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Journal of Gerontology: PSYCHOLOGICAL SCIENCES1999, Vol. 54B, No. 1, P44-P54

Copyright 1999 by The Gerontological Society of America

Psychological Predictors of Mortality in Old Age

Heiner Maier and Jacqui Smith

Max Planck Institute for Human Development, Berlin, Germany.

Cox regression models examined associations between 17 indicators of psychological functioning (intellectual abili-ties, personality, subjective well-being, and social relations) and mortality. The sample (N = 516, age range 70-103years) comprised participants in the Berlin Aging Study assessed between 1990 and 1993. By 1996, 50% had died.Eleven indicators were identified as mortality risk factors at the zero-order level and six when age was controlled.Low perceptual speed and dissatisfaction with aging were uniquely significant after controls for age, SES, health,and the 16 other psychological factors. Low intellectual functioning was a greater risk for individuals aged 70-84years than for the oldest old (over 85 years). The effects of psychological risk factors did not diminish over time.Future research should focus on the mechanisms and time frames that underlie the death-relatedness of intellec-tual functioning and self-evaluation.

EPIDEMIOLOGISTS are increasingly interested in pat-terns of late-life mortality and the health predictors

thereof (e.g., Manton, 1992; Manton, Stallard, Woodbury,& Dowd, 1994). In the gerontological literature there arealso several proposals suggesting that, in old age, lowerlevels of psychological functioning are associated with im-minent death. Cognitive functioning (Swan, Carmelli, &LaRue, 1995; White & Cunningham, 1988), personalitycharacteristics (Berkman, 1988; Friedman et al., 1995;Swan & Carmelli, 1996), social integration (Seeman, Ka-plan, Knudsen, Cohen, & Guralnik, 1987), and indicatorsof subjective well-being (Mossey & Shapiro, 1982; Pal-more & Jeffers, 1971) have all been implicated as relevantpredictors of mortality. To date, there have been few stud-ies with the explicit goal of comparing the predictivepower of these psychological domains in relation to mortal-ity in very old age. The present article is a first step towardaddressing some of the many open questions in this area.Of particular interest is the question whether the associa-tion with mortality in very old age is general to many do-mains of psychological functioning, or whether it is spe-cific to a few psychological characteristics. It is also notknown whether psychological predictors of mortality aredifferent for the old and the oldest old. A further questionconcerns the time frame of the predictive period (Ljungquist,Berg, & Steen, 1996). Does the mortality risk associatedwith lower psychological functioning change over time?

Previous research investigating psychological function-ing in relation to mortality has used several different per-spectives. Some studies have compared groups of survivorsand dropouts from longitudinal studies (e.g., Kleemeier,1962; Riegel, Riegel, & Meyer, 1967), whereas others haveused a dynamic terminal decline framework relating de-creases in functioning to the imminence of death (e.g.,Deeg, Hofman, & van Zonneveld, 1990). A third group ofstudies has examined associations between static baselinefunctioning and follow-up mortality information using sur-vival analysis (e.g., Swan et al., 1995; Wolinsky & John-son, 1992). In relating baseline functioning to prospec-tively collected mortality information, the present study

follows the latter, static approach. Data reported were col-lected in the Berlin Aging Study (BASE; Baltes & Mayer,1999; Baltes & Smith, 1997). These data provided a con-text for examining predictors of mortality in a sample (Mage = 85 years) that had survived longer than the averagelife expectancy for their birth cohorts.

Many studies have emphasised the role of chronologicalage and how best to incorporate age into a general frame-work explaining the relationships between functioning andmortality (e.g., Berkman, 1988; Manton, 1992; Palmore &Jeffers, 1971). Because there are age-associated changes inmany areas of psychological functioning, and because ageitself is a risk factor for death, it could be argued that it isonly interesting to examine predictors of mortality afterthese age-associated changes are taken into account. On theother hand, partialing age out of the relationship betweenpsychological functioning and mortality may result in lossof the very information that is most interesting to life-spanresearchers. Age, as an interindividual differences charac-teristic, reflects a complex host of resources, roles, and ac-cumulated experiences (Wohlwill, 1973).

The solution adopted in the present study attempted acompromise between these two positions regarding the roleof age. In a first set of analyses, we report zero-order andage-controlled associations between functioning on 17psychological dimensions and mortality. The set of age-controlled analyses takes into account the association be-tween age and distance from death. A further set of analy-ses explicitly examined the risk patterns for two age groups(70-84 years and 85-103 years). This latter strategy wasadopted in line with recent suggestions that, as a group, theoldest old (i.e., persons over 85 years) are very differentfrom persons between the ages of 70 and 84 (Suzman,Willis, & Manton, 1992).

Intellectual functioning is the psychological domain thathas received the most attention in its relation to mortality(for reviews see Berg, 1996; Siegler, 1975). Researchersstudying large enough samples with appropriate statisticaltechniques have found that reduced intellectual functioningis associated with nearness to death (e.g., Berg, 1987; Swan

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PREDICTORS OF MORTALITY P45

et al., 1995; White & Cunningham, 1988). There is somedebate, however, whether the association is more pro-nounced for some abilities than for others. Using a commondistinction between the fluid-like mechanics and the crys-tallized pragmatics of intelligence (Cattell, 1971), it hasbeen argued that the fluid abilities, as indicators of gen-eral brain functioning, should be most predominantly re-lated to mortality (Botwinick, West, & Storandt, 1978;Kleemeier, 1962). Others have suggested that the associa-tion with mortality is more salient for the crystallized prag-matics of intelligence, in particular verbal ability. Crystal-lized abilities are thought to be less affected by normalaging. As a consequence, death-related effects in these abil-ities should be less obscured by "normal" aging decline(Cooney, Schaie, & Willis, 1988; White & Cunningham,1988). Finally, Berg (1987) has hypothesized that the asso-ciation between intellectual functioning and mortality ispervasive, and that it can be observed for all intellectualabilities.

