Trends and practices in the use of non-prescription drugs among university students in the United Arab Emirates

Background: A wide variety of medication, from vitamins to analgesics and anti-inflammatory drugs, can be purchased by users without a medical prescription. These are referred to as Oral Non-Prescription Drugs (ONPD). While this may empower patients to treat themselves, when used irrationally these medications can have a negative health impact. Previous research on higher education students, particularly healthcare students, has demonstrated that they might be a high-risk population for irrationally use of ONPD. In 2004, the World Health Organisation issued specific guidelines to address research in this area. However, recent investigations still indicate that irrational use of medication occurs among this population. Therefore, the current thesis will be guided by the WHO framework in an attempt to develop a strategy to address this problem. Aim: The aim of this thesis is to determine the prevalence of irrational use of medication sold without a prescription in UAE to university students and to identify the reasons for this behaviour. A secondary aim of this investigation is to develop, implement and evaluate the effectiveness of an educational intervention to improve knowledge and awareness of, as well as attitudes and practice towards, rational use of ONPD medication by university students in UAE. To reach the aims of the study, a health behavioural model was used together with qualitative and quantitative methods. Methodology Study One: The aim of this study was to determine the prevalence and risk factors of four types of irrational use (incautious use, inappropriate use, use of antibiotics without prescription and polypharmacy) of ONPD among undergraduate students in UAE. This study used a cross-sectional design employing a randomised sampling technique (n=2875). Statistical analysis was used to analyse this data. Results obtained from this study indicated that 85.9% of students used ONPD, with 38.6% using antibiotics without a prescription. Based on WHO risk assessment criteria, this behaviour was found to the most severe form of irrational use. Additional findings indicated that female participants were 34% less likely to be incautious users (OR =0.344, 95% CI: 0. 244-0.486, p≤0.001), which set males at a higher risk of engaging in this behaviour. Not verifying the expiration date also increased the likelihood of being an incautious user by as much as 51%. Seeking drug information from health care professionals was found to be a protective factor against incautious ONPD use (OR =0. 798, 95% CI: 0.540-0.967, p967, p≤0.05). At the same time, not seeking information on cautious use of ONPD either from medical books or the internet was associated with a higher risk of incautious use (OR = 1.914, 95% CI: 1.353-2.708, p≤0.001). Being a healthcare student significantly increased the odds of being an incautious user of ONPD (OR = 1.561, 95% CI: 1.103-2.208, p≤0.05). Using antibiotics without a prescription was reported among 35.9% of the sample, with no statistically significant difference being observed between healthcare and non-healthcare students. Study Two: Based on the WHO Severity Rating Matrix, the use of antibiotics without prescription was found to be the most significant risk for personal and population health. Therefore, the aim of this study was to further explore the reasons for use of antibiotics without prescription among healthcare university students. This study used a qualitative design employing an interview method and a purposive sample selection technique (n=15) which included only the population of students who used antibiotics without a prescription. Thematic analysis was used to analyse the data. Five main themes emerged from this study: knowledge, awareness, attitude, views, and perceptions, as well as possible strategies to decrease their misuse of antibiotics. Study Three: The aim of this study was to develop and test an intervention for reducing the use of antibiotics without prescription based on the findings of study 1 and 2. The intervention was carried out for 14 weeks. Each session was delivered on a weekly basis and comprised of a 15 minutes PowerPoint presentation followed by 10 minutes of discussion. A quasi-experimental design with purposive sampling was used in which participants (n=140) were assessed at baseline for knowledge, awareness, attitude, and practice of using antibiotics without prescription. Results obtained through comparing baseline measures with post-intervention measures demonstrated a statistically significant (p<0.05) improvement in reducing the use of antibiotic without prescription among the sample. Moderate improvements were also noted in knowledge, attitude, and awareness of antibiotic use. Conclusion: This thesis has demonstrated that the prevalence of ONPD is high among university students in the UAE. This is particularly significant as this increased prevalence occurs concomitantly with irrational use. The most significant risk was related to using antibiotics without prescription. Although the intervention to change this behaviour was successful, other issues such as access to health care and lack of time to see medical practitioners may still promote the use of antibiotics without prescription. Recommendations underlined in this investigation include educating pharmacists to provide information to ONPD buyers

Self-medication with oral antibiotics among University students in United Arab Emirates

Purpose: To investigate the prevalence of antibiotic use without prescriptions and to identify factors associated with this behavior among university students using oral non-prescription drugs (ONPD). Methods: A cross-sectional study was conducted among the students of major universities in UAE. A multistage sampling technique was used in the present study. Results: Out of 2875 students, only 2355 (81.9 %) questionnaire were fully answered and included. Of 2355, more than half (1348; 57.2 %) of the participants reported using ONPD. More than one-third (484, 35.9 %) of 1348 participants used antibiotics without a prescription during the 90 days prior to the present study. Binary logistic regression identified nine statistically significant variables: nationality (OR = 0.471, 95 % CI: 0.326 - 0.681, p &lt; 0.001); cost-influence behavior (OR = 1.716, 95 % CI: 1.175 - 2.508, p &lt; 0.005); belief in ONPD effectiveness (OR = 0.332, 95 % CI: 0 .135 - 0.815, p &lt; 0.05); year of study (OR = 0.310, 95 %, CI: 0.141 - 0.681, p &lt; 0.004); medication knowledge (OR = 0.619, 95 % CI: 0.443 - 0.866, p &lt; 0.005); self-care orientation (OR=1.878, 95 % CI: 1.304 - 2.706, p &lt; 0.001); using ONPD helps to save money (OR=1.665, 95 % CI: 1.047-2.649, p&lt;0.04); and urgency of use (OR = 1.644, 95 %, CI: 1.144 - 2.363, p &lt; 0.007); as well as being healthcare students (OR = 1.465, 95 %, CI: 1.012 - 2.120, p &lt; 0.05). Conclusion: There is a need for educational intervention to improve students’ knowledge, attitude, and awareness regarding the risk of using antibiotics without prescriptions

