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The existence of health gradients within health care is a universal constant. Many of the determinants of ill health were identified by Edwin Chadwick in his studies of public health in Victorian England: poverty, housing, water, sewerage, the environment, safety and food. In addition, we recognize today that illiteracy, tobacco, AIDS/HIV, immunization, medication and health services are also important (Ferriman, 2007).

Recent studies of the social determinants of health have pinpointed various kinds of inequity. The first of these is based on socio-economic status (SES): people who are higher up the ‘pecking order’ of wealth, education and status have better health and live longer than those at the lower end of the scale. To illustrate this, Figure 4.3 shows a map of the Jubilee Line, which travels along an east–west axis across London. If you travel eastwards along this tube line from Westminster to Canning Town, the life expectancy of the local population is reduced by one year for every stop.

Health gradients are found in all societies. Wealthier groups always have the best health; poorer groups have the worst health. These differentials occur in both illness and death rates, and health gradients are equally dramatic in both rich and poor countries. The majority of studies have been carried out in rich countries.

Figure 4.3 Differences in life expectancy within a small area of London

Source: Department of Health (2008)

BOX 4.2 KEY STUDY: The Whitehall studies

The Whitehall studies investigated social class, psychosocial factors and lifestyle as determinants of disease. The first Whitehall study of 18,000 men in the Civil Service was set up in the 1960s. The Whitehall I study showed a clear gradient in which men employed in the lowest grades were much more likely to die prematurely than men in the highest grades.

The Whitehall II study started in 1985 with the aim of determining the causes of the social gradient and also included women, including potential psychological mediators. A total of 10,308 employees participated, two-thirds men and one-third women. The cohort was followed up over time with medical examinations and surveys. Most participants are now retired or approaching retirement.

There have been many phases of data collection, alternating postal self-completion questionnaires with medical screenings and questionnaires. In addition to cardiovascular measures, blood pressure, blood cholesterol, height, weight and ECGs were taken, along with tests of walking, lung function and mental functioning, questions about diet, and diabetes screening.

Figure 4.4 Death rates (%) vs. employment grades over a 25-year period in the Whitehall studies

Source: Ferrie (2004). Reproduced with permission

The Whitehall studies found that an imbalance between demands and control lead to illness. Control is less when a worker is lower in the hierarchy and so a worker in a lower position is unable to respond effectively if demands are increased, supporting Karasek and Theorell’s (1990) demand–control model. Other mechanisms can buffer the effect of work stress on mental and physical health: social support (Stansfeld et al., 2000), effort–reward balance (Kuper and Marmot, 2003), job security and organizational stability (Ferrie et al., 2002). Figure 4.4 shows the gradient of death rates versus employment grades over a 25-year period in men from the Whitehall studies. The death rate is shown relative to the whole Civil Service population (reproduced from Ferrie, 2004).

Virtanen et al. (2015) examined whether midlife adversity predicts post-retirement depressive symptoms in 3,939 Whitehall II participants (mean age 67.6 years at follow-up). Strong associations occurred between midlife adversities and post-retirement depressive symptoms, including low occupational position, poor standard of living, high job strain and few close relationships. Associations between socio-economic, psychosocial, work-related or non-work-related exposures and depressive symptoms were of similar strength. The data suggest that socio-economic and psychosocial risk factors for symptoms of depression post-retirement can be detected in midlife.

Source: Ferrie (2004)

There are relatively few studies of health gradients in poor countries. The data are cross-sectional rather than longitudinal, but show a similar pattern to those observed with Whitehall civil servants. One of the authors analysed data from the Demographic and Health Surveys (DHS) programme of the World Bank (2002) (Marks, 2004). These are large-scale household sample surveys carried out periodically in 44 countries across Asia, Africa, the Middle East, Latin America and the former Soviet Union. Socio-economic status was evaluated using answers about assets given by the head of each household. The asset score reflected the household’s ownership of consumer items ranging from a fan to a television and car, dwelling characteristics such as flooring material, type of drinking water source and toilet facilities used, and other characteristics related to wealth. Each household was assigned a score for each asset and scores were summed for each household; individuals were ranked according to the total score of the household in which they resided. The sample was divided into population wealth quintiles – five groups with the same number of individuals in each.

The gradient of under-5 mortality rates (U5MRs) for 22 countries in sub-Saharan Africa are shown collectively in Figure 4.5. The U5MR indicator is the number of deaths of children under 5 years of age per 1,000 live births. This figure shows gradients in all countries. A wide gap in health outcomes exists between the rich and the poor even within these very poor countries. Similar gradients exist for countries in Latin America and the Caribbean and throughout the 44 countries included in the DHS. Infant mortality is halved between quintiles 1 and 5, representing the poorest of the poor and the wealthiest of the poor.

Figure 4.5 Under-5 mortality gradients for sub-Saharan Africa plotted against asset quintile. The area under each line represents the individual country rates. Quintile 1 has the least assets, quintile 5 the most

Source: Marks (2004)

An interesting set of relationships was observed between the U5MRs, literacy and resources (Marks, 2004). The U5MRs in 44 countries were positively correlated with female illiteracy rates and the proportion of households using bush, field or traditional pit latrines, and negatively correlated with the proportion of households having piped domestic water, national health service expenditure, the number of doctors per 100,000 people, the number of nurses per 100,000 people and immunization rates.

The most important predictors of infant survival are educational and environmental. The most effective long-term structural interventions to combat inequality are to improve the educational opportunities for women and to improve the supply of drinking water. High literacy among mothers and access to water supplies and toilets are highly associated with low infant mortality. High numbers of doctors and nurses, immunization rates and health service expenditure are associated with lower mortality rates, but these health service variables are less influential, statistically speaking, than literacy, domestic water and sanitation. The latter provide the foundations of good health, while health services are the bricks and mortar.