On the calculation of the prevalence of transsexualism
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On the Calculation of the Prevalence of Transsexualism Prof. Femke Olyslager 1 , PhD, and Prof. em. Lynn Conway 2 1 Department of Information Technology, Ghent University, Ghent, Belgium 2 Department of Electrical Engineering and Computer Science,

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On the Calculation of the Prevalence of Transsexualism

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On the calculation of the prevalence of transsexualism l.jpg
On the Calculation of the Prevalence of Transsexualism

Prof. Femke Olyslager1, PhD, and Prof. em. Lynn Conway2

1Department of Information Technology,

Ghent University, Ghent, Belgium

2Department of Electrical Engineering and Computer Science,

University of Michigan, Ann Arbor, Michigan, USA

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Introduction l.jpg
Introduction

  • Introductory remark:

    • Only male-to-female transsexualism is discussed here!

  • The key-question:

    “What are the chances that my child is transsexual?”

    =

    “The prevalence of transsexualism.”

  • Observation:

    • Literature(1):P = 1 in 11,900

    • In discrepancy with simple sanity-checks!(2)

  • Actions:

    • Analyse the old reports on prevalence

    • Develop a mathematical framework

    • Derive commensurable prevalence numbers

      (1) A. Bakker et al., “The prevalence of transsexualism in the Netherlands,” Acta Psych. Scand., 1993.

      (2) L. Conway, “How frequently does transsexualism occur?,” LynnConway.com, 2001.

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Deriving prevalence l.jpg

1986

1980

1983

1990

1 in 18,000

1 in 45,000

1 in 26,000

1 in 11,900

# patients receiving treatment

P =

total population

Deriving prevalence

  • How was prevalence derived?

  • Problem A:

    • E.g. the Netherlands(1,2):

      “Prevalence increases over time, as more patients treated.”

  • Problem B:

    • Who is counted?

      Those satisfying TS definition, those on hormone therapy, those having SRS, ... ?

      “Leads to incommensurable numbers.”

      (1) A. Bakker et al., “The prevalence of transsexualism in the Netherlands,” Acta Psych. Scand., 1993.

      (2) P.L.E. Eklund et al., “Prevalence of transsexualism in the Netherlands,” Brit. J. Psych., 1988.

P =

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Deriving prevalence4 l.jpg
Deriving prevalence

  • Types of prevalence:

    • P(TS) = the prevalence of transsexualism

    • P(SH) = the prevalence of transsexual people who have sought help

    • P(HT) = the prevalence of those on hormone therapy

    • P(ST) = the prevalence of those who have socially transitioned

    • P(SRS) = the prevalence of those who have undergone SRS

  • Inequalities (in general):

    P(TS) > P(SH) > P(HT) > P(ST) > P(SRS)

    or:

    P(TS) > P(SH) > P(ST) > P(HT) > P(SRS)

    • Allows e.g. to derive P(HT) from P(SRS) if P(SRS)/P(HT) is known

    • Provides lower bounds for P(TS)

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Deriving prevalence5 l.jpg

# transsexual babies per year

P(TS) =

# births per year

# identified individuals per year

P(TS) =

# births per year

# identified individuals per year

P(TS) =

# births per year L years ago

# flu cases per year

duration flu in days

x

P(flu) =

total population

365

Deriving prevalence

  • Suppose transsexuality were visible at birth:

  • Suppose transsexuality is always identified at some point in life:

    • Assumes constant demographics

    • Assumes regime situation for # identified individuals

  • Correction for non-constant demographics:

    L = average age of identification

  • Note: This is a different from recurring conditions such as the flu, where:

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Inherent vs active prevalence l.jpg

# identified individuals

P(TSA) =

total population

_

E L

P(TSA) =

x P(TSI)

E

Inherent vs. active prevalence

  • “Inherent” prevalence:

    • P(TSI) = 1 in 4,000 & population = 4,000,000

      • 1,000 identified transsexuals? No!

