003eBacterial\u003c/p\u003e \u003cp\u003eNon-bacterial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1417\u003c/p\u003e \u003cp\u003e1063\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.1\u003c/p\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory investigations requested\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e285\u003c/p\u003e \u003cp\u003e2195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e11.5\u003c/b\u003e\u003c/p\u003e \u003cp\u003e88.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory investigations requested per directorate\u003c/b\u003e\u003c/p\u003e \u003cp\u003eInternal medicine\u003c/p\u003e \u003cp\u003ePaediatrics\u003c/p\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003cp\u003e161\u003c/p\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.2\u003c/p\u003e \u003cp\u003e56.5\u003c/p\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory investigations performed\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e239\u003c/p\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.9\u003c/p\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAntibiotic prescribed (n\u0026thinsp;=\u0026thinsp;1479)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCefixime\u003c/p\u003e \u003cp\u003eLevofloxacin\u003c/p\u003e \u003cp\u003eAzithromycin\u003c/p\u003e \u003cp\u003eAmoxicillin\u003c/p\u003e \u003cp\u003eAmoxicillin/flucloxacillin\u003c/p\u003e \u003cp\u003eAmoxicillin/clavulanic acid\u003c/p\u003e \u003cp\u003eCiprofloxacin\u003c/p\u003e \u003cp\u003eOthers\u003cb\u003e#\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMetronidazole***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e321\u003c/p\u003e \u003cp\u003e196\u003c/p\u003e \u003cp\u003e188\u003c/p\u003e \u003cp\u003e124\u003c/p\u003e \u003cp\u003e180\u003c/p\u003e \u003cp\u003e150\u003c/p\u003e \u003cp\u003e94\u003c/p\u003e \u003cp\u003e226\u003c/p\u003e \u003cp\u003e313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e21.7\u003c/b\u003e\u003c/p\u003e \u003cp\u003e13.3\u003c/p\u003e \u003cp\u003e12.7\u003c/p\u003e \u003cp\u003e8.4\u003c/p\u003e \u003cp\u003e12.2\u003c/p\u003e \u003cp\u003e10.0\u003c/p\u003e \u003cp\u003e6.4\u003c/p\u003e \u003cp\u003e15.3\u003c/p\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAWaRe antibiotic Classification\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAccess\u003c/p\u003e \u003cp\u003eWatch\u003c/p\u003e \u003cp\u003eReserve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e590\u003c/p\u003e \u003cp\u003e889\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.0\u003c/p\u003e \u003cp\u003e\u003cb\u003e60.0\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003e*\u003c/b\u003eRCT\u003cb\u003e-\u003c/b\u003eroot canal treatment\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e**\u003c/b\u003e Other diagnoses included; bacteremia, septiceamia, skin sepsis, dog bite, gastritis, hypertension, epilepsy, diabetes mellitus, and otitis media externa.\u003c/p\u003e \u003cp\u003e***Metronidazole was prescribed with other antibiotics like cefixime, amoxicillin, levofloxacin, amoxicillin/flucloxacillin, ampicillin/cloxacillin, and cefuroxime.\u003c/p\u003e \u003cp\u003e \u003cb\u003e#\u003c/b\u003e Other antibiotics included; cefuroxime, cefpodoxime, cephalexin, clarithromycin, ampicillin/cloxacillin, cloxacillin, ofloxacin, doxycycline, neomycin, gentamycin, chloramphenicol, tetracycline, erythromycin, nitrofurantoin, moxifloxacin, and erythromycin.\u003c/p\u003e \u003cp\u003eThe most common diagnosis was respiratory tract infection (16.7%, 414/2480); while 57.1% (1417/2480) were diagnosed with bacterial infections, and cefixime was the most prescribed antibiotic (21.7%, 321/1479). Furthermore; most of the prescribed antibiotics were in the WHO Watch category (60%,889/1479) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence of antibiotic prescription\u003c/h2\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\u003ePrevalence of antibiotic prescription among 2480 participants in Mulago Hospital\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrevalence (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% Confidence interval\u003c/p\u003e \u003cp\u003eLower bound Upper bound\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAntibiotic prescription\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e60.4 (1497)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e39.6 (983)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.4 62.3\u003c/p\u003e \u003cp\u003e37.7 41.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDirectorate antibiotic prescription\u003c/b\u003e\u003c/p\u003e \u003cp\u003eInternal Medicine\u003c/p\u003e \u003cp\u003ePaediatrics\u003c/p\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.6 (529)\u003c/p\u003e \u003cp\u003e\u003cb\u003e69.5 (540)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e54.5 (428)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.4 60.8\u003c/p\u003e \u003cp\u003e66.2 72.6\u003c/p\u003e \u003cp\u003e51.0 58.