Socioeconomic Status and Health
86
Socioeconomic Status and Health An overview of the evidence for a connection between wealth and health Ottawa, August, 2006
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Socioeconomic Status and Health. An overview of the evidence for a connection between wealth and health Ottawa, August, 2006. Sections. Which indicators of “health” & wealth to use? Individual evidence for link between SES and health Comparisons between societies - PowerPoint PPT Presentation
Transcript of Socioeconomic Status and Health
Socioeconomic Status and Health
An overview of the evidencefor a connection
between wealth and health
Ottawa, August, 2006
Sections
• Which indicators of “health” & wealth to use?
• Individual evidence for link between SES and health– Comparisons between societies– Comparisons within societies (Britain, USA,
Canada)
• Societal level income inequality and health
Health Indicators• All-cause mortality
– Gives an overview; non-specific; doesn’t weight by age
• Infant mortality– Sensitive to socio-economic development & to medical
care
• PYLL– Selects causes; weights by age at death
• Morbidity indicators– Usually partial coverage; how available?
• QoL– Captures non-fatal outcomes; subjective (bias?)
Socioeconomic Indicators• No ideal indicator. Some options:
• Wealth– Income readily measurable (in most societies), but only covers part of
the picture; doesn’t apply well to elderly, to housewives, etc. Individual or family income? How to correct for family size?
• Occupation– Reasonably comparable across countries; may have direct relevance to
health (exposures, hazards); difficult to classify & score; doesn’t apply well to retired, housewives, children, etc.
• Education– May be driving force behind occupation and income; permanent &
unaffected by market fluctuations; applies to those not in labour force; established early in life so may not reflect subsequent changes
• Composite indicators– Blend of above; choice of weights for components is difficult.
1. Socioeconomic Status and Health
(1a) Comparisons Between Societies
Infant Mortality Rate, per Thousand, for 56
Countries, 1980
0
50
100
150
200
250
0 2000 4000 6000 8000 10000 12000 14000 16000
GNP per capita, 1980
IMR
Data source: World Bank Report, 1983
The Preston Curve (Preston SH. Population Studies 1975;29:231-248)Note the non-linearity of the relationship.
This becomes crucial in subsequent argumentsas we compare individual and aggregate statistics
Infant Mortality Rates in 56 Countries,
by GNP Per Capita, 1996
0
50
100
150
200
0 5000 10000 15000 20000 25000 30000 35000 40000
GNP per Capita
IMR
Source: 1998 World Bank Report
Sixteen years later: have things changed?
As before, the health of the rich is not much affected by changes in income, so transfers from rich to poor would improve overall health.
Hence, poverty is important in poor countries and the equity of income distribution is important in richer countries.
Will throwing money at it help? Expenditures & Health Outcomes
The link between nationalhealth care spendingand level of health is
curvilinear.Among poor countries,expenditures quickly
reduce infant mortalityand this greatly extends average life expectancy.
But once infant mortality islow, increasing expenditures
have less effect.
So, will paying doctors more make them work harder, or will they say “Thanks!” and go play golf?
Is life expectancy the best health indicator? What would you suggest?
Compare Cuba with the US.How does Cuba do it?
(1b) Inequalities Within Societies
(i) Data from Britain, where most
of the analyses began.
Consider mortality in ages 15 – 64,
i.e. adult, but premature mortality
Have things improved? Certainly!
