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Quantitative intersectionality research: Past, present, and future directions.

Dougie Zubizarreta1, Ariel L Beccia2, Jonathan M Platt3

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Summary
This summary is machine-generated.

Quantitative intersectionality research faces measurement and analysis challenges. Addressing these is crucial for advancing health equity by understanding how social identities impact health outcomes.

Keywords:
EpidemiologyHealth equityI-MAIHDAIntersectionality

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Area of Science:

  • Public Health
  • Epidemiology
  • Social Justice Research

Background:

  • Intersectionality, originating from Black feminist scholarship, provides a framework to analyze how interconnected systems of oppression affect health.
  • Current quantitative intersectionality research in public health and epidemiology encounters significant measurement and analysis hurdles.
  • Existing studies often use simplistic, individual-level identity measures, failing to capture complex, structural oppression dynamics.

Purpose of the Study:

  • To highlight the critical measurement and analysis challenges in quantitative intersectionality research.
  • To advocate for the development of lifecourse-informed, multidimensional, and structurally-focused measures.
  • To emphasize the need for addressing epidemiologic biases in intersectionality research for actionable evidence.

Main Methods:

  • Discusses the limitations of current static, unidimensional measures of social identities.
  • Introduces advanced analytic methods like Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (I-MAIHDA).
  • Highlights the scarcity of causal inference methods for quantifying intervention impacts in this field.

Main Results:

  • Researchers face substantial measurement and analysis challenges in quantitative intersectionality.
  • Existing methods often rely on inadequate proxies for exposure to systems of oppression.
  • There's a recognized need for lifecourse-informed, multidimensional, and structurally-focused measurement approaches.

Conclusions:

  • Addressing measurement and analysis challenges is vital for valid evidence in intersectionality research.
  • Developing sophisticated, lifecourse-oriented, and structural measures is essential.
  • Thorough examination of epidemiologic biases is necessary to advance health equity.