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Antimicrobial resistance (AMR) is a natural phenomenon [], to which overuse and misuse of antibiotics contribute and augment [-]. Globally, antibiotic consumption has increased (eg, by 65% from 2000 to 2015), and 30% to 50% of antibiotic prescriptions were used either inappropriately or unnecessarily [], further resulting in increased inappropriate use [,] and the development of selective pressure on antibiotics [-]. Inappropriate prescribing is a key contributing factor to the emergence of AMR [,,] and varies from 62.8% for respiratory tract infections to 78.5% in patients with skin and soft tissue infections []. This would strengthen the belief that antibiotics ought to be prescribed and are effective in circumstances when they are not []. Physicians’ prescribing behaviors impact not only patient health but also medical expenses and health resources []. It is recommended to monitor antibiotic prescribing in hospitals to improve the quality of antibiotic prescribing through education and practice changes [12]. Identifying key behaviors and drivers for the behaviors that may be amenable to change and improve prescribing decisions is an important component of interventions in health care practice to mitigate the burden of AMR [,,]. Antibiotic stewardship programs (ASPs), which are among the most common interventions in health facilities to optimize antibiotic use, are effective, low-cost methods to change behaviors that drive excessive prescribing of antibiotics in health facilities [].
Human behavior is guided by beliefs about the likely consequence of the behavior (behavioral beliefs), beliefs about the normative expectations of others (normative beliefs), beliefs about the presence of factors that may facilitate or impede the performance of the behavior (control beliefs), shaping attitudes, subjective norms (SNs), and perceived behavioral control (PBC) []. It is reported that these behavioral beliefs (attitudes, PBC, and SN) of physicians are predictors of indiscriminate antibiotic prescribing behaviors in hospitals [,]; thus, campaigns that address both health service personnel and the general population should take this into account []. A high level of knowledge is known to be associated with a more positive attitude and behavioral intention for reducing antibiotic prescriptions and was linked with less complacency, less fear, and less ignorance, although it had indirect effects on intentions to prescribe antibiotics through the attitude of ignorance []. On the other hand, perceived higher patient pressure negatively affects attitudes toward the rational use of antibiotics and promotes higher use of antibiotics []. Thus, characterizing and designing behavior change interventions based on the behavior change wheel model and theory of planned behavior (TPB) serve as a framework for modeling the antibiotic prescribing behaviors of physicians [,,]. Optimizing antibiotic consumption and reducing the rate of AMR are currently global issues [,]. In low- and middle-income countries, the prescribing of antibiotics is highly influenced by inadequate diagnostic facilities, lack of guidelines, difficulty monitoring patient progress, poor intensive care facilities, patient demand for quick relief, perceived patient expectations from past prescriptions, and fear of losing patients to competition [,]. This results in high mortality and morbidity due to inadequate regulation, limited access to diagnostic facilities, and antimicrobial over-prescription [,]. Based on the behavior change wheel, once a problem is identified and context is considered, functions and policies may be implemented as interventions to understand and change prescribing behavior and improve antibiotic consumption [,]. This requires the design and implementation of sustained awareness campaigns to change behaviors and improve health outcomes [].
In sub-Saharan Africa, physicians still prescribe antibiotics based only on a simple assessment of patients’ symptoms, just as they used to when antibiotics first became commonly used in the 1950s [], due to a lack of diagnostic and antibiotic susceptibility tests, resulting in up to 95% of antibiotic prescriptions as unnecessary []. Prescribing antibiotics requires balancing physician, patient, and facility-related factors []. In Ethiopia, antibiotic prescribing in hospitals may account for 52.39% of all prescriptions [], and one-half of prescribed antibiotics might not be needed []. Although behavior change campaigns can be very cost-effective for changing antibiotic prescribing practices, based on identified gaps [], in Ethiopia, to our knowledge, there have been no studies to model the antibiotic prescribing behavior of physicians other than determining the perceptions of health professionals on AMR and antibiotic use [,]. Modeling behavior is needed to help clinical leaders drive ASP and design educational programs to help standardize and improve antibiotic prescribing behaviors in health facilities []. Thus, this study assessed the determinants of antibiotic prescribing behavior among physicians serving in outpatient departments (OPDs) in hospitals in northwest Ethiopia using a structural equation modeling (SEM) approach.
