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Prevalence and Risk Factors for Depression in a Longitudinal, Population-Based Study Including Individuals in the Community and Residential Care
Prevalence and Risk Factors for Depression in a Longitudinal, Population-Based Study Including Individuals in the Community and Residential Care Kaarin J. Anstey, Ph.D., Chwee von Sanden, B.Sc. Hons, Kerry Sargent-Cox, B.A. Hons, Mary A. Luszcz, Ph.D.
Objective: The authors report the population prevalence of depression in older adults living in the community and in residential care. Demographic, medical, health behavior, functional and cognitive measures, and transition to residential care are evaluated as risk factors for depression over eight years. Methods: Depression prevalence estimates were obtained from the initial electoral role sample of the Australian Longitudinal Study of Ageing that included persons living in residential care. A subsample (N ⫽1,116) based on follow-up data were included in longitudinal multilevel analyses that evaluated between-person and within-person predictors associated with scores from the Center for Epidemiology–Depression Scale. Results: At wave 1, 14.4% of community-dwelling and 32.0% of residential care-dwelling participants were depressed (15.2% of total cases). Increase in depression was associated with antidepressant status, sex, education, and marital status, but not history of hypertension, stroke, diabetes, heart disease, or smoking. Time-varying predictors, including residential care, activities of daily living, instrumental activities of daily living, self-rated health, and Mini-Mental State Examination, predicted depressive symptoms both between and within persons. Conclusions: Depression is strongly linked with factors indicating increased dependency. Risk assessment and targeting of intervention strategies to prevent depression in late life should incorporate changes in functional capacity, mental status, and need for residential care. (Am J Geriatr Psychiatry 2007; 15:497–505) Key Words: Depression, longitudinal study, prevalence, epidemiology, activities of daily living, institutionalization
Received May 31, 2006; revised September 26, 2006; accepted October 25, 2006. From the Centre for Mental Health Research, Australian National University, Canberra, Australia (KJA, CvS, KS-C); and the School of Psychology and Centre for Ageing Studies, Flinders University, Adelaide, South Australia (MAL). Send correspondence and reprint requests to Associate Professor Kaarin J. Anstey, Ageing Research Unit, Centre for Mental Health Research, Australian National University, Canberra ACT 0200, Australia. e-mail: [email protected] © 2007 American Association for Geriatric Psychiatry
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Prevalence and Eight-Year Depression Risk
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ommunity studies estimating the prevalence of depression in the elderly have found lower rates in community samples compared with the high rates reported in studies of institutionalized adults.1,2 Exclusion of institutionalized adults from epidemiologic studies leads to difficulty in obtaining accurate figures for prevalence of depression in the population.3 Different measurement instruments make comparisons between studies difficult, and measurement instruments may not be valid in different settings. The present study used the Center for Epidemiological Studies–Depression Scale (CES-D), which has been validated in samples of older adults who are frail4 or who have poor physical health and functioning5 to obtain estimates of the prevalence of depression from a population-based sample that includes both institutionalized and community-dwelling adults. Although there is strong evidence for high rates of depression in institutions6 and a strong association between chronic illness and disability and depression,7 only a few epidemiologic studies have examined the relationship between institutionalization and depression prospectively. A recent report from the Cardiovascular Health Study found different trajectories of functional disability associated with different trajectories of depressive symptoms over time.8 These authors stress the need for longitudinal studies of depression in relation to disability. The present study therefore evaluated predictors of change in depressive symptoms over time in a very old sample, including demographic factors, antidepressant use, medical conditions, health behaviors, activities of daily living (ADL), instrumental activities of daily living (IADL), residential care status, cognitive decline, and self-rated health (SRH). An additional aim was to identify whether transition to residential care predicted change in depressive symptoms independently of functional and cognitive decline.
METHOD Sample The sample of the Australian Longitudinal Study of Ageing has been fully described elsewhere.9 The
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South Australian Electoral Roll (voting is compulsory in Australia) was used as a sampling frame to identify households with residents over 65 years of age. The sample was stratified by age and domicile into four five-year cohorts aged 65– 69, 70 –74, 75–79, 80 – 84, and 85 and older. The study comprises seven waves of data collection: the baseline, between September 1992 and March 1993, and six subsequent waves of data collected at approximately 12- to 18month intervals with the seventh wave conducted in 2003. Data from waves 1, 3, 6, and 7 are used in this study because they included the full interview, whereas the other waves were telephone interviews. A trained interviewer conducted a comprehensive two-hour home interview followed by an individual clinical assessment conducted approximately two weeks later. Participants were invited to opt into the clinical assessment. Only participants alive at wave 6 were included in longitudinal modeling (N⫽1,131). Participant data from waves in which they scored below the cutoff for dementia were excluded. There were 15 participants with Mini-Mental State Examination (MMSE) scores below 24 at waves 1, 3, and 6, so they were excluded from the sample, leaving a total of 1116. Data from wave 7 was excluded as a result of a large proportion of missing data. Measures Time Invariant (wave 1 only). Marital status was coded as married/de facto, separated/divorced, widowed, or never married. Education was the total number of years of formal education (range: 0 –20). Cardiovascular risk factors measured in a self-report questionnaire10,11 included heart condition/attack, hypertension, stroke/transient ischemic attack, and diabetes. Information was also obtained on current smoking and frequency of alcohol consumption (1⫽ ⱕ4 times per month; 2⫽2–3 times per week; 3⫽ⱖ4 times per week). Time Varying (waves 1, 3, and 6). Participants were classified as either living in the community (living in a private dwelling) or in residential care (i.e., private rest homes, hostels, nursing homes, hospitals, or boarding houses). Antidepressant use was coded as a binary variable. Depression was measured using the CES-D, a 20-item questionnaire designed to measure depression in community-based epidemiologic stud-
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Anstey et al. ies.12 Item responses are in reference to the way the individual felt in the last week. A four-point Likert scale is used with answers extending from rarely or none of the time (0) to most of the time (3) with scores ranging from 0 – 60. SRH was measured on a five-point scale reverse-coded for analyses so that higher scores represent better health (5⫽ excellent, 4⫽ very good, 3⫽good, 2⫽fair, 1⫽poor). The ADL measure required respondents to report if they had difficulty performing everyday activities, including bathing, grooming, dressing, eating, using the toilet, getting around or away from home, and getting from a bed to a chair. IADL questions included 10 activities regarding housework, meal preparation, money management, and the use of public transport. A four-point Likert scale was used for both measures with answers ranging from “no difficulty at all” to “a lot of difficulty.”13 ADL total scores ranged from 0 – 8, and IADL total scores ranged from 0 –10. For statistical analyses, these were dichotomized so that 0 ⫽no difficulty and 1 ⫽any difficulty. A 21-item version of the MMSE was administered at wave 114 and the 30-item version was administered at waves 3 and 6. The 21-item version was converted to a 30-item scale using the following formula: [(MMSE score/21) ⫻ 30] to match scores from waves 3 and 6. Statistical Analyses Descriptive analyses compared baseline characteristics of the sample used in longitudinal analyses with those not selected (N ⫽971) and, within the selected sample, between depressed and nondepressed groups at wave 1. t tests were used for continuous variables and Pearson 2 for categorical variables. For these analyses, the CES-D was treated as dichotomous using the clinical cutoff of ⱖ16.15,16 Longitudinal modeling was conducted within a multilevel modeling framework17 using the SPSS MIXED procedure in SPSS version 14.0. This approach allows for within-person change and between-person differences to be estimated simultaneously. The CES-D was treated as a continuous variable18 and change was modeled as a function of years of follow up. There were three stages to the analyses. First, a baseline model was determined a priori consisting of variables that required adjusting to interpret the findings relating to predictors of depression over time. Both fixed and random effects Am J Geriatr Psychiatry 15:6, June 2007
of time were estimated. Demographic variables (age, sex, education, marital status) and their interactions with time were tested and antidepressant status was included as a time-varying predictor. The interaction of the between-person effect and time was also tested to determine if those who were ever on antidepressants differed in their rate of change in depression from those who never reported using antidepressants. Second, wave 1 medical conditions and health behaviors were evaluated as predictors of depression over time adjusting for the baseline model. In the final stage, the key predictors of interest (residential care, ADL, IADL, SRH, MMSE) were analyzed in separate models as time varying (measurements for each predictor included at each wave) after adjusting for the baseline model. For each of these variables, the between-person and within-person fixed effects and the between-person effect by time interactions were evaluated. For continuous time-varying predictors (SRH and MMSE), the between-person differences in the within-person relationship were also estimated. The significance of betas in multilevel models is determined by dividing by the standard error, which provides a t statistic. The significance of random slopes added to the model is evaluated by the difference between the ⫺2 restricted log likelihood measures for models including and excluding the random slope(s).19 To enable interpretation of regression coefficients, age was centered at 75, years of education was centered at nine years, SRH was centered at three, and the MMSE was centered at 27. Missing data were imputed using the expectation maximization algorithm20 using all available measures. An alpha of 0.05 was used for selection of covariates (baseline model only) to ensure adequate control of potentially confounding variables. An alpha of 0.01 was used to evaluate the health variables and key predictors of interest.
