Health inequalities in the older population: the role of personal capital, social resources and socio-economic circumstances

Social Science & Medicine 56 (2003) 935–947

Health inequalities in the older population: the role of personalcapital, social resources and socio-economic circumstances

Emily Grundy*, Andy Sloggett

Centre for Population Studies, London School of Hygiene and Tropical Medicine, 49-51 Bedford Square, London WC1B 3DP, UK

Abstract

Older people now constitute the majority of those with health problems in developed countries so an understanding

of health variations in later life is increasingly important. In this paper, we use data from three rounds of the Health

Survey for England, a large nationally representative sample, to analyse variations in the health of adults aged 65–84 by

indicators of attributes acquired in childhood and young adulthood, termed personal capital; and by current social

resources and current socio-economic circumstances, while controlling for smoking behaviour and age. We used six

indicators of health status in the analysis, four based on self-reports and two based on nurse collected data, which we

hypothesised would identify different dimensions of health. Results showed that socio-economic indicators, particularly

receipt of income support (a marker of poverty) were most consistently associated with raised odds of poor health

outcomes. Associations between marital status and health were in some cases not in the expected direction. This may

reflect bias arising from exclusion of the institutional population (although among those under 85 the proportion in

institutions is very low) but merits further investigation, especially as the marital status composition of the older

population is changing.

Analysis of deviance showed that social resources (marital status and social support) had the greatest effect on the

indicator of psychological health (GHQ) and also contributed significantly to variation in self-rated health, but among

women not to variation in taking three or more medicines and among men not to self-reported long-standing illnesses.

Smoking, in contrast, was much more strongly associated with these indicators than with self-rated health. These results

are consistent with the view that self-rated health may provide a holistic indicator of health in the sense of well-being,

whereas measures such as taking prescribed medications may be more indicative of specific morbidities. The

results emphasise again the need to consider both socio-economic and socio-psychological influences on later life health.

r 2002 Elsevier Science Ltd. All rights reserved.

Keywords: Older people; Health status; Social support; Marital status; Inequalities in health

Introduction

The increased representation of older people in the

population of Britain and other developed countries,

coupled with epidemiological changes which mean that

older people constitute a large majority of those in poor

health, has led to a growing concern with identifying

determinants of health, and inequalities in health, in

later life (Acheson, 1998; Department of Health, 1999).

Variations in the mortality and morbidity of the elderly

population by indicators of socio-economic status based

on past occupation, education, housing tenure, income

and wealth have been reported in a wide range of studies

with those in the most socio-economically disadvan-

taged groups also suffering the greatest health disad-

vantage (Fox, Goldblatt, & Jones, 1985; Arber & Ginn,

1993; Menchik, 1993; Martelin, 1994; Elo & Preston,

1996; Marmot & Shipley, 1996; Rogers, 1996; Sundquist

& Johansson, 1997; Grundy & Glaser, 1999; Grundy &

Holt, 2000). In general, associations between health and

socio-economic characteristics seem less pronounced

than in young or middle-aged groups, but because

*Corresponding author. Tel.: +44-20-7299-4668; fax: +44-

20-7299-4637.

E-mail address: [email protected] (E. Grundy).

0277-9536/03/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved.

PII: S 0 2 7 7 - 9 5 3 6 ( 0 2 ) 0 0 0 9 3 - X

morbidity and mortality is concentrated in elderly age

groups, attributable differences are greater.

A substantial and to some extent parallel literature,

has shown that socio-demographic and social-psycho-

logical characteristics such as marital status, household

composition and social support are also associated with

differentials in health and mortality in older age groups.

In general, married people have the best health, followed

by the never married and then the formerly married.

Hypothesised reasons for these associations include both

selection factors—good health increases the chances of

marrying (including remarrying) and remaining married

for longer—and the protective effects of care and

support (Verbrugge, 1979; Hu & Goldman, 1990;

Umberson, 1992; Hahn, 1993; Gliksman, Lazarus,

Wilson, & Leeder, 1975; Waite, 1995; Cheung, 2000).

Although these latter effects might be supposed to be

particularly important in older age groups, some studies

suggest a weaker, or even reversed, relationship between

health and marriage with increasing age. Goldman,

Korenman and Weinstein (1995), for example, found

that never-married older women had better health

outcomes than their married counterparts, a result they

attributed to more extensive social ties built up over the

lifetime as an alternative to marriage. However, their

analysis was based on a sample that excluded the

institutional population, which, as the unmarried are

over-represented in institutions, may have biased results.

Analyses of British data on differentials in limiting long

standing illness which included the whole population

have shown a continuing, although weaker, advantage

for the married, even in the oldest age groups (Murphy,

Glaser, & Grundy, 1997).

Associations between other indicators of social con-

nectedness, including social network size and character-

istics and church or club membership, and both

mortality and other health indicators have also been

reported (Blazer, 1982; Seeman, Kaplan, Knudsen,

Cohen, & Guralnik, 1987; House & Landis, 1988;

Grundy, Bowling, & Farquhar, 1996; Sugisawa, Liang,

& Liu, 1994). However, in some studies effects appear

much weaker, or non-existent, in older than in younger

groups, possibly because of inconsistencies in the

measures of health and social support used and the

relatively small numbers of older people included in

some analyses (Orth-Gomer & Unden, 1987; O’Reilly,

1988; Bowling & Grundy, 1998).

A wide range of studies thus show variations in the

health of elderly people according to differences in

socio-economic circumstances and socio-demographic

or socio-psychological characteristics, although in both

cases variations seem less marked than in younger age

groups. These domains, although often considered

separately (Preston & Taubman, 1994), are clearly

intertwined in several ways. It is known, for example,

that there are social class differences in patterns of social

interaction and in marriage and divorce patterns (House

& Landis, 1988; Ben-Shlomo, Smith, Shipley, &

Marmot, 1993; Schoeni, 1995; Stansfield, 1999). Among

elderly women, in particular, being married is associated

with indicators of economic advantage such as income

and housing tenure (Hahn, 1993; Murphy et al., 1997).

Apart from these interrelationships, there may be

factors which influence both socio-economic and socio-

demographic circumstances at older ages and exert an

influence on health, such as legacies from earlier life.

