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Environmental Epidemiology (environmental + epidemiology)

Distribution by Scientific Domains
Mathematics and Statistics80%

Selected Abstracts

Statistical Methods for Environmental Epidemiology with R: A Case Study in Air Pollution and Health by PENG, R. D. and DOMINICI, F.

BIOMETRICS, Issue 3 2009
David Buckeridge
No abstract is available for this article. [source]

Environmental burden of disease: HRQoL and statistical perspectives

ENVIRONMETRICS, Issue 5 2004
Pranab Kumar Sen
Abstract Environmental toxicity and pollution mingled with substandard sanitation and public health practice can lead to serious health problems. Some of these toxics can be identified and subjected to preventive measures but together with some other major factors they form the environmental burden of disease, more seriously in developing countries. As a result, in health related quality of life risk assessments, especially relating to cancer of various types, as well as chronic and intestinal diseases, we need to incorporate toxicology as well as environmental epidemiology. Statistical perspectives in this challenging task are appraised with special attention to the arsenite contamination of the groundwater problem. Copyright © 2004 John Wiley & Sons, Ltd. [source]

A novel study design to investigate the early-life origins of asthma in children (SAGE study)

ALLERGY, Issue 8 2009
A. 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]

Estimation of Spatial Variation in Risk Using Matched Case-control Data

BIOMETRICAL JOURNAL, Issue 8 2002
Mikala F. Jarner
Abstract A common problem in environmental epidemiology is to estimate spatial variation in disease risk after accounting for known risk factors. In this paper we consider this problem in the context of matched case-control studies. We extend the generalised additive model approach of Kelsall and Diggle (1998) to studies in which each case has been individually matched to a set of controls. We discuss a method for fitting this model to data, apply the method to a matched study on perinatal death in the North West Thames region of England and explain why, if spatial variation is of particular scientific interest, matching is undesirable. [source]

Bayesian Distributed Lag Models: Estimating Effects of Particulate Matter Air Pollution on Daily Mortality

BIOMETRICS, Issue 1 2009
L. J. Welty
Summary A distributed lag model (DLagM) is a regression model that includes lagged exposure variables as covariates; its corresponding distributed lag (DL) function describes the relationship between the lag and the coefficient of the lagged exposure variable. DLagMs have recently been used in environmental epidemiology for quantifying the cumulative effects of weather and air pollution on mortality and morbidity. Standard methods for formulating DLagMs include unconstrained, polynomial, and penalized spline DLagMs. These methods may fail to take full advantage of prior information about the shape of the DL function for environmental exposures, or for any other exposure with effects that are believed to smoothly approach zero as lag increases, and are therefore at risk of producing suboptimal estimates. In this article, we propose a Bayesian DLagM (BDLagM) that incorporates prior knowledge about the shape of the DL function and also allows the degree of smoothness of the DL function to be estimated from the data. We apply our BDLagM to its motivating data from the National Morbidity, Mortality, and Air Pollution Study to estimate the short-term health effects of particulate matter air pollution on mortality from 1987 to 2000 for Chicago, Illinois. In a simulation study, we compare our Bayesian approach with alternative methods that use unconstrained, polynomial, and penalized spline DLagMs. We also illustrate the connection between BDLagMs and penalized spline DLagMs. Software for fitting BDLagM models and the data used in this article are available online. [source]