A randomized controlled trial to assess the clinical and cost effectiveness of a nurse-led Antenatal Asthma Management Service in South Australia (AAMS study)

Background: Pregnancy presents a unique situation for the management of asthma as it can alter the course of asthma severity and its treatment, which in turn can affect pregnancy outcomes. Despite awareness of the substantial adverse effects associated with asthma during pregnancy, little has been done to improve its management and reduce associated perinatal morbidity and mortality. The aim of this randomized controlled trial is to evaluate the clinical and cost effectiveness of an Antenatal Asthma Management Service. Methods/design: Design: Multicentre, randomized controlled trial. Inclusion criteria: Women with physician diagnosed asthma, which is not currently in remission, who are less than 20 weeks gestation with a singleton pregnancy and do not have a chronic medical condition. Trial entry and randomization: Eligible women with asthma, stratified by treatment site, disease severity and parity, will be randomized into either the ‘Standard Care Group’ or the ‘Intervention Group’. Study groups: Both groups will be followed prospectively throughout pregnancy. Women in the ‘Standard Care Group’ will receive routine obstetric care reflecting current clinical practice in Australian hospitals. Women in the ‘Intervention Group’ will receive additional care through the nurse-led Antenatal Asthma Management Service, based in the antenatal outpatient clinic. Women will receive asthma education with a full assessment of their asthma at 18, 24, 30 and 36 weeks gestation. Each antenatal visit will include a 60 min session where asthma management skills are assessed including: medication adherence and knowledge, inhaler device technique, recognition of asthma deterioration and possession of a written asthma action plan. Furthermore, subjects will receive education about asthma control and management skills including trigger avoidance and smoking cessation counseling when appropriate. Primary study outcome: Asthma exacerbations during pregnancy. Sample size: A sample size of 378 women will be sufficient to show an absolute reduction in asthma exacerbations during pregnancy of 20% (alpha 0.05 two-tailed, 90% power, 5% loss to follow-up). Discussion: The integration of an asthma education program within the antenatal clinic setting has the significant potential to improve the participation of pregnant women in the self-management of their asthma, reduce asthma exacerbations and improve perinatal health outcomes.Luke E Grzeskowiak, Gustaaf Dekker, Karen Rivers, Kate Roberts-Thomson, Anil Roy, Brian Smith, Jeffery Bowden, Robert Bryce, Michael Davies, Justin Beilby, Anne Wilson, Philippa Middleton, Richard Ruffin, Jonathan Karnon, Vicki L Clifton and for the AAMS study grou

Spatially Explicit Analyses of Anopheline Mosquitoes Indoor Resting Density: Implications for Malaria Control

Background: The question of sampling and spatial aggregation of malaria vectors is central to vector control efforts and estimates of transmission. Spatial patterns of anopheline populations are complex because mosquitoes' habitats and behaviors are strongly heterogeneous. Analyses of spatially referenced counts provide a powerful approach to delineate complex distribution patterns, and contributions of these methods in the study and control of malaria vectors must be carefully evaluated. Methodology/Principal Findings: We used correlograms, directional variograms, Local Indicators of Spatial Association (LISA) and the Spatial Analysis by Distance IndicEs (SADIE) to examine spatial patterns of Indoor Resting Densities (IRD) in two dominant malaria vectors sampled with a 565 km grid over a 2500 km(2) area in the forest domain of Cameroon. SADIE analyses revealed that the distribution of Anopheles gambiae was different from regular or random, whereas there was no evidence of spatial pattern in Anopheles funestus (Ia = 1.644, Pa0.05, respectively). Correlograms and variograms showed significant spatial autocorrelations at small distance lags, and indicated the presence of large clusters of similar values of abundance in An. gambiae while An. funestus was characterized by smaller clusters. The examination of spatial patterns at a finer spatial scale with SADIE and LISA identified several patches of higher than average IRD (hot spots) and clusters of lower than average IRD (cold spots) for the two species. Significant changes occurred in the overall spatial pattern, spatial trends and clusters when IRDs were aggregated at the house level rather than the locality level. All spatial analyses unveiled scale-dependent patterns that could not be identified by traditional aggregation indices. Conclusions/Significance: Our study illustrates the importance of spatial analyses in unraveling the complex spatial patterns of malaria vectors, and highlights the potential contributions of these methods in malaria control

Probabilistic Inference for Future Climate Using an Ensemble of Climate Model Evaluations

This paper describes an approach to computing probabilistic assessments of future climate, using a climate model. It clarifies the nature of probability in this context, and illustrates the kinds of judgements that must be made in order for such a prediction to be consistent with the probability calculus. The climate model is seen as a tool for making probabilistic statements about climate itself, necessarily involving an assessment of the model’s imperfections. A climate event, such as a 2^C increase in global mean temperature, is identified with a region of ‘climate-space’, and the ensemble of model evaluations is used within a numerical integration designed to estimate the probability assigned to that region

Multiscale Graphical Modeling in Space: Applications to Command and Control

Recently, a class of multiscale tree-structured models was introduced in terms of scale-recursive dynamics defined on trees. The main advantage of these models is their association with a fast, recursive, Kalmanfilter prediction algorithm. In this article, we propose a more general class of multiscale graphical models over acyclic directed graphs, for use in command and control problems. Moreover, we derive the generalized-Kalmanfilter algorithm for graphical Markov models, which can be used to obtain the optimal predictors and prediction variances for multiscale graphical models. 1 Introduction Almost every aspect of command and control (C2) involves dealing with information in the presence of uncertainty. Since information in a battlefield is never precise, its status is rarely known exactly. In the face of this uncertainty, commanders must make decisions, issue orders, and monitor the consequences. The uncertainty may come from noisy data or, indeed, regions of the battle space whe..