Attributes present or acquired in childhood may have an

important and lasting effect on life chances, health

behaviours and coping mechanisms, and so on health,

throughout the life course, as well as exerting a strong

influence on adult socio-economic and socio-demo-

graphic experiences and later life circumstances (Barker,

1992; Bartley, Blane, & Montgomery, 1999; Bosma, van

de Mheen, & Mackenbach, 1999; Brunner, Shipley,

Blane, Smith, & Marmot, 1999; Davey Smith, Hart,

Blane, Gillis, & Hawthorne, 1997; Wadsworth, 1997).

Education, for example, may increase feelings of

personal control and promote better health behaviours

as well as providing a route to higher status well paid

occupations and so to accumulated wealth and better

pensions in later life (Bosma, Schrijvers, & Mackenbach,

1999). Height, often used as an indicator of childhood

circumstances and development, is associated with both

adult social class and with the marriage chances of men,

as well as with health (Blane et al., 1996; Kuh &

Wadsworth, 1989; Murray, 2000; Phillips et al., 2001).

As well as common, or overlapping, pathways to

particular health, socio-economic and socio-demo-

graphic statuses in adult life, there may also be common

mechanisms whereby socio-economic and socio-demo-

graphic or psychological characteristics influence health.

Most obviously, social class, education and housing

tenure are all strongly associated with smoking (Blaxter,

1990; Bennett, Dodd, Flatley, Freeth, & Bolling, 1995);

so too is marital status and, among elderly men,

household type (Umberson, 1992). Smoking is clearly

not the only explanation for variations in health as

persistent socio-economic inequalities in mortality and

morbidity in the whole adult population are found even

when smoking is allowed for (Blaxter, 1990; Suadicani,

Hein, & Gyntelberg, 1994). Similarly, studies have

reported associations between social and blood pressure

after adjustment for smoking, exercise, alcohol use and

body mass index (Hanson, Isacsson, Janzon, Lindell, &

Rastam, 1988).

Synergistic or offsetting interactions between econom-

ic and socio-psychological domains may also be

important (Roberts, Dunkle, & Haug, 1994). Material

advantages not only enable the purchase of better food

and housing, but also the purchase of services that may

preserve feelings of control and autonomy and enable

social participation, all factors hypothesised to have

E. Grundy, A. Sloggett / Social Science & Medicine 56 (2003) 935–947936

important influences on health behaviours and physio-

logical functions (Seeman, 2000). Supportive networks

may buffer the effects of stress, including socio-

economic stress, and enhance the operation of immu-

nological functions, as well as being a potential source of

practical help and advice and so an alternative to

purchased assistance (Berkman, Leo-Summers, & Hor-

owitz, 1992; Uchino, Cacioppo, & Kiecolt-Glaser,

1996). Conversely, the combination of poor socio-

economic and poor socio-psychological circumstances

may be particularly harmful (Ben-Shlomo et al., 1993;

Martikainen & Valkonen, 1998).

For all these reasons it would seem essential to

consider both socio-economic and socio-demographic

factors in analyses of health differentials in later life and

preferably also indicators of childhood legacies and

health behaviours. This type of analysis is, however,

relatively unusual, partly due to data limitations

(Preston & Taubman, 1994). Here we use data from a

large nationally representative sample of the older

population of England to investigate the effect of

attributes acquired in childhood and young adulthood,

which we term personal capital, current social resources

and current socio-economic circumstances on health

variation in later life, while also controlling for smoking

behaviour, one of the mechanisms whereby the other

domains may influence health. The personal capital

variables used were height and educational qualifica-

tions. Educational qualifications may be gained in

adulthood, but in the cohorts with which we are

concerned this was relatively unusual and highest

qualification obtained is a good indicator of educational

experiences and outcomes in childhood and early

adulthood, themselves strongly influenced by social

class of origin (White, Blane, Morris, & Mourouga,

1999).

As already noted there is an extensive literature on

links between marital status and health, although still

some uncertainty as to whether associations are

attenuated, or even reversed in older age groups. We

used this, together with a variable measuring perceived

social support, as indicators of social resources.

Housing tenure has been shown in numerous studies

to be associated with other indicators of socio-economic

status, such as income and social class, and to be

strongly associated with differentials in health (Fox

et al., 1985). It has the advantage, in comparison with

occupationally based measures, of relating to current

material circumstances and applying equally to men and

women (Arber & Ginn, 1993; Grundy & Holt, 2001). We

used this as an indicator of socio-economic circum-

stances together with receipt of income support, a means

tested benefit paid to those on low incomes.

Our aims were to quantify the extent of inequalities in

the health of older people, see which of the broad

domains considered was most strongly associated with

health in later life, and investigate possible interactive

effects of socio-economic and socio-demographic dis-

advantage on health in later life. Health, whether

conceptualised negatively as the absence of disease,

positively as a complete state of well-being, or norma-

tively as the average, is a multidimensional concept that

is difficult to measure (Ware, Allyson, & Robert, 1980).

It is well recognised, for example, that self-reported and

observational measures produce different results,

although which gives a better indicator of ‘true’ health

status remains a matter of debate. Several analysts have

concluded that self-reported and directly measured

indicators of physical function represent different

dimensions of health (Guralnik, Branch, Cummings, &

Curb, 1989; Merrill, Seeman, Kasl, & Berkman, 1997).

In this study we use a range of six indicators of health

status, four based on self-reports and two based on

nurse collected data, which we hypothesised would

identify different dimensions of health. We aimed to

investigate both associations between the explanatory

domains and these outcome indicators and whether the

explanatory variables had consistent or varying effects

on different indicators of health status.

Data and methods

The data we use come from the 1993–95 rounds of the

Health Survey for England (HSfE) (Bennett et al., 1995).

We chose this study because it is nationally representa-

tive, includes a range of indicators of health status,

together with information on health-related behaviours

and socio-demographic and socio-economic character-

istics, and has a large enough sample size, when, as here,

3 years data are combined to allow detailed analysis.

Data in the HSfE are collected through a questionnaire,

nearly all interviewer administered, and a second nurse

visit during which prescribed medicines are counted and

blood pressure taken. Nurses also took blood samples

but blood analyte data were unavailable for over 30% of

elderly respondents and so are not used here.

