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Multilevel grouped regression for analyzing self-reported health in relation to environmental factors: the model and its application

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Author: Miedema, H.M.E. · Groothuis-Oudshoorn, C.G.M.
Type:article
Date:2006
Institution: TNO Bouw en Ondergrond
Source:Biometrical Journal, 1, 48, 67-82
Identifier: 470387
Keywords: Acoustics and Audiology · Confidence and tolerance intervals · Exposure-response relationships · Grouped regression · Polychotomous · Random effects · Self-reported health · algorithm · article · biological model · biometry · computer simulation · environmental exposure · evaluation · health survey · human · mathematical computing · mental stress · methodology · Netherlands · regression analysis · risk assessment · statistical analysis · statistical model · statistics · traffic noise · Algorithms · Biometry · Computer Simulation · Data Interpretation, Statistical · Environmental Exposure · Health Status Indicators · Health Surveys · Humans · Models, Biological · Models, Statistical · Netherlands · Noise, Transportation · Numerical Analysis, Computer-Assisted · Regression Analysis · Risk Assessment · Stress, Psychological · Urban Development · Built Environment

Abstract

A method for modeling the relationship of polychotomous health ratings with predictors such as area characteristics, the distance to a source of environmental contamination, or exposure to environmental pollutants is presented. The model combines elements of grouped regression and multilevel analysis. The statistical model describes the entire response distribution as a function of the predictors so that any measure that summarizes this distribution can be calculated from the model. With the model, polychotomous health ratings can be used, and there is no need for a priori dichotomizing such variables which would lead to loss of information. It is described how, according to the model, various measures describing the response distribution are related to the exposure, and the confidence and tolerance intervals for these relationships are presented. Specific attention is given to the incorporation of random factors in the model. The application that here serves as an example, concerns annoyance from transportation noise. Exposure – response relationships obtained with the described method of modeling are presented for aircraft, road traffic, and railway noise