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Evaluation of antibiotic use and prescription patterns using World Health Organization indicators for paediatric inpatients in a tertiary care teaching hospital
*Corresponding author: Dr. Jammula Mounika, Doctor of Pharmacy, Department of Pharmacy Practice, Bapuji Pharmacy College, SS Layout, Davangere, Karnataka, India mounikajammula99@gmail.com
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Received: ,
Accepted: ,
How to cite this article: Mounika J, Manjabhovi D, Mathew A, Biju J, Moncy SK, Evaluation of antibiotic use and prescription pattern using World Health Organization indicators for paediatric inpatients in a tertiary care teaching hospital. South Asian J Health Sci. doi: 10.25259/SAJHS_25_2024
Abstract
Objectives:
Antibiotics play a crucial role in treating infectious diseases in paediatrics. A thorough understanding of prescribing patterns is imperative to enhance awareness and ensure the prudent use of antibiotics to continue deriving their benefits while preventing antibiotic resistance. Hence, evaluating the prescription pattern helps in identifying and discerning the occurrence of such adverse outcomes. Our study aimed to evaluate antibiotic prescribing patterns in paediatric patients using World Health Organisation (WHO) prescribing indicators.
Material and Methods:
This hospital-based prospective cross-sectional descriptive study was conducted for 6 months among 300 paediatric inpatients. The data on ongoing prescription trends were collected using a self-designed data collection form and compared with the WHO core prescribing indicators. Furthermore, we classified antibiotics based on the Anatomical Therapeutic Classification.
Results:
Of the 300 participants, 60.0% were male, and 40.0% were female. Infants aged 1 month to 1 year constituted 45.0% (135/300) of the cohort and were more prone to infections. When classified by system affected, respiratory tract infections were most prevalent, with 62.3%, and the most common infection was pneumonia, accounting for 26.3%. In this study, the prescribing pattern of antibiotics in the hospital deviates from and is non-compliant with the standards endorsed by WHO.
Conclusion:
The study shows that the prescribing pattern of antibiotics in paediatrics deviates from the endorsed standard WHO prescribing indicators, which explains the need to establish an antimicrobial stewardship program in the paediatric setting to avoid Antibiotic resistance.
Keywords
Anatomical therapeutic classification
Antibiotic resistance
Paediatrics
Prescription pattern
WHO core prescribing Indicators
INTRODUCTION
Infectious diseases are common among paediatric patients in India and contribute to the total mortality rate, which is the highest in the world.[1] Antibiotics play a vital part in the treatment of infectious diseases encyclopedically.[2] Medicine use is regarded as rational when patients receive drugs that are appropriate for their clinical condition, in suitable doses and durations, tailored to individual needs and at an affordable cost.[3] When one or more of these requirements are not met, medicine use is considered irrational or inappropriate.[4] According to data from the World Health Organisation (WHO), more than half of all medications are either supplied, distributed, or administered improperly.[5] Other studies also demonstrated that inappropriate antibiotic use can result in superinfections due to antibiotic-resistant bacteria, opportunistic fungi, unnecessary health care expenditure, treatment failures, and adverse drug effects.[3] Irrational and inappropriate use of antibiotics has contributed largely to the development of antibiotic resistance (ABR).[6] An increase in infections caused by resistant bacteria has called for rational and applicable use of antibiotics in paediatrics.[7] Rational prescribing of antibiotics is vital in controlling ABR.[8] Numerous prescribers fail to abide by rational prescribing of antibiotics despite numerous countries promoting good antimicrobial stewardship.[6] Thus, prescribers need to have detailed knowledge of antibiotic prescribing patterns.[9] As an integral part of the medical audit, the study of prescribing patterns aims to track, assess, and, if required, recommend changes to prescribing procedures to improve the rationality and economy of medical care.[10] To ameliorate overall medicine use, especially in developing countries, transnational agencies like the WHO and the International Network for the Rational Use of Medicines have developed a set of drug prescribing indicators.[11] The indicators can be swiftly and efficiently used in numerous settings to assess implicit problems in drug use and to prioritise and concentrate on consecutive efforts to correct these problems.[12]
Thus, this study aimed to evaluate the appropriateness and prescribing pattern of antibiotics in paediatric patients using the WHO core prescribing indicators in a tertiary care teaching Hospital.
