Socio-economic position and its relationship to physical capacity among elderly people living in Jyväskylä, Finland: five- and ten-year follow-up studies

https://doi.org/10.1016/j.socscimed.2004.11.029Get rights and content

Abstract

Socio-economic differences in self-reported disability are well described but much less is known about their associations with more objective measures of physical capacity. The aim was to study socio-economic differences in performance-based physical capacity in 75-year-old persons, examining changes in performance at five- and ten-year follow-up intervals. At the baseline 350 residents of the city of Jyväskylä, Finland, aged 75 were interviewed and 295 of them took part in clinical examinations. The corresponding figures at the five-year follow-up were 234 and 191 and at the ten-year follow-up 139 and 103. The statistical significance of differences in physical capacity between the socio-economic groups and genders were tested using ANOVA in univariate and repeated measures models and ANCOVA, with confounders added to the models. Generally, higher education and income were separately related to better maximal walking speed and vital capacity at every measurement point. In addition, higher income was related to better maximal isometric hand grip strength at both follow-ups. When education and income were in the same model, only income was related to physical capacity, almost without exception. Similarly, in the five- and ten-year follow-up periods, both education and income groups showed a parallel decline in physical capacity. The association between income and physical capacity remained even after adjusting for smoking, physical activity and number of chronic diseases. The results indicate that elderly people in disadvantaged socio-economic groups show lower levels of performance in almost all domains of physical capacity, but change in capacity over time does not differ significantly between either markers of socio-economic position.

Introduction

Many studies have shown an inverse relationship between socio-economic factors and different indicators of health status (Cairney & Arnold, 1996; Dahl & Birkelund, 1997; Thorslund & Lundberg, 1994) and mortality in elderly people (Martelin, 1994; Martelin, Koskinen, & Valkonen, 1998). Recent studies have also pointed out that socio-economic factors at baseline are important predictors of functioning in the future in older people (Deeg et al., 1992; Grundy & Glaser, 2000; Guralnik & Kaplan, 1989; Guralnik et al., 1993; Harris, Kovar, Suzman, Kleinman, & Feldman, 1989; Ho, Woo, Yuen, Sham, & Chan, 1997; Palmore, Nowlin, & Wang, 1985; Rogers, Rogers, & Belanger, 1992; Seeman et al., 1994).

In these studies both socio-economic position and functional capacity have been approached and conceptualised in different ways. According to Grundy and Holt (2001) the most useful pair of socio-economic variables in studies of health inequalities in older people are educational qualification or social class paired with a deprivation indicator. Functional capacity is on the other hand, a very broad concept and may comprise, for example physical, cognitive and social functioning as well as performance of activities of daily living. Often functioning is measured by a composite, multidimensional outcome variable or it is approached from the point of view of disability. It is usually assessed with self-reported measures (Grundy & Glaser, 2000; Guralnik & Kaplan, 1989; Guralnik et al., 1993; Harris et al., 1989; Rogers et al., 1992) and less frequently with performance-based measurements (Seeman et al., 1994).

In general persons with a high socio-economic position at baseline maintain better functional capacity during follow-up than persons with a low position (Deeg et al., 1992; Grundy & Glaser, 2000; Guralnik & Kaplan, 1989; Guralnik et al., 1993; Harris et al., 1989; Ho et al., 1997; Palmore et al., 1985; Rogers et al., 1992; Seeman et al., 1994). However, gender differences may exist and control for other factors such as onset of diseases and life style factors has not been conducted in many studies and might lead to different findings. For example, Strawbridge, Camacho, Cohen, and Kaplan (1993) reported that family income and education had stronger associations with 6-year change in functioning among older men than women.

Furthermore, the rate of change in functional capacity may differ between socio-economic groups. For example, the rate of change in cognitive functioning during a ten-year follow-up was predicted by the occupation of longest duration in a study of Japanese men aged 69–71 at baseline. Men who had worked in blue collar occupations showed more decline in cognitive functioning than men in white collar occupations. In addition, a high level of education was associated with good maintenance of physical functioning among women (Deeg et al., 1992). Hemingway, Stafford, Stansfeld, Shipley, and Marmot (1997) reported similar results in middle-aged persons; persons in the lower employment grades showed a greater decline in almost all scales of health functioning than persons with higher employment status during an average 36 month's follow-up. Seeman et al. (1994) found that a higher level of education was associated with a somewhat larger decline in physical performance during a three-year follow-up, but the most educated still had better physical performance.

Physical capacity is an essential domain of functional capacity from the point of view of the autonomy and quality of life of elderly people. Examination of longitudinal changes in physical capacity in elderly people is useful in predicting the need for health and social care. Performance-based measures are important, because they provide information not available in self-report items (Guralnik et al., 1994). For example, these measures have identified a nondisabled group at high risk of progressing to disability (Guralnik, Ferrucci, Simonsick, Salive, & Wallace, 1995), nursing home admission and mortality (Guralnik et al., 1994). Therefore, the effect of socio-economic position on specific areas of performance-based indicators of physical capacity may be informative in understanding patterns of functional decline in elderly people.

