Cognition and survival: an exploration in a large multicentre study of the population aged 65 years and over

Medical Research Council Cognitive Function Ageing Study Writing Committee:R Nealea, C Brayneb and AL Johnsonc

a Queensland Institute of Medical Research, Post Office, Royal Brisbane Hospital, QLD 4029, Australia. E-mail: racheln{at}qimr.edu.au
b Department of Public Health & Primary Care, University of Cambridge, Institute of Public Health, Forvie Site, Robinson Way, Cambridge CB2 2SR, UK. E-mail: carol.brayne{at}medschl.cam.ac.uk
c Medical Research Council Biostatistics Unit, Institute of Public Health, Forvie Site, Robinson Way, Cambridge CB2 2SR, UK. E-mail: tony.johnson{at}mrc-bsu.cam.ac.uk

Abstract

Background Understanding the patterns in determinants of survival becomes increasingly important as the population ages. Dementia is known to shorten survival as is impaired cognition. Whether this is a continuous phenomenon and independent of other explanatory variables is less clear.

Objectives To examine a population-based dataset in which a measure of cognitive function (Mini-Mental State Examination [MMSE]), self-reported physical health and lifestyle variables were measured at outset, with monitoring for mortality thereafter.

Methods The five identical sites of the Medical Research Council Cognitive Function and Ageing Study (MRC CFAS) were analysed, with populations in rural Cambridgeshire, Gwynedd, Newcastle, Nottingham and Oxford. Survival curves were modelled and stratified analyses carried out, with physical disease, sociodemographic variables and lifestyle variables as covariates.

Results There was a strong and consistent reduction in survival probability for each decrement in MMSE. Adjustment for known confounders did not alter this pattern. Social class and education in particular had no additional effect. Self-reported health was the only other associated variable.

Conclusion Cognitive function appears to be a marker of capacity for survival in the UK. Terminal decline can account for some of this. Actuarial survival provided here can give carers and service providers an idea of prognosis at given ages and levels of cognition, and provide baseline data for those planning interventions in similar groups.

Keywords MRC CFAS, MMSE, survival, self-reported health, epidemiology

Accepted 1 May 2001

During the last 50 years the survival rates, and consequently the demographic profile of the British population, has changed dramatically. In particular, the proportion of older people in the population has increased, with the number of people aged >=80 years increasing by 245%.1 A greater understanding of predictors of survival is important to understanding risks and health in the elderly, and to enable an assessment of the impact of future interventions and changes in the risk factor profile of the population.

A variety of populations have been studied to show the relation between cognitive impairment and survival using a number of different tests of cognitive function. It is now generally accepted that overt cognitive dysfunction leads to significantly shortened survival.2,3 A number of studies have also demonstrated a relation between cognitive impairment and survival but the relation between very mild levels of impairment and survival is less well established.4 Recently a number of studies have been reported in which the survival of those whose cognition was considered to be in the ‘normal’ range, but was at the lower end, was shown to be reduced compared to those with higher performance.5,6 However, one of these studies was restricted to those aged over 85 years,5 and the other used a sample selected from a geriatric rehabilitation unit.6 A comprehensive systematic review has confirmed these observations but notes that little exploration of the effects and interrelationships of age, subtype of dementia and comorbidity has been carried out. The Medical Research Council Cognitive Function and Ageing Study (MRC CFAS)7 comprises a very large population-based sample encompassing a broad age range, giving an opportunity to explore further the relationship between cognitive function and survival among different age and sex groups within the population, accounting for the variables of importance in survival.

Methods

The methods of the CFAS have been previously reported.7 Each centre received Local Research Ethics Committee approval. Informed consent was received from participants. Briefly, participants in their 65th year and above were randomly selected from Family Health Service Authority lists in five areas of England and Wales (rural Cambridgeshire, Gwynedd, Newcastle, Nottingham and Oxford). These lists constitute a comprehensive population register, particularly in older age groups, as medical care is dependent on registration with a general practitioner. Stratification ensured equal numbers in the 65–74 and >=75 years age groups.