Co-variates used in the analysis

Height was measured by interviewers using a portable

stadiometer. As height varies by cohort, age and sex, the

indicator of height we derived for our analyses was

tertile of the height distribution for those of the same

gender and 10-year age group. Those for whom a

measurement of height was lacking were identified

separately. As a large proportion of today’s elderly

population have no formal educational qualifications,

we distinguished only two qualification groups, those

with and those without an educational qualification of

any kind. The size of the sample meant that we were able

to use a fourfold classification of marital status

E. Grundy, A. Sloggett / Social Science & Medicine 56 (2003) 935–947 937

(currently married or cohabiting; never married; wi-

dowed; divorced or separated) rather than amalgamat-

ing the unmarried groups. The perceived social support

measure used in the HSfE was developed for the Health

and Lifestyle Survey and was based on seven questions

on support and encouragement from family and friends,

each with three possible responses. Responses were

combined into a single scale ranging from 0 to 21. We

followed developers of the scale in categorising those

with scores of 21 as having no lack of social support,

those with scores of 18–20 as having some lack of social

support and those with scores of less than 18 as having a

severe lack of social support (Blaxter, 1990; Bennett

et al., 1995). Three tenure groups were used in the

analysis, owner–occupiers, tenants of local authorities or

housing associations, and other tenants. The second

socio-economic indicator used distinguished recipients

of income support from non-recipients.

Indicators of health status

The indicators of health status we analyse comprised

four based on self-reports and two based on nurse

collected data. The self-reported measures were presence

of a long-standing illness; number of specific long-

standing conditions (with those with no long-standing

illness coded as zero), and self-reported general health

(dichotomised into ‘bad’ or’ very bad’ rather than ‘very

good’, ‘good’ or ‘fair’). The fourth measure was self-

completed score on the General Health Questionnaire

(12-item version); a well validated instrument for

measuring minor psychiatric morbidity. In line with

accepted practice we dichotomised responses into those

of four or above, a threshold taken to indicate

‘caseness’, and those with lower scores (Goldberg &

Williams, 1988).

The observational indicators were high blood pressure

(systolic blood pressure >159mm Hg or diastolic blood

pressure >94 mm Hg, or taking anti-hypertensive

medication) and number of prescribed medications

being taken. These data were collected during the nurse

visit that followed administration of the main ques-

tionnaire. Nurses measured blood pressure using a

Dinamap 8100 monitor and recorded information on

all medications being taken. Number of medications is

here dichotomised into 0–2 and 3 and more.

The Health Survey for England includes only those in

private households. Some 20% of those aged 85 and

over live in institutions, and as entry to an institution is

strongly associated with health, with marital status and,

less strongly, with housing tenure (Grundy & Glaser,

1997), analysis of differentials in the health of the oldest

old by these characteristics will be biased if based on

samples, like the HSfE, which exclude the institutional

population. Our concerns about this were compounded

by the extent of information missing for those aged 85

and over who were included in the survey (20% had no

height measurement; 26% no count of medicines and

46% no blood pressure measurement). We therefore

restricted our analyses to those aged 65–84. In this

broad age group only 2% of men and 4% of women

lived in institutions in 1991 (Grundy, Glaser, & Murphy,

2000).

Response rates and missing data

Response rates to the HsfE, reported in detail

elsewhere, are good and exceed 80% in elderly age

groups (Bennett et al., 1995). However, some of the

variables we used were based on measurements which

were lacking for some of the sample. This is shown in

Table 1 which gives details of the proportions with

missing information on height (measured by inter-

viewers during the first visit); number of medications

and blood pressure (measured by the nurse during the

second visit) and GHQ (measured using a self-comple-

tion booklet included in the main interview) together

with the proportion of respondents and non-respon-

dents to these items who reported their health as bad or

very bad. The proportions with missing blood pressure

measurements were quite high and those lacking this

measure included an over representation of people

Table 1

Percentage of sample with missing measurements and prevalence (%) of self-reported bad or very bad health by whether a measure was

recorded or unrecorded

Age Blood pressure Height Medicines GHQ

Missing

(%)

Bad health (%) Missing

(%)


(%)


(%)

Bad health (%)

Rec. Unrec. Rec. Unrec. Rec. Unrec. Rec. Unrec.

65–9 25 7 14 4 8 27 15 13 8 4 8 26

70–4 27 7 13 5 8 24 15 10 10 4 8 21

75–9 29 9 15 6 9 39 16 14 11 6 9 19

80–4 38 10 16 11 10 35 19 15 12 7 11 23


reporting their health as bad or very bad. The

proportions with missing height or GHQ information

were much lower but, particularly for height, those

without a measurement were much more likely than

those with one to report bad or very bad health.

Information on medicines was missing for some 17% of

the sample, but there was no difference between those

with and without this measure in reporting of bad or

very bad health. Here, those with missing height data

have been included and are shown as a separate group.

Those with missing outcome data have had to be

excluded from analyses of that outcome.

We first calculated the prevalence of each indicator of

health according to our indicators of personal capital,

social resources and socio-economic circumstances using

linear regression to adjust for age and survey year. In

subsequent multivariate analyses we used logistic

regression to estimate odds of poor outcomes in models

including all the variables, together with age group and

survey year. Analysis of deviance was used to identify

which domain was most strongly associated with

variation in the outcome variables. Appendix A shows

the distribution of the sample by variables used in the

analysis (except height which, as explained above, was

coded into tertiles).

Results

Fig. 1 shows the age-adjusted prevalence of self-

reported bad or very bad health according to the pairs

of variables representing the three domains of interest.

Among both men and women a higher proportion of

those with no educational qualifications rated their

health bad or very bad and among men and, to a lesser

extent women, there was a gradient in prevalence with

height (Fig. 1, Panel a). Short men and women with no

qualifications were twice as likely to report poor health

as their tall counterparts with qualifications. The

prevalence of bad health was very high among those

with no measure of height, a group which would include

the chairbound and those too frail to stand unaided.