0
5
10
15
20
25
1841 1851 1861 1871 1881 1891 1901 1911 1921 1931 1941 1951 1961 1971 1981
Source: Townsend P, Davidson N. Inequalities in health: the Black Report. Penguin books, 1992
Standardized mortality rates, England and Wales, 1841 to 1971
Males
Females
However: there are major inequities. An early example: the Black Report
Occupational class Men Women Ratio M / F
I Professional 3.98 2.15 1.85
II Intermediate 5.54 2.85 1.94
IIIN Skilled Non-manual 5.80 2.76 1.96
IIIM Skilled Manual 6.08 3.41 1.78
IV Partly Skilled 7.96 4.27 1.87
V Unskilled 9.88 5.31 1.86
Ratio V / I 2.5 2.5
Age-Standardized Mortality Rates per 1,000 at Ages 15 to 64by Occupational Class, United Kingdom, 1971
Source: Townsend P, Davidson N. Inequalities in health: the Black Report. Penguin books, 1992
Life expectancy in England and Wales, by social class, 1972-76 and 1992-96
Males Females
Class 1972-76 1992-96 1972-76 1992-96
I 72.0 77.7 79.2 83.4
II 71.7 75.8 77.0 81.1
III non-man. 69.5 75.0 78.0 80.4
III manual 69.8 73.5 75.1 78.8
IV 68.4 72.6 75.0 77.7
V 66.5 68.2 73.9 77.0Difference I-V 5.5 9.5 5.3 6.4
Source: Marmot M. Perspec Biol Med 2003; 46 (Suppl 3): Table 1
The Side-effects of Success:Mortality from cardiovascular disease,
England and Wales
0
100
200
300
Professional Managerial Non-man. Manual Partly skilled Unskilled
1971 1991
• In 1971, cardiovascular disease showed relatively little SES gradient. • By 1991, a strong gradient had appeared, due to the differential
success in prevention across the occupational categories. There was almost no reduction in mortality among unskilled people, but people in the professional category had reduced their mortality risk to one-third.
• So, much of the SES gradient we see today results from differential access to, and uptake of, preventive care across social groups.
The effect holds for both sexes:SMR by Occupational Class for Ages 15
to 64, England & Wales, 1970-72
0
20
40
60
80
100
120
140
160
SMR
Male Female
IIIIIInIIImIVV
And for many individual causes of death: Respiratory Deaths for Ages 15-64 by
Occupational Class,England & Wales, 1970-72
020406080
100120140160180200
SMR
Male Female
IIIIIInIIImIVV
And also among children All-cause SMRs (ages 0 – 14) by occupational
class, England & Wales, 1970-72
0
20
40
60
80
100
120
140
160
SMR
Male Female
IIIIIInIIImIVV
Most of the Effect Lies at Low Income Levels:
Earnings and SMRs (UK, 1970)
50
70
90
110
130
150
170
190
10 30 50 70
1970 Earnings: Pounds per Week
Source: Wilkinson: Class and Health,1986: pg. 110
SMR
Is it only premature mortality that shows a social gradient?
SMRs by occupational class and age at death. England & Wales, 1981-83
0
20
40
60
80
100
120
25-64 65-74 75+
I & IIIV & V
Occupationalclass
Age at death
Source: Whitehead M. The Health Divide, table 11. Penguin books, 1992.
The class gradientcontinues up to include deaths
at old age
And disparities appear to be increasing…Trends in SMRs over Timein UK Men Aged 15 - 64
0
20
40
60
80
100
120
140
160
180
1931 1951 1961 1971 1981
IntermediateProfessional
Unskilled
Partly SkilledSkilled Manual
Source: Wilkinson RG: Class and Health. London, Tavistock, 1986: Table 1.1
The effect occurs from birth:Perinatal Death Rates (up to day 7) by
Occupational Class: England & Wales, 1970-79
0
5
10
15
20
25
30
35
1971 1975 1979
Class I
Class V
Source: Wilkinson RG: Class and Health. London, Tavistock, 1986: Table 6.8
Postneonatal Death Rates (28 days-1yr.) by Social Class: England & Wales, 1970-79
0123456789
1011121314
1971 1975 1979
S.C. I
S.C. V
Source: Wilkinson RG: Class and Health. London, Tavistock, 1986: Table 6.7
Whitehall 2 Cohort Study: Mortality Trends over Time in Men Initially Aged 40-64
0
5
10
15
20
25
30
35
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Administrative
“Other”
Clerical
Source: Marmot et al. Lancet 1991;337:1387-1393
Professional& Executive
Cumulative Probabilityof death (per cent)
Year of follow-up
Potential Years of Life Lost (All Causes) England & Wales, 1971 – 1991
Message: there are two-fold differences in mortality rates across occupational groups. The deficit occurs mainly from the lowest class. While overall mortality rates have
fallen over the 20 years, the inequality has remained.