RESULTS Prevalence Rates in the Australian Longitudinal Study of Ageing Table 1 shows the rates of depression by age group at waves 1, 3, 6, and 7 of the study. There was a 499
Prevalence and Eight-Year Depression Risk
TABLE 1.
Depression Prevalence by Age Group and Domicile at Waves 1, 3, 6, and 7 Domicile N (%) Age
N
65–69 70–74 75–79 80–84 85–89 90⫹ Total
140 560 518 425 323 98 2,064
65–69 70–74 75–79 80–84 85–89 90⫹ Total
54 380 426 357 239 109 1,565
70–74 75–79 80–84 85–89 90⫹ Total
22 179 228 141 74 644
75–79 80–84 85–89 90⫹ Total
41 188 122 62 413
Community
Residential Care
Full Sample
N ⫽ 1,942 19 (13.6) 69 (12.3) 78 (15.5) 54 (13.4) 49 (18.0) 10 (15.4) 279 (14.4) N ⫽ 1,454 7 (13.0) 44 (11.7) 55 (13.2) 53 (16.0) 51 (25.2) 12 (16.4) 222 (15.3) N ⫽ 601 5 (22.7) 21 (11.7) 34 (15.1) 13 (10.2) 6 (12.8) 79 (31.1) N ⫽ 382 7 (17.1) 26 (14.2) 25 (21.9) 7 (15.9) 65 (17.0)
N ⫽122 0 0 3 (20.0) 8 (38.1) 19 (37.3) 9 (27.3) 39 (32.0) N ⫽111 0 0 3 (30.0) 6 (24.0) 13 (35.1) 15 (41.7) 37 (33.3) N ⫽ 43 0 0 1 (33.3) 2 (15.4) 7 (25.9) 10 (23.3) N ⫽ 31 0 1 (20.0) 1 (12.5) 2 (11.1) 4 (12.9)
N ⫽ 2,064 19 (13.6) 69 (12.3) 81 (15.6) 62 (14.6) 68 (21.1) 19 (19.4) 318 (15.2) N ⫽ 1,565 7 (13.0) 44 (11.6) 58 (13.6) 59 (16.5) 64 (26.8) 27 (21.1) 259 (16.5) N ⫽ 644a 5 (22.7) 21 (11.7) 36 (15.7) 15 (10.3) 13 (17.6) 90 (13.8) N ⫽ 413 7 (17.1) 27 (14.4) 26 (21.3) 9 (10.2) 69 (16.7)
Wave 1
Wave 3
Wave 6
Wave 7
a There were nine missing values for the residential care variable. Percentages (shown in parentheses) are based on the proportion of participants within each domicile in each age group.
greater prevalence of depression at wave 1 among those living in residential care compared with community-dwelling adults (2[1] ⫽31.99, p ⬍0.01).
Sample Attrition and Missing Data At wave 1 only, 23 of 2,087 participants did not complete the CES-D. At wave 3, 1,679 participants completed the interview, 1,565 completed the CES-D, 125 participants refused to participate, 43 had moved or were not contactable, and 240 participants were dead. At wave 6, 791 participants completed the interview, 653 completed the CES-D, 276 refused to complete the study, 64 had moved or were not contactable, and 956 were deceased. At wave 7, 489 completed the interview, 413 completed the CES-D, 282 refused to complete the interview, 89 had
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moved or were not contactable, and 1,227 were deceased. Analyses were conducted to evaluate how the sample used in longitudinal analyses (N⫽1,116) differed from the remainder of the sample (N⫽971) at baseline. The selected sample was younger (mean age 75.43, standard deviation [SD]: 5.53 compared with 81.30 [SD: 6.54], t[2086] ⫽21.97, p ⬍0.01) and more likely to be female (2 [1] ⫽59.24, p ⬍0.01), married (2 [3] ⫽61.16, p ⬍0.01), and to be living in the community (2 [1] ⫽75.31, p ⬍0.01). They were less likely to report experiencing a heart attack (2 [1] ⫽26.63), p ⬍0.01), stroke (2 [1] ⫽49.43, p ⬍0.01), diabetes (2[1] ⫽21.09, p ⬍0.01), or be current smokers (2 [1] ⫽12.71, p ⬍0.01). They were less likely to report difficulties with ADL (2 [1] ⫽ 84.33, p ⬍0.01) or IADL (2 [1] ⫽73.68, p ⬍0.01). They also reported better SRH (t[2080] ⫽11.39, Am J Geriatr Psychiatry 15:6, June 2007
Anstey et al. p ⬍0.01), higher MMSE (t [1903] ⫽11.72, p ⬍0.01), and fewer depressive symptoms (7.06, SD: 6.76, compared with 9.94, SD: 8.17, t [2063] ⫽ 8.76, p ⬍0.01).
cated by the significant random effect of time (variance of slope ⫽ 0.047, 2 [2] ⫽8.68, p ⬍0.01).
Comparison of Depressed and Nondepressed on Risk Factors at Wave 1
Baseline Model Including Demographic Predictors and Antidepressant Use
Those classified as depressed at wave 1 were more likely to be female (2 [1] ⫽6.50, p ⬍0.01), be on antidepressants (2 [1] ⫽3.89, p ⬍0.01), rate their health as poorer (t [1115] ⫽9.44, p ⬍0.01), and report difficulties with ADL (2 [1] ⫽35.16, p ⬍0.01) and IADL (2 [1] ⫽ 29.21, p ⬍0.01) than those who were not depressed at baseline. They did not differ in age, education, marital status, residential care status, hypertension, stroke, smoking status, alcohol consumption, diabetes, or MMSE.
Table 2 shows the results of the baseline model in which predictors were time-invariant except for antidepressant use. All interactions with time were tested. Nonsignificant interactions are not shown. Compared with males, females scored 1.29 points higher on the CES-D scale. For every year of education above nine years, the CES-D score decreased by 0.22 points. The significant age ⫻ time interaction shows that the rate of change in depression score increased such that for every year above 75 years, the effect of time increased by 0.01 points. The significant interaction between marital status and time showed that the rate of change in depression was dependent on wave 1 marital status. Those who were married or in a de facto relationship and those who were widowed at wave 1 experienced a greater increase in CES-D scores over time compared with those who were never married. Examination of the simple slopes indicated that depressive symptoms in married/ de facto participants increased over time, whereas participants who were never married at wave 1 experienced a significant decrease in CES-D scores over
Descriptive Model of Depression Over Time With No Predictors Included Before examining predictors, a model of depression over time was estimated for descriptive purposes. This showed that 53.6% of the variability in CES-D scores over the eight-year follow-up period was the result of between-person differences and 47.4% was the result of within-person change. There were significant differences in the rate of change in CES-D scores over time between individuals as indi-
TABLE 2.
