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The present brief article sketches the various ways in which health has been defined and measured in quantitative linked register and survey research. Health, mortality, morbidity, functioning, quality of life and biomarkers are considered. A case is made for using the survey questionnaire measure of limiting long-standing illness in labour market economics. Il presente articolo delinea i vari modi in cui la salute è stata definita e misurata nella ricerca su dati di tipo survey e quantitativi di fonte amministrativa. Sono presi in considerazione salute, mortalità, morbilità, capacità funzionali, qualità della vita e biomarcatori. Negli studi di economia del lavoro, si sostiene l'opportunità di utilizzare le misure – derivanti da questionario – relative alle malattie di lungo corso che comportano limitazioni per i lavoratori.

There is widespread recognition that the most powerful determinants of health today are to be found in social, economic and cultural circumstances. These include: ecnomic growth, income distribution, consumption, work oganisation, unemployment and job insecurity, social and family structure, education and deprivation, and they are all aspects of 'social organisation'. In ^Health and Social Organisation leading British and North American researchers who bring together an invaluable collection of data o

- The present paper reviews the development of life course epidemiology since its origins during the 1990s from biological programming, birth cohort research and the study of health inequalities. Methods of studying the life course are examined, including birth cohort studies, linked register datasets and epidemiological archaeology. Three models of life course epidemiology are described: critical periods, accumulation, and pathways. Their conceptual and empirical differentiation can be difficult, but it is argued that accumulation is the underlying social process driving life course trajectories, while the critical period and pathway models are distinguished by their concern with specific types of aetiological process. Among the advantages of the accumulation model are predictive power, aetiological insights, contributions to health inequality debates and social policy implications. It is emphasised that the life course approach is not opposed to, or an alternative to, a concern with cross-sectional and current effects; major social disruption can have a large and immediate impact on health. Other limitations of the life course approach include a spectrum of impact (life course effects can be strong in relation to physiology, but often are weaker in relation to behaviour and psychological reactions to everyday life) and, more speculatively, the possibility that life course effects are diluted in the older age groups where morbidity and mortality are highest. Three issues for the future of life course epidemiology are identified. Many life course data are collected retrospectively. We need to know which items of information are recalled with what degree of accuracy over how many decades; and what methods of collecting these retrospective data maximise accuracy and duration. Second, the two partners in life course research need to take more seriously each other's disciplines. Social scientists need to be more critical of such measures as self-assessed health, which lacks an aetiology and hence biological plausibility. Natural scientists need to be more critical of such concepts as socio-economic status, which lacks social plausibility because it fails to distinguish between social location and social prestige. Finally, European comparative studies can play an important part in the future development of life course epidemiology if they build on the emerging infrastructure of European comparative research. Key words: life course epidemiology, life course trajectories, life course data, social inequalities, accumulation model, socio-economic status. Parole chiave: epidemiologia del corso di vita, traiettorie di vita, dati del corso di vita, disuguaglianze sociali, modello di accumulazione, status socio-economico

The increase in life expectancy at middle age has added quality of life to the policy concerns related to older ages. The present article asks whether this quality of life varies with socio-economic position. Using data from the English Longitudinal Study of Ageing (n.d.), the article answers this question in terms of the National Statistics Socio-Economic Classification and CASP-19, a measure of positive quality of life in early old age. Among all respondents aged 50 to 75 years, quality of life was found to be graded by social position, with the difference in quality of life between the higher managerial and professional group and the routine group being of comparable size to having a limiting long-standing illness; with the size of this difference varying between labour market sub-groups. The implication of these findings for ageing policy and for the National Statistics Socio-Economic Classification are discussed.

