The Maori preventable
mortality project:
an indigenous partnership
initiative.
A Sporle, C Kiro, N Pearce, A Lee, D Jansen
IAOS 2014
1
The Context of Maori Health Development
Persistent
Disparities
Intra Ethnic
Variation
National Health
Policies + Regional
Funders
Maori Service Providers
– local
– prevention focussed
URGENTLY Need more ….
• Information that is
• Accessible
• MAORI focussed
• Timely
• LOCAL
• Usable
• Flexible
• Rates not just counts
• Future predictions
2
Persistent Disparities
SRR Maori : NonMaori
Rate Ratio
3.5
3.0
2.5
2.0
1.5
Source MoH 2010
1.0
0.5
Male
Female
0.0
1996
1997
1998
1999
2000
2001
Year
2002
2003
2004
2005
2006
3
Amenable Mortality Rate
per 105
Intra-Ethnic Variation in Outcomes
1600
1400
1200
1000
Non-Maori
Maori
800
600
400
200
0
1-2
3
4
5
6
Social Class
4
Indigenous Diversity by Geography
Differences in:
• Colonisation history
• Years of contact
• Social conditions
• Language
• Land retention
• Governance
• Resources
5
Why do this?
“to transform Maori mortality
rates from being a measure of
disparity to being a driver for action to
improve Maori health at the local and
national levels. “
6
Needs to:
• plan services
• monitor outcomes
Requires information:
• local
• recent
• high quality
7
Why Mortality?
Key Public Health Measure:
- Good quality ethnic data
- Accessible by researchers
- Regional data not published
Preventability:
50-60% of difference in mortality
due to PREVENTABLE causes
Great EXEMPLAR for
improving access and use
8
The Research Team
Maori Provider
Organisation
Researchers
Andrew Sporle – social epidemiology
Alan Lee – statistician
(Auckland)
Neil Pearce - epidemiology
(LSHTM)
Cindy Kiro – social policy
(Victoria)
Tony Blakely - epidemiologist
(Otago Uni)
Antony Raymont – health services
District
Health
Board
Advisory Groups
Stakeholders – StatsNZ, Govt.
ministries, local Maori organisations
Maori Researchers
9
Research Questions
How does preventable mortality vary by:
• Time
• Gender
• Age group / cohort
• Level of preventability
• REGION
Purpose:
To identify the best places
and ways to intervene
10
www.stat.auckland.ac.nz/~wild/iNZight/
© Departments of Statistics, The University of Auckland
S11
Data Visualiser
Select:
Population
Region
Gender
Age Range
Males
Māori
Non-Māori
Ratio
Pacific
Non-Pacific
Ratio
Asian
Non-Asian
Ratio
Amenable
320.5
296.7–345.7
113.6
109.1–118.3
2.82
248.2
216.8–282.9
128.2
123.5–133.0
1.94
63.8
51.5–78.0
138.3
133.3–143.4
0.46
Non-amenable
249.0
228.4–270.9
123.5
118.8–128.3
2.02
216.9
187.7–249.3
133.1
128.3–137.9
1.63
76.2
62.7–91.7
140.5
135.6–145.6
0.54
Females
Amenable
Non-amenable
189.6
197.9
172.2–208.3
180.1–217.1
63.1
88.3
59.8–66.5
84.4–92.2
3.00
2.24
160.0
139.5
136.1–187.0
117.5–164.5
72.4
97.2
69.0–76.0
93.3–101.2
2.21
1.44
43.8
44.2
34.1–55.3
34.2–56.3
78.5
103.0
74.8–82.2
98.9–107.2
0.56
0.43
Male, total New Zealand, 2006
Age standardised rates per 100,000
Standard Population
700
600
500
400
Comparison
300
200
Projection
100
0
Amenable
Non-amenable
Maori
Total
Amenable
Non-amenable
NonMaori
Total
12
Improving Data Access
Secure Data Archive
Datasets
Epi Methods
Protocols
Data
visualiser
Regional
Funders
© Departments of Sociology and Statistics, The University of Auckland
Public Access
Interface
• Region
• Gender
• Age
• Ref Population
• Relative Risk
• Confidence Ints.
• Graphs and Tables
Maori
Providers
S13
Project Impact in 12 months
Results:
• Regional differences in
data quality
• Data visualiser pilot
• Too early for other results
Other Projects:
- Mortality by migration
and age cohort
- Longitudinal mortality
study
Student placements
with providers:
Teaching
• Community
• Providers
• Students
Networks
• Data users
• Maori researchers
14
Maori Wellbeing Research
Independent Maori
Statutory Board
Maori Development Plan
•
•
•
•
•
Maori Values
Key Directions
Domains and Focus Areas
Maori Outcomes
Indicator wish list
15
© Departments of Statistics, The University of Auckland
S16
The Future ?
Improving responsive of official
statistics leads to
- Increased use
- Improved perception
- Improved quality
- New ideas
If there is data – then
make it available and
useable
17