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Population Health (population + health)
Terms modified by Population Health Selected AbstractsOutcomes of genetics services: Creating an inclusive definition and outcomes menu for public health and clinical genetics services,AMERICAN JOURNAL OF MEDICAL GENETICS, Issue 3 2009Kerry Silvey Abstract Third party payers, funding agencies, and lawmakers often require clinicians and public health agencies to justify programs and services by documenting results. This article describes two assessment tools,"Defining Genetics Services Framework" and "Genetics Services Outcomes Menu," created to assist public health professionals, clinicians, family advocates, and researchers to plan, evaluate, and demonstrate the effectiveness of genetics services. The tools were developed by a work group of the Western States Genetics Services Collaborative (WSGSC) consisting of public health genetics and newborn screening professionals, family representatives, a medical geneticist, and genetic counselors from Alaska, California, Hawaii, Idaho, Oregon, and Washington. The work group created both tools by an iterative process of combining their ideas with findings from a literature and World Wide Web review. The Defining Genetics Services Framework reflects the diversity of work group members. Three over-lapping areas of genetics services from public health core functions to population screening to clinical genetics services are depicted. The Genetics Services Outcomes Menu lists sample long-term outcomes of genetics services. Menu outcomes are classified under impact areas of Knowledge and Information; Financing; Screening and Identification; Diagnosis, Treatment, and Management; and Population Health. The WSGSC incorporated aspects of both tools into their Regional Genetics Plan. © 2009 Wiley-Liss, Inc. [source] Emerging Issues in Population Health: National and Global Perspectives: A Tribute to Gene W. MatthewsTHE JOURNAL OF LAW, MEDICINE & ETHICS, Issue 4 2003Lawrence O. Gostin Guest EditorArticle first published online: 24 JAN 200 First page of article [source] From Public Health to Population Health: How Law Can Redefine the Playing FieldTHE JOURNAL OF LAW, MEDICINE & ETHICS, Issue 2003Daniel M. Fox First page of article [source] Is Income Inequality a Determinant of Population Health?THE MILBANK QUARTERLY, Issue 1 2004Part 1. This article reviews 98 aggregate and multilevel studies examining the associations between income inequality and health. Overall, there seems to be little support for the idea that income inequality is a major, generalizable determinant of population health differences within or between rich countries. Income inequality may, however, directly influence some health outcomes, such as homicide in some contexts. The strongest evidence for direct health effects is among states in the United States, but even that is somewhat mixed. Despite little support for a direct effect of income inequality on health per se, reducing income inequality by raising the incomes of the most disadvantaged will improve their health, help reduce health inequalities, and generally improve population health. [source] European Mathematical Genetics Meeting, Heidelberg, Germany, 12th,13th April 2007ANNALS OF HUMAN GENETICS, Issue 4 2007Article first published online: 28 MAY 200 Saurabh Ghosh 11 Indian Statistical Institute, Kolkata, India High correlations between two quantitative traits may be either due to common genetic factors or common environmental factors or a combination of both. In this study, we develop statistical methods to extract the contribution of a common QTL to the total correlation between the components of a bivariate phenotype. Using data on bivariate phenotypes and marker genotypes for sib-pairs, we propose a test for linkage between a common QTL and a marker locus based on the conditional cross-sib trait correlations (trait 1 of sib 1 , trait 2 of sib 2 and conversely) given the identity-by-descent sharing at the marker locus. The null hypothesis cannot be rejected unless there exists a common QTL. We use Monte-Carlo simulations to evaluate the performance of the proposed test under different trait parameters and quantitative trait distributions. An application of the method is illustrated using data on two alcohol-related phenotypes from the Collaborative Study On The Genetics Of Alcoholism project. Rémi Kazma 1 , Catherine Bonaďti-Pellié 1 , Emmanuelle Génin 12 INSERM UMR-S535 and Université Paris Sud, Villejuif, 94817, France Keywords: Gene-environment interaction, sibling recurrence risk, exposure correlation Gene-environment interactions may play important roles in complex disease susceptibility but their detection is often difficult. Here we show how gene-environment interactions can be detected by investigating the degree of familial aggregation according to the exposure of the probands. In case of gene-environment interaction, the distribution of genotypes of affected individuals, and consequently the risk in relatives, depends on their exposure. We developed a test comparing the risks in sibs according to the proband exposure. To evaluate the properties of this new test, we derived the formulas for calculating the expected risks in sibs according to the exposure of probands for various values of exposure frequency, relative risk due to exposure alone, frequencies of latent susceptibility genotypes, genetic relative risks and interaction coefficients. We find that the ratio of risks when the proband is exposed and not exposed is a good indicator of the interaction effect. We evaluate the power of the test for various sample sizes of affected individuals. We conclude that this test is valuable for diseases with moderate familial aggregation, only when the role of the exposure has been clearly evidenced. Since a correlation for exposure among sibs might lead to a difference in risks among sibs in the different proband exposure strata, we also add an exposure correlation coefficient in the model. Interestingly, we find that when this correlation is correctly accounted for, the power of the test is not decreased and might even be significantly increased. Andrea Callegaro 1 , Hans J.C. Van Houwelingen 1 , Jeanine Houwing-Duistermaat 13 Dept. of Medical Statistics and Bioinformatics, Leiden University Medical Center, The Netherlands Keywords: Survival analysis, age at onset, score test, linkage analysis Non parametric linkage (NPL) analysis compares the identical by descent (IBD) sharing in sibling pairs to the expected IBD sharing under the hypothesis of no linkage. Often information is available on the marginal cumulative hazards (for example breast cancer incidence curves). Our aim is to extend the NPL methods by taking into account the age at onset of selected sibling pairs using these known marginal hazards. Li and Zhong (2002) proposed a (retrospective) likelihood ratio test based on an additive frailty model for genetic linkage analysis. From their model we derive a score statistic for selected samples which turns out to be a weighed NPL method. The weights depend on the marginal cumulative hazards and on the frailty parameter. A second approach is based on a simple gamma shared frailty model. Here, we simply test whether the score function of the frailty parameter depends on the excess IBD. We compare the performance of these methods using simulated data. Céline Bellenguez 1 , Carole Ober 2 , Catherine Bourgain 14 INSERM U535 and University Paris Sud, Villejuif, France 5 Department of Human Genetics, The University of Chicago, USA Keywords: Linkage analysis, linkage disequilibrium, high density SNP data Compared with microsatellite markers, high density SNP maps should be more informative for linkage analyses. However, because they are much closer, SNPs present important linkage disequilibrium (LD), which biases classical nonparametric multipoint analyses. This problem is even stronger in population isolates where LD extends over larger regions with a more stochastic pattern. We investigate the issue of linkage analysis with a 500K SNP map in a large and inbred 1840-member Hutterite pedigree, phenotyped for asthma. Using an efficient pedigree breaking strategy, we first identified linked regions with a 5cM microsatellite map, on which we focused to evaluate the SNP map. The only method that models LD in the NPL analysis is limited in both the pedigree size and the number of markers (Abecasis and Wigginton, 2005) and therefore could not be used. Instead, we studied methods that identify sets of SNPs with maximum linkage information content in our pedigree and no LD-driven bias. Both algorithms that directly remove pairs of SNPs in high LD and clustering methods were evaluated. Null simulations were performed to control that Zlr calculated with the SNP sets were not falsely inflated. Preliminary results suggest that although LD is strong in such populations, linkage information content slightly better than that of microsatellite maps can be extracted from dense SNP maps, provided that a careful marker selection is conducted. In particular, we show that the specific LD pattern requires considering LD between a wide range of marker pairs rather than only in predefined blocks. Peter Van Loo 1,2,3 , Stein Aerts 1,2 , Diether Lambrechts 4,5 , Bernard Thienpont 2 , Sunit Maity 4,5 , Bert Coessens 3 , Frederik De Smet 4,5 , Leon-Charles Tranchevent 3 , Bart De Moor 2 , Koen Devriendt 3 , Peter Marynen 1,2 , Bassem Hassan 1,2 , Peter Carmeliet 4,5 , Yves Moreau 36 Department of Molecular and Developmental Genetics, VIB, Belgium 7 Department of Human Genetics, University of Leuven, Belgium 8 Bioinformatics group, Department of Electrical Engineering, University of Leuven, Belgium 9 Department of Transgene Technology and Gene Therapy, VIB, Belgium 10 Center for Transgene Technology and Gene Therapy, University of Leuven, Belgium Keywords: Bioinformatics, gene prioritization, data fusion The identification of genes involved in health and disease remains a formidable challenge. Here, we describe a novel bioinformatics method to prioritize candidate genes underlying pathways or diseases, based on their similarity to genes known to be involved in these processes. It is freely accessible as an interactive software tool, ENDEAVOUR, at http://www.esat.kuleuven.be/endeavour. Unlike previous methods, ENDEAVOUR generates distinct prioritizations from multiple heterogeneous data sources, which are then integrated, or fused, into one global ranking using order statistics. ENDEAVOUR prioritizes candidate genes in a three-step process. First, information about a disease or pathway is gathered from a set of known "training" genes by consulting multiple data sources. Next, the candidate genes are ranked based on similarity with the training properties obtained in the first step, resulting in one prioritized list for each data source. Finally, ENDEAVOUR fuses each of these rankings into a single global ranking, providing an overall prioritization of the candidate genes. Validation of ENDEAVOUR revealed it was able to efficiently prioritize 627 genes in disease data sets and 76 genes in biological pathway sets, identify candidates of 16 mono- or polygenic diseases, and discover regulatory genes of myeloid differentiation. Furthermore, the approach identified YPEL1 as a novel gene involved in craniofacial development from a 2-Mb chromosomal region, deleted in some patients with DiGeorge-like birth defects. Finally, we are currently evaluating a pipeline combining array-CGH, ENDEAVOUR and in vivo validation in zebrafish to identify novel genes involved in congenital heart defects. Mark Broom 1 , Graeme Ruxton 2 , Rebecca Kilner 311 Mathematics Dept., University of Sussex, UK 12 Division of Environmental and Evolutionary Biology, University of Glasgow, UK 13 Department of Zoology, University of Cambridge, UK Keywords: Evolutionarily stable strategy, parasitism, asymmetric game Brood parasites chicks vary in the harm that they do to their companions in the nest. In this presentation we use game-theoretic methods to model this variation. Our model considers hosts which potentially abandon single nestlings and instead choose to re-allocate their reproductive effort to future breeding, irrespective of whether the abandoned chick is the host's young or a brood parasite's. The parasite chick must decide whether or not to kill host young by balancing the benefits from reduced competition in the nest against the risk of desertion by host parents. The model predicts that three different types of evolutionarily stable strategies can exist. (1) Hosts routinely rear depleted broods, the brood parasite always kills host young and the host never then abandons the nest. (2) When adult survival after deserting single offspring is very high, hosts always abandon broods of a single nestling and the parasite never kills host offspring, effectively holding them as hostages to prevent nest desertion. (3) Intermediate strategies, in which parasites sometimes kill their nest-mates and host parents sometimes desert nests that contain only a single chick, can also be evolutionarily stable. We provide quantitative descriptions of how the values given to ecological and behavioral parameters