Healthcare In Britain Social Studies Essay Question

Abstract

Background. Ethnic minority patients seem to be confronted with barriers when using health services. Yet, care providers are often oblivious to these barriers, although they may share to some extent the burden of responsibility for them. In order to enlighten care providers, as to the potential pitfalls that may exist, there is a need to explore the different factors in the creation of the barriers.

Objective. Therefore, the objective of this paper is to present an overview of the potential barriers and the factors, which may restrict ethnic minority patients from using health services, according to the literature available.

Methods. Articles published from 1990 to 2003 were identified by searching electronic databases and selected through titles and abstracts. The articles were included if deemed to be relevant to study health services use by ethnic minorities, i.e. the different factors in the creation of a barrier.

Results. There were 54 articles reviewed. They reported on studies carried out in different countries and among different ethnic minorities. Potential barriers occurred at three different levels: patient level, provider level and system level. The barriers at patient level were related to the patient characteristics: demographic variables, social structure variables, health beliefs and attitudes, personal enabling resources, community enabling resources, perceived illness and personal health practices. The barriers at provider level were related to the provider characteristics: skills and attitudes. The barriers at system level were related to the system characteristics: the organisation of the health care system.

Conclusion. This review has the goal of raising awareness about the myriad of potential barriers, so that the problem of barriers to health care for different ethnic minorities becomes transparent. In conclusion, there are many different potential barriers of which some are tied to ethnic minorities. The barriers are all tied to the particular situation of the individual patient and subject to constant adjustment. In other words, generalizations should not be made.

Potential barriers, health services use, ethnic minorities

Introduction

Populations in western industrialized countries become increasingly multi-ethnic as a result of the internationalization of the market place and the successive opening of borders.1 The rise in migration is, contrary to popular belief, not a new phenomenon. It has taken on many forms, from labour migration in countries like the UK and France to the immigration of settlers in the USA, Canada and Australia. There has been the migration of refugees fleeing from hostilities and of asylum seekers seeking refuge in countries such as Sweden and the United States.2–4 In receiving countries, newly arrived migrants have often been concentrated in poor, low status regions of major cities. They usually live in low standard accommodation and under less favourable living conditions and health.5 The World Health Organizations objective of ‘Health for all by the year 2000’ suggests that we should ensure that ‘ethnic minorities’ also have equal access to health services, regardless of their standing in society.1 Equal access to health care is a fundamental human right.6

Although migration is the norm and health care a natural right of every individual, ethnic minority patients seem to be confronted with barriers when using health services. Their use of health services is also lower, when compared with their non-immigrant counterparts.4,7–10 Yet, care providers often are oblivious to these barriers, although they may share to some extent the burden of responsibility for them. Most of their attention is directed towards language discordance and cultural differences, which can lead to biased or false conclusions.1 Language and culture are by no means the only factors that may act as a barrier. In order to enlighten care providers, as to the potential pitfalls that may exist, there is a need to explore the different factors in the creation of the barriers. Therefore, the objective of this paper is to present an overview of potential barriers and the factors, which may restrict ethnic minority patients from using health services, according to the literature available.

Methods

Definitions

Potential barrier

If patients' expectations or health beliefs are not in line with what is proposed by the care provider, they may experience barriers to the use of health services. When the end result is not in line with the treatment received, barriers may also come into existence. A barrier, as it is used in this paper, restricts the use of health services. It is a wall or limit that prevents people from going into an area or doing what they want to do. The lack of health insurance, for example, can prevent people from using health services. The limitation to speak the local language, for example, can prevent people from communicating adequately with their physician.

A potential barrier is a barrier that only afflicts us under certain circumstances or only afflicts some of us, mostly the socioeconomic vulnerable ones. As we will see, a barrier that only afflicts us under certain circumstances is, for instance, irregular public transport. If there is no need to use the public transport, irregular public transport does not act as a barrier (e.g. to car owners). If public transport is needed, irregular public transport acts as a barrier. A barrier that only afflicts some of us is for instance health insurance coverage. For the socioeconomic vulnerable ones, the price of health services can act as a barrier if a health service is not covered by their health insurance, or is only partly reimbursed.

