Red meat consumption: An overview of the risks and benefits

Red meat is long established as an important dietary source of protein and essential nutrients including iron, zinc and vitamin B12, yet recent reports that its consumption may increase the risk of cardiovascular disease (CVD) and colon cancer have led to a negative perception of the role of red meat in health. The aim of this paper is to review existing literature for both the risks and benefits of red meat consumption, focusing on case-control and prospective studies. Despite many studies reporting an association between red meat and the risk of CVD and colon cancer, several methodological limitations and inconsistencies were identified which may impact on the validity of their findings. Overall, there is no strong evidence to support the recent conclusion from the World Cancer Research Fund (WCRF) report that red meat has a convincing role to play in colon cancer. A substantial amount of evidence supports the role of lean red meat as a positive moderator of lipid profiles with recent Studies identifying it as a dietary source of the anti-inflammatory long chain (LC) n-3 PUFAs and conjugated linoleic acid (CIA). In conclusion. moderate consumption of lean red meat as part of a balanced diet is unlikely to increase risk for CVD or colon cancer, but may positively influence nutrient intakes and fatty acid profiles, thereby impacting positively on long-term health. (C) 2009 Elsevier Ltd. All rights reserved

Assessment of the benefits of Discrete Conditional Survival Models in modelling ambulance response times

Many of the challenges faced in health care delivery can be informed through building models. In particular, Discrete Conditional Survival (DCS) models, recently under development, can provide policymakers with a flexible tool to assess time-to-event data. The DCS model is capable of modelling the survival curve based on various underlying distribution types and is capable of clustering or grouping observations (based on other covariate information) external to the distribution fits. The flexibility of the model comes through the choice of data mining techniques that are available in ascertaining the different subsets and also in the choice of distribution types available in modelling these informed subsets. This paper presents an illustrated example of the Discrete Conditional Survival model being deployed to represent ambulance response-times by a fully parameterised model. This model is contrasted against use of a parametric accelerated failure-time model, illustrating the strength and usefulness of Discrete Conditional Survival models.