Neuropsychological Functioning of Homeless Men

Numerous biological and psychological factors associated with impaired neurological functioning have been identified as common among the homeless, but there has been relatively little systematic examination of the cognitive functioning of homeless people. This study explored the neuropsychological functioning of 90 homeless men. There was great variability in their test scores, but the presence of possible cognitive impairment was detected in 80% of the sample. Average general intellectual functioning and reading abilities were found to be relatively low, and the incidence of impairments in reading, new verbal learning, memory, and attention and concentration was high. These findings suggest that the homeless men in this study had considerable assessment and treatment needs that were not being met by most of the health and social services offered to them

Obesity remains underdiagnosed: discordant documentation of obesity body mass index and obesity diagnosis in patients\u27 electronic medical record

Background: Obesity remains a public health crisis in the United States with 64% of adults in the U.S. being overweight or obese with its corresponding economic impacts. Purpose: This study examined concordance between obesity body mass index (BMI \u3e 30) in the patient’s electronic medical record (EMR) and a documented diagnosis of obesity in the EMR. Methods: We conducted a retrospective record review of a large health care system EMR for the period of one year (2012). A total of 397,313 patients met criteria including having at least one physician visit, at least 18 years of age and not being pregnant. Of those patients, 158,372 had a BMI \u3e 30 (39.86%). We examined BMI obesity and obesity diagnosis on the EMR concordance as well as demographics and comorbid diagnoses for their ability to predict obesity diagnosis. Results: Obesity was on the problem list for only 35% of patients with a BMI \u3e 30. Obesity was documented more frequently in women, more frequently in middle-aged patients, and more frequently for blacks/African Americans. Obesity on the problem list was greater for some comorbidities (e.g. sleep apnea, hypertension, diabetes) and less for others (e.g. coronary artery disease, osteoarthritis); there was a significant positive association between the number of comorbid diagnoses and obesity diagnosis on the problem list. Conclusion: Obesity remains underdiagnosed despite the presence of obesity BMI in the patient’s EMR. Patient demographics and comorbidities should be considered when identifying new best practices for screening, diagnosing, documenting, intervening and monitoring weight management. New practices should be patient-centered and consider cultural context and social and physical resources available to patients – all crucial for enacting systems change in a true accountable care environment

The Effect of Alcohol Treatment on Social Costs of Alcohol Dependence: Results From the COMBINE Study

The COMBINE (Combined Pharmacotherapies and Behavioral Intervention) clinical trial recently evaluated the efficacy of pharmacotherapies, behavioral therapies, and their combinations for the treatment of alcohol dependence. Previously, the cost and cost-effectiveness of COMBINE have been studied. Policy makers, patients, and nonalcohol-dependent individuals may be concerned not only with alcohol treatment costs but also with the impact of alcohol interventions on broader social costs and outcomes

Discovering Outliers of Potential Drug Toxicities Using a Large-scale Data-driven Approach

We systematically compared the adverse effects of cancer drugs to detect event outliers across different clinical trials using a data-driven approach. Because many cancer drugs are toxic to patients, better understanding of adverse events of cancer drugs is critical for developing therapies that could minimize the toxic effects. However, due to the large variabilities of adverse events across different cancer drugs, methods to efficiently compare adverse effects across different cancer drugs are lacking. To address this challenge, we present an exploration study that integrates multiple adverse event reports from clinical trials in order to systematically compare adverse events across different cancer drugs. To demonstrate our methods, we first collected data on 186,339 clinical trials from ClinicalTrials.gov and selected 30 common cancer drugs. We identified 1602 cancer trials that studied the selected cancer drugs. Our methods effectively extracted 12,922 distinct adverse events from the clinical trial reports. Using the extracted data, we ranked all 12,922 adverse events based on their prevalence in the clinical trials, such as nausea 82%, fatigue 77%, and vomiting 75.97%. To detect the significant drug outliers that could have a statistically high possibility of causing an event, we used the boxplot method to visualize adverse event outliers across different drugs and applied Grubbs’ test to evaluate the significance. Analyses showed that by systematically integrating cross-trial data from multiple clinical trial reports, adverse event outliers associated with cancer drugs can be detected. The method was demonstrated by detecting the following four statistically significant adverse event cases: the association of the drug axitinib with hypertension (Grubbs’ test, P < 0.001), the association of the drug imatinib with muscle spasm ( P < 0.001), the association of the drug vorinostat with deep vein thrombosis ( P < 0.001), and the association of the drug afatinib with paronychia ( P < 0.01)

