Antibiotic prescribing trends and determinants in a regional hospital in Ghana: An eight- year retrospective analysis with cross-sectional survey

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Monitoring prescribing patterns using standardised metrics such as World Health Organisation (WHO) prescribing indicators and Defined Daily Dose (DDD) supports antimicrobial stewardship. Evidence on outpatient antibiotic prescribing in parts of Ghana remains limited. This study assessed antibiotic prescribing trends and associated prescriber factors at a regional hospital in Ghana. Methods A hospital-based observational study was conducted at Bono Regional Hospital, Sunyani. Retrospective outpatient antibiotic prescription data (January 2014–December 2021) were extracted from the Hospital Administration Management Software. Antibiotic utilisation was quantified using DDD per 1,000 inhabitants per day. The study employed a cross-sectional survey of prescriber characteristics that influenced antibiotic prescribing practices. Descriptive statistics summarised prescribing patterns. Associations were examined using chi-square tests and binary logistic regression at p < 0.05. Results A total of 1,014,368 outpatient antibiotic prescriptions were analysed. Antibiotic consumption increased over time, reaching 11 DDD per 1,000 inhabitants per day in 2021. The study determined that amoxicillin/clavulanic acid (14.39%) served as the most commonly prescribed antibiotic, whereas erythromycin (11.44%), ciprofloxacin (11.36%), and amoxicillin (10.83%) followed. Five antibiotics accounted for more than half of total prescriptions. Among 35 prescribers (response rate: 64.8%), age and years of practice were associated with prescribing in bivariate analysis but were not significant in multivariable models. Delayed laboratory results were the most frequently reported barrier to rational prescribing. Conclusions Outpatient antibiotic prescribing increased over time with substantial reliance on broad-spectrum agents. There is a need for better management of healthcare resources and enhanced diagnostic facilities, which will help achieve proper antibiotic usage and control antimicrobial resistance. Antibiotic prescribing Defined Daily Dose Antimicrobial resistance Rational drug use Ghana Figures Figure 1 Figure 2 Figure 3 Introduction Antibiotics function as medications which treat bacterial infections through their ability to stop bacteria from growing or to eliminate bacterial organisms [1], [2]. The process of selecting antibiotics for patient treatment presents clinicians with challenges because they must make decisions during times when their medical diagnosis remains uncertain and other clinical obligations demand their attention [3], [4]. The rational use of medicines requires that patients receive their necessary medications through proper dosing and treatment length, which costs them and society the least amount of money [5]. According to this principle, prescribers must first confirm an infection exists before they can use diagnostic tools to assess infection status and base their treatment decisions on established clinical guidelines, which help reduce unnecessary antimicrobial contact [1], [3], [6]. Antimicrobial resistance (AMR) represents a significant global public health danger, which occurs through improper antimicrobial usage that includes both unnecessary drug prescriptions and wrong medication dosages and breaches of established treatment protocols [7], [8]. The overuse of antibiotics has resulted in diminished effectiveness with various antibacterial medications because of emerging resistant pathogens which spread throughout populations [5], [9], [10]. Antibacterial resistance creates multiple dangers, which include treatment failure and patient death and extended hospital duration, and increased medical expenses thus raising worries about a future "post-antibiotic era" where ordinary infections turn into hard-to-treat conditions [11]. Pathogenic bacteria develop resistance through the selective pressure which arises from antibiotic misuse, thus demonstrating that healthcare facilities must restrict their antibiotic usage to essential and vital treatment needs [12], [13]. Outpatient evidence demonstrates that excessive antibiotic prescription practices continue to exist as a major problem. The consensus study conducted in India discovered that excessive prescription practices serve as a primary factor which leads to resistance development [14]. In the United States, the Centres for Disease Control and Prevention estimated that 30% of outpatient antibiotic prescriptions are unnecessary [15]. In many low- and middle-income settings, antibiotic exposure is even more extensive: antibiotics are prescribed for a large proportion of hospitalised patients and frequently inappropriately [16], and it has been projected that a sizeable share of medicines in Africa, including antibiotics, are used inappropriately [17]. Prescribing patterns also vary across countries and levels of care, reflecting differences in disease epidemiology, access to diagnostics, and implementation of clinical guidelines [18], [19], [20]. Ghana uses policy instruments which promote rational prescribing through its Essential Medicines List and Standard Treatment Guidelines [21]. Yet empirical evidence shows that medical professionals continue to prescribe antibiotics at high rates, which differ between different medical facilities [22], [23], [24], [25], [26]. The prescription practices at medical facilities result from various factors which include the prescribers' knowledge base and training level and practical experience as well as their access to drug guidelines and drug information and drug and therapeutics committees and their existing facility restrictions which include restricted diagnostic resources and slow laboratory analysis processing times [27], [28], [29], [30], [31], [32]. These influences can create two effects, which include rising empirical prescribing practices and healthcare providers who use only limited antibiotic options, which leads to faster development of resistance [33], [34] Current research about antibiotic prescription in Ghana has increased, but there are still knowledge gaps that affect specific geographical areas and healthcare facilities. Existing research lacks sufficient organised studies that evaluate outpatient antibiotic prescription practices in the Bono Region through standardised assessment methods and World Health Organisation prescribing standards. The [35] states that organisations need to conduct regular assessments of their prescribing practices so they can discover specific medication usage issues, which will help them develop effective antibiotic management programs and make policy decisions. The Defined Daily Dose (DDD) metric enables researchers to measure antibiotic consumption, but it does not reflect how much doctors prescribe to their patients because DDD represents the typical daily dose for the primary medical usage of a medication [36], [37]. The development of specific evidence for each facility throughout multiple years, together with its connection to prescriber-reported factors, will create practical solutions that help organisations achieve better antibiotic usage. Accordingly, this study assessed outpatient antibiotic prescribing/consumption patterns at the Bono Regional Hospital, Sunyani, over an eight-year period, and examined prescriber-related factors associated with antibiotic prescribing practices using WHO rational drug-use prescribing indicators [35]. Study aim To assess antibiotic prescribing patterns at the Bono Regional Hospital, Sunyani. Specific objectives To determine the defined daily doses (DDD) of outpatient antibiotic use/prescriptions at the Bono Regional Hospital. To identify the most commonly prescribed antibiotics at the hospital. To examine factors associated with clinicians’ antibiotic prescribing practices. Methods Study design We conducted an observational study which included two distinct components. The first component involved a retrospective examination of outpatient antibiotic dispensing and prescribing records, which were maintained in an electronic hospital system throughout eight years from January 2014 until December 2021. The second component involved a facility-based cross-sectional survey, which evaluated prescriber characteristics that affected their antibiotic prescribing behaviour between February and July 2021. The World Health Organization WHO [35] rational drug-use prescribing indicators framework was used to evaluate antibiotic prescribing patterns. Study setting The study was undertaken at the Bono Regional Hospital, Sunyani, located in the Bono Region of Ghana. Sunyani is the regional capital, and the hospital provides outpatient and inpatient services to residents within the metropolis and surrounding districts [38]. Study Population Secondary (electronic) data The electronic dataset comprised outpatient records for patients aged ≥ 18 years who received an antibiotic prescription/dispensing episode at the hospital outpatient department (OPD) within January 2014–December 2021. Adults were selected because WHO-defined daily dose (DDD) metrics are primarily standardised for adult use and are not routinely determined for children [36]. Primary (prescriber survey) data The survey population comprised prescribers working in the hospital during the study period, including medical doctors and physician assistants. Prescriber characteristics were collected to examine associations with antibiotic prescribing practices. Eligibility criteria Inclusion criteria For the electronic records, OPD encounters for patients aged ≥ 18 years with an antibiotic dispensed/prescribed for a new episode of illness between January 2014 and December 2021 were eligible. For each patient, the first eligible visit with an antibiotic record within the period was selected, where applicable. For the prescriber survey, clinicians/physician assistants present during data collection who consented to participate were eligible. Exclusion criteria Electronic records were excluded if they represented review visits, originated from other departments outside the OPD but were dispensed in the OPD pharmacy, had missing medication information in the system, or did not meet eligibility criteria. Prescribers who were absent during the survey period or declined consent were excluded. Sample size For the electronic component, no sample size calculation was performed because the study extracted all eligible antibiotic dispensing/prescribing records within the specified period from the hospital electronic system. For the prescriber survey, the hospital’s human resource directorate listed 54 prescribers (doctors and physician assistants). A census approach (complete enumeration) was intended; 35 prescribers participated (response rate: 64.8%). Data sources and measurement Electronic prescribing/dispensing data (2014–2021) The Hospital Administration Management Software (HAMS) provided data about antibiotic usage. The researchers created a data extraction checklist (see Supplementary File 2_Data Extraction Tool) that used WHO prescribing indicators from 1993 and national and international prescribing assessment methods for its structure [21], [22], [39]. The researchers extracted multiple variables, which included the date of prescribing and dispensing the antibiotic, together with its name and category and the required dosage information needed for DDD calculations, the route of administration, the diagnosis, laboratory test results, and the total number of medications prescribed during each patient visit and the available drug prices. The researchers used DDD methodology from the World Health Organization 2020) to summarise antibiotic consumption, which they reported as DDD per 1 000 inhabitants per day. This method establishes a standard usage metric which enables both benchmarking and trend analysis [37]. Prescriber survey (February – July 2021) The research team created their questionnaire (see Supplementary File 1_Prescriber Survey), which they distributed through Google Forms. The study collected data about socio-demographic factors and professional characteristics through items that included age, sex, profession, years of practice, patient load, and enabling/facility factors, which supported rational use of medicines by documenting the existence of both a drug information unit and a drug and therapeutics committee [20], [29]. Prescribers reported their constraints, which impacted their ability to follow the rational use of medicines standards. Data quality assurance The researchers tested the data extraction checklist through its application on the HAMS system at Ahafo Regional Hospital, which they used to examine whether all needed data points could be collected. The team confirmed that all extracted information met requirements for completeness, consistency and accuracy before proceeding with the analysis. The researchers examined the prescriber questionnaire to improve its clarity through a design that would reduce the chances of respondents leaving questions unanswered. Variables and operational definitions Antibiotic utilisation (DDD metrics) : computed per WHO DDD definitions and summarised as DDD/1,000 inhabitants/day [36], [37]. Common antibiotics prescribed : frequency and percentage distribution of antibiotic types over 2014–2021. Prescribing practice outcome (survey) : binary indicator reflecting whether the prescriber reported antibiotic prescribing in the assessed context (Yes/No), used for association testing with prescriber and facility factors. Explanatory variables (survey) : age group, sex, profession, years of practice, patient load, consultation duration, and reported presence of DIU/DTC, and client demand. Bias and how it was addressed The researchers decreased selection bias from the electronic