Abstract
Antibiotics are the most prescribed medications worldwide. Global consumption rose by 65% in 76 low and middle-income countries between the years 2000 and 2015. According to the World Health Organization, improper administration of antibiotics occurs in over 60% of people with upper respiratory tract infections. Inadvertent antibiotic use has been identified as a contributor to antimicrobial resistance. Outpatient antibiotic use accounts for around 80-90% of all antibiotic use in patients. Clinical officers are non-physician healthcare workers who have received less training, have a more restricted scope of practice than physicians. Clinical officers are key service providers in this country especially at the primary healthcare level.
The study assessed the factors that influence antibiotic prescribing for upper respiratory tract infections by clinical officers.
A prospective study was carried out at 20 public hospitals in Kiambu County, on 36 clinical officers and 600 patient prescriptions. The parameters measured were patient factors, prescriber factors, institutional factors and how they affected the antibiotic prescribing practices by either being rational or irrational. Rational prescribing was identified as prescribing the right drug, at the right frequency, in the right duration, right dose for the right indication. Prescriptions were considered irrational if they did not satisfy any of the rational indices. Data was collected via a questionnaire from the clinical officers while WHO prescription checklist was used to collect data from patient encounters. Data was analyzed using Statistical Package for Social Sciences version 22.0 (SPSS v22.0) with P-value, Confidence Interval and Odds Ratio.
A total of 600 patient encounters were recorded and 79.8% of the 479 encounters had an antibiotic prescription for URTI, 91% of the antibiotics prescribed were the right dose, 98% had the right frequency, 75% had the right duration, and only 23% had the right indication. Patients above 65 years were more likely to receive an antibiotic prescription OR 3.98 CI 0.91,17.41 P=0.17 compared to children under 12 years old. Males were more likely to receive an antibiotic, but this was not significant OR 1.06 CI 0.70, 1.59 P=0.79. A total of 28 (4.6%) patients had fever, and all received antibiotics.
A total of 36 clinical officers were sampled and only 5 (13.8%) were found to have rational prescriptions (P=0.63), prescriber age (P=0.92), prescriber level of education (P=0.99) and prescriber work experience (P=0.22) were not associated with antibiotic prescription. As per institutional factors, availability of antibiotics (P=0.026) and availability of prescription guidelines (P=0.012) were associated with rational prescription of antibiotics.
The study indicated that there was a high antibiotic prescription rate deviating from the WHO standard. It demonstrated that most antibiotic prescriptions were irrational.
Author Contributions
Copyright© 2024
Murigi Kevin, et al.
License
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Competing interests The authors have declared that no competing interests exist.
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Introduction
The worldwide antibiotic consumption rose by 65% from 2000 to 2015 Irrational prescribing is defined as prescribing without achieving proper quality of therapy Viruses account for 80% of URTIs The aim of this study was to find out which factors influence antibiotic prescriptions for upper respiratory tract infections by clinical officers.
Materials And Methods
This was a descriptive cross-sectional public hospital-based study conducted among clinical officers that work in the outpatient departments in their respective hospitals within Kiambu County, Kenya. The study analyzed the antibiotic prescribing practices of clinical officers from September to October 2022. The study followed the methodology recommended by the WHO for investigating drug use in hospitals The outcome variable was antibiotic prescribing practice, whether rational or irrational. WHO core prescribing indicators checklist on rational use of medicine was used to assess rational antibiotic drug use. The independent variables which were assessed are socio-demographic attributes of the prescriber (age, gender, work experience, cadre and level of education), patient factors (age, gender, temperature, signs and symptoms) and institutional factors (availability of antibiotics, availability of hospital guidelines, and laboratory support). The Statistical package for Social Science version 22.0 (SPSS v22.0) was used for analyzing the data. Descriptive statistics were applied to test differences of patient age, sex, temperature, prescriber age, sex, work experience and level of education and availability of antibiotics and availability of prescription guidelines. The association between antibiotic prescribing and the prescribing factors was assessed using logistic regression. Odds ratio with 95% confidence interval was reported in the logistic regression analysis. All analyses were considered significant when P<0.05. Permission to conduct the study was acquired from Kenyatta University graduate school and the university Ethics Review Committee, National Council for Science and Technology and Kiambu County health research office.
