Diagnostic Utility of Cerebrospinal Fluid Procalcitonin in Neonatal Meningitis

We aimed to study the diagnostic utility of cerebrospinal fluid (CSF) procalcitonin in neonates with meningitis. All the neonates with sepsis who qualified for lumbar puncture were prospectively evaluated. The neonates were classified as Meningitis and No meningitis group based on predefined criteria. CSF procalcitonin was estimated in these neonates along with cytological and biochemical parameters. A total of 113 neonates were included in the study with 29 in meningitis group and 84 in no meningitis group. The median procalcitonin levels was higher in babies with meningitis as compared to those without meningitis [0.194 (0.034 - 0.534) in meningitis group vs 0.012 (0.012-0.012) ng/ml in no meningitis group, p< 0.001]. The area under curve (AUC) for CSF procalcitonin was 0.867 (0.77 -0.95) and at a cut off level of 0.120 ng/ml CSF procalcitonin had a sensitivity of 83%, specificity of 84% and positive and negative predictive likelihood ratios of 5.35 and 0.20 respectively for the diagnosis of meningitis.Conclusion: CSF procalcitonin has a good diagnostic accuracy similar to other parameters in the diagnosis of neonatal meningitis and can be considered as an additional diagnostic marker particularly, when CSF culture is negative and cytochemical analysis is inconclusive.Trial registration number and date: CTRI/2018/09/015720 ; 14/09/2018

JUST HOW RISKY? COMPARATIVE INSTITUTIONAL RISKS OF MISSION-BASED DEPOSITORY INSTITUTIONS

We examine the relative institutional failure risks for three sets of bank depositories: Community Development Banking Institutions (CDBIs), Minority Depositories (MDIs) and what we term Non-Mission Depository Institutions (NMDIs). CDBIs have primary missions of community development and serving underserved populations; MDIs are typically led by minorities and serve minority populations (a single institution can be both a CDBI and an MDI, either or neither). In this analysis, NMDIs represent all other depository banks. Given their operation within lower-income and minority communities, MDIs and CDBIs appear, prima facie, to face greater institutional failure risks. We examine these risks across each set of institutions, ceteris paribus. Utilizing data from a number of sources, including the Reports of Condition and Income (call reports) for a substantial set of FDIC-insured banks in the United States, we apply a modified Capital, Assets, Management, Earnings and Liquidity model (CAMEL) to measure the predictive likelihood of failure. Recognizing that MDIs are not homogeneous, we also examine relative institutional failure across types of depositories. The results indicate that CDBIs and MDIs are systematically at lower failure risks and that there are differences across service designations.

A Predictive Likelihood Approach to Bayesian Averaging

Multivariate time series forecasting is applied in a wide range of economic activities related to regional competitiveness and is the basis of almost all macroeconomic analysis. In this paper we combine multivariate density forecasts of GDP growth, inflation and real interest rates from four various models, two type of Bayesian vector autoregression (BVAR) models, a New Keynesian dynamic stochastic general equilibrium (DSGE) model of small open economy and DSGE-VAR model. The performance of models is identified using historical dates including domestic economy and foreign economy, which is represented by countries of the Eurozone. Because forecast accuracy of observed models are different, the weighting scheme based on the predictive likelihood, the trace of past MSE matrix, model ranks are used to combine the models. The equal-weight scheme is used as a simple combination scheme. The results show that optimally combined densities are comparable to the best individual models.