ArticleJournal of racial and ethnic health disparities2026
Assessing Measurement Invariance in Allostatic Load between Black and White Adolescents.
Article in Journal of racial and ethnic health disparities, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundAllostatic load (AL) reflects the cumulative dysregulation of physiological systems due to repeated stress responses. Early life exposure to adversity can increase AL, causing premature aging and heightened mortality risks, especially among Black adolescents. AL has potential to be a useful proxy measure of health risk. However, for it to be used in racially-diverse populations, measures of AL must be invariant across racial groups, particularly when comparisons are being made between the groups. PURPOSE: This exploratory study examines whether AL displays properties of measurement invariance across Black and White adolescents.
methodsWe used a nationally representative sample drawn from the National Health and Nutrition Examination Survey dataset to establish the optimal model for this sample. We then tested for measurement invariance by conducting a multigroup model.
resultsThe construct of AL displayed weak factorial invariance, such that the same biomarkers indexed AL for both groups (Likelihood Ratio Test: (χ²(3) = 7.02, p = .071). AL did not display strong factorial invariance, suggesting that mean comparisons are not valid between these two groups.
conclusionThese initial results underscore the importance of testing assumptions about measurement invariance before comparing levels of AL between Black and White adolescents. Research assessing AL during adolescence may help clarify researchers' and clinicians' understanding of the interplay between early life adversity, puberty, and stress-related biomarkers that contribute to AL. This construct may facilitate more effective identification and prediction of adolescents' disease risk, but it remains critical to first establish how to accurately model and apply AL in racially-diverse populations.
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