dataset by extracting all OPD antibiotic records which met the research criteria during the designated time period. Information bias was minimised through the use of a standardised extraction checklist based on WHO indicators [35]. The researchers tried to eliminate bias from non-response in the survey by conducting a complete assessment of all prescribers and providing a short electronic tool which would increase participation. Statistical analysis The researchers used Microsoft Excel to export data and conducted their analysis with STATA version 17. The researchers calculated descriptive statistics, which they presented through categorical variables as frequencies and percentages and through continuous variables as means and standard deviations. The prescriber survey component used chi-square tests to examine bivariate relationships between prescriber and facility characteristics and their antibiotic prescribing patterns. The study used binary logistic regression to examine variables that showed significant relationships and all major theoretical predictors by calculating crude odds ratios and adjusted odds ratios with 95% confidence intervals. The researchers established statistical significance at p < 0.05. Ethics approval and consent to participate This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Ethical approval was obtained from the Institutional Review Board of Kwame Nkrumah University of Science and Technology (CHRPE/AP/098/21). Institutional permission was obtained from the Bono Regional Hospital administration and the regional directorate of health services. For the electronic records, patient identifiers were not extracted; confidentiality was maintained through the use of anonymised identifiers and secure storage. For the prescriber survey, informed consent was obtained electronically, and participation was voluntary. All data were used strictly for academic purposes and stored in password-protected electronic formats with access limited to the research team. Results Overview A total of 1,014,368 outpatient antibiotic prescription records from January 2014 to December 2021 were extracted from the Hospital Administration Management Software (HAMS). In addition, 35 out of 54 eligible prescribers participated in the survey (response rate: 64.8%). Results are presented in four sections: (i) prescriber characteristics, (ii) antibiotic consumption measured using DDD, (iii) commonly prescribed antibiotics and trends, and (iv) factors associated with antibiotic prescribing practices. Prescriber characteristics Among the 35 prescribers (see Table 1), the majority were male and medical doctors. The mean age was 30.11 years (SD ± 6.63), and the mean years of practice was 5.17 years (SD ± 4.79). Most prescribers had fewer than six years of practice and reported seeing ≤20 patients per day. A large proportion reported the availability of a Drug Information Unit (DIU) and a Drug and Therapeutic Committee (DTC) within the facility. Approximately half indicated that clients demand antibiotics during consultations. Table 1: Socio-demographic Characteristics of Prescribers at Bono Regional Hospital, 2021 Characteristics Frequency (n=35) Percentage Age in years Mean (SD) 30.11 ± 6.63 20-25 12 34.29 26-30 10 28.57 31-35 5 14.29 ≥36 8 22.86 Sex Female 11 31.43 Male 24 68.57 Profession Doctor 25 71.43 Physician Assistant 10 28.57 Years of practice Mean (SD) 5.17 ± 4.79 <6 23 65.71 ≥6 12 34.29 Number of patients seen during a day Mean (SD) 27.26 ± 33.21 ≤20 18 51.43 21-30 10 28.57 ≥31 7 20.00 Average duration to attend to a patient (minutes) Mean (SD) 15.51 ± 19.48 ≤10 18 51.43 11-20 12 34.29 ≥21 5 14.29 Availability of DIU in the facility No 9 25.71 Yes 26 74.29 Availability of DTC in the facility No 8 22.86 Yes 27 77.14 Clients demand antibiotics No 17 48.57 Yes 18 51.43 DIU: Drug Information Unit; DTC: Drug and Therapeutic Com mittee; SD: Standard Deviation Antibiotic consumption (Defined Daily Dose – DDD) Overall trend in antibiotic consumption Antibiotic consumption expressed as DDD per 1,000 inhabitants per day demonstrated fluctuations across the eight-year period (see Figure 1). A progressive increase was observed from 2014, with a temporary decline in 2020, followed by a marked rise in 2021. In 2021, antibiotic consumption was equivalent to 11 DDD per 1,000 inhabitants per day. Antibiotic-specific DDD trends Across the study period, antibiotic-specific consumption varied considerably. Amoxicillin, doxycycline, and cefuroxime showed substantial increases during peak years. In 2021, doxycycline, amoxicillin, erythromycin, ciprofloxacin, cefuroxime, azithromycin, and clindamycin contributed significantly to overall DDD values (see Table 2). Table 2: Antibiotic Consumption (DDD per 1,000 Inhabitants per Day) by Antibiotic Type, 2014–2021 Antibiotics 2014 2015 2016 2017 2018 2019 2020 2021 Cefuroxime 3.00 56.00 68.00 77.00 100.00 130.00 130.00 83.00 Amoxicillin 54.00 82.00 42.00 174.00 156.00 196.00 270.00 214.00 Ciprofloxacin 12.00 32.00 43.00 52.00 48.00 70.00 73.00 65.00 Flucloxacillin 13.00 12.00 5.00 10.00 26.00 23.00 32.00 33.00 Erythromycin 13.00 32.00 10.00 64.00 50.00 90.00 0.00 116.00 Doxycycline 66.00 270.00 186.00 186.00 274.00 500.00 170.00 42.00 Clarithromycin 7.00 24.00 10.00 32.00 20.00 66.00 16.00 21.00 Azithromycin 7.00 36.00 21.00 76.00 62.00 59.00 60.00 81.00 Clindamycin 12.00 13.00 30.00 43.00 56.00 60.00 56.00 90.00 Commonly prescribed antibiotics Across the eight-year period (see Table 3), Amoxicillin/clavulanic acid was the most frequently prescribed antibiotic, followed by erythromycin, ciprofloxacin, and amoxicillin. A relatively small number of antibiotics accounted for a substantial proportion of total prescriptions. Table 3: Most Commonly Prescribed Antibiotics at Bono Regional Hospital, 2014-2021 Antibiotics Frequency Percentage Amoxicillin + clavulanic acid 146924 14.39 Erythromycin 116816 11.44 Ciprofloxacin 116003 11.36 Amoxicillin 110527 10.83 Cefuroxime 94136 9.22 Clindamycin 92772 9.09 Flucloxacillin 92332 9.04 Amoxicillin 91203 8.93 Doxycycline 62366 6.11 Azithromycin 35195 3.45 Clindamycin 33534 3.28 Clarithromycin 29120 2.85 Trend in total antibiotic prescriptions The total number of antibiotic prescriptions increased steadily from 2014 to 2019, declined in 2020, and rose again in 2021, reaching the highest level observed during the study period (see Figure 2). Yearly distribution of antibiotics The most frequently prescribed antibiotic varied by year. Flucloxacillin predominated in 2014; ciprofloxacin was most frequent in 2015 and 2016; Amoxicillin/clavulanic acid dominated in 2017 and 2018; while erythromycin and amoxicillin were prominent in later years (see Table 4). Table 4: Distribution of Antibiotics Prescribed by Year at Bono Regional Hospital, 2014–2021 Antibiotics Prescribed Year 2014 2015 2016 2017 2018 2019 2020 2021 N (%) N (%) N (%) N (%) N (%) N (%) N (%) N (%) Cefuroxime 728 (1.23) 10140 (10.54) 10296 (16.74) 10556 (8.41) 14664 (10.47) 18512 (10.14) 17108 (10.77) 11492 (6.04) Amoxicillin + clavulanic acid 14560 (24.56) 20696 (21.51) 1456 (2.37) 18200 (14.50) 18200 (12.99) 24960 (13.67) 20696 (13.03) 26936 (14.15) Amoxicillin 4368 (7.37) 2184 (2.27) 6552 (10.65) 13104 (10.44) 14196 (10.13) 6552 (3.59) 21840 (13.75) 21840 (11.48) Amoxicillin 4888 (8.25) 6916 (7.19) 4732 (7.69) 16380 (13.05) 13364 (9.54) 24336 (13.32) 28704 (18.08) 10556 (5.55) Ciprofloxacin 5096 (8.60) 11752 (12.22) 12896 (20.96) 14248 (11.35) 13936 (9.95) 20020 (10.96) 19240 (12.12) 18096 (9.51) Flucloxacillin 11648 (19.65) 8736 (9.08) 2912 (4.73) 5512 (4.39) 15184 (10.84 13104 (7.17) 16640 (10.48) 18096 (9.51) Erythromycin 5824 (9.82) 11648 (12.11) 2912 (4.73) 17472 (13.92) 14560 (10.39) 26208 (14.35) 5824 (3.67) 32032 (16.83) Doxycycline 2912 (4.91) 9984 (10.38) 5616 (9.13) 5096 (4.06) 8008 (5.72) 14224 (7.79) 4472 (2.82) 11648 (6.12) Clarithromycin 1456 (2.46) 4368 (4.54) 1456 (2.37) 4368 (3.48) 2912 (2.08) 9360 (5.12) 2080 (1.31) 2912 (1.53) Azithromycin 936 (1.58) 3952 (4.11) 1872 (3.04) 6292 (5.01) 5408 (3.86) 4992 (2.73) 4680 (2.95) 6760 (3.55) Clindamycin 3952 (6.67) 2912 (3.03) 5824 (9.47) 4472 (3.56) 2184 (1.56) 4368 (2.39) 2940 (1.85) 6656 (3.50) Clindamycin 2912 (4.91) 2912 (3.03) 4992 (8.11) 9828 (7.83) 17472 (12.47) 16016 (8.77) 14560 (9.17) 23296 (12.24) Total 59280 (100) 96200 (100) 61516 (100) 125528 (100) 140088 (100) 182652 (100) 158784 (100) 190320 (100) N: Total number of antibiotics prescribed; %: Percen tage of antibiotics prescribed Reasons for non-adherence to rational use of medicines (RUM) standards The most commonly reported reason for not fully adhering to RUM standards was delayed laboratory results (see Figure 3). Other contributing factors were reported less frequently. Factors associated with antibiotic prescribing practices Bivariate analysis (see Table 5) demonstrated statistically significant associations between antibiotic prescribing and prescribers’ age, years of practice, and availability of a Drug and Therapeutic Committee. No significant association was observed with sex, patient load, consultation duration, or availability of a Drug Information Unit. Table 5: Bivariate Analysis of Factors Associated with Antibiotic Prescribing Practices at Bono Regional Hospital, 2021 Antibiotics prescribed P-value Variables No Yes n (%) n (%) Age in years 0.005* 20-25 9 (47.37) 3 (18.75) 26-30 8 (42.11) 2 (12.50) 31-35 1 (5.26) 4 (25.00) 36+ 1 (5.26) 7 (43.75) Sex 0.983 Female 6 (31.58) 5 (31.25) Male 13 (68.42) 11 (68.75) Years of practice 0.003* <6 17 (89.47) 6 (37.50) 6+ 2 (10.53) 10 (62.50) Number of patients seen during a day 0.905 20 9 (47.37) 9 (56.25) 21-30 6 (31.58) 4 (25.00) 31+ 4 (21.05) 3 (18.75) Average duration to attend to a patient (minutes) 0.806 ≤10 10 (52.63) 8 (50.00) 11-20 7 (36.84) 5 (31.25) ≥21 2 (10.53) 3 (18.75) Availability of DIU in the facility 0.460 No 6 (31.58) 3 (18.75) Yes 13 ()68.42 13 (81.25) Availability of DTC in the facility 0.047* No 7 (36.84) 1 (6.25) Yes 12 (63.16) 15 (93.75) Clients Demand Antibiotics 0.877 No 9 (47.37) 8 (50.00) Yes 10 (52.63) 8 (50.00) DIU: Drug Information Unit; DTC: Drug and Therapeutic Committee Logistic regression analysis In crude (bivariate) logistic regression (see Table 6), prescribers aged ≥36 years and those with ≥6 years of practice experience had significantly higher odds of prescribing antibiotics compared to their reference groups. However, in the adjusted model, none of the independent variables remained statistically significant after controlling for confounders. Table 6: Bivariate and Multivariable Logistic Regression of Factors Associated with Antibiotic Prescribing Practices, 2021 Variables Bivariate Logistic Regression Multiple Logistic Regression COR (95% CI) p-value AOR (95% CI) p-value Age in years 20-25 1 (Ref.) 1 (Ref.) 26-30 0.75 0.10 – 5.69 0.781 1.10 0.11 – 10.59 0.933 31-35 12.00 0.94 – 15.89 0.056 6.78 0.33 – 13.39 0.214 36+ 21.00 1.78 – 48.10 0.016 * 13.40 0.21 – 87.69 0.223 Sex Female 1 (Ref.) Male 1.02 0.24 – 4.26 0.983 Years of practice <6 1 (Ref.) 1 (Ref.) 6+ 14.17 2.39 – 84.07 0.004 * 1.96 0.09 – 44.31 0.672 Availability of DTC in the facility No 1 (Ref.) 1 (Ref.) Yes 8.75 0.94 – 81.26 0.056 7.17 0.42 – 12.83 0.173 * Statistically significant at p<0.05; DTC: Drug and Therapeutic Committee; Ref: Base Reference; COR: Crude Odds Ratio; AOR: Adjusted Odds Ratio; CI: Confidence Interval Discussions The research examined outpatient antibiotic prescribing patterns at a regional hospital in Ghana over an eight-year period while investigating factors that influenced prescribers’ antibiotic prescribing behaviour. Three principal findings emerged. First, outpatient antibiotic consumption, expressed as DDD per 1,000 inhabitants per day, increased over time with a marked rise in 2021. Second, a relatively small group of antibiotics, particularly amoxicillin/clavulanic acid, erythromycin, ciprofloxacin, and amoxicillin, accounted for a substantial proportion of prescriptions. The bivariate analysis showed that prescriber age and years of experience affected antibiotic prescribing, but the statistical significance of these relationships disappeared after adjustment. The total drug consumption of DDD in 2021 reached a value of 11 DDD per 1000 inhabitants per day. DDD serves as a standardised measurement unit which does not show the actual daily medication requirements for individual patients [36] but functions as a benchmarking tool that helps track changes in antibiotic use patterns [37]. The upward trajectory that researchers observed throughout the research period indicates that hospital facilities experienced higher rates of antibiotic treatment for patients who visited outpatient services. The healthcare system experienced a temporary decline in 2020, but medical services showed to have recovered by 2021. The study however, did not intend to determine what specific factors brought about these changes. DDD values require careful interpretation because they fail to include clinical severity assessment and case-mix analysis together with pharmacokinetic data from individual patients [36]. The study period saw amoxicillin/clavulanic acid become the most common antibiotic prescription, which was followed by erythromycin and ciprofloxacin and amoxicillin. The pattern demonstrates similarity with research results from different locations, which show that penicillins and fluoroquinolones represent the main antibiotics used in outpatient settings [25], [33], [40], [41]. Ghanaian hospitals have reported similar patterns, which researchers have documented [25], [26]. The concentration of prescribing within a limited number of antibiotic classes raises stewardship concerns. The persistent administration of a limited selection of broad-spectrum medications will result in more frequent development of antimicrobial resistance because microbiological testing occurs at rare intervals within these settings [8], [34]. The