Zika virus infection in pregnant women in Honduras: study protocol

Background: Although there is increasing evidence for a relationship between symptomatic Zika virus (ZIKV) maternal infection, and microcephaly, a firm causal relation has yet to be established by epidemiologic studies. Studies also need to be conducted in recently infected settings. Our objectives are to assess the frequency of ZIKV infection during pregnancy in Honduras and the association of microcephaly with ZIKV infection. Methods/Design: We will perform a prospective study enrolling pregnant women at their first antenatal visit and following them up until delivery. At the time of enrollment, women will be interviewed to collect socio-demographic data, data needed to locate them for potential additional follow-up, and data about ZIKV symptoms during pregnancy. We will also collect maternal blood as soon as possible after enrollment. A probable maternal ZIKV infection will be defined as positive for maternal ZIKV IgM. A confirmed maternal ZIKV infection will be defined as positive for ZIKV IgM confirmed by plaque reduction neutralization test. Microcephaly at birth will be defined as an occipito-frontal circumference <2SD for sex and gestational age. Our objective is to enroll 2000 pregnant women. In a first step, we will follow a case cohort design and only analyze blood samples for cases and a sub-cohort of 200 women randomly selected. Blood samples for the entire population will be analyzed at a later stage if funds are available. Discussion: This protocol was designed to be implemented with minimal resources. It allows a cohort to be built, which could be a foundation for future in-depth and follow-up studies.Fil: Buekens, Pierre. University of Tulane; Estados UnidosFil: Alger, Jackeline. Universidad Nacional Autónoma de Honduras; HondurasFil: Althabe, Fernando. Instituto de Efectividad Clínica y Sanitaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bergel, Eduardo. Instituto de Efectividad Clínica y Sanitaria; ArgentinaFil: Berrueta, Amanda Mabel. Instituto de Efectividad Clínica y Sanitaria; ArgentinaFil: Bustillo, Carolina. Hospital Escuela. Departamento de Ginecología y Obstetricia; HondurasFil: Cafferata, Maria Luisa. Hospital de Clínicas. Unidad de Investigación Clínica y Epidemiológica Montevideo; UruguayFil: Harville, Emily. University of Tulane; Estados UnidosFil: Rosales, Karla. Región Sanitaria Metropolitana Distrito Central de Francisco Morazán; HondurasFil: Wesson, Dawn M.. University of Tulane; Estados UnidosFil: Zuniga, Concepcion. Hospital Escuela Universitario. Instituto de Enfermedades Infecciosas y Parasitología Antonio Vidal; Hondura

Prediction with Expert Advice under Discounted Loss

We study prediction with expert advice in the setting where the losses are accumulated with some discounting---the impact of old losses may gradually vanish. We generalize the Aggregating Algorithm and the Aggregating Algorithm for Regression to this case, propose a suitable new variant of exponential weights algorithm, and prove respective loss bounds.Comment: 26 pages; expanded (2 remarks -> theorems), some misprints correcte

Fast and accurate modelling of longitudinal and repeated measures neuroimaging data

Despite the growing importance of longitudinal data in neuroimaging, the standard analysis methods make restrictive or unrealistic assumptions (e.g., assumption of Compound Symmetry—the state of all equal variances and equal correlations—or spatially homogeneous longitudinal correlations). While some new methods have been proposed to more accurately account for such data, these methods are based on iterative algorithms that are slow and failure-prone. In this article, we propose the use of the Sandwich Estimator (SwE) method which first estimates the parameters of interest with a simple Ordinary Least Square model and second estimates variances/covariances with the “so-called” SwE which accounts for the within-subject correlation existing in longitudinal data. Here, we introduce the SwE method in its classic form, and we review and propose several adjustments to improve its behaviour, specifically in small samples. We use intensive Monte Carlo simulations to compare all considered adjustments and isolate the best combination for neuroimaging data. We also compare the SwE method to other popular methods and demonstrate its strengths and weaknesses. Finally, we analyse a highly unbalanced longitudinal dataset from the Alzheimer's Disease Neuroimaging Initiative and demonstrate the flexibility of the SwE method to fit within- and between-subject effects in a single model. Software implementing this SwE method has been made freely available at http://warwick.ac.uk/tenichols/SwE

A Closed-Form Solution of the Multi-Period Portfolio Choice Problem for a Quadratic Utility Function

