Presentation on theme: "Socioeconomic Status and Health An overview of the evidence for a connection between wealth and health Ottawa, August, 2006."— Presentation transcript:
Socioeconomic Status and Health An overview of the evidence for a connection between wealth and health Ottawa, August, 2006
Sections 1.Which indicators of “health” & wealth to use? 2.Individual evidence for link between SES and health 1.Comparisons between societies 2.Comparisons within societies (Britain, Canada, USA) 3.Societal level income inequality and health 1.Between countries 2.Within countries 4.Potential explanations
1. 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.
2. Socioeconomic Status and Health (2.1) Comparisons Between Societies
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 arguments as we compare individual and aggregate statistics
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 national health care spending and level of health is curvilinear. Among poor countries, expenditures quickly reduce infant mortality and this greatly extends average life expectancy. But once infant mortality is low, 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?
2.2 Comparisons 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! 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 Age-Standardized Mortality Rates per 1,000 at Ages 15 to 64 by 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 MalesFemales Class 1972-761992-961972-761992-96 I 72.077.779.283.4 II 71.775.877.081.1 III non-man. 69.575.078.080.4 III manual 69.873.575.178.8 IV 68.472.675.077.7 V 66.568.273.977.0 Difference I-V 184.108.40.206.4 Source: Marmot M. Perspec Biol Med 2003; 46 (Suppl 3): Table 1
The Side-effects of Success: Mortality from cardiovascular disease, England and Wales 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
And for many individual causes of death: Respiratory Deaths for Ages 15-64 by Occupational Class, England & Wales, 1970-72
And also among children All-cause SMRs (ages 0 – 14) by occupational class, England & Wales, 1970-72
Most of the Effect Lies at Low Income Levels: Earnings and SMRs (UK, 1970) 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 Occupational class Age at death Source: Whitehead M. The Health Divide, table 11. Penguin books, 1992. The class gradient continues up to include deaths at old age
And disparities appear to be increasing… Trends in SMRs over Time in UK Men Aged 15 - 64 Intermediate Professional Unskilled Partly Skilled Skilled 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 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 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 Administrative “Other” Clerical Source: Marmot et al. Lancet 1991;337:1387-1393 Professional & Executive Cumulative Probability of 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. Occupational Class V IV III II I
Potential Years of Life Lost (Accidents & Violence). England & Wales, 1971 - 1991 Social Class V IV III II I
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 Deaths per 1000 population
Age-standardized mortality rates from cardiovascular disease, Canada, 1951-1995 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 FemalesTotal < 1 2,336 1,680 4,016 1 - 14 896 599 1,495 15 - 34 1,373 822 2,195 35 - 54 5,547 2,597 8,144 55 - 7412,265 7,23819,503 75 + 5,707 12,03717,744 Totals28,124 24,97353,097
Life expectancy at birth by age and sex, Canada, 1921-2000 Life expectancy (years)
So, what about Social Class? Life Expectancy at Birth, Canada, 1971 and 1986 Years Females, 1986 Females, 1971 Males, 1986 Males, 1971 (High) Income Quintiles (Low)
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 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 Weeks since beginning of pregnancy Per 1000 total births
Infant Mortality by quintiles of wealth, Canada 1971 - 1996 per 1,000 Source; Russ Wilkins, “Socioeconomic inequality in health outcomes.” Statistics Canada, 2003
Potentially Modifiable Mortality 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-RichTotal-RichExcess YearRD RRRD RRDeaths 19719.81.974.81.472028 19864.81.821.71.29 666 19912.91.641.31.29 577 19962.61.671.31.33 513 RD = 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 Vanier Ottawa Gloucester Kanata Percentage of Families Below Low Income Cutoff % Rates of Low Birth Weight, 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) 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 Vanier Ottawa Kingston Gloucester Kanata Percentage of Families Below Low Income Cutoff % of children scoring below Ontario standards (Educational Quality Assurance Office of Ontario)` Cornwall
Prevalence of obesity among women, by SES and by SES of parents 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 Source: Federal Task force on Population Health, 1996 Years of Education Percentage
Prevalence of Activity Limitation (ages 15+), Canada, 1991 Percentage (High) Income Quintiles (Low) Statistics Canada. Report of 1991GSS.
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 White Females Black Females White Males Black Males <$10,000 $10,000- $15,000- 25,000+ $14,999 $24,999 Family Income Life Expectancy at age 45 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 White Black Hispanic Native Asian Low Birthweight per 1,000 Live Births 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 <7.51015202530 Annual family income in thousands of US dollars
3. 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) 0100
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
Sweden Japan Finland Norway Spain France Germany NL Austria Belgium DK CH Greece Canada Ireland NZ Is UK Australia Portugal USA Singapore CH–Switzerland DK–Denmark Is–Israel NL-Netherlands NZ–New Zealand UK–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 Zealand UK–United Kingdom USA-United States of America Japan Sweden Finland Norway D Belgium Spain Austria Canada NL CH F Denmark Greece Ireland Australia Portugal USA Singapore Is Italy UK NZ (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) Japan Belgium Canada USA Italy UK France New Zealand Australia Netherlands Spain Germany (r = 0.73)
Income Inequality and Index of Social Problems in 21 wealthy countries (Data from Equality Trust www.equalitytrust.org.uk) Worse Better LowHigh Sweden Norway Spain Austria Switzerland Denmark Greece Ireland Australia Portugal USA UK New Zealand Italy France Canada D NL Belgium Finland 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.
Income Inequality and Educational Attainment in 22 wealthy countries (Data from Equality Trust www.equalitytrust.org.uk) Lower Higher LowHigh Sweden Norway Spain Austria CH DK Greece Ireland Australia Portugal USA UK NZ Italy France Canada D NL Belgium Finland CH = Switzerland; D = Germany; DK = Denmark; NZ = New Zealand; UK = United Kingdom Japan Israel r = -0.45
Life Expectancy and Income Inequality, 1970 NL Sweden Norway Canada UK Japan Australia USA W.Germany Spain France Gini coefficients of inequality of distribution of income, standardized for household size More equalLess equal Life expectancy (M & F combined) Adapted from Wilkinson R. Unhealthy societies: the afflictions of inequality. London, Routledge, 1996, p 84. r = -0.81
Income Inequality and Life Expectancy, 1981 W. Germany USA UK Australia Canada NL Switzerland Sweden Norway r = 0.86
Occupational Class Differences in IMR in England & Wales, Compared to Sweden Sweden England & Wales Deaths per 1000 live 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 United Kingdom United States Canada Australia Japan Austria France Denmark, Sweden Germany 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.
3.2 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 development http://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 …?
4. Categories of Explanation 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 μ B B+$100A-$100 A The blue line shows the concave relationship of 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 APopulation B Mean income Population A shows a narrow spread of incomes: little income inequality. No-one falls below the poverty line and 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 and accordingly 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. Low Source: 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 $ 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 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 % regular smokers 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 Civil service occupational categories: ‘Other’ ‘Clerical’ ‘Professional’ 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. 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? Other B.P. Smoking Cholesterol Unexplained RR
Hungary, 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 Age-Standardized Mortality Rates per 1,000 at Ages 15 to 64 by 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?