of the host-parasite system influence the likelihood of each strategy and compare our results with real host-brood parasite associations in nature. Martin Harrison 114 Mathematics Dept, University of Sussex, UK Keywords: Brood parasitism, games, host, parasite The interaction between hosts and parasites in bird populations has been studied extensively. Game theoretical methods have been used to model this interaction previously, but this has not been studied extensively taking into account the sequential nature of this game. We consider a model allowing the host and parasite to make a number of decisions, which depend on a number of natural factors. The host lays an egg, a parasite bird will arrive at the nest with a certain probability and then chooses to destroy a number of the host eggs and lay one of it's own. With some destruction occurring, either natural or through the actions of the parasite, the host chooses to continue, eject an egg (hoping to eject the parasite) or abandon the nest. Once the eggs have hatched the game then falls to the parasite chick versus the host. The chick chooses to destroy or eject a number of eggs. The final decision is made by the host, choosing whether to raise or abandon the chicks that are in the nest. We consider various natural parameters and probabilities which influence these decisions. We then use this model to look at real-world situations of the interactions of the Reed Warbler and two different parasites, the Common Cuckoo and the Brown-Headed Cowbird. These two parasites have different methods in the way that they parasitize the nests of their hosts. The hosts in turn have a different reaction to these parasites. Arne Jochens 1 , Amke Caliebe 2 , Uwe Roesler 1 , Michael Krawczak 215 Mathematical Seminar, University of Kiel, Germany 16 Institute of Medical Informatics and Statistics, University of Kiel, Germany Keywords: Stepwise mutation model, microsatellite, recursion equation, temporal behaviour We consider the stepwise mutation model which occurs, e.g., in microsatellite loci. Let X(t,i) denote the allelic state of individual i at time t. We compute expectation, variance and covariance of X(t,i), i=1,,,N, and provide a recursion equation for P(X(t,i)=z). Because the variance of X(t,i) goes to infinity as t grows, for the description of the temporal behaviour, we regard the scaled process X(t,i)-X(t,1). The results furnish a better understanding of the behaviour of the stepwise mutation model and may in future be used to derive tests for neutrality under this model. Paul O'Reilly 1 , Ewan Birney 2 , David Balding 117 Statistical Genetics, Department of Epidemiology and Public Health, Imperial, College London, UK 18 European Bioinformatics Institute, EMBL, Cambridge, UK Keywords: Positive selection, Recombination rate, LD, Genome-wide, Natural Selection In recent years efforts to develop population genetics methods that estimate rates of recombination and levels of natural selection in the human genome have intensified. However, since the two processes have an intimately related impact on genetic variation their inference is vulnerable to confounding. Genomic regions subject to recent selection are likely to have a relatively recent common ancestor and consequently less opportunity for historical recombinations that are detectable in contemporary populations. Here we show that selection can reduce the population-based recombination rate estimate substantially. In genome-wide studies for detecting selection we observe a tendency to highlight loci that are subject to low levels of recombination. We find that the outlier approach commonly adopted in such studies may have low power unless variable recombination is accounted for. We introduce a new genome-wide method for detecting selection that exploits the sensitivity to recent selection of methods for estimating recombination rates, while accounting for variable recombination using pedigree data. Through simulations we demonstrate the high power of the Ped/Pop approach to discriminate between neutral and adaptive evolution, particularly in the context of choosing outliers from a genome-wide distribution. Although methods have been developed showing good power to detect selection ,in action', the corresponding window of opportunity is small. In contrast, the power of the Ped/Pop method is maintained for many generations after the fixation of an advantageous variant Sarah Griffiths 1 , Frank Dudbridge 120 MRC Biostatistics Unit, Cambridge, UK Keywords: Genetic association, multimarker tag, haplotype, likelihood analysis In association studies it is generally too expensive to genotype all variants in all subjects. We can exploit linkage disequilibrium between SNPs to select a subset that captures the variation in a training data set obtained either through direct resequencing or a public resource such as the HapMap. These ,tag SNPs' are then genotyped in the whole sample. Multimarker tagging is a more aggressive adaptation of pairwise tagging that allows for combinations of two or more tag SNPs to predict an untyped SNP. Here we describe a new method for directly testing the association of an untyped SNP using a multimarker tag. Previously, other investigators have suggested testing a specific tag haplotype, or performing a weighted analysis using weights derived from the training data. However these approaches do not properly account for the imperfect correlation between the tag haplotype and the untyped SNP. Here we describe a straightforward approach to testing untyped SNPs using a missing-data likelihood analysis, including the tag markers as nuisance parameters. The training data is stacked on top of the main body of genotype data so there is information on how the tag markers predict the genotype of the untyped SNP. The uncertainty in this prediction is automatically taken into account in the likelihood analysis. This approach yields more power and also a more accurate prediction of the odds ratio of the untyped SNP. Anke Schulz 1 , Christine Fischer 2 , Jenny Chang-Claude 1 , Lars Beckmann 121 Division of Cancer Epidemiology, German Cancer Research Center (DKFZ) Heidelberg, Germany 22 Institute of Human Genetics, University of Heidelberg, Germany Keywords: Haplotype, haplotype sharing, entropy, Mantel statistics, marker selection We previously introduced a new method to map genes involved in complex diseases, using haplotype sharing-based Mantel statistics to correlate genetic and phenotypic similarity. Although the Mantel statistic is powerful in narrowing down candidate regions, the precise localization of a gene is hampered in genomic regions where linkage disequilibrium is so high that neighboring markers are found to be significant at similar magnitude and we are not able to discriminate between them. Here, we present a new approach to localize susceptibility genes by combining haplotype sharing-based Mantel statistics with an iterative entropy-based marker selection algorithm. For each marker at which the Mantel statistic is evaluated, the algorithm selects a subset of surrounding markers. The subset is chosen to maximize multilocus linkage disequilibrium, which is measured by the normalized entropy difference introduced by Nothnagel et al. (2002). We evaluated the algorithm with respect to type I error and power. Its ability to localize the disease variant was compared to the localization (i) without marker selection and (ii) considering haplotype block structure. Case-control samples were simulated from a set of 18 haplotypes, consisting of 15 SNPs in two haplotype blocks. The new algorithm gave correct type I error and yielded similar power to detect the disease locus compared to the alternative approaches. The neighboring markers were clearly less often significant than the causal locus, and also less often significant compared to the alternative approaches. Thus the new algorithm improved the precision of the localization of susceptibility genes. Mark M. Iles 123 Section of Epidemiology and Biostatistics, LIMM, University of Leeds, UK Keywords: tSNP, tagging, association, HapMap Tagging SNPs (tSNPs) are commonly used to capture genetic diversity cost-effectively. However, it is important that the efficacy of tSNPs is correctly estimated, otherwise coverage may be insufficient. If the pilot sample from which tSNPs are chosen is too small or the initial marker map too sparse, tSNP efficacy may be overestimated. An existing estimation method based on bootstrapping goes some way to correct for insufficient sample size and overfitting, but does not completely solve the problem. We describe a novel method, based on exclusion of haplotypes, that improves on the bootstrap approach. Using simulated data, the extent of the sample size problem is investigated and the performance of the bootstrap and the novel method are compared. We incorporate an existing method adjusting for marker density by ,SNP-dropping'. We find that insufficient sample size can cause large overestimates in tSNP efficacy, even with as many as 100 individuals, and the problem worsens as the region studied increases in size. Both the bootstrap and novel method correct much of this overestimate, with our novel method consistently outperforming the bootstrap method. We conclude that a combination of insufficient sample size and overfitting may lead to overestimation of tSNP efficacy and underpowering of studies based on tSNPs. Our novel approach corrects for much of this bias and is superior to the previous method. Sample sizes larger than previously suggested may still be required for accurate estimation of tSNP efficacy. This has obvious ramifications for the selection of tSNPs from HapMap data. Claudio Verzilli 1 , Juliet Chapman 1 , Aroon Hingorani 2 , Juan Pablo-Casas 1 , Tina Shah 2 , Liam Smeeth 1 , John Whittaker 124 Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, UK 25 Division of Medicine, University College London, UK Keywords: Meta-analysis, Genetic association studies We present a Bayesian hierarchical model for the meta-analysis of candidate gene studies with a continuous outcome. Such studies often report results from association tests for different, possibly study-specific and non-overlapping markers (typically SNPs) in the same genetic region. Meta analyses of the results at each marker in isolation are seldom appropriate as they ignore the correlation that may exist between markers due to linkage disequlibrium (LD) and cannot assess the relative importance of variants at each marker. Also such marker-wise meta analyses are restricted to only those studies that have typed the marker in question, with a potential loss of power. A better strategy is one which incorporates information about the LD between markers so that any combined estimate of the effect of each variant is corrected for the effect of other variants, as in multiple regression. Here we develop a Bayesian hierarchical linear regression that models the observed genotype group means and uses pairwise LD measurements between markers as prior information to make posterior inference on adjusted effects. The approach is applied to the meta analysis of 24 studies assessing the effect of 7 variants in the C-reactive protein (CRP) gene region on plasma CRP levels, an inflammatory biomarker shown in observational studies to be positively associated with cardiovascular disease. Cathryn M. Lewis 1 , Christopher G. Mathew 1 , Theresa M. Marteau 226 Dept. of Medical and Molecular Genetics, King's College London, UK 27 Department of Psychology, King's College London, UK Keywords: Risk, genetics, CARD15, smoking, model Recently progress has been made in identifying mutations that confer susceptibility to complex diseases, with the potential to use these mutations in determining disease risk. We developed methods to estimate disease risk based on genotype relative risks (for a gene G), exposure to an environmental factor (E), and family history (with recurrence risk ,R for a relative of type R). ,R must be partitioned into the risk due to G (which is modelled independently) and the residual risk. The risk model was then applied to Crohn's disease (CD), a severe gastrointestinal disease for which smoking increases disease risk approximately 2-fold, and mutations in CARD15 confer increased risks of 2.25 (for carriers of a single mutation) and 9.3 (for carriers of two mutations). CARD15 accounts for only a small proportion of the genetic component of CD, with a gene-specific ,S, CARD15 of 1.16, from a total sibling relative risk of ,S= 27. CD risks were estimated for high-risk individuals who are siblings of a CD case, and who also smoke. The CD risk to such individuals who carry two CARD15 mutations is approximately 0.34, and for those carrying a single CARD15 mutation the risk is 0.08, compared to a population prevalence of approximately 0.001. These results imply that complex disease genes may be valuable in estimating with greater precision than has hitherto been possible disease risks in specific, easily identified subgroups of the population with a view to prevention. Yurii Aulchenko 128 Department of Epidemiology & Biostatistics, Erasmus Medical Centre Rotterdam, The Netherlands Keywords: Compression, information, bzip2, genome-wide SNP