Use of health services

The use of health services is defined as the process of seeking professional health care and submitting oneself to the application of regular health services, with the purpose to prevent or treat health problems. In this paper we focus on all possible barriers in relation to this process. Although the decision to use health services is stated to be an individual choice, we imagine that these choices are mostly framed in the social context through cultural, social and family ties; especially for ethnic minorities.11 Many ethnic minorities first try to solve health problems on their own, or in the circle of family members and friends. If one does not succeed, the help of a ‘great’ man in the community is usually called upon (preachers, spiritual healers). The help of regular health services is often only called upon after an escalation of the complaints of illness.12

Ethnic minority

The concept ‘ethnic minority’ is broadly defined in this paper. It refers to many different ethnic groups of extreme heterogeneity. The concept is used for groups that share minority status in their country of residence due to ethnicity, place of birth, language, religion, citizenship and other (cultural) differences. It sets apart a particular group in both numerical and (often) socioeconomical terms. Members of these groups are considered to practice different cultural norms and values from the majority culture and (often) a different mother tongue.1,4,13 Ethnic minorities vary in duration of stay and acculturation and between different ethnic minorities there exist different degrees of access to the majority culture. The concept ‘ethnic minority’ includes groups from newly arrived immigrants to (minority) groups that have been a part of a country's history for hundreds of years. Examples of the second type of these groups are the Aboriginals in Australia or American Indians in the USA. They are in fact the original inhabitants of the country.

Patient, provider and system level

Barriers can present themselves to patients, health care providers and the organization of health services, in other words the health care system itself. Therefore we say that barriers occur at patient level, provider level and system level. By patient level we mean related to patient characteristics, such as sex, ethnicity, income, etc. By provider level we mean related to provider characteristics, such as sex, skills, attitudes, etc. By system level we mean related to system characteristics, such as policy, organizational factors, structural factors, etc.

Search and selection

Research question

The research question of the literature research was ‘What is known about the factors that hinder the use of health services among ethnic minorities?’

Search strategy

To answer the research question, articles were identified by searching the databases Medline, Embase, Psycinfo, Cinahl and Web of Science. The searches were limited to articles published between 1990 and 2003 and performed by the first author of this paper in September 2003. The databases were searched using keywords that covered the domains ‘health services’, ‘use’ of health services and ‘ethnic minorities’. The different keywords used to search are presented in the appendix.

Selection

The articles were selected through titles and abstracts by the first author of this paper. The selection was based on inclusion and exclusion criteria. The results of the search were completed by tracking references from studies already included.

Inclusion criteria

The articles had to report on the results of research and contain information pertaining to migrants, health care and factors that may hinder health services use. The following inclusion criteria were employed in this study. Publication date: 1990–2003. The articles had to be published between 1990 and 2003. Type of population: ethnic minorities. The articles had to report on the use of health services by ethnic minorities. Type of study: all types of health research. The study of potential barriers to the use of health services among ethnic minorities is still a relatively uncharted course. Therefore, not only articles on quantitative research were included, but also articles on qualitative research, as well as literature reviews and a few published essays too. The studies had to report on health research, i.e. the use of health services. Type of outcome measures: potential barriers and the factors. Outcome measures had to be factors that hinder the use of health services and that can act as a barrier.

Exclusion criteria

The following exclusion criteria were employed in this study. Type of study: summaries. Articles in summary form only were not included in this study. Type of intervention: health education. Articles on health education were excluded.

Analysis

Quality assessment

Due to the heterogeneity of the included studies, the studies are not sufficiently comparable to each other. Therefore, the assessment of the methodological quality of each study seemed not appropriate to us. Although the literature search, the selection of studies and the extraction of data were done systematically, the review cannot to be compared with a systematic review; there was no quality assessment done. The aim of the study was to explore and identify as many (potential) barriers as possible. Also, the extracted (potential) barriers are not exclusively evidence-based phenomena.

Data extraction

Data extraction of the articles was compiled by the first author of this paper. The first author read the available titles and abstracts identified in the different database searches, as well as the selected articles. The articles were screened for the different variables as presented by the theoretical framework used.

Theoretical framework

We used Andersen's behaviour model of health services use as the theoretical framework.14–16 The aim of using the Andersen-model is to reveal conditions that hinder the use of health services. The model is a valuable tool to select, identify and sequence the relevant variables in the process of health services use.

In the Andersen-model the use of health services is related to four main components: (i) ‘Population characteristics’; (ii) ‘Environment’; (iii) ‘Health Behaviour’ and (iv) ‘Health outcomes’. (i) Population characteristics consists of ‘predisposing characteristics’ (demographic variables, social structure variables and health belief variables), ‘enabling characteristics’ (personal or family enabling resources, community enabling resources) and ‘need characteristics’ (individual perceived need, professional evaluated need). (ii) Environment consists of ‘external environment’ (physical, political and economic) and ‘health care system’ (policy, resources and organization). (iii) Health behaviour consists of ‘use of health services’ (type, site, purpose and time interval) and ‘personal health practices’ (do-it-yourself remedies). (iv) Health outcomes consist of ‘consumer satisfaction’ (convenience, availability, financing, provider characteristics and quality), health status’ and ‘perceived health status’.14–17

The Andersen-model was also used by us to help arrange the potential barriers. We present the barriers under the subject headings of the Andersen-model. We condensed the subject headings into three main groups which we have called ‘Patient level’, ‘Provider level’ and ‘System level’. By doing so, the myriad of potential barriers is easier to oversee.