The prediction of mortality has also been examined forother domains of psychological functioning. With respect topersonality functioning, Friedman and colleagues (1995)reported that conscientiousness was positively associatedwith longevity in the Terman sample, but that there were noeffects for neuroticism, extraversion, and optimism. Swanand Carmelli (1996) reported that state curiosity, an aspectof openness, was related to longevity. It has long beenrecognized that, in old age, it is important to have closeconfidants in a social network who provide different as-pects of instrumental and emotional support (Antonucci,1990). In this regard, several epidemiological surveys haverevealed associations between lack of social contacts andmortality (Berkman, 1988; Seeman et al., 1987). Otherstudies found mortality to be associated with perceived lackof support (Yasuda, Zimmerman, Hawkes, Fredman, Hebel,& Magaziner, 1997), and a sense of loneliness (Olsen,Olsen, Gunnar-Svenson, & Waldstr0m, 1991).

There is mixed evidence with respect to associations be-tween subjective evaluations of functioning and mortality.Troll and Skaff (1997) found that perceived personalitychanges were unrelated to mortality in a sample of personsaged over 85 years. On the other hand, there is a large bodyof research indicating that subjective evaluations of healthpredict mortality above and beyond objective health status(Borawski, Kinney, & Kahana, 1996; Idler, 1992; Idler &Kasl, 1991; Mossey & Shapiro, 1982; Wolinsky & Johnson,1992). There are also reports that perceived problems withaging (Deeg, van Zonneveld, van der Maas, & Habbema,1989), reduced morale and life satisfaction (Mossey &Shapiro, 1982; Shahtahmasebi, Davies, & Wenger, 1992),as well as feelings of uselessness and unhappiness (Pal-more, 1982), are predictive of mortality in old age.

In the majority of studies cited above, the specific goalwas not to examine the associations between psychologicalfunctioning and mortality or to compare the relative risksassociated with predictions from different psychologicaldomains. These were the goals of the present study. Psy-chosocial measures included in epidemiological studiestypically involve single items which are subsequently en-tered in regression models as dichotomized covariates (e.g.,

beliefs in control vs no control; Wolinsky & Johnson, 1992).In the context of BASE, we had the advantage of having ac-cess to psychometrically developed measures of functioningin the domains of intelligence, subjective well-being, person-ality, and social relations.

Cox regression analysis (Cox, 1972) was used to exam-ine the associations between functioning on 17 indices ofpsychological functioning and subsequent risk of death inthe years following baseline measurement. We examinewhether mortality effects are specific to intellectual func-tioning or general across several areas of psychologicalfunctioning and whether the results change after the rela-tionship between age and mortality is taken into account.We also introduce controls for sociodemographic andhealth factors. Finally, we report exploratory analyses ad-dressing the length of the predictive period associated withpsychological risk factors.

METHOD

SampleData considered in this article stem from participants in

the Berlin Aging Study (BASE). Detailed information aboutthe BASE design, sample selectivity, and assessment proce-dure is available in Baltes and Mayer (1999), Baltes andSmith (1997), and Lindenberger, Gilberg, Little, Nuthmann,Potter, and Baltes (1999). Three general features of theBASE sample and the initial cross-sectional data set makeit worthwhile to explore questions about psychologicalcharacteristics associated with mortality and survival: localrepresentativeness, sample heterogeneity, and an age by sexstratified sample of individuals aged 70-103 years (M = 85years). Local representativeness and heterogeneity wereachieved by locating the sample through available obliga-tory city (state) registry records.

From 1990 to 1993, a multidisciplinary assessment bat-tery involving a total of 14 sessions over a period of threeto five months was completed by 516 individuals. At thisbaseline measurement there were an equal number of men(n = 43) and women (n = 43) in each of six age/cohortgroups: 70-74 years (born 1915-1922), 75-79 years (born1910-1917), 80-84 years (born 1905-1913), 85-89 years(born 1900-1908), 90-94 years (born 1896-1902), and95-103 years (born 1883-1897).

Mortality status information for BASE participants andthe date of death for the deceased participants were obtainedfrom the State Registry office. By October 1996 (which rep-resents a 3-6 year period after baseline assessment), 257 in-dividuals, or 49.8% of this sample, were registered in thestate records as deceased, and 256 individuals, or 49.6%,were registered as alive. Mortality information for three per-sons could not be obtained, because these individuals hadmoved out of the Berlin area. The three individuals withmissing mortality information were removed from the anal-yses. Thus, the present study utilized a sample of 513 per-sons (257 men and 256 women). Their estimated mediansurvival time after baseline assessment was 62 months. As isto be expected for the advanced age of this sample, a largerproportion of the deceased was from the oldest old (older

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than 85 years: n = 194 decedents vs n = 61 survivors) com-pared to the younger age groups (70-84 years: n = 63 dece-dents v s n = 195 survivors). As of October 1996, less than20% of individuals aged 90-103 years at baseline assess-ment were still alive. In contrast, more than 75% of individ-uals aged 70-79 years at baseline had survived.

Measures of Psychological FunctioningPsychological data collected in BASE included measures

of intellectual abilities, subjective well-being, personalitydispositions, and social relations. We selected 17 variablesfrom the BASE data protocol to represent these measures.Measures of psychological functioning were always givenin the same order and were collected in 4 of 14 individualtest sessions (over a period of 3-5 months) carried out atthe participants' place of residence (see Baltes & Smith,1997).

For all analyses, measures of psychological functioningwere standardized to M = 4, SD = 1 (see Appendix, Note 1),and the scales were reversed so that higher values reflectedlower, or less desirable, functioning. Below, we presentgeneral information about the scales, tests, and proceduresof assessment (for further information and descriptivestatistics for the BASE sample see Baltes & Mayer, 1999;Lindenberger & Baltes, 1997; Smith & Baltes, 1997).