The initial screening interview was carried out between 1991 and 1994 on the entire sample by trained interviewers using laptop computers in participants' homes. All interviewers were trained by the National or Local Co-ordinator. Work was monitored by checks of paperwork, data and taped interviews, and by accompanied interviews. Quality control sessions were held, where all interviewers rated the same tape and discrepancies were discussed and resolved. Centre performance was monitored via regular checks on progress, refusal rates, and duration of interview.

The interview included items on sociodemographic variables, lifestyle and health factors. Cognitive function was estimated using the Mini-Mental State Examination (MMSE).8 Missing MMSE items that could not be asked because of impairment due to blindness, deafness, stroke and neuromusculoskeletal disorders were set to zero although interviewers were instructed not to assume items could not be attempted. If a MMSE item was missing for reasons other than physical impairment the whole MMSE was coded as missing. Refusal to attempt an item was coded as error.

Participants were flagged on the Office of National Statistics National Health Service Central Register, resulting in automatic notification of death. To allow for a lag between death and notification, this analysis was restricted to deaths occurring prior to 1 July 1997; all participants alive at this date were censored in the analysis.

The association between cognitive function and survival was assessed by the Cox Proportional Hazards regression model using the PHREG procedure in SAS.9 Both simple stratified analyses and full modelling procedures were used. Departures from the proportional hazards assumption were assessed by examination of log(-ve log) survival curves: none were found. Variables were included in multivariate models on the basis of their effect on the estimated association between MMSE and survival, as well as their effect on the association between age and MMSE found in previous analyses.7

A variety of covariates reporting comorbidity were considered in the analysis. A history of chronic disease was assigned if a participant reported ever having been diagnosed with myocardial infarction, angina, stroke, intermittent claudication, transient ischaemic attack, Parkinson's disease, epilepsy, asthma, chronic bronchitis or diabetes. Although questions about cancer were not specifically asked at interview, a review of responses to a question asking about any other significant illnesses revealed very few participants with a history of cancer. Social class was assigned according to the Registrar General's method on the basis of occupation during most of working life, and for married women, husband's occupations. Parental social class was not collected. Education was measured by the number of years of full-time education. Smoking was categorized into current, past or never. Housing was separated into community or residential care (including nursing homes and long-stay hospitals).

Results

Version 4.1 of the dataset is used. From a sampling frame of 123 691 people on the Family Health Services Authority (FHSA) lists in the five identical CFAS centres, 20 234 were randomly sampled. After duplicate records, deaths and migrants from the areas had been omitted an eligible sample of 16 258 was eligible for approach. In all, 13 006 participants (80%) agreed to the first interview (prevalence screen). Sixty per cent of the sample was female and ages ranged from 64 to 105 with a median of 75 years. The follow-up time from interview to the cut-off date of 30 June 1997, ranged from 3 to 6.5 with a median of 4.7 years. Of the participants, 3446 (26.5%) died during this period. There were MMSE scores available for 12 552 participants (96.5%) (Table 1Go).


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Table 1 Number of participants and deaths in sex, age and Mini-Mental State Examination (MMSE) strata
 
Cognitive dysfunction measured by MMSE was divided into none (MMSE 28–30); mild (24–27); moderate (19–23); and severe (0–18); these groups were chosen on the basis that they represent high functioning, intermediate, mild and severe impairment, and are similar to those used for identification of dementia in screening studies. There was a further group in whom MMSE could not be tested (MMSE missing). There was a strong and consistent reduction in survival probability associated with a lower MMSE. Compared with those with no cognitive dysfunction, the age- and sex-adjusted hazard ratios among the mild, moderate and severely impaired groups were 1.24 (95% CI : 1.13–1.36), 1.77 (95% CI : 1.60–1.97) and 3.20 (95% CI : 2.82–3.63) respectively. Further adjustment for identified confounders reduced the strength of these associations, although the trend of decreasing survival with decreasing cognition remained (Figure 1Go).



