Analyses and Comparison of Imputation-Based Association Methods

Genotype imputation methods have become increasingly popular for recovering untyped genotype data. An important application with imputed genotypes is to test genetic association for diseases. Imputation-based association test can provide additional insight beyond what is provided by testing on typed tagging SNPs only. A variety of effective imputation-based association tests have been proposed. However, their performances are affected by a variety of genetic factors, which have not been well studied. In this study, using both simulated and real data sets, we investigated the effects of LD, MAF of untyped causal SNP and imputation accuracy rate on the performances of seven popular imputation-based association methods, including MACH2qtl/dat, SNPTEST, ProbABEL, Beagle, Plink, BIMBAM and SNPMStat. We also aimed to provide a comprehensive comparison among methods. Results show that: 1). imputation-based association tests can boost signals and improve power under medium and high LD levels, with the power improvement increasing with strengthening LD level; 2) the power increases with higher MAF of untyped causal SNPs under medium to high LD level; 3). under low LD level, a high imputation accuracy rate cannot guarantee an improvement of power; 4). among methods, MACH2qtl/dat, ProbABEL and SNPTEST perform similarly and they consistently outperform other methods. Our results are helpful in guiding the choice of imputation-based association test in practical application

Poverty related risk for potentially preventable hospitalisations among children in Taiwan

<p>Abstract</p> <p>Background</p> <p>This study investigated the incidence of potentially preventable hospitalisations in the first two years of life among children in the National Health Insurance (NHI) system of Taiwan. It also examined income disparities in potentially preventable hospitalisations across four economic categories: below a government-established poverty line and low-, middle-, and upper-income. Five major diseases causing potentially preventable hospitalisations were investigated: gastroenteritis and dehydration, asthma and chronic bronchitis, acute upper respiratory infections, lower respiratory infections, and acute injuries and poisonings.</p> <p>Methods</p> <p>NHI data on enrolee registrations and use of ambulatory and hospital care by all children born between July 1, 2003 and June 30, 2004 (n = 218,158) was used for the study. The negative binomial regression method was used to identify factors associated with total inpatient care and the severity level for various types of potentially preventable hospitalisations during the first two years of life.</p> <p>Results</p> <p>This study found high inpatient expenses for lower respiratory infections for children in all income categories. Furthermore, results from the multivariate analysis indicate that children in the lowest economic category used inpatient care to a much greater extent than better-off children for problems considered potentially avoidable through primary prevention or through timely outpatient care. This was especially true for acute injuries and poisonings and for lower respiratory infections. On average, and controlling for other variables, a child in poverty spent 6.1 times more days in inpatient care for acute injuries and poisonings (p < 0.01) and 2.7 times more days for lower respiratory infections (p < 0.01) before age two, compared with a similarly-aged high-income child. The results also suggest a connection between economic status and the severity of a condition causing a potentially avoidable hospital admission. On average, length of stay for each admission for gastroenteritis and dehydration for children in poverty was 1.3 times that for high-income children (p < 0.01). Both the ratios for lower respiratory infections and for acute upper respiratory infections were 1.2 (p < 0.01 for both).</p> <p>Conclusions</p> <p>There were high hospital admission rates and lengths of stays for lower respiratory infections among young children in all income categories. Hospital care use of young children in the poorest category was significantly higher for acute injuries and poisonings as well as for lower respiratory infections, compared with those of better-off children. The findings suggest the need for increased attention to these two disease types. It particularly calls for more research on the causes of high hospital care use for lower respiratory infections and on the reasons for large economic disparities in hospital care use for acute injuries and poisonings.</p

Multidimensional screening in a monopolistic insurance market

Support from the Government of Catalonia project 2005SGR00836 and the Barcelona GSE Research Network, as well as from the Ministerio de Educación y Ciencia, project ECO2009-07616 and CONSOLIDER-INGENIO 2010(CSD2006-0016)We consider a population of individuals who differ in two dimensions, their risk type (expected loss) and their risk aversion, and solve for the profit-maximising menu of contracts that a monopolistic insurer puts out on the market. Our findings are threefold. First, it is never optimal to fully separate all the types. Second, if heterogeneity in risk aversion is sufficiently high, then some high-risk individuals (the risk-tolerant ones) will obtain lower coverage than some low-risk individuals (the risk-averse ones). Third, because women tend to be more risk averse than men (in that the risk aversion distribution for women first-order stochastically dominates that for men), gender discrimination may lead to a Pareto improvement