A cross-sectional study was conducted from September 2022 to October 2022 in 4 hospitals: Felege Hiwot Comprehensive Specialized Hospital, Tibebe Ghion Specialized Hospital, Debre Markos Comprehensive Specialized Hospital, and Injibara General Hospital. Except for attempts to implement ASPs in inpatient wards in some of the hospitals, there is currently no system to monitor antibiotic prescribing or enabling factors for prescribing antibiotics in OPDs. This survey assessed the knowledge, attitudes, SN, and PBC of physicians and their intention to prescribe antibiotics as possible factors for antibiotic prescribing behaviors to provide insights into the driving forces of antibiotic prescribing as a complementary factor for antibiotic consumption, which together constitute baseline information to design effective ASPs to tackle AMR.
Study Participants, Sample Size Determination, and Sampling ProceduresPhysicians (general practitioners and residents) working in OPDs of internal medicine, pediatrics, gynecology and obstetrics, and surgical departments were included in the study. The sample size for health professionals was determined based on the following formula for a finite population:
n = χ2NP (1-P)/ (d2 (N – 1) + χ2P (1 – P))where n is the sample size and χ2 is the table value of the chi square for 1 degree of freedom at the desired confidence level (1.96 × 1.96=3.84), N is the total population, P is the population proportion (27%), and d is the degree of accuracy expressed as a proportion (0.05). According to Gebretekle et al [], physicians estimate that they prescribe antibiotics to about 27% of their patients. Thus, a prevalence of 27% was used to calculate the sample size in this study. Accordingly, n was calculated as follows:
n=(1.96 × 1.96) × 487 × 0.27(1–.27)/((0.05×0.05) × (487–1) + (1.96 × 1.96) × 0.27(1–0.27))n=181.26 or ~182To account for the nonresponse rate, 10% was added; thus, the total sample size was 200.
Data Collection Instruments and ProcessesData collection was based on the study by Liu et al [,] and customized to local scenarios in Ethiopian hospital settings. Questionnaires consisted of 4 behavioral aspects leading to antibiotic prescribing based on the TPB, namely attitudes (the degree to which a prescriber is in favor of the use of antibiotics), SN (perceived social pressure to which a prescriber is subject to prescribe antibiotics), PBC (the ease or difficulty of making a rational decision on antibiotic prescriptions), and intentions (the degree to which a prescriber is willing to prescribe or reduce antibiotics). The questionnaire for professionals was designed on a Likert scale with a 5-point response format, ranging from 1 (strongly disagree) to 5 (strongly agree) for attitudes about and intention to prescribe antibiotics, and a 5-point response format (from always to never) for SN and PBC. In addition, physicians were asked to estimate the number of patients who receive antibiotics from their weekly encounters that involve prescriptions and the number of patients for whom they prescribe antibiotics from 10 encounters with patients with symptoms of upper respiratory tract infections (URTIs) to assess their antibiotic prescribing behavior or practices. To assess physicians’ knowledge, 11 questions were used, attitude was assessed using 7 questions, SN was assessed using 8 questions, PBC was assessed using 5 questions, and there were 3 questions each to measure intentions to reduce and prescribe antibiotics.
Physicians (general practitioners and residents) working in internal medicine, pediatrics, gynecology and obstetrics, and surgical OPDs in the hospital were approached to participate in the study. The questionnaire was distributed while they were on duty. The completeness of the data was monitored on a daily basis. Finally, the data were compiled, and the behavioral constructs were linked with the percentages of physicians’ perceived antibiotic prescribing behaviors and practices using SEM based on modified TPB (MTPB).
The Theoretical Framework for Structural Equation ModelingAttitude, SN, and PBC were shown to be related to appropriate sets of salient behavioral, normative, and control beliefs about a behavior. PBC, together with behavioral intention, can be used directly to predict behavioral achievement. Attitude is defined as the degree to which a prescriber is in favor of the use of antibiotics in outpatient encounters, whereas SN and PBC measure the perceived social pressure to which a prescriber is subject to prescribe antibiotics and the perceived ease or difficulty of making a rational decision during antibiotic prescriptions, respectively. A behavioral intention that is intermediate measures the degree to which a prescriber is willing to prescribe antibiotics []. Thus, the theoretical framework was adopted from the TPB model [], and links between knowledge and attitude, SN, and PBC were explored. However, since the comparative fit indexes (CFIs) were low, knowledge was linked to SN and PBC in relation to antibiotic use, and attitude, SN, and PBC were linked to intentions to prescribe antibiotics and finally to behaviors influencing antibiotic prescribing.
