# Prevalence and patterns of antibiotic prescriptions in Conakry hospitals, Guinea: a multicentre cross-sectional survey

Thibaut Armel Chérif Gnimadi, Kadio Jean-Jacques Olivier Kadio, Aïssata Camara, Castro Gbêmêmali Hounmenou, Saidouba Cherif Camara, Salifou Talassone Bangoura, Elsie Hermine Ogoumma, Alpha Kabiné Keita, Djiba Kaba, Tiguidanke Camara, et al.

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Thibaut Armel Chérif Gnimadi, Kadio Jean-Jacques Olivier Kadio, Aïssata Camara, Castro Gbêmêmali Hounmenou, Saidouba Cherif Camara, et al. Prevalence and patterns of antibiotic prescriptions in Conakry hospitals, Guinea: a multicentre cross-sectional survey. JAC-Antimicrobial Resistance, 2025, 7 (6), pp.dlaf223. 10.1093/jacamr/dlaf223. hal-05377034

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# Prevalence and patterns of antibiotic prescriptions in Conakry hospitals, Guinea: a multicentre cross-sectional survey

Thibaut Armel Chérif Gnimadi ID <fnref n="1" />,²*, Kadio Jean-Jacques Olivier Kadio<fnref n="1" />,³, Aïssata Camara³,⁴,
Castro Gbêmêmali Hounmenou<fnref n="1" />,⁵, Saidouba Cherif Camara<fnref n="1" />, Salifou Talassone Bangoura<fnref n="1" />,³,
Elsie Hermine Ogoumma<fnref n="1" />, Alpha Kabiné Keita<fnref n="1" />,³, Djiba Kaba ID <fnref n="1" />,³, Tiguidanke Camara<fnref n="1" />, Yamoussa Youla<fnref n="1" />,³,
Amadou Daye Diallo⁶, Mamadou Saliou Sow<fnref n="1" />,⁶, Mano Joseph Mathew²,⁷, Alpha Kabinet Keita<fnref n="1" />,³
and Abdoulaye Touré<fnref n="1" />,³

*Corresponding author. E-mail: armel.gnimadi@cerfig.org

<figure><img src="image_2.png" /></figure>

Received 29 June 2025; accepted 4 November 2025

**Background:** Inappropriate use of antibiotics is a major driver of antimicrobial resistance (AMR), particularly in low- and middle-income countries. Understanding antibiotic use and patterns in hospital settings is essential for promoting rational use and optimizing antimicrobial stewardship (AMS). This study aims to assess the extent of antibiotic prescribing in secondary and tertiary hospitals in Conakry, Guinea, and to evaluate the appropriateness of these prescriptions on the basis of WHO recommendations via the Access, Watch, Reserve (AWaRe) classification of antibiotics.

**Methods:** A multicentre cross-sectional survey was conducted from June to October 2024 to assess patient anti-biotic use levels across six hospital wards in Conakry, capital of Guinea. The prevalence of antibiotic prescriptions, with 95% confidence intervals (CI), was compared across patient, prescriber and ward variables. Antibiotic use was categorized by Anatomic Therapeutic Chemical and AWaRe classifications. Associations between categorical variables were assessed using the Chi-square or Fisher's exact test. Univariate and multivariate logistic regression were used to analyse factors associated with antibiotic prescription.

**Results:** Of 1482 patients surveyed, the overall prevalence of antibiotic prescriptions was 35.0% (95% CI: 32.6-37.5), with significant differences between inpatients (83.4%, 95% CI: 78.1-87.6) and outpatients (25.1%, 95% CI: 22.7-27.6). The total number of antibiotics prescribed was 669, and the most commonly prescribed antibiotics were beta-lactams/beta-lactamase inhibitors (24.2%), followed by third-generation cephalosporins (21.7%), imidazoles (18.5%), and penicillins (13.6%). Almost all antibiotic courses (99.4%) were started empirically, without microbiological testing to guide choice. Regarding the AWaRe classification of all prescribed antibiotics, Access antibiotics accounted for 64.3% (430/669), and 33.3% (223/669) were from the Watch group.

**Conclusions:** The results of this study, conducted in six hospital departments, provide an overview of antibiotic prescriptions in Conakry hospitals, with a high prevalence of antibiotic prescription, particularly among inpatients and almost all courses were initiated empirically without microbiological guidance. These findings underscore the urgent need for AMS programs and interventions.

<footnote n="1">¹Centre de Recherche et de Formation en Infectiologie de Guinée (CERFIG), Université Gamal Abdel Nasser de Conakry, Conakry, Guinea;
²Laboratoire de Génomique, Bioinformatique et Chimie Moléculaire, EA7528, Conservatoire National des Arts et Métiers, HESAM Université, 2 Rue Conté, Paris 75003, France;
³Faculté des Sciences et Techniques de la Santé, Université Gamal Abdel Nasser de Conakry, Conakry, Guinea;
⁴Parasitology Unit, Institut Pasteur of Guinea, Conakry, Guinea;
⁵Université de Labé, Département Informatique, Labé, Guinea;
⁶Service des Maladies Infectieuses et Tropicales, Hôpital National Donka, Conakry, Guinea;
⁷EFREI Research Lab, Panthéon Assas University, 30-32 Avenue de la République, Villejuif 94800, France</footnote>
## Introduction

Antimicrobial resistance (AMR) is a serious public health problem worldwide, and it is considered one of the top 10 threats to global public health, and is projected to cause nearly 39 million deaths between 2025 and 2050.¹ According to a report published in 2019, almost 1.27 million deaths were associated with antibiotic-resistant bacterial infections, much higher than the death rate attributed to HIV or malaria.² The highest burden of deaths attributable to AMR organisms is observed in sub-Saharan Africa.²,³

Beyond regional disparities, the global public health impact of excessive, inappropriate and unnecessary antimicrobial use has been demonstrated by the risks of the development and emergence of pathogen resistance to antimicrobials, increased hospitalization rates, increased treatment costs, adverse events, and wasted resources.⁴,⁵ Despite this existing evidence, the use of antibiotics in resource-limited countries is weakly regulated and poorly tracked.⁶ Antibiotic consumption increased by 65% between 2000 and 2015, with the most significant increases observed in low- and middle-income countries, where antibiotics are prescribed to 25%-50% of patients and may represent up to 30% of total medication expenditures.⁷,⁸ Studies have also shown that over 50% of outpatient prescriptions are dispensed inappropriately.⁹ Between 20% and 50% of all antibiotics prescribed in acute care hospitals are unnecessary or inappropriate.¹⁰,¹¹

In Guinea, a lower-middle-income country in West Africa with an estimated population of 14 million, evidence shows that the prevalence of antibiotic prescriptions in healthcare facilities varies by context and level of care. Conakry, the capital city, concentrates most tertiary-level hospitals, while rural areas remain largely underserved. The country adopted a National Action Plan on antimicrobial resistance in line with the WHO Global Action Plan,¹² although implementation and antimicrobial stewardship activities remain very limited. The healthcare system is predominantly financed through out-of-pocket payments, with minimal government-funded insurance coverage.¹³ A study carried out in rural areas revealed that 62.9% of febrile patients were prescribed at least one antibiotic,¹⁴ while another study in an urban setting, in one department of a national hospital in Conakry, reported an antibiotic prescription frequency of 21%.¹⁵

Many prescriptions of antibiotics are not aligned with evidence-based guidelines.¹⁶ To address this issue and mitigate the global threat of AMR, WHO has developed initiatives aimed at supporting countries and hospitals in monitoring antibiotic use, promoting appropriate prescribing practices and evaluating the effectiveness of antimicrobial stewardship programs. Among these initiatives are the establishment of the Global Antimicrobial Resistance and Use Surveillance System (GLASS) in 2015 and the introduction of the Access, Watch, and Reserve (AWaRe) antibiotic classification framework in 2017.¹⁷⁻¹⁹ Under the AWaRe framework, Access antibiotics includes narrow-spectrum antibiotics recommended as first- and second-line empiric treatments for the most common clinical syndromes.²⁰ Watch antibiotics generally include broad-spectrum antibiotics, which are critically important for human medicine but have a greater potential to cause AMR and thus should be used for critically ill patients in hospital settings. The Reserve antibiotics include antibiotics used as a last resort and are reserved for multidrug-resistant pathogens.²⁰ WHO recommends that at least 70% of national antibiotic consumption

should come from Access group antibiotics, with minimal use of Reserve antibiotics.²¹ By stratifying antibiotics into Access, Watch, and Reserve groups, the AWaRe tool guides prescribers and policymakers towards rational prescribing.²² This framework promotes rational use, supports antimicrobial stewardship (AMS), and facilitates monitoring of antibiotic consumption at national and global levels.²³ Despite efforts to promote and operationalize these tools, implementation and integration remain a challenge in many resource-limited countries.

