Abstract
Introduction:
Little is known about how expansion of telemedicine services during the COVID-19 pandemic has affected access to gender-affirming care for transgender and gender-diverse (TGD) youth. The purpose of this study was to explore differences in demographic characteristics and visit completion rates at a multidisciplinary gender clinic before and after telemedicine implementation in March 2020 and among telemedicine users and nonusers.
Methods:
Data were from electronic health records of Seattle Children’s Gender Clinic (SCGC) patients seen between April 2019 and February 2021. We assessed differences in demographic characteristics and care utilization (i.e., encounter type and status) between April 2019 and February 2020 (pre-telemedicine) and April 2020 and February 2021 (post-telemedicine).
Results:
Of the 1,051 unique patients seen at SCGC during this time period, majority groups were as follows: 62% identified as transmasculine/male, 68% were non-Hispanic White, and 76% resided within 50 miles of the clinic. Statistically significant differences were observed in patient pronouns and insurance type when comparing the pre- and post-telemedicine periods (p < 0.01). Half (52%) of post-telemedicine period encounters were conducted through telemedicine, and telemedicine encounters were significantly more likely to be completed (72% vs. 50%) and less likely to be canceled (21% vs. 46%) compared with in-person encounters.
Conclusions:
Telemedicine services facilitated continued access to gender-affirming care services for TGD youth during the COVID-19 pandemic. Although the introduction of telemedicine did not exacerbate demographic disparities in access to this care, further research and interventions are warranted to address the ongoing disparities in access to gender-affirming care for youth of color and rural youth.
Keywords: telemedicine, telehealth, gender-affirming care, transgender and gender-diverse youth, adolescent health
Introduction
Transgender and gender-diverse (TGD) youth experience numerous health disparities compared with their cisgender peers.1–3 Many TGD youth have negative experiences with health care systems, which are often the result of transphobia, cisnormativity, and other forms of discrimination,4,5 leading to reticence in seeking medical care and further perpetuating health disparities.1,6,7 Due to a number of sociostructural barriers, few TGD youth, ~1 in 5, who desire gender-affirming medical interventions are able to access them.8
For those who do access gender-affirming medical care, many youth receive care in specialty gender clinics,9 which often have long waiting lists and are typically located in large urban areas, thereby reducing access for TGD youth who live outside these urban hubs.10–12
The COVID-19 pandemic has led to unprecedented utilization of patient-to-provider telemedicine, defined as the use of audio–video technology for direct medical care, in many hospital systems.13–17 Even before the pandemic, pediatric providers found many benefits to using telemedicine, such as improved reach to rural communities18 and access to subspecialty care,19 as well as the ability to serve patients who may have difficulties traveling to a clinic.20,21
However, the recent scale-up and expansion of telemedicine due to the COVID-19 pandemic have offered new and exciting opportunities to provide health care to populations that have historically been neglected or are harder to reach.
A recent review of telemedicine in gender-affirming care settings indicated that telemedicine has been employed in the gender-affirming care context in a variety of ways, including patient-to-provider telemedicine and peer support.22 Studies have also shown that clinicians, TGD youth, and their caregivers are interested in providing and receiving care through telemedicine.23,24
Telemedicine may be an ideal way to bridge certain barriers to pediatric gender-affirming care, including increasing access for those who have been historically underrepresented in pediatric gender clinics such as TGD youth of color secondary to the intersectional oppression of racism and transphobia and those living in rural areas due to travel restrictions.25
However, little research has explored whether the introduction of telemedicine has had this intended impact on access to gender-affirming care. Such research is crucial to help us understand the extent to which telemedicine has affected inequities in access to gender-affirming care services; the necessary next steps for improving access to gender-affirming care moving forward; and more broadly, if and how telemedicine services should be used to provide this care in the future.
In March 2020, the Seattle Children’s Gender Clinic (SCGC) implemented telemedicine in response to the COVID-19 pandemic. The purpose of this study was to investigate if implementing telemedicine impacted either the demographic characteristics of patients seen at our clinic or visit completion rates.
We hypothesized that the introduction of telemedicine services would result in greater use of gender-affirming care among groups of youth who have been disproportionately underrepresented in gender clinics (i.e., youth of color and rural youth) and increased visit completion rates and decreased cancelation rates for telemedicine encounters compared with in-person encounters.