Fixed Effects From Multilevel Baseline Model of Depression Over Time
Variable Intercept Time Age (75 years) Female Education (9 years) Marital status (never married reference group) Married/de facto Separated/divorced Widowed (Age ⫺75 years) ⫻ time Marital status ⫻ time Married/de facto ⫻ time Separated/divorced ⫻ time Widowed ⫻ time Between-person never on antidepressant Within-person ever on antidepressant Between-person never on antidepressant ⫻ time
 (standard error)
t (df )
p
9.382 (1.219) ⫺0.334 (0.156) 0.058 (0.035) 1.287 (0.327) ⫺0.215 (0.063) F (3, 1088.47) ⫽ 3.797a ⫺1.059 (1.070) 1.953 (1.584) 0.060 (1.115) 0.010 (0.004) F (3, 1058.358) ⫽ 9.232a 0.533 (0.138) 0.226 (0.200) 0.320 (0.144) ⫺4.513 (0.753) 1.491 (0.583) 0.166 (0.085)
7.694 (1145.578) ⫺2.146 (1120.499) 1.658 (1123.699) 3.933 (1108.178) ⫺3.435 (1112.954)
⬍0.001 0.032 0.098 ⬍0.001 0.001 0.010 0.322 0.218 0.957 0.029 ⬍0.001 ⬍0.001 0.257 0.027 ⬍0.001 0.011 0.049
⫺0.990 (1091.116) 1.233 (1073.437) 0.054 (1098.785) 2.189 (1084.527) 3.851 (1080.627) 1.134 (1048.582) 2.216 (1085.894) ⫺5.992 (1927.034) 2.557 (2337.674) 1.969 (1227.484)
a
Omnibus test results displayed for variables with more than two categories. Where interactions with time are present, the main effects of the predictors are interpreted in relation to wave 1 scores.
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Prevalence and Eight-Year Depression Risk time. Participants who were divorced or separated experienced no change in depression scores. Antidepressant use was associated with change in depression scores both between and within persons. Individuals who changed from not taking antidepressants to taking antidepressants had an average 1.49-point increase in their depression score. There were significant between-person differences in change of depression over time (Table 2). Examination of the simple slopes showed that depression scores for those who did not take antidepressants at any wave did not change, whereas those who did take antidepressants at any wave experienced a significant decrease in depression score. Baseline Model With the Addition of Health Variables Table 3 shows the results of analyses evaluating medical and health factors at wave 1. For each predictor, the main effect and the interaction with time was tested in a separate model (nonsignificant interactions not shown). There was a significant interaction between time and history of heart condition or attack; however, review of the slopes showed that those reporting no history of heart condition or attack at wave 1 experienced a decrease in depression score over time, whereas those reporting a history of heart condition or attack remained stable. There was a significant interaction between alcohol consumption and time. Review of the slopes showed that those with lower drinking frequency (ⱕ4 times per month) decreased in depressive symptoms, whereas those who consumed alcohol 2–3 TABLE 3.
times per week or ⱖ4 times per week showed no change in level of depressive symptoms. Modeling of Time-Varying Predictors: Residential Care, Activities of Daily Living, Instrumental Activities of Daily Living, and Self-Rated Health The final set of analyses examined time-varying predictors separately after adjustment for the baseline model (Table 4). There were significant withinperson and between-person effects of residential care. Compared with participants who were in residential care at any point during the period of the study, those who were not in residential care scored on average 2.44 units lower on the CES-D. The within-person effect indicated that a participant moving into residential care increased by 1.73 points on the CES-D. The rate of change in depression score was also dependent on whether participants had difficulty with at least one ADL or IADL over the study period. The significant main effects of between-person ADL and IADL indicate that compared with those with difficulties in either ADL or IADL, those with no difficulties scored 3.86 and 2.54 points lower on wave 1 CES-D score, respectively. There was a significant difference in the change of depression scores over time for those with difficulties compared with those with no difficulties in ADL but not IADL (Table 4). Examination of simple slopes showed that those with no difficulties in ADL experienced no change in depression scores, but those who did have at least one difficulty with ADL at any measurement occasion
Multilevel Models of Health Variables as Predictors of Depression
Variable No heart condition/attack No heart condition/attack ⫻ time No hypertension No stroke/transient ischemic attack No diabetes Not current smoker Alcohol ⱖ4 times per week (reference group) ⱕ4 times per month 2–3 times per week Alcohol frequency x time ⱕ4 times per month x time 2–3 times per week x time
 (standard error)
t (df)
p
⫺0.776 (0.428) ⫺0.180 (0.053) ⫺0.548 (0.338) ⫺1.005 (0.627) ⫺0.605 (0.660) ⫺0.646 (0.641) F (2,1096.181) ⫽ 0.923a 0.372 (0.422) ⫺0.351 (0.645) F (2,1049.459) ⫽ 9.437a ⫺0.073 (0.051) 0.246 (0.080)
⫺1.812 (1084.388) ⫺3.377 (1048.732) ⫺1.623 (1099.117) ⫺1.603 (1115.718) ⫺0.916 (1099.554) ⫺1.008 (1085.683)
0.070 0.001 0.105 0.109 0.360 0.314 0.398 0.378 0.587 ⬍0.001 0.157 0.002
0.881 (1100.313) ⫺0.544 (1085.436) ⫺1.417 (1046.809) 3.064 (1048.195)
a
Omnibus test results displayed for variables with more than two categories. Where interactions with time are present, the main effects of the predictors are interpreted in relation to wave 1 scores.
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TABLE 4.
Fixed Effects from Multilevel Models of Residential Care, Activities of Daily Living, Instrumental Activities of Daily Living, Self-Rated Health, and Mini-Mental State Examination Predicting Depression
Variables BP no residential care at any wave WP residential care BP no difficulty ADL BP No difficulty ADL ⫻ time WP difficulty ADL BP no difficulty IADL BP no difficulty IADL ⫻ time WP difficulty IADL BP SRH (centered at 3) WP SRH BP MMSE (centered at 27) WP MMSE
 (standard error)
t (df )
p
⫺2.441 (0.627) 1.732 (0.586) ⫺3.864 (0.438) 0.158 (0.054) 1.842 (0.284) ⫺2.536 (0.609) 0.161 (0.075) 1.052 (0.227) ⫺2.674 (0.145) ⫺1.641 (0.141) ⫺0.267 (0.082) ⫺0.269 (0.073)
⫺3.896 (1,370.718) 2.955 (1,655.903) ⫺8.827 (1,650.027) 2.946 (1,351.472) 6.488 (2,408.850) ⫺4.164 (1,203.215) 2.148 (1,117.819) 4.638 (2,317.864) ⫺18.471 (1,240.155) ⫺11.657 (616.140) ⫺3.267 (1,062.278) ⫺3.685 (953.905)
⬍0.001 0.003 ⬍0.001 0.003 ⬍0.001 ⬍0.001 0.032 ⬍0.001 ⬍0.001 ⬍0.001 0.001 ⬍0.001
Note: Results for random effects for SRH and MMSE not shown. Where interactions with time are present, the main effects of the predictors are interpreted in relation to wave 1 scores. BP: between-person; WP: within-person; ADL: activities for daily living; IADL: instrumental activities of daily living; MMSE: Mini-Mental State Examination; SRH: self-rated health.
