Adult life experiences and health in early old age in Great Britain

Adult life experiences and health in early old age inGreat Britain

Emily Grundy*, Gemma Holt

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

Abstract

In Britain and other developed countries older people comprise a large majority of all those reporting long termillness or disability. However, most studies of socio-demographic variations in health have focussed on those inyounger age groups. Moreover approaches to the study of health variations are often fragmented. In this study we

have adopted a life course approach to analyse di�erentials in health in early old age.The data comes from the Retirement and Retirement Plans Survey and follow-up, a two-wave study of persons

aged 55±69 in 1988/9. As well as information on current circumstances, the data set includes occupational, marital,

and fertility history information. At baseline a nationally representative sample of the population of Great Britainwere interviewed at home by trained interviewers (n = 3543). The sample was followed up and in 1994, 2247survivors were re-interviewed, a response rate of 70% (of survivors). The data were weighted to adjust for non-

response bias.Two outcome measures were used: self rated health and presence or absence of disability assessed from a scale

derived from detailed questions on thirteen domains of disability. The severity score used was that developed for the1985/6 ONS Surveys of Disability.

The ®ndings indicate that health and disability status at baseline and at follow up were associated with socio-economic and geographic variables, such as proportion of adult life spent unemployed and residence outside theSoutheast of England; demographic factors, such as early age at marriage and high parity; and experience of

adverse events, such as the death of a child and being dismissed from work.The results show that socio-economic, demographic, and geographical and `life events' factors are all associated

with health status in early old age and that integrated, rather than bifurcated, approaches to the study of health

di�erentials are needed. 7 2000 Elsevier Science Ltd. All rights reserved.

Keywords: Socio-economic status inequality; Life events; Elderly; Disability; Social class; Great Britain

Introduction

Socio-economic di�erentials in indicators of health

status have long been a focus of enquiry in Britain and

have recently returned to the forefront of the policyarena (Department of Health, 1999; Acheson, 1998).

In 1997 83% of all deaths in England and Wales were

Social Science & Medicine 51 (2000) 1061±1074

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* Corresponding author. Tel: +44-0-20-7299-4668; fax:

+44-0-20-7299-4637.

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

of people aged 65 years and over, with a further 8%

occurring among people aged 55±64 (ONS, 1999).Those aged 55 years and over also account for 68% ofthe population with a limiting long-term illness or dis-

ability and 58% of those who consider their healthbad or very bad (Bennett, Dodd, Flatley, Freeth &Bolling, 1995).

It necessarily follows from this demographic contextthat much of the overall variation in health re¯ects

di�erentials among those in older age groups. Despitethis, studies of health variations in the elderly popu-lation are sparse relative to research on younger people

(Medical Research Council, 1994).The relative neglect of variations in the health status

of older adults stems in part from the historical predo-minance of studies of socio-economic factors and geo-graphic mortality di�erentials based on the decennial

supplements produced by the Registrar General. Thedi�culties involved in assigning occupationally de®nedSocial Class to those who have long left the labour

market and numerator±denominator bias in the esti-mation of mortality rates make these sources in-

adequate for studying socio-economic variations inmortality at older ages. As a result such di�erencesreceived little attention in the Black report and other

in¯uential publications (Townsend and Davidson,1982; Wilkinson, 1996, Kunst, 1997). The availabilityof better data sets, particularly the ONS Longitudinal

Study (Fox, Goldblatt & Jones, 1985) has shown de®-nitively that socio-economic variations in mortality

persist beyond the age of 65.Analyses of General Household Survey data have

also shown variations in elderly age groups in indi-

cators of health, such as self reported long-standing ill-ness and recent restricted activity (Grundy, 1987;

Victor, 1989; Arber & Ginn, 1993), as have studies ofparticular cohorts (Marmot & Shipley, 1996).Apart from studies of socio-economic di�erences in

health status, there is a substantial literature on vari-ations according to family and household character-istics, particularly marital status. Unravelling causal

associations is complex because of the need to allowfor health related selection between relevant status

groups, but in general the currently married have beenfound to have better health than the unmarried, par-ticularly the divorced (Goldman, Korenman & Wein-

stein, 1995; Murphy, Glaser & Grundy, 1997; Glaser,Murphy & Grundy, 1997; Grundy, 1993). A few stu-dies have investigated links between reproductive his-

tory and mortality and found a J shaped relationshipwith nulliparous and high parity women having above

average risks (Green, Beral & Moser, 1988; Lund,Arnesen & Borgan, 1990). An association betweenhigh parity (six or more births) and worse health was

also found in an analysis of women aged 50 and overincluded in the US Panel Study of Income Dynamics

(Kington, Lillard & Rogouski, 1997). Related to

research on associations between family and householdstatus and health, is a burgeoning body of research onlinks between indicators of social support and health

(see Grundy, Bowling & Farquhar, 1996 for a recentreview). This latter research ®eld has included con-sideration of the possible protective e�ect that social

ties may have in times of adversity and so overlaps tosome extent with the literature on the e�ects of stress-

ful life events on health (Markides & Cooper, 1989;Kivela, Kongas-Saviaro, Laippala, Pahkala & Kesti,1996).