0
20
40
60
80
100
120
1971 1981 1991
OccupationalClass
V
IVIII
III
Potential Years of Life Lost (Accidents & Violence). England & Wales, 1971 - 1991
0
5
10
15
20
25
30
1971 1981 1991
SocialClass
V
IVIIIIII
• The Black Report was published in 1980 and, despite government attempts to hide it, produced significant reactions
• For example, the British Health Education Council published The Health Divide in 1988. It focused on ‘inequities’ (inequalities perceived as being unfair)
• Other countries in Europe began to investigate whether they, too, experienced health disparities. Many countries reported to the WHO that health disparities increased during the 1980s.
• This shifted health disparities up the political agenda• Marmot (2003): “The point I wish to draw out of these
figures is that if the life expectancy gap can increase, it can, in principle, decrease. If we think this is a problem worth tackling, the challenge is to understand the reasons for the social gradient in order to do something about it.”
(ii) Data from Canada, where Statistics Canada began to
take notice in the 1990s
Crude and age- standardized mortality rates, Canada, 1920-2000
0
2
4
6
8
10
12
14
StandardizedCrude
Deaths per 1000 population
Age-standardized mortality rates from cardiovascular disease, Canada, 1951-1995
0
100
200
300
400
500
600
700
MalesFemales
Deaths per 100,000 population
Deaths avoided due to declining death rates in Canada: Numbers of deaths that would have occurred in 1989 if 1971 rates had
applied.
Age Males Females Total
< 1 2,336 1,680 4,016 1 - 14 896 599 1,49515 - 34 1,373 822 2,19535 - 54 5,547 2,597 8,14455 - 74 12,265 7,238 19,503 75 + 5,707 12,037 17,744Totals 28,124 24,973 53,097
Life expectancy at birth by age and sex, Canada, 1921-2000
50
55
60
65
70
75
80
85
FemalesMales
Life expectancy(years)
So, what about Social Class?Life Expectancy at Birth, Canada, 1971 and 1986
60
65
70
75
80
85
1 2 3 4 5
Years
Females, 1986
Females, 1971
Males, 1986
Males, 1971
(High) Income Quintiles (Low)
40
42
44
46
48
50
52
54
56
58
60
62
1 2 3 4 5 1 2 3 4 5 Income adequacy quintiles
Men
Women
Remaining life expectancy at age 25 in Canada by sex and income quintile,non-institutionalized population, 1991 to 2001
Life expectancy at birth, by income quintile, urban Canada, 1971 & 1986
686970717273747576777879
1 2 3 4 5
19711986
• Income classified by proportion of census tract falling below Stats Canada low income threshold
• Quintiles within each CMA
• Apparently, gradient leveled somewhat by 1986
• Wilkins et al. Health Reports 1988;1:137
High Low
Cumulative fetal and infant mortality by weeks since beginning of pregnancy, by maternal education,
Québec, 1990-91
0
2
4
6
8
10
12
16 21 26 31 36 41 46 51 56 61 66 71 76 81
0-10 yrseducation
11 yrs
12-13 yrs
14+ yrs
Weeks since beginning of pregnancy
Per 1000 total births
Infant Mortality by quintiles of wealth, Canada 1971 - 1996
0
2
4
6
8
10
12
14
16
18
20
1971 1976 1981 1986 1991 1996
Q1 - Richest
Q2
Q3
Q4
Q5 - Poorest
per 1,000
Source; Russ Wilkins, “Socioeconomic inequality in health outcomes.”Statistics Canada, 2003
Potentially Modifiable Mortality
0 20 40
%
OtherCongenitalPerinatalCirculatoryAccidentsNeoplasms
• Potential years of life lost, Canada 1986, prior to age 75
• Includes infant deaths• For each cause they
subtracted rates in quintile 1 from other quintiles. The result is expressed as a percentage: how much improvement would occur if everyone had the rate in the highest income quintile?