had a significant decrease in depression ( ⫽⫺0.46, standard error: 0.16, t [1135.06] ⫽⫺2.95, p ⬍0.01), possibly as a result of regression to the mean from their higher starting point. Significant within-person relationships between ADL and depression and IADL and depression were also found. When a participant changed from having no difficulty to having difficulty with at least one ADL or at least one IADL, their CES-D score increased by 1.84 points and 1.05 points, respectively. There were significant effects of SRH on CES-D scores. On average, participants who scored one point more on the SRH scale scored 2.67 points less on the CES-D scale. The within-person effect showed that participants who increased their health rating by one unit experienced an associated decrease in CES-D score of 1.64 points. The significant variance in the slope (2 [3] ⫽36.81, p ⬍0.001) demonstrated interindividual variability in rates of change in CES-D scores. There were significant effects of MMSE on CES-D scores. For every point scored above 27, there was an associated decrease of 0.27 points on the CES-D. The within-person effect showed that when a participant performed better on the MMSE by one unit, they experienced a decrease in CES-D score of 0.27 points. The significant variance in the slope (2 [3] ⫽11.66, p ⫽ 0.005) indicated that this within-person effect varied between individuals. To evaluate whether transition to residential care was associated with change in depressive symptoms
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independent of functional and cognitive status, an additional model was evaluated that adjusted for ADL, IADL, and MMSE. Both the between-person ( ⫽⫺1.75, SE: 0.62, t [1450.52] ⫽⫺2.82, p⫽0.005) and within-person effects of institionalization on depression remained significant ( ⫽1.61, SE: 0.59, t [1563.09] ⫽2.73, p ⫽0.006).
CONCLUSIONS The present study reports depression prevalence from a population-based sample of older Australian adults and evaluates predictors of change in level of depressive symptoms over time. Our particular interest was in time-varying predictors associated with increasing dependency and move to residential care. First, although the prevalence of depression in residential care was 32% compared with 14.4% in the community, this difference did not lead to a large overall inflation in the population prevalence because the number of participants in residential care was small. This finding should not diminish the significance of the high prevalence of depression in residential care, but suggests that community-based studies only underestimate depression prevalence in the population by a small amount. The prevalence rate observed in this study is within 1%–2% of that observed in several other studies using the same measure of depression.5,21–23 The rates of depression
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Prevalence and Eight-Year Depression Risk found among older adults are comparable to those of younger adults when assessed with the CES-D.21,24 Second, our results described the trajectory of depression within a cohort of survivors. The sample included in our analyses was healthier at baseline than those who were excluded. This is typical of analyses based on longitudinal studies of very old adults and is largely accounted for by the exclusion of participants who did not survive to wave 6. We have previously reported that depressed participants are more likely to be missing from follow-up assessments from this sample.25 Therefore, the results probably underestimate the prevalence and severity of depression in the age group studied, but this would also be the case in most population-based studies. The first stage of our multilevel analyses described our baseline model. Cross-sectionally, there was no increase in depressive symptoms with age. This was probably the result of selection effects occurring at baseline whereby older participants were healthier than younger participants by virtue of their survival. However, age differences emerged longitudinally, because older participants had a greater increase in depressive symptoms relative to younger participants. There was also an overall increase in depression over time independent of age indicating the whole sample became more depressed over time. Those who were partnered had a greater increase in depressive symptoms, possibly as a result of their greater likelihood of losing a partner during the course of the study. The second stage of our analyses revealed relatively few medical conditions associated with depression. The lack of an association between depression and hypertension or diabetes was consistent with some previous literature.26 Neither stroke nor smoking was associated with depression, possibly as a result of the self-report nature of our measures.26,27 Heart disease did not have a main effect in the multilevel model, which adjusted for demographic variables, and did not increase the risk of depression longitudinally, which differs from some previous reports.26,28 Our main interest was in the depressive risk associated with increasing functional dependence, cognitive decline, poor SRH, and transition to residential care. In contrast to the measures of physical health, these were all associated with depression prospectively (both between and within persons). Our results clearly show that functional impairment and
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cognitive decline are associated with increasing depression in late life in those without cognitive impairment. It is noteworthy that perceptions of health were more important than the presence or absence of medical conditions for predicting depression. These findings are consistent with a four-year follow up of the Cardiovascular Health Study that showed how persistent depressive symptoms were associated with increasing disability.8 Our results also showed that transition to residential care was associated with increased depressive symptoms after adjusting for functional impairment and cognitive decline. Thus, the transition to residential care predicts depression independent of the factors that lead to institutionalization, which also predict depression. This points to a cascade of risk factors for depression occurring in a vulnerable group of older adults who experience functional and cognitive decline. The present study is limited by sample attrition and the lack of clinical assessments of depression and medical conditions. The number of statistical tests means that some significant results are likely to be the result of type 1 errors. Nevertheless, the results show a consistent pattern of indicators of dependency being associated with depressive symptoms prospectively. Despite being a longitudinal study, it is still not possible to firmly determine causality from observed associations. It remains possible that other factors cause both depression and dependency or that depression causes disability. Our findings reiterate the significance of late-life depression as a public health problem that is strongly linked with decline in functional and cognitive capacity and self-perceived health. In addition to history of depression and antidepressant use, risk assessment and targeting of intervention strategies for older adults should incorporate gender, marital status, changes in functional capacity and mental status, SRH, and need for residential care. The ALSA is funded by the South Australian Health Commission, the Australian Rotary Health Research Fund, the U.S. National Institute of Health (Grant No. AG 08523-02), and the National Health and Medical Research Council Grants #229936 and #179839. The authors thank the study participants and the Centre for Ageing Studies, Flinders University, Adelaide, Australia.
