socioeconomic gradient of health a brief summary

8/7/2019 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.

socioeconomic gradient of health a brief summary

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