Study design and period
An explanatory sequential mixed-methods study was conducted in the Ophthalmology outpatient department of the University of Gondar Comprehensive and Specialized Hospital (UoGCSH), a tertiary care teaching hospital in Gondar City, from March 2024 to June 2024. The quantitative part of the study focused on assessing the prescription and drug utilization practices of ophthalmic medication, while the qualitative study was employed to gain a deeper understanding of the factors identified in the quantitative phase and explore the perspectives and experiences of patients regarding ophthalmic care, medication use, and their understanding of eye health.
Study setting
The study was conducted at the UoGCSH, located in Gondar town, approximately 738 km northwest of Addis Ababa and 180 km north of Bahir Dar, within the Central Gondar administrative zone of the Amhara Regional State, Ethiopia. Established in 1954, UoGCSH is a tertiary referral hospital with over 950 beds, providing specialized medical care to nearly five million residents in the North Gondar zone and neighboring areas.
Population
Source population
The source population consisted of patients attending the Ophthalmology outpatient department of UoGCSH.
Study population
The study included adult ophthalmic patients with completed prescriptions who consented to participate during the data collection period. The exclusion criteria were emergency patients, those unable to communicate, and individuals with severe illnesses.
Sample size determination
Sample sizes were calculated using a single population proportion formula by considering the response distribution, P = 0.5 (50%), and at a 95% confidence interval, the marginal error was 5% for the two-tailed type-I error (Zα = 1.96). The sample size was 385. Finally, considering the potentia 10% nonresponse to the interview and/or missing or lost data on the patient’s medical record, 423 patients were approached in the final study [34].
Sampling procedure
A systematic random sampling method was used to select the participants. Approximately 1496 patients receive service in the outpatient department each month [35]. The patient’s follow-up registration book was used as a sampling frame. Among these patients, the sampling fraction, K, was calculated using the total number of patients in three months = 4488 (so K = 4488/423 ~ 11). The first patient was selected using the lottery method and the next patient was selected at 11 intervals on each day within the study period.
Data collection tool and procedure
Data were collected through face-to-face interviews using a pretested, structured questionnaire, supplemented by data abstraction from patient records and prescriptions [26, 27, 36]. The questionnaire, developed in English and then translated into Amharic and back into English by language experts for accuracy, was pretested with 5% of the sample at Felege Hiwot Referral Hospital, leading to necessary revisions. Five trained optometrists conducted the interviews, and the data collectors and supervisors received three days of training. Daily checks ensure data completeness, accuracy, and clarity.
The study collected sociodemographic data, prescription details, and ophthalmic medication types. The prescribing patterns were analyzed using WHO indicators, (Supplementary file 3) including the average number of drugs prescribed per prescription, percentage of generic drugs prescribed, and antibiotic use. The dependent variable was post-dispensing knowledge, which assessed the effectiveness of the dispensing process [27, 28]. While there is a considerable amount of research on Knowledge, Attitudes, and Practices (KAP), there is a limited body of data specifically focused on the tool we used to measure post-dispensing knowledge in this study area. To address this gap, we referenced previous studies that employed the same tool to measure outcomes. We then used their classification methods as a guide to assess and categorize post-dispensing knowledge in our study, ensuring consistency and validity in our approach (Supplementary file 2). The Independent variables included sociodemographic characteristics, eye problems and medication use history, drug prescription information, and prescriber information.
For the qualitative component of the study, key informants were identified, and informed consent was obtained before their participation. The study explored barriers to effective ophthalmic care and medication understanding, with themes focusing on patients’ lack of awareness and education about medications, age-related concerns about eye health, and trust in simplified prescriptions. Additional sub-themes included perceptions of affordability and trust in generic medications, overuse of antibiotics, confusion over prescription instructions and, lack of confidence in treatment plans. Furthermore, challenges in post-dispensing knowledge and literacy-related issues hindered patients’ understanding and proper use of medications. The principal investigator conducted face-to-face interviews using a semi-structured interview guide questionnaire (Supplementary file 1). Each interview lasted approximately 35 min. To ensure accuracy, all interviews were audio-recorded and transcribed verbatim. The key informants included ophthalmologists, optometrists, medical doctors, pharmacists, and patients, who were chosen for their relevant roles and insights into the study context. The semi-structured interview guide was designed to gather detailed responses through probing questions tailored to the participants’ areas of expertise. Separate questionnaires were used to collect comparable position-specific information from each group of informants. This approach ensured a nuanced understanding of the reasons influencing unused medicines, providing complementary qualitative data to enrich the quantitative findings.
Operational definitions
Good post-dispensing knowledge
In this study, this refers to respondents scoring below the mean on questions assessing their understanding of medication frequency, dose, administration, and how to administer the medication.
The scoring system is based on a binary classification, with “Yes” scored as 1 and “No” as 2. The total score ranges from 9 to 18. A score equal to or below the mean indicates good knowledge, while scores above the mean suggest poorer knowledge.
Percentage of drugs prescribed by generic name
This was computed by dividing the number of drugs prescribed by generic name by the total number of drugs prescribed, multiplied by 100.
Percentage of drugs prescribed from an essential drug list
This was computed by dividing the numbers of drugs prescribed that are in the essential drug list by the total number of drugs prescribed, multiplied by 100.
Average consultation time
This was computed by dividing the total time for consecutive consultations in minutes by the number of consultations.
Average dispensing time
This was computed by dividing the total time for dispensing medicines to a sequence of patients in seconds by the number of encounters.
Data analysis
The data were thoroughly checked, coded, and cleaned for inconsistencies and missing values before being entered into EpiData version 4.6.0.0. After processing, the data were exported to SPSS version 25 for analysis. The WHO drug use indicators, including the mean number of drugs per encounter, the percentage of drugs prescribed by generic name, the percentage of encounters with antibiotics prescribed, and the percentage of drugs prescribed from the national essential drug list, were analyzed, along with the dispensing time and counseling time. Descriptive statistics (mean, frequency, proportion, 95% confidence interval, and standard deviation) were used to summarize the data. Bivariate and multivariate logistic regression analyses were performed to identify the independent variables associated with the dependent variable. The independent variables included sociodemographic characteristics, eye problems and medication use history, drug prescription information, and prescriber information. Independent variables with a p-value < 0.2 in the bivariate analysis were included in the multivariate model. The Hosmer-Lemeshow goodness-of-fit test (p = 0.815) confirmed the model’s adequacy. Statistical significance was set at a p-value < 0.05.
For qualitative analyses, the first step involved the transcription of the recorded interviews verbatim. The transcripts were then translated into English for further analysis. After the translation was completed, four investigators (AFB, TTA, GWG, AK, and SAW) independently developed initial codes based on the qualitative data. In cases where there were uncertainties or doubts regarding the coding, five additional authors (ATG, HSA, DG, and ATB) were involved in the decision-making process. Any disagreements in the coding were resolved through discussion before the final analysis commenced. To ensure the accuracy and completeness of the qualitative findings, the research team conducted a thorough verification process. This involved reviewing the participants’ responses in detail and familiarizing themselves with the data, making careful notes where necessary to ensure a comprehensive understanding of the findings. The translated data were analyzed thematically using the OpenCode software version 4.2 or handled manually. The initial codes were developed based on the original terms and concepts identified during the transcription and translation process. These codes were subsequently reviewed, discussed, and validated by the research team to ensure consistency and reliability. From the open codes, sub-themes were generated to organize the data into meaningful categories. The main themes then emerged through an iterative process of identifying patterns and relationships between the subthemes.
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