Suchergebnisse

This book provides a coherent description of the main concepts and statistical methods used to analyse economic performance. The focus is on measures of performance that are of practical relevance to policy makers. Most, if not all, of these measures can be viewed as measures of productivity and/or efficiency. Linking fields as diverse as index number theory, data envelopment analysis and stochastic frontier analysis, the book explains how to compute measures of input and output quantity change that are consistent with measurement theory. It then discusses ways in which meaningful measures of productivity change can be decomposed into measures of technical progress, environmental change, and different types of efficiency change. The book is aimed at graduate students, researchers, statisticians, accountants and economists working in universities, regulatory authorities, government departments and private firms. The book contains many numerical examples. Computer codes and datasets are available on a companion website.--

Softcover version of the second edition Hardcover.Incorporates a new author, Dr. Chris O'Donnell, who brings considerable expertise to the project in the area of performance measurement. Numerous topics are being added and more applications using real data, as well as exercises at the end of the chapters. Data sets, computer codes and software will be available for download from the web to accompany the volume.

AIM: Million Veteran Program (MVP) is the largest ongoing mega-cohort biobank program in the US with 570,131 enrollees as of May 2017. The primary aim is to describe demographics, military service, and major diseases and comorbidities of the MVP cohort. Our secondary aim is to examine body mass index (BMI), a proxy for general health, among enrollees. MATERIALS AND METHOD: The study population consists of Veterans who actively use the Veterans Health Administration in the US. Data evaluated in this paper combine health information from multiple sources to provide the most comprehensive demographic profile and information on height and weight of MVP enrollees. A standardized cleaning algorithm was used to curate the demographic variables for each participant in MVP. For height and weight, we derived a final data point for each participant to evaluate BMI. STATISTICAL ANALYSIS USED: Multivariable logistic regression was used to compare the differences in BMI categories across enrollment years adjusting for gender, race, and age. P < 0.05 was considered statistically significant. All analyses were conducted using Statistical Analysis System 9.2. RESULTS: The MVP cohort consists of 90.4% of males with an average age of 61.9 years (standard deviation [SD] = 13.9). MVP is the largest multiethnic biobank cohort within the Veteran population with 73.9% White, 19.0% Black, and 6.5% Hispanic. The most common self-reported disease was hypertension (62.6%) for males and depression (47.5%) for females. Mean BMI was 29.7 kg/m(2) (SD = 5.8) with 38.2% obese and 42.3% overweight. CONCLUSIONS: Our findings suggest that demographic representation in MVP is similar to the Veterans Health Administration population and contrasts with the overall National Health and Nutrition Examination Survey US population. The prevalence of overweight and obese is high among US Veterans, and future studies will examine the role of BMI and disease risk in the Veteran population.

The Million Veteran Program (MVP) was established in 2011 as a national research initiative to determine how genetic variation influences the health of U.S. military veterans. We genotyped 312,571 MVP participants using a custom biobank array and linked the genetic data to laboratory and clinical phenotypes extracted from electronic health records covering a median of 10.0 years of follow-up. Among 297,626 veterans with at least 1 blood lipid measurement including 57,332 blacks and 24,743 Hispanics, we tested up to ~32 million variants for association with lipid levels and identified 118 novel genome-wide significant loci after meta-analysis with data from the Global Lipids Genetics Consortium (total N > 600,000). Through a focus on mutations predicted to result in a loss of gene function and a phenome-wide association study, we propose novel indications for pharmaceutical inhibitors targeting PCSK9 (abdominal aortic aneurysm), ANGPTL4 (type 2 diabetes), and PDE3B (triglycerides and coronary disease).

Abstract Background Coronary heart disease (CHD) is the major cause of death in the United States. Coronary artery calcification (CAC) scores are independent predictors of CHD. African Americans (AA) have higher rates of CHD but are less well-studied in genomic studies. We assembled the largest AA data resource currently available with measured CAC to identify associated genetic variants. Methods We analyzed log transformed CAC quantity (ln(CAC + 1)), for association with ~2.5 million single nucleotide polymorphisms (SNPs) and performed an inverse-variance weighted meta-analysis on results for 5,823 AA from 8 studies. Heritability was calculated using family studies. The most significant SNPs among AAs were evaluated in European Ancestry (EA) CAC data; conversely, the significance of published SNPs for CAC/CHD in EA was queried within our AA meta-analysis. Results Heritability of CAC was lower in AA (~30%) than previously reported for EA (~50%). No SNP reached genome wide significance (p < 5E-08). Of 67 SNPs with p < 1E-05 in AA there was no evidence of association in EA CAC data. Four SNPs in regions previously implicated in CAC/CHD (at 9p21 and PHACTR1 ) in EA .

Abstract Background Coronary heart disease (CHD) is the major cause of death in the United States. Coronary artery calcification (CAC) scores are independent predictors of CHD. African Americans (AA) have higher rates of CHD but are less well-studied in genomic studies. We assembled the largest AA data resource currently available with measured CAC to identify associated genetic variants. Methods We analyzed log transformed CAC quantity (ln(CAC + 1)), for association with ~2.5 million single nucleotide polymorphisms (SNPs) and performed an inverse-variance weighted meta-analysis on results for 5,823 AA from 8 studies. Heritability was calculated using family studies. The most significant SNPs among AAs were evaluated in European Ancestry (EA) CAC data; conversely, the significance of published SNPs for CAC/CHD in EA was queried within our AA meta-analysis. Results Heritability of CAC was lower in AA (~30%) than previously reported for EA (~50%). No SNP reached genome wide significance (p < 5E-08). Of 67 SNPs with p < 1E-05 in AA there was no evidence of association in EA CAC data. Four SNPs in regions previously implicated in CAC/CHD (at 9p21 and PHACTR1) in EA reached nominal significance for CAC in AA, with concordant direction. Among AA, rs16905644 (p = 4.08E-05) had the strongest association in the 9p21 region. Conclusions While we observed substantial heritability for CAC in AA, we failed to identify loci for CAC at genome-wide significant levels despite having adequate power to detect alleles with moderate to large effects. Although suggestive signals in AA were apparent at 9p21 and additional CAC and CAD EA loci, overall the data suggest that even larger samples and an ethnic specific focus will be required for GWAS discoveries for CAC in AA populations.