1. Incorporating equity into our work Assoc Prof Sue Crengle
HQSC
Whakakotahi workshop
Wellington
23rd May 2017

2. Outline
What is equity
How do we assess equity
What do we need to be able to do this?
An example
Discussion

3. What is equity – WHO http://www. who
Equity is the absence of avoidable or remediable differences among groups of people
groups may be defined socially, economically, demographically, or geographically.
more than inequality with respect to health determinants, access to the resources needed to improve and maintain health or health outcomes.
also entail a failure to avoid or overcome inequalities that infringe on fairness and human rights norms.

4. What is equity – Boston Public Health Commission http://www. bphc
everyone has a fair opportunity to live a long, healthy life.
health should not be compromised or disadvantaged because of an individual or population group's race, ethnicity, gender, income, sexual orientation, neighborhood or other social condition.
Achieving health equity requires creating fair opportunities for health and eliminating gaps in health outcomes between different social groups.
It also requires solutions outside of the health care system
e.g. transportation, housing sectors, wider determinants of health

5. Inequity vs. inequality
Inequalities - differences in the presence of disease, health outcomes, or access to health care between population groups.
Inequities - differences in health that are unnecessary, avoidable, unfair, unjust, amenable to change.

7. Assessing inequities
Consider inequities in early stages of project
Need data to assess for inequities and to assess impact of your project

8. Data of interest is high quality
Data considerations
Data of interest is high quality
ethnicity data misclassification
numerator – denominator bias
Impact of misclassification
Think about differences in age structure
Use appropriate denominator
e.g. people with disease c.f. total population
Accuracy and completeness of classification coding, screening data entry etc.

9. Summary changes in ethnicity classification
2016 (black font) and 2017 (red font) after re-registration
Total
Change n %
Māori Males
913 50
1016
Māori females
899
1007
Total Maori
1812
100
2023
+ 211
Non-Māori males
6095
5728
Non-Māori females
6057
5646
12152
11374
-778
TOTAL PRACTICE
13964
13397
-567
Increase in Māori registrations = 211
New Māori registrations = 38
Increase due to correction misclassification = 173
173 / = 8.6%
were misclassified as non-Maori

10. Ensuring high quality ethnicity data
Training for staff
Needs to be repeated
Primary care ethnicity data audit tool
Staff awareness
Processes are correct
Caveat re 100 consecutive patient recount
Check – Māori in ethnicity2 and ethnicity3 fields (MedTech) (registered, enrolled, funded)

11. Process - PDSA
what’s the question(s)?
Choose your indicator(s)
Identify the denominator
Assess the quality of the data
Analyse the data
Decide actions where indicated
Repeat analyses at suitable time point
Decide actions…..

12. IMC Māori health plan
Concrete indicators
Good news, bad news
Associated actions
About to re-run analyses
Then study and act on the findings

13. Utilisation rate by age group in previous 12 months (at 1 July 2016)
 Age (y)
00-04
05-14
15-24
25-44
45-64
65+
Māori
2.4
1.2
1.0
1.8
3.1
5.0
Non-Māori
2.5
1.1
1.6
2.8
5.1
High needs
3.2
5.2
Non-high needs
1.3
2.7
No differences – when might expect higher utilisation rates by Māori, high needs groups

15. KIA ORA