There is an obvious need to integrate theseapproaches to the study of variations in health and

consider the contribution of socio-economic factors,socio-demographic circumstances and exposure toadverse life events together. However, many research

approaches to the study of health variations in laterlife are highly fragmented; Preston and Taubman(1994), for example, draw attention to the `bifurcation

of literatures' on socio-economic di�erences in mor-tality and health on the one hand and social±psycho-

logical studies on the other.Recently new approaches, which have emphasised

the role of life course factors as a whole, have led to

some integration of di�erent conceptual approaches tothe study of health (Wunsch, Duchene, Thiltges &Salhi, 1996; Davey Smith, Hart, Blane, Gillis &

Hawthorne, 1997; Wadsworth, 1997). Forsdahl (1977)and more recently Barker (1992) has emphasised the

importance of very early life environments on sub-sequent risk of speci®ed diseases and have proposedparticular etiological pathways which could explain

some observed variations. Other analysts have empha-sised the importance of accumulated risk over the lifecourse (Vagero & Lundberg, 1995; Bartley, Blane &

Montgomery, 1997) and it is this approach whichunderlies the research reported here.

In this paper we analyse variations in indicators ofhealth and disability at two points in time in a rela-tively large, and nationally representative, study of

British adults in late middle and early old age. This isan important group to study as it is in these age

groups that much chronic morbidity and prematuremortality is manifest. In previous analyses (Grundy &Glaser, 1998, 2000) we have shown that the extent of

disability, and of change in disability, in the samplestudied varied considerably according to a number ofsocio-economic indicators, such as housing tenure and

social class. Here our focus is on variations in healthand disability status according to socio-economic and

demographic history throughout adult life.Two recent studies have also considered those in this

age group and have adopted a lifecourse approach to

analysis and so merit particular consideration here.Blane, Berney, Davey Smith, Gunnell & Holland,

E. Grundy, G. Holt / Social Science & Medicine 51 (2000) 1061±10741062

(1999) followed up a sample of some 300 65±74 yearolds who as children had been included in the Boyd±

Orr 1937±39 survey of families. They used a lifegridmethod to gather retrospective information on the oc-cupational and residential histories of survivors from

the original survey whom they re-interviewed in 1995.This study has the advantage of including indicators ofchildhood circumstances and also physiological and

anthropometric measurements taken in childhood andin early old age. Preliminary results suggest that,although the sample is not a random one, its charac-

teristics are similar to those of the national populationof the same age, work on analysing the data are inprogress. Davey Smith et al. (1997) also used a lifecourse approach to analyse variations in the morbidity

and mortality of workers screened in 1970±73, whenthey were aged 35±64, as part of a workplace study inthe west of Scotland. Deaths of study members were

followed up and analysed in relation to social class inchildhood, at entry to the labour market (informationgathered retrospectively) and at the time of screening.

The authors reported a cumulative e�ect of these threeindicators (although their results show little e�ect ofsocial class at age 25) and concluded that studies with

data on socio-economic circumstances at only onestage of life àre inadequate for fully elucidating thecontribution of socio-economic factors to health'.The research reported here is based on analyses of a

data set that does not include any information onchildhood circumstances (apart from educational quali-®cations) or any physiological measures of health.

However, unlike the two studies referred to above, it isbased on a nationally representative sample (and asample some ten times larger than the Boyd±Orr

cohort) and includes detailed retrospectively gatheredinformation on both work and marital and fertility his-tories. Generally associations between these domainsof life and health have been analysed separately, here

we take account of both.

Data and methods

This analysis is based on data from the baseline and

follow up rounds of the Retirement and RetirementPlans Survey conducted in 1988/9 and 1994. The sur-

vey was commissioned by the Department of SocialSecurity and carried out by the O�ce for NationalStatistics. The baseline survey, described in detail else-

where (Bone, Gregory, Gill & Lader, 1992), compriseda nationally representative sample of 3543 adults aged55±69 who answered a detailed interviewer-adminis-

tered questionnaire including extensive questions onoccupational history, ®nancial circumstances, disabil-ity, and marital and fertility history. As there is no

comprehensive list by age of the population of GreatBritain, obtaining the sample required a prior sift of arandom sample of addresses in order to identify house-holds including 55±69 year olds. Through this method,

2717 households containing an eligible person wereidenti®ed and survey interviews were achieved at 88%of these households. All household members within the

age range were recruited to the study so the sampleincludes a high proportion of couples. In this analysiswe have excluded those spouses whose age was outside

the target range, although they were included in thesurvey. Five and a half years after baseline, survivingrespondents were traced and re-interviewed (Disney,

Grundy & Johnson, 1997). By the time of the follow-up, 11% of the original sample were known to havedied and a further 26% could either not be traced orrefused, so the response rate was 70% (of those known

to have survived). Unfortunately, although not unex-pectedly, response rates at follow-up were lower in lessadvantaged social groups and varied by age and mari-

tal and health status. In order to minimise the biasarising from these variations in response, a re-weight-ing procedure was used which, in essence, adjusts the

numbers in speci®ed categories upwards or downwardsin order to allow for under or over representation inthe sample rising from di�erential response. The pro-gramme used to undertake this was the CALMAR

macro developed by the French national statisticsagency INSEE which is also used by the Departmentof Social Security (Deville, Sarndal & Sautory, 1993).

