VA Palo Alto Healthcare System

About: VA Palo Alto Healthcare System is a healthcare organization based out in Palo Alto, California, United States. It is known for research contribution in the topics: Population & Health care. The organization has 2548 authors who have published 4605 publications receiving 209938 citations.

State-Transition Modeling: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force–3

Abstract: State-transition modeling (STM) is an intuitive, flexible, and transparent approach of computer-based decision-analytic modeling, including both Markov model cohort simulation as well as individual-based (first-order Monte Carlo) microsimulation. Conceptualizing a decision problem in terms of a set of (health) states and transitions among these states, STM is one of the most widespread modeling techniques in clinical decision analysis, health technology assessment, and health-economic evaluation. STMs have been used in many different populations and diseases, and their applications range from personalized health care strategies to public health programs. Most frequently, state-transition models are used in the evaluation of risk factor interventions, screening, diagnostic procedures, treatment strategies, and disease management programs.

The effect of automated calls with telephone nurse follow-up on patient-centered outcomes of diabetes care: a randomized, controlled trial.

Abstract: Objective.We evaluated the impact of automated telephone disease management (ATDM) calls with telephone nurse follow-up as a strategy for improving outcomes such as mental health, self-efficacy, satisfaction with care, and health-related quality of life (HRQL) among low-income patients with diabetes

Who searches the internet for health information

Abstract: Objective. To determine what types of consumers use the Internet as a source of health information. Data Sources. A survey of consumer use of the Internet for health information conducted during December 2001 and January 2002. Study Design. We estimated multivariate regression models to test hypotheses regarding the characteristics of consumers that affect information seeking behavior. Data Collection. Respondents were randomly sampled from an Internet-enabled panel of over 60,000 households. Our survey was sent to 12,878 panel members, and 69.4 percent of surveyed panel members responded. We collected information about respondents' use of the Internet to search for health information and to communicate about health care with others using the Internet or e-mail within the last year. Principal Findings. Individuals with reported chronic conditions were more likely than those without to search for health information on the Internet. The uninsured, particularly those with a reported chronic condition, were more likely than the privately insured to search. Individuals with longer travel times for their usual source of care were more likely to use the Internet for health-related communication than those with shorter travel times. Conclusions. Populations with serious health needs and those facing significant barriers in accessing health care in traditional settings turn to the Internet for health information.

Evidence for improved muscle activation patterns after retraining of reaching movements with the MIME robotic system in subjects with post-stroke hemiparesis

Abstract: Previously, we reported that chronic stroke subjects had significant improvements in isometric strength, free reaching extent, and clinical evaluations of function after training in the mirror-image movement enabler (MIME) robotic device. Our primary goal in this analysis was to investigate the hypothesis that the robotic training promoted improved muscle activation patterns. To this end, we examined the interaction forces, kinematics, and electromyograms recorded during training of eight different movement patterns in active-constrained mode. In this mode, the robot constrained the reaching movements to be toward the target, and the movement velocity was proportional to the force produced along the trajectory. Thirteen chronic stroke subjects trained in MIME for 24 1-h sessions over an eight-week period. Work output was significantly increased by week five in all eight movement patterns. Low-level subjects increased their extent of reach, while high-level subjects increased their speed. Directional errors in force production were reduced in six of eight movement patterns. Electromyographic data provided evidence for improved muscle activation patterns in the four movement patterns that started at tabletop level and ended at shoulder level. In contrast, there was no evidence of improved muscle activation patterns in any of the tabletop movements, with increased activation of antagonists in two movement patterns. This dichotomy may have been related to compensation at the shoulder girdle during movements that remained at tabletop level. A simple biomechanical model will be introduced to demonstrate the likelihood of this possibility.

Genetics of 35 blood and urine biomarkers in the UK Biobank

Abstract: Clinical laboratory tests are a critical component of the continuum of care. We evaluate the genetic basis of 35 blood and urine laboratory measurements in the UK Biobank (n = 363,228 individuals). We identify 1,857 loci associated with at least one trait, containing 3,374 fine-mapped associations and additional sets of large-effect (>0.1 s.d.) protein-altering, human leukocyte antigen (HLA) and copy number variant (CNV) associations. Through Mendelian randomization (MR) analysis, we discover 51 causal relationships, including previously known agonistic effects of urate on gout and cystatin C on stroke. Finally, we develop polygenic risk scores (PRSs) for each biomarker and build 'multi-PRS' models for diseases using 35 PRSs simultaneously, which improved chronic kidney disease, type 2 diabetes, gout and alcoholic cirrhosis genetic risk stratification in an independent dataset (FinnGen; n = 135,500) relative to single-disease PRSs. Together, our results delineate the genetic basis of biomarkers and their causal influences on diseases and improve genetic risk stratification for common diseases.