Case management for emergency department frequent users : a randomized controlled trial

Background: Emergency department frequent users (EDFUs) account for a disproportionally high number of emergency department (ED) visits, contributing to overcrowding and high health-care costs. At the Lausanne University Hospital, EDFUs account for only 4.4% of ED patients, but 12.1% of all ED visits. Our study tested the hypothesis that an interdisciplinary case management intervention red. Methods: In this randomized controlled trial, we allocated adult EDFUs (5 or more visits in the previous 12 months) who visited the ED of the University Hospital of Lausanne, Switzerland between May 2012 and July 2013 either to an intervention (N=125) or a standard emergency care (N=125) group and monitored them for 12 months. Randomization was computer generated and concealed, and patients and research staff were blinded to the allocation. Participants in the intervention group, in addition to standard emergency care, received case management from an interdisciplinary team at baseline, and at 1, 3, and 5 months, in the hospital, in the ambulatory care setting, or at their homes. A generalized, linear, mixed-effects model for count data (Poisson distribution) was applied to compare participants' numbers of visits to the ED during the 12 months (Period 1, P1) preceding recruitment to the numbers of visits during the 12 months monitored (Period 2, P2).

The Effect of Cigarette Smoking on Diabetic Peripheral Neuropathy: A Systematic Review and Meta-Analysis.

OBJECTIVE: Studies suggest that smoking may be a risk factor for the development of microvascular complications such as diabetic peripheral neuropathy (DPN). The objective of this study was to assess the relationship between smoking and DPN in persons with type 1 or type 2 diabetes. RESEARCH DESIGN AND METHODS: A systematic review of the PubMed, Embase, and Cochrane clinical trials databases was conducted for the period from January 1966 to November 2014 for cohort, cross-sectional and case-control studies that assessed the relationship between smoking and DPN. Separate meta-analyses for prospective cohort studies and case-control or cross-sectional studies were performed using random effects models. RESULTS: Thirty-eight studies (10 prospective cohort and 28 cross-sectional) were included. The prospective cohort studies included 5558 participants without DPN at baseline. During follow-up ranging from 2 to 10 years, 1550 cases of DPN occurred. The pooled unadjusted odds ratio (OR) of developing DPN associated with smoking was 1.26 (95% CI 0.86-1.85; I(2) = 74%; evidence grade: low strength). Stratified analyses of the prospective studies revealed that studies of higher quality and with better levels of adjustment and longer follow-up showed a significant positive association between smoking and DPN, with less heterogeneity. The cross-sectional studies included 27,594 participants. The pooled OR of DPN associated with smoking was 1.42 (95% CI 1.21-1.65; I(2) = 65%; evidence grade: low strength). There was no evidence of publication bias. CONCLUSIONS: Smoking may be associated with an increased risk of DPN in persons with diabetes. Further studies are needed to test whether this association is causal and whether smoking cessation reduces the risk of DPN in adults with diabetes.

The effects of physiologically plausible connectivity structure on local and global dynamics in large scale brain models.

Functionally relevant large scale brain dynamics operates within the framework imposed by anatomical connectivity and time delays due to finite transmission speeds. To gain insight on the reliability and comparability of large scale brain network simulations, we investigate the effects of variations in the anatomical connectivity. Two different sets of detailed global connectivity structures are explored, the first extracted from the CoCoMac database and rescaled to the spatial extent of the human brain, the second derived from white-matter tractography applied to diffusion spectrum imaging (DSI) for a human subject. We use the combination of graph theoretical measures of the connection matrices and numerical simulations to explicate the importance of both connectivity strength and delays in shaping dynamic behaviour. Our results demonstrate that the brain dynamics derived from the CoCoMac database are more complex and biologically more realistic than the one based on the DSI database. We propose that the reason for this difference is the absence of directed weights in the DSI connectivity matrix.