Methods
STUDY POPULATION AND PROCEDURES
This secondary analysis used patient- and encounter-level data from electronic health records (EHRs) of patients who were scheduled to be seen in the SCGC between April 2019 and February 2021. Specifically, patients were included in these analyses if they had an International Classification of Diseases-10 diagnosis of gender dysphoria and were scheduled to be seen for a visit with an SCGC medical provider during this period.
The authors acknowledge the controversy of using gender dysphoria diagnosis codes, but ultimately decided that this was the most accessible way of identifying patients given their use for the purpose of insurance billing.26,27 Encounters were included if the visit was scheduled as a new or returning medical encounter either in-person or through telemedicine. Telemedicine encounters included appointments that were scheduled to be conducted by phone or by audio–video telemedicine with an SCGC medical provider (Doctor of Medicine, Doctor of Osteopathic Medicine, Physician Assistant, or Advanced Registered Nurse Practitioner).
Visits were excluded if the reason for the visit was something other than gender care (e.g., eating disorder). Visits that occurred during March 2020 were characterized as a washout period and excluded from these analyses to account for any variation in service use during the transition to telemedicine at the onset of the COVID-19 pandemic.
All study procedures were reviewed by the Seattle Children’s Hospital Institutional Review Board and deemed exempt.
VARIABLES
Patient-level data.
Demographic information, including age in years, sex assigned at birth, gender identity, pronouns, race and ethnicity, language for care, insurance type, and zip code, were extracted from EHRs. Due to small cell sizes and to reduce the risk of deductive disclosure, the American Indian/Alaska Native (AI/AN) and Native Hawaiian/Pacific Islander (NH/PI) race and ethnicity categories were aggregated for all analyses. Similarly, Spanish and all other language groups were aggregated into a “Language other than English” category.
Zip codes were used to allocate counties through US Department of Housing and Urban Development-US Postal Service ZIP Code Crosswalk data28 to identify and categorize the distance from the clinic in miles. Zip codes were also allocated to US Department of Agriculture Economic Research Service’s Rural–Urban Continuum Codes29 to identify metro/nonmetro and urban/rural statuses.29
Because gender identity information was missing for most patients, manual chart review of clinician and referral notes was used to complete this field. Following discussion with the SCGC Youth Advisory Board (YAB), we decided to use the first gender identity reported in a clinician note for patients with multiple encounters during the study period.
Due to small sample sizes for certain gender identities and to reduce the risk of deductive disclosure, we aggregated certain gender identities into larger categories for analysis, as directed by the YAB.
Encounter-level data.
Encounter type was labeled as “New” if the encounter represented the patient’s first encounter with an SCGC medical provider and “Returning” for all subsequent visits.
Encounter status was categorized as completed for encounters that were conducted at the scheduled date and time; canceled for encounters that were scheduled, but canceled before the time of the visit; and no-show for encounters that did not occur at the scheduled date and time, but were not canceled in advance of the visit. Canceled visits were further categorized into patient initiated and provider/practice initiated based on the reason provided for cancelation, as documented in the EHRs.
STATISTICAL ANALYSES
Descriptive analyses, including frequencies and percentages, of data were performed. Chi-square tests were used to assess differences in demographic and care utilization characteristics for patients who completed visits in the pre- and post-telemedicine periods. We also tested for differences in demographic characteristics among those who completed encounters in the post-telemedicine period after stratifying those who had at least one completed visit through telemedicine (telemedicine users) and those who never used telemedicine (nontelemedicine users).
Finally, we examined differences in encounter type and status among scheduled clinic encounters during the post-telemedicine period by modality (telemedicine vs. in person). All analyses were conducted using R software, version 1.3.1.30
Results
PATIENT-LEVEL RESULTS
Data of patients who completed an encounter at the SCGC during the entire study period (N = 1,051) are shown in Table 1. The mean age at first encounter was 15.7 years (range 10–21 years). Most patients (62%) identified as transmasculine/male, 24% identified as transfeminine, and 8% had a gender-diverse identity such as nonbinary, genderfluid, and more.
Table 1.
