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Record Count: 11
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header (Title, Principal Investigator, Institution, City, ST, Award Code, or
Pubs).
DESCRIPTION (provided by applicant): This project will develop statistical methods relevant to two common forms of environmental epidemiologic studies. The primary goal is to provide methods that extract a concise assessment of health risks associated with environmental exposures, supplemented by appropriate statistical inference. The first topic will evaluate the association between exposure and the risk of a health outcome using diagnosis data on a cohort of individuals supplemented with screening information on undiagnosed participants. The methods will be applied to data from the Seveso Women's Health Study which addresses health risks in women exposed to high levels of dioxin. It is intended that the statistical methods will apply generally to similar studies that include a combination of diagnostic and screening data. The second project concerns statistical techniques to investigate the effects of multiple environmental exposures on health and developmental outcomes. The ideas will be applied to data from the CHAMACOS study of Latino women and their children in California, where information has been collected on environmental (largely pesticide) exposures, in utero and in childhood, for a cohort of women and their infants. Statistical issues involve estimation and ranking-in importance-of suitable causal effects of each exposure, supplemented by a rigorous assessment of which of these represent real effects rather than spurious associations, allowing appropriately for multiple comparisons. Both studies involve the study of vulnerable populations exposed to above average environmental exposures with the potential for elevated risk for poor health outcomes. Statistical and computational algorithms will be developed and provided in an open source user-friendly format allowing their rapid dissemination and use by other investigators. The relevance to public health is two-fold: first, the research will allow environmental epidemiologists to accurately describe the effects of (i) acute dioxin exposure on the reproductive health of women, in particularly on the onset of fibroids, and of (ii) pesticide exposures on birth outcomes and subsequent neurodevelopment of children born to Latino women, in a farmworking community. Second, the proposed research will provide appropriate statistical tools and software to allow other investigators to apply these complex methods to similar studies of the effects of environmental exposures in a wide variety of settings.
Crisp Terms/Key Words: computational biology, Hispanic American, leiomyoma, Italy, children, environmental exposure, women's health, human data, female, embryo /fetus toxicology, pesticide biological effect, dioxin, statistics /biometry, health science research analysis /evaluation, disease /disorder proneness /risk, computer program /software
DESCRIPTION (provided by applicant): Evidence from environmental epidemiology research often contributes to the foundation of major policy decisions, driving policy makers to pose challenging questions to researchers. These questions are often best answered by using statistical methods that characterize the risk of a targeted environmental agent while taking other environmental variables into account. The nature and characteristics of environmental data and health outcomes make the risk estimation challenging and require the development of novel statistical methods. The purpose of this research is to develop models for integrated analyses of Spatio temporal data on exposure, health outcomes and covariates, incompletely observed and available at different levels of aggregation. Such models are needed for addressing a broad class of environmental agents that vary over time and across geographical regions. The focus is on the development of new statistical methods for: 1) estimating temporal associations between health outcomes and current and past environmental exposures, when the underlying function is unknown and exposure is measured with error (Aim A.1); 2) estimating spatial associations between health outcomes and environmental exposures which take proper account of non-random sampling designs (Aim A.2); and 3) conducting integrated analyses of spatio-temporal data on health and environmental exposures taking into account sources of bias arising from spatial and temporal aggregations (Aim A.3). We apply the proposed statistical methods to data on air pollution, mortality and temperature (Aim A.4).
This research will provide a unified statistical framework for analyses of environmental epidemiological data of practical importance. The work proposed here will contribute statistical methodology to the field of environmental epidemiology, and will provide evidence on health effects of air pollution and temperature through the application of the proposed methods to various data sets available to us.
Crisp Terms/Key Words: environmental exposure, clinical research, human data, temperature, human mortality, epidemiology, mathematical model, model design /development, statistics /biometry, environmental health, disease /disorder proneness /risk, air pollution
DESCRIPTION (provided by applicant): Environmental epidemiological data need to be collected over time and across different geographic domains. These data need to be analyzed in order to determine important aspects of national environmental policy, aspects that protect the health of citizens and prevent damage to infrastructure and the environment. The purpose of this research is to develop a statistical framework and methodology for integrated analyses of spatial temporal data on air pollution concentrations and other environmental agents, exposure, health outcomes and covariate information. Generally, these various data layers are temporally misaligned and are observed at different spatial scales. The focus of this research is:
[1] the development of new statistical methods and models for the investigation of the spatial and temporal association between environmental stressors, taking into account human activity, and adverse human health outcomes in the context of two case studies: *study of the impact of ozone and PM (fine, course and ultrafine) on cardiovascular mortality across the conterminuous U.S. *study of the impact of ozone and PM (fine, course and ultra fine) on asthma, cardiovascular and cerebrovascular diseases in the state of Wisconsin.