data, statistical genetics With advances in molecular technology, studies accessing millions of genetic polymorphisms in thousands of study subjects will soon become common. Such studies generate large amounts of data, whose effective storage and management is a challenge to the modern statistical genetics. Standard file compression utilities, such as Zip, Gzip and Bzip2, may be helpful to minimise the storage requirements. Less obvious is the fact that the data compression techniques may be also used in the analysis of genetic data. It is known that the efficiency of a particular compression algorithm depends on the probability structure of the data. In this work, we compared different standard and customised tools using the data from human HapMap project. Secondly, we investigate the potential uses of data compression techniques for the analysis of linkage, association and linkage disequilibrium Suzanne Leal 1 , Bingshan Li 129 Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, USA Keywords: Consanguineous pedigrees, missing genotype data Missing genotype data can increase false-positive evidence for linkage when either parametric or nonparametric analysis is carried out ignoring intermarker linkage disequilibrium (LD). Previously it was demonstrated by Huang et al (2005) that no bias occurs in this situation for affected sib-pairs with unrelated parents when either both parents are genotyped or genotype data is available for two additional unaffected siblings when parental genotypes are missing. However, this is not the case for consanguineous pedigrees, where missing genotype data for any pedigree member within a consanguinity loop can increase false-positive evidence of linkage. The false-positive evidence for linkage is further increased when cryptic consanguinity is present. The amount of false-positive evidence for linkage is highly dependent on which family members are genotyped. When parental genotype data is available, the false-positive evidence for linkage is usually not as strong as when parental genotype data is unavailable. Which family members will aid in the reduction of false-positive evidence of linkage is highly dependent on which other family members are genotyped. For a pedigree with an affected proband whose first-cousin parents have been genotyped, further reduction in the false-positive evidence of linkage can be obtained by including genotype data from additional affected siblings of the proband or genotype data from the proband's sibling-grandparents. When parental genotypes are not available, false-positive evidence for linkage can be reduced by including in the analysis genotype data from either unaffected siblings of the proband or the proband's married-in-grandparents. Najaf Amin 1 , Yurii Aulchenko 130 Department of Epidemiology & Biostatistics, Erasmus Medical Centre Rotterdam, The Netherlands Keywords: Genomic Control, pedigree structure, quantitative traits The Genomic Control (GC) method was originally developed to control for population stratification and cryptic relatedness in association studies. This method assumes that the effect of population substructure on the test statistics is essentially constant across the genome, and therefore unassociated markers can be used to estimate the effect of confounding onto the test statistic. The properties of GC method were extensively investigated for different stratification scenarios, and compared to alternative methods, such as the transmission-disequilibrium test. The potential of this method to correct not for occasional cryptic relations, but for regular pedigree structure, however, was not investigated before. In this work we investigate the potential of the GC method for pedigree-based association analysis of quantitative traits. The power and type one error of the method was compared to standard methods, such as the measured genotype (MG) approach and quantitative trait transmission-disequilibrium test. In human pedigrees, with trait heritability varying from 30 to 80%, the power of MG and GC approach was always higher than that of TDT. GC had correct type 1 error and its power was close to that of MG under moderate heritability (30%), but decreased with higher heritability. William Astle 1 , Chris Holmes 2 , David Balding 131 Department of Epidemiology and Public Health, Imperial College London, UK 32 Department of Statistics, University of Oxford, UK Keywords: Population structure, association studies, genetic epidemiology, statistical genetics In the analysis of population association studies, Genomic Control (Devlin & Roeder, 1999) (GC) adjusts the Armitage test statistic to correct the type I error for the effects of population substructure, but its power is often sub-optimal. Turbo Genomic Control (TGC) generalises GC to incorporate co-variation of relatedness and phenotype, retaining control over type I error while improving power. TGC is similar to the method of Yu et al. (2006), but we extend it to binary (case-control) in addition to quantitative phenotypes, we implement improved estimation of relatedness coefficients, and we derive an explicit statistic that generalizes the Armitage test statistic and is fast to compute. TGC also has similarities to EIGENSTRAT (Price et al., 2006) which is a new method based on principle components analysis. The problems of population structure(Clayton et al., 2005) and cryptic relatedness (Voight & Pritchard, 2005) are essentially the same: if patterns of shared ancestry differ between cases and controls, whether distant (coancestry) or recent (cryptic relatedness), false positives can arise and power can be diminished. With large numbers of widely-spaced genetic markers, coancestry can now be measured accurately for each pair of individuals via patterns of allele-sharing. Instead of modelling subpopulations, we work instead with a coancestry coefficient for each pair of individuals in the study. We explain the relationships between TGC, GC and EIGENSTRAT. We present simulation studies and real data analyses to illustrate the power advantage of TGC in a range of scenarios incorporating both substructure and cryptic relatedness. References Clayton, D. G.et al. (2005) Population structure, differential bias and genomic control in a large-scale case-control association study. Nature Genetics37(11) November 2005. Devlin, B. & Roeder, K. (1999) Genomic control for association studies. Biometics55(4) December 1999. Price, A. L.et al. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics38(8) (August 2006). Voight, B. J. & Pritchard, J. K. (2005) Confounding from cryptic relatedness in case-control association studies. Public Library of Science Genetics1(3) September 2005. Yu, J.et al. (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics38(2) February 2006. Hervé Perdry 1 , Marie-Claude Babron 1 , Françoise Clerget-Darpoux 133 INSERM U535 and Univ. Paris Sud, UMR-S 535, Villejuif, France Keywords: Modifier genes, case-parents trios, ordered transmission disequilibrium test A modifying locus is a polymorphic locus, distinct from the disease locus, which leads to differences in the disease phenotype, either by modifying the penetrance of the disease allele, or by modifying the expression of the disease. The effect of such a locus is a clinical heterogeneity that can be reflected by the values of an appropriate covariate, such as the age of onset, or the severity of the disease. We designed the Ordered Transmission Disequilibrium Test (OTDT) to test for a relation between the clinical heterogeneity, expressed by the covariate, and marker genotypes of a candidate gene. The method applies to trio families with one affected child and his parents. Each family member is genotyped at a bi-allelic marker M of a candidate gene. To each of the families is associated a covariate value; the families are ordered on the values of this covariate. As the TDT (Spielman et al. 1993), the OTDT is based on the observation of the transmission rate T of a given allele at M. The OTDT aims to find a critical value of the covariate which separates the sample of families in two subsamples in which the transmission rates are significantly different. We investigate the power of the method by simulations under various genetic models and covariate distributions. Acknowledgments H Perdry is funded by ARSEP. Pascal Croiseau 1 , Heather Cordell 2 , Emmanuelle Génin 134 INSERM U535 and University Paris Sud, UMR-S535, Villejuif, France 35 Institute of Human Genetics, Newcastle University, UK Keywords: Association, missing data, conditionnal logistic regression Missing data is an important problem in association studies. Several methods used to test for association need that individuals be genotyped at the full set of markers. Individuals with missing data need to be excluded from the analysis. This could involve an important decrease in sample size and a loss of information. If the disease susceptibility locus (DSL) is poorly typed, it is also possible that a marker in linkage disequilibrium gives a stronger association signal than the DSL. One may then falsely conclude that the marker is more likely to be the DSL. We recently developed a Multiple Imputation method to infer missing data on case-parent trios Starting from the observed data, a few number of complete data sets are generated by Markov-Chain Monte Carlo approach. These complete datasets are analysed using standard statistical package and the results are combined as described in Little & Rubin (2002). Here we report the results of simulations performed to examine, for different patterns of missing data, how often the true DSL gives the highest association score among different loci in LD. We found that multiple imputation usually correctly detect the DSL site even if the percentage of missing data is high. This is not the case for the naďve approach that consists in discarding trios with missing data. In conclusion, Multiple imputation presents the advantage of being easy to use and flexible and is therefore a promising tool in the search for DSL involved in complex diseases. Salma Kotti 1 , Heike Bickeböller 2 , Françoise Clerget-Darpoux 136 University Paris Sud, UMR-S535, Villejuif, France 37 Department of Genetic Epidemiology, Medical School, University of Göttingen, Germany Keywords: Genotype relative risk, internal controls, Family based analyses Family based analyses using internal controls are very popular both for detecting the effect of a genetic factor and for estimating the relative disease risk on the corresponding genotypes. Two different procedures are often applied to reconstitute internal controls. The first one considers one pseudocontrol genotype formed by the parental non-transmitted alleles called also 1:1 matching of alleles, while the second corresponds to three pseudocontrols corresponding to all genotypes formed by the parental alleles except the one of the case (1:3 matching). Many studies have compared between the two procedures in terms of the power and have concluded that the difference depends on the underlying genetic model and the allele frequencies. However, the estimation of the Genotype Relative Risk (GRR) under the two procedures has not been studied. Based on the fact that on the 1:1 matching, the control group is composed of the alleles untransmitted to the affected child and on the 1:3 matching, the control group is composed amongst alleles already transmitted to the affected child, we expect a difference on the GRR estimation. In fact, we suspect that the second procedure leads to biased estimation of the GRRs. We will analytically derive the GRR estimators for the 1:1 and 1:3 matching and will present the results at the meeting. Family based analyses using internal controls are very popular both for detecting the effect of a genetic factor and for estimating the relative disease risk on the corresponding genotypes. Two different procedures are often applied to reconstitute internal controls. The first one considers one pseudocontrol genotype formed by the parental non-transmitted alleles called also 1:1 matching of alleles, while the second corresponds to three pseudocontrols corresponding to all genotypes formed by the parental alleles except the one of the case (1:3 matching). Many studies have compared between the two procedures in terms of the power and have concluded that the difference depends on the underlying genetic model and the allele frequencies. However, the estimation of the Genotype Relative Risk (GRR) under the two procedures has not been studied. Based on the fact that on the 1:1 matching, the control group is composed of the alleles untransmitted to the affected child and on the 1:3 matching, the control group is composed amongst alleles already transmitted to the affected child, we expect a difference on the GRR estimation. In fact, we suspect that the second procedure leads to biased estimation of the GRR. We will analytically derive the GRR estimator for the 1:1 and 1:3 matching and will present the results at the meeting. Luigi Palla 1 , David Siegmund 239 Department of Mathematics,Free University Amsterdam, The Netherlands 40 Department of Statistics, Stanford University, California, USA Keywords: TDT, assortative mating, inbreeding, statistical power A substantial