Inequalities in health (e.g. by region, ethnicity, soci-economic position or gender) and in access to health care, including their causes

Inequalities in health (e.g. by region, ethnicity, socio-economic position or gender) and in access to health care, including their causes
Equality, Equity and Policy: Inequalities in health and in access to health care, including their causes

The distribution of health is determined by a wide variety of individual, community, and national factors (See Figure 1). There is a growing body of evidence documenting inequalities in both the distribution of health (i.e. health outcomes) and access to health care both internationally and in the UK. Access to health care is a supply side issue indicating the level of service which the health care system offers the individual.

Figure 1: Determinants of health

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Inequalities in the distribution of health

Researchers have documented inequalities in the distribution of health by social class, gender, and ethnicity. Inequalities in health have been measured using many different outcomes including infant deaths, mortality rates, morbidity, disability, and life expectancy.

Social class (including income, wealth and education)

Research on socio-economic inequalities in health in the UK has a long history. For over 150 years, inequality in health outcomes have been a concern since the early Medical Officer of Health reports (Wellcome Trust). Health outcomes generally worsened with greater socioeconomic disadvantage. In the early part of the 20th century the British government introduced questions on occupation in the decennial census. This allowed researchers to examine health outcomes by social class. The five-class scheme Registrar General’s Social Class (RGSC) was created in 1911 and a variation of this scheme was still used until 2001. The National Statistics Socio-Economic Classification (NS-SEC) has now replaced the RGSC. For a description of the current scheme see:

http://www.ons.gov.uk/methodology/classificationsandstandards/otherclassifications/thenationalstatisticssocioeconomicclassificationnssecrebasedonsoc2010

Table 1: Classifications of Social Classes.

RGSC	NC-SEC
I Professional occupations	1 Higher managerial, administrative and professional occupations
II Managerial and technical occupations	2 Lower managerial, administrative and professional occupations
III Skilled occupations	3 Intermediate occupations
manual (M) and non-manual (N)	4 Small employers and own account workers
IV Partly-skilled occupations	5 Lower supervisory and technical occupations
V Unskilled occupations	6 Semi-routine occupations
	7 Routine occupations
	8 Never worked and long-term unemployed

The 1970-1972 Decennial Supplement of occupational Mortality (OCPS) showed that men in social class V (unskilled) were 2.5 times as likely to die before the age of 65 than those in social class I (professional). Children in social class V families were twice as likely to die as those in social class I.

Table 2 shows the relationship between social class and death.
Bartley and Blane (2008).

Table 2: Social class and health, 1991-1993 and 1993-1995

Social Class	Still-birth rate	Infant mortality rate	Mortality rate (1-15 years)	Standardised mortality ratio (men 20-64 years)
I	4	4	18	66
II	4	5	16	72
IIIN	5	5	16	100
IIIM	5	6	26	117
IV	6	7	22	116
V	8	8	42	189
N=non-manual; M=manual Still birth rate = number of deaths per 1000 live and death births, 1993-5 Infant mortality rate = number of deaths in the first year of life per 1000 live births, 1993-5 Mortality rate (1-15 years) = number of deaths per 100,000 population aged 1-15 years, 1991-3 Standardised mortality ratio (men 20-64 years) = The ratio of the observed mortality rate in a social class to its expected rate from the total population, multiplied by 100, 1991-3 Source: Bartley and Blane, 2008

Social class inequalities in the UK persist at every age and for all the major diseases. An analysis of health outcomes in England for the Global Burden of Disease study showed that males living in the most deprived region of England in 2013 had a life expectancy 8.2 years shorter than those living in the least deprived region, which was as large a difference as seen in 1990. Life expectancy for women living in the most deprived region in 2013 was 6.9 years shorter than for those in the least deprived region, an improvement since 1990 when the difference was 7.2 years. (Newton JN et al., 2015)

The inverse relationship between deprivation and health outcomes though well established as shown above (Table 2 and recently in Newton JN et al 2015) is also slightly more complex as shown below. (Tables 2b, 3b and 4b).

The table of Life Expectancy (LE) and Healthy Life Expectancy (HLE) at birth for both genders and by national deciles of area deprivation in England over a 3 year period (2009-2011) shows there is a difference in life expectancy by gender and level of deprivation throughout.