[2] The development of a broad statistical framework to study the association of environmental factors and adverse health outcomes. This general framework incorporates parametric and nonparametric ial dependence structure for environmental processes, taking into account spatial misalignment, spatial and temporal change of support, and lack of stationarity and lack of separability in the space-time covariance function. An exposure simulator model is used to characterize population exposure levels.
[3] The model fitting, estimation and prediction of multivariate space-time environmental epidemiological data.
[4] The statistical assessment of the performance of deterministic and stochastic models, and model diagnostics. In aims 2-4 we establish general statistical frameworks that will be implemented to the case studies introduced in aim 1.
DESCRIPTION (provided by applicant): We propose a four-year interdisciplinary research program developing statistical methodology for disease ecology, the study of environmental and ecological impacts on disease incidence and spread. Many infectious diseases involve multiple hosts and vectors, each with unique behaviors, and each impacted by climate and landscape. The proposed research draws from the fields of ecology, conservation biology, environmental health, remote sensing, epidemiology, and global health. Geographic space links these disparate fields of inquiry and we use utilize spatial statistics to achieve our three specific aims: 1) Spatio-temporal inference for the local phylogeography of emerging diseases. We propose methods quantifying landscape impacts on the genetic structure of a virus in the ongoing disease outbreak of raccoon rabies in the eastern United States as motivation and application for methodological development, application and evaluation. Associations of interest involve geographic and genetic bottlenecks, and the impact of intervention programs in stalling disease spread. 2) Spatial inference linking disease incidence and environmental/ecological data from imperfectly measured systems. We propose to develop models linking spatial disease surveillance data to environmental landscapes, vectors, and reservoir hosts. We focus on surveillance data subject to spatially varying levels of quality, e.g., spatially varying probabilities of diagnosis and reporting. We use ongoing field data regarding Buruli ulcer in Ghana, West Africa to motivate and illustrate the proposed methodology. 3) Assess spatial design and performance criteria for the developed techniques. For the proposed techniques to have broad impact, it is essential to measure their performance in the context within which they will be applied. We focus primarily on spatial criteria: e.g., where one needs to collect more information and where methods reduce probability of detection or increase rates of false alarms. The results of this program will allow researchers to measure the impact of landscape features on the spread of an emerging disease allowing more accurate predictions of spread, planning of appropriate responses, and improved design of intervention strategies. The public health data gathered during this study will be used to design models, analytic techniques, and software that will assist ministries of health, non- government organizations, and researchers identify areas in which to focus surveillance, determine placement/enhancement of health treatment facilities, increase laboratory capacity, provide educational outreach activities, and organize trainings for healthcare workers, outreach coordinators, and laboratory personnel.SPATIAL STATISTICS FOR DISEASE ECOLOGY
PROJECT NARRATIVE
The proposed research project will develop analytic methods for mapping and quantifying the spread of infectious diseases in time and space through a complicated landscape. The project focuses on two diseases: a strain of rabies that is typically found in raccoons (but transmittable to humans) in the eastern United States and Buruli ulcer (a bacterial infection resulting in deep skin wounds) in Ghana, West Africa. The primary goal of the research is to develop accurate measurements of the impact of environmental factors (such as rivers and mountain ranges) on the spread of diseases across a diverse landscape in order to design effective, geographically specific public health responses.
