Revision of clinical case definitions: influenza-like illness and severe acute respiratory infection

Citations Over TimeTop 1% of 2017 papers

Abstract

The formulation of accurate clinical case definitions is an integral part of an effective process of public health surveillance. Although such definitions should, ideally, be based on a standardized and fixed collection of defining criteria, they often require revision to reflect new knowledge of the condition involved and improvements in diagnostic testing. Optimal case definitions also need to have a balance of sensitivity and specificity that reflects their intended use. After the 2009-2010 H1N1 influenza pandemic, the World Health Organization (WHO) initiated a technical consultation on global influenza surveillance. This prompted improvements in the sensitivity and specificity of the case definition for influenza - i.e. a respiratory disease that lacks uniquely defining symptomology. The revision process not only modified the definition of influenza-like illness, to include a simplified list of the criteria shown to be most predictive of influenza infection, but also clarified the language used for the definition, to enhance interpretability. To capture severe cases of influenza that required hospitalization, a new case definition was also developed for severe acute respiratory infection in all age groups. The new definitions have been found to capture more cases without compromising specificity. Despite the challenge still posed in the clinical separation of influenza from other respiratory infections, the global use of the new WHO case definitions should help determine global trends in the characteristics and transmission of influenza viruses and the associated disease burden.

Related Papers

• → ML interpretability: Simple isn't easy(2024)29 cited
• → When consumers need more interpretability of artificial intelligence (AI) recommendations? The effect of decision-making domains(2023)7 cited
• → Measures of Model Interpretability for Model Selection(2018)10 cited
• → Measuring Interpretability for Different Types of Machine Learning Models(2018)15 cited
• → ML Interpretability: Simple Isn't Easy(2022)2 cited