Statistical AnalysisData were coded, entered, cleaned, and transferred to STATA version 14.0 (Stata Corp) for SEM analyses, but descriptive statistics were analyzed using SPSS version 23 (IBM Corp). ANOVA and chi-squared tests were performed to determine the difference in the mean measuring knowledge, attitudes, SN, PBC, and behavioral intentions of the participants according to age, gender, city, professional status, workplace, and duration of clinical practice. For knowledge, the percentage of respondents who answered correctly and the total number of correct answers per respondent were calculated. In addition, correct answers were coded as 1, and incorrect answers were coded as 0 for the SEM. Each attitude item was coded using a 5-point Likert scale (1=strongly agree, 5=strongly disagree), then recoded (–2=strongly disagree, 2=strongly agree), with a negative score indicating disagreements and a positive score indicating agreement with the average scores (ranging from –2 to 2). Intentions to reduce and prescribe antibiotics were coded similarly as the attitude measurements, with a negative score indicating refusal and a positive score indicating support for reducing antibiotic prescriptions (from –2 to 2). SN and PBC were measured from 1 to 5, with 1 indicating always and 5 indicating never, then recoded from 0 to 4, where 0 denotes never and 4 represents always. Behaviors around antibiotic prescriptions were measured using the percentage of antibiotic prescriptions for URTIs, per every 10 patients, and the percentage of antibiotic prescriptions among the estimated weekly visits.
Each variable was modeled separately to exclude factor loadings <0.3. Finally, SEM was applied to establish the associations between knowledge, attitudes, and practices. Standardized path coefficients with statistical significance (P<.05) were used. The maximum likelihood method was used to estimate the parameters. The fitness of the data in the SEM model was assessed using model fitness indexes based on recommended level acceptances such as P>χ2 (P>.05), standardized root mean squared residual <0.09, and root mean squared error of approximation <0.08; Tucker-Lewis index >0.90; CFI>0.90; and coefficient of determination ≥0.7. In addition, descriptive analysis was used.
Operational DefinitionsWe considered attitude to be the degree to which a participant had a positive or negative evaluation of indiscriminate antibiotic use. SNs were participants’ beliefs about whether significant others would approve or disapprove of indiscriminate antibiotic use (ie, the perceived social pressure to which a prescriber is subject to prescribe antibiotics). PBC was the participant’s beliefs regarding the ease or difficulty of making a rational decision about antibiotic prescriptions. Knowledge was considered participants’ understanding and awareness regarding indiscriminate antibiotic use and AMR. The level of knowledge was determined based on the average score for all the questions (ie, physicians who answered at least or above the average score were considered to have good knowledge). Behavioral intentions around antibiotic prescriptions were the degree to which a prescriber was willing to prescribe antibiotics. Behaviors were documented as physicians’ self-reported antibiotic prescribing behaviors.
Ethical ConsiderationsEthical approval was obtained from the College of Health Sciences (protocol code: 106/22/SoP) and the School of Pharmacy (protocol code: ERB/SOP/472/14/2022) of Addis Ababa University. A support letter to the hospitals was obtained from the Amhara Public Health Institute. During data collection, physicians’ names were deidentified. All participants provided informed consent prior to participating in this study. The information obtained was kept confidential and used only for research purposes. Ethical issues like privacy and confidentiality were considered during data collection in order not to disclose information about people outside the research.
Among 200 planned respondents, 185 completed the questionnaires. Thus, the overall response rate was 92.5%.
General Characteristics of Study ParticipantsThe majority of physicians (153/185, 82.7%) were men, and their average age was 30.3 (3.9) years. Overall, physicians had been in their current roles an average of 3.0 (2.2) years and had worked at their current hospital for an average of 2.0 (1.8) years. The majority of physicians (149/185, 80.5%) had worked in their current roles for <5 years; 87 (87/185, 47%) and 55 (55/185, 29.9%) were from Tibebe Ghion Specialized Hospital and the gynecology and obstetrics department, respectively. Of the patients who visited the OPDs, the physicians estimated that 9896 (9896/18,049 54.8%) had received at least one antibiotic ().