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the 2480 participants enrolled in the study, 60.4% (n\u0026thinsp;=\u0026thinsp;1497; 95%CI: 58.4, 62.3) were prescribed at least one antibiotic from outpatient clinics. From the study, 69.5% (540/777; 95%CI: 66.2, 72.6) of the participants in the directorate of paediatrics were prescribed at least one antibiotic \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCompliance of antibiotic prescriptions with the Uganda Clinical Guidelines\u003c/h2\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\u003eCompliance with UCG among 1479 outpatient antibiotic prescriptions at Mulago Hospital\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrevalence (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% Confidence interval\u003c/p\u003e \u003cp\u003eLower bound Upper bound\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCompliance with UCG\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e57.5 (861)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e42.5 (636)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.0 60.0\u003c/p\u003e \u003cp\u003e40.0 45.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCompliance with UCG per directorate\u003c/b\u003e\u003c/p\u003e \u003cp\u003eInternal medicine\u003c/p\u003e \u003cp\u003ePaediatrics\u003c/p\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.6 (310)\u003c/p\u003e \u003cp\u003e\u003cb\u003e47.4 (256)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e68.9 (295)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.3 62.7\u003c/p\u003e \u003cp\u003e43.2 51.6\u003c/p\u003e \u003cp\u003e64.4 78.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the 1479 participants, 57.5% (n\u0026thinsp;=\u0026thinsp;861, 95%CI: 55\u0026ndash;60) of the antibiotic prescriptions were written in compliance with the UCG 2023, while only 47.4% (256/540) of the antibiotic prescriptions in the directorate of paediatrics were appropriately written \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAppropriateness of antibiotic prescriptions\u003c/h2\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\u003eAppropriateness of antibiotic prescription among 1479 outpatients at Mulago Hospital\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrevalence (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003cp\u003eLower bound Upper bound\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight antibiotic\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87.5 (1310)\u003c/p\u003e \u003cp\u003e12.5 (187)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.8 89.1\u003c/p\u003e \u003cp\u003e10.8 14.2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight antibiotic per directorate\u003c/p\u003e \u003cp\u003eInternal medicine\u003c/p\u003e \u003cp\u003ePaediatrics\u003c/p\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.0 (434)\u003c/p\u003e \u003cp\u003e88.5 (478)\u003c/p\u003e \u003cp\u003e93.0 (398)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.5 85.1\u003c/p\u003e \u003cp\u003e85.5 90.9\u003c/p\u003e \u003cp\u003e90.1 95.1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight dose\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.2 (916)\u003c/p\u003e \u003cp\u003e\u003cb\u003e38.8 (581)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.7 63.7\u003c/p\u003e \u003cp\u003e36.3 41.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight dose per directorate\u003c/b\u003e\u003c/p\u003e \u003cp\u003eInternal medicine\u003c/p\u003e \u003cp\u003ePaediatrics\u003c/p\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.0 (34.4)\u003c/p\u003e \u003cp\u003e\u003cb\u003e48.1 (260)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e72.9 (312)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.8 68.9\u003c/p\u003e \u003cp\u003e43.9 52.4\u003c/p\u003e \u003cp\u003e68.5 76.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight duration\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.6 (1191)\u003c/p\u003e \u003cp\u003e\u003cb\u003e20.4 (306)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.5 81.6\u003c/p\u003e \u003cp\u003e18.4 22.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight duration per directorate\u003c/b\u003e\u003c/p\u003e \u003cp\u003eInternal medicine\u003c/p\u003e \u003cp\u003ePaediatrics\u003c/p\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e70.7 (374)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e83.7 (452)\u003c/p\u003e \u003cp\u003e85.3 (365)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.7 74.4\u003c/p\u003e \u003cp\u003e80.3 86.6\u003c/p\u003e \u003cp\u003e81.6 88.