amount of Assortative Mating (AM) is often recorded on physical and psychological, dichotomous as well as quantitative traits that are supposed to have a multifactorial genetic component. In particular AM has the effect of increasing the genetic variance, even more than inbreeding because it acts across loci beside within loci, when the trait has a multifactorial origin. Under the assumption of a polygenic model for AM dating back to Wright (1921) and refined by Crow and Felsenstein (1968,1982), the effect of assortative mating on the power to detect genetic association in the Transmission Disequilibrium Test (TDT) is explored as parameters, such as the effective number of genes and the allelic frequency vary. The power is reflected by the non centrality parameter of the TDT and is expressed as a function of the number of trios, the relative risk of the heterozygous genotype and the allele frequency (Siegmund and Yakir, 2007). The noncentrality parameter of the relevant score statistic is updated considering the effect of AM which is expressed in terms of an ,effective' inbreeding coefficient. In particular, for dichotomous traits it is apparent that the higher the number of genes involved in the trait, the lower the loss in power due to AM. Finally an attempt is made to extend this relation to the Q-TDT (Rabinowitz, 1997), which involves considering the effect of AM also on the phenotypic variance of the trait of interest, under the assumption that AM affects only its additive genetic component. References Crow, & Felsenstein, (1968). The effect of assortative mating on the genetic composition of a population. Eugen.Quart.15, 87,97. Rabinowitz,, 1997. A Transmission Disequilibrium Test for Quantitative Trait Loci. Human Heredity47, 342,350. Siegmund, & Yakir, (2007) Statistics of gene mapping, Springer. Wright, (1921). System of mating.III. Assortative mating based on somatic resemblance. Genetics6, 144,161. Jérémie Nsengimana 1 , Ben D Brown 2 , Alistair S Hall 2 , Jenny H Barrett 141 Leeds Institute of Molecular Medicine, University of Leeds, UK 42 Leeds Institute for Genetics, Health and Therapeutics, University of Leeds, UK Keywords: Inflammatory genes, haplotype, coronary artery disease Genetic Risk of Acute Coronary Events (GRACE) is an initiative to collect cases of coronary artery disease (CAD) and their unaffected siblings in the UK and to use them to map genetic variants increasing disease risk. The aim of the present study was to test the association between CAD and 51 single nucleotide polymorphisms (SNPs) and their haplotypes from 35 inflammatory genes. Genotype data were available for 1154 persons affected before age 66 (including 48% before age 50) and their 1545 unaffected siblings (891 discordant families). Each SNP was tested for association to CAD, and haplotypes within genes or gene clusters were tested using FBAT (Rabinowitz & Laird, 2000). For the most significant results, genetic effect size was estimated using conditional logistic regression (CLR) within STATA adjusting for other risk factors. Haplotypes were assigned using HAPLORE (Zhang et al., 2005), which considers all parental mating types consistent with offspring genotypes and assigns them a probability of occurence. This probability was used in CLR to weight the haplotypes. In the single SNP analysis, several SNPs showed some evidence for association, including one SNP in the interleukin-1A gene. Analysing haplotypes in the interleukin-1 gene cluster, a common 3-SNP haplotype was found to increase the risk of CAD (P = 0.009). In an additive genetic model adjusting for covariates the odds ratio (OR) for this haplotype is 1.56 (95% CI: 1.16-2.10, p = 0.004) for early-onset CAD (before age 50). This study illustrates the utility of haplotype analysis in family-based association studies to investigate candidate genes. References Rabinowitz, D. & Laird, N. M. (2000) Hum Hered50, 211,223. Zhang, K., Sun, F. & Zhao, H. (2005) Bioinformatics21, 90,103. Andrea Foulkes 1 , Recai Yucel 1 , Xiaohong Li 143 Division of Biostatistics, University of Massachusetts, USA Keywords: Haploytpe, high-dimensional, mixed modeling The explosion of molecular level information coupled with large epidemiological studies presents an exciting opportunity to uncover the genetic underpinnings of complex diseases; however, several analytical challenges remain to be addressed. Characterizing the components to complex diseases inevitably requires consideration of synergies across multiple genetic loci and environmental and demographic factors. In addition, it is critical to capture information on allelic phase, that is whether alleles within a gene are in cis (on the same chromosome) or in trans (on different chromosomes.) In associations studies of unrelated individuals, this alignment of alleles within a chromosomal copy is generally not observed. We address the potential ambiguity in allelic phase in this high dimensional data setting using mixed effects models. Both a semi-parametric and fully likelihood-based approach to estimation are considered to account for missingness in cluster identifiers. In the first case, we apply a multiple imputation procedure coupled with a first stage expectation maximization algorithm for parameter estimation. A bootstrap approach is employed to assess sensitivity to variability induced by parameter estimation. Secondly, a fully likelihood-based approach using an expectation conditional maximization algorithm is described. Notably, these models allow for characterizing high-order gene-gene interactions while providing a flexible statistical framework to account for the confounding or mediating role of person specific covariates. The proposed method is applied to data arising from a cohort of human immunodeficiency virus type-1 (HIV-1) infected individuals at risk for therapy associated dyslipidemia. Simulation studies demonstrate reasonable power and control of family-wise type 1 error rates. Vivien Marquard 1 , Lars Beckmann 1 , Jenny Chang-Claude 144 Division of Cancer Epidemiology, German Cancer Research Center (DKFZ) Heidelberg, Germany Keywords: Genotyping errors, type I error, haplotype-based association methods It has been shown in several simulation studies that genotyping errors may have a great impact on the type I error of statistical methods used in genetic association analysis of complex diseases. Our aim was to investigate type I error rates in a case-control study, when differential and non-differential genotyping errors were introduced in realistic scenarios. We simulated case-control data sets, where individual genotypes were drawn from a haplotype distribution of 18 haplotypes with 15 markers in the APM1 gene. Genotyping errors were introduced following the unrestricted and symmetric with 0 edges error models described by Heid et al. (2006). In six scenarios, errors resulted from changes of one allele to another with predefined probabilities of 1%, 2.5% or 10%, respectively. A multiple number of errors per haplotype was possible and could vary between 0 and 15, the number of markers investigated. We examined three association methods: Mantel statistics using haplotype-sharing; a haplotype-specific score test; and Armitage trend test for single markers. The type I error rates were not influenced for any of all the three methods for a genotyping error rate of less than 1%. For higher error rates and differential errors, the type I error of the Mantel statistic was only slightly and of the Armitage trend test moderately increased. The type I error rates of the score test were highly increased. The type I error rates were correct for all three methods for non-differential errors. Further investigations will be carried out with different frequencies of differential error rates and focus on power. Arne Neumann 1 , Dörthe Malzahn 1 , Martina Müller 2 , Heike Bickeböller 145 Department of Genetic Epidemiology, Medical School, University of Göttingen, Germany 46 GSF-National Research Center for Environment and Health, Neuherberg & IBE-Institute of Epidemiology, Ludwig-Maximilians University München, Germany Keywords: Interaction, longitudinal, nonparametric Longitudinal data show the time dependent course of phenotypic traits. In this contribution, we consider longitudinal cohort studies and investigate the association between two candidate genes and a dependent quantitative longitudinal phenotype. The set-up defines a factorial design which allows us to test simultaneously for the overall gene effect of the loci as well as for possible gene-gene and gene time interaction. The latter would induce genetically based time-profile differences in the longitudinal phenotype. We adopt a non-parametric statistical test to genetic epidemiological cohort studies and investigate its performance by simulation studies. The statistical test was originally developed for longitudinal clinical studies (Brunner, Munzel, Puri, 1999 J Multivariate Anal 70:286-317). It is non-parametric in the sense that no assumptions are made about the underlying distribution of the quantitative phenotype. Longitudinal observations belonging to the same individual can be arbitrarily dependent on one another for the different time points whereas trait observations of different individuals are independent. The two loci are assumed to be statistically independent. Our simulations show that the nonparametric test is comparable with ANOVA in terms of power of detecting gene-gene and gene-time interaction in an ANOVA favourable setting. Rebecca Hein 1 , Lars Beckmann 1 , Jenny Chang-Claude 147 Division of Cancer Epidemiology, German Cancer Research Center (DKFZ) Heidelberg, Germany Keywords: Indirect association studies, interaction effects, linkage disequilibrium, marker allele frequency Association studies accounting for gene-environment interactions (GxE) may be useful for detecting genetic effects and identifying important environmental effect modifiers. Current technology facilitates very dense marker spacing in genetic association studies; however, the true disease variant(s) may not be genotyped. In this situation, an association between a gene and a phenotype may still be detectable, using genetic markers associated with the true disease variant(s) (indirect association). Zondervan and Cardon [2004] showed that the odds ratios (OR) of markers which are associated with the disease variant depend highly on the linkage disequilibrium (LD) between the variant and the markers, and whether the allele frequencies match and thereby influence the sample size needed to detect genetic association. We examined the influence of LD and allele frequencies on the sample size needed to detect GxE in indirect association studies, and provide tables for sample size estimation. For discordant allele frequencies and incomplete LD, sample sizes can be unfeasibly large. The influence of both factors is stronger for disease loci with small rather than moderate to high disease allele frequencies. A decline in D' of e.g. 5% has less impact on sample size than increasing the difference in allele frequencies by the same percentage. Assuming 80% power, large interaction effects can be detected using smaller sample sizes than those needed for the detection of main effects. The detection of interaction effects involving rare alleles may not be possible. Focussing only on marker density can be a limited strategy in indirect association studies for GxE. Cyril Dalmasso 1 , Emmanuelle Génin 2 , Catherine Bourgain 2 , Philippe Broët 148 JE 2492 , Univ. Paris-Sud, France 49 INSERM UMR-S 535 and University Paris Sud, Villejuif, France Keywords: Linkage analysis, Multiple testing, False Discovery Rate, Mixture model In the context of genome-wide linkage analyses, where a large number of statistical tests are simultaneously performed, the False Discovery Rate (FDR) that is defined as the expected proportion of false discoveries among all discoveries is nowadays widely used for taking into account the multiple testing problem. Other related criteria have been considered such as the local False Discovery Rate (lFDR) that is a variant of the FDR giving to each test its own measure of significance. The lFDR is defined as the posterior probability that a null hypothesis is true. Most of the proposed methods for estimating the lFDR or the FDR rely on distributional assumption under the null hypothesis. However, in observational studies, the empirical null distribution may be very different from the theoretical one. In this work, we propose a mixture model based approach that provides estimates of the lFDR and the FDR in the context of large-scale variance component linkage analyses. In particular, this approach allows estimating the empirical null distribution, this latter being a key quantity for any simultaneous inference procedure. The proposed method is applied on a real dataset. Arief Gusnanto 1 , Frank Dudbridge 150 MRC Biostatistics Unit, Cambridge UK Keywords: Significance, genome-wide, association, permutation, multiplicity Genome-wide association scans have introduced statistical challenges, mainly in the multiplicity of thousands of tests. The question of what constitutes a significant finding remains somewhat unresolved. Permutation testing is very time-consuming, whereas