Table 1. Characteristics of the 185 physician respondents in hospital outpatient departments in 2022.VariablesResultsSex, n (%)aGP: general practitioner.
bFHCSH: Felege Hiwot Comprehensive Specialized Hospital.
cTGSH: Tibebe Ghion Specialized Hospital.
d DMCSH: Debre Markos Comprehensive Specialized Hospital.
eIGH: Injibara General Hospital.
fGyne/obs: gynecology and obstetrics.
Knowledge of Physicians About Antibiotic PrescriptionThe majority of physicians agreed that amoxicillin is safe for pregnant patients (169/185, 91.4%), metronidazole has the best activity against anaerobes (166/185, 89.7%), and antibiotics should not be prescribed for nonfebrile diarrhea (151/185, 81.6%). However, none of the physicians answered, “Aminoglycosides are very active if they are administered parenterally once daily.” Physicians answered 5 of 11 (46%) questions correctly. Based on this, 121 (65.4%) of the 185 physicians had good knowledge, based on the cutoff of a mean ≥5; however, for 64 (34%) of the 185 physicians, knowledge was poor ().
Table 2. Knowledge about antibiotic prescriptions of 185 physicians in hospital outpatient departments in 2022.CodeItems to assess knowledge levelsResponseFactor loadingP valueThe mean response for attitude questions was 2.5 (0.4). Of the 185 physicians, 88 (47.6%) perceived that microbiology results are important for treating infectious diseases, 95 (51.4%) believed that over-prescribing of antibiotics contributes to the generation of antibiotic resistance, and 89 (48.1%) believed that over-prescription of antibiotics leads to the development of resistance. Regarding intention to prescribe antibiotics, the mean score for intention to reduce antibiotics was 2.4 (0.9), whereas the mean score for intention to prescribe antibiotics 2.5 (0.8). Of the 185 physicians, 133 (71.9%) wanted to reduce antibiotic consumption, 132 (71.4%) expected to reduce antibiotic consumption, and 117 (63.2%) planned to reduce antibiotic consumption for outpatients; however, 107 (57.8%) wanted to prescribe antibiotics, 103 (55.6%) expected to prescribe antibiotics, and 102 (55.1%) planned to prescribe antibiotics to their patients ().
Table 3. Physicians’ (n=185) responses to individual items about attitudes and behavioral intentions about antibiotic prescribing in hospital outpatient departments in 2022.Measurement and itemsCodeResponse score, mean (SD)Responses, n (%)Factor loadingPaOverall score: mean 2.5 (SD 0.4).
bAMR: antimicrobial resistance.
cOverall score: mean 2.4 (SD 0.9).
dOverall score: mean 2.5 (SD 0.8).
Subjective Norms and Perceived Behavioral Control of PhysiciansThe mean scores for SNs and PBC were 4.3 (0.8) and 4.0 (1.1), respectively. Of the 185 physicians, 133 (71.9%), 128 (69.2%), 126 (68.1%), and 125 (67.6%) never prescribed antibiotics based on patients’ expectations, based on patient pressure, based on patients’ requests for antibiotics, and to make patients trust them, respectively. Similarly, 119 (64.3%) of the 185 physicians never prescribed antibiotics to avoid being perceived as doing nothing for patients. Only a limited number of physicians agreed that they prescribed antibiotics based on patients’ expectations or pressure ().
Table 4. Physicians’ (n=185) responses to individual items about subjective norms and perceived behavioral control for intention to prescribe antibiotics in hospital outpatient departments in 2022.Measurement and itemsCodesResponse score, mean (SD)Responses, n (%)Factor loadingP valueaOverall score: mean 4.3 (SD 0.8).
bOverall score: mean 4.0 (SD 1.1).
