Pattern of use and awareness of side-effects of non-steroidal anti-inflammatory drugs in the Jordanian population.
Randa I FarahAseil E KhatibHiba J Abu ZiyadDareen K JiadLara R Al QusousAli Jamal AbabnehSalma AjarmehPublished in: Annals of medicine (2023)
Background: Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly utilized to reduce pain, inflammation, and fever. This study aimed to assess patterns of use and awareness of NSAID-related side-effects in an adult Jordanian. And the associations with sociodemographic factors. Methods: This cross-sectional study among a representative sample of 604 adults >18 years. A validated, self-administered questionnaire was used to collect basic sociodemographic data from the participants, as well as information regarding NSAID use. Results: Most respondents were NSAID users (65.7%), female (53.4%) and under 50 years of age (74.5%). Overall, 42.6% had been prescribed NSAIDs by a physician. Male gender and smoking were negatively correlated with NSAIDs use (multivariable odds ratio [OR]: 0.5, 95% confidence interval [CI]: 0.4-0.8, p = 0.001 and OR: 0.6, 95% CI 0.4-0.8, p = 0.003). In contrast, the Ministry of Health Insurance was associated with NSAIDs use with OR: 1.6, 95% CI: 1.1-2.6, p = 0.03. Overall, 65.1% were aware of kidney NSAID-related side-effects and 22.4% were aware of the increased risk of asthma and allergy. Conclusion: Despite the high frequency of NSAID use in the Jordanian general population, there is limited knowledge of their side-effects as well as drug interactions. This is cause for concern, particularly as many participants reported having been prescribed NSAIDs by physicians without adequate patient safety education.
Keyphrases
- anti inflammatory drugs
- patient safety
- high frequency
- health insurance
- quality improvement
- primary care
- healthcare
- transcranial magnetic stimulation
- oxidative stress
- chronic pain
- emergency department
- cross sectional
- magnetic resonance
- chronic obstructive pulmonary disease
- affordable care act
- mental health
- lung function
- machine learning
- spinal cord
- deep learning
- social media
- psychometric properties
- postoperative pain