Of importance was the largest differences in healthy life expectancy between neighbouring deciles were found between the most deprived area groupings.

Table 2b: Life Expectancy (LE) and Healthy Life Expectancy (HLE) at birth for males and females by national deciles of area deprivation in England, 2009-2011

Decile	LE (MALE)	LE (FEMALE)	HLE (MALE)	HLE (FEMALE)	Proportion of life in 'Good' health (%)-MALES	Proportion of life in 'Good' health (%)-FEMALES
1	73.4	78.9	52.1	52.5	70.9	66.5
2	75.5	80.4	55.8	56.1	73.9	69.7
3	76.8	81.2	58.4	59.7	76.0	73.4
4	78.0	82.1	61.2	61.7	78.4	75.1
5	79.0	83.0	63.5	64.3	80.4	77.4
6	79.8	83.4	64.9	66.0	81.3	79.1
7	80.6	84.0	66.8	67.7	82.9	80.6
8	81.1	84.3	67.7	68.6	83.4	81.4
9	81.5	84.9	68.4	69.8	83.9	82.2
10	82.7	85.7	70.5	71.5	85.2	83.4

Source: http://www.ons.gov.uk/ons/taxonomy/index.html?nscl=Subnational+Health+Expectancies

These are the first intercensal estimates of inequality in healthy life expectancy by deciles of deprivation to be produced by ONS using clusters of Lower Super Output Areas (LSOAs) by the English Index of Multiple Deprivation (IMD).

Above add to the debate of the complex relationship between health outcomes, gender and social class. Previous studies have shown that causes of death differ in their relationship to social class.

Erikson and Torssander (2008) in the European Journal of Public Health describe this relationship as a ‘variation lacking in detail’. They found in their European study using data from a decade (1990-2003) a clear mortality gradient among employees for the majority of causes; from low relative risk of death among higher managerial and professional occupations to relatively high risks for the unskilled working class.

The authors noted exceptions to the general pattern and discovered causes of death in which higher social classes were at a greater risk, or in which there was a very small or no mortality gradient.

(Eur J Public Health (2008) 18 (5): 473-478. doi: 10.1093/eurpub/ckn053)

Efforts have been made to reduce health inequalities through policies and interventions dating back to the 1980 Black Report. Although notable improvements across society in indicators such as life expectancy (ONS, 2013) have occurred, a large, persistent health gap remains.

The Health and Social Care Act 2012 introduced legal duties on health organisations to have regard to the need to reduce health inequalities. Reducing differences in health between populations is a key policy objective for NHS England (NHS England, 2014) and Public Health England (PHE).

There are four major models used to explain social class inequalities in health (Bartley and Blane, 2008; Bartley, 2004).