Abstract Measurement of quality of life has become a major feature of much social and epidemiological research in health and social care settings. It is seen as an important alternative to more process‐based outcome measures but remains poorly defined. A major weakness is the absence of any coherent theoretical underpinning whether sociological, psychological or philosophical. Into this conceptual vacuum proxies for quality of life have been introduced. Quality of life [QoL] research into older populations has focused on measures of health and illness as equivalents of QoL. This paper argues that this response is inadequate as it reduces old age to a dimension of health, disability and disease. Instead, we argue that it is necessary to create a theoretically based measure of QoL in early old age which relates to those aspects of later life that are not defined by health. We present a model of QoL that is derived from aspects of contemporary social theory as they relate to the ontology of late modernity. In particular, we utilize a model based upon needs satisfaction. The model contains four domains: Control, Autonomy, Pleasure and Self‐realization. The measure consists of a 19‐item scale. The four domains load on to a single latent QoL factor. We argue that the CASP 19 scale offers an approach to QoL that integrates a sociologically based model of quality of life with a meaningful and valid research instrument.

This book examines health trajectories and health transitions at different stages of the life course, including childhood, adulthood and later life. It provides findings that assess the role of biological and social transitions on health status over time. The essays examine a wide range of health issues, including the consequences of military service on body mass index, childhood obesity and cardiovascular health, socio-economic inequalities in preventive health care use, depression and anxiety during the child rearing period, health trajectories and transitions in people with cystic fibrosis, and oral health over the life course. The book addresses theoretical, empirical and methodological issues as well as examines different national contexts, which help to identify factors of vulnerability and potential resources that support resilience available for specific groups and/or populations. Health reflects the ability of individuals to adapt to their social environment. This book analyzes health as a dynamic experience. It examines how different aspects of individual health unfold over time as a result of aging but also in relation to changing socioeconomic conditions. It also offers readers potential insights into public policies that affect the health status of a population

The present text builds on an earlier publication* which had the same aim: namely, to encourage clarity and coherence in the interdisciplinary area we called social-to-biological transitions. This burgeoning area of research involves a complex workforce with differing career levels and disciplinary traditions, reflecting which the present authors comment from different perspectives (one author from each of early career research, epidemiology, biology and public health) and invite debate. (* Blane, D., Kelly-Irving, M., d'Errico, A., Bartley, M. and Montgomery, S. (2013) Social-biological transitions: how does the social become biological?, Longitudinal and Life Course Studies, 4(2): 136–46.)

This book examines health trajectories and health transitions at different stages of the life course, including childhood, adulthood and later life. It provides findings that assess the role of biological and social transitions on health status over time.The essays examine a wide range of health issues, including the consequences of military service on body mass index, childhood obesity and cardiovascular health, socio-economic inequalities in preventive health care use, depression and anxiety during the child rearing period, health trajectories and transitions in people with cystic fibrosis, and oral health over the life course. The book addresses theoretical, empirical and methodological issues as well as examines different national contexts, which help to identify factors of vulnerability and potential resources that support resilience available for specific groups and/or populations. Health reflects the ability of individuals to adapt to their social environment. This book analyzes health as a dynamic experience. It examines how different aspects of individual health unfold over time as a result of aging but also in relation to changing socioeconomic conditions. It also offers readers potential insights into public policies that affect the health status of a population.

PurposeThe aim of this paper is to describe the use of sequence analysis to model trajectories of life‐course economic activity status, within a broader research agenda aimed at improving understanding of the relationship between socioeconomic position and health.Design/methodology/approachThe analysis used data on 288 participants of the Boyd Orr Stratified Sub‐Sample, comprising a combination of prospective and retrospective information on economic activity status, as well as health in early old age. Economic activity was coded as a time‐based sequence of states for each participant based on six‐month periods throughout their lives. Economic activity was classified as: pre‐labour market; full‐time employment; part‐time employment; housewife; made redundant; stopped work due to illness; retired; other unemployed; or not applicable. Optimal matching analysis was carried out to produce a matrix of distances between each sequence, which was then used as the basis for cluster analysis.FindingsThe optimal matching analysis resulted in the classification of individuals into five economic activity status trajectories: full‐time workers (transitional exit), part‐time housewives, career breakers, full‐time workers (late entry, early exit), and full‐time housewives.Originality/valueThe paper presents the case for using sequence analysis as a methodological tool to facilitate a more interdisciplinary approach to the measurement of the life‐course socioeconomic position, in particular attempting to integrate the empirical emphasis of epidemiological research with the more theoretical contributions of sociology. This may in turn help generate a framework within which to examine the relationships between life‐course socioeconomic position and outcomes such as health in later life.