Bayesian arguments struggle to distinguish direct from indirect association. It seems attractive to summarise the multiplicity in a simple form that allows users to avoid time-consuming permutations. A standard significance level would facilitate reporting of results and reduce the need for permutation tests. This is potentially important because current scans do not have full coverage of the whole genome, and yet, the implicit multiplicity is genome-wide. We discuss some proposed summaries, with reference to the empirical null distribution of the multiple tests, approximated through a large number of random permutations. Using genome-wide data from the Wellcome Trust Case-Control Consortium, we use a sub-sampling approach with increasing density to estimate the nominal p-value to obtain family-wise significance of 5%. The results indicate that the significance level is converging to about 1e-7 as the marker spacing becomes infinitely dense. We considered the concept of an effective number of independent tests, and showed that when used in a Bonferroni correction, the number varies with the overall significance level, but is roughly constant in the region of interest. We compared several estimators of the effective number of tests, and showed that in the region of significance of interest, Patterson's eigenvalue based estimator gives approximately the right family-wise error rate. Michael Nothnagel 1 , Amke Caliebe 1 , Michael Krawczak 151 Institute of Medical Informatics and Statistics, University Clinic Schleswig-Holstein, University of Kiel, Germany Keywords: Association scans, Bayesian framework, posterior odds, genetic risk, multiplicative model Whole-genome association scans have been suggested to be a cost-efficient way to survey genetic variation and to map genetic disease factors. We used a Bayesian framework to investigate the posterior odds of a genuine association under multiplicative disease models. We demonstrate that the p value alone is not a sufficient means to evaluate the findings in association studies. We suggest that likelihood ratios should accompany p values in association reports. We argue, that, given the reported results of whole-genome scans, more associations should have been successfully replicated if the consistently made assumptions about considerable genetic risks were correct. We conclude that it is very likely that the vast majority of relative genetic risks are only of the order of 1.2 or lower. Clive Hoggart 1 , Maria De Iorio 1 , John Whittakker 2 , David Balding 152 Department of Epidemiology and Public Health, Imperial College London, UK 53 Department of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, UK Keywords: Genome-wide association analyses, shrinkage priors, Lasso Testing one SNP at a time does not fully realise the potential of genome-wide association studies to identify multiple causal variants of small effect, which is a plausible scenario for many complex diseases. Moreover, many simulation studies assume a single causal variant and so more complex realities are ignored. Analysing large numbers of variants simultaneously is now becoming feasible, thanks to developments in Bayesian stochastic search methods. We pose the problem of SNP selection as variable selection in a regression model. In contrast to single SNP tests this approach simultaneously models the effect of all SNPs. SNPs are selected by a Bayesian interpretation of the lasso (Tibshirani, 1996); the maximum a posterior (MAP) estimate of the regression coefficients, which have been given independent, double exponential prior distributions. The double exponential distribution is an example of a shrinkage prior, MAP estimates with shrinkage priors can be zero, thus all SNPs with non zero regression coefficients are selected. In addition to the commonly-used double exponential (Laplace) prior, we also implement the normal exponential gamma prior distribution. We show that use of the Laplace prior improves SNP selection in comparison with single -SNP tests, and that the normal exponential gamma prior leads to a further improvement. Our method is fast and can handle very large numbers of SNPs: we demonstrate its performance using both simulated and real genome-wide data sets with 500 K SNPs, which can be analysed in 2 hours on a desktop workstation. Mickael Guedj 1,2 , Jerome Wojcik 2 , Gregory Nuel 154 Laboratoire Statistique et Génome, Université d'Evry, Evry France 55 Serono Pharmaceutical Research Institute, Plan-les-Ouates, Switzerland Keywords: Local Replication, Local Score, Association In gene-mapping, replication of initial findings has been put forwards as the approach of choice for filtering false-positives from true signals for underlying loci. In practice, such replications are however too poorly observed. Besides the statistical and technical-related factors (lack of power, multiple-testing, stratification, quality control,) inconsistent conclusions obtained from independent populations might result from real biological differences. In particular, the high degree of variation in the strength of LD among populations of different origins is a major challenge to the discovery of genes. Seeking for Local Replications (defined as the presence of a signal of association in a same genomic region among populations) instead of strict replications (same locus, same risk allele) may lead to more reliable results. Recently, a multi-markers approach based on the Local Score statistic has been proposed as a simple and efficient way to select candidate genomic regions at the first stage of genome-wide association studies. Here we propose an extension of this approach adapted to replicated association studies. Based on simulations, this method appears promising. In particular it outperforms classical simple-marker strategies to detect modest-effect genes. Additionally it constitutes, to our knowledge, a first framework dedicated to the detection of such Local Replications. Juliet Chapman 1 , Claudio Verzilli 1 , John Whittaker 156 Department of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, UK Keywords: FDR, Association studies, Bayesian model selection As genomewide association studies become commonplace there is debate as to how such studies might be analysed and what we might hope to gain from the data. It is clear that standard single locus approaches are limited in that they do not adjust for the effects of other loci and problematic since it is not obvious how to adjust for multiple comparisons. False discovery rates have been suggested, but it is unclear how well these will cope with highly correlated genetic data. We consider the validity of standard false discovery rates in large scale association studies. We also show that a Bayesian procedure has advantages in detecting causal loci amongst a large number of dependant SNPs and investigate properties of a Bayesian FDR. Peter Kraft 157 Harvard School of Public Health, Boston USA Keywords: Gene-environment interaction, genome-wide association scans Appropriately analyzed two-stage designs,where a subset of available subjects are genotyped on a genome-wide panel of markers at the first stage and then a much smaller subset of the most promising markers are genotyped on the remaining subjects,can have nearly as much power as a single-stage study where all subjects are genotyped on the genome-wide panel yet can be much less expensive. Typically, the "most promising" markers are selected based on evidence for a marginal association between genotypes and disease. Subsequently, the few markers found to be associated with disease at the end of the second stage are interrogated for evidence of gene-environment interaction, mainly to understand their impact on disease etiology and public health impact. However, this approach may miss variants which have a sizeable effect restricted to one exposure stratum and therefore only a modest marginal effect. We have proposed to use information on the joint effects of genes and a discrete list of environmental exposures at the initial screening stage to select promising markers for the second stage [Kraft et al Hum Hered 2007]. This approach optimizes power to detect variants that have a sizeable marginal effect and variants that have a small marginal effect but a sizeable effect in a stratum defined by an environmental exposure. As an example, I discuss a proposed genome-wide association scan for Type II diabetes susceptibility variants based in several large nested case-control studies. Beate Glaser 1 , Peter Holmans 158 Biostatistics and Bioinformatics Unit, Cardiff University, School of Medicine, Heath Park, Cardiff, UK Keywords: Combined case-control and trios analysis, Power, False-positive rate, Simulation, Association studies The statistical power of genetic association studies can be enhanced by combining the analysis of case-control with parent-offspring trio samples. Various combined analysis techniques have been recently developed; as yet, there have been no comparisons of their power. This work was performed with the aim of identifying the most powerful method among available combined techniques including test statistics developed by Kazeem and Farrall (2005), Nagelkerke and colleagues (2004) and Dudbridge (2006), as well as a simple combination of ,2-statistics from single samples. Simulation studies were performed to investigate their power under different additive, multiplicative, dominant and recessive disease models. False-positive rates were determined by studying the type I error rates under null models including models with unequal allele frequencies between the single case-control and trios samples. We identified three techniques with equivalent power and false-positive rates, which included modifications of the three main approaches: 1) the unmodified combined Odds ratio estimate by Kazeem & Farrall (2005), 2) a modified approach of the combined risk ratio estimate by Nagelkerke & colleagues (2004) and 3) a modified technique for a combined risk ratio estimate by Dudbridge (2006). Our work highlights the importance of studies investigating test performance criteria of novel methods, as they will help users to select the optimal approach within a range of available analysis techniques. David Almorza 1 , M.V. Kandus 2 , Juan Carlos Salerno 2 , Rafael Boggio 359 Facultad de Ciencias del Trabajo, University of Cádiz, Spain 60 Instituto de Genética IGEAF, Buenos Aires, Argentina 61 Universidad Nacional de La Plata, Buenos Aires, Argentina Keywords: Principal component analysis, maize, ear weight, inbred lines The objective of this work was to evaluate the relationship among different traits of the ear of maize inbred lines and to group genotypes according to its performance. Ten inbred lines developed at IGEAF (INTA Castelar) and five public inbred lines as checks were used. A field trial was carried out in Castelar, Buenos Aires (34° 36' S , 58° 39' W) using a complete randomize design with three replications. At harvest, individual weight (P.E.), diameter (D.E.), row number (N.H.) and length (L.E.) of the ear were assessed. A principal component analysis, PCA, (Infostat 2005) was used, and the variability of the data was depicted with a biplot. Principal components 1 and 2 (CP1 and CP2) explained 90% of the data variability. CP1 was correlated with P.E., L.E. and D.E., meanwhile CP2 was correlated with N.H. We found that individual weight (P.E.) was more correlated with diameter of the ear (D.E.) than with length (L.E). Five groups of inbred lines were distinguished: with high P.E. and mean N.H. (04-70, 04-73, 04-101 and MO17), with high P.E. but less N.H. (04-61 and B14), with mean P.E. and N.H. (B73, 04-123 and 04-96), with high N.H. but less P.E. (LP109, 04-8, 04-91 and 04-76) and with low P.E. and low N.H. (LP521 and 04-104). The use of PCA showed which variables had more incidence in ear weight and how is the correlation among them. Moreover, the different groups found with this analysis allow the evaluation of inbred lines by several traits simultaneously. Sven Knüppel 1 , Anja Bauerfeind 1 , Klaus Rohde 162 Department of Bioinformatics, MDC Berlin, Germany Keywords: Haplotypes, association studies, case-control, nuclear families The area of gene chip technology provides a plethora of phase-unknown SNP genotypes in order to find significant association to some genetic trait. To circumvent possibly low information content of a single SNP one groups successive SNPs and estimates haplotypes. Haplotype estimation, however, may reveal ambiguous haplotype pairs and bias the application of statistical methods. Zaykin et al. (Hum Hered, 53:79-91, 2002) proposed the construction of a design matrix to take this ambiguity into account. Here we present a set of functions written for the Statistical package R, which carries out haplotype estimation on the basis of the EM-algorithm for individuals (case-control) or nuclear families. The construction of a design matrix on basis of estimated haplotypes or haplotype pairs allows application of standard methods for association studies (linear, logistic regression), as well as statistical methods as haplotype sharing statistics and TDT-Test. Applications of these methods to genome-wide association screens will be