197,304 research outputs found

    Non-Emergency Medical Transportation Needs of Middle-Aged and Older Adults: A Rural-Urban Comparison in Delaware, USA.

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    Background: Older adults in rural areas have unique transportation barriers to accessing medical care, which include a lack of mass transit options and considerable distances to health-related services. This study contrasts non-emergency medical transportation (NEMT) service utilization patterns and associated costs for Medicaid middle-aged and older adults in rural versus urban areas. Methods: Data were analyzed from 39,194 NEMT users of LogistiCare-brokered services in Delaware residing in rural (68.3%) and urban (30.9%) areas. Multivariable logistic analyses compared trip characteristics by rurality designation. Results: Rural (37.2%) and urban (41.2%) participants used services more frequently for dialysis than for any other medical concern. Older age and personal accompaniment were more common and wheel chair use was less common for rural trips. The mean cost per trip was greater for rural users (difference of $2910 per trip), which was attributed to the greater distance per trip in rural areas. Conclusions: Among a sample who were eligible for subsidized NEMT and who utilized this service, rural trips tended to be longer and, therefore, higher in cost. Over 50% of trips were made for dialysis highlighting the need to address prevention and, potentially, health service improvements for rural dialysis patients

    Spartan Daily, March 4, 1981

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    Volume 76, Issue 26https://scholarworks.sjsu.edu/spartandaily/6730/thumbnail.jp

    End-to-end informed VM selection in compute clouds

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    The selection of resources, particularly VMs, in current public IaaS clouds is usually done in a blind fashion, as cloud users do not have much information about resource consumption by co-tenant third-party tasks. In particular, communication patterns can play a significant part in cloud application performance and responsiveness, specially in the case of novel latencysensitive applications, increasingly common in today’s clouds. Thus, herein we propose an end-to-end approach to the VM allocation problem using policies based uniquely on round-trip time measurements between VMs. Those become part of a userlevel ‘Recommender Service’ that receives VM allocation requests with certain network-related demands and matches them to a suitable subset of VMs available to the user within the cloud. We propose and implement end-to-end algorithms for VM selection that cover desirable profiles of communications between VMs in distributed applications in a cloud setting, such as profiles with prevailing pair-wise, hub-and-spokes, or clustered communication patterns between constituent VMs. We quantify the expected benefits from deploying our Recommender Service by comparing our informed VM allocation approaches to conventional, random allocation methods, based on real measurements of latencies between Amazon EC2 instances. We also show that our approach is completely independent from cloud architecture details, is adaptable to different types of applications and workloads, and is lightweight and transparent to cloud providers.This work is supported in part by the National Science Foundation under grant CNS-0963974
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