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Death, Dropout, and Longitudinal Measurements of Cognitive Change in Old Age

¹ Department of Experimental Psychology, University of Oxford, England and University of W. Australia.
² Department of Statistics, University of Oxford, England.

Address correspondence to P. M. A. Rabbitt, Department of Experimental Psychology, University of Oxford, England. E-mail: Patrick.Rabbitt{at}psy.ox.ac.uk

	Abstract

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During a 20-year longitudinal study of cognitive change in old age 2,342 of 5,842 participants died and 3,204 dropped out. To study cognitive change as death approaches, we grouped participants by survival, death, dropout, or dropout followed by death. Linear mixed-effects pattern-mixture models compared rates of cognitive change before death and dropout from four quadrennial administrations of tests of fluid intelligence, vocabulary, and verbal learning. After we took into account the significant effects of age, gender, demographics, and recruitment cohorts, we found that approach to death and dropout caused strikingly similar reductions in mean test scores and amounts of practice gains between successive quadrennial testing sessions. Participants who neither dropped out nor died showed significant but slight cognitive declines. These analyses illustrate how neglect of dropout miscalculates effects of death, of worsening health, and of all other factors affecting rates of cognitive change.

Key Words: Cognitive change • Death • Dropout

Many studies of changes preceding death in old age have found that individuals who are between 18 months and 11 years from death score less well on tests of mental abilities than survivors do (e.g., Johansson & Berg, 1989; Lieberman, 1965; Palmore & Cleveland, 1976; Rabbitt, Watson, Donlan et al. 2002; Reimanis & Green, 1971; Riegel & Riegel, 1972; Riegel, Riegel, & Meyer, 1967; Siegler & Botwinick, 1979; Small, Fratiglioni, von Strauss, & Backman, 2002). The typical methodology has been to assess participants only once and to compare the scores of survivors and decedents at a later, arbitrary, census date. This underestimates the effects of death, because younger decedents are compared against younger survivors who will survive longer beyond the census date whereas elderly decedents are compared against elderly survivors who are likely to die soon after census (Rabbitt, Lunn, & Wong, 2005; Rabbitt, Watson, Donlan, Bent, & McInnes, 1994). This explains the otherwise paradoxical findings that differences in ability between deceased persons and survivors are larger in younger than in older samples (e.g., Riegel & Riegel; Riegel et al.).

Longitudinal studies in which participants are repeatedly assessed over many years avoid these problems but encounter other methodological difficulties. Participants improve with practice as a result of repeated testing (Rabbitt, Diggle, Holland, McInnes, Bent, et al., 2004); recruitment cohorts may markedly differ in ability; and participants typically drop out of studies because of deteriorating health and survivors become increasingly elite and able (Lachman, Lachman, & Taylor, 1982; Rabbitt, Watson, Donlan, Bent, & McInnes, 1994; Schaie, Labouvie, & Barrett, 1973). Thus, if scores of decedents are compared against those of all survivors, including less able dropouts, then the effects of approaching death are underestimated (Rabbitt et al., 2005). Demographic factors must be taken into consideration. Women, the socioeconomically advantaged, and the most able live longer, so deaths selectively alter sample composition (Hart et al., 2003). More and less advantaged individuals tend to die from different causes and so experience different patterns and rates of terminal declines in health and cognition (Nagi & Stockwell, 1973; Pincus, Callahan, & Birkhauser, 1987; Snowden, Ostwald, Kane, & Keenan, 1989). Differences in age must be considered because, independent of approach to death, age accelerates the rate of change in performance over time; the effects of pathologies, and so of approaching death, may also alter as age advances (Rabbitt, Diggle, Holland, & McInnes, 2004).

Data from the University of Manchester Longitudinal Study, described in detail elsewhere (Rabbitt, Diggle, Holland, McInnes, Bent, et al., 2004), allowed us to make analyses of rates of change preceding death and dropout after the effects of practice, age, cohort effects, and demographics had also been considered.

