An integrated Bayesian model for estimating the long-term health effects of air pollution by fusing modelled and measured pollution data: A case study of nitrogen dioxide concentrations in Scotland

Spatial and Spatio-temporal Epidemiology

Volumn 14-15, Issue , 2015, Pages 63-74

  Huang, Guowen ^a   Lee, Duncan ^a   Scott, Marian ^a  

^a UNIVERSITY OF GLASGOW   (United Kingdom)

Author keywords

Air pollution; Health effects; Respiratory disease; Spatial temporal model

Indexed keywords

NITROGEN DIOXIDE; AIR POLLUTANT; PARTICULATE MATTER;

AIR POLLUTION; ARTICLE; ATTRIBUTABLE RISK; BAYESIAN LEARNING; CONCENTRATION (PARAMETERS); CORRELATION FUNCTION; COVARIANCE; DISEASE MODEL; HEALTH IMPACT ASSESSMENT; HOSPITAL ADMISSION; HUMAN; MAJOR CLINICAL STUDY; PREDICTION; PRIORITY JOURNAL; PROBABILITY; PROCESS MODEL; RESPIRATORY TRACT DISEASE; STANDARDIZED INCIDENCE RATIO; STATISTICAL MODEL; UNITED KINGDOM; VALIDATION STUDY; ADVERSE EFFECTS; AIR POLLUTANT; ALGORITHM; ANALYSIS; BAYES THEOREM; CHEMICALLY INDUCED; DRUG EFFECTS; ENVIRONMENTAL EXPOSURE; HOSPITALIZATION; INTOXICATION; PARTICULATE MATTER; POLLUTION; PREDICTIVE VALUE; RESPIRATORY SYSTEM; RESPIRATORY TRACT DISEASES; SCOTLAND; SENSITIVITY AND SPECIFICITY; SPATIOTEMPORAL ANALYSIS; STATISTICS AND NUMERICAL DATA; THEORETICAL MODEL; TIME FACTOR;

AIR POLLUTANTS; AIR POLLUTION; ALGORITHMS; BAYES THEOREM; ENVIRONMENTAL EXPOSURE; ENVIRONMENTAL POLLUTION; HOSPITALIZATION; HUMANS; MODELS, THEORETICAL; NITROGEN DIOXIDE; PARTICULATE MATTER; PREDICTIVE VALUE OF TESTS; RESPIRATORY SYSTEM; RESPIRATORY TRACT DISEASES; SCOTLAND; SENSITIVITY AND SPECIFICITY; SPATIO-TEMPORAL ANALYSIS; TIME FACTORS;

EID: 84944076740     PISSN: 18775845     EISSN: 18775853     Source Type: Journal    
DOI: 10.1016/j.sste.2015.09.002     Document Type: Article

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