Sex and Gender in Health Outcomes

Overview

Sex and gender are often erroneously used interchangeably in health research, yet both independently influence disease risk, symptom presentation, treatment response, and health outcomes. This research program—part of the GOING-FWD consortium (Gender Outcomes INternational Group: to Further Well-being Development)—develops methodologies and generates evidence on how sex and gender shape health across cardiovascular, metabolic, renal, and neurodegenerative diseases in Austria, Canada, Spain, and Sweden (Raparelli et al., 2021).

Key Contributions

• GOING-FWD Framework: Developed a standardized 5-step methodology to retrospectively identify and analyze gender-related factors (identity, roles, relations, institutionalized gender) in existing health datasets
• Gender-disease interactions: Demonstrated that socioeconomic gender variables (education, marital status, employment, household size) differentially impact chronic disease associations between males and females
• Cross-country analysis: Enabled privacy-preserving international comparisons using federated analysis and synthetic data generation
• Cardiovascular health metrics: Assessed country-level differences in cardiovascular health using modified CANHEART indices, revealing distinct sex-gender patterns across populations

Methodology

The research integrates multiple complementary approaches:

1. Gender Variable Identification: Compile “wish lists” of gender-related factors based on the Women Health Research Network framework covering identity, roles, relations, and institutionalized gender

2. Data Harmonization: Align variables across international datasets using Maelström research guidelines for retrospective harmonization

3. Privacy-Preserving Analysis: Apply federated analysis (DataSHIELD) and synthetic data generation to enable cross-jurisdictional pooling while respecting GDPR and local privacy regulations (Lindner & others, 2023)

4. Statistical Modeling: Use multivariate regression with interaction terms to quantify how gender variables modify disease associations differently for males and females

Results

Gender factors—independent of biological sex—emerge as powerful predictors of health outcomes. In a cross-sectional analysis of 74,748 people with hypertension from the European Health Interview Survey across 30 countries (Lindner & others, 2024), we identified 47 significant sex differences in how socioeconomic variables relate to comorbidities including myocardial infarction, arthrosis, renal disease, and depression. Key findings:

• Married women with hypertension face 30% higher odds of the condition compared to single women; in men, only the loss of a partner (widowed/divorced) is associated with elevated risk
• Employment relates to cardiac risk differently by sex and country-level gender inequality—consistent with a double-burden hypothesis for women
• Higher education paradoxically increases arthrosis odds in hypertensive women but not men
• All effects are amplified in countries with high Gender Inequality Index, pointing to structural context rather than individual biology as a key driver

These effects vary by country-level gender inequality, underscoring that gender is a social—not just biological—determinant of health. The interactive network below visualizes the full pattern of associations. Full context in the accompanying blog post.

The interactive bipartite network connects socioeconomic gender factors (left) to hypertension comorbidities (right). Color encodes the group with the significant association (blue = male, orange = female, pink = both), line style encodes direction (solid = risk, dashed = protective), and amber highlights mark associations where the odds ratio ratio between sexes was itself significant. Hover over any node to see exact odds ratios.

Technologies

• R / Python
• DataSHIELD (Federated Analysis)
• Synthetic Data Generation
• European Health Interview Survey
• Machine Learning