Demographic Characteristics of Patients Who Completed a Gender Clinic Visit in the Year Before and Year Following the Onset of the COVID-19 Pandemic (N = 1,051)
| PRE-TELEMEDICINE: APRIL 2019-FEBRUARY 2020, n (%) (N = 709) | POST-TELEMEDICINE: APRIL 2020-FEBRUARY 2021, n (%) (N = 788) | OVERALL, n (%) (N = 1,051)a | P * | |
|---|---|---|---|---|
| Age (years) | 0.70 | |||
| ≤12 | 58 (8.2) | 60 (7.6) | 93 (8.8) | |
| 13–15 | 166 (23.4) | 182 (23.1) | 276 (26.3) | |
| 16–17 | 322 (45.4) | 380 (48.2) | 507 (48.2) | |
| ≥18 | 163 (23.0) | 166 (21.1) | 175 (16.7) | |
| Sex assigned at birth | 0.55 | |||
| Female | 491 (69.3) | 566 (71.8) | 748 (71.2) | |
| Male | 216 (30.5) | 220 (27.9) | 301 (28.6) | |
| Choose not to disclose | 2 (0.3) | 2 (0.3) | 2 (0.2) | |
| Gender identityb | 0.62 | |||
| Transmasculine/male | 451 (63.6) | 497 (63.1) | 651 (61.9) | |
| Transfeminine/female | 184 (26.0) | 188 (23.9) | 255 (24.3) | |
| Nonbinary | 38 (5.4) | 61 (7.7) | 81 (7.7) | |
| Gender exploring | 12 (1.7) | 14 (1.8) | 21 (2.0) | |
| Genderfluid/genderflux/bigender | 9 (1.3) | 7 (0.9) | 13 (1.2) | |
| Genderqueer/gender-diverse | 4 (0.6) | 7 (0.9) | 10 (1.0) | |
| Agender | 3 (0.4) | 5 (0.6) | 6 (0.6) | |
| Demi-boy/demi-girl | 3 (0.4) | 5 (0.6) | 6 (0.6) | |
| Another genderc | 2 (0.3) | 4 (0.5) | 5 (0.5) | |
| Pronouns | 0.008* | |||
| He | 449 (63.3) | 468 (59.4) | 637 (60.6) | |
| She | 173 (24.4) | 174 (22.1) | 235 (22.4) | |
| They | 73 (10.3) | 105 (13.3) | 134 (12.7) | |
| He/they | 9 (1.3) | 27 (3.4) | 29 (2.8) | |
| She/they | 2 (0.3) | 8 (1.0) | 8 (0.8) | |
| Pronouns not listed above | 1 (0.1) | 3 (0.4) | 3 (0.3) | |
| Race and ethnicity | 0.97 | |||
| Non-Hispanic White | 483 (68.1) | 545 (69.2) | 711 (67.6) | |
| Hispanic/Latinx | 75 (10.6) | 83 (10.5) | 117 (11.1) | |
| Two or more races | 52 (7.3) | 57 (7.2) | 75 (7.1) | |
| Asian | 21 (3.0) | 23 (2.9) | 34 (3.2) | |
| Black/African American | 14 (2.0) | 15 (1.9) | 23 (2.2) | |
| AI/AN and NH/PI | 8 (1.1) | 5 (0.6) | 8 (0.8) | |
| Race and ethnicity not listed above | 21 (3.0) | 18 (2.3) | 26 (2.5) | |
| Unknown/refused | 35 (4.9) | 42 (5.3) | 57 (5.4) | |
| Language for care | 0.86 | |||
| English | 700 (98.7) | 781 (99.1) | 1,039 (98.9) | |
| Language other than Englishd | 6 (0.8) | 5 (0.6) | 8 (0.8) | |
| Insurance type | 0.002* | |||
| Commercial | 457 (64.5) | 491 (62.3) | 670 (63.7) | |
| Medicaid/healthy options | 226 (31.9) | 239 (30.3) | 320 (30.4) | |
| Other government | 14 (2.0) | 15 (1.9) | 20 (1.9) | |
| Self-pay/charity care | 12 (1.7) | 43 (5.5) | 41 (3.9) | |
| Distance from the clinic (miles)e | 0.72 | |||
| <25 | 389 (54.9) | 433 (54.9) | 572 (54.4) | |
| 25–49 | 162 (22.8) | 164 (20.8) | 228 (21.7) | |
| 50–99 | 90 (12.7) | 110 (14.0) | 138 (13.1) | |
| 100+ | 67 (9.5) | 80 (10.2) | 111 (10.6) | |
| Residence type | 0.96 | |||
| Metro (≥1,000,000) | 544 (76.7) | 595 (75.5) | 798 (75.9) | |
| Metro (250,000–999,999) | 57 (8.0) | 62 (7.9) | 80 (7.6) | |
| Metro (<250,000) | 54 (7.6) | 62 (7.9) | 85 (8.1) | |
| Nonmetro (≥20,000) | 42 (5.9) | 52 (6.6) | 68 (6.5) | |
| Nonmetro (≤19,999) | 12 (1.7) | 16 (2.0) | 19 (1.8) | |
| Missing | 1 (0.1) | 1 (0.1) | ||
| Urban/rural status | 0.53 | |||
| Urban | 655 (92.4) | 719 (91.2) | 963 (91.6) | |
| Rural | 54 (7.6) | 68 (8.6) | 87 (8.3) | |
Minimal missingness was reported for the following variables: gender identity (n = 3), pronouns (n = 5), language (n = 5), distance from the clinic (n = 2), residence type (n = 1), region (n = 1), and urban/rural status (n = 1).
