Socioeconomic Gradient of Health A Brief Summary
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The Socioeconomic Gradient of Health: A
BriefSummaryby Abraham J. Nunes (abrahamjnunes.wordpress.com)
Introduction
The quality and quantity of health services, and indirectly, health itself,
improve roughly in parallel with a countrys gross domestic product (Figs.
1-3). However, alone, GDP cannot account for the seeming improvement in
these health indicators. For instance, Portuguese and Japanese gross
domestic products are significantly less than that of the United States, but
life expectancies in these nations are not appreciably different. Moreover,
little to no correlation can be found between disability adjusted life years
(DALY; years of life lost due to either early mortality or disability; fig. 4)
and national GDP. DALY is widely recognized as a more robust measure of
population health, compared to life expectancy.
The explanatory factor is likely due to the socioeconomic gradient of health.For example, when analyzing the WHO mortality regions, Reidpath and
Allotey (2007; Fig 5) found that the poorest world regions suffered the
greatest DALY losses when compared to their wealthiest counterparts.
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Figure 1: Higher life expectancies are associated with greater per capita income across
nations.
Figure 2. Increased per capita income is associated with a decreased all cause
newborn mortality rate across nations.
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Figure 3: Lower values of all cause mortality in children are associated with increases in
per capita income, when measured across nations.
Figure 4: Disability Adjusted Life Years Against National GDP: Data courtesy of WHO
and Wolfram Alpha
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Summary ofThe Socioeconomic Gradient in Population Health:
Explaining Health InequalitiesThroughout history, observers have noted that health status improves
incrementally with increases in socioeconomic status (SES), and that this
gradient is evident for a myriad of afflictions: from the perinatal period
until elderlylife. Longitudinal evidence supports this theory. Aprominent
UK study (aka Whitehall; Marmot et al., 1978), showed that, although
affluence levels of all subjects were high when compared to other nations,
the remaining SES gradient (based on occupational status, education, etc.)
continued to affect health outcomes in a roughly linear fashion, consistent
with the SES gradient of health theory. In short, the SES gradient exists
regardless of whether it is analyzed with respect to income, occupation, or
education (Fig. 6).
Figure 5: Disability Adjusted Life Years Across WHO Mortality Subregions (Reidpath
and Allotey, 2007)
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Status differences may be observed at any stage of the life cycle:
Perinatally: status differences are associated with differential stability
and securityin the home. This impacts future school readiness (Fig.7; Marmot, 2011; Washbrook and Waldfogel, 2008).
Parental status throughout infancy and childhood is reflected in the
neighbourhood of residence. This affects the social networks,
opportunities, and community values offered to a child.
During adolescence and into adulthood, a child begins to define his or
her own status. Individuals from lower SES strata tend to acquire jobs
with high demands, and which offer little, if any, autonomy.
During late adulthood, individuals who occupy the aforementioned
positions begin to demonstrate poor health and absenteeism.
Ultimately, these individuals die prematurely.
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Figure 6: Infant mortality rate according to maternal years of education in Chile
1990-1995 (Nolen et al., 2005; Hollstein et al., 1998)
Particularly interesting is the slope of the socioeconomic gradient, and its
relation to the overall mean health of a population: the shallower the SESinequality slope, the greater the mean level of that populations health.
Therefore, some authors suggest that an SES gradient may be used as a
measure of public health, similar to BMI and infant mortality levels.
Figure 7: School Readiness per Parental Income Bracket (Marmot, 2011; Washbrook
and Waldfogel, 2008)
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As mentioned above, the SES gradient may be observed at any age:
Perinatally: gradient exists for infant mortality and low birth weight
Childhood: gradient exists for injurious death and socio-emotional
development (Fig. 8; Marmot et al. 2010; Power and Matthews,
1997).
Early adulthood: gradient exists for injury and mental health related
deaths.
Late adulthood (age 45-74): gradient exists for morbidity and
mortality related to premature chronic degenerative diseases (i.e.
stroke, heart attack, arthritis, cancer). This period represents the
most significant observable gradient in health outcomes related to
SES. Data from the 25 year Whitehall follow-up report demonstrate a
readily observable gradient for coronary heart disease, when
measured against occupational rank (Fig. 9; Marmot, 2000)
Elderly life (75 +): a gradient exists for dementia and other
degenerative conditions.
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Figure 8: Childrens' Poor Social Adjustment Based on Parental Occupational Status
(Marmot et al. 2011; Power and Matthews, 1997)
Figure 9: The Mortality Gradient for Coronary Heart Disease Based on Occupational
Status (Whitehall 25 Year Follow-Up Report). Note that administrators were used as the
reference (RR=1.0).
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Although late adulthood represents the most significant gradient in health
outcomes related to SES, the individuals comprising this demographic have
expressed multiple sources of heterogeneity throughout their life cycles.
These sources of heterogeneity are known as pathways of diseaseexpression. These pathways are encompassed in six explanatory theories
(Public Health Agency of Canada).
1. Health selection
2. Differential susceptibility
3. Individual lifestyle preferences
4. Physical environment differences
5. Differences in access to health services
6. Socioeconomic-psychosocial condition
Health selection refers to the potential trend for unhealthy individuals and
their families to move into lower income areas. This has been refuted based
on the fact that the SES gradient persists following statistical adjustment
for income (although income is the primary determinant of a
socioeconomic gradient).
Differential susceptibility suggests that upward socioeconomic mobility is
based on individuals favourable genetic characteristics. This may be
partially true, since factors such as height have been shown to contribute to
future health and SES. This contribution, however, is statistically minimal,
and may be better reflected in an individuals socioeconomic-psychosocialcondition.