Effects of alternative sets of climatic predictors on species distribution models and associated estimates of extinction risk: a test with plants in an arid environment

Aim To evaluate the effects of using distinct alternative sets of climatic predictor variables on the performance, spatial predictions and future projections of species distribution models (SDMs) for rare plants in an arid environment. . Location Atacama and Peruvian Deserts, South America (18º30'S - 31º30'S, 0 - 3 000 m) Methods We modelled the present and future potential distributions of 13 species of Heliotropium sect. Cochranea, a plant group with a centre of diversity in the Atacama Desert. We developed and applied a sequential procedure, starting from climate monthly variables, to derive six alternative sets of climatic predictor variables. We used them to fit models with eight modelling techniques within an ensemble forecasting framework, and derived climate change projections for each of them. We evaluated the effects of using these alternative sets of predictor variables on performance, spatial predictions and projections of SDMs using Generalised Linear Mixed Models (GLMM). Results The use of distinct sets of climatic predictor variables did not have a significant effect on overall metrics of model performance, but had significant effects on present and future spatial predictions. Main conclusion Using different sets of climatic predictors can yield the same model fits but different spatial predictions of current and future species distributions. This represents a new form of uncertainty in model-based estimates of extinction risk that may need to be better acknowledged and quantified in future SDM studies.

Species distribution models reveal apparent competitive and facilitative effects of a dominant species on the distribution of tundra plants

Abiotic factors are considered strong drivers of species distribution and assemblages. Yet these spatial patterns are also influenced by biotic interactions. Accounting for competitors or facilitators may improve both the fit and the predictive power of species distribution models (SDMs). We investigated the influence of a dominant species, Empetrum nigrum ssp. hermaphroditum, on the distribution of 34 subordinate species in the tundra of northern Norway. We related SDM parameters of those subordinate species to their functional traits and their co-occurrence patterns with E. hermaphroditum across three spatial scales. By combining both approaches, we sought to understand whether these species may be limited by competitive interactions and/or benefit from habitat conditions created by the dominant species. The model fit and predictive power increased for most species when the frequency of occurrence of E. hermaphroditum was included in the SDMs as a predictor. The largest increase was found for species that 1) co-occur most of the time with E. hermaphroditum, both at large (i.e. 750 m) and small spatial scale (i.e. 2 m) or co-occur with E. hermaphroditum at large scale but not at small scale and 2) have particularly low or high leaf dry matter content (LDMC). Species that do not co-occur with E. hermaphroditum at the smallest scale are generally palatable herbaceous species with low LDMC, thus showing a weak ability to tolerate resource depletion that is directly or indirectly induced by E. hermaphroditum. Species with high LDMC, showing a better aptitude to face resource depletion and grazing, are often found in the proximity of E. hermaphroditum. Our results are consistent with previous findings that both competition and facilitation structure plant distribution and assemblages in the Arctic tundra. The functional and co-occurrence approaches used were complementary and provided a deeper understanding of the observed patterns by refinement of the pool of potential direct and indirect ecological effects of E. hermaphroditum on the distribution of subordinate species. Our correlative study would benefit being complemented by experimental approaches.

Effects of brood size manipulation and common origin on phenotype and telomere length in nestling collared flycatchers.

BACKGROUND: Evidence is accumulating that telomere length is a good predictor of life expectancy, especially early in life, thus calling for determining the factors that affect telomere length at this stage. Here, we investigated the relative influence of early growth conditions and origin (genetics and early maternal effects) on telomere length of collared flycatchers (Ficedula albicollis) at fledging. We experimentally transferred hatchlings among brood triplets to create reduced, control (i.e. unchanged final nestling number) and enlarged broods. RESULTS: Although our treatment significantly affected body mass at fledging, we found no evidence that increased sibling competition affected nestling tarsus length and telomere length. However, mixed models showed that brood triplets explained a significant part of the variance in body mass (18%) and telomere length (19%), but not tarsus length (13%), emphasizing that unmanipulated early environmental factors influenced telomere length. These models also revealed low, but significant, heritability of telomere length (h(2) = 0.09). For comparison, the heritability of nestling body mass and tarsus length was 0.36 and 0.39, respectively, which was in the range of previously published estimates for those two traits in this species. CONCLUSION: Those findings in a wild bird population demonstrate that telomere length at the end of the growth period is weakly, but significantly, determined by genetic and/or maternal factors taking place before hatching. However, we found no evidence that the brood size manipulation experiment, and by extension the early growth conditions, influenced nestling telomere length. The weak heritability of telomere length suggests a close association with fitness in natural populations.

Protective and Aggravating Effects of Nlrp3 Inflammasome Activation in IBD Models: Influence of Genetic and Environmental Factors.