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom the study, 12.5% (187/1417) of the participants were not prescribed the right antibiotic medicine, 38.8% (581/1417) were not prescribed the right dose, and 20.4% (306/1417) were not prescribed the right duration of the medicine \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFactors associated with antibiotic prescription among outpatients at Mulago Hospital Uganda\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable analysis for factors associated with antibiotic prescription among outpatients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAntibiotic prescription\u003c/p\u003e \u003cp\u003eYes, n (%) \u0026nbsp;No, n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCrude Prevalence ratio, cPR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted prevalence ratio aPR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge in years\u003c/p\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003cp\u003e6\u0026ndash;17\u003c/p\u003e \u003cp\u003e18\u0026ndash;35\u003c/p\u003e \u003cp\u003e36\u0026ndash;49\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (5.8)\u003c/p\u003e \u003cp\u003e140 (9.4)\u003c/p\u003e \u003cp\u003e262 (12.5)\u003c/p\u003e \u003cp\u003e174 (11.6)\u003c/p\u003e \u003cp\u003e834 (55.7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (3.1)\u003c/p\u003e \u003cp\u003e51 (5.2)\u003c/p\u003e \u003cp\u003e138 (14.1)\u003c/p\u003e \u003cp\u003e94 (9.6)\u003c/p\u003e \u003cp\u003e667 (68.1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.582 (0.425 0.796)\u003c/p\u003e \u003cp\u003e0.601 (0.472 0.765)\u003c/p\u003e \u003cp\u003e0.776 (0.671 0.899)\u003c/p\u003e \u003cp\u003e0.789 (0.664 0.988)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.034 (0.962 1.110)\u003c/p\u003e \u003cp\u003e1.052 (0.991 1.118)\u003c/p\u003e \u003cp\u003e1.042 (0.995 1.090)\u003c/p\u003e \u003cp\u003e1.031 (0.986 1.079)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e641 (42.8)\u003c/p\u003e \u003cp\u003e856 (57.2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e371 (37.7)\u003c/p\u003e \u003cp\u003e612 (62.3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.879 (0.795 0.973)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.012 (0.979 1.046)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDirectorate\u003c/b\u003e\u003c/p\u003e \u003cp\u003eInternal medicine\u003c/p\u003e \u003cp\u003ePaediatrics\u003c/p\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e529 (35.3)\u003c/p\u003e \u003cp\u003e540 (36.1)\u003c/p\u003e \u003cp\u003e428 (28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e389 (39.6)\u003c/p\u003e \u003cp\u003e237 (24.1)\u003c/p\u003e \u003cp\u003e357 (36.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.720 (0.632 0.820)\u003c/p\u003e \u003cp\u003e1.017 (0.964 1.120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.962 (0.918 1.009)\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.955 (0.919 0.993)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of diagnosis\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBacterial\u003c/p\u003e \u003cp\u003eNon-bacterial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1373 (91.7)\u003c/p\u003e \u003cp\u003e124 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (4.5)\u003c/p\u003e \u003cp\u003e939 (95.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.306 (7.038 9.803)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e8.083 (6.833 9.560)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of drugs\u003c/b\u003e\u003c/p\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e801 (53.5)\u003c/p\u003e \u003cp\u003e696 (46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e750 (76.3)\u003c/p\u003e \u003cp\u003e233 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.512 (0.459 0.586)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e\u003cb\u003e1.133 (1.093 1.175\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMultivariable analysis showed that directorate of surgery (aPR: 0.955; 95%CI:0.919, 0.993), bacterial diagnosis (aPR: 8.083; 95%CI:6.833, 9.560), and number of drugs (aPR: 1.133; 95%CI: 1.093, 1.1750) were significantly associated with antibiotic prescription. The analysis showed that gender; and age confounded the directorate from which the medicine was prescribed. \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence of antibiotic prescription\u003c/h2\u003e \u003cp\u003eApproximately 61 out of 100 outpatients at Mulago Hospital during the study period were prescribed at least one antibiotic medicine. From this study, 57 out of 100 patients were diagnosed with bacterial infections; hence the high prescription of antibiotic medicines. However, 8.3% of the patients were prescribed antibiotic medicines yet they did not have any bacterial infection, which led to wastage. This wastage of antibiotic medicines can lead to other patients who require these medicines missing treatment because of faster antibiotic stockouts. On the other hand, 4.9% of the patients who were not prescribed antibiotic medicines were diagnosed with bacterial infections. This can lead to increased morbidity and mortality among the patients. However, a global point prevalence survey of 17 hospitals across Ghana, Uganda, Zambia, and Tanzania; about antimicrobial use reported an overall prevalence of antibiotic prescription of 50%, with Uganda\u0026rsquo;s rate standing at 45% [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The difference might be due to the changes in prescribing patterns over the years, and improved availability of antibiotics in Mulago Hospital.