research results from Ghana show that ampicillin and cotrimoxazole, which are standard antibiotics, face increasing resistance rates that demonstrate the necessity for continuous monitoring and responsible use programs [42], [43], [44]. The study found two prescriber factors, which included their age together with their total years of practice, that influenced their decision to prescribe antibiotics. The study showed that older prescribers, together with those who possessed six or more years of experience, had a higher probability of prescribing antibiotics than their younger counterparts who had less work experience. The practice of prescribing in medical contexts shows similar patterns which emerge as doctors gain more clinical experience together with their professional development [45], [46]. The multivariable model revealed no significant relationships because the survey sample size restricted statistical analysis power, while potential confounding factors remained present. The adjusted analysis produced broad confidence intervals, which indicated measurement uncertainty and demanded careful assessment. The primary reason for medical professionals to fail to follow the rational use of medicines standards involved late laboratory results. Diagnostic confirmation delays lead to increased empirical antibiotic prescribing, which researchers have found to exist in other low-resource environments [31], [32]. The practice of empirical prescribing without conducting antimicrobial susceptibility testing results in two negative outcomes, which include improper antibiotic usage and development of resistance selection pressure [8]. Policy and stewardship implications The institutional stewardship mechanisms which include active Drug and Therapeutic Committees [29], [30] and better execution of national Standard Treatment Guidelines [21] demonstrate their institutional value. The World Health Organization [35] recommends routine monitoring of prescribing indicators which together with periodic antibiotic utilisation audits will enable better antibiotic usage. The implementation of faster laboratory test results will help decrease the need for empirical antibiotic treatment, which leads to unnecessary antibiotic use. Strengths and limitations The research benefits from its extensive eight-year retrospective dataset, which enables researchers to track outpatient antibiotic usage patterns at the regional referral hospital. The combination of DDD usage data with prescriber survey information creates a comprehensive view. The study includes multiple limitations which require examination. The DDD system fails to measure actual patient dosing practices and appropriate clinical use (World Health Organization, 2020). The prescriber survey produced moderate response rates yet generated insufficient data to achieve reliable statistical outcomes. The study conducted its research at one regional hospital, which restricted its findings to that specific location. The research created no link between antibiotic resistance data and prescribing records, which stopped researchers from analysing the connection between antibiotic use and resistance development. Conclusion Outpatient antibiotic prescribing at Bono Regional Hospital increased over the eight-year study period, with amoxicillin/clavulanic acid and other broad-spectrum agents accounting for a substantial share of prescriptions. Although prescriber age and experience were associated with antibiotic prescribing in unadjusted analyses, these associations did not persist after adjustment. Delayed laboratory results were frequently reported as a barrier to rational prescribing. The implementation of enhanced antimicrobial stewardship programs, together with regular prescription audits and better diagnostic capabilities, will result in more effective antibiotic management, which will help reduce the spread of antimicrobial resistance. Declarations Consent for Publication Not applicable. Availability of Data and Materials The datasets generated and/or analysed during the current study are not publicly available due to institutional data protection policies but are available from the corresponding author on reasonable request and with permission from Bono Regional Hospital. Conflict of Interest / Competing Interests The authors declare no competing interests concerning the work presented in this manuscript. Funding The authors declare that no funding was received for this manuscript's research, authorship, or publication. Author Contributions PG conceived the study, coordinated data collection, and contributed to manuscript drafting. YMB performed the statistical analyses and contributed to the interpretation of results. JL contributed to data acquisition and assisted with data validation. MBU conceptualised and supervised the study, provided methodological oversight, contributed to data analysis and interpretation, and critically revised the manuscript for important intellectual content. GOT contributed to the literature review, data interpretation, and manuscript revision. YGF and WKA supported data collection and contributed to manuscript editing. All authors read and approved the final manuscript. Acknowledgements The authors thank the management and staff of Bono Regional Hospital for granting permission to access prescription records. We also acknowledge the prescribers who participated in the survey. References E. Getachew, S. Aragaw, W. Adissie, Agalu, and A, “Antibiotic prescribing pattern in a referral hospital in Ethiopia,” 7(38) , vol. 2657, pp. 2657–2661, 2013, doi: 10.5897/AJPP12.505. S. Leekha, C. L. Terrell, Edson, and R. S, “General principles of antimicrobial therapy,” Mayo Clin Proc , vol. 86, pp. 156–167, 2011, doi: 10.4065/mcp.2010.0639. T. Alameri, Narayana, and G, “Assessment of Antibiotic Prescribing Pattern in Pediatric Patients : A Cross sectional Hospital based Survey,” 2017, doi: 10.4103/cjhr.cjhr. S. Malo, L. Bjerrum, C. Feja, M. J. Lallana, A. Poncel, and M. J. Rabanaque, “Antibiotic prescribing in acute respiratory tract infections in general practice ଝ,” Anales de Pediatría (English Edition) , vol. 82, no. 6, pp. 412–416, 2015, doi: 10.1016/j.anpede.2015.05.009. World Health Organization, “National Drug Policy,” 2003. M. Adorka, K. Allen, M. Lubbe, Serfontein, and J, “The Impact of Healthcare Providers Knowledge on Appropriate Prescribing of Antibiotics,” J Pharm Care , vol. 1, no. 4, 2013. The World Health Organisation, “Antibiotic resistance: multi-country public awareness survey,” 2015. C. L. Ventola, “The antibiotic resistance crisis: part 1: causes and threats,” Pharmacy and Therapeutics , vol. 40, no. 4, 2015. A. N. Kimanga, “‘A situational analysis of antimicrobial drug resistance in Africa: are we losing the battle?’ Ethiopian Journal of Health Sciences, 22(2), 135–143,” 2012. N. D. Amaha, D. G. Weldemariam, N. Abdu, and E. H. Tesfamariam, “Prescribing practices using WHO prescribing indicators and factors associated with antibiotic prescribing in six community pharmacies in Asmara , Eritrea : a cross-sectional study,” 5 , vol. 1, pp. 1–7, 2019. D. Jasovsk?, J. Littmann, A. Zorzet, Cars, and O, “Antimicrobial resistancea threat to the worlds sustainable development,” Ups. J. Med. Sci. , vol. 121, no. 3, 2016. M. Chlabicz, Chlabicz, and S, ““Outpatient use of systemic antibiotics in Poland: 2004-2008,” ” Epidemiological Review , vol. 68, no. 1, pp. 435–441, 2014. C. R. Lee, J. H. Lee, L. W. Kang, B. C. Jeong, and S. H. Lee, “Educational effectiveness, target, and content for prudent antibiotic use,” 2015. A. Kotwani, Holloway, and K, “Access to antibiotics in New Delhi, India: implications for antibiotic policy,” J. Pharm. Policy Pract. , vol. 6, no. 1, 2013. Centers for Disease Control and Prevention, “Antibiotic use in the United States, 2017: progress and opportunities,” 2017. B. E. Ider, A. Clements, J. Adams, M. Whitby, Muugolog, and T, “Prevalence of hospital-acquired infections and antibiotic use in two tertiary Mongolian hospitals,” J Hosp Infect. , vol. 75, no. 214, p. 016, 2010, doi: 10.1016/j.jhin.2010.01.016. Kairuki and S, “Situation analysis and recommendations: antibiotic use and resistance in,” 2011. [Online]. Available: https://cddep.org/publications/%0Asituation_analysis_and_recommendations_antibiotic_use_%0Aand_resistance_kenya/ E. Admassie, B. Begashaw, Hailu, and W, “Assessment of drug use practices and completeness of prescriptions in Gondar University Teaching Referral Hospital,” Int. J. Pharm. Sci. Res. , vol. 4, no. 1, 2013. Drug Administration and Control Authority of Ethiopia, “Management Sciences for Health, Strengthening Pharmaceutical Systems,” 2019. Holloway and K, “The world medicines situation 2011 rational use of medicines,” 2011. [Online]. Available: http://www.who.int/medicines/areas/policy/world_medicines_situation/WMS_ch14_w%0ARational.pdf Ghana National Drugs Programme, “Ghana National Drugs Programme (GNDP), Standard Treatment Guidelines,” 2010. E. O. Afriyie et al. , “Antibiotics Availability and Usage in Health Facilities: A Case of the Offinso-South Municipality of Ghana,” J Biol , vol. 5, no. 2, pp. 132–138, 2015. M. A. Ahiabu, B. P. Tersbl, R. Biritwum, I. C. Bygbjerg, Magnussen, and P, “A retrospective audit of antibiotic prescriptions in primary health-care facilities in Eastern Region, Ghana,” Health Policy Plan. , vol. 31, no. 2, 2016. R. Ofori-Asenso and A. A. Agyeman, “A review of injection and antibiotic use at primary health care (public and private) centers in Africa,” J. Pharm. Bioallied Sci. , vol. 7, no. 3, 2015. J. Prah, J. Kizzie-Hayford, E. Walker, Ampofo-Asiama, and A, “Antibiotic prescription pattern in a Ghanaian primary health care facility,” Pan African Medical Journal , vol. 28, no. 1, pp. 1–10, 2017, doi: 10.11604/pamj.2017.28.214.13940. A. Labi et al. , “Antibiotic prescribing in paediatric inpatients in Ghana?: a multi-centre point prevalence survey,” 2018. A. E. Joda and R. I. Aderemi-williams, “Full Length Research Paper A comparative study of prescribing patterns in two tertiary care teaching hospitals in Lagos , Nigeria,” 2(1) , vol. 41, pp. 41–46, 2013. A. A. Desalegn, “Assessment of drug use pattern using WHO prescribing indicators at Hawassa University teaching and referral hospital, south Ethiopia: a cross-sectional study,” BMC Health Serv. Res. , vol. 13, no. 1, pp. 1–6, 2013. The World Health Organisation, “Promoting rational use of medicines : core components Patient Care Indicators : WHO Policy Perspectives of Medicines,” 2002. [Online]. Available: http://apps.who.int/medicinedocs/pdf/h3011e/h3011e.pdf The World Health Organisation, “Antibiotic resistance threats in the United States,2013,” 2014. [Online]. Available: http://www.bbc.com/news/health-%0A20354536 L. W. Van Buul et al. , “Factors influencing antibiotic prescribing in long-term care facilities: A qualitative in-depth study,” BMC Geriatr. , vol. 14, no. 1, pp. 1–11, 2014, doi: 10.1186/1471-2318-14-136. E. D. Chem, D. N. Anong, and J. F. K. T. Akoachere, “Prescribing patterns and associated factors of antibiotic prescription in primary health care facilities of Kumbo East and Kumbo West Health Districts, North West Cameroon,” PLoS One , vol. 13, no. 3, pp. 1–18, 2018, doi: 10.1371/journal.pone.0193353. F. Worku, Tewahido, and D, “Retrospective Assessment of Antibiotics Prescribing at Public Primary Healthcare Facilities in Addis Ababa , Ethiopia,” 2018. C. F. Amábile-Cuevas, Global perspectives of antibiotic resistance . 2010. The World Health Organisation, “WHO_DAP_93,” 1993. The World Health Organisation, “Defined Daily Dose (DDD),” 2020. [Online]. Available: https://www.who.int/toolkits/atc-ddd-toolkit/about-ddd W H O Collaborating Centre for Drug Statistics Methodology, “Guidelines for ATC classification and DDD assignment,” Vegetatio , vol. 70, p. 70, 2010. Ghana Statistical Service, “2010 Population and Housing Census Report,” 2014. D. A. and C. A. of Ethiopia, “Drug Administration and Control Authority of Ethiopia; Management Sciences for Health, Strengthening Pharmaceutical Systems,” 2019. L. Dong, H. Yan, Wang, and D, “Antibiotic prescribing patterns in village health clinics across 10 provinces of Western China,” Journal of Antimicrobial Chemotherapy , vol. 62, no. 2, pp. 410–415, 2008. S. Mollahaliloglu, A. Alkan, B. Donertas, S. Ozgulcu, Akici, and A, “Assessment of antibiotic prescribing at different hospitals and primary health care facilities,” Saudi Pharmaceutical Journal , vol. 21, no. 3, pp. 281–291, 2013. M. J. Newman, E. Frimpong, E. S. Donkor, J. A. Opintan, Asamoah-Adu, and A, “Resistance to antimicrobial drugs in Ghana,” Infect. Drug Resist. , vol. 4, no. 215, 2011. Dayie et al, “Multidrug-resistant Streptococcus Pneumoniae isolates from healthy Ghanaian preschool children,” Microb Drug Resist. , vol. 21, pp. 636–642, 2015. Ayie et al, “Penicillin resistance and serotype distribution of Streptococcus Pneumoniae in Ghanaian children less than six years of age,” BMC Infect Disease , vol. 13, p. 490, 2013. G. Cadieux, R. Tamblyn, D. Dauphinee, Libman, and M, “Predictors of inappropriate antibiotic prescribing among primary care physicians,” Cmaj , vol. 177, no. 8, pp. 877–883, 2007. M. M. Opoku, H. A. Bonful, and K. A. Koram, “Antibiotic prescription for febrile outpatients: a health facility-based secondary data analysis for the Greater Accra region of Ghana,” BMC Health Serv. Res. , vol. 20, no. 1, pp. 1–11, 2020, doi: 10.1186/s12913-020-05771-9. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile1PrescriberSurvey.pdf SupplementaryFile2DataExtractionTool.