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Original Investigation |

The Trajectory of Depressive Symptoms Across the Adult Life Span FREE

Angelina R. Sutin, PhD1,3; Antonio Terracciano, PhD2,3; Yuri Milaneschi, PhD3,4; Yang An, MS3; Luigi Ferrucci, MD, PhD3; Alan B. Zonderman, PhD3
[+] Author Affiliations
1Department of Medical Humanities and Social Sciences, Florida State University College of Medicine, Tallahassee
2Department of Geriatrics, Florida State University College of Medicine, Tallahassee
3National Institute on Aging, National Institutes of Health, Baltimore, Maryland
4VU University Medical Center, Amsterdam, the Netherlands
JAMA Psychiatry. 2013;70(8):803-811. doi:10.1001/jamapsychiatry.2013.193.
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Importance  Long-term longitudinal studies are needed to delineate the trajectory of depressive symptoms across adulthood and to individuate factors that may contribute to increases in depressive symptoms in older adulthood.

Objectives  To estimate the trajectory of depressive symptoms across the adult life span; to test whether this trajectory varies by demographic factors (sex, ethnicity, and educational level) and antidepressant medication use; and to test whether disease burden, functional limitations, and proximity to death explain the increase in depressive symptoms in old age.

Design  Longitudinal study.

Setting  Community.

Participants  The study included 2320 participants (47.0% female; mean [SD] age at baseline, 58.1 [17.0] years; range, 19-95 years) from the Baltimore Longitudinal Study of Aging.

Main Outcomes and Measures  Estimated trajectory of depressive symptoms modeled from 10 982 assessments (mean [SD] assessments per participant, 4.7 [3.6]; range, 1-21) based on the Center for Epidemiologic Studies Depression scale and 3 subscales (depressed affect, somatic complaints, and interpersonal problems).

Results  The linear (γ10 = 0.52; P < .01) and quadratic (γ20 = 0.43; P < .01) terms were significant, which indicated that depressive symptoms were highest in young adulthood, decreased across middle adulthood, and increased again in older adulthood. The subscales followed a similar pattern. Women reported more depressed affect at younger ages, but an interaction with age suggested that this gap disappeared in old age. Accounting for comorbidity, functional limitations, and impending death slightly reduced but did not eliminate the uptick in depressive symptoms in old age.

Conclusions and Relevance  Symptoms of depression follow a U-shaped pattern across adulthood. Older adults experience an increase in distress that is not due solely to declines in physical health or approaching death.

Figures in this Article

Depression is a common mental disorder that is among the leading causes of disability worldwide.1,2 The burden of depression and depressive symptoms is pervasive and varied, ranging from decreased socioemotional well-being3 to impaired physical health2 to lower productivity in the workplace.4 Given that depressive symptoms are associated with important outcomes at every stage of life, there has been great interest in understanding the trajectory of depressive symptoms across adulthood.

Epidemiological evidence suggests that the prevalence of major depressive disorder declines with age.5 In contrast, depressive symptoms, after a midlife decline, may increase again at older ages.68 Longitudinal studies of depressive symptoms, however, have focused primarily on a single segment of the adult life span9,10 or on transition points (eg, from adolescence to young adulthood11). Repeated assessments performed for a substantial period are needed to reliably estimate the trajectory of depressive symptoms across adulthood. In addition, it is likely that not everyone is changing in the same way. Previous research suggests that mean levels of depressive symptoms differ by sex,12,13 ethnicity,14 and educational level,15,16 but it is less clear whether these differences increase or decrease over time.7,1620

Depressive symptoms in older adulthood are linked to a number of consequential outcomes, including decreased quality of life,21 greater disease burden,22 less ability to cope with illness,23 and premature mortality.23 If depressive symptoms increase in older age, it is important to determine whether the increase is due primarily to declines in physical health. Although persons with chronic diseases8 and functional limitations24 are more prone to experiencing depressive symptoms, such burdens may not fully explain the increase in old age.8 Furthermore, neuroticism (ie, a general tendency to experience negative affect) tends to increase and well-being (eg, life satisfaction, happiness) tends to decline sharply with impending death.25 Thus, the uptick in depressive symptoms in old age may reflect end-of-life factors related to deteriorating health and/or proximity to death.