The re-weighting used for the Retirement Surveys,which is more fully detailed in the survey report (Dis-ney et al., 1997), involved adjustment for di�erential

loss to follow-up by social class, income quartile, agegroup, gender, marital status and presence/absence ofdisability. While this method corrects for under/over

representation of certain groups, the possibility offurther unknown di�erences between respondents andthose lost to follow-up cannot be allowed for, we havetherefore erred on the side of caution in the presen-

tation and interpretation of results.1

Measures of disability and health

Two indicators of health status were available. The

1 Extensive comparisons of raw (unweighted) and weighted

data were undertaken. These showed that di�erences in results

were in fact very slight, largely because the numbers in cat-

egories with response rates that di�ered substantially from

average were very small. The weighted data have been used in

preference to the raw data in accordance with statistical

advice and common practice as they do mean that some com-

ponents of bias are adjusted for. The derivation of weights

was undertaken by colleagues in the Institute of Fiscal Studies

and the Department of Social Security.

E. Grundy, G. Holt / Social Science & Medicine 51 (2000) 1061±1074 1063

®rst came from responses to a simple question on self-perceived health in the preceding 12 months. Respon-

dents were asked `Òver the last 12 months would yousay that on the whole your health has been: good;fairly good; or not good?'' The question has been used

widely in surveys, including the British General House-hold Survey. Numerous studies have shown associ-ations between self rated health and other health

indicators including use of health services, functionaldisability, and mortality (Blaxter, 1985; Idler & Kasl,1995; Idler & Benyamini, 1997; Manderbacka, Lund-

berg & Martikainen, 1999). Information on the disabil-ity status of respondents was collected using the scalesdeveloped for the ONS Disability Surveys carried outin 1985/6 (Martin, Meltzer & Elliot, 1988). A detailed

set of questions was asked on thirteen domains of dis-ability in order to rate the respondent's degree of dis-ability in each. A weighted score derived from

responses to these thirteen sub-scales was then assignedto a severity category ranging from 0 (no disability) to10 (most severe). In this paper, we have dichotomised

the severity of disability score distinguishing those withsome disability (scores of 1 or more) from those withnone.

Health and disability status are not equivalent con-cepts. Disability involves some limitation of functionarising from impairment. Such impairments may bethe result of congenital abnormality or trauma, but in

the age groups considered here will most usually be theconsequence of acquired chronic disease. Conceptuallyself-perceived health status may be an indicator of

impairment (albeit a rather imperfect one), and disabil-ity an indicator of the sequelae of impairment. In rec-ognition of this we have included baseline health status

as a co-variate in models of disability status at follow-up, but not disability in 1988/9 as a `precursor' ofhealth status in 1994.

Variables used in the analysis

The employment history data available were used

to construct a range of indicators of social class[based on the OPCS Classi®cations of Occupations,(OPCS, 1980)] at key points in adult life and

throughout adult life. These included social class atage 25 (approximately)2, social class of last or currentoccupation, and àverage' lifetime social class. This last

was derived by summing the social class value relatingto each of the respondent's jobs and dividing by the

total number of jobs that that respondent had held.

Additionally a variable indicating the proportion ofworking life (since ®rst job) spent unemployed wasconstructed. A number of `dummy' variables relating

to employment related experiences, including whetherrespondents had ever been unemployed, been dismissedor made redundant were also created. Married women

were assigned to social class groups on the basis oftheir own occupations, rather than that of their hus-

bands. Four per cent of women (40) and 1% of men(11) had never been employed, and had to be excludedfrom analyses using occupationally based variables.

Other indicators of socio-economic status usedincluded educational quali®cation level, housing tenure

and income quartile. We distinguished between foureducational quali®cation groups: those with A'level(exams taken at ages 17±19) equivalent or higher quali-

®cations; those with O'level (exams taken at age 16)or equivalent quali®cations; those with trade, clericalor commercial quali®cations and those with no quali-

®cations. In most of the analyses we have simplydichotomised between those with some and those

with no quali®cations (women) or, for men, thosewith academic quali®cations (O'level and equivalentor higher) and those with other or no quali®cations.

