RESPUESTAS POR SERIE
¿EL NO CONTAR CON UNA ADECUADA IMPLEMENTACIÓN DEL SISTEMA DE CONTROL
5.9.1. Objetivo general: Determinar de qué manera el Sistema de
Measuring health by socioeconomic status often uses individual level data such as
education, income or class to classify people into socioeconomic groups. These are
discussed below.
Education and health
There is a positive association between education and health with well-educated
people often experiencing better health than the poorly educated, indicated by
higher levels of self-assessed good health and low levels of disease. A meta-analysis
percent odds risk reduction in the likelihood of developing a chronic disease among
the more educated compared to the less well-educated. The education gradient
remained robust across genders. Conversely, low educational attainment is
associated with higher rates of infectious and chronic diseases, poor self-assessed
health and premature mortality (Nagel et al., 2008).
Educational attainment may affect health for three main reasons; (1) work and
economic conditions, (2) social-psychological resources, and (3) health behaviours.
Firstly, well-educated people are more likely to be in employment, specifically full-
time employment, have subjectively rewarding jobs, high incomes, and lower
economic hardship (Ross and Wu, 1995). Secondly, well-educated people generally
report a greater sense of control over their personal lives and health, with greater
social support networks. Thirdly, well-educated people are less likely to engage in
health hindering behaviours such as tobacco smoking, physical inactivity, and
excessive alcohol consumption, contributing to better health. While much of the
association between health and education correlates with these factors, education
continues to have a strong and positive direct effect on health even after accounting
for these factors (Ross and Mirowsky, 1999). Therefore, educational attainment has
both a direct and indirect effect on health.
Health literacy and work
Health literacy is an important determinant of health although defining it appears
more complex. A systematic review (Sorensen et al., 2012) of definitions and
conceptual frameworks of health literacy identified 17 definitions and 12 conceptual
the ability to regularly update oneself on determinants of health in the social
and physical environment…and derive meaning, to interpret and evaluate
information on determinants of health in the social and physical environment,
and…to make informed decisions on health determinants in the social and
physical environment” (Sorensen et al., 2012, p. 10).
This definition links to the determinants of health models discussed in section 2.1.
In accordance with the definition provided above, the occupational groups included
in the analysis presented in this thesis, which can be categorised as health literate,
are nurses, nursing and midwifery professionals, and other health professionals.
Theoretically, the analysis presented in this thesis should show that these
occupational groups have lower percentages of workers with long-term conditions
and better health behaviours than non-health literate occupations such as nursing
auxiliaries, and care assistants and home carers.
The burden of lower health literacy is unequally distributed across society with high
rates of limited (low or marginal) health literacy associated with older age (OR=5.74;
95% CI 3.90, 8.43; p˂0.001), lower educational level (OR=6.94; 95% CI 4.74, 10.14;
p˂0.001), lower income (OR=3.11; 95% CI 2.09, 4.62; p˂0.001) and perceived poor
health (OR=5.28; 95% CI 3.00, 9.29; p˂0.001) (Protheroe et al., 2016). The effect of
health literacy on health is potentially confounded by occupation and education.
Income and health
There is a wealth of evidence confirming the association between income and health
with those on lower incomes experiencing poorer health, economic strain and
indicated that people in midlife with lower incomes and greater subjective financial
difficulties have a higher risk of poor health; conversely, the effect of income on
health in later life is mediated entirely through subjective financial wellbeing.
Alternatively, there is weak evidence to suggest that income inequalities can have
positive effects on economic growth by providing incentives to work, potentially
improving health. Nonetheless, the association between income and health might
be down to health and social problems leading to lower income rather than vice
versa. In general, evidence indicates that “socioeconomic disadvantage precedes
poorer health…[but] this does not exclude reverse causation – poor health does affect
earnings – but it is not the primary mechanism behind the association between
income and health” (Lynch et al., 2004, pp. 9-10). The effect of income on health is
potentially confounded by occupation and education.
Social class and health
Categorising people by social class whereby individuals are allocated to a social class
based on their role in the labour market is a common feature in most European social
class research. Employing a structured occupational typology can enhance
occupational health research and improve comparability between studies. One of
the most influential class typologies developed was the Erikson Goldthrope
Portocarero (EGP) Schema. The typology had no commonly agreed method which
contributed to low consistency when using EGP across different developed countries
(Erikson and Goldthorpe, 1992). This reduced its usefulness and reliability for use in
research. Later occupational classifications– including National Statistics
as a result, the European Union Sixth Framework Programme project has produced
comparative European research (Rose and Harrison, 2011). For example, NS-SEC
classified occupations into eight classes based on the title of the role and the role
description (shown in Table 2.4), from doctors in the highest and unemployed
individuals in the lowest classifications.
The Whitehall Studies were fundamental to epidemiology research interested in the
relationship between social class and health. The first of the Whitehall Studies,
Whitehall I, showed a steep inverse association between social classes, assessed by
grade of employment and mortality from a variety of diseases. Men in the lowest
grade of employment experienced three to six times higher coronary health disease
mortality compared to those in the highest grade of employment (Marmot et al.,
1978). In the same study, compared to those in the highest grade of employment,
those in the lowest grade exhibited four main risk factors – they were heavier for
their height, had higher blood pressure, smoked more, and reported less leisure-time
physical activity. Findings such as these demanded further explanation and thus
Whitehall II was established.