The present research uses more than 30 years of depressive symptom assessments from the Baltimore Longitudinal Study of Aging (BLSA). Using more than 10 000 repeated assessments of the Center for Epidemiologic Studies Depression (CES-D) scale performed during a 30-year period (mean [SD] assessments per participant, 4.7 [3.6]; range, 1-21), we examined the trajectory of depressive symptoms across the adult life span. In addition to the CES-D total scale score, we examine 3 subscales that tap into different types of depressive symptoms: depressed affect, somatic complaints, and interpersonal problems. These aspects of depressive symptoms may not necessarily follow the same trajectory over the life span. It is particularly important to separate the somatic aspects from other types of symptoms because such items may also reflect changes in physical health that are more prevalent with aging.26 We also examine differences in this trajectory across demographic characteristics (sex, ethnicity, and educational level) and the use of antidepressant medication.27 Finally, we test whether increases in depressive symptoms in old age could be accounted for by disease burden, functional limitations, and proximity to death.

Participants and Procedure

A total of 2320 community-dwelling volunteers from the BLSA participated in the study. Started in 1958, the BLSA is an ongoing multidisciplinary study of aging administered by the National Institute on Aging. This study was approved by the local institutional review board, and all participants provided informed consent. The current sample is 47.0% female, 73.4% white (20.0% black and 6.6% other ethnicities; all self-reported), and well educated (mean [SD], 16.5 [2.4] years of education). The CES-D assessments started in 1979; data used in the present study were collected between January 10, 1979, and December 27, 2011, at regularly scheduled visits. As of 2011, the rate of attrition was approximately 15%. After we controlled for age, sex, ethnicity, and educational level, there were no differences in the CES-D total scale score or the subscale scores between participants who dropped out and those who stayed in the study (see Supplement for detailed attrition analyses).

The mean (SD) age at the first CES-D assessment was 58.1 (17.0) years (range, 19-95 years), and the mean age at the most recent assessment was 70.0 (15.9) years (range, 24 -101 years). Participants completed up to 21 assessments of the CES-D (mean, 4.7 [3.6] assessments per participant; range, 1-21) for a total of 10 982 assessments of depressive symptoms across more than 30 years. The mean interval between administrations was 2.7 (2.2) years (range, 4 months to 21 years) (Table 1). Morbidity analyses (described below) focused on a subset of 1482 participants aged 60 years or older (mean age, 74.7 [8.6] years; 42.3% female). (See Supplement for additional information about the BLSA.)

Table Graphic Jump LocationTable 1.  Mean Follow-up and Number of Assessments by Baseline Age in 2320 Participants
Depressive Symptoms

Depressive symptoms were measured with the CES-D scale.28 This 20-item scale assesses the frequency of a variety of depressive symptoms within the previous week. Items are rated on a 4-point scale from 0 (rarely) to 3 (most or all of the time). A score of 16 is typically considered the threshold for severe depressive symptoms.29 In addition to the total scale score, we examined 3 subscales that tap into different aspects of depressive symptoms,28,30 including depressed affect (7 items; eg, “I felt sad”), somatic complaints (7 items; eg, “My sleep was restless”), and interpersonal problems (2 items; eg, “I felt that people disliked me”). At baseline, the mean (SD) scores were 7.0 (6.9; range, 0-50) for CES-D total scale score, 1.6 (2.7; range, 0-20) for depressed affect, 3.1 (2.9; range, 0-20) for somatic complaints, and 0.2 (0.7; range, 0-6) for interpersonal problems.

Antidepressant Medication

Information on antidepressant medication use was available for most visits (10 442 visits). Participants reported using antidepressant medication at approximately 8% of these visits (826 visits; 404 participants).

Illness Burden

Illness burden was assessed with the Charlson Comorbidity Index (CCI).31 The CCI is the weighted sum of 19 clinical conditions found to increase risk of mortality, including myocardial infarct, congestive heart failure, peripheral vascular disease, dementia, cerebrovascular disease, chronic pulmonary disease, connective tissue disease, ulcer disease, mild liver disease, diabetes mellitus, hemiplegia, moderate or severe renal disease, diabetes with end-organ damage, any tumor, leukemia, lymphoma, moderate or severe liver disease, metastatic solid tumor, and AIDS. We used an adapted version of the CCI, which defines each condition by the International Classification of Diseases, Ninth Revision diagnosis codes and combines leukemia and lymphoma with any tumor.32 This version consistently predicts mortality.33,34 We calculated the CCI from the medical history obtained by a certified nurse practitioner at each visit. For the morbidity analyses, we focused on 1482 participants aged 60 years or older at the time of assessment. The CCI and CES- D were available concurrently at 6523 visits. The mean (SD) CCI was 0.7 (1.1) diseases (range, 0-8) at the first assessment and 1.5 (1.5) diseases (range, 0-10) at the most recent assessment.

Functional Limitations

Data on difficulties with activities of daily living (ADLs35; eg, bathing) and instrumental ADLs (IADLs36; eg, meal preparation) were available for a subset of 972 participants aged 60 years or older (2286 visits). At the first assessment, ADLs had a mean (SD) of 0.1 (0.5) limitations (range, 0-5) and IADLs had a mean of 0.2 (0.6; range, 0-7). At the most recent assessment, ADLs had a mean of 0.2 (0.8) limitations (range, 0-5) and IADLs had a mean of 0.3 (0.9; range 0-7).

