Assessing Measurement Invariance in Allostatic Load between Black and White Adolescents
Abbey N Collins1, Steven J Holochwost2, Matthew G Graham3
1Department of Psychology, North Carolina State University, Campus Box 7650, Raleigh, NC, 27695-7650, USA. acollin2@ncsu.edu.
Journal of Racial and Ethnic Health Disparities
|February 9, 2026
Summary
Allostatic load (AL) measures are not equivalent across racial groups. This study found that while the same biomarkers indicate AL in Black and White adolescents, mean comparisons between groups are not yet valid due to a lack of strong factorial invariance.
Area of Science:
- Psychological Science
- Public Health
- Biomedical Science
Background:
- Allostatic load (AL) quantifies physiological dysregulation from chronic stress.
- Early life adversity elevates AL, increasing premature aging and mortality risks, particularly in Black adolescents.
- Accurate AL measurement across diverse populations requires demonstrated measurement invariance.
Purpose of the Study:
- To investigate measurement invariance of allostatic load (AL) between Black and White adolescents.
- To determine if AL models are equivalent across racial groups for health risk assessments.
Main Methods:
- Utilized the National Health and Nutrition Examination Survey (NHANES) dataset.
- Employed a multigroup model to test for measurement invariance in a nationally representative sample.
- Established an optimal AL model for the adolescent sample.
Main Results:
- Allostatic load (AL) demonstrated weak factorial invariance across Black and White adolescents.
- The same biomarkers were found to index AL in both racial groups.
- Strong factorial invariance was not achieved, precluding valid mean-level comparisons between groups.
Conclusions:
- Testing for measurement invariance is crucial before comparing AL levels between racial groups.
- Understanding AL in adolescence can clarify the links between early adversity, puberty, and stress biomarkers.
- Further research is needed to accurately model and apply AL in diverse adolescent populations for effective disease risk prediction.
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