PMID- 17032699
OWN - NLM
STAT- MEDLINE
DCOM- 20070926
LR  - 20161019
IS  - 1465-4644 (Print)
IS  - 1465-4644 (Linking)
VI  - 8
IP  - 3
DP  - 2007 Jul
TI  - An informative Bayesian structural equation model to assess source-specific 
      health effects of air pollution.
PG  - 609-24
AB  - A primary objective of current air pollution research is the assessment of health 
      effects related to specific sources of air particles or particulate matter (PM). 
      Quantifying source-specific risk is a challenge because most PM health studies do 
      not directly observe the contributions of the pollution sources themselves. 
      Instead, given knowledge of the chemical characteristics of known sources, 
      investigators infer pollution source contributions via a source apportionment or 
      multivariate receptor analysis applied to a large number of observed elemental 
      concentrations. Although source apportionment methods are well established for 
      exposure assessment, little work has been done to evaluate the appropriateness of 
      characterizing unobservable sources thus in health effects analyses. In this 
      article, we propose a structural equation framework to assess source-specific 
      health effects using speciated elemental data. This approach corresponds to 
      fitting a receptor model and the health outcome model jointly, such that 
      inferences on the health effects account for the fact that uncertainty is 
      associated with the source contributions. Since the structural equation model 
      (SEM) typically involves a large number of parameters, for small-sample settings, 
      we propose a fully Bayesian estimation approach that leverages historical 
      exposure data from previous related exposure studies. We compare via simulation 
      the performance of our approach in estimating source-specific health effects to 
      that of 2 existing approaches, a tracer approach and a 2-stage approach. 
      Simulation results suggest that the proposed informative Bayesian SEM is 
      effective in eliminating the bias incurred by the 2 existing approaches, even 
      when the number of exposures is limited. We employ the proposed methods in the 
      analysis of a concentrator study investigating the association between 
      ST-segment, a cardiovascular outcome, and major sources of Boston PM and discuss 
      the implications of our findings with respect to the design of future PM 
      concentrator studies.
FAU - Nikolov, Margaret C
AU  - Nikolov MC
AD  - Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, 
      USA. meg.nikolov@gmail.com
FAU - Coull, Brent A
AU  - Coull BA
FAU - Catalano, Paul J
AU  - Catalano PJ
FAU - Godleski, John J
AU  - Godleski JJ
LA  - eng
GR  - P30 ES000002/ES/NIEHS NIH HHS/United States
GR  - ES012972/ES/NIEHS NIH HHS/United States
GR  - ES07142/ES/NIEHS NIH HHS/United States
GR  - ES012044/ES/NIEHS NIH HHS/United States
GR  - P01 ES008129/ES/NIEHS NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20061010
PL  - England
TA  - Biostatistics
JT  - Biostatistics (Oxford, England)
JID - 100897327
SB  - IM
MH  - Air Pollution/*adverse effects
MH  - Animals
MH  - *Bayes Theorem
MH  - *Biometry
MH  - Dogs
MH  - Environmental Health
MH  - Humans
MH  - *Models, Biological
MH  - Myocardial Ischemia/etiology/physiopathology
EDAT- 2006/10/13 09:00
MHDA- 2007/09/27 09:00
CRDT- 2006/10/13 09:00
PHST- 2006/10/13 09:00 [pubmed]
PHST- 2007/09/27 09:00 [medline]
PHST- 2006/10/13 09:00 [entrez]
AID - kxl032 [pii]
AID - 10.1093/biostatistics/kxl032 [doi]
PST - ppublish
SO  - Biostatistics. 2007 Jul;8(3):609-24. doi: 10.1093/biostatistics/kxl032. Epub 2006 
      Oct 10.