Prevalence of Psychosocial Distress in Transmen Seeking Gender-Affirming Mastectomy

Abstract

Background: Although the benefits of gender-affirming surgery may be apparent to patients and providers, there remains a paucity of studies assessing the impact of these procedures. As an initial step, preoperative patient-reported outcomes using validated measures of depression, anxiety, and body image were used and compared to cisgender normative data. Methods: Patients presenting for gender-affirming mastectomy were approached and surveyed using validated instruments measuring anxiety, depression, and body image. In addition, clinical data were collected from the medical record. Results were compared to published instrument norms in the general cisgender population. Results: One hundred three patients completed the preoperative assessment; 70.3 percent and 66.3 percent of the cohort screened positive for mild to severe depression and anxiety, respectively. Only 25 percent and 29.8 percent of the cohort, respectively, had a previous diagnosis of depression and anxiety. The rates of depression and anxiety were significantly higher than those in cisgender normative data [mean Patient Health Questionnaire score, 2.7 (p < 0.0001); mean Generalized Anxiety Disorder Scale 7 score, 2.66 (p < 0.0001)]. Body Image Quality of Life Index and BREAST-Q scores were also significantly lower than those in cisgender normative data. Conclusions: Patients seeking gender-affirming mastectomy have a significant mental health burden that appears to be underdiagnosed. They further have significant challenges with body image compared with cisgender normative data. These findings signify dramatic mental health disparities in the preoperative transgender population and the need for ongoing prospective research of gender-affirming surgery.

Gender Affirming Mastectomy Improves Quality of Life in Transmasculine Patients

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Gender‐affirming hormone therapy: An updated literature review with an eye on the future

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Predicting Patient Demographics From Chest Radiographs With Deep Learning

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