Consequently, ongoing monitoring on the basis of descriptive studies of their frequency of use is essential to understand how they are prescribed and used in line with recommendations. In these times of emerging infectious diseases, such an approach seems crucial to prevent the emergence of bacterial resistance, guarantee the effectiveness of treatments and protect the health of the population. We aimed to assess the extent of antibiotic prescribing in secondary and tertiary hospitals in Conakry, Guinea, and to evaluate the appropriateness of these prescriptions on the basis of WHO recommendations via the AWaRe classification of antibiotics.

## Materials and methods

### Study design, setting, and approach

This study was conducted in two health facilities in Conakry, Guinea, where public healthcare comprises health posts and health centres (primary level), prefectural and regional hospitals (secondary level) and national hospitals (tertiary level). The study focused on secondary and tertiary levels.

At the secondary level, data were collected from the Municipality Medical Centre Matam (CMC/Matam), specifically within the general medicine and gynaecology wards. At the tertiary level, the study was conducted in two national hospitals, the National Hospital of Donka and the Sino-Guinean Friendship Hospital, within the dermatology, traumatology, infectious disease and emergency wards.

This was a cross-sectional study including both inpatients and outpatients who received their first antibiotic prescription after visiting the wards. Outpatients were eligible if they attended the department between 8:00 a.m. and 2:00 p.m., whereas inpatients were eligible if they had been admitted the previous day or before 8:00 a.m. and had received their first antibiotic prescription during that period. Patients who had been hospitalized for several days and were already receiving antibiotic treatment, as well as those with known histories of HIV infection or tuberculosis, were excluded.

### Sampling size and data collection

The sample size was calculated based on patients as the unit of analysis. For each department, the Schwartz formula was applied using the reported frequency of antibiotic prescription from a previous study conducted in an internal medicine ward in Guinea,¹⁵ with a 10% margin of error. This resulted in a required sample size of 280 patients per ward, applied to both levels of facility included in the study.

Data were collected from June to October 2024 via individual survey forms supplemented by direct patient interviews and information extracted from medical records and prescriptions. The data collection tool was developed based on a modified WHO Methodology for Point Prevalence Surveys (WHO-PPS) on Antibiotic Use in Hospitals, version 1.1.²⁴

Before full implementation, two final-year pharmacy students were trained on the questionnaire and data collection procedures. Pharmacy students were selected as data collectors due to their background knowledge of antibiotics. A pilot test was conducted to refine the tool and ensure its appropriateness and reliability.
## Data analysis

Descriptive statistics were used to summarize the characteristics of patients, healthcare providers, healthcare facilities and prescribed antibiotics. The prevalence of antibiotic prescriptions, distribution of antibiotic characteristics by patients and prescribers, and information by hospital wards and AWaRe classification were determined at the patient level (i.e. the number of participants with at least one antibiotic prescribed). The proportion of prescribed antibiotics, AWaRe antibiotics, classification by classes and distribution across hospital wards and patients' status at the antibiotic level (total number of individual prescribed antibiotics).

Comparisons were made by using the Chi-square test. For contingency tables including small, expected cell counts, particularly in the analysis of antibiotics distribution by patient status and across hospital wards, Fisher's exact test with Monte Carlo simulation (10000 replicates) was used to ensure the robustness of the statistical inference. Factors associated with antibiotic prescription were analysed with univariate and multivariate logistic regression.

A significance level of 0.05 was considered, and results were interpreted at a 95% Confidence intervals (CI). Finally, we use the WHO ATC and AWaRe classification¹⁷ to categorize and evaluate the practice of antibiotic prescription in this study. All analyses were performed using R statistical software (version 4.4.1).²⁵

## Ethical consideration and consent to participate

The study was approved by the National Ethics Committee for Health Research of Guinea (Comité National d'Éthique pour la Recherche en Santé—CNERS) under registration number **127/CNERS/24**. Before data collection, official authorization was obtained from the administrative leadership of each participating hospital.

All participants were provided with information and a consent form that explained the purpose of the study. Patients were included only after providing their informed consent, in accordance with the principles of ethical research.

## Results

### Demographic characteristics of patients, prescribers and hospital structures

A total of 1482 patients received medication prescriptions. The patient's age ranged from 1 month to 97 years, with a mean age of 35.5 ± 18.7 years. Females accounted for 57.6% of all participants. These patients were recruited from six different departments as follows: 19.6% (291/1482) from dermatology, 19.9% (295/1482) from emergency medicine, 8.4% (124/1482) from general medicine, 18.7% (277/1482) from gynaecology, 19.8% (294/1482) from traumatology and 13.6% (201/1482) from infectious diseases. The majority of patients were seen in outpatient consultations (82.9%, n=1229/1482), whereas 17% (n=253/1482) were hospitalized.

Regarding the prescriber profiles, general practitioners represented 60% (n=888/1482), followed by specialists (24.5%, n=363/1482) and medical interns (24.5%, n=230/1482). Furthermore, 71.3% (n=1056/1482) of prescribers had more than 5 years of professional experience (Table 1).

### Prevalence of antibiotic use

Among the 1482 patients included, 519 (35.0%; 95% CI: 32.6–37.5) received at least one prescribed antibiotic. The proportion

