Hepatitis C Diagnoses in an American Indian Primary Care Population

BACKGROUND: Despite large disparities in the burden of chronic liver disease, data on hepatitis C virus (HCV) infection among American Indians (AIs) are lacking. We reviewed hepatitis C diagnoses in 35,712 AI/AN primary care patients. MAIN FINDINGS: At least one HCV-associated ICD-9 code was recorded in 251 (1%) patients between October 1, 2001 and September 30, 2003. An HCV enzyme-linked immunoassay (HCVEIA) was sent in 209 (83.0%); 206/209 (99%) were positive. Confirmatory testing was performed in 144/206 (70%) HCV-EIA positive patients; HCV infection was confirmed in 144 (100%). In the 90/144 (63%) charts with risk factor documentation, injection drug use was the most common risk factor (61/90, 68%). Deficiencies were present in hepatitis B and HIV testing, and hepatitis A and B vaccination. PRINCIPAL CONCLUSIONS: Improvements in laboratory workup of HCV and co-infections, risk factor ascertainment and documentation, and adult vaccination are needed to address HCV effectively in this population

A Smoothed-Distribution Form of Nadaraya-Watson Estimation

Given observation-pairs (xi ,yi ), i = 1,...,n , taken to be independent observations of the random pair (X ,Y), we sometimes want to form a nonparametric estimate of m(x) = E(Y/ X = x). Let YE have the empirical distribution of the yi , and let (XS ,YS ) have the kernel-smoothed distribution of the (xi ,yi ). Then the standard estimator, the Nadaraya-Watson form mNW(x) can be interpreted as E(YE?XS = x). The smoothed-distribution estimator ms (x)=E(YS/XS = x) is a more general form than  mNW (x) and often has better properties. Similar considerations apply to estimating Var(Y/X = x), and to local polynomial estimation. The discussion generalizes to vector (xi ,yi ).nonparametric regression, Nadaraya-Watson, kernel density, conditional expectation estimator, conditional variance estimator, local polynomial estimator

Wage Dispersion in a Partially Unionized Labor Force

Taking as our point of departure a model proposed by David Card (2001), we suggest new methods for analyzing wage dispersion in a partially unionized labor market. Card's method disaggregates the labor population into skill categories, which procedure entails some loss of information. Accordingly, we develop a model in which each worker individually is assigned a union-membership probability and predicted union and nonunion wages. The model yields a natural three-way decomposition of variance. The decomposition permits counterfactual analysis, using concepts and techniques from the theory of factorial experimental design. We examine causes of the increase in U.K. wage dispersion between 1983 and 1995. Of the factors initially considered, the most influential was a change in the structure of remuneration inside both the union and nonunion sectors. Next in importance was the decrease in union membership. Finally, exogenous changes in labor force characteristics had, for most groups considered, only a small negative effect. We supplement this preliminary three-factorial analysis with a five-factorial analysis that allows us to examine effects from the wage-equation parameters in greater detail.wage dispersion, three-way variance decomposition, bivariate kernel density smoothing, union membership, deunionization, factorial experimental design

Wage Dispersion in a Partially Unionized Labor Force

Wage Dispersion in a Partially Unionized Labor Force This paper critiques Card’s (2001) method for analyzing wage dispersion in a partially unionized labor market based on a disaggregation of the population into skill categories. We argue that disaggregation is a good idea, the use of skill categories less so. We offer a modified model in which each worker is assigned a union-membership probability, a predicted union wage, and a predicted nonunion wage. Our model provides a natural three-way decomposition of variance, and is also suited to counterfactual analysis. By way of an application, we examine the effect of de-unionization on wage dispersion in the United Kingdom between 1983 and 1995, reporting that the decline in membership accounts for only about one-fifth of the observed increase in wage dispersion.wage dispersion, three-way variance decomposition, bivariate kernel density smoothing, union membership, deunionization.

SNIFFER WFD119: Enhancement of the River Invertebrate Classification Tool (RICT)

