Mixed-Method, Multilevel Clustered-Randomized Control Trial for Menstrual Health Disparities.

Menstrual cycle characteristics are largely considered unmodifiable reproductive factors, a framing that prevents exploration of the ways structural factors interfere with menstrual health. Given the role of structural factors like healthy food and healthcare access on reproductive health and the grave need for structural interventions to known reproductive health disparities that disproportionately target cisgender women racialized as Black, it is imperative that science begin to examine how structural factors influence menstrual health. To explore such research, we employ critical race theory and intersectionality to illustrate what a structural intervention to improve menstrual cycle health could look like. Centering those with the greatest need, persons racialized as Black and/or LatinX living in food and healthcare deserts in Northern Manhattan, our illustrative sample includes four groups of persons who menstruate (e.g., cisgender girls and women) that are pre-menarche, pre-parous, postpartum, or perimenopausal. We describe a hypothetical, multilevel clustered-randomized control trial (cRCT) that provides psychoeducation on racism-related trauma and free delivered groceries to both treatment and control groups, while randomizing 30 clusters of housing associations to receive either sexual health clinics at their housing association or free vouchers for healthcare. We embed mixed methods (diaries, interviews, surveys, mobile apps, observation) into the design to evaluate the effectiveness of the 1-year intervention, in addition to determining the impact on participants through their perspectives. Through this illustration, we provide a novel example of how structural interventions can apply mixed methods to evaluate effectiveness while delivering services to populations impacted by multiple structural factors. We demonstrate how qualitative and quantitative approaches can be paired in clustered RCTs and how a living logic model can empirically incorporate the population perspective into more effective interventions. Lastly, we reveal how sensitive menstrual health is to structural factors and how upstream improvements will trickle down to potentially reduce health disparities in reproductive health.