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

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 manuscript, 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 estimate for the combined effect of missing data, outcome misclassification, and nonparticipation. Results 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). 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.

Assessment of adverse events for a home‐use intense pulsed light hair removal device using postmarketing surveillance

“…Techniques exist to adjust study results for biases in epidemiologic studies including bias analyses, 41,42 yet challenges with specifying the bias parameters remain. 43 Various data sources are needed to develop and understand overall AE profiles of consumer products. Inconsistent coding terminology to describe AEs across data sources precluded our ability to make direct analytical comparisons.…”

“…The reported AE verbatims are then coded to MedDRA lower‐level terms and aligned to PTs that may be incorrect and result in further classification bias. Techniques exist to adjust study results for biases in epidemiologic studies including bias analyses, 41,42 yet challenges with specifying the bias parameters remain 43 …”

Data Quality in Electronic Health Record Research: An Approach for Validation and Quantitative Bias Analysis for Imperfectly Ascertained Health Outcomes Via Diagnostic Codes

“…In fact, quantitative bias analyses extend beyond the bounds of data accuracy: similar approaches can handle cases of selection bias and residual confounding (again, see Lash et al, 2009 , and Smith et al, 2021 ). Beyond the case study demonstrated in this article, we refer readers to these other examples of quantitative bias analysis applied to misclassified outcome data ( Bodnar et al, 2010 ; Burstyn et al, 2020 ; Goldstein et al, 2016 ; Goldstein et al, 2021 ; Jurek & Maldonado, 2016 ; Srugo et al, 2021 ; Wesselink et al, 2018 ).…”

Evolving Trends and Research Hotspots in Disaster Epidemiology From 1985 to 2020: A Bibliometric Analysis

“…Disaster epidemiology is defined as the epidemiologic investigation of disaster forecasting and warning, emergency responses according to the different phases of disasters, and the short-and long-term adverse health effects of disasters on the population (7). Although disaster epidemiology is an evolving field, it still faces significant challenges (8).…”