1. New Zealand Mental Health & Addiction data and metrics
IIMHL, Stockholm, Sweden 29 May 2018

3. Te Pou o te Whakaaro Nui Our People Our Purpose Our Provenience
Our Products

4. Worth stressing the small size, ethnic mix and Auckland centric nature of the demographics

5. Self explanatory

6. People seen in mental health & addiction services, Jan - Dec 2017
DHB & NGO
19%
32,977
NGO only
18%
30,781
DHB only
63%
111,053
 DHB %
Mental health only
79%
Alcohol and other drug only
15%
Both 6%
Total
100%
Forensic 5%
 NGO %
Mental health only
60%
Alcohol and other drug only
34%
Both 6%
Total
100%
Forensic 1%
Note: People with any type of activity excluding do not attends and leave.
Source: Ministry of Health, PRIMHD extract 9 April 2018, analysed and formatted by Te Pou.

7. Current Drivers 1)New Coalition Government
As a result a number of policy frameworks are being revised as the new Government’s priorities are implemented
2) National Mental Health and Addiction inquiry
Independent Inquiry with broad terms of reference. The inquiry covers the breadth of current New Zealand policies, services, programmes, and interventions in the mental health and addiction area
3) Mental Health Commission
The NZ government has asked the Independent Mental Health and Addiction Inquiry to include recommendations on the roles and responsibilities of a re-established Mental Health Commission for NZ.
4)Health Workforce New Zealand
Developed the Mental Health and Addiction Workforce Action Plan 2017–2021 (the Action Plan) is to identify the priority areas and actions required to develop an integrated, competent, capable, high-quality and motivated workforce focused on improving health and wellbeing

9. Future directions Stronger emphasis on: Improving Equity of outcomes,
a life course approach to prevention of mental illness and distress alongside support for those who experience mental illness
multi sector and agency co-ordination

10. There are three main strands to the outcomes and indicator framework in NZ: the national collection; outcome measures and KPI’s.
The outcome measures are based on 5 domains: clinical (this applies to the HoNOS family of measures), alcohol and drugs (ADOM –alcohol and drug outcome measure), maori ( Hua Oranga), functioning and self rated for which no measures have currently been chosen.

11. PRIMHD is a patient centric data collection system, not only collecting outcome data but the other areas you can see here
With the data collected here we can see if anyone is better off
We are working towards also understanding the type of activity that may make a positive difference eg talking therapies. We are working closely with Australia on these aspects
You will see here the ability to identify not only patients via a unique encrypted identifying number but also clinicians. This can help us understand our workforce development needs.
Services are also required to ensure treatment plans are up to date in the system. This is a national health target.
ADOM
continue to work with DHBs
promote data utility
develop workforce data sets
alignment with broader national outcomes work and MOH direction.
project management of PRIMHD projects on behalf of MOH
support to develop and manage communications around projects
implementation of two workforce projects:
national consistency of codes
national training resource.
housing and employment measures
collection guidelines
sector engagement and awareness.

12. Statistics NZ - Integrated Data Infrastructure (IDI)

14. Mental health and addiction quality improvement programme: HQSC
Five year programme
Designed to focus on five themes across the MH&A sector
Eliminate seclusion by 2021
client transitions,
prescribing and medication management
serious adverse event responses
physical health
Building on the work of Te Pou and National KPI Programme
National collaborative- 20 DHBs
The programme is based on the successful Scottish Patient Safety Programme: Mental Health
This 5 year programme will see the Health Quality and Safety Commission work together with DHBs and local providers to identify areas of innovative and effective practice for sharing more widely, identify other opportunities for quality improvement, reduce unwarranted variation, with the goals of improving outcomes, reducing harm, improving safety, and addressing inequity (needs more). We have recruited a MHA Team.
As well as a strong emphasis on leading and supporting quality improvement, the programme also seeks to build QI capability in the sector. (MHA QIF participants ).

16. Measuring Users of Services Experience
Mārama collects real-time feedback from your consumers and their family / whānau and presents the results in attractive graphical displays

17. Each organisation has 8 standard questions and up to 2 custom questions

19. “…some of this work is about system development, most of it is about culture and attitude change.
Only by asking and using the information we receive, do we create an environment of continuous improvement.”

20. Referrals with seclusion as a percentage of all referrals for non-forensic tāngata whai ora aged 18 to 64 years, all DHBs, year ending December, 2010 to 2017
Note: Data may not represent full seclusion data for some DHBs. Only data that is captured electronically is included. Referrals have a bed night activity in the period. Exclude team type codes: 03, 05, 15, 16. Source: Ministry of Health, PRIMHD extract 9 April 2018, extracted and formatted by Te Pou.