DESCRIPTION (provided by applicant): Space-Time Clustering of Testicular Cancer Using Residential Histories Abstract The primary objective of the proposed research project is to generate insights concerning the etiology of testicular cancer by conducting a large, population-wide case-control study in Denmark investigating space- time clustering of cases and controls using residential histories. Testicular cancer is the most common cause of cancer in men aged 15-34 yet relatively little is known about its etiology. The established and presumptive risk factors, taken as a whole, account for only a small proportion of the total cases of testicular cancer, and novel techniques are needed to solve this public health enigma. Our research team has recently developed Q-statistics and Space-Time Information System (STIS) technology that enable space-time cluster analyses. Other methods for analyzing cancer clusters typically ignore residential mobility, and almost exclusively work with static spatial point distributions of place-of-residence at time of diagnosis or time of death. In addition, few spatial techniques adequately account for known risk factors and covariates. Our approach addresses each of these needs by utilizing the residential history of the participants represented as a life-line, and thus evaluates space-time clustering at any moment in the life-course of the residential histories of the cases relative to the residential histories of the controls. In addition, in place of the widely used (but often inappropriate) null hypothesis of spatial randomness, Q-statistics can incorporate each individual's probability of being a case based on his/her risk factors and covariates. This project will apply the innovative Q-statistics and STIS technology to identify spatial clustering of testicular cancer by comparing the residential histories of the cases with those of the controls. The proposed research will select testicular cancer cases (3100) from the nation-wide Danish Cancer Registry (diagnosed in 1994-2003) and controls (3100, matched on age) from the general population. The cases and controls will be linked with residential history information available in the Danish Central Population Registry and Q-statistics will be implemented to examine clustering. Local authorities and registers at the Danish Environmental Protection Agency will be asked to identify possible patterns of environmental contamination surrounding the most significant clusters to generate hypotheses about environmental causes of testicular cancer. Future research will address such environmental hypotheses by collecting biomarkers and modeling environmental contaminants within an epidemiologic framework. This research will, for the first time ever, allow epidemiologists to assess space-time clusters of testicular cancer using detailed residential histories, with the intended purpose of generating fresh hypotheses about potential etiologic factors associated with testicular cancer to be investigated in future studies. Space-Time Clustering of Testicular Cancer Using Residential Histories Relevance The scientific innovations from this research are expected to dramatically improve our understanding of testicular cancer, the most common type of cancer in men aged 15-34. This research will enable environmental and spatial epidemiologists to investigate spatial clusters of testicular cancer by comparing residential histories of cases with residential histories of controls, with the intended purpose of generating fresh hypotheses concerning the etiology of testicular cancer.
DESCRIPTION (provided by applicant): The goal of this proposal is to develop statistical methods for environmental health data when the health effects of interest are complex. The Specific Aims are motivated by problems arising in toxicological and environmental epidemiological studies of the health effects of airborne particulate matter. Specific aims of the project are the development of (i) Wavelet-based historical functional data models for assessing high-dimensional associations between exposure and health; (ii) Hierarchical hidden Markov models for analyzing multivariate functional data arising from animal particulate matter concentrator studies; (iii) Methods to address exposure measurement error arising from spatial and temporal misalignment in particulate matter epidemiology studies. We will apply the proposed methods to several data sets for which existing analysis methods do not make full use of the data, including (i) semi-continuous heart- rate variability data from matter in sensitive subpopulations. In the motivating applications, the methods will provide insight into two scientifically pressing issues in environmental health research: the identification of biologic mechanisms of morbidity and mortality of air particles, and identification of pollution sources responsible for observed health effects. More generally, the proposed methods represent advancements in the areas of functional data analysis, hidden Markov modeling, and measurement error modeling that are applicable in a variety of biomedical research settings involving high-dimensional data.
DESCRIPTION (provided by applicant): The long-term objective of this research is to develop powerful statistical methods for the analysis of data from genetic epidemiology studies. While voluminous data are becoming available owing to the Human Genome Project and rapid advancement of high throughput genotyping technology, powerful statistical methods are needed for ultimate success in identifying predisposing genetic variants and their environmental modifiers. This project focuses on developing statistical methods for analyzing genetic association studies on perinatal or early-life diseases. These studies very often adopt a retrospective case-control design, but they have a distinct feature in that offspring of mother cases/controls (for perinatal diseases) or parents of offspring cases/controls (for early-life diseases) are also recruited. Thus these studies have information on both unrelated case-control comparisons and genotype/haplotype transmissions within families. Another important feature of these studies is that the covariate distribution in the study population is structured so that genetic and environmental variables are usually independent within families. The fact that such independence does not hold in the case population under the alternative hypothesis provides further information on the association beyond standard case-control comparison. These studies usually seek to evaluate effects of both maternal and offspring genotypes/haplotypes, their interactions, and gene-environment interactions. Building on currently available approaches for analysis of case-control association studies and case-parent triads, we propose novel efficient estimation and testing methods that can account for the retrospective case-control design and incorporate the family information on the genotype/haplotype transmission and the structure in the covariate distribution. Classical logistic regression for case-control studies applies for most of the analysis but is less efficient due to the ignorance of family information and covariate structure. The Transmission/Disequilibrium type test or likelihood-based methods for analyzing case-parent triads discard the controls and/or their parents and cannot estimate all parameters of interest (e.g., main effects of environmental exposures). Our methods range from profile-likelihood methods and estimating-function based methods to hybrid methods based on the conditional likelihood for case triads and pseudo-likelihoods. This project is motivated by and will be applied to ongoing scientific studies at the University of Pennsylvania on which the PI is collaborating, and the phenotypes include pre-term birth, preeclampsia, hypospadias, and asthma. Our methods also have broad implications to the study of phenotypes other than perinatal and early-life diseases. We will develop large sample theories for the proposed methods, evaluate their finite sample performance by simulation studies, and demonstrate their usefulness using real data. Fully documented software to implement these methods for public use will be provided using freely available statistical package R. PUBLIC HEALTH RELEVANCE: This project proposes novel statistical methods for the analysis of data arising from case- control genetic epidemiology studies of perinatal or early childhood diseases. Data usually consist of case and control mothers and their respective offspring or consist of both case-parent triads and control-parent triads. The proposed methods are for the estimation and testing of maternal and offspring genotype/haplotype main effects and interactions and interaction effects between genotypes/haplotypes and environment variables.