<table><caption>**Table 1.** Characteristics of patients, prescribers, and hospitals with antibiotic prescribing in Conakry, Guinea (June–October 2024)</caption><thead><tr><th rowspan="2">Characteristic</th><th rowspan="2">n (%)</th><th colspan="2">Antibiotics prescribing</th><th rowspan="2">P value</th></tr><tr><th>No n (%)</th><th>Yes n (%)</th></tr></thead><tbody><tr><td>Overall</td><td>1482</td><td>963 (65.0)</td><td>519 (35.0)</td><td></td></tr><tr><td>Sex</td><td></td><td></td><td></td><td>0.030</td></tr><tr><td>Male</td><td>629 (42.4)</td><td>389 (61.8)</td><td>240 (38.2)</td><td></td></tr><tr><td>Female</td><td>853 (57.6)</td><td>574 (67.3)</td><td>279 (32.7)</td><td></td></tr><tr><td>Age group</td><td></td><td></td><td></td><td>0.4</td></tr><tr><td>1-23 months</td><td>13 (0.89)</td><td>8 (61.5)</td><td>5 (38.6)</td><td></td></tr><tr><td>2-18 years</td><td>177 (12.16)</td><td>106 (59.9)</td><td>71 (40.1)</td><td></td></tr><tr><td>18-49 years</td><td>949 (65.2)</td><td>627 (66.0)</td><td>322 (34)</td><td></td></tr><tr><td>&gt;50 years</td><td>317 (21.7)</td><td>207 (65.3)</td><td>110 (34.7)</td><td></td></tr><tr><td>Occupation</td><td></td><td></td><td></td><td>0.2</td></tr><tr><td>Civil servant</td><td>300 (20.2)</td><td>197 (65.7)</td><td>103 (34.3)</td><td></td></tr><tr><td>Marketer</td><td>182 (12.3)</td><td>124 (68.1)</td><td>58 (31.9)</td><td></td></tr><tr><td>Self-employed professional</td><td>597 (40.3)</td><td>397 (66.5)</td><td>200 (33.5)</td><td></td></tr><tr><td>Unemployed</td><td>403 (28.2)</td><td>245 (60.8)</td><td>158 (39.2)</td><td></td></tr><tr><td>Study level</td><td></td><td></td><td></td><td>0.6</td></tr><tr><td>Educated</td><td>896 (60.5)</td><td>577 (64.4)</td><td>319 (35.6)</td><td></td></tr><tr><td>No educated</td><td>586 (39.5)</td><td>386 (65.9)</td><td>200 (34.1)</td><td></td></tr><tr><td>Marital status</td><td></td><td></td><td></td><td>0.047</td></tr><tr><td>Married</td><td>913 (61.6)</td><td>611 (67.0)</td><td>302 (33.0)</td><td></td></tr><tr><td>Single</td><td>569 (38.4)</td><td>352 (61.9)</td><td>217 (38.1)</td><td></td></tr><tr><td>Patient status</td><td></td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>Outpatient</td><td>1229 (82.9)</td><td>921 (74.9)</td><td>308 (25.1)</td><td></td></tr><tr><td>Inpatient</td><td>253 (17.0)</td><td>42 (16.6)</td><td>211 (83.4)</td><td></td></tr><tr><td>Prescriber</td><td></td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>Specialist</td><td>363 (24.5)</td><td>198 (54.55)</td><td>165 (45.45)</td><td></td></tr><tr><td>General practitioner</td><td>888 (60.0)</td><td>605 (68.1)</td><td>283 (31.9)</td><td></td></tr><tr><td>Medical interns</td><td>230 (15.5)</td><td>160 (69.6)</td><td>70 (30.4)</td><td></td></tr><tr><td>Prescribers' experience</td><td></td><td></td><td></td><td>0.2</td></tr><tr><td>&lt;5 years</td><td>426 (28.7)</td><td>288 (67.6)</td><td>138 (32.4)</td><td></td></tr><tr><td>&gt;5 years</td><td>1056 (71.3)</td><td>675 (64)</td><td>381 (36.0)</td><td></td></tr><tr><td>Prescription wards</td><td></td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>Dermatology</td><td>291 (19.6)</td><td>211 (72.5)</td><td>80 (27.5)</td><td></td></tr><tr><td>Emergency</td><td>295 (19.9)</td><td>174 (59)</td><td>121 (41.0)</td><td></td></tr><tr><td>General medicine</td><td>124 (8.4)</td><td>100 (80.65)</td><td>24 (19.35)</td><td></td></tr><tr><td>Gynaecology</td><td>277 (18.7)</td><td>178 (64.3)</td><td>99 (35.7)</td><td></td></tr><tr><td>Traumatology</td><td>294 (19.8)</td><td>146 (49.7)</td><td>148 (50.3)</td><td></td></tr><tr><td>Infectious diseases</td><td>201 (13.6%)</td><td>154 (76.6)</td><td>47 (23.4)</td><td></td></tr><tr><td>Hospital level</td><td></td><td></td><td></td><td>0.033</td></tr><tr><td>Secondary hospital</td><td>401 (27.1)</td><td>278 (69.3)</td><td>123 (30.7)</td><td></td></tr><tr><td>Tertiary hospital</td><td>1081 (72.9)</td><td>685 (63.4)</td><td>396 (36.6)</td><td></td></tr></tbody></table>

of prescriptions containing antibiotics was not statistically influenced by patients' age, profession or level of education. However, it was significantly higher among hospitalized patients (83.4%; 95% CI: 77.1–87.6) than among outpatients (25.1%; 95% CI: 22.7–27.6; *P*<0.001). Medical specialists and general practitioners were the leading prescribers of antibiotics, followed by medical interns (45.4%, 31.9%, and 30.4%, respectively; *P*<0.001). Furthermore, antibiotic prescription frequencies varied by the level of healthcare facility, with higher rates observed in
<table><caption>**Table 2.** Multivariable logistic regression analysis of factors associated with antibiotic prescribing in Conakry, Guinea (June-October 2024)</caption><thead><tr><th>Characteristic</th><th>Unadjusted OR (95% CI)</th><th>P-value</th><th>Adjusted OR (95% CI)</th><th>P-value</th></tr></thead><tbody><tr><td>Patient Sex</td><td></td><td></td><td></td><td></td></tr><tr><td>Female</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Male</td><td>1.27 (1.02 – 1.57)</td><td>0.030</td><td>1.21 (0.91 – 1.61)</td><td>0.2</td></tr><tr><td>Patient Marital status</td><td></td><td></td><td></td><td></td></tr><tr><td>Single</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Married</td><td>0.80 (0.64 – 1.00)</td><td>0.047</td><td>0.91 (0.67 – 1.24)</td><td>0.6</td></tr><tr><td>Patient status</td><td></td><td></td><td></td><td></td></tr><tr><td>Outpatient</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Inpatient</td><td>15.0 (10.6 – 21.7)</td><td>&lt;0.001</td><td>23.7 (15.8 – 36.5)</td><td>&lt;0.001</td></tr><tr><td>Prescriber</td><td></td><td></td><td></td><td></td></tr><tr><td>Medical interns</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Specialist physician</td><td>1.90 (1.35 – 2.71)</td><td>&lt;0.001</td><td>0.47 (0.21 – 0.96)</td><td>0.049</td></tr><tr><td>General practitioner</td><td>1.07 (0.78 – 1.47)</td><td>0.700</td><td>0.19 (0.05 – 0.73)</td><td>0.019</td></tr><tr><td>Prescribers' experience</td><td></td><td></td><td></td><td></td></tr><tr><td>≤5 years</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>&gt;5 years</td><td>1.18 (0.93 – 1.50)</td><td>0.2</td><td>0.94 (0.59 – 1.48)</td><td>0.8</td></tr><tr><td>Hospital ward</td><td></td><td></td><td></td><td></td></tr><tr><td>General medicine</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Infectious diseases</td><td>1.27 (0.74 – 2.24)</td><td>0.4</td><td>0.44 (0.22 – 0.87)</td><td>0.017</td></tr><tr><td>Emergency</td><td>2.90 (1.78 – 4.87)</td><td>&lt;0.001</td><td>3.68 (2.08 – 6.70)</td><td>&lt;0.001</td></tr><tr><td>Dermatology</td><td>1.58 (0.96 – 2.69)</td><td>0.082</td><td>0.33 (0.07 – 1.41)</td><td>0.15</td></tr><tr><td>Gynaecology</td><td>2.32 (1.41 – 3.92)</td><td>0.001</td><td>1.72 (0.97 – 3.11)</td><td>0.067</td></tr><tr><td>Traumatology</td><td>4.22 (2.60 – 7.10)</td><td>&lt;0.001</td><td>0.67 (0.17 – 2.41)</td><td>0.6</td></tr></tbody></table>

tertiary hospitals (36.6%) compared to secondary hospitals (30.7%; P=0.033) (Table 1).