	METHODS

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Participants and Procedure
A panel of 5,842 volunteers, that is, 2,615 residents of Greater Manchester and 3,227 residents of Newcastle-upon-Tyne, United Kingdom, were all sufficiently healthy and motivated to travel independently to the University of Newcastle-upon-Tyne or the University of Manchester, where they were given cognitive tests in groups of 10 to 20. There were 1,711 men aged between 49 and 93 years (M = 65.6, SD = 7.7) and 4,131 women aged between 49 and 92 years (M = 64.4, SD = 7.8). They were each paid £5 (UK) per session to cover expenses. A search by Her Majesty's Registry Office UK obtained exact dates and proximate causes for all 2,342 deaths between 1983, when the study began, and the census date, July 2004. Of 3,204 dropouts, 1,208 also died before the census date. Most dropouts only appeared as failures to answer invitations for testing and so could only be dated from the last session attended. Consequently, to compare rates of changes preceding dropout and death, we had to use the same dating. We could not compare changes preceding dropouts from different causes because many participants did not reveal them. However, a previous survey found that, although most respondents before 1993 cited poor health as their reason for dropping out, many cited positive reasons such as taking up new employment (Rabbitt et al., 1994). Thus, data pooled over all dropouts underestimates the effects of illness and increasing frailty. The remaining 1,996 participants did not drop out before July 2003 and also survived the July 2004 census of deaths. During the course of the study from 1983 to 2004, 7 participants were identified as suffering from dementias, either by death certificates or information from relatives, and we excluded these. Since 2004, all survivors have been screened every 6 months on the Mini-Mental State Examination and other assessment protocols for dementias, and so far nine cases have been identified. We have also retrospectively excluded these. Although it is impossible to be certain that no individuals contributing data did not also suffer from dementia, the latter figure suggests that the incidence was sufficiently low as to have hardly any effect on the comparisons described.

Materials
Some of us have given details of the entire study elsewhere (Rabbitt, Diggle, Holland, McInnes, Bent, et al., 2004). The data analyzed here are from tests of general fluid intelligence, that is, the Heim (1970) AH4-1 intelligence test; of vocabulary, that is, the Raven (1965) Mill Hill B Vocabulary Test; and cumulative verbal learning, that is, the Cumulative Verbal Learning task (CVL task) administered on four successive occasions at 4-year intervals between 1983–1985 and 2003. The AH4-1 test consists of 64 logic, verbal comparisons, and arithmetic problems. Scores are the percentages of correct answers given within 10 minutes. The Mill Hill B Vocabulary Test requires correct definitions for each of 34 words with no time limit. For the CVL task, 15 three-syllable words matched for frequency (1/100,000) and concreteness are projected, one at a time, on a screen at a rate of 1/1.5 s. Participants recall as many words as possible and the words are then shown three times in different random orders and recalled without sight of previous attempts. Scores are percentages of correct answers.

Levels of socioeconomic advantage (SEA) are indexed by the Office of Population Censuses and Surveys (1980) in their classification of occupational categories. These categories are as follows: C1, made up of professionals such as doctors, lawyers, senior managers, and academics; C2, which consists of other professionals such as schoolteachers, junior managers, and pharmacists; C3N, made up of skilled nonmanual workers such as secretaries and clerical workers; C3M, consisting of skilled manual workers such as plumbers, craftsmen, joiners, fitters, and machinists; C4, made up of nonskilled, nonmanual workers such as security guards; and C5, which consists of nonskilled manual workers such as cleaners. We categorized those participants who did not reveal occupations as nonresponders (labeled NR). We include SEAs, city of residence, gender, and recruitment cohort in all analyses.

We divided participants into 11 groups according to their histories of survival, dropout, and death, logged with respect to the four quadrennial assessment time points: T1, T2, T3, and T4. The groups, labeled as D (for death), W (for withdrawal), or WD (for withdrawal followed by death), are as follows: D1 completed T1 but died before T2; D2 completed T1 and T2 but died before T3; D3 completed T1, T2, and T3 but died before T4; D4 completed T1, T2, T3, and T4 but died before the close of the census of deaths in 2004; WD1 completed T1 but withdrew before T2 and also died before the 2004 census; WD2 completed T1 and T2 but withdrew and then died before the 2004 census; WD3 completed T1, T2, and T3 but withdrew and then died before the 2004 census; W1 completed T1 and withdrew before T2 but survived the 2004 census; W2 completed T1 and T2, withdrew before T3, but survived the 2004 census; W3 completed T1, T2, and T3, withdrew before T4, but survived the 2004 census. Group C was a control group whose members completed all four assessments and survived the 2004 census.

