Statistical Methods for Linking Health Effects to Air Pollution
Statistical Models and Applications with Estimates
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Statistical models play important role in linking health effects to environmental risk factors like air pollution. Associating ambient air pollution to human health based upon short term effect can underestimate health effect coefficients. In this scenario, generalized linear and additive models with distributed lag effect of ambient air pollution are realistic in estimating these coefficients. Environmental burden of disease studies based upon such effects produce pragmatic effects. Indoor air pollution (IAP) is a major health threat in developing countries where most people use biomass fuels...
Statistical models play important role in linking health effects to environmental risk factors like air pollution. Associating ambient air pollution to human health based upon short term effect can underestimate health effect coefficients. In this scenario, generalized linear and additive models with distributed lag effect of ambient air pollution are realistic in estimating these coefficients. Environmental burden of disease studies based upon such effects produce pragmatic effects. Indoor air pollution (IAP) is a major health threat in developing countries where most people use biomass fuels for cooking. Effects from IAP can be estimated using statistical models which incorporate multiple modeling tools of logit model and principal component. Moreover, optimally scaled categorical regression is suitable for predicting pollutant concentrations inside kitchens when measurements are costly. Estimates are derived by applying above models to some rural and urban areas of Nepal. The book is useful to students, professionals, and researchers involved in studies linking health effects to air pollution.
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