Including race, ethnicity data in screening model may reduce disparities in lung cancer screening

A counterfactual approach to the ‘life-years gained from screening–computed tomography’ (LYFS-CT) lung cancer screening model can maintain the model’s accuracy and reduce disparities for African-Americans without reducing eligibility for Hispanic and Asian Americans.

These new findings came from a recent study conducted to determine the most effective strategies for incorporating race and ethnicity into the LYFS-CT lung cancer prediction model, a model recommended by the American College of Chest Physicians for personalizing patients’ shared decision-making in screening for lung cancer.

Screenings for lung cancer involve asymptomatic patients, and the harms of screening for these individuals must not exceed the potential benefits. Merely lowering the eligibility risk-or-benefit thresholds and thus allowing for more minority patients could, consequently, be a bad strategy if those individuals would likely not benefit.

Current guidelines usually recommend 5 - 10 years minimum life expectancy to prevent those more likely to die from comorbid conditions to be negatively affected. That said, some racial groups often lack access to health care and preventing them may unjustly penalize those projected to have lower life expectancy due only to their race or ethnicity.

The new research into the LYFS-CT model was conducted to address this. It was led by Rebecca Landy, PhD, from the Division of Cancer Epidemiology and Genetics at the National Cancer Institute in Bethesda, Maryland.

“LYFS-CT is particularly interesting to examine from the perspective of fairness because, unlike risk models, it explicitly incorporates life expectancy,” Landy and colleagues wrote. “Here, we examined the effect of different approaches to incorporating race and ethnicity in the LYFS-CT model on screening eligibility for a contemporary representative US population.”

The investigators set out to assess lung cancer screening eligibility within a contemporary representative US population using two different approaches. In the first approach, the team refitted the LYFS-CT screening model by excluding patients’ ethnicity and race.

The second approach involved a counterfactual eligibility method that would recalculate life expectancy for racial and ethnic minority patients. It would substitute the White racial group and utilize the higher predicted expectancies of this group to prevent historically disadvantaged racial and ethnic groups from being underserved.

The research team used a comprehensive analysis with two submodels within LYFS-CT. The submodels were recalibrated and then validated without considering patient ethnicity or race. The team’s first submodel looked at risk of cancer death and looked at those drawn from a large clinical trial in the period between 1993 - 2001, with a smoking history (n = 39,180).

The second submodel focused on all-cause mortality among patients, and it was developed using data taken from the National Health Interview Survey (NHIS) from 1997 - 2001. It covered individuals age 40 to 80 who may have ever smoked (n = 74,842) and used follow-up data from subsequent years through the end of 2006.

The investigators assessed individuals’ screening eligibility among NHIS participants from 2015 - 2018, in the age range of 50 - 80 years with a history of smoking. The data analysis from the team’s entire study was done between June 2021 and September 2022.

The research team’s primary outcomes were determined to be related to calibration, with comparisons of the LYFS-CT ‘NoRace’ model and the counterfactual approach, with a focus placed on the ratio of expected to observed (E/O) patient outcomes. The team also looked at the effects of such approaches on eligibility for screening and predicted the potential rise in life expectancy resulting from screening as estimated by the LYFS-CT model.

Overall, with the team’s NHIS 2015-2018 dataset covering former smokers in the age range of 50 - 80, the elimination of race or ethnicity from the submodels resulted in underestimated risks for lung cancer death and all-cause mortality in African Americans and overestimated mortality risks in those identified as Asian and Hispanic Americans.

Consequently, the investigators found that the LYFS-CT NoRace screening model improved eligibility for Hispanic and Asian Americans by 108% and 73% but reduced it by 39% for African Americans.

But they also found that the LYFS-CT using the counterfactual all-cause mortality model was able to better maintain calibration, with the team noting an increase in African American patient eligibility by a rate of 13% without reducing eligibility for Asian or Hispanic American individuals.

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“Race and ethnicity can represent risk pathways, such as environmental factors or other social determinants of health, that are difficult to ascertain in the clinic,” they wrote. “Thoughtful use of race and ethnicity data may account for important historical factors that have led to disparities. Thus, appropriate use of prediction models that include race and ethnicity can provide a powerful tool for reducing disparities.”