Diminishing Disparities in Infant Mortality, Canada 1971 - 1996
Poor-Rich Total-RichExcess
Year RD RR RD RR Deaths
• 1971 9.8 1.97 4.8 1.47 2028
• 1986 4.8 1.82 1.7 1.29 666
• 1991 2.9 1.64 1.3 1.29 577
• 1996 2.6 1.67 1.3 1.33 513RD = difference in infant mortality rates between rich and poor; RR = ratio of mortality rates, poor : rich;Excess deaths = number of deaths that would have been avoided had death rates for rich applied to all deaths
Source: Russ Wilkins, “Socioeconomic inequality in health outcomes”, 2003
Low Income and Low Birth Weight Ottawa Area, 1991
2
4
6
8
10
0102030
Vanier
OttawaGloucester
Kanata
Percentage of Families Below Low Income Cutoff
% Ratesof LowBirthWeight,1990-92
(Ross & Wolfson, Statistics Canada)
Nepean
The Barker hypothesis. Why is birth weight important?Death rates from IHD by birth weight (n = 15,726)
0
20
40
60
80
100
120
<2.5 3 3.4 3.9 4.3 >4.3
Source: Barker DJP et al. Weight in infancy and death from ischaemic heart disease. Lancet 1989;I:577-580
Birthweight (kg)
Death
Rate
Examples of associations between SES indicators: Income and School Achievement
Eastern Ontario, 1996-97
0
10
20
30
40
50
010203040
Vanier
Ottawa
Kingston
Gloucester
Kanata
Percentage of Families Below Low Income Cutoff
% ofchildrenscoringbelow Ontariostandards
(Educational Quality Assurance Office of Ontario)`
Cornwall
Prevalence of obesity among women, by SES and by SES of parents
0
5
10
15
20
25
30
35
Low Medium High
Own SESSES of origin
Source: Goldblatt PB et al. Social factors in obesity. JAMA 1965;192:1039-1044.
Socioeconomic status
Prevalence%
(N in each group ranges from 291 to 362)
Note that both obesity, and improvement in obesity, are related to SES. Lower SES women are more often obese than their parents; higher SES slightly less obese
Prevalence of high blood pressure, high cholesterol and obesity, Canada, 1986-92, by
educational level
42
33
22
29
14
18
22
1014
0
5
10
15
20
25
30
35
40
45
Hi BP Hi Chol BMI > 27
0 - 11
12 - 15
16 +
Source: Federal Task force on Population Health, 1996
Years of Education
Percentage
Prevalence of Activity Limitation (ages 15+), Canada, 1991
79
13
19
25
0
5
10
15
20
25
30
1 2 3 4 5
Percentage
(High) Income Quintiles (Low)
Statistics Canada. Report of 1991GSS.
(iii) U.S.A.
0
0.0002
0.0004
0.0006
0.0008
0.001
0.0012
0.0014
0.0016
0.0018
0.002
0 10 20 30 40 50 60 70
Males
Females
Source: Deaton A. Health, inequality and economic development www2.cid.harvard.edu/cidmh/wg1_paper3.pdf
The effect of income is much greater among poor people. Data from U.S. National Longitudinal Mortality Survey (1980-1990)
(graph based on a logit model of the data)
Family Income in 1980 $
10-year age-adjusted probability of dying
And race has a greater effect among the poor:Life Expectancy at age 45 by Family Income, Race and Sex.
United States, averaged over 1979-89
20
25
30
35
40
White FemalesBlack FemalesWhite MalesBlack Males
<$10,000 $10,000- $15,000- 25,000+ $14,999 $24,999
Family Income
Life
Exp
ect
an
cy a
t ag
e 4
5
Source: GA Kaplan et al. In: Promoting Health: Intervention Strategies from Social and Behavioral Research. Institute of Medicine, 2000, page 40
Low Birth weight, by Education and Race / Ethnicity, United States, 1996
0
2
4
6
8
10
12
14
16
18
20
< 121213-1516+
White Black Hispanic Native AsianLow
Bir
thw
eig
ht
per
1,0
00
Liv
e B
irth
s
Years of Education
Source: GA Kaplan et al. In: Promoting Health: Intervention Strategies from Social and Behavioral Research. Institute of Medicine, 2000, page 44
Mortality by family income, MRFIT
0
0.5
1
1.5
2
2.5
RR
<7.5 10 15 20 25 30
Annual family income in thousands of US dollars
2. Income Inequality and Health
Hypothesis since late 1970s – Rodgers, Flegg and others. Mortality rises with range of incomes (Gini coefficient) seen
in societies.