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 12 May, 2026 Reviews received at journal 28 Apr, 2026 Reviews received at journal 20 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviews received at journal 19 Mar, 2026 Reviewers agreed at journal 15 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers invited by journal 03 Mar, 2026 Editor invited by journal 03 Mar, 2026 Editor assigned by journal 02 Mar, 2026 Submission checks completed at journal 02 Mar, 2026 First submitted to journal 23 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Sunyani","correspondingAuthor":false,"prefix":"","firstName":"Wollie","middleName":"Kwadwo","lastName":"Abraham","suffix":""}],"badges":[],"createdAt":"2026-02-23 17:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8949575/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8949575/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104072804,"identity":"05eae628-5cd5-4087-bb9c-3c22b357bc23","added_by":"auto","created_at":"2026-03-06 12:18:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42499,"visible":true,"origin":"","legend":"\u003cp\u003eAntibiotic Consumption Rate at Bono Regional Hospital OPD (2014–2021) Expressed as DDD per 1,000 Inhabitants per Day\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8949575/v1/e7421756c379da77a445cb99.png"},{"id":104072805,"identity":"ba13912f-52da-448e-b976-ebf3fcaa6dcf","added_by":"auto","created_at":"2026-03-06 12:18:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54349,"visible":true,"origin":"","legend":"\u003cp\u003eSecular Trend in Total Number of Antibiotic Prescriptions at Bono Regional Hospital, 2014–2021\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8949575/v1/32099f31c409a3988d016ac2.png"},{"id":104072806,"identity":"06e17870-86b3-4b50-a9aa-1878d7cf89a2","added_by":"auto","created_at":"2026-03-06 12:18:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53041,"visible":true,"origin":"","legend":"\u003cp\u003eReported Reasons for Non-Adherence to Rational Use of Medicines Standards among Prescribers, 2021\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8949575/v1/852a43f2f069c2da5384d7d6.png"},{"id":104072808,"identity":"9dcbd494-7355-4c8a-8e33-402f9ab28a39","added_by":"auto","created_at":"2026-03-06 12:18:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":122009,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1PrescriberSurvey.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8949575/v1/812a66bab20ee620dc74a69f.pdf"},{"id":104402577,"identity":"6cc5b501-139c-4770-ae98-c2ad19364b6c","added_by":"auto","created_at":"2026-03-11 12:15:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":155667,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile2DataExtractionTool.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8949575/v1/8a69f8c7818ccd55872e07c4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Antibiotic prescribing trends and determinants in a regional hospital in Ghana: An eight- year retrospective analysis with cross-sectional survey","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAntibiotics function as medications which treat bacterial infections through their ability to stop bacteria from growing or to eliminate bacterial organisms [1], [2]. The process of selecting antibiotics for patient treatment presents clinicians with challenges because they must make decisions during times when their medical diagnosis remains uncertain and other clinical obligations demand their attention [3], [4]. The rational use of medicines requires that patients receive their necessary medications through proper dosing and treatment length, which costs them and society the least amount of money [5]. According to this principle, prescribers must first confirm an infection exists before they can use diagnostic tools to assess infection status and base their treatment decisions on established clinical guidelines, which help reduce unnecessary antimicrobial contact [1], [3], [6].\u003c/p\u003e \u003cp\u003eAntimicrobial resistance (AMR) represents a significant global public health danger, which occurs through improper antimicrobial usage that includes both unnecessary drug prescriptions and wrong medication dosages and breaches of established treatment protocols [7], [8]. The overuse of antibiotics has resulted in diminished effectiveness with various antibacterial medications because of emerging resistant pathogens which spread throughout populations [5], [9], [10]. Antibacterial resistance creates multiple dangers, which include treatment failure and patient death and extended hospital duration, and increased medical expenses thus raising worries about a future \"post-antibiotic era\" where ordinary infections turn into hard-to-treat conditions [11]. Pathogenic bacteria develop resistance through the selective pressure which arises from antibiotic misuse, thus demonstrating that healthcare facilities must restrict their antibiotic usage to essential and vital treatment needs [12], [13].\u003c/p\u003e \u003cp\u003eOutpatient evidence demonstrates that excessive antibiotic prescription practices continue to exist as a major problem. The consensus study conducted in India discovered that excessive prescription practices serve as a primary factor which leads to resistance development [14]. In the United States, the Centres for Disease Control and Prevention estimated that 30% of outpatient antibiotic prescriptions are unnecessary [15]. In many low- and middle-income settings, antibiotic exposure is even more extensive: antibiotics are prescribed for a large proportion of hospitalised patients and frequently inappropriately [16], and it has been projected that a sizeable share of medicines in Africa, including antibiotics, are used inappropriately [17]. Prescribing patterns also vary across countries and levels of care, reflecting differences in disease epidemiology, access to diagnostics, and implementation of clinical guidelines [18], [19], [20].\u003c/p\u003e \u003cp\u003e Ghana uses policy instruments which promote rational prescribing through its Essential Medicines List and Standard Treatment Guidelines [21]. Yet empirical evidence shows that medical professionals continue to prescribe antibiotics at high rates, which differ between different medical facilities [22], [23], [24], [25], [26]. The prescription practices at medical facilities result from various factors which include the prescribers' knowledge base and training level and practical experience as well as their access to drug guidelines and drug information and drug and therapeutics committees and their existing facility restrictions which include restricted diagnostic resources and slow laboratory analysis processing times [27], [28], [29], [30], [31], [32]. These influences can create two effects, which include rising empirical prescribing practices and healthcare providers who use only limited antibiotic options, which leads to faster development of resistance [33], [34]\u003c/p\u003e \u003cp\u003eCurrent research about antibiotic prescription in Ghana has increased, but there are still knowledge gaps that affect specific geographical areas and healthcare facilities. Existing research lacks sufficient organised studies that evaluate outpatient antibiotic prescription practices in the Bono Region through standardised assessment methods and World Health Organisation prescribing standards. The [35] states that organisations need to conduct regular assessments of their prescribing practices so they can discover specific medication usage issues, which will help them develop effective antibiotic management programs and make policy decisions. The Defined Daily Dose (DDD) metric enables researchers to measure antibiotic consumption, but it does not reflect how much doctors prescribe to their patients because DDD represents the typical daily dose for the primary medical usage of a medication [36], [37]. The development of specific evidence for each facility throughout multiple years, together with its connection to prescriber-reported factors, will create practical solutions that help organisations achieve better antibiotic usage. Accordingly, this study assessed outpatient antibiotic prescribing/consumption patterns at the Bono Regional Hospital, Sunyani, over an eight-year period, and examined prescriber-related factors associated with antibiotic prescribing practices using WHO rational drug-use prescribing indicators [35].\u003c/p\u003e\n\u003ch3\u003eStudy aim\u003c/h3\u003e\n\u003cp\u003eTo assess antibiotic prescribing patterns at the Bono Regional Hospital, Sunyani.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSpecific objectives\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo determine the defined daily doses (DDD) of outpatient antibiotic use/prescriptions at the Bono Regional Hospital.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo identify the most commonly prescribed antibiotics at the hospital.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo examine factors associated with clinicians\u0026rsquo; antibiotic prescribing practices.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eWe conducted an observational study which included two distinct components. The first component involved a retrospective examination of outpatient antibiotic dispensing and prescribing records, which were maintained in an electronic hospital system throughout eight years from January 2014 until December 2021. The second component involved a facility-based cross-sectional survey, which evaluated prescriber characteristics that affected their antibiotic prescribing behaviour between February and July 2021. The World Health Organization WHO [35] rational drug-use prescribing indicators framework was used to evaluate antibiotic prescribing patterns.\u003c/p\u003e \u003c/div\u003e \n\u003ch3\u003eStudy setting\u003c/h3\u003e\n\u003cp\u003e The study was undertaken at the Bono Regional Hospital, Sunyani, located in the Bono Region of Ghana. Sunyani is the regional capital, and the hospital provides outpatient and inpatient services to residents within the metropolis and surrounding districts [38].\u003c/p\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSecondary (electronic) data\u003c/h2\u003e \u003cp\u003eThe electronic dataset comprised outpatient records for patients aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years who received an antibiotic prescription/dispensing episode at the hospital outpatient department (OPD) within January 2014\u0026ndash;December 2021. Adults were selected because WHO-defined daily dose (DDD) metrics are primarily standardised for adult use and are not routinely determined for children [36].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePrimary (prescriber survey) data\u003c/h2\u003e \u003cp\u003eThe survey population comprised prescribers working in the hospital during the study period, including medical doctors and physician assistants. Prescriber characteristics were collected to examine associations with antibiotic prescribing practices.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEligibility criteria\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eInclusion criteria\u003c/h2\u003e \u003cp\u003eFor the electronic records, OPD encounters for patients aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years with an antibiotic dispensed/prescribed for a new episode of illness between January 2014 and December 2021 were eligible. For each patient, the first eligible visit with an antibiotic record within the period was selected, where applicable.\u003c/p\u003e \u003cp\u003eFor the prescriber survey, clinicians/physician assistants present during data collection who consented to participate were eligible.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eExclusion criteria\u003c/h2\u003e \u003cp\u003eElectronic records were excluded if they represented review visits, originated from other departments outside the OPD but were dispensed in the OPD pharmacy, had missing medication information in the system, or did not meet eligibility criteria. Prescribers who were absent during the survey period or declined consent were excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSample size\u003c/h2\u003e \u003cp\u003eFor the electronic component, no sample size calculation was performed because the study extracted all eligible antibiotic dispensing/prescribing records within the specified period from the hospital electronic system.\u003c/p\u003e \u003cp\u003eFor the prescriber survey, the hospital\u0026rsquo;s human resource directorate listed 54 prescribers (doctors and physician assistants). A census approach (complete enumeration) was intended; 35 prescribers participated (response rate: 64.8%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eData sources and measurement\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eElectronic prescribing/dispensing data (2014\u0026ndash;2021)\u003c/h2\u003e \u003cp\u003eThe Hospital Administration Management Software (HAMS) provided data about antibiotic usage. The researchers created a data extraction checklist (see Supplementary File 2_Data Extraction Tool) that used WHO prescribing indicators from 1993 and national and international prescribing assessment methods for its structure [21], [22], [39]. The researchers extracted multiple variables, which included the date of prescribing and dispensing the antibiotic, together with its name and category and the required dosage information needed for DDD calculations, the route of administration, the diagnosis, laboratory test results, and the total number of medications prescribed during each patient visit and the available drug prices.\u003c/p\u003e \u003cp\u003eThe researchers used DDD methodology from the World Health Organization 2020) to summarise antibiotic consumption, which they reported as DDD per 1 000 inhabitants per day. This method establishes a standard usage metric which enables both benchmarking and trend analysis [37].