Sukupuoli-identiteetin häiriöt nuorissa Saksassa: yleisyys ja suuntaukset, 2013–2022

Sukupuoli-identiteetin häiriöt nuorissa Saksassa: esiintyvyys ja suuntaukset, 2013–2022

Kohortti- ja rekisteritutkimukset Lapsuusiän dysforian laantuminen Kasvavat diagnoosit & sosiaalinen vaikutus

Authors: Christian J. Bachmann, Yulia Golub, Jakob Holstiege, Falk Hoffmann
Journal: Deutsches Ärzteblatt International
Year: 2024
Source: di.aerzteblatt.de/int/archive/article/239563

Summary

Using German public health insurance billing records on about 14 million people aged 5 to 24, researchers tracked diagnoses of 'gender identity disorder' from 2013 to 2022. Confirmed diagnoses rose about eight-fold, from about 1 in 4,400 to about 1 in 570, with the steepest rise in teenage girls. In 2022, 72.4% of people with the diagnosis also had another mental health diagnosis, most often depression and anxiety. Of those diagnosed in 2017, only 36.4% still had the diagnosis five years later. The authors say the data cannot explain the rise. It could reflect a real increase, more awareness, less stigma, better services or over-diagnosis. They say how few kept the diagnosis, and how common other mental health problems were, should shape advice on starting medical transition. As a short look at billing codes, it shows population trends but little about individual people's histories.

Key findings

Vahvistetut ”sukupuoli-identiteettihäiriö”-diagnoosit vakuutetuilla 5–24-vuotiailla kasvoivat 22,5:stä 175,7:ään 100 000:ta kohden (noin yhdestä 4 400:sta yhteen 570:stä) vuosina 2013–2022, kahdeksankertainen kasvu; tiukemmalla laskentatavalla (diagnosoitu vähintään kahdella kolmen kuukauden laskutusjaksolla) se nousi 15,2:sta 132,6:een 100 000:ta kohden.
Luvut olivat korkeimmat 15–19-vuotiailla tytöillä lähes joka vuosi, saavuttaen 452,6 per 100 000 (noin yksi 220:stä) vuonna 2022; pojat olivat tyttöjä useammin ennen murrosikää, tytöt poikia useammin murrosiästä alkaen, ja sukupuolet tasaantuivat nuoressa aikuisuudessa.
Vanhempi, liittyvä diagnoosikoodi laski 216,7:stä 73,7:ään 100 000:ta kohden samoina vuosina, minkä kirjoittajat selittävät pääasiassa lääkäreiden koodinvaihdolla; silti kaksi koodia yhdessä nousi edelleen tiukemmalla laskentatavalla (51,9:stä 149,8:aan 100 000:ta kohden).
Vuonna 2022 72,4 %:lla 24 624 diagnoosin saaneesta oli vähintään yksi muu mielenterveysdiagnoosi: masennus 49,3 %:lla miehistä ja 57,5 %:lla naisista, ahdistus 23,5 %:lla ja 34,0 %:lla sekä epävakaa persoonallisuushäiriö 12,1 %:lla ja 17,6 %:lla.
7 885:stä vuonna 2017 diagnosoidusta vain 36,4 %:lla oli diagnoosi edelleen vuonna 2022; tämä oli alle puolet jokaisessa ikäryhmässä ja alhaisin (27,3 %) 15–19-vuotiailla tytöillä.
Kirjoittajat sanovat, ettei laskutustieto voi selittää kasvua, ja suosittelevat, että teinien lääketieteellisen korjauksen aloittamista koskevissa neuvoissa otetaan huomioon, kuinka usein diagnoosi ei pysy ja kuinka usein on muita mielenterveysongelmia.

Conclusion

Our data do not allow any conclusions to be drawn about possible causes (for example, actual prevalence increase, increased awareness, decreased stigmatization, improved health care services, overdiagnosis) for the increase in the prevalence of F64 diagnoses. ... The diagnostic persistence over the 5-year follow-up period of less than 50% in all age groups is in line with the literature and presumably reflects the fluidity of the concept of gender identity in childhood and adolescence; however, it could also be interpreted as an indication of the need for a comprehensive, standardized diagnostic procedure. ... There is a need for further research into what causes the low diagnostic persistence and the observed increase in prevalence. In the meantime, diagnostic stability and the high prevalence of concomitant mental disorders should be taken into account in the recommendations on the initiation of gender reassignment treatment in adolescence.

Methodological limitations

Billing records, not medical checks: Diagnoses are codes sent in for payment, not diagnoses made for research. The authors note they could not check whether the codes were medically accurate.
A dropped code is not the same as growing out of it: A missing code five years later could mean the person stopped seeing outpatient doctors, finished treatment, moved to care not in the data, or was coded under another label. The letter does not tell these apart.
Some care is missing: Data from psychiatric hospital outpatient clinics, and from hospital stays, were not included. The authors say this probably makes the numbers too low and may affect the figures on lasting diagnoses and other conditions.
Public insurance only: People with private insurance (about 1 in 10 Germans) are not included. This could skew the figures if they use care differently.
Doctors changed codes over time: The older related code fell as the newer one rose, which suggests a change in how doctors code. Under the looser count, the two codes combined hardly changed, so part of the rise reflects re-coding.
Nothing on individual care: The data say nothing about assessments, social or medical transition, treatment, gender identity or how severe symptoms were. So keeping or losing the diagnosis cannot be linked to treatment or to the person's situation.
Other conditions counted at one point: Other mental health diagnoses were counted in 2022 only. So it is unknown whether they came before or after the gender diagnosis, and the letter gives no comparison with young people without the diagnosis.
Sex records unclear: About 0.3% of insured people in 2022 had their sex recorded as unknown, undetermined or diverse. The data also cannot tell birth sex from a changed registered sex.
Very short report: As a two-page letter, it gives little detail on methods (for example, how the 2017 group or other diagnoses were defined). It also has no statistical modelling, no measure of uncertainty, and no adjustment for other factors.
Causes not studied: The authors say plainly that the data cannot tell a real increase apart from more awareness, less stigma, better services or over-diagnosis.

detrans.ai

Summary

Key findings

Conclusion