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The BRAVO risk engine for the US diabetes cohort provides an alternative to the UKPDS risk engine. It can be applied to assist clinical and policy decision making such as cost-effective resource allocation in USA.

With a goal of improving operational numerical weather prediction (NWP), the Developmental Testbed Center (DTC) has been working with operational centers, including, among others, the National Centers for Environmental Prediction (NCEP), National Oceanic and Atmospheric Administration (NOAA), National Aeronautics and Space Administration (NASA), and the U.S. Air Force, to support numerical models/systems and their research, perform objective testing and evaluation of NWP methods, and facilitate research-to-operations transitions. This article introduces the first attempt of the DTC in the data assimilation area to help achieve this goal. Since 2009, the DTC, NCEP’s Environmental Modeling Center (EMC), and other developers have made significant progress in transitioning the operational Gridpoint Statistical Interpolation (GSI) data assimilation system into a community-based code management framework. Currently, GSI is provided to the public with user support and is open for contributions from internal developers as well as the broader research community, following the same code transition procedures. This article introduces measures and steps taken during this community GSI effort followed by discussions of encountered challenges and issues. The purpose of this article is to promote contributions from the research community to operational data assimilation capabilities and, furthermore, to seek potential solutions to stimulate such a transition and, eventually, improve the NWP capabilities in the United States.

Purpose The use of technology to implement cost-effective health care management on a large scale may be an alternative for diabetes management but needs to be evaluated in controlled trials. This study assessed the utility and cost-effectiveness of an automated Diabetes Remote Monitoring and Management System (DRMS) in glycemic control versus usual care. Methods In this randomized, controlled study, patients with uncontrolled diabetes on insulin were randomized to use of the DRMS or usual care. Participants in both groups were followed up for 6 months and had 3 clinic visits at 0, 3, and 6 months. The DRMS used text messages or phone calls to remind patients to test their blood glucose and to report results via an automated system, with no human interaction unless a patient had severely high or low blood glucose. The DRMS made adjustments to insulin dose(s) based on validated algorithms. Participants reported medication adherence through the Morisky Medication Adherence Scale-8, and diabetes-specific quality of life through the diabetes Daily Quality of Life questionnaire. A cost-effectiveness analysis was conducted based on the estimated overall costs of DRMS and usual care. Findings A total of 98 patients were enrolled (59 [60%] female; mean age, 59 years); 87 participants (89%) completed follow-up. HbA1c was similar between the DRMS and control groups at 3 months (7.60% vs 8.10%) and at 6 months (8.10% vs 7.90%). Changes from baseline to 6 months were not statistically significant for self-reported medication adherence and diabetes-specific quality of life, with the exception of the Daily Quality of Life–Social/Vocational Concerns subscale score (P = 0.04). Implications An automated system like the DRMS may improve glycemic control to the same degree as usual clinic care and may significantly improve the social/vocational aspects of quality of life. Cost-effectiveness analysis found DRMS to be cost-effective when compared to usual care and suggests DRMS has a good scale of economy for program scale up. Further research is needed to determine how to sustain the benefits seen with the automated system over longer periods.

We dissected 72 upper limbs of fresh cadavers and found 17 cases with a Martin-Gruber communicating branch (23.6%). These were classified into 4 types: type I (n = 5, 29.4%): communicating branch between the anterior interosseous and ulnar nn, type II (n = 3, 17.6%): Communicating branch between the median and ulnar nn., type III (n = 3, 17.6%): Communicating branch between the muscular branches to the flexor digitorum profundus m., type IV (n = 6, 35.3%): combination of type I or II and type III. At histologic examination the number and size of the nerve bundles each communicating branch contained proved to be very different. In one case of type II only a single nerve bundle was found. We suggest that the different numbers of nerve bundles innervate different amounts of the intrinsic hand musculature. The communicating branch with a single nerve bundle probably innervated only the first dorsal interosseous muscle.

Results from our review are consistent with previous studies, as we found that many of our studies produced moderate to high correlation between both SRQs and monitoring devices [Farmer, Clin Ther 21(6):1074-90 (1999), IMS Institute for Healthcare Informatics. Avoidable costs in US health care (2012), Patel et al., Respirology 18(3):546-52 (2013), Siracusa et al., J Cyst Fibros 14(5):621-6 (2015), Smith et al., Int J Cardiol 145(1):122-3 (2010)]. Our findings demonstrate that self-reported adherence produces comparable results to electronic monitoring devices. As there is not yet a 'gold standard' measure for monitoring patient adherence, SRQs and Medication Event Monitoring Systems (MEMS) operating together continue to emerge as the preferred effective method for measuring medication adherence.

Objective-Health utility decrements associated with diabetes complications are essential for calculating quality-adjusted life years (QALYs) in patients for use in economic evaluation of diabetes interventions. Previous studies mostly focused on assessing the impact of complications on health utility at event year based on cross-sectional data. This study aimed to separately estimate health utility decrements associated with current and previous diabetes complications.Research Design and Methods-The Health Utilities Index Mark 3 (HUI-3) was used to measure heath utility in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (N=8,713). Five macrovascular complications (myocardial infarction (MI), congestive heart failure (CHF), stroke, angina, and revascularization surgery (RS)) and three microvascular complications (nephropathy (renal failure), retinopathy (severe vision loss), and neuropathy (severe pressure sensation loss)) were included in a set of alternative modelling approaches including ordinary least squares (OLS) model, fixed effects model and random effects model to estimate the complicationrelated health utility decrements.

We found significant differences in the odds of 30-day readmissions on the basis of race, socioeconomic status, and payer. As readmissions penalties become widely adopted, payers need to be mindful of their effects on vulnerable populations.