Behavioural model: There are social class differences in health damaging or health promoting behaviours such as dietary choices, consumption of drugs, alcohol and tobacco, active leisure time pursuits, and use of immunisation, contraception and antenatal services. However, long-term studies (like the Whitehall study described below) have found that differences in health behaviour explain only one-third of social class differences in mortality. Furthermore, evaluations of interventions that seek to change health behaviours have rarely found clear cut improvements in health that would be predicted by the behavioural model.
Materialist model: Poverty exposes people to health hazards. Disadvantaged people are more likely to live in areas where they are exposed to harm such as air-pollution and damp housing. The Black Report (see below) found materialist explanations to be the most important in explaining social class differences in health. There is some specific evidence for materialist explanations. For example, many studies have associated higher rates of childhood respiratory disease with damp housing. The full impact of living standards, however, can only be understood over the course of the life term. While most experts in public health agree that materialist explanations play a role in explaining health inequalities, many find a simple materialist model to be insufficient. In the UK, relatively disadvantaged people receive various kinds of state help (rent, school meals etc) which, some argue, makes diet or poor housing unlikely to account for all inequalities health outcomes. Furthermore, in the UK and internationally, inequalities in health tend to follow a steady gradient, rather than there being poor outcomes for the most disadvantaged and equally good outcomes for the rest of society.
Psycho-social model: Social inequality may affect how people feel which in turn can affect body chemistry. For example, stressful social circumstances produce emotional responses which bring about biological changes that increase risk of heart disease. Psycho-social risk factors include social support, control and autonomy at work, the balance between home and work, and the balance between efforts and rewards. There has been a plethora of research exploring associations between psycho-social factors and health. Evidence shows that people who have good relationships with family and friends, and who participate in the community, have longer life expectancies than those who are relatively isolated. Evidence of an association between stress at work and health is less clear, but most well designed studies show a higher risk of heart disease among individuals who work in jobs where demands are high and control is low. Furthermore, a number of studies have shown that an imbalance between effort and reward at work tends to be linked to high blood pressure, fibrinogen and a more adverse blood fat profile.
Life-course model: Health reflects the patterns of social, psycho-social and biological advantages and disadvantages experienced by an individual over time. The chances of good or poor health are influenced by what happened to a child in-utero and in early childhood and disadvantages are likely to accumulate through childhood and adulthood. For example, individuals who experienced poor home conditions in childhood are more likely to experience occupational disadvantage. The life-course model was developed relatively recently and studies investigating life-course explanations require detailed longitudinal data. Regardless, several studies have shown that health disadvantage accumulates over time.
A life course approach underpins the recommendations made in the Marmot Review on reducing health inequalities in England. The review states that ‘action to reduce health inequalities must begin before birth and continue through the life of the child. Only then can the close links between early disadvantage and poor outcomes throughout life be broken’. (Marmot review, 2010). Similarly, the Welsh Adverse Childhood Experiences (ACE) Study, 2015) highlights the impact of adverse childhood experiences on individuals’ risks of developing health harming behaviours in adult life. ACEs are stressful experiences occurring during childhood that directly harm a child (e.g. sexual or physical abuse) or affect the environment in which they live (e.g. growing up in a house with domestic violence).

Landmark studies in social class inequalities in health in the UK include:

The Black Report
The Black Report, published in 1980 confirmed social class health inequalities in overall mortality (and for most causes of death) and showed that health inequalities were widening. The report set out four possible mechanisms to explain widening socio-economic health inequalities:

Artefact: Population information came from the decennial census while death and cause of death information came from death certificates. An individual may have been described in different ways in the two data sources leading to numerator-denominator bias. The report also noted widening inequalities may be explained by the shrinking of social class V. With fewer people who were completely unskilled, the average health of social class V moved further from social class I. Furthermore, the report noted that the meaning of social class may have changed over time as some jobs disappear and others emerge.

Social selection: Health determines social position. Somewhat similar to Darwin’s ‘natural selection’, i.e. healthy people are more likely to get promoted while unhealthy people are more likely to lose their jobs.

Behaviour: individuals in the lower social classes indulge in comparatively more health damaging behaviour (see behavioural model above).

Material circumstances: poverty causes poor health (see materialist model above).

Whitehall Study of British Civil Servants
The ongoing Whitehall Study of British Civil Servants http://www.ucl.ac.uk/whitehallII/ is a cohort study following British civil servants over a long period of time. It collects detailed information on risk factors such as weight, cholesterol, smoking, and blood pressure. The study found inequalities in health and mortality between employment grades and found that risk factors could only explain one-third of the observed variation in health by employment grade.

The Acheson Report
The Acheson Report published in 1988 found that mortality had decreased in the last 50 years but that inequalities in health remained, and in some instances health inequalities had widened. The report recommended:

evaluating all policies likely to affect health in terms of their impact on inequalities
giving high priority to the health of families with children
the government should take steps to reduce income inequalities and improve living conditions in poor households.