DESCRIPTION (provided by investigator): This application constitutes a renewal application for the previously funded study entitled "Analytic Methods: Environmental/Reproductive Epidemiology". The initial funding cycle facilitated a productive collaboration, and these efforts have revealed promising new directions for research to more fully encompass the multiple challenges posed by exposure and reproductive health data collected under motivating studies such as the Michigan PBB Studies (MIPBB) and the Mount Sinai Study of Women Office Workers (MSSWOW). As in many longitudinal studies, exposure assays utilized in the MIPBB underwent an evolution over time so that data obtained via the original and more recent assays are recorded at different levels of resolution. In particular, data obtained earlier in the study were primarily "heaped", due to assay limitations that effectively led values to be rounded to the nearest integer. Proper analysis of the longitudinal data should attribute the correct level of resolution to each data point, based on the assay used to record it. In epidemiologic studies, it is also common to observe highly skewed exposure data. The simultaneous features of heavy skewness, detection limit issues, changing assay resolution over time, and heaping due to rounding require flexible and innovative modeling, with the ultimate aim of improved prediction and valid determination of associations between exposure and reproductive health outcomes (Aim 1). Our research to date motivated by the MSSWOW study has identified new avenues of research into the modeling of time-to-pregnancy and menstrual cycle length data. In such studies, time-to-pregnancy is typically recorded in terms of a number of cycles as opposed to being measured in days or weeks, so that methods for discrete data survival analysis are required. Modeling innovations are needed in order to relate environmental exposures and other covariates to fertility in such contexts (Aim 2). Repeated menstrual cycle length data tend to be characterized by heterogeneity not only in average length, but in the level of variability as well. This motivates a need for flexible modeling and improved methods for classifying women into menstrual cycle length and variability subgroups, and brings attention to potential misclassification error (Aim3). This renewal application continues to seek improved analytic methods for epidemiologic research by means of an effective balance between statistical theory and application in the environmental and reproductive health areas. We consider both parametric and semi-parametric approaches, noting that both have their advantages in this context and that each approach has the potential to inform and augment the other. While intended to be of direct benefit to the motivating studies, the methods to be developed address issues that are common and fundamental enough to make them of broader interest in statistical and epidemiologic practice. PUBLIC HEALTH RELEVANCE Environmental exposures can have a major impact on various aspects of public health,
including women's reproductive health. This application aims to address multiple unique challenges in the analysis of exposure and reproductive health outcome data stemming from two landmark motivating studies. The statistical methods to be developed will also have broader implications toward public health studies that collect exposure and outcome data over time.
DESCRIPTION (provided by applicant): The long term objective of this project is to develop powerful and computationally efficient statistical methods of identifying genes, environmental risk factors and their interactions underlying complex traits related to human diseases and health. The specific aim of this project is to continue to develop survival analysis models to incorporate age of onset data, environmental covariates information, gene-gene and gene-environment interactions into haplotype-based genetic association analysis, analysis of single nucleotide polymorphisms (SNPs) and admixture mapping of complex traits in population-based cohort studies. The project also evaluates different study designs in genetic association studies. The proposed methods build on our current methods and hinge on novel integration of methods in survival analysis, high dimensional data analysis and methods in human genetics. The focus will be on the development of rigorous and comprehensive statistical inference procedures for haplotype analysis, gene-gene and gene-environment interaction analysis and admixture mapping in cohort studies of unrelated individuals sampled by different study designs, including case-cohort and nested case-control designs. Likelihood-based inferences, hidden Markov models and threshold gradient descent methods will be developed for these aims. The project will also investigate the robustness, power and efficiencies of these methods. In addition, this project will develop practical and feasible computer programs in order to implement the proposed methods, to evaluate the performance of these methods through simulation and application to real data on breast and ovarian cancer risks among the BRCA1/2 carriers and to data sets in the area of pharmacogenomics. The work proposed here will contribute both statistical methodology to studying complex traits and methods for high-dimensional data analysis, and offer insight into each of the clinical areas represented by the various data sets to evaluate these new methods. All programs developed under this grant and detailed documentations will be made available free-of-charge to interested researchers via the World Wide Web.
