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.

Treatment options for HER-2-positive metastatic breast cancer (mBC) patients have expanded markedly since trastuzumab approval in 1998. Several other regimens are now available, including pertuzumab plus trastuzumab plus docetaxel, T-DM1, capecitabine plus lapatinib, and trastuzumab plus lapatinib. This study assesses the cost-effectiveness of four treatment sequences for HER-2-positive mBC according to the Taiwanese National Health Insurance Administration (TNHIA). Costs (U.S. Dollars) and effectiveness (quality-adjusted life years) of four treatment sequences for HER-2-positive mBC patients were examined using a Markov model over a lifetime horizon. Transition probabilities, disease progression, and probability of adverse events and survival were derived from clinical trial data. Costs and health utilities were estimated from TNHIA, Taipei Medical University Hospital, and the literature. Deterministic, probabilistic sensitivity analyses and a scenario analysis examined parameter uncertainty and accounted for drug wastage in dosage and cost calculations. Sequence 3 (1st line: trastuzumab plus docetaxel; 2nd line: T-DM1; 3rd line: trastuzumab plus lapatinib) was the most cost-effective sequence followed by sequence 1 (1st line: pertuzumab plus trastuzumab plus docetaxel; 2nd line: T-DM1; 3rd line: capecitabine plus lapatinib), and sequence 4 (1st line: trastuzumab plus docetaxel; 2nd line: trastuzumab plus lapatinib; 3rd line: trastuzumab plus capecitabine), respectively. The model was sensitive to costs and transition probabilities, but not particularly sensitive to the wastage assumption. From the perspective of the TNHIA, trastuzumab plus docetaxel as 1st line followed by T-DM1 and trastuzumab plus lapatinib as 2nd and 3rd line represents the most cost-effective strategy among the four sequences considered for treating HER-2-positive mBC patients.

How impactful is the use of Sertraline, Fluoxetine, and Escitalopram monotherapy on psychological distress among adults with depression in the real world? Selective serotonin reuptake inhibitors (SSRIs) are the most commonly prescribed antidepressants. Medical Expenditure Panel Survey (MEPS) longitudinal data files from 1 January 2012 to 31 December 2019 (panel 17–23) were used to assess the effects of Sertraline, Fluoxetine and Escitalopram on psychological distress among adult outpatients diagnosed with a major depressive disorder. Participants aged 20–80 years without comorbidities, who initiated antidepressants only at rounds 2 and 3 of each panel, were included. The impact of the medicines on psychological distress was assessed using changes in Kessler Index (K6) scores, which were measured only in rounds 2 and 4 of each panel. Multinomial logistic regression was conducted using the changes in the K6 scores as a dependent variable. A total of 589 participants were included in the study. Overall, 90.79% of the study participants on monotherapy antidepressants reported improved levels of psychological distress. Fluoxetine had the highest improvement rate of 91.87%, followed by Escitalopram (90.38%) and Sertraline (90.27%). The findings on the comparative effectiveness of the three medications were statistically insignificant. Sertraline, Fluoxetine, and Escitalopram were shown to be effective among adult patients suffering from major depressive disorders without comorbid conditions.

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.