The average raw scores for each of these death and dropout groups at each of the four testing occasions, that is, T1, T2, T3, and T4, are plotted for the AH4-1 test in Figure 1, for the CVL task in Figure 2, and for the Mill Hill B Vocabulary Test in Figure 3. Note that we have not taken effects of differences in age, recruitment cohort, and demographics into consideration. Although these means illustrate the logic of grouping individuals in this way, they only provide very approximate indications of specific comparisons between data points. Results of comparisons between these means, after age, cohort, and demographics have been considered, are described in the following paragraphs.

View larger version (9K): [in this window] [in a new window]   Figure 1. Profiles of percentage scores on the Heim AH4-1 test (AH41) of fluid intelligence across testing sessions for survivor (C), death (D), and dropout (W) groups

View larger version (9K): [in this window] [in a new window]   Figure 2. Profiles of percentage scores on the Cumulative Verbal Learning task across testing sessions for survivor (C), death (D), and dropout (W) groups

View larger version (9K): [in this window] [in a new window]   Figure 3. Profiles of percentage scores on the Mill Hill B Vocabulary Test (MHB) across testing sessions for survivor (C), death (D), and dropout (W) groups

	RESULTS

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Table 1<--CO?5--> shows details of the demographic and age categories, and death and withdrawal categories, and mean scores of these subgroups on the AH4-1 test, the Mill Hill B Vocabulary Test, and the CVL task.

The aim is to determine the effect of imminent death on a participant's cognitive performance. However, some participants who withdraw survive beyond the census date whereas others do not, giving three mechanisms by which they fail to complete the tests: death, withdrawal, and withdrawal followed by death. The statistical method determines whether or not these three mechanisms have similar effects on the test scores.

There are at least two possible approaches to handling missing data. One uses a selection model, and the other uses a pattern-mixture model. We are interested in a retrospective analysis of the effects of death and dropout, and so a pattern-mixture model is an ideal tool. The probability density function is factored a<--CO?1-->s f(y, d) = f(y|d)f(d), where y represents the response and d represents the dropout data. We will be interested in the first factor on the right-hand side of the equation, which represents the response conditional on the dropout pattern. In this data set, dropouts occur between test sessions or between the last session and the end of the study, but they are also of three types: death, dropout followed by death, and dropout with survival after census. We can thus classify volunteers into groups by dropout type or pattern. We model the conditional density as we did in Rabbitt and colleagues (2001), that is,

where the mean value of Y_ij is given by

Index i is the identifier of the volunteer, index j gives the occasion of the test (j = 1, ..., 4), and x_ijk denotes the values of the explanatory variables, where k runs from 1 to p and g_im is an indicator, a value 1 denoting to which of r type or pattern groups the ith volunteer belongs. Only one of these indicators is nonzero. The first two variables x_ij1 and x_ij2 are related to age and age squared, with a value of zero corresponding to age 49, which is close to the mean age.

As in Rabbitt and colleagues (2001), the random effects (Level 2) are given by A_i + B_ix_ij1 , where (A_i, B_i) are bivariate normal with a mean of zero and covariance matrix

and are independent of the Level 1 error, E_ij, which follows a univariate normal distribution with a mean of zero and variance _E².

This allows us to quantify the mean effect of death or withdrawal, including also the test occasion by which this has occurred. It also allows us to make a direct comparison of the effects of death and of withdrawal, as we shall see.

We have also considered interactions between the various explanatory variables and these are included in the final models.

Analysis of AH4-1 intelligence test scores
To illustrate general trends common to all tasks, we give analyses of AH4-1 scores in detail. Scores for vocabulary and cumulative learning are jointly discussed in the subsequent text. Table 2<--CO?6--> shows results from a linear mixed-effects pattern-mixture model comparing percentages of correct unadjusted AH4-1 scores for the death and dropout groups after the effects of age, gender, occupational category, city of residence, recruitment cohort, and practice are considered. Age was centered at 49, the lowest recorded in the study. Variance between individuals has been modeled with random effects because these are longitudinal data and measurements from the same individual are correlated.