Intellectual functioning.—Five factor scores of psycho-metric intelligence were included in the present analyses tocharacterize performance in two broad categories of intel-lectual abilities—the fluid-like mechanics and the crystal-lized pragmatics of intelligence. The factors PerceptualSpeed, Reasoning, and Memory were selected to character-ize functioning associated with the fluid mechanics of intel-ligence. The factors Knowledge and Fluency were selectedto capture aspects related to the crystallized pragmatics ofintelligence. In all cases, higher scores in the present analy-ses indicated lower functioning.

A total of 14 subtests were administered to assess thefive intelligence factors (see Lindenberger & Baltes, 1997).Perceptual Speed was measured by the Digit-Letter Test,Digit Symbol Substitution, and Identical Pictures. Reason-ing was measured by Figural Analogies, Letter Series, andPractical Problems. Memory was measured by ActivityRecall, Memory for Text, and a Paired-Associates Task.Knowledge was measured by a practical knowledge test,Spot-a-Word, and a vocabulary test. Fluency was measuredby a Categories and a Word Beginnings test (Letter "S").Stimulus presentation and data collection were supportedby a Macintosh SE/30 equipped with a touch-sensitivescreen.

Subjective well-being.—Five constructs were included torepresent different aspects of subjective well-being. Factorscores for Positive Affect and Negative Affect were ob-tained using a translated version of the Positive Affect Neg-ative Affect Schedule (PANAS; Watson, Clark, & Tellegen,1988). Factor scores for Agitation, Dissatisfaction withAging and Dissatisfaction with Life were obtained with aGerman translation of the 15-item Philadelphia GeriatricCenter Morale Scale (PGCMS: Lawton, 1975; Liang &

Bollen, 1983). For all measures of subjective well-being,participants were asked to indicate how well items de-scribed them using a 5-point scale. Each item was readaloud by an interviewer, and the participant's response wasrecorded. In the present analyses, scores were reversed sothat higher scores indicated low subjective well-being (i.e.,Low Positive Affect, Negative Affect, Agitation, Dissatis-faction with Aging, Dissatisfaction with Life).

Personality.—Personality dispositions were representedby three factors: Neuroticism (derived from responses tosix items assessing the facets anxiety, depressivity, vulnera-bility, and hostility), Extraversion ( derived from responsesto six items assessing the facets gregariousness, positiveemotionality, assertiveness, and activity), and Openness(derived from responses to six items assessing the facetsfantasy, ideas, feelings, aesthetics, and actions). Items to as-sess these dimensions were selected from the NEO (Costa& McCrae, 1985). For all personality measures, partici-pants were asked to indicate how well items described themusing a 5-point scale. Each item was read aloud by an inter-viewer, and the individual's response was recorded. Scoresfor extraversion and openness were reversed, with highervalues indicating less extraversion and less openness.

Social relationships.—Four constructs were chosen torepresent the social relationship domain: reported numberof close confidants, perceived receipt of physical and emo-tional support, social loneliness, and emotional loneliness.These indices characterize the individual's subjective eval-uation and reports of his/her intimate social ecology and so-cial networks.

The "Circle Task," or Hierarchical Mapping Technique(Antonucci, 1986), was used to generate the names of allcurrent intimates, friends, and acquaintances of the individ-ual. Participants were shown a diagram of concentric cir-cles, and were told that they should imagine that they stoodin the center. They were then asked to name the personsthat they considered to be extremely close and important intheir lives (the first inner circle), followed by those some-what less close (second circle), and finally those personswho, although still an important part of their lives, weremore distant (third outer circle). In the present analyses, weincluded the number of Close Confidants nominated in theinner circle. Perceived Support (especially receipt of physi-cal and personal comfort) was assessed with three ques-tions. Participants were asked whether, in the last threemonths, (a) they had spoken to someone about personalworries and concerns, (b) whether someone had cheeredthem up at a time when they were feeling sad, and (c)whether someone had given them a kiss or a cuddle, orshown them some tenderness. In the present analyses, scalescores were reversed so that higher scores indicated per-ceived lack of support and few close confidants.

Items selected from the UCLA Loneliness Scale (Rus-sell, Cutrona, Rose, & Yurko, 1984) were used to assesstwo aspects of loneliness: Social Loneliness (factor scorederived from four items asking about perceptions of be-longing to a social group and general availability of trustedothers) and Emotional Loneliness (factor score derived

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from four items dealing with feelings of isolation, beingalone, and being secluded from contact with others).

Sociodemographic Characteristics and Health MeasuresIndicators of sociodemographic characteristics and health

measures available in the BASE data protocol were used ascontrol measures. Gender, marital status (married vs notmarried), and three indicators of socioeconomic resources(years of education, equivalent income, and occupationalprestige) were selected to represent sociodemographic char-acteristics. Equivalent income was computed by weightingthe net household income by the number of people sharingthe household (Baltes & Mayer, 1999). The social status rat-ing (Wegener, 1985) of the participant's last occupation be-fore retirement formed the measure of occupational prestige.In the case of individuals who were never part of the laborforce, the prestige of the last occupation of the spouse (latespouse if widowed) was used as a substitute.

BASE assessment included comprehensive clinical ex-aminations by physicians. For the present analyses, we in-cluded an objective, externally assessed health measure(Number of Illnesses) and a subjective health measure(Self-Rated Health). The Number of Illnesses measure rep-resents physician-observed ICD-9 (WHO, 1980) diagnosesof illnesses, weighted by severity. The diagnoses were de-termined in a clinical examination and supported by addi-tional blood and saliva laboratory assessments. Self-RatedHealth was measured with the standard single item question("How would you rate your health at the present time?")using a 5-point response scale (see also Idler, 1992).