In the present paper, we derive a closed-form solution of the multi-period portfolio choice problem for a quadratic utility function with and without a riskless asset. All results are derived under weak conditions on the asset returns. No assumption on the correlation structure between different time points is needed and no assumption on the distribution is imposed. All expressions are presented in terms of the conditional mean vectors and the conditional covariance matrices. If the multivariate process of the asset returns is independent it is shown that in the case without a riskless asset the solution is presented as a sequence of optimal portfolio weights obtained by solving the single-period Markowitz optimization problem. The process dynamics are included only in the shape parameter of the utility function. If a riskless asset is present then the multi-period optimal portfolio weights are proportional to the single-period solutions multiplied by time-varying constants which are depending on the process dynamics. Remarkably, in the case of a portfolio selection with the tangency portfolio the multi-period solution coincides with the sequence of the simple-period solutions. Finally, we compare the suggested strategies with existing multi-period portfolio allocation methods for real data.Comment: 38 pages, 9 figures, 3 tables, changes: VAR(1)-CCC-GARCH(1,1) process dynamics and the analysis of increasing horizon are included in the simulation study, under revision in Annals of Operations Researc

Second Annual Seminar on Estate Planning

Schedule and materials from the Second Annual Seminar on Estate Planning held by UK/CLE on July 18-19, 1975

Bayesian estimation of genetic parameters for multivariate threshold and continuous phenotypes and molecular genetic data in simulated horse populations using Gibbs sampling

<p>Abstract</p> <p>Background</p> <p>Requirements for successful implementation of multivariate animal threshold models including phenotypic and genotypic information are not known yet. Here simulated horse data were used to investigate the properties of multivariate estimators of genetic parameters for categorical, continuous and molecular genetic data in the context of important radiological health traits using mixed linear-threshold animal models via Gibbs sampling. The simulated pedigree comprised 7 generations and 40000 animals per generation. Additive genetic values, residuals and fixed effects for one continuous trait and liabilities of four binary traits were simulated, resembling situations encountered in the Warmblood horse. Quantitative trait locus (QTL) effects and genetic marker information were simulated for one of the liabilities. Different scenarios with respect to recombination rate between genetic markers and QTL and polymorphism information content of genetic markers were studied. For each scenario ten replicates were sampled from the simulated population, and within each replicate six different datasets differing in number and distribution of animals with trait records and availability of genetic marker information were generated. (Co)Variance components were estimated using a Bayesian mixed linear-threshold animal model via Gibbs sampling. Residual variances were fixed to zero and a proper prior was used for the genetic covariance matrix.</p> <p>Results</p> <p>Effective sample sizes (ESS) and biases of genetic parameters differed significantly between datasets. Bias of heritability estimates was -6% to +6% for the continuous trait, -6% to +10% for the binary traits of moderate heritability, and -21% to +25% for the binary traits of low heritability. Additive genetic correlations were mostly underestimated between the continuous trait and binary traits of low heritability, under- or overestimated between the continuous trait and binary traits of moderate heritability, and overestimated between two binary traits. Use of trait information on two subsequent generations of animals increased ESS and reduced bias of parameter estimates more than mere increase of the number of informative animals from one generation. Consideration of genotype information as a fixed effect in the model resulted in overestimation of polygenic heritability of the QTL trait, but increased accuracy of estimated additive genetic correlations of the QTL trait.</p> <p>Conclusion</p> <p>Combined use of phenotype and genotype information on parents and offspring will help to identify agonistic and antagonistic genetic correlations between traits of interests, facilitating design of effective multiple trait selection schemes.</p

Postpartum mental health after Hurricane Katrina: A cohort study

<p>Abstract</p> <p>Background</p> <p>Natural disaster is often a cause of psychopathology, and women are vulnerable to post-traumatic stress disorder (PTSD) and depression. Depression is also common after a woman gives birth. However, no research has addressed postpartum women's mental health after natural disaster.</p> <p>Methods</p> <p>Interviews were conducted in 2006–2007 with women who had been pregnant during or shortly after Hurricane Katrina. 292 New Orleans and Baton Rouge women were interviewed at delivery and 2 months postpartum. Depression was assessed using the Edinburgh Depression Scale and PTSD using the Post-Traumatic Stress Checklist. Women were asked about their experience of the hurricane with questions addressing threat, illness, loss, and damage. Chi-square tests and log-binomial/Poisson models were used to calculate associations and relative risks (RR).</p> <p>Results</p> <p>Black women and women with less education were more likely to have had a serious experience of the hurricane. 18% of the sample met the criteria for depression and 13% for PTSD at two months postpartum. Feeling that one's life was in danger was associated with depression and PTSD, as were injury to a family member and severe impact on property. Overall, two or more severe experiences of the storm was associated with an increased risk for both depression (relative risk (RR) 1.77, 95% confidence interval (CI) 1.08–2.89) and PTSD (RR 3.68, 95% CI 1.80–7.52).</p> <p>Conclusion</p> <p>Postpartum women who experience natural disaster severely are at increased risk for mental health problems, but overall rates of depression and PTSD do not seem to be higher than in studies of the general population.</p