The Wilkinson Hypothesis (1990s): for defined geographical areas, mortality rises with the level of disparity
in incomes.
Corollary: occupation and education gradients in health do not occur in societies with low income disparities.
As countries become wealthier and move through the epidemiologic transition, the leading cause of differences in
mortality changes from material deprivation to social disadvantage.
Material deprivation provokes poverty and infectious disease; social disadvantage provokes stress and chronic
disease.
One measure of Income Inequality: Gini Coefficient
• L(s) lies below line of equality when income inequality favours the rich
• Gini coefficient is twice the area between the curve and the line of equality
• It is about 0.32 for Canada (2006)
% of income
% of population
L(s)
0 100
100
Source: Wikipedia http://en.wikipedia.org/wiki/File:Gini_Coefficient_World_CIA_Report_2009.png
(2 a) Comparisons Across Countries
Gini coefficients for the World
2.00
3.00
4.00
5.00
6.00
7.00
8.00
3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00
Income Inequality
Infa
nt M
orta
lity
SwedenJapan
Finland NorwaySpainFranceGermany
NLAustriaBelgium
DKCH Greece
Canada
Ireland NZ
Is UK
Australia
Portugal
USA
Singapore
CH–Switzerland DK–Denmark Is–Israel NL-Netherlands NZ–New ZealandUK–United Kingdom USA-United States of America
Income Inequality and Infant Mortality in 23 selected wealthy countries (Data from Equality Trust www.equalitytrust.org.uk)
(r = 0.4)
Income Inequality and Life Expectancy in 23 selected wealthy countries (Data from Equality Trust www.equalitytrust.org.uk)
CH–Switzerland D–Germany Is–Israel NL-Netherlands NZ–New ZealandUK–United Kingdom USA-United States of America
75.00
76.00
77.00
78.00
79.00
80.00
81.00
82.00
3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00
Income Inequality
Life
Exp
ecta
ncy
Japan
Sweden
Finland
NorwayD
BelgiumSpain
Austria
Canada
NL
CHF
Denmark
Greece
Ireland
Australia
Portugal
USA
Singapore
IsItaly
UKNZ
(r = –0.4)
Income Inequality and self-reported “mental illness” (in previous 12 months) in 12 wealthy countries
(Data from Equality Trust www.equalitytrust.org.uk)
5.00
10.00
15.00
20.00
25.00
30.00
3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00
Income Inequality
Per
cent
age
repo
rtin
g M
enta
l Ill
ness
(las
t 12
mon
ths)
Japan
Belgium
Canada
USA
Italy
UK
France
New Zealand
Australia
Netherlands
SpainGermany
(r = 0.73)
Income Inequality
Ind
ex o
f S
oci
al P
rob
lem
sIncome Inequality and Index of Social Problems
in 21 wealthy countries (Data from Equality Trust www.equalitytrust.org.uk)
Worse
Better
Low High
SwedenNorway
SpainAustria
SwitzerlandDenmark
GreeceIreland
Australia
Portugal
USA
UK
New Zealand
ItalyFranceCanadaD
NLBelgiumFinland
Japan D-Germany NL-Netherlands
Index of social problems: Life expectancy; Mental illness; Level of trust; Obesity rates; Children’s educational performance; Teenage births; Homicides; Imprisonment rates; Social mobility.
430.00
450.00
470.00
490.00
510.00
530.00
550.00
3.00 4.00 5.00 6.00 7.00 8.00 9.00
Ave
rag
e M
ath
an
d L
iter
acy
Sco
res
Income Inequality
Income Inequality and Educational Attainment in 22 wealthy countries
(Data from Equality Trust www.equalitytrust.org.uk)
Lower
Higher
Low High
SwedenNorway
SpainAustria
CHDK
Greece
Ireland
Australia
Portugal
USA
•UKNZ
Italy
France
Canada
D
NLBelgium
Finland
CH = Switzerland; D = Germany; DK = Denmark; NZ = New Zealand; UK = United Kingdom
Japan
Israelr = -0.45
Life Expectancy and Income Inequality, 1970
70
71
72
73
74
75
26 31 36 41
NL
SwedenNorway
Canada
UK
Japan
Australia
USAW.Germany
Spain
France
Gini coefficients of inequality of distribution of income, standardized for household size
More equal Less equal
Life expectancy(M & Fcombined)
Adapted from Wilkinson R. Unhealthy societies: the afflictions of inequality. London, Routledge, 1996, p 84.
