Elsevier

Social Science & Medicine

Volume 62, Issue 2, January 2006, Pages 329-336
Social Science & Medicine

Life-course influences on mortality at older ages: Evidence from the Oslo Mortality Study

https://doi.org/10.1016/j.socscimed.2005.10.003Get rights and content

Abstract

Several studies have investigated the cumulative influence of disadvantage acting from early to adult life. And they suggest that risk of death increases in a cumulative fashion. Few studies have investigated the life-course influences on social inequality in mortality in early old age. In this study we wanted to follow a cohort through their adult working age into retirement and investigate pathways that may give increased risk of mortality. A cohort of all inhabitants in the age range 68–72 who lived in the municipality of Oslo on 1st January 1990 was selected. Data were obtained by linking censuses from 1960, 1970 and 1980 with tax registry in 1990 and death registry 1990–1998. Independent variables were occupational class in 1960, 1970 and 1980 and household income in 1990. Occupational class was coded as manual and non-manual and household income as above or below median household income. A cumulative model was developed by adding times of disadvantage. Then a pathway model was developed which investigated the specific trajectories individuals followed. Most of the increased risk of death was explained by individuals’ social conditions in 1990. In the cumulative model, there was no clear gradient in the groups between the bottom and top categories. This suggests that the cumulative model is not important in the high mortality age and that the relative importance of a cumulative effect varies by stages of the life course.

Introduction

Recent years of research into inequality in health has seen a growing interest in how social and biological factors act together throughout the life course and influence health (Ben-Shlomo & Kuh, 2002; Davey Smith, Gunnel, & Ben-Shlomo, 2000; Power & Hertzman, 1997). In Western countries where population health is dominated by chronic diseases, extending the time frame from risk factors in adult life to factors all along the life course has been suggested as a better model of explaining social inequality in adult health. Researchers have previously looked at how adverse social factors early in life may have a long-term impact on an individual's health (Barker, 1998; Forsdahl, 1977). The foetal origin's hypothesis proposes that adverse social circumstances in early life and especially poor maternal nutrition during pregnancy lead to impaired foetal growth, biological programming of the foetus and an increased risk of coronary heart disease, hypertension and diabetes in adult life. The life-course approach explicitly takes a broader perspective into account than this by including the socially and biologically transmitted chains of risk throughout the whole life (Ben-Shlomo & Kuh, 2002).

Several theories of life-course processes have been suggested. Some researchers have compared social conditions in childhood and adulthood and found an independent effect of childhood, pointing to this period as a sensitive period for various health outcomes in adult life (Ben-Shlomo & Davey Smith, 1991; Claussen, Davey Smith, & Thelle, 2003; Kuh, Hardy, & Langenberg, 2002). A growing body of research has shown some evidence of a cumulative effect of social disadvantage acting throughout the life (Bartley & Plewis, 2002; Davey Smith, & Hart, 2002; Davey Smith, Hart, Blane, Gillis, & Hawthorne, 1997; Hart, Davey Smith, & Blane, 1998; Lynch, Kaplan, & Shema, 1997; Power, Manor, & Matthews, 1999). According to this accumulative model, risk of morbidity and mortality is influenced from each stage of the life course. In the West of Scotland Collaborative Study, Davey Smith et al. (1997) found an increased risk of death from all causes when number of occasions in manual class were added using father's occupational class, first occupational class at labour entry and occupational class at screening. The population studied included 5766 men in the age range of 35–64 years at screening with 21 years of follow-up. The study added up number of occasions in manual class and demonstrated a graded increase in mortality in all causes of death, cardiovascular causes, cancer, non-cardiovascular and non-cancer causes. Bartley & Plewis (2002) looked at the 1958 birth cohort with information on social and demographic circumstances in the period 1971–1991 in 500,000 individuals. This study investigated if labour market disadvantage in each time period had an independent effect of the other periods in the life course when they were all mutually adjusted. They found an independent effect of each five occasions of disadvantage in the labour market on limiting long-term illness.

The cumulative model has some potential when explaining distribution of various chronic diseases in western populations, but it fails to take into account the temporal sequence of causal influences. Wunch and Duchenne used Norwegian linked registry data from Censuses in 1960, 1970 and 1980, and the death registry looking at deaths until 1985 (Sahli et al. (1992), Sahli et al. (1995); Thiltges, Duchene, & Wunsch, 1992; Wunsch, Duchene, & Thiltges, 1996). They suggested a pathway approach which specifies more in detail how risk may vary between different trajectories. Such an approach may enable us to identify, in detail, stages in the life course that has a detrimental influence. Furthermore, it may provide deeper exploration of trajectories that have similarities in their influence on risk of ill health. Few studies have investigated this pathway approach empirically. Due to increased detail in the model, it demands large data to conduct.

The studies so far have mostly focused on mortality and morbidity in adult life with rather wide age distribution. It is not yet clear weather the cumulative effect is similarly seen in all age groups. In early old age where the majority of deaths increasingly occur, accumulation of risk remains to be investigated. This age group is of particular importance as the main burden of ill health will probably be found there. We wanted in this study to follow an age cohort with a rather narrow age span through their active working age into retirement and investigate how mortality after retirement is affected by the life course from adult to early old age. For this purpose we want to take advantage of Norwegian linked registry data and investigate the trajectories this cohort follow using the pathway model originally suggested by Wunch and Duchenne (Sahli et al. (1992), Sahli et al. (1995)).

Section snippets

Population and methods

A cohort of all inhabitants in the age range 68–72 who lived in the municipality of Oslo on January 1, 1990 was chosen as the study population. This age cohort was chosen because their social conditions could be followed during their working age and after they had just left the labour force. Workers retired at that time in Norway in most cases at the age of 67. They were 38–42 years in 1960, 48–52 years in 1970, 58–62 years in 1980. In all, 24,547 individuals were selected. Data were obtained

Results

Table 1 shows age-adjusted mortality in 1990–1998 for the cohort by their occupational class in 1960, 1970 and 1980 and median household income in 1990. Thus, this cohort is followed by social conditions through their adult life into retirement whereas mortality is recorded in the high mortality period after retirement. In both men and women, disadvantaged conditions earlier in life predicted higher mortality after retirement. Particularly in men, income in 1990 had stronger effect on mortality

Discussion

In this study we found most of the increased risk at the proximal time point which is after the age of retirement. We found no clear evidence of independent risk through this period when mutually adjusting all time points of disadvantage was measured in the Censuses. The cumulative model did not show a stepwise gradient between the top and bottom categories. The pathway model suggested that the trajectories with highest risk where those that had experienced proximal disadvantage.

Lack of a

Acknowledgements

We would like to thank Statistics Norway for providing the data. We gratefully acknowledge comments on an earlier draft from Tiina Pensola and Martijn Huisman.

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