How to Ensure Inclusivity in Large-Scale General Population Cohort Studies? Lessons Learned with Regard to Including and Assessing Sex, Gender, and Sexual Orientation

Despite recent advances in the measurement of sex, gender, and sexual orientation in large-scale cohort studies, the three concepts are still gaining relatively little attention, may be mistakenly equated, or non-informatively operationalized. The resulting imprecise or lacking information hereon in studies is problematic, as sex, gender, and sexual orientation are important health-related factors. Omission of these concepts from general population cohort studies might dismiss participants' identity and experiences and pushes research on sexual or gender minority populations toward purposive sampling, potentially introducing selection bias. It also reinforces the unintentional notion of irrelevance of these concepts to health research, ultimately disadvantaging sexual and gender minority populations. Similarly, a lack of uniform measures on sex, gender, and sexual orientation hampers multi-cohort studies in which data from multiple studies are combined, facilitating increased statistical power. This paper discusses the encountered pitfalls and lessons learned on including and assessing sex, gender, and sexual orientation in large-scale general population cohort studies, exemplified by the Dutch Lifelines Cohort Study. Additionally, we propose hands-on strategies on how to operationalize these concepts in an inclusive manner that is useful for large-scale general population cohort studies.

Automated summary

Despite recent advances, sex, gender, and sexual orientation still receive little attention in large-scale cohort studies and may be mistakenly equated or non-informatively operationalized. This lack of attention and imprecise measurement hamper the understanding of health-related factors and disadvantage sexual and gender minority populations. Additionally, a lack of uniform measures hampers data integration in multi-cohort studies.