Financial incentives tied to Medicare star ratings: impact on influenza vaccination uptake in Medicare beneficiaries.

OBJECTIVES: To evaluate the impact of the star rating bonus payment policy on annual influenza vaccination rates before and after the policy was adopted for Medicare Advantage (MA) plans in 2012. STUDY DESIGN: Observational study using data from the Medicare Current Beneficiary Survey from 2007 to 2015 to test whether the bonus payment policy led to higher flu vaccination rates in MA prescription drug (MAPD) plans vs fee-for-service prescription drug plans (PDPs), which were ineligible for bonus payments. METHODS: Mean preperiod (2007-2011) and postperiod (2012-2015) influenza vaccination rates were compared for enrollees in both types of plans using descriptive and multivariate difference-in-difference (DID) equations. The experimental effect of the MA bonus payment policy was estimated as the interaction between plan type (MAPD plan vs PDP) and period (pre- vs post period) controlling for the main effects of plan type (MAPD vs PDP), timing of the observation (pre- vs post period), and other potential confounders. RESULTS: The study sample included 40,369 person-years of data in the preperiod and 27,703 person-years of data in the post period. Vaccination rates increased by 3.8% in MAPD plans compared with 2.7% in PDPs, leading to a relative MAPD-favored difference that was nonsignificant (P = .31). However, the effect was statistically significant (odds ratio [OR], 1.12; P = .03) in the main multivariate DID model. A larger relative difference was observed among beneficiaries 75 years and older (OR, 1.18; P = .03). CONCLUSIONS: The Medicare bonus payment policy led to a small increase in beneficiaries' flu vaccination rates, suggesting that expanding the star measure set could be an effective way to increase uptake for other recommended adult vaccines.

Prediction of pharmaceutical solubility Via NRTL-SAC and COSMO-SAC.

Solid phase solubility is a fundamental parameter in the design of crystallization processes. The development and optimization of crystallization processes requires screening of numerous solvent systems for which the solubility of the compound of interest has to be measured as a function of temperature and solvent composition. Tools that quickly estimate the solubility in different solvents can be very useful in the initial phases of the solvent system selection process. In this paper, we report our experience applying two thermodynamic models in the solubility estimation of pharmaceutical compounds: the NRTL-SAC method (Chen and Song, 2004, Ind Eng Chem Res 43: 8354) which provides a correlative and predictive model from limited solubility measurements, and the COSMO-SAC (Lin and Sandler, 2002, Ind Eng Chem Res 41: 899) method which predicts solubility from ab initio calculations. These theoretical methods, coupled with rapid experimental measurement for verification, provide a powerful solubility screening protocol for the development of crystallization processes.