\u003c/p\u003e \u003cp\u003eAccording to the WHO AWaRe antibiotic classification, Access antibiotics were prescribed to 40% of the patients versus the WHO-recommended country target of at least 60% [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This low prescription of Access antibiotics implies that the Watch category is prescribed mostly to patients. This could have been due to either, Access antibiotics becoming more resistant to bacterial infections, or the hospital availed more of the Watch compared to Access antibiotics. This can increase the cost of treatment to the hospital, and have more financial implications for the patients in case the prescribed medicines are out of stock. This may cause medicine non-adherence, hence the development of antibacterial resistance. This is consistent with a point prevalence survey to assess antibiotic use in 13 hospitals in Uganda which reported that the \u0026ldquo;Watch\u0026rdquo; antibiotics were used for 44% of prescriptions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOnly 11.2% of the patients who were prescribed antibiotic medicine had laboratory investigations requested before diagnosis. This led to the prescription of antibiotics in the absence of bacterial infections, which might lead to toxicity, and increase unnecessary costs to the patient in case of medicine stockouts. There is a possibility that sometimes laboratory reagents are stocked out, and when patients are sent for investigations, they are bounced back which reduces laboratory investigation requests. This coupled with the high patient load at the laboratory which increases patient waiting time, deters requests for laboratory investigations; hence the prescription of antibiotics based on the clinical presentation of the patient. This increases the prescription of antibiotics in the absence of confirmed bacterial infections, hence leading to antibiotics stockouts in the hospital. This can result in patients who genuinely require antibiotics not accessing them from the hospital. The majority of the patients cannot afford to buy these antibiotics from pharmacies outside the hospital, so they either buy half a dose or none at all. This can increase antibiotic resistance, morbidity, and mortality.\u003c/p\u003e \u003cp\u003eResults are consistent with studies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and ([\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] which reported that the prescription of Access antibiotics was below the WHO recommended level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCompliance with the Uganda Clinical Guidelines\u003c/h2\u003e \u003cp\u003eResults of the study show that 58 out of 100 outpatient antibiotic prescriptions were written as per the UCG 2023. These patient prescriptions had the right antibiotic prescribed for the diagnosis, in the right dose, and with the right duration of treatment.\u003c/p\u003e \u003cp\u003eThe majority of the outpatients (87.5%) were prescribed the right antibiotic for the diagnosis, with the WHO Watch category prescribed up to a level of 60%. However; 39 out of 100 were not prescribed the right dose, especially in paediatrics. This is possibly due to the weight-dose calculation for paediatric patients. Mulago Hospital is a teaching health facility with many medical students, spanning from years three to five, from intern doctors to senior house officers. There is a possibility that some of these prescriptions originated from these students who are still being perfected in prescription writing; hence this high level of incorrect dose among the prescriptions. Out of all the patients prescribed antibiotics, 20% of the prescriptions did not have the right treatment duration. This is possibly due to a lack of reference UCG for the prescribers. The UCG has just been reviewed, new copies have not yet been disseminated to the public hospitals, and even the old ones are limited; so, they cannot easily be accessed by the prescribers for reference. Therefore, doctors will have to rely on their knowledge, and experience to prescribe treatment for the patients. This can increase antibiotic resistance, and transmission of resistant bacterial strains; hence increasing the burden of bacterial infections.\u003c/p\u003e \u003cp\u003eMost patients had shorter than the recommended duration for treatment, especially those diagnosed with peptic ulcer disease, and cystitis. Some respiratory tract infections had treatment for only three days. This breeds antibacterial resistance, increased morbidity, and less human productivity, reducing the country\u0026rsquo;s gross domestic product.\u003c/p\u003e \u003cp\u003eThe results are consistent with [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] who reported a UCG compliance of 30%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFactors associated with antibiotic prescription\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eDirectorate\u003c/h2\u003e \u003cp\u003eThe prevalence of antibiotic prescription was 3.8% lower in the directorate of paediatric patients; and 4.5% lower in the directorate of surgery patients than in those in the directorate of internal medicine.