Section snippets

General description of survey area and target population

The city of Jyväskylä, which is an educational and industrial centre is located in Central Finland. In 1989 the city had about 66,000 inhabitants, of which 12.4% were aged 65 or over (Heikkinen, 1997). In 1989 the percentages of Finns aged 65 years or older with their basic level of education equivalent to graduation from senior secondary school, vocational school or university were 4.4, 14.3 and 2.3, respectively (Central Statistical Office of Finland, 1991). Percentages were similar for

Results

At the baseline, 91.6% (119 men, 231 women) of those eligible participated in the interview and 77.2% (104 men and 191 women) also took part in the examinations at the study centre. At the five-year and ten-year follow-ups, 87.3% and 79.9%, respectively, of those eligible took part in the interviews, and 71.3% and 59.2% took part in the examinations. Fig. 1 shows participation in interviews and examinations during the study together with deaths and other attrition. Smoking was much more common

Discussion

High levels of education and income were separately related to better maximal walking speed and vital capacity at the baseline and both follow-ups. In addition, persons whose income was high had better maximal hand grip strength in the five- and ten-year follow-ups than those with low income. However, these differences were not very great. When education and income were included in the same model, only income in general was related to physical capacity. Higher income was related to better

Acknowledgements

The authors wish to thank Timo Törmäkangas for his valuable statistical advice and comments during the preparation of this manuscript. The Evergreen project has been supported by the Academy of Finland, the Ministry of Social Affairs and Health, the Ministry of Education, the Social Insurance Institution, the Scandinavian Red Feather project, the Association of Finnish Lions Clubs and the City of Jyväskylä. The present study was financially supported by the University of Jyväskylä and the

References (37)

  • Finnish Centre for Pensions & The Social Insurance Institution. (2003). Statistical yearbook of pensioners in Finland...
  • G. Grimby

    Physical activity and muscle training in the elderly

    Acta Medica Scandinavica

    (1986)
  • E. Grundy et al.

    Socio-demographic differences in the onset and progression of disability in early old agea longitudinal study

    Age and Ageing

    (2000)
  • E. Grundy et al.

    The socioeconomic status of older adultshow should we measure it in studies of health inequalities?

    Journal of Epidemiology and Community Health

    (2001)
  • J.M. Guralnik et al.

    Lower-extremity function in persons over the age of 70 years as a predictor of subsequent disability

    New England Journal of Medicine

    (1995)
  • J.M. Guralnik et al.

    Predictors of healthy agingprospective evidence from the Alameda County Study

    American Journal of Public Health

    (1989)
  • J.M. Guralnik et al.

    Maintaining mobility in late life. I. Demographic characteristics and chronic conditions

    American Journal of Epidemiology

    (1993)
  • J.M. Guralnik et al.

    A short physical performance battery assessing lower extremity functionassociation with self-reported disability and prediction of mortality and nursing home admission

    Journal of GerontologyMedical Sciences

    (1994)
  • Cited by (41)

    • Relationships between hand-grip strength, socioeconomic status, and depressive symptoms in community-dwelling older adults

      2019, Journal of Affective Disorders
      Citation Excerpt :

      This implies that low income level may cause low HGS, which leads to depressive symptoms among older adults independent of the direct effects of income level on depressive symptoms. Various evidence has suggested that SES is a predictor of HGS in older adults (Haas et al., 2012; Mohd Hairi et al., 2010), and previous studies reported that lower income was associated with low HGS (Rautio et al., 2005; Thorpe et al., 2016). Serial associations among low income, low HGS, and depressive symptoms may provide indirect evidence about the mediation pathway.

    • The impact of economic conditions on the disablement process: A Markov transition approach using SHARE data

      2017, Health Policy
      Citation Excerpt :

      A growing number of studies attempt to demonstrate a causal relationship between socioeconomic status (SES) and health throughout the life course, including during later life (see e.g.; [9,10,17,21,38,39,46,47].

    • Diet, Social Inequalities, and Physical Capability in Older People

      2013, Bioactive Food as Dietary Interventions for the Aging Population
    • Diet, Social Inequalities, and Physical Capability in Older People

      2012, Bioactive Food as Dietary Interventions for the Aging Population: Bioactive Foods in Chronic Disease States
    • Childhood socioeconomic disadvantage as a determinant of late-life physical function in older Japanese people

      2020, Archives of Gerontology and Geriatrics
      Citation Excerpt :

      Maintenance of physical function is a key feature of healthy aging because poor physical function increases the risk for adverse health outcomes such as mortality, disability, and hospitalization (Cesari et al., 2009; Cooper, Kuh, Hardy, & Mortality Review Group on behalf of the FALCon and HALCyon study teams, 2010; Onder et al., 2005). It is also widely known that socioeconomic status (SES) in adulthood is a powerful determinant of late-life physical function (Brunner et al., 2009; Coppin et al., 2006; Hurst et al., 2013; Rautio, Heikkinen, & Ebrahim, 2005; Russo et al., 2006; Stringhini et al., 2018). For example, Stringhini et al. (2018) showed that adult SES is as strong and consistent a risk factor for physical function in old age as several established non-communicable disease risk factors (e.g., smoking, obesity, physical inactivity, and diabetes).

    View all citing articles on Scopus
    View full text