MATERIAL AND METHODS
This prospective cross-sectional descriptive study was conducted at the Shyamanuru Shivashankarappa Institute of Medical Sciences and Research Centre, a tertiary care teaching hospital of Davangere, Karnataka, from March 2023 to September 2023. The ethical clearance for the study was attained from the Institutional Ethics Committee of Bapuji Pharmacy College and SS Institute of Medical Sciences and Research Centre, Davangere. Based on the prevalence of 26.3% the sample size was calculated using a single proportional sample determination formula, i.e., n= Zα2 p(1-p)/d2 where Z 2 is Z statistic for a level of confidence interval at 95% i.e. 1.96, P is the Expected prevalence or proportion of prescribing antibiotics in paediatric population and d is precision or absolute error (If precision is 5%, then d= 0.05) a total of 300 participants were enrolled in our study as per study criteria.
The study included paediatric inpatients of both sexes, aged 13 years or below, who were hospitalised in either paediatric intensive care unit, neonatal intensive care unit, or general paediatric ward and received at least one systemic antibiotic during their hospital stay.
The study excluded prescriptions with incomplete data and children whose parents or guardians had not signed the informed consent. Patients who are critically ill and have complicated medical backgrounds were also excluded, as their treatment plans and medication sheets were significantly more complicated and altered regularly in the intensive care unit, which would have necessitated an alternate study methodology that would have ensured that the prescribing behaviours were accurately recorded.
Data collection
A self-designed data collection form was used to collect the data from the case files of each patient and assess the prescription pattern, including demographic details, clinical diagnosis, and antibiotic prescription data. The form was developed under the guidance of the Head of the Department of Paediatrics, based on WHO core prescribing indicators and previous paediatric antibiotic-use studies, and was reviewed and approved as part of the protocol by the Institutional Review Board of Bapuji Pharmacy College and Shyamanuru Shivashankarappa Institute of Medical Sciences and Research Centre. The provisional diagnosis was based on assimilating patient signs and symptoms along with laboratory examinations. Microbiological culture and antibiotic susceptibility testing are not normally ordered in our setting until a preliminary empirical therapy has been undertaken. The response of the child should then be observed by clinicians over about 3-4 days, and only culture testing is recommended in case the clinical improvement is not sufficient. Since microbiological studies are very costly to most families, a lot of time usually goes to waste in counselling and seeking parental consent before samples can be taken. The approval is received, and blood is sampled and sent to the microbiology lab, and the lab processing time to get final results on the identification and susceptibility of the result is an additional 3-5 days. In this way, the total time of starting an empirical treatment process to available usable culture and sensitivity report on the ward often ranges between 10-15 days, despite the inherent laboratory processing time being less. This causes a large number of outcomes to be available later in the admission or after discharge, and are irregularly documented. For this reason, culture and sensitivity results were not systematically collected or analysed. The final diagnosis was reached after the consensus of health care professionals. The data regarding prescribed antibiotics was collected from the patient treatment chart and was cross-verified with the discharge summary.
Statistical analysis
All the gathered results were introduced to Microsoft Excel and calculated using Statistical Package for the Social Sciences 28 Windows version. Categorical variables (sex, age group, system of infection, antibiotic class and route of administration) were described as frequencies and percentages, whereas continuous variables (number of drugs per encounter) were described as mean and standard deviation. The statistical analysis plan was created following consultation with a statistician who recommended paying attention to descriptive statistics, with the main purpose of evaluating the prescribing patterns based on the WHO core indicators. No inferential tests (Analysis Of Variance or non-parametric alternatives) were used, and thus, the comparisons of subgroups can only be considered as a description. Any exploratory analysis which was statistically significant had to have a P < 0.05.
The assessment of prescribing patterns utilised WHO prescribing indicators, which were determined using the following formulas.
The average number of drugs per encounter was calculated by dividing the total number of drugs prescribed by the total number of encounters; drug combinations used for one health problem were counted as a single prescription.
