A structural hierarchical approach to longitudinal modeling of effects of air pollution on health outcomes

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

In the paper we present the methodology of construction and interpretation of models for the study of air pollution effects on health outcomes and their applications. According to the main assumption of the model, every health outcome is an element of the multivariate hierarchical system and depends on the system meteorology, pollution, geophysical, socio-cultural and other factors. The given model is built on system approach using GEE-technique and time-series analysis. The model is tested by the data collected from lung function measurements of the group of 48 adults with vulnerable respiratory system in Leipzig, Germany, over the period from October 1990 till April 1991 (the total of 10,080 individual daily records). The meteorological variables comprise temperature and humidity, while the pollution variables are made of the Total Suspended Paniculate Matter and Sulfur Dioxide airborne concentration. Results of the models, constructed separately for morning, noon, and evening, demonstrate direct and indirect influence of air pollution on the lung function under the certain meteorological, individual factors and seasonal changes.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsJose M. Barreiro, Fernando Martin-Sanchez, Victor Maojo, Ferran Sanz
PublisherSpringer Verlag
Pages243-250
Number of pages8
ISBN (Print)9783540305477
DOIs
StatePublished - 1 Jan 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3337
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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