21. Referrals with seclusion as a percentage of all referrals for non-forensic tāngata whai ora aged 18 to 64 years, by ethnicity, all DHBs, year ending December, 2010 to 2017
Note: Data may not represent full seclusion data for some DHBs. Only data that is captured electronically is included. Referrals have a bed night activity in the period. Exclude team type codes: 03, 05, 15, 16. Source: Ministry of Health, PRIMHD extract 9 April 2018, extracted and formatted by Te Pou.

22. DHB variation in seclusion rates
Median = 7.8%
Mark Using PRIMHD (2015) data to look at DHB variation in rates of seclusion.
As shown in the earlier slide there is wide variation in DHB seclusion rates
DHBs were grouped based on their seclusion rates (as shown in graph)
The rates of seclusion in the groups vary from 2.6% to 22.2%
The research was interested in finding out more about the potential reasons for variation in rates
Possible explanations may include
(a) different client characteristics within DHBs
(b) Different DHB policies and practices with regards to seclusion
(c) A combination of a and b
The client and service characteristics examined included:
Maori/non-Maori, age, MH Act: Section 30, Total HoNOS scores, Bed Nights
Evidence indicated that the odds (or likelihood) of being secluded were higher for:
Maori, Males, being under Section 30 of the act, HoNOS total scores (14+), and number of bed nights
E.g., odds of being secluded is approx. 50% higher for Maori, about 2 times higher for 8+ bed nights, and higher total HoNOS scores (of 14+), and 2.8 times higher for those under S30 of the MH act

23. Likelihood of seclusion
When all these factors (e.g., ethnicity, age, HoNOS, bed nights, MH Act) were into account the study still found differences in DHB seclusion rates
After statistically controlling for all these factors:
The odds (or likelihood of being secluded) were 11 x higher in DHB group 4 compared to DHB group 1
About 3.8 x higher in DHB group 3 compared to DHB group 1
About 2.65 x higher in DHB group 2 compared to DHB group 1
The Confidence Intervals (Cis) in the graph also show that all ORs are well estimated.
That is, differences amongst clients does not fully explain differences in DHB seclusion rates
Question – how much of this may be attributed to DHB policies and practices, or are there other possible explanations?
This study also illustrates how data can be better used to understand service delivery and outcomes for consumers.

24. Number of seclusion events
Number of seclusion events per shift by day of week, non-forensic tāngata whai ora 18 to 64 years, all DHBs, Jan to Dec 2017
Shift1
Number of seclusion events
Events per day
Weekdays
Weekends
morning
683
291
137
146
afternoon
660
232
132
116
night
237 86 47 43
Morning shift 7am to 3pm, Afternoon shift 3pm to 11 pm, Night 11pm to 7am. Source: Ministry of Health, PRIMHD extract 9 April 2018, extracted and formatted by Te Pou.

25. Percentage of people admitted who are secluded within 24 hours, non-forensic tāngata whai ora 18 to 64 years, by ethnicity, all DHBs, Jan to Dec 2017
Note: For admissions which start in the period and have seclusion, check to see time from the beginning of bednights to time of seclusion is less than 24 hours. Source: Ministry of Health, PRIMHD extract 9 April 2018, extracted and formatted by Te Pou.

26. Percentage of people secluded, once admitted to inpatient units, previous admissions and first inpatient admission comparison, all DHBs, Jan to Dec 2017
Note: Previous admission is defined as any previous inpatient non-forensic admissions. These admissions could be either a short or long period before admission from July 2008 onwards. Source: Ministry of Health, PRIMHD extract 9 April 2018, extracted and formatted by Te Pou.

27. Percentage of collections in the clinical range for HoNOS items at inpatient admission, secluded versus not secluded, all DHBs, Jan to Dec 2017
Note: Percentage of collections in the clinical range (2, 3 or 4) for each HoNOS items. Referrals have a bed night activity in the period. Exclude team type codes: 03, 05, 15, Source: Ministry of Health, PRIMHD extract 9 April 2018 extracted and formatted by Te Pou.

28. Percentage of collections in clinical range on each HoNOS item, at admission, New Zealand, Jan - Dec 2017
Note: Percentage of service users in the clinical range (2, 3 or 4) for each HoNOS item.
Source: Ministry of Health, PRIMHD extract 9 April 2018, analysed and formatted by Te Pou.

29. Not to use data is unethical

31. Help
Currently working on a restraint measure(s) for national collection. What are others doing?
Is anyone collecting family involvement indicators?