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Anstey et al. References 1. Beekman A, Deeg D, van Tilburg T, et al: Major and minor depression in later life: a study of prevalence and risk factors. J Affect Disord 1995; 36:65–75 2. Kay D, Henderson AS, Scott R, et al: Dementia and depression among the elderly living in the Hobart community: the effect of the diagnostic criteria on the prevalence rates. Psychol Med 1985; 15:771–788 3. Snowden J, Lane F: The prevalence and outcome of depression and dementia in Botany’s elderly population. Int J Geriatr Psychiatry 2001; 16:293–299 4. Davidson H, Feldman PH, Crawford S: Measuring depressive symptoms in the frail elderly. J Gerontol 1994; 49:P159 –P164 5. Berkman LF, Berkman CS, Kasl S, et al: Depressive symptoms in relation to physical health and functioning in the elderly. Am J Epidemiol 1986; 124:372–388 6. Ames D: Depressive disorders among elderly people in long-term institutional care. Aust N Z J Psychiatry 1993; 27:379 –391 7. Beekman A, Penninx B, Deeg D, et al: The impact of depression on the well-being, disability and use of services in older adults: A longitudinal perspective. Acta Psychiatr Scand 2002; 105:20 –27 8. Lenze EJ, Schulz R, Martire LM, et al: The course of functional decline in older people with persistently elevated depressive symptoms: longitudinal findings from the Cardiovascular Health Study. J Am Geriatr Soc 2005; 53:569 –575 9. Andrews G, Henderson AS, Hall W: Prevalence, comorbidity, disability and service utilisation: overview of the Australian National Mental Health Survey. Br J Psychiatry 2001; 178:145–153 10. Palmore E: Normal Aging: Reports From the Duke Longitudinal Study, 1955–1969. Durham, NC, Duke University Press, 1970 11. National Institute on Aging: Established Populations for Epidemiologic Studies of the Elderly Resource Data Book. Washington, DC, National Institute of Health, 1986, pp. 86 –2443 12. Radloff L: The CES-D Scale: a self report depression scale for research in the general population. Appl Psych Meas 1977; 3:385–401 13. Fillenbaum GG: Multidimensional Functional Assessment of Older Adults. Hillsdale, NJ, Lawrence Erlbaum Associates, 1988 14. Folstein MF, Robins LN, Helzer JE: The Mini-Mental State Examination. Arch Gen Psychiatry 1983; 40:811–812 15. Beekman A, Copeland J, Prince MJ: Review of community prev-
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alence of depression in later life. Br J Psychiatry 1999; 174:307– 311 16. Penninx BW, Kritchevsky SB, Yaffe K, et al: Inflammatory markers and depressed mood in older persons: results from the Health, Aging and Body Composition study. Biol Psychiatry 2003; 54: 566 –572 17. Singer JD, Willett JB: Applied Longitudinal Data Analysis: Modelling Change and Event Occurrence. Oxford, Oxford University Press, 2003 18. Duncan B, Rees DI: The effect of smoking on depressive symptomatology: a re-examination of data from the National Longitudinal Study of Adolescent Health. Am J Epidemiol 2005; 162:461– 470 19. Snijders TAB, Bosker RJ: Multilevel Analyses: An Introduction to Basic and Advanced Multilevel Modelling. London, Sage Publications, 2004 20. Graham J, Hofer S, Piccinin A: Analysis with missing data in drug prevention research. National Institute of Drug and Alcohol Research Monographs 1994; 142:13–63 21. Gatz M, Hurwicz M-L: Are old people more depressed? Crosssectional data on Centre for Epidemiological Studies Depression Scale factors. Psychol Aging 1990; 5:284 –290 22. Kennedy GJ, Kelman HR, Thomas C, et al: Hierarchy of characteristics associated with depressive symptoms in an urban elderly sample. Am J Psychiatry 1989; 146:220 –225 23. Murrell S, Himmelfarb S, Wright K: Prevalence of depression and its correlates in older adults. Am J Epidemiol 1983; 117:173–185 24. Eaton WW, Kessler LG: Rates of symptoms of depression in a national sample. Am J Epidemiol 1981; 114:528 –538 25. Anstey KJ, Luszcz MA: Mortality risk varies according to gender and change in depressive status in very old adults. Psychosom Med 2002; 64:880 –888 26. Kim J-M, Stewart R, Kim S-W, et al: Vascular risk factors and incident late-life depression in a Korean population. Br J Psychiatry 2006; 189:26 –30 27. Klungsoyr O, Nygard JF, Sorensen T, et al: Cigarette smoking and incidence of first depressive episode: an 11-year, populationbased follow-up study. Am J Epidemiol 2006; 163:421–432 28. Bremmer MA, Hoogendijk WJ, Deeg DJ, et al: Depression in older age is a risk factor for first ischemic cardiac events. Am J Geriatr Psychiatry 2006; 14:523–530
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Objective: The authors report the population prevalence of depression in older adults living in the community and in residential care. Demographic, medical, health behavior, functional and cognitive measures, and transition to residential care are evaluated as risk factors for depression over eight years. Methods: Depression prevalence estimates were obtained from the initial electoral role sample of the Australian Longitudinal Study of Ageing that included persons living in residential care. A subsample (N ⫽1,116) based on follow-up data were included in longitudinal multilevel analyses that evaluated between-person and within-person predictors associated with scores from the Center for Epidemiology–Depression Scale. Results: At wave 1, 14.4% of community-dwelling and 32.0% of residential care-dwelling participants were depressed (15.2% of total cases). Increase in depression was associated with antidepressant status, sex, education, and marital status, but not history of hypertension, stroke, diabetes, heart disease, or smoking. Time-varying predictors, including residential care, activities of daily living, instrumental activities of daily living, self-rated health, and Mini-Mental State Examination, predicted depressive symptoms both between and within persons. Conclusions: Depression is strongly linked with factors indicating increased dependency. Risk assessment and targeting of intervention strategies to prevent depression in late life should incorporate changes in functional capacity, mental status, and need for residential care. (Am J Geriatr Psychiatry 2007; 15:497–505) Key Words: Depression, longitudinal study, prevalence, epidemiology, activities of daily living, institutionalization
Received May 31, 2006; revised September 26, 2006; accepted October 25, 2006. From the Centre for Mental Health Research, Australian National University, Canberra, Australia (KJA, CvS, KS-C); and the School of Psychology and Centre for Ageing Studies, Flinders University, Adelaide, South Australia (MAL). Send correspondence and reprint requests to Associate Professor Kaarin J. Anstey, Ageing Research Unit, Centre for Mental Health Research, Australian National University, Canberra ACT 0200, Australia. e-mail: [email protected] © 2007 American Association for Geriatric Psychiatry
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ommunity studies estimating the prevalence of depression in the elderly have found lower rates in community samples compared with the high rates reported in studies of institutionalized adults.1,2 Exclusion of institutionalized adults from epidemiologic studies leads to difficulty in obtaining accurate figures for prevalence of depression in the population.3 Different measurement instruments make comparisons between studies difficult, and measurement instruments may not be valid in different settings. The present study used the Center for Epidemiological Studies–Depression Scale (CES-D), which has been validated in samples of older adults who are frail4 or who have poor physical health and functioning5 to obtain estimates of the prevalence of depression from a population-based sample that includes both institutionalized and community-dwelling adults. Although there is strong evidence for high rates of depression in institutions6 and a strong association between chronic illness and disability and depression,7 only a few epidemiologic studies have examined the relationship between institutionalization and depression prospectively. A recent report from the Cardiovascular Health Study found different trajectories of functional disability associated with different trajectories of depressive symptoms over time.8 These authors stress the need for longitudinal studies of depression in relation to disability. The present study therefore evaluated predictors of change in depressive symptoms over time in a very old sample, including demographic factors, antidepressant use, medical conditions, health behaviors, activities of daily living (ADL), instrumental activities of daily living (IADL), residential care status, cognitive decline, and self-rated health (SRH). An additional aim was to identify whether transition to residential care predicted change in depressive symptoms independently of functional and cognitive decline.
