Output-based surveillance standards provide a mechanism to achieve harmonised and comparable surveillance (which meets a defined objective) while allowing flexible approaches that are adapted to the different populations under surveillance. When correctly implemented, they can result in lower cost and greater protection against disease spread. This paper presents examples of how risk-based sampling can improve the efficiency of surveillance, and describes the evolution of output-based surveillance standards for demonstration of freedom from disease in terms of three generations of approach: surveillance sensitivity, probability of freedom, and expected cost of error. These three approaches progressively capture more of the factors affecting the final outcome. The first two are relatively well accepted but the third is new and relates to the consequences of infection. There has been an increased recognition of the value of risk-based sampling for demonstration of freedom from disease over the last decades, but there has been some disagreement about practical definitions and implementation, in particular as to whether 'risk-based' implies probability of infection or probability and consequences. This paper argues that risk-based sampling should be based solely on the probability of infection of a unit within the population, while the consequences of infection should be used to set the target probability of freedom. This approach provides a quantitative framework for planning surveillance which is intuitively understandable. The best way to find disease, if it is present, is to focus on those units that are most likely to be infected. However, if the purpose of surveillance includes mitigating the risk of a disease outbreak, we want to ensure that that risk is smallest in those populations where the consequences of failure to detect are greatest.
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