Housing tenure, which has been shown to be associ-ated with health status in a wide range of studies(Arber & Ginn, 1993; Fox et al., 1985), was grouped

into three categories; owner occupiers; local authorityor housing association tenants, and a residual cat-

egory largely comprising private sector tenants. Grossincome from all sources was included in the calcu-lation of income quartile. In the case of couples, indi-

vidual income was set at 80% of the joint income, inaccordance with the scale devised by McClements(1978).

The demographic variables used in the analysisincluded age, measured in single years and treated

as a continuous variable, and a range of indicatorsbased on marital and fertility history. These datawere collected from all women and from unmarried

men. Men in ®rst marriages were assigned the samehistory as that of their wife, but obviously this

would not have been appropriate for the 10% in asecond or later marriage, so fertility history datafor this group are not available. Co-variates used in

the analysis included: current marital status, dichot-omised into married and not married; the pro-

portion of adult life (16+) spent married; whetherrespondents had been married more than once, ex-perience of divorce or death of a spouse; experience

of the death of a child; young age at marriage(under 21 years for women and under 25 years formen); number of children borne; and age at ®rst

and last birth. Additionally, those living in theSoutheast of England, the most prosperous region

2 A number of male respondents were in the armed

forces at the age of 25 (1944±58) In these cases social

class either immediately prior to enlistment or directly

after demobilisation, whichever was closest to age 25, was

used.


of Great Britain, were distinguished from those liv-ing elsewhere.

All covariates used in the analysis refer to status atbaseline in 1988/9. The analysis was carried out usingSAS version 6.12.

Results

Characteristics of the study population

The vast majority of sample members had married(93%) and had had at least one child (79%). Twelveper cent had experienced divorce; 16% had been

widowed and 6% had experienced the death of a child.

A quarter of respondents lived in the Southeast. Some

60% of respondents were owner-occupiers, 22% had

an academic quali®cation (O'level or equivalent orhigher) and 60% no quali®cations (a proportion which

was higher among women than men). A third had ex-

perienced redundancy, a third unemployment and 6%

had at some time been dismissed from a job. Higherproportions of men compared with women had been

unemployed and had ever been dismissed or made

redundant. On the basis of last or current occupation,

just over half of all respondents were assigned to amanual social class group. Men were most likely to

have been employed in skilled manual jobs (IIIm)

throughout their working lives, while women were

more likely to have been employed in skilled non-man-

Fig. 1. Distribution of health and disability status at baseline and follow-up.


ual jobs (IIIn). The distribution of the sample by mari-tal status, housing tenure and self reported health at

baseline and at follow-up was very similar to thatfound in the General Household Surveys for 1988 and1994 respectively (Bennett, Jarvis, Rowlands, Singleton

& Haselden, 1996 and Foster, Wilmot & Dobbs,1990). More detailed information on the distributionof respondents by demographic and socio-economic

characteristics is available on request from the authors.

Health and disability status

Fig. 1 shows the distribution of men and women inthe sample by health and disability status at baselineand at follow up. In 1988/9 half the sample membersrated their health as `good' and two-thirds were free of

disability as measured on the scale used. By follow up,the proportion reporting good health was lower (42%)

and the proportion assessed as having some disability

higher, even so quite high proportions remained ingood health and free of disability.

Tables 1 and 2 show what proportion of the baselineand follow-up samples had some disability or reported

fair or poor health according to a number of demo-graphic and socio-economic characteristics and experi-

ences. Self-reported health and disability at bothbaseline and follow-up were associated to some extent

with marital status and history as reported in the 1988/89. For example, 41% of women who were not mar-

ried at baseline (single, widowed or divorced) hadsome disability, compared with 30% of those who

were currently married. The prevalence of poor or fairhealth and disability was also higher among women

who had been married more than once than amongthose married once only, and higher among those who

had married and started childbearing at relatively

Table 1

Health & disability status in 1988/9 & 1994 by selected demographic characteristics & experiencesa

