Gender-related variables for health research

Abstract

Background: In this paper, we argue for Gender as a Sociocultural Variable (GASV) as a complement to Sex as a Biological Variable (SABV). Sex (biology) and gender (sociocultural behaviors and attitudes) interact to influence health and disease processes across the lifespan-which is currently playing out in the COVID-19 pandemic. This study develops a gender assessment tool-the Stanford Gender-Related Variables for Health Research-for use in clinical and population research, including large-scale health surveys involving diverse Western populations. While analyzing sex as a biological variable is widely mandated, gender as a sociocultural variable is not, largely because the field lacks quantitative tools for analyzing the influence of gender on health outcomes.

Methods: We conducted a comprehensive review of English-language measures of gender from 1975 to 2015 to identify variables across three domains: gender norms, gender-related traits, and gender relations. This yielded 11 variables tested with 44 items in three US cross-sectional survey populations: two internet-based (N = 2051; N = 2135) and a patient-research registry (N = 489), conducted between May 2017 and January 2018.

Results: Exploratory and confirmatory factor analyses reduced 11 constructs to 7 gender-related variables: caregiver strain, work strain, independence, risk-taking, emotional intelligence, social support, and discrimination. Regression analyses, adjusted for age, ethnicity, income, education, sex assigned at birth, and self-reported gender identity, identified associations between these gender-related variables and self-rated general health, physical and mental health, and health-risk behaviors.

Conclusion: Our new instrument represents an important step toward developing more comprehensive and precise survey-based measures of gender in relation to health. Our questionnaire is designed to shed light on how specific gender-related behaviors and attitudes contribute to health and disease processes, irrespective of-or in addition to-biological sex and self-reported gender identity. Use of these gender-related variables in experimental studies, such as clinical trials, may also help us understand if gender factors play an important role as treatment-effect modifiers and would thus need to be further considered in treatment decision-making.

Keywords: Biomedical outcomes; Gender measures; Sex differences.

Fig. 1

Gender-related variables capturing specific behaviors and attitudes. The figure displays the z-scores for the seven gender-related variables for respondents seeing themselves as men (green), women (orange) and gender fluid/Non-binary (grey) in sample 1 (N = 1893)

Fig. 2

Adjusted incidence rate ratios of associations with recent physical health, mental health, and activity limitations. This figure displays the outcomes of the negative binomial regressions predicting health outcomes in the combined sample (sample 1 + sample 2) (Physical health, N = 3879; mental health, N = 3880; activity limitations, N = 3876). Error bars represent 99.9% confidence intervals. See Tables S37-S39 for model specifications

Fig. 3

Adjusted odds ratios of associations with health status, smoking, vaping, binge drinking, and BMI. This figure displays the outcomes of the binary logistic regressions predicting health outcomes in the combined sample (sample 1 + sample 2) (health status, N = 3,894; smoking, N = 3,892 vaping, N = 3,891; binge drinking, N = 3,887; BMI, N = 3,851. Error bars represent 99.9% confidence intervals. See Tables S40-S44 for model specifications