Multipollutant Measurement Error in Air Pollution Epidemiology Studies Arising from Predicting Exposures with Penalized Regression Splines

Details

• Personal Author:
  Bergen S ; Kaufman JD ; Sheppard L ; Szpiro AA
• Description:
  Air pollution epidemiology studies are trending towards a multipollutant approach. In these studies, exposures at subject locations are unobserved and must be predicted by using observed exposures at misaligned monitoring locations. This induces measurement error, which can bias the estimated health effects and affect standard error estimates. We characterize this measurement error and develop an analytic bias correction when using penalized regression splines to predict exposure. Our simulations show that bias from multipollutant measurement error can be severe, and in opposite directions or simultaneously positive or negative. Our analytic bias correction combined with a non-parametric bootstrap yields accurate coverage of 95% confidence intervals. We apply our methodology to analyse the association of systolic blood pressure with PM2.5 and NO2 levels in the National Institute of Environmental Health Sciences Sister Study. We find that NO2 confounds the association of systolic blood pressure with PM2.5 levels and vice versa. Elevated systolic blood pressure was significantly associated with increased PM2.5 and decreased NO2 levels. Correcting for measurement error bias strengthened these associations and widened 95% confidence intervals. [Description provided by NIOSH]
• Subjects:
  Air Pollution Epidemiology Occupational Health Particulate Matter Safety
• Keywords:
  Air Pollution Author Keywords: Air Pollution Epidemiology Exposure Assessment Measurement Error Bias Models Multipollutant Measurement Particulates Penalized Regression Splines Statistical Analysis
• ISSN:
  0035-9254
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Minnesota ; OSHA Region 10 ; OSHA Region 5 ; Washington
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  65
• Issue:
  5
• NIOSHTIC Number:
  nn:20063686
• Citation:
  J R Stat Soc Ser C Appl Stat 2016 Nov; 65(5):731-753
• Contact Point Address:
  Silas Bergen, Department of Mathematics and Statistics, Winona State University, 175 West Mark Street, Winona, MN 55987, USA
• Email:
  SBergen@winona.edu
• CAS Registry Number:
  Nitrogen dioxide (CAS RN 10102-44-0)
• Federal Fiscal Year:
  2017
• Performing Organization:
  University of Washington
• Peer Reviewed:
  False
• Start Date:
  20050701
• Source Full Name:
  Journal of the Royal Statistical Society. Series C, Applied Statistics
• End Date:
  20250630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:99e995221dcc7e78d40b4ea4b55a465056bd3aa85f0cf3413a622f4d0fde93f214577910758c280e20508598a957ada3577fc3379ebffa195b55662edf519834
• Download URL:
  https://stacks.cdc.gov/view/cdc/222247/cdc_222247_DS1.pdf
• File Type:
  [PDF - 1.77 MB ]