The Marmot Review
The Marmot Review was commissioned in 2008 to provide evidence-based recommendations for a strategy to reduce health inequalities in England. The review found that:

Health inequalities must be addressed in the interests of fairness and social justice.
There exists a social gradient in health: health improves as social status goes up.
Social inequalities result in health inequalities; therefore to reduce health inequalities we must consider all the social determinants of health.
Health inequalities cannot be properly addressed by only targeting those worst off. Reducing the steepness of the social gradient in health requires universal actions, concentrated according to levels of deprivation (‘proportionate universalism’).
Taking action to reduce health inequalities will have a positive effect on society in many ways, such as bringing economic benefits by reducing population illness and increasing productivity.
A country’s success is measured by more than economic growth: fair distribution of health, wellbeing and sustainability are also important. Climate change and social inequalities in health should be addressed simultaneously.
Policy to reduce health inequalities must cover all of the following objectives:
- Give every child the best start in life
- Enable all children young people and adults to maximise their capabilities and have control over their lives
- Create fair employment and good work for all
- Ensure healthy standard of living for all
- Create and develop healthy and sustainable places and communities
- Strengthen the role and impact of ill health prevention
These policy objectives can only be delivered through effective involvement of central and local government, the NHS, third and private sectors, individuals and communities.

(Marmot, 2010)

Gender
Much research has shown that in industrialised countries women live longer than men (tables 3 and 3B) but appear to experience more ill health. While men have higher mortality from the most common single causes of death (ischemic heart disease and lung cancer), more women than men suffer from somatic complaints such as tiredness, headache, muscular aches and pains. However, some researchers have raised questions about the validity of studies that show higher illness rates in women, as many different health outcome variables have been used and not all show gender differences. There is more consistency in studies that examine minor psychological illness, anxiety, sickness absence from work, functional limitation, and depression (Bartley, 2004).

Table 3: Selected developed countries by order of female to male difference (in years) of life expectancy at birth in 1980 and 1996

	1996		1980
Country	Female-Male difference	Ranking	Female-Male difference	Ranking
United Kingdom	4.9	1	6	4
Sweden	5	2	6	4
Denmark	5.2	3	6.1	6
Greece	5.3	4	4.6	1
Ireland	5.3	5	5.5	3
Netherlands	5.6	6	6.6	8
United States	6	7	7.4	12
Austria	6.3	8	7.1	11
Italy	6.4	9	6.8	10
Japan	6.5	10	5.3	2
Germany	6.5	11	6.8	9
Spain	7.2	12	6.1	7
Finland	7.5	13	9.1	14
France	7.8	14	8.2	13
Mean	6.2	6.6
Standard Deviation	0.987	1.156
Range	3	4.5
	Source: Gjonca et al, 1999.

In the UK, mortality is greater in males than in females at all ages. In youth and early adulthood, males are more likely to die from motor vehicle accidents, other injury (such as fire and flames, accidental drowning and submersion), and suicide, contributing to higher mortality rates among young men and boys. Across the whole of adult life, mortality rates are higher for men than women for all the major causes of death including cancers and cardiovascular disease. However, women have much higher rates of disability than men, especially at older ages. Women have more morbidity from poor mental health, particularly those related to anxiety and depressive disorder (Acheson, 1998).

WHO (2008) suggests that gender differences in health are a result of both (1) biology and (2) social factors (distinct roles and behaviours of a men and women a given culture, dictated by that cultures gender norms and values).

More recently, the ONS (published 2015) focused on inequality in Healthy life expectancy (HLE) at birth by deciles of area deprivation in England and for both genders. This data shows that the effects of inequality have different magnitudes for males compared to females (Table 3b). Inequality has a greater effect on life expectency in men than in women, but for healthy life expectancy, inequality creates a greater difference in women than in men.

TABLE 3b-Slope Index of Inequality (SII) and Range for Life Expectancy (LE) and Healthy Life Expectancy (HLE) at birth for males and females by national deciles of area deprivation in England, 2009-2011

DECILE	Range²	SII³	Lower 95 % Confidence Interval	Upper 95 % Confidence Interval
LE(Male)	9.2	9.4	7.9	10.9
LE(Female)	6.8	6.9	5.9	7.9
HLE(Male)	18.4	19.3	16.1	22.6
HLE(Female)	19.0	20.1	16.7	23.5
Proportion of life in 'Good' health -Male (%)	14.3	15.2	12.2	18.3
Proportion of life in 'Good' health -Female (%)	16.9	18.0	14.4	21.6

Source: http://www.ons.gov.uk/ons/taxonomy/index.html?nscl=Subnational+Health+Expectancies

This analysis reveals that:

Males in the most advantaged areas can expect to live 19.3 years longer in ‘Good’ health than those in the least advantaged areas as measured by the slope index of inequality (SII). For females this was 20.1 years.
Males in the most deprived areas have a life expectancy 9.2 years shorter (when measured by the range) than males in the least deprived areas, they also spend a smaller proportion of their shorter lives in ‘Good’ health (70.9% compared to 85.2%).