1988/9 % Reporting 1994 % Reporting

Fair/Poor health Some disability Fair/Poor health Some disability

Males Females Males Females Males Females Males Females

Age at baseline

55±59 43.3 48.6 28.7 27.6 53.1 52.8 44.2 40.9

60±64 49.0 52.9 36.1 31.9 59.8 57.4 55.6 46.5

65±69 52.6 54.1 41.5 40.0 61.4 64.6 57.0 57.3

Marital status

Married 46.5 49.9 34.7 29.6 57.4 57.1 51.7 46.7

Not married 55.8 55.9 37.8 40.9 59.5 60.6 51.8 51.2

Marital history

Never married 50.1 39.2 31.3 35.6 58.6 52.1 45.4 45.8

Married once only 47.4 51.8 35.4 32.1 57.8 56.9 52.3 46.6

Married twice or more 52.3 60.6 37.0 41.4 56.6 72.8 51.9 62.6

Age at ®rst marriage

< 21F/< 25 M 52.1 62.3 39.1 40.0 62.5 73.7 58.8 56.5

e21F/e25 M 45.5 48.8 32.6 31.3 54.6 54.0 47.1 45.9

Age at ®rst birth

< 23 years 47.4 64.4 40.4 42.8 65.7 76.5 67.0 56.8

e23/no children 48.3 49.1 34.8 31.2 57.2 54.5 50.6 46.4

Age at last birth

> 39 years 54.0 57.0 36.3 39.0 64.7 62.7 54.4 53.5

E39/no children 47.3 51.5 35.1 32.8 56.7 57.9 51.3 47.7

Number of children

< 5 children 47.9 50.8 34.8 32.2 56.8 56.6 50.8 47.0

5 or more children 53.7 67.2 42.3 48.7 75.7 81.7 71.2 65.1

Death of a child

Yes 60.2 63.8 43.8 42.2 58.2 74.1 53.7 58.7

No 47.5 52.5 35.7 32.6 59.3 56.8 52.9 46.8

Region of residence

South-east of England 37.5 43.4 26.8 28.6 41.5 49.3 42.2 41.4

Other region of GB 51.7 54.5 38.1 34.7 63.3 61.1 55.0 50.3

N (weighted) 812 961 593 615 591 708 531 586

a Bold denotes statistically signi®cant from other category or statistically signi®cant trend ( p<0.05).


young ages. In 1994, for example, 77% of women who

had had their ®rst child before the age of 23 had somedisability, compared with 55% of other women. Rates

of disability and poor or fair health were also higher

among women who had had ®ve or more children andthose who had experienced the death of a child. Men

and women who lived in the South East of England

had lower rates of fair/poor health or disability thandid those living elsewhere in Britain. In assessing the

importance of these bi-variate associations, the relativesize of the groups with the characteristic in question

should be borne in mind. For example, only 6% of the

sample had experienced the death of a child whereas

25% were not currently married.

As shown in Table 2, di�erentials by indicators ofsocio-economic circumstances and experiences were

also quite marked. Reported fair or poor health anddisability in 1988 and 1994 was associated with hous-

ing tenure, educational quali®cation level, income

quartile, and all the social class variables considered.Those who had ever been dismissed from a job and,

among men, those who had experienced unemploymentalso included higher proportions reporting poor or fair

health or disability.

Table 2

Health & disability status in 1988/9 & 1994 by selected socio-economic indicators & experiencesa

1988/9 % Reporting 1994 % Reporting

Fair/Poor health Some disability Fair/Poor health Some disability

Males Females Males Females Males Females Males Females

Educational quali®cations

A'level+ 34.8 35.5 20.8 21.0 41.9 43.2 35.7 29.4

O'level 30.7 39.6 25.3 24.5 47.2 44.9 36.1 39.6

Trade 49.7 45.0 40.0 23.6 62.8 48.0 54.7 44.2

None 54.7 57.4 39.1 38.3 62.9 65.1 59.9 53.2

Housing tenure

Owner occupier 43.0 45.2 30.8 27.0 53.0 51.7 46.7 42.5

LA/HA tenant 58.8 63.9 45.7 47.9 69.5 69.6 66.6 60.0

Income quartile

Lowest 62.7 62.2 46.5 43.1 71.8 66.4 61.8 53.2

Second 56.9 56.1 46.6 37.9 63.3 62.4 54.7 54.0

Third 46.2 48.4 34.1 29.7 57.5 57.5 51.9 46.7

Highest 35.7 35.1 21.5 18.9 45.6 40.6 41.9 31.6

Ever been made redundant

Yes 47.9 51.5 36.3 36.5 60.4 61.1 53.5 50.2

No 48.4 52.0 34.4 32.1 55.6 57.1 50.3 47.4

Ever been dismissed

Yes 68.4 60.5 55.9 45.3 78.0 69.2 61.6 61.8

No 46.6 51.5 33.7 32.7 56.2 57.8 51.0 47.7

Ever unemployed

Yes 51.2 53.0 38.2 36.1 63.3 59.1 53.2 52.7

No 46.1 51.5 33.2 32.3 53.9 57.9 50.8 46.5

Social class at age 25

Non-manual 39.5 42.0 26.8 25.4 50.3 50.2 41.6 42.6

Manual/not working 51.5 56.4 38.5 36.9 60.8 61.9 56.0 50.7

Àverage' social class

I 14.5 19.7 16.6 0 28.4 45.5 34.9 45.5

II 35.2 34.0 18.0 22.3 40.9 44.5 34.0 38.8

IIInm 40.1 43.2 26.5 26.4 48.4 49.6 45.5 41.0

IIIm 50.8 56.9 40.1 34.8 61.4 62.3 51.1 50.3

IV 57.8 60.2 42.7 42.3 70.9 67.3 67.2 56.4

V 68.6 69.6 45.5 41.5 60.7 75.3 80.4 60.0

Social class of last/current job

Non-manual 40.0 45.7 27.1 27.4 46.6 51.9 41.6 42.3

Manual 53.5 58.6 41.0 39.6 65.1 64.8 59.4 54.3

N (weighted) 812 961 593 615 591 708 531 586

a Bold denotes statistically signi®cant from other category or statistically signi®cant trend ( p<0.05).


