A review of methods suggested in the literature for sequential detection of changes in public health surveillance data is presented. Many researchers have noted the need for prospective methods. In recent years there has been an increased interest in both the statistical and the epidemiological literature concerning this type of problem. However, most of the vast literature in public health monitoring deals with retrospective methods, especially spatial methods. Evaluations with respect to the statistical properties of interest for prospective surveillance are rare. The special aspects of prospective statistical surveillance and different ways of evaluating such methods are described. Attention is given to methods that include only the time domain as well as methods for detection where observations have a spatial structure. In the case of surveillance of a change in a Poisson process the likelihood ratio method and the Shiryaev-Roberts method are derived. Copyright 2003 Royal Statistical Society.

In this paper we describe Bonferroni-based multiple testing procedures (MTPs) as strategies to split and recycle test mass. Here, 'test mass' refers to (parts of) the nominal level alpha at which the family-wise error rate is controlled. Briefly, test mass is split between different null hypotheses, and whenever a null hypothesis is rejected, the part of alpha allocated to it may be recycled to the testing of other hypotheses. These recycling MTPs are closed testing procedures based on raw p-values associated with testing the individual null hypotheses, and the class of such MTPs includes, for example, serial and parallel gatekeeping, fallback and Holm procedures. Graphical displays and a concise algebraic notation are provided for such MTPs. This recycling approach has pedagogical advantages and may facilitate the tailoring of MTPs for different purposes.

BackgroundA pre-specified meta-analysis of cardiovascular (CV) events from 21 phase 2b/3 dapagliflozin clinical trials was undertaken to characterise the CV profile of dapagliflozin. This showed no increase in CV risk with dapagliflozin compared with control (placebo or comparator treatment) with or without background glucose-lowering therapies. The analysis reported here aimed to characterise the CV profile of dapagliflozin in subgroups of patients in these 21 studies grouped by degree of CV risk, based on both baseline and in-study risk factors (including hypoglycaemic events), with a focus on major adverse CV events (MACE).MethodsPatients with type 2 diabetes, both overall and with different levels of CV risk, including CV disease (CVD) history, age and other CV risk factors, were analysed. A further analysis compared CV risk in patients who experienced a hypoglycaemic event prior to MACE and those who did not. Analyses were based on time to first event using a Cox proportional hazards model stratified by study comparing dapagliflozin versus control.ResultsIn total, 9339 patients were included in this meta-analysis; 5936 patients received dapagliflozin 2.5–10 mg (6668 patient–years) and 3403 received control (3882 patient–years). Dapagliflozin is not associated with increased CV risk and results further suggest the potential for a beneficial effect both in the overall population [Hazard Ratio (HR) 0.77; 95 % CI (0.54, 1.10) for MACE] and in those with a history of CVD [HR 0.80 (0.53, 1.22)]. These findings were consistent in patients with varying degrees of CV risk, including age, number and type of CVD events in medical history and number of CV risk factors present. Furthermore, there was no increased risk of MACE in patients who experienced a hypoglycaemic event compared with those who did not.ConclusionsThere was no suggestion of increased risk for MACE with dapagliflozin compared with control in any of the populations investigated. In addition, the results suggest the potential for a beneficial CV effect which is consistent with the multifactorial benefits on CV risk factors associated with sodium–glucose cotransporter-2 (SGLT2) inhibitors.Electronic supplementary materialThe online version of this article (doi:10.1186/s12933-016-0356-y) contains supplementary material, which is available to authorized users.

Generic substitution has been implemented in practice although it did not reach full dividend during the first year. The potential savings from extended use of generic substitution are substantial.

Several methods for timely detection of emerging clusters of diseases have recently been proposed. We focus our attention on one of the most popular types of method; a scan statistic. Different ways of constructing space-time scan statistics based on surveillance theory are presented. We bridge the ideas from space-time disease surveillance, public health surveillance and industrial quality control and show that previously suggested space-time scan statistics methods can be fitted into a general CUSUM framework. Crucial differences between the methods studied are due to different assumptions about the spatial process. An example is the specification of the spatial regions of interest for a possible cluster, another is the increased rate to be detected within a cluster. We evaluate the detection ability of the methods considering the possibility of a cluster emerging at any time during the surveillance period. The methods are applied to the detection of an increased incidence of Tularemia in Sweden.