Activity-Based Funding of Hospitals and Its Impact on Mortality, Readmission, Discharge Destination, Severity of Illness, and Volume of Care: A Systematic Review and Meta-Analysis

Background: Activity-based funding (ABF) of hospitals is a policy intervention intended to re-shape incentives across health systems through the use of diagnosis-related groups. Many countries are adopting or actively promoting ABF. We assessed the effect of ABF on key measures potentially affecting patients and health care systems: mortality (acute and post-acute care); readmission rates; discharge rate to post-acute care following hospitalization; severity of illness; volume of care. &nbsp; &nbsp; Methods: We undertook a systematic review and meta-analysis of the worldwide evidence produced since 1980. We included all studies reporting original quantitative data comparing the impact of ABF versus alternative funding systems in acute care settings, regardless of language. We searched 9 electronic databases (OVID MEDLINE, EMBASE, OVID Healthstar, CINAHL, Cochrane CENTRAL, Health Technology Assessment, NHS Economic Evaluation Database, Cochrane Database of Systematic Reviews, and Business Source), hand-searched reference lists, and consulted with experts. Paired reviewers independently screened for eligibility, abstracted data, and assessed study credibility according to a pre-defined scoring system, resolving conflicts by discussion or adjudication. &nbsp; &nbsp; Results: Of 16,565 unique citations, 50 US studies and 15 studies from 9 other countries proved eligible (i.e. Australia, Austria, England, Germany, Israel, Italy, Scotland, Sweden, Switzerland). We found consistent and robust differences between ABF and no-ABF in discharge to post-acute care, showing a 24% increase with ABF (pooled relative risk = 1.24, 95% CI 1.18–1.31). Results also suggested a possible increase in readmission with ABF, and an apparent increase in severity of illness, perhaps reflecting differences in diagnostic coding. Although we found no consistent, systematic differences in mortality rates and volume of care, results varied widely across studies, some suggesting appreciable benefits from ABF, and others suggesting deleterious consequences. &nbsp; &nbsp; Conclusions: Transitioning to ABF is associated with important policy- and clinically-relevant changes. Evidence suggests substantial increases in admissions to post-acute care following hospitalization, with implications for system capacity and equitable access to care. High variability in results of other outcomes leaves the impact in particular settings uncertain, and may not allow a jurisdiction to predict if ABF would be harmless. Decision-makers considering ABF should plan for likely increases in post-acute care admissions, and be aware of the large uncertainty around impacts on other critical outcomes

Flower proteome: changes in protein spectrum during the advanced stages of rose petal development

Flowering is a unique and highly programmed process, but hardly anything is known about the developmentally regulated proteome changes in petals. Here, we employed proteomic technologies to study petal development in rose ( Rosa hybrida ). Using two-dimensional polyacrylamide gel electrophoresis, we generated stage-specific (closed bud, mature flower and flower at anthesis) petal protein maps with ca. 1,000 unique protein spots. Expression analyses of all resolved protein spots revealed that almost 30% of them were stage-specific, with ca. 90 protein spots for each stage. Most of the proteins exhibited differential expression during petal development, whereas only ca. 6% were constitutively expressed. Eighty-two of the resolved proteins were identified by mass spectrometry and annotated. Classification of the annotated proteins into functional groups revealed energy, cell rescue, unknown function (including novel sequences) and metabolism to be the largest classes, together comprising ca. 90% of all identified proteins. Interestingly, a large number of stress-related proteins were identified in developing petals. Analyses of the expression patterns of annotated proteins and their corresponding RNAs confirmed the importance of proteome characterization.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47485/1/425_2005_Article_1512.pd