0

5

10

15

20

25

30

35

Owner occ Priv. Tenant LA tenant Owner occ Priv. Tenant LA tenant

Housing tenure

(c) by housing tenure and receipt of income support

No inc. support Has inc. support

Men Women

0

5

10

15

20

25

30

35

%%

Married Single WidowedDivorced Married Single Widowed Divorced

Marital status

(b) by marital status and perceived social support

No lack Some or severe lack

WomenMen

0

5

10

15

20

25

30

35

Tall Mid Short Unmeasured Tall Mid Short Unmeasured

Height tercile

(a) by education and height

Has educ. Qual. No quals.

Men Women

(a) (b)

(c)

Fig. 1. Age adjusted prevalence of self-reported bad or very bad health among men and women aged 65–84: (a) by education and

height; (b) by marital status and perceived social support; (c) by housing tenure and receipt of income support.


Among men differentials by marital status, shown in

the second panel of the figure, were as expected with the

highest prevalence of bad health among the divorced

and the lowest among the married. Among women,

however, the proportion reporting bad or very bad

health was lowest among single women and highest

among the divorced. For both men and women in all

marital status categories those reporting a lack of social

support had worse health on this indicator, but

differences were not large. Socio-economic differences

in bad health, shown in the third panel, were substantial.

More tenants than owner–occupiers reported bad health

and prevalence rates were markedly raised among those

in receipt of income support.

Parallel analyses of variations in the other five

indicators of health status were undertaken but, in the

interests of brevity, are not presented here. Results were

similar to those shown in Fig. 1, although there were

some differences in their magnitude and ordering. There

was less variation in the prevalence of hypertension than

in the other indicators, especially the self-reported ones.

For example among men the prevalence of hypertension

by marital status and social support ranged from 50%

among married men with no lack of social support to

58% among widowers with some or a severe lack of

support. Variation in the prevalence of GHQ scores of

four or more was much greater (8% among married men

with no lack of social support compared with 24%

among less well-supported widowers).

This descriptive analysis showed that all the variables

chosen to represent important domains of life were

associated with the health indicators considered, to

varying degrees. There were of course strong correla-

tions between the co-variates considered. Receipt of

income support, for example, was associated with

educational qualifications, housing tenure and marital

status as well as with age and gender and perceived

social support was associated with marital status,

particularly for men.

Logistic regression analysis was undertaken to esti-

mate the effect of each variable on the outcomes

considered, after adjustment for the other co-variates,

including 5 year age group, smoking status and survey

year. Initial analyses showed that unmeasured height

was, as might be expected, strongly associated with

significantly higher odds of several of the six health

indicators used. However, measured height was only

significantly associated with one indicator—high blood

pressure—among women and among men with none of

the health indicators. We therefore decided to drop this

variable and the results presented below thus include

only one variable (education) to represent the personal

capital domain. We included a control for a number of

physical conditions reported in the model of GHQ

because of known strong associations between physical

and mental health.

Results from these models for men, presented in

Table 2, show lack of an educational qualification was

associated with GHQ scores above three and with taking

three or more medicines. Widowers had significantly

higher odds of bad health, taking three or more

medicines, high blood pressure and psychiatric morbid-

ity than married men (the reference group). Men

reporting some lack of social support had higher risks

of a poor GHQ score. Local authority tenure was

associated with higher odds of reporting bad health and

with GHQ. Receipt of income support was associated

with raised odds of five of the six indicators, the

exception being raised blood pressure. Of the control

variables, older age was associated with higher odds of

taking three or more medicines and former smoking

habit with all indicators except blood pressure and

GHQ.

Results for women presented in Table 3 show that

education was associated with three of the six indicators.

Lack of social support was positively associated with

GHQ and with bad health, less strongly with reported

long-standing illness and negatively with raised blood

pressure. The marital status results suggest, after control

for all the other variables in the model, no advantage for

the currently married. Single and widowed women had

lower odds of reporting bad or very bad health, single

women also had lower odds of a raised GHQ score and

of reporting two or more conditions. Divorced women

had a reduced risk of taking three or more medicines.

Local authority tenants and those in receipt of income

support had raised odds of five out of six of the health

indicators considered.

One of our research questions was whether disadvan-

tage in one sphere compounded disadvantage in another

and whether strengths in, for example socio-economic

circumstances, could compensate for weaknesses in

social resources. In order to analyse this we tested for

interactions in all the models fitted. However, for neither

men nor women did we find any interactions that were

statistically significant.

These results show that the indicators of personal

capital, social resources and socio-economic resources

were associated to varying degrees with higher odds of

poor health in models including all co-variates. How-

ever, some of the marital status associations were not in

the hypothesised direction. The co-variates most con-

sistently associated with higher odds of poor health

indicators were receipt of income support, local author-

ity tenure and some lack of social support. The patterns

of associations varied considerably depending on which

health indicator was considered. Thus there were more

significant associations between the explanatory vari-

ables considered and proportions in bad or very bad

health and proportions with GHQ scores of 4 or more

than with the other two indicators based on self-

reported information. On this criteria taking more than


three medicines appears more sensitive than the other

nurse measured indicator, high blood pressure, although

the extent of missing blood pressure measurements may

be an influence on this.

We also wished to examine the contribution of each

of these domains, and of smoking, to the overall

variation in health in this sample of elderly people. We

therefore undertook an analysis of the contribution of

each to reductions in the deviance (variation) in the

sample. Results, presented in Table 4, show that all the

domains, and smoking contributed significantly to

reductions in the deviance when variations in bad or

very bad health were modelled. The contribution of the

socio-economic resources indicator was particularly

strong, whereas the contribution of the smoking

variable, although statistically significant, was weaker

than that of the three other domains. When long-

standing illness was taken as an outcome, however,

among men only smoking contributed significantly to

reductions in the deviance and among both men and

women the education variable was not significantly

associated with a reduction in deviance in either long-

standing illness or reporting of two more conditions.

When taking more than three medicines was used as an

outcome, the contributions of education and socio-

economic resources were significant but less important

than in the models fitted to self-reported health. The

effect of smoking however, was, much more important.

Among women the social resources domain did not

contribute significantly to improving the fit of this

model. In the analysis of hypertension smoking and

social resources were both associated with significant

(but not large) reductions in the deviance for men, while

among women significant reductions were associated

with education and socio-economic resources. Social

resources were, for both men and women, the most

important domain associated with reductions in the

deviance for GHQ.

