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The needs for statistical surveillance in different areas of medicine are described. The predictive value of an alarm and other measures for the evaluation of alarm procedures are suggested. The measures are used to evaluate some common methods of continual surveillance of time series. It is demonstrated that some methods have about the same properties at the start of the surveillance period as later. This is however not the case for all methods. For some methods the consequence of an early alarm should be quite different from that of a late one.

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Biometrika Trust is collaborating with JSTOR to digitize, preserve and extend access to Biometrika. SUMMARY Continual surveillance to detect some event is of interest in quite different situations in industry, medicine, economics and other fields. A general method with an optimality property is described. The implications of the method for some different situations are derived. Some commonly used methods turn out to be special cases.

We describe and discuss statistical models of Swedish influenza data, with special focus on aspects which are important in on-line monitoring. Earlier suggested statistical models are reviewed and the possibility of using them to describe the variation in influenza-like illness (ILI) and laboratory diagnoses (LDI) is discussed. Exponential functions were found to work better than earlier suggested models for describing the influenza incidence. However, the parameters of the estimated functions varied considerably between years. For monitoring purposes we need models which focus on stable indicators of the change at the outbreak and at the peak. For outbreak detection we focus on ILI data. Instead of a parametric estimate of the baseline (which could be very uncertain), we suggest a model utilizing the monotonicity property of a rise in the incidence. For ILI data at the outbreak, Poisson distributions can be used as a first approximation. To confirm that the peak has occurred and the decline has started, we focus on LDI data. A Gaussian distribution is a reasonable approximation near the peak. In view of the variability of the shape of the peak, we suggest that a detection system use the monotonicity properties of a peak.

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Twenty-four wrists in 19 patients with rheumatoid arthritis affecting the wrist were treated by arthroscopic synovectomy. Range of motion, subjective pain, wrist function and X-ray changes were recorded preoperatively and at an average of 3.8 years after operation. Arthroscopic synovectomy of the rheumatoid wrist reduced pain and improved wrist function in the majority of patients. Progress of arthritic degeneration was significantly less common in patients with no, or very early changes at the time of surgery.