Gender reassignment hormones

Date sent: 31 October 2019

OIA response

Response to a request for information about sex change treatments.

31 October 2019

[name and contact details redacted]

Dear [name redacted]

REQUEST FOR INFORMATION

Thank you for your request dated 15 October 2019 under the Official Information Act 1982 (OIA) for information relating hormonal treatments for gender reassignment. You asked for:

a breakdown of prescription numbers of medication for the purpose of sex change/ gender transition medications over the last 5 years, broken down by age please. For those aged 0-18 please break this down by year. For those over 18, 5 year categories is sufficient.
biological females that are being prescribed ongoing testosterone treatment;
biological females that are being prescribed: estrogen, progesterones, GnRH Agonists (ie Lupron), anti-androgens (spironolactone, bicalumide, flutamide, finasteride). Broken by type of above.
If it’s possible to know which are prescribed together (ie for same person) that would also be interesting.

PHARMAC does not collect data on what medicines are prescribed for, so we are unable to provide data to answer your first question. Accordingly, this part of your request is refused under section 18(g) of the OIA, because PHARMAC does not hold this information and has no reason to believe any other Government agency will.

We have also conducted analysis for question 4, and it shows that only a very small number of patients were dispensed two or more of these drugs over five years. As these numbers are less than 10, this data will not be provided, because we consider this is necessary to protect the privacy of natural persons (section 9(2)(a)).

The below data provides information to answer your other questions.

PHARMAC relies on Ministry of Health data for classifications of individuals’ gender. Data in the tables below reflect these classifications. This may or may not correspond to your intention in seeking information relating to ‘biological females’.

As required under the OIA, we considered whether, in the circumstances, the withholding of this information was outweighed by other considerations which render it desirable, in the public interest, to make this information available. In this case we did not consider that the public interest outweighed the reasons for withholding the information. Please note you have the right, by way of complaint under section 28(3) of the OIA to an Ombudsman, to seek an investigation and review of our decision.

We trust that this information answers your queries.

Yours sincerely

Alison Hill
Director, Engagement and Implementation

Question 2: Females that are being prescribed ongoing testosterone treatment, by age

Age	14	15	16	17	18	19 to 25	26 to 30	31 to 35	36 to 40	41 to 45	46 to 50	51 to 55	56 to 60	61 to 65	66 to 70	above 70
2014/15				< 10	< 10	38	20	< 10	< 10	< 10	16	22	12	< 10	< 10	< 10
2015/16		< 10	< 10	< 10	< 10	47	14	< 10	< 10	< 10	11	17	18	< 10	< 10	< 10
2016/17		< 10	< 10	< 10	11	51	19	12	< 10	< 10	12	< 10	< 10	< 10	< 10	< 10
2017/18	< 10		< 10	< 10	15	53	14	< 10	16	12	16	16	14	< 10	< 10	< 10
2018/19		< 10	< 10	11	< 10	61	15	12	< 10	15	12	15	23	< 10	< 10	< 10

Notes: Where numbers are less than 10, we have used <10 as there is a risk of identifying individuals where numbers are very small.

There were no results for patients under 14.

Question 3: Females that are being prescribed: estrogen, progesterones, GnRH Agonists (ie Lupron), anti-androgens (spironolactone, bicalumide, flutamide, finasteride), by age

Age		0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18
2014/15	Anti androgens	12	13	< 10	< 10	< 10	< 10	< 10	< 10	< 10	< 10	< 10		< 10	< 10	12	15	24	43	46
	Gnrh analogues				< 10	< 10	< 10	< 10	< 10	14	17	26	18	12	< 10	< 10	10	14	13	20
	oestrogens																	< 10	< 10
	progesterone																	< 10	< 10	< 10
2015/16	Anti androgens	17	12	< 10	< 10	< 10	< 10	< 10	< 10	< 10	< 10	< 10	< 10	< 10	< 10	< 10	16	27	38	48
	Gnrh analogues				< 10	< 10	< 10	< 10	< 10	24	23	23	25	14	< 10	11	< 10	17	24	17
	oestrogens																< 10		< 10	< 10
	progesterone																	< 10		< 10
2016/17	Anti androgens	13	11	< 10	< 10	< 10	< 10		< 10	< 10		< 10	< 10	< 10	< 10	< 10	10	27	30	41
	Gnrh analogues					< 10	< 10	< 10	< 10	15	35	34	20	20	< 10	< 10	18	16	30	21
	oestrogens																< 10	< 10
	progesterone																	< 10		< 10
2017/18	Anti androgens	15	16	< 10	< 10	< 10		< 10	< 10	< 10	< 10	< 10	< 10	< 10	< 10	< 10	11	19	35	39
	Gnrh analogues		< 10		< 10		< 10	< 10	12	15	22	32	32	15	23	14	20	33	20	33
	oestrogens																	< 10	< 10	< 10
	progesterone																		< 10	< 10
2018/19	Anti androgens	14	16	< 10	< 10	< 10	< 10	< 10	< 10	< 10		< 10	< 10	< 10	< 10	< 10	11	21	30	49
	Gnrh analogues			< 10		< 10	< 10	< 10	< 10	24	26	36	33	25	27	22	18	29	38	24
	oestrogens																		< 10
	progesterone																			< 10

		19 to 25	26 to 30	31 to 35	36 to 40	41 to 45	46 to 50	51 to 55	56 to 60	61 to 65	66 to 70	above 70
2014/15	Anti androgens	632	606	548	625	775	888	1083	1155	1313	1590	6871
	Gnrh analogues	133	70	129	199	291	253	150	17	< 10	< 10	< 10
	oestrogens	18	14	13	48	154	324	626	616	563	528	618
	progesterone	31	60	68	39	15	< 10
2015/16	Anti androgens	688	608	542	638	803	946	1092	1165	1389	1677	6906
	Gnrh analogues	162	108	124	211	293	286	166	27	< 10	< 10	< 10
	oestrogens	14	< 10	17	28	129	294	604	538	508	457	614
	progesterone	33	69	90	42	< 10	< 10
2016/17	Anti androgens	688	658	598	651	858	965	1088	1193	1399	1660	6978
	Gnrh analogues	173	80	154	214	263	328	170	32	< 10	< 10	< 10
	oestrogens	17	10	10	30	113	251	530	502	446	403	606
	progesterone	44	79	100	59	11
2017/18	Anti androgens	717	631	590	665	813	981	1057	1272	1422	1655	7147
	Gnrh analogues	164	106	163	272	314	354	196	37	< 10	< 10	< 10
	oestrogens	12	12	13	30	94	255	513	538	405	399	648
	progesterone	37	71	98	74	13	< 10	< 10
2018/19	Anti androgens	711	689	657	658	791	954	1126	1322	1448	1636	7423
	Gnrh analogues	150	109	208	270	380	398	227	40	< 10	< 10	< 10
	oestrogens	11	< 10	14	29	68	218	386	466	360	334	631
	progesterone	50	87	98	66	17			< 10

Notes: Where numbers are less than 10, we have used <10 as there is a risk of identifying individuals where numbers are very small. The data is extracted according to the number of patients receiving the prescriptions in each financial year. Thus, the patient can appear in each year's data so a sum of numbers over multiple years does not give a meaningful conclusion.