Cox Regression MethodThere are two characteristics of the present sample and

design that needed to be considered in choosing an appro-priate data analytic strategy. First, baseline measures fordifferent individuals were administered over a period oftime (1990-1993), whereas mortality status assessment wasobtained at a fixed point in time (October 1996). As a re-sult, individuals differed in the length of time they were inthe study. Second, data from half of the sample were right-censored (i.e., they had not yet died at the end of the pre-sent study). No right-censoring occurred in the first threeyears following baseline assessment. Censoring became in-creasingly prominent, however, four and more years afterbaseline assessment (n = 26 at 4 years, n = 96 at 5 years, n= 95 at 6 years, and n = 38 at 7 years).

Cox regression models (Allison, 1995; Cox, 1972) wereselected as a comprehensive method to relate baseline psy-chological functioning to mortality information that takescensored cases into account. With this method, parameterestimates were derived utilizing the full baseline sample (N= 513). The hazard or risk of dying was the dependent vari-able in all Cox models reported in this study. Cox regres-sion models are well suited to analyze data from individu-als who were in the study for different lengths of timebecause they model the hazard rather than mortality statusas the outcome variable.

Parameter estimates obtained from Cox regression mod-els were used to describe the associations between indica-tors of psychological functioning and mortality. We report

the risk ratio (RR, obtained by exponentiating the parameterestimate) and the 95% confidence limit for the risk ratio(CT). Risk ratios can be interpreted with respect to a one-unit increase in the scale of the predictor. Predictors enteredinto Cox regression models were expressed on a scale withM = 4, SD = 1 (see also Appendix, Note 1), and risk ratioscan be interpreted with respect to standard deviations of thebaseline sample. For example, a risk ratio of 1.5 indicatesthat with every one standard deviation increase in the pre-dictor, the mortality risk is 1.5 times higher. For all indica-tors of psychological functioning, higher scores indicatedlower functioning.

RESULTS

Our findings are reported in four sections. First, we ex-amine associations between mortality risk and four do-mains of psychological functioning (intellectual function-ing, subjective well-being, personality dispositions, andsocial relations, respectively) in a multivariate hypothesistesting framework. We evaluate the robustness of the ef-fects stemming from these four domains by introducingcontrols for age, sociodemographic characteristics, andhealth measures. In the second section, we turn to the indi-vidual 17 psychological indicators that were discussed inprior research as potential predictors of mortality and sur-vival. We report the risk ratios associated with each psy-chological indicator, again with and without statistical con-trols for age, sociodemographic characteristics, and healthmeasures. Thirdly, we examine the risk patterns associatedwith lower or less desirable functioning separately for theold and the oldest old. In a final section, we explorewhether associations between psychological risk factorsand mortality vary with time.

Domains of Psychological Functioning and Risk of DyingA first set of analyses addressed the general question

whether or not functioning in four psychological domains(intellectual functioning, subjective well-being, personalitydispositions, and social relations) was associated with riskof dying. Indicators of each domain were entered into theanalyses as a block. Three models were run initially foreach domain: one model addressing the zero-order relation-ship between the domain and mortality, one model address-ing the relationship controlled for age alone, and one modeladdressing the relationship controlled for age, sociodemo-graphic characteristics, and health measures. We applied anested model test procedure using the likelihood ratio test(Allison, 1995) to examine whether a domain was associ-ated with mortality above and beyond the respective covari-ates. Columns two through four of Table 1 provide the re-sults of these tests.

Cox regression models suggested that level of function-ing in each of the four domains was related to an elevatedmortality risk (column two of Table 1). When controls forthe relationship between chronological age and distancefrom death were introduced (column three of Table 1), thedomains of personality and social relationships were nolonger significant. Intellectual functioning and subjectivewell-being were associated with an increased mortality riskafter statistical controls for age (column three of Table 1), as

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Table 1. Multivariate Associations Between Four Domains of Psychological Functioning and Mortality Risk

Domain

Intellectual functioningSubjective well-beingPersonality characteristicsSocial relations

Model x2 (df)

137.1 (5),/?< .0144.7 (5),p<.0118.9 (3), p<. 0117.8 (4),p<.01

Age

24.0(5),/><.0113.5(5),p<.055.1 (3), n.s.0.5 (4), n.s.

Model x2 (df) Adjusted for

Age, SES, Health

31.3 (5),p<.0111.2(5),/?<.05

Age, Intellectualfunctioning

12.5 (5),p<.05

Age, SES, Health,Intellectual functioning

10.6 (5), n.s.

Note. Domain indicators were coded in the direction of low or less desirable functioning. Domain indicators were entered as blocks to obtain multivari-ate tests of their effects. Model fits were obtained from Cox regression models. Effects of domain indicators adjusted for covariates were evaluated bylikelihood ratio tests comparing nested models (model "covariates only" vs model "covariates plus domain indicators"). SES = five sociodemographiccharacteristics, Health = a physician-assessed and a subjective health measure (see text for explanation of measures).

well as after additional controls for sociodemographic char-acteristics and health measures (column four of Table 1).

Two additional analyses focused on the observed effectsof subjective well-being. Specifically, we wondered whetherindicators of subjective well-being predicted mortality riskover and above the prediction stemming from intellectualfunctioning. As can be seen in column five of Table 1, sub-jective well-being was associated with mortality risk in amodel that controlled for age and intellectual functioning.When additional controls for health measures were intro-duced, however, subjective well-being was no longer asso-ciated with mortality (see column six of Table 1).

Risk Ratios Associated with IndividualPsychological Indicators

The next set of analyses focused on the risk ratios associ-ated with each of the 17 psychological indicators. Analysesinvolving the individual 17 psychological indicators weredesigned to serve three major purposes. First, the indicatorswere selected from the BASE data protocol in order to testspecific hypotheses in the literature about their relation tomortality. Second, it is of interest whether the associationpatterns obtained from multivariate analyses were in fact inthe expected direction (i.e., lower or less desirable func-tioning associated with an increased hazard of dying). Third,it is possible that potential confounder covariates disatten-uate the effects for some psychological indicators, but notfor others. To explore this possibility, we report the resultsof a series of Cox regression models in which the effectsfor the indicators are examined at a zero-order level, afterage was controlled, as well as in the context of additionalcontrols.

