Demographic Conscious Analysis: A New Paradigm for Health Research.

Abstract

Race and ethnicity are frequently misused in research as biological determinants, reinforcing essentialist assumptions and embedding structural factors into clinical guidelines and tools. Disparities in health outcomes increasingly reflect lived experiences and structural inequities, yet standard analytic approaches have not meaningfully evolved. We introduce demographic conscious analysis (DCA), a methodological framework replacing the use of social demographics as predictor variables. Race/ethnicity is a "social demographic" variable that acts as a proxy for causative variables. The social demographic is excluded from primary analytic models, which instead incorporate causative variables; for race/ethnicity, these might include socioeconomic status, geographic deprivation, chronic stress, and accelerated biological aging. The social demographic is added in a secondary diagnostic analysis to assess residual confounding. A residual association signals unmeasured or inadequately captured causal mechanisms. Within a study, DCA provides a practical method to evaluate a model. Across studies, DCA offers a way to assess progress toward comprehensive measurement of the causative factors for which a given social demographic variable serves as a proxy. Overall, DCA replaces unchangeable social demographic variables with potentially modifiable causative variables. (Am J Public Health. Published online ahead of print October 15, 2026:e1-e10. https://doi.org/10.2105/AJPH.2026.308767).