• DOI:10.1001/JAMA.294.22.2879
• Corpus ID: 20588890

Socioeconomic status in health research: one size does not fit all.

@article{Braveman2005SocioeconomicSI,
  title={Socioeconomic status in health research: one size does not fit all.},
  author={Paula Braveman and Catherine Cubbin and Susan Egerter and Sekai Chideya and Kristen S. Marchi and Marilyn M Metzler and Samuel F. Posner},
  journal={JAMA},
  year={2005},
  volume={294 22},
  pages={
          2879-88
        }
}

• P. Braveman, C. Cubbin, +4 authors S. Posner
• Published 14 December 2005
• Economics
• JAMA

Problems with measuring socioeconomic status (SES)-frequently included in clinical and public health studies as a control variable and less frequently as the variable(s) of main interest-could affect research findings and conclusions, with implications for practice and policy. We critically examine standard SES measurement approaches, illustrating problems with examples from new analyses and the literature. For example, marked racial/ethnic differences in income at a given educational level and in wealth at a given income level raise questions about the socioeconomic comparability of individuals who are similar on education or income alone. Evidence also shows that conclusions about nonsocioeconomic causes of racial/ethnic differences in health may depend on the measure-eg, income, wealth, education, occupation, neighborhood socioeconomic characteristics, or past socioeconomic experiences-used to "control for SES," suggesting that findings from studies that have measured limited aspects of SES should be reassessed. We recommend an outcome- and social group-specific approach to SES measurement that involves (1) considering plausible explanatory pathways and mechanisms, (2) measuring as much relevant socioeconomic information as possible, (3) specifying the particular socioeconomic factors measured (rather than SES overall), and (4) systematically considering how potentially important unmeasured socioeconomic factors may affect conclusions. Better SES measures are needed in data sources, but improvements could be made by using existing information more thoughtfully and acknowledging its limitations.