Asthma among World Trade Center First Responders: A Qualitative Synthesis and Bias Assessment.
Hyun KimNavneet Kaur BaidwanDavid KriebelManuel CifuentesSherry BaronPublished in: International journal of environmental research and public health (2018)
The World Trade Center (WTC) disaster exposed the responders to several hazards. Three cohorts i.e., the Fire Department of New York (FDNY), the General Responder Cohort (GRC), and the WTC Health Registry (WTCHR) surveyed the exposed responder population. We searched Pubmed and Web of Science for literature on a well-published association between the WTC exposures and asthma, focusing on new-onset self-reported physician-diagnosed asthma. The resulting five articles were qualitatively assessed for potential biases. These papers were independently reviewed by the co-authors, and conclusions were derived after discussions. While, the cohorts had well-defined eligibility criteria, they lacked information about the entire exposed population. We conclude that selection and surveillance biases may have occurred in the GRC and WTCHR cohorts, but were likely to have been minimal in the FDNY cohort. Health care benefits available to responders may have increased the reporting of both exposure and outcome in the former, and decreased outcome reporting in the FDNY cohort. Irrespective of the biases, the studies showed similar findings, confirming the association between WTC exposure and self-reported physician-diagnosed asthma among responders. This suggests that health data gathered under great duress and for purposes other than epidemiology can yield sound conclusions. Potential biases can, however, be minimized by having validated survey instruments and worker registries in place before events occur.
Keyphrases
- healthcare
- chronic obstructive pulmonary disease
- public health
- lung function
- allergic rhinitis
- emergency department
- health information
- primary care
- human health
- mental health
- air pollution
- systematic review
- adverse drug
- risk assessment
- risk factors
- randomized controlled trial
- big data
- artificial intelligence
- health insurance
- deep learning