Background and Objective: There is no accepted nutrition approach for wound healing in children. Our aims were to determine optimal nutrition support for pediatric wound healing. Methods: We applied local methods to create evidence- and consensus-based recommendations, supported by implementation tools, including algorithms, clinical decision supports, and measures. We applied these recommendations to the care of 49 patients from December 5, 2011, to December 5, 2012. Results: Six articles were found that addressed our clinical questions, and we formulated 5 clinical recommendations. Evidence supported evaluating patients for vitamin C, zinc, and protein deficiency. Of the patients where laboratory values were checked, 9 patients were zinc deficient (33%) and 12 patients were vitamin C deficient (48%). Discussion and Practical Application: The implementation of our recommendations has led to increased identification of micronutrient deficiencies and closer monitoring of nutrition status and intake. Online clinical decision supports can accelerate the adoption of clinical recommendations and reduce provider practice variation.

AbstractThe negative consequences of financial instability for the world economy during the recent financial crisis have highlighted the need for a better understanding of financial conditions. We use a financial conditions index (FCI) for South Africa previously constructed from 16 financial variables to test whether the South African economy responds in a nonlinear and asymmetric way to unexpected changes in financial conditions. To this end, we make use of a nonlinear logistic smooth transition vector autoregressive model (LSTVAR), which allows for a smooth evolution of the economy, governed by a chosen switching variable between periods of high and low financial volatility. We find that the South African economy responds nonlinearly to financial shocks, and that manufacturing output growth and Treasury Bill rates are more affected by financial shocks during upswings. Inflation responds significantly more to financial changes during recessions.

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The global financial crisis that began in 2007-08 demonstrated how severe the impact of financial markets' stress on real economic activity can be. In the wake of the financial crisis policy-makers and decision-makers across the world identified the critical need for a better understanding of financial conditions, and more importantly, their impact on the real economy. To this end, we have constructed a financial conditions index (FCI) for the South African economy, to enable the gauging of financial conditions and to better understand the macro-financial linkages in the country. The FCI is constructed using monthly data over the period 1966 to 2011, and is based on a set of sixteen financial variables, which include variables that define the state of international financial markets, asset prices, interest rate spreads, stock market yields and volatility, bond market volatility and monetary aggregates. We explore different methodologies for constructing the FCI, and find that recursive principal components analysis (PCA) yields the best result. We furthermore investigate whether it is beneficial to purge the FCI of the real effects of inflation, economic growth and interest rates, and use the identified FCI in causality testing with three macroeconomic variables.

The importance of financial instability for the world economy has been severely demonstrated since the 2007/08 global financial crisis, highlighting the need for a better understanding of financial conditions. We consider a financial conditions index (FCI) for South Africa which is constructed from 16 financial variables and test whether the FCI does better than its individual financial components in forecasting the key macroeconomic variables of output growth, inflation and interest rates. Two sets of out-of-sample forecasts are obtained -one from a benchmark AR model and one from a nested ARDL model which includes one financial variable at a time. This concept of forecast encompassing is used to examine the out-of-sample forecasting ability of these financial variables as well as of the FCI, while also controlling for data-mining. We find that the FCI has good out-of-sample forecasting ability with respect to manufacturing output growth at the one, three and six month horizons, but has no forecasting ability with respect to inflation and interest rates 1 .