Quantitative bias analysis in an asthma study of rescue-recovery workers and volunteers from the 9/11 World Trade Center attacks

Anne M. Jurek, George Maldonado

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Purpose When learning bias analysis, epidemiologists are taught to quantitatively adjust for multiple biases by correcting study results in the reverse order of the error sequence. To understand the error sequence for a particular study, one must carefully examine the health study's epidemiologic data-generating process. In this article, we describe the unique data-generating process of a man-made disaster epidemiologic study. Methods We described the data-generating process and conducted a bias analysis for a study associating September 11, 2001 dust cloud exposure and self-reported newly physician-diagnosed asthma among rescue-recovery workers and volunteers. We adjusted an odds ratio (OR) estimate for the combined effect of missing data, outcome misclassification, and nonparticipation. Results Under our assumptions about systematic error, the ORs adjusted for all three biases ranged from 1.33 to 3.84. Most of the adjusted estimates were greater than the observed OR of 1.77 and were outside the 95% confidence limits (1.55, 2.01). Conclusions Man-made disasters present some situations that are not observed in other areas of epidemiology. Future epidemiologic studies of disasters could benefit from a proactive approach that focuses on the technical aspect of data collection and gathers information on bias parameters to provide more meaningful interpretations of results.

Original languageEnglish (US)
Pages (from-to)794-801
Number of pages8
JournalAnnals of epidemiology
Issue number11
StatePublished - Nov 1 2016

Bibliographical note

Funding Information:
This work was supported by the National Institutes of Health —National Institute for Occupational Safety and Health to Dr. Hyun Kim, grant number U01OH010730 .

Publisher Copyright:
© 2016 Elsevier Inc.


  • 9/11
  • Asthma
  • Bias analysis
  • Outcome misclassification
  • Selection bias
  • Sensitivity analysis
  • World Trade Center

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