IJE Advance Access originally published online on January 19, 2005
International Journal of Epidemiology 2005 34(2):276-283; doi:10.1093/ije/dyh328
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Article |
Area deprivation, social class, and quality of life among people aged 75 years and over in Britain
1 Centre for Ageing and Public Health, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
2 University Department of Geriatric Medicine, Llandough Hospital, Penlan Road, Cardiff, Wales, CF64 2XX UK
3 Section of Care of the Elderly, Faculty of Medicine, Imperial College, Hammersmith Campus, Du Cane Road, London W12 0NN, UK
4 Public Health and Epidemiology Research Unit, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
* Corresponding author. Dr E Breeze, Centre for Ageing and Public Health, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK. E-mail: elizabeth.breeze{at}lshtm.ac.uk
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Abstract |
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Background There is a shortage of research studies that assess how selected characteristics of neighbourhood and personal social circumstances contribute towards health-related quality of life (QoL) among older people.
Methods Analysis of baseline data for 5581 people aged >75 years and over from the Trial of Assessment and Management of Older People in the Community. The scores for four dimensions from the UK version of the Sickness Impact Profile and for the Philadelphia Geriatric Morale Scale were analysed in relation to individual social class and the Carstairs score of socioeconomic deprivation for the enumeration district of residence.
Results In age and sex adjusted analyses, the proportion of participants of social class IV/V living in the most deprived areas who were in the quintile with worst QoL scores was more than double that among those from social class I/II living in the least deprived areas. Individual social class and area deprivation score contributed roughly equally to this doubling for home management, self-care and social interaction, whereas social class appeared a stronger determinant for mobility. Adjustment for living circumstances, health symptoms, and health behaviours substantially reduced the excess risk associated with social class and area deprivation. Being in a rural area was associated with lower risk of poor morale.
Conclusion Poor socioeconomic characteristics of both the area and the individual are associated with worse functioning (QoL) of older people in the community. This is not fully explained by health status. Policy should consider community-level interventions as well as those directed at individuals.
Keywords Quality of life, socioeconomic factors, older people, urban (rural), morale, deprivation, Sickness Impact Profile
Accepted 5 August 2004
Theoretical reasoning and limited empirical evidence suggest that health and well-being are influenced by local environmental characteristics as well as by personal circumstances.1–4 Research on such influences in the elderly is sparse although they are a growing part of the population who may be particularly vulnerable to poor functioning by virtue of low income and worsening health. Improved understanding about the contribution of individual and area characteristics to the well-being of the elderly population has evident policy importance. For example, benefits may accrue from initiatives that improve services and local environmental conditions. More deprived communities might offer less opportunity for healthy living because of high crime rates, poor housing conditions, environmental pollution, or lack of services, all of which are in some degree amenable to local intervention. The specific policy implications depend on whether collective resources dominate such that living in a deprived area equally affects all residents, or whether relative inequality dominates such that people of low socioeconomic position are at a greater relative disadvantage in non-deprived areas.5 Research in Britain to date has focused on the whole adult population6,7 or on people of working age.5,8–10 Here we report an analysis of quality of life (QoL) in relation to individual and area socioeconomic circumstances using data from a large-scale trial of people aged 75 years and over in primary care in Great Britain.
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Methods |
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The Medical Research Council (MRC) Trial of the Assessment and Management of Older People in the Community evaluated a package of multidimensional assessment and management of older people in the context of the 1990 contract of service that required family doctors [general practitioner (GP)] to offer annual health checks to people aged 75 years and over. The design and methods are fully described elsewhere.11 Through the MRC General Practice Research Framework 106 practices were recruited, selected to be representative of the joint tertiles of Jarman Scores12 (derived from 8 items of Census data that are correlated with GP workload) and Standard Mortality Ratios for practice locations. Over 1000 volunteer family doctor practices belong to the Framework, meaning that they are available to take part in research concerning primary care. People eligible for the annual health check, unless in nursing homes or terminally ill, were invited to complete a brief assessment. People in residential homes and sheltered accommodation were included. In a random subsample of 23 practices throughout Britain there was a longitudinal QoL component. Ethics committee approval was obtained for all practices.