METHOD Sample The sample of the Australian Longitudinal Study of Ageing has been fully described elsewhere.9 The
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South Australian Electoral Roll (voting is compulsory in Australia) was used as a sampling frame to identify households with residents over 65 years of age. The sample was stratified by age and domicile into four five-year cohorts aged 65– 69, 70 –74, 75–79, 80 – 84, and 85 and older. The study comprises seven waves of data collection: the baseline, between September 1992 and March 1993, and six subsequent waves of data collected at approximately 12- to 18month intervals with the seventh wave conducted in 2003. Data from waves 1, 3, 6, and 7 are used in this study because they included the full interview, whereas the other waves were telephone interviews. A trained interviewer conducted a comprehensive two-hour home interview followed by an individual clinical assessment conducted approximately two weeks later. Participants were invited to opt into the clinical assessment. Only participants alive at wave 6 were included in longitudinal modeling (N⫽1,131). Participant data from waves in which they scored below the cutoff for dementia were excluded. There were 15 participants with Mini-Mental State Examination (MMSE) scores below 24 at waves 1, 3, and 6, so they were excluded from the sample, leaving a total of 1116. Data from wave 7 was excluded as a result of a large proportion of missing data. Measures Time Invariant (wave 1 only). Marital status was coded as married/de facto, separated/divorced, widowed, or never married. Education was the total number of years of formal education (range: 0 –20). Cardiovascular risk factors measured in a self-report questionnaire10,11 included heart condition/attack, hypertension, stroke/transient ischemic attack, and diabetes. Information was also obtained on current smoking and frequency of alcohol consumption (1⫽ ⱕ4 times per month; 2⫽2–3 times per week; 3⫽ⱖ4 times per week). Time Varying (waves 1, 3, and 6). Participants were classified as either living in the community (living in a private dwelling) or in residential care (i.e., private rest homes, hostels, nursing homes, hospitals, or boarding houses). Antidepressant use was coded as a binary variable. Depression was measured using the CES-D, a 20-item questionnaire designed to measure depression in community-based epidemiologic stud-
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Anstey et al. ies.12 Item responses are in reference to the way the individual felt in the last week. A four-point Likert scale is used with answers extending from rarely or none of the time (0) to most of the time (3) with scores ranging from 0 – 60. SRH was measured on a five-point scale reverse-coded for analyses so that higher scores represent better health (5⫽ excellent, 4⫽ very good, 3⫽good, 2⫽fair, 1⫽poor). The ADL measure required respondents to report if they had difficulty performing everyday activities, including bathing, grooming, dressing, eating, using the toilet, getting around or away from home, and getting from a bed to a chair. IADL questions included 10 activities regarding housework, meal preparation, money management, and the use of public transport. A four-point Likert scale was used for both measures with answers ranging from “no difficulty at all” to “a lot of difficulty.”13 ADL total scores ranged from 0 – 8, and IADL total scores ranged from 0 –10. For statistical analyses, these were dichotomized so that 0 ⫽no difficulty and 1 ⫽any difficulty. A 21-item version of the MMSE was administered at wave 114 and the 30-item version was administered at waves 3 and 6. The 21-item version was converted to a 30-item scale using the following formula: [(MMSE score/21) ⫻ 30] to match scores from waves 3 and 6. Statistical Analyses Descriptive analyses compared baseline characteristics of the sample used in longitudinal analyses with those not selected (N ⫽971) and, within the selected sample, between depressed and nondepressed groups at wave 1. t tests were used for continuous variables and Pearson 2 for categorical variables. For these analyses, the CES-D was treated as dichotomous using the clinical cutoff of ⱖ16.15,16 Longitudinal modeling was conducted within a multilevel modeling framework17 using the SPSS MIXED procedure in SPSS version 14.0. This approach allows for within-person change and between-person differences to be estimated simultaneously. The CES-D was treated as a continuous variable18 and change was modeled as a function of years of follow up. There were three stages to the analyses. First, a baseline model was determined a priori consisting of variables that required adjusting to interpret the findings relating to predictors of depression over time. Both fixed and random effects Am J Geriatr Psychiatry 15:6, June 2007
of time were estimated. Demographic variables (age, sex, education, marital status) and their interactions with time were tested and antidepressant status was included as a time-varying predictor. The interaction of the between-person effect and time was also tested to determine if those who were ever on antidepressants differed in their rate of change in depression from those who never reported using antidepressants. Second, wave 1 medical conditions and health behaviors were evaluated as predictors of depression over time adjusting for the baseline model. In the final stage, the key predictors of interest (residential care, ADL, IADL, SRH, MMSE) were analyzed in separate models as time varying (measurements for each predictor included at each wave) after adjusting for the baseline model. For each of these variables, the between-person and within-person fixed effects and the between-person effect by time interactions were evaluated. For continuous time-varying predictors (SRH and MMSE), the between-person differences in the within-person relationship were also estimated. The significance of betas in multilevel models is determined by dividing by the standard error, which provides a t statistic. The significance of random slopes added to the model is evaluated by the difference between the ⫺2 restricted log likelihood measures for models including and excluding the random slope(s).19 To enable interpretation of regression coefficients, age was centered at 75, years of education was centered at nine years, SRH was centered at three, and the MMSE was centered at 27. Missing data were imputed using the expectation maximization algorithm20 using all available measures. An alpha of 0.05 was used for selection of covariates (baseline model only) to ensure adequate control of potentially confounding variables. An alpha of 0.01 was used to evaluate the health variables and key predictors of interest.
RESULTS Prevalence Rates in the Australian Longitudinal Study of Ageing Table 1 shows the rates of depression by age group at waves 1, 3, 6, and 7 of the study. There was a 499
Prevalence and Eight-Year Depression Risk
TABLE 1.
Depression Prevalence by Age Group and Domicile at Waves 1, 3, 6, and 7 Domicile N (%) Age
N
65–69 70–74 75–79 80–84 85–89 90⫹ Total
140 560 518 425 323 98 2,064
65–69 70–74 75–79 80–84 85–89 90⫹ Total
54 380 426 357 239 109 1,565
70–74 75–79 80–84 85–89 90⫹ Total
22 179 228 141 74 644
75–79 80–84 85–89 90⫹ Total
41 188 122 62 413
Community
Residential Care
Full Sample
N ⫽ 1,942 19 (13.6) 69 (12.3) 78 (15.5) 54 (13.4) 49 (18.0) 10 (15.4) 279 (14.4) N ⫽ 1,454 7 (13.0) 44 (11.7) 55 (13.2) 53 (16.0) 51 (25.2) 12 (16.4) 222 (15.3) N ⫽ 601 5 (22.7) 21 (11.7) 34 (15.1) 13 (10.2) 6 (12.8) 79 (31.1) N ⫽ 382 7 (17.1) 26 (14.2) 25 (21.9) 7 (15.9) 65 (17.0)
N ⫽122 0 0 3 (20.0) 8 (38.1) 19 (37.3) 9 (27.3) 39 (32.0) N ⫽111 0 0 3 (30.0) 6 (24.0) 13 (35.1) 15 (41.7) 37 (33.3) N ⫽ 43 0 0 1 (33.3) 2 (15.4) 7 (25.9) 10 (23.3) N ⫽ 31 0 1 (20.0) 1 (12.5) 2 (11.1) 4 (12.9)
N ⫽ 2,064 19 (13.6) 69 (12.3) 81 (15.6) 62 (14.6) 68 (21.1) 19 (19.4) 318 (15.2) N ⫽ 1,565 7 (13.0) 44 (11.6) 58 (13.6) 59 (16.5) 64 (26.8) 27 (21.1) 259 (16.5) N ⫽ 644a 5 (22.7) 21 (11.7) 36 (15.7) 15 (10.3) 13 (17.6) 90 (13.8) N ⫽ 413 7 (17.1) 27 (14.4) 26 (21.3) 9 (10.2) 69 (16.7)
Wave 1
Wave 3
Wave 6
Wave 7
a There were nine missing values for the residential care variable. Percentages (shown in parentheses) are based on the proportion of participants within each domicile in each age group.
greater prevalence of depression at wave 1 among those living in residential care compared with community-dwelling adults (2[1] ⫽31.99, p ⬍0.01).
Sample Attrition and Missing Data At wave 1 only, 23 of 2,087 participants did not complete the CES-D. At wave 3, 1,679 participants completed the interview, 1,565 completed the CES-D, 125 participants refused to participate, 43 had moved or were not contactable, and 240 participants were dead. At wave 6, 791 participants completed the interview, 653 completed the CES-D, 276 refused to complete the study, 64 had moved or were not contactable, and 956 were deceased. At wave 7, 489 completed the interview, 413 completed the CES-D, 282 refused to complete the interview, 89 had
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moved or were not contactable, and 1,227 were deceased. Analyses were conducted to evaluate how the sample used in longitudinal analyses (N⫽1,116) differed from the remainder of the sample (N⫽971) at baseline. The selected sample was younger (mean age 75.43, standard deviation [SD]: 5.53 compared with 81.30 [SD: 6.54], t[2086] ⫽21.97, p ⬍0.01) and more likely to be female (2 [1] ⫽59.24, p ⬍0.01), married (2 [3] ⫽61.16, p ⬍0.01), and to be living in the community (2 [1] ⫽75.31, p ⬍0.01). They were less likely to report experiencing a heart attack (2 [1] ⫽26.63), p ⬍0.01), stroke (2 [1] ⫽49.43, p ⬍0.01), diabetes (2[1] ⫽21.09, p ⬍0.01), or be current smokers (2 [1] ⫽12.71, p ⬍0.01). They were less likely to report difficulties with ADL (2 [1] ⫽ 84.33, p ⬍0.01) or IADL (2 [1] ⫽73.68, p ⬍0.01). They also reported better SRH (t[2080] ⫽11.39, Am J Geriatr Psychiatry 15:6, June 2007
Anstey et al. p ⬍0.01), higher MMSE (t [1903] ⫽11.72, p ⬍0.01), and fewer depressive symptoms (7.06, SD: 6.76, compared with 9.94, SD: 8.17, t [2063] ⫽ 8.76, p ⬍0.01).
cated by the significant random effect of time (variance of slope ⫽ 0.047, 2 [2] ⫽8.68, p ⬍0.01).
Comparison of Depressed and Nondepressed on Risk Factors at Wave 1
Baseline Model Including Demographic Predictors and Antidepressant Use
Those classified as depressed at wave 1 were more likely to be female (2 [1] ⫽6.50, p ⬍0.01), be on antidepressants (2 [1] ⫽3.89, p ⬍0.01), rate their health as poorer (t [1115] ⫽9.44, p ⬍0.01), and report difficulties with ADL (2 [1] ⫽35.16, p ⬍0.01) and IADL (2 [1] ⫽ 29.21, p ⬍0.01) than those who were not depressed at baseline. They did not differ in age, education, marital status, residential care status, hypertension, stroke, smoking status, alcohol consumption, diabetes, or MMSE.
Table 2 shows the results of the baseline model in which predictors were time-invariant except for antidepressant use. All interactions with time were tested. Nonsignificant interactions are not shown. Compared with males, females scored 1.29 points higher on the CES-D scale. For every year of education above nine years, the CES-D score decreased by 0.22 points. The significant age ⫻ time interaction shows that the rate of change in depression score increased such that for every year above 75 years, the effect of time increased by 0.01 points. The significant interaction between marital status and time showed that the rate of change in depression was dependent on wave 1 marital status. Those who were married or in a de facto relationship and those who were widowed at wave 1 experienced a greater increase in CES-D scores over time compared with those who were never married. Examination of the simple slopes indicated that depressive symptoms in married/ de facto participants increased over time, whereas participants who were never married at wave 1 experienced a significant decrease in CES-D scores over
Descriptive Model of Depression Over Time With No Predictors Included Before examining predictors, a model of depression over time was estimated for descriptive purposes. This showed that 53.6% of the variability in CES-D scores over the eight-year follow-up period was the result of between-person differences and 47.4% was the result of within-person change. There were significant differences in the rate of change in CES-D scores over time between individuals as indi-
TABLE 2.
Fixed Effects From Multilevel Baseline Model of Depression Over Time
Variable Intercept Time Age (75 years) Female Education (9 years) Marital status (never married reference group) Married/de facto Separated/divorced Widowed (Age ⫺75 years) ⫻ time Marital status ⫻ time Married/de facto ⫻ time Separated/divorced ⫻ time Widowed ⫻ time Between-person never on antidepressant Within-person ever on antidepressant Between-person never on antidepressant ⫻ time
 (standard error)
t (df )
p
9.382 (1.219) ⫺0.334 (0.156) 0.058 (0.035) 1.287 (0.327) ⫺0.215 (0.063) F (3, 1088.47) ⫽ 3.797a ⫺1.059 (1.070) 1.953 (1.584) 0.060 (1.115) 0.010 (0.004) F (3, 1058.358) ⫽ 9.232a 0.533 (0.138) 0.226 (0.200) 0.320 (0.144) ⫺4.513 (0.753) 1.491 (0.583) 0.166 (0.085)
7.694 (1145.578) ⫺2.146 (1120.499) 1.658 (1123.699) 3.933 (1108.178) ⫺3.435 (1112.954)
⬍0.001 0.032 0.098 ⬍0.001 0.001 0.010 0.322 0.218 0.957 0.029 ⬍0.001 ⬍0.001 0.257 0.027 ⬍0.001 0.011 0.049
⫺0.990 (1091.116) 1.233 (1073.437) 0.054 (1098.785) 2.189 (1084.527) 3.851 (1080.627) 1.134 (1048.582) 2.216 (1085.894) ⫺5.992 (1927.034) 2.557 (2337.674) 1.969 (1227.484)
a
Omnibus test results displayed for variables with more than two categories. Where interactions with time are present, the main effects of the predictors are interpreted in relation to wave 1 scores.
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Prevalence and Eight-Year Depression Risk time. Participants who were divorced or separated experienced no change in depression scores. Antidepressant use was associated with change in depression scores both between and within persons. Individuals who changed from not taking antidepressants to taking antidepressants had an average 1.49-point increase in their depression score. There were significant between-person differences in change of depression over time (Table 2). Examination of the simple slopes showed that depression scores for those who did not take antidepressants at any wave did not change, whereas those who did take antidepressants at any wave experienced a significant decrease in depression score. Baseline Model With the Addition of Health Variables Table 3 shows the results of analyses evaluating medical and health factors at wave 1. For each predictor, the main effect and the interaction with time was tested in a separate model (nonsignificant interactions not shown). There was a significant interaction between time and history of heart condition or attack; however, review of the slopes showed that those reporting no history of heart condition or attack at wave 1 experienced a decrease in depression score over time, whereas those reporting a history of heart condition or attack remained stable. There was a significant interaction between alcohol consumption and time. Review of the slopes showed that those with lower drinking frequency (ⱕ4 times per month) decreased in depressive symptoms, whereas those who consumed alcohol 2–3 TABLE 3.
times per week or ⱖ4 times per week showed no change in level of depressive symptoms. Modeling of Time-Varying Predictors: Residential Care, Activities of Daily Living, Instrumental Activities of Daily Living, and Self-Rated Health The final set of analyses examined time-varying predictors separately after adjustment for the baseline model (Table 4). There were significant withinperson and between-person effects of residential care. Compared with participants who were in residential care at any point during the period of the study, those who were not in residential care scored on average 2.44 units lower on the CES-D. The within-person effect indicated that a participant moving into residential care increased by 1.73 points on the CES-D. The rate of change in depression score was also dependent on whether participants had difficulty with at least one ADL or IADL over the study period. The significant main effects of between-person ADL and IADL indicate that compared with those with difficulties in either ADL or IADL, those with no difficulties scored 3.86 and 2.54 points lower on wave 1 CES-D score, respectively. There was a significant difference in the change of depression scores over time for those with difficulties compared with those with no difficulties in ADL but not IADL (Table 4). Examination of simple slopes showed that those with no difficulties in ADL experienced no change in depression scores, but those who did have at least one difficulty with ADL at any measurement occasion
Multilevel Models of Health Variables as Predictors of Depression
Variable No heart condition/attack No heart condition/attack ⫻ time No hypertension No stroke/transient ischemic attack No diabetes Not current smoker Alcohol ⱖ4 times per week (reference group) ⱕ4 times per month 2–3 times per week Alcohol frequency x time ⱕ4 times per month x time 2–3 times per week x time
 (standard error)
t (df)
p
⫺0.776 (0.428) ⫺0.180 (0.053) ⫺0.548 (0.338) ⫺1.005 (0.627) ⫺0.605 (0.660) ⫺0.646 (0.641) F (2,1096.181) ⫽ 0.923a 0.372 (0.422) ⫺0.351 (0.645) F (2,1049.459) ⫽ 9.437a ⫺0.073 (0.051) 0.246 (0.080)
⫺1.812 (1084.388) ⫺3.377 (1048.732) ⫺1.623 (1099.117) ⫺1.603 (1115.718) ⫺0.916 (1099.554) ⫺1.008 (1085.683)
0.070 0.001 0.105 0.109 0.360 0.314 0.398 0.378 0.587 ⬍0.001 0.157 0.002
0.881 (1100.313) ⫺0.544 (1085.436) ⫺1.417 (1046.809) 3.064 (1048.195)
a
Omnibus test results displayed for variables with more than two categories. Where interactions with time are present, the main effects of the predictors are interpreted in relation to wave 1 scores.