Background: Inflammatory bowel disease (IBD) is characterized by chronic intestinal inflammation due to dysregulation of the mucosal immune system. The cytokines IL-1β and IL-18 appear early in intestinal inflammation and their pro-forms are processed via the caspase-1-activating multiprotein complex, the Nlrp3 inflammasome. Previously, we reported that the uptake of dextran sodium sulfate (DSS) by macrophages activates the Nlrp3 inflammasome and that Nlrp3(-/-) mice are protected in the acute DSS colitis model. Of note, other groups have reported opposing effects in regards to DSS susceptibility in Nlrp3(-/-) mice. Recently, mice lacking inflammasomes were found to develop a distinct intestinal microflora. Methods: To reconcile the contradicting observations, we investigated the role of Nlrp3 deficiency in two different IBD models: acute DSS colitis and TNBS (2,4,6-trinitrobenzene sulfonic acid)-induced colitis. In addition, we investigated the impact of the intestinal flora on disease severity by performing cohousing experiments of wild-type and Nlrp3(-/-) mice, as well as by antibiotic treatment. Results: Nlrp3(-/-) mice treated with either DSS or TNBS exhibited attenuated colitis and lower mortality. This protective effect correlated with an increased frequency of CD103+ lamina propria dendritic cells expressing a tolerogenic phenotype in Nlrp3(-/-) mice in steady state conditions. Interestingly, after cohousing, Nlrp3(-/-) mice were as susceptible as wild-type mice, indicating that transmission of endogenous bacterial flora between the two mouse strains might increase susceptibility of Nlrp3(-/-) mice towards DSS-induced colitis. Accordingly, treatment with antibiotics almost completely prevented colitis in the DSS model. Conclusions: The composition of the intestinal microflora significantly influences disease severity in IBD models comparing wild-type and Nlrp3(-/-) mice. This observation may - at least in part - explain contradictory results concerning the role of the inflammasome in different labs. Further studies are required to define the role of the Nlrp3 inflammasome in noninflamed mucosa under steady state conditions and in IBD.

Metabolic effects of a mixed and a high-carbohydrate low-fat diet in man, measured over 24 h in a respiration chamber.

1. The relation between dietary carbohydrate: lipid ratio and the fuel mixture oxidized during 24 h was investigated in eleven healthy volunteers (six females, and five males) in a respiration chamber. Values of the fuel mixture oxidized were estimated by continuous indirect calorimetry and urinary nitrogen measurements. 2. The subjects, were first given a mixed diet for 7 d and spent the last 24 h of the 7 d period in a respiration chamber for continuous gas-exchange measurement. The fuels oxidized during 2.5 h or moderate exercise were also measured in the respiration chamber. After an interval of 2 weeks from the end of the mixed-diet period, the same subjects were given an isoenergetic high-carbohydrate low-fat diet for 7 d, and the same experimental regimen was repeated. 3. Dietary composition markedly influenced the fuel mixture oxidized during 24 h and this effect was still present 12 h after the last meal in the postabsorptive state. However, the diets had no influence on the substrates oxidized above resting levels during exercise. With both diets, the 24 h energy balance was slightly negative and the energy deficit was covered by lipid oxidation. 4. With the high-carbohydrate low-fat diet, the energy expenditure during sleep was found to be higher than that with the mixed diet. 5. It is concluded that: (a) the composition of the diet did not influence the fuel mixture utilized for moderate exercise, (b) the energy deficit calculated for a 24 h period was compensated by lipid oxidation irrespective of the carbohydrate content of the diet, (c) energy expenditure during sleep was found to be higher with the high-carbohydrate low-fat diet than with the mixed diet.

