No abstract

Objectives The study objectives were to (1) describe the characteristics of the pharmacy professionals and (2) explore the association between job satisfaction and factors, such as work control, work stress, workload and organization and professional commitments. Methods This study was a cross-sectional design. The survey items were mainly adapted from the US National Pharmacist Workforce Survey. An electronic (Qualtrics) questionnaire was posted on pharmacist social media in several Arab countries. The survey link was posted from 22 March 2021 to 1 May 2021. The multiple linear regression measured the association between 12 independent variables and pharmacist job satisfaction. Key findings A total of 2137 usable surveys were received from pharmacists (54.7% female) working in 18 Arabic countries. The job satisfaction rate varied among countries in the Arab world. The fields with the highest satisfaction average included pharmaceutical marketing, academia and the pharmaceutical industry. At the same time, pharmacists working in community pharmacy and Ministry of Health/administrative positions had the lowest satisfaction rates. Overall, pharmacist satisfaction was average (3.1 out of 5). The pharmacists had the lowest satisfaction averages with income and job expectations. The pharmacists with bachelor’s degrees had significantly lower satisfaction than pharmacists with postgraduate degrees. Male pharmacists had significantly higher job satisfaction compared with female pharmacists. Workload and the feelings of organization and professional commitments had significant positive associations with job satisfaction. Conclusions The pharmacy profession in Arabic countries faced several challenges that negatively impacted job satisfaction. Improving work environment, professional management, income and organization loyalty is necessary to enhance pharmacist job satisfaction.

There is a paucity of predictive models for uncontrolled diabetes mellitus. The present study applied different machine learning algorithms on multiple patient characteristics to predict uncontrolled diabetes. Patients with diabetes above the age of 18 from the All of Us Research Program were included. Random forest, extreme gradient boost, logistic regression, and weighted ensemble model algorithms were employed. Patients who had a record of uncontrolled diabetes based on the international classification of diseases code were identified as cases. A set of features including basic demographic, biomarkers and hematological indices were included in the model. The random forest model demonstrated high performance in predicting uncontrolled diabetes, yielding an accuracy of 0.80 (95% CI: 0.79–0.81) as compared to the extreme gradient boost 0.74 (95% CI: 0.73–0.75), the logistic regression 0.64 (95% CI: 0.63–0.65) and the weighted ensemble model 0.77 (95% CI: 0.76–0.79). The maximum area under the receiver characteristics curve value was 0.77 (random forest model), while the minimum value was 0.7 (logistic regression model). Potassium levels, body weight, aspartate aminotransferase, height, and heart rate were important predictors of uncontrolled diabetes. The random forest model demonstrated a high performance in predicting uncontrolled diabetes. Serum electrolytes and physical measurements were important features in predicting uncontrolled diabetes. Machine learning techniques may be used to predict uncontrolled diabetes by incorporating these clinical characteristics.

No abstract

Objective: To explore the impact of different lung cancer treatment modalities on survival time and mortality rates in older patients. Methods: The Surveillance Epidemiology and End Results (SEER) database was used to identify lung cancer patients aged ≥50 years old in the United States. Descriptive statistics and trend charts from 2000 to 2016 were generated. Regression analysis was performed among lung cancer patients to explore the association between survival time and treatment utilization (chemotherapy, radiation, and surgery). A regression model was also applied to explore the association between treatment modalities and odds of dying. Results: A total of 826,217 patients were diagnosed with lung cancer between 2000-2016. The number of lung cancer cases increased by 7%, and the average annual frequency was 48,529 cases per year. Survival, mortality, and treatment utilization varied over the years based on demographic, clinical characteristics, and social status. Five-year survival rate was less than 10% among the study population, and 84% of included lung cancer patients died. Chemotherapy was more commonly used (62%), followed by radiation (35%) and surgical interventions (22%). Chemotherapy and surgery showed a survival advantage. The odds of dying were two times higher among patients treated with surgery than those who were not (OR: 2.62, 95%Cl: 2.58- 2.67). Conclusion: This study highlighted the importance of considering treatment modalities and individual patient characteristics, which may impact survival times and mortality rates among older lung cancer patients.