The multivariate analyses showed that compared to medical interns, both specialists (aOR=0.47; 95% CI: 0.21-0.96; P=0.049) and general practitioners (aOR=0.19; 95% CI: 0.05-0.73; P=0.019) were significantly less likely to prescribe antibiotics. Regarding the medical wards, patients treated in the emergency ward had a substantially higher risk of receiving at least one antibiotic (aOR=8.35; 95% CI: 4.77-15.0; P<0.001) compared to patients treated in general medicine. Those managed in the infectious diseases department were significantly less likely to receive antibiotics (aOR=0.44, 95% CI: 0.22-0.87; P=0.017). Finally, hospitalized patients had more than 23 times the odds of being prescribed an antibiotic compared to outpatients (aOR=23.8; 95% CI: 15.9-36.7; P<0.001) (Table 2).

Data expressed by wards shows that empirical prescribing was the leading practice in all wards, representing a total proportion of 99.4% (516/519). Except for the gynaecology, medicine and infectious diseases wards, prescribing a single antibiotic was significantly predominant. The grouped proportion of prescriptions containing a single antibiotic line was 75.5%. The route of administration varied from ward to ward, with an overall predominance of the oral route at 62.2% (323/519), followed by the parenteral route at 41.4% (215/519). In all wards and according to the WHO AWaRe classification, the majority of prescribed antibiotics belonged to the Access group, at 68.2% (354/519), followed by the Watch group, at 42.1% (219/519). No antibiotics were prescribed to the Reserve group.

The most common clinical indications or reasons for antibiotic prescription were fractures of the knee, ulna or bones (19.3%), skin infections and diseases (13.9%), gastrointestinal infections (9.8%), gynaecological and obstetric infections (9.5%) and malaria (5.8%). However, a considerable proportion (31.2%) of antibiotic prescriptions were associated with other, unspecified reasons (Table 3).

## Characteristics of antibiotics prescribed by the WHO AWaRe classification groups

Analysis of all prescriptions containing at least one antibiotic line, according to the WHO AWaRe classification, showed that the proportion of outpatients who received Access group antibiotics (67.5%) was lower compared to inpatients (68.7%), with P<0.010. However, for antibiotics in the Watch group, the prescribing trend was higher for outpatients, 44.5% versus 38.9% for inpatients; P=0.084. Secondary hospitals prescribed Access group antibiotics predominantly (80.5%), whereas the proportion was lower in tertiary hospitals (64.4%). Conversely, Watch group antibiotics were more frequently prescribed in tertiary-level hospitals (45.7%) than in secondary hospitals (30.9%), P<0.001. General practitioners were the highest prescribers of Access antibiotics (74.2%). Whereas medical interns (34.3%) frequently prescribed antibiotics from the Watch group compared to specialists (43.0%) and general practitioners (43.5%), the difference was statistically significant (P<0.001).

Regarding the route of administration, a higher proportion of Access antibiotics (81.4%) were prescribed orally, whereas
<table><caption>**Table 3.** Prevalence and characteristics of prescribed antibiotics by hospital wards in Conakry, Guinea (June–October 2024)</caption><thead><tr><th rowspan="2">Characteristics</th><th rowspan="2">n=519</th><th colspan="6">Antibiotics Prescription Department</th><th rowspan="2">P value</th></tr><tr><th>Dermatology<br>n (%)</th><th>Emergency<br>n (%)</th><th>Gynaecology<br>n (%)</th><th>Infectious<br>diseases<br>n (%)</th><th>Medicine<br>n (%)</th><th>Traumatology<br>n (%)</th></tr></thead><tbody><tr><td>Number of antibiotics per patient</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>One antibiotic</td><td>388 (75.5)</td><td>71 (18.3)</td><td>93 (23.97)</td><td>37 (9.5)</td><td>18 (4.6)</td><td>21 (5.4)</td><td>148 (38.1)</td><td></td></tr><tr><td>Two antibiotics</td><td>109 (21.2)</td><td>9 (8.3)</td><td>21 (19.3)</td><td>57 (52.3)</td><td>20 (18.3)</td><td>2 (1.8)</td><td>0 (0.0)</td><td></td></tr><tr><td>Three antibiotics and more</td><td>17 (3.3)</td><td>0 (0.0)</td><td>3 (17.6)</td><td>4 (23.5)</td><td>9 (52.9)</td><td>1 (5.9)</td><td>0 (0.0)</td><td></td></tr><tr><td>Type of treatment</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.11</td></tr><tr><td>Directed</td><td>3 (0.6)</td><td>0 (0.0)</td><td>0 (0.0)</td><td>0 (0.0)</td><td>0 (0.0)</td><td>1 (33.3)</td><td>2 (66.7)</td><td></td></tr><tr><td>Empirical</td><td>516 (99.4)</td><td>80 (15.5)</td><td>121 (23.4)</td><td>99 (19.2)</td><td>47 (9.1)</td><td>23 (4.5)</td><td>146 (28.3)</td><td></td></tr><tr><td>WHO AWaRe classification</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Access group</td><td>354 (68.2)</td><td>50 (14.2)</td><td>80 (22.7)</td><td>82 (23.2)</td><td>36 (10.2)</td><td>16 (4.5)</td><td>89 (25.2)</td><td>0.004</td></tr><tr><td>Watch group</td><td>219 (42.2)</td><td>38 (16.6)</td><td>65 (28.4)</td><td>26 (11.3)</td><td>26 (11.3)</td><td>12 (5.2)</td><td>62 (27.0)</td><td>&lt;0.001</td></tr><tr><td>Patient status</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>Outpatient</td><td>308 (59.3)</td><td>79 (25.6)</td><td>120 (39.0)</td><td>54 (17.5)</td><td>9 (2.9)</td><td>22 (7.1)</td><td>24 (7.8)</td><td></td></tr><tr><td>Inpatient</td><td>211 (40.7)</td><td>1 (0.5)</td><td>1 (0.5)</td><td>45 (21.3)</td><td>38 (18.0)</td><td>2 (0.9)</td><td>124 (58.8)</td><td></td></tr><tr><td>Route of administration</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Oral</td><td>323 (62.2)</td><td>76 (23.5)</td><td>66 (20.4)</td><td>94 (29.1)</td><td>21 (6.5)</td><td>19 (5.8)</td><td>47 (14.5)</td><td>&lt;0.001</td></tr><tr><td>Parenteral</td><td>215 (41.4)</td><td>1 (0.5)</td><td>70 (32.6)</td><td>1 (0.5)</td><td>33 (15.3)</td><td>7 (3.2)</td><td>103 (47.9)</td><td>&lt;0.001</td></tr><tr><td>Others</td><td>29 (5.6)</td><td>11 (37.9)</td><td>2 (6.9)</td><td>14 (48.3)</td><td>2 (6.9)</td><td>0 (0.0)</td><td>0 (0.0)</td><td>&lt;0.001</td></tr><tr><td>Reason for prescription</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>Fractures of the knee, ulna or bones</td><td>100 (19.3)</td><td>0 (0.0)</td><td>7 (7.0)</td><td>0 (0.0)</td><td>0 (0.0)</td><td>0 (0.0)</td><td>93 (93.0)</td><td></td></tr><tr><td>Gastrointestinal infections</td><td>51 (9.8)</td><td>0 (0.0)</td><td>42 (82.3)</td><td>0 (0.0)</td><td>2 (3.9)</td><td>7 (13.7)</td><td>0 (0.0)</td><td></td></tr><tr><td>Pulmonary Infections</td><td>7 (1.3)</td><td>0 (0.0)</td><td>3 (42.8)</td><td>0 (0.0)</td><td>0 (0.0)</td><td>4 (57.1)</td><td>0 (0.0)</td><td></td></tr><tr><td>Infections of the central nervous system</td><td>5 (0.9)</td><td>0 (0.0)</td><td>0 (0.0)</td><td>0 (0.0)</td><td>5 (100.0)</td><td>0 (0.0)</td><td>0 (0.0)</td><td></td></tr><tr><td>Malaria</td><td>30 (5.8)</td><td>0 (0.0)</td><td>20 (66.7)</td><td>0 (0.0)</td><td>9 (30.0)</td><td>1 (3.3)</td><td>0 (0.0)</td><td></td></tr><tr><td>Obstetric or gynaecological infections</td><td>49 (9.5)</td><td>0 (0.0)</td><td>0 (0.0)</td><td>44 (89.8)</td><td>4 (8.2)</td><td>1 (2.0)</td><td>0 (0.0)</td><td></td></tr><tr><td>Other reasons are not clearly defined</td><td>162 (31.2)</td><td>11 (6.8)</td><td>29 (17.9)</td><td>51 (31.5)</td><td>20 (12.3)</td><td>7 (4.3)</td><td>44 (27.1)</td><td></td></tr><tr><td>Otolaryngology infections</td><td>6 (1.2)</td><td>0 (0.0)</td><td>6 (100.0)</td><td>0 (0.0)</td><td>0 (0.0)</td><td>0 (0.0)</td><td>0 (0.0)</td><td></td></tr><tr><td>Sexually transmitted diseases</td><td>13 (2.5)</td><td>0 (0.0)</td><td>4 (30.7)</td><td>1 (7.7)</td><td>5 (38.4)</td><td>3 (23.0)</td><td>0 (0.0)</td><td></td></tr><tr><td>Skin diseases, infectious</td><td>72 (13.9)</td><td>69 (95.8)</td><td>2 (2.7)</td><td>1 (1.4)</td><td>0 (0.0)</td><td>0 (0.0)</td><td>0 (0.0)</td><td></td></tr><tr><td>Surgical site infections</td><td>12 (2.3)</td><td>0 (0.0)</td><td>1 (8.3)</td><td>0 (0.0)</td><td>0 (0.0)</td><td>0 (0.0)</td><td>11 (91.7)</td><td></td></tr><tr><td>Influenza</td><td>3 (0.5)</td><td>0 (0.0)</td><td>2 (66.6)</td><td>1 (33.3)</td><td>0 (0.0)</td><td>0 (0.0)</td><td>0 (0.0)</td><td></td></tr><tr><td>Intra-abdominal sepsis</td><td>3 (0.5)</td><td>0 (0.0)</td><td>1 (33.3)</td><td>0 (0.0)</td><td>2 (66.7)</td><td>0 (0.0)</td><td>0 (0.0)</td><td></td></tr></tbody></table>