\u003c/p\u003e \u003cp\u003eMost of the prescriptions in internal medicine (849/918) are written based on clinical presentation without laboratory investigations. This leads to a high antibiotic prescription. The prescribers in the directorate of internal medicine have different qualifications including clinical officers, and intern doctors, especially Mac adult. These clinical officers have worked in this department for more than 20 years without any further knowledge improvement, and no continuous medical education about rational prescription writing. They end up prescribing antibiotics even when there is no need, hence the high antibiotic prescription.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eType of diagnosis\u003c/h2\u003e \u003cp\u003eThe odds of antibiotic prescription were 8 times higher in patients with bacterial diagnosis than in patients without bacterial diagnosis.\u003c/p\u003e \u003cp\u003eBacterial diagnoses should be prescribed antibiotics following the WHO AWaRe classification with reference to the UCG. In this study, most of the antibiotic prescriptions were from the Watch category which did not follow the WHO guidelines [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Some of the patients without bacterial diagnoses were prescribed antibiotics which can breed resistance. However, none of the patients were prescribed Reserve antibiotics.\u003c/p\u003e \u003cp\u003eThe prescription of antibiotic medicines in non-bacterial diagnoses shows how blindly prescriptions are written, specifically with no laboratory investigations. This implies that; either some prescriptions presented to the pharmacies do not originate from the hospital health care workers, or there is a knowledge gap amongst the prescribers which leads to antibiotic wastage, and faster stockouts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eNumber of drugs prescribed\u003c/h2\u003e \u003cp\u003eThe prevalence of antibiotic prescription was 13.3% higher in patients who were prescribed four or more drugs than in those prescribed one to three drugs. Most of the patients prescribed more than three drugs had more than one diagnosis, and most probably one was bacterial, hence the higher antibiotic prescription than their counterparts. Sometimes antibiotics were indicated for diagnoses that were not bacterial; like malaria, hypertension, and diabetes mellitus which increased the antibiotic prescription. This irrational antibiotic use leads to wastage; and faster stockout of medicines which can lead to increased morbidity and mortality.\u003c/p\u003e \u003cp\u003eThis is consistent with studies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003ePatient age\u003c/h2\u003e \u003cp\u003eThe prevalence of antibiotic prescription was 3.4% higher among patients aged 0 to 5 years; 5.25% higher among patients who were aged 6 to 17 years; 4.2% higher in patients who were aged 18 to 35 years; and 3.1% higher in patients who were aged 36 to 49 years than in those aged 50 years and above.\u003c/p\u003e \u003cp\u003eMost paediatric patients presented with respiratory tract infections, hence the high prescription of antibiotics compared to adults. Bacterial infections especially respiratory tract infections spread easily in paediatrics because of their low immunity, interactions at school, and social behavior, hence high antibiotic prescriptions. Urinary tract infections can also be high because of using the same toilets at school, hence the high prevalence of antibiotic prescriptions. The spread of bacterial infections is also rampant in 18 to 35 years because of workplace interactions, and the social lifestyles of the youth, hence high antibiotic prescription. Some youths don\u0026rsquo;t care so much about their health and thus, do not engage so much in disease prevention measures. They too, present mostly with bacterial infections which warrant an antibiotic prescription. At the ages of 36 years and above, people begin to be keener about their lifestyle, engage in more disease preventive measures, and hence can avoid some diseases like respiratory tract infections, and urinary tract infections. This leads to fewer antibiotic prescriptions. Our findings are consistent with studies done in the USA which showed that children below 18 years of age had more antibiotic prescriptions than older people [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the results are contrary to a study done in Uganda which reported that the age group of 18\u0026ndash;59 years was associated with antibiotic prescription [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This might be due to the different facilities used in the two studies.