Table 2

Odds ratios from logistic regression models of indicators of poor health, men aged 65–84

Co-variates Bad/very bad

health

Long-

standing

illness

2+Conditions 3+Medicines High blood

pressure

Psychiatric

morbidity

(GHQ 4+)

Age 70–4 0.88 1.13 1.19 1.25* 1.12 1.10

70–5 0.86 1.07 1.24* 1.49** 1.17 1.18

80–4 0.96 0.83 1.07 1.44* 1.10 1.58*

Smoking Ex 1.49* 1.19 1.32** 1.47** 1.17 1.05

Current 1.29 0.77* 1.07 0.82 0.85 1.07

Number

physical

conditions

— — — — — 1.71***

Personal capital

Education No quals 1.26 0.98 1.06 1.31** 1.07 1.41**

Social resources

Marital status Single 0.92 0.88 0.89 0.86 1.69** 0.75

Widowed 1.41* 1.13 1.14 1.37** 1.30* 1.73***

Div./sep 1.44 1.19 1.18 1.16 1.19 1.40

Social support Some lack 0.90 0.95 1.18 1.32** 1.03 1.37**

Severe lack 1.10 1.11 1.17 0.98 1.04 1.91***

Socio economic resources

Housing tenure LA tenant 2.00*** 1.14 1.11 1.17 1.01 1.34*

Other tenant 1.42 0.97 0.81 0.75 0.96 1.22

Income support Receives 2.87*** 1.35* 1.38** 1.52** 1.19 1.83***

N 3706 3708 3701 3244 2760 3608

Reference categories: age 65–9; never smoked; (no physical conditions, GHQ model only); has a qualification; married; no lack of

social support; owner–occupier; not in receipt of income support.

***Po0:001; **Po0:01; *Po0:05:


Discussion

In this paper we have used data from a large

nationally representative sample to analyse differentials

in the health of older adults according to variables

selected to represent personal capital, social resources

and socio-economic resources. Unlike many analyses

which have considered only one outcome, we analysed

variations in six indicators of health status including

both self-reported and nurse-measured variables. We

also controlled for smoking status as well as for age.

Our initial analyses showed that all the variables we

selected as indicative of the domains we wanted to

consider were associated with at least some of our

indicators of health status. In multivariate analyses

socio-economic indicators, particularly receipt of

income support (a marker of poverty) appeared

most consistently associated with raised odds of poor

health outcomes. Of our two indicators of childhood

legacies, measured stature was only associated with

blood pressure in women (which is consistent with

documented physiological associations between stature

and cardiovascular performance) and, was dropped

from the multivariate analysis. This does not, of course,

mean that legacies from childhood are unimportant in

explaining health differentials in later life and indeed we

found associations between educational qualification,

our second indicator of personal capital, and several

health indicators. Our analysis was limited by the fact

that other indicators of childhood experience were

unavailable. Moreover, childhood experiences may

influence health in later life indirectly through their

effects on current socio-economic status, which was

taken account of in our analyses (Bosma et al., 1999;

Ross & Mirowsky, 1999).

A surprising finding was of weak or counterintuitive

associations between marital status and health indica-

tors. Some previous studies have also reported better

health among single than married elderly women in

studies, like this one, including only the private house-

Table 3

Odds ratios from logistic regression models of indicators of poor health, women aged 65–84

Co-variates Bad/very bad

health

Long-

standing

illness

2+Conditions 3+Medicine High blood

pressure

Psychiatric

morbidity

(GHQ 4+)

Age 70–4 1.27 1.01 0.97 1.10 1.24* 1.11

70–5 1.69*** 1.34** 1.18 1.23 1.60** 1.43**

80–4 1.64** 1.50*** 1.19 1.95*** 2.11*** 1.60***

Smoking Ex 1.44** 1.16* 1.18* 1.56*** 0.89 1.08

Current 1.20 0.89 0.74** 0.88 0.82 1.20

No. physical

conditions

— — — — — 1.53***

Personal capital

Education No quals 1.63** 0.91 0.97 1.12 1.35*** 1.35**

Social resources

Marital status Single 0.58* 0.82 0.71* 0.71 0.85 0.45***

Widowed 0.67*** 0.99 1.04 1.02 1.05 1.03

Div./sep 0.93 1.09 1.47* 0.65* 0.81 1.17

Social support Some lack 1.05 1.04 1.12 1.09 0.88 1.38**

Severe lack 1.68*** 1.23* 1.04 1.03 0.79* 2.57***

Socio economic resources

Housing tenure LA tenant 1.84*** 1.39*** 1.57*** 1.35** 1.42*** 1.13

Other tenant 1.16 1.08 1.27* 1.38* 1.30* 1.04

Income support Receives 2.26*** 1.59*** 1.40*** 1.71*** 1.13 1.28*

N 4966 4976 4964 4147 3541 4812

Reference categories: age 65–9; never smoked; (no physical conditions, GHQ model only); medium height; has a qualification;

married; no lack of social support; owner–occupier; not in receipt of income support.

