Race, Ethnicity, and Complications Following Gender-Affirming Surgery: A National Surgical Quality Improvement Program (NSQIP) Analysis

Yu Jui Kung; Bashar Hassan; Calvin Schuster; Claudia Taccheri; Essie Ghafoor; Mona Ascha; Fan Liang

doi:10.7759/cureus.96699

. 2025 Nov 12;17(11):e96699. doi: 10.7759/cureus.96699

Race, Ethnicity, and Complications Following Gender-Affirming Surgery: A National Surgical Quality Improvement Program (NSQIP) Analysis

Yu Jui Kung ¹, Bashar Hassan ¹, Calvin Schuster ¹, Claudia Taccheri ¹, Essie Ghafoor ¹, Mona Ascha ², Fan Liang ^1,^✉

Editors: Alexander Muacevic, John R Adler

PMCID: PMC12701913 PMID: 41399564

Abstract

Background: Transgender and non-binary (TGNB) individuals face disproportionately limited access to gender-affirming surgery (GAS) due to systemic inequities. This study examines how these inequities, such as race and ethnicity, impact the utilization of GAS and associated surgical complications across different GAS subtypes.

Methods: Using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database, we retrospectively reviewed TGNB individuals who underwent GAS from 2012 to 2021. The primary outcome was the incidence of major complications (e.g., unplanned reoperation and readmission) and minor complications (e.g., wound complications) within 30 days postoperatively. Bivariate and multivariable tests were used to compare the frequency of GAS and complications across racial and ethnic groups.

Results: Among n = 6013 TGNB individuals, most were White (n = 3994, 66.4%), followed by Black (n = 880, 14.6%) and Hispanic (n = 788, 13.1%). Chest masculinization surgery was the most common procedure (n = 2395, 39.8%), followed by genital masculinization surgery (n = 1298, 21.6%), genital feminization surgery (n = 958, 15.9%), chest feminization surgery (n = 843, 14%), and facial feminization surgery (n = 396, 6.6%). White individuals were significantly more likely to undergo chest and genital masculinization surgery than Hispanic and Black individuals. Black individuals undergoing chest masculinization surgery had a higher frequency of unplanned reoperation (n = 14, 4.5%; n = 28, 1.7%; p = 0.042) and readmission (n = 7, 2.3%; n = 8, 0.5%; p = 0.048) compared with White individuals. For genital feminization surgery, Hispanic and Black individuals experienced significantly higher frequencies of wound disruption than White individuals (n = 12, 9.8%; n = 12, 8.7%; n = 21, 3.3%; p = 0.006). No significant disparities were observed for chest feminization, genital masculinization, or facial feminization surgeries.

Conclusion: Racial and ethnic disparities exist in surgical utilization and complications among TGNB individuals, highlighting the need for additional research and possibly targeted interventions to address these inequities.

Keywords: gender-affirming surgery, national surgical quality improvement program (nsqip), postoperative complications, racial and ethnic disparities, transgender and non-binary

Introduction

According to the Centers for Disease Control and Prevention (CDC)’s Behavioral Risk Factor Surveillance System (BRFSS) and Youth Risk Behavior Survey (YRBS), over 1.6 million individuals, ages 13 and above, identify as transgender and non-binary (TGNB) [1]. This community faces significant challenges associated with gender dysphoria, including heightened rates of mental health conditions such as anxiety, depression, and suicidality-conditions often exacerbated by societal stigma, discrimination, and systemic inequities [2]. For many TGNB individuals, gender-affirming care (GAC) helps address the incongruence between their gender identity and physical characteristics, which can alleviate gender dysphoria and improve mental health outcomes [2]. GAC includes hormone replacement therapy (HRT) and gender-affirming surgeries (GAS) such as chest surgery, genital surgery, head and neck surgery, and body contouring. For those who pursue it, GAC is associated with significantly reduced rates of depression, suicidality, and gender dysphoria, as well as enhanced quality of life [2].

Despite its benefits, access to GAC remains limited, particularly for racial and ethnically marginalized TGNB individuals. Barriers such as financial constraints, gaps in insurance coverage, provider shortages, and pervasive discrimination within healthcare settings disproportionately impact these individuals, compounding the challenges faced by the broader TGNB community [3,4]. The intersectionality of being TGNB and a member of a racially or ethnically marginalized group magnifies the effects of structural racism, socioeconomic disadvantages, and medical discrimination, creating additional barriers to care [5].

Racial and ethnic disparities in surgical complications are well-documented across various medical fields, including oncologic, orthopedic, and cardiovascular surgeries. Black and Hispanic individuals consistently experience higher rates of complications, prolonged hospital stays, and unplanned reoperations, even after controlling for access and socioeconomic factors [6-9]. Although research has begun to examine disparities in TGNB populations undergoing GAS, these studies often do not delve into the nuances of how such disparities manifest in surgical complications across specific surgical subtypes to comprehensively evaluate the compounded barriers faced by these individuals [10,11]. Existing studies on GAS complications have primarily grouped procedures into broad categories, such as masculinizing or feminizing, without accounting for the wide variability in surgical techniques, risks, and complications associated with specific procedures (e.g., chest, genital, and facial surgeries) [10,11].

To address these gaps, this study leverages the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database to examine racial and ethnic disparities in the utilization of GAS and associated surgical complications, with a focus on stratifying specific surgical subtypes to gain a more detailed understanding of these inequities. This represents the first comprehensive analysis of GAS stratified by surgical subtype. The observed associations highlight emerging trends that may guide future investigations aimed at understanding and mitigating these disparities. The objective of this study is to evaluate racial and ethnic disparities in the utilization and 30-day postoperative complications of GAS among TGNB individuals, stratified by surgical subtype, to identify procedure-specific inequities that may inform efforts to advance equitable surgical care.

Materials and methods

Population source

We queried the ACS-NSQIP database from 2012 to 2021. The ACS-NSQIP collects risk-adjusted, prospectively gathered data on patient demographics, comorbidities, operative characteristics, and 30-day postoperative outcomes to improve the quality of surgical care [12]. Trained surgical clinical reviewers at more than 750 voluntarily participating hospitals, including critical access, community, and large academic teaching hospitals across the United States, Canada, and 11 other countries, collect these data [12]. This study utilized de-identified patient data with an exempt approval obtained from the Johns Hopkins Institutional Review Board (IRB).

Study population

TGNB individuals within the ACS-NSQIP database were first identified using International Classification of Diseases (ICD)-9 and ICD-10 codes for gender dysphoria. These individuals were analyzed overall and across six subtypes of gender-affirming procedures: chest surgery in general, chest masculinization surgery, chest feminization surgery, genital masculinization surgery, genital feminization surgery, and facial feminization surgery. Patient selection and stratification were based on ICD and current procedural terminology (CPT) codes detailed in Table 1.

Table 1. ICD-9, ICD-10, and CPT codes used for patient selection and stratification.

ICD: International Classification of Diseases; CPT: current procedural terminology; CMS: chest masculinization surgery; CFS: chest feminization surgery

ICD and CPT codes were selected by the study team based on clinical relevance and coding guidelines [13]

Code category	ICD-9, ICD-10, and CPT codes
ICD-9 codes for gender dysphoria	302.50, 302.51, 302.52, 302.53, 302.6, 302.85
ICD-10 codes for gender dysphoria	F64.0, F64.1, F64.2, F64.8, F64.9
CPT codes for CMS	15200, 15201, 19300, 19301, 19303, 19304, 19318, 19350
CPT codes for CFS	19324, 19325, 19340, 19342, 19366, 19370, 19371
CPT codes for genital masculinization surgery	15374, 15378, 15757, 15758, 53410, 53430, 53520, 55980, 56620, 57106, 57107, 57110, 57111, 58150, 58260, 58262, 58263, 58270, 58290, 58542, 58552, 58571, 58572, 58573, 58661
CPT codes for genital feminization surgery	53415, 54125, 54520, 54522, 54530, 55970, 56805, 57291, 57292, 57335
CPT codes for facial feminization surgery	21120, 21121, 21137, 21138, 21139, 21172, 21175, 21179, 21180, 21193, 21198, 21206, 21208, 21209, 21256, 31599, 31750, 31899

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Individuals were categorized into six racial and ethnic categories per the U.S. Office of Management and Budget (OMB) 1997 standards: Hispanic, Non-Hispanic American Indian and Alaska Native, Non-Hispanic Asian, Non-Hispanic Black or African American, Non-Hispanic Native Hawaiian and Other Pacific Islander, and Non-Hispanic White [14]. The words “Hispanic” and “Latinx” were both used to refer to the same ethnic group. For the purposes of this study, American Indian or Alaska Native refers to Non-Hispanic American Indian and Alaska Native, Asian refers to Non-Hispanic Asian, Black refers to Non-Hispanic Black or African American, Native Hawaiian and Other Pacific Islander refers to Non-Hispanic Native Hawaiian and Other Pacific Islander, and White refers to Non-Hispanic White. Race and ethnicity classifications in the ACS-NSQIP are derived from administrative and clinical documentation at participating hospitals.

Covariates

Data on patient demographics, comorbidities, and surgical characteristics were collected, including age, body mass index (BMI), total operating time, hypertension, diabetes mellitus, smoking history within one year of surgery, current steroid use for chronic condition, preoperative dialysis, preoperative dyspnea, history of chronic obstructive pulmonary disease, history of a bleeding disorder, transfusion of one or more units of packed red blood cells within 72 hours preoperatively, >10% body weight loss within six months preoperatively, functional health status, surgical specialty, elective versus non-elective surgery, wound classification, and American Society of Anesthesiologists (ASA) Classification.

Outcomes

Our primary outcome was the incidence of major and minor complications within 30 days postoperatively. Major complications included cerebrovascular accident (CVA)/stroke, myocardial infarction (MI), cardiac arrest requiring cardiopulmonary resuscitation (CPR), sepsis, septic shock, pulmonary embolism (PE), deep venous thrombosis (DVT), acute renal failure, bleeding requiring transfusion, unplanned reoperation, unplanned readmission, unplanned intubation, ventilator-assisted respirations for >48 hours, and prolonged hospital stay (>30 days). Minor complications included pneumonia, wound disruption, surgical site infection (SSI), urinary tract infection (UTI), and Clostridioides difficile (C. diff) infection. Variable definitions followed ACS-NSQIP standards [15].

Statistical analysis

Patient demographics, comorbidities, surgical characteristics, major, and minor complications were compared across racial and ethnic groups using bivariate analysis. Descriptive statistics were calculated for the overall cohort and for each racial and ethnic group. Normally distributed continuous variables were presented as mean ± standard deviation (SD) and compared using the analysis of variance (ANOVA) test. Non-normally distributed continuous variables were presented as median (interquartile range, IQR) and compared using the Kruskal-Wallis test. Categorical variables were summarized as counts and percentages and compared using the chi-square or Fisher’s exact test as appropriate.

Pairwise comparisons among racial and ethnic groups were analyzed using Bonferroni post hoc analysis. Covariates with a p-value of <0.2 in bivariate analysis were included in multivariable logistic regression models to examine the potential association between race/ethnicity and the odds of complications. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) quantified the strength of these associations. A two-sided significance level was set at p < 0.05. All analyses were performed using IBM SPSS Statistics for Windows, Version 29 (Released 2022; IBM Corp., Armonk, New York, United States) [16].

Results

Between 2012 and 2021, the ACS-NSQIP database identified 7508 individuals with gender dysphoria who underwent GAS. Those with unknown ethnicity (n = 1111, 14.79%) or unknown race (n = 384, 5.11%) were excluded, resulting in a final cohort of 6013 TGNB individuals eligible for analysis. Surgical outcomes were stratified by specific surgical subtypes and racial/ethnic categories to investigate disparities in surgical utilization and postoperative complications.

Patient demographics and surgical characteristics

Table 2 summarizes the frequency of GAS procedures by racial and ethnic groups. White individuals accounted for the majority (n = 3994, 66.4%) of the cohort, followed by Black individuals (n = 880, 14.6%) and the other groups. Chest masculinization surgery was the most commonly performed procedure (n = 2395, 39.8%), followed by genital masculinization (n = 1298, 21.6%) and feminization (n = 958, 15.9%) surgeries, chest feminization surgery (n = 843, 14%), and facial feminization surgery (n = 396, 6.6%). White individuals were significantly more likely to undergo chest masculinization surgery compared with Hispanic and Black individuals (n = 1661, 41.6%; n = 276, 35.0%; n = 311, 35.3%; p <0.001). The same pattern was observed for genital masculinization surgery (n = 980, 24.5%; n = 109, 13.8%; n = 150, 17.0%; p < 0.001). However, chest feminization surgery utilization was disproportionately lower among White individuals compared with Hispanic and Black individuals (n = 399, 10.0%; n = 195, 24.7%; n = 195, 22.2%; p < 0.001). Native Hawaiian or Pacific Islander individuals were more likely to undergo facial feminization surgery compared to Hispanic (n = 6, 28.6%; n = 71, 9.0%; p < 0.001) and White individuals (n = 6, 28.6%; n = 237, 5.9%; p < 0.001).

Table 2. Subtypes of gender-affirming surgeries stratified by racial and ethnic groups.