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eGender\u003c/h2\u003e \u003cp\u003eThe prevalence of antibiotic prescription was 1.2% higher in male patients compared to their female counterparts. In Uganda, most male patients visit private facilities, and if they visit public facilities; they present late when they are too sick [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. They also have poor health-seeking behavior compared to females. They therefore; miss several health education talks done routinely in health facilities about disease-preventive practices. Despite the introduction of sensitization programs to the public by the Ministry of Health through the media, males tend not to engage in them especially hand washing, and wearing masks, and hence end up getting more bacterial infections. This explains the higher antibiotic prescriptions in males compared to females.\u003c/p\u003e \u003cp\u003eThis is consistent with a study carried out at Mbarara Hospital [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, our findings are contrary to a study done in the USA which showed that female patients had a higher overall rate of antibiotic visits than male patients [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe prevalence of antibiotic prescriptions was high, while antibiotic appropriateness was moderate. The factors associated with antibiotic prescription were; patient age, gender, type of diagnosis, directorate from which the prescription was written, and number of medicines prescribed.\u003c/p\u003e \u003cp\u003eAll prescribers should avail their authentic signatures to the pharmacy department for faster therapeutic intervention. Constant availability of laboratory reagents in the hospital, and refresher training in rational prescription writing are needed. The current UCG 2023 copies should be availed to all prescribers, and antibiotic prescription among inpatients should be investigated.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e \u003cp\u003e Ethical approval was obtained from the Mulago Hospital Research and Ethics Committee, and permission was obtained from the Uganda National Council of Science and Technology (Ref: HS3440ES). A waiver of informed consent was obtained from the ethics committee.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAuthors information\u003c/h2\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003e Mulago National Referral Hospital provided funding for the study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eNE designed and conceptualized the study, and performed data cleaning, data management, and preliminary analysis of the data. She also wrote the first draft of the paper. NE and ES contributed to the data analysis and report writing. All authors contributed to the interpretation of the findings. ES, KE, and KJ reviewed, revised, and contributed to writing the paper. All authors read and approved the final manuscript. NE, ES, KE, and KJ read and met the ICMJE criteria for authorship.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003e The authors extend their sincere thanks to the research participants, hospital staff, and the management of Mulago National Referral Hospital for funding, and supporting the implementation of the study.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMurray CJL, et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet. 2022;399(10325):629\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoH. \u003cem\u003eAntimicrobial Resistance National Action Plan\u003c/em\u003e. 2018, Ministry of Health. p. 132.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiguba R, Karamagi C, Bird SM. Extensive antibiotic prescription rate among hospitalized patients in Uganda: but with frequent missed-dose days. J Antimicrob Chemother. 2016;71(6):1697\u0026ndash;706.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiggundu R et al. Point Prevalence Survey of Antibiotic Use across 13 Hospitals in Uganda. Antibiot (Basel), 2022. 11(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIHME. The burden of antimicrobial resistance (AMR) in Uganda. Global Research on Antimicrobial Resistance University of Oxford; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO, WHO Global Action Plan on Antimicrobial Resistance. 2015. p. 28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. \u003cem\u003eThe WHO AWaRe (Access, Watch, Reserve) antibiotic book\u003c/em\u003e. 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson W, et al. Factors associated with antibiotic prescribing for adults with acute conditions: an umbrella review across primary care and a systematic review focusing on primary dental care. J Antimicrob Chemother. 2019;74(8):2139\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuwanguzi TE, Yadesa TM, Agaba AG. Antibacterial prescription and the associated factors among outpatients diagnosed with respiratory tract infections in Mbarara Municipality, Uganda. BMC Pulm Med. 2021;21(1):374.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026rsquo;Arcy N, et al. Antibiotic Prescribing Patterns in Ghana, Uganda, Zambia and Tanzania Hospitals: Results from the Global Point Prevalence Survey (G-PPS) on Antimicrobial Use and Stewardship Interventions Implemented. Antibiotics. 