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Implications of Race Adjustment in Lung-Function Equations.

James A Diao1, Yixuan He1, Rohan Khazanchi1

  • 1From the Department of Biomedical Informatics, Harvard Medical School (J.A.D., P.R., L.M.-K., C.J.P., M.F., A.K.M.), the Computational Health Informatics Program, Boston Children's Hospital (J.A.D., A.K.M.), the Analytic and Translational Genetics Unit (Y.H., A.R.M.) and the Division of Pulmonary and Critical Care Medicine, Department of Medicine (M.F.), Massachusetts General Hospital, Harvard Internal Medicine-Pediatrics Combined Residency Program, Brigham and Women's Hospital, Boston Children's Hospital, and Boston Medical Center (R.K.), the François-Xavier Bagnoud Center for Health and Human Rights, Harvard University (R.K.), the Department of Medicine (M.J.N.T.) and the Channing Division of Network Medicine and the Division of Pulmonary and Critical Care Medicine, Department of Medicine (M.H.C., E.K.S.), Brigham and Women's Hospital, and the Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Beth Israel Deaconess Medical Center (S.L.), Boston, and the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge (Y.H., A.R.M.) - all in Massachusetts; the Departments of Pediatrics (J.I.W.), Medicine (J.R.E., E.G.B.), and Bioengineering and Therapeutic Sciences (J.R.E., E.G.B.), University of California, San Francisco, San Francisco; the Department of Computer Science, Cornell University, Ithaca (E.P.), and the Department of Population Health Sciences, Weill Cornell Medical College (E.P.), and the Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (L.N.B.), New York - all in New York; the Department of Engineering Science, University of Oxford, Oxford, United Kingdom (L.M.-K.); and the Medical Scientist Training Program, University of Illinois at Chicago, Chicago (A.Y.).

The New England Journal of Medicine
|May 20, 2024
PubMed
Summary

Race-neutral lung function equations may reclassify millions, impacting disease classification and disability compensation differently across racial groups. While predictive accuracy for respiratory outcomes remains similar, the shift affects millions, particularly Black and White individuals.

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Area of Science:

  • Pulmonary Medicine
  • Health Equity
  • Biostatistics

Background:

  • Race-based adjustments in lung function testing are discouraged.
  • The implications of adopting race-neutral equations require comprehensive quantification.

Purpose of the Study:

  • To compare the impact of race-based (GLI-2012) and race-neutral (GLI-Global) lung function equations.
  • To evaluate reclassifications in clinical, occupational, and financial outcomes.
  • To assess individual lung-allocation scores and prediction accuracy.

Main Methods:

  • Utilized longitudinal data from 369,077 participants across multiple large health studies.
  • Compared Global Lung Function Initiative 2012 (GLI-2012) race-based equations with 2022 race-neutral (GLI-Global) equations.
  • Evaluated outcomes including clinical, occupational, and financial reclassifications, and prediction concordance statistics.

Main Results:

  • Race-neutral equations could reclassify ventilatory impairment for 12.5 million US individuals.
  • Significant racial disparities observed: nonobstructive impairment classifications increased 141% for Black persons, decreased 69% for White persons.
  • Annual disability payments projected to increase by over $1 billion for Black veterans and decrease by $0.5 billion for White veterans.
  • Both equation sets showed similar discriminative accuracy for respiratory outcomes.

Conclusions:

  • Race-based and race-neutral lung function equations yield similar respiratory outcome predictions.
  • However, race-neutral equations significantly alter disease classifications, occupational eligibility, and disability compensation for millions.
  • These effects diverge notably based on race, highlighting equity considerations.