Mapping the gender gradient in posttraumatic stress disorder prevalence: A machine learning approach.

Abstract

The prevalence of posttraumatic stress disorder (PTSD) among women is over twice that of men, but the underlying mechanisms for these differences remain poorly understood. This study introduces a novel approach to examining gender and PTSD, moving beyond the binary group labels of male and female to explore the summative impact of gender-linked sociocultural factors. Using supervised machine learning, we modeled gender from theoretical and empirically selected predictors reflecting the roles, relationships, and institutional facets of gender. This model produced continuous gender scores reflecting the social circumstances typical of male (lower scores) or female (higher scores) individuals. We then examined how well these scores were associated with past-year PTSD among trauma-exposed men and women (N = 23,936) and compared effects to those obtained using binary sex. The results revealed a clear dose-response relationship between the social circumstances typical of female gender and past-year PTSD. Main effects for gender scores, adjusted odds ratio (aOR) = 4.03, 95% CI [2.64, 6.15], were substantially larger than main effects for binary sex, aOR = 2.69, 95% CI [1.96, 3.68], z = 2.30, p = .021, even after accounting for trauma exposure and other risk factors. This study highlights the importance of quantitative approaches that move beyond binary comparisons of male and female to better elucidate sociocultural determinants of traumatic stress.