The brief assessments included self-reported health, current alcohol consumption and cigarette smoking, availability of support for carers, and frequency of contact with family and friends outside the household. In half the practices a detailed assessment was routinely offered as well; this paper does not use the detailed assessment data because it was only available for 12 QoL practices.
QoL component
Trained interviewers, independent of the practice, administered QoL interviews in privacy in the patient's homes prior to the brief assessment, then 18 and 36 months later. This paper refers to the baseline QoL interview (undertaken 1995–1999). The core interview included four dimensions from the UK version of the Sickness Impact Profile (SIP) (Home management, Mobility, Self-care, Social interaction), and the Philadelphia Geriatric Morale Scale,13 a 17-item measure specifically developed for use with older people. These dimensions reflect quite disparate outcomes and were separately analysed.
Information was also collected on current living circumstance, previous housing tenure, main occupation of self and male spouse during working life, recent use of selected services, and regular informal help. We coded social class from job titles and brief job descriptions using the 1991 classification of occupations;14 where possible ever-married women were assigned their husband's social class because of the numbers with limited experience of paid work. Home addresses were postcoded (equivalent to zip coding) and thereby linked to 1991 Enumeration District (ED) census data and hence to population density and the Carstairs Deprivation Index. The Carstairs score is a summation of the normalized prevalence of unemployment, low social class, no car, and overcrowding as measured in 1991.15 The ED is the smallest area for which population data are available from the Census and on average covers around 140 households (400 people).
Objectives
The overall aim of our study was to assess how selected characteristics of one's neighbourhood and personal social circumstances combine to contribute towards health-related QoL.
The specific questions addressed were:
- Is the Carstairs deprivation score of the ED of residence associated with health-related QoL independently of individual social class?
- What is the cumulative strength of association of being in a low social class and in a deprived area compared with being in a high social class and undeprived area?
- Is the association with area deprivation greater, the same, or less among people with higher socioeconomic position than among those with lower socioeconomic position and vice versa?
- Does prevalence of poor QoL differ between rural and urban areas and is the relationship between deprivation and QoL different according to population density?
Recommended weightings, derived to reflect severity,17 were used for the SIP scores and the sum converted to a percentage of the maximum possible score for that person, given the items they answered. The morale score was a count of the number of answers indicating negative morale. Less than 1% of the participants could not be assigned a score because they answered fewer than half the component items of any one scale. These people were excluded from the analysis. Higher scores indicated worse QoL and being in the worst quintile of the scores in this sample was designated as poor quality. Area deprivation categories were defined by quartiles of the individuals' scores within the analysis sample. Smoothed local area population densities over a standard radius (5 km) were calculated from census data, using the mean population density of all ED's whose centroid falls within a 5 km radius of the ED in which the participant resided. This was categorized into four mutually exclusive groups: <250 people/km2 (most rural); 250–999 people/km2; <1000–2499 people/km2; >2500 people/km2 (most urban).
The participants included in the analyses are shown in Figure 1. In the 23 QoL practices 9547 people on the age-sex registers were eligible to participate in the trial of whom 91% were interviewed for QoL and 64% also took part in a brief assessment. Missing data on individual variables reduced the analysis sample to 5581 (58% of those from relevant practices).
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Statistical methods
After tabulating the characteristics of the study population, generalized estimating equations based on Poisson regression with robust standard errors were used to model the prevalence of poor QoL relative to the person's social class and the ED Carstairs deprivation category—this is an alternative to logistic regression and enables direct estimation of risk ratios. All models took into account the clustering by Practice using robust standard errors.18 The main results are shown for prevalence ratios adjusted for gender and age and also with additional adjustment for living circumstances, i.e. housing tenure and with whom the respondent lived, six self-reported symptoms and number of prescribed medicines (the latter an indicator of general frailty), self-reported smoking and alcohol consumption, and population density category.