There are, however, some significant interactions of age with practice gains. The interaction between age and the difference between scores at T1 and T2 is not significant, but the interactions between age and the T3 versus T1 difference (p =.0086) and between age and the T4 versus T1 difference (p =.0089) are significant. In survivors, deceased individuals, and dropouts alike, the effect of age on test scores increases with the interval over which it is measured. Overall, men (M = 50.9, SD = 17.7) scored higher than women did (M = 47.4, SD = 17.4). The significant Age x Sex interaction shows that even when longevity has been taken into account, women decline less as they age. Mancunians or persons from Greater Manchester (M = 49.6, SD = 17.7) score higher than Novocastrians or persons from Newcastle (M = 47.5, SD = 17.3). Scores range from 61.1, SD = 14.0, for C1 (the most advantaged SEA group), to 31.0, SD = 16.6, for SEA C5. This is reflected in the main model, shown in Table 2. There, we can see that the mean score of C5 is over 14% lower than that of C3 (both C3N and C3M), the baseline class, and that of C1 is 9.5% higher than C3 (C3N and C3M). There are significant differences between recruitment waves with performance for entry years 1990 and 1992 that are greater than those for 1985 (baseline). There is a highly significant practice gain between T1 and T2 followed by rather less substantial gains (less than 2%) between T2 and T3 and between T3 and T4.

After we consider these effects and interactions, then we compare the death, withdrawal, or death followed by withdrawal groups against those for the control group of survivors, Group C. Table 3<--CO?7--> compares average scores throughout the study between Group C survivors and all others by using t values computed from the main model. Group C survivors score higher than any of the members of the death and withdrawal groups except Group D4, whose members also completed all four assessments but then died within the 12 months before the death census in July 2004. Table 3 shows comparisons by two-tailed t tests between all other death and withdrawal groups. After applying a Bonferroni correction for multiple testing, we take the threshold level for significance for these multiple comparisons as p =.001.

Similar results can be seen for comparisons between Group C and (dropout and survivor) Groups W1 through W3. Rather more pronounced results of a similar nature are also seen with Groups WD1 through WD3 (participants who dropped out and subsequently died).

Both from the raw data (Figure 1) and from the main model (Table 2), we see that death, dropout, and dropout with subsequent death persons do indeed model in much the same way as do overall survivors who did not withdraw.

Analyses for cumulative verbal learning and vocabulary
Tables 4<--CO?8--> and 5 and Tables 6 and 7 show the same analyses for correct percentage scores on the CVL task and Mill Hill B Vocabulary Test, respectively.

Tables 5 and 7 show the results when t values from the main models for the CVL task and the Mill Hill B Vocabulary Test are used to compare overall scores between all death or withdrawal groups throughout the study. The corrected threshold for significance is p =.001.

For the CVL task, Tables 4 and 5 show that overall average scores for the D3 and D4 groups are significantly higher than for the D1 and D2 groups and that the D2 group scores higher than the D1 group, although this last item is not significant at the corrected level of p =.001. In other words, mean declines in verbal learning scores accelerate as death approaches. There are similar graded effects of dropout, with scores for the W3 group being higher than those for the W2 or W1 group and scores for the W2 group being higher than those for W1. The D1 and the W1 groups do not differ, suggesting that over this brief interval the effects of impending withdrawal are as severe as those of impending death. For the Mill Hill B Vocabulary Test, Table 6 shows that the mean differences follow similar patterns. Even scores on a test of crystallized intelligence, production vocabulary, fall as death approaches. There are no differences between those groups who drop out and then die shortly thereafter and those who die without first dropping out.

	GENERAL DISCUSSION

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These analyses replicate the main findings of Rabbitt and colleagues (2005) for AH4-1 intelligence test scores on a much larger sample, extend and compare these to the CVL task and production vocabulary (Mill Hill B Vocabulary Test), and examine the time courses of changes in greater detail, concluding that mean scores on all analyzed cognitive tests decline according to nearness of death or dropout.

Time Courses of Effects of Death and Dropout
There are significant declines up to 8 years preceding death, but participants who survived for more than 8 years after their first assessment (T1) performed almost as well as those who survived the final testing session. Because there is no Age x Death or Age x Dropout interaction, there is no evidence, in this study of atypically healthy participants, of change in the sizes or the time courses of the cognitive effects of approaching death between the ages of 49 and 93 years.

