Multi-city time series analyses of air pollution and mortality data using generalized geoadditive mixed models Public Deposited

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  • March 21, 2019
Creator
  • Chien, Lung-Chang
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
Abstract
  • Background Here we introduce the generalized geoadditive mixed model (GGAMM), a combination of generalized additive model and linear mixed model with unified model structure for more flexible applications, to alternatively examine the influence of air pollution to human health. Methods Extant air pollution and mortality data came from the National Morbidity, Mortality, and Air Pollution Study for 15 U.S. cities in 1991-1995. The PM10 main model, distributed lag model and four co-pollutant models used the GGAMM approach to analyze the effect of PM10, lag effects and co-pollutants on several mortalities, adjusting for day-of-week, calendar time and temperature. Objectives First, the effects of PM10 on mortality are preliminarily examined; second, a jackknife-bootstrap method and a principal component analysis are proposed to handle potential convergence problems; third, some missing data imputation methods are evaluated in the GGAMM; fourth, the issues of multicollinearity and concurvity in our models are examined; fifth, comparisons of the GGAMM and 2-stage Bayesian hierarchical model are performed; sixth, three simulations are accomplished for investigating the influence of concurvity, multicollinearity and missing data imputation methods on estimates and smoothing functions. Results First, the effects of PM10 on mortality are preliminarily examined; second, a jackknife-bootstrap method and a principal component analysis are proposed to handle potential convergence problems; third, some missing data imputation methods are evaluated in the GGAMM; fourth, the issues of multicollinearity and concurvity in our models are examined; fifth, comparisons of the GGAMM and 2-stage Bayesian hierarchical model are performed; sixth, three simulations are accomplished for investigating the influence of concurvity, multicollinearity and missing data imputation methods on estimates and smoothing functions. Conclusions The GGAMM provides an integrate model structure to concern national average estimates, city-specific estimates, smoothing and spatial functions simultaneously. Geographical data can immediately be used in the GGAMM without being affected by missing data, and nation-level smoothing functions can be fitted well by enough valid observations from all cities. These properties are not offered by 2-stage Bayesian hierarchical models, and recommended by using spatio-temporal data.
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  • In Copyright
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  • Bangdiwala, Shrikant
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  • University of North Carolina at Chapel Hill
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