antibiotics administered parenterally were more often from the Watch group (63.3%; P<0.001). Prescriptions for ordinary and routine infections, such as gastrointestinal infections (70.6%), pulmonary diseases (100%), sexually transmitted infections (76.9%) and other reasons not clearly defined, predominantly involved Access antibiotics, and Watch group antibiotics were prescribed mainly for reasons, such as intra-abdominal sepsis, malaria, and gynaecological diseases (Table 4).

## Distribution of antibiotic classes prescribed

The most frequently prescribed antibiotic classes in the hospital facilities included were beta-lactams/beta-lactamase-inhibitor (24.2%, 162/669), followed by third generation cephalosporins (21.7%, 145/669), imidazoles, particularly metronidazole (18.5%, 124/669) and penicillins (13.6%, 91/669) [Figure 1, Table S1 (available as Supplementary data at JAC-AMR Online)].

The distribution of prescribed antibiotic classes varied considerably between hospital wards. beta-lactams/beta-lactamase inhibitors, 3GCs, were the most commonly prescribed antibiotics in the majority of the hospital wards, followed by imidazoles. On the other hand, penicillins are the second most widely used antibiotics in general medicine wards, while macrolides are commonly used in gynaecology (Figure 2). Among inpatients, prescriptions were dominated by beta-lactams/beta-lactamase-inhibitor combinations, imidazoles, and 3GCs. In contrast, outpatient prescriptions exhibited greater diversity, with higher proportions of macrolides, fluoroquinolones and cotrimoxazole (Figure 3). A Fisher's exact test with a Monte Carlo simulation revealed a significant association between patient status (inpatient versus outpatient) and hospital wards versus the distribution of the prescribed antibiotics (P<0.0001). The magnitude of these associations, as measured by Cramer's V, was moderate for patient status (V=0.36) and strong for hospital wards (V=0.46),
<table><caption>**Table 4.** Characteristics of prescribed antibiotics by the WHO AWaRe classification in Conakry, Guinea (June–October 2024)</caption><thead><tr><th rowspan="3">Variable</th><th colspan="6">WHO AWaRe classification of antibiotics</th></tr><tr><th colspan="3">Access group</th><th colspan="3">Watch group</th></tr><tr><th>No<br>n (%)</th><th>Yes<br>n (%)</th><th>P value</th><th>No<br>n (%)</th><th>Yes<br>n (%)</th><th>P value</th></tr></thead><tbody><tr><td>Patient status</td><td></td><td></td><td>0.010</td><td></td><td></td><td>0.084</td></tr><tr><td>Inpatient</td><td>66 (31.3)</td><td>145 (68.7)</td><td></td><td>129 (61.1)</td><td>82 (38.9)</td><td></td></tr><tr><td>Outpatient</td><td>99 (32.1)</td><td>209 (67.9)</td><td></td><td>171 (55.5)</td><td>137 (44.5)</td><td></td></tr><tr><td>Hospital type</td><td></td><td></td><td>&lt;0.001</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>Secondary hospital</td><td>24 (19.5)</td><td>99 (80.5)</td><td></td><td>85 (69.1)</td><td>38 (30.9)</td><td></td></tr><tr><td>Tertiary hospital</td><td>141 (35.6)</td><td>255 (64.4)</td><td></td><td>215 (54.3)</td><td>181 (45.7)</td><td></td></tr><tr><td>Prescriber</td><td></td><td></td><td>0.009</td><td></td><td></td><td>0.4</td></tr><tr><td>Medical interns</td><td>26 (37.1)</td><td>44 (62.9)</td><td></td><td>46 (65.7)</td><td>24 (34.3)</td><td></td></tr><tr><td>General practitioner</td><td>73 (25.8)</td><td>210 (74.2)</td><td></td><td>160 (56.5)</td><td>123 (43.5)</td><td></td></tr><tr><td>Specialist</td><td>65 (39.4)</td><td>100 (60.6)</td><td></td><td>94 (56.9)</td><td>71 (43.0)</td><td></td></tr><tr><td>Route of administration</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Oral</td><td>60 (18.6)</td><td>263 (81.4)</td><td>&lt;0.001</td><td>220 (68.1)</td><td>103 (31.9)</td><td>&lt;0.001</td></tr><tr><td>Parenteral</td><td>103 (47.9)</td><td>112 (52.1)</td><td>&lt;0.001</td><td>79 (36.7)</td><td>136 (63.3)</td><td>&lt;0.001</td></tr><tr><td>Others</td><td>8 (27.6)</td><td>21 (72.4)</td><td>0.8</td><td>19 (65.)</td><td>10 (34.4)</td><td>0.5</td></tr><tr><td>Reason for prescription</td><td></td><td></td><td>&lt;0.001</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>Fractures of the knee, ulna or bones</td><td>45 (45.0)</td><td>55 (55.0)</td><td></td><td>53 (53.0)</td><td>47 (47.0)</td><td></td></tr><tr><td>Gastrointestinal infections</td><td>15 (29.4)</td><td>36 (70.6)</td><td></td><td>22 (43.1)</td><td>29 (56.9)</td><td></td></tr><tr><td>Pulmonary infections</td><td>0 (0.0)</td><td>7 (100.0)</td><td></td><td>7 (100.0)</td><td>0 (0.0)</td><td></td></tr><tr><td>Infections of the central nervous system</td><td>2 (40.0)</td><td>3 (60.0)</td><td></td><td>1 (20.0)</td><td>4 (80.0)</td><td></td></tr><tr><td>Malaria</td><td>10 (33.3)</td><td>20 (66.7)</td><td></td><td>12 (40.0)</td><td>18 (60.0)</td><td></td></tr><tr><td>Obstetric or gynaecological infections</td><td>17 (34.7)</td><td>32 (65.3)</td><td></td><td>19 (38.8)</td><td>30 (61.2)</td><td></td></tr><tr><td>Other reasons are not clearly defined</td><td>31 (19.1)</td><td>130 (80.9)</td><td></td><td>116 (70.6)</td><td>46 (28.4)</td><td></td></tr><tr><td>Otolaryngology infections</td><td>4 (66.7)</td><td>2 (33.3)</td><td></td><td>4 (66.7)</td><td>2 (33.3)</td><td></td></tr><tr><td>Sexually transmitted diseases</td><td>3 (23.1)</td><td>10 (76.9)</td><td></td><td>8 (61.5)</td><td>5 (38.5)</td><td></td></tr><tr><td>Skin diseases, infectious</td><td>28 (38.9)</td><td>44 (61.1)</td><td></td><td>46 (63.9)</td><td>26 (36.1)</td><td></td></tr><tr><td>Surgical site infections</td><td>5 (41.7)</td><td>7 (58.3)</td><td></td><td>7 (58.3)</td><td>5 (41.7)</td><td></td></tr><tr><td>Influenza</td><td>1 (33.3)</td><td>2 (66.7)</td><td></td><td>1 (33.3)</td><td>2 (66.7)</td><td></td></tr><tr><td>Intra-abdominal sepsis</td><td>1 (33.3)</td><td>2 (66.7)</td><td></td><td>0 (0.0)</td><td>3 (100.0)</td><td></td></tr></tbody></table>