Females in the most deprived areas have a life expectancy 6.8 years shorter (when measured by the range) than females in the least deprived areas. They also expect to spend 16.9% less of their life in ‘Good’ health (66.5% compared to 83%).

The SII for males and females was computed using Stata software. This software automatically weights the population of each decile used in the regression analysis which had been manually weighted by ONS, causing a slight increase in the SII to the second decimal place. These updated tables were published 8th June 2015 by ONS and this analysis covers the time period 2009-11; the next update will provide figures for the period 2010-12.

Another analysis by ONS further highlights the intricate nature of explaining the relationship between gender and life outcomes. (Please see table 4b). Compared with 2001–03, male mortality rates in 2008–10 were lower in most socio-economic classes across the English regions and Wales; only the Intermediate class in the East region remained constant.

In females, mortality decreased between 2001–03 and 2008–10 in all classes in only London and the South West. Increases in mortality were observed in the Intermediate; Lower Supervisory and Technical; and the Semi-routine classes in several regions.

The absolute inequality between the most and least advantaged men generally decreased across most English regions between 2001-03 and 2008-10. For women, the inequality decreased in some regions but showed an increase in others.

Erickson and Torssander‘sSwedish study noted that the effect of gender and class was disease specific. They found considerable variations in the strength of the association between class and cause of death. For example, with diseases such as malignant melanoma, breast cancer and transport accidents among women, no clear class differences were found. At the other extreme, mental and behavioural disorders, endocrine, nutritional and metabolic diseases and diseases of the respiratory system all show steep slopes for both men and women.

Social factors used to explain higher mortality rates in men (Scambler, 2008):

Employment: More occupations typically followed by men involve direct risk to life (such as dangerous machinery, weather, environmental hazards, and exposure to toxic chemicals).
Risk taking behaviour: Men are more socialised to participate in dangerous sports like motorbike racing, rock climbing etc. Men are at higher risk of road traffic injury and tend to drive more and faster when under the influence of alcohol compared to women.
Smoking: In the past, men had much higher smoking rates than women. However, the gender gap between men and women in smoking has narrowed in recent years, particularly in high-income countries.
Alcohol: Men drink significantly more than women in all age groups and are more likely than women to exceed their recommended daily alcohol intake.

Ethnicity and Culture
There is a growing body of evidence documenting ethnic inequalities in health outcomes in the UK, and internationally, despite difficulties with the conceptualisation and measurement of ethnicity as an epidemiological variable (see Box 1).

Box 1: Difficulties with the conceptualisation and measurement of ethnicity in health research.

Ethnicity is a fluid concept and takes on different meanings in different contexts. For example, a person may be considered (or consider his/herself) Pakistani when filling out the UK Census. The same person may be considered Asian on the US census or South Asian on other UK surveys. The definition of ethnicity is influenced by both historical value systems and the current social and political context (Bradby, 2003). Definitions of ethnicity change, but are likely to involve dimensions of race, skin colour, language, religion, nationality, country of origin, and ‘culture’. Each of these dimensions may have implications for health. A major limitation of the concept of ethnicity in practice is that research specific definitions are often not clearly stated. Bhopal (1997) claims that ethnicity is “a euphemism for race”. Indeed, in a four year review of the literature, Comstock and colleagues (2004) found that researchers “frequently failed to differentiate between the concepts of race and ethnicity”.