Results
In the analysis of the demographic characteristics of the patient, their median age was 17 years, where females were 29% more than males. The median temperature was 36.5 degrees Celsius; ( Those above the age of 65 years were most likely to receive an antibiotic prescription (OR 3.98, 95% CI 0.91 to 17.41). Adolescents (13-18 years) were more likely to receive an antibiotic prescription than children (OR 1.27 95% CI O.73 to 2.22). Adults were more likely to receive an antibiotic prescription than children (OR 1.12 95% CI 0.72 to 1.72). However, these results were not statistically significant (P = 0.17). Similarly, male patients were more likely to receive an antibiotic prescription than females, but this was not statistically significant (OR 1.06, 95% CI 0.70 to 1.59, P = 0.79). All patients with high temperatures received antibiotics. The logistic regression gives an output of 1 and no standard error, it means that the model is perfectly predicting antibiotic prescription for the temperature. As such, the odds ratio cannot be computed because the model perfectly separates the two categories of the outcome variable. Most of the patients (91.2%) were given the correct dose of the antibiotics prescribed. Of the six patients given erythromycin, two were incorrectly prescribed dosage. Of the patients prescribed for antibiotics, there were a total of seven patients that had incorrect frequency (under 2%). These include one patient on amoxyl, three on co-amoxyclav, one on cotrimoxazole, and two on erythromycin. Of the patients prescribed for antibiotics, 360 (75.2%) received the right duration. Most of the patients had incorrect antibiotic indications (n=362; 75.6%) whilst 114 (23.8%) had correct indications, there were 24 (4.4%) and 49 (21.4%) of the patients with correct indications for amoxyclav and amoxyl respectively. All the patients given cefixime (100%), Ampiclox (100%), flagyl (100%), floxapen (100%) and tunaclav (100%) had incorrect indications. Antibiotics were prescribed in 479 encounters. Most of the total antibiotics prescribed were generic (96.8%) and almost all the prescribed antibiotics (96.6%) were on the Kenya Essential Medicines List. Median (IQR) or Frequency (%) Fisher's exact test A total of 36 clinicians were included in the study, of whom 21 (58.3%) were females. Most of them were either in the age-group 26-30 years (n=13; 36.1%) or 31-35 years (n=12; 33.3%). The majority (n=30; 83.3%) had diploma level of education and had worked for a duration of 6-10 years (n=19; 52.8%). Two (5.5%) prescribers had a bachelor’s degree in clinical medicine. There was an association between availability of antibiotics and antibiotic prescribing, P = 0.026. Availability of prescription guidelines significantly affected prescribing of antibiotics, P = 0.012.
Age, Median (IQR)
17 (9-31)
Gender, n (%)
Female
388 (64.7)
Male
212 (35.3)
Temperature (°C), Median (IQR)
36.5 (36.4-36.8)
Diagnosis, n (%)
Cold
21 (3.5)
Cough
28 (4.7)
Flu
2 (0.3)
Pharyngitis
8 (1.3)
Rhinitis
23 (3.8)
Sinusitis
1 (0.2)
Sore throat
3 (0.5)
Tonsillitis
128 (21.3)
URTI
386 (64.3)
P Value
0.17
52(24.2)
163 (75.8)
23(20.0)
92(80.0)
1.27 (0.73, 2.22)
54(22.2)
189(77.8)
1.12 (0.72, 1.72)
2(7.4)
25(92.6)
3.98 (0.91, 17.41)
0.79
45(21.23)
167(78.77)
1.06 (0.70, 1.59)
86(22.2)
302(77.8)
Reference Category
No p value
131(22.9)
441(77.1)
No odds ratio
0
28(100)
Amoxyclav
4(7.4)
0 (0.0)
0 (0.0)
30 (55.6)
50 (92.6)
54 (100.0)
54 (100.0)
24 (44.4)
Amoxyl
9 (3.9)
0 (0.0)
1 (0.4)
179 (78.2)
203 (88.6)
229 (100.0)
227 (99.1)
49 (21.4)
Ampiclox
0 (0.0)
0 (0.0)
0 (0.0)
1 (100.0)
1 (100.0)
1 (100.0)
1 (100.0)
0 (0.0)
Augmentin
0 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
1 (100.0)
1 (100.0)
1 (100.0)
1 (100.0)
Azithromycin
3 (4.4)
10 (14.7)
0 (0.0)
40 (58.8)
64 (94.1)
58 (85.3)
68 (100.0)
28 (41.2)
Cefclav
0 (0.0)
0 (0.0)
0 (0.0)
2 (40.0)
1(20.0)
5 (100.0)
5 (100.0)
3 (60.0)
Cefixime
0 (0.0)
0 (0.0)
0 (0.0)
8 (100.0)
8 (100.0)
8 (100.0)
8 (100.0)
0 (0.0)
Cefuroxime
0 (0.0)
1 (100.0)
0 (0.0)
0 (0.0)
1 (100.0)
0 (0.0)
1 (100.0)
1 (100.0)
Clarithromycin
0 (0.0)
4 (100.0)
0 (0.0)
1 (25.0)
4 (100.0)
0 (0.0)
4 (100.0)
3 (75.0)
Clindamycin