Crisp Terms/Key Words: disease /disorder onset, linkage disequilibrium, gene environment interaction, human data, family genetics, spondylitis, rheumatoid arthritis, narcolepsy, computer assisted sequence analysis, mathematical model, genetic model, model design /development, data collection methodology /evaluation, linkage mapping, genetic disorder, celiac disease, insulin dependent diabetes mellitus, computer program /software, computer system design /evaluation, biotechnology
DESCRIPTION (provided by applicant): Many aspects of the social world bear on important health outcomes, but for most, the exact nature of their impact is still unclear. Although much has been learned through statistical approaches, the literature falls short of a causal understanding of how macro and micro processes interrelate in affecting health. The proposed research uses a spatially explicit agent-based modeling approach, informed by insights from sociology, geography, and economics. Dynamic social networks are included endogenously, an especially innovative element. Through the construction of a model in one setting, a set of modeling tools will be developed that will be applicable across a wide range of settings. These new tools will be made available through a public website. Once the agent-based model is constructed, it will be used to study interconnections among the social, spatial, and biophysical dimensions of the local context, taking into account migration, residential choice, land use, and household wealth, and feedbacks therein. In the process will come a better understanding of the consequences of oversimplification in standard statistical models of community effects. The model will also be used to conduct experiments about the effects of a sudden shift in infant mortality and shifts in economic conditions. The model will be field tested twice, the first time to validate key model assumptions and the second time to explore unexpected results. Indeed, an advantage of the agent-based approach is the possibility of "emergence" at the system or community level (i.e., the integration of macro and micro processes producing new structures not anticipated based on a simple aggregation of individual and household behaviors). Finally, borrowing and extending tools from the fields of meteorology and control theory, new methods for testing model sensitivity that are more computationally efficient than those now in use will be developed, applied, and made broadly available. The proposed project will develop tools to study social processes involving individuals, households, social networks, and communities in relation to health. The application of these tools will help us better understand and interpret the research literature connecting community factors with health outcomes and provide a complement to the standard statistical approaches.
DESCRIPTION (provided by applicant):
The proposed research aims to address critical gaps in scientific knowledge of ozone and health with a national assessment of ozone and mortality and hospital admissions through epidemiological studies and biostatistical analysis. Specifically, the primary objectives are to investigate the relationship between ozone and mortality and hospital admissions, with respect to mortality displacement, longer-term exposure, the shape of the distributed lag curve, potential interaction with particulate matter (PM), heterogeneity among community-specific estimates, and threshold effects. Recent synthesis of scientific information on ozone and health by the United States EPA revealed considerable gaps in the literature on ozone and human health, including those outlined in the specific aims. This research directly relates to the NIEHS goals of reducing the burden of environmentally associated disease, contributing to career enhancement of young researchers, and providing a sound scientific foundation to support policy. More than 100 million people in the United States reside in areas exceeding the ozone standard, making ozone one of the nation's most pressing air pollution problems. A key component of the proposal is to investigate potential interaction between ozone and PM through the development of interaction models and through study of whether variation in PM composition can explain spatial and temporal heterogeneity in ozone effect estimates. This work is consistent with NIEHS initiatives to identify the health effects from complex mixtures and to develop statistical and mathematical models that accommodate complex mixtures. Results from this research are anticipated to be highly policy-relevant, both with respect to ozone and in the long-term with regards to air pollution and health more generally. An overall objective of this work is to contribute statistical techniques that could be applied broadly in environmental health research, such as to other health outcomes and other pollutants. Relevance to public health: This research investigates how hospital admissions and mortality are affected by ozone, a common urban air pollutant. The goal of this work is to provide policy-makers, scientists, and the public with a better understanding of how ozone affects health in order to aid more effective decisions regarding protection of human health.
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