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Transcript of Researching ethnic differences in health: finding the right data Mark Brown, CCSR CCSR Seminar...
Researching ethnic differences in health: finding the right data
Mark Brown, CCSR
CCSR Seminar Series: 2004
Ethnic differences in health
Context…
1999 study of Black and Minority Ethnic Housing needs in Manchester
Health emerges as a key issue
Motivation for a more systematic investigation – using survey data
1999 Health Survey for England (with a boosted B&ME sample)
Ethnic differences in health
Presentation… an exemplar of the problem of small samples
in surveys
a new appreciation of the census
Ethnicity and health
Long literature but little consensus
about nature or extent of ethnic differences
explanations for those differences
Explaining ethnic differences
Ethnic differences in health status
Explanation
Artefact
Material
Cultural
Migration
Genetic
after controlling for age
Ethnicity and health research questions with obvious policy relevance
are service needs among B&ME groups different? • in scale? • in the nature of need? • in terms of appropriate forms of delivery?
heightened interest in light of an ageing population
Ageing minorities Aged 60+
Aged 75+
1991 %
2001 %
% increase
1991-2001
1991 %
2001 %
% increase
1991-2001
White 22.2
22.1 0.6 7.5 8.2 10.3
Non-white
5.9 8.0 108 0.8 1.5 175
Indian 6.8
10.2 86 1.2 2.0 119
Pakistani
3.7 6.6 179 0.4 1.1 340
Bangla 3.3 5.9 312 0.2 0.6 457
Contrasting age structures
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
0 years
4 years
8 years
12 years
16 years
20 years
24 years
28 years
32 years
36 years
40 years
44 years
48 years
52 years
56 years
60 years
64 years
68 years
72 years
76 years
80 years
84 years
88 years
Series2
Series1
-1.5 -1 -0.5 0 0.5 1 1.5
0 years
4 years
8 years
12 years
16 years
20 years
24 years
28 years
32 years
36 years
40 years
44 years
48 years
52 years
56 years
60 years
64 years
68 years
72 years
76 years
80 years
84 years
88 years
Series2
Series1
General Pakistani
Measuring health differences: data issues
Reliance on country of birth statistics (relatively recent adoption of ethnicity in population and health statistics)
Small numbers - especially in national sample surveys e.g. General Household Survey - B&ME sample <1000 hs
But a growing interest in ethnic comparisons... surveys designed to facilitate analysis of ethnic
difference
• 1994 PSI National Survey of Ethnic Minorities (B&ME sample size > 5,000)
• 1999 Health Survey for England (HSE) (B&ME sample size = >8,000)
bigger samples
B&ME specific questions
1999 Health Survey for England
Aim to compare ethnic groups
for selected measures of health (mental as well as physical health)
age dimension - health better or worse for the B&ME elderly?
Why the Elderly?
The demographic situation: Asian elderly ‘time bomb’
evidence of a deprived and needy group hard living conditions - overcrowding poor physical health - heart disease, stress
related illness loneliness, cultural alienation suffering a ‘double’ or even ‘triple jeopardy’
(being in a poor environment, being old, being non-white)
Why Mental wellbeing?
Case for a broad definition of health - taking in mental wellbeing / happiness / quality of life - especially among frail elderly
impact of factors such as overcrowding and cultural alienation on health likely to be psychological as much as physical
topical… Bennett Inquiry…
Why Mental wellbeing?
Bennett Inquiry, 2004:
‘Many mental patients face difficulties in accessing appropriate treatment, but black and ethnic minority communities suffer by far the most serious problems’
Guardian Leader
Measuring psychological wellbeing in a survey? General Health Questionnaire 12 (GHQ 12)
designed to detect possible psychiatric morbidity
12 questions about general levels of happiness, depression, anxiety and sleep disturbance over the past four weeks.
a score of 4 or more used to identify informants with a possible psychiatric disorder
but question over validity for B&ME groups
Ethnic differences? Figure from main report
Ethnic differences?
Statistically significant differences after controlling for age
Bangladeshi men twice as likely to have a high GHQ12 score
But what relationship with age?
Masked by standardisation
From the Official 1999 HSE report...
“In the general population, the proportion with a high GHQ12 score did not vary much with age, but among men of South Asian origin and Black Caribbean men, it appeared to increase with increasing age... However, the samples of each sex in the oldest age group (55 and over) were small, and should be treated with caution.”
Report generally offers very little detailed analysis by age... but this is vital information
• for the description of difference• for understanding difference• in terms of a policy response?
High GHQ12 score: % by age by ethnic group (men)
0
10
20
30
40
50
60
General BlackCaribbean
Indian Pakistani Bangladeshi Chinese Irish
16-34 35-49 50-64 65+
High GHQ12 score: % by age by ethnic group (men)
0
10
20
30
40
50
60
General BlackCaribbean
Indian Pakistani Bangladeshi Chinese Irish
16-34 35-49 50-64 65+
34 cases
12 cases
High GHQ12 score: % by age by ethnic group (men)
0
10
20
30
40
50
60
General BlackCaribbean
Indian Pakistani Bangladeshi Chinese Irish
16-34 35-49 50-64 65+
34 cases
12 cases
High GHQ12 score: % by age by ethnic group
Striking differences, but... Based on very small numbers. Standard errors are big; overlapping confidence
intervals
Response: do some heavy re-coding on age on ethnic group sexes combined
at what price?