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Results |
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The median age was 80.2 years and 40% were men. The characteristics of the sample are analysed in Table 1. The subsample useable for analysis was similar in distributions of potential explanatory factors to the full sample of nearly 33 000 people in 106 practices. People in the 23 practices who did not participate in a brief assessment were more likely to have poor QoL than those who did participate (prevalences standardized for sex and age among those excluded were 24–25% compared with 17–20% of those included in the analyses).
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One-third of the analysis sample (which omits those without a class) was in social classes I/II and one-sixth in social classes IV/V (Table 2). Six percent of men and women were in the worst personal and area socioeconomic position, 14% of men and 10% of women in the best position. The lowest percentage scores for people classified as having poor QoL were 54% for home management, 38% for mobility, 23% for self-care, 22% for social interaction, and 9/17 for morale. Although these thresholds were only quarter to half way up the scale, people in the worst quintile were much more likely to have any one of the limitations or negative morale elements than the other 80% of the sample (not shown).
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Relative risks of poor QoL by personal and area socioeconomic factors
Table 3 shows the percentage of participants in the quintile of highest (worst) QoL scores by social class and quartile of area deprivation. For clarity we show only the data for social class groups I/II and IV/V and for the top and bottom Carstairs quartiles. The variation of the influence of social class by area deprivation was examined by testing for interactions but for each outcome the interactions were neither statistically significant (P > 0.10 for all) nor substantial. The relative risk of a poor outcome if doubly disadvantaged (i.e. social class IV/V and most deprived quartile of deprivation) was 2.4–2.5 for home management, self-care, and social interaction, 2.1 for mobility and 1.7 for morale (Table 3 in bold). The relative risk (RR) from being in social class IV/V (RR 1.8, 95% Confidence interval (CI) 1.5–2.2) was markedly greater than that from being in the most deprived areas (RR 1.2, 95% 0.9–1.5) for mobility and somewhat greater for morale.
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The excess risks of being in the most deprived quintile on poor QoL outcomes were partially explained by the clustering of respondents by social class within areas. Excess risk of poor mobility and morale in the most deprived areas was marginal once social class had been taken into account. Living circumstances accounted for about a third of the remaining excess risk of poor self-care and social interaction in the most deprived areas but, surprisingly, only about a sixth of the excess for poor home management (not shown). Health symptoms accounted for about 30% of the excess risk of poor home management and self-care in the most deprived areas and 20% of the excess risk of social interaction. After adjustment for both living circumstances and health factors, the excess risk was about 30% for home management, self-care, and social interaction but of borderline statistical significance. These factors also attenuated the social class effect for all the outcomes. After additional adjustment for health behaviours (right hand model of Table 3) there remained unexplained the association of low social class with home management, mobility, and social interaction. Those with double disadvantage had increased prevalence of poor SIP states, even after allowing for the potential explanatory variables.
The risk of poor self-care was lower in the most urban areas once greater prevalence of health symptoms, low social class and rented housing were accounted for. In contrast, the risk of poor morale was lower in the more rural areas than in any other type of area before and after accounting for potential explanatory variables (Table 4). In these models neither Carstairs category nor social class were significant. There were no interactions between population density and either Carstairs quartile or social class with respect to associations with poor QoL.
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Discussion |
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This study provides new evidence about possible influences of individual-level and area deprivation on health-related QoL in the older population. People from the most deprived EDs who were also in social class IV/V had approximately double the risk of poor home management, mobility, self-care, and social interaction and almost double the risk of poor morale. Statistically, both the social class of the individual and the socioeconomic characteristics of the area contribute roughly equally to this doubling for three outcomes whereas social class dominated for mobility. Further adjustment for urban-rural location suggested that being in a rural area is more protective against poor morale than either high social class or being in an area of low deprivation.