Evaluating effects of spectral training data distribution on continuous field mapping performance

Continuous field mapping has to address two conflicting remote sensing requirements when collecting training data. On one hand, continuous field mapping trains fractional land cover and thus favours mixed training pixels. On the other hand, the spectral signature has to be preferably distinct and thus favours pure training pixels. The aim of this study was to evaluate the sensitivity of training data distribution along fractional and spectral gradients on the resulting mapping performance. We derived four continuous fields (tree, shrubherb, bare, water) from aerial photographs as response variables and processed corresponding spectral signatures from multitemporal Landsat 5 TM data as explanatory variables. Subsequent controlled experiments along fractional cover gradients were then based on generalised linear models. Resulting fractional and spectral distribution differed between single continuous fields, but could be satisfactorily trained and mapped. Pixels with fractional or without respective cover were much more critical than pure full cover pixels. Error distribution of continuous field models was non-uniform with respect to horizontal and vertical spatial distribution of target fields. We conclude that a sampling for continuous field training data should be based on extent and densities in the fractional and spectral, rather than the real spatial space. Consequently, adequate training plots are most probably not systematically distributed in the real spatial space, but cover the gradient and covariate structure of the fractional and spectral space well. (C) 2009 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

Effects of a school-based physical activity program on physical and psychosocial quality of life in elementary school children: a cluster-randomized trial.

The present study tested the effect of a school-based physical activity (PA) program on quality of life (QoL) in 540 elementary school children. First and fifth graders were randomly assigned to a PA program or a no-PA control condition during one academic year. QoL was assessed by the Child Health Questionnaire at baseline and postintervention. Based on mixed linear model analyses, physical QoL in first graders and physical and psychosocial QoL in fifth graders were not affected by the intervention. In first graders, the PA intervention had a positive impact on psychosocial QoL (effect size [d], 0.32; p < .05). Subpopulation analyses revealed that this effect was caused by an effect in urban (effect size [d], 0.38; p < .05) and overweight first graders (effect size [d], 0.45; p < .05). In conclusion, a school-based PA intervention had little effect on QoL in elementary school children.

Particulate matter exposure in cars is associated with cardiovascular effects in healthy young men

Exposure to fine airborne particulate matter (PM(2.5)) is associated with cardiovascular events and mortality in older and cardiac patients. Potential physiologic effects of in-vehicle, roadside, and ambient PM(2.5) were investigated in young, healthy, nonsmoking, male North Carolina Highway Patrol troopers. Nine troopers (age 23 to 30) were monitored on 4 successive days while working a 3 P.M. to midnight shift. Each patrol car was equipped with air-quality monitors. Blood was drawn 14 hours after each shift, and ambulatory monitors recorded the electrocardiogram throughout the shift and until the next morning. Data were analyzed using mixed models. In-vehicle PM(2.5) (average of 24 microg/m(3)) was associated with decreased lymphocytes (-11% per 10 microg/m(3)) and increased red blood cell indices (1% mean corpuscular volume), neutrophils (6%), C-reactive protein (32%), von Willebrand factor (12%), next-morning heart beat cycle length (6%), next-morning heart rate variability parameters, and ectopic beats throughout the recording (20%). Controlling for potential confounders had little impact on the effect estimates. The associations of these health endpoints with ambient and roadside PM(2.5) were smaller and less significant. The observations in these healthy young men suggest that in-vehicle exposure to PM(2.5) may cause pathophysiologic changes that involve inflammation, coagulation, and cardiac rhythm.

Effects of sample size on the performance of species distribution models

A wide range of modelling algorithms is used by ecologists, conservation practitioners, and others to predict species ranges from point locality data. Unfortunately, the amount of data available is limited for many taxa and regions, making it essential to quantify the sensitivity of these algorithms to sample size. This is the first study to address this need by rigorously evaluating a broad suite of algorithms with independent presence-absence data from multiple species and regions. We evaluated predictions from 12 algorithms for 46 species (from six different regions of the world) at three sample sizes (100, 30, and 10 records). We used data from natural history collections to run the models, and evaluated the quality of model predictions with area under the receiver operating characteristic curve (AUC). With decreasing sample size, model accuracy decreased and variability increased across species and between models. Novel modelling methods that incorporate both interactions between predictor variables and complex response shapes (i.e. GBM, MARS-INT, BRUTO) performed better than most methods at large sample sizes but not at the smallest sample sizes. Other algorithms were much less sensitive to sample size, including an algorithm based on maximum entropy (MAXENT) that had among the best predictive power across all sample sizes. Relative to other algorithms, a distance metric algorithm (DOMAIN) and a genetic algorithm (OM-GARP) had intermediate performance at the largest sample size and among the best performance at the lowest sample size. No algorithm predicted consistently well with small sample size (n < 30) and this should encourage highly conservative use of predictions based on small sample size and restrict their use to exploratory modelling.