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePrescriber survey (February \u0026ndash; July 2021)\u003c/h2\u003e \u003cp\u003eThe research team created their questionnaire (see Supplementary File 1_Prescriber Survey), which they distributed through Google Forms. The study collected data about socio-demographic factors and professional characteristics through items that included age, sex, profession, years of practice, patient load, and enabling/facility factors, which supported rational use of medicines by documenting the existence of both a drug information unit and a drug and therapeutics committee [20], [29]. Prescribers reported their constraints, which impacted their ability to follow the rational use of medicines standards.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eData quality assurance\u003c/h2\u003e \u003cp\u003eThe researchers tested the data extraction checklist through its application on the HAMS system at Ahafo Regional Hospital, which they used to examine whether all needed data points could be collected. The team confirmed that all extracted information met requirements for completeness, consistency and accuracy before proceeding with the analysis. The researchers examined the prescriber questionnaire to improve its clarity through a design that would reduce the chances of respondents leaving questions unanswered.\u003c/p\u003e \u003cp\u003e \u003cb\u003eVariables and operational definitions\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAntibiotic utilisation (DDD metrics)\u003c/b\u003e: computed per WHO DDD definitions and summarised as DDD/1,000 inhabitants/day [36], [37].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCommon antibiotics prescribed\u003c/b\u003e: frequency and percentage distribution of antibiotic types over 2014\u0026ndash;2021.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePrescribing practice outcome (survey)\u003c/b\u003e: binary indicator reflecting whether the prescriber reported antibiotic prescribing in the assessed context (Yes/No), used for association testing with prescriber and facility factors.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eExplanatory variables (survey)\u003c/b\u003e: age group, sex, profession, years of practice, patient load, consultation duration, and reported presence of DIU/DTC, and client demand.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eBias and how it was addressed\u003c/h2\u003e \u003cp\u003eThe researchers decreased selection bias from the electronic dataset by extracting all OPD antibiotic records which met the research criteria during the designated time period. Information bias was minimised through the use of a standardised extraction checklist based on WHO indicators [35]. The researchers tried to eliminate bias from non-response in the survey by conducting a complete assessment of all prescribers and providing a short electronic tool which would increase participation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe researchers used Microsoft Excel to export data and conducted their analysis with STATA version 17. The researchers calculated descriptive statistics, which they presented through categorical variables as frequencies and percentages and through continuous variables as means and standard deviations. The prescriber survey component used chi-square tests to examine bivariate relationships between prescriber and facility characteristics and their antibiotic prescribing patterns. The study used binary logistic regression to examine variables that showed significant relationships and all major theoretical predictors by calculating crude odds ratios and adjusted odds ratios with 95% confidence intervals. The researchers established statistical significance at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Ethical approval was obtained from the Institutional Review Board of Kwame Nkrumah University of Science and Technology (CHRPE/AP/098/21). Institutional permission was obtained from the Bono Regional Hospital administration and the regional directorate of health services. For the electronic records, patient identifiers were not extracted; confidentiality was maintained through the use of anonymised identifiers and secure storage. For the prescriber survey, informed consent was obtained electronically, and participation was voluntary. All data were used strictly for academic purposes and stored in password-protected electronic formats with access limited to the research team.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eOverview\u003c/h2\u003e\n\u003cp\u003eA total of 1,014,368 outpatient antibiotic prescription records from January 2014 to December 2021 were extracted from the Hospital Administration Management Software (HAMS). In addition, 35 out of 54 eligible prescribers participated in the survey (response rate: 64.8%). Results are presented in four sections: (i) prescriber characteristics, (ii) antibiotic consumption measured using DDD, (iii) commonly prescribed antibiotics and trends, and (iv) factors associated with antibiotic prescribing practices.\u003c/p\u003e\n\u003ch2\u003ePrescriber characteristics\u003c/h2\u003e\n\u003cp\u003eAmong the 35 prescribers (see Table 1), the majority were male and medical doctors. The mean age was 30.11 years (SD \u0026plusmn; 6.63), and the mean years of practice was 5.17 years (SD \u0026plusmn; 4.79). Most prescribers had fewer than six years of practice and reported seeing \u0026le;20 patients per day. A large proportion reported the availability of a Drug Information Unit (DIU) and a Drug and Therapeutic Committee (DTC) within the facility. Approximately half indicated that clients demand antibiotics during consultations.\u003c/p\u003e\n\u003cp\u003eTable 1: Socio-demographic Characteristics of Prescribers at Bono Regional Hospital, 2021\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eFrequency (n=35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge in years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e30.11 \u0026plusmn; 6.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e20-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e34.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e26-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e28.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e31-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e14.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026ge;36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e22.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e31.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e68.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProfession\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003eDoctor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e71.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003ePhysician Assistant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e28.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYears of practice\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e5.17 \u0026plusmn; 4.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026lt;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e65.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026ge;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e34.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of patients seen during a day\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e27.26 \u0026plusmn; 33.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026le;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e51.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e21-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e28.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026ge;31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage duration to attend to a patient (minutes)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e15.51 \u0026plusmn; 19.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026le;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e51.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e11-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e34.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026ge;21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e14.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAvailability of DIU in the facility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e25.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e74.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAvailability of DTC in the facility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e22.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e77.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClients demand antibiotics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e48.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 366px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e51.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cem\u003eDIU: Drug Information Unit; DTC: Drug and Therapeutic Com\u003c/em\u003e\u003cem\u003emittee; SD: Standard Deviation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch2\u003eAntibiotic consumption (Defined Daily Dose \u0026ndash; DDD)\u003c/h2\u003e\n\u003ch3\u003eOverall trend in antibiotic consumption\u003c/h3\u003e\n\u003cp\u003eAntibiotic consumption expressed as DDD per 1,000 inhabitants per day demonstrated fluctuations across the eight-year period (see Figure 1). A progressive increase was observed from 2014, with a temporary decline in 2020, followed by a marked rise in 2021. In 2021, antibiotic consumption was equivalent to 11 DDD per 1,000 inhabitants per day.\u003c/p\u003e\n\u003ch3\u003eAntibiotic-specific DDD trends\u003c/h3\u003e\n\u003cp\u003eAcross the study period, antibiotic-specific consumption varied considerably. Amoxicillin, doxycycline, and cefuroxime showed substantial increases during peak years. In 2021, doxycycline, amoxicillin, erythromycin, ciprofloxacin, cefuroxime, azithromycin, and clindamycin contributed significantly to overall DDD values (see Table 2).\u003c/p\u003e\n\u003cp\u003eTable 2: Antibiotic Consumption (DDD per 1,000 Inhabitants per Day) by Antibiotic Type, 2014\u0026ndash;2021\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"678\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eAntibiotics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eCefuroxime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e56.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e68.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e77.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e130.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e130.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e83.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eAmoxicillin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e54.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e82.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e42.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e174.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e156.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e196.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e270.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e214.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eCiprofloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e12.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e32.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e43.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e52.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e48.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e70.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e73.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e65.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eFlucloxacillin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e13.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e12.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e26.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e23.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e32.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e33.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eErythromycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e13.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e32.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e64.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e50.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e90.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e116.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eDoxycycline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e66.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e270.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e186.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e186.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e274.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e500.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e170.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e42.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eClarithromycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e24.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e32.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e66.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e16.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e21.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eAzithromycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e36.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e21.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e76.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e62.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e59.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e60.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e81.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eClindamycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e12.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e13.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e30.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e43.