Frequency data are reported as No. (%). P-values were calculated using Fisher’s exact test due to low expected cell counts across groups. Racial and ethnic categories that do not share the same subscript letter differ significantly at p < .05

Type of gender-affirming surgery	Hispanic (n = 788)	Non-Hispanic American Indian or Alaska Native (n = 38)	Non-Hispanic Asian (n = 292)	Non-Hispanic Black or African American (n = 880)	Non-Hispanic Native Hawaiian or Pacific Islander (n = 21)	Non-Hispanic White (n = 3994)	Total (n = 6013)
Chest masculinization surgery	276 (35.0)_a	14 (36.8)_a,b	124 (42.5)_a,b	311 (35.3)_a	9 (42.9)_a,b	1661 (41.6)_b	2395 (39.8)
Chest feminization surgery	195 (24.7)_a	6 (15.8)_a,b,c	46 (15.8)_c	195 (22.2)_a,c	2 (9.5)_a,b,c	399 (10.0)_b	843 (14.0)
Genital masculinization surgery	109 (13.8)_a	9 (23.7)_a,b	49 (16.8)_a	150 (17.0)_a	1 (4.8)_a,b	980 (24.5)_b	1298 (21.6)
Genital feminization surgery	123 (15.6)_a	8 (21.1)_a	45 (15.4)_a	138 (15.7)_a	3 (14.3)_a	641 (16.0)_a	958 (15.9)
Facial feminization surgery	71 (9.0)_a	1 (2.6)_a,b	22 (7.5)_a,b	59 (6.7)_a,b	6 (28.6)_c	237 (5.9)_b	396 (6.6)
Other/unclear	14 (1.8)_a	0 (0.0)_a	6 (2.1)_a	27 (3.1)_a	0 (0.0)_a	76 (1.9)_a	123 (2.0)

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Table 3 compares demographics, comorbidities, and surgical characteristics across racial and ethnic groups. Black and White individuals who underwent GAS were significantly older than Asian individuals (median (IQR) age: 30 (25-36), 28 (23-36), 27 (23-34) years; p = 0.002). Black individuals had significantly greater BMI at surgery compared with White and Hispanic individuals, who had significantly greater BMI compared with Asian individuals (median (IQR) BMI: 28.2 (24.1-33.1), 26.7 (23.2-31.9), 27.2 (23.6-31.4), 24.4 (21.7-27.9) kg/m2; p < 0.001). Hypertension was most prevalent among Black individuals compared with Hispanic, White, and Asian individuals (n = 87, 9.9%; n = 37, 4.7%; n = 278, 7.0%; n = 8, 2.7%; p < 0.001). Additionally, Black individuals were significantly more likely to be smokers compared with Hispanic, White, and Asian individuals (n = 176, 20%; n = 106, 13.5%; n = 479, 12.0%; n = 31, 10.6%; p < 0.001). Finally, ASA classifications were significantly higher among Black and White individuals compared with Asian individuals (p < 0.001). A similar comparative analysis of demographics, comorbidities, and surgical characteristics for individual GAS procedures is provided in Tables 4-8.

Table 3. Demographics, comorbidities, and surgical characteristics of patients who underwent any gender-affirming surgery across racial and ethnic groups.

IQR: interquartile range; BMI: body mass index; COPD: chronic obstructive pulmonary disorder; PRBC: packed red blood cells; ASA: American Society of Anesthesiologists

Frequency data are reported as No. (%). Likelihood ratio chi-square test in logistic regression for surgical specialty: χ²(6) = 55.50. If chi-square test statistics are not provided, p-values were calculated using Fisher’s exact test due to low event counts. Racial and ethnic categories that do not share the same subscript letter “'a', b', 'c', etc.” differ significantly at p < .05

Racial and ethnic categories		Hispanic (n = 788)	Non-Hispanic American Indian or Alaska Native (n = 38)	Non-Hispanic Asian (n = 292)	Non-Hispanic Black or African American (n = 880)	Non-Hispanic Native Hawaiian or Pacific Islander (n = 21)	Non-Hispanic White (n = 3994)	P
Age, median (IQR), years		29 (24.0-37.0)_ab	30.5 (23.0-37.0)_ab	27 (23.0-33.8)_a	30 (25.0-36.0)_b	25 (21.0-33.5)_ab	28 (23.0-36.0)_b	.002
BMI, median (IQR), kg/m²		27.2 (23.6-31.4)_a	27.1 (23.4-31.3)_abc	24.4 (21.7-27.9)_b	28.2 (24.1-33.1)_c	27.3 (23.7-31.2)_abc	26.7 (23.2-31.9)_a	< .001
Operating time, median (IQR), minutes		139 (90.3-212.0)_a	106 (74.5-194.8)_a	138.5 (90.0-197.8)_a	130 (88.0-201.0)_a	107 (83.0-158.0)_a	128 (88.0-190.0)_a	.209
Surgical specialty	General surgery	26 (3.3)_a	0 (0.0)_a	10 (3.4)_a	43 (4.9)_a	0 (0.0)_a	144 (3.6)_a	< .001
	Gynecology	82 (10.4)_a	8 (21.1)_ab	34 (11.6)_a	97 (11.0)_a	0 (0.0)_ab	811 (20.3)_ab
	Obstetric surgery	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	3 (0.1)_a
	Orthopedic surgery	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	2 (0.1)_a
	Otolaryngology (ENT)	54 (6.9)_a	1 (2.6)_abcd	21 (7.2)_ad	21 (2.4)_c	5 (23.8)_bd	181 (4.5)_ac
	Plastic surgery	590 (74.9)_a	24 (63.2)_ab	210 (71.9)_ab	655 (74.4)_a	13 (61.9)_ab	2549 (63.8)_b
	Urology	36 (4.6)_a	5 (13.2)_ab	17 (5.8)_ab	64 (7.3)_ab	3 (14.3)_ab	304 (7.6)_b
Elective surgery		785 (99.6)_a	38 (100.0)_a	292 (100.0)_a	880 (100.0)_a	21 (100.0)_a	3983 (99.7)_a	.404
Hypertension requiring medication		37 (4.7)_a	3 (7.9)_ab	8 (2.7)_a	87 (9.9)_b	1 (4.8)_ab	278 (7.0)_a	< .001
Diabetes	On insulin	9 (1.1)_a	1 (2.6)_a	2 (0.7)_a	5 (0.6)_a	0 (0.0)_a	48 (1.2)_a	.689
Diabetes	On oral agents	11 (1.4)_a	1 (2.6)_a	4 (1.4)_a	17 (1.9)_a	0 (0.0)_a	76 (1.9)_a	.689
Dyspnea	Moderate exertion	216 (27.4)_a	8 (21.1)_a	79 (27.1)_a	213 (24.2)_a	10 (47.6)_a	1075 (26.9)_a	.316
Dyspnea	At rest	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	3 (0.1)_a	.316
Current smoker within 1 year prior to surgery		106 (13.5)_a	5 (13.2)_ab	31 (10.6)_a	176 (20.0)_b	4 (19.0)_ab	479 (12.0)_a	< .001
Functional health status prior to surgery		786 (99.7)_a	38 (100.0)_a	292 (100.0)_a	878 (99.8)_a	21 (100.0)_a	3977 (99.6)_a	.815
History of severe COPD		1 (0.1)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	12 (0.3)_a	.469
Preoperative dialysis		1 (0.1)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	.189
Steroid use for chronic condition		3 (0.4)_a	0 (0.0)_a	1 (0.3)_a	6 (0.7)_a	0 (0.0)_a	56 (1.4)_a	.070
>10% body weight loss within 6 months prior to surgery		216 (27.4)_a	8 (21.1)_a	78 (26.7)_a	214 (24.3)_a	10 (47.6)_a	1069 (26.8)_a	.173
Bleeding disorder		2 (0.3)_ab	1 (2.6)_b	2 (0.7)_ab	1 (0.1)_a	0 (0.0)_ab	29 (0.7)_ab	.061
Transfusion >/= 1 units of PRBCs in 72 Hours prior to surgery		0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.1)_a	0 (0.0)_a	0 (0.0)_a	.336
Wound Classification	Clean	607 (77.0)_a	26 (68.4)_ab	229 (78.4)_ab	684 (77.7)_a	18 (85.7)_ab	2871 (71.9)_b	< .001
	Clean/contaminated	177 (22.5)_a	12 (31.6)_ab	63 (21.6)_ab	193 (21.9)_a	3 (14.3)_ab	1114 (27.9)_b
	Contaminated	4 (0.5)_a	0 (0.0)_a	0 (0.0)_a	2 (0.2)_a	0 (0.0)_a	8 (0.2)_a
	Dirty/infected	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.1)_a	0 (0.0)_a	1 (0.0)_a
ASA Classification	I	189 (24.0)_a	10 (26.3)_ab	113 (38.7)_b	200 (22.7)_a	7 (33.3)_ab	1067 (26.7)_a	< .001
	II	554 (70.3)_a	26 (68.4)_ab	172 (58.9)_b	602 (68.4)_a	13 (61.9)_ab	2607 (65.3)_ab
	III	42 (5.3)_ab	2 (5.3)_ab	7 (2.4)_b	78 (8.9)_a	1 (4.8)_ab	317 (7.9)_a
	IV	2 (0.3)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	3 (0.1)_a
	None assigned	1 (0.1)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a

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Table 4. Demographics, comorbidities, and surgical characteristics of patients who underwent chest masculinization surgery across racial and ethnic groups.

IQR: interquartile range; BMI: body mass index; COPD: chronic obstructive pulmonary disorder; PRBC: packed red blood cells; ASA: American Society of Anesthesiologists

Frequency data are reported as No. (%). p-values were calculated using Fisher’s exact test due to low expected cell counts across groups. Racial and ethnic categories that do not share the same subscript letter “'a', 'b', 'c', etc.” differ significantly at p < .05

Covariate		Hispanic (n = 276)	Non-Hispanic American Indian or Alaska Native (n = 14)	Non-Hispanic Asian (n = 124)	Non-Hispanic Black or African American (n = 311)	Non-Hispanic Native Hawaiian or Pacific Islander (n = 9)	Non-Hispanic White (n = 1661)	p
Age, median (IQR), years		24 (21.0-30.0)_a	26 (20.5-31.8)_ab	25 (21.0-30.0)_ab	27 (23.0-32.0)_b	23 (20.0-29.5)_ab	25 (21.0-30.0)_a	.013
BMI, median (IQR), kg/m²		27.4 (23.7-32.4)_a	29 (26.2-35.7)_ab	25.4 (22.5-28.8)_b	28.3 (24.3-33.6)_a	27.3 (23.7-31.7)_ab	27.4 (23.5-32.5)_a	< .001
Operating time, median (IQR), minutes		152 (119.0-189.8)_a	125 (91.5-141.0)_ab	142 (110.0-169.0)_ab	147 (116.0-189.0)_a	107 (98.5-138.0)_ab	134 (105.5-170.0)_b	< .001
Surgical specialty	General surgery	18 (6.5)_a	0 (0.0)_a	9 (7.3)_a	26 (8.4)_a	0 (0.0)_a	127 (7.6)_a	.940
	Gynecology	1 (0.4)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_ab	2 (0.1)_a
	Obstetric surgery	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.1)_a
	Plastic surgery	257 (93.1)_a	14 (100.0)_a	115 (92.7)_a	285 (91.6)_a	9 (100.0)_a	1530 (92.1)_a
	Urology	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.1)_a
Elective surgery		276 (100.0)_a	14 (100.0)_a	124 (100.0)_a	311 (100.0)_a	9 (100.0)_a	1657 (99.8)_a	1.0
Hypertension requiring medication		8 (2.9)_a	0 (0.0)_a	1 (0.8)_a	21 (6.8)_a	0 (0.0)_a	59 (3.6)_a	.054
Diabetes	On insulin	3 (1.1)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	14 (0.8)_a	.468
Diabetes	On oral agents	4 (1.4)_a	1 (7.1)_a	1 (0.8)_a	3 (1.0)_a	0 (0.0)_a	22 (1.3)_a	.468
Dyspnea	Moderate exertion	87 (31.5)_a	3 (21.4)_a	32 (25.8)_a	79 (25.4)_a	4 (44.4)_a	431 (25.9)_a	.490
Dyspnea	At rest	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.1)_a	.490
Current smoker within 1 year prior to surgery		30 (10.9)_a	3 (21.4)_a	13 (10.5)_a	59 (19.0)_a	2 (22.2)_a	228 (13.7)_a	.036
Functional health status prior to surgery		274 (99.3)_a	14 (100.0)_a	124 (100.0)_a	311 (100.0)_a	9 (100.0)_a	1654 (99.6)_a	.531
History of severe COPD		0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	2 (0.1)_a	1.0
Preoperative dialysis		0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	-
Steroid use for chronic condition		2 (0.7)_a	0 (0.0)_a	0 (0.0)_a	3 (1.0)_a	0 (0.0)_a	25 (1.5)_a	.652
>10% body weight loss within 6 months prior to surgery		87 (31.5)_a	3 (21.4)_a	32 (25.8)_a	79 (25.4)_a	4 (44.4)_a	428 (25.8)_a	.306
Bleeding disorder		1 (0.4)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	10 (0.6)_a	.695
Transfusion ≥ 1 units of PRBCs in 72 hours prior to surgery		0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	-
Wound classification	1-Clean	274 (99.3)_a	14 (100.0)_a	123 (99.2)_a	311 (100.0)_a	9 (100.0)_a	1653 (99.5)_a	.570
	2-Clean/contaminated	2 (0.7)_a	0 (0.0)_a	1 (0.8)_a	0 (0.0)_a	0 (0.0)_a	7 (0.4)_a
	3-Contaminated	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a
	4-Dirty/infected	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.1)_a
ASA classification	I	77 (27.9)_a	3 (21.4)_a	52 (41.9)_a	89 (28.6)_a	3 (33.3)_a	514 (30.9)_a	.899
	II	187 (67.8)_a	10 (71.4)_a	69 (55.6)_a	206 (66.2)_a	5 (55.6)_a	1051 (63.3)_a
	III	11 (4.0)_a	1 (7.1)_a	3 (2.4)_a	16 (5.1)_a	1 (11.1)_a	94 (5.7)_a
	IV	1 (0.4)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	2 (0.1)_a

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Table 8. Demographics, comorbidities, and surgical characteristics of patients who underwent facial feminization surgery across racial and ethnic groups.