demonstrated. Manuela Zucknick 1 , Chris Holmes 2 , Sylvia Richardson 163 Department of Epidemiology and Public Health, Imperial College London, UK 64 Department of Statistics, Oxford Center for Gene Function, University of Oxford, UK Keywords: Bayesian, variable selection, MCMC, large p, small n, structured dependence In large-scale genomic applications vast numbers of markers or genes are scanned to find a few candidates which are linked to a particular phenotype. Statistically, this is a variable selection problem in the "large p, small n" situation where many more variables than samples are available. An additional feature is the complex dependence structure which is often observed among the markers/genes due to linkage disequilibrium or their joint involvement in biological processes. Bayesian variable selection methods using indicator variables are well suited to the problem. Binary phenotypes like disease status are common and both Bayesian probit and logistic regression can be applied in this context. We argue that logistic regression models are both easier to tune and to interpret than probit models and implement the approach by Holmes & Held (2006). Because the model space is vast, MCMC methods are used as stochastic search algorithms with the aim to quickly find regions of high posterior probability. In a trade-off between fast-updating but slow-moving single-gene Metropolis-Hastings samplers and computationally expensive full Gibbs sampling, we propose to employ the dependence structure among the genes/markers to help decide which variables to update together. Also, parallel tempering methods are used to aid bold moves and help avoid getting trapped in local optima. Mixing and convergence of the resulting Markov chains are evaluated and compared to standard samplers in both a simulation study and in an application to a gene expression data set. Reference Holmes, C. C. & Held, L. (2006) Bayesian auxiliary variable models for binary and multinomial regression. Bayesian Analysis1, 145,168. Dawn Teare 165 MMGE, University of Sheffield, UK Keywords: CNP, family-based analysis, MCMC Evidence is accumulating that segmental copy number polymorphisms (CNPs) may represent a significant portion of human genetic variation. These highly polymorphic systems require handling as phenotypes rather than co-dominant markers, placing new demands on family-based analyses. We present an integrated approach to meet these challenges in the form of a graphical model, where the underlying discrete CNP phenotype is inferred from the (single or replicate) quantitative measure within the analysis, whilst assuming an allele based system segregating through the pedigree. [source] Do the learning needs of rural and urban general practitioners differ?AUSTRALIAN JOURNAL OF RURAL HEALTH, Issue 6 2005James A. Allan Abstract Introduction:,The challenges of rural general practice have given rise to a separate rural training stream and a separate rural professional body. The differences are characterised by the nature of the work undertaken by rural GPs and reflected in the continuing medical education topic choices made when surveyed. Methods:,In 2001 a survey was designed and distributed by the Royal Australian College of General Practitioners and Divisions of General Practice in South Australia and Northern Territory. The survey utilised a list of 104 topics. The topic choices of rural and urban GPs were compared. Results:,The survey was distributed to approximately 1762 GPs and yielded 578 responses (33%). Rural GPs were more likely to select the following topics: Anaesthetics, Aboriginal Torres Strait Islander health, Population Health, Renal medicine, Cardiology, Teaching skills, Obstetrics, Neonates, Arrhythmias, Fracture management, Tropical medicine and Therapeutics. Urban GPs were more likely to select Menopause, Travel medicine and Palliative care (P < 0.05). Discussion:,Many of the areas of difference reflected aspects of rural general practice. There were also many similarities in topic choices between these two groups. [source] Population health, communities and health promotionAUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH, Issue 5 2009Article first published online: 6 OCT 200 No abstract is available for this article. [source] Non-remission of depression in the general population as assessed by the HAMD-7 scaleDEPRESSION AND ANXIETY, Issue 5 2008Andrew G. Bulloch Ph.D. Abstract Remission from the symptoms of depression is the optimal outcome for depression treatment. Many studies have assessed the frequency of treatment, but there are none that have estimated the frequency of treated remission in the general population. We addressed this issue in the population of Alberta using a brief Hamilton Depression Rating Scale (HAMD)-7 scale (recently validated against the HAMD-17 scale in a clinical setting) that has been proposed as a suitable indicator for remission in primary care. We used data from a survey conducted within the Alberta Depression Initiative in 2005 (n=3,345 adults), to produce a population-based estimate of the number of respondents taking antidepressant medication for depression. From this group we selected a subpopulation that did not screen positive when the MINI module for major depression was administered (i.e., who did not have an active episode). Non-remission in this subpopulation was assessed with a version of the HAMD-7 scale adapted for telephone administration by a nonclinician. Of the survey respondents, 189 reported taking antidepressant medication for depression. Of these, 115 were found not to have an active episode. However, 49.0% of this subpopulation was not in remission as evaluated by the HAMD-7. We estimate that 1.3% (95% confidence interval, 0.9,2.0%) of the population is in treated non-remission for depression. Our study indicates a substantial degree of non-remission from depression in individuals taking antidepressants in the general population. This suggests that, in addition to increasing the frequency of treatment, increasing the effectiveness of treatment can have an impact on population health. Depression and Anxiety 0:1,5, 2007. © 2007 Wiley-Liss, Inc. [source] Cost-effectiveness of interventions to prevent alcohol-related disease and injury in AustraliaADDICTION, Issue 10 2009Linda Cobiac ABSTRACT Aims To evaluate cost-effectiveness of eight interventions for reducing alcohol-attributable harm and determine the optimal intervention mix. Methods Interventions include volumetric taxation, advertising bans, an increase in minimum legal drinking age, licensing controls on operating hours, brief intervention (with and without general practitioner telemarketing and support), drink driving campaigns, random breath testing and residential treatment for alcohol dependence (with and without naltrexone). Cost-effectiveness is modelled over the life-time of the Australian population in 2003, with all costs and health outcomes evaluated from an Australian health sector perspective. Each intervention is compared with current practice, and the most cost-effective options are then combined to determine the optimal intervention mix. Measurements Cost-effectiveness is measured in 2003 Australian dollars per disability adjusted life year averted. Findings Although current alcohol intervention in Australia (random breath testing) is cost-effective, if the current spending of $71 million could be invested in a more cost-effective combination of interventions, more than 10 times the amount of health gain could be achieved. Taken as a package of interventions, all seven preventive interventions would be a cost-effective investment that could lead to substantial improvement in population health; only residential treatment is not cost-effective. Conclusions Based on current evidence, interventions to reduce harm from alcohol are highly recommended. The potential reduction in costs of treating alcohol-related diseases and injuries mean that substantial improvements in population health can be achieved at a relatively low cost to the health sector. [source] Estimating the burden of disease attributable to illicit drug use and mental disorders: what is ,Global Burden of Disease 2005' and why does it matter?ADDICTION, Issue 9 2009Louisa Degenhardt ABSTRACT Background The estimated impact of illicit drug use and mental disorders upon population health needs to be understood because there is evidence that they produce substantial loss of life and disability, and information is needed on the comparative population health impact of different diseases and risk factors to help focus policy, service and research planning and execution. Aims To provide an overview of a global project, running since the end of 2007,Global Burden of Disease (GBD) 2005. Methods The new GBD aims to update comprehensively the findings of the first GBD exercise. It aims to provide regional and global estimates of the burden of disease attributable to hundreds of diseases, injuries and their risk factors. Groups have been assembled to provide expert advice on the parameters needed to inform these estimates; here, we provide a brief summary of the broad range of work being undertaken by the group examining illicit drug use and mental disorders. Discussion The estimates of the contribution of mental disorders and illicit drugs to GBD will inform and potentially shape the focus of researchers, clinicians and governments in the years to come. We hope that interested readers might be encouraged to submit new data or feedback on the work completed thus far, as well as the work that is still under way and yet to be completed. [source] Standardized health check data from community-dwelling elderly people: the potential for comparing populations and estimating needHEALTH & SOCIAL CARE IN THE COMMUNITY, Issue 1 2000Peter Bath PhD Abstract The main aim of this study was to compare EASY-Care data obtained during nurse-administered annual health checks in two populations of older people. A secondary aim was to determine whether a standardized assessment system administered as part of routine practice by a trained nurse during the over-75 health check could generate useful information for comparing population health and functional status of community-dwelling-older people. One hundred and seventy-nine elderly people (aged 75 years and over) from the Woodstock ward, Belfast, having relatively high deprivation; and 238 elderly people from south Hampshire, ranging from affluent wards in New Forest to inner city wards, were assessed using the EASY-Care assessment system as part of their annual health check. There was a high response rate to the standardized assessment in both populations (75% and 79%). Compared to people in south Hampshire, the people in Belfast had higher relative risk of having fair/poor self-rated health, and lower relative risk of having good/sufficient accommodation and of having difficulty chewing. People in Belfast had a higher relative risk of being dependent for six of the seven IADL items and for continence of urine, bathing, grooming, use of the stairs and dressing among the ADL items. The results demonstrate the ability of data generated by assessment system to discriminate between populations of older people when used as part of routine practice. Differences in health and functional status may be associated with deprivation. Data collected during the annual health check about the health and functional status of older people could provide a useful adjunct to census and survey data to measure population needs and to support locality planning. [source] Investment in quality improvement: how to maximize the returnHEALTH ECONOMICS, Issue 1 2010Afschin Gandjour Abstract Today, one of the most pressing concerns of health-care policymakers in industrialized countries are deficits in the quality of health care. This paper presents a decision program that addresses the question in which disease areas and at what intensity to invest in quality improvement (QI) in order to maximize population health. The decision program considers both a budget constraint as well as time constraints of educators and health professionals to participate in educational activities. The calculations of the model are based on a single assumption which is that more intense quality efforts lead to larger QIs, but with diminishing returns. This assumption has been validated by previous studies. All other relationships described by the model are deduced from this assumption. The model uses data from QI trials published in the literature. Thus, it is able to assess how the vast number of published QI strategies compare in terms of their value. Copyright © 2009 John Wiley & Sons, Ltd. [source] Schooling, cognitive ability and healthHEALTH ECONOMICS, Issue 10 2005M. Christopher Auld Abstract A large literature documents a strong correlation between health and educational outcomes. In this paper we investigate the role of cognitive ability in the health-education nexus. Using NLSY data, we show that one standard deviation increase in cognitive ability is associated with roughly the same increase in health as two years of schooling and that cognitive ability accounts for roughly one quarter of the association between schooling and