Statistically significant at the p < 0.05 level.
Since these data are based on the patient’s first encounter, patients may be included in both time periods. The overall column does not reflect the total between these two time periods.
Patients can be included in multiple gender categories.
Another gender includes "Other," "boy or other," and "does not identify with she/her."
Language other than English includes ASL, English/PSE, and Spanish.
Based on zip code.
AI/AN, American Indian and Alaska Native; ASL, American Sign Language; NH/PI, Native Hawaiian/Pacific Islander; PSE, Pidgin Signed English.
Over two-thirds of patients (68%) identified as non-Hispanic White, 11% Hispanic/Latinx, 7% two or more races, 3% Asian, 2% Black/African American, and 0.8% AI/AN and NH/PI. Roughly 3 of every 4 patients (76%) resided within 50 miles of the SCGC, and a majority (64%) used commercial health insurance.
Temporal trends in telemedicine utilization among completed encounters, including the washout period in March 2020, are shown in Figure 1. Overall, a similar number of patients were seen in the pre- and post-telemedicine periods (n = 709 vs. n = 788). There were statistically significant differences in demographic characteristics between the pre-telemedicine and post-telemedicine periods by both pronouns and insurance type.
Fig. 1.
Completed telemedicine (dark gray) versus in-person (light gray) encounters during the period April 2019–February 2021, with a washout period in March 2020.
Specifically, a slightly larger proportion of youth seen in the post-telemedicine period used “they” pronouns (13% vs. 10% for “they” only, 3% vs. 1% for “he/they,” and 1% vs. 0.3% for “she/they,” p < 0.01) and a higher proportion of patients used self-pay/charity care insurance (6% vs. 2%, p < 0.01) compared with the pre-telemedicine period. No other statistically significant demographic differences emerged between the pre- and post-telemedicine periods.
In the post-telemedicine period, there were no significant differences in demographic characteristics between patients who ever used telemedicine services (52%) and those who never used telemedicine services (48%; Supplementary Table S1).
ENCOUNTER-LEVEL RESULTS
Encounter-level characteristics comparing the pre- and post-telemedicine periods and by telemedicine utilization during the post-telemedicine period are shown in Table 2 (N = 4,288 patient visits). The volume of scheduled encounters was similar between the pre- (n = 2,208) and post-telemedicine periods (n = 2,080) and most encounters were typed as return visits (79%).
Table 2.
Characteristics of Scheduled Gender Clinic Encounters between April 2019 and February 2021, By Telemedicine Period and Utilization
| PRE-TELEMEDICINE | POST-TELEMEDICINE | OVERALL | |||||
|---|---|---|---|---|---|---|---|
| TOTAL,a n (%) (N = 2,208, 51.5% OF ALL ENCOUNTERS) | TOTAL, n (%) (N = 2,080, 48.5% OF ALL ENCOUNTERS) | TELEMEDICINE, n (%) (N = 1,082, 52.0% OF POST-TELEMEDICINE ENCOUNTERS) | IN PERSON, n (%) (N = 998, 48% OF POST-TELEMEDICINE ENCOUNTERS) | p-VALUE COMPARING IN-PERSON VS. TELEMEDICINE UTILIZATION | TOTAL, N (%) (N = 4,288) | p-VALUE COMPARING PRE-TELEMEDICINE AND POST-TELEMEDICINE PERIODS | |
| Encounter type | |||||||
| New | 452 (20.5) | 441 (21.2) | 221 (20.4) | 220 (22.0) | 0.37 | 893 (20.8) | 0.56 |
| Returning | 1,756 (79.5) | 1,639 (78.8) | 861 (79.6) | 778 (78.0) | 3,395 (79.2) | ||
| Encounter status | |||||||
| Completed | 1,385 (62.7) | 1,278 (61.4) | 775 (71.6) | 503 (50.4) | <0.01* | 2,663 (62.1) | 0.58 |
| No-show | 113 (5.1) | 118 (5.7) | 81 (7.5) | 37 (3.7) | 231 (5.4) | ||
| Canceled | 710 (32.2) | 684 (32.9) | 226 (20.9) | 458 (45.9) | 1,394 (32.5) | ||
| Patient initiated | 483 (68.0) | 395 (57.7) | 126 (55.8) | 269 (58.7) | 0.46 | 878 (63.0) | <0.01* |
| Provider/practice initiated | 227 (32.0) | 289 (42.3) | 100 (44.2) | 189 (41.3) | 516 (37.0) | ||
Statistically significant at the p < 0.05 level.
