Quantitative Bias Analysis in an Asthma Study of Rescue-Recovery Workers and Volunteers from the 9/11 World Trade Center Attacks
Published Date:Sep 21 2016
Source:Ann Epidemiol. 26(11):794-801.
Pubmed Central ID:PMC5135411
Funding:U01 OH010730/OH/NIOSH CDC HHS/United States
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 manuscript, we describe the unique data-generating process of a man-made disaster epidemiologic study.
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 estimate for the combined effect of missing data, outcome misclassification, and nonparticipation.
Under our assumptions about systematic error, the odds ratios adjusted for all three biases ranged from 1.33 to 3.84. Most of the adjusted estimates were greater than the observed odds ratio of 1.77 and were outside the 95% confidence limits (1.55, 2.01).
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.
Supporting Files:No Additional Files
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