Practical scores for the detection of malnutrition

Malnutrition occurs in 30 - 60 % of hospitalized medical or surgical patients, as well as out-patients. Serious consequences at various levels were observed. Malnutrition influences negatively the quality of life, the immune system, muscle strength and worsens the prognosis of the patient. Interventions for a rapid and simple identification and effective treatment of this condition are essential and cost saving. Screening tools for the identification of patients at nutritional risk are very useful in daily practice. The systematic identification of patients with potential or apparent malnutrition is very important allowing an effective nutritional treatment at an early time. The medical team in charge should perform the nutritional risk screening and the following assessment to recognize the nutritional problems and to solve them in an interdisciplinary and -professional team.

A practical approach to designing syndromic surveillance systems for livestock and poultry

The field of animal syndromic surveillance (SyS) is growing, with many systems being developed worldwide. Now is an appropriate time to share ideas and lessons learned from early SyS design and implementation. Based on our practical experience in animal health SyS, with additions from the public health and animal health SyS literature, we put forward for discussion a 6-step approach to designing SyS systems for livestock and poultry. The first step is to formalise policy and surveillance goals which are considerate of stakeholder expectations and reflect priority issues (1). Next, it is important to find consensus on national priority diseases and identify current surveillance gaps. The geographic, demographic, and temporal coverage of the system must be carefully assessed (2). A minimum dataset for SyS that includes the essential data to achieve all surveillance objectives while minimizing the amount of data collected should be defined. One can then compile an inventory of the data sources available and evaluate each using the criteria developed (3). A list of syndromes should then be produced for all data sources. Cases can be classified into syndrome classes and the data can be converted into time series (4). Based on the characteristics of the syndrome-time series, the length of historic data available and the type of outbreaks the system must detect, different aberration detection algorithms can be tested (5). Finally, it is essential to develop a minimally acceptable response protocol for each statistical signal produced (6). Important outcomes of this pre-operational phase should be building of a national network of experts and collective action and evaluation plans. While some of the more applied steps (4 and 5) are currently receiving consideration, more emphasis should be put on earlier conceptual steps by decision makers and surveillance developers (1-3).