IQR: interquartile range; BMI: body mass index; COPD: chronic obstructive pulmonary disorder; PRBC: packed red blood cells; ASA: American Society of Anesthesiologists

Covariate		Hispanic (n = 71)	Non-Hispanic American Indian or Alaska Native (n = 1)	Non-Hispanic Asian (n = 22)	Non-Hispanic Black or African American (n = 59)	Non-Hispanic Native Hawaiian or Pacific Islander (n = 6)	Non-Hispanic White (n = 237)	p
Age, median (IQR), years		31 (26.0-40.0)	31 (31.0-31.0)	26 (22.8-36.8)	32 (27.0-36.0)	29.5 (23.3-32.3)	31 (27.0-39.0)	.394
BMI, median (IQR), kg/m²		27.8 (23.6-30.4)	23.4 (23.4-23.4)	23.5 (21.0-25.8)	27.5 (23.5-32.0)	26 (22.1-31.0)	24.8 (22.0-28.7)	.003
Operating time, median (IQR), minutes		300 (208.0-367.0)	464 (464.0-464.0)	264 (106.5-323.3)	198 (130.0-291.0)	277.5 (144.5-387.5)	288 (124.0-408.0)	.012
Surgical specialty	General surgery	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.4)_a	< .001
	Otolaryngology (ENT)	54 (76.1)_a	1 (100.0)_ab	21 (95.5)_a	21 (35.6)_b	5 (83.3)_ab	178 (75.1)_a
	Plastic surgery	17 (23.9)_a	0 (0.0)_ab	1 (4.5)_a	38 (64.4)_b	1 (16.7)_ab	57 (24.1)_a
	Urology	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.4)_a
Elective surgery		71 (100.0)_a	1 (100.0)_a	22 (100.0)_a	59 (100.0)_a	6 (100.0)_a	237 (100.0)_a	-
Hypertension requiring medication		2 (2.8)_a	0 (0.0)_a	0 (0.0)_a	3 (5.1)_a	0 (0.0)_a	8 (3.4)_a	.844
Diabetes	On insulin	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	3 (1.3)_a	.905
Diabetes	On oral agents	2 (2.8)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	6 (2.5)_a	.905
Dyspnea	Moderate exertion	30 (42.3)_a	0 (0.0)_a	8 (36.4)_a	17 (28.8)_a	3 (50.0)_a	63 (26.6)_a	.110
Current smoker within 1 year prior to surgery		4 (5.6)_a	1 (100.0)_b	1 (4.5)_a	8 (13.6)_ab	0 (0.0)_ab	20 (8.4)_a	.131
Functional health status prior to surgery		71 (100.0)_a	1 (100.0)_a	22 (100.0)_a	59 (100.0)_a	6 (100.0)_a	237 (100.0)_a	-
History of severe COPD		0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	-
Preoperative dialysis		0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	-
Steroid use for chronic condition		0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.4)_a	1.0
>10% body weight loss within 6 months prior to surgery		30 (42.3)_a	0 (0.0)_a	8 (36.4)_a	17 (28.8)_a	3 (50.0)_a	63 (26.6)_a	.110
Bleeding disorder		0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	2 (0.8)_a	1.0
Transfusion ≥1 units of PRBCs in 72 hours prior to surgery		0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	-
Wound classification	1-Clean	55 (77.5)_a	1 (100.0)_a	18 (81.8)_a	42 (71.2)_a	5 (83.3)_a	161 (67.9)_a	.513
Wound classification	2-Clean/contaminated	16 (22.5)_a	0 (0.0)_a	4 (18.2)_a	17 (28.8)_a	1 (16.7)_a	76 (32.1)_a	.513
ASA classification	I	23 (32.4)_a	0 (0.0)_a	8 (36.4)_a	13 (22.0)_a	3 (50.0)_a	63 (26.6)_a	.236
	II	40 (56.3)_a	1 (100.0)_a	14 (63.6)_a	37 (62.7)_a	3 (50.0)_a	158 (66.7)_a
	III	7 (9.9)_a	0 (0.0)_a	0 (0.0)_a	9 (15.3)_a	0 (0.0)_a	16 (6.8)_a
	IV	1 (1.4)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a

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Table 5. Demographics, comorbidities, and surgical characteristics of patients who underwent chest feminization surgery across racial and ethnic groups.

IQR: interquartile range; BMI: body mass index; COPD: chronic obstructive pulmonary disorder; PRBC: packed red blood cells; ASA: American Society of Anesthesiologists

Covariate		Hispanic (n = 195)	Non-Hispanic American Indian or Alaska Native (n = 6)	Non-Hispanic Asian (n = 46)	Non-Hispanic Black or African American (n = 195)	Non-Hispanic Native Hawaiian or Pacific Islander (n = 2)	Non-Hispanic White (n = 399)	P
Age, median (IQR), years		32 (25.0-40.0)_a	31.5 (23.0-39.0)_ab	32 (26.8-39.0)_a	31 (27.0-39.0)_a	38.5 (22.0-38.5)_ab	37 (29.0-51.0)_b	< .001
BMI, median (IQR), kg/m²		26.9 (23.6-32.2)_a	26.7 (19.8-29.6)_ab	23.1 (21.1-27.0)_b	27.6 (24.0-31.9)_a	22.3 (20.3-22.3)_ab	26 (22.4-30.4)_b	< .001
Operating time, median (IQR), minutes		86 (63.0-106.0)_a	67.5 (58.8-153.0)_a	80 (63.3-121.5)_a	86 (70.0-115.0)_a	77.5 (74.0-77.5)_a	81 (62.0-112.0)_a	.139
Surgical specialty	General surgery	4 (2.1)_ab	0 (0.0)_ab	1 (2.2)_ab	10 (5.1)_b	0 (0.0)_ab	3 (0.8)_a	.048
	Orthopedic surgery	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.3)_a
	Plastic surgery	191 (97.9)_ab	6 (100.0)_ab	45 (97.8)_ab	185 (94.9)_b	2 (100.0)_ab	395 (99.0)_a
Elective surgery		194 (99.5)_a	6 (100.0)_a	46 (100.0)_a	195 (100.0)_a	2 (100.0)_a	395 (99.0)_a	.719
Hypertension requiring medication		13 (6.7)_a	1 (16.7)_a	4 (8.7)_a	22 (11.3)_a	0 (0.0)_a	55 (13.8)_a	.138
Diabetes	On insulin	0 (0.0)_a	0 (0.0)_a	1 (2.2)_a	2 (1.0)_a	0 (0.0)_a	7 (1.8)_a	.538
Diabetes	On oral agents	4 (2.1)_a	0 (0.0)_a	1 (2.2)_a	6 (3.1)_a	0 (0.0)_a	16 (4.0)_a	.538
Dyspnea	Moderate exertion	56 (28.7)_ab	2 (33.3)_ab	10 (21.7)_ab	42 (21.5)_b	1 (50.0)_ab	137 (34.3)_a	.017
Current smoker within 1 year prior to surgery		40 (20.5)_ab	0 (0.0)_ab	8 (17.4)_ab	55 (28.2)_b	1 (50.0)_ab	61 (15.3)_a	.004
Functional health status prior to surgery		195 (100.0)_a	6 (100.0)_a	46 (100.0)_a	194 (99.5)_a	2 (100.0)_a	399 (100.0)_a	.527
History of severe COPD		1 (0.5)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	3 (0.8)_a	.855
Preoperative dialysis		0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	-
Steroid use for chronic condition		0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.5)_a	0 (0.0)_a	3 (0.8)_a	.855
>10% body weight loss within 6 months prior to surgery		56 (28.7)_ab	2 (33.3)_ab	10 (21.7)_ab	43 (22.1)_b	1 (50.0)_ab	136 (34.1)_a	.029
Bleeding disorder		0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	3 (0.8)_a	.536
Transfusion ≥1 units of PRBCs in 72 hours prior to surgery		0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	-
Wound classification	1-Clean	193 (99.0)_a	5 (83.3)_b	44 (95.7)_ab	190 (97.4)_ab	2 (100.0)_ab	383 (96.0)_ab	.055
	2-Clean/contaminated	2 (1.0)_a	1 (16.7)_b	2 (4.3)_ab	3 (1.5)_ab	0 (0.0)_ab	16 (4.0)_ab
	3-Contaminated	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a
	4-Dirty/infected	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a
ASA classification	I	43 (22.1)_a	2 (33.3)_a	14 (30.4)_a	35 (17.9)_a	0 (0.0)_a	86 (21.6)_a	-
	II	141 (72.3)_a	3 (50.0)_a	30 (65.2)_a	145 (74.4)_a	2 (100.0)_a	270 (67.7)_a
	III	10 (5.1)_a	1 (16.7)_a	2 (4.3)_a	15 (7.7)_a	0 (0.0)_a	43 (10.8)_a
	None assigned	1 (0.5)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a

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Table 6. Demographics, comorbidities, and surgical characteristics of patients who underwent genital masculinization surgery across racial and ethnic groups.

IQR: interquartile range; BMI: body mass index; COPD: chronic obstructive pulmonary disorder; PRBC: packed red blood cells; ASA: American Society of Anesthesiologists

Frequency data are reported as No. (%). Pearson’s chi-square test comparing race/ethnicity and surgical subspecialty: χ²(25) = 40.2, p = .028; Cramér’s V = 0.79. Pearson’s chi-square test comparing race/ethnicity and ASA Classification: χ²(10) = 21.598, p = .017; Cramér’s V = 0.091. If chi-square test statistics are not provided, p-values were calculated using Fisher’s exact test due to low event counts. Racial and ethnic categories that do not share the same subscript letter “'a', 'b', 'c', etc.” differ significantly at p < .05

Covariate		Hispanic (n = 109)	Non-Hispanic American Indian or Alaska Native (n = 9)	Non-Hispanic Asian (n = 49)	Non-Hispanic Black or African American (n = 150)	Non-Hispanic Native Hawaiian or Pacific Islander (n = 1)	Non-Hispanic White (n = 980)	p
Age, median (IQR), years		28 (24.0-36.0)	30 (26.0-37.0)	26 (23.0-34.0)	30 (25.0-37.3)	41 (41.0-41.0)	27 (23.0-34.0)	.168
BMI, median (IQR), kg/m²		28.3 (23.6-31.9)	21.7 (20.8-28.0)	25.2 (22.6-29.8)	29.7 (24.3-34.1)	30.8 (30.8-30.8)	27.6 (24.0-32.6)	.008
Operating time, median (IQR), minutes		134 (95.0-184.0)	109 (79.5-228.0)	130 (88.0-199.5)	143.5 (96.8-247.3)	137 (137.0-137.0)	120.5 (88.0-176.8)	.009
Surgical specialty	General surgery	1 (0.9)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	3 (0.3)_a	.028
	Gynecology	81 (74.3)_ab	8 (88.9)_ab	34 (69.4)_ab	96 (64.0)_b	0 (0.0)_ab	804 (82.0)_a
	Obstetric surgery	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	2 (0.2)_a
	Orthopedic surgery	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.1)_a
	Plastic surgery	23 (21.1)_ab	1 (11.1)_ab	13 (26.5)_ab	47 (31.3)_b	1 (100.0)_ab	145 (14.8)_a
	Urology	4 (3.7)_a	0 (0.0)_a	2 (4.1)_a	7 (4.7)_a	0 (0.0)_a	25 (2.6)_a
Elective surgery		109 (100.0)_a	9 (100.0)_a	49 (100.0)_a	150 (100.0)_a	1 (100.0)_a	977 (99.7)_a	1.0
Hypertension requiring medication		6 (5.5)_a	1 (11.1)_ab	3 (6.1)_a	17 (11.3)_ab	1 (100.0)_b	80 (8.2)_a	.100
Diabetes	On insulin	1 (0.9)_a	0 (0.0)_a	0 (0.0)_a	1 (0.7)_a	0 (0.0)_a	15 (1.5)_a	.888
Diabetes	On oral agents	0 (0.0)_a	0 (0.0)_a	1 (2.0)_a	2 (1.3)_a	0 (0.0)_a	12 (1.2)_a	.888
Dyspnea	Moderate exertion	27 (24.8)_a	2 (22.2)_a	15 (30.6)_a	56 (37.3)_a	0 (0.0)_a	260 (26.5)_a	.217
Dyspnea	At rest	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.1)_a	.217
Current smoker within 1 year prior to surgery		17 (15.6)_a	0 (0.0)_a	6 (12.2)_a	24 (16.0)_a	0 (0.0)_a	101 (10.3)_a	.179
Functional health status prior to surgery		109 (100.0)_a	9 (100.0)_a	49 (100.0)_a	150 (100.0)_a	1 (100.0)_a	976 (99.6)_a	1.0
History of severe COPD		0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.1)_a	1.0
Preoperative dialysis		1 (0.9)_a	0 (0.0)_ab	0 (0.0)_ab	0 (0.0)_ab	0 (0.0)_ab	0 (0.0)_b	.129
Steroid use for chronic condition		1 (0.9)_a	0 (0.0)_a	1 (2.0)_a	1 (0.7)_a	0 (0.0)_a	8 (0.8)_a	.578
>10% body weight loss within 6 months prior to surgery		27 (24.8)_a	2 (22.2)_a	14 (28.6)_a	56 (37.3)_a	0 (0.0)_a	260 (26.5)_a	.118
Bleeding disorder		0 (0.0)_a	0 (0.0)_a	2 (4.1)_a	0 (0.0)_a	0 (0.0)_a	9 (0.9)_a	.140
Transfusion ≥1 unit of PRBCs in 72 hours prior to surgery		0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.7)_a	0 (0.0)_a	0 (0.0)_a	.245
Wound classification	1-Clean	38 (34.9)_ab	2 (22.2)_ab	20 (40.8)_ab	75 (50.0)_b	0 (0.0)_ab	326 (33.3)_a	.008
	2-Clean/contaminated	69 (63.3)_ab	7 (77.8)_ab	29 (59.2)_ab	75 (50.0)_b	1 (100.0)_ab	649 (66.2)_a
	3-Contaminated	2 (1.8)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	5 (0.5)_a
ASA classification	I	26 (23.9)_a	2 (22.2)_ab	24 (49.0)_b	37 (24.7)_a	0 (0.0)_ab	243 (24.8)_a	.017
	II	77 (70.6)_a	7 (77.8)_a	24 (49.0)_a	94 (62.7)_a	1 (100.0)_a	644 (65.7)_a
	III	6 (5.5)_a	0 (0.0)_a	1 (2.0)_a	19 (12.7)_a	0 (0.0)_a	93 (9.5)_a