indicating that antibiotic class prescription varies substantially according to both hospitalization status and clinical service.

Discussion

Evidence on the prevalence of antibiotic prescribing and the most commonly used classes of antibiotics in hospitals is essential for each country and health system. This enables the assessment of the level of antibiotic consumption and measures the impact of initiatives aimed at promoting appropriate antibiotic use. In this study, we conducted a point prevalence survey to evaluate anti-biotic use and compliance with WHO guidelines in public hospitals in Conakry, the capital of the Republic of Guinea.

Among the 1482 patients included, 35% were receiving at least one antibiotic; this proportion reached 83% among hospitalized patients. The global rate observed in our study is above that reported in South Korea (14.1% of all patients, with 50.8% of prescriptions relating to inpatients) and 21.1% in Malaysia.²⁶,²⁷

Among outpatients, the antibiotic prescription rate is 25.1%. These figures are favourable compared to the international

norm, which suggests that less than 30% of consultations should result in the prescription of antibiotics.²⁸,²⁹ Also, this proportion observed in our study is lower than those reported in other similar studies.³⁰⁻³² This comparatively low rate could be explained by the specific medical wards included and the level of consultation in our study.

For inpatients, WHO recommends that the prescription of antibiotics should be 40% or less.²⁴ However, our data show that this proportion is doubled for patients treated on an inpatient basis in Guinea. Similar trends can be observed in other countries, including Benin, which has 82.9%, Ghana, with 68.5%, Zambia (75%), Ethiopia (65%) and other West African countries.³³⁻³⁷ Some countries have reported lower rates of antibiotic prescribing among hospitalized patients, suggesting that significant efforts have been made to ensure more rational prescribing. This is the case in South Africa (33.6%), Tanzania (47%) and Senegal (45.5%).⁶,³⁸,³⁹

The high prevalence of antibiotic prescribing among inpatients could be explained by factors such as the lack of targeted education for physicians and difficulties in accessing laboratories and microbiological results. Also, hospitalized patients are often in a
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Proportion of prescribed antibiotics class in six Conakry hospital wards in Conakry, Guinea (June–October 2024).</figcaption></figure>

more serious clinical condition, and when faced with a patient with an infectious syndrome, doctors prescribe antibiotics even without laboratory results. Furthermore, other factors that may be personal, psychological and organizational have been reported,⁴⁰ including work experience, knowledge, availability of and compliance with guidelines, perceived pressure from patients and explicit requests for antibiotics by patients.⁴¹,⁴²

The most commonly prescribed antibiotics are beta-lactams/
beta-lactamase inhibitors, 3GCs, imidazoles (metronidazole), pe-
nicillins and macrolides. Other studies also highlighted the every-
day use of these antibiotics. ¹⁴,²⁷,⁴³ Moreover, a systematic review
of 17 studies of patterns of antimicrobial prescription in Africa
also reported beta-lactam penicillin (amoxicillin, clavulanic
acid) and 3GCs as the most commonly used antibiotics. ⁴⁴ The
23-fold greater odds of receiving at least one antibiotic for hospi-
talized patients could essentially be attributable to the nature of
illness, diagnostic processes, comorbidities and prophylaxis.

In addition, almost all antibiotic prescriptions were empirical,
with no prior biological investigations. Bacterial culture and anti-
biotic susceptibility testing were requested for only 3 patients.
This finding has also been reported in several other studies.⁸,¹⁵,³³,⁴⁵
Knowing that this practice could significantly increase the risk of
AMR development, particularly in the context of the frequent pre-
scription of broad-spectrum antibiotics such as ceftriaxone, cefix-
ime, azithromycin and clarithromycin. It should be noted that
studies in Guinea have reported a high prevalence of bacteria re-
sistant to broad-spectrum antibiotics, such as *Escherichia coli*,

Klebsiella pneumoniae, Staphylococcus aureus and Pseudomonas aeruginosa, resistant to 3GCs and carbapenems.⁴⁶,⁴⁷ These pre-scription practices, especially in low- and middle-income countries, can often be driven by the clinical urgency or limited access to adequately equipped diagnostic laboratories. Moreover, the noted reasons for antibiotic prescribing, such as malaria, influenza, or other reasons that are not clearly defined, highlight the urgent need for awareness-raising actions and a solid surveillance system to be implemented in the Guinea hospital system.

According to the WHO AWaRE classification, 64.3% of the anti-
biotics prescribed fall under the Access group, and 33.3% fall un-
der the Watch group. In the rural healthcare district of Forécariah
in Guinea, a previous study reported 73.6% for Access and 27.6%
for Watch antibiotic prescription for febrile patients. ¹⁴ In
Tanzania, authors reported proportions of 60.8% and 33.3%, re-
spectively, for the Access and Watch groups.⁸ Although the cur-
rent proportion of Access group antibiotics (64.3% of
prescriptions) aligns with the WHO minimum target of ≥60%
for antibiotic consumption,⁴⁸ it is vital to continue efforts to
achieve the ambitious new goal (≥70%) of the Global multisec-
toral action needed to reach targets by 2030.⁴⁹ Concurrently,
the proportion of Watch group antibiotics (33.3% of prescriptions)
represents an area requiring significant action. Limiting their use
is imperative, as these antibiotics are under close surveillance due
to their high potential to promote the emergence and spread of
antimicrobial resistance.
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Proportion of prescribed antibiotic classes in Conakry hospitals by prescription ward in Conakry, Guinea, from June to October 2024 (*P* value < 0.0001; Cramer's V = 0.461).</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Figure 3.** Proportion of prescribed antibiotic classes in Conakry hospitals by participants' status, from June to October 2024 (*P* value < 0.0001; Cramer's V = 0.3648).</figcaption></figure>
We also found that antibiotic prescribing differed significantly by hospital type, with secondary hospitals predominantly prescribing antibiotics for the Access group, whereas the proportion of Watch antibiotics was higher in tertiary hospitals.

