Income, Wealth and Health

• Income - flow of money received by an individual or household. Can be given weekly, monthly or annually. For most, it is a wage/salary, but it can also be pensions, interest on investments of benefits.
  • In places with subsistence agriculture, cash income may be a poor reflection of resources.
• Wealth - stock of money and assets that a person or household has, including housing. It can also include savings in a bank, assets in the form of stocks/ISAs/bonds.
  • Also difficult to measure reliably as wealth can be held in goods and these appreciate/depreciate over time.
• Income and wealth often go hand in hand but sometimes they don't. They can be measured for individuals or households.
• Income data in the UK is not routinely collected, so is not very reliable. Census questions rarely ask about it as many people are reticent about divulging their income. This differs in the US where their census always asks about income, so data is more reliable.
• Asset based wealth indices use information from surveys about housing, other amenities and assets to produce a composite score which places households in one area of the wealth distribution e.g. in 1 of 5 wealth quintiles. These aren't really used in wealthy countries.
  • Different resources are often used in different places (due to differing circumstances e.g. urban vs rural, different cultures), meaning that the indices might be composed of different indicators.
  • This makes direct comparability between places difficult.

• Instead of wealth indices, countries such as the UK use socio-economic status. This is used in both poor and rich countries.
• SES is based on occupation as this is much easier to gather than information on income and wealth.
  • Works by using hierarchical systems e.g. social groups/classes formed by grouping together occupations. For example, doctors and lawyers are in the 'professional' group
  • Resulting SES groups tend to be quite well correlated with income and wealth
  • Less likely to be discriminatory for women than men as it can measure fewer differences for women than for men as it is more likely that a woman's occupation doesn't reflect her resources (e.g. in the 1900's most women were housekeepers but this didn't show their access to resources).
• Education is another common measure, and can be more indicative of individual level attributes. Therefore, it is more useful for women.
  • Can be highly correlated with income and wealth but not always.
• Graphs created by Katikireddi et al. (2017) show that all of the occupations in the highest social group have low mortality and vice versa. There is a strong socio-economic gradient in mortality. Men with no occupation have by far the highest mortality. Also, the social gradient for women is much smaller than for men due to reasons previously mentioned.

• Income, wealth, SES and education are all correlated with health but might impact it in different ways.
  • Health care (access, quality, time taken).
  • Nutrition/diet (can be affected by income but also education).
  • Behaviour (whether knowledge/agency are acted upon). Might influence things such as smoking and exercise (but these can also be affected by income-access to facilities).
  • Housing (facilities/quality/overcrowding).
  • Environment/location.
  • Stress.
  • Knowledge (about health/services - often shaped by education and social influences).
  • Agency (ability to seek healthcare) - linked to female empowerment. Linked to income/wealth but especially education.
• Important to note that health can also affect income - poor health might mean people are unable to access high incomes for various reasons.

• Proposed by Link and Phelan (1995).
• Argued that SES is the fundamental cause of disease as it affects multiple diseases via multiple risk factors (i.e. when one intermediate factor is dealt with, another one takes over as the fundamental social causes haven't been addressed).
• The diseases and mechanisms which produce SES differences in health change over time and vary with place.
• Higher SES people are always in a better position to access knowledge and resources to reduce their risks.
• Link and Phelan argued that in the early and mid 20th century, social class differences in mortality in the USA were strongly determined by access to public health services e.g. public sanitation and vaccination.
• However, once these issues were addressed, differences in health weren't eliminated but instead there were new, intervening mechanisms e.g. smoking, exercise, diet.
• An attractive theory as it offers a link between the epidemiologic and demographic transitions.