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Table 7. Demographics, comorbidities, and surgical characteristics of patients who underwent genital feminization surgery across racial and ethnic groups.

IQR: interquartile range; BMI: body mass index; COPD: chronic obstructive pulmonary disorder; PRBC: packed red blood cells; ASA: American Society of Anesthesiologists

Frequency data are reported as No. (%). Pearson’s chi-square test comparing race/ethnicity and surgical subspecialty: χ²(20) = 30.629, p = .06; Cramér’s V = 0.089. Pearson’s chi-square test comparing race/ethnicity and ASA Classification: χ²(15) = 17.660, p = .281; Cramér’s V = 0.078. If chi-square test statistics are not provided, p-values were calculated using Fisher’s exact test due to low event counts. Racial and ethnic categories that do not share the same subscript letter “'a', 'b', 'c', etc.” differ significantly at p < .05

Covariate		Hispanic (n = 123)	Non-Hispanic American Indian or Alaska Native (n = 8)	Non-Hispanic Asian (n = 45)	Non-Hispanic Black or African American (n = 138)	Non-Hispanic Native Hawaiian or Pacific Islander (n = 3)	Non-Hispanic White (n = 641)	p
Age, median (IQR), years		31 (27.0-42.0)_ab	40 (30.0-48.5)_ab	28 (25.0-36.0)_ab	34.5 (28.0-46.0)_a	27 -	34 (27.0-47.0)_b	.013
BMI, median (IQR), kg/m²		26.6 (23.5-29.1)_abc	28.8 (25.5-33.6)_abc	22.5 (20.4-26.2)_a	27.6 (24.5-30.8)_b	31.6 -	25.7 (22.2-29.8)_c	< .001
Operating time, median (IQR), minutes		256 (112.0-313.0)_a	96.5 (44.0-256.0)_ab	285 (175.5- 363.0)_a	197 (59.8-307.5)_ab	32 -	169 (52.0-292.0)_b	< .001
Surgical specialty	General surgery	1 (0.8)_a	0 (0.0)_a	0 (0.0)_a	5 (3.6)_a	0 (0.0)_a	8 (1.2)_a	.060
	Gynecology	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	2 (0.3)_a
	Otolaryngology (ENT)	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.2)_a
	Plastic surgery	94 (76.4)_a	3 (37.5)_ab	32 (71.1)_ab	78 (56.6)_b	0 (0.0)_b	372 (58.0)_b
	Urology	28 (22.8)_a	5 (62.5)_ab	13 (28.9)_ab	55 (39.9)_b	3 (100.0)_b	258 (40.2)_b
Elective surgery		122 (99.2)_a	8 (100.0)_a	45 (100.0)_a	138 (100.0)_a	3 (100.0)_a	641 (100.0)_a	.187
Hypertension requiring medication		5 (4.1)_a	1 (12.5)_a	0 (0.0)_a	19 (13.8)_a	0 (0.0)_a	64 (10.0)_a	.011
Diabetes	On insulin	4 (3.3)_a	1 (12.5)_a	1 (2.2)_a	2 (1.4)_a	0 (0.0)_a	9 (1.4)_a	.269
Diabetes	On oral agents	1 (0.8)_a	0 (0.0)_a	1 (2.2)_a	6 (4.3)_a	0 (0.0)_a	17 (2.7)_a	.269
Dyspnea	Moderate exertion	13 (10.6)_a	1 (12.5)_abc	12 (26.7)_abc	17 (12.3)_ac	2 (66.7)_bc	164 (25.6)_b	< .001
Dyspnea	At rest	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.2)_a	< .001
Current smoker within 1 year prior to surgery		15 (12.2)_a	1 (12.5)_a	2 (4.4)_a	25 (18.1)_a	1 (33.3)_a	65 (10.1)_a	.040
Functional health status prior to surgery		123 (100.0)_a	8 (100.0)_a	45 (100.0)_a	137 (99.3)_a	3 (100.0)_a	635 (99.1)_a	.895
History of severe COPD		0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	4 (0.6)_a	1.0
Preoperative dialysis		0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	-
Steroid use for chronic condition		0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.7)_a	0 (0.0)_a	16 (2.5)_a	.334
>10% body weight loss within 6 months prior to surgery		13 (10.6)_a	1 (12.5)_abc	12 (26.7)_abc	17 (12.3)_ac	2 (66.7)_bc	162 (25.3)_b	< .001
Bleeding disorder		1 (0.8)_ab	1 (12.5)_b	0 (0.0)_ab	0 (0.0)_a	0 (0.0)_ab	5 (0.8)_a	.136
Transfusion ≥1 units of PRBCs in 72 hours prior to surgery		0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	-
Wound classification	1-Clean	38 (30.9)_a	4 (50.0)_ab	19 (42.2)_ab	49 (35.5)_ab	2 (66.7)_ab	295 (46.0)_b	.037
	2-Clean/contaminated	84 (68.3)_a	4 (50.0)_ab	26 (57.8)_ab	88 (63.8)_ab	1 (33.3)_ab	344 (53.7)_b
	3-Contaminated	1 (0.8)_a	0 (0.0)_a	0 (0.0)_a	1 (0.7)_a	0 (0.0)_a	2 (0.3)_a
ASA classification	I	18 (14.6)_a	3 (37.5)_a	14 (31.1)_a	23 (16.7)_a	1 (33.3)_a	142 (22.2)_a	.281
	II	99 (80.5)_a	5 (62.5)_a	30 (66.7)_a	99 (71.7)_a	2 (66.7)_a	435 (67.9)_a
	III	6 (4.9)_a	0 (0.0)_a	1 (2.2)_a	16 (11.6)_a	0 (0.0)_a	63 (9.8)_a
	IV	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.2)_a

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Postoperative complications

Perioperative and 30-day postoperative complications were compared across racial and ethnic groups, stratified by type of GAS. For each complication, multivariable logistic regression was performed to adjust for potential confounders, which were identified based on covariates that had a p < 0.2 upon comparison.

Bivariate analysis of postoperative complications following chest masculinization surgery (Table 9) revealed that Black individuals were significantly more likely to experience unplanned reoperation (n = 14, 4.5%; n = 28, 1.7%; p = 0.042) and unplanned readmission (n = 7, 2.3%; n = 8 0.5%; p = 0.048) compared to White individuals. These differences were not significant in multivariable analysis (Table 10). The frequency of wound disruption (n = 2, 0.1%) and SSI (n = 22, 0.9%) remained low, with no significant differences across racial and ethnic groups. Table 11 shows no significant differences in complications across racial and ethnic groups following chest feminization surgery. Multivariable analysis confirmed the absence of disparities across groups (Table 12).

Table 9. Patients’ postoperative complications following chest masculinization surgery across racial and ethnic groups.

SSI: surgical site infection; DVT: deep vein thrombosis; OR: operating room; C. diff: Clostridioides difficile; CVA: cerebrovascular accident

Outcome	Racial and ethnic categories						Total	p
Outcome	Hispanic (n = 276)	Non-Hispanic American Indian or Alaska Native (n = 14)	Non-Hispanic Asian (n = 124)	Non-Hispanic Black or African American (n = 311)	Non-Hispanic Native Hawaiian or Pacific Islander (n = 9)	Non-Hispanic White (n = 1661)	Total	p
Wound disruption	1 (0.4)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.1)_a	2 (0.1)	.339
SSI	5 (1.8)_a	0 (0.0)_a	0 (0.0)_a	1 (0.3)_a	0 (0.0)_a	16 (1.0)_a	22 (0.9)	.399
Occurrences pneumonia	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	-
Unplanned intubation	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	-
Pulmonary embolism	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	-
Occurrences ventilator > 48 Hours	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	-
Acute renal failure	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	-
Urinary tract infection	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.3)_a	0 (0.0)_a	5 (0.3)_a	6 (0.3)	1.0
CVA	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	-
Cardiac arrest requiring CPR	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	-
Myocardial infarction	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	-
Bleeding transfusions	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.3)_a	0 (0.0)_a	2 (0.1)_a	3 (0.1)	.667
DVT/thrombophlebitis	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.1)_a	1 (0.0)	1.0
Sepsis	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.3)_a	0 (0.0)_a	1 (0.1)_a	2 (0.1)	.519
Septic shock	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	-
Return to OR	9 (3.3)_ab	0 (0.0)_ab	4 (3.2)_ab	14 (4.5)_b	0 (0.0)_ab	28 (1.7)_a	55 (2.3)	.042
Still in hospital > 30 days	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	-
Unplanned readmission	1 (0.4)_ab	0 (0.0)_ab	1 (0.8)_ab	7 (2.3)_b	0 (0.0)_ab	8 (0.5)_a	17 (0.7)	.048
C. diff colitis	2 (0.7)_a	0 (0.0)_a	2 (1.6)_a	9 (2.9)_a	0 (0.0)_a	46 (2.8)_a	59 (2.5)	.359

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Table 10. Multivariate logistic regression for the incidence of complications following chest masculinization surgery.

SSI: surgical site infection; PE: pulmonary embolism; progressive renal insuff: progressive renal insufficiency; UTI: urinary tract infection; bleeding + transfusion: bleeding requiring transfusion; DVT: deep venous thrombosis; LOS: total length of hospital stay; C. diff: Clostridioides difficile; BMI: body mass index; aOR: adjusted odds ratio

Outcome		Wound disruption	SSI	Pneumonia	PE	UTI	Bleeding + transfusion	DVT	Sepsis	Unplanned reoperation	LOS >30 days	Unplanned readmission	C. diff colitis
Outcome		aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)
Age		0.9 (0.71-1.23)	1.0 (1.00-1.08)	-	-	1.0 (0.90-1.10)	0.9 (0.8-1.1)	1.0 (0.88-1.23)	0.83 (0.57-1.21)	1.0 (0.94-1.02)	-	1.0 (0.94-1.06)	1.0 (1.0-1.02)
BMI		0.8 (0.56-1.17)	1.0 (0.99-1.10)	-	-	1.1 (1.03-1.20)	1.1 (1.02-1.25)	0.9 (0.68-1.34)	1.0 (0.82-1.24)	1.0 (0.97-1.05)	-	1.1 (1.02-1.14)	1.0 (0.94-1.01)
Total operation time		1.0 (0.98-1.0)	1.0 (1.00-1.01)	-	-	1.0 1.0-1.02	1.0 (0.98-1.02)	1.0 (0.96-1.04)	1.0 (0.97-1.02)	1.0 (0.99-1.0)		1.0 (1.0-1.01)	1.0 (1.0-1.01)
Smoking		10.2 (0.54- >100)	1.0 (0.30-3.52)	-	-	-	-	-	-	0.5 (0.16-1.26)	-	0.8 (0.17-3.39)	1.9 (1.02-3.69)
Hypertension		1.0 -	0.5 (0.06-4.38)	-	-	-	4.5 (0.24-87.07)	-	-	1.0 (0.22-4.81)	-	0.7 (0.08-6.22)	0.5 (0.11-2.23)
Racial and ethnic categories	Non-Hispanic American Indian or Alaska Native	1.0 -	-	-	-	1.1 -	0.9 -	1.3 -	0.9 -	0.0 -	-	0.0 -	0.0 -
	Non-Hispanic Asian	1.0 -	-	-	-	1.5 -	1.8 -	0.9 -	1.0 -	1.0 (0.30-3.28)	-	2.9 (0.18-47.04)	2.4 (0.34-17.75)
	Non-Hispanic Black or African American	1.0 -	0.2 (0.02-1.36)	-	-	>100 -	>100 -	1.1 -	>100 -	1.5 (0.64-3.59)	-	6.2 (0.75-51.28)	4.0 (0.9-18.91)
	Non-Hispanic Native Hawaiian or Pacific Islander	1.0 -	-	-	-	2.6 -	2.0 -	1.1 -	0.8 -	0.0 -	-	0.0 -	0.0 -
	Non-Hispanic White	0.2 (0.0-2.28)	0.5 (0.19-1.44)	-	-	>100 -	>100 -	>100 -	>100 -	0.5 (0.23-1.07)	-	1.3 (0.16-10.75)	4.4 (1.04-18.18)
Constant		2.4	0.001	-	-	0.0	0.0	0.0	0.0	0.07	-	0.0	0.001

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Table 11. Patients’ postoperative complications following chest feminization surgery across racial and ethnic groups.