These findings further underscore that antibiotic prescribing practices may vary considerably across healthcare settings and are influenced by hospital type and prescribers' experience. Consequently, interventions aimed at reducing antibiotic overuse and antimicrobial resistance should be carefully tailored and context-specific.

In Guinea, a Multisectoral National Action Plan against antimicrobial resistance was developed in 2020 within a One Health framework and aims to improve coordination across the human, animal and environmental health sectors, strengthen antimicrobial use surveillance, and build laboratory capacity for AMR detection.¹² However, unlike in some other health systems, there is no publicly available evidence of established and functional AMS committees at either the national or hospital level, nor of national antibiotic prescribing guidelines. These contextual gaps, combined with challenges such as limited infection prevention and control measures, limited access to microbiology laboratory and resource constraints, likely contribute to the patterns of empirical prescribing observed in our study. In this way, AMS programs should be implemented, particularly in tertiary hospitals where antibiotic use is more common.

This study has some limitations. Firstly, it did not cover all hospital wards; key departments, such as paediatrics or surgery, were not included, which limits the generalizability of the findings to the entire hospital population. Secondly, its cross-sectional design does not allow an assessment of whether the antibiotics prescribed were used by the patients and does not provide information on the clinical appropriateness of the prescriptions.

Despite these limitations, the study also has notable strengths. It included a large sample of more than 1400 patients, which increased the statistical power and reliability of the analysis. It was also based on a standardized WHO methodology, which ensured good data collection and allowed the indicators obtained to be compared with those reported in the regional and international literature.

## Conclusion

This study highlights a high prevalence of antibiotic prescriptions in hospital settings in Conakry, Guinea, which is particularly pronounced among hospitalized patients compared with outpatients. Our findings suggest an urgent need to improve antibiotic prescription practices, strengthen diagnostic capacities, educate physicians and patients, and implement antimicrobial stewardship programs in hospitals. These strategies align with national policies and regional stewardship frameworks, are crucial for reducing empirical antibiotic prescribing practices, preserving antibiotic effectiveness, and preventing the emergence of new antimicrobial resistance and the public health threats it poses.

## Acknowledgements

We sincerely thank the management of all participating hospitals for permitting us to collect data at their facilities.

## Funding

This study was supported by internal funding.

## Transparency declarations

The authors declare no conflicts of interest.

## Supplementary data

Table S1 is available as Supplementary data at JAC-AMR Online.

## References

1. Naghavi M, Vollset SE, Ikuta KS et al. Global burden of bacterial antimicrobial resistance 1990–2021: a systematic analysis with forecasts to 2050. *Lancet* 2024; **404**: 1199–226. https://doi.org/10.1016/S0140-6736(24)01867-1

2. Murray CJL, Ikuta KS, Sharara F et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. *Lancet* 2022; **399**: 629–55. https://doi.org/10.1016/S0140-6736(21)02724-0

3. Kariuki S, Kering K, Wairimu C et al. Antimicrobial resistance rates and surveillance in Sub-Saharan Africa: where are we now? *Infect Drug Resist* 2022; **15**: 3589–609. https://doi.org/10.2147/IDR.S342753

4. Sweileh WM. Global research publications on irrational use of antimicrobials: call for more research to contain antimicrobial resistance. *Glob Health* 2021; **17**: 94. https://doi.org/10.1186/s12992-021-00754-9

5. Chang TI, Park H, Kim DW et al. Polypharmacy, hospitalization, and mortality risk: a nationwide cohort study. *Sci Rep* 2020; **10**: 18964. https://doi.org/10.1038/s41598-020-75888-8

6. Katyali D, Kawau G, Blomberg B et al. Antibiotic use at a tertiary hospital in Tanzania: findings from a point prevalence survey. *Antimicrob Resist Infect Control* 2023; **12**: 112. https://doi.org/10.1186/s13756-023-01317-w

7. Sakly H, Rebaia F, Ben Jeddou K. Antibiothérapie en milieu hospitalier: évaluation des prescriptions d'antibiotiques et détermination des facteurs influençant la pertinence. *Ann Pharm Fr* 2022; **80**: 478–85. https://doi.org/10.1016/j.pharma.2021.08.005

8. Khalfan MA, Sasi PG, Mugusi SF. The prevalence and pattern of antibiotic prescription among insured patients in Dar es Salaam Tanzania. *Pan Afr Med J* 2021; **40**: 140. https://doi.org/10.11604/pamj.2021.40.140.29584

9. Bara W, Brun-Buisson C, Coignard B et al. Outpatient antibiotic prescriptions in France: patients and providers characteristics and impact of the COVID-19 pandemic. *Antibiotics* 2022; **11**: 643. https://doi.org/10.3390/antibiotics11050643

10. Hulscher MEJL, Grol RPTM, van der Meer JWM. Antibiotic prescribing in hospitals: a social and behavioural scientific approach. *Lancet Infect Dis* 2010; **10**: 167–75. https://doi.org/10.1016/S1473-3099(10)70027-X

11. Schuts EC, Hulscher MEJL, Mouton JW et al. Current evidence on hospital antimicrobial stewardship objectives: a systematic review and meta-analysis. *Lancet Infect Dis* 2016; **16**: 847–56. https://doi.org/10.1016/S1473-3099(16)00065-7

12. Guinea: Multisectoral National Action Plan Against Antimicrobial Resistance 2020–2024 (French). https://www.who.int/publications/m/item/multisectoral-national-action-plan-against-antimicrobial-resistance-2020-2024

13. Generis Global Legal Services. *An Overview of the Healthcare System in Guinea*. Generis Global Legal Services, 2024. https://generisonline.com/an-overview-of-the-healthcare-system-in-guinea/
**14** Kourouma K, Grovogui FM, Delamou A et al. Management of febrile illness in rural Guinea over a seven-year period: a retrospective study. *PLOS Glob Public Health* 2022; **2**: e0001133. https://doi.org/10.1371/journal. pgph.0001133

**15** Bangoura ST, Toure A, Sidibé S et al. Frequency and determinants of prescribing antibiotics for internal medicine at Donka National Hospital (Guinea). *Cent Afr J Public Health* 2020; **6**: 1-4. https://doi.org/10.11648/j.cajph.20200601.11

**16** Zarb P, Amadeo B, Muller A et al. Identification of targets for quality improvement in antimicrobial prescribing: the web-based ESAC Point Prevalence Survey 2009. *J Antimicrob Chemother* 2011; **66**: 443-9. https://doi.org/10.1093/jac/dkq430

**17** AWaRe Classification of Antibiotics for Evaluation and Monitoring of Use. 2023. https://www.who.int/publications/i/item/WHO-MHP-HPS-EML-2023.04

**18** Mudenda S, Wataya MD, Mufwambi W et al. The World Health Organization access, watch, and reserve classification of antibiotics: an awareness survey among pharmacy professionals in a sub-Saharan country, Zambia. *Antimicrob Steward Healthc Epidemiol* 2024; **4**: e212. https://doi.org/10.1017/ash.2024.403

**19** Global Antimicrobial Resistance and Use Surveillance System (GLASS). https://www.who.int/initiatives/glass.

**20** Zanichelli V, Sharland M, Cappello B et al. The WHO AWaRe (access, watch, reserve) antibiotic book and prevention of antimicrobial resistance. *Bull World Health Organ* 2023; **101**: 290-6. https://doi.org/10.2471/BLT.22.288614

**21** Antibiotics Most Responsible for Drug Resistance are Overused—WHO Report. https://www.who.int/news/item/29-04-2025-antibiotics-most-responsible-for-drug-resistance-are-overused—who-report.