All encounters in the pre-telemedicine period were conducted in person.
The status for the majority (62%) of encounters was completed, while a third (33%) were canceled and 5% were characterized as no-shows. Among canceled appointments, nearly two-thirds were patient initiated (63%). During the post-telemedicine period, approximately half (52%) of all visits were conducted through telemedicine.
Comparing the pre- and post-telemedicine periods, there were no significant differences in encounter type and encounter status. However, among canceled visits, significantly more cancelations were patient initiated in the pre-telemedicine period compared with the post-telemedicine period (68% vs. 58%, p < 0.01).
During the post-telemedicine period, there were no significant differences in encounter type between telemedicine and in-person encounters. Encounters that occurred through telemedicine were not only more likely to be completed (72% vs. 50%, p < 0.01) and less likely to be canceled (21% vs. 45%, p < 0.01), compared with in-person encounters, but were also more likely to result in no-shows (8% vs. 4%, p < 0.01).
Among canceled visits, there were no significant differences in cancelation initiation (patient vs. provider/practice) between the telemedicine and in-person modalities.
Discussion
The purpose of this study was to assess differences in patient- and encounter-level characteristics among telemedicine users and nonusers at the SCGC before and after the clinic implemented telemedicine options in response to the COVID-19 pandemic. Overall, our findings suggest that the expansion of telemedicine services has allowed for continued access to services during the COVID-19 pandemic.
Our results also show that telemedicine has had a significant impact on visit completion as encounters conducted through telemedicine were more likely to be completed and less likely to be canceled, suggesting an overall improvement in care access. These results are in line with existing research highlighting the pivotal role telemedicine has played in facilitating continued access to routine care during the COVID-19 pandemic31 and further suggest the utility of telemedicine in the gender-affirming care setting. As such, these data will be particularly useful in informing health system-level changes in telemedicine utilization moving forward.
There are, however, ongoing disparities in service access that have not been addressed by the introduction of telemedicine services. Our results showed few differences in demographic characteristics of patients who completed encounters at the SCGC between the pre- and post-telemedicine periods. This suggests that while the introduction of telemedicine did not introduce additional disparities in access to gender-affirming care, it also did not reduce disparities for groups of youth who have been historically underrepresented in pediatric gender clinics as we had hypothesized.
Similar to other pediatric gender clinics in the Unites States, the SCGC patient population continues to disproportionately comprise White privately insured patients living in urban areas.32,33 Data from the Youth Risk Behavior Survey24 suggest that a higher proportion of youth of color identify as TGD compared with their White peers.4
In addition, another national survey found that racial and ethnic minority TGD youth reported lower rates of receiving gender-affirming care compared with White youth,8 highlighting ongoing disparities in access to gender-affirming care for youth of color. These disparities indicate a critical need for future research to better understand why these disparities exist31 and to develop community-informed interventions to improve access.
Furthermore, although telemedicine encounters were more likely to be completed and less likely to be canceled than in-person encounters, telemedicine encounters were also more likely to result in no-shows. These results suggest a potential need to develop new reminder systems to promote visit attendance during telemedicine visits as existing reminder systems have been designed for in-person visits and may not be meeting the needs of patients and their families who are utilizing telemedicine.
These findings also highlight a need to improve health system-level and health information technology systems to more seamlessly allow patients to pivot from in-person to telemedicine visits as their needs change.
