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. 2016 Nov;26(11):794-801.
doi: 10.1016/j.annepidem.2016.09.002. Epub 2016 Sep 21.

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

Anne M Jurek  1 , George Maldonado  2
Affiliations

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

Anne M Jurek et al. Ann Epidemiol. 2016 Nov.

Abstract

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.

Keywords: 9/11; Asthma; Bias analysis; Outcome misclassification; Selection bias; Sensitivity analysis; World Trade Center.

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Figures

Figure 1
Figure 1
Selection Bias Fourfold Tables Note. Numbers of cases from Wheeler et al. [12]. Numbers of noncases were not provided; estimated from given data.
Figure 2
Figure 2
Identifying Misclassification during Selection Bias for a World Trade Center Health Registry Study of Asthma Note. Numbers of cases from Wheeler et al. [12]. Numbers of noncases were not provided; estimated from given data.
Figure 3
Figure 3
Quantitative Bias Analysis Adjustment Steps for a World Trade Center Health Registry Study of Asthma Note. Numbers of cases from Wheeler et al. [12]. Numbers of noncases were not provided; estimated from given data.
Figure 4
Figure 4
Step-by-Step Examples of Cell Frequencies and Odds Ratios Calculations for Adjusting an Odds Ratio for Missing Data, Outcome Misclassification, and Nonparticipation using Data from the World Trade Center Health Registry Note. Numbers of cases from Wheeler et al. [12]. Numbers of noncases were not provided; estimated from given data. Secloud = outcome sensitivity for rescue-recovery workers and volunteers self-reporting exposure to 9/11 dust cloud; Senoncloud = outcome sensitivity for rescue-recovery workers and volunteers self-reporting not exposed to 9/11 dust cloud; Spcloud = outcome specificity for rescue-recovery workers and volunteers self-reporting exposure to 9/11 dust cloud; Spnoncloud = outcome specificity for rescue-recovery workers and volunteers self-reporting not exposed to 9/11 dust cloud; ORM = odds ratio adjusted for missing data. ORMO = odds ratio adjusted for missing data and newly self-reported physician-diagnosed asthma (outcome) misclassification. ORMON = odds ratio adjusted for missing data, newly self-reported physician-diagnosed asthma misclassification, and non-participation.
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