Results

Out of the 309 titles and abstracts, a total of 56 articles were selected for inclusion. Finally, 54 articles were reviewed, as 2 of the articles were not available through Dutch university libraries.

The articles were classified into four different types of studies: Quantitative studies (n = 28); Qualitative studies (n = 10); Combined studies (n = 6), that combine quantitative and qualitative methods and Other studies (n = 8), like literature studies and essays. The reviewed studies were carried out in 11 different countries and a great number of ethnic minorities were involved. Different types of health services were studied. The different types were Health care in general; Preventive care; Dental care; Prenatal care; Primary health care; Care for the children; Care for the elderly and Mental health care.

A great number of potential barriers were identified. The identified potential barriers referred to population or patient characteristics (i.e. predisposing characteristics, enabling characteristics and need characteristics); health behaviour (i.e. patients' personal health practices); health outcomes (i.e. provider characteristics) and environment (i.e. the organizational factors of the health care system). The barriers are presented in three groups of barriers: (1) potential barriers at patient level; (2) potential barriers at provider level and (3) potential barriers at system level. An inventory of the potential barriers can be found in Table 1. The characteristics of the articles reviewed are summarized in Table 2.

Table 1

Inventory of potential barriers to the use of health services among ethnic minorities

Patient level Provider level System level 
Demographic variablesProvider characteristicsMedical paradigm44
Age9,18Medical procedures and practices9,18,37,50,62Consumerist approach42
Gender9,19,20Organisational factors
Marital status8,20,21Orientation on immediate complaint42Referral system10,38
Social structure variablesIntake procedure and opening hours27,35,62
Ethnicity22Program orientation and ethnic matching40Consultancy appointments and waiting time7,8,10,12,18,24,28,31,34,35,37
Education8,23,24Skills1,8,27,28,31,35,36,48,63,64The length of consultation and treatment7,18,27,28,35
Social class and economic status9,21,23,2527Behaviour7,8,27,28,3538,44,48,64Printed materials and other media forms38
Living conditions28Communication style62Translation7,30,35,62
Life style28,29Style of providing information38
Family and social support2830Client approach30,51
Culture31Bilingualism40,47,62
Duration of stay8,11,12,26,3234Translation23,43
Acculturation10,20,24,32,35,36Cultural knowledge9,25,30
Family involvement25,30,51
Religion/spirituality10,28
Parallel sets of belief and practices9,2628,36,49
Local language skills1,810,26,28,30,31,3335,3745
Communication42,43,46,47
Translation9,23,35,38,39,42,43
Health beliefs and attitudes
Time orientation and concepts of achievement8,10,30
Values concerning health and illness5,810,12,21,2628,36,44,4853
Perceptions and attitudes towards health services and personnel9,18,24,26,27,30,35,38
Knowledge about physiology and disease9,29,30,37,50
Personal enabling resources
Immigration rules9,25,30,35
Income/financial means8,10,24,25,2831,35,37,38,5457
Entry to health insurance25,37
Health insurance benefits8,9,18,21,24,25,35,55,57,58
Sources of advise and regular source of care8,21,30
Knowledge of health services and how to use them27,28,31,37,39,44,49,59,60
Available time and stress constraint9,10,29,35,37,50
Community enabling resources
Availability and delivery of services8,54
Price of health services29,35,38
Transportation and travel time8,9,10,18,28,31,35,37,54
Perceived illness
Perceived cause31
Personal health practices
Traditional remedies and self-treatment8,10,21,28,30,51,60,61
Patient level Provider level System level 
Demographic variablesProvider characteristicsMedical paradigm44
Age9,18Medical procedures and practices9,18,37,50,62Consumerist approach42
Gender9,19,20Organisational factors
Marital status8,20,21Orientation on immediate complaint42Referral system10,38
Social structure variablesIntake procedure and opening hours27,35,62
Ethnicity22Program orientation and ethnic matching40Consultancy appointments and waiting time7,8,10,12,18,24,28,31,34,35,37
Education8,23,24Skills1,8,27,28,31,35,36,48,63,64The length of consultation and treatment7,18,27,28,35
Social class and economic status9,21,23,2527Behaviour7,8,27,28,3538,44,48,64Printed materials and other media forms38
Living conditions28Communication style62Translation7,30,35,62
Life style28,29Style of providing information38
Family and social support2830Client approach30,51
Culture31Bilingualism40,47,62
Duration of stay8,11,12,26,3234Translation23,43
Acculturation10,20,24,32,35,36Cultural knowledge9,25,30
Family involvement25,30,51
Religion/spirituality10,28
Parallel sets of belief and practices9,2628,36,49
Local language skills1,810,26,28,30,31,3335,3745
Communication42,43,46,47
Translation9,23,35,38,39,42,43
Health beliefs and attitudes
Time orientation and concepts of achievement8,10,30
Values concerning health and illness5,810,12,21,2628,36,44,4853
Perceptions and attitudes towards health services and personnel9,18,24,26,27,30,35,38
Knowledge about physiology and disease9,29,30,37,50
Personal enabling resources
Immigration rules9,25,30,35
Income/financial means8,10,24,25,2831,35,37,38,5457
Entry to health insurance25,37
Health insurance benefits8,9,18,21,24,25,35,55,57,58
Sources of advise and regular source of care8,21,30
Knowledge of health services and how to use them27,28,31,37,39,44,49,59,60
Available time and stress constraint9,10,29,35,37,50
Community enabling resources
Availability and delivery of services8,54
Price of health services29,35,38
Transportation and travel time8,9,10,18,28,31,35,37,54
Perceived illness
Perceived cause31
Personal health practices
Traditional remedies and self-treatment8,10,21,28,30,51,60,61