Whitehall II studied 10,314 (6,900 men, 3,414 women) British Civil Servants in
London aged 35-55 (Marmot et al., 1991), advancing the available evidence about
the health of workers at that time. The studies found that health was related to
some degree to health behaviours and monotonous work characterised by low
control, satisfaction and social support, with workers in lower classes at greater
risk. Arguably this led to increased interest among the research community
Table 2.4 National Statistics Socioeconomic Classification Eight-Class Grouping.
Classification title Description Occupations
1 Higher managerial and professional occupations
This includes employers in large occupations, managerial professionals and higher professional occupations.
Doctors 2 Lower managerial and
professional occupations
This includes lower professional and higher technical occupations, lower managerial occupations and higher supervisory occupations.
Nurses
School teachers 3 Intermediate occupations These are positions in clerical, sales and intermediate technical occupations that do not
involve general planning or supervisory powers.
Auxiliary nurses 4 Small employers and own
account work
Small employers are those, other than higher or lower professionals, who employ others and so assume some degree of control over them.
Own account workers are self-employed people engaged in any (non-professional) trade, personal service or semi-routine, routine or other occupation but have no employees other than family workers.
Self-employed builders Hairdressers
Shopkeepers – own shop
5 Lower supervisory and technical occupations
Lower supervisory occupations have titles such as ‘foreman’ and ‘supervisor’ and have formal and immediate supervision over those in classes 6 and 7.
Train driver
Employed plumbers or electricians
6 Semi-routine occupations The work involved requires at least some element of employee discretion/decision making. Care assistants 7 Routine occupations Positions with a basic labour contract, in which employees are paid for the specific service.
Employee discretion/decision-making less relevant here.
Bus drivers Waitresses 8 Never worked and long-
term unemployed
People in this category have never had an occupation or have been unemployed for an extended period and can therefore not be assigned to an NS-SEC category. ‘Long-term’ can be defined as any period of time but is generally one or two years.
Later studies on social class and health have provided inconsistent findings indicating
the absence of an absolute graded relationship. For example, a Spanish cross-
sectional study (Casado, González and de la Torre Esteve, 2015) of 52,121 people
aged 16 or above found that the percentage of females reporting self-assessed poor
or very poor health increased as class fell (Class I [professionals and managerial]
4.3%; 95% CI 3.2, 5.5 and Class V [unskilled manual workers] 15.2%; 95% CI 13.6,
16.9). A similar finding was seen for women reporting three or more health problems
(Class I 8.5%; 95% CI 6.7, 10.3 and Class V 26.6%; 95% CI 24.2, 29.0). There appeared
to be no graded relationship between social class and self-assessed health although
the lowest percentage was reported by Class I (3.6%; 95% CI 2.6, 4.6) and the highest
by Class V (10.7%; 95% CI 9.2, 12.3). The percentage of males reporting three or
more health problems increased as class fell (Class I 6.7%; 95% CI 5.0, 8.4 and Class V
12.8%; 95% CI 10.9, 14.8). While the study used a reliable and validated
questionnaire, the results may not be generalisable outside of the Spanish study
area.
Richards and Paskov’s (2016) UK cross-sectional study of 131,898 (120,921 in English
Health Survey and 10,977 in British Household Panel Survey) people aged 25-65
showed no social class gradient for psychological wellbeing. Some research has
shown that being in a lower social class is associated with having older identities with
individuals more likely to classify themselves as “old”, “elderly” or report feeling
older than their chronological age (Barrett, 2003). These differences are more
pronounced among older adults. In addition, in a cross-sectional study of 5,412
adults aged 50-60 in Denmark, by Hansen et al. (2014), found no interaction between
Lower social classes often display more harmful health behaviours than higher
socioeconomic groups. Nandi, Glymour and Subramanian’s (2014) study of 8,037
people aged 50 or above in 1992 and resident in the United States found that,
compared to higher socioeconomic groups, people in lower socioeconomic groups
had a mortality risk ratio of 2.84 (95% CI 2.23, 3.60). Unhealthy behaviours including
tobacco smoking, alcohol consumption of any level, and physical inactivity explained
68 percent (95% CI 35, 104) of this variance, leaving a risk ratio of 1.59 (95% CI 1.03,
2.45) for lower socioeconomic status. The study findings are ungeneralisable to the
current general population because of the age inclusion criteria used; furthermore
sufficient time has elapsed that behaviour may have changed substantially.
Deprivation and health
The effect of deprivation on health in the UK has been firmly established, with those
living in more deprived areas at greater risk of morbidity and mortality (Barnett et
al., 2012; Carstairs and Morris, 1990; Doebler and Glasgow, 2016; Kuo and Chiang,
2013; Stafford and Marmot, 2003) irrespective of measure used (e.g. Townsend
deprivation index or Carstairs-Morris index). For example, compared to the most
affluent areas, those living in the most deprived areas experience the onset of multi-
morbidity ten to 15 years earlier (Barnett et al., 2012) and spend twice as many years
in poor health (Bajekal, 2005; The Scottish Government, 2010). This is potentially
confounded by neighbourhood deprivation (Shouls, Congdon and Curtis, 1996)
where there is a dependency on collective neighbourhood resources (Stafford and
and past deprivation, such as poor housing (Marsh, Gordon, Heslop and Patazis,
2000).