There are a number of concerns about the reliability and validity of measurements of ethnicity. Researcher-assigned ethnic identities may not match respondent self-defined identities, threatening validity. Even when ethnicity is self-identified, the same person may use different ethnic identities in different situations at different times, compromising reliability. Fixed response categories such as those found in the UK Census and many other quantitative surveys have particular validity concerns. Bradby (2003) notes that the lack of theoretical coherence in defining fixed-response categories is a major problem in ethnicity related research. This has led some observers to describe data collection in the UK as ‘ad-hoc’ (Sheldon, 1992). While fixed response categories facilitate comparisons over time, and potentially across surveys, mutually exclusive groups cannot reflect mixed ethnic identities. Furthermore, fixed response categories such as ‘black’, ‘white’, or ‘Asian’ may mask considerable within-group differences and emphasise between-group differences. Ellison (2005) notes that the validity and reliability of ethnicity data depend on measurement techniques as well as the population. Broad categories, objective techniques and group homogeneity can improve validity and reliability of ethnicity measurement. Furthermore, qualitative research into ethnic identification and monitoring of open-ended ‘other-specify’ survey responses may help to define more accurate fixed-response categories (Aspinall, 1997).

These limitations of measurement, and the changing multidimensional nature of ethnicity, mean that quantitative researchers may never have a totally unbiased ethnicity variable. However, taking account of the methodological limitations and social context, these variables can be useful as a proxy for the complex concept of ethnicity

(Ellison, 2005).

Ethnicity is not recorded on UK death certificates, and mortality data uses country of birth as a proxy, thus failing to identify ethnic minorities born in the UK.

There are some repeatedly documented findings on ethnic inequalities in mortality (Kelly & Nazroo, 2008):

Men and women born in the Caribbean have high rates of mortality from stroke. Men born in the Caribbean have low rates of mortality overall and low rates of mortality from coronary heart disease.
Individuals born in West/South Africa have high overall mortality rates, high mortality rates from stroke, but low mortality rates from coronary heart disease.
Individuals born in South Asia have high mortality rates form coronary heart disease and stroke.
Non-white migrant groups tend to have lower mortality rates from respiratory disease and lung cancer but higher mortality rates for conditions relating to diabetes.

Table 4: Standardised mortality ratios by country of origin, England and Wales, 1989-1992.

	Cause of death
	All		Coronary heart disease		Lung cancer		Breast cancer
	Men	Women	Men	Women	Men	Women	Women
All	100	100	100	100	100	100	100
Scotland	132	136	120	130	149	169	114
Ireland	139	120	124	120	151	147	92
East Africa	110	103	131	105	42	17	84
West Africa	113	126	56	62	62	51	125
Caribbean	77	91	46	71	49	31	75
South Asia	106	100	146	151	45	33	59

Source: Wild and McKeigue (1997:705) in Bartly (2004)

Combining national origin data with data on social class (which is only available for men because social class is poorly recorded on women’s death certificates), Bartley (2004) reports that the relatively high mortality in men born in Scotland, Ireland, and South Asia is only seen outside of social classes I and II.

Region
Within the UK, more recent analysis based on the seven-class reduced National Statistics Socio-economic Classification (NS-SEC) shows regional trends in estimates of mortality rates of working age men in English regions and Wales, from 2001-03 to 2008-10.

TABLE 4b - Relative ratio of mortality rates between NS-SEC classes for males aged 25 to 64, English regions and Wales, 2001-03 to 2008-10

Table 4b: Relative Index of Inequality (RII)

	2001-03	2002-04	2003-05	2004-06	2005-07	2006-08	2007-09	2008-10
England and Wales	4.3	4.5	4.6	4.8	4.8	5.0	5.1	5.3
North East	4.9	5.0	5.5	6.0	6.6	6.4	6.1	5.5
North West	4.9	5.1	5.4	5.6	5.7	5.6	5.9	6.4
Yorkshire and the Humber	5.0	5.3	5.2	5.1	5.1	4.8	4.8	5.3
East Midlands	3.0	3.5	3.8	4.3	4.0	4.1	3.8	3.9
West Midlands	4.4	4.3	4.1	4.4	4.9	5.6	5.6	5.6
East	3.3	3.6	4.0	4.0	4.2	4.5	4.4	4.3
London	5.5	5.8	5.6	5.2	4.8	4.8	5.1	5.9
South East	3.0	2.9	3.2	3.3	3.5	3.6	4.2	4.6
South West	4.9	5.2	5.0	4.9	5.0	5.1	5.0	5.0
Wales	4.0	4.0	3.6	3.8	4.0	4.4	4.7	5.7