These bi-variate analyses suggest health at bothbaseline and follow-up was associated with a range of

indicators of demographic history, socio-economic cir-cumstances and experiences and life events. Clearlymany of these factors are interrelated so multivariate

analyses are needed in order to disentangle the e�ectsof the variables considered. Tables 3±6 present theresults of best-®t models of self reported health and

disability in 1988 and in 1994 for men and women re-spectively.

Multivariate analysis

The outcomes modelled were proportion in fair/poor

health and proportion with any disability. Several ofthe variables used in our initial analyses, for examplethose relating to social class, were highly interrelated.

In such cases our strategy was to select for inclusion inthe ®nal models presented below the variable mostconsistently, and strongly, associated with the outcome

variables. Of the variables included, a number werecontinuous, or could be treated as continuous, (age;proportion of adult life spent married; proportion of

adult life spent unemployed; average social class), theremainder were dichotomised. Overall the models

accounted for some 7±16% of variation in the out-come measures. Although statistically highly signi®-

cant, such values indicate that factors other than thosewhich could be examined in this analysis are importantin explaining the observed variation in self-rated health

and disability. Odds ratios presented are adjusted forall the co-variates shown in the relevant part of thetable.

Self-perceived health status

Tables 3 and 4 show for men and women respect-ively the results from ®tting logistic regression modelsto the data on reported health status at baseline in1988/9 and follow-up in 1994. The odds of poor or

fair health increased with age, although this was notsigni®cant for women at baseline, and were consider-ably higher for those living outside the Southeast, par-

ticularly among men.Among men (Table 3) the odds of poor or fair

health at baseline and at follow-up were associated

with average lifetime social class (higher odds withlower social class) and increased with percentage ofadult life spent unemployed. Those who had been dis-

missed at least once during their working lives had ahigher likelihood of poor or fair self-rated health. At

Table 3

Results from logistic regression models of poor/fair health, males at baseline and follow-up

Explanatory factor 1988/9 Odds ratio 1994 Odds ratio

Socio-demographic factors

Age in 1988 1.041b 1.045b

Live outside the SouthEast 1.517c 2.072c

No educational quali®cations 1.418b 1.228ns

Family factors

Married young 1.390b 1.412b

Proportion of adult life spent married 0.664b 0.954ns

Child born >39 yr 1.282ns 1.484a

Employment factors

Below median income 1.425b 1.276ns

Average social class 1.176b 1.287b

Percentage of adult life spent unemployed 1.019b 1.025b

Life events

Ever been dismissed 1.740b 1.926b

Death of a child 1.632b 0.960ns

-2 Log Likelihood 2172.909 1278.278

df 12 12

R2 0.0872 0.1004

Weighted N 1676 1033

a p<0.1.b p<0.05.c p<0.001.ns p>0.1.


Table 4

Results from logistic regression models of poor/fair health, females at baseline and follow-up



Age in 1988 1.015ns 1.037b

Live outside the SouthEast 1.367b 1.481b

No quali®cations 1.249a 1.524b

Owner occupier 0.768b 0.676b

Family factors

Married more than once 1.401b 1.801b

Child born <23 yr 1.375b 1.890c

Had more than four children 1.362ns 2.013b

Employment factors


Average social class 1.173b 1.041ns

Life events

Death of a child 1.450a 1.499ns

-2 Log Likelihood 2387.298 1510.816

df 11 11

R2 0.0669 0.0932


a p<0.1.b p<0.05.c p<0.001.ns p>0.1.

Table 5

Results from logistic regression models of some disability, males at baseline and follow-up



Age in 1988 1.051c 1.057c

Live outside the SouthEast 1.393b 1.251ns

No educational quali®cations 1.288ns 1.694b

Owner occupier 0.918ns 0.697b

Family factors

Married young 1.312b 1.429b

Had more than four children 0.985ns 1.936a

Employment factors

Below median income 1.505c 0.847ns

Average social class 1.187b 1.160a

Percentage of adult life spent unemployed 1.020b 1.005ns

Life events

Ever been dismissed 1.849b 0.839ns

Health in 1988/9

In fair/poor health in 1988/9 ± 3.224c

-2 Log Likelihood 2034.381 1248.160

df 11 12

R2 0.0807 0.1484


a p<0.1.b p<0.05.c p<0.001.ns p>0.1.


baseline, but not at follow-up, having no academicquali®cations and an income below the median were

also associated with higher odds. A young age at mar-riage was positively associated with higher odds ofpoor or fair health in both 1988 and 1994 while the

proportion of adult life spent married was negativelyassociated with this outcome at baseline. The oddsratio at baseline was also signi®cantly raised among

those who had experienced the death of a child. Menwho had had a child at the age of 40 or older werealso more likely to report fair or poor health in 1994,

although the strength of this association did not reachthe 5% level of signi®cance.Factors associated with increased probability of

female fair/poor self rated health included: having no

quali®cations; having been married more than once;and being under 23 years of age at ®rst birth. At base-line, but not at follow-up, having an income below the

median, and average social class were also associatedwith higher odds of fair or poor health, as was experi-ence of a death of a child, although this was only sig-

ni®cant at the 10% level. High parity (®ve or morechildren) increased the risk of poor or fair health atfollow-up. At both points in time, owner-occupiers

had lower odds of poor or fair health than did tenants.