These effects were adjusted for individual health status, which suggests that poor physical illness is not the only driver of the socioeconomic associations although the information on physical health was not sufficiently detailed to be sure that there is no residual confounding. However, the greatest attenuation after adjustment from health symptoms and behaviours occurred for morale. Macleod et al.19 caution against reading too much into attenuation of associations between self-reported psychosocial and physical health factors; it may be that low morale increases the tendency to report physical symptoms and vice versa. It is also of interest that morale had a weaker association with socioeconomic position than the SIP measures although the latter were themselves highly correlated with morale—this begs the question of the place of psychosocial measures in health inequalities.20
For home management and self-care the risk ratios comparing people in the most and least deprived extremes were higher (
2.4–2.5) in the analysis subsample than in the fuller sample that included non-respondents to the brief assessment (2.2). This difference is too small for selection bias to account for all the excess risks among the most disadvantaged people.
Misclassification of QoL was most likely where proxy interviews had taken place but fewer than 5% were in this position. Moreover, morale was thought too subjective to be collected by proxy. Misclassification of Carstairs category could occur if the address was out-of-date—however, the respondents had to be still living within the GP catchment area so misclassification in terms of 1991 classification is thought to be small. On the other hand, by the time of data collection between 1995 and 1999 some areas may have changed their deprivation profiles, for example if there had been regeneration projects in areas formerly heavily dependent on one industry. Misclassification of social class was possible because of long-term recall and proxy information. It has been found for death certificates, and may apply here, that proxy informers tend to inflate the status of others. This would again tend to dilute socioeconomic differences. However, we took extra care in looking at job descriptions as well as titles when coding managerial and engineering jobs, two groups that are particularly prone to status inflation.
There may have been some dilution of area effect from inclusion of people in residential or sheltered housing since these may be clustered in certain types of area and/or their residents less affected by their location. People in nursing homes, perhaps those least affected by the physical environment, were excluded from the study. If data had been available we would have explored whether effects were stronger for those who have lived in an area longer21 and whether cumulative history was a more powerful discriminator than current situation.22
We cannot be sure whether socioeconomic factors are causative partly because the data were cross-sectional. Indeed, cross-sectional data make reverse causation a theoretical possibility, e.g. people with functioning limitations may have moved to be near relatives or services. This might explain the small contrast between the least and most deprived areas for mobility. People living in the most rural areas who are emotionally adversely affected by ageing may move closer to people and facilities leaving behind those with higher morale. Social class should not be subject to reverse causation, as most limitations would develop after their working history was complete.
While there is literature on socioeconomic differentials in functional limitations among older people, some of it in relation to mobility23–26 and/or self-care27,28 suggesting that more socioeconomically disadvantaged people have worse outcomes, they have tended to look only at individual factors. Our study adds to this by providing evidence about independent associations of individual and area socioeconomic influences.
In four mortality studies that have separated out effects for older (usually 
65 years) and younger people, area associations were weak or non-existent among the older age groups but not the younger ones.29,30 This has been attributed to a survival effect22 but it is possible that the QoL of survivors still differs.
More generally, studies with outcomes varying from all-cause mortality to self-reported health have usually found a separate effect of area deprivation that is somewhat smaller than individual effects.29 In this study the area effect appeared as great as that of low social class, indicating double disadvantage of low social class people residing in poor areas. One possibility is that area has important and appreciable influences on health independent of social class; these could be material, such as poor housing and heavy traffic, cultural, or behavioural (i.e. conditions not conducive to health-promoting behaviours). On the other hand, the area measure could be acting as proxy for unmeasured aspects of individual social position.31 Reijneveld32 found that income attenuated parameters for area-level socioeconomic effects more than occupational status and educational level. In our study, adding in living circumstances (housing tenure and who lived with) reduced the area differentials and partially adjusted for another aspect of individual socioeconomic circumstance. However, housing tenure is not a straightforward measure of socioeconomic position in old age because those in supported accommodation may, for health reasons, no longer own their homes.