The percentage of drugs prescribed by generic name was calculated by dividing the number of drugs written by their generic name by the total number of prescribed drugs and then multiplying by 100.
The percentage of encounters with an antibiotic prescribed was calculated by dividing the number of encounters in which at least one antibiotic was given by the total number of encounters and then multiplying by 100.
The percentage of encounters with an injection prescribed was calculated by dividing the number of encounters in which at least one injection was given by the total number of encounters, and then multiplying by 100
The percentage of drugs prescribed from the essential medicines list was calculated by dividing the number of drugs taken from the essential medicines list by the total number of prescribed drugs and then multiplying by 100.
RESULTS
Out of 300 paediatric inpatients, 180 (60.0%) were male, and 120 (40.0%) were female. The highest proportion of patients was in the age group of 1 month–1 year, with 135/300 (45.0%), followed by 2–5 years with 87/300 (29.0%), and 6–13 years with 78/300 (26.0%), as shown in Figure 1. We have classified the diseases concerning the system affected, and among the patients, respiratory system infection was highest with 62.3%, followed by urinary system infection with 15.0%. Among 187 patients infected with respiratory system infections, the most prevalent disease was pneumonia (26.3%), followed by bronchiolitis (10.6%). Among 45 patients infected with the urinary tract system, the most prevalent disease was urinary tract infection with 13.3%, followed by acute glomerulonephritis and acute post-streptococcal glomerulonephritis with each 0.3%. Among 34 patients infected with the gastrointestinal system, the most prevalent disease was acute gastroenteritis 10.6% followed by acute colitis and bacillary dysentery, equally distributed with 0.3% respectively. Some of the infections which didn’t belong to the above three systems of illness are included in other system infections, among which enteric fever was most prevalent, with 3.6%, followed by meningitis and orbital cellulitis, with 1.6% cases each, as shown in Table 1.
| System infections | No of patients (N=300) | Percentage (%) |
|---|---|---|
| A. Respiratory system infections | 187 | 62.3% |
| Pneumonia | 79 | 26.3% |
| Bronchiolitis | 32 | 10.6% |
| Respiratory tract infection | 17 | 5.6% |
| Bronchitis | 15 | 5.0% |
| Upper respiratory tract infection | 15 | 5.0% |
| Lower respiratory tract infection | 10 | 3.3% |
| Pharyngitis | 6 | 2.0% |
| Tonsilitis | 5 | 1.6% |
| Acute otitis media | 5 | 1.6% |
| Whooping cough | 1 | 0.3% |
| Croup | 1 | 0.3% |
| Sinusitis | 1 | 0.3% |
| B. Urinary tract System infections | 45 | 15% |
| Urinary tract infection | 43 | 14.3% |
| Acute post-streptococcal glomerulonephritis | 1 | 0.3% |
| Acute glomerulonephritis | 1 | 0.3% |
| C. Gastrointestinal System infections | 34 | 11.3% |
| Acute gastroenteritis | 32 | 10.6% |
| Acute colitis | 1 | 0.3% |
| Bacillary dysentery | 1 | 0.3% |
| D. Other infections | 34 | 11.3% |
| Enteric fever | 11 | 3.6% |
| Infective diarrhea | 7 | 2.3% |
| Orbital cellulitis | 5 | 1.6% |
| Meningitis | 5 | 1.6% |
| Rickettsia fever | 3 | 1.0% |
| Cervical lymphadenitis | 2 | 0.6% |
| Bacterial appendicitis | 1 | 0.3% |
Bold values in the table indicate the Types of systemic infections and Number of pediatrics effected through each systemic infection.

- Sociodemographic characteristics of study population.