A new finding is that amounts of decline preceding dropout closely resemble those before death in all tests. Because only some participants gave reasons for dropout, we could not compare the decline of individuals who dropped out for different reasons. However, some of us (Rabbitt et al., 1994) found that the group of dropouts who gave reasons for withdrawal included relatively healthy and able people who withdrew for reasons such as employment. Because these analyses include such "robust dropouts," the actual similarity of declines preceding dropout caused by illness or frailty to those preceding death must be even closer than these analyses suggest. The finding that amounts of declines preceding impending death and dropout are so strikingly similar suggests that they are both caused by declining health. On this interpretation, death and dropout are empirically useful, but rough and indirect, markers for worsening health. The effects of approach to death, on their own, tell us little about the functional causes of cognitive decline. To learn more we must study precisely how particular terminal illnesses affect our brains and central nervous systems.

Are Different Cognitive Abilities Differentially Affected by Death and Dropout?
Declines with age for production vocabulary (Mill Hill B Vocabulary Test) are much less (and nonsignificant) than those for intelligence and cumulative verbal learning. Vocabulary, a skill crystallized because it is acquired early in life and maintained by continual practice into old age (Horn, 1987; Horn, Donaldson, & Engstrom, 1981), is not only relatively resistant to "normal" aging but also to the pathologies that accompany aging and that may terminate in death. The effect of death or withdrawal group is also weaker in Mill Hill B Vocabulary Test.

This result differs from previous findings that vocabulary test scores may be especially sensitive to approaching death (Berg, 1987; Birren, 1965; Siegler, McCarty, & Logue, 1982). It is noteworthy that these previous studies were cross-sectional and used relatively elderly samples and brief census periods of 24 months or less. It is therefore possible that participants in these studies were quite near to their deaths but that this could not be observed because the census periods were so short. It is therefore plausible that declines in vocabulary only become marked very shortly before death and so were less pronounced in the present study, in which the times of measurement were 4 years or longer. It seems likely that individuals who are, as yet, only experiencing declines in so-called fluid abilities are relatively further from death than those whose terminal pathologies have become severe enough to affect even their crystallized abilities. Accepting this context declines in vocabulary are particularly sensitive markers of approaching death.

Effects of Gender and Demographic Variables
Women perform better than men on the CVL task and on the Mill Hill B Vocabulary Test (the latter at a marginal significance level), and men perform better than women on the AH4-1 intelligence test. On the AH4-1 task, although women score lower on average than men do, they also decline more slowly as they grow older. This is informative because the effects of differences in death and dropout have been taken into consideration, as the slower declines of women of advancing age cannot be attributed simply to their greater longevity or lower dropout rate. This raises a fruitful topic for further investigation. It is possible that examining the differences in the nature of terminal pathologies in men and women would be informative.

A different point is that significant gender advantages in baseline scores are opposite on different cognitive tests. This means that if we do not take into account the typical progressive, age-related increases in the proportion of women to men during longitudinal studies, then our speculations as to which cognitive abilities are most and least sensitive to age and pathology will be insecure.

Recruitment cohorts differ markedly in cognitive test scores and, as exploratory analyses show, also in average levels of SEA. Because mortality is strongly associated with level of general intellectual ability (Hart et al., 2003) and with SEA (e.g., Nagi & Stockwell, 1973), it is essential that researchers check for differences between recruitment cohorts when analyzing the effects of impending death on cognitive performance.

Methodological Issues
Most studies have found that particular pathologies that become common in later life, such as diabetes (e.g., Bent, Rabbitt, & Metcalf, 2000), hypertension and other cardiovascular problems (Fahlander et al., 2000; Hertzog, Schaie, & Gribbin, 1978; Lopez et al., 2003), respiratory problems (Holland & Rabbitt, 1991), and undifferentiated health problems (McInnes & Rabbitt, 1997), have significant but surprisingly small effects on cognitive performance. Most studies have compared patients and healthy controls only at a single time point, and the few longitudinal studies have ignored death and dropout. The current analyses suggest that if individuals in so-called patient groups who die or withdraw are not included in comparisons against healthy controls, then the true effects of pathologies must be severely underestimated. If dropouts and deaths are excluded from analyses then the comparisons will involve only patients whose conditions are, as yet, relatively mild. The effects of terminal declines and of early stages of pathologies can only be compared if all deaths and dropouts are logged and taken into account.

	Footnotes

 
Decision Editor: Thomas M. Hess, PhD

Received for publication June 28, 2005. Accepted for publication January 30, 2008.

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