Measuring Socioeconomic Status in Health Disparities Research

Unlocking the Impact of Socioeconomic Status on Health Disparities: A Critical Analysis

Health disparities represent a significant challenge in public health, with socioeconomic status (SES) frequently implicated as a major contributor to unequal health outcomes among racial/ethnic minorities, women, and elderly populations. However, studies examining the role of SES and health disparities have often yielded inconsistent results.

Dr. Vickie L. Shavers’ insightful article, “Measurement of Socioeconomic Status in Health Disparities Research,” published in the Journal of the National Medical Association, offers an expert examination into the reasons behind these inconsistencies and provides crucial guidance for more effective research.

This article is essential reading for anyone seeking a deeper understanding of:

• Key Challenges in SES Measurement: It comprehensively identifies and discusses the six primary reasons for inconsistent findings in SES and health disparities research, including the lack of precision and reliability of measures, difficulties in collecting individual SES data (such as high nonresponse rates for income), the dynamic nature of SES over a lifetime, challenges in classifying diverse populations (e.g., women, children, retired, unemployed persons), poor correlation between individual SES measures, and inaccurate interpretation of study results.
• Types of SES Measures: The article details the strengths and limitations of various approaches, distinguishing between:
  • Compositional SES measures: These focus on individual socioeconomic and behavioral characteristics like occupation, education, and income.
  • Contextual SES measures: These refer to characteristics of an individual’s environment, such as neighborhoods, counties, and economic conditions within those areas.
  • Composite SES measures: These combine information from several SES indicators, categorized either by material and social deprivation or social standing/prestige.
• The Nuance of SES and Race/Ethnicity: Dr. Shavers highlights the significant analytical issues arising from the interplay between race/ethnicity and SES. The article argues against simply using SES as a covariate with race and strongly advocates for multivariate analyses stratified by race/ethnicity, gender, or age. This approach allows researchers to examine the specific effect of SES measures both across and within different groups, providing more meaningful information and revealing interactive effects.
• SES Across the Life Course: The paper emphasizes that SES is dynamic, with the effects of socioeconomic disadvantage potentially beginning in childhood and accumulating over time, influencing future opportunities and health.
• Informing Interventions: Ultimately, the article urges researchers to move beyond simply documenting disparities to understand the specific mechanisms through which low SES impacts health. By focusing on factors directly influenced by SES, such as transportation to medical appointments, type of health insurance, access to childcare, and health literacy, researchers can design more tailored and effective interventions aimed at reducing health disparities.

This article is an indispensable resource for researchers, public health officials, and policymakers dedicated to effectively addressing health disparities by improving the measurement, analysis, and interpretation of socioeconomic status in health research. It underscores that choosing the most appropriate SES measure is dependent on its relevance to the population and outcomes under study, as well as the likely causal pathways.

Why SES Findings Are Inconsistent

Shavers reviews how socioeconomic status (SES) is measured in health disparities research and identifies six reasons why results across studies often conflict: imprecise or unreliable measures; the difficulty of collecting individual-level SES data; changes in SES across the life course; the problem of classifying women, children, retirees and unemployed people; weak correlation between different SES indicators in some groups; and inaccurate or misleading interpretation of results.

Strengths and Limits of Each Measure

Income gives access to goods and services that affect health, but it is age-dependent, unstable, has high non-response, ignores wealth and benefits, and buys poorer-quality goods at higher prices in low-income and minority neighbourhoods. Education is easy to measure, stable after early adulthood and less affected by illness, yet its economic returns differ by race, ethnicity and gender. Occupation links education to income and captures working conditions and decision latitude, but occupational classes are heterogeneous and most schemes were developed on working men, which makes homemakers and retirees hard to classify. Composite indices are useful for area-wide planning but can hide which component drives an association.

Compositional and Contextual Approaches

Following Kaplan, the article separates compositional measures, which describe the individual, from contextual measures, which describe the environment such as neighbourhood poverty. Each has strengths and limitations, and combining individual and area-level data can give additional insight.

Practical Guidance

The conclusion is that the choice of SES indicator should follow the likely causal pathway and its relevance to the population and outcome. The article cites evidence that conclusions about the role of SES in racial and ethnic disparities in birth outcomes changed depending on which SES measure was used. Researchers should therefore justify their indicator, consider more than one, and avoid treating a single SES variable as a full adjustment for social disadvantage.

Reference for the Article: Shavers, V. L. (2007). Measurement of Socioeconomic Status in Health Disparities Research. Journal of the National Medical Association, 99(9), 1013–1020.