Applied Epidemiology 304 Inequalities research Research involving Maori participants Adapted from...

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