applied epidemiology 304 inequalities research research involving maori participants adapted from...
TRANSCRIPT
Applied Epidemiology 304
Inequalities researchResearch involving Maori participants
Adapted from slides from Dr Sue CrengleSept 2013
What lecture will cover?
• Rationale for inequalities research• What do you need to undertake inequalities
research?• Examples
– Population based cross-sectional survey– Cohort study– Intervention trial
Rationale
• Why should we undertake inequalities research?
Rationale
• Why should we undertake inequalities research? – Social justice*
• Inequalities are unjust or unfair• Ethical and moral dilemma for doctors• Inequalities affect everyone• Inequalities are expensive• Inequalities are avoidable
* Woodward and Kawachi JECH 2000; 54:923-929 * IOM Unequal Treatment 2003
Rationale
• Why should we undertake inequalities research? – Social justice– NZ Public Health and Disability Act
• reducing disparities between population groups
Rationale
• Why should we undertake inequalities research? – Social justice– NZ Public Health and Disability Act
• reducing disparities between population groups– Rights – human, Indigenous, Treaty rights
Rationale
• Why should we undertake inequalities research? – Social justice– NZ Public Health and Disability Act– Rights – human, Indigenous, Treaty rights
– Health care that is not well-considered and responsive to Māori needs likely to increase inequalities
– Move from simple description to understanding– Identify effective interventions
What do we need to do inequalities research?
• Sufficient explanatory power• Accurate and complete exposure
– Ethnicity data– SES data– Other exposures of relevance to outcome
• E.g. Cancer , CVD procedures
• Relevant questions• Non-deficit approach
•
Appropriate explanatory power
• Māori statistical needs have equal status with those of the total NZ population
• Enables research to generate results that are as productive for Māori health development as for non-Māori
• Surveys and trials based on random samples of population produce non-Māori profiles of exposures, access to social determinants of health, health behaviours, health service access and outcomes
Appropriate explanatory power
• Findings based on this data will more closely reflect non-Māori than Māori realities
• Services, interventions, policy, and programmes developed from these research findings will be more likely to meet non-Māori than Māori needs
Appropriate explanatory power
• Appropriate numbers of non-Māori and Māori in surveys and trials will allow:– Equal statistical power for both population groups– Develop appropriate services, interventions, policy,
and programmes for each group– Provide ethnic specific baseline estimates for
subsequent surveys and trials
Ethnicity data collection
• “The term ethnic group has a wide meaning. It is not the same as nationality, race or place of birth. Ethnic groups are… …people who have culture, language, history or traditions in common. These people have a ‘sense of belonging’ to the group… It is possible to belong to more than one ethnic group. At different times of their life people may wish to identify with other groups”. (NZHIS, 1996)
Ethnicity data collection
• Why?– data analyses in research– planning and developing appropriate services– determining quality of services, tracking outcomes,
monitoring inequalities – development of health policies– highlighting areas of clinical intervention e.g. sickle
cell disease
Ethnicity data can…
• Introduce bias if same ethnicity question not used for numerator and denominators
• Change if person changes their ethnic affiliation over time – this is OK!
Validity of ethnicity data can be affected if…
• Wrong question used• Data collector guesses rather than asks person
to self-identify• Only one ethnic group is allowed• Changes are made to the question (response
categories themselves or the order of the categories)
Collecting and using ethnicity data
• Self-identification essential– As many as applies to them
• Should be checked at each interaction• Must use same question –NZ Census question
– Same wording, layout• Classification as per ethnicity data protocol
More information…
• ‘Ethnicity data protocols for the Health and Disability Sector’ Ministry of Health Feb 2004
Available onhttp://www.moh.govt.nz/moh.nsf/
49ba80c00757b8804c256673001d47d0/038aa30b8a5ef30dcc256e7e007c98c4?OpenDocument
2001 2006
Some examples
Cross-sectional survey – determinant of health and contribution to inequalities
Exaimination of CVD procedures and inequalities in procedures
Intervention trial
NZHS 2002/03 (Harris et al, 2006a and
2006b)
• National Health Survey• August 2002 to January 2004• Adults aged 15 years and over• Approx. 12,500 respondents
– Māori 4000– Pacific 1000– Asian 1000
• Response rate 72%
Racial Discrimination Questions (Harris et al, 2006)
• Have you ever been the victim of an ethnically motivated attack (verbal or physical abuse to the person or property) in New Zealand?
• Have you ever been treated unfairly (e.g. treated differently, kept waiting) by a health professional (e.g. doctor, nurse, dentist etc.) because of your ethnicity in New Zealand?
• Have you ever been treated unfairly at work or been refused a job because of your ethnicity in New Zealand?
• Have you ever been treated unfairly when renting or buying housing because of your ethnicity in New Zealand?
Prevalence of physical and verbal attack (ever) by ethnic group
0
5
10
15
20
25
30
Physical Verbal
per
cen
t (%
)
Maori
Pacific
Asian
European/Other
Prevalence of unfair treatment in institutional settings (ever) by ethnic group
0
2
4
6
8
10
12
14
Health Work Housing
per
cen
t (%
)
Maori
Pacific
Asian
European/Other
Levels of self-reported exposure to any racial discrimination by ethnic group
Level** Māori Pacific Asian European/Other*
2 only
21.1%1 only
3 or more
8.3%
4.5%
17.2%
4.4%
2.8%
21.1%
5.2%
1.6%
11.6%
2.5%
0.5%
*Includes all non-Māori, non-Pacific, non-Asian
**Number of racial discrimination variables to which respondents were exposed
Odds ratio of experience of racial discrimination with health outcomes*
*Adjusted for sex, age, dep, ethnicity # not statistically significant at the 95% level
Physical attack
Verbal attack
Unfair Treatment
Health
Work
Housing
Poor/fair self-rated health
Poor physical functioning
Poor mental health
Current smoking
CVD
Overall discrim.