r = -0.81
Income Inequality and Life Expectancy, 1981
72
73
74
75
76
77
44 45 46 47 48 49 50 51Percentage of income received by least well-off 70% of
families
Lif
e E
xpec
tan
cy a
t B
irth
, 198
1
W. Germany
USAUK
Australia Canada
NL
Switzerland Sweden
Norway
r = 0.86
Occupational Class Differences in IMR in England & Wales, Compared to Sweden
0
2
4
6
8
10
12
14
16
I II IIIN IIIM IV V Singleparents
Sweden
England& Wales
Deaths per 1000live births
Note: Income inequality is substantially higher in Britain than in Sweden
Source: R. Wilkinson. Unhealthy societies: the afflictions of inequality. Routledge, 1996
Changes in the Dispersion of Income, 1980 - 1991
0.9
0.95
1
1.05
1.1
1.15
1.2
1.25
1.3
1.35
1980 1991
United Kingdom
United States
CanadaAustraliaJapan
AustriaFranceDenmark, SwedenGermany Norway
Note: the chart shows the ratio of the earnings of someone at the 90th centile of income to the earnings of someone at the 10th centile, artificially set at
1 for 1980. Source: OECD
Some difficulties in nation-level studies• Lack of good quality international data, collected in
consistent manner in different countries. E.g., in some studies Sweden is rated very egalitarian, in others less so than Britain!
• Results seem to vary according to era from which data taken
• Failures to replicate. Mellor & Milyo found that controlling for education removes effect (for infant mortality). Judge et al found correlation of -0.17 (n.s.). Snowdon shows alternatives for most of the Equality Trust graphs that reduce or remove the associations.
• General conclusion: income inequality does not appear to drive overall mortality in industrial countries; may do so for infant mortality. This theory may have outlived its usefulness.
2 (b) Comparisons Within Countries
These appear to avoid some of the difficulties in cross-national comparisons: data are usually collected by a single agency (e.g.,
Statistics Canada)Income data usually collected via the census (rather than surveys). Correlation usually found; lots of replications. Usually around 0.7
(i.e. ‘explains’ half of the difference between areas). Wagstaff: “The first point to emerge from these studies is that they
all confirm that income inequality is strongly associated with mortality, even after controlling for the average level of
community income.” (Annu Rev Public Health 2000;21:554)
Illustration of Within-Country Results: Inequality and the log-odds of mortality. U.S., 1990
Source: Deaton A. Health, inequality and economic developmenthttp://www.cmhealth.org/docs/wg1_paper3.pdf
Questions & Concerns• Data are pooled across ethnic (etc) groups: presumably income
inequality is a proxy for various other factors. As you focus down onto selected groups the association (not surprisingly?) is reduced. So, if the effect comes from inequality between groups (e.g., blacks & whites in the US), is this merely a proxy for race, and does income inequality have no direct effect?
• There is an issue of scale – what inequality should we use when analysing individual data (country level, state level, community or neighbourhood level?) What is the person’s reference point?
• Individual-level analyses generally show very modest inequality effects (RR 1.2, etc)
• General conclusion is that health is an increasing, nonlinear function of absolute income
• So, there may be no direct effect of income inequality at all, but race, geography, social support services, or …?
Categories of Explanation (course topic 2)
1. Theories that explain the pattern of relationships between SES and health – cf. the economic literature (e.g., Wagstaff, below). “What form does it take?”
2. Theories of mechanisms for the link – e.g., lifestyles, genetics, access to care. “How does it work?”
3. Theories on determinants of the relationship – the field of population health. “Why does it arise?”
The concave income-health relationship explanation
Income
Health
μB B+$100 A-$100 A
The blue line shows the concave relationshipof income and health
Two people: A and B. Mean income μ.
Their aggregate health is represented by the green line.
Redistribute $100 from A to B (dotted arrows),reducing income inequity. μ stays the same.
Average health now shown by red line.
See, e.g., Wagstaff and van Doorslaer. Annual Review of Public Health 2000; 21: 543.