***Po0:001; **Po0:01; *Po0:05:


hold population. We restricted our analyses to those

aged 65–84 in order to reduce bias arising from

differential exclusion as a result of residence in an

institution. In this age group the proportion of people in

institutions, and so excluded from surveys such as the

Health Survey for England, is small. Nevertheless, as

institutional residence is associated with both marital

status and health and 10% of single men and women

Table 4

Analysis of deviance in proportions with poor health by indicator of poor health

Men Women

Model Deviance/change

in deviance

Df/change in df Po Deviance/change

in deviance

Df/change In df Po

(a) Self reported ‘bad’ or ‘very bad’ health

Age group and year 2360 3700 0.001 3086 4960 0.001

+Personal capital �20 �1 0.001 �40 �1 0.001

+Social resources �23 �5 0.001 �24 �5 0.001

+Socio-economic

resources

�87 �3 0.001 �99 �3 0.001

+Smoking �6 �2 0.05 �11 �2 0.005

(b) Long-standing illness

Age group and year 4855 3702 6533 4970

+Personal capital �0 �1 NS �0 �1 NS

+Social resources �7 �5 NS �12 �5 0.05

+Socio-economic

resources

�7 �3 NS �56 �3 0.001

+Smoking �28 �2 0.001 �10 �2 0.05

(c) 2+Conditions


+Personal capital �2 �1 NS �3 �1 NS

+Social resources �10 �5 NS �25 �5 0.001

+Socio-economic

resources

�11 �3 0.05 �59 �3 0.001

+Smoking �11 �2 0.01 �23 �2 0.001

(d) Taking three or more prescribed medicines

Age group and year 3250 3238 0.001 4233 4141 0.001


+Social resources �18 �5 0.01 �7 �5 NS

+Socio-economic

resources

�12 �3 0.05 �49 �3 0.001

+Smoking �31 �2 0.001 �36 �2 0.001

(e) Hypertension


+Personal capital �2 �1 NS �28 �1 0.001

+Social resources �14 �5 0.05 �10 �5 NS

+Socio-economic

resources

�1 �3 NS �19 �3 0.001

+Smoking �12 �2 0.005 �5 �2 NS

(f) GHQ Score of 4+



+Social resources �53 �5 0.001 �87 �5 0.001

+Socio-economic

resources

�23 �3 0.001 �9 �3 0.001

+Smoking �0 �2 NS �3 �2 NS

Df=degrees of freedom.


aged 65–84 were in institutions in 1991 (compared with

2% of all men and 4% of all women), this limitation of

the data may have had some effect on our results

(Grundy et al., 2000). It is also important to remember

that the odds ratios shown in Tables 3 and 4 are those

from the full models, that is after allowance for socio-

economic factors and social support, which have not

been taken into account in many other earlier studies of

links between marital status and health.

Apart from exclusion of the institutional population,

other forms of selection may also influence health

differentials in the older population, most obviously

selective survival. Estimates from the Government

Actuary’s Department show that 69% of men and

78% of women born in 1930 survived to aged 65; of

those born in 1911, the oldest birth cohort represented in

our analysis, only 14% of men and 31% of women

survived to age 84. Results will therefore be influenced

by prior mortality of the unhealthiest. This, together

with the fact that poor health provides a major impetus

for giving up smoking, is likely to account for the

increased odds of poor health outcomes among ex, but

not current, smokers.

We used six indicators of health status in our analysis

and our results have implications both for selection

of the most sensitive indicators to use in surveys

and suggest that pathways to particular indicators

of poor health may vary. On the first count the

self-rated general health indicator appeared to perform

well, particularly in comparison with the widely used

long-standing illness indicator. On the second our

analysis of deviance showed that social resources

contributed significantly to variation in self-rated health,

but among women not to the three or more medicines

indicator (after allowance for personal capital, socio-

economic resources and smoking) and among men not

to long-standing illness or reporting two or more

conditions. Marital status and lack of social support

had the greatest effect on the GHQ variable, which is

consistent with what is known about links between

social support and psychological health. However,

it must be recognised that the social support measure

was based on self-reports and it may well be that

people who are depressed are more likely to perceive

and report that their levels of social support are low

simply because of their mental state. Smoking was

much more strongly associated with the medicines

indicator and with long-standing illness (men) or

reporting two or more conditions (women) than with

the self-rated health variable. These results are consis-

tent with the view that self-rated health measures

provide holistic indicators of health in the sense of well

being, whereas indicators such as taking medicines

may be more indicative of specific morbidities

(Idler & Benyamini, 1997). Factors which influence

these different dimensions of health may not be the

same, just as specific diseases and causes of death

seem to relate to the life course in different ways

(Blane, 1999; Davey Smith, Gunnell, & Ben-Shlomo,

2001).

These results are based on cross sectional data and

clearly longitudinal data would be much more suitable

for unravelling the pathways to good and bad health in

later life. Currently, large, nationally representative

longitudinal data on the older population are not

available in Britain, a gap the new English Longitudinal

Study of Ageing (based on a follow up of the 2000

HSfE) is designed to fill. Other limitations of the study

not already referred to include the rather restricted

choice of indicators available to represent personal

capital and social resources and lack of information

about social capital and other area characteristics which

may also influence health. Additionally, as already

noted, perceptions of social support may be influenced

by psychological health, and both physical and psycho-

logical health may influence social participation and

opportunities for developing and maintaining the social

networks from which social support is drawn. In this

analysis it is not possible to identify causal pathways

between these variables.

Despite these limitations, these results, based on a

large nationally representative sample and including

both observational and self-reported indicators of

health, extend our knowledge in several ways. Firstly,

our results demonstrate unequivocally the strong link

between poverty in old age and poor health, even after

control for smoking and other relevant factors. Sec-

ondly, the increased odds of psychiatric morbidity

among those with no educational qualifications suggests

a link between early life circumstances and development,

perhaps including development of coping strategies, and

psychological well-being in later life. A third finding of

interest was the lack of health advantage for married

older people, particularly among women, and, again

among women, the possible advantage of the never

married. The effects of marital status and history on

health and support needs in later life are a very

important issue for the future because of large differ-

ences in the marital experiences of cohorts born in the

inter-war and immediate post-war period and those

born from the mid 1950s onward (Grundy, 1996).

Further work on the selective and protective effects of

marriage and development of alternative sources of

support would therefore seem important. These results

also indicate the complexity of measuring health status

in later life and the possibly differing influences on

various types of health indictor. Finally, our analyses

emphasise again the need to consider both socio-

economic and socio-psychological influences on health

and health inequalities and the need for both further

research and policy initiatives to reduce the extent of

health inequalities in later life.


Acknowledgements

This research was supported by the Economic and

Social Research council, grant reference number L128

251040, as part of its Health Variations (Phase II)

Programme. Gemma Holt provided valuable assistance

with data management and preparation in the early

stages of the work reported here.

Appendix

See (Table 5).

References

Acheson, D. (1998). Independent inquiry into inequalities in

health report. London: The Stationery Office.

Arber, S., & Ginn, J. (1993). Gender and inequalities in health

in later life. Social Science & Medicine, 36, 33–46.

Barker, D. J. P. (1992). Fetal and infant origins of adult diseases.

London: British Medical Journal Publications.

Bartley, M., Blane, D., & Montgomery, S. (1997). Socio-

economic determinants of health: Health and the life course:

Why safety nets matter. British Medical Journal, 314,

1194–1198.