Understanding Health Care Costs in a Wisconsin Acute Leukemia Population

Purpose We investigated factors driving health care costs of patients with a diagnosis of acute myeloid and acute lymphoblastic leukemia. Methods Standard costs identified in insurance claims data obtained from the Wisconsin Health Information Organization were used in a sample of 837 acute leukemia patients from April 2009 to June 2011. The Andersen behavioral model of health care utilization guided selection of patient and community factors expected to influence health care costs. A generalized linear model fitting gamma-distributed data with log-link technique was used to analyze cost. Results Type of treatment received and disease severity represented significant cost drivers, and patients receiving at least some of their treatment from academic medical centers experienced higher costs. Inpatient care and pharmacy costs of patients who received treatment from providers located in areas of higher poverty experienced lower costs, raising questions of potential treatment and medical practice disparities between provider locations. Directions of study findings were not consistent between different types of services received and underscore the complexity of investigating health care cost. Conclusions While prevalence of acute leukemia in the United States is low compared to other diseases, its extreme high cost of treatment is not well understood and potentially influences treatment decisions. Acute leukemia health care costs may not follow expected patterns; further exploration of the relationship between cost and the treatment decision, and potential treatment disparities between providers in different socioeconomic locations, is needed

Population Analysis of Adverse Events in Different Age Groups Using Big Clinical Trials Data

BACKGROUND: Understanding adverse event patterns in clinical studies across populations is important for patient safety and protection in clinical trials as well as for developing appropriate drug therapies, procedures, and treatment plans. OBJECTIVES: The objective of our study was to conduct a data-driven population-based analysis to estimate the incidence, diversity, and association patterns of adverse events by age of the clinical trials patients and participants. METHODS: Two aspects of adverse event patterns were measured: (1) the adverse event incidence rate in each of the patient age groups and (2) the diversity of adverse events defined as distinct types of adverse events categorized by organ system. Statistical analysis was done on the summarized clinical trial data. The incident rate and diversity level in each of the age groups were compared with the lowest group (reference group) using t tests. Cohort data was obtained from ClinicalTrials.gov, and 186,339 clinical studies were analyzed; data were extracted from the 17,853 clinical trials that reported clinical outcomes. The total number of clinical trial participants was 6,808,619, and total number of participants affected by adverse events in these trials was 1,840,432. The trial participants were divided into eight different age groups to support cross-age group comparison. RESULTS: In general, children and older patients are more susceptible to adverse events in clinical trial studies. Using the lowest incidence age group as the reference group (20-29 years), the incidence rate of the 0-9 years-old group was 31.41%, approximately 1.51 times higher (P=.04) than the young adult group (20-29 years) at 20.76%. The second-highest group is the 50-59 years-old group with an incidence rate of 30.09%, significantly higher (P CONCLUSION: The results show that there is a significant adverse event variance at the population level between different age groups in clinical trials. The data suggest that age-associated adverse events should be considered in planning, monitoring, and regulating clinical trials

Impact of the Heart WATCH Program on Patients at Risk of Developing Metabolic Syndrome, Prediabetes or Cardiovascular Disease

Purpose Metabolic syndrome is a set of metabolic risk factors associated with increased risk of developing cardiovascular disease and type 2 diabetes mellitus. We retrospectively evaluated the effectiveness of a lifestyle modification program (Heart WATCH) geared toward reducing development of chronic disease in women deemed at risk for metabolic syndrome, prediabetes and/or cardiovascular disease. Methods Our institution’s Heart WATCH program consists of screening sessions with a multidisciplinary team (physician/nurse, nutritionist and psychologist), a minimum of three visits with a nurse practitioner and weekly follow-up phone calls for a 14-week period. Sociodemographic variables were obtained at initial visit. Biometric testing indices and self-reported clinical and behavioral health measures were recorded pre- and postintervention, and compared using paired t-tests or McNemar’s test as appropriate. Results Heart WATCH enrolled 242 women from November 2006 to April 2014, and 193 (80%) completed all phases of the 14-week lifestyle intervention. Postintervention, participants demonstrated improved health status in all areas and improved significantly in the following areas: diet/nutrition (P=0.014), exercise (P\u3c0.001), stress (P\u3c0.0001), quality of life (P=0.003), weight (P\u3c0.0001), waist circumference (P=0.01) and total cholesterol (P=0.019). Clinically meaningful improvements were realized by participants who moved to a healthier classification in a number of vital signs and blood panel indices. Conclusions These findings suggest the “elevated risk profile” for women with components of metabolic syndrome can be reversed through a lifestyle program focused on reducing risk factors associated with cardiovascular disease and prediabetes. Future research is needed to determine mechanisms of risk reduction as well as optimal patient-centered and culturally appropriate approaches to weight management