**22** Mudenda S, Daka V, Matafwali SK. World Health Organization AWaRe framework for antibiotic stewardship: where are we now and where do we need to go? An expert viewpoint. *Antimicrob Steward Healthc Epidemiol ASHE* 2023; **3**: e84. https://doi.org/10.1017/ash.2023.164. eCollection 2023

**23** Mendelson M, Lewnard JA, Sharland M et al. Ensuring progress on sustainable access to effective antibiotics at the 2024 UN General Assembly: a target-based approach. *Lancet* 2024; **403**: 2551-64. https://doi.org/10.1016/S0140-6736(24)01019-5

**24** World Health Organization. *WHO Methodology for Point Prevalence Survey on Antibiotic Use in Hospitals*. World Health Organization, 2018. https://www.who.int/publications/i/item/WHO-EMP-IAU-2018.01.

**25** R Core Team. *R: A Language and Environment for Statistical Computing*. R Foundation for Statistical Computing, 2024. https://www.R-project.org/.

**26** Park SY, Moon SM, Kim B et al. Appropriateness of antibiotic prescriptions during hospitalization and ambulatory care: a multicentre prevalence survey in Korea. *J Glob Antimicrob Resist* 2022; **29**: 253-8. https://doi.org/10.1016/j.jgar.2022.03.021

**27** Ab Rahman N, Teng CL, Sivasampu S. Antibiotic prescribing in public and private practice: a cross-sectional study in primary care clinics in Malaysia. *BMC Infect Dis* 2016; **16**: 208. https://doi.org/10.1186/s12879-016-1530-2

**28** Ofori-Asenso R, Brhlikova P, Pollock AM. Prescribing indicators at primary health care centers within the WHO African region: a systematic analysis (1995-2015). *BMC Public Health* 2016; **16**: 724. https://doi.org/10.1186/s12889-016-3428-8

**29** Sulis G, Adam P, Nafade V et al. Antibiotic prescription practices in primary care in low- and middle-income countries: a systematic review and meta-analysis. *PLoS Med* 2020; **17**: e1003139. https://doi.org/10.1371/journal.pmed.1003139

**30** Zhang HL, Bodinayake C, Wijayaratne GB et al. Point-prevalence survey of outpatient antibiotic prescription at a tertiary medical center in

Sri Lanka: opportunities to improve prescribing practices for respiratory illnesses. *BMC Infect Dis* 2021; **21**: 97. https://doi.org/10.1186/s12879-021-05804-6

**31** Di Martino M, Lallo A, Kirchmayer U et al. Prevalence of antibiotic prescription in pediatric outpatients in Italy: the role of local health districts and primary care physicians in determining variation. A multilevel design for healthcare decision support. *BMC Public Health* 2017; **17**: 886. https://doi.org/10.1186/s12889-017-4905-4

**32** Chen R, Li J, Wang C et al. Global antibiotic prescription practices in hospitals and associated factors: a systematic review and meta-analysis. *J Glob Health* 2025; **15**: 04023. https://doi.org/10.7189/jogh.15.04023

**33** Gnimavo MS, Boya B, Mudenda S et al. Antibiotic use at the Centre Hospitalier Universitaire de Zone d'Abomey Calavi/Sò-Ava (CHUZ/AS) in Benin: a point prevalence survey. *JAC Antimicrob Resist* 2025; **7**: dlae220. https://doi.org/10.1093/jacamr/dlae220

**34** Abubakar U, Salman M. Antibiotic use among hospitalized patients in Africa: a systematic review of point prevalence studies. *J Racial Ethn Health Disparities* 2024; **11**: 1308-29. https://doi.org/10.1007/s40615-023-01610-9

**35** Siachalinga L, Godman B, Mwita JC et al. Current antibiotic use among hospitals in the sub-Saharan Africa region; findings and implications. *Infect Drug Resist* 2023; **16**: 2179-90. https://doi.org/10.2147/IDR.S398223

**36** Mudenda S, Kapolowe K, Chirwa U et al. Antimicrobial stewardship impact on antibiotic use in three tertiary hospitals in Zambia: a comparative point prevalence survey. *Antibiot Basel Switz* 2025; **14**: 284. https://doi.org/10.3390/antibiotics14030284

**37** Merga KH, Getachew EM, Fujita AW et al. A high prevalence of antibiotic use at two large teaching hospitals in Addis Ababa, Ethiopia: a point prevalence survey. *Antimicrob Steward Healthc Epidemiol* 2024; **4**: e180. https://doi.org/10.1017/ash.2024.432

**38** Skosana PP, Schellack N, Godman B et al. A point prevalence survey of antimicrobial utilisation patterns and quality indices amongst hospitals in South Africa; findings and implications. *Expert Rev Anti Infect Ther* 2021; **19**: 1353-66. https://doi.org/10.1080/14787210.2021.1898946

**39** Diop M, Wone F, Bassoum O et al. WHO-point prevalence survey of antibiotic use in a Senegalese tertiary care hospital. *PLOS Glob Public Health* 2025; **5**: e0004887. https://doi.org/10.1371/journal.pgph.0004887

**40** Lansink C, Sinha B, Meessen N et al. Why do physicians prescribe antibiotics? A systematic review of the psycho-socio-organisational factors related to potentially inappropriate prescribing of antimicrobials in Europe. *Infect Dis Rep* 2024; **16**: 664-83. https://doi.org/10.3390/ida16040051

**41** Hulscher MEJL, van der Meer JWM, Grol RPTM. Antibiotic use: how to improve it? *Int J Med Microbiol* 2010; **300**: 351-6. https://doi.org/10.1016/j.ijmm.2010.04.003

**42** Little P, Dorward M, Warner G et al. Importance of patient pressure and perceived pressure and perceived medical need for investigations, referral, and prescribing in primary care: nested observational study. *BMJ* 2004; **328**: 444. https://doi.org/10.1136/bmj.38013.644086.7C

**43** Donkor ES, Osman AH, Aglomasa BC et al. Improving antibiotic utilization in West Africa: enhancing interventions through systematic review and evidence synthesis. *Antimicrob Resist Infect Control* 2025; **14**: 5. https://doi.org/10.1186/s13756-024-01504-3

**44** Okedo-Alex IN, Akamike IC, Iyamu I et al. Pattern of antimicrobial prescription in Africa: a systematic review of point prevalence surveys. *Pan Afr Med J* 2023; **45**: 67. https://doi.org/10.11604/pamj.2023.45.67.36191

**45** Gwebu PC, Meyer JC, Schellack N et al. A web-based point prevalence survey of antimicrobial use and quality indicators at Raleigh Fitkin Memorial Hospital in the Kingdom of Eswatini and the implications. *Hosp Pract* 2022; **50**: 214-21. https://doi.org/10.1080/21548331.2022.2069247

**46** Kaba D, Niabaly O, Diallo I et al. Resistance profile of urine isolate enterobacterial strains at Donka University teaching hospital in Conakry,
Guinea. Afr J Microbiol Res 2024; **18**: 87-95. https://doi.org/10.5897/AJMR2024.9746

**47** Diallo MB, Camara A, Oumar DB et al. Prévalence et facteurs de risque des infections associées aux soins dans trois hôpitaux nationaux de la ville de Conakry. Guinée. Rev Int Sci Méd Abidj. 2022; **24**(2): 175-83.

**48** Thirteenth General Programme of Work, 2019-2023. https://www.who.int/about/general-programme-of-work/thirteenth

**49** World Leaders Commit to Decisive Action on Antimicrobial Resistance. https://www.who.int/news/item/26-09-2024-world-leaders-commit-to-decisive-action-on-antimicrobial-resistance