Sentiment Analysis in Comments Associated to News Articles: Application to Al Jazeera Comments

Sentiment analysis is a very important research task that aims at understanding the general sentiment of a specific community or group of people. Sentiment analysis of Arabic content is still in its early development stages. In the scope of Islamic content mining, sentiment analysis helps understanding what topics Muslims around the world are discussing, which topics are trending and also which topics will be trending in the future. This study has been conducted on a dataset of 5000 comments on news articles collected from Al Jazeera Arabic website. All articles were about the recent war against the Islamic State. The database has been annotated using Crowdflower which is website for crowdsourcing annotations of datasets. Users manually selected whether the sentiment associated with the comment was positive or negative or neutral. Each comment has been annotated by four different users and each annotation is associated with a confidence level between 0 and 1. The confidence level corresponds to whether the users who annotated the same comment agreed or not (1 corresponds to full agreement between the four annotators and 0 to full disagreement). Our method represents the corpus by a binary relation between the set of comments (x) and the set of words (y). A relation exists between the comment (x) and the word (y) if, and only if, (x) contains (y). Three binary relations are created for comments associated with positive, negative and neutral sentiments. Our method then extracts keywords from the obtained binary relations using the hyper concept method [1]. This method decomposes the original relation into non-overlapping rectangles and highlights for each rectangle the most representative keyword. The output is a list of keywords sorted in a hierarchical ordering of importance. The obtained keyword list associated with positive, negative and neutral comments are fed into a random forest classifier of 1000 random trees in order to predict the sentiment associated with each comment of the test set. Experiments have been conducted after splitting the database into 70% training and 30% testing subsets. Our method achieves a correct classification rate of 71% when considering annotations with all values of confidence and even 89% when only considering the annotation with a confidence value equal to 1. These results are very promising and testify of the relevance of the extracted keywords. In conclusion, the hyper concept method extracts discriminative keywords which are used in order to successfully distinguish between comments containing positive, negative and neutral sentiments. Future work includes performing further experiments by using a varying threshold level for the confidence value. Moreover, by applying a part of speech tagger, it is planned to perform keyword extraction on words corresponding to specific grammatical roles (adjectives, verbs, nouns… etc.). Finally, it is also planned to test this method on publicly available datasets such as the Rotten Tomatoes Movie Reviews dataset [2]. Acknowledgment This contribution was made possible by NPRP grant #06-1220-1-233 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.qscienc

Sentiment analysis of islamic news data using hyper-concepts

Sentiment Analysis is the extraction of writers feeling from a written manuscript. It aims at predicting the sentiment of a particular text using automated means. There are two main ways to make predictions: (1) lexicon-based techniques; (2) machine learning based techniques. Our paper contributes in the machine learning techniques, which are more accurate. Furthermore, we have adopted a hyper-conceptual method as our primary feature extraction technique. This method extracts the keywords in a hierarchical ordering of importance. Classification is then performed using the Random Forest classifier that predicts the sentiment of each document. We were able to obtain an accuracy of 90% on an comments collected from Al Jazeera News website.Qatar National Research Fund, QNRF& Qatar Foundation, QFScopu

Anticholinergic burden risk and prevalence of medications carrying anticholinergic properties in elderly cancer patients in Jordan

Background: Geriatric cancer patients are susceptible to adverse drug events due to the complexity of their chemotherapy regimens and collateral treatments for their comorbid conditions. Prescribing medications with anticholinergic burden characteristics can complicate their condition, leading to negative impacts on their health outcomes and quality of life, including an increase in adverse drug event frequency, physical and cognitive impairments. Objective: This study aims to examine the prevalence of anticholinergic prescribing and identify the cumulative anticholinergic load risk associated with drugs prescribed to elderly cancer patients. Also, to identify the predictors that might lead to raised anticholinergic burden in these patients. Methodology: This retrospective cross-sectional study included elderly patients (age ≥ 65) diagnosed with cancer and admitted to the adult oncology unit at King Abdullah University Hospital (KAUH) in Jordan during the period between (January 1st, 2019, and January 1st, 2022). The medication charts of 420 patients were evaluated for study outcomes. Results: Of the total subjects, females represented 49.3%, and the average age was 72.95 (SD = 7.33). A total of 354 (84.3%) patients were prescribed at least one drug carrying anticholinergic burden properties. Median for anticholinergic medications was 3 (IQR = 4). Our study found that 194 (46.2%) patients were at a high risk of adverse events associated with anticholinergic load (cumulative score ≥ 3). Metoclopramide, furosemide, and tramadol were the most frequently prescribed drugs with anticholinergic properties. Alimentary tract drugs with anticholinergic action were the most commonly encountered items in our study population. Conclusion: Our study revealed a significantly high prevalence of anticholinergic prescribing among elderly cancer patients. Nearly half of the patients were at high risk of developing serious effects related to anticholinergic activity from the drugs administered. Polypharmacy was strongly associated with increased anticholinergic burden score. Evidence-based recommendations utilizing prescribing strategies for safer alternatives and deprescribing of inappropriate medications could reduce such inappropriate prescribing