Higher body mass index (BMI) increases the risk of cardiometabolic diseases, but nearly a third of the people living with obesity (BMI: ≥30 kg/m2) are metabolically healthy (MHO). Extreme sleep durations and poor sleep quality are associated with higher bodyweight and cardiometabolic dysfunction, but the full extent to which sleep habits may help differentiate those with MHO versus metabolically abnormal obesity (MAO) is not yet known. Data from the U.S. National Health and Nutritional Examination Survey 2005–08 was used (BMI: ≥30 kg/m2; ≥20 y; N = 1,777). The absence of metabolic syndrome was used to define MHO. Those with MHO tended to be younger, female, Non-Hispanic Black, never smokers, more physically active, and with less physician diagnosed sleep disorders than MAO. Neither sleep duration nor overall sleep quality was related to MHO in crude or multivariable adjusted analyses; however, reporting “almost always” to having trouble falling asleep (OR (95% CI): 0.40 (0.20–0.78)), waking up during the night (0.38 (0.17–0.85)), feeling unrested during the day (0.35 (0.18–0.70)), and feeling overly sleepy during the day (0.35 (0.17–0.75)) was related to lower odds of MHO. Selected sleep quality factors, but not sleep quantity or overall sleep quality, are associated with the MHO phenotype.

BackgroundHealth care organizations gather large volumes of data, which has been traditionally stored in legacy formats making it difficult to analyze or use effectively. Though recent government-funded initiatives have improved the situation, the quality of most existing data is poor, suffers from inconsistencies, and lacks integrity. Generating reports from such data is generally not considered feasible due to extensive labor, lack of reliability, and time constraints. Advanced data analytics is one way of extracting useful information from such data.ObjectiveThe intent of this study was to propose how Business Intelligence (BI) techniques can be applied to health system infrastructure data in order to make this information more accessible and comprehensible for a broader group of people.MethodsAn integration process was developed to cleanse and integrate data from disparate sources into a data warehouse. An Online Analytical Processing (OLAP) cube was then built to allow slicing along multiple dimensions determined by various key performance indicators (KPIs), representing population and patient profiles, case mix groups, and healthy community indicators. The use of mapping tools, customized shape files, and embedded objects further augment the navigation. Finally, Web forms provide a mechanism for remote uploading of data and transparent processing of the cube. For privileged information, access controls were implemented.ResultsData visualization has eliminated tedious analysis through legacy reports and provided a mechanism for optimally aligning resources with needs. Stakeholders are able to visualize KPIs on a main dashboard, slice-and-dice data, generate ad hoc reports, and quickly find the desired information. In addition, comparison, availability, and service level reports can also be generated on demand. All reports can be drilled down for navigation at a finer granularity.ConclusionsWe have demonstrated how BI techniques and tools can be used in the health care environment to make informed decisions with reference to resource allocation and enhancement of the quality of patient care. The data can be uploaded immediately upon collection, thus keeping reports current. The modular design can be expanded to add new datasets such as for smoking rates, teen pregnancies, human immunodeficiency virus (HIV) rates, immunization coverage, and vital statistical summaries.

No abstract

No abstract

Due to ever increasing demand for network capacity, the congestion problem is inflating. Congestion results in queuing within the network, packet loss and increased delays. It should be controlled to increase the system throughput and quality of service. The existing congestion control approaches such as source throttling and rerouting focus on controlling congestion after it has already happened. However, it is much more desirable to predict future congestion based on the current state and historical data, so that efficient controlling techniques can be applied to prevent congestion from happening in future. We have proposed a Neural Network Prediction-based routing (NNPR) protocol to predict as well as control the network traffic in distributed real time environment. A distributed real time transaction processing simulator (DRTTPS) has been used as the test-bed. For predictions, multi-step neural network model is developed in SPSS Modeler, which predicts congestion in future. ADAPA (Adaptive Decision and Predictive Analytics) scoring engine has been used for real-time scoring. An ADAPA wrapper calls the prediction model through web services and predicts the congestion in real-time. Once predicted results are obtained, messages are rerouted to prevent congestion. To compare our proposed work with existing techniques, two routing protocols are also implemented-Dijkstra's Shortest Path (DSP) and Routing Information Protocol (RIP). The main metric used to analyze the performance of our protocol is the percentage of transactions which complete before their deadline. The NNPR protocol is analyzed with various simulation runs having parameters both inside and outside the neural network input training range. Various parameters which can cause congestion were studied. These include bandwidth, worksize, latency, max active transactions,