Disability

In Tables 5 and 6 the outcome modelled is presenceor absence of a disability in the relevant year. As dis-cussed in the section on health and disability measures,

we included baseline health status as a variable whenmodelling disability at follow-up. Men in fair or poorhealth in 1988 were over three times as likely as other

men to be disabled at follow-up; among women theincrease in odds of disability in 1994 was even greater.Health at baseline was of course associated with many

of the socio-demographic variables considered, asreported above, and, once baseline health status wascontrolled for, fewer of these variables were signi®-cantly associated with disability at follow-up than in

the baseline models. Even so, disability at follow-upamong men was positively associated with having noacademic quali®cations; and with a young age at mar-

riage and, at the 10% level of signi®cance, with aver-age social class and having had ®ve or more children.Owner occupation was associated with reduced odds

of disability. The odds of disability at baselineincreased with time spent unemployed and with lowersocial class. They were also raised for those with below

median incomes, those who lived outside the South-

Table 6

Results from logistic regression models some disability, females at baseline and follow-up



Age in 1988 1.036b 1.067c

No quali®cations 1.629c 1.305a

Owner occupier 0.695b 0.777a

Family factors

Currently married 0.749b 1.137ns

Married more than once 1.377a 1.745b

Parous 0.946ns 0.708a

Child born <23 yr 1.277a 1.074ns

Had more than four children 1.535b 1.699a

Employment factors


Life events

Ever unemployed 1.239a 1.300a

Health in 1988/9

In fair/poor health in 1988/9 ± 3.881c

-2 Log Likelihood 2227.219 1482.849

df 11 12

R2 0.0659 0.1560


a p<0.1.b p<0.05.c p<0.001.ns p>0.1.


east, those who had married young and those who hadexperienced dismissal from a job.

Among women, owner occupiers had a reduced riskof disability at baseline and at follow-up as, in case ofdisability status at baseline, did currently married

women. Those without quali®cations and with incomesbelow the median had higher odds of disability atbaseline. Family factors associated with baseline dis-

ability included high parity and, at the 10% level ofsigni®cance, having being married more than once andyoung age at ®rst marriage. The odds of disability at

follow-up were raised in high parity women but loweramong the parous than the childless.Taken together the results of these analyses of vari-

ations in health and disability status show that among

both men and women health and disability at twopoints in time were associated with socio-economicand demographic history and circumstances and with

experience of adverse life events. Among men associ-ations with employment related factors and regionwere stronger than among women, while family related

factors tended to be more strongly associated withvariations in the health and disability status of women.Comparing baseline and follow-up models, income was

more consistently associated with baseline outcomesthan with health and disability in 1994 (by which timevery few sample members were working).

Discussion

There are a number of limitations to the study wereport on here. Firstly, the information on health and

disability comes from self-reports (albeit very detailedself-reports in the case of disability). Although self-reported health status has been shown in a number ofstudies to be correlated with other health indicators

(Blaxter, 1991; Idler & Kasl, 1995; Sundquist &Johansson, 1997), clearly there may be associationsbetween socio-economic factors and perceptions of

health which could confound our results. If, as seemsprobable, those in less advantaged social groups havelower health expectations, the e�ect of any such bias

would mean that some of the results reported heremay be too conservative, rather than exaggerated.However, the lack of any observational data on health,or of information on health related behaviour such as

smoking, remains a limitation.A further possible data problem is the e�ect of recall

bias. In a population such as that of contemporary

Great Britain, misreporting of events such as marriageand childbearing is not likely to be serious. Berney andBlane (1997) reported good recall of social circum-

stances 50 years earlier when a lifegrid method of ret-rospective data collection was used. However, recall ofthe details of work many decades earlier may be more

of a problem. Respondents were asked to consult rel-

evant documents, such as payslips, pension books, sav-ings certi®cates and bank statements, when answeringthe questions of employment and ®nances, even so

some degree of misreporting seems likely. A furtherproblem is di�erential loss to follow-up, although wehave tried to adjust for this, the adjustment process

involves making various assumptions which may notbe correct. As with all studies of particular cohorts, it

is unclear whether the results are generalisable to thoseborn in di�erent time periods and with di�erent ex-posures to events such as war, conscription and econ-

omic shock. Finally, as our results come from anobservational study, we cannot show cause and e�ect.