Antibiotic Prescribing Practices of PhysiciansOf the 18,049 patients seen in the OPDs, 9896 (54.8%), or an average of 60.1 (53.5%) patients per week, were estimated to receive at least one antibiotic. Using an estimate of 10 patients for each of the 185 physicians, for a total of 1850 patients with URTIs, about 916 (49.5%) were estimated to be prescribed at least one antibiotic. Accordingly, 178 (96.2%) of the 185 physicians estimated that they prescribed antibiotics for at least one patient out of every 10 patients who presented with symptoms of a URTI, with a mean score of 5.9 (SD 2.2); 142 (142/185, 76.8%) physicians believed they would prescribe antibiotics for >3 patients; and 43 (43/185, 23.3%) physicians estimated they would prescribe antibiotics for 0 to 3 patients. The majority of physicians (56/185, 30.3%) said they would prescribe antibiotics to 5 patients out of 10 encounters with patients with URTIs in the OPDs ().
Table 5. Estimated prescriptions of antibiotics for upper respiratory tract infections (URTIs) out of every 10 patients by 185 physicians in hospital outpatient departments.Number of patients prescribed antibiotics per every 10 patientsPhysicians who estimated they would prescribe antibiotics, n (%)07 (3.8)14 (2.2)29 (4.9)323 (12.4)Total for ≤3 encounters (30% of patients) with a URTI43 (23.3)431 (16.8)556 (30.3)620 (10.8)710 (5.4)812 (6.5)92 (1.1)1011 (5.9)Total for >3 encounters with a URTI142 (76.8)Structural Equation ModelingThe SEM using MTPB confirmed the theoretical framework for the antibiotic prescribing behaviors of physicians with some modifications. Based on the coefficient of determination (R2), 94.6% of the variation in antibiotic prescribing behavior could be explained by all the exogenous variables. Data in the MTPB model had good fit, with P>χ2 (P>0.0001), a root mean squared error of approximation of 0.049, a standardized root mean squared residual of 0.071, a CFI of 0.91, and a Tucker-Lewis index of 0.901 ().
The MTPB model indicated that only physician knowledge was associated with PBC and SN. There was covariance between SNs and PBC (P<.001). Attitudes, SNs, and PBC were not associated with intentions to prescribe or reduce use of antibiotics. Similarly, intentions to prescribe or reduce use of antibiotics was not associated with the estimated number of antibiotic prescriptions for URTIs or during weekly visits (). Physician age (P=.004) and professional level (P<.02) were predictors of the number of estimated prescriptions for URTIs, and physician age (P=.001), sex (P=.03), and professional level (P=.02) were predictors of the estimated number of prescriptions during weekly OPD visits. Knowledge was a direct predictor of SNs (P<.001) and PBC (P<.001). There was no indirect relationship between prescriber behaviors and knowledge, attitude, SN, and PBC ().
Based on the information in , for the 49.5% of the 1850 patients with URTIs who were estimated to be prescribed at least one antibiotic, physicians older than 30 years were more likely to prescribe antibiotics (51/100, 51%) than those ≤30 years old (48/100, 48%). Based on professional level, residents (51/100, 51%) were more likely to prescribe antibiotics than general practitioners (47/100, 47%). Similarly, for the estimated 54.8% (9896/18,049) of weekly OPD visits that had an antibiotic prescription, physicians older than 30 years were more likely to prescribe antibiotics (57/100, 57%) than those ≤30 years old (54/100, 54%). Women (63/100, 63%) and residents (57/100, 57%) were also more likely to prescribe antibiotics than men (53/100, 53%) and general practitioners (53/100, 53%), respectively. Good knowledge was a direct predictor of SNs (mean 4.4, SD 0.6) and PBC (mean 4.1, SD 1.1), both of which are in contrast for those with poor knowledge (mean 4.0, SD 0.9) and (mean 3.8, SD 1), respectively. However, intentions to reduce and prescribe antibiotics were not affected by attitudes, SNs, nor PBC, and perceived antibiotic prescribing behavior was not related to intentions to reduce or prescribe antibiotics.
Table 6. The model goodness of fit indexes for antibiotic prescribing behaviors of 185 physicians in hospital outpatient departments.Fit statisticsValueDescriptionStandardLikelihood ratioaNot applicable.
Figure 1. SEM of Antibiotic Prescribing Behavior in hospital outpatient departments using standardized coefficients in 2022. This SEM is also available as . SEM: Structural Equation Modeling. Table 7. Effects of direct and indirect variables on the intention to prescribe antibiotics by 185 physicians in hospital outpatient departments in 2022.FactorEffects of covariates on each other
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