Systematic review of methods used to study the intersecting impact of sex and social locations on health outcomes
Purpose Independent health impacts of sex or social circumstances are well studied, particularly among older adults. Less theorized or examined is how combinations or intersections of these underpin differential health effects. Nevertheless, and often without naming it as such, an intersectional framework aligns with studies of social determinants of health, life course epidemiology and eco epidemiology. In this systematic review we examined and aimed to identify research methods used to operationalize, whether intentionally or inadvertently, interconnected effects of sex and social locations on health outcomes for 45+ year olds. Methods Using broad search terms, numerous databases, and following Prisma guidelines, 732 of 9214 papers initially identified, met inclusion criteria for full review. Results Of the 501 papers included after full review, methods used in considering intersections of sex and social circumstances/location(s) included regression (112 of 365 papers) growth curves (7 of 22) multilevel (15 of 25) decomposition (6 of 9) mediation (10 of 17) structural equation modelling (23 of 25) and other (2 of 3) Most (n 157) approximated intersectional analyses by including interaction terms or sex stratifying results. Discussion Few authors used the inherent strength of some study methods to examine intersecting traits. As even fewer began with an intersectionality framework their subsequent failure to deliver cannot be faulted, despite many studies including data and methodologies that would support intersectional analyses. There appeared to be a gap, not in analytic potential but rather in theorizing that differential distributions of social locations describe heterogeneity within the categories 'men' and 'women' that can underlie differential, gendered effects on older adults' health. While SEM, mediation and decomposition analyses emerged as particularly robust methods, the unexpected outcome was finding how few researchers consider intersectionality as a potential predictor of health.
SSM population health