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e56.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e60.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e56.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e90.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch2\u003eCommonly prescribed antibiotics\u003c/h2\u003e\n\u003cp\u003eAcross the eight-year period (see Table 3), Amoxicillin/clavulanic acid was the most frequently prescribed antibiotic, followed by erythromycin, ciprofloxacin, and amoxicillin. A relatively small number of antibiotics accounted for a substantial proportion of total prescriptions.\u003c/p\u003e\n\u003cp\u003eTable 3: Most Commonly Prescribed Antibiotics at Bono Regional Hospital, 2014-2021\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"510\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 293px;\"\u003e\n \u003cp\u003eAntibiotics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 293px;\"\u003e\n \u003cp\u003eAmoxicillin + clavulanic acid\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e146924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e14.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 293px;\"\u003e\n \u003cp\u003eErythromycin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e116816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e11.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 293px;\"\u003e\n \u003cp\u003eCiprofloxacin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e116003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e11.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 293px;\"\u003e\n \u003cp\u003eAmoxicillin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e110527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e10.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 293px;\"\u003e\n \u003cp\u003eCefuroxime\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e94136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e9.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 293px;\"\u003e\n \u003cp\u003eClindamycin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e92772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e9.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 293px;\"\u003e\n \u003cp\u003eFlucloxacillin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e92332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e9.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 293px;\"\u003e\n \u003cp\u003eAmoxicillin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e91203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e8.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 293px;\"\u003e\n \u003cp\u003eDoxycycline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e62366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e6.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 293px;\"\u003e\n \u003cp\u003eAzithromycin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e35195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 293px;\"\u003e\n \u003cp\u003eClindamycin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e33534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 293px;\"\u003e\n \u003cp\u003eClarithromycin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e29120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eTrend in total antibiotic prescriptions\u003c/h2\u003e\n\u003cp\u003eThe total number of antibiotic prescriptions increased steadily from 2014 to 2019, declined in 2020, and rose again in 2021, reaching the highest level observed during the study period (see Figure 2).\u003c/p\u003e\n\u003ch2\u003eYearly distribution of antibiotics\u003c/h2\u003e\n\u003cp\u003eThe most frequently prescribed antibiotic varied by year. Flucloxacillin predominated in 2014; ciprofloxacin was most frequent in 2015 and 2016; Amoxicillin/clavulanic acid dominated in 2017 and 2018; while erythromycin and amoxicillin were prominent in later years (see Table 4).\u003c/p\u003e\n\u003cp\u003eTable 4: Distribution of Antibiotics Prescribed by Year at Bono Regional Hospital, 2014\u0026ndash;2021\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"990\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntibiotics Prescribed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" valign=\"bottom\" style=\"width: 864px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003eCefuroxime\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e728 (1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e10140 (10.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e10296 (16.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e10556 (8.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e14664 (10.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e18512 (10.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e17108 (10.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e11492 (6.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003eAmoxicillin + clavulanic acid\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e14560 (24.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e20696 (21.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1456 (2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e18200 (14.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e18200 (12.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e24960 (13.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e20696 (13.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e26936 (14.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003eAmoxicillin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4368 (7.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2184 (2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6552 (10.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e13104 (10.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e14196 (10.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6552 (3.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e21840 (13.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e21840 (11.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003eAmoxicillin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4888 (8.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6916 (7.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4732 (7.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e16380 (13.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e13364 (9.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e24336 (13.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e28704 (18.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e10556 (5.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003eCiprofloxacin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5096 (8.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e11752 (12.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e12896 (20.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e14248 (11.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e13936 (9.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e20020 (10.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e19240 (12.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e18096 (9.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003eFlucloxacillin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e11648 (19.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e8736 (9.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2912 (4.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5512 (4.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e15184 (10.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e13104 (7.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e16640 (10.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e18096 (9.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003eErythromycin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5824 (9.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e11648 (12.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2912 (4.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e17472 (13.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e14560 (10.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e26208 (14.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5824 (3.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e32032 (16.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003eDoxycycline\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2912 (4.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e9984 (10.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5616 (9.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5096 (4.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e8008 (5.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e14224 (7.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4472 (2.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e11648 (6.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003eClarithromycin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1456 (2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4368 (4.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1456 (2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4368 (3.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2912 (2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e9360 (5.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2080 (1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2912 (1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003eAzithromycin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e936 (1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3952 (4.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1872 (3.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6292 (5.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5408 (3.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4992 (2.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4680 (2.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6760 (3.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003eClindamycin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3952 (6.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2912 (3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5824 (9.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4472 (3.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2184 (1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4368 (2.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2940 (1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6656 (3.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003eClindamycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2912 (4.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2912 (3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4992 (8.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e9828 (7.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e17472 (12.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e16016 (8.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e14560 (9.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e23296 (12.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e59280 (100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e96200 (100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e61516 (100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e125528 (100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e140088 (100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e182652 (100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e158784 (100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e190320 (100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"bottom\" style=\"width: 990px;\"\u003e\n \u003cp\u003e\u003cem\u003eN: Total number of antibiotics prescribed; %: Percen\u003c/em\u003e\u003cem\u003etage of antibiotics prescribed\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch2\u003eReasons for non-adherence to rational use of medicines (RUM) standards\u003c/h2\u003e\n\u003cp\u003eThe most commonly reported reason for not fully adhering to RUM standards was delayed laboratory results (see Figure 3). Other contributing factors were reported less frequently.\u003c/p\u003e\n\u003ch2\u003eFactors associated with antibiotic prescribing practices\u003c/h2\u003e\n\u003cp\u003eBivariate analysis (see Table 5) demonstrated statistically significant associations between antibiotic prescribing and prescribers\u0026rsquo; age, years of practice, and availability of a Drug and Therapeutic Committee. No significant association was observed with sex, patient load, consultation duration, or availability of a Drug Information Unit.\u003c/p\u003e\n\u003cp\u003eTable 5: Bivariate Analysis of Factors Associated with Antibiotic Prescribing Practices at Bono Regional Hospital, 2021\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntibiotics prescribed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge in years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.005*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e20-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e9 (47.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e3 (18.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e26-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e8 (42.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2 (12.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e31-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1 (5.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e4 (25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e36+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1 (5.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e7 (43.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e6 (31.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e5 (31.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e13 (68.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e11 (68.