Estimating Acute Air Pollution Health Effects from Cohort Study Data

Details

• Personal Author:
  Adar SD ; Kaufman JD ; Sheppard L ; Szpiro AA
• Description:
  Traditional studies of short-term air pollution health effects use time series data, while cohort studies generally focus on long-term effects. There is increasing interest in exploiting individual level cohort data to assess short-term health effects in order to understand the mechanisms and time scales of action. We extend semiparametric regression methods used to adjust for unmeasured confounding in time series studies to the cohort setting. Time series methods are not directly applicable since cohort data are typically collected over a prespecified time period and include exposure measurements on days without health observations. Therefore, long-time asymptotics are not appropriate, and it is possible to improve efficiency by exploiting the additional exposure data. We show that flexibility of the semiparametric adjustment model should match the complexity of the trend in the health outcome, in contrast to the time series setting where it suffices to match temporal structure in the exposure. We also demonstrate that pre-adjusting exposures concurrent with the health endpoints using trends in the complete exposure time series results in unbiased health effect estimation and can improve efficiency without additional confounding adjustment. A recently published article found evidence of an association between short-term exposure to ambient fine particulate matter (PM2.5) and retinal arteriolar diameter as measured by retinal photography in the Multi-Ethnic Study of Atherosclerosis (MESA). We reanalyze the data from this article in order to compare the methods described here, and we evaluate our methods in a simulation study based on the MESA data. [Description provided by NIOSH]
• Subjects:
  Air Pollution Cohort Studies Epidemiology Occupational Health Particulate Matter Safety
• Keywords:
  Air Pollution Author Keywords: Air Pollution Cohort Studies Environmental Epidemiology Exposure Assessment Generalized Least Squares Health Effects Mixed Models Particulates Semiparametric Regression Time Series Unmeasured Confounding

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• ISSN:
  0006-341X
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Michigan ; OSHA Region 10 ; OSHA Region 5 ; Washington
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  164-174
• Volume:
  70
• Issue:
  1
• NIOSHTIC Number:
  nn:20055122
• Citation:
  Biometrics 2014 Mar; 70(1):164-174
• Contact Point Address:
  Adam A. Szpiro, Department of Biostatistics, University of Washington, Seattle, Washington, U.S.A.
• Email:
  aszpiro@u.washington.edu
• Federal Fiscal Year:
  2014
• Performing Organization:
  University of Washington
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Biometrics
• End Date:
  20250630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:6ea1e5faa2374b84c7c1b867fddbcb677c3616201e874030d8a3b51116779a3c7e5dab695e2f068ce6fc8a94b3ed495e0d9172d7d0bcf26029cfcfa01a61a783
• Download URL:
  https://stacks.cdc.gov/view/cdc/217489/cdc_217489_DS1.pdf
• File Type:
  [PDF - 659.75 KB ]