Impact of Preferential Sampling on Exposure Prediction and Health Effect Inference in the Context of Air Pollution Epidemiology

Details

• Personal Author:
  Kim SY ; Lee A ; Sheppard L ; Szpiro A
• Description:
  Preferential sampling has been defined in the context of geostatistical modeling as the dependence between the sampling locations and the process that describes the spatial structure of the data. It can occur when networks are designed to find high values. For example, in networks based on the US Clean Air Act, monitors are sited to determine whether air quality standards are exceeded. We study the impact of the design of monitor networks in the context of air pollution epidemiology studies. The effect of preferential sampling has been illustrated in the literature by highlighting its impact on spatial predictions. In this paper, we use these predictions as input in a second-stage analysis, and we assess how they affect health effect inference. Our work is motivated by data from two US regulatory networks and health data from the Multi-Ethnic Study of Atherosclerosis and Air Pollution. The two networks were designed to monitor air pollution in urban and rural areas, and we found that the health analysis results based on the two networks can lead to different scientific conclusions. We use preferential sampling to gain insight into these differences. We designed a simulation study and found that the validity and reliability of the health effect estimate can be greatly affected by how we sample the monitor locations. To better understand its effect on second-stage inference, we identify two components of preferential sampling that shed light on how preferential sampling alters the properties of the health effect estimate. [Description provided by NIOSH]
• Subjects:
  Air Pollution Cohort Studies Environmental Monitoring Epidemiology Occupational Health Particulate Matter Safety
• Keywords:
  Air Monitoring Air Pollution Air Sampling Air Sampling Equipment Author Keywords: Air Pollution Epidemiology Berkson-like Error Cohort Studies Geostatistical Modeling Health Effects Network Design Particulates Preferential Sampling

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• ISSN:
  1180-4009
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  OSHA Region 10 ; Washington
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  255-267
• Volume:
  26
• Issue:
  4
• NIOSHTIC Number:
  nn:20054806
• Citation:
  Environmetrics 2015 Jun; 26(4):255-267
• Contact Point Address:
  L. Sheppard, Department of Biostatistics, University of Washington, Seattle, WA 98195, U.S.A.
• Email:
  adellee@uw.edu
• Federal Fiscal Year:
  2015
• Performing Organization:
  University of Washington
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Environmetrics
• End Date:
  20250630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:706893f566346ddf64a15a2b6b8fd65c2992e097982e8f0f98a6358632b37ba8bf6aeb9c2d30c93f24f94ff1726c1ccc366494464b13004feadffea02d8c05bb
• Download URL:
  https://stacks.cdc.gov/view/cdc/217236/cdc_217236_DS1.pdf
• File Type:
  [PDF - 520.74 KB ]