Statins are drugs of choice in treating hyperlipidemia and preventing or reducing cardiovascular diseases. Explore statins utilization and expenditure trends in the United States before and after the publication of the 2013 ACC/AHA guidelines. A retrospective, cross-sectional study of US noninstitutionalized civilians was conducted using MEPS data from 2008 to 2019. Adults who were ≥ 40 years old and who reported taking statins were included in the study. Statins use patterns, total cost, and out-of-pocket spending in the general adults who reported taking statins medications. Expenditures were expressed in 2019 US dollars. In this study, 409,804 individuals were eligible to be included (mean age [SE], 59 [0.1] years; 54% female). Of those participants, 22% reported taking statin therapy, and 11% of them filled only one statin prescription. The number of individuals in the general population who reported taking any statin climbed from 31 million (12%) in 2008–2009 to 92 million (35%) in 2018–2019, representing a 197% increase. After 2013, the number of individuals who used statins increased by 149%, from 37 million in 2012–2013 to 92 million users in 2018–2019. The annual number of statins prescriptions increased from 461 million to 818 million (77%; p = 0.000) between 2008 and 2019. Atorvastatin was the most prescribed medication in the statins class (36%), followed by simvastatin (34%). The moderate-intensity statins were the most used by the participants (60%). The total statins cost in 2013 was $8 billion and increased to $10 billion in 2019 (25%; p = 0.000). The total OOP expenditure trend sloped from $4.0 billion in the 2008–2009 cycle to $3.1 billion in 2018–2019. The average OOP paid by Asians was higher than that of other races at $141. The proportion of individuals who used statins significantly increased following the adoption of the 2013 ACC/AHA guidelines. The findings, however, demonstrated suboptimal prescribing trends of high-intensity statins, which need to be addressed by the stakeholders to maximize medication outcomes. Statins expenditures, especially the co-payments, significantly decreased. The results have shown that revised or new regulations have a substantial impact on the healthcare industry.

This study was undertaken to investigate in-depth the factors impacting job satisfaction among pharmacists in the Arab world and the challenges they encounter in their career path. The outcome of this study should help the local policymakers to take corrective actions to improve pharmacist’s satisfaction and therefore enhance quality of patient care. This qualitative study collected responses of pharmacists from 12 Arab countries, as part of a large quantitative survey. Participants added comments to an optional open-ended question regarding work satisfaction. The Qualtrics Survey Software was used to collect the responses. The survey was distributed from March to May 2021 through multiple online channels for filling. The responses collected were analysed to develop themes. An inductive constructivist approach was used for the conceptual thematic analysis as the methodological orientation. A total of 110 responses/comments were received from the study participants. The two largest practice settings of the participants were from hospitals (44.5%) and community pharmacies (28.2%). Almost 40% of responses came from pharmacists practising in Qatar (21.8%) and UAE (18.1%). The survey data demonstrated several reasons impacting job satisfaction among pharmacists practising in the Arab countries. Underestimation of the pharmacists' role, low salaries, lack of motivation and excessive workload were reported as major contributors to job dissatisfaction. On the other hand, professional commitment and the culture of the work setting were the major contributors to job satisfaction. The study provides valuable insights into the aspects concerning pharmacists’ satisfaction in the Arab world. Policymakers and other stakeholders need to act upon aspects of pharmacists’ job satisfaction and dissatisfaction to ensure potentially better working environment and patient outcomes.

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.

Background: The older adult population is rapidly expanding in the United States (US), with a high prevalence of high blood pressure, high cholesterol, and diabetes. Medication nonadherence is prevalent in this population, with less evidence on the influence of social determinants of health (SDoH). Thus, the objective of this study was to identify and prioritize SDoH associated with medication adherence among US older adults with these comorbidities. Method: Using the World Health Organization Commission on Social Determinants of Health and Pharmacy Quality Alliance Medication Access Conceptual Frameworks, publicly available National Health and Nutrition Examination Survey datasets (2009–2018) were cross-sectionally analyzed among respondents aged 65 and older who were diagnosed with study diseases. Data analyses included descriptive statistics, and logistic regression using an alpha level of 0.05. Result: Analyses included 5513 respondents’ data. Bivariate analysis revealed significant differences in medication adherence based on several structural (e.g., ethnicity) and intermediary (e.g., disability status) determinants of health. Multivariable analysis revealed significant differences in medication adherence for alcohol consumption (p = 0.034) and usual healthcare place (p = 0.001). Conclusions: The study findings underscore pertinent implications for public health and policy, with specific SDoH being the most likely to affect medication adherence in common chronic conditions among older adults in the US.

Introduction. Due to limited information on mental health in rural areas, this study innovates by applying validated tools previously unused in rural Honduras to assess levels of anxiety and depression among rural residents of Ojojona, Honduras. Methods. The Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder Scale-7 (GAD-7) were used to assess depression and anxiety, respectively. Twenty-one participants were obtained through convenience sampling and door-to-door surveys. Results. The prevalence of depression and anxiety was 47.7% and 47.6%, respectively. 29% have co-occurring depression and anxiety. Conclusion. Additional research is needed to effectively understand the high prevalence of mental health disorders in rural Honduran communities and improve access to healthcare and health research technology in rural areas.