Racial and Ethnic Disparities in Lung Cancer Screening by the 2021 USPSTF Guidelines Versus Risk-Based Criteria: The Multiethnic Cohort Study
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: In 2021, the US Preventive Services Task Force (USPSTF) revised its lung cancer screening guidelines to expand screening eligibility. We evaluated screening sensitivities and racial and ethnic disparities under the 2021 USPSTF criteria vs alternative risk-based criteria in a racially and ethnically diverse population. METHODS: In the Multiethnic Cohort, we evaluated the proportion of ever-smoking lung cancer cases eligible for screening (ie, screening sensitivity) under the 2021 USPSTF criteria and under risk-based criteria through the PLCOm2012 model (6-year risk ≥1.51%). We also calculated the screening disparity (ie, absolute sensitivity difference) for each of 4 racial or ethnic groups (African American, Japanese American, Latino, Native Hawaiian) vs White cases. RESULTS: Among 5900 lung cancer cases, 43.3% were screen eligible under the 2021 USPSTF criteria. Screening sensitivities varied by race and ethnicity, with Native Hawaiian (56.7%) and White (49.6%) cases attaining the highest sensitivities and Latino (37.3%), African American (38.4%), and Japanese American (40.0%) cases attaining the lowest. Latino cases had the greatest screening disparity vs White cases at 12.4%, followed by African American (11.2%) and Japanese American (9.6%) cases. Under risk-based screening, the overall screening sensitivity increased to 75.7%, and all racial and ethnic groups had increased sensitivities (54.5%-91.9%). Whereas the screening disparity decreased to 5.1% for African American cases, it increased to 28.6% for Latino cases and 12.8% for Japanese American cases. CONCLUSIONS: In the Multiethnic Cohort, racial and ethnic disparities decreased but persisted under the 2021 USPSTF lung cancer screening guidelines. Risk-based screening through PLCOm2012 may increase screening sensitivities and help to reduce disparities in some, but not all, racial and ethnic groups. Further optimization of risk-based screening strategies across diverse populations is needed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it