Prevalence and Determinants of Self-Medication Practices among Cardiovascular Patients from Béja, North West Tunisia: A Community-Pharmacy-Based Survey.
Maria SuciuLavinia VlaiaEya BoujnehLiana SuciuValentina Oana BudaNarcisa JianuVicențiu VlaiaCarmen CristescuPublished in: Pharmacy (Basel, Switzerland) (2024)
In Tunisia, self-medication is a common practice, and there is a continual rise in the prevalence of cardiovascular disease. Given the lack of data on the self-medication practices (SMPs) among cardiovascular patients in this area, the present study aimed to identify the prevalence and determinants of SMPs among cardiovascular patients in the city of Béja. A community-pharmacy-based survey was conducted among selected cardiovascular patients in Béja, Tunisia, from May 2021 to June 2021. Data were collected using a self-administered questionnaire provided by pharmacists during in-person surveys with patients. Descriptive statistics were used to summarize the data, while Fisher's exact test was used for categorical variables, with the significance level set at p < 0.05. The frequency of self-medication among the 150 respondents was 96%; 70.14% of participants reported that the primary reason why people engage in self-medication is the existence of an old prescription. The most prevalent conditions leading patients to self-medicate were headaches (100%), fever (83.33%), toothache (65.97%), and dry cough (47.92%). The most frequently self-administered drugs were paracetamol (100%), antibiotics (56.94%), and antitussives (47.92%). The results of our study indicate that SMPs among Tunisian cardiovascular patients have a high prevalence. With this in mind, healthcare practitioners should ask their patients about their self-medication practices and advise cardiovascular patients about the risks and benefits associated with this practice.
Keyphrases
- healthcare
- end stage renal disease
- newly diagnosed
- ejection fraction
- cardiovascular disease
- chronic kidney disease
- primary care
- emergency department
- peritoneal dialysis
- type diabetes
- risk factors
- deep learning
- cross sectional
- coronary artery disease
- patient reported outcomes
- metabolic syndrome
- risk assessment
- quality improvement
- social media
- machine learning
- big data
- drug induced
- data analysis
- general practice
- climate change
- human health
- molecular dynamics