Survival on four-times-per-week compared with three times a week haemodialysis in high ultrafiltration patients : an observational study

Background The harm caused by the long interdialytic interval in three-times-per-week haemodialysis regimens (3×WHD) may relate to fluid accumulation and associated high ultrafiltration rate (UFR). Four-times-per-week haemodialysis (4×WHD) may offer a solution, but its impact on mortality, hospitalization and vascular access complications is unknown. Methods From the AROii cohort of incident in-centre haemodialysis patients, 3×WHD patients with a UFR >10 mL/kg/h were identified. The hazard for the outcomes of mortality, hospitalization and vascular access complications in those who switched to 4×WHD compared with staying on 3×WHD was estimated using a marginal structural Cox proportional hazards model. Adjustment included baseline patient and treatment characteristics with inverse probability weighting used to adjust for time-varying UFR and cardiovascular comorbidities. Results From 10 637 European 3×WHD patients, 3842 (36%) exceeded a UFR >10 mL/kg/h. Of these, 288 (7.5%) started 4×WHD and at baseline were more comorbid. Event rates while receiving 4×WHD compared with 3×WHD were 12.6 compared with 10.8 per 100 patient years for mortality, 0.96 compared with 0.65 per year for hospitalization and 14.7 compared with 8.0 per 100 patient years for vascular access complications. Compared with 3×WHD, the unadjusted hazard ratio (HR) for mortality on 4×WHD was 1.05 [95% confidence interval (CI) 0.78–1.42]. Following adjustment for baseline demographics, time-varying treatment probability and censoring risks, this HR was 0.73 (95% CI 0.50–1.05; P = 0.095). Despite these adjustments on 4×WHD, the HR for hospitalization remained elevated and vascular access complications were similar to 3×WHD. Conclusions This observational study was not able to demonstrate a mortality benefit in patients switched to 4×WHD. To demonstrate the true benefits of 4×WHD requires a large, well-designed clinical trial. Our data may help in the design of such a study

Local spatial regression models : a comparative analysis on soil contamination

Spatial data analysis focuses on both attribute and locational information. Local analyses deal with differences across space whereas global analyses deal with similarities across space. This paper addresses an experimental comparative study to analyse the spatial data by some weighted local regression models. Five local regression models have been developed and their estimation capacities have been evaluated. The experimental studies showed that integration of objective function based fuzzy clustering to geostatistics provides some accurate and general models structures. In particular, the estimation performance of the model established by combining the extended fuzzy clustering algorithm and standard regional dependence function is higher than that of the other regression models. Finally, it could be suggested that the hybrid regression models developed by combining soft computing and geostatistics could be used in spatial data analysis

Analyzing regional economic development patterns in a fast developing province of China through geographically weighted principal component analysis

Understanding the spatial structure of regional economic development is of importance for regional planning and provincial development strategy making. Taking Jiangsu Province in the economically richest Yangtze Delta as a case study, this paper aims to explore regional economic development level on a provincial scale. Using the data sets from provincial statistical yearbook of 2010, eleven variables are selected for statistical and spatial analyses at a county level. Both the traditional principal component analysis (PCA) and its local version—geographically weighted PCA (GWPCA)—are employed to these analyses for the purpose of comparison. The results have confirmed that GWPCA is an effective means of analyzing regional economic development level through mapping its local principal components. It is also concluded that the regional economic development in Jiangsu Province demonstrates spatial inequality between the North and South

Modelling malaria treatment practices in Bangladesh using spatial statistics

<p>Abstract</p> <p>Background</p> <p>Malaria treatment-seeking practices vary worldwide and Bangladesh is no exception. Individuals from 88 villages in Rajasthali were asked about their treatment-seeking practices. A portion of these households preferred malaria treatment from the National Control Programme, but still a large number of households continued to use drug vendors and approximately one fourth of the individuals surveyed relied exclusively on non-control programme treatments. The risks of low-control programme usage include incomplete malaria treatment, possible misuse of anti-malarial drugs, and an increased potential for drug resistance.</p> <p>Methods</p> <p>The spatial patterns of treatment-seeking practices were first examined using hot-spot analysis (Local Getis-Ord Gi statistic) and then modelled using regression. Ordinary least squares (OLS) regression identified key factors explaining more than 80% of the variation in control programme and vendor treatment preferences. Geographically weighted regression (GWR) was then used to assess where each factor was a strong predictor of treatment-seeking preferences.</p> <p>Results</p> <p>Several factors including tribal affiliation, housing materials, household densities, education levels, and proximity to the regional urban centre, were found to be effective predictors of malaria treatment-seeking preferences. The predictive strength of each of these factors, however, varied across the study area. While education, for example, was a strong predictor in some villages, it was less important for predicting treatment-seeking outcomes in other villages.</p> <p>Conclusion</p> <p>Understanding where each factor is a strong predictor of treatment-seeking outcomes may help in planning targeted interventions aimed at increasing control programme usage. Suggested strategies include providing additional training for the Building Resources across Communities (BRAC) health workers, implementing educational programmes, and addressing economic factors.</p

Intra-week spatial-temporal patterns of crime

Since its original publication, routine activity theory has proven most instructive for understanding temporal patterns in crime. The most prominent of the temporal crime patterns investigated is seasonality: crime (most often assault) increases during the summer months and decreases once routine activities are less often outside. Despite the rather widespread literature on the seasonality of crime, there is very little research investigating temporal patterns of crime at shorter time intervals such as within the week or even within the day. This paper contributes to this literature through a spatial-temporal analysis of crime patterns for different days of the week. It is found that temporal patterns are present for different days of the week (more crime on weekends, as would be expected) and there is a spatial component to that temporal change. Specifically, aside from robbery and sexual assault at the micro-spatial unit of analysis (street segments) the spatial patterns of crime changed. With regard to the spatial pattern changes, we found that assaults and theft from vehicle had their spatial patterns change in predictable ways on Saturdays: assaults increased in the bar district and theft from vehicles increased in the downtown and recreational car park areas