Our findings are consistent with other studies showing socioeconomic area parameters are attenuated by individual ones, suggesting part of the area variation is due to the population composition.9,10,30,32 The findings on differential effects of area according to socioeconomic position or vice versa have been mixed. We found no interactions between social class and Carstairs. Humphreys and Carr-Hill7 obtained inconclusive results as to whether or not area deprivation would make a difference for poor but not rich individuals. Reijneveld32 had inconclusive results. On the other hand, gradients by personal position were shallower in deprived than non-deprived areas for long-term limiting illness.6,9 In a Finnish study there was a negative gradient in mortality by education among people aged >64 years in the socially cohesive (possibly less deprived) areas but not in the un-cohesive ones.30 In contrast, there was some evidence from the second Whitehall cohort that area deprivation had a greater effect on people of lower socioeconomic position than on those of higher socioeconomic position,5 which they attributed to greater vulnerability to local conditions. Older people of lower socioeconomic status might be particularly affected by local conditions but our results were not consistent with this. Wen et al.33 argue that there needs to be a ‘concentration of affluence’ to generate sufficient social support before all the inhabitants can be favourably affected. Our study may have under-represented those in the most affluent areas and hence missed this effect.
There are debates about the most appropriate way to capture socioeconomic position in an elderly population34,35 that is some years beyond retirement age and where area-based Census statistics reflect characteristics of a younger part of the population. A combination of measures gives the best prediction34 and in younger populations measures taken at different stages of the life course have helped elucidate the times at which exposures may have most influence.36 Although more work is needed to develop measures, it is reasonable to conclude that the gradients we observed are likely to underestimate the true total strength of socioeconomic associations.
There will be more detailed area indicators in future37 so that pathways can be explored using more direct measures of integral aspects of the area such as service availability, housing and leisure facilities, and crime levels (inhibiting use of services).4 Other explanations that could be explored further include the possibility of poor health arising from disagreement between people's usual expectations of a place and the lived in reality38 or from negative perceptions of the community.39,40
In our study health symptoms and, to a minor extent, behaviour further attenuated area effects. Reijneveld32 found that an association of smoking with area deprivation was not explained away by individual socioeconomic position. In our age-group history of smoking rather than current smoking may have had an indirect effect via the contributions of health symptoms. Health symptoms are likely to be on the pathway between socioeconomic position and QoL—part of the attenuating effect of living circumstances may also reflect dependence among people living in sheltered accommodation or with relatives other than spouse.
Administratively defined areas are unlikely to coincide with individuals' perceptions of neighbourhood. We chose EDs, i.e. small areas, thinking that older people would be more locally oriented. However, features of larger areas—such as whether the wider area is one of retirement homes, or declining industry, or a commuter belt—could affect QoL via the cohesiveness of communities, informal support available, and opportunity to accumulate wealth.3
These results have implications for policies aimed at tackling health inequalities among older people. They support current policies that aim to alter both physical and cultural community environments (e.g. the Neighbourhood Renewal Strategy41), as well as to empower individuals to retain independence (e.g. the National Service Framework for Older People42 Standard 8). Risk of poor morale was strongly associated with the urban/rural divide rather than deprivation or social class so may need a different approach.
KEY MESSAGES
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Acknowledgments |
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The secondary-analysis project ‘Inequalities in quality of life among older people in Britain’ was funded by the Economic Social and Research Council, grant no L480254018, as part of its Growing Older programme. The trial was funded by the MRC, the Department of Health, and the Scottish Office, grant no G9223939. Apart from the investigators, the members of the Steering Committee of the Trial were Professor Sir John Grimley Evans (University of Oxford), Professor Carol Brayne (University of Cambridge), Professor Karen Luker (University of Manchester), Professor Mike Drummond (University of York). Interviewers managed by Dr Dee Jones' team undertook the QoL interviews. Data collection for the main trial was undertaken by research nurses from The General Practice Research Framework under the guidance of its Director Dr Madge Vickers and the Senior Research Nurse Nicola Fasey. P.W. is supported by a Public Health Career Scientist Award (NHS Executive CCB/BS/PJCS031).
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