The parameters that define the optimal prescription of antibiotics according to WHO were compared with the prescription of antibiotics in our study site. A total of 1737 drugs were prescribed with an average of 5.79 drugs per encounter. The percentage of antibiotics prescribed was 92.3%. Of the 1678 antibiotics, 30% were injectable, 85.8% were prescribed with generic names, and 96.6% of antibiotics were prescribed from the EML, as shown in Table 2 Among 300 prescriptions, cephalosporins were the leading class of antibiotics prescribed, followed by penicillins. As shown in Table 3, Figure 2, out of 218 cephalosporin antibiotics, it was revealed that ceftriaxone was the most prescribed cephalosporin at 53.2%, followed by cefotaxime at 21.5%, and so on.
| Prescribing indicators | No of drugs, N=1737 | Percentage (%) | WHO standards |
|---|---|---|---|
| Percentage of antibiotics from EML | 1678/1737 | 96.6% | 100% |
| Percentage of antibiotics prescribed | 277/300 | 92.3% | 20-26.8% |
| The percentage of antibiotics prescribed by generic name | 1491/1737 | 85.8% | 100% |
| Percentage of encounters with an injection prescribed | 90/300 | 30.0% | 13.4-24.1% |
| Average number of drugs per encounter | 5.79 | - | 1.6-1.8 |
WHO: World Health Organization, EML: Essential medicines list
| Different classes of antibiotics | No. of drugs |
|---|---|
| Cephalosporins | 218 |
| Penicillins | 89 |
| Aminoglycosides | 81 |
| Macrolides | 56 |
| Carbapenem | 39 |
| Fluoroquinolones | 29 |
| Glycopeptides | 24 |
| Nitroimidazoles | 24 |
| Tetracyclines | 14 |
| TMP-SMX | 9 |
| Nitrofurans | 7 |
TMP-SMX: Trimethoprim–sulfamethoxazole.

- Most prescribed cephalosporin antibiotics
To facilitate comprehensive drug utilisation research, the Anatomical Therapeutic Classification system was employed in our study. As shown in As shown in Figure 2 and Table 4, Ceftriaxone of anatomical therapeutic chemical (ATC) code J01DD04 was prescribed in 116 patients and was the most frequently prescribed antibiotic. From figure 3 and table 4 it is understood that amoxicillin + Clavulanic acid with ATC code J01CR02 was the commonest antibiotic combination prescribed in 43 patients followed by piperacillin + tazobactum in 29 patients.

- Most commonly used antibiotic combination
| Category | Drug | ATC Code | Number | Percentage |
|---|---|---|---|---|
| Cephalosporins | ||||
| Ceftriaxone | J01DD04 | 116 | 19.6 % | |
| Cefotaxime | J01DD01 | 47 | 7.9 % | |
| Ceftriaxone + | J01DD63 | 22 | 3.7% | |
| Sulbactum | ||||
| Cefixime | J01DD08 | 9 | 1.5 % | |
| Cefuroxime | J01DC02 | 6 | 1.0% | |
| Cefpodoxime | J01DD13 | 6 | 1.0 % | |
| Ceftazidime + | J01DD52 | 4 | 0.6 % | |
| Tazobactum | ||||
| Ceftazidime | J01DD02 | 4 | 0.6 % | |
| Ceftriaxone + | J01DD64 | 4 | 0.6 % | |
| Tazobactum | ||||
| Aminoglycosides | ||||
| Amikacin | J01GB06 | 69 | 11.6 % | |
| Gentamicin | J01GB03 | 7 | 1.1 % | |
| Tobramycin | J01GB01 | 5 | 0.8% | |
| Macrolides | ||||
| Azithromycin | J01FA10 | 50 | 8.4 % | |
| Clarithromycin | J01FA09 | 3 | 0.5 % | |
| Erythromycin | J01FA01 | 3 | 0.5% | |
| Penicillins | ||||
| Amoxicillin + | J01CR02 | 43 | 7.2 % | |
| clavulanic acid | ||||
| Piperacillin + | J01CR05 | 29 | 4.9 % | |
| tazobactum | ||||
| Ampicillin | J01CA01 | 9 | 1.52% | |
| Benzathine | J01CE08 | 4 | 0.6 % | |
| benzylpenicillin | ||||
| Amoxicillin | J01CA04 | 4 | 0.6 % | |
| Carbapenem | ||||
| Meropenem | J01DH02 | 39 | 6.6 % | |
| Nitroimidazoles | ||||
| Metronidazole | J01XD01 | 24 | 4.0 % | |
| Glycopeptide antibiotics | ||||
| Vancomycin | J01XA01 | 24 | 4.0 % | |
| Tetracycline | ||||
| Doxycycline | J01AA02 | 14 | 2.3 % | |
| Fluoroquinolones | ||||
| Levofloxacin | J01MA12 | 11 | 1.8 % | |
| Moxifloxacin | J01MA14 | 10 | 1.6 % | |
| Ciprofloxacin | J01MA02 | 8 | 1.3% | |
| TMP-SMX | ||||
| Cotrimoxazole | J01EE01 | 9 | 1.5 % | |
| Nitrofuran derivatives | ||||
| Nitrofurantoin | J01XE01 | 7 | 1.1 % | |
ATC: Anatomical and therapeutic classification
DISCUSSION
The present study sought to determine the prescription pattern of antibiotics in paediatric inpatients using the WHO core prescribing indicators in paediatric patients with infectious diseases.