Effect of Propranolol on Functional Connectivity in Autism Spectrum Disorder—A Pilot Study

A decrease in interaction between brain regions is observed in individuals with autism spectrum disorder (ASD), which is believed to be related to restricted neural network access in ASD. Propranolol, a beta-adrenergic antagonist, has revealed benefit during performance of tasks involving flexibility of access to networks, a benefit also seen in ASD. Our goal was to determine the effect of propranolol on functional connectivity in ASD during a verbal decision making task as compared to nadolol, thereby accounting for the potential spurious fMRI effects due to peripheral hemodynamic effects of propranolol. Ten ASD subjects underwent fMRI scans after administration of placebo, propranolol or nadolol, while performing a phonological decision making task. Comparison of functional connectivity between pre-defined ROI-pairs revealed a significant increase with propranolol compared to nadolol, suggesting a potential imaging marker for the cognitive effects of propranolol in ASD

Population, behavioural and environmental drivers of malaria prevalence in the Democratic Republic of Congo

<p>Abstract</p> <p>Background</p> <p>Malaria is highly endemic in the Democratic Republic of Congo (DRC), but the limits and intensity of transmission within the country are unknown. It is important to discern these patterns as well as the drivers which may underlie them in order for effective prevention measures to be carried out.</p> <p>Methods</p> <p>By applying high-throughput PCR analyses on leftover dried blood spots from the 2007 Demographic and Health Survey (DHS) for the DRC, prevalence estimates were generated and ecological drivers of malaria were explored using spatial statistical analyses and multilevel modelling.</p> <p>Results</p> <p>Of the 7,746 respondents, 2268 (29.3%) were parasitaemic; prevalence ranged from 0-82% within geographically-defined survey clusters. Regional variation in these rates was mapped using the inverse-distance weighting spatial interpolation technique. Males were more likely to be parasitaemic than older people or females (p < 0.0001), while wealthier people were at a lower risk (p < 0.001). Increased community use of bed nets (p = 0.001) and community wealth (p < 0.05) were protective against malaria at the community level but not at the individual level. Paradoxically, the number of battle events since 1994 surrounding one's community was negatively associated with malaria risk (p < 0.0001).</p> <p>Conclusions</p> <p>This research demonstrates the feasibility of using population-based behavioural and molecular surveillance in conjunction with DHS data and geographic methods to study endemic infectious diseases. This study provides the most accurate population-based estimates to date of where illness from malaria occurs in the DRC and what factors contribute to the estimated spatial patterns. This study suggests that spatial information and analyses can enable the DRC government to focus its control efforts against malaria.</p

Spatiotemporal climate and vegetation greenness changes and their nexus for Dhidhessa River Basin, Ethiopia

Background Understanding spatiotemporal climate and vegetation changes and their nexus is key for designing climate change adaptation strategies at a local scale. However, such a study is lacking in many basins of Ethiopia. The objectives of this study were (i) to analyze temperature, rainfall and vegetation greenness trends and (ii) determine the spatial relationship of climate variables and vegetation greenness, characterized using Normalized Difference in Vegetation Index (NDVI), for the Dhidhessa River Basin (DRB). Quality checked high spatial resolution satellite datasets were used for the study. Mann–Kendall test and Sen’s slope method were used for the trend analysis. The spatial relationship between climate change and NDVI was analyzed using geographically weighted regression (GWR) technique. Results According to the study, past and future climate trend analysis generally showed wetting and warming for the DRB where the degree of trends varies for the different time and spatial scales. A seasonal shift in rainfall was also observed for the basin. These findings informed that there will be a negative impact on rain-fed agriculture and water availability in the basin. Besides, NDVI trends analysis generally showed greening for most climatic zones for the annual and main rainy season timescales. However, no NDVI trends were observed in all timescales for cool sub-humid, tepid humid and warm humid climatic zones. The increasing NDVI trends could be attributed to agroforestry practices but do not necessarily indicate improved forest coverage for the basin. The change in NDVI was positively correlated to rainfall (r2 = 0.62) and negatively correlated to the minimum (r2 = 0.58) and maximum (r2 = 0.45) temperature. The study revealed a strong interaction between the climate variables and vegetation greenness for the basin that further influences the biophysical processes of the land surface like the hydrologic responses of a basin. Conclusion The study concluded that the trend in climate and vegetation greenness varies spatiotemporally for the DRB. Besides, the climate change and its strong relationship with vegetation greenness observed in this study will further affect the biophysical and environmental processes in the study area; mostly negatively on agricultural and water resource sectors. Thus, this study provides helpful information to device climate change adaptation strategies at a local scale

the prevention of chronic diseases through ehealth a practical overview

Disease prevention is an umbrella term embracing individual-based or population-based interventions aimed at preventing the manifestation of diseases (primary prevention), reducing the impact of a disease that has arisen (secondary prevention), or mitigating the impact of an ongoing illness (tertiary prevention). Digital health has the potential to improve prevention of chronic diseases. Its application ranges from effective mHealth weight-loss intervention to prevent or delay the onset of diabetes in overweight adults to the cost-effective intervention on the provision of mental-health care via mobile-based or Internet-based programs to reduce the incidence or the severity of anxiety. The present contribution focuses on the effectiveness of eHealth preventive interventions and on the role of digital health in improving health promotion and disease prevention. We also give a practical overview on how eHealth interventions have been effectively implemented, developed, and delivered for the primary, secondary, and tertiary prevention of chronic diseases