reanalysis of the data with a synthesized adjusted population of 13.4% Black participation demonstrated the study undervalued the impact of LDLCS with an estimated relative reduction in mortality of 18%—12.5% higher than the initial measured reduction of 16%. In populations such as Washington, D.C., which has a Black representation of 46.3%, the estimated reduction was determined to be 26%.8


Other studies have shown that Black Americans have a significantly higher baseline risk for developing lung cancer. In 2019, a study revealed that in patients diagnosed with lung cancer, 56% of white patients met criteria for LDLCS, while in Black patients, only 32% met criteria. This suggested that screening guidelines were too conservative for Black patients.10 Accordingly, the USPSTF changed the screening criteria, lowering the entry age from 55 years to 50 years and reducing smoking exposure from 30 pack-years to 20 pack-years.9


The phenomenon of confounding can also play a role in how medical research can unwittingly distort the truth of data. Confounding is a distortion of the relationship between an exposure and observed health outcome by an unrelated, third variable called a confounder. For example, a limited interpretation of data suggests that non-smokers who sustain acute hip fractures have a higher mortality rate than smokers. Such a narrow analysis may not account for the confounding dynamic that smokers on average present with hip fractures 10 years younger than non-smokers. When the data is reanalyzed and adjusted for age, smokers with hip fractures have a higher mortality than non-smokers.12


Lastly, the impact of artificial intelligence is just starting to evolve within healthcare. Statistical and computational biases reflect human and systemic bias, and rooting out bias in AI demands addressing these issues. Even code that is utterly neutral in its approach may lead to disparate outcomes. Analyzing the outcomes of AI and modifying platforms accordingly will be integral to future healthcare equity.


10 IRQ | SPRING 2023


Mitigating the effects of bias For our patients, if left unchecked, bias can lead to disparities in diagnoses, treatment, outcomes and participation in clinical trials.


So, what can we do as individuals and within our healthcare organizations to mitigate bias? Perhaps the first and easiest step is to strive to learn more about the nature of bias and engage in open discussions with colleagues. Raising awareness of how unconscious bias operates is essential. Become attuned to situations such as deadlines and high-stress events that can trigger unconsciously biased behaviors. Positive contact with members of stigmatized groups can be a powerful way to break the spell of unhelpful stereotypes.11 Knowing someone as an individual directly confronts any stereotypes we may hold and allows us to deconstruct our innate bias.


When it comes to medical research, become a savvy consumer. Examine the study design to discern if bias may have affected the conclusions. When appropriate, examine the study’s methods to understand if the collection of demographical data was part of the study design and if efforts were made to achieve diverse representation among enrollees. If there is demographical data, compare the composition of the enrollees to the population you treat. When designing studies, actively include language and practices that strive to create a diverse study group when appropriate. Lastly, be aware that the effects of confounding may distort our understanding and hide a deeper truth.


Our vision With our imaging training, we strive to see things as they truly are. I think of bias like a fog or defective lens that distorts our perception of reality. As practitioners, bias can hinder us from delivering effective remedies. Bias can perpetuate healthcare disparities. Bias can impair our ability to realize the fullness of human potential. Bias interferes with our ability to perceive the world clearly as it really exists.


It’s important to remember that none of us created bias. It lies as an innate part of our humanity. Hopefully, with intention,


determination and goodwill, we can create a future where the presence of bias in healthcare will be less impactful than it has been in the past.


References


1. Centre for Evidence-Based Medicine. Catalogue of Bias. catalogofbias.org


2. U.S. Food & Drug Administration. Center for Drug Evaluation and Research Drug Trials Snapshots; summary report 2021. fda.gov/ media/158482/download


3. The need for clinical trial diversity: How we got here and where we’re headed. The Boston Globe. sponsored.bostonglobe.com/citeline/ clinical-trial-diversity/


4. National Institute on Minority Health and Health Disparities. Diversity and inclusion in clinical trials. nimhd.nih.gov/resources/understanding- health-disparities/diversity-and-inclusion-in- clinical-trials.html


5. Image; Distribution of U.S. population by race/ ethnicity, 2010. The Henry J. Kaiser Family Foundation. image1.slideserve.com/2474275/ distribution-of-u-s-population-by-race- ethnicity-2010-l.jpg


6. Turner BE, Steinberg JR, Weeks BT, Rodriguez F, Cullen MR. Race/ethnicity reporting and representation in US clinical trials: A cohort study. Lancet. 2022;11: 100252. doi.org/10.1016/j. lana.2022.100252


7. Humes KR, Jones NA, Ramirez RR. Overview of race and Hispanic origin: 2010. Census Briefs. 2011. census.gov/content/dam/Census/library/ publications/2011/dec/c2010br-02.pdf


8. Prosper AE, Inoue K, Brown K, Bui AA, Aberle D, Hsu W. Association of inclusion of more black individuals in lung cancer screening with reduced mortality. JAMA Netw Open. 2021;4(8):e2119629. doi:10.1001/ jamanetworkopen.2021.19629


9. U.S. Preventative Services Task Force. Final recommendation statement; Lung cancer: screening. 2021. uspreventiveservicestaskforce. org/uspstf/recommendation/ lung-cancer-screening


10. Aldrich MC, Mercaldo SF, Sandler KL, Blot WJ, Grogan EL, Blume JD. Evaluation of USPSTF lung cancer screening guidelines among African American adult smokers. JAMA Oncol. 2019;5(9):1318–1324. doi:10.1001/ jamaoncol.2019.140


11. Finnegan E, Oakhill J, Garnham A. Counter- stereotypical pictures as a strategy for overcoming spontaneous gender stereotypes. Front. Psychol. 2015;5. frontiersin.org/ articles/10.3389/fpsyg.2015.01291


12. Zhang N, Liu Y, Yang C, et al. Association between cigarette smoking and mortality in patients with hip fracture: A systematic review and meta-analysis. Tobacco Induced Diseases. 2022;20:110. doi:10.18332/tid/156030.


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