The individual lifestyle preferences theory suggests that individuals in
lower socioeconomic strata adopt poorer lifestyle habits, compared to
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people in higher SES brackets. Although this theory explains a significant
portion of the SES health gradient, we must remember that the SES health
gradient is not confined only to diseases which have lifestyle risk factors.
Furthermore, following statistical adjustment for lifestyle factors, thegradient has persisted in studies. Differences in lifestyle preference are said
to simply augment the effects of any existing gradient in SES.
Physical environment differences exist between SES strata. Those on the
lower end of the scale typically undergo proportionally higher toxic
exposures. Deaths in OECD countries which are attributable to these
exposures, however, is low, and cannot account for the observed SES health
gradient.
Figure 10: Levels of Air Pollution Concentration Between 2003-2007 by Socioeconomic
Region of London, UK (Marmot et al., 2011; Tonne et al., 2008)
The theory of differential access to health care is quite weak, for in nations
with universal access to health care, stark SES health gradients persist.
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Furthermore, medically avoidable deaths account for a very small portion
of the SES health gradient.
The mere persistence of the SES health gradient through statisticaladjustments for other explanatory variables suggests that factors
intrinsically coupled to the low SES circumstance, and its product bio-
psycho-social environment, account for a majority of the observed SES
health gradient. This is the basic rationale for the socioeconomic-
psychosocial condition theory. Two explanatory models exist:
1. The latency model: states that early experiences will affect well-being
and health outcomes in later life regardless of further intervention.
This supports the notion of a critical period in early development.
2. The pathway model: states that the cumulative effect of events and
experiences throughout ones life is the prime determinant of end
well-being and health outcomes. This model intrinsically supports the
efficacy of interventions targeted at preventing or treating critical
negative events which may occur at important transition points
during human development.
Resolutions
The most likely reality is that both the latency and pathway models operate
to some extent, and that a disproportionately high investment in an
individuals early life, followed by ongoing investment in his or her wellbeing are both required for achievement of optimal SES and health
outcomes. Implied in this statement are changes in individual needs
throughout the life cycle (see also Fig. 11):
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During early age, infants require much attention, care, security, and
to a healthy degree, spoiling, dare I say it.
As they age, children receive progressively less influence from the
home, and begin to absorb (a) community characteristics, (b) labourmarket forces, and most importantly, (c) the influence of peer
relationships. The authors of this full manuscript suggest that these
factors are likely to contribute the greatest cumulative effect on future
well-being and health outcomes.
Figure 11: Graphical representation of the life cycle and its associated developmental
facets, with performance suggestions (Marmot et al., 2011)
In their extensive review of current evidence regarding intervention,
Marmot et al. (2010) devised 6 policy objectives aimed to reduce inequity in
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the social determinants of health (See References for a link to the full
report, and Fig. 12 for graphical summary):
1. Give every child the best start in life2. Enable all children, young people and adults to maximise their
capabilities and have control over their lives
3. Create fair employment and good work for all
4. Ensure a healthy standard of living for all
5. Create and develop healthy and sustainable places and communities
6. Strengthen the role and impact of ill health prevention
Figure 12: Graphical representation of performance objectives and health inequity
reducing strategy by Marmot et al. (2011)
In summary, the health of a population may be said to generally increase in
parallel with the respective nations GDP. However, measures of social
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inequity must be considered in order to more accurately model population
health improvements. The socioeconomic gradient of health may be
measured using multiple indicators: income, education, occupational
status, neighbourhood of residence, etc. Regardless of the metric used, agradient is demonstrated. That is, income disparities alone do not fully
describe the behaviour of health outcomes. Improved equity within a nation
has been associated with overall improvements in population health.
Therefore, it is likely that providing interventions for the reduction in social
inequality will improve population health metrics as a whole. Several
models provide explanations for the socioeconomic gradient of health, and
it is likely that the poor health behaviours of the poor, along with their
socioeconomic-psychosocial environments provide the greatest influence
on future health outcomes. Several authors have provided policy
recommendations, the most substantial of which are likely to come from Sir
Michael Marmot et al. (2011).
Currently, Im reviewing how microcredit based interventions for the
reduction in social inequity might affect health behaviours, self-reported
measures of psychosocial and physical health, and long-term health
outcomes. It is likely that my next post on this topic will involve some of
those findings.
I hope you enjoyed this post!
AN
References
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MainlyThe Socioeconomic Gradient in Population Health: Explaining
Health Inequalities, with further contributions from the following sources:
Hollstein RD, et al. Social inequalities and health. Socioeconomic level andinfant mortality in Chile in 19851995. Revista Medica de Chile
1998;126:333-40
Marmot, M. et al. Employment grade and coronary heart disease in British
civil servants. Journal of Epidemiology and Community Health 1978;32:
24449.
Marmot, M. Multilevel Approaches to Understanding Social Determinants.
in Social Epidemiology, eds. L. Berkman and I. Kawachi. 2000;Oxford:
Oxford University Press.
Marmot, M. et al. Fair Society, Healthy Lives: The Marmot Review. 2010
(Download the Full Marmot Review Here)
Nolen et al. Strengthening health information systems to address health
equity challenges. Bulletin of the World Health Organization. 2005;83(8)
Power, C. and Matthews, S. Origins of health inequalities in a national
population sample. The Lancet 1997; 350:1584-9.
Reidpath, DD., Allotey, P. Measuring global health inequity. Int J Equity
Health. 2007;6:16.
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Tonne, C., Beevers, S., Armstrong, B., Kelly, F., Wilkinson, P. Air pollution
and mortality benefits of the London Congestion Charge: Spatial and
socioeconomic inequalities. Occupational and Environmental Medicine
1998;65: 620-627.