    • Age of identification L = 35 years & life expectancy E = 70 years

      • 500 identified transsexuals!

  • “Active” prevalence:

  • Relation between “active” and “inherent” prevalence:

    L = average age of identification

    E = life expectancy

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An example l.jpg

507 patients in 1976 - 1990

6,019,546 males > 15 years

34

P(HT in Bakker et. al.(1)) =

= 1 in 11,900

120,000

P(HTI) =

=

= 1 in 3,500

P(HTA) =

x P(HTI) =

x

= 1 in 6,200

_

75 32

_

34

# new HT per year

E L

75

120,000

# births per year L years ago

E

An example

  • Prevalence as given in Bakker et. al.(1):

  • Inherent prevalence:

    • 34 new patients annually (1,2)

    • Average age to start HT: L = 32 years

    • Male births in 1990 – 32 = 1958 (3): 120,000

  • Active prevalence:

    • If life expectancy E = 75 years, then

      (1) A. Bakker et al., “The prevalence of transsexualism in the Netherlands,” Acta Psych. Scand., 1993.

      (2) P.L.E. Eklund et al., “Prevalence of transsexualism in the Netherlands,” Brit. J. Psych., 1988.

      (3) Central bureau for statistics in the Netherlands, http://www.cbs.nl.

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Other methods l.jpg

P(SRSA) =

= 1 in 1,800

3

5,500

Other methods

  • Other methods to estimate the prevalence of transsexualism:

    “triangulations and sanity-checks”

  • Example 1: Fellows of the IEEE(1)

    • 5,500 Fellows (mostly males)

    • 3 Fellows had SRS

    • Estimate:

    • If P(SRSA) = 1 in 11,900 then only 1.6% chance to have 3 cases

      (1) IEEE: Institute for Electrical and Electronics Engineers.

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Other methods9 l.jpg

# SRS per year

1,500

P(SRSI) =

>

> 1 in 1,300

# births per year

2,000,000

Other methods

  • Example 2: SRS surgeries on US residents(1)

    • Over 1,500 SRS on US residents

      = over 1,000 SRS in US + over 500 SRS elsewhere

    • 2,000,000 US male births per year

    • Since P(TSI) > P(SHI) > P(HTI) > P(STI) > P(SRSI):

      P(TSI) > 1 in 500

      (1) L. Conway, “How frequently does transsexualism occur?,” LynnConway.com, 2001.

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Other methods10 l.jpg

6

1,000

73

_

73

24.1

Other methods

  • Example 3: estimates based on Thai Kathoey

    • Counting Kathoey (1): 6 per 1,000

      P(STA) = =1 in 167

    • Demographic profile of Kathoey (2): 27.7% of Kathoey had SRS

      P(SRSA) = 0.277 x P(STA) = 1 in 600

    • Average age of SRS L = 24.1 years & Life expectancy E = 73 years

      P(SRSI) = x P(SRSA) = 1 in 400

    • Demographic profile of Kathoey (2): 48.3% of Kathoey who had no SRS want SRS

      P(TSI) = 1 in 200

      (1) S. Winter, “Counting Kathoey,” Transgender Asia papers, 2002.

      (2) S. Winter, “Thai Transgenders in focus, Demographics, Transitions and Identities,” IJT, 2006.

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Conclusions l.jpg
Conclusions

  • Past reports failed to answer the key question:

    “What are the chances that my child is transsexual?”

  • Analysis of past reports:

    P(SRSI) =1 in 4,500 to 1 in 2,000P(TSI) =1 in 2,000 to 1 in 1,000

  • Estimates from more recent reports:

    P(TSI) =1 in 500

    Transsexuality is not that uncommon!

    Important for transgender health care and recognition by society!

  • Further reading (draft hardcopies available from authors):

    F. Olyslager and L. Conway, “On the Calculation of the Prevalence of Transsexualism,” submitted to the International Journal of Transgenderism.

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