METHODS: A cross-sectional survey using a self-administered questionnaire was conducted with 300 community pharmacists in the Klang Valley, Malaysia using a stratified sampling approach. The questionnaire consisted of 36 questions with three sections: demographic data, adoption of mHealth applications and perception towards mHealth applications. Descriptive and inferential tests as well as exploratory factor analysis were used to analyse the data.
KEY FINDINGS: Adoption of mHealth applications by community pharmacists for both professional and personal use was relatively high at 79.7%. Utilised mHealth applications were primarily from the medical references category, while applications for patient monitoring, personal care and fitness were used to a lesser degree. Among mHealth application users, only 65.7% recommended them to their patients. Overall perception towards mHealth applications was positive, but perception towards the benefits and favour of mHealth applications for their patients was lower. This was corroborated by the factor analysis, which identified four main factors explaining 59.9% of variance in the dataset. These factors were perception towards use in their own professional practice, perception on benefits and use in their patients, perception on specific features of mHealth applications, and reliability of mHealth applications.
CONCLUSIONS: Adoption of mHealth applications among community pharmacists in Malaysia is high. Community pharmacists are more likely to use mHealth applications professionally and personally but less likely to recommend them to patients due to less favourable perceptions on how patients will benefit from mHealth applications.
METHODS: This cross-sectional study employed a validated, self-administered questionnaire which was administered to 543 first-year pharmacy students from nine different private universities. Factor analysis was utilised to extract key factors from the responses. Descriptive and inferential statistics were used to analyse the data.
KEY FINDINGS: Eight factors motivating students' decision to study pharmacy emerged from the responses, accounting for 63.8% of the variance observed. Students were primarily motivated by intrinsic interests, with work conditions and profession attributes also exerting significant influence. In terms of choice of private university, nine factors were identified, accounting for 73.8% of the variance observed. The image of the school and university were most influential factors in this context, followed by university safety, programme attributes and financial factors.
CONCLUSIONS: First-year pharmacy students in the private higher education sector are motivated by intrinsic interest when choosing to study pharmacy over other courses, while their choice of private university is influenced primarily by the image of the school and university.
METHODS: The study was conducted using a self-administered questionnaire. Knowledge of PIMs was assessed using 10 clinical vignettes based on the 2015 Beers Criteria. Practice behaviour towards older customers was assessed using 10 items with a 5-point Likert scale. Descriptive and inferential statistics were used to analyse the data.
RESULTS: A total of 277 community pharmacists participated in the study. Only 27.1% of the pharmacists were aware of Beers Criteria, and of these, only 37.3% were aware of the latest 2015 update. The respondents demonstrated moderate knowledge of PIMs with a mean total score of 5.46 ± 1.89 out of a maximum of 10. Pharmacists who were aware of Beers Criteria had significantly higher scores (6.31 vs 5.14, P
METHODS: Medical claims records from February 2019 to February 2020 were extracted from a health insurance claims database. Data cleaning and data analysis were performed using Python 3.7 with the Pandas, NumPy and Matplotlib libraries. The top five most common diagnoses were identified, and for each diagnosis, the most common medication classes and medications prescribed were quantified. Potentially inappropriate prescribing practices were identified by comparing the medications prescribed with relevant clinical guidelines.
KEY FINDINGS: The five most common diagnoses were upper respiratory tract infection (41.5%), diarrhoea (7.7%), musculoskeletal pain (7.6%), headache (6.7%) and gastritis (4.0%). Medications prescribed by general practitioners were largely as expected for symptomatic management of the respective conditions. One area of potentially inappropriate prescribing identified was inappropriate antibiotic choice. Same-class polypharmacy that may lead to an increased risk of adverse events were also identified, primarily involving multiple paracetamol-containing products, non-steroidal anti-inflammatory drugs (NSAIDs), and antihistamines. Other areas of non-adherence to guidelines identified included the potential overuse of oral corticosteroids and oral salbutamol, and inappropriate gastroprotection for patients receiving NSAIDs.
CONCLUSIONS: While prescribing practices are generally appropriate within the private primary care sector, there remain several areas where some potentially inappropriate prescribing occurs. The areas identified should be the focus in continuing efforts to improve prescribing practices to obtain the optimal clinical outcomes while reducing unnecessary risks and healthcare costs.
METHODS: The study utilized the docked dataset (Induced Fit Docking with Glide XP scoring function) from Loo et al., consisting of 46 ligands-23 agonists and 23 antagonists. The equilibrated structures from Loo et al. were subjected to 30 ns production simulations using GROMACS 2018 at 300 K and 1 atm with the velocity rescaling thermostat and the Parinello-Rahman barostat. AMBER ff99SB*-ILDN was used for the proteins, General Amber Force Field (GAFF) was used for the ligands, and Slipids parameters were used for lipids. MM/PBSA and MM/GBSA binding free energies were then calculated using gmx_MMPBSA. The solute dielectric constant was varied between 1, 2, and 4 to study the effect of different solute dielectric constants on the performance of MM/PB(GB)SA. The effect of entropy on MM/PB(GB)SA binding free energies was evaluated using the interaction entropy module implemented in gmx_MMPBSA. Five GB models, GBHCT, GBOBC1, GBOBC2, GBNeck, and GBNeck2, were evaluated to study the effect of the choice of GB models in the performance of MM/GBSA. Pearson correlation coefficients were used to measure the correlation between experimental and predicted binding free energies.