health. Both schooling and ability are strongly associated with health at low levels but less related or unrelated at high levels. Estimates treating schooling as endogenous to health suggest that much of the correlation between schooling and health is attributable to unobserved heterogeneity; the causal effect of schooling on health is large only for respondents with low levels of schooling and low cognitive ability. An implication is that policies which increase schooling will only increase health to the extent that they increase the education of poorly-educated individuals. Subsidies to college education, for example, are unlikely to increase population health. Copyright © 2005 John Wiley & Sons, Ltd. [source] The dynamics of the health labour marketINTERNATIONAL JOURNAL OF HEALTH PLANNING AND MANAGEMENT, Issue 2 2006Marko Vujicic Abstract One of the most important components of health care systems is human resources for health (HRH),the people that deliver the services. One key challenge facing policy makers is to ensure that health care systems have sufficient HRH capacity to deliver services that improve or maintain population health. In a predominantly public system, this involves policy makers assessing the health care needs of the population, deriving the HRH requirements to meet those needs, and putting policies in place that move the current HRH employment level, skill mix, geographic distribution and productivity towards the desired level. This last step relies on understanding the labour market dynamics of the health care sector, specifically the determinants of labour demand and labour supply. We argue that traditional HRH policy in developing countries has focussed on determining the HRH requirements to address population needs and has largely ignored the labour market dynamics aspect. This is one of the reasons that HRH policies often do not achieve their objectives. We argue for the need to incorporate more explicitly the behaviour of those who supply labour,doctors, nurses and other providers,those who demand labour, and how these actors respond to incentives when formulating health workforce policy. Copyright © 2006 John Wiley & Sons, Ltd. [source] Two millennia of male stature development and population health and wealth in the Low CountriesINTERNATIONAL JOURNAL OF OSTEOARCHAEOLOGY, Issue 4 2005G. J. R. MaatArticle first published online: 5 AUG 200 Abstract This paper offers a review of shifts in average male stature and their relationship with health and wealth in the Low Countries from AD 50 to 1997. Twenty-one population samples were studied to cover the full time span. To make data compatible, so-called ,virtual statures' were used, i.e. the statures which adult males were supposed to have had at the end of their growth period, before they started shrinking by ageing. Original data were extracted from ,in situ measured statures', ,calculated statures' and ,corrected cadaveric statures'. If possible, maximum femoral lengths were also collected from the same population samples to check whether trends in stature development were in agreement with raw skeletal data. A long phase of stature decrease from ca. 176,cm to 166,cm, a so-called ,negative secular trend', was noticed from the Roman Period up to and including the first half of the 19th century. This was followed by a sharp and still ongoing increase in stature to 184,cm, a typical ,positive secular trend', from the second half of the 19th century to the present time. General shifts in stature and ,outliers' illustrative for the process are viewed in the context of socio-economic, demographic, health and nutritional factors. Copyright © 2005 John Wiley & Sons, Ltd. [source] The potential power of social policy programmes: income redistribution, economic resources and healthINTERNATIONAL JOURNAL OF SOCIAL WELFARE, Issue 2010Olle Lundberg Lundberg O, Fritzell J, Ĺberg Yngwe M, Kölegĺrd ML. The potential power of social policy programmes: income redistribution, economic resources and health Int J Soc Welfare 2010: ,,: ,,,,,© 2010 The Author(s), Journal compilation © 2010 Blackwell Publishing Ltd and International Journal of Social Welfare. This Supplement includes a number of articles dealing with the role of social policy schemes for public health across the life course. As a key social determinant of health, poverty and its consequences have historically been at the forefront of the public health discussion. But also in rich countries today, economic resources are likely to be important for health and survival, both on an individual and an aggregate level. This introductory article serves as a background for the more specific analyses that follow. The focus is on why income and income inequality could have an effect on individual and population health. We discuss relationships between the individual and population levels and between income and health, and some of the possible mechanisms involved. We also present arguments for why welfare state institutions may matter. [source] Improving the health of populations , evidence for policy and practice actionJOURNAL OF EVIDENCE BASED MEDICINE, Issue 4 2009Gilbert Ramírez The evidence-based movement has greatly improved how health scientists review literature. It has also resulted in improvements in the conduct and reporting of primary studies. Its impact in the practice of clinical medicine has been greater than in public health, both in terms of practice and policy decisions. In order to substantially improve population health, there needs to be a paradigm shift in academia , evidence should be guided by the needs of practitioners and policymakers in terms of relevance and timeliness. [source] Hierarchical related regression for combining aggregate and individual data in studies of socio-economic disease risk factorsJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 1 2008Christopher Jackson Summary., To obtain information about the contribution of individual and area level factors to population health, it is desirable to use both data collected on areas, such as censuses, and on individuals, e.g. survey and cohort data. Recently developed models allow us to carry out simultaneous regressions on related data at the individual and aggregate levels. These can reduce ,ecological bias' that is caused by confounding, model misspecification or lack of information and increase power compared with analysing the data sets singly. We use these methods in an application investigating individual and area level sociodemographic predictors of the risk of hospital admissions for heart and circulatory disease in London. We discuss the practical issues that are encountered in this kind of data synthesis and demonstrate that this modelling framework is sufficiently flexible to incorporate a wide range of sources of data and to answer substantive questions. Our analysis shows that the variations that are observed are mainly attributable to individual level factors rather than the contextual effect of deprivation. [source] A novel study design to investigate the early-life origins of asthma in children (SAGE study)ALLERGY, Issue 8 2009A. L. Kozyrskyj This is a description of the Study of Asthma, Genes and the Environment (SAGE), a novel birth cohort created from provincial healthcare administrative records. It is a general population-based cohort, composed of children at high and low risk for asthma, living in urban and rural environments in Manitoba, Canada. The SAGE study captures the complete longitudinal healthcare records of children born in 1995 and contains detailed information on early-life exposures, such as antibiotic utilization and immunization, in relationship to the development of asthma. Nested within the birth cohort is a case-control study, which was created to collect information on home environmental exposures from detailed surveys and home dust sampling, to confirm asthma status in children and use this data to validate healthcare database measures of asthma, to determine differences in immune system responsiveness to innate and adaptive immune stimuli in asthma, to genotype children for genes likely associated with the development of asthma and to study the epigenetic regulation of pre-established protective vs allergic immune responses. The SAGE study is a multidisciplinary collaboration of researchers from pediatric allergy, population health, immunology, and genetic and environmental epidemiology. As such, it serves as a fertile, interdisciplinary training ground for graduate students, and postdoctoral and clinician fellows. [source] Optimal and sub-optimal control in Dengue epidemicsOPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 2 2001Marco Antonio Leonel Caetano Abstract This work concerns the application of the optimal control theory to Dengue epidemics. The dynamics of this insect-borne disease is modelled as a set of non-linear ordinary differential equations including the effect of educational campaigns organized to motivate the population to break the reproduction cycle of the mosquitoes by avoiding the accumulation of still water in open-air recipients. The cost functional is such that it reflects a compromise between actual financial spending (in insecticides and educational campaigns) and the population health (which can be objectively measured in terms of, for instance, treatment costs and loss of productivity). The optimal control problem is solved numerically using a multiple shooting method. However, the optimal control policy is difficult to implement by the health authorities because it is not practical to adjust the investment rate continuously in time. Therefore, a suboptimal control policy is computed assuming, as the admissible set, only those controls which are piecewise constant. The performance achieved by the optimal control and the sub-optimal control policies are compared with the cases of control using only insecticides when Breteau Index is greater or equal to 5 and the case of no-control. The results show that the sub-optimal policy yields a substantial reduction in the cost, in terms of the proposed functional, and is only slightly inferior to the optimal control policy. Copyright © 2001 John Wiley & Sons, Ltd. [source] Changing patterns of inequality in birthweight and its determinants: a population-based study, Scotland 1980,2000PAEDIATRIC & PERINATAL EPIDEMIOLOGY, Issue 5 2005Lesley Fairley Summary Birthweight is used as an indicator of individual and population health and is known to be strongly correlated with adult cardiovascular disease. This paper uses routinely collected maternity discharge data from Scotland between 1980 and 2000 to look at birthweight trends and the changes in the distribution of maternal risk factors for birthweight. We also examine the contributions of each of the risk factors to birthweight trends and investigate whether there has been a reduction in inequality in birthweight over time. Data from 1 282 172 singleton live births were used in the analysis. Both mean birthweight and low birthweight (LBW: <,2500 g) were used as outcomes. The risk factors studied were maternal age, parity, maternal height, marital status and occupational social class of the father. The slope and relative indices of inequality were used to measure the change in inequalities over time. Mean birthweight increased from 3320 g in 1980 to 3410 g in 2000, while the percentage LBW decreased slightly from 5.7% in 1980 to 5.4% in 2000. The prevalence of many risk factors changed; there has been an increase in the proportion of older mothers, single mothers, taller mothers and mothers with undetermined social class. Although most risk factors had a significant change in effect over time, the inequalities in birthweight between groups did not appear to diminish over time. Both the slope and relative index of inequality had a quadratic relationship over time, with the inequalities in birthweight being greatest in the early 1980s and late 1990s. [source] Immigration, employment relations, and health: Developing a research agendaAMERICAN JOURNAL OF INDUSTRIAL MEDICINE, Issue 4 2010Joan Benach Abstract Background International migration has emerged as a global issue that has transformed the lives of hundreds of millions of persons. Migrant workers contribute to the economic growth of high-income countries often serving as the labour force performing dangerous, dirty and degrading work that nationals are reluctant to perform. Methods Critical examination of the scientific and "grey" literatures on immigration, employment relations and health. Results Both lay and scientific literatures indicate that public health researchers should be concerned about the health consequences of migration processes. Migrant workers are more represented in dangerous industries and in hazardous jobs, occupations and tasks. They are often hired as labourers in precarious jobs with poverty wages and experience more serious abuse and exploitation at the workplace. Also, analyses document migrant workers' problems of social exclusion, lack of health and safety training, fear of reprisals for demanding better working conditions, linguistic and cultural barriers that minimize the effectiveness of training, incomplete OHS surveillance of foreign workers and