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYears of practice\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.003*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u0026lt;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e17 (89.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e6 (37.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e6+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2 (10.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e10 (62.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of patients seen during a day\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e9 (47.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e9 (56.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e21-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e6 (31.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e4 (25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e31+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e4 (21.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e3 (18.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage duration to attend to a patient (minutes)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u0026le;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e10 (52.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e8 (50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e11-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e7 (36.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e5 (31.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u0026ge;21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2 (10.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e3 (18.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAvailability of DIU in the facility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e6 (31.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e3 (18.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e13 ()68.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e13 (81.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAvailability of DTC in the facility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.047*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e7 (36.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1 (6.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e12 (63.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e15 (93.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClients Demand Antibiotics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e9 (47.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e8 (50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e10 (52.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e8 (50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cem\u003eDIU: Drug Information Unit; DTC:\u0026nbsp;\u003c/em\u003e\u003cem\u003eDrug and Therapeutic Committee\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eLogistic regression analysis\u003c/h2\u003e\n\u003cp\u003eIn crude (bivariate) logistic regression (see Table 6), prescribers aged \u0026ge;36 years and those with \u0026ge;6 years of practice experience had significantly higher odds of prescribing antibiotics compared to their reference groups. However, in the adjusted model, none of the independent variables remained statistically significant after controlling for confounders.\u003c/p\u003e\n\u003cp\u003eTable 6: Bivariate and Multivariable Logistic Regression of Factors Associated with Antibiotic Prescribing Practices, 2021\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"637\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 12.5841%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 29.7443%;\"\u003e\n \u003cp\u003eBivariate Logistic Regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 26.6555%;\"\u003e\n \u003cp\u003eMultiple Logistic Regression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4657%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1265%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0377%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.0929%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5545%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8937%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5841%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge in years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4657%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.0929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8937%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5841%;\"\u003e\n \u003cp\u003e20-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4657%;\"\u003e\n \u003cp\u003e1 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.0929%;\"\u003e\n \u003cp\u003e1 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8937%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5841%;\"\u003e\n \u003cp\u003e26-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4657%;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1265%;\"\u003e\n \u003cp\u003e0.10 \u0026ndash; 5.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0377%;\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.0929%;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5545%;\"\u003e\n \u003cp\u003e0.11 \u0026ndash; 10.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8937%;\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5841%;\"\u003e\n \u003cp\u003e31-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4657%;\"\u003e\n \u003cp\u003e12.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1265%;\"\u003e\n \u003cp\u003e0.94 \u0026ndash; 15.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0377%;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.0929%;\"\u003e\n \u003cp\u003e6.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5545%;\"\u003e\n \u003cp\u003e0.33 \u0026ndash; 13.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8937%;\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5841%;\"\u003e\n \u003cp\u003e36+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4657%;\"\u003e\n \u003cp\u003e21.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1265%;\"\u003e\n \u003cp\u003e1.78 \u0026ndash; 48.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0377%;\"\u003e\n \u003cp\u003e0.016 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.0929%;\"\u003e\n \u003cp\u003e13.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5545%;\"\u003e\n \u003cp\u003e0.21 \u0026ndash; 87.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8937%;\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5841%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4657%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.0929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8937%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5841%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4657%;\"\u003e\n \u003cp\u003e1 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.0929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8937%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5841%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4657%;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1265%;\"\u003e\n \u003cp\u003e0.24 \u0026ndash; 4.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0377%;\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.0929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8937%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5841%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYears of practice\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4657%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.0929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8937%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5841%;\"\u003e\n \u003cp\u003e\u0026lt;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4657%;\"\u003e\n \u003cp\u003e1 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.0929%;\"\u003e\n \u003cp\u003e1 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8937%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5841%;\"\u003e\n \u003cp\u003e6+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4657%;\"\u003e\n \u003cp\u003e14.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1265%;\"\u003e\n \u003cp\u003e2.39 \u0026ndash; 84.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0377%;\"\u003e\n \u003cp\u003e0.004 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.0929%;\"\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5545%;\"\u003e\n \u003cp\u003e0.09 \u0026ndash; 44.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8937%;\"\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5841%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAvailability of DTC in the facility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4657%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.0929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8937%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5841%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4657%;\"\u003e\n \u003cp\u003e1 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.0929%;\"\u003e\n \u003cp\u003e1 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8937%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5841%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4657%;\"\u003e\n \u003cp\u003e8.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1265%;\"\u003e\n \u003cp\u003e0.94 \u0026ndash; 81.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0377%;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.0929%;\"\u003e\n \u003cp\u003e7.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5545%;\"\u003e\n \u003cp\u003e0.42 \u0026ndash; 12.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8937%;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 69.5559%;\"\u003e\n \u003cp\u003e\u003cem\u003e* Statistically significant at p\u0026lt;0.05; DTC: Drug and Therapeutic Committee; Ref: Base Reference; COR: Crude Odds Ratio; AOR: Adjusted Odds\u0026nbsp;\u003c/em\u003e\u003cem\u003eRatio; CI: Confidence Interval\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussions","content":"\u003cp\u003eThe research examined outpatient antibiotic prescribing patterns at a regional hospital in Ghana over an eight-year period while investigating factors that influenced prescribers\u0026rsquo; antibiotic prescribing behaviour. Three principal findings emerged. First, outpatient antibiotic consumption, expressed as DDD per 1,000 inhabitants per day, increased over time with a marked rise in 2021. Second, a relatively small group of antibiotics, particularly amoxicillin/clavulanic acid, erythromycin, ciprofloxacin, and amoxicillin, accounted for a substantial proportion of prescriptions. The bivariate analysis showed that prescriber age and years of experience affected antibiotic prescribing, but the statistical significance of these relationships disappeared after adjustment.\u003c/p\u003e\n\u003cp\u003eThe total drug consumption of DDD in 2021 reached a value of 11 DDD per 1000 inhabitants per day. DDD serves as a standardised measurement unit which does not show the actual daily medication requirements for individual patients [36] but functions as a benchmarking tool that helps track changes in antibiotic use patterns [37]. The upward trajectory that researchers observed throughout the research period indicates that hospital facilities experienced higher rates of antibiotic treatment for patients who visited outpatient services.\u003c/p\u003e\n\u003cp\u003eThe healthcare system experienced a temporary decline in 2020, but medical services showed to have recovered by 2021. The study however, did not intend to determine what specific factors brought about these changes. DDD values require careful interpretation because they fail to include clinical severity assessment and case-mix analysis together with pharmacokinetic data from individual patients [36].\u003c/p\u003e\n\u003cp\u003eThe study period saw amoxicillin/clavulanic acid become the most common antibiotic prescription, which was followed by erythromycin and ciprofloxacin and amoxicillin. The pattern demonstrates similarity with research results from different locations, which show that penicillins and fluoroquinolones represent the main antibiotics used in outpatient settings [25], [33], [40], [41]. Ghanaian hospitals have reported similar patterns, which researchers have documented [25], [26].\u003c/p\u003e\n\u003cp\u003eThe concentration of prescribing within a limited number of antibiotic classes raises stewardship concerns. The persistent administration of a limited selection of broad-spectrum medications will result in more frequent development of antimicrobial resistance because microbiological testing occurs at rare intervals within these settings [8], [34]. The research results from Ghana show that ampicillin and cotrimoxazole, which are standard antibiotics, face increasing resistance rates that demonstrate the necessity for continuous monitoring and responsible use programs [42], [43], [44].\u003c/p\u003e\n\u003cp\u003eThe study found two prescriber factors, which included their age together with their total years of practice, that influenced their decision to prescribe antibiotics. The study showed that older prescribers, together with those who possessed six or more years of experience, had a higher probability of prescribing antibiotics than their younger counterparts who had less work experience. The practice of prescribing in medical contexts shows similar patterns which emerge as doctors gain more clinical experience together with their professional development [45], [46].\u003c/p\u003e\n\u003cp\u003eThe multivariable model revealed no significant relationships because the survey sample size restricted statistical analysis power, while potential confounding factors remained present. The adjusted analysis produced broad confidence intervals, which indicated measurement uncertainty and demanded careful assessment.