2021;10(9):1122.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUrooj, Sajjad et al. Evaluation of antibiotic prescription patterns using WHO AWaRe classification. EMHJ, 2024. 30 (2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKizito M et al. Antibiotic Prevalence Study and Factors Influencing Prescription of WHO Watch Category Antibiotic Ceftriaxone in a Tertiary Care Private Not for Profit Hospital in Uganda. Antibiot (Basel), 2021. 10(10).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldstein E, et al. Prescribing for different antibiotic classes across age groups in the Kaiser Permanente Northern California population in association with influenza incidence, 2010\u0026ndash;2018. Epidemiol Infect. 2022;150:e180.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoung EH et al. National Disparities in Antibiotic Prescribing by Race, Ethnicity, Age Group, and Sex in United States Ambulatory Care Visits, 2009 to 2016. Antibiot (Basel), 2022. 12(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkiring J, et al. Gender difference in the incidence of malaria diagnosed at public health facilities in Uganda. Malar J. 2022;21(1):22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGertrude N, Lubega et al. \u003cem\u003eDeterminants of health seeking behaviour among men\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003ein\u003c/em\u003e Luwero District. J Educ Res Behav Sci 2015. 4(2).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"antibiotics, antibiotic prescription, antibiotic prescription prevalence, burden of antibiotics prescription, Mulago National Referral Hospital","lastPublishedDoi":"10.21203/rs.3.rs-4840000/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4840000/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003e The burden of antibiotic prescription in Uganda ranges between 12\u0026ndash;79%, and compliance with the Uganda treatment guidelines (UCG) is still low; at 30%. There is limited information about antibiotic prescription levels and their appropriateness in public health facilities. This study, therefore, aimed to determine the prevalence of antibiotic prescription, compliance with the Uganda treatment guidelines; and factors associated with antibiotic prescription among outpatients at Mulago National Referral Hospital, Uganda.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe employed a cross-sectional design, and collected quantitative data at Mulago National Referral Hospital, among 2480 outpatients. We used a data abstraction tool to collect data from systematically sampled patient prescriptions. Ethical approval was obtained from the Mulago Hospital Research and Ethics Committee, and permission was sought from the Uganda National Council of Science and Technology (Reference: HS3440ES). Data were entered into Epidata software, and analysed in STATA, using Modified Poisson regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe median age of 2480 participants was 62 years (IQR: 56\u0026ndash;68), and 60.6% (1501/2479) were 50 and older. The prevalence of antibiotic prescription among outpatients was 60.4% (1479/2480). The compliance with the UCG was 57.5% (861/1479). The factors associated with antibiotic prescription were; prescription from the directorate of surgery (aPR: 0.995; 95%CI:0.919, 0.993), bacterial infection diagnosis (aPR: 8.083; 95%CI: 6.833, 9.560), prescription of three or more drugs (aPR: 1.133, 95%CI: 1.093, 1.175), patient age of 6 to 17 years (aPR:1.052; 95%CI: 0.991, 1.118), and gender (aPR: 1.012; 95%CI:0.979, 1.046),\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAntibiotic prescription prevalence was high while compliance to the UCG was moderate. All prescribers should present their authentic signatures to the pharmacy department to strengthen therapeutic intervention. Constant availability of laboratory reagents in the hospital; and refresher training in rational prescription writing are needed. Sensitization of the public about disease preventive measures should be strengthened. The current UCG 2023 copies should be available to all prescribers, and antibiotic prescriptions among inpatients should be investigated.\u003c/p\u003e","manuscriptTitle":"Burden of antibiotic prescription, associated factors, and compliance with the Uganda Clinical Guidelines among outpatients at Mulago National Referral Hospital, Uganda. A cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-29 16:31:19","doi":"10.21203/rs.3.rs-4840000/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c4d0eb6d-7486-4727-a570-09bc45524906","owner":[],"postedDate":"August 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-12T18:23:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-29 16:31:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4840000","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4840000","identity":"rs-4840000","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}