Ben-Shlomo, Y., Smith, G. D., Shipley, M., & Marmot, M. G.

(1993). Magnitude and cause of mortality differences

between married and unmarried men. Journal of Epidemiol-

ogy and Community Health, 47, 200–205.

Bennett, N., Dodd, T., Flatley, J., Freeth, S., & Bolling, K.

(1995). Health survey for England 1993. London: HMSO.

Berkman, L. F., Leo-Summers, L., & Horowitz, R. I. (1992).

Emotional support and survival after myocardial infarction.

Annals of Internal Medicine, 117, 1003–1009.

Blane, D. (1999). The life course, the social gradient, and

health. In M. Marmot, & R. G. Wilkinson (Eds.), Social

determinants of health (pp. 64–80). Oxford: Oxford

University Press.

Blane, D., Hart, C. L., Smith, G. D., Gillis, C. R., Hole, D. J.,

& Hawthorne, V. M. (1996). Association of cardiovascular

disease risk factors with socioeconomic position during

childhood and during adulthood. British Medical Journal,

313, 1434–1438.

Blaxter, M. (1990). Health and lifestyles. London: Tavistock/

Routledge.

Blazer, D. G. (1982). Social support and mortality in an elderly

community population. American Journal of Epidemiology,

115, 684–694.

Bosma, H., van de Mheen, D. H., & Mackenbach, J. (1999).

Social class in childhood and general health in adulthood:

Questionnaire study of contribution of psychological

attributes. British Medical Journal, 318(7175), 18–22.

Bosma, H., Schrijvers, C., & Mackenbach, P. (1999). Socio-

economic inequalities in mortality and importance of

Table 5

Percent distribution of the sample by variables used in the analysis, by broad age group and sex

Men Women

65–74 75–84 65–74 75–84

Smoking status Never 19.0 22.3 47.3 57.4

Ex 53.5 55.4 34.1 30.6

Current 27.5 22.3 18.6 12.0

Education Qualification 42.0 39.2 28.9 22.2

No qualification 58.0 60.8 71.1 77.8

Tenure Owner–occupier 71.8 64.8 67.6 58.4

Private tenant 7.0 10.8 7.7 13.8

LA tenant 21.2 24.4 24.7 27.8

Receipt of income support No 92.0 87.5 86.8 76.8

Yes 8.0 12.5 13.2 23.2

Marital status Married 76.5 68.2 55.4 28.7

Single 6.8 5.0 5.8 7.4

Widowed 12.3 24.1 33.3 61.3

Div./sep. 4.5 2.7 5.4 2.7

Perceived social support No lack 52.4 53.7 61.6 61.9

Some lack 29.2 28.2 25.7 24.4

Severe lack 18.4 18.1 12.7 13.6

N (=100%) 2694 1213 3271 1951


perceived control: Cohort study. British Medical Journal,

319, 1469–1470.

Bowling, A., & Grundy, E. (1998). The association between

social networks and mortality in later life. Reviews in

Clinical Gerontology, 8, 353–361.

Brunner, E., Shipley, M. J., Blane, D., Smith, G. D., &

Marmot, M. G. (1999). When does cardiovascular risk

start? Past and present socio economic circumstances and

risk factors in adulthood. Journal of Epidemiology and

Community Health, 53, 695–757.

Cheung, Y. B. (2000). Marital status and mortality in British

women: A longitudinal study. International Journal of

Epidemiology, 29, 93–99.

Davey Smith, G., Gunnell, D., & Ben-Shlomo, Y. (2001). Life-

course approaches to socio-economic differentials in cause-

specific adult mortality. In D. Leon, & G. Walt (Eds.),

Poverty, inequality and health (pp. 88–124). Oxford: Oxford

University Press.

Davey Smith, G., Hart, C., Blane, D., Gillis, C., & Hawthorne,

V. (1997). Lifetime socioeconomic position and mortality:

Prospective observational study. British Medical Journal,

314, 547–550.

Department of Health, 1999. NHS R&D Strategic Review,

Ageing and age associated diseases and disability. Depart-

ment of Health, London.

Elo, I. T., & Preston, S. H. (1996). Educational differentials

in mortality, United States, 1979–85. Social Science &

Medicine, 42, 47–57.

Fox, A. J., Goldblatt, P. O., & Jones, D. R. (1985). Social class

mortality differentials: Artefact, selection or life circum-

stances? Journal of Epidemiology and Community Health, 39,

1–8.

Gliksman, M. D., Lazarus, R., Wilson, A., & Leeder, S. (1995).

Social support, marital status and living arrangement

correlates of cardiovascular disease risk factors in the

elderly. Social Science & Medicine, 40(6), 811–814.

Goldberg, D. P., & Williams, P. (1988). A user’s guide to the

general health questionnaire. Windsor: NFER-Nelson.

Goldman, N., Korenman, S., & Weinstein, R. (1995). Marital

status and health among the elderly. Social Science &

Medicine, 40, 1717–1730.

Grundy, E. (1996). Population review: The population aged 60

and over. Population Trends, 84, 14–20.

Grundy, E., & Glaser, K. (1997). Trends in, and transitions to,

institutional residence among older people in England and

Wales, 1971 to 1991. Journal of Epidemiology and Commu-

nity Health, 51, 531–540.

Grundy, E., & Glaser, K. (1999). Differentials in the onset and

progression of disability in early old age in Great Britain.

Age and Ageing, 29, 149–157.

Grundy, E., & Holt, G. (2000). Adult life experiences and

health in early old age in Great Britain. Social Science &

Medicine, 51, 1061–1074.

Grundy, E., & Holt, G. (2001). The socio-economic status of

older adults: How should we measure it in studies of health

inequalities? Journal of Epidemiology and Community

Health, 55, 895–904.

Grundy, E., Bowling, A., & Farquhar, M. (1996). Social

support, life satisfaction and survival at older ages. In G.

Casselli, & A. Lopez (Eds.), Health and mortality among

elderly populations (pp. 135–156). Oxford: Clarendon Press.

Grundy, E., Glaser, K., & Murphy, M. (2000). The importance

of institutional populations in analyses of health in later life.

