Massimo Alfonso Russo

Hospital-acquired malnutrition in children with mild clinical conditions

OBJECTIVE Little is known about the incidence and risk factors of hospital-acquired malnutrition in children with mild illness (grade 1 clinical conditions) and its timing of occurrence. The aim of this study was to recognize any early stage of denutrition and possible risk factors leading to nutritional deterioration in children hospitalized due to mild clinical conditions. METHODS Four hundred ninety-six children (age 1-192 mo) with mild clinical conditions were studied. Weight and height were measured. Weight was assessed daily and body mass index (BMI) Z-score was calculated for all patients. RESULTS Children with a BMI Z-score <-2 SD on admission showed a mean BMI decrease at the end of their hospital stay, which was significantly higher than in children who showed a better nutritional condition at admission. Risk factors for hospital-acquired malnutrition were an age <24 mo, a duration of hospital stay >5 d, fever, and night-time abdominal pain. CONCLUSION Hospital stay has an impact on the nutritional status of children affected by mild clinical conditions. Children already malnourished on admission were found to be at risk for further nutritional deterioration during their hospital stay; and in all groups of children identified by their BMI Z-score at admission, nutritional status declined progressively.

Decision Making in Financial Markets Through Multivariate Ordering Procedure

One of the main problems in managing multidimensional data for decision making is that it is impossible to define a complete ordering on multidimensional Euclidean spaces. In order to solve this problem, the scientific community has devolped more and more sofisticated tecniques belonging to the wide framework of Multivariate Statistics. Recently some authors [DR04] have proposed an ordering procedure in which the “meaningful direction” is the “worst-best”. The aim of this paper is to extend this approach considering that, especially in financial applications, variables are quantified using different scales and, as we will show, this can lead to undesired results. As a matter of fact, we show that, without an appropriate rescaling, variables with a large range of variation (rv) are “overweighted” with respect to variables with a small one.

Using CATPCA to Evaluate Market Regulation

One of the most interesting research area in economics concerns the measurement of relative competiveness of different economic systems. Among the several proposed indicators, a particularly relevant one is the Product Market Regulation (PMR) proposed by the OECD, calculated on the basis of a rich database. This paper uses the same database to compute alternative indicators. The main difference with the OECD indicator is that we propose a less invasive statistical methodology (CATPCA). suitable for the treatment of qualitative data. In addition we remove several arbitrary manipulations of basic data. The calculation delivered a new ranking of the 21 countries analyzed and some new interesting evidence.

ADMINISTRATIVE DATA AND MODEL BASED ESTIMATION IN ITALIAN AGRICULTURE STATISTICS

Actually, agricultural surfaces are estimated by the Italian National Statistical Institute (ISTAT) through experts evaluations. The present work has two purposes: 1) to improve the use of administrative data for increasing reliability of crop statistics; 2) to improve methodology for releasing crop early estimates. IACS administrative data are available within a short time lag, cover all the main crops and may substitute estimates gradually. As regards early estimates, the usual design based estimation strategy may be improved through double sampling and model based regression. Results show good reliability of administrative data and decrease of estimates variances using model based estimation. JEL Codes: Q10, Q11, C13, C83.

Financial time series and neural networks in a minority game context

In this paper we consider financial time series from U.S. Fixed Income Market, S&P500, DJ Eurostoxx 50, Dow Jones, Mibtel and Nikkei 225. It is well known that financial time series reveal some anomalies regarding the Efficient Market Hypothesis and some scaling behaviour, such as fat tails and clustered volatility, is evident. This suggests that financial time series can be considered as “pseudo”-random. For this kind of time series the prediction power of neural networks has been shown to be appreciable [10]. At first, we consider the financial time series from the Minority Game point of view and then we apply a neural network with learning algorithm in order to analyse its prediction power. We prove that the Fixed Income Market shows many differences from other markets in terms of predictability as a measure of market efficiency.