1.93
1.96
3.46
2.21
1.27
2.15
1.55
1.47
2.43
1.70
1.42
2.16
1.73
2.75
1.73
1.39
2.16
1.48
1.70
1.35
1.73
1.29#
1.69
1.79
1.31
0.68#
1.59 1.77 1.67 1.38
Odds ratios for increasing exposure to racial discrimination with health outcomes*
*Adjusted for sex, age, dep, ethnicity # Not statistically significant at the 95% level
None
One
Two
Three+
Poor/fairself-rated health
Poor physical functioning
Poor mental health
Current smoking
CVD
1.00
2.02
2.26
3.60
1.00
1.48
1.91
2.15
1.00
1.56
2.47
2.95
1.00
1.61
1.59
2.93
1.00
1.17#
2.36
1.41#
Odds ratio of ethnicity (Māori vs European) on health outcomes
0
0.5
1
1.5
2
2.5
poor/fair selfrated health
poor physicalfunctioning
poor mentalhealth
CVD
age, sex
age, sex, racism
age, sex, dep
age, sex, dep, racism
Harris R, Tobias M, Jeffreys M, Waldegrave K, Karlsen S, and Nazroo J
Effects of self-reported racial discrimination and deprivation on Māori health and inequalities in New Zealand: cross-sectional study
The Lancet 2006; 367:2005-2009. http://www.thelancet.com.ezproxy.auckland.ac.nz/journals/
lancet/article/PIIS0140673606688909/fulltext
Harris R, Tobias M, Jeffreys M, Waldegrave K, Karlsen S, and Nazroo J
Racism and health: The relationship between experience of racialdiscrimination and health in New ZealandSocial Science & Medicine 63 (2006) 1428–1441
Ischaemic heart disease and intervention (Harwood et al 2006)
Follow 8,000 Māori and 90,000 non-Māori patients admitted to hospital for IHD between 1996 and 2004
From first admission and up to 9 years
Controlled for various factors including age, sex, disease and co-morbid condition
Data SourcesQuality ethnicity data – Ever Māori
New Zealand Health Information Service: Public Hospital Discharges
All principal and secondary diagnoses (ICD-9 and ICD10)
All procedures (ICD-9 and ICD-10)
Demographic factors (age, sex, ethnicity, domicile code)
Mortality Underlying cause of death, other contributing causes, other relevant
conditions, cancer as a non-contributing cause of death
National Health Index
IHD procedure receipt during 1st hospital admission
ProcedureMāori %n=8,224
Non-Māori % n=90,014
Relative Rate (95% CI)
Angiography 17.5 22.3 0.79(0.75-0.83)
Angioplasty 3.2 6.0 0.53(0.46-0.59)
CABG 1.3 1.9 0.68(0.56-0.82)
Procedure Receipt during 1st admission – Māori : non-Māori Ratios
ProcedureHR
Age, sex adjusted
HRAge, sex, diagnosis adjusted
HRAge, sex,
diagnosis, co-morbidity
Angiography 0.60
Angioplasty 0.39
CABG 0.59
Procedure Receipt during 1st admission – Māori : non-Māori Ratios
ProcedureHR
Age, sex adjusted
HRAge, sex, diagnosis adjusted
HRAge, sex,
diagnosis, co-morbidity
Angiography 0.60 0.62
Angioplasty 0.39 0.39
CABG 0.59 0.58
Procedure Receipt during 1st admission – Māori : non-Māori Ratios
ProcedureHR
Age, sex adjusted
HRAge, sex, diagnosis adjusted
HRAge, sex,
diagnosis, co-morbidity
Angiography 0.60 0.62 0.68 (0.65-0.72)
Angioplasty 0.39 0.39 0.43 (0.38-0.49)
CABG 0.59 0.58 0.64 (0.53-0.79)
Māori/non-Māori ratios for IHD Procedure (Ever)
Procedure
Adjust for age, sex, diagnosis
& Co-morbid as secondary diagnosis on index admission
& Co-morbid as any diagnosis
on index or earlier
admission
Angiography 0.74(0.71-0.76)
0.77(0.74-0.79)
0.78(0.76-0.81)
PCI 0.53(0.50-0.57)
0.55(0.52-0.59)
0.57(0.54-0.61)
CABG 0.82(0.77-0.88)
0.80(0.75-0.86)
0.84(0.79-0.90)
Māori/non-Māori ratios for deaths from IHD 1996 to 2003
TimeHR
Age, sex adjusted
HRAge, sex, diagnosis adjusted
HRAge, sex,
diagnosis, co-morbidity
First admission
1.40(1.21-1.62)
1.40(1.21-1.62)
1.35(1.16-1.)
After first admission
1.85(1.69-2.02)
1.80(1.65-1.97)
1.72(1.57-1.88)
Possible interventions
• Focus on clinical audit and a web based clinical decision support programme…
Summary
• Inequalities research is important
• Explanatory power is essential
• Accurate, complete, valid exposure ascertainment important