Development and Assessment of a Patient-Reported Outcome Instrument for Gender-Affirming Care

Importance: There is an urgent need for a validated gender-affirming care-specific patient-reported outcome measure (PROM). Objective: To field test the GENDER-Q, a new PROM for gender-affirming care, in a large, international sample of transgender and gender diverse (TGD) adults and evaluate its psychometric properties. Design, Setting, and Participants: This international cross-sectional study was conducted among TGD adults aged 18 years and older who were seeking or had received gender-affirming care within the past 5 years at 21 clinical sites across Canada, the United States, the Netherlands, and Spain; participants were also recruited through community groups (eg, crowdsourcing platform, social media). The study was conducted between February 2022 and March 2024. Participants had to be capable of completing the instrument in English, Danish, Dutch, or French-Canadian. Eligible participants accessed an online REDCap survey to complete sociodemographic questions and questions about gender-affirming care they had received or sought (ie, to look, function, or feel masculine, feminine, gender fluid, or another way). Main Outcome and Measures: Branching logic was used to assign relevant instrument scales. Rasch measurement theory (RMT) analysis was used to examine the fit of the observed data to the Rasch model for each scale. Test-retest reliability and hypothesis-based construct validity of instrument scales were examined. The hypothesis was that instrument scale scores would increase with better outcomes on corresponding categorical questions. Results: A total of 5497 participants (mean [SD] age, 32.8 [12.3] years; 1837 [33.4%] men; 1307 [23.8%] nonbinary individuals; and 2036 [37.0%] women) completed the field test survey. Participants sought or had the following types of gender-affirming care: 2674 (48.6%) masculinizing, 2271 (41.3%) femininizing, and 552 (10.0%) other. RMT analysis led to the development of 54 unidimensional scales and 2 checklists covering domains of health-related quality of life, sexual, urination, gender practices, voice, hair, face and neck, body, breasts, genital feminization, chest, genital masculinization, and experience of care. Test-retest reliability of the scales (intraclass correlation coefficient [average] &gt;0.70) was demonstrated. Only 1 item (phalloplasty donor flap) had an ICC less than 0.70. As hypothesized, scores increased incrementally with better associated self-reported categorical responses. For example, among 661 participants who reported poor psychological well-being, the mean (SD) scale score was 45 (18) points; for those who reported excellent psychological well-being, the mean (SD) scale score was 85 (16) points (P &lt;.001). Conclusions and Relevance: In this cross-sectional study of 5497 TGD adults, the instrument demonstrated reliability and validity. The instrument was validated in an international sample and is designed to collect and compare evidence-based outcome data for gender-affirming care from the patients' perspective.</p

Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder

\ua9 2015 The Authors. This is an open access article under the CC BY-NC-ND license. Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk

Joint Analysis Of Psychiatric Disorders Increases Accuracy Of Risk Prediction For Schizophrenia, Bipolar Disorder, And Major Depressive Disorder

Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk