Health and Subjective Well-Being in Later Adulthood; Different Health States

Health and Subjective Well-Being inLater Adulthood: Different Health

States—Different Needs?

Benjamin Schüz,* Susanne Wurm, Lisa M. Warner andClemens Tesch-Römer

German Centre of Gerontology, Berlin, Germany

From midlife on, health problems become more prevalent. Health is one of thekey determinants of subjective well-being (SWB), but examining the relationbetween health and SWB in later adulthood is complicated by the clusteringof multiple illnesses. This article proposes Latent Class Analysis (LCA) for aparsimonious description of adult health. This article compares SWB in healthclasses and examines the relative importance of socioeconomic resources, painand coping (flexible goal adjustment) for SWB. Data stem from a nation-widerepresentative sample of adults aged 40–85 (German Ageing Survey, DEAS;N = 3,084). LCA was used to examine different configurations of health. Mul-tiple regression analyses in latent classes were conducted to examine predictorsof SWB. LCA generated four distinct classes of health conditions: No disease(n = 807), cardiovascular diseases (n = 405), joint problems (n = 1,612) and mul-tiple illnesses (n = 258). As expected, only small mean differences in SWB indi-cators were found, whereas discontinuous predictors of SWB were detected:Coping was more strongly associated with SWB in individuals with higherillness burden. LCA can be applied to describe health in later adulthood.Differential prediction patterns suggest distinct factors for SWB depending onindividual health status.

Keywords: adult health, developmental psychology, Germany, health psychol-ogy, health status, latent class analysis, later adulthood, resource appraisal,subjective well-being

INTRODUCTION

The general increase in life expectancy is accompanied by a diversification ofhealth. Some people stay relatively healthy up to old age, whereas otherssuffer from multiple illnesses already from midlife on (differential aging;Whitebourne, 2001). Nevertheless, only small differences in subjective well-

* Address for correspondence: Benjamin Schüz, German Centre of Gerontology, Manfred-von-Richthofen-Str. 2, 12101 Berlin, Germany. Email: [email protected]

APPLIED PSYCHOLOGY: HEALTH AND WELL-BEING, 2009, 1 (1), 23–45doi:10.1111/j.1758-0854.2009.01004.x

© 2009 The Authors. Journal compilation © 2009 International Association of AppliedPsychology. Published by Blackwell Publishing Ltd., 9600 Garsington Road, Oxford OX4 2DQ,UK and 350 Main Street, Malden, MA 02148, USA.

mailto:[email protected]

being emerge between age groups, at least for some dimensions (Kunzmann,Little, & Smith, 2000).

However, the variability in individual health states can complicatethe examination of the relation between health and subjective well-being.Additionally, illnesses tend to cluster with age (Laux, Kuehlein, Rosemann,& Szecsenyi, 2008). Therefore this paper aims at identifying health-relatedsimilarities and differences using latent class analysis. Moreover, the studyexamines whether subjective well-being differs according to health status anddetermines differential effects of socioeconomic and psychological resourcesfor subjective well-being.

Health in Later AdulthoodIncreasing age is often, but not normatively, associated with decreases inhealth status and increasing morbidity. It has been claimed that theco-occurrence of severe medical conditions is present in the majority of olderadults (Fried, 2000). Large-scale studies underline this (e.g. van den Akker,Buntinx, Metsemakers, Roos, & Knotterus, 1998). They found that individu-als in the age group between 20 and 39 years suffered on average from less thanone illness, the group between 40 and 59 from around 1.3 illnesses, the groupbetween 60 and 79 from 2.42 (male) and 2.61 (female) and the group over 80from 3.24 (male) and 3.57 (female) illnesses. Such an accumulation of condi-tions has severe effects both on mortality (Schneeweiss, Wang, Avorn, &Glynn, 2003) and quality of life (Fortin et al., 2006). However, most studies sofar have relied on examining the relation between morbidity and subjectivewell-being by either relating a morbidity count (weighted or unweighted; e.g.Tooth, Hockey, Byles, & Dobson, 2008) to SWB or on examining differencesin individuals with or without the most common diseases in adulthood (Alonsoet al., 2004). With increasing age, however, many people tend to suffer frommore than one condition, which increases the complexity of the picture.

For example, Laux et al. (2008) found typical age-related clustering ofhealth problems in a large sample (N = 39,699), indicating that, for instance,the likelihood of developing diabetes type 2 increases tenfold in women withhypertension, and about eightfold in men with hypertension. It has thus beenargued that the effects of multiple conditions consist of more than just thesum of single conditions (Fortin, Dubois, Hudon, Soubhi, & Almirall, 2007).However, so far only a small amount of work has been conducted to system-atically examine the impact of differing, multiple conditions on SWB.

Analysing Multiple Illnesses: More than Just theSum of Illnesses?As outlined above, illnesses tend to cluster with age (Laux et al. 2008), im-plying differential effects on subjective well-being. However, the examination

24 SCHÜZ ET AL.

© 2009 The Authors. Journal compilation © 2009 International Association of AppliedPsychology.

of specific combinations of illnesses is associated with some complexity. Onekey problem involves the almost infinite number of possible cells of combina-tions of any two or three illnesses.1 Such an examination strategy wouldinevitably face the problem of very small cell sizes or require very largesamples. An additional difficulty is posed by illnesses with shared symptoms,or rather asymptomatic conditions such as hypertension (Meyer, Leventhal, &Gutmann, 1985), and by shared effects of illnesses in terms of functionallimitations. Here, it might be problematic for people to disentangle the effectsof different illnesses on their subjective well-being.

It might therefore be helpful to examine the phenomenon of multipleillnesses in an accumulative way, i.e. by grouping illnesses (Deeg, Portrait, &Lindeboom, 2002). Such a procedure would also suggest that the accumula-tion of conditions results in qualitatively different health states. Thisgrouping can be organised according to the organ system which has beenaffected most (Charlson, Szatrowski, Peterson, & Gold, 1994), according tosimilar medical aetiologies (Kriegsman, Deeg, & Stalman, 2004), or, relyingon epidemiological data (i.e. on empirical co-occurrences), on conditionsfrequently being present in combination (Deeg et al., 2002; Portrait, Linde-boom, & Deeg, 2001).

While the first two approaches represent top-down classifications alongpredefined combination rules, the latter one is a bottom-up approach thatrelies solely on empirical health indicators. This paper uses a bottom-upapproach to examine the effects of multiple illnesses on subjective well-beingand to determine whether there are differential resources for SWB accordingto health status. It has to be noted that the procedure of conjoining individualinformation in groups is accompanied by a reduction in the informationavailable from the indicators (Agresti, 2002), thus being similar to factoranalysis. This reduction of information, however, needs to be justifiedthrough practical implications. This would be the case if individuals in dif-ferent latent classes of health and illness relied on different resources forsubjective well-being, which would suggest differential psychosocial interven-tions. Hence, the question is whether the identification of latent classes offersmore distinct information than the arrangement of the classes on an under-lying continuum, for example illness burden. A procedure of testing suchqualitative differences between distinct categories as compared to anassumed underlying continuum has been proposed by Weinstein, Rothman,and Sutton (1998) in the context of stage theories of health behaviour. Onekey criterion of qualitative differences is the identification of discontinuous

1 For example, using the popular Charlson Comorbidity Index assessing 19 different condi-tions, (19 * (19 - 1))/2 = 171 cells would be needed to examine all possible non-redundant com-binations of two, and (19!/(6*16!)) = 969 cells for all possible non-redundant combinations ofthree illnesses.

HEALTH AND SUBJECTIVE WELL-BEING 25


patterns between the categories if aligned on a hypothetical underlying con-tinuum in terms of means or regression weights in multigroup models.

Subjective Well-Being and its Correlates inLater AdulthoodSubjective well-being is usually considered as consisting of cognitive andemotional components (E. Diener, Kesebir, & Lucas, 2008). The cognitivecomponent describes the outcomes of subjective evaluations of concurrent orgeneral states, whereas the affective component describes both state-likeaffective reactions to circumstances and relatively stable affective disposi-tions. The present paper considers both components of subjective well-being.Health is one of the key determinants of subjective well-being (E. Diener et al.,2008), affecting subjective well-being both directly and indirectly (e.g. medi-ated by functional limitations). However, it has been discussed that the impactof severe health conditions is transitory, with levels of subjective well-beingreturning to initial levels (or levels close to the initial levels) after some time(Frederick & Loewenstein, 1999). Thus, a cross-sectional perspective onhealth and well-being should reveal only small differences between individualswith different health states. As the objective and subjective resource status ofindividuals varies with their health status, however, it has been suggested thatalthough mean levels of subjective well-being might be similar, the individualresources and evaluations underlying these similar levels can differ (Schwartzet al., 2006).

A plethora of factors that affect subjective well-being have been discussed.Here, we concentrate on three central correlates of subjective well-being inlater life: first, the effects of individuals’ appraisals of their economic andsocial situation, second, the impact of pain due to illness, and finally, theeffects of coping with adversity, namely the ability to adjust personal goals tothe individual resource status (flexible goal adjustment; Brandtstädter &Rothermund, 1994).

Socioeconomic Situation and Subjective Well-BeingThe individual social and economic situation constitutes an importantresource for subjective well-being (Biswas-Diener, 2008). Several pathwaysand mediators have been suggested. For example, material wealth couldcontribute to experiences of relative autonomy and progress towards per-sonally relevant goals (E. Diener et al., 2008). Additionally, the fulfilmentof basic needs of everyday living and having sufficient access to necessaryresources should contribute to subjective well-being (Maslow, 1954).However, in terms of this basic needs theory, the relative importance ofindividuals’ appraisals of their material wealth for subjective well-being

26 SCHÜZ ET AL.


should depend on their life circumstances: for example, in individuals withstrong health-related limitations, appraisals of the economic situation shouldbe less important for subjective well-being than more proximal predictorssuch as their social situation or coping styles. Rather than objective measuresof resources, however, subjective evaluations of domains have been shownto predict subjective well-being (Smith, Fleeson, Geiselmann, Settersten, &Kunzmann, 1999).

Similarly, being embedded in a satisfying and rich social network has beenshown to be an important prerequisite for subjective well-being (E. Diener &Seligman, 2002; M.L. Diener, Diener McGavran, Eid, & Larsen, 2008).Apart from this generic effect, it has been suggested that emotionally sup-portive relations such as friendships are increasingly important with decreas-ing time perspective due to age or illness (Carstensen, 1995; Löckenhoff &Carstensen, 2004). Thus, such resources should be more important for sub-jective well-being with increasing illness burden. In particular, self-selectedresources such as friendship compared to family is assumed to provide emo-tional support; thus friendship appraisals might be differentially affectingsubjective well-being compared to family support.

Pain and Subjective Well-BeingPain related to an illness has a strong impact on subjective well-being, bothdirectly (Niv & Kreitler, 2001) and indirectly, mediated by reduced masteryof everyday life (Windle & Woods, 2004). However, the effects of pain onsubjective well-being might be more strongly pronounced in otherwise rela-tively healthy individuals compared to individuals with multiple conditions,where other factors can have stronger relative impact on subjective well-being(Schwartz et al., 2006).

Coping: Flexible Goal AdjustmentFlexible goal adjustment describes the individual ability to rescale personalgoals and strivings to available resources and thus cope with diminishingresources. The concept is part of a dual framework of goal attainment with anassimilative and an accommodative pathway (Brandtstädter & Rothermund,2002). Accommodative activities are related to rescaling desired outcomes inorder to adjust them to available resources. In terms of life-course dynamics,it has been proposed that in the face of potential losses and health threats inolder age, accommodative tendencies such as flexible goal adjustment becomemore important for maintaining subjective well-being than strategies to tena-ciously adhere to desired but unaccomplishable goals. The accommodativeprocess of rescaling or adjusting personal goals according to availableresources predicts higher subjective well-being in general (Wrosch, Scheier,



Miller, Schulz, & Carver, 2003) and in the face of age-related developmentallosses (Brandtstädter & Renner, 1990).

Research QuestionsThe main research aims of this study therefore are threefold: first, to deter-mine whether the health status of older individuals can be grouped intohomogeneous subpopulations according to multidimensional indicators ofhealth using latent class analysis; second, whether these subpopulations differwith regard to their subjective well-being; and third, whether the resources forsubjective well-being differ between these subpopulations.

H1: Latent Class Analysis for Health Indicators. We expect qualitativelydifferent latent classes of health in later adulthood. The qualitative differ-ences are evaluated using suggestions for detecting qualitative differencesbetween categories (Weinstein et al., 1998).

H2: Subjective Well-Being. With regard to subjective well-being, weexpect little differences between the classes on all indicators of subjectivewell-being because, as outlined above, individuals often adapt to chronicillnesses. However, individuals with many illnesses should score lower onSWB indicators than relatively healthy individuals.

H3: Resources for Subjective Well-Being. Here, we hypothesise that indi-viduals’ appraisals of emotional resources (friends and family) should bemore strongly attenuated in individuals with limited time perspective (dueto age and/or illness burden), whereas individuals’ appraisals of tangibleresources (economic situation) should be more important in individuals withextended time perspective (younger and/or fewer illnesses; Löckenhoff &Carstensen, 2004). With regard to accommodative coping strategies in termsof rescaling goals in accordance to the current situation (Brandtstädter &Renner, 1990; Brandtstädter & Rothermund, 2002), we hypothesise thatindividuals in classes with significant health-related losses, in particular, willshow stronger associations between accommodative coping and satisfactionwith life.

METHOD

Participants and ProcedureAll analyses are based on data from the second wave of the German AgeingSurvey (DEAS) conducted in 2002. DEAS is a nation-wide and population-based survey of the adult population between the ages of 40 and 85 (Engstler

28 SCHÜZ ET AL.


& Wurm, 2006) and is funded by the German Federal Ministry of FamilyAffairs, Senior Citizens, Women and Youth.2 The sample for this study wasdrawn by a national probability sampling stratified by age, sex, and place ofresidence (Eastern, i.e. former GDR or Western Germany). The study com-prised a standardised interview and an additional questionnaire.

MeasuresHealth Status. Health is a multidimensional construct, consisting of

several psychosocial facets (World Health Organization, 1946). For thepurpose of this study, we concentrated on physical and psychological com-ponents of health, with the physical component decomposed into functionalstatus and a disease list, and the psychological component assessed viadepressive symptomatology and cognitive capacity. The LCA therefore wasbased on 14 indicators, namely a list of 11 diseases, a measure of physicalfunctioning and two indicators of psychological health, depressive symp-toms, and cognitive capacity.

The disease list consisted of 11 conditions (e.g. cardiovascular diseases,diabetes, cancer, respiratory diseases, eye diseases, hearing problems) andwas presented to participants in the questionnaire. It was informed by thedisease list for the Charlson Comorbidity Index (Charlson et al., 1994), whichis widely used to determine the occurrence of comorbid conditions. Thephysical functioning subscale of the short-form health survey SF-36 (Ware &Sherbourne, 1992) was used to indicate functional status (above/belowmedian). Depressive symptoms were assessed by the Centre for Epidemiologi-cal Studies-Depression scale (CES-D; Radloff, 1977). For the purpose of thisstudy, we dichotomised the scale at the 15-item short-version adjusted clinicalcut-off point for the German norm population of one standard deviationabove the mean (Hautzinger & Bailer, 1993). Cognitive capacity was assessedvia the digit-symbol test (D-S-T) from the Wechsler Adult Intelligence Scale-revised (WAIS-R; Wechsler, 1981). Age-adjusted cut-off scores for anaverage intelligence score were derived from Sattler (1982) and the individualdigit-symbol scores were dichotomised into 0 (below average) and 1 (averageand above).

Subjective Well-Being and Predictors. Satisfaction with life was assessedin the questionnaire using the satisfaction with life scale (SWLS; E. Diener,Emmons, Larsen, & Griffin, 1985). The SWLS consists of five items witha 5-point response format. It has been used in numerous studies (for an

2 All instruments and sample descriptions (in German) are available online (www.german-ageing-survey.org).



http://www.german-ageing-survey.org



overview, see e.g. Pavot & Diener, 1993) and forms a reliable and change-sensitive indicator of general satisfaction with life.

Positive and negative affect were assessed in the questionnaire using thePANAS scale consisting of 20 items (10 items positive, 10 items negativeaffect; Watson, Clark, & Tellegen, 1988). Individuals were asked to indicatehow often in the last month they experienced a range of affects (e.g. “enthu-siastic” for positive or “hostile” for negative affect) on a scale ranging from1 (“never”) to 5 (“very often”).

Pain was assessed in the interview using a single item: “To what extent doyou feel that physical pain prevents you from doing what you need to do?”based on the WHOQoL-BREF questionnaire (World Health Organization,1993). Participants answered this item on a scale ranging from 1 (“not at all”)to 5 (“an extreme amount”).

Individual appraisals of family, friends, and the economic situation wereassessed with three single items in the interview: “On an overall level, howwould you rate . . .” (a) “. . . your relation to your family”, (b) “. . . yourrelation to your friends”, (c) “. . . your economic situation?” Answers weregiven on 5-point scales from 1 (“very bad”) to 5 (“very good”).

Flexible goal adjustment was assessed in the questionnaire using the 10-itemscale as reported by Freund and Baltes (2002). The items are to be answeredon a 5-point Likert scale from 1 (“not at all true”) to 5 (“totally true”). Anexample item is, “Even if everything goes wrong, I still can find somethingpositive about the situation”.

Analytical ProcedureIn order to summarise the information obtained by the health status indica-tors, we conducted Latent Class Analyses (LCA) using Mplus Version 5.0(Muthén & Muthén, 2007). Missing data (less than 10% on any of theindicator variables) were imputed using the Full Information MaximumLikelihood method (FIML). Similar to factor analysis, LCA aims at findinga reduced set of dimensions that explains the relations between the variables.Unlike factor analysis, LCA assumes that the latent variable is categorical,and indicators can be nominal. The number of latent classes in LCA can beobtained by comparing fit indices of solutions with varying numbers ofclasses. Fit indices for LCA are the likelihood ratio c2 statistic, the AkaikeInformation Criterion (AIC) and the Bayesian information criterion (BIC).Because the likelihood ratio c2 statistic is sample-size dependent, it tends to beless informative the larger the sample size gets. Additionally, the Lo-Mendell-Rubin adjusted likelihood ratio test (Lo, Mendell, & Rubin, 2001) wasused to determine the number of latent classes in the model, because it allowsone to compare models with differing numbers of latent classes, with

30 SCHÜZ ET AL.


non-significant values (p < .05) indicating that the model with k - 1 classesshould be accepted.

ANOVAs between classes were conducted to compare age, number ofillnesses, appraisals of life standard, family and friends, satisfaction with life,positive and negative affect, and flexible goal adjustment. Post-hoc tests(Tukey HSD or Tamhane T2) were performed according to the assumptionof equality of variances between groups.

Finally, satisfaction with life and positive as well as negative affect wereregressed on the predictors while controlling for the stratification factors age,gender, and region of residence. Regression analyses were performed sepa-rately in each latent class in order to examine differential resource patterns.Regression coefficients were compared with regard to the 95 per cent confi-dence interval for the unstandardised regression coefficients, adjusted formultiple comparisons.

RESULTS

Characteristics of Study SampleOf the 3,084 participants who took part in the study, 2,787 (90.4%) answeredboth the interview questions and the questionnaire, whereas 297 (9.6%) didnot fill in the questionnaire. These participants had to be dropped from allanalyses, because several variables were assessed in the questionnaire. Logis-tic regression analysis was used to examine differences between participantsand non-participants in the questionnaire by predicting participation with allvariables that were available from the interview. Participants with higherappraisals of their living standard (OR = 1.34, p < .05) were significantlymore likely to partake in the questionnaire, whereas participants with higherdepressive symptomatology were more likely to refuse the questionnaire(OR = .91, p < .01); all other predictors did not reach significance. Partici-pants were aged between 40 and 85 years (M = 61.38). About half of theparticipants were female (49.8%), about 67.2 per cent of participants lived informer Western Germany, and 32.8 per cent lived in former EasternGermany. The high proportion of older adults and people living in EasternGermany is due to sample stratification. Age, gender, and place of residenceare therefore treated as control variables in the following analyses. Table 1sets out other characteristics of the study sample and the intercorrelations ofthe study variables.

Latent Class AnalysesThe results of the Latent Class Analyses suggest that a model with four latentclasses fits the data better than the three- or five-class solutions (see Table 2),



TAB

LE1

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=3,

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ber

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ness

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32 SCHÜZ ET AL.


because the Lo-Mendell-Rubin test for the five-class solution was not signifi-cant, suggesting that we accept the solution with k - 1 classes. Class orderwas rearranged in order to reflect increasing illness burden from class 1 to 4.

The four latent classes differ with regard to the probability of the preva-lence of different health conditions (Figure 1). Individuals whose most likely

TABLE 2Results from Latent Class Analyses

Model AIC BIC L2 df% Reduction

in L2

Lo-Mendell-RubinTest for k-1

classes

One-class 34983.83 35068.30 2821.27 16163 0 –/–Two-class 32863.60 33038.57 2446.73 16240 14.28 2132.53***Three-class 32569.35 32835.11 2209.82 16229 21.71 321.29**Four-class 32466.83 32922.79 2079.96 16211 26.31 131.73*Five-class 32429.03 32875.49 2030.27 16198 28.04 67.24 ns

Note: *** p < .001; ** p < .01; * p < .05.

FIGURE 1. Item profile for 4-class solution: Relative prevalence of conditionsin classes.

Note: D-S-T: Digit-Symbol-Test scores with age-adjusted cut-off scores forabove/below average; low PF: SF-36 physical functioning subscale belowmedian; CES-D: Centre for Epidemiological Studies-Depression scale withclinical cut-off score.



class membership is in latent class 1 (n = 807) have low incidence probabilitiesfor all illnesses and have the highest probabilities for being above the medianof the SF-36 physical functioning subscore and the Digit-Symbol-Test score.Latent class 2 (n = 1,612) is characterised by moderate incidence probabilitiesfor all illnesses, but has a high likelihood of joint problems. Latent class 3(n = 405) is characterised by the highest incidence rates of cardiovascularproblems and the highest probability of scoring below the median on theSF-36 PF subscale, indicating low functional status. Additionally, individualsin this class have the highest probability of suffering from diabetes. Latentclass 4 (n = 258) is characterised by the highest relative incidence rates for allillnesses except diabetes and cardiovascular diseases, and has the highestprobabilities of sight as well as hearing problems, and of scoring below themedian on the SF-36 physical functioning subscale.

Mean differences between individuals according to their most likely classmembership are depicted in Table 3. We will only point to some noteworthyfindings: individuals in latent classes 3 and 4 are not significantly differentwith regard to age, but individuals in latent class 4 report significantly moreillnesses (5.99) than those in latent class 3 (3.8). All classes differed signifi-cantly with regard to pain, with class 1 reporting the lowest levels and class 4reporting the highest levels of limitations due to pain. The other predictorswere mainly similar between classes, with low mean differences.

In terms of indicators of subjective well-being, individuals in latent class 1scored highest on indicators of well-being (life satisfaction and positive affect)and individuals in latent class 4 scored highest on negative affect. The highestmean differences between the latent classes are around half a scale point

TABLE 3Means of Study Variables According to Most Likely Class Membership

VariableLatent Class 1

(n = 807)Latent Class 2

(n = 1,612)Latent Class 3

(n = 405)Latent Class 4

(n = 258)

Age1 54.05a 61.75b 69.68c 68.86b

Number of illnesses1 .29a 1.87b 3.8c 5.99d

Pain1 1.10a 1.77b 2.43c 2.74d

Appraisal economy1 3.85a 3.73a 3.56b 3.59b

Appraisal friends1 4.17a 4.10a,b 4.03a,b 4.00b

Appraisal family1 4.09a 4.02a,b 3.99a,b 3.91b

Flexible goal adjustment2 2.37a 2.39a,b 2.45a,b 2.47b

Satisfaction with life1 4.02a 3.84b 3.50c 3.54c

Positive affect1 3.66a 3.46b 3.11c 3.28d

Negative affect1 1.83a 1.98b 2.10c 2.28d

Note: Means with different subscripts differ at p < .05, based on 1 Tamhane T2 post-hoc tests or 2 TukeyHSD post-hoc tests.

34 SCHÜZ ET AL.


(.52 for satisfaction with life between classes 1 and 3, .55 for positive affectbetween classes 1 and 3, and .45 for negative affect between classes 1 and 4),and the mean levels are above the theoretical mean of the scale (3), indicatingrelatively high subjective well-being.

Predicting Subjective Well-BeingIn a series of multiple regression analyses, satisfaction with life, positive andnegative affect were regressed on age, sex, region of residence, pain, indi-vidual appraisals of living standard, family and friends, as well as flexible goaladjustment for the four latent classes separately (see Table 4). The predictionpatterns of the different indicators of subjective well-being suggest differen-tial impact of pain, subjective appraisals of economic situation, friends andfamily on subjective well-being in the latent classes.

Regarding satisfaction with life, the appraisal of friends predicted satisfac-tion with life only in classes 2 (b = .08, p < .01) and 4 (b = .25, p < .001), andthese regression coefficients were significantly different from those in theother classes. Flexible goal adjustment predicted satisfaction with life signifi-cantly better in classes 3 (b = .45, p < .001) and 4 (b = .39, p < .001) than inclasses 1 (b = .25, p < .001) and 2 (b = .29, p < .001). This differential predic-tion pattern supports qualitative differences between both latent classes 2 and3 as well as latent classes 3 and 4. Individuals in all classes profited fromhigher appraisal of their economic situation and positive appraisals of theirfamily relations.

With regard to positive affect, the appraisal of the economic situationsignificantly predicted positive affect in individuals in classes 1, 2, and 3, withthe non-significant predictor in class 4 (b = .05, ns) being significantly lowerthan the coefficient in class 1 (b = .24, p < .001). The prediction pattern offlexible goal adjustment closely matches that of the prediction of satisfactionwith life, with the predictor being significantly stronger in classes 3 (b = .44,p < .001) and 4 (b = .47, p < .001) than in class 1 (b = .34, p < .001).

With regard to the prediction of negative affect, pain significantly predictednegative affect in classes 1 (b = .15, p < .001) and 2 (b = .10, p < .001), with theregression coefficient in class 4 being significantly lower than in class 1.Appraisal of the relation to friends was only predictive of negative affect(inversely related) in class 4 (b = -.28, p < .001), with this regression coeffi-cient being significantly higher than all others.

DISCUSSION

The main research aims of this study were to examine configurations ofhealth indicators in older individuals, their impact on life satisfaction, and thedegree to which pain, subjective appraisals of individuals’ social as well as



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emb

ers

of

the

Late

nt

Cla

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1–4,

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ifica

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der

,R

egio

no

fR

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ence

)

Indi

cato

rP

redi

ctor

Lat

ent

Cla

ss1

(n=

807)

“No

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esse

s”L

aten

tC

lass

3(n

=1,

612)

“Joi

ntD

isea

ses”

Lat

ent

Cla

ss2

(n=

405)

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diov

ascu

lar

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ease

s”L

aten

tC

lass

4(n

=25

8)“M

ulti

ple

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esse

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BS

EB

bB

SE

Bb

BS

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bB

SE

Bb

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sfac

tion

wit

hlif

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ain

-.08

.06

-.04

-.09

.02

-.11

***

-.09

.04

-.11

*-.

10.0

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13**

App

rais

alec

onom

y.4

1.0

3.4

0***

.37

.02

.36*

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9.0

6.2

3***

.26

.07

.21*

**A

ppra

isal

frie

nds

.04 a

.04

.04

.09 b

.03

.08*

*.0

2 a.0

6.0

2.2

6 b.0

6.2

5***

App

rais

alfa

mily

.11

.03

.11*

**.0

6.0

2.0

6**

.16

.05

.15*

*.0

5.0

6.0

6F

lexi

ble

goal

adju

stm

ent

.37 a

.05

.25*

**.4

4 a.0

4.2

9***

.77 b

.08

.45*

**.6

6 b.0

9.3

9***

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j..3

9***

.32*

**.3

7***

.35*

**

Pos

itiv

eaf

fect Pai

n-.

10.0

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07-.

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*-.

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11*

-.09

.03

-.17

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omy

.19 a

.04

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3.0

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-.03

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.00

.04

.00

36 SCHÜZ ET AL.


TAB

LE4

Co

nti

nu

ed

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cato

rP

redi

ctor

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ent

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ss1

(n=

807)

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ent

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ss3

(n=

1,61

2)L

aten

tC

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2(n

=40

5)L

aten

tC

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4(n

=25

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itiv

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xibl

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just

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7***

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Neg

ativ

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fect

Pai

n.2

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2 b.0

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ppra

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-.11

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-.16

***

-.02

.02

-.03

-.09

.05

-.10

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5-.

08A

ppra

isal

frie

nds

.01 a

.03

.01

-.01

a.0

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01a

.05

-.01

-.21

b.0

5-.

28**

*A

ppra

isal

fam

ily-.

01.0

3-.

01-.

00.0

2-.

00-.

02.0

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4**

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xibl

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just

men

t-.

23.0

4-.

23**

*-.

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3-.

24**

*-.

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22**

*.2

9.0

7-.

24**

*

R2 ad

j..1

6***

.14*

**.2

3***

.25*

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Not

e:*

p<

.05;

**p

<.0

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*p

<.0

01.S

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.:St

anda

rder

ror

ofre

gres

sion

wei

ght.

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tand

ardi

sed

regr

essi

onw

eigh

ts(B

)pri

nted

inbo

ldbe

arin

gdi

ffer

ents

ubsc

ript

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<.0

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post

-hoc

test

s.



economic situation, and the ability to flexibly adjust personal goals differen-tially predicted different aspects of subjective well-being. This study adds tothe existing literature by suggesting qualitatively different groups of healthstatus and by providing evidence for the differential effects of personalresources on SWB depending on individual health status.

Health Status and Subjective Well-Being (H1 and H2)LCA identified four latent classes of individuals according to their configu-ration on several health indicators. As with all classification approaches, areduction of information compared to the information available from theoriginal indicators occurs. In contrast to predefined grouping criteria as inclassifications according to shared risk factors or affected organ systems(top-down approaches), we employed an epidemiology based (bottom-up)approach by identifying configurations of health indicators according to theirfrequency of co-occurrence.

For the examination of the relation between health status and SWB, thisapproach might have advantages over the other approaches. A bottom-upapproach as employed here allows us to examine this relation without theneed for a full examination of all combinations of single conditions, whichwould result in a very large number of possible cells.

We aimed at a multidimensional operationalisation of health (WorldHealth Organization, 1946), incorporating indicators of functional and psy-chological health along with a disease list. While such a multidimensionalapproach might be criticised for mixing up differential aspects of health, weexplicitly aimed at including functional and psychological indicators ofhealth to account for differential subjective experiences with multiple ill-nesses, thus accounting for the notion that multimorbidity might be morethan just the sum of illnesses (Fortin et al., 2006, 2007).

With regard to the validity of the latent classes, it has to be ensured that thedistinction between classes offers more information than arranging individu-als on an underlying continuum of illness burden. Table 3 suggests that themean differences follow a linear pattern, and post-hoc trend analyses revealedstrong significant linear trends for all variables. However, the examination ofthe prediction patterns of SWB reveals discontinuity between the classes:while flexible goal adjustment and economic appraisal could be aligned on alinear continuum, both pain and the appraisal of friendship relation follow asequence different from what an underlying continuum would suggest (seeTable 4). This finding suggests qualitative differences between the classes(Weinstein et al., 1998).

The four classes identified here approximately correspond to those identi-fied in other studies (Deeg et al., 2002; Portrait, Lindeboom, & Deeg, 1999).It is notable that there is a substantial proportion of individuals (latent class

38 SCHÜZ ET AL.


1, n = 807; 26.2%) who enjoy good health with high functional status. Thisclass also enjoyed significantly higher levels of satisfaction with life than theother classes. However, a substantial proportion of individuals (latent class 4,n = 258; 23.11%) suffer from multiple conditions, especially cardiovasculardiseases and joint problems; moreover, they have a high prevalence of respi-ratory, gastric, diabetes, and gall bladder problems. Accordingly, functionallimitations were most prominent among individuals in class 4. With regard tolife satisfaction, however, individuals in this class scored similarly to those inlatent class 3, with mainly cardiovascular diseases. This is consistent withprevious studies which found fatal or fatally perceived diagnoses to be asso-ciated with low satisfaction with life. The mean differences between theclasses might seem small, with the greatest difference spanning over roughlyhalf a scale point (Table 3). However, such relatively small mean differencesin subjective well-being do in fact reflect substantial differences (Lucas, 2007).

A substantial proportion of individuals is affected by cardiovascular dis-eases (latent class 2, 13.1%). This class also shows the highest incidence ofdiabetes, which suggests that the diseases of individuals in these classes mightbe lifestyle-related (Taylor, 2008).

The largest proportion of individuals (52.3%) had their most likely classmembership in class 2, which is characterised by relatively high incidenceprobabilities for joint problems and moderate to low incidence rates for otherdiseases. This pattern points to age-degenerative symptoms, which is alsounderlined by the finding that individuals in latent class 2 were significantlyolder than those in the “healthy” class 1, but also significantly younger thanthose in the other two classes. In terms of life satisfaction, individuals inlatent class 2 scored lower than those in latent class 1, but higher than theindividuals in the other classes. Longitudinal studies might be able toexamine possible class transitions between the classes and the question ofwhich variables would determine such a transition pattern or deviations.

Predicting Subjective Well-Being (H3)This article operationalised subjective well-being with the facets satisfactionwith life, positive affect, and negative affect. The intercorrelations of thesefacets (Table 1) suggest that these facets tap into related, albeit substantiallydistinct, aspects of subjective well-being.

In terms of predictors of subjective well-being, there were no significantdifferences between the groups with regard to the appraisal of family rela-tions, which yielded comparably small coefficients for all aspects of subjectivewell-being. Although there are studies which suggest the important role offamily relations for subjective well-being (for an overview, see M.L. Dieneret al., 2008), our findings suggest that subjective appraisal of friendship might



be a more important predictor of subjective well-being, especially in individu-als with limited time perspective due to higher age or higher illness burden(Löckenhoff & Carstensen, 2004).

The other predictors (pain, economic appraisal, friendship appraisal, flex-ible goal adjustment) were differentially predictive of subjective well-beingaccording to classes: the importance of pain for subjective well-being isespecially pronounced in the facet of negative affect. Here, pain was predic-tive in latent classes 1 and 2, but not in classes 3 and 4. Furthermore, thecoefficients between classes 1 and 4 were significantly different. This points tothe notion that although individuals in classes 3 and 4 experienced higherlevels of limitations due to pain than the other classes, this aspect might beinferior for their construction of negative affect, possibly due to habituationeffects or response shifts for the construction of subjective well-being.

The appraisal of the individual’s economic situation showed indifferentprediction patterns in terms of satisfaction with life and negative affect. Thispoints to the importance of being satisfied with one’s economic situation forsubjective well-being, which is not surprising, given that this has been afundamental tenet of social psychology since basic needs theory was pro-pounded (Maslow, 1954). However, in terms of positive affect, there weresignificant differences between the classes, with the economic appraisal beinga significant predictor in individuals in latent classes 1, 2, and 3, but not forindividuals in latent class 4 (b = .05, ns). This finding is noteworthy, as ourhypotheses would predict that in classes with limited time perspective (3 and4), such tangible resources would be less important. However, individuals inclass 3 profited from higher appraisals of their economic situation, whichpoints to qualitative differences between classes 3 and 4. Here, we can onlyspeculate that in terms of time perspective, individuals with mainly cardio-vascular diseases perceive these as more controllable and thus base theirsubjective well-being more on tangible resources (Löckenhoff & Carstensen,2004).

There were also differential prediction patterns for satisfaction with lifewith regard to the appraisal of friendship, with individuals in classes 2 and 4profiting more from positive appraisals of friendship than individuals in theother two classes. While the importance of emotional resources such asfriendship support (rather than family support alone) for individuals withlimited time perspective has been well documented (Carstensen, 1995), we canonly speculate about the importance of friendship appraisal in individuals inclass 2 (mainly joint problems). It may be possible that in individuals in thisclass, age-related decline symptoms such as joint problems signal beginninghealth problems, which might trigger first notions of limitations in their timeperspective rather than in individuals in class 3, which has manifest healthproblems, but who might on the other hand have adapted to their relativelycontrollable problems of cardiovascular diseases in terms of an extending

40 SCHÜZ ET AL.


time perspective. Individuals in class 4 are challenged by an especially frailhealth status and limited time perspective, thus preferring emotionalresources over tangible ones (Löckenhoff & Carstensen, 2004). In terms ofnegative affect, friendship appraisal constituted a protective factor only inindividuals in class 4, which points to the idea that social support is especiallybeneficial in individuals with high illness burden (Cohen & Wills, 1985).

Flexible goal adjustment was more strongly associated with subjectivewell-being in individuals with high illness burden (classes 3 and 4), bothfor positive affect and satisfaction with life. This is consistent with the dualframework of development (Brandtstädter & Rothermund, 2002), whichsuggests that accommodative processes of reframing unrealistic goals when-ever resources are low is beneficial for subjective well-being. Theory suggeststhat this might work in two ways—freeing of resources otherwise boundby untenable goals, and preventing setbacks due to goal non-attainment.The finding that this resource is significantly more important for subjectivewell-being in relatively ill individuals also points to the idea of differentialintervention contents. In addition to the development-related argumenta-tion in the original theory, our findings suggest that health status might bean important mediator in explaining age-related effects of flexible goaladjustment.

Limitations and Suggestions for Further ResearchThis study has some limitations which need to be noted. First, it is based ona cross-sectional data set, which neither allows causal inferences nor offerschange perspectives. Clearly, replication of the current findings and researchon the transitions between classes in a longitudinal setting are called for.However, the data set is based on a representative sample of the Germanadult population aged 40 and older, which allows for some generalisability ofthe findings. A second limitation is that all illnesses were self-reported, andno objective verification between diagnoses was available. Comparativestudies suggest high consistencies between self-reported and record-basedcondition lists (Chaudhry, Jin, & Meltzer, 2005), which speaks in favour ofthe usability of self-reported data. Nevertheless, it would be fruitful toexamine the validity of health status classes with objectively measured healthdata. Third, the LCA is based on binary indicators, which is associated withinformation loss for the scales that have been categorised (WAIS, CES-D,and SF-36). However, we used evidence-based and age-adjusted cut-offscores whenever available (Hautzinger & Bailer, 1993; Sattler, 1982). Finally,subjective appraisals and subjective well-being might share conceptualoverlap, with individuals high in subjective well-being also appraising theirsubjective situation more positively. However, both causal directions arepossible, and we chose to employ subjective assessments of the social and



economic situation in order to avoid the problem of individual set-points andrelative standards in the individual evaluation of material and social resourcestatus (Biswas-Diener, 2008).

ImplicationsDespite these limitations, we think this study has some practically relevantimplications. Our findings suggest that describing adult health status with alimited set of dimensions might be a viable and parsimonious alternative tomere illness counts. Qualitative differences between latent classes of healthwere supported by discontinuous prediction patterns for subjective well-being. These class-specific prediction patterns also suggest that examining theeffects of differential interventions targeting, e.g. mobilisation of socialsupport in individuals with beginning age-degenerative problems as well asmultimorbid individuals and self-regulation in terms of rescaling unrealisticgoals in individuals with high illness burden (Wrosch et al., 2003) might bebeneficial for subjective well-being.

ACKNOWLEDGEMENTS

The data used in this article are from the German Ageing Survey (DEAS),available via www.ageing-survey.org. The German Ageing Survey is fundedby the German Federal Ministry for Family Affairs, Senior Citizens, Womenand Youth, Grant 301-1720-2/2. The first and third authors are funded by theGerman Federal Ministry of Education and Research, Grant 01ET0702.

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Documents

Health and Subjective Well-Being in Later Adulthood; Different Health States