In our study, there was a male preponderance over females. The findings coincide with previous studies conducted by Mishra et al.[13], Shah et al.[14], Pedro et al.[15]
This similarity in findings indicates that male children are more likely to get infections than female children. This is because most male children are more active, and as such, they come into contact with various objects and people who are likely to transmit various infections to them.[16] From this research, it was found that the major part of paediatric patients affected were those within the age group of 1 month to 1 year. There are studies claiming an elevated rate of antibiotic use in the age group of 1 month to 12 months. It could be a reflection of higher susceptibility to infections below the age of 1 and suggests that infant health should be a healthcare priority.[17] A study conducted by Mishra et al.[13] also supported our findings. Our study detected 25 infections in 300 patients. The focus of infection in the majority of the patients was the respiratory system, urinary system, and gastrointestinal system, respectively. Epidemiological factors in resource-scarce environments also contribute to this, according to recent Indian studies.[17] This coincides with the study's findings conducted by Kalonga et al.[6] and Ganjoo et al.[18] A study conducted by Thapaliya et al.[19] In patients with respiratory infections, it was found that 22.5% of the participants had pneumonia, a more prevalent finding in our study, which is 26.3%.
We used the WHO core prescribing indicators to analyse the prescription pattern as a tool similar to that of Mathew et al.[17] The ideal WHO standard value of average drugs per encounter is 1.6-1.8.[20] An average of 5.79 drugs per patient encounter in this study signifies the presence of polypharmacy. However, similar findings were reported in studies conducted by Badar et al.[21] The use of injectables in our study was 30.0%, which was higher than the WHO acceptable range. Excessive use of injectables may lead to a higher probability of blood-borne diseases, the development of complications, and increased costs.[22] With the limited availability of oral formulations in paediatrics, poor compliance towards oral therapy and emergent action in severe conditions are a few reasons associated with the increased prescription of injectables.[23] The percentage of encounters with antibiotics was 92.3%, not per the recommended standard of 20- 26%. These results were like that of the study conducted by Badar et al.[21] On the contrary, studies conducted by Chandika et al.[24] in India found the prescribing of antibiotics to be 22.7%. Mohajer and colleagues found the prescribing of antibiotics to be 18.5% in Saudi Arabia.[25] Most of the drugs prescribed by generic name were found to be 85.8%, which was inconsistent with the study conducted by Chandika et al.[24] Prescribing by generic name is known to reduce the confusion and cost of the drug treatment and to rationalise drug therapy.[26] In our study, 96.6% of drugs were prescribed from the Essential Medicine List (EML). Drug prescription from EML is beneficial in terms of cost-effectiveness and safety of drugs.[27] In our setting, this pattern is likely influenced by multiple local factors, including diagnostic uncertainty in febrile paediatric patients, delayed or incomplete microbiological results due to long turnaround times, and prescriber concern about rapid clinical deterioration in young children. Limited availability of rapid diagnostics and guideline-adapted decision support may further reinforce a preference for broad empirical coverage. Recognising these context-specific drivers is essential for designing effective antimicrobial stewardship interventions that can safely reduce unnecessary antibiotic exposure while maintaining adequate coverage for severe infections.[28,29] Cephalosporins were the most commonly prescribed family of antibiotics in our cohort, followed by penicillins, and the most common diagnosis amongst respiratory infections was pneumonia. Combined, this implies that we are very much dependent on the broad-spectrum cephalosporins to treat paediatric pneumonia in our environment.[19] Although this use is perhaps justified in severe cases or when the local resistance patterns justify the general coverage, first-line utilisation in all cases of pneumonia may not conform to the principles of antimicrobial stewardship, suggesting the use of narrower-spectrum agents wherever possible. In our study, ceftriaxone (19.6%) with ATC Code J01DD04 was the leading antibiotic prescribed, followed by cefotaxime (7.9%) with ATC Code J01DD01 and other antibiotics. Similar conclusions were established by Yehualaw et al.[3] and Thapiliya et al.[19]
The utmost used combination was Amoxicillin + clavulanic acid, followed by piperacillin+ Tazobactam. The studies conducted by Thapaliya et al.[18] and Yehualaw et al.[3] showed analogous conclusions.