\u003c/p\u003e\n\u003cp\u003eThe primary reason for medical professionals to fail to follow the rational use of medicines standards involved late laboratory results. Diagnostic confirmation delays lead to increased empirical antibiotic prescribing, which researchers have found to exist in other low-resource environments [31], [32]. The practice of empirical prescribing without conducting antimicrobial susceptibility testing results in two negative outcomes, which include improper antibiotic usage and development of resistance selection pressure [8].\u003c/p\u003e\n\u003ch2\u003ePolicy and stewardship implications\u003c/h2\u003e\n\u003cp\u003eThe institutional stewardship mechanisms which include active Drug and Therapeutic Committees [29], [30] and better execution of national Standard Treatment Guidelines [21] demonstrate their institutional value. The World Health Organization [35] recommends routine monitoring of prescribing indicators which together with periodic antibiotic utilisation audits will enable better antibiotic usage. The implementation of faster laboratory test results will help decrease the need for empirical antibiotic treatment, which leads to unnecessary antibiotic use.\u003c/p\u003e\n\u003ch2\u003eStrengths and limitations\u003c/h2\u003e\n\u003cp\u003eThe research benefits from its extensive eight-year retrospective dataset, which enables researchers to track outpatient antibiotic usage patterns at the regional referral hospital. The combination of DDD usage data with prescriber survey information creates a comprehensive view.\u003c/p\u003e\n\u003cp\u003eThe study includes multiple limitations which require examination. The DDD system fails to measure actual patient dosing practices and appropriate clinical use (World Health Organization, 2020). The prescriber survey produced moderate response rates yet generated insufficient data to achieve reliable statistical outcomes. The study conducted its research at one regional hospital, which restricted its findings to that specific location. The research created no link between antibiotic resistance data and prescribing records, which stopped researchers from analysing the connection between antibiotic use and resistance development.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOutpatient antibiotic prescribing at Bono Regional Hospital increased over the eight-year study period, with amoxicillin/clavulanic acid and other broad-spectrum agents accounting for a substantial share of prescriptions. Although prescriber age and experience were associated with antibiotic prescribing in unadjusted analyses, these associations did not persist after adjustment. Delayed laboratory results were frequently reported as a barrier to rational prescribing. The implementation of enhanced antimicrobial stewardship programs, together with regular prescription audits and better diagnostic capabilities, will result in more effective antibiotic management, which will help reduce the spread of antimicrobial resistance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConsent for Publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of Data and Materials\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to institutional data protection policies but are available from the corresponding author on reasonable request and with permission from Bono Regional Hospital.\u003c/p\u003e\n\u003ch2\u003eConflict of Interest / Competing Interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests concerning the work presented in this manuscript.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe authors declare that no funding was received for this manuscript\u0026apos;s research, authorship, or publication.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003ePG conceived the study, coordinated data collection, and contributed to manuscript drafting. YMB performed the statistical analyses and contributed to the interpretation of results. JL contributed to data acquisition and assisted with data validation. MBU conceptualised and supervised the study, provided methodological oversight, contributed to data analysis and interpretation, and critically revised the manuscript for important intellectual content. GOT contributed to the literature review, data interpretation, and manuscript revision. YGF and WKA supported data collection and contributed to manuscript editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors thank the management and staff of Bono Regional Hospital for granting permission to access prescription records. We also acknowledge the prescribers who participated in the survey.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eE. Getachew, S. Aragaw, W. Adissie, Agalu, and A, \u0026ldquo;Antibiotic prescribing pattern in a referral hospital in Ethiopia,\u0026rdquo; \u003cem\u003e7(38)\u003c/em\u003e, vol. 2657, pp. 2657\u0026ndash;2661, 2013, doi: 10.5897/AJPP12.505.\u003c/li\u003e\n\u003cli\u003eS. Leekha, C. L. Terrell, Edson, and R. S, \u0026ldquo;General principles of antimicrobial therapy,\u0026rdquo; \u003cem\u003eMayo Clin Proc\u003c/em\u003e, vol. 86, pp. 156\u0026ndash;167, 2011, doi: 10.4065/mcp.2010.0639.\u003c/li\u003e\n\u003cli\u003eT. Alameri, Narayana, and G, \u0026ldquo;Assessment of Antibiotic Prescribing Pattern in Pediatric Patients : A Cross sectional Hospital based Survey,\u0026rdquo; 2017, doi: 10.4103/cjhr.cjhr.\u003c/li\u003e\n\u003cli\u003eS. Malo, L. Bjerrum, C. Feja, M. J. Lallana, A. Poncel, and M. J. Rabanaque, \u0026ldquo;Antibiotic prescribing in acute respiratory tract infections in general practice ଝ,\u0026rdquo; \u003cem\u003eAnales de Pediatr\u0026iacute;a (English Edition)\u003c/em\u003e, vol. 82, no. 6, pp. 412\u0026ndash;416, 2015, doi: 10.1016/j.anpede.2015.05.009.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization, \u0026ldquo;National Drug Policy,\u0026rdquo; 2003.\u003c/li\u003e\n\u003cli\u003eM. Adorka, K. Allen, M. Lubbe, Serfontein, and J, \u0026ldquo;The Impact of Healthcare Providers Knowledge on Appropriate Prescribing of Antibiotics,\u0026rdquo; \u003cem\u003eJ Pharm Care\u003c/em\u003e, vol. 1, no. 4, 2013.\u003c/li\u003e\n\u003cli\u003eThe World Health Organisation, \u0026ldquo;Antibiotic resistance: multi-country public awareness survey,\u0026rdquo; 2015.\u003c/li\u003e\n\u003cli\u003eC. L. 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Res.\u003c/em\u003e, vol. 4, no. 1, 2013.\u003c/li\u003e\n\u003cli\u003eDrug Administration and Control Authority of Ethiopia, \u0026ldquo;Management Sciences for Health, Strengthening Pharmaceutical Systems,\u0026rdquo; 2019.\u003c/li\u003e\n\u003cli\u003eHolloway and K, \u0026ldquo;The world medicines situation 2011 rational use of medicines,\u0026rdquo; 2011. [Online]. Available: http://www.who.int/medicines/areas/policy/world_medicines_situation/WMS_ch14_w%0ARational.pdf\u003c/li\u003e\n\u003cli\u003eGhana National Drugs Programme, \u0026ldquo;Ghana National Drugs Programme (GNDP), Standard Treatment Guidelines,\u0026rdquo; 2010.\u003c/li\u003e\n\u003cli\u003eE. O. Afriyie \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Antibiotics Availability and Usage in Health Facilities: A Case of the Offinso-South Municipality of Ghana,\u0026rdquo; \u003cem\u003eJ Biol\u003c/em\u003e, vol. 5, no. 2, pp. 132\u0026ndash;138, 2015.\u003c/li\u003e\n\u003cli\u003eM. A. Ahiabu, B. P. Tersbl, R. 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[Online]. Available: http://apps.who.int/medicinedocs/pdf/h3011e/h3011e.pdf\u003c/li\u003e\n\u003cli\u003eThe World Health Organisation, \u0026ldquo;Antibiotic resistance threats in the United States,2013,\u0026rdquo; 2014. [Online]. Available: http://www.bbc.com/news/health-%0A20354536\u003c/li\u003e\n\u003cli\u003eL. W. Van Buul \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Factors influencing antibiotic prescribing in long-term care facilities: A qualitative in-depth study,\u0026rdquo; \u003cem\u003eBMC Geriatr.\u003c/em\u003e, vol. 14, no. 1, pp. 1\u0026ndash;11, 2014, doi: 10.1186/1471-2318-14-136.\u003c/li\u003e\n\u003cli\u003eE. D. Chem, D. N. Anong, and J. F. K. T. 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Drug Resist.\u003c/em\u003e, vol. 4, no. 215, 2011.\u003c/li\u003e\n\u003cli\u003eDayie et al, \u0026ldquo;Multidrug-resistant Streptococcus Pneumoniae isolates from healthy Ghanaian preschool children,\u0026rdquo; \u003cem\u003eMicrob Drug Resist.\u003c/em\u003e, vol. 21, pp. 636\u0026ndash;642, 2015.\u003c/li\u003e\n\u003cli\u003eAyie et al, \u0026ldquo;Penicillin resistance and serotype distribution of Streptococcus Pneumoniae in Ghanaian children less than six years of age,\u0026rdquo; \u003cem\u003eBMC Infect Disease\u003c/em\u003e, vol. 13, p. 490, 2013.\u003c/li\u003e\n\u003cli\u003eG. Cadieux, R. Tamblyn, D. Dauphinee, Libman, and M, \u0026ldquo;Predictors of inappropriate antibiotic prescribing among primary care physicians,\u0026rdquo; \u003cem\u003eCmaj\u003c/em\u003e, vol. 177, no. 8, pp. 877\u0026ndash;883, 2007.\u003c/li\u003e\n\u003cli\u003eM. M. Opoku, H. A. Bonful, and K. A. Koram, \u0026ldquo;Antibiotic prescription for febrile outpatients: a health facility-based secondary data analysis for the Greater Accra region of Ghana,\u0026rdquo; \u003cem\u003eBMC Health Serv. Res.\u003c/em\u003e, vol. 20, no. 1, pp. 1\u0026ndash;11, 2020, doi: 10.1186/s12913-020-05771-9.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Antibiotic prescribing, Defined Daily Dose, Antimicrobial resistance, Rational drug use, Ghana","lastPublishedDoi":"10.21203/rs.3.rs-8949575/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8949575/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eInappropriate antibiotic use is a key driver of antimicrobial resistance (AMR), a growing global public health threat. Monitoring prescribing patterns using standardised metrics such as World Health Organisation (WHO) prescribing indicators and Defined Daily Dose (DDD) supports antimicrobial stewardship. Evidence on outpatient antibiotic prescribing in parts of Ghana remains limited. This study assessed antibiotic prescribing trends and associated prescriber factors at a regional hospital in Ghana.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA hospital-based observational study was conducted at Bono Regional Hospital, Sunyani. Retrospective outpatient antibiotic prescription data (January 2014\u0026ndash;December 2021) were extracted from the Hospital Administration Management Software. Antibiotic utilisation was quantified using DDD per 1,000 inhabitants per day. The study employed a cross-sectional survey of prescriber characteristics that influenced antibiotic prescribing practices. Descriptive statistics summarised prescribing patterns. Associations were examined using chi-square tests and binary logistic regression at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 1,014,368 outpatient antibiotic prescriptions were analysed. Antibiotic consumption increased over time, reaching 11 DDD per 1,000 inhabitants per day in 2021. The study determined that amoxicillin/clavulanic acid (14.39%) served as the most commonly prescribed antibiotic, whereas erythromycin (11.44%), ciprofloxacin (11.36%), and amoxicillin (10.83%) followed. Five antibiotics accounted for more than half of total prescriptions. Among 35 prescribers (response rate: 64.8%), age and years of practice were associated with prescribing in bivariate analysis but were not significant in multivariable models. Delayed laboratory results were the most frequently reported barrier to rational prescribing.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOutpatient antibiotic prescribing increased over time with substantial reliance on broad-spectrum agents. There is a need for better management of healthcare resources and enhanced diagnostic facilities, which will help achieve proper antibiotic usage and control antimicrobial resistance.\u003c/p\u003e","manuscriptTitle":"Antibiotic prescribing trends and determinants in a regional hospital in Ghana: An eight- year retrospective analysis with cross-sectional survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-06 12:18:49","doi":"10.21203/rs.3.rs-8949575/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-12T19:26:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T09:54:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-20T23:50:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17775486587575214509548893105617950166","date":"2026-04-02T11:30:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223699311219716101693367217404104884573","date":"2026-03-30T19:32:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"225584958211872532379831692736010603107","date":"2026-03-30T07:04:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-19T20:32:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213502509108542735724528591232270574586","date":"2026-03-15T05:59:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155972706767283707562340483550001428877","date":"2026-03-06T20:20:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-03T18:59:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-03T12:52:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-02T09:11:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-02T09:10:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2026-02-23T17:24:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"48ae7086-9f2a-42be-909f-676484ab92c9","owner":[],"postedDate":"March 6th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-12T19:26:26+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T19:38:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-06 12:18:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8949575","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8949575","identity":"rs-8949575","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}