ORIGINAL ARTICLE

Group-Based Trajectory Modeling of CaregiverPsychological Distress Over Time

Chien-Wen J. Choi, MS & Roslyn A. Stone, PhD &

Kevin H. Kim, PhD & Dianxu Ren, MD, PhD &

Richard Schulz, PhD & Charles W. Given, PhD &

Barbara A. Given, PhD, RN, FAAN &

Paula R. Sherwood, PhD, RN, CNRN, FAAN

Published online: 15 May 2012# The Society of Behavioral Medicine 2012

AbstractBackground Competing theories of adaptation and wear-and-tear describe psychological distress patterns amongfamily caregivers.Purpose This study seeks to characterize psychological dis-tress patterns in family caregivers and identify predictors.Methods One hundred three caregivers of care recipients withprimary malignant brain tumors were interviewed within 1, 4,8, and 12 months post-diagnosis regarding psychologicaldistress; care recipients were interviewed regarding clinical/functional characteristics. Group-based trajectory modelingidentified longitudinal distress patterns, and weighted

logistic/multinomial regression models identified predictorsof distress trajectories.Results Group-based trajectory modeling identified high-decreasing (51.1 % of caregivers) and consistently low(48.9 %) depressive symptom trajectories, high-decreasing(75.5 %) and low-decreasing (24.5 %) anxiety trajectories,and high (37.5 %), moderate (40.9 %), and low-decreasing (21.6 %) caregiver burden trajectories. Highdepressive symptoms were associated with high trajec-tories for both anxiety and burden, lower caregivers age,income, and social support, and lower care recipientfunctioning.

C. J. ChoiDepartment of Acute and Tertiary Care School of Nursing,University of Pittsburgh,Pittsburgh, PA, USA

R. A. StoneDepartment of Biostatistics Graduate School of Public Health,University of Pittsburgh,Pittsburgh, PA, USA

K. H. KimDepartment of Psychology in Education School of Education,University of Pittsburgh,Pittsburgh, PA, USA

D. RenDepartment of Health and Community Systems School of NursingDepartment of Biostatistics Graduate School of Public Health,University of Pittsburgh,Pittsburgh, PA, USA

R. SchulzUniversity Center for Social and Urban Research,University of Pittsburgh,Pittsburgh, PA, USA

C. W. GivenDepartment of Family Medicine, College of Human Medicine,Michigan State University,East Lansing, MI, USA

B. A. GivenCollege of Nursing, Michigan State University,East Lansing, MI, USA

P. R. SherwoodDepartment of Acute and Tertiary Care, School of NursingDepartment of Neurosurgery, School of Medicine,University of Pittsburgh,Pittsburgh, PA, USA

C. J. Choi (*)School of Nursing, University of Pittsburgh,3500 Victoria Street, Suite 336,Pittsburgh, PA 15217, USAe-mail: cjk28@pitt.edu

ann. behav. med. (2012) 44:73–84DOI 10.1007/s12160-012-9371-8

Conclusions Our data support the adaptation hypothesis;interventions should target those at risk for persistentdistress.

Keywords Longitudinal data . Family caregivers .

Primary malignant brain tumors . Depressive symptoms .

Caregiver burden . Anxiety

Introduction

In the USA, 44.4 million caregivers provide care to familymembers with a chronic illness [1]. Taking on the role offamily caregiver is associated with psychological distress(operationalized here as depressive symptoms, anxiety, andschedule burden, because they are the most common out-comes in both descriptive and intervention caregiver re-search) [2]. In addition to caring for their loved ones,family caregivers often assume primary responsibility forsuch tasks as managing household finances, ensuring em-ployment and insurance coverage, and childcare. Associa-tions between high psychological distress and poor physicalhealth in family caregivers [3–6] may diminish caregivers’ability to provide quality care, but may be ameliorated byearly intervention.

Most studies of family caregiving, particularly in oncol-ogy, have focused on cross-sectional relationships betweenthe provision of care and distress at specific points in time,with little attention given to the pattern of caregiver distressthroughout the course of caregiving. Understanding howdistress changes over time is vital to designing interventionsthat can provide timely and appropriate support for caregivers[7, 8]. Two hypotheses attempt to explain how caregivers copewith stress over time. According to the adaptation hypothesis[9], the caregiver must learn to cope with the devastating newsof the care recipient’s diagnosis; initially high levels of psy-chological distress decrease over time, as the caregivingdemands are assimilated and caregivers adjust to the situationand added role responsibilities. According to the wear-and-tear hypothesis [10], caregivers experience low levels of psy-chological distress at the time of diagnosis, as they employcoping strategies and resources. However, as the care situationprogresses, the chronic stress, accumulating care demands,and progression of the care recipient’s illness begin to erodethese coping strategies, as well as the caregiver’s psycholog-ical well-being. This drain on the caregiver may lead toincreasing feelings of depression, anxiety, and burden. Thesehypotheses suggest that distinct subgroups of caregivers couldexperience different patterns of psychological distressthroughout the course of care.

Few studies have characterized the individual patterns ofdepressive symptoms, anxiety, or burden among caregiversover time. Commonly, longitudinal data are summarized in

terms of population averages at serial time points for pre-defined groups (i.e., repeated measures ANOVA [11–13]),correlation coefficients between mean outcomes at varioustime points [14], and mean population growth curves andindividual variations about these means [15]. Analyses as-suming that caregivers behave homogeneously over timecan produce misleading results when the population con-tains distinct subgroups [16]. Northouse et al. [12] analyzedthe effectiveness of a family intervention for prostate cancerpatients and caregivers over 4 time points, using randomeffects regression models to estimate individual trajectories(i.e., estimated curves over time). However, only time-specific means were reported, and trajectory groups werenot identified.

Trajectory analysis, or group-based trajectory modeling,simultaneously estimates patterns over time and identifiesunobserved subgroups of individuals with similar trajecto-ries [16]. Group-based trajectory modeling is based on finitemixture modeling of unobserved subpopulations, and hy-potheses regarding trajectory shape and the number of tra-jectory groups can be tested using maximum likelihood.Although group-based trajectory modeling is widely used,to our knowledge this technique has not been used to char-acterize psychological distress in caregivers of patients withprimary malignant brain tumors throughout the course ofcaregiving. Also, few studies relate caregiver psychologicaldistress over time to either the adaptation or the wear-and-tearhypothesis.

How caregivers respond to the sudden and traumaticdiagnosis of a primary malignant brain tumor in their lovedones depends on both the caregiver’s personal and socialcharacteristics and their care recipient’s disease character-istics. The Adapted Pittsburgh Mind-Body Model has beenused to describe the relationships between factors associatedwith the emotional and physical stress response in caring forsomeone with a primary malignant brain tumor [7].

According to this conceptual model, caregiver personaland social characteristics may indicate the type of attitudescaregivers will have towards the care situation, and theamount of outside support they will have. Female caregiverswho are younger, have a lower income, or are married to thecare recipient have been associated with greater risk ofcaregiver burden, depressive symptoms, anxiety, and sleeppattern changes [2, 17–21]. Mastery describes the level ofcontrol or ability the caregiver perceives to have in order tofulfill the role and challenge of the care situation. Caregiverswith high levels of mastery generally feel more prepared andready to face the care demands and challenges ahead and areless likely to have poor psychological response to the caresituation [22–24]. Social support describes the perceivedavailability and willingness of friends and family to provideemotional support to the caregiver, and has been associatedwith caregiver burden and depressive symptoms in the

74 ann. behav. med. (2012) 44:73–84

presence of care recipient neurological status [25, 26].Greater social support may provide the caregiver with reliefwhen care demands are high.

The demands of the care situation will be dependent onthe care recipient’s functional, neurologic, symptom, andtumor status. Tumor grade, location, and treatment optionshave been associated with aggressiveness of tumor recur-rence, mortality and physical, cognitive, and functionalchanges in the care recipient. Care recipients with lowerfunctionality and cognitive ability generally require morehelp with activities of daily living and make more demandson the caregiver [7]. Care recipient disease characteristicsmay describe the severity of the deterioration of the carerecipient, which may lead to more caregiver distress. Carerecipient tumor type and neuro-cognitive status can be usedas proxies for the degree of caregiving required [27].

The purpose of this analysis is to (1) characterize patternsof change over time in caregiver psychological distressthroughout the course of caregiving and (2) identify the care-giver and care recipient characteristics that are associated withvarious caregiver psychological distress trajectories over time.

Methods

Dyads of persons with primary malignant brain tumors andtheir family caregivers were recruited from suburban neuro-surgery and neuro-oncology clinics in Western Pennsylvaniaas part of a descriptive longitudinal study (R01 CA118711, PISherwood). Data collection began in 2005, with 15 dyadsrecruited for data collection at baseline and 4 months. AfterNIH funding was obtained in 2007, follow-up data collectionat 8 and 12 months was added to the protocol. Subsequentdyads were recruited within a month of each patient’s diag-nosis, and data collection occurred at baseline, 4, 8, and12 months after diagnosis in either private clinic exam-ination rooms or in the caregiver’s home. Each dyad receivedquestionnaires specific to the caregiver regarding socio-demographic characteristics, personal characteristics, psycho-logical responses, behavioral responses, biologic responses,and overall physical health; and questionnaires specific to thecare recipient regarding tumor grade, functional and neuro-logical ability, and symptom status. Approval from the insti-tutional review board at the University of Pittsburgh andinformed consent from participants were obtained prior toparticipant recruitment and data collection.

Participants

Caregivers were not required to be legally related to or livewith care recipients, but were required to be nonprofessional,non-paid caregivers over 21 years of age, English-speaking,

and not a primary caregiver for anyone else other than childrenunder 21 years of age. Care recipients were required to be over21 years of age and newly diagnosed (within 1 month) with aprimary malignant brain tumor verified by pathology report.When a care recipient died, the corresponding caregiver couldcontinue to participate in this study if they wished.

Measures

Caregiver

Baseline caregiver sociodemographic information includedage, gender, relationship to care recipient, income, and yearsof education. Income was quantified as percentage abovethe poverty level using the 2011 Federal Poverty [28] guide-lines accounting for family size (e.g., 200 % of povertyrepresents a family earning twice the poverty level). Person-al attributes of the caregiver included mastery and socialsupport. Mastery was measured using the 11-item Masterscale [29], which rates the degree of caregiver perception ofcontrol over the care situation on a scale of 10stronglydisagree to 40strongly agree. Social support was measuredusing the Interpersonal Evaluation List [30], which rates thedegree of caregiver perception of emotional support andwillingness available from friends and family on a scale of10definitely false to 40definitely true.

Caregiver psychological responses were self-reported.Depressive symptoms were measured using the short formfor the Center for Epidemiologic Studies-Depression scale,a 10-item questionnaire (CESD-10), rating the caregiver’sexperience of such symptoms as feeling “lonely,” “fearful,”and “sad” on a 4-point scale [31]. Anxiety was measuredusing a shortened version of the anxiety subscale of theProfile of Moods States scale [32], a 3-item questionnairethat rates a caregiver’s report of being: “on edge,” “nervous,”and “tense”, on a 5-point scale. Caregiver burden was mea-sured using the Caregiver Reaction Assessment scale, whichmeasures the caregiver’s perception of the positive and nega-tive effects of providing care on five areas of life: self-esteem,schedule, finances, feelings of abandonment, and health on a5-point scale [33]. This analysis focuses on the schedulesubscale, which measures the perception of burden on thecaregiver’s daily activities as a result of providing care. Foreach of these scales/subscales, a higher score indicates agreater level of the corresponding attribute/response.

Care Recipient

Care recipients’ sociological, demographic, and clinicalcharacteristics were collected at baseline; disease character-istics and functional, neurological, and symptom status werecollected at every follow-up time. Disease characteristics

ann. behav. med. (2012) 44:73–84 75

were ascertained from the care recipient’s medical recordsand pathology reports. Care recipient cognitive functionswere assessed using the Neurobehavioral Cognitive StatusExamination [34], which captures a profile of cognitiveability in the domains of level of consciousness, orientation,attention, language (comprehension, naming, repetition),constructional ability, memory, calculations, and reasoning(similarities and judgment). Lower scores indicate neuro-behavioral dysfunction.

Descriptive Analysis

For each caregiver, time-specific depression, anxiety, andschedule burden scores were plotted from the time of diagnosisto 12 months, using Stata version 9.0. Because follow-up visitswere conducted within 2 weeks of each caregiver’s scheduledfollow-up dates, time was defined as the number of monthsbetween the baseline measurement and each follow-up visit.

Trajectory Model Selection

A separate censored normal trajectory model was estimatedfor each measure of psychological distress using SAS ProcTraj [35]. Caregivers with only single time point measure-ments were excluded from the model to preserve the longi-tudinal aspect of the analyses. Model selection involved theiterative estimation of (1) the number of trajectory groupsand (2) the shape/order of each trajectory group using bothstatistical [36] and non-statistical considerations [37].

Statistical criteria for ascertaining the best fitting modelincluded four log-likelihood statistics (Akaike’s InformationCriterion [AIC] [38], Bayesian Information Criterion [BIC][39], the sample-size adjusted BIC [ssBIC] [40], and theconsistent AIC [CAIC] [41]) which include a penalty formodel complexity, and three classification statistics (classi-fication likelihood criterion (CLC), integrated classificationlikelihood adjusting the BIC (ICL-BIC) [42], and entropy[43]). Smaller values of AIC, BIC, ssBIC, and CAIC denotebetter models. Entropy is an index used in classificationaccuracy based on posterior probabilities, with higher valuesdenoting better classification. Currently, there is no com-monly accepted single gold standard model fit statistic, butsuggestions from simulation studies exist [36, 44].

Other criteria included non-overlapping confidence inter-vals, reasonable sample sizes in each identified trajectorygroup, and distinct average posterior probabilities acrossgroups. Subjective judgment is important in trajectory modelselection because the aim is to identify a useful and parsimo-nious model [37]. Depending on the outcome measure, eithera clinically relevant cutoff or previous literature guided thenumber of trajectory groups chosen.

Associations Between Trajectory Groups

For each outcome, associations between trajectory groupswere assessed using chi-square tests. To account for uncer-tainty in the assignment to trajectory groups, cross-tabulationswere weighted by their average posterior probabilities.p values≤0.05 were considered to be statistically significantthroughout.

Predictors of Trajectory Group Membership

Predictors of trajectory group membership were identifiedusing weighted binary logistic regression for outcomes withtwo trajectory groups and weighted multinomial logisticregression for outcomes with more than two trajectorygroups. Caregiver risk factors included age, gender, relation-ship to care recipient (spouse vs. other), years of education,income, and baseline mastery and social support scores. Carerecipient risk factors included tumor type (astrocytoma I–II,oligodendroglioma, other versus astrocytoma III–IV) and cog-nitive domains. To reduce collinearity between predictors,continuous predictors were centered at their respective means.

The logistic and multinomial regression models wereestimated including all caregiver and care recipient charac-teristics listed in Table 1, while treating caregiver age,gender, years of education, and care recipient tumor typeas covariates. If needed, continuous predictors were log-transformed to correct for non-normal distributions. Missingbaseline covariate data were multiply imputed using MarkovChain Monte Carlo. Model selection was conducted usingall possible subsets regressions [45] selecting the lowestMallow’s Cp criterion [46]. Multicollinearity was evaluatedusing variance inflation factor scores. In a sensitivity analysis,the final models were refit excluding influential cases (definedas deviance deletion >3.84). These models were fit using SASversion 9.2.

Caregiver bereavement was evaluated as a potential cova-riate by (1) treating bereavement status as a time-varyingcovariate using Proc Traj and by (2) comparing the associationbetween bereavement status and group membership at eachfollow-up time using weighted chi-square tests. A dropoutanalysis alsowas conducted comparing trajectory groupmem-bership between those who completed and those who droppedout of the study using weighted chi-square tests.

Results

Sample

This analysis includes a total of 124 caregiver-care recipientdyads (See Fig. 1 for flow chart of study design). Of these,

76 ann. behav. med. (2012) 44:73–84

16 dyads withdrew before completion of their baseline inter-view, 2 were no longer eligible once the care recipient’s tumordiagnosis was re-evaluated by a secondary pathology labora-tory, and 3 were diagnosed outside of the 1-month window.

The remaining 103 caregivers completed baseline assess-ments. Among these dyads, 25 care recipients died duringthe study period (8 by 4 months, 8 by 8 months, and 9 by12 months). At 4 months, 94 caregivers completed follow-up assessments (including 3 bereaved caregivers) and 9caregivers dropped out (3 due to feeling overwhelmed, 4due to bereavement, and 2 for other reasons). At 8 months,78 caregivers completed follow-up assessments (including 7bereaved caregivers), 4 caregivers dropped out due to feel-ing overwhelmed, and 12 caregivers who participated in thepilot study were not followed past 4 months. At 12 months,75 caregivers completed assessments (including 8 bereavedcaregivers) and 3 caregivers dropped out (2 due to feelingoverwhelmed and 1 due to bereavement).

The trajectory models for both the depressive symptomsand anxiety outcomes included a total of 94 caregivers whoprovided baseline and at least one follow-up measurement.For the caregiver reaction assessment outcome, a total of 88caregivers were included in the trajectory model: 6 care-givers with measurements at a single time point were ex-cluded from the final model (3 were bereaved after baseline

and did not receive the schedule burden subscale, and 3 didnot complete their 4-month follow-up and were bereaved by8 months).

Socio-Demographic and Clinical Characteristics

The majority of caregivers was female (n076, 73.8 %) andcaring for spouses (n077, 74.8 %). As shown in Table 1, theaverage age of caregivers was 51.4 years (SD011.4; range,21–77), and caregivers completed 14.3 years of educationon average (SD02.7, range, 5–23). The majority of carerecipients was diagnosed with an astrocytoma grade III–IV(n069, 67.0 %).

Descriptive Analysis

Cronbach’s alpha reliability coefficients for depressivesymptoms, anxiety, and schedule burden scores were .85,.91 and .76, respectively. “Spaghetti” plots of the predictedslopes for each psychological outcome scale from baselineto 12 months revealed diverse patterns of individual change,i.e., increasing, decreasing, and flat trajectories over time(Fig. 2). For each scale, the thick line represents the popu-lation average decreases over time.

Trajectory Analysis

Depressive Symptoms

A two-group linear trajectory model best fit caregiver depres-sive symptoms (Fig. 3; Table 2). Figure 3 shows the scatter ofdepressive symptoms scores by time from diagnosis inmonths. About half (n048, 5 l.1 %) of the caregivers wereassigned to the high-decreasing depressive symptoms group.The “average” caregiver in the high-decreasing group has adepressive symptoms score of 13.25 at baseline that signifi-cantly decreases by approximately 0.28 points per month. Thepredicted depressive symptoms score of 9.96 at 1 year remainsabove the threshold for clinical depression (CESD-10 score≥8). The other 48.9 % of the caregivers follow a trajectory thatstarts low at diagnosis and remains low, with no significantchange over time.

Anxiety

A two-group linear trajectory model best fit caregiver anx-iety over time (Fig. 4; Table 2). The low-decreasing grouprepresents the 24.5 % (n023) of caregivers who expressedlow levels of anxiety at baseline and improved significantlyover time (slope B0−0.16, p0 .001). The majority (n071,75.5 %) of caregivers were assigned to the high-decreasinggroup, who reported high anxiety at baseline and signifi-cantly improved by 12 months (slope B0−0.1; p0 .003).

Table 1 Baseline summary of caregivers and care recipients (N0103)

Characteristics M SD

Caregiver

Age 51.4 11.4

Years of education 14.3 2.5

Income 312.5 193.7

Social support 35.5 4.5

Mastery 20.9 2.8

Care recipient

Tumor type (N, %)

Astrocytoma I–II, ligodendroglioma, other 34 33.0

Astrocytoma III–IV 69 67.0

Cognitive domains

Orientation 11.6 1.1

Attention 6.9 1.6

Language 11.4 1.1

Comprehension 5.6 0.7

Repetition 11.5 1.2

Naming 7.7 0.8

Judgment 4.5 1.3

Reasoning 6.4 1.3

Similarities 6.8 1.6

Constructional ability 4.5 1.7

Memory 7.6 3.4

Calculations 3.6 0.9

ann. behav. med. (2012) 44:73–84 77

Because no cutoff point has been established for being atrisk for a clinical anxiety disorder, the clinical significanceof these changes cannot be ascertained.

Schedule Burden

A three-group linear trajectory model best fit caregiver sched-ule burden scores over time (Fig. 5; Table 2). The low-decreasing burden group (n019, 21.6 %) reported low burdenat baseline with a significant decrease over time (slopeB0−0.03; p0 .002). Approximately 41 % (n036) of caregiversreported moderate scores at baseline and 37.5 % (n033)reported high scores; in both groups these scores did not changesignificantly over time (p00.86 and p00.35, respectively).

Associations Between Trajectory Groups

A strong pair-wise association was observed between tra-jectory group membership for depressive symptoms and

anxiety (χ2(1, N094)031.80, p<.001), with all caregiversin the high-decreasing depressive symptoms group being inthe high-decreasing anxiety group (Table 3). Caregivers inthe low depressive symptoms trajectory group were approx-imately evenly split between the high-decreasing and low-decreasing anxiety trajectory groups. There were no signif-icant associations between trajectory group membership forschedule burden and depressive symptoms (χ2(2, N088)03.93, p0 .14) or anxiety (χ2(2, N088)04.62, p0 .10).

Predictors of Trajectory Group Membership

Depressive Symptoms

Relationship to care recipient and caregiver gender weresignificantly associated (Fisher’s exact test p0 .02), care-givers of spouses were more likely to be female (data notshown). Relationship to care recipient was therefore excludedfrom all predictive analyses to reduce collinearity. The final

Care recipient deaths (n=9)

4 month follow-up (n=94) • Bereaved caregivers (n=3)

Caregiver and care recipient dyads assessed for eligibility

(n=124)

Completed baseline assessments (n=103)

Discontinued (n=9) • Caregiver overwhelmed (n=3) • Care recipient death (n=4) • Other reasons (n=2)

Excluded (n=21) • Caregiver withdrew before completing

baseline assessments (n=16) • Care recipient no longer eligible after re-

evaluation (n=2) • Care recipient was diagnosed past 1

month window (n=3)

Care recipient deaths (n=8)

Discontinued (n=16) • Caregiver overwhelmed (n=4) • Pilot study (n=12)

Discontinued (n=3) • Caregiver overwhelmed (n=2) • Care recipient death (n=1)

Care recipient deaths (n=8)

8 month follow-up (n=78) • Bereaved caregivers (n=7)

12 month follow-up (n=75) • Bereaved caregivers (n=8)

Fig. 1 Flow diagram of studydesign

78 ann. behav. med. (2012) 44:73–84

logistic regression model included income, social support andcare recipient calculations score, in addition to caregiver age,gender, education and care recipient tumor type. Caregiverswere more likely to belong to the high depressive symptomstrajectory group with lower income (odds ratio (OR)00.34,p0 .02), and lower social support (OR00.85, p0 .02). A bor-derline association was found between younger age and be-longing to the high trajectory group (OR00.96, p0 .06, seeTable 4).

Anxiety

The final logistic regression model included social support,and care recipient orientation, attention, language and cal-culations scores. There were no significant associationsbetween caregiver and care recipient characteristics and

belonging to the high anxiety group. Younger caregivers(OR00.95, p0 .08) and lower social support (OR00.88,p0 .09) were somewhat more likely to belong in the highanxiety group.

Schedule Burden

The final multinomial logistic model selected included carerecipient constructional ability score while adjusting forcovariates to predict schedule burden trajectory group. Carerecipients who perform poorly on the constructional abilitytest were more likely to belong to the high (risk ratio (RR)00.48, p0 .03) or moderate (RR00.53, p0 .05) schedule bur-den trajectory group than the low trajectory group. Addi-tionally, caregivers of care recipients diagnosed with anastrocytoma grade III–IV were significantly more likely tobelong to the high schedule burden group than the lowburden group (RR04.09, p0 .04). This association was notreflected in the moderate schedule burden trajectory group.

Bereavement

A total of 18 caregivers were bereaved in this analysis, 3 at4 months, 7 at 8 months, and 8 at 12 months. The effect ofbereavement as a time-varying covariate was significant inboth trajectory groups for the depressive symptoms trajec-tory model but not for the anxiety trajectory model (data notshown). Bereavement was not tested in the schedule burdentrajectory model since this questionnaire is not given tobereaved caregivers. Testing bereavement at each time pointagainst the trajectory group using Fisher’s exact testsshowed non-significant results. Trajectory models were also

Anx

iety

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ptom

s S

core

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edul

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12840

Time since diagnosis (months)

Fig. 2 Predicted (a) depressive symptoms, (b) anxiety, and (c) sched-ule burden scores for each caregiver over time, based on randomeffects models. Each population average (thick line) decreases overtime

0

5

10

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Dep

ress

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pto

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Time since diagnosis (months)

Low High-decreasing

Fig. 3 Trajectory plots of depressive symptoms from diagnosis to1 year. Dots and diamonds denote the individual scores. For eachtrajectory group, the solid line represents the predicted trajectory andthe dashed lines represent the 95 % confidence interval

ann. behav. med. (2012) 44:73–84 79

rerun after censoring bereaved caregivers. These trajectorymodels showed little change from the original models withoutcensoring, since SAS Proc Traj imputes for missing values.Therefore, no adjustment for bereavement was included in theprediction models.

Sensitivity Analyses

Sensitivity analyses of potentially influential observationswere conducted for depressive symptoms (n02), anxiety(n04), and schedule burden (n011); a total of 16 caregivers.Similar results were obtained when these observations wereexcluded. Dropout analyses revealed no significant differ-ences in trajectory group membership between those whocompleted versus those who dropped out of the study fordepressive symptoms, anxiety, and schedule burden outcomes.

Discussion

Group-based trajectory modeling revealed patterns that havenot been fully explored in family caregiving. There appearto be distinct subsets of caregivers at risk; some caregiversdisplay persistent distress throughout the year followingdiagnosis, while another group of caregivers did not displayhigh levels of distress as a result of providing care.

Approximately one half of the family caregivers experi-enced low levels of depressive symptoms upon care recipientdiagnosis that remained low across the disease trajectory. Theremaining half reported scores above the established cutoff forbeing at risk for clinical depression at the time of care recipientdiagnosis and remained above the cutoff, despite some im-provement over time. Anxiety scores showed a similar pattern,despite the lack of an established clinical cutoff. Caregivers

Table 2 Summary of group-based trajectory analysisfor each psychologicaldistress outcome

The slope reflects thechange per month

*p<.05; **p<.005

Trajectorygroup

InterceptB (SE)

SlopeB (SE)

Predicted scoreat 12 months

SlopeB p

Depressive symptoms (Range, 0–30)

Low 3.83 (0.68) −0.08 (0.09) 2.93 .39

High-decreasing 13.25 (0.69) −0.28 (0.09)** 9.96 .001

Anxiety (Range, 3–15)

Low-decreasing 5.95 (0.44) −0.16 (0.06)** 4.02 .004

High-decreasing 9.66 (0.23) −0.01 (0.03)** 8.47 .002

Schedule burden (Range, 5–25)

Low-decreasing 10.73 (0.61) −0.34 (0.09)** 6.71 <.001

Moderate 13.88 (0.51) −0.01 (0.07) 13.7 .86

High 18.91 (0.50) 0.07 (0.07) 19.7 .35

3

6

9

12

15

12840

An

xiet

y S

core

Time since diagnosis (months)Low-decreasing High-decreasing

Fig. 4 Trajectory plots of anxiety from diagnosis to 1 year. Dots anddiamonds denote the individual scores. For each trajectory group, thesolid line represents the predicted trajectory and the dashed linesrepresent the 95 % confidence interval

5

10

15

20

25

12840

Sch

edu

le B

urd

en S

core

Time since diagnosis (months)Low-decreasing Moderate High

Fig. 5 Trajectory plots of schedule burden scores from diagnosis to1 year. Dots and diamonds denote the individual scores. For eachtrajectory group, the solid line represents the predicted trajectory andthe dashed lines represent the 95 % confidence interval

80 ann. behav. med. (2012) 44:73–84

reporting high levels of depressive symptoms also report highlevels of anxiety. However, caregivers reporting low levels of

depressive symptoms evenly report both low and high levelsof anxiety.

Table 3 Pair-wise associations between trajectory group memberships for depressive symptoms, anxiety and schedule burden

Trajectory group Schedule burden Anxiety

Low-decreasing Moderate High Low-decreasing High-decreasing

n % n % n % n % n %

Depressive symptoms

Low 11 25.0 21 47.7 12 27.3 23 50.0 23 50.0

26.0 46.6 27.5 51.6 48.4

High-decreasing 8 18.2 15 34.1 21 47.7 0 0.0 48 100.0

17.6 33.4 49.1 0.0 100.0

χ2(2, N088)03.93, p0 .14 χ2(1, N094)031.77, p<.001

χ2(2, N082.04)04.06, p0 .13 χ2(1, N089.67)031.84, p<.001

Anxiety

Low-decreasing 8 34.8 10 43.5 5 21.7 – – – –

34.9 43.3 21.8

High-decreasing 11 16.9 26 40.0 28 43.1 – – – –

16.9 38.6 44.5

χ2(2, N088)04.62, p0 .10

χ2(2, N082.72)04.57, p0 .11

Un-weighted and weighted row percentages and χ2 statistics are shown, respectively

Table 4 Predictors of high depressive symptoms (vs. low) and high anxiety (vs. low) based on logistic regression models, and high (vs. low) andmoderate (vs. low) schedule burden based on a multinomial regression model

Characteristics Depressive symptoms Anxiety Schedule burden

High vs. low Moderate vs. low

OR p OR p RR p RR p

Age 0.96 .06 0.95 .08 1.00 .95 0.98 .42

Gender (male) 0.85 .79 0.40 .12 0.33 .21 1.09 .92

Education (years) 0.84 .11 0.90 .37 1.22 .18 0.94 .69

Tumor type (astrocytoma III–IV)a 1.84 .25 0.89 .84 4.09* .04 2.47 .16

Incomeb 0.34* .02

Social support 0.85* .02 0.88 .09

Care recipient cognitive domains

Orientation (0–12) 0.46 .26

Attention (0–8) 0.91 .67

Language (0–12) 0.60 .28

Constructional ability (0–6) 0.48* 0.03 0.53 .05

Calculations (0–4) 1.63 .13 1.54 .32

Score ranges are listed in parentheses for neuropsychological tests. Memory, reasoning andmastery were not significant predictors in anymodel considered

OR odds ratio, RR risk ratio

*p<.05a vs. astrocytoma I–II, oligodendroglioma, otherb Income is log-transformed

ann. behav. med. (2012) 44:73–84 81

Schedule burden scores behaved differently, with care-givers who scored low at baseline continuing to decreasesignificantly and caregivers who scored moderate to high atbaseline experiencing no significant change over time. Ourresults lend support to the adaptation hypothesis, suggestingmost caregivers learn to adjust and cope with the demandsof the care situation over time. However, some caregivers donot adapt over time, but continue to experience levels ofmoderate to high feelings of schedule burden.

Contrary to some other research in the field [47–49], wedid not find a strong positive association between caregiverburden with depressive symptoms and anxiety. Interestingly,Given et al. [50] reported only a modest correlation of .14between schedule burden and depressive symptoms in can-cer caregivers of patients at end of life. The lack of anassociation is likely due to the measures chosen for thestudy. A large number of tools measuring caregiver burdenare uni-dimensional and focus on the care recipient’s cogni-tive dysfunction, which has shown a clear association withcaregiver burden. Unlike other studies, we measured thecare recipient’s cognitive status with neuropsychologicaltests. Our measure of caregiver burden focuses on the degreeto which providing care disrupts the caregiver’s schedule. Itmay be that this dimension of burden has a lower associationwith depressive symptoms or precedes depressive symptom insome way. Further research is required to parse out the way inwhich varying dimensions of burden contribute to depressivesymptoms and anxiety.

To our knowledge, no other caregiver study has usedgroup-based trajectory modeling of longitudinal caregivingdata to estimate distinct trajectories of depressive symptoms,anxiety scores, and caregiver burden scores over time incaregivers of persons with a primary malignant brain tumor.Recent studies have used growth curve modeling [51, 52]and generalized estimating equation models [53], both ofwhich characterize patterns over time for subgroups defineda priori. Growth curve modeling may be appropriate formonotonic longitudinal data (i.e., language development)but may not be appropriate in situations where the patternof change over time is non-monotonic or unknown [54].

Our findings suggest that caregivers with higher incomeand social support are more likely to report fewer depressivesymptoms over time. These findings are consistent withprevious literature [7]. Contrary to our expectations, ouranalysis failed to identify significant associations betweenrisk factors that have been shown to be associated withdepressive symptoms and anxiety in caregivers (e.g., gender,relationship to care recipient, and care recipient tumor type). Atrend of association between caregiver age and anxiety trajec-tory group suggests that younger caregivers may be morelikely to report high anxiety over time. Younger caregivershave been shown to be more likely to be highly anxious,regardless of care recipient health [55, 56].

Schedule burden was predicted only by care recipientcharacteristics, specifically tumor type and cognitive func-tion. Care recipients with more aggressive tumor types willmost likely have more frequent doctor’s visits, treatmentappointments and shorter survival time, which may explainwhy caregivers feel burden on their schedules. In addition,poor performance on constructional ability (i.e., the inabilityto assemble shapes to copy a two-dimensional drawing)suggests that care recipients have both cognitive and func-tional limitations, specifically in the use of tools, and will bemore likely to require help from the caregiver in performingactivities of daily living, such as dressing, bathing, andeating. This places a greater burden on the caregiver sincethe care recipient requires constant attention and help. Clini-cians and other health-related professionals commonly usethe Neurobehavioral Cognitive Status Examination as abrief screening tool to capture a profile of specific abilitiesand disabilities of the patient. Therefore, these scores maynot be equivalent to a formal neuro-cognitive functioningassessment.

Longitudinal data that describe the natural response tocare demands are vital for designing and implementinginterventions that target specific caregivers at risk for dis-tress. Caregivers have been shown to benefit more frompersonalized and targeted interventions; however, currenthealthcare systems may lack the amount of time, personneland financial resources to offer such interventions to helpindividuals adjust to the psychological demands of caregiving[52, 57, 58]. Our data suggests caregivers follow the adapta-tion hypothesis upon diagnosis of a primary malignant braintumor in their loved one and identified a number of risk factorsat baseline associated with trajectory group membership.Caregivers at risk of experiencing high distress can be identi-fied early on, suggesting an early intervention facilitatingcaregiver ability to adapt to the caregiving situation wouldbe preferable to a later-stage intervention.

In our population of caregivers of persons with a primarymalignant brain tumor, early intervention could be targetedto the caregivers upon diagnosis of their care recipient.Group-based trajectory analyses and plots revealed caregiverswho report high levels of depressive symptoms (51.1 %),anxiety (75.5 %), and/or schedule burden (37.5 %) at baselinemay not clinically improve by 1 year. Without having tofollow these caregivers for a year, clinicians can assess care-givers for psychological distress upon diagnosis and identifythose in need of intervention if they perform within the 95 %confidence range of scores in the high trajectory groups. Thesedistinct trajectory groups can be used as a screening tool toidentify caregivers who may be at increased risk of psycho-logical distress over time.

Our analysis also indicates that some caregivers do notdisplay negative psychological responses as a result of pro-viding care. While these caregivers still may have needs

82 ann. behav. med. (2012) 44:73–84

related to the care situation, they may be able to employadequate coping skills without outside intervention. Thesefindings could help to explain the inconsistent effect sizesacross prior intervention studies.

Since depressive symptoms and anxiety tend to be associ-ated with certain personality types, such as neuroticism, thesepatterns of distress may also be present in other caregivingsituations. However, since the onset of illness and length ofthe care situation differ greatly among caregiving populations(e.g., dementia, stroke, traumatic brain injury, and cancer) theanalysis should be replicated in those caregiving situations todetermine whether they follow similar trajectory patterns.

Limitations

One limitation of this analysis is that the small number ofmeasurement time points affects trajectory shape, groupmembership, and peaks. Because follow-up assessments inthis study spanned 1 year, only linear trajectory shapes wereconsidered. In future research, a prospective validation com-ponent could test whether trajectory group membership canbe extrapolated to new caregiver and care recipient dyads,and assess whether the patterns observed within 12 monthspersist over longer periods of time.

Potential selection bias may have also occurred in thisanalysis. Sixteen caregivers dropped out of the study afterconsenting but before baseline measurements could beassessed. These caregivers could be more highly stressedwithin the first month of their care recipient diagnosis thanothers. If these caregivers tend to be those who are experi-encing more caregiver distress, then our sample would bebiased in favor of caregivers who are more able to handlethe stress of caregiving and participating in a longitudinalstudy. However, we observed no difference in psychologicaldistress by whether caregivers stayed or left the studythroughout the follow-up period.

Conclusions

Caregivers in this study typically followed a trajectory ofdecreasing psychological distress over 12 months followingdiagnosis, lending support to the adaptation hypothesis. Ourfindings indicate group-based trajectory modeling is an ef-fective technique to estimate distinct trajectories of longitu-dinal caregiver psychological distress, and when coupledwith predictive models to examine associated risk factorscan lead to the development of targeted interventions andscreening tools customized to caregivers most in need.

Acknowledgements Support for this research was provided by theNational Cancer Institute (R01 CA118711-01A1) as part of the Mind-Body Interactions in Neuro-Oncology Family Caregivers study,

Principal Investigator Paula R. Sherwood. The authors wish to thankthe Innovations in Cancer Care team for data collection and manage-ment, and our participants who donated their time and insight to thisproject.

Conflict of Interest Statement The authors have no conflict ofinterest to disclose.

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