Source: http://www.ons.gov.uk/ons/rel/health-ineq/health-inequalities/trends-in-all-cause-mortality-by-ns-sec-for-english-regions-and-wales--2001-03-to-2008-10/index.html

Conclusion

In England and Wales, there were statistically significant decreases in all-cause mortality rates for men across all socio-economic classes between 2001–03 and 2008–10.
Across the regions, the North West had the highest mortality rates in almost all classes for both sexes for the majority of the 2001–03 to 2008–10 period.
Conversely, the South East and East regions had the lowest mortality rates in most of the classes for both sexes for the majority of the period.
Over the same period, the relative inequality increased for both sexes however the absolute inequality in mortality between the Higher Managerial and Professional class (most advantaged) and the Routine class (least advantaged) narrowed.

RII was chosen as an inequality measure as it uses and takes into account mortality rates data of all the intervening classes, in addition to the most and least advantaged NS-SEC classes; the 'Higher Managerial and Professional' and 'Routine' class respectively.

Mortality rates used were age-standardised per 100,000 population, according to the European Standard Population and of working age men 25 to 64.

Further information on data and methodology, please visit the link, http://www.ons.gov.uk/ons/rel/health-ineq/health-inequalities/trends-in-all-cause-mortality-by-ns-sec-for-english-regions-and-wales--2001-03-to-2008-10/index.html

Explanations for ethnic or regional inequalities in health include:

They are a statistical artefact.
They are a consequence of the migration process.
They are due to genetic/biological differences between ethnic groups.
They are due to differences in culture and health behaviours.
They are a consequence of socioeconomic disadvantage.
Experiences of racism result in health differences.
Level of Education.

Inequalities in health care and its access

Health care access is a supply side issue indicating the level of service which the health care system offers the individual. While the concept of equity in access to health care (horizontal equity) has been a central objective of the NHS since it began, inequalities in health care access persist. The inverse care law, first described by Julian Tudor Hart in 1971, states: The availability of good medical care tends to vary inversely with the need for it in the population served.

Of significance are the ‘hard to reach’, or ‘seldom heard’ groups of people. As research strongly suggests, they suffer poorer health outcomes and access services less for various reasons. Hard-to-reach groups vary widely: Black and minority ethnic (BAME) groups, the homeless, asylum seekers, adolescents with eating disorders, not in employment, education or training (NEETs), the elderly, people with medically unexplained symptoms, people with advanced cancers, those with sensory impairments, people with learning disabilities, people with mental health or substance misuse problems, and older people with a variety of physical, sensory, intellectual and mental health difficulties.

Equality of access requires that, for different communities (Wonderling et al, 2005):

Travel distance to facilities is equal.
Transport and communication services are equal.
Waiting times are equal.
Patients are equally informed about the availability and effectiveness of treatments.
Charges are equal (with equal ability to pay).

Many studies investigating access to health care use treatment received (i.e. utilisation) as a proxy for access. However, utilisation of health services may vary for many several reasons (such as perceptions of benefits or availability, availability of alternative therapies or services) and is an imperfect measure of access. Nonetheless, it is commonly used as such.

Goddard and Smith (2001) outline reasons for variations in access to health care:

Availability: Some health care services may not be available to some population groups, or clinicians may have different propensities to offer treatment to patients from different population groups, even where they have identical needs.

Quality: The quality of services offered to patients may vary between population groups.

Costs: The health care services may impose costs (financial or otherwise) which vary between population groups.

Information: The health care organisations may fail to ensure that all population groups are equally aware of the services available.

Certain groups within society may be described as ‘hard-to-reach’: a term that is difficult to define but can include the homeless, individuals with problem drug or alcohol use, people living with HIV, asylum seekers and refugees, people from black and minority ethnic groups and people from sexual minority communities. These individuals may face barriers to engaging with services (for instance language or cultural barriers), or are reluctant to engage with services and therefore deemed ‘hard-to-reach’ from a societal perspective.

Engaging with socially excluded and marginalised populations presents a major challenge but increasing flexibility of services, working with voluntary sector organisations and user involvement can be effective mechanisms for reducing inequalities in access to healthcare.

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