SSI: surgical site infection; DVT: deep vein thrombosis; OR: operating room; C. diff: Clostridioides difficile; CVA: cerebrovascular accident

Outcome	Racial and ethnic categories						Total	p
Outcome	Hispanic (n = 195)	Non-Hispanic American Indian or Alaska Native (n = 6)	Non-Hispanic Asian (n = 46)	Non-Hispanic Black or African American (n = 195)	Non-Hispanic Native Hawaiian or Pacific Islander (n = 2)	Non-Hispanic White (n = 399)	Total	p
Wound disruption	1 (0.5)_a	0 (0.0)_a	0 (0.0)_a	1 (0.5)_a	0 (0.0)_a	0 (0.0)_a	2 (0.2)	.338
SSI	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	4 (2.1)_a	0 (0.0)_a	4 (1.0)_a	8 (0.9)	.296
Occurrences of pneumonia	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	-
Unplanned intubation	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	-
Pulmonary embolism	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	-
Occurrences ventilator > 48 hours	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	-
Acute renal failure	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	-
Urinary tract infection	2 (1.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.5)_a	0 (0.0)_a	4 (1.0)_a	7 (0.8)	1.0
CVA	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	-
Cardiac arrest requiring CPR	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	-
Myocardial infarction	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	-
Bleeding transfusions	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	-
DVT/thrombophlebitis	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	-
Sepsis	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.5)_a	0 (0.0)_a	1 (0.3)_a	2 (0.2)	.776
Septic shock	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	-
Return to OR	3 (1.5)_a	0 (0.0)_a	0 (0.0)_a	6 (3.1)_a	0 (0.0)_a	3 (0.8)_a	12 (1.4)	.263
Still in hospital >30 days	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	-
Unplanned readmission	1 (0.5)_a	0 (0.0)_a	0 (0.0)_a	5 (2.6)_a	0 (0.0)_a	2 (0.5)_a	8 (0.9)	.189
C. diff colitis	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	8 (2.0)_a	8 (0.9)	.092

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Table 12. Multivariate logistic regression for the incidence of complications following chest feminization surgery.

Outcome		Wound disruption	SSI	Pneumonia	PE	UTI	Bleeding + transfusion	DVT	Sepsis	Unplanned reoperation	LOS >30 days	Unplanned readmission	C. diff colitis
Outcome		aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR(95% CI)	aOR (95% CI)	aOR (95% CI)
Age		-	1.0 (0.95-1.08)	-	-	1.0 (0.93-1.07)	-	-	0.9 (0.57-1.32)	1.0 (1.01-1.13)	-	1.0 (0.97-1.12)	1.0 (0.89-1.03)
BMI		-	0.9 (0.82-1.08)	-	-	1.0 (0.91-1.12)	-	-	1.1 (0.62-2.0)	0.9 (0.79-1.02)	-	0.9 (0.81-1.09)	1.0 (0.82-1.13)
Total operation time		-	1.0 (1.0-1.02)	-	-	1.0 (1.0-1.02)	-	-	1.0 (0.99-1.06)	1.0 (0.99-1.01)	-	1.0 (1.0-1.02)	1.0 (1.0-1.02)
Smoking		-	3.5 (0.67-18.63)	-	-	4.1 (0.81-20.71)	-	-	0.0 -	1.9 (0.51-7.02)	-	2.4 (0.46-12.14)	0.0 -
Hypertension		-	0.9 (0.07-11.71)	-	-	3.0 (0.39-22.63)	-	-	0.0 -	0.0 -	-	0.0 -	1.5 (0.08-29.46)
>10% body weight loss		-	0.5 (0.0- >100)	-	-	6.0 (0.0- >100)	-	-	0.0 -	0.0 -	-	0.0 -	0.6 -
Dyspnea on moderate exertion		-	1.5 (0.0- >100)	-	-	0.5 (0.0- >100)	-	-	0.0 -	>100 -	-	>100 -	0.0 -
Surgical specialty	Orthopedic surgery	-	0.2 -	-	-	>100 -	-	-	>100 -	0.1 -	-	0.1 -	0.0 -
Surgical specialty	Plastic surgery	-	>100 -	-	-	>100 -	-	-	>100 -	>100 -	-	>100 -	>100 -
Wound classification	2-Clean/contaminated	-	1.3 (0.0- 17.04)	-	-	0.0 -	-	-	0.0 -	4.2 (0.31-55.49)	-	1.9 (0.11-31.47)	0.7 (0.09-6.27)
	3-Contaminated	-	0.0 -	-	-	0.0 -	-	-	>100 -	0.0 -	-	0.0 -	>100 -
	4-Dirty/infected	-	0.0 -	-	-	0.0 -	-	-	>100 -	>100 -	-	>100 -	>100 -
Racial and ethnic categories	Non-Hispanic American Indian or Alaska Native	-	0.5 -	-	-	0.0 -	-	-	3.3 -	0.0 -	-	0.0 -	1.1 -
	Non-Hispanic Asian	-	0.8 -	-	-	0.0 -	-	-	0.8 -	0.0 -	-	0.0 -	0.6 -
	Non-Hispanic Black or African American	-	>100 -	-	-	0.3 (0.02-4.40)	-	-	0.6 -	2.4 (0.55-10.68)	-	4.1 (0.44-38.93)	0.8 -
	Non-Hispanic Native Hawaiian or Pacific Islander	-	0.5 -	-	-	0.0 -	-	-	>100 -	0.0 -	-	0.0 -	>100 -
	Non-Hispanic White	-	>100 -	-	-	0.7 (0.12-4.71)	-	-	>100 -	0.3 (0.04-1.43)	-	0.5 (0.04-6.6)	>100 -
Constant			0.0	-	-	0.0	-	-	0.0	0.0	-	0.0	0.0

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Bivariate analysis of postoperative complications following genital masculinization surgery shows no significant differences across racial and ethnic groups (Table 13). Multivariable analysis confirmed the absence of disparities across groups (Table 14). Among individuals who underwent genital feminization surgery, Hispanic and Black individuals were significantly more likely to experience wound disruption compared with White individuals (n = 12, 9.8%; n = 12, 8.7%; n = 21, 3.3%; p = 0.006; Table 15). Multivariable regression indicated that Non-Hispanic White patients remained at lower odds of wound disruption than Hispanic patients (aOR: 0.5, 95% CI: 0.18-0.84; p < 0.05; Table 16).

Table 13. Patients’ postoperative complications following genital masculinization surgery across racial and ethnic groups.

SSI: surgical site infection; DVT: deep vein thrombosis; OR: operating room; C. diff: Clostridioides difficile

Frequency data are reported using No. (%). P-values were calculated using Fisher’s exact test due to low expected cell counts across groups. Racial and ethnic categories that do not share the same subscript letter “'a', 'b', 'c', etc.” differ significantly at p < .05

Outcome	Racial and ethnic categories						Total	p
Outcome	Hispanic (n = 109)	Non-Hispanic American Indian or Alaska Native (n = 9)	Non-Hispanic Asian (n = 49)	Non-Hispanic Black or African American (n = 150)	Non-Hispanic Native Hawaiian or Pacific Islander (n = 1)	Non-Hispanic White (n = 980)	Total	p
Wound disruption	2 (1.8)_a	0 (0.0)_a	0 (0.0)_a	2 (1.3)_a	0 (0.0)_a	6 (0.6)_a	10 (0.8)	.303
SSI	4 (3.7)_a	0 (0.0)_a	2(4.1)_a	4 (2.7)_a	0 (0.0)_a	26 (2.7)_a	36 (2.8)	.773
Occurrences of pneumonia	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.7)_a	0 (0.0)_a	2 (0.2)_a	3 (0.2)	.570
Occurrences of unplanned intubation	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.1)_a	0 (0.0)_a	5 (0.1)_a	6 (0.1)	1.0
Occurrences of pulmonary embolism	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	1.0
Occurrences of ventilator > 48 hours	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.1)_a	1 (0.1)	1.0
Occurrences of cerebrovascular accident	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	1.0
Occurrence of myocardial infarction	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	1.0
Acute renal failure	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.1)_a	1 (0.1)	1.0
Urinary tract infection	0 (0.0)_a	0 (0.0)_a	1 (2.0)_a	2 (1.3)_a	0 (0.0)_a	20 (2.0)_a	23 (1.8)	.566
Cardiac arrest requiring CPR	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.1)_a	1 (0.1)	1.0
Bleeding transfusions	1 (0.9)_a	0 (0.0)_a	1 (2.0)_a	1 (0.7)_a	0 (0.0)_a	11 (1.1)_a	14 (1.1)	.759
DVT/thrombophlebitis	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.1)_a	1 (0.1)	1.0
Sepsis	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	2 (0.2)_a	2 (0.2)	1.0
Septic shock	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.1)_a	1 (0.1)	1.0
Return to OR	5 (4.6)_a	0 (0.0)_a	2 (4.1)_a	7 (4.7)_a	0 (0.0)_a	26 (2.7)_a	40 (3.1)	.416
Still in hospital > 30 days	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.7)_a	0 (0.0)_a	2 (0.2)_a	3 (0.2)	.570
Unplanned readmission	3 (2.8)_a	0 (0.0)_a	0 (0.0)_a	5 (3.3)_a	0 (0.0)_a	23 (2.3)_a	31 (2.4)	.737
C. diff colitis	2 (1.8)_a	0 (0.0)_a	4 (8.2)_a	4 (2.7)_a	1 (100.0)_a	34 (3.5)_a	45 (3.5)	.036

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Table 14. Multivariate logistic regression for the incidence of complications following genital masculinization surgery.

