Unveiling unique scaling behavior in miscible, volatile Marangoni spreading.

We present a novel observation of the expansion of the outer tip radius of a fast-spreading ethanol-water film spreading over a deep substrate of water. The experimentally measured radius of the outer tip of the film (ro) and its velocity (Uo) display a complex scaling with time and drop properties. The variation showed by ro differed from the commonly observed scalings of t3/4 and t1/4. We propose novel scaling laws for ro and Uo by expressing ro as the sum of the radius of the stable part of the film rf and the length of the unstable part lp at the periphery of the stable part of the film, that had azimuthally uniformly spaced plumes. The radius of the stable part of the film scales as rf ∼ t1/4 since, while the film expands, the Marangoni stresses are balanced by viscous stresses within the film thickness. At the same time, lp ∼ t3/4 since the plumes grow at the periphery of the stable part of the film, with the driving surface tension stresses balanced by the viscous stresses in a shear layer below the plumes. Combining these two scaling laws yielded a novel, two-term scaling law for ro, which is close to a single power-law scaling ro ∼ t1/2. We obtain an expression for the dimensionless mean outer tip radius as , where t* = t/tξ, tξ = (rd4ρwµw/Δσ2)1/3 being the time scale. Similarly, we show that the dimensionless velocity scales as with the variables λ1 and λ2 being functions of t* and drop properties. These proposed scaling laws are shown to match our measurements, thereby validating the phenomenology of such miscible, volatile spreading.

Effects of utilization management on health outcomes: evidence from urinary tract infections and community-acquired pneumonia.

BACKGROUND: Utilization management policies are pervasive in the Medicare Part D program. We assess the effect of utilization management restrictions in the Medicare Part D program on the quality of care in two clinical areas - community-acquired pneumonia (CAP) and urinary tract infections (UTI). METHODS: In this study, we identified new cases of CAP and UTI from Medicare claims data from 2010 to 2016. We assessed the relationship between exposure to utilization management for antibiotic medications suitable for treating these conditions and adverse health outcomes, based on the Agency for Healthcare Research and Quality prevention quality indicators. RESULTS: We identified 147,526 cases of CAP and 632,407 UTI cases in our data. In these samples, the adverse event rate varied from 3.6 to 5.7%. The probability of an adverse event increased by 0.75 (p = 0.061) percentage points for each ten percentage point increase in exposure to quantity limits (one form of utilization management) among people with CAP. There was no relationship between utilization management and adverse events in the UTI cohort. CONCLUSIONS: In some circumstances, exposure to utilization management policies-particularly quantity limits-may adversely affect health.

Association of opioid utilization management with prescribing and overdose.

OBJECTIVES: Deaths from prescription opioids have reached epidemic levels in the United States, yet little is known about how insurers' coverage policies may affect rates of fatal and nonfatal overdose among individuals filling an opioid prescription. STUDY DESIGN: Retrospective cohort study using 2010-2016 Medicare claims data for beneficiaries with 1 or more filled prescriptions for a Schedule II opioid. METHODS: Outcomes were opioid volume dispensed in morphine milligram equivalents (MME), number of days supplied, and number of pills dispensed on each prescription and emergency department or inpatient stay associated with an opioid overdose during a prescription or within 7 days of the end of the prescription. RESULTS: A total of 7.03 million prescriptions for Schedule II opioids were dispensed over 1.87 million Part D beneficiary-years. The 7.03 million opioid prescriptions were associated with 8.5 opioid overdoses per 10,000 prescriptions. Prior authorization was associated with larger opioid volumes per prescription (103.6 MME; 95% CI, 36.2-171.0). Step therapy was associated with a greater number of days supplied (0.62 days; 95% CI, 0.10-1.13) and more pills dispensed (6.12 pills; 95% CI, 2.17-10.1). Quantity limits were associated with smaller opioid volumes (24.3 MME; 95% CI, 12.3-36.3) and fewer pills dispensed (2.35 pills; 95% CI, 1.77-2.93). In adjusted models, beneficiaries filling an opioid requiring prior authorization experienced 3.3 fewer overdoses per 10,000 prescriptions (95% CI, 0.41-6.2). CONCLUSIONS: Opioid utilization management among these beneficiaries was associated with mixed effects on opioid prescribing, and prior authorization was associated with a decreased likelihood of subsequent overdose. Further work exploring the impact of utilization management and insurer policies is needed.

Machine Learning Prediction of Treatment Outcome in Late-Life Depression.

Background: Recent evidence suggests that integration of multi-modal data improves performance in machine learning prediction of depression treatment outcomes. Here, we compared the predictive performance of three machine learning classifiers using differing combinations of sociodemographic characteristics, baseline clinical self-reports, cognitive tests, and structural magnetic resonance imaging (MRI) features to predict treatment outcomes in late-life depression (LLD). Methods: Data were combined from two clinical trials conducted with depressed adults aged 60 and older, including response to escitalopram (N = 32, NCT01902004) and Tai Chi (N = 35, NCT02460666). Remission was defined as a score of 6 or less on the 24-item Hamilton Rating Scale for Depression (HAMD) at the end of 24 weeks of treatment. Features subsets were constructed from baseline sociodemographic and clinical features, gray matter volumes (GMVs), or both. Three classification algorithms were compared: (1) Support Vector Machine-Radial Bias Function (SVMRBF), (2) Random Forest (RF), and (3) Logistic Regression (LR). A repeated 5-fold cross-validation approach with a wrapper-based feature selection method was used for model fitting. Model performance metrics included Area under the ROC Curve (AUC) and Matthews correlation coefficient (MCC). Cross-validated performance significance was tested by permutation analysis. Classifiers were compared by Cochran's Q and post-hoc pairwise comparisons using McNemar's Chi-Square test with Bonferroni correction. Results: For the RF and SVMRBF algorithms, the combined feature set outperformed the clinical and GMV feature sets with a final cross-validated AUC of 0.83 ± 0.11 and 0.80 ± 0.11, respectively. Both classifiers passed permutation analysis. The LR algorithm performed best using GMV features alone (AUC 0.79 ± 0.14) but failed to pass permutation analysis using any feature set. Performance of the three classifiers differed significantly for all three features sets. Important predictive features of treatment response included anterior and posterior cingulate volumes, depression characteristics, and self-reported health-related quality scores. Conclusion: This preliminary exploration into the use of ML and multi-modal data to identify predictors of general treatment response in LLD indicates that integration of clinical and structural MRI features significantly increases predictive capability. Identified features are among those previously implicated in geriatric depression, encouraging future work in this arena.