EXECUTIVE SUMMARY Project funders/partners: Environment Agency (EA), Northern Ireland Environment Agency (NIEA), Scotland & Northern Ireland Forum for Environmental Research (SNIFFER), Scottish Environment Protection Agency (SEPA) Background to research The Regulatory Agencies in the UK (the Environment Agency; Scottish Environment Protection Agency; and the Northern Ireland Environment Agency) now use the River Invertebrate Classification Tool (RICT) to classify the ecological quality of rivers for Water Framework Directive compliance monitoring. RICT incorporates RIVPACS IV predictive models and is a highly capable tool written in a modern software programming language. While RICT classifies waters for general degradation and organic pollution stress, producing assessments of status class and uncertainty, WFD compliance monitoring also requires the UK Agencies to assess the impacts of a wide range of pressures including hydromorphological and acidification stresses. Some of these pressures alter the predictor variables that current RIVPACS models use to derive predicted biotic indices. This project has sought to broaden the scope of RICT by developing one or more RIVPACS model(s) that do not use predictor variables that are affected by these stressors, but instead use alternative GIS based variables that are wholly independent of these pressures. This project has also included a review of the wide range of biotic indices now available in RICT, identifying published sources, examining index performance, and where necessary making recommendations on further needs for index testing and development. Objectives of research •To remove and derive alternative predictive variables that are not affected by stressors, with particular emphasis on hydrological/acidification metric predictors. •To construct one or more new RIVPACS model(s) using stressor independent variables. •Review WFD reporting indices notably AWIC(species), LIFE (species), PSI & WHPT. Key findings and recommendations : Predictor variables and intellectual property rights : An extensive suite of new variables have been derived by GIS for the RIVPACS reference sites that have been shown to act as stressor-independent predictor variables. These include measures of stream order, solid and drift geology, and a range of upstream catchment characteristics (e.g. catchment area, mean altitude of upstream catchment, and catchment aspect). It is recommended that decisions are reached on which of the newly derived model(s) are implemented in RICT so that IPR issues for the relevant datasets can be quickly resolved and the datasets licensed. It is also recommended that licensing is sought for a point and click system (where the dataset cannot be reverse engineered) that is capable of calculating any of the time-invariant RIVPACS environmental predictor variables used by any of the newly derived (and existing) RIVPACS models, and for any potential users. New stressor-independent RIVPACS models : Using the existing predictor variables, together with new ones derived for their properties of stressor-independence, initial step-wise forward selection discriminant models suggested a range of 36 possible models that merited further testing. Following further testing, the following models are recommended for assessing watercourses affected by flow/hydromorphological and/or acidity stress: • For flow/hydromorphological stressors that may have modified width, depth and/or substrate in GB, it is suggested that a new ‘RIVPACS IV – Hydromorphology Independent’ model (Model 24) is used (this does not use the predictor variables width, depth and substratum, but includes a suite of new stressor-independent variables). • For acidity related stressors in GB, it is suggested that a new ‘RIVPACS IV – Alkalinity Independent’ model (Model 35) is used (this does not use the predictor variable alkalinity, but includes new stressor-independent variables). • For flow/hydromorphological stressors and acidity related stressors in GB, it is suggested that a new ‘RIVPACS IV – Hydromorphology & Alkalinity Independent’ model (Model 13) is used (this does not use the predictor variables width, depth, substratum and alkalinity, but includes a suite of new stressor-independent variables). • Reduced availability of appropriate GIS tools at this time has meant that no new models have been developed for Northern Ireland. Discriminant functions and end group means have now been calculated to enable any of these models to be easily implemented in the RICT software. Biotic indices : The RIVPACS models in RICT can now produce expected values for a wide range of biotic indices addressing a variety of stressors. These indices will support the use of RICT as a primary tool for WFD classification and reporting of the quality of UK streams and rivers. There are however a number of outstanding issues with indices that need to be addressed: • There is a need to develop a biotic index for assessing metal pollution. • WFD EQR banding schemes are required for many of the indices to report what is considered an acceptable degree of stress (High-Good) and what is not (Moderate, Poor or Bad). • A comprehensive objective testing process needs to be undertaken on the indices in RICT using UK-wide, large-scale, independent test datasets to quantify their index-stressor relationships and their associated uncertainty, for example following the approach to acidity index testing in Murphy et al., (in review) or organic/general degradation indices in Banks & McFarland (2010). • Following objective testing, the UK Agencies should make efforts to address any index under-performance issues that have been identified, and where necessary new work should be commissioned to modify existing indices, or develop new ones where required so that indices for all stress types meet certain minimum performance criteria. • Testing needs to be done to examine index-stressor relationships with both observed index scores and RIVPACS observed/expected ratios. Work should also be done to compare the existing RIVPACS IV and the new stressor-independent models (developed in this project) as alternative sources of the expected index values for these tests. • Consideration should be given to assessing the extent to which chemical and biological monitoring points co-occur. Site-matched (rather than reach-matched) chemical and biological monitoring points would i) generate the substantial training datasets needed to refine or develop new indices and ii) generate the independent datasets for testing

Novel porcine repetitive elements

BACKGROUND: Repetitive elements comprise ~45% of mammalian genomes and are increasingly known to impact genomic function by contributing to the genomic architecture, by direct regulation of gene expression and by affecting genomic size, diversity and evolution. The ubiquity and increasingly understood importance of repetitive elements contribute to the need to identify and annotate them. We set out to identify previously uncharacterized repetitive DNA in the porcine genome. Once found, we characterized the prevalence of these repeats in other mammals. RESULTS: We discovered 27 repetitive elements in 220 BACs covering 1% of the porcine genome (Comparative Vertebrate Sequencing Initiative; CVSI). These repeats varied in length from 55 to 1059 nucleotides. To estimate copy numbers, we went to an independent source of data, the BAC-end sequences (Wellcome Trust Sanger Institute), covering approximately 15% of the porcine genome. Copy numbers in BAC-ends were less than one hundred for 6 repeat elements, between 100 and 1000 for 16 and between 1,000 and 10,000 for 5. Several of the repeat elements were found in the bovine genome and we have identified two with orthologous sites, indicating that these elements were present in their common ancestor. None of the repeat elements were found in primate, rodent or dog genomes. We were unable to identify any of the replication machinery common to active transposable elements in these newly identified repeats. CONCLUSION: The presence of both orthologous and non-orthologous sites indicates that some sites existed prior to speciation and some were generated later. The identification of low to moderate copy number repetitive DNA that is specific to artiodactyls will be critical in the assembly of livestock genomes and studies of comparative genomics