Outcome		Wound disruption	SSI	Pneumonia	PE	UTI	Bleeding + transfusion	DVT	Sepsis	Unplanned reoperation	LOS >30 days	Unplanned readmission	C. diff colitis
Outcome		aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR(95% CI)	aOR (95% CI)	aOR (95% CI)
BMI		1.0 (0.90-1.012)	1.0 (0.95-1.06)	0.9 (0.75-1.16)	-	1.0 (0.95-1.07)	0.8 (0.74-0.96)	3.8 (0.0- >100)	0.8 (0.34-1.68)	1.0 (0.9-1.01)	1.0 (0.9-1.21)	0.9 (0.86-0.99)	1.0 (0.89-1.02)
Total operation time		1.0 (1.0-1.0)	1.0 (1.0-1.0)	1.0 (1.0-1.01)	-	1.0 (1.0-1.01)	1.0 (1.01-1.02)	1.2 (0.0- >100)	1.0 (1.0-1.05)	1.0 (1.0-1.0)	1.0 (1.0-1.02)	1.0 (1.0-1.01)	1.0 (1.0-1.01)
Smoking		0.9 (0.09-10.34)	1.9 (0.73-4.74)	3.8 (0.18-82.91)	-	1.8 (0.58-5.41)	1.3 (0.12-13.48)	>100 -	1.6 (0.01- >100)	0.7 (0.19-2.27)	0.0 -	1.0 (0.31-3.03)	1.5 (0.56-4.12)
Hypertension		0.6 (0.05-7.33)	0.5 (0.12-1.73)	36.8 (1.45- >100)	-	0.6 (0.13-3.18)	0.6 (0.08-4.34)	0.0 -	19.2 (0.0- >100)	0.6 (0.15-2.11)	5.8 (0.26->100)	1.6 (0.5-5.10)	0.4 (0.05-3.72)
Preoperative dialysis		0.0 -	0.0 -	0.0 -	-	0.7 -	0.0 -	>100 -	>100 -	0.0 -	0.4 -	0.0 -	0.0 -
>10% body weight loss		1.4 (0.17- 11.79)	1.8 (0.51- 6.37)	17.3 (0.0- >100)	-	0.6 (0.09- 3.96)	1.4 (0.26- 7.81)	>100 -	0.7 -	0.9 (0.32-2.48)	>100 -	0.7 (0.15-3.56)	0.0 -
Bleeding disorders		0.0 -	0.0 -	0.0 -	-	0.0 -	0.0 -	>100 -	0.0 -	0.0 -	0.0 -	0.0 -	>100 -
Surgical specialty	Gynecology	>100 -	>100 -	>100 -	-	>100 -	>100 -	0.0 -	>100 -	>100 -	>100 -	>100 -	>100 -
	Obstetric surgery	0.7 -	0.4 -	3.0 -	-	0.4 -	0.1 -	0.0 -	>100 -	0.5 -	2.2 -	1.1 -	1.1 -
	Orthopedic surgery	0.7 -	0.8 -	2.3 -	-	0.4 -	0.2 -	>100 -	0.0 -	0.5 -	0.8 -	0.4 -	0.3 -
	Plastic surgery	>100 -	>100 -	>100 -	-	>100 -	>100 -	0.0 -	0.0 -	>100 -	>100 -	>100 -	>100 -
	Urology	0.7 -	>100 -	0.2 -	-	0.5 -	>100 -	0.0 -	>100 -	>100 -	0.3 -	>100 -	0.9 -
Wound classification	2-Clean/contaminated	2.6 (0.45- 14.77)	1.7 (0.5- 5.77)	14.4 (0.0- >100)	-	1.1 (0.19-5.91)	0.6 (0.12- 3.44)	0.0 -	>100 -	0.8 (0.31-2.11)	>100 -	1.9 (0.48-7.47)	5.3 (0.62- 44.7)
Wound classification	3-Contaminated	0.0 -	0.0 -	0.0 -	-	0.0 -	0.0 -	>100 -	6.1 -	0.0 -	0.1 -	0.0 -	0.0 -
ASA classification	2-Mild disturbance	1.9 (0.30-12.24)	1.2 (0.48-2.93)	0.2 (0.0-5.61)	-	1.5 (0.46-4.64)	0.4 (0.09-1.97)	0.0 -	>100 -	0.9 (0.42-1.95)	>100 -	2.2 (0.77-6.18)	0.6 (0.27-1.21)
ASA classification	3-Severe disturbance	3.7 (0.21-67.83)	4.7 (1.32-16.43)	0.4 (0.0-26.09)	-	2.5 (0.45-14.10)	10.9 (1.31-91.31)	>100 -	0.0 -	3.0 (0.82-10.69)	>100 -	3.73 (0.75-18.47)	0.8 (0.15-4.56)
Racial and ethnic categories	Non-Hispanic American Indian or Alaska Native	0.0 -	0.0 -	0.9 -	-	1.1 -	0.0 -	>100 -	0.4 -	0.0 -	0.3 -	0.0 -	0.0 -
	Non-Hispanic Asian	0.0 -	1.0 (0.17-6.21)	0.7 -	-	>100 -	0.5 (0.01-27.39)	0.0 -	2.6 -	0.5 (0.08-3.27)	0.5 -	0.0 -	2.2 (0.29-17.07)
	Non-Hispanic Black or African American	0.4 (0.05-3.11)	0.5 (0.12-2.16)	>100 -	-	>100 -	0.4 (0.02-7.35)	8.2 -	10.5 -	0.7 (0.21-2.47)	>100 -	1.0 (0.23-4.63)	1.7 (0.3-9.67)
	Non-Hispanic Native Hawaiian or Pacific Islander	0.0 -	0.0 -	0.3 -	-	2.9 -	0.0 -	>100 -	>100 -	0.0 -	0.1 -	0.0 -	>100 -
	Non-Hispanic White	0.3 (0.05-1.79)	0.7 (0.24-2.23)	>100 -	-	>100 -	1.6 (0.15-17.19)	0.0 -	>100 -	0.6 (0.2-1.56)	>100 -	0.8 (0.23-2.78)	1.4 (0.32-6.05)
Constant		0.0	0.0	0.0	-	0.0	0.0	-	0.0	0.0	0.0	0.0	0.0

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Table 15. Patients’ postoperative complications following genital feminization surgery across racial and ethnic groups.

SSI: surgical site infection; DVT: deep vein thrombosis; OR: operating room; C. diff.: Clostridioides difficile

Outcome	Racial and ethnic categories						Total	p
Outcome	Hispanic (n = 123)	Non-Hispanic American Indian or Alaska Native (n = 8)	Non-Hispanic Asian (n = 45)	Non-Hispanic Black or African American (n = 138)	Non-Hispanic Native Hawaiian or Pacific Islander (n = 3)	Non-Hispanic White (n = 641)	Total	p
Wound disruption	12 (9.8)_a	0 (0.0)_a,b	0 (0.0)_a,b	12(8.7)_a	0(0.0)_a,b	21(3.3)_b	45(4.7)	.006
SSI	3 (2.4)_a	0 (0.0)_a,b	1(2.2)_a,b	7(5.1)_a,b	1(33.3)_b	17 (2.7)_a	29(3.0)	.147
Occurrences of pneumonia	0 (0.0)_a	0(0.0)_a	0 (0.0)_a	0(0.0)_a	0(0.0)_a	2 (0.3)_a	2(0.2)	1.0
Occurrences of unplanned intubation	0 (0.0)_a	0(0.0)_a	0 (0.0)_a	0(0.0)_a	0(0.0)_a	1 (0.2)_a	1(0.1)	1.0
Occurrences of pulmonary embolism	0 (0.0)_a	0(0.0)_a	0 (0.0)_a	1(0.7)_a	0(0.0)_a	0 (0.0)_a	1(0.1)	.331
Occurrences ventilator > 48 hours	0 (0.0)_a	0(0.0)_a	0 (0.0)_a	0(0.0)_a	0(0.0)_a	1 (0.1)_a	1(0.1)	1.0
Occurrences of cerebrovascular accident	0 (0.0)_a	0(0.0)_a	0 (0.0)_a	0(0.0)_a	0(0.0)_a	0 (0.0)_a	0(0.0)	1.0
Occurrences of myocardial infarction	0 (0.0)_a	0(0.0)_a	0 (0.0)_a	0(0.0)_a	0(0.0)_a	0 (0.0)_a	0(0.0)	1.0
Acute renal failure	0 (0.0)_a	0(0.0)_a	0 (0.0)_a	0(0.0)_a	0(0.0)_a	1 (0.1)_a	1(0.1)	1.0
Urinary tract infection	2 (1.6)_a	0(0.0)_a	0 (0.0)_a	3(2.2)_a	0(0.0)_a	6 (0.9)_a	11(1.1)	.549
Cardiac arrest requiring CPR	0 (0.0)_a	0(0.0)_a	0 (0.0)_a	0(0.0)_a	0(0.0)_a	1 (0.1)_a	1(0.1)	1.0
Bleeding requiring transfusion	2 (1.6)_a	0(0.0)_a	1 (2.2)_a	5(3.6)_a	0(0.0)_a	10 (1.6)_a	18(1.9)	.486
DVT/thrombophlebitis	0 (0.0)_a	0(0.0)_a	0 (0.0)_a	1(0.7)_a	0(0.0)_a	1 (0.2)_a	2(0.2)	.553
Sepsis	0 (0.0)_a	0(0.0)_a	0 (0.0)_a	1(0.7)_a	0(0.0)_a	3 (0.5)_a	4(0.4)	.800
Septic shock	0 (0.0)_a	0(0.0)_a	0 (0.0)_a	0(0.0)_a	0(0.0)_a	0 (0.0)_a	0(0.0)	1.0
Return to OR	4 (3.3)_a	1(12.5)_a	0(0.0)_a	7(5.1)_a	0(0.0)_a	23 (3.6)_a	35(3.7)	.370
Still in hospital >30 days	0 (0.0)_a	0(0.0)_a	0 (0.0)_a	0(0.0)_a	0(0.0)_a	0 (0.0)_a	0(0.0)	1.0
Unplanned readmission	4 (3.3)_a	0(0.0)_a	1(2.2)_a	7(5.1)_a	0(0.0)_a	21(3.3)_a	33(3.4)	.830
C. diff. colitis	2 (1.6)_a	2(25.0)_b	1(2.2)_a,b	2(1.4)_a	0(0.0)_a,b	20 (3.1)_a	27(2.8)	.076

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Table 16. Multivariate logistic regression for the incidence of complications following genital feminization surgery.

SSI: surgical site infection; PE: pulmonary embolism; UTI: urinary tract infection; bleeding + transfusion: bleeding requiring transfusion; DVT: deep venous thrombosis; LOS: total length of hospital stay; C. diff: Clostridioides difficile; ASA: American Society of Anesthesiologists; BMI: body mass index; aOR: adjusted odds ratio

Outcome		Wound disruption	SSI	Pneumonia	PE	UTI	Bleeding + transfusion	DVT	Sepsis	Unplanned reoperation	LOS >30 days	Unplanned readmission	C. diff. colitis
Outcome		aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)
Age		1.0 (0.99-1.04)	1.0 (1.0-1.06)	0.9 (0.86-1.10)	9.4 (0.0- >100)	1.0 (0.97-1.07)	1.0 (0.91-1.03)	1.1 (1.0-1.24)	1.0 (0.90-1.07)	1.0 (0.99-1.05)	-	1.0 (1.0-1.06)	1.0 (0.98-1.05)
BMI		1.0 (0.95-1.07)	1.0 (0.94-1.08)	1.3 (0.93-1.82)	0.0 (0.0- >100)	1.0 (0.88-1.11)	1.0 (0.85-1.07)	1.0 (0.74-1.45)	1.0 (0.89-1.23)	1.1 (1.0-1.11)	-	1.1 (1.06-1.12)	1.0 (0.97-1.11)
Total operation time		1.0 (1.0-1.01)	1.0 (1.0-1.01)	1.0 (1.0-1.03)	1.2 (0.0- >100)	1.0 (1.0-1.01)	1.0 (1.01-1.02)	1.0 (0.98-1.01)	1.0 (1.0-1.01)	1.0 (1.0-1.01)	-	1.0 (1.0-1.01)	1.0 (0.99-1.0)
Elective surgery		>100 -	>100 -	0.2 -	0.0 -	>100 -	>100 -	0.3 -	1.4 -	>100 -	-	>100 -	>100 -
Smoking		1.8 (0.79-4.23)	3.5 (1.41-8.44)	0.0 -	0.0 -	0.9 (0.10-7.24)	0.5 (0.06-4.21)	0.0 -	0.0 -	1.1 (0.37-3.28)	-	1.1 (0.38-3.36)	2.7 (0.92-8.08)
Hypertension		1.3 (0.47-3.82)	1.1 (0.32-3.86)	0.0 -	0.5 -	3.3 (0.60-17.96)	2.3 (0.30-17.44)	0.0 -	0.0 -	1.4 (0.49-3.76)	-	1.1 (0.40-3.17)	1.2 (0.27-5.09)
Dyspnea	Moderate exertion	0.0 -	>100 -	0.0 -	>100 -	0.0 -	0.0 -	>100 -	0.7 -	0.0 -	-	0.0 -	0.0 -
	At rest	0.0 -	0.0 -	>100 -	>100 -	0.0 -	0.0 -	>100 -	0.9 -	0.0 -	-	0.0 -	>100 -
>10% body weight loss		>100 -	0.0 -	0.0 -	0.0 -	2.0 -	>100 -	0.0 -	0.0 -	>100 -	-	>100 -	>100 -
Bleeding disorders		0.0 -	0.0 -	0.0 -	0.0 -	0.0 -	0.0 -	0.0 -	0.0 -	0.0 -	-	0.0 -	>100 -
Surgical specialty	Gynecology	1.0 -	0.6 -	11.0 -	>100 -	1.2 -	0.0 -	5.9 -	1.8 -	>100 -	-	1.0 -	0.5 -
Otolaryngology (ENT)	2.5 -	1.9 -	56.8 -	>100 -	2.4 -	0.0 -	5.6 -	0.7 -	>100 -	-	2.3 -	1.0 -
Plastic surgery	>100 -	>100 -	>100 -	0.0 -	>100 -	0.1 (0.0-4.60)	>100 -	>100 -	0.3 (0.04-2.69)	-	>100 -	>100 -
Urology	>100 -	>100 -	2.0 -	0.0 -	>100 -	0.0 (0.0-2.47)	0.1 -	>100 -	0.2 (0.03-2.20)	-	>100 -	>100 -
Wound classification	2-Clean/contaminated	1.4 (0.58- 3.34)	2.4 (0.65- 8.81)	>100 -	0.0 -	1.2 (0.24-6.26)	3.4 (0.34- 35.06)	>100 -	1.3 (0.11-14.43)	1.2 (0.48-3.27)	-	0.8 (0.34-2.04)	0.8 (0.27- 2.25)
Wound classification	3-Contaminated	0.0 -	0.0 -	>100 -	>100 -	0.0 -	0.0 -	16.8 -	0.0 -	0.0 -	-	0.0 -	0.0 -
Racial and ethnic categories	Non-Hispanic American Indian or Alaska Native	0.0 -	0.0 -	8.5 -	>100 -	0.0 -	0.0 -	11.0 -	0.8 -	7.2 (0.63-81.88)	-	0.0 -	17.2 (0.84- >100)
	Non-Hispanic Asian	0.0 -	1.2 (0.12-12.82)	0.3 -	>100 -	0.0 -	1.5 (0.09-26.52)	1.2 -	1.0 -	0.0 -	-	1.0 (0.11-9.70)	5.7 (0.32->100)
	Non-Hispanic Black or African American	0.9 (0.39-2.28)	2.1 (0.5-8.47)	0.1 -	>100 -	1.2 (0.18-7.68)	4.8 (0.54-41.58)	>100 -	>100 -	1.4 (0.40-5.24)	-	1.5 (0.41-5.36)	1.5 (0.13-17.45)
	Non-Hispanic Native Hawaiian or Pacific Islander	0.0 -	28.1 (1.50- >100)	>100 -	>100 -	0.0 -	0.0 -	>100 -	1.2 -	0.0 -	-	0.0 -	0.0 -
	Non-Hispanic White	0.5 (0.18-0.84)	1.1 (0.31-4.10)	>100 -	0.0 -	0.6 (0.11-3.19)	1.8 (0.24-14.16)	>100 -	>100 -	0.6 (0.40-3.66)	-	1.0 (0.34-3.20)	3.1 (0.39-25.13)
Constant		-	-	0.0	>100	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

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As shown in Table 17, there were no significant differences in the frequency of complications following facial feminization surgery across racial and ethnic groups. Multivariable analysis confirmed the absence of disparities across groups (Table 18).