LIMITATIONS
Our results should be interpreted within the context of the following limitations. Although our study compared pre- and post-telemedicine time periods, our post-telemedicine findings were limited to only 1 year, which may not have been sufficient time to detect changes in demographic characteristics. We must acknowledge that some of the changes seen between pre- and post-telemedicine periods may be related to temporal trends rather than the introduction of telemedicine.
Additionally, our study relied on EHR-abstracted data, which may have led to inaccurate interpretation and categorization of certain variables.34 For example, EHR gender identity data were quite limited during the study period, requiring us to conduct chart reviews of clinician notes to identify gender identity. Therefore, our reliance on provider-reported data may not adequately reflect a patient’s self-reported gender identity.
More recently, adolescents seen at the Seattle Children’s Hospital with access to their EHR portal can now independently edit their gender identity and pronoun fields using an embedded Sexual Orientation and Gender Identity smart form. Such tools may provide opportunities for researchers using secondary EHR data to capture the gold standard, self-reported gender identity, in future research.
Similarly, small sample sizes required us to aggregate certain gender identity and race and ethnicity categories to protect patient confidentiality, which limited our ability to understand variation in these groups. Therefore, future research with longer study periods and larger datasets may allow for more robust subgroup analyses.
Conclusions
Overall, this study highlights the importance of telemedicine in the ongoing provision of gender-affirming care services for TGD youth. Future research is needed to improve our understanding of how telemedicine use impacts access to care.
In the meantime, these data can be used to inform the development of interventions to improve access to and utilization of pediatric gender-affirming care services, especially among youth who have been disproportionately underrepresented in pediatric gender care settings.
Supplementary Material
Acknowledgments
The authors would like to thank Kristyn Simmons from the Analytics team of SCGC for providing the data and the SCGC YAB for providing input on the reporting of gender identities. The authors received no financial support for the research, authorship, or publication of this manuscript.
Funding Information
The authors received no financial support for the research, authorship, or publication of this manuscript.
Footnotes
Disclosure Statement
All authors declare that they have no conflicts of interest.
REFERENCES
- 1.Rider GN, McMorris BJ, Gower AL, et al. Health and care utilization of transgender and gender nonconforming youth: A population-based study. Pediatrics 2018;141(3):e20171683. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Newcomb ME, Hill R, Buehler K, et al. High burden of mental health problems, substance use, violence, and related psychosocial factors in transgender, non-binary, and gender diverse youth and young adults. Arch Sex Behav 2020;49(2):645–659. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Clark H, Babu AS, Wiewel EW, et al. Diagnosed HIV infection in transgender adults and adolescents: Results from the National HIV Surveillance System, 2009–2014. AIDS Behav 2017;21(9):2774–2783. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Hendricks ML, Testa RJ. A conceptual framework for clinical work with transgender and gender nonconforming clients: An adaptation of the minority stress model. Profess Psychol Res Pract 2012;43(5):460. [Google Scholar]
- 5.Tan KK, Treharne GJ, Ellis SJ, et al. Gender minority stress: A critical review. J Homosex 2020;67(10):1471–1489. [DOI] [PubMed] [Google Scholar]
- 6.Lykens JE, Leblanc AJ, Bockting WO. Healthcare experiences among young adults who identify as genderqueer or nonbinary. LGBT Health 2018;5(3):191–196. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Clark BA, Veale JF, Greyson D, et al. Primary care access and foregone care: A survey of transgender adolescents and young adults. Fam Pract 2018;35(3):302–306. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Green AE, DeChants JP, Price MN, et al. Association of gender-affirming hormone therapy with depression, thoughts of suicide, and attempted suicide among transgender and nonbinary youth. J Adolesc Health 2022;70(4):643–649. [DOI] [PubMed] [Google Scholar]
- 9.Hsieh S, Leininger J. Resource list: Clinical care programs for gender-nonconforming children and adolescents. Pediatr Ann 2014;43(6):238–244. [DOI] [PubMed] [Google Scholar]
- 10.Chong LS, Kerklaan J, Clarke S, et al. Experiences and perspectives of transgender youths in accessing health care: A systematic review. JAMA Pediatr 2021;175(11):1159–1173. [DOI] [PubMed] [Google Scholar]
- 11.Inwards-Breland DJ, DiVall S, Salehi P, et al. Youth and parent experiences in a multidisciplinary gender clinic. Transgend Health 2019;4(1):100–106. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Inwards-Breland DJ, Karrington B, Sequeira GM. Access to care for transgender and nonbinary youth: Ponder this, innumerable barriers exist. JAMA Pediatr 2021;175(11):1112–1114. [DOI] [PubMed] [Google Scholar]
- 13.Hanson S Pandemic Catapults Rapid Expansion of Telehealth Care. Seattle Children’s On the Pulse: Seattle, WA, USA; 2020. [Google Scholar]
- 14.Hollander JE, Carr BG. Virtually perfect? Telemedicine for COVID-19. N Engl J Med 2020;382(18):1679–1681. [DOI] [PubMed] [Google Scholar]
- 15.Geisinger Landi H., UPMC among Health Systems Fast-Tracking Tech, Telehealth Projects for COVID-19. Fierce Healthcare; 2020. Available from: https://www.fiercehealthcare.com/tech/health-system-cios-covid-19-response-we-ve-never-experienced-anything-like [Last accessed: November 19, 2021].