At the zero-order level, 11 out of the 17 indicators ofpsychological functioning were found to be associated withmortality risk, with lower or less desirable functioning re-lated to an increased hazard of dying (column two of Table2). Associations were most pronounced for intellectual abil-ities, with lower Perceptual Speed, lower Reasoning, lowerMemory, lower Knowledge, and lower Fluency all beingrelated to an increased hazard of dying (see Appendix, Note2). Every one standard deviation decrease in PerceptualSpeed (here coded so that higher scores indicate lowerfunctioning) was associated with a mortality risk 2.03 timeshigher. Six other psychological indicators (lower PositiveAffect, Dissatisfaction with Aging, Dissatisfaction with Life,

lower Extraversion, lower Openness, and Emotional Lone-liness) were also associated with an increased hazard ofdying.

Five of the relationships disappeared when we controlledfor the effects of chronological age on mortality risk (seecolumn three of Table 2). Lower functioning on all intellec-tual abilities and Dissatisfaction with Aging, however, re-mained associated with an increased mortality risk. As forthe crude risk ratios, the risk associated with dissatisfactionwith aging after the age control was somewhat lower thanthe risks associated with indicators of lower intellectualfunctioning.

We also tried to evaluate comparatively the predictivestrength of prototypical markers of the fluid-like mechanicsand of the crystallized pragmatics of intelligence. Percep-tual speed was selected as a marker of the fluid-like me-chanics and Knowledge as a marker of the crystallizedpragmatics (see Lindenberger & Baltes, 1997, for a similarselection of markers). A Cox regression model includingLow Perceptual Speed and Low Knowledge as predictorsindicated that Low Perceptual Speed was still associatedwith mortality risk (RR = 2.05, 95% CI = 1.72-2.46) butLow Knowledge was not (RR = 0.99, 95% CI = 0.83-1.16).A similar pattern was obtained when we additionally con-trolled for chronological age (Low Perceptual Speed: RR =1.37, 95% CI = 1.12-1.68; Low Knowledge: RR = 1.02,95% CI = 0.86-1.21). Thus, our analyses support the argu-ment that fluid-like abilities are most predominantly relatedto mortality.

The final columns of Table 1 indicate the robustness ofthe core set of psychological indicators in Cox regressionmodels that controlled for age and sociodemographic char-acteristics (column four), age, sociodemographic character-istics, and health measures (column five), and finally age,sociodemographic characteristics, and health measures aswell as the 16 other psychological risk factors (column 6).The addition of controls for sociodemographic characteris-tics did not alter the effects of psychological risk factors—lower functioning on all intellectual abilities and Dissatis-faction with Aging were still associated with an increasedmortality risk. When health measures were also included,lower functioning on all intellectual abilities continued to beassociated with an elevated mortality risk. The risk ratio forDissatisfaction with Aging, however, was no longer signifi-cantly different from 1 (RR =1.16, 95% CI =0.99-1.34, p =

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Table 2. Relationships Between 17 Indicators of Psychological Functioning and Time to Death: Zero-order and Adjusted Risk Ratios

Low intellectual functioningLow perceptual speed

Low reasoning

Low memory

Low knowledge

Low fluency

Low subjective well-beingLow positive affect

Negative affect

Agitation

Dissatisfaction with aging

Dissatisfaction with life

Less desirable personality characteristicsNeuroticism

Low extraversion

Low openness

Less desirable social relationsEmotional loneliness

Social loneliness

Low support

Few close confidants

Zero-orderRisk Ratio

2.031.79-2.30

1.931.66-2.25

1.88

1.64-2.161.61

1.43-1.811.92

1.67-2.21

1.251.11-1.42

0.910.81-1.04

0.910.81-1.03

1.361.20-1.53

1.181.04-1.33

1.110.99-1.26

1.151.02-1.30

1.281.14-1.45

1.281.14-1.44

1.110.99-1.25

1.020.90-1.16

1.130.99-1.29

Age

1.401.20-1.62

1.331.13-1.57

1.33

1.14-1.541.22

1.08-1.391.37

1.18-1.59

—

—

—

1.201.06-1.35

—

—

—

—

—

—

—

—

Risk Ratio Adjusted for

Age, SES

1.501.27-1.77

1.421.19-1.71

1.35

1.15-1.581.32

1.14-1.531.50

1.27-1.77

—

—

—

1.241.09-1.41

—

—

—

—

—

—

—

—

Age, SES,Health

1.531.29-1.81

1.371.15-1.65

1.39

1.19-1.631.33

1.15-1.541.50

1.27-1.78

—

—

—

1.16"0.99-1.34

—

—

—

—

—

—

—

—

Age, SES, Health,Other Psychological

Risk Factors

1.321.03-1.68

—

—

—

—

—

—

0.790.65-0.96

1.221.02-1.45

—

—

—

—

—

—

—

—

Note. Risk ratios and their 95% CI are shown. Adjusted risk ratios that were not significantly different from 1 are omitted for clarity. SES = five socio-demographic characteristics, Health = a physician-assessed and a subjective health measure (see text for explanation of measures).

'p = .06.

.06). In the final hard test of the robustness of the separatepsychological risk factors, which included additional adjust-ments for the respective other psychological constructs,lower perceptual speed, lower agitation (see Appendix, Note3), and dissatisfaction with aging were significant.