Statistical Overview

We used hierarchical linear modeling (HLM)37,38 to estimate the trajectory of depressive symptoms across the adult life span; HLM is a flexible approach that can be applied to evaluate within-individual change or growth trajectories. In HLM analyses, the number and spacing of measurement observations may vary across persons, given that the time-series observations in each individual are used to estimate the individual trajectories (level 1), and the individual parameters are the basis of group estimates (level 2). Even data from individuals who were tested on only a single occasion can be used to stabilize estimates of the mean and variance. In this way, all available data can be included in the analyses. This is a major advantage of conducting analyses within the HLM framework; by contrast, missing data and varying timing pose major problems in conventional repeated measures analyses of variance.39 Furthermore, longitudinal HLM can estimate age trajectories over a broad age span with data collected in a relatively shorter time interval.

We conducted the analyses using HLM, version 6.40 To evaluate the longitudinal trajectories, we first defined the level 1 model and then tested possible level 2 predictors. At level 1, we fit a quadratic model for the CES-D total score and separately for each subscale to test for potential nonlinear changes in depressive symptoms across the life span.41,42 At level 2, we entered characteristics of the individual (sex, ethnicity, and educational level) as independent variables to explain between-subject variation in the intercept and linear and quadratic slopes. We centered age in decades on the grand mean age (66.4 years) to minimize the correlation between the linear and quadratic terms. Antidepressant medication use, illness burden, and functional limitations were entered at level 1 as time-varying covariates to test their effect on the trajectory of depressive symptoms.

Trajectory of Depressive Symptoms

The estimates for the trajectory of depressive symptoms across adulthood are shown in Table 2 (the eTable in the Supplement shows the deviance statistics for all models). As depicted in Figure 1, depressive symptoms were the highest in early adulthood, declined in middle adulthood, and then increased in older adulthood (figure available from the corresponding author [http://med.fsu.edu/userFiles/file/SutinSupplementalMaterial.pdf] shows the scales in the raw metric and eFigure 1 in the Supplement shows spaghetti plots of the raw data). The intercept indicated that, at about age 66 years, participants scored approximately 5.8 on the CES-D scale. At the subscale level, depressed affect and interpersonal problems followed a similar trajectory to that of the total CES-D. Somatic complaints also followed a similar trajectory, but increased slightly more in older adulthood. The intercept, linear, and quadratic slope estimates were similar when sex, ethnicity, educational level, antidepressant medication use, disease burden, and functional limitations were controlled for (table available from the corresponding author).

Table Graphic Jump LocationTable 2.  HLM Coefficients and Variance Estimates of Intercept, Linear, and Quadratic Equations Predicting Depressive Symptoms From Age and Age Squared (in Decades) in 2320 Participants1
Place holder to copy figure label and caption
Figure 1.
Scores for the Center for Epidemiologic Studies Depression Total Scale and 3 Subscales Across Adulthood

Estimated trajectory of scores for the Center for Epidemiologic Studies Depression (CES-D) total scale and 3 subscales across adulthood. Raw scored were z-transformed so that all scales could be plotted on the same axis. (eFigure 1 in the Supplement shows the estimated trajectories of each scale in the original metric.)

Graphic Jump Location
Predictors of the Trajectory of Depressive Symptoms
Demographics

We first tested the effect of sex, educational level, and ethnicity on the intercept and slopes of the CES-D scale and the subscales (Table 3 and Table 4). There was no effect of sex on the intercept of the CES-D, which indicated that men and women experienced depressive symptoms to a similar extent. The subscales, however, revealed that women reported more depressed affect than did men. We reported elsewhere43 that women in the BLSA also tended to report greater well-being, which, when combined with the negative affect items in the total scale score, obscured the association with total depressive symptoms. Indeed, in the present study, when the 4 positively valenced items were removed from the total scale score, women had significantly more depressive symptoms than men (β = .43 [SE = .18]; P < .01).

Table Graphic Jump LocationTable 3.  Effect of Demographic Factors, Antidepressant Medication Use, Disease Burden, and Death on the Intercepts and Slopes of Depressive Symptoms Overall and Depressed Affect in 2320 Participants
Table Graphic Jump LocationTable 4.  Effect of Demographic Factors, Antidepressant Medication Use, Disease Burden, and Death on the Intercepts and Slopes of Somatic Complaints and Interpersonal Problems in 2320 Participants

There was also a significant effect of sex on the slope of depressed affect (