High GHQ12 score: % by age by ethnic group (men)
reduced detail on age
0
10
20
30
40
50
60
General BlackCaribbean
Indian Pakistani Bangladeshi Chinese Irish
16-34 35-54 55 +
76 cases51 cases
0
10
20
30
40
50
60
General BlackCaribbean
Indian Pakistani Bangladeshi Chinese Irish
16-34 35-54 55 +
High GHQ12 score: % by age by ethnic group (women)
reduced detail on age
28 cases38 cases
High GHQ12 score: % by age by ethnic group (women) reduced detail on ethnic group
0
10
20
30
40
50
60
General Black Caribbean Indian Pak&Bang Chinese Irish
16-34 35-49 50-64 65+
18 cases
High GHQ12 score: % by age by ethnic group (women)
reduced detail on age and ethnic group
0
10
20
30
40
50
60
General Black Caribbean Indian Pak&Ban Chinese Irish
16-34 35-54 55 +
66 cases
High GHQ12 score: % by age by ethnic group (men)
reduced detail on age and ethnic group
0
10
20
30
40
50
60
General Black Caribbean Indian Pak&Ban Chinese Irish
16-34 35-54 55 +
127 cases
Statistical significance: at a price?
Recoding to retain adequate sample size --> A blurring/changing of the story
Masks diversity
generates groupings that are conceptually and theoretically unsound:
A single over 55 category for the elderly? Pakistanis and Bangladeshis combined
1994 National Survey of Ethnic Minorities: the same story
Table 1 Relative risk of ‘fair or poor self-reportedhealth’ among ethnic minorities compared with whites
Caribbean Indian/AfricanAsian
Pakistani/Bangladeshi
All ethnicminorities
Age & sex 1.25 0.99 1.45 1.17
Standardof living,age & sex
1.15 0.94 1.24 1.08
(Data reproduced from Berthoud et al 1996)
Observations Even in targeted surveys, small sample size is a
far greater barrier to the researcher than the conceptual or operational crudeness of variable definitions
strategies to retain adequate sample size mean we never get near exploiting the full detail available in the variables
not even able to properly describe the differences I wanted to explain
Census to the rescue? Commissioned tables from the 2001 Census
Long Term Limiting Illness by Age by sex by ethnic group (England and Wales)
General Health by Age by sex by ethnic group (England and Wales)
HSE shows fairly strong association between ‘poor’ General Health and a ‘high’ GHQ12 score
Census to the rescue?
100% data allows measurement of health differences with…
the full 16 point ethnic classification the mixed the Chinese the Non-British whites
Detail on age including: 55-65; 66-75; 75+
men: % ‘not good’ general health
0
5
10
15
20
25
30
35
40
45
50
0 to 15 16 to 24 25 to 34 35 to 54 55 to 59 60 to 64 65 to 74 75 andover
Irish Indian Pakistani Bangladeshi Black Caribbean Black African Chinese
women: % ‘not good’ general health
0
5
10
15
20
25
30
35
40
45
50
0 to 15 16 to 24 25 to 34 35 to 54 55 to 59 60 to 64 65 to 74 75 and over
Irish Indian Pakistani Bangladeshi Black Caribbean Black African Chinese
difference in % with ‘not good’ general health Indian v general population
-5
0
5
10
15
20
25
30
0 to 15 16 to 24 25 to 34 35 to 54 55 to 59 60 to 64 65 to 74 75 +
difference in % with ‘not good’ general health Pakistani v general population
-5
0
5
10
15
20
25
30
0 to 15 16 to 24 25 to 34 35 to 54 55 to 59 60 to 64 65 to 74 75 +
difference in % with ‘not good’ general health Bangladeshi v general population
-5
0
5
10
15
20
25
30
0 to 15 16 to 24 25 to 34 35 to 54 55 to 59 60 to 64 65 to 74 75 +
difference in % with ‘not good’ general health Black Caribbean v general population
-5
0
5
10
15
20
25
30
0 to 15 16 to 24 25 to 34 35 to 54 55 to 59 60 to 64 65 to 74 75 +
difference in % with ‘not good’ general health Black African v general population
-5
0
5
10
15
20
25
30
0 to 15 16 to 24 25 to 34 35 to 54 55 to 59 60 to 64 65 to 74 75 +
difference in % with ‘not good’ general health Chinese v general population
-10
-5
0
5
10
15
20
25
30
0 to 15 16 to 24 25 to 34 35 to 54 55 to 59 60 to 64 65 to 74 75 +
difference in % with ‘not good’ general health Irish v general population
-5
0
5
10
15
20
25
30
0 to 15 16 to 24 25 to 34 35 to 54 55 to 59 60 to 64 65 to 74 75 +
Male : Female Ratio of proportion
with ‘not good’ general health
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0 to 15 16 to 24 25 to 34 35 to 54 55 to 59 60 to 64 65 to 74 75 and over
General Irish Indian Pakistani Bangladeshi
male health worse
female health worse
Explaining ethnic differences?
Complex relationships - won’t get very far without micro-data - bring on SARs!
but a detailed description of patterns is a good starting point in research.
Extra detail revealed by Census is basis for more focussed questions
Final thoughts on sample surveys the balance between variable detail and sample size
in the 1999 HSE and the 1994 NSEM seems wrong! We can never get near to the potential promised by the variables
effect of inadequate sample size is that theory underpinning research questions is sacrificed in the pursuit of statistical significance?
But a statistically significant finding may not be interesting or valid.
Just because a finding lacks statistical significance doesn’t make it invalid.