Comparison of the Old and the Oldest OldInterpretation of age-controlled risk ratios may be mis-

leading if the involved association patterns are fundamen-tally different across age groups. Suzman, Willis, and Man-ton (1992) suggested that this may indeed be the case forthe oldest old (older than 85 years). We tried to address thisissue empirically by calculating the associations between

psychological functioning and mortality separately for theold (70-84 years) and the oldest old (85-103 years). Riskratios obtained from these analyses suggested that associa-tions were similar for the old and the oldest old for 15 ofthe psychological dimensions. Differences were revealedon two intellectual abilities, however. The risk ratio for theoldest old was lower for Low Perceptual Speed (85-103years: RR = 1.43), and the 95% confidence interval aroundthis ratio did not include the estimated risk ratio for theyounger group (70-84 years: RR = 2.02). Similarly, the riskratio for the oldest old was lower for Low Fluency (85-103years: RR = 1.43), and the 95% confidence interval aroundthis ratio also did not include the estimated risk ratio for the

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younger group (70-84 years: RR = 1.80). Low PerceptualSpeed and Low Fluency appear to be less predominant pre-dictors of mortality among oldest old individuals (see Ap-pendix, Note 4).

Do the Effects of Psychological Risk FactorsVary With Time?

A further exploratory set of analyses addressed the lengthof the predictive period associated with psychological riskfactors. We defined a time-dependent covariate for eachpsychological risk factor as the product of the risk factorand time (see Allison, 1995). For each of the 17 psycholog-ical risk factors, we then calculated a Cox regression modelthat included the respective risk factor and the associatedtime-dependent covariate as predictors. None of the effectsassociated with the time-dependent covariates was statisti-cally significant. This set of analyses suggested that the re-ported effects for psychological risk factors neither dimin-ished nor increased over time. A better test of the timeframe of the psychological risk factors would, of course, re-quire longitudinally repeated measures of the psychologicalconstructs.

DISCUSSION

Taken together, these results from the BASE cross-sec-tional data protocol suggest that there are some aspects ofpsychological functioning in old age that appear to be asso-ciated with impending death. At a zero-order level, 11 of the17 constructs that we examined showed a significant rela-tionship to survival status. For each construct, the relation-ship was in the expected direction: namely, lower levels offunctioning or less positive self-evaluations were associatedwith a higher risk of subsequent death. Longitudinal assess-ment of functioning would be needed to determine whetherthese lower levels indeed reflect death-related decline.

Psychological Predictors of MortalityOur analyses suggest that psychological predictors of

mortality in old age may not just be specific to intellectualfunctioning, but rather that they extend to self-related eval-uations of personal well-being. As a domain, intellectualfunctioning certainly provided the strongest and most ro-bust set of predictors. Effects associated with predictorsfrom the personality, self-related, and social domains ap-pear to be more subtle. With one exception (dissatisfactionwith aging), in the present study, predictors from these lat-ter domains were only revealed as significant risks in analy-ses that did not first control for associations between ageand mortality. The predictive effect of dissatisfaction withaging, however, remained significant even after statisticalcontrols for age, sociodemographic characteristics, healthmeasures, and the 16 other psychological variables. This isstrong evidence in support of the proposal that intelligencemay not be the only aspect of psychological functioningthat is death-related.

Intellectual functioning is the main area in psychologicalresearch in which hypotheses about terminal decline andthe prediction of mortality have been tested (Berg, 1996;Riegel et al., 1967; Siegler, 1975). In many early studies,however, the number of cases was small, samples were

highly selected, and the age range limited. In general, thepresent study suggested that effects associated with intel-lectual functioning are pervasive rather than ability-specific(see also Berg, 1987). The zero-order risk levels were simi-lar across the five abilities examined in the present studyand were reduced to a similar degree by various controls(see columns two through five in Table 2). This pervasive-ness may be specific to the older age range of this sample,where it has been argued that due to the common cause ofbrain aging, all intellectual abilities converge towards ahigher commonality (e.g., Balinsky 1941; Lindenberger &Baltes, 1997; Reinert, 1970). Several mechanisms couldunderlie our findings. At one level, lower intellectual func-tioning that is death-related could be indicative of generalprocesses of brain atrophy and system breakdown. At apragmatic and everyday level, lower intellectual function-ing could also put individuals at risk for other factors thatare more direct causes of death. For example, poor memoryand reduced reasoning capacity could have substantial im-plications for the correct usage of medication and otherhealth-related behavior.

A specific question in the domain of intellectual func-tioning concerns the relative predictive power of measuresassociated with fluid versus crystallized abilities. Some re-searchers argue that fluid abilities are more age-related thandeath-related, and that the strongest predictors of mortalityare among the crystallized abilities (e.g., Cooney et al.,1988; White & Cunningham, 1988). Our results did not re-veal stronger predictions from the crystallized than fromthe fluid abilities. Quite to the contrary, if we consideredonly perceptual speed (as a marker of the fluid-like me-chanics) and knowledge (as a marker of the crystallizedpragmatics), it appeared that the stronger prediction of mor-tality was from the fluid domain. Furthermore, only thefluid ability of perceptual speed remained significant incontrol analyses that included age, sociodemographic char-acteristics, health, and the 16 other psychological variables.The measures of crystallized abilities in BASE, however,may not be extensive enough to cover the effects reportedin previous studies.

As might be expected from the extensive literature con-cerning the predictive links between subjective health andmortality (e.g., Idler, 1992), risks associated with subjectiveevaluations of well-being were found to be strong in thepresent study. In particular, dissatisfaction with aging, asmeasured by the subscale of the PGCMS (Lawton, 1975;Liang & Bollen, 1983), remained as a significant risk factorafter controls for age and sociodemographic characteristics(columns three and four of Table 2). The effect becamemarginally significant when additional controls for healthwere added (column five), but then stood the hardest test(column 6) when pitted against age, sociodemographiccharacteristics, health, and the 16 other psychological vari-ables. Few other studies have investigated this aspect ofperceived well-being in relation to mortality. In the DutchLongitudinal Study, Deeg and colleagues (1989) found thata single item asking about perceived problems with agingpredicted mortality after aspects of health were controlled.