Despite these limitations, a number of importantconclusions may be drawn from the analyses. Firstly,it seems that demographic history and events, as well

as socio-economic history and circumstances, are as-sociated with health and disability status in early oldage, particularly among women. As reported in a num-

ber of other studies, marriage was generally associatedwith better health outcomes. However, having married

young, a young age at childbearing and having mar-ried more than once were in some cases associatedwith a higher odds of poor health or disability, even

after allowing for age, educational, employment andincome related factors. The ®nding of an association

between high parity and poorer health among womenin the study is consistent with earlier work on thistopic (Kington et al., 1997). Kington et al. (1997) also

found an association between experience of the deathof an infant and poorer health among women aged 50and over, we found a relationship with experience of

death of a child at any age. A range of factors mightaccount for this, including the greater risks of death

among infants whose mothers have poor health orunhealthy behaviours (particularly smoking), possibleintergenerational congruence in health related beha-

viour, risk taking and genetic vulnerability and ®nally,the adverse e�ect of a child's death on the parent'smental health and well-being. We also found some as-

sociation between late paternity and health, althoughthis e�ect was not strong. Some recent studies have

suggested that daughters born to older fathers havereduced longevity (Gavrilov & Gavrilova, 1997) but weare not aware of research showing an implication for

the health of the father. It seems most likely that thispossible association, which needs con®rmation, re¯ects

the operation of various selection factors not con-trolled for in this analysis, rather than a direct e�ect.Associations between health status and the socio-econ-

omic and employment related factors considered weregenerally in the expected direction. Average lifetimesocial class, few or no educational quali®cations and

low income increased the odds of being in poor healthor having some disability at baseline and at follow-up


while owner occupation was associated with a reduced

risk, particularly among women. The stronger associ-ation between tenure and health outcomes amongwomen as compared with men may re¯ect a lesser sal-

ience of occupationally based variables, and the lesserheterogeneity in social class, in women in thesecohorts. Length of time spent unemployed increased

the odds of poor outcomes to a large extent, a ®ndingconsistent with other work on links between unemploy-

ment and health related outcomes (Moser, Goldblatt,Fox & Jones, 1987).Those living outside the Southeast of England, par-

ticularly men, also had higher odds of poor health ordisability. This is consistent with our knowledge of re-

gional variations in mortality and may re¯ect the oper-ation of a range of factors, such as di�erentialmigration and the e�ect of living in a `depressed' area.

Analyses of larger data sets allowing greater areal dis-aggregation are needed to investigate this ®ndingfurther.

It is likely that some of the associations found re¯ectthe operation of various selection factors, rather than,

or in addition to, causally important in¯uences. Insome cases the direction of the causation may bereversed. Poor health or unhealthy behaviours, such as

heavy drinking, may, for example, increase both therisk of being dismissed from a job and the risk of poorhealth in later life. However, there are also plausible

causal pathways that may underlie the associationsfound. Stress resulting from lack of status or control

at work, for example, has been associated with poorerhealth outcomes hypothesised to operate through de-pressed immunological responses, as well as beha-

vioural di�erences (Marmot & Theorell, 1988).Accumulated lifetime stresses and insults arise in otherspheres of life, as well as work. Stress arising from

family related experiences, such as early marriage andchildbearing, both of which are risk factors for marital

breakdown and so presumably for marital con¯ict,(Murphy, 1985) may have a similar e�ect. It is alsoknown that events such as widowhood and divorce are

associated with adverse behavioural changes, includingincreased smoking and drinking (Bowling, 1987).Large family size may itself cause stress in a society

with generally low fertility, as well as having directe�ects on the risks of developing certain chronic dis-

eases, such as diabetes and cardiovascular disease(Kritz-Silverstein, Barrett-Connor, Wingard & Fried-lander, 1994; Ness et al., 1993).

These results show clearly that socio-economic,demographic, and `life events' factors are all associated

with health status in early old age and that integrated,rather than bifurcated, approaches to the study ofhealth di�erentials are needed. Further analyses of

changes in health indicators between baseline and fol-low-up, and of associations with characteristics at fol-

low-up currently in progress may allow moreexploration of possible causal pathways and shed

further light on these associations. Studies based onother data sets are also needed.

Acknowledgements

The research reported here was supported by theEconomic and Social Research Council (UK) as partof its Health Variations Programme (Phase 2), grantreference number L128 25 1040. The British Retire-

ment Survey was commissioned by the UK Depart-ment of Social Security (DSS) and carried out by theSocial Survey Division of the O�ce of National Stat-

istics (formerly the O�ce of Population Censuses andSurveys). Respondents were interviewed twice: once in1988/89 and again in 1994. The DSS commissioned the

Institute of Fiscal Studies and the Age Concern Insti-tute of Gerontology to analyse the combined datasetand ®ndings were published in the report `Thedynamics of retirement' edited by R. Disney, E.

Grundy and P. Johnson, DSS Research Report No.72, The Stationery O�ce. Views expressed in thispaper are not necessarily those of the DSS or any

other government department.

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Adult life experiences and health in early old age in Great Britain