Historical evidence for the theory:

• Suggested that social differences in mortality emerged during the industrial period.
• Antonovsky argued that socio-economic differences depend entirely on what health technologies were available. In times when medical treatment was ineffective/there was little useful knowledge about disease causation and control, there was very little that the rich could do to protect themselves. It was only the growth of medical knowledge and the growth of effective cures and preventative techniques that provided opportunities for those who were better off to benefit.
• Antonovsky's (1967) 3-stage model: divergence then convergence - predicted that there would be an evolution from the situation before around 1650 where there were no real social status differentials in health and between 1650 and 1850, these differentials would widen, then narrow again from 1850 and the technological benefits began to trickle down to the poorest.
• Woods and Williams (1995) argued that this depended on the type of health or mortality measure considered.

• Clouston, Rubin, Phelan and Link (2016) produced a version of fundamental cause theory which shows more sensitivity to the historic evidence.
• Each cause follows a pattern...
  • Natural mortality. Characterised by a lack of knowledge about risk factors/prevention/treatment in the population so no opportunity for elite groups to gain an advantage so no difference sin mortality between social groups.
  • Producing inequalities, due to unequal diffusion of innovations about healthcare/health knowledge.
  • Reducing inequalities, as health knowledge becomes accessible to everybody.
  • Reduced mortality/disease elimination. Where prevention and treatment is widely available and accessible to everybody. Too optimistic?

• Models such as the fundamental cause theory model don't pay much attention to plac,e instead focusing on knowledge and access to healthcare and technology.
• Fundamental causes has been interpreted as predicting that the health of places is a product of the class composition of places. This is shown in some studies.
• Other studies show that the physical or social aspects of place have an effect on the health of inhabitant, irrespective of their class or status.
• When we look at different sorts of places (e.g. agricultural vs industrial), there is very little class differences within them (shown by Reid, A, 1997). This suggests that class differences in mortality depend on where people live rather than social class.
• It isn't that simple - manual labourers needed to live near their work and these are the places that tended to have high environmental pollution and poor sanitation and the people living there had little opportunity to change this.

• Haven't changed much between 1901 and 2010/2012.
• Generally, places with the 'worst health' were industrial hotspots in the past and are now less economically buoyant post industrial places.
• E.g. coal mining areas in the South Wales Valleys.

• Most common is the IMD.
• Combines information on...
  • Income
  • Employment
  • Education
  • Health
  • Crime
  • Barriers to housing and services
  • Living environment (each factor has varying weights)
• People are then put in one of ten deprivation deciles.
• There is a strong health gradient with IMD deciles.
  • Marmot (2020) emphasizes the political and societal influences which operate at an areal level - the most deprived areas are forced to cut their spending by much more than the least deprived areas. This led to reductions in life expectancy in these areas.

• The relationship between income and health on a national level is the same as on an individual level - wealthier countries will have more money to spend and will also have individuals with more money to afford good services etc.
• Preston curves (Preston, 1975) show that income has a non-linear effect on health.
  • Also shows that the relationship between income and health has changed over time.
  • Rises in income accounted for a small fraction of improvements in health between 1930 and 1960. Most of it was due to advances in and diffusion of knowledge and technology.
  • Income plays a larger role in declines in income which take place later in the epidemiologic transition (Mackenback et al. 2013).
  • Lutz and Kebede (2018) state that improvements in education can explain both rising GDP and improving health.
• In effect, health is getting 'cheaper' over time at every level of income. Preston attributed this to changes in technology.

• Caldwell (1986) identified countries which 'over achieved' or 'under achieved' in terms of health relative to their GDP.
• Identified two routes to low mortality...
  • 'Open' societies e.g. Sri lanka, Kerala, Costa Rica
  • Communist states e.g. China, Vietnam, Cuba
• High health achiever groups were characterised by...
  • High % GDP devoted to health and education
  • Egalitarian access to healthcare
  • Nutritional safety net or egalitatian food distribution
  • Effective immunisation programs and delivery of health services
  • 'Sufficient' levels of female autonomy
• Follow up studies have highlighted the complexity of health determinants (e.g. Kuhn, 2010 or Freeman et al. 2020)
  • Good health achievers at all points in time promote basic healthcare and a strong civil society