The development of an early warning system for climate-sensitive disease risk with a focus on dengue epidemics in Southeast Brazil.

Previous studies demonstrate statistically significant associations between disease and climate variations, highlighting the potential for developing climate-based epidemic early warning systems. However, limitations include failure to allow for non-climatic confounding factors, limited geographical/temporal resolution, or lack of evaluation of predictive validity. Here, we consider such issues for dengue in Southeast Brazil using a spatio-temporal generalised linear mixed model with parameters estimated in a Bayesian framework, allowing posterior predictive distributions to be derived in time and space. This paper builds upon a preliminary study by Lowe et al. but uses extended, more recent data and a refined model formulation, which, amongst other adjustments, incorporates past dengue risk to improve model predictions. For the first time, a thorough evaluation and validation of model performance is conducted using out-of-sample predictions and demonstrates considerable improvement over a model that mirrors current surveillance practice. Using the model, we can issue probabilistic dengue early warnings for pre-defined 'alert' thresholds. With the use of the criterion 'greater than a 50% chance of exceeding 300 cases per 100,000 inhabitants', there would have been successful epidemic alerts issued for 81% of the 54 regions that experienced epidemic dengue incidence rates in February-April 2008, with a corresponding false alarm rate of 25%. We propose a novel visualisation technique to map ternary probabilistic forecasts of dengue risk. This technique allows decision makers to identify areas where the model predicts with certainty a particular dengue risk category, to effectively target limited resources to those districts most at risk for a given season.

Effect of preload reduction by haemodialysis on new indices of diastolic function.

Assessment of mitral annular motion diastolic velocities by M-mode or tissue Doppler imaging and the propagation velocity of early diastolic filling (Vp) by colour M-mode have been proposed as preload-independent indices of diastolic function. The aim of the present study was to determine the effects of preload reduction by haemodialysis on these new echocardiographic indices and to assess the relationship between these indices. The study group comprised 17 patients with chronic renal failure in sinus rhythm with normal left ventricular systolic function who underwent echocardiography 30 min prior to and 30 min following haemodialysis. Following dialysis there were significant reductions in weight (P<0.001), left atrial diameter (P=0.001), the peak Doppler velocity of early diastolic transmitral flow (P=0.005) and the ratio of Doppler velocities of early to late diastolic transmitral flow (P=0.02), consistent with a reduction in intravascular volume. There was no change after dialysis in early diastolic mitral annular velocity using M-mode (P=0.19) or tissue Doppler imaging from either the septal or lateral walls (P=0.88 and P=0.15 respectively), but there was a reduction in Vp after dialysis (55 to 49 cm/s; P=0.04). There were only weak correlations between Vp and the early diastolic mitral annular velocities (r<0.6 for all). We conclude that the assessment of diastolic function by the mitral annular early diastolic velocity appears to be preload-independent, that Vp may be affected by preload and that there is only a weak relationship between Vp and the early diastolic mitral annular velocity.