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TABLE 4.
Fixed Effects from Multilevel Models of Residential Care, Activities of Daily Living, Instrumental Activities of Daily Living, Self-Rated Health, and Mini-Mental State Examination Predicting Depression
Variables BP no residential care at any wave WP residential care BP no difficulty ADL BP No difficulty ADL ⫻ time WP difficulty ADL BP no difficulty IADL BP no difficulty IADL ⫻ time WP difficulty IADL BP SRH (centered at 3) WP SRH BP MMSE (centered at 27) WP MMSE
 (standard error)
t (df )
p
⫺2.441 (0.627) 1.732 (0.586) ⫺3.864 (0.438) 0.158 (0.054) 1.842 (0.284) ⫺2.536 (0.609) 0.161 (0.075) 1.052 (0.227) ⫺2.674 (0.145) ⫺1.641 (0.141) ⫺0.267 (0.082) ⫺0.269 (0.073)
⫺3.896 (1,370.718) 2.955 (1,655.903) ⫺8.827 (1,650.027) 2.946 (1,351.472) 6.488 (2,408.850) ⫺4.164 (1,203.215) 2.148 (1,117.819) 4.638 (2,317.864) ⫺18.471 (1,240.155) ⫺11.657 (616.140) ⫺3.267 (1,062.278) ⫺3.685 (953.905)
⬍0.001 0.003 ⬍0.001 0.003 ⬍0.001 ⬍0.001 0.032 ⬍0.001 ⬍0.001 ⬍0.001 0.001 ⬍0.001
Note: Results for random effects for SRH and MMSE not shown. Where interactions with time are present, the main effects of the predictors are interpreted in relation to wave 1 scores. BP: between-person; WP: within-person; ADL: activities for daily living; IADL: instrumental activities of daily living; MMSE: Mini-Mental State Examination; SRH: self-rated health.
had a significant decrease in depression ( ⫽⫺0.46, standard error: 0.16, t [1135.06] ⫽⫺2.95, p ⬍0.01), possibly as a result of regression to the mean from their higher starting point. Significant within-person relationships between ADL and depression and IADL and depression were also found. When a participant changed from having no difficulty to having difficulty with at least one ADL or at least one IADL, their CES-D score increased by 1.84 points and 1.05 points, respectively. There were significant effects of SRH on CES-D scores. On average, participants who scored one point more on the SRH scale scored 2.67 points less on the CES-D scale. The within-person effect showed that participants who increased their health rating by one unit experienced an associated decrease in CES-D score of 1.64 points. The significant variance in the slope (2 [3] ⫽36.81, p ⬍0.001) demonstrated interindividual variability in rates of change in CES-D scores. There were significant effects of MMSE on CES-D scores. For every point scored above 27, there was an associated decrease of 0.27 points on the CES-D. The within-person effect showed that when a participant performed better on the MMSE by one unit, they experienced a decrease in CES-D score of 0.27 points. The significant variance in the slope (2 [3] ⫽11.66, p ⫽ 0.005) indicated that this within-person effect varied between individuals. To evaluate whether transition to residential care was associated with change in depressive symptoms
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independent of functional and cognitive status, an additional model was evaluated that adjusted for ADL, IADL, and MMSE. Both the between-person ( ⫽⫺1.75, SE: 0.62, t [1450.52] ⫽⫺2.82, p⫽0.005) and within-person effects of institionalization on depression remained significant ( ⫽1.61, SE: 0.59, t [1563.09] ⫽2.73, p ⫽0.006).
CONCLUSIONS The present study reports depression prevalence from a population-based sample of older Australian adults and evaluates predictors of change in level of depressive symptoms over time. Our particular interest was in time-varying predictors associated with increasing dependency and move to residential care. First, although the prevalence of depression in residential care was 32% compared with 14.4% in the community, this difference did not lead to a large overall inflation in the population prevalence because the number of participants in residential care was small. This finding should not diminish the significance of the high prevalence of depression in residential care, but suggests that community-based studies only underestimate depression prevalence in the population by a small amount. The prevalence rate observed in this study is within 1%–2% of that observed in several other studies using the same measure of depression.5,21–23 The rates of depression
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Prevalence and Eight-Year Depression Risk found among older adults are comparable to those of younger adults when assessed with the CES-D.21,24 Second, our results described the trajectory of depression within a cohort of survivors. The sample included in our analyses was healthier at baseline than those who were excluded. This is typical of analyses based on longitudinal studies of very old adults and is largely accounted for by the exclusion of participants who did not survive to wave 6. We have previously reported that depressed participants are more likely to be missing from follow-up assessments from this sample.25 Therefore, the results probably underestimate the prevalence and severity of depression in the age group studied, but this would also be the case in most population-based studies. The first stage of our multilevel analyses described our baseline model. Cross-sectionally, there was no increase in depressive symptoms with age. This was probably the result of selection effects occurring at baseline whereby older participants were healthier than younger participants by virtue of their survival. However, age differences emerged longitudinally, because older participants had a greater increase in depressive symptoms relative to younger participants. There was also an overall increase in depression over time independent of age indicating the whole sample became more depressed over time. Those who were partnered had a greater increase in depressive symptoms, possibly as a result of their greater likelihood of losing a partner during the course of the study. The second stage of our analyses revealed relatively few medical conditions associated with depression. The lack of an association between depression and hypertension or diabetes was consistent with some previous literature.26 Neither stroke nor smoking was associated with depression, possibly as a result of the self-report nature of our measures.26,27 Heart disease did not have a main effect in the multilevel model, which adjusted for demographic variables, and did not increase the risk of depression longitudinally, which differs from some previous reports.26,28 Our main interest was in the depressive risk associated with increasing functional dependence, cognitive decline, poor SRH, and transition to residential care. In contrast to the measures of physical health, these were all associated with depression prospectively (both between and within persons). Our results clearly show that functional impairment and
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cognitive decline are associated with increasing depression in late life in those without cognitive impairment. It is noteworthy that perceptions of health were more important than the presence or absence of medical conditions for predicting depression. These findings are consistent with a four-year follow up of the Cardiovascular Health Study that showed how persistent depressive symptoms were associated with increasing disability.8 Our results also showed that transition to residential care was associated with increased depressive symptoms after adjusting for functional impairment and cognitive decline. Thus, the transition to residential care predicts depression independent of the factors that lead to institutionalization, which also predict depression. This points to a cascade of risk factors for depression occurring in a vulnerable group of older adults who experience functional and cognitive decline. The present study is limited by sample attrition and the lack of clinical assessments of depression and medical conditions. The number of statistical tests means that some significant results are likely to be the result of type 1 errors. Nevertheless, the results show a consistent pattern of indicators of dependency being associated with depressive symptoms prospectively. Despite being a longitudinal study, it is still not possible to firmly determine causality from observed associations. It remains possible that other factors cause both depression and dependency or that depression causes disability. Our findings reiterate the significance of late-life depression as a public health problem that is strongly linked with decline in functional and cognitive capacity and self-perceived health. In addition to history of depression and antidepressant use, risk assessment and targeting of intervention strategies for older adults should incorporate gender, marital status, changes in functional capacity and mental status, SRH, and need for residential care. The ALSA is funded by the South Australian Health Commission, the Australian Rotary Health Research Fund, the U.S. National Institute of Health (Grant No. AG 08523-02), and the National Health and Medical Research Council Grants #229936 and #179839. The authors thank the study participants and the Centre for Ageing Studies, Flinders University, Adelaide, Australia.
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