In A. Dale (Ed.), Analyzing census microdata (pp. 188–195).

London: Arnold.

Guralnik, J., Branch, L., Cummings, S., & Curb, J. (1989).

Physical performance measures in aging research. Journal of

Gerontology, 44, M141–146.

Hahn, B. A. (1993). Marital status and women’s health: Effect

of economic and marital acquisitions. Journal of Marriage

and the Family, 55, 495–504.

Hanson, B. S., Isacsson, S., Janzon, L., Lindell, S., & Rastam,

L. (1988). Social anchorage and blood pressure in elderly

men: A population study. Journal of Hypertension, 6,

503–510.

House, J. S., & Landis, K. L. (1988). Social relationships and

health. Science, 241, 540–545.

Hu, Y., & Goldman, N. (1990). Mortality differentials by

marital status: An international comparison. Demography,

27(2), 233–250.

Idler, E. L., & Benyamini, Y. (1997). Self-rated health and

mortality: A review of twenty-seven community studies.

Journal of Health and Social Behaviour, 38, 21–37.

Kuh, D., & Wadsworth, M. (1989). Parental height: Childhood

environment and subsequent adult height in a national birth

cohort. International Journal of Epidemiology, 18, 663–668.

Marmot, M. G., & Shipley, M. J. (1996). Do socioeconomic

differences in mortality persist after retirement? 25 Year

follow up of civil servants from the first Whitehall study.

British Medical Journal, 313, 1177–1180.

Martelin, T. (1994). Mortality by indicators of socio-economic

status among the Finnish elderly. Social Science & Medicine,

38(9), 1257–1278.

Martikainen, P., & Valkonen, T. (1998). Do education and

income buffer the effects of death of spouse on mortality?

Epidemiology, 9, 530–534.

Menchik, P. L. (1993). Economic status as a determinant of

mortality among black and white older men: Does poverty

kill? Population Studies, 47, 427–436.

Merrill, S., Seeman, T., Kasl, S., & Berkman, L. (1997). Gender

differences in the comparison of self-reported disability and

performance measures. Journal of Gerontology, 52, M19–26.

Murphy, M., Glaser, K., & Grundy, E. (1997). Marital status

and long-term illness in Britain. Journal of Marriage and the

Family, 59, 156–164.

Murray, J. E. (2000). Marital protection and marital selection:

Evidence from a historical-prospective sample of American

men. Demography, 37, 511–521.

O’Reilly, P. (1988). Methodological issues in social support and

social network research. Social Science & Medicine, 26(8),

863–873.

Orth-Gomer, K., & Unden, A. L. (1987). The measurement of

social support in population surveys. Social Science &

Medicine, 24(1), 83–94.

Phillips, D. I. W., Handelsman, D. J., Eriksson, J. G., Forsten,

T., Osmond, C., & Barker, D. J. (2001). Prenatal growth

and subsequent marital status: Longitudinal study. British

Medical Journal, 322, 771.

Preston, S. H., & Taubman, P. (1994). Socioeconomic

differences in adult mortality and health status. In L. G.

Martin, & S. H. Preston (Eds.), The demography of aging

(pp. 279–318). Washington, DC: National Academy Press.


Roberts, B. L., Dunkle, R., & Haug, M. (1994). Physical,

psychological and social resources as moderators of the

relationship of stress to mental health of the very old.

Journal of Gerontology, 49(1), S35–43.

Rogers, R. G. (1996). The effects of family composition, health,

and social support linkages on mortality. Journal of Health

and Social Behavior, 37, 326–338.

Ross, C. E., & Mirowsky, J. (1999). Refining the association

between education and health: The effects of quantity,

credential, and selectivity. Demography, 36, 445–460.

Schoeni, R. (1995). Marital status and earnings in developed

countries. Journal of Population Economics, 8, 351–359.

Seeman, T. E. (2000). Health promoting effects of friends and

family on health outcomes in older adults. American Journal

of Health Promotion, 14, 362–370.

Seeman, T. E., Kaplan, G. A., Knudsen, L., Cohen, R., &

Guralnik, J. (1987). Social network ties and morbidity

among the elderly in the Alameda county study. American

Journal of Epidemiology, 126, 714–723.

Stansfield, S. (1999). Social support and social cohesion. In M.

Marmot, & R. G. Wilkinson (Eds.), Social determinants of

health (pp. 155–178). Oxford: Oxford University Press.

Sugisawa, H., Liang, J., & Liu, X. (1994). Social networks,

social support and mortality among older people in Japan.

Journal of Gerontology, 49(1), S3–13.

Suadicani, P., Hein, H., & Gyntelberg, F. (1994). Serum

validated tobacco use and social inequalities in risk of

ischaemic heart disease. International Journal of Epidemiol-

ogy, 23, 293–300.

Sundquist, J., & Johansson, S. E. (1997). Self reported health

and low educational level predictors for mortality: A

population based follow up study of 39,156 people in

Sweden. Journal of Epidemiology and Community Health,

51, 35–40.

Uchino, B. N., Cacioppo, J. R., & Kiecolt-Glaser, J. K. (1996).

The relationship between social support and physiological

processes: A review with emphasis on underlying mechan-

isms and implications for Health. Psychology Bulletin, 119,

488–531.

Umberson, D. (1992). Gender, marital status and the social

control of health behavior. Social Science & Medicine, 34(8),

907–917.

Verbrugge, L. M. (1979). Marital status and health. Journal of

Marriage and the Family, 41, 267–285.

Wadsworth, M. E. (1997). Health inequalities in the life course

perspective. Social Science & Medicine, 44, 859–869.

Waite, L. (1995). Does marriage matter? Demography, 32,

483–507.

Ware, J., Allyson, D., & Robert, B. (1980) Conceptualization

and measurement of health status for adults in the health

insurance study: Vol. VI. Analysis of relationships among

health status measures. Santa Monica, CA: RAND

(R-1987/6-HEW).

White, I. R., Blane, D., Morris, J. N., & Mourouga, P.

(1999). Educational attainment, deprivation-affluence

and self reported health in Britain: A cross sectional

study. Journal of Epidemiology and Community Health, 53,

535–541.


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Health inequalities in the older population: the role of personal capital, social resources and socio-economic circumstances