Elsevier

Annals of Epidemiology

Volume 25, Issue 9, September 2015, Pages 674-680.e1
Annals of Epidemiology

Original article
Contribution of socioeconomic position over life to frailty differences in old age: comparison of life-course models in a French sample of 2350 old people

https://doi.org/10.1016/j.annepidem.2015.05.006Get rights and content

Abstract

Purpose

To assess the impact of socioeconomic position (SEP) over life on a measure of frailty in old age.

Methods

This is a cross-sectional population study of people aged 70 years and more in which 2350 respondents were interviewed in 2008 to 2010. The relationships between different indicators of SEP (childhood standard of living, level of education, occupational class, and current affluence) and quartiles of a frailty index including 43 variables were assessed in ordinal regression models adjusted for potential confounders.

Results

Mean age of the population was 83.3 ± 7.5 years, with 59.4% of women. The mean value of the frailty index was 0.19 ± 0.13, with values ranging between 0 and 0.65. All periods of social disadvantage were associated with increasing frailty in bivariate analysis. In multivariate analysis, a poor level financial security in the old age was the SEP indicator the most strongly associated with frailty (odds ratio [OR]: 2.81, 95% confidence interval [CI]: 2.20–3.59), followed by a low level of education (OR: 1.45, 95% CI: 1.17–1.80) and occupation during active life (OR: 1.38, 95% CI: 1.06–1.79).

Conclusions

Socioeconomic inequalities over life affect health capital in old age. The most important risk factor identified in this study, contemporary financial difficulties, was also the most accessible to prevention.

Introduction

Socioeconomic factors are important determinants of health status, throughout adulthood and old age [1]. European studies have shown that disadvantaged people die earlier than people in advantageous socioeconomic circumstances (high level of education or income) and that groups of lower socioeconomic status are more likely to report poorer health [2]. Socioeconomic inequalities can be enhanced in old age. Indeed, aging is often associated with a loss of income because of retirement or widowhood, and old people are more likely to face financial difficulties compared with their midlife counterparts. Data from two English cohorts have actually highlighted social gradients in both walking speed [3], [4] and incidence of functional impairment [5].

A growing body of evidence suggests that health inequalities in later life cannot be fully understood without taking into account earlier life experiences. For instance, data from the Wisconsin Longitudinal Study showed that exercise in later life is influenced by socioeconomic status at the age of 18 years, an association partly mediated by socioeconomic resources and health in midlife [6]. In a Swedish cohort of men and women born in the period of 1915 to 1929, Mishra et al. [7] examined the effect of socioeconomic position (SEP) across lifetime on mortality in old age. Findings indicated that SEP at birth, in adulthood, and in later life independently increased mortality risk.

Among health measures in old age, frailty is increasingly used in both clinical and epidemiologic studies. Frailty is a geriatric concept that refers to the increased vulnerability to stressors [8]. This is an age-related predictor of adverse health outcomes and mortality in old age, which is the result of a decrease in physiological reserves of multiple systems [9]. Although the existence of a social gradient in frailty in old age is well documented [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], only a few studies have examined the effect of the period of exposure to social inequalities [19], [21].

In this context, this study aims to investigate the effect of SEP at each life stage on health in old age, measured using a frailty index (FI).

Section snippets

Study design and population

This work is part of a cross-sectional study carried out to characterize health and functional independence among people aged 70 years and older (SIPAF study, French acronym for “Système d’Information sur la Perte d’Autonomie Fonctionnelle de la personne âgée,” meaning “information system for loss of functional autonomy of the elderly”). Subjects were selected at random among participants in a supplementary pension fund, AG2R La Mondiale (Paris, France), with over-representation of strata of

Study population

A total of 2350 people agreed to participate in the study (participation rate: 18.9%). The main reasons for nonparticipation were the lack of interest in the study (28.3% of the nonparticipants), followed by a state of frailty (10.8%) and the refusal of a close relative (7.3%). Participation was better in low-populated areas and in departments where the population is aging or with a lower standard of living. Besides, participants were on average 2.3 years younger than nonparticipants (P

Discussion

In addition to a number of health variables, this study assessed different carriers of social inequalities in health, namely childhood deprivation, low level of education, low occupational class, and financial insecurity in old age. By using life-course models, this framework offered the possibility to test hypotheses with regard to the specific effect of each period of social disadvantage, as well as hypotheses with regard to their combined effects. Finally, results indicated that each period

Acknowledgment

This work was supported by AG2R La Mondiale (staffing, data management) and Université Versailles St-Quentin-en-Yvelines (data analyses). In addition to our sources of funding, we thank Jean-Pierre Audran for his involvement in setting up the study, Isabelle Remy for her logistical support, and Frédéric Simoes Da Gama for his contribution to data management.

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      An age, sex, and accelerometer wear time adjusted model was initially completed for each exposure variable individually, followed by a fully adjusted model. We included the following variables in the fully adjusted models based on previous literature which have shown a relationship with the FI (example references are provided): age (Kehler et al., 2017; Rockwood et al., 2011), sex (Gordon et al., 2017), ethnicity (Shamliyan et al., 2013; Espinoza and Hazuda, 2008), education (Rockwood et al., 2011), income (Hubbard et al., 2014), marital status (Rockwood et al., 2011), smoking status (Hubbard et al., 2009), alcohol consumption (Herr et al., 2015), body mass index (Hubbard et al., 2010), and total sedentary time (Blodgett et al., 2015). Accelerometer wear time was also added to the fully adjusted model to control for differences in wear time across study participants.

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