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This Handbook is the definitive resource for anyone wishing to quickly look up and understand key concepts and measurements relating to socioeconomic position and inequalities.

A range of key concepts is defined and measures of socioeconomic position and inequality described. Alphabetical listings, cross-referencing, graphs and worked examples, references to web and other sources of further information, all contribute to making the Handbook both engaging and accessible for a wide audience.

For students, academics and others involved in social science research it answers questions such as:

'What's the official government measure of poverty?'

'What factors make up the Townsend Index of Deprivation?'

'What is a gini coefficient?'

'I have to write a report on tackling inequalities in my area - what are the key issues I should consider before I begin?' For practitioners, policy makers, journalists and others who must read, understand and use research in fields as diverse as health, criminology, education, the environment, transport and housing it provides a one-stop, authoritative guide to making sense of and evaluating the significance of often complex methodologies. The authors are all eminent researchers in the field of health inequalities. They have together produced two glossaries for the Journal of Epidemiology and Community Health and have published a large number of books and articles in learned academic journals.

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‘Ethnicity’ derives from the Greek word ‘ethnos’ and means ‘nation’ or ‘people’. An ethnic group is now generally considered to be a community who share a number of characteristics, for example, some, but not necessarily all, of language, history, religion, geographical origin, mythologies, traditions and political system. The term is often linked to identity, in the formulation ‘ethnic identity’, and relates to the reporting of the experience of group membership. It is also often linked to the concept of minority status as in ‘ethnic minorities’, indicating people who live in a country that is not generally their land of family origin and in which they are not the majority population. These extended definitions are of course not automatically linked, and the majority population in a country has an ethnic identity.

Ethnicity is not a measure of socioeconomic position (SEP), but in some contexts, particularly within the US, it has been used as a proxy measure of SEP. In the US a portmanteau term ‘race/ethnicity’ is sometimes used, reflecting a notion of difference that extends beyond that of identity alone. In the US the use of ethnicity as an index of SEP is, in part, due to the historical relative absence of socioeconomic data in routine data sources in that country. For example, in a US Department of Health and Human Services report entitled Health status of the disadvantaged, a high proportion of tables present health indicators by what is referred to as ‘race’ and not by any explicitly socioeconomic measure (US DHHS, 1990).

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Both absolute and relative differences are measures of inequality since they examine the absolute or relative difference in an outcome (for example, health or disease status) by categories of an exposure (for example, social class groups).

An absolute difference (also known as absolute effect) refers to the consequence of an exposure, expressed as the difference between proportions, means, rates, risk, etc, as opposed to being expressed as a ratio of these (see 3.9 Relative differences). The exposure of interest, forming the categories we wish to compare, could be a measure of socioeconomic position (SEP), sex, ethnicity, age, geographical area, time period and so on.

Since the absolute difference depends on how common the outcome of interest is in the population under study it may vary even when the relative difference remains constant. For example, a relative risk of 2 for heart disease comparing the least affluent to the most affluent groups may occur if the risk of heart disease is 50 per 1,000 in the least affluent group and 25 per 1,000 in the most affluent group (absolute difference = 25 per 1,000) or if the risk is 5 per 1,000 in the least affluent group and 2.5 per 1,000 in the most affluent group (absolute difference = 2.5 per 1,000). It is also possible that over time, as the frequency of an outcome decreases, the absolute difference could decrease as the relative difference increases (Regidor, 2004).

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A number of household amenities (or assets) are used as markers of material circumstances. These often include features such as access to hot and cold water in the house, having central heating and carpets, sole use of bathrooms and toilets, whether the toilet is inside or outside the home, and having a refrigerator, washing machine, or telephone. These household amenities are seen as direct measures of socioeconomic position (SEP) but they may also be seen as important because they may be associated with health outcomes through specific aetiological mechanisms. For example, lack of running water or lack of a household toilet may be associated with increased risk of infection.

The meaning of these amenities will vary by context and cohort. Very few people in contemporary advanced industrial societies will be without running hot water, an indoor toilet or bathroom facilities and, therefore, these measures are not able to differentiate individuals within these populations. However, such measures may still have relevance as indicators of childhood SEP among older adults living in the UK.

The census in the UK has traditionally included questions on household amenities. In 1971, for example, it included whether the household had a cooker, sink, bath/shower, toilet and central heating. The list of amenities included has decreased since 1971, reflecting the fact that some amenities have become features of virtually all households and hence no longer distinguish SEP. In non-industrialised countries, assets that have been used as indicators of SEP in health-related research include the number of livestock, owning a bicycle, refrigerator, radio, sewing machine, television or clock

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One or a combination of age, period or cohort effects may drive changes in trends over time, for example, producing a widening or narrowing of socioeconomic differences in an outcome or leading to an increase or decrease in an outcome for one socioeconomic group alone.

Age effects mean that the change occurs in all individuals at a particular age irrespective of when they were born or the current time period. For example, an increase in socioeconomic differentials occurring at the age of retirement (irrespective of what time period is being examined or which birth cohort individuals were born in) would be described as an age effect.

A cohort is a group of people defined by a particular attribute (or set of attributes). In the context of age-period cohort effects, ‘cohort’ refers to birth cohort. Thus, the group (cohort) are defined by the period (usually the year) in which they were born.

Cohort analysis is the tabulation and analysis of data, such as morbidity and mortality rates or educational outcomes, in relation to the birth cohorts of the individuals concerned.

Cohort effect (also known as generational effect) refers to variations in health status that arise from the different causal factors to which each birth cohort in the population is exposed. Every birth cohort is exposed to different social and environmental circumstances that coincide with its life span. Because the meaning of socioeconomic measurements varies by birth cohorts (for example, a female who was born in 1910 obtaining a university degree is likely to have been from a different social stratum to a female born in 1975 obtaining a university degree) cohort effects are important to consider when examining the association of socioeconomic position (SEP) with health and other outcomes.

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