Limitations
There are a few limitations to this study. To begin with, patients with complicated medical histories and critically ill children were not considered, which probably skewed the sample to milder cases and could have underrepresented how broad-spectrum and combination antibiotics are used in the sickest inpatients. Second, the data were gathered by means of a self-designed form that, though created with the help of the specialists and accepted by the Institutional Review Board, was not validated or tested in terms of reliability, which can lead to information bias. Third, microbiological culture and sensitivity data were not systematically included because cultures were typically requested only after several days of empirical therapy and then required additional time for parental consent and laboratory processing, so many reports became available late in the hospital stay or after discharge and were incompletely documented in the records. Therefore, the appropriateness of antibiotics could only be determined indirectly via prescribing indicators and not pathogen-specific susceptibility. Lastly, only descriptive statistics were applied; the absence of inferential analyses limits our ability to conclude differences in prescribing between subgroups (e.g. across age groups or types of infection).
CONCLUSION
The significance and worth of antibiotics cannot be overrated; we are reliant on them for treating infectious diseases. But over the years, these microorganisms evolved and developed resistance by various mechanisms. Greater awareness and judicious use of antibiotics are required if we are to continue to derive benefits from antibiotics for the forestallment of antibiotic resistance. Studying prescribing patterns helps us simplify the rational use of antibiotics and ensure their judicious use. As seen in our study, we found that prescription practice was less satisfactory, as it showed that the drug utilisation pattern was not optimal under the values of the WHO prescribing indicators. Polypharmacy was observed to be more advanced than the recommended values. This issue can be addressed by implementing an Antimicrobial Stewardship Program (ASP), promoting the use of antibiotics based on culture and sensitivity tests, and creating institutional guidelines. Therefore, this study provides a substantiation for the necessity and outlines a path forward for the establishment of an ASP in the hospital.
Acknowledgement:
We author, humbly grab this opportunity to acknowledge and express our deep and sincere thanks to Dr N K Kalappanavar (HOD of the paediatrics department) and express our profound gratitude to our guide, Dr Asha Mathew (Assistant professor, department of Pharmacy Practice) for the valuable advice and tremendous contribution to our project. Finally, we would like to extend our appreciation to all the study participants and parents for sharing their valuable time to give their responses for the completion of the research work. The study described in the manuscript has not been previously presented in a meeting or published as an abstract.
Author's contributions:
JM, DM, JB: Data acquisition; SKM, JM, JB: Analysis and data interpretation; JM: Drafting the article; JM DM, AM: Revising it critically for important intellectual content; AM: Approved final version of the manuscript.
Ethical approval:
The research/study was approved by the Institutional and Ethics Committee of Bapuji Pharmacy College, Davangere, number BPC/IEC no. 92, dated 14th March 2023.
Declaration of patient consent:
The authors certify that they have obtained all appropriate patient consent forms. In the form, the patient has given consent for clinical information to be reported in the journal. The patient understands that the patient’s names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.
Conflicts of interest:
There are no conflicts of interest.
Use of artificial intelligence (AI)-assisted technology for manuscript preparation:
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.
Financial support and sponsorship: Nil.
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