Evaluation and bias correction of global climate models in the CMIP5 over the Indian Ocean region.

Global climate model (GCM) simulations driven by various emission scenarios are widely used for the projections of future climate change. In this study, an assessment was carried out by using 35 GCMs under Coupled Model Intercomparison Project (CMIP5) in reproducing the present day wind speed changes over six selected regions in the Indian Ocean region based on altimetry-measured merged wind speed product in the Indian Ocean. The relative ranking of the GCMs is performed based on the evaluation of the CMIP5 historical simulations for the period 1993-2005. The skill level of GCMs in representing the various metrics such as annual mean, mean seasonal cycle, linear trend, correlation coefficient, and seasonal standard deviations was accounted for the relative ranking of the GCMs. The models CMCC-CESM, HadGEM2-ES, and GFDL-ESM2G are found to be better for the Arabian Sea region. The GCM products such as HadCM3, CSIRO-Mk3.6.0, HadGEM2-CC, HadGEM2-AO, and MIROC5 were noticed better for the Bay of Bengal (BoB) region. Large bias in wind speed (~ 3 m/s) is observed for the head BoB and the Southern Ocean region. Bias corrections for the present-day Representative Concentration Pathway (RCP) simulations (2006-2016) were performed based on quantile mapping (QM) method, and the present-day wind changes are also compared with observations. The findings from study recommend that suitable bias correction for different GCMs is an essential pre-requisite for climate change studies.

Addressing Medicaid Expansion from the Perspective of Patient Experience in Hospitals.

BACKGROUND: More Medicaid holders are entering the healthcare system consequential to Medicaid expansion. Their experience has financial consequences for hospitals and crucial implications for the provision of patient-centered care. This study examined how the hospital characteristics, especially the rates of Medicaid coverage and racial/ethnic minorities, impact the quality of inpatient care. METHODS: Using data for years 2009-2011 for 870 observations of California hospitals, and data collected from patients via the Hospital Consumer Assessment of Healthcare Providers and Systems survey coupled with data from the Healthcare Cost and Utilization Project and American Hospital Association Annual Survey, we used a generalized estimating equation approach to evaluate patients' experience with hospital care. Our multivariate model includes a comprehensive set of characteristics capturing market, structural, process, and patient demographics associated with the patient's hospital stay. RESULTS: The findings indicate that high concentrations of Medicaid patients in the hospital negatively impact the perceived patient experience. In addition, all things being equal, hospitals with higher concentrations of Hispanic, Black, and Asian patients received lower patient satisfaction results on 28 of the 30 regression coefficients capturing patient satisfaction, with 22 of the 30 negative coefficients statistically significant. CONCLUSIONS: Hospitals serving higher concentrations of Medicaid patients and more racial/ethnic diverse patients experienced a less satisfactory patient experience than patients utilizing other payers or patients who were White. Our research magnifies the challenge for addressing the disparities that exist in healthcare. Further research is called for clarifying the underlying reasons for these disparities and the optimal strategies for addressing these problems.