Relative or Absolute Income?
• In very poor places, it is logical that there is a minimum income required for basic amenities.
• But in richer places does health reflect absolute wealth, or relative?
• Marmot, 2003: The GNP in Costa Rica is about $2,000 per person; life expectancy for men is 74 years. Among black men in the U.S., mean income is around $26,000 and life expectancy is 66. Adjusting for different buying power brings the Costa Rica figure to about $6,000 per person, still one-quarter of the US figure.
(continued)
• A consistent finding is that within countries or states, individual health is related to individual income, but comparing between states average health is independent of average income, but is negatively related to income inequality. I.e., it depends on which comparison you are making (within or across places)
• Wilkinson noted “Mortality is associated with relative income. Someone with an absolute income that equals half of the US average income might do better to be moderately well off in Greece or Spain than poor in the US” (BMJ 1998; 316: 1611) and “health is powerfully affected by social position”
• “Relative inequality in income may correspond to absolute discrimination and social exclusion.” (Marmot, Perspec Biol Med 2003;46 (suppl 3):S17).
Wagstaff & van Doorslaer’s hypotheses
• Economic perspective: what is the main driver in the relationship?– Absolute income hypothesis– Relative income– Deprivation hypothesis– Relative position – Income inequality
• Conclusion: it depends very much whether you are explaining individual health, or community, or population health patterns– Annual Review of Public Health 2000;21:543
The poverty explanation for the link between income inequality and health
Poverty line
Population A Population B
Mean income
Population A shows a narrow spread of incomes:little income inequality.No-one falls below the poverty lineand health is reasonably good
Population B shows a much wider spread of incomes: high income inequality. Substantial numbers of people fall below the poverty line andaccordingly their health suffers, pulling the average health statistics downward
Two populations, equal in mean income, but different in levels of income inequality
Deaton’s presentation of relative income.
Income
Health
Group 1
Group 2
Two groups differ in average income, and within each group health rises with income (solid sloping lines). But they have equal average health (the ellipses are the same vertical height). However, when you combine the two groups, increasing income inequality, the association between health and income is reduced (dotted line). Hence, within each group, relative income is more important than absolute income, but combining groups income inequality becomes more significant.
LowSource: Deaton A. Health, inequality & economic development www2.cid.harvard.edu/cidmh/wg1_paper3.pdf
Interrelated processes: the challenge of young parenthood changes over time.
Difference between family income of parents and overall median income, by age of mother
-25000
-20000
-15000
-10000
-5000
0
5000
10000
15000
20000
1971 1996
<2525-3030-35> 35
$
Age
Source: C. Lochhead. ISUMA 2000;1:41-44. (www.isuma.net/v01n02/index.htm)
Is it the indicator of Class?
• Goldblatt (1990) compared professional men living in their own home and who had access to a car (SMR = 67) to all men who lacked access to a car and lived in rented accommodation (SMR = 123). This gradient similar to that based on the occupational classification
• Other studies (Carstairs; Townsend) used area-based indicators of social and material deprivation. Consistent relationship found with health indicators.
Here an index of deprivation is based on nine variables. Life Expectancy at Birth, by deprivation decile & gender. New
Zealand, 1995-97
60
65
70
75
80
85
1 2 3 4 5 6 7 8 9 10
MalesFemales
Number of years
Deprivation decile (composite score of nine variables)
Source: Social inequalities in Health: New Zealand 1999. N.Z. Ministry of Health
Health inequalities: mediated by health behaviours?
• Behaviours are correlated with SES
• Alameda County & Whitehall studies
• Behavioral factors unclear for some diseases: spina bifida
• Lifestyle: “patterns of health-related behaviour, values and attitudes in response to social, cultural & economic environment”
• Connections among risk behaviours
Do conventional risk factors account for link between SES and mortality? Prevalence of Regular Smoking, by
deprivation decile & gender (ages 45-64) New Zealand, 1996
0
5
10
15
20
25
30
35
40
1 2 3 4 5 6 7 8 9 10
MalesFemales
% regularsmokers
Deprivation decile (composite score of nine variables)
Source: Social Inequalities in Health: New Zealand 1999. N.Z. Ministry of Health
Ten-Year Relative Risks of Death (all causes) in Whitehall I Cohort (a) unadjusted, and (b) adjusting for CVD risk factors
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
40- 50- 60+
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
40- 50- 60+
Civil service occupational categories: ‘Other’ ‘Clerical’ ‘Professional’
Relative risk
Relative risk(a) Raw data (b) Adjusted for smoking,
BP & cholesterol
Are class mortality gradients mediated by smoking?
