Gender-based analysis when gender data are unavailable

• Amanda Doggett
• Anaïs Lacasse

For our October journal club session we will discuss a paper on conducting gender-based analysis of existing databases when self-reported gender data are unavailable.

Paper

Conducting gender-based analysis of existing databases when self-reported gender data are unavailable: the GENDER Index in a working population

Objectives

Growing attention has been given to considering sex and gender in health research. However, this remains a challenge in the context of retrospective studies where self-reported gender measures are often unavailable. This study aimed to create and validate a composite gender index using data from the Canadian Community Health Survey (CCHS).

Methods

According to scientific literature and expert opinion, the GENDER Index was built using several variables available in the CCHS and deemed to be gender-related (e.g., occupation, receiving child support, number of working hours). Among workers aged 18–50 years who had no missing data for our variables of interest (n = 29,470 participants), propensity scores were derived from a logistic regression model that included gender-related variables as covariates and where biological sex served as the dependent variable. Construct validity of propensity scores (GENDER Index scores) were then examined.

Results

When looking at the distribution of the GENDER Index scores in males and females, they appeared related but partly independent. Differences in the proportion of females appeared between groups categorized according to the GENDER Index scores tertiles (p < 0.0001). Construct validity was also examined through associations between the GENDER Index scores and gender-related variables identified a priori such as choosing/avoiding certain foods because of weight concerns (p < 0.0001), caring for children as the most important thing contributing to stress (p = 0.0309), and ability to handle unexpected/difficult problems (p = 0.0375).

Conclusion

The GENDER Index could be useful to enhance the capacity of researchers using CCHS data to conduct gender-based analysis among populations of workers.