Why are evaluations of subjective well-being associatedwith increased rates of subsequent mortality? These evalua-

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tions might reflect quite accurate summary perceptionsabout individuals' present status with respect to functioningin a variety of domains. Negative evaluations themselvesare probably not the cause for an increased mortality risk,but they may reflect potential causes from other domains offunctioning (e.g., intellectual, health, or biological func-tioning).

Findings reported in the literature with regard to predic-tors associated with personality dimensions and social rela-tionships are less consistent than those for intelligence. Inpart, this may be due to between-study variations in con-struct measurement, sample characteristics, and analysisstrategies (e.g., whether controls were introduced for age).At a zero-order level, for example, we found significantrisks associated with lower levels of extraversion and open-ness to new experience. These risks did not remain after thecontrol for age. For these dimensions, our finding is similarto that of Friedman and associates (1995).

We had expected that indicators of social support, loneli-ness, and the presence of close confidants would also be re-lated to mortality. Only emotional loneliness proved to be arisk at the zero-order level. Although several studies havereported associations between perceived lack of support,loneliness, and the absence of close confidants (e.g., Berk-man, 1988; Yasuda et al., 1997), it is also recognized thatsubjective reports of social support may not fully reflect ac-tual availability of support. Furthermore, social networksand perceived social support may have an indirect effecton mortality through physical health and health behavior(Cohen & Willis, 1985; Sugisawa, Liang, & Liu, 1994).Had we examined other indicators of integration in a socialnetwork in the present study or explored the compensatoryeffects of professional care provision, we might have ob-served different relationships between social indicators andmortality.

The Role of Socioeconomic Resources and HealthThe association between socioeconomic resources (years

of education, equivalent income, occupational prestige) andmortality was relatively small in this sample of older indi-viduals from West Berlin. Controlling for these resources,therefore, did not substantially alter the effects of psycho-logical characteristics on mortality. Weak associations be-tween socioeconomic resources and mortality are some-what in contrast to reports relying on United States samples(e.g., Kitagawa & Hauser, 1973). It is important to note,however, that most of these studies involved younger agegroups than those included in BASE. Further, the weak as-sociations found in the present analyses are consistent withother BASE analyses, showing that the prevalence of ill-ness and frailty is minimally associated with socioeco-nomic resources (Baltes & Mayer, 1999). It is possible thathealth outcomes in very old age are increasingly dependenton genetically-determined processes, which are relativelyindependent of socioeconomic conditions.

Introducing physician-assessed and subjective healthmeasures as additional covariates provided a strong test forthe robustness of psychological predictors of mortality. Theadditional controls for health (after age and sociodemo-graphic characteristics) did not alter the risk ratios associ-

ated with the intellectual abilities, but they did reduce therisk associated with dissatisfaction with aging. Similar tosubjective health (Idler, 1992), dissatisfaction with agingcould also be in part a reflection of an individual's poorhealth status. Rather than attributing psychological func-tioning to health, however, we consider it more likely thatgenetic and life history factors may act as additional vari-ables, orchestrating the intertwined relationships amonghealth, psychological functioning, and mortality (see Chris-tensen & Vaupel, 1996; McClearn, Johansson, Berg, Peder-sen, Ahern, Petrill, & Plomin, 1997).

Admittedly, we did not explore a large variety of objectiveand subjective health measures as potential confounders(see for example Idler, 1992, p. 44). The two indicators se-lected, however, are recognized as prominent exemplars ofexternal and self-assessed health. In older adults (70-100years), chronological age is itself a substantial carrier of ad-ditional health information, especially functional health. Itseems, then, that after controlling for the effects of age andhealth status, psychological functioning does make a differ-ence in terms of survival.

The Role of AgeWe believe that age is a critical variable to consider

when patterns of mortality are investigated. Age is relatedboth to functioning and to death in complex ways that can-not be addressed easily in a single set of analyses. At a verygeneral level, increasing age is associated with greater riskof death and a shorter average life expectancy (residual lifetime) after baseline assessment. Life tables for the Berlinpopulation indicate that the average life expectancy for a70-year-old person is 12.2 years, whereas for a person aged80 it is 6.6 years and at age 95, 2.5 years. In the presentBASE sample, 89.1% of participants aged 70-84 years atbaseline were alive three years after baseline assessmentand 75.6% survived until the time of the present study. Aswas to be expected, fewer participants older than 85 yearsof age (54.9%) were alive three years after baseline assess-ment, and only 23.9% had survived until the time of thepresent study.

In general, our results indicated that controlling for therelationship between age and death reduced the magnitudeof six effects of psychological functioning on mortality riskand eliminated five other effects (six indicators were notsignificant at the zero-order level). That the effects did notvanish altogether suggests that the associations betweenlower psychological functioning and mortality cannot beequated with age decrements in psychological functioning.

Furthermore, selective survival may also have an effecton the range of functioning observed in different age groups—the oldest old survivors may represent a restricted pro-portion (positive selection) of the performance distributionof their birth cohort. Changes in cohort membership arequite dramatic in the 30 years from age 70 to age 100. Inthe Berlin population, 70-year-olds represent on averageapproximately 70% of their birth cohort, whereas 90-year-olds represent 12%.

In the present study, these dynamics of aging and theirrelationship to death were also explored by the separatepredictive analyses for the old (70-84 years) and oldest old

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(85-100+ years). This set of analyses suggested that, as awhole, the associations between psychological functioningand mortality risk were fairly similar for the two age groups.However, it is of interest that we found age group differ-ences for two indicators of intellectual functioning—bothperceptual speed and fluency appeared to be less predomi-nant predictors of mortality among the oldest old. This de-crease in the strength of predictors of death for the oldestold does not seem to be restricted to intellectual function-ing. Other research groups examined various indicators ofhealth and functional capacity as mortality predictors andreported a similar decrease in predictive strength among theoldest old (e.g., Dontas, Toupadaki, Tzonou, & Kasviki-Charvati, 1996; Manton, Stallard, Woodbury, & Dowd, 1994).It is possible that mortality risk in younger age groups isrelatively closely tied to the level of functioning, whiledeath strikes more randomly in advanced age (Riegel &Riegel, 1972).

