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

Abstract

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 genderbased analysis among populations of workers. Résumé Objectifs Dans le domaine de la recherche en santé, une attention grandissante est portée à l'importance de tenir compte à la fois du sexe et du genre. Toutefois, ceci représente un défi quand on travaille avec des données existantes qui ne contiennent pas toujours de mesure autoraportée de genre. L'objectif de cette étude était donc de développer et valider un indice composite de genre grâce aux données de l'Enquête sur la santé dans les collectivités canadiennes (ESCC).

Gender score development in the Berlin Aging Study II: a retrospective approach

“…In the present paper, we therefore searched to develop a method to operationalize gender in the context of clinical studies by developing a gender score, following previously published concepts [ 3 , 17 , 18 ].…”

“…Since most study databases include a large number of psychosocial and/or socioeconomic variables that are potentially gender related, it is possible to construct a score to measure gender. Two recent studies have done this [ 17 , 18 ]. In one study, a large population-based database was used, the Canadian Labour Force survey, to extract the most suitable variables related to the gender dimensions described above and to derive a gender score from these variables [ 18 ].…”

“…In one study, a large population-based database was used, the Canadian Labour Force survey, to extract the most suitable variables related to the gender dimensions described above and to derive a gender score from these variables [ 18 ]. Most variables available in this dataset were related to work and living conditions, with a focus on gender roles and institutionalized gender [ 17 , 18 ].…”

Systematic review of methods used to study the intersecting impact of sex and social locations on health outcomes

“…Increasingly, there have been calls from funders and journals for research to account for sex and/or gender. This often translates into categorizing study participants by sex ( CIHR, 2012 ; Lacasse et al, 2020 ; Tannenbaum et al, 2016 ). Such a binary division of study populations is, however, generally of limited explanatory value.…”

Gender-based analysis using existing public health datasets

“…Finally, capacity must be built for interpreting gender-based analyses in a way that thoughtfully and concretely informs policies and programs tailored to gender. Lacasse et al (2020) tackle the challenge of developing methods for analyzing gender in datasets that a priori do not collect data on gender. The authors scoured three iterations of the Canadian Community Health Survey (CCHS) for variables that might serve as proxy representations of gender and be included in a gender index.…”