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Figure 1 Actuarial survival for various categories of scores in the Mini-Mental State Examination (MMSE), adjusted for age, sex, education, self-perceived health, history of chronic disease, housing and smoking. (Hazard ratios and 95% CI relative to MMSE 28–30 are MMSE 24–27: 1.14 [95% CI : 1.04–1.25]; MMSE 19–23: 1.49 [95% CI : 1.33–1.66]; MMSE 0–18: 2.11 [95% CI : 1.82–2.45])

 
Stratified analyses were conducted to examine strata-specific associations between MMSE and survival. There was little evidence for modification of the effect of MMSE by education, smoking, marital status, housing, chronic disease or self-assessed health (data not shown). However, there was some indication that the association was reduced among the older old (those >85 years), particularly among men. Inclusion of an interaction term in the complete model presented in the Figure supported this finding, but resulted in instability of the model. Although stratification by age, sex and MMSE resulted in small numbers in some categories (Table 1Go), the number of deaths in most of these was sufficient to allow a meaningful analysis, and age- and sex-specific models have therefore been presented (Table 2Go). In all categories, other than older men, an MMSE of 0–18 conferred an approximately two-fold hazard, while moderate impairment resulted in about a 50% increase in the hazard rate. Even those with only minor impairment of cognition experienced an increase in the hazard rate of approximately 10%. This pat-tern was not observed in men aged >=85 years in whom there was no significant association between MMSE and survival. Table 3Go presents actuarial percentage survival at 1, 3 and 5 years from the screening interview by sex, age group and MMSE.


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Table 2 Multivariate models within separate age and sex strata
 

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Table 3 Actuarial percentage survival at 1,3 and 5 years from screening interview by sex, age group and Mini-Mental State Examination (MMSE)
 
The proportionality assumption for the effect of MMSE on mortality was tested using time-dependent covariates to code the interaction between MMSE and survival time; no evidence for departure from proportional hazards was found ({chi}2 = 0.79, d.f. = 3, P = 0.85).

The proportion of participants with MMSE missing was higher in those who died (9% compared with 2% in those alive) and in older participants (percentages missing were 1, 4 and 12 in 65–74, 75–84 and >85 year olds respectively). There were insufficient numbers to enable a stratified analysis of this group. However, the hazard ratio for those with missing MMSE (compared with the group with no impairment) adjusted for all the covariates included in the above models was 1.61 (95% CI : 1.28–2.02).

Although education confounded the MMSE-survival relationship, there was no evidence for effect modification and it was not itself associated with survival in this analysis. Social class had no additional effect and was not included in the model. The covariate demonstrating the strongest and most consistent association with survival was self-perceived health; there was an approximate three-fold increase in risk in men and women <85 years who reported that their health was poor in comparison with others of their age, and a doubling of risk in the older ages. The relationship with chronic disease was stronger in men than in women, and again non-significant in older participants. The expected association between survival and smoking was observed in this study; there was a reduced although still significant survival decrement among those who had given up smoking compared with those who were current smokers (Table 2Go).

The MMSE remains a predictor of mortality even when restricted to subjects with a low level of education (<=8 years). Elimination of MMSE from the multivariate model reported in Table 2Go gives {chi}2 = 18.9, d.f. = 3, P < 0.001. However, within the MMSE categories used here mild cognitive decline (MMSE 24–27) is not a significant predictor in those with a low level of education (HR = 0.80, 95% CI : 0.55–1.16, P = 0.25).

Social class was examined only in subsidiary analyses. It is correlated with years of education and this variable is preferred since it has fewer missing data and it avoids the problem of coding social class in a group which has retired. The multivariate models reported in Table 2Go include variables possibly related to mortality; in practice all were statistically significant except for education. This can be seen from the reported odds ratios, none of which is significantly different from one. Forward stepwise selection of variables including social class (in a slightly reduced dataset of 11 933 subjects) yields a multivariate model which includes all the variables presented in Table 2Go other than education; social class also is not selected.