- 16.Curfman AL, Hackell JM, Herendeen NE, et al. Telehealth: Improving access to and quality of pediatric health care. Pediatrics 2021;148(3):e2021053129. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.HealthIT. What’s the Difference between Telemedicine and Telehealth? 2019. Available from: HealthIT.gov [Last accessed: November 19, 2021].
- 18.Marcin JP, Shaikh U, Steinhorn RH. Addressing health disparities in rural communities using telehealth. Pediatr Res 2016;79(1):169–176. [DOI] [PubMed] [Google Scholar]
- 19.Ray KN, Demirci JR, Bogen DL, et al. Optimizing telehealth strategies for subspecialty care: Recommendations from rural pediatricians. Telemed J E Health 2015;21(8):622–629. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Winegard B, Miller EG, Slamon NB. Use of telehealth in pediatric palliative care. Telemed J E Health 2017;23(11):938–940. [DOI] [PubMed] [Google Scholar]
- 21.Simacek J, Dimian AF, McComas JJ. Communication intervention for young children with severe neurodevelopmental disabilities via telehealth. J Autism Dev Disord 2017;47(3):744–767. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Stoehr JR, Hamidian Jahromi A, Hunter EL, et al. Telemedicine for gender-affirming medical and surgical care: A systematic review and call-to-action. Transgend Health 2022;7(2):117–126. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Stephenson R, Todd K, Kahle E, et al. Project Moxie: Results of a feasibility study of a telehealth intervention to increase HIV testing among binary and nonbinary transgender youth. AIDS Behav 2020;24(5):1517–1530. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Sequeira GM, Kidd KM, Coulter RWS, et al. Transgender youths’ perspectives on telehealth for delivery of gender-affirming care. J Adolesc Health 2021;68(6):1207–1210. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Gill-Peterson J Histories of the Transgender Child. University of Minnesota Press: Minneapolis and London; 2018. [Google Scholar]
- 26.Davy Z, Toze M. What is gender dysphoria? A critical systematic narrative review. Transgend Health 2018;3(1):159–169. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Suess Schwend A, Winter S, Chiam Z, et al. Depathologising gender diversity in childhood in the process of ICD revision and reform. Glob Public Health 2018;13(11):1585–1598. [DOI] [PubMed] [Google Scholar]
- 28.Anonymous. HUD USPS ZIP Code Crosswalk Files. n.d Available from: https://www.huduser.gov/portal/datasets/usps_crosswalk.html [Last accessed: October 10, 2021].
- 29.Anonymous. USDA ERS. n.d. Available from: https://www.ers.usda.gov/data-products/rural-urban-continuum-codes.aspx [Last accessed: October 10, 2021].
- 30.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, Austria; 2020. [Google Scholar]
- 31.Ortega G, Rodriguez JA, Maurer LR, et al. Telemedicine, COVID-19, and disparities: Policy implications. Health Policy Technol 2020;9(3):368–371. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Handler T, Hojilla JC, Varghese R, et al. Trends in referrals to a pediatric transgender clinic. Pediatrics 2019;144(5):e20191368. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Chen M, Fuqua J, Eugster EA. Characteristics of referrals for gender dysphoria over a 13-year period. J Adolesc Health 2016;58(3):369–371. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Alzu’bi AA, Watzlaf VJM, Sheridan P. Electronic health record (EHR) abstraction. Perspect Health Inf Manag 2021;18:1g. [PMC free article] [PubMed] [Google Scholar]
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