Seminar Recording: Gender Analysis in Health Data

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Seminar Recording: Gender Analysis in Health Data

1 hour 27 minutes

This seminar provides an overview of what gender analysis in health data means and methods to apply gender analyses to large health datasets, with a focus on the differences between sex disaggregation and gender analysis using gender equity indicators.

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Key takeaways

Gender analysis of population-based health data is often limited to biological sex disaggregation, omitting gender dimensions and indicators.
Cause of death data does not always accurately reflect the reproductive and maternal health-related deaths of women, girls, and non-binary people.
USAID highlights five dimensions to consider when conducting a gendered data analysis: 1) Laws, regulations, and institutional practices; 2) Cultural norms and beliefs; 3) Gender roles, responsibilities, and time; 4) Access to and control over assets and resources; and 5) Patterns of power and decision-making.

This seminar first aired live on April 21st, 2022.

Moderator: Vidhi Maniar, Data for Health, Johns Hopkins Bloomberg School of Public Health Speakers:

Dr. Rosemary Morgan, Associate Scientist, Johns Hopkins Bloomberg School of Public Health
Dr. Gayane Yenokyan, Senior Biostatistician, Johns Hopkins Bloomberg School of Public Health
Onikepe Owolabi, Program Director, Data Driven Policy Initiative to Improve Women’s Health, Vital Strategies

Seminar Synopsis: This seminar provides an overview of what gender analysis in health data means and methods to apply gender analyses to large health datasets. The focus is on the differences between sex disaggregation and gender analysis using gender equity indicators. Speakers provide examples of gender analyses with country and regional datasets from Africa to showcase how this type of analysis can positively affect the health and well-being of women and girls and non-binary people.

Methods for gender analysis described include use of proxy indicators, data triangulation, cross-examination, and cross-referencing of multiple data sources within a country or region. Comparison of data sources and gender equity indicators can complete a more representative picture of health and disease in a country or region. This type of gender analysis of population data provides critical context for the creation of gender-responsive public health policies and programs.

Focusing only on biological sex differences limits our understanding of the gender dimensions that continue to propel health inequities for men, women, and non-binary people.

TAGS:

AfricaData Analysis

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GENDER EQUITY UNIT