Message: as before, smoking is very important, but class seems even more so.
0
2
4
6
8
10
12
14
All N.S.
Admin.
Exec.
Clerical
Other
• All = whole sample
• N.S. = never smoked
• Smoking is linked to SES
• Gradient remains for never-smokers (it’s actually even stronger)
• Whitehall I Study: Carroll et al. Psychology & Health 1993;9:295.
Mortality per 1000
Relative Risk of Death from CVD by Occupational Grade, Showing Differences that can be Explained by Conventional Risk Factors.
Whitehall I Study.Message: most of the variation is not explained by common risk factors. So, what
does class represent?
1
2
3
4
5
Admin. Prof. Clerical Other
OtherB.P.SmokingCholesterol
Unexplained
RR
1
10
100
1000Hungary, Czech,Poland
Canada, U.S.
Germany, U.K.
Hungary, Czech,Poland
Germany, U.K.
Canada, U.S.
The impact of medical care:Trends in age-standardized mortality from causes that are, and are not,
amenable to medical treatment
Deaths unrelated to quality of care
Deaths amenable to care
Source: Boys RJ et al. Br Med J 1991;303:879-883.
“The contribution of medical care is to treat illness when it occurs, not to prevent its occurrence” M. Marmot.
Complications! Interaction between occupational class and country of origin for
immigrants to Britain. Black Report
Occupational class India & Pakistan
West Indies
Europe Ireland All
Professional 122 267 121 118 100
Intermediate 127 163 109 112 100
Skilled Nonmanual 114 135 98 111 100
Skilled Manual 105 87 83 118 100
Partly Skilled 93 71 81 115 100
Unskilled 73 75 82 110 100
Age-Standardized Mortality Rates per 1,000 at Ages 15 to 64by Occupational Class and Country of Immigration, United Kingdom,
1971
Why the reverse trend for immigrants?
Some conclusions• Poverty is clearly linked to health• Income inequality is a useful marker of risk, but represents
the likely occurrence of other factors that adversely affect health
• Depends on level of phenomenon: individual vs community-level vs population health
• Sen (Development and freedom, 1999): relief from any one of several interlinked deprivations helps to promote relief from the others
• This course will try to identify the inter-linked deprivations that affect health
• Theories of What?, How? and Why?
Bits & Pieces
And for premature mortality:Age-Standardized Mortality Rates per
thousand, Ages 15 to 64, by Occupational Class. U.K., 1971
0123456789
10
Mortalityratesper
thousand
Male Female
IIIIIInIIImIVV
Overview of Canadian Labour Force, 1998
Job > 6 m os .8 ,0 4 4 ,4 0 0
(5 6 .2 % )
Job < 6 M os .7 9 0 ,9 0 0
F u ll-t im e8 ,8 3 5 ,3 0 0
(6 1 .7 % )
P art-t im e1 ,5 7 1 ,1 0 0
(9 .9 % )
P erm an en t1 0 ,4 0 6 ,4 0 0
(7 2 .6 % )
Tem p orary1 ,3 9 4 ,8 0 0
(9 .7 % )
E m p loyees1 1 ,8 0 1 ,2 0 0
(8 2 .4 % )
O w n -accou n t1 ,6 7 4 ,7 0 0
(1 1 .7 % )
E m p loyers8 5 0 ,6 0 0(5 .9 % )
S e lf-em p loyed2 ,5 2 5 .2 0 0
(1 7 .6 % )
E m p loyed1 4 ,3 2 6 ,4 0 0
(1 0 0 % )
U n em p loyed1 ,3 0 5 ,1 0 0
Tota l L ab ou r F orce1 5 ,6 3 1 ,5 0 0
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Overall Dependency Ratios. Canada, 1881 - 2031