Discussion

These results confirm the previously reported association between cognition and survival, although comparisons with most other reports are problematic due to the numerous differing methods of measuring cognitive function. Most studies have found a two- to three-fold relative risk among those with the lowest cognitive function10–13 but the association with marginal cognitive function is less clear. Goodwin et al.10 found a relative risk of 1.00 in people with only one error on the brief mental state test used. Similarly a marginal score in the Hodkinson abbreviated mental test showed no decrease in survival13 whereas others found an increase in risk of 30%11 to 50%.12

Two studies were found which categorized MMSE in the same way as this study, enabling a more direct comparison.5,6 The age- and sex-adjusted analysis in the Leiden 85-plus study5 resulted in higher estimates of the relative risk in the mildly and moderately impaired groups. If the age- and sex-adjusted analysis in CFAS data was restricted to those >85 years, the estimates were lower still (data not shown). The differences may be due to differences in MMSE in translation, administration or measurement. Control for other potential confounders in the previous study would enable further interesting comparisons. The study by Rossini et al.6 also found the risk associated with low cognitive function to be higher. However, the smaller sample size resulted in a greater degree of uncertainty regarding the true size of the relative risks. Additionally the hospital-based sample selection may explain a proportion of the greater risk, and this study should not be generalized to a randomly-selected population-based sample.

The possibility that the demonstrated relation between cognitive function and survival may be due to the confounding effects of chronic disease has been hypothesized, but assessments of this conjecture give somewhat conflicting results. Liu et al.12 found that removing sufferers of chronic disease from the analysis, or adjusting for chronic disease in a multivariate analysis, did not decrease the risk associated with poor cognition. By contrast, in another study, the inclusion of chronic diseases seemed to reduce markedly the relative risks for the moderately and severely affected groups, although the risk in the group with minor cognitive impairment was unaffected.6 The analysis presented above showed a marked reduction in risk when factors in addition to age and sex were included in the model, with the addition of self-perceived health having the most marked effect. This measure has consistently been shown to be associated with health and survival14–16 and may be a better indicator of underlying health status than attempted ascertainment of particular diseases. Specific measurement of depressive symptoms was not included in the screen and thus any contribution of depression on mortality cannot be estimated in CFAS. Nevertheless, the possibility that a more comprehensive assessment of current disease in this study would have further reduced the association between MMSE and survival cannot be disregarded, as suggested in analysis by Smits and colleagues.3

It is of interest that education and social class did not appear to remain important once other more clinical measures were made, supporting the view that much of the variation seen is accounted for by the dual impact on health.

The relationship between cognition and survival was relatively uniform across all age groups and both sexes in this study, with the exception of men aged >=85 years. This is almost certainly due to the markedly reduced life expectancy of all individuals in this age group and has been reported for survival with Alzheimer's disease in the very old in another study17 in which the relative risk almost drops to unity. Despite the lower relative risks, this study is consistent with previous findings and extends them to a multicentre longitudinal study. Even a minor impairment in cognitive function has detrimental effects on survival. The finding is robust even after accounting for a range of other characteristics.

It seems that cognitive function itself is a marker of the human organism's capacity to survive. To some extent cognitive function itself is a marker of cumulative exposure and this may explain the findings. Only very long-term cohorts from earlier ages can clarify how independent the effect is. Late terminal decline accounts for some of the findings and longitudinal analysis in this cohort will clarify the relationship of decline to mortality in the population. The value of this study is to provide estimates of the size of the mortality effect, as well as actuarial percentages surviving to 5 years from screening interview; these will be helpful to carers and service providers, providing some idea of prognosis. For those assessing the impact of new therapies for cognitive decline it should be useful for estimating drop-out and sensible trial duration;18 it will also be useful for comparing the performance of other population cohorts.


KEY MESSAGES

  • Mini-Mental State Examination performance is associated with survival at all levels.
  • Social class and education are not significantly independently related nor do they modify the effect of cognition on mortality.
  • Self-rated health is associated with survival independently of cognition and the other variables measured.
  • Actuarial survival in this population can be useful to clinicians and those planning interventions.

 

Acknowledgments

The MRC CFA Study is supported by major awards from the Medical Research Council and the Department of Health.

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