difficulty accessing care and compensation when injured. Therefore migrant status can be an important source of occupational health inequalities. Conclusions Available evidence shows that the employment conditions and associated work organization of most migrant workers are dangerous to their health. The overall impact of immigration on population health, however, still is poorly understood and many mechanisms, pathways and overall health impact are poorly documented. Current limitations highlight the need to engage in explicit analytical, intervention and policy research. Am. J. Ind. Med. 53:338,343, 2010. © 2009 Wiley-Liss, Inc. [source] Sources of variation in fecal cortisol levels in howler monkeys in belizeAMERICAN JOURNAL OF PRIMATOLOGY, Issue 7 2010Alison M. Behie Abstract High cortisol levels are known to cause low fecundity and increased mortality; thus, the prospect of using cortisol as a measure of population health is an exciting one. However, because so many factors can interact to influence cortisol release, it can be difficult to interpret what exactly is creating changes to cortisol levels. This study investigates variation in fecal cortisol levels in a population of black howlers (Alouatta pigra) from 350 fecal samples collected from 33 individuals in more than 4 years. A general linear mixed model revealed that cortisol varied significantly with fruit availability and contact with tourists. When fruit availability was low, cortisol increased, likely because when fruit availability is low monkeys eat less fruit, thus obtaining less sugar. This result may simply reflect cortisol's metabolic function of mobilizing glucose. It also indicates that these monkeys may be experiencing periods of food stress throughout the year, which was earlier thought to be minimal for a primarily folivorous species. Presence of tourists was the only other factor found to lead to high cortisol; with exposure to tourists increasing stress levels. These results highlight the importance of understanding how physiological factors can influence cortisol, making it easier to interpret results and determine the external social or ecological stressors that may increase cortisol. Am. J. Primatol. 72:600,606, 2010. © 2010 Wiley-Liss, Inc. [source] Making the Case for Laws That Improve Health: A Framework for Public Health Law ResearchTHE MILBANK QUARTERLY, Issue 2 2010SCOTT BURRIS Context: Public health law has received considerable attention in recent years and has become an essential field in public health. Public health law research, however, has received less attention. Methods: Expert commentary. Findings: This article explores public health law research, defined as the scientific study of the relation of law and legal practices to population health. The article offers a logic model of public health law research and a typology of approaches to studying the effects of law on public health. Research on the content and prevalence of public health laws, processes of adopting and implementing laws, and the extent to which and mechanisms through which law affects health outcomes can use methods drawn from epidemiology, economics, sociology, and other disciplines. The maturation of public health law research as a field depends on methodological rigor, adequate research funding, access to appropriate data sources, and policymakers' use of research findings. Conclusions: Public health law research is a young field but holds great promise for supporting evidence-based policymaking that will improve population health. [source] Upstream Solutions: Does the Supplemental Security Income Program Reduce Disability in the Elderly?THE MILBANK QUARTERLY, Issue 1 2008PAMELA HERD Context: The robust relationship between socioeconomic factors and health suggests that social and economic policies might substantially affect health, while other evidence suggests that medical care, the main focus of current health policy, may not be the primary determinant of population health. Income support policies are one promising avenue to improve population health. This study examines whether the federal cash transfer program to poor elderly, the Supplemental Security Income (SSI) program, affects old-age disability. Methods: This study uses the 1990 and 2000 censuses, employing state and year fixed-effect models, to test whether within-state changes in maximum SSI benefits over time lead to changes in disability among people aged sixty-five and older. Findings: Higher benefits are linked to lower disability rates. Among all single elderly individuals, 30 percent have mobility limitations, and an increase of $100 per month in the maximum SSI benefit caused the rate of mobility limitations to fall by 0.46 percentage points. The findings were robust to sensitivity analyses. First, analyses limited to those most likely to receive SSI produced larger effects, but analyses limited to those least likely to receive SSI produced no measurable effect. Second, varying the disability measure did not meaningfully alter the findings. Third, excluding the institutionalized, immigrants, individuals living in states with exceptionally large benefit changes, and individuals living in states with no SSI supplements did not change the substantive conclusions. Fourth, Medicaid did not confound the effects. Finally, these results were robust for married individuals. Conclusions: Income support policy may be a significant new lever for improving population health, especially that of lower-income persons. Even though the findings are robust, further analyses are needed to confirm their reliability. Future research should examine a variety of different income support policies, as well as whether a broader range of social and economic policies affect health. [source] Is Income Inequality a Determinant of Population Health?THE MILBANK QUARTERLY, Issue 1 2004Part 1. This article reviews 98 aggregate and multilevel studies examining the associations between income inequality and health. Overall, there seems to be little support for the idea that income inequality is a major, generalizable determinant of population health differences within or between rich countries. Income inequality may, however, directly influence some health outcomes, such as homicide in some contexts. The strongest evidence for direct health effects is among states in the United States, but even that is somewhat mixed. Despite little support for a direct effect of income inequality on health per se, reducing income inequality by raising the incomes of the most disadvantaged will improve their health, help reduce health inequalities, and generally improve population health. [source] Social Determinants and Their Unequal Distribution: Clarifying Policy UnderstandingsTHE MILBANK QUARTERLY, Issue 1 2004HILARY GRAHAM Public health policy in older industrialized societies is being reconfigured to improve population health and to address inequalities in the social distribution of health. The concept of social determinants is central to these policies, with tackling the social influences on health seen as a way to reduce health inequalities. But the social factors promoting and undermining the health of individuals and populations should not be confused with the social processes underlying their unequal distribution. This distinction is important because, despite better health and improvement in health determinants, social disparities persist. The article argues that more emphasis on social inequalities is required for a determinants-oriented approach to be able to inform policies to address health inequalities. [source] Life Course Health Development: An Integrated Framework for Developing Health, Policy, and ResearchTHE MILBANK QUARTERLY, Issue 3 2002Neal Halfon This article describes the Life Course Health Development (LCHD) framework, which was created to explain how health trajectories develop over an individual's lifetime and how this knowledge can guide new approaches to policy and research. Using recent research from the fields of public health, medicine, human development, and social sciences, the LCHD framework shows that ,Health is a consequence of multiple determinants operating in nested genetic, biological, behavioral, social, and economic contexts that change as a person develops. ,Health development is an adaptive process composed of multiple transactions between these contexts and the biobehavioral regulatory systems that define human functions. ,Different health trajectories are the product of cumulative risk and protective factors and other influences that are programmed into biobehavioral regulatory systems during critical and sensitive periods. ,The timing and sequence of biological, psychological, cultural, and historical events and experiences influence the health and development of both individuals and populations. The life course health development (LCHD) framework organizes research from several fields into a conceptual approach explaining how individual and population health develops and how developmental trajectories are determined by interactions between biological and environmental factors during the lifetime. This approach thus provides a construct for interpreting how people's experiences in the early years of life influence later health conditions and functional status. By focusing on the relationship between experiences and the biology of development, the LCHD framework offers a better understanding of how diseases occur. By suggesting new strategies for health measurement, service delivery, and research, as well as for improving health outcomes, this framework also supports health care-purchasing strategies to develop health throughout life and to build human health capital. [source] Living longer with a greater health burden , changes in the burden of disease and injury in the Northern Territory Indigenous population between 1994,1998 and 1999,2003AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH, Issue 2010Yuejen Zhao Abstract Objective: To measure changes over time in the burden of disease for Northern Territory (NT) Indigenous and non-Indigenous population. Methods: The numbers, and crude and age-adjusted rates of disability adjusted life years (DALY) were calculated for periods 1994,1998 and 1999,2003. A measure of information bias was developed to adjust for the tendency of years lost to disability (a component of DALY) to increase over time because of increasing data availability. The jackknife method was used for DALY uncertainty assessment. Results: The all-cause DALY rate was stable for the non-Indigenous population, but increased for the Indigenous population. For both populations, the burden of premature death decreased while the burden of disability increased. For the Indigenous population, there were substantial increases in DALY rates for type 2 diabetes, depression, nephritis/nephrosis, suicide and sense organ disorders. Conclusions: The burden of disease for Indigenous people increased over the study periods, with improvement in the burden of fatal outcomes more than offset by substantial increase in the prevalence and severity of non-fatal conditions. Implications: The paradoxical shift of living longer with a greater health burden has not been previously reported for Indigenous Australians, and highlights the critical importance of prevention for sustaining life expectancy improvement and managing escalation of health costs. This study also demonstrated the usefulness of the DALY to monitor population health. [source] Cost-effectiveness of Weight Watchers and the Lighten Up to a Healthy Lifestyle programAUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH, Issue 3 2010Linda Cobiac Abstract Objective: Intensive weight loss programs that incorporate dietary counselling and exercise advice are popular and are supported by evidence of immediate weight loss benefits. We evaluate the cost-effectiveness of two weight loss programs, Lighten Up to a Healthy Lifestyle and Weight Watchers. Methods: Health gains from prevention of chronic disease are modelled over the lifetime of the Australian population. These results are combined with estimates of intervention costs and cost offsets (due to reduced rates of lifestyle-related diseases) to determine the dollars per disability-adjusted life year (DALY) averted by each intervention program, from an Australian health sector perspective. Results: Both weight loss programs produced small improvements in population health compared to current practice. The time and travel associated with attending group-counselling sessions, however, was costly for patients, and overall the cost-effectiveness ratios for Lighten Up ($130,000/DALY) and Weight Watchers ($140,000/DALY) were high. Conclusion: Based on current evidence, these intensive behavioural counselling interventions are not very cost-effective strategies for reducing obesity, and the potential benefits for population health are small. Implications: It will be critical to consider other strategies (e.g. changing the ,obesogenic' environment) or explore alternative methods of intervention delivery (e.g. Internet) to see if they offer a more cost-effective approach by effectively reaching a high number of people at a low cost. [source] | |||||||||||||||||||