Table 17. Patients’ postoperative complications following facial feminization surgery across racial and ethnic groups.

SSI: surgical site infection; DVT: deep vein thrombosis; OR: operating room; C. diff: Clostridioides difficile

Outcome	Racial and ethnic categories						Total	p
Outcome	Hispanic (n = 71)	Non-Hispanic American Indian or Alaska Native (n = 1)	Non-Hispanic Asian (n = 22)	Non-Hispanic Black or African American (n = 59)	Non-Hispanic Native Hawaiian or Pacific Islander (n = 6)	Non-Hispanic White (n = 237)	Total	p
Wound disruption	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	1.0
SSI	2 (2.8)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	7 (3.0)_a	9 (2.3)	.701
Occurrences of pneumonia	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	1.0
Occurrences of unplanned intubation	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	1.0
Occurrences of pulmonary embolism	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.4)_a	1 (0.3)	1.0
Occurrences of ventilator >48 hours	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	1.0
Occurrences of cerebrovascular accident	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	1.0
Occurrences of myocardial infarction	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	1.0
Acute renal failure	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	1.0
Urinary tract infection	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.4)_a	1 (0.3)	1.0
Cardiac arrest requiring CPR	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	1.0
Bleeding transfusions	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	1 (0.4)_a	1 (0.3)	1.0
DVT/thrombophlebitis	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	1.0
Sepsis	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	1.0
Septic shock	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	1.0
Return to OR	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	1.0
Still in hospital >30 days	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	1.0
Unplanned readmission	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)	1.0
C. diff colitis	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	0 (0.0)_a	3 (1.3)_a	3 (0.8)	1.0

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Table 18. Multivariate logistic regression for the incidence of complications following facial feminization surgery.

Outcome		Wound disruption	SSI	Pneumonia	PE	UTI	Bleeding + transfusion	DVT	Sepsis	Unplanned reoperation	LOS >30 days	Unplanned readmission	C. diff colitis
Outcome		aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)
BMI		-	1.0 (0.94-1.15)	-	1.1 (0.0- >100)	1.2 (0.95-1.44)	1.1 (0.0- >100)	-	-	-	-	-	1.0 (0.78-1.40)
Total operation time		-	1.0 (1.0-1.01)	-	1.2 (0.0- >100)	1.0 (0.98-1.01)	1.2 (0.0- >100)	-	-	-	-	-	1.0 (0.99-1.01)
Smoking		-	3.6 (0.66-19.17)	-	0.0 -	0.0 -	0.0 -	-	-	-	-	-	19.1 (1.15- >100)
Dyspnea on moderate exertion		-	0.2 (0.03-1.87)	-	0.0 -	0.0 -	0.0 -	-	-	-	-	-	0.0 -
Surgical specialty	Otolaryngology (ENT)	-	>100 -	-	0.0 -	4.3 -	0.0 -	-	-	-	-	-	0.0 -
	Plastic surgery	-	>100 -	-	0.0 -	0.0 -	0.0 -	-	-	-	-	-	11.1 -
	Urology	-	0.1 -	-	0.0 -	0.0 -	0.0 -	-	-	-	-	-	0.0 -
Racial and ethnic categories	Non-Hispanic American Indian or Alaska Native	-	0.0 -	-	>100 -	>100 -	>100 -	-	-	-	-	-	>100 -
	Non-Hispanic Asian	-	0.0 -	-	>100 -	0.6 -	>100 -	-	-	-	-	-	6.6 -
	Non-Hispanic Black or African American	-	0.0 -	-	36.9 -	1.2 -	36.9 -	-	-	-	-	-	0.1 -
	Non-Hispanic Native Hawaiian or Pacific Islander	-	0.0 -	-	0.5 -	2.3 -	0.5 -	-	-	-	-	-	1.5 -
	Non-Hispanic White	-	0.9 (0.18-4.84)	-	0.0 -	>100 -	0.0 -	-	-	-	-	-	>100 -
Constant		-	0.0	-	0.0	0.0	0.0	-	-	-	-	-	0.0

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Discussion

The steady increase in the number of GAS performed in recent years reflects its recognized role in addressing gender dysphoria and improving the quality of life for many TGNB individuals [17]. As access to GAS expands, understanding disparities in surgical access and outcomes is crucial to ensuring equitable care. Racial and ethnic disparities in healthcare are well-documented across numerous surgical specialties, yet their impact within GAS has been understudied [6-8]. This gap in the literature is particularly concerning given the compounded inequities faced by individuals who identify as members of multiple marginalized groups [11]. These individuals often experience worse health outcomes due to the intersecting effects of systemic barriers, discrimination, and limited access to care [11]. To address this gap, we analyzed data from 6013 TGNB individuals who underwent GAS between 2012 and 2021, focusing on how racial and ethnic disparities manifest in GAS utilization and complications across various surgical subtypes. These observed associations represent trends that may inform future studies and guide the development of more robust models to better understand and address these disparities.

Overall, our analysis revealed significant disparities in GAS utilization and postoperative complications among TGNB individuals from different racial and ethnic groups. White individuals were more likely to undergo chest and genital masculinization surgeries than Hispanic and Black individuals. Postoperatively, Black individuals were more likely to experience wound disruption following genital feminization surgery, and Hispanic individuals undergoing the same procedure had higher odds of this complication compared to White individuals. Additionally, Black individuals experienced a higher frequency of unplanned reoperations and readmissions following chest masculinization surgeries, although these associations were not statistically significant after multivariable adjustment, likely due to sample size limitations and small group comparisons after stratification.

Patient-level factors, including preexisting comorbidities, are closely linked to systemic inequities and likely contribute to disparities in surgical complications [18]. In our study, Black individuals had a higher frequency of hypertension, elevated body mass index (BMI), and smoking history, key predictors of poorer surgical outcomes [19,20]. These findings reflect broader inequities in the distribution of income, education, and employment, which disproportionately impact the health status of racial and ethnic minorities [18]. As a result, marginalized TGNB individuals are more likely to have limited access to resources such as nutritious food, stable housing, reliable transportation, and effective healthcare management, further increasing the risk of complications and limiting access to optimal postoperative care [18]. Our finding of increased wound disruption among Black and Hispanic individuals undergoing genital feminization surgery may reflect the impact of unmanaged comorbidities on wound healing, as hypertension and elevated BMI have been associated with wound complications in plastic surgery [21]. Efforts to address these social determinants of health must accompany clinical interventions to achieve equitable outcomes.

Evidence from prior research suggests that systemic inequities in healthcare infrastructure, particularly in insurance coverage and access to high-volume centers (HVCs), perpetuate disparities in surgical utilization and complications for marginalized TGNB individuals [22]. HVCs are associated with better outcomes, including lower complication rates and shorter hospital stays [22]. These inequities disproportionately affect Black and Hispanic individuals, who are also more likely to rely on Medicaid or be uninsured, further limiting their access to experienced providers [17]. Individuals treated at HVCs are more frequently White, privately insured, and within the highest income quartile, whereas individuals who are Black, Hispanic, or Medicaid-insured have significantly lower odds of receiving care at HVCs [22]. These insurance-related barriers compound inequities in surgical care and likely partly explain the lower frequencies of genital masculinization surgery among Hispanic and Black individuals in our cohort, as these procedures require extensive preoperative evaluations, insurance authorizations, and long-term follow-ups. Addressing these systemic issues through equitable insurance policies and increased access to HVCs could significantly enhance access to care, addressing the underlying barriers limiting marginalized TGNB populations’ ability to undergo necessary procedures.

Implicit bias and discrimination within healthcare settings exacerbate these disparities. Provider-level factors, such as stereotypes or assumptions about TGNB individuals of color, contribute to less thorough preoperative counseling and fewer referrals for complex procedures [23]. This bias deepens mistrust of the healthcare system among TGNB individuals, with higher rates of healthcare avoidance reported by people of color due to fear of mistreatment [11]. Furthermore, medical professionals often face challenges in identifying postsurgical complications in individuals with darker skin, which may contribute to disparities in surgical complications. For instance, several studies have found Black race to be associated with higher rates of postoperative hematomas in head and neck surgeries, often requiring unplanned reoperations [24]. One potential reason for this disparity is that changes in skin color, such as signs of poorer perfusion or ecchymosis, can be harder to discern in patients with darker skin tones, making it more challenging to recognize complications [25]. Additionally, disparities in representation may further contribute to inequities in care. Trilles et al. highlighted that while it has been estimated that 45% of TGNB individuals in the United States identify as people of color, just 29% of photographs and 16% of graphics depict individuals of color in TGNB-related medical literature [11]. This lack of representation may reflect broader systemic inequities that perpetuate implicit biases and reduce providers’ ability to diagnose complications in racial and ethnic minorities [11,24].

Previous studies have demonstrated that social and cultural factors also influence GAS utilization and postoperative complications. Many individuals in marginalized communities face significant challenges in receiving adequate social support due to stigma and discrimination associated with their gender identity [26]. For instance, Black and Hispanic individuals often navigate unique cultural pressures surrounding GAS, especially for visible surgeries like chest masculinization [27]. Our results highlight these cultural influences, demonstrating that Black and Hispanic individuals are less likely to undergo chest masculinization surgery compared to White individuals. These patient populations have the highest rate of hiding their transgender identity, as the internalization of stigma and negative attitudes toward their gender identity can hinder them from seeking care and moving forward with their transition [27,28]. Additionally, individuals from marginalized communities may often have increased postoperative complications due to the lack of social support [27,28]. Our findings of higher readmission frequency among Black individuals following chest masculinization surgeries support the possibility that a lack of robust social networks to assist with postoperative care increases the risk of complications [11]. Social networks provide emotional and practical resources, and their absence disproportionately affects marginalized TGNB individuals [28]. Therefore, interventions that focus on building robust social networks and community support systems are crucial.

Our study advances the field by providing a granular analysis of disparities across specific gender-affirming surgical subtypes. While previous research grouped procedures broadly into masculinizing or feminizing categories, we demonstrate that disparities are not uniform across all GAS procedures and identify disparities that might be obscured in broader analyses. For example, we highlight pronounced disparities in genital feminization surgery, where Hispanic and Black individuals face significantly higher risks of complications. This level of detail pinpoints areas where targeted interventions are most urgently needed, such as enhancing postoperative care for genital feminization surgery, increasing access to HVCs for genital masculinization surgery, and implementing cultural competency training for providers. However, our findings must be interpreted considering the study’s limitations. First, stratifying individuals by surgical subtype resulted in smaller sample sizes for some groups, reducing statistical power and potentially obscuring additional significant associations. Second, the ACS-NSQIP database primarily includes HVCs, which may underestimate disparities that are more pronounced in smaller or lower-resourced practices [29]. Third, the database captures only 30-day postoperative outcomes, precluding an analysis of long-term complications [12]. Finally, the ACS-NSQIP lacks data on social determinants of health and access to care, such as educational attainment, occupation, insurance status, and income, limiting our ability to fully assess the systemic and patient-level factors contributing to disparities [30]. Because this study includes only TGNB individuals who underwent surgery, it cannot assess access disparities among those who did not receive care, and related interpretations remain speculative and literature-based. Moving forward, we plan on addressing these gaps by developing a prospective longitudinal registry to better evaluate chronic complications and long-term surgical and psychosocial outcomes in TGNB individuals undergoing GAS. We also plan on integrating the patient-reported outcome measures in our data collection to better evaluate the role of social determinants of health in postoperative outcomes.

Conclusions

Our study identifies persistent racial and ethnic disparities in GAS utilization and surgical complications. White individuals were more likely to undergo genital masculinization surgery, while Hispanic and Black individuals faced significant barriers to accessing care and higher postoperative complication rates, particularly for genital feminization and chest masculinization surgeries. By stratifying surgical complications by subtype, we contextualize our findings within broader systemic inequities while also identifying the specific procedures where disparities in complications are most pronounced. These insights offer a framework for more granular analysis of patient outcomes and provide a foundation for targeted interventions to improve access to care, reduce complications, and achieve equitable surgical outcomes for TGNB individuals from marginalized racial and ethnic backgrounds.