0 (0.0)
3 (100.0)
0 (0.0)
2 (66.7)
3 (100.0)
0 (0.0)
3 (100.0)
0 (0.0)
CO-amoxyclav
1 (20.0)
3(60.0)
3(60.0)
3(60.0)
4(80.0)
2 (40.0)
2 (40.0)
2 (40.0)
Cotrimoxazole
0 (0.0)
90 (100.0)
1 (1.1)
88 (97.8)
89 (98.9)
0 (0.0)
89 (98.9)
2 (2.2)
Erythromycin
2 (33.3)
6 (100.0)
2 (33.3)
4(66.7)
4(66.7)
0 (0.0)
4(66.7)
1 (16.7)
Flagyl
0 (0.0)
0 (0.0)
0 (0.0)
1 (100.0)
1 (100.0)
1 (100.0)
1 (100.0)
0 (0.0)
Floxapen
0 (0.0)
0 (0.0)
0 (0.0)
1 (100.0)
1 (100.0)
1 (100.0)
1 (100.0)
0 (0.0)
Tunaclav
0 (0.0)
2 (100.0)
0 (0.0)
2 (100.0)
2 (100.0)
0 (0.0)
2 (100.0)
0 (0.0)
Total
19 (4.0)
119 (24.8)
7 (1.5)
362 (75.6)
437 (91.2)
360 (75.2)
471 (98.3)
114 (23.8)
479
79.8%
20-26.8
45.9
464
96.8%
100
-
463
96.6%
100
-
0.63
Male
15 (41.7)
12 (38.7)
3 (60.0)
Female
21 (58.3)
19 (61.3)
2 (40.0)
0.92
Under 25 years
6 (16.7)
5 (16.1)
1 (20.0)
26-30 years
13 (36.1)
11 (35.5)
2 (40.0)
31-35 years
12 (33.3)
11 (35.5)
1 (20.0)
36-40 years
5 (13.9)
4 (12.9)
1 (20.0)
0.99
Diploma
30 (83.3)
25 (80.6)
5 (100.0)
Higher diploma
4 (11.1)
4 (12.9)
0 (0.0)
Bachelor's degree
2 (5.6)
2 (6.5)
0 (0.0)
0.22
Under 1 year
6 (16.7)
5 (16.1)
1 (20.0)
1-5 years
8 (22.2)
6 (19.4)
2 (40.0)
6 - 10 years
19 (52.8)
18 (58.1)
1 (20.0)
11-15 years
3 (8.3)
2 (6.5)
1 (20.0)
0.53
Strongly agree
5 (13.9)
5 (16.1)
0 (0.0)
Agree
18 (50.0)
16 (51.6)
2 (40.0)
Neutral
9 (25.0)
7 (22.6)
2 (40.0)
Disagree
4 (11.1)
3 (9.7)
1 (20.0)
0.026
Strongly agree
2 (5.6)
1 (3.2)
1 (20.0)
Agree
20 (55.6)
17 (54.8)
3 (60.0)
Neutral
13 (36.1)
13 (41.9)
0 (0.0)
Disagree
1 (2.8)
0 (0.0)
1 (20.0)
Strongly disagree
0 (0.0)
0 (0.0)
0 (0.0)
16 (44.4)
11 (35.5)
5 (100.0)
0.012
Discussion
The antibiotic prescribing rate (79.8%) exceeded that set by the WHO of under 30% The results showed that 91% of antibiotics prescribed were the right dose, less than two percent had incorrect frequency, and 75.2% had the right duration. Over 75% of the antibiotics were wrongly prescribed. Research from Turkey found that 11.5% of antibiotic prescriptions failed to comply with current guidelines and 29.7% of antibiotic prescriptions were inadequate This study found that the male prescribers were more rational than females. This is contrary to a study done in the Netherlands that found female practitioners are less likely to prescribe antibiotics than males In this study all patients with fever were prescribed an antibiotic. This is similar to a study done in Malta that found patients with fever were more likely to receive an antibiotic There was no significant difference between the age groups of receiving an antibiotic prescription. The lack of a difference in antibiotic prescriptions across the different age groups may be due to a few reasons. Firstly, the prescribers may have prescribed antibiotics based on the symptoms presented by the patient rather than the age. Second, antibiotics may be prescribed at similar rates across all the age groups due to a perceived high prevalence of infectious disease in the population. Lastly, it may be due to the lack of specific guidelines for prescribing antibiotics to different age groups. Availability of antibiotics influenced those over 30 years to prescribe antibiotics. A study in Malta found older practitioners had higher antibiotic prescribing rates while female practitioners were twice as likely to write an antibiotic prescription for URTI In conclusion, there was a high antibiotic prescription rate deviating from the WHO standard. This study demonstrated that most antibiotic prescriptions were irrational. Despite prescribing antibiotics when not indicated, the dose, frequency and duration were noted to be correct in most prescriptions. Only five out of thirty-six clinical officers were rational in their approach to prescribing antibiotics. Availability of antibiotics and availability of guidelines were noted to influence antibiotic prescribing for URTIs by clinical officers. Fever was associated with prescribing of antibiotics for patients with URTIs. Institutional factors such as availability of antibiotics and availability of hospital guidelines significantly affected antibiotic prescribing. Prescriber factors did not influence prescribing of antibiotics.