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Table 2

Characteristics of the articles reviewed: health care sector, country, ethnic minority, level of occurrence and potential barrier, type of study, first author and reference

HC sector Country Ethnic minority Level of occurrence, potential barrier Type of study First author Ref 
HCG Australia Thai migrant women Patient level Combined study Jirojwong (2002) 38
    Local language skills 
    Translation 
    Perceptions and attitudes towards health services and personnel 
    Income/financial means 
    Price of health services 
Provider level 
    Behaviour 
    Style of providing information 
System level 
    Referral system 
    Printed materials and other media forms 
Canada Immigrants: born outside Canada or whose mother tongue (still understood) was neither French nor English Patient level Quantitative study Blais (1999) 1
    Local language skills 
Provider level 
    Skills 
Newcomers: Arab 42%; Spanish 19%; Polish 15%; Chinese 6%; East Indian 2%; Vietnamese 2%; Eastern Europe/South and Central America/Africa 14% Patient level Quantitative study Matuk (1996) 34
    Duration of stay 
    Local language skills 
System level 
    Consultancy appointments and waiting time 
Germany Turkish (im)migrant women Patient level Other study Berg (1997) 52

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

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.

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 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).

  1. 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.
  2. 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.
  3. 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.
  4. 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:

  1. evaluating all policies likely to affect health in terms of their impact on inequalities
  2. giving high priority to the health of families with children
  3. 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:

  1. Health inequalities must be addressed in the interests of fairness and social justice.
  2. There exists a social gradient in health: health improves as social status goes up.
  3. Social inequalities result in health inequalities; therefore to reduce health inequalities we must consider all the social determinants of health.
  4. 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’).
  5. 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.
  6. 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.
  7. 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
  8. 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

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:

  1. 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.
  2. 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)

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.

References

  • Acheson D (1998). Independent inquiry into inequalities in health report. London: The Stationary Office.
  • Aspinall PJ (1997). “The conceptual basis of ethnic group terminology and classifications” Social Science and Medicine,45(5)
  • Bartley M, Blane D (2008). Inequality and social class in Scambler G (ed) Sociology as applied to medicine. Elsevier Limited.
  • Bartley M (2004). Health inequality: an introduction to theories, concepts, and methods. Cambridge: Polity Press.
  • Bhopal R. (1997). “Is research into ethnicity and health racist, unsound, or important science?” BMJ, 314.
  • Bradby H. (2003) “Describing ethnicity in health research.” Ethnicity and Health, 8(1).
  • Comstock RD, Castillo EM, Lindsay SP (2004). “Four-year review of the use of race and ethnicity in epidemiologic and public health research” American Journal of Epidemiology. Vol. 159, No. 6.
  • Dalgren G (1995). European Health Policy Conference. Opportunities for the Future Vol 1-Intersectorial Action for Health, Copenhagen: WHO Regional Office for Europe.
  • Department of Health and Human Services (DHHS) (1980). Inequalities in health: report of a research working group. (The Black Report). HMSO, London.
  • Ellison, GTH (2005). “Population profiling and public health risk: when and how should we use race/ethnicity? Critical Public Health, 15(1).

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

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

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.

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