Acknowledgments

Data are available upon request.

Disclosures

Human subjects: Informed consent for treatment and open access publication was obtained or waived by all participants in this study.

Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue.

Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following:

Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work.

Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work.

Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.

Author Contributions

Concept and design: Yu Jui Kung, Bashar Hassan, Claudia Taccheri, Essie Ghafoor, Mona Ascha, Fan Liang, Calvin Schuster

Acquisition, analysis, or interpretation of data: Yu Jui Kung, Bashar Hassan, Claudia Taccheri, Essie Ghafoor, Mona Ascha, Fan Liang, Calvin Schuster

Drafting of the manuscript: Yu Jui Kung, Bashar Hassan, Fan Liang

Critical review of the manuscript for important intellectual content: Yu Jui Kung, Bashar Hassan, Claudia Taccheri, Essie Ghafoor, Mona Ascha, Fan Liang, Calvin Schuster

Supervision: Fan Liang

References

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26.Interpersonal relationships and social support in transitioning narratives of black transgender women in Detroit. Graham LF, Crissman HP, Tocco J, Hughes LA, Snow RC, Padilla MB. Int J Transgend. 2014;15:100–113. [Google Scholar]
27.Racial disparities in the 30-day outcomes of gender-affirming chest surgeries. Jolly D, Boskey ER, Ganor O. Ann Surg. 2023;278:0–202. doi: 10.1097/SLA.0000000000005512. [DOI] [PubMed] [Google Scholar]
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[REF1] 1.James SE, Herman JL, Rankin S, Keisling M, Mottet L, Anafi M. Washington, DC: National Center for Transgender Equality; 2016. The report of the 2015 U.S. transgender survey; pp. 11–21. [Google Scholar]

[REF2] 2.Association between gender confirmation treatments and perceived gender congruence, body image satisfaction, and mental health in a cohort of transgender individuals. Owen-Smith AA, Gerth J, Sineath RC, et al. J Sex Med. 2018;15:591–600. doi: 10.1016/j.jsxm.2018.01.017. [DOI] [PMC free article] [PubMed] [Google Scholar]

[REF3] 3.Barriers to gender-affirming care for transgender and gender nonconforming individuals. Puckett JA, Cleary P, Rossman K, Newcomb ME, Mustanski B. Sex Res Social Policy. 2018;15:48–59. doi: 10.1007/s13178-017-0295-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[REF4] 4.Racial and ethnic differences in experiences of discrimination in accessing health services among transgender people in the united states. Kattari SK, Walls NE, Whitfield DL, Langenderfer-Magruder L. Int J Transgend. 2015;16:68–79. [Google Scholar]

[REF5] 5.Intersecting experiences of healthcare denials among transgender and nonbinary patients. Kattari SK, Bakko M, Hecht HK, Kinney MK. Am J Prev Med. 2020;58:506–513. doi: 10.1016/j.amepre.2019.11.014. [DOI] [PubMed] [Google Scholar]

[REF6] 6.Unequal treatment: confronting racial and ethnic disparities in health care. Nelson A. https://pmc.ncbi.nlm.nih.gov/articles/PMC2594273/pdf/jnma00325-0024.pdf. J Natl Med Assoc. 2002;94:666–668. [PMC free article] [PubMed] [Google Scholar]

[REF7] 7.What happens to racial and ethnic minorities after cancer surgery at American College of Surgeons National Surgical Quality Improvement Program hospitals? Parsons HM, Habermann EB, Stain SC, Vickers SM, Al-Refaie WB. J Am Coll Surg. 2012;214:539–547. doi: 10.1016/j.jamcollsurg.2011.12.024. [DOI] [PubMed] [Google Scholar]

[REF8] 8.Assessing short- and long-term outcomes among black vs white Medicare patients undergoing resection of colorectal cancer. Schneider EB, Haider AH, Hyder O, Efron JE, Lidor AO, Pawlik TM. Am J Surg. 2013;205:402–408. doi: 10.1016/j.amjsurg.2012.08.005. [DOI] [PMC free article] [PubMed] [Google Scholar]

[REF9] 9.Racial disparities in readmissions and site of care for major surgery. Girotti ME, Shih T, Revels S, Dimick JB. J Am Coll Surg. 2014;218:423–430. doi: 10.1016/j.jamcollsurg.2013.12.004. [DOI] [PubMed] [Google Scholar]

[REF10] 10.Gender affirmation surgery: a synopsis using American College of Surgeons National Surgery Quality Improvement Program and National Inpatient Sample Databases. Tran BN, Epstein S, Singhal D, Lee BT, Tobias AM, Ganor O. Ann Plast Surg. 2018;80:0–35. doi: 10.1097/SAP.0000000000001350. [DOI] [PubMed] [Google Scholar]

[REF11] 11.Recognizing racial disparities in postoperative outcomes of gender affirming surgery. Trilles J, Chaya BF, Brydges H, et al. LGBT Health. 2022;9:333–339. doi: 10.1089/lgbt.2021.0396. [DOI] [PubMed] [Google Scholar]

[REF12] 12.Large surgical databases with direct data abstraction: VASQIP and ACS-NSQIP. Habermann EB, Harris AH, Giori NJ. J Bone Joint Surg Am. 2022;104:9–14. doi: 10.2106/JBJS.22.00596. [DOI] [PubMed] [Google Scholar]

[REF13] 13.ICD-10-CM official guidelines for coding and reporting. [ Feb; 2025 ]. 2024. https://www.cms.gov/files/document/fy-2024-icd-10-cm-coding-guidelines-updated-02/01/2024.pdf https://www.cms.gov/files/document/fy-2024-icd-10-cm-coding-guidelines-updated-02/01/2024.pdf

[REF14] 14.Revisions to the standards for the classification of Federal data on race and ethnicity. [ Dec; 2024 ];https://obamawhitehouse.archives.gov/omb/fedreg_1997standards Fed Regist. 1997 62:58782–58790. [Google Scholar]

[REF15] 15.ACS NSQIP Participant Use Data File. [ Oct; 2024 ]. 2013. https://www.facs.org/quality-programs/data-and-registries/acs-nsqip/participant-use-data-file/ https://www.facs.org/quality-programs/data-and-registries/acs-nsqip/participant-use-data-file/

[REF16] 16.Stehlik-Barry K, Babinec AJ. Ltd. Birmingham, UK: Packt Publishing; 2017. Data analysis with IBM SPSS statistics. [Google Scholar]

[REF17] 17.Assessing racial disparities in gender-affirming surgery utilization and hospital-level experience. Shamamian PE, Kwon DY, Oleru O, et al. J Plast Reconstr Aesthet Surg. 2025;100:16–23. doi: 10.1016/j.bjps.2024.10.034. [DOI] [PubMed] [Google Scholar]

[REF18] 18.Unmet social health needs as a driver of inequitable outcomes after surgery: a cross-sectional analysis of the national health interview survey. Taylor KK, Neiman PU, Bonner S, Ranganathan K, Tipirneni R, Scott JW. Ann Surg. 2023;278:193–200. doi: 10.1097/SLA.0000000000005689. [DOI] [PMC free article] [PubMed] [Google Scholar]

[REF19] 19.Isolated and combined breast augmentation in transgender patients: multi-institutional insights into early outcomes and risk factors. Knoedler S, Knoedler L, Geldner B, et al. J Plast Reconstr Aesthet Surg. 2024;90:149–160. doi: 10.1016/j.bjps.2024.01.026. [DOI] [PubMed] [Google Scholar]

[REF20] 20.Association of high body mass index with postoperative complications after chest masculinization surgery. Hassan B, Schuster CR, Ascha M, Del Corral G, Fischer B, Liang F. Ann Plast Surg. 2024;92:174–180. doi: 10.1097/SAP.0000000000003737. [DOI] [PubMed] [Google Scholar]

[REF21] 21.The association between metabolic derangement and wound complications in elective plastic surgery. Sergesketter AR, Geng Y, Shammas RL, Denis GV, Bachelder R, Hollenbeck ST. J Surg Res. 2022;278:39–48. doi: 10.1016/j.jss.2022.03.017. [DOI] [PMC free article] [PubMed] [Google Scholar]

[REF22] 22.Race, insurance, and sex-based disparities in access to high-volume centers for pancreatectomy. Williamson CG, Ebrahimian S, Sakowitz S, Aguayo E, Kronen E, Donahue TR, Benharash P. Ann Surg Oncol. 2023;30:3002–3010. doi: 10.1245/s10434-022-13032-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[REF23] 23.Healthcare experiences of transgender people of color. Howard SD, Lee KL, Nathan AG, Wenger HC, Chin MH, Cook SC. J Gen Intern Med. 2019;34:2068–2074. doi: 10.1007/s11606-019-05179-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

[REF24] 24.Neck hematoma after major head and neck surgery: risk factors, costs, and resource utilization. Shah-Becker S, Greenleaf EK, Boltz MM, Hollenbeak CS, Goyal N. Head Neck. 2018;40:1219–1227. doi: 10.1002/hed.25102. [DOI] [PubMed] [Google Scholar]

[REF25] 25.Predicting alternate light absorption in areas of trauma based on degree of skin pigmentation: not all wavelengths are equal. Scafide KN, Downing NR, Kutahyalioglu NS, Sheridan DJ, Langlois NE, Hayat MJ. Forensic Sci Int. 2022;339:111410. doi: 10.1016/j.forsciint.2022.111410. [DOI] [PubMed] [Google Scholar]

[REF26] 26.Interpersonal relationships and social support in transitioning narratives of black transgender women in Detroit. Graham LF, Crissman HP, Tocco J, Hughes LA, Snow RC, Padilla MB. Int J Transgend. 2014;15:100–113. [Google Scholar]

[REF27] 27.Racial disparities in the 30-day outcomes of gender-affirming chest surgeries. Jolly D, Boskey ER, Ganor O. Ann Surg. 2023;278:0–202. doi: 10.1097/SLA.0000000000005512. [DOI] [PubMed] [Google Scholar]

[REF28] 28.Loneliness, lack of social and emotional support, and mental health issues-United States, 2022. Bruss KV, Seth P, Zhao G. MMWR Morb Mortal Wkly Rep. 2024;73:539–545. doi: 10.15585/mmwr.mm7324a1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[REF29] 29.Evaluation of hospitals participating in the American College of Surgeons National Surgical Quality Improvement Program. Sheils CR, Dahlke AR, Kreutzer L, Bilimoria KY, Yang AD. Surgery. 2016;160:1182–1188. doi: 10.1016/j.surg.2016.04.034. [DOI] [PubMed] [Google Scholar]

[REF30] 30.Integrating equity frameworks into surgical quality improvement and health administrative databases: a narrative review. Bondzi-Simpson A, Ribeiro T, Coburn NG, Hallet J. Am J Surg. 2024;236:115421. doi: 10.1016/j.amjsurg.2023.08.009. [DOI] [PubMed] [Google Scholar]

PERMALINK

Race, Ethnicity, and Complications Following Gender-Affirming Surgery: A National Surgical Quality Improvement Program (NSQIP) Analysis

Yu Jui Kung

Bashar Hassan

Calvin Schuster

Claudia Taccheri

Essie Ghafoor

Mona Ascha

Fan Liang

Abstract

Introduction

Materials and methods

Table 1. ICD-9, ICD-10, and CPT codes used for patient selection and stratification.

Results

Table 2. Subtypes of gender-affirming surgeries stratified by racial and ethnic groups.

Table 3. Demographics, comorbidities, and surgical characteristics of patients who underwent any gender-affirming surgery across racial and ethnic groups.

Table 4. Demographics, comorbidities, and surgical characteristics of patients who underwent chest masculinization surgery across racial and ethnic groups.

Table 8. Demographics, comorbidities, and surgical characteristics of patients who underwent facial feminization surgery across racial and ethnic groups.

Table 5. Demographics, comorbidities, and surgical characteristics of patients who underwent chest feminization surgery across racial and ethnic groups.

Table 6. Demographics, comorbidities, and surgical characteristics of patients who underwent genital masculinization surgery across racial and ethnic groups.

Table 7. Demographics, comorbidities, and surgical characteristics of patients who underwent genital feminization surgery across racial and ethnic groups.

Table 9. Patients’ postoperative complications following chest masculinization surgery across racial and ethnic groups.

Table 10. Multivariate logistic regression for the incidence of complications following chest masculinization surgery.

Table 11. Patients’ postoperative complications following chest feminization surgery across racial and ethnic groups.

Table 12. Multivariate logistic regression for the incidence of complications following chest feminization surgery.

Table 13. Patients’ postoperative complications following genital masculinization surgery across racial and ethnic groups.

Table 14. Multivariate logistic regression for the incidence of complications following genital masculinization surgery.

Table 15. Patients’ postoperative complications following genital feminization surgery across racial and ethnic groups.

Table 16. Multivariate logistic regression for the incidence of complications following genital feminization surgery.

Table 17. Patients’ postoperative complications following facial feminization surgery across racial and ethnic groups.

Table 18. Multivariate logistic regression for the incidence of complications following facial feminization surgery.

Discussion

Conclusions

Acknowledgments

Disclosures

Author Contributions

References

ACTIONS

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