Age is also implicated in more complex ways in terms oflevel of functioning and the relationship between function-ing and death. Different aspects of observed functioning de-cline in older adults may be age- and death-related (Pal-more & Jeffers, 1971). Research on terminal decline (Berg,1996; Siegler, 1975) has often adopted the premise thatthere are age-related declines in functioning ("normal agingtrack") that can be distinguished from death-related de-clines ("death track"), with the latter exposing a steeper de-cline trajectory than the former. Apparently it is very diffi-cult to determine whether an individual is still on the"normal aging track" or has already switched to the "deathtrack". In the present study we did not explicitly attempt toseparate the two tracks, because we doubt that such an en-terprise would be successful in the absence of longitudinaldata on functioning. Our exploratory analyses addressingthe length of the predictive period suggest, however, thatthe "death track" may extend over a considerable time pe-riod. Participants in the present study were followed be-tween three and seven years, and there was no indicationthat the effects of psychological predictors of mortality di-minished over this period. Subsequent analyses will be ableto examine whether the predictive period extends for aneven longer time span, and whether there are domain differ-ences in the length of the predictive period. We consider itplausible that a temporal sequence of steps involves first adecline in intellectual functioning, next a decline in subjec-tive well-being, and finally death.

ACKNOWLEDGMENTS

The research presented here was conducted in the context of the BerlinAging Study (BASE), which was initiated in 1989 by the Academy of Sci-ences and Technology in Berlin. BASE is currently sponsored by theBerlin-Brandenburg Academy of Sciences and has been financially sup-ported by two German federal departments: the Department of Researchand Technology (1988-1991) and the Department of Family Affairs, Se-nior Citizens, Women and Youth (1992 to present: grant 314-1722-109/9,9A). The primary institutions involved are the Free University of Berlinand the Max Planck Institute for Human Development. The study is di-rected by a steering committee co-chaired by Paul B. Baltes and Karl Ul-rich Mayer. The full steering committee consists of the directors of fourco-operating research units (P. B. Baltes and J. Smith, psychology; H.Helmchen and M. Linden, psychiatry; K. U. Mayer and M. Wagner, soci-

ology and social policy; and E. Steinhagen-Thiessen and M. Borchelt, in-ternal medicine and geriatrics) and the project administrator, R. Nuthmann(until 1996) and K. M. Neher (since 1997).

Current members of the BASE Psychology Unit include Paul B. Baltes(co-director), Alexandra M. Freund, Ulman Lindenberger, Shu-Chen Li,Heiner Maier, Jacqui Smith (co-director), and Ursula M. Staudinger. Wethank our colleagues in the Psychology Unit for their many contributions.In addition, we express our appreciation to Paul B. Baltes, Ulman Linden-berger and John R. Nesselroade for useful comments on an earlier draft.

Heiner Maier is now at the Max Planck Institute for Demographic Re-search, Rostock, Germany.

Address correspondence to Jacqui Smith, Max Planck Institute forHuman Development, Lentzeallee 94, D-14195 Berlin, Germany. E-mail:[email protected]

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Received May 29, 1997Accepted August 10, 1998

Appendix

Notes1. Standardization to M = 4, SD = 1 does not alter the reported

findings in terms of their statistical significance. It does, however,does affect the size of the parameter estimates associated with pre-dictors of mortality. The original, raw data metric of our measuresof psychological functioning is arbitrary. We chose to standardizebecause once in standardized metric, individuals' scores can be in-terpreted with respect to standard deviation units of the baselinesample. Additionally, it is more convenient to evaluate the strengthof different mortality predictors comparatively if the associatedparameter estimates are based on scores that are expressed in acomparable metric.

2. We recalculated all models, excluding n = 109 individualswith diagnoses of different levels of dementia. In BASE, diag-noses of suspected dementia (n = 109) were differentiated at threelevels: Level 1 (n - 37) indicated participants with symptomaticmemory difficulties and Levels 2 and 3 (n = 33 and 39 respec-tively) indicated cases of moderate to severe symptomatology(DSM-III-R). The reported pattern of findings did not change,suggesting that these effects cannot be explained by an increasedmortality risk for individuals with different levels of dementia.

3. In analyses controlling for age, sociodemographic character-istics, health measures and 16 other psychological indicators,higher Agitation was related to survival and lower Agitation todeath. It is possible that lower Agitation, or "calmness," character-

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izes the period prior to death. However, we recommend caution ininterpreting this finding. The effect was in the opposite directionfrom what we had originally hypothesized, and it was present onlyin the Cox regression model with the maximum number of covari-ates (column six of Table 2) and absent in all other, simpler mod-els (columns two to five of Table 2).

4. Complete data are available from the authors. The decreasein predictive strength for perceptual speed and fluency in the old-est old might be due to floor effects in the measures. We cannotrule out this possibility but the empirical evidence for floor effectsis sparse. Inspection of the univariate distributions for the oldestold did not reveal that there was a noticeable clustering of individ-uals at the lower end of the perceptual speed or of the fluency

measure. Lindenberger and Baltes (1997, pp. 420-421) used vari-ous approaches to examine the possible existence of age differ-ences in interindividual variability in these measures, and con-cluded that interindividual variability was remarkably stable withage. We also calculated the risk ratios excluding groups of lowperformers (lowest 5% and lowest 10%), separately for perceptualspeed and fluency. These analyses showed that the age group dif-ferences in predicting mortality persisted for the fluency measurebut not for the perceptual speed measure. This might be taken asan indication of floor effects in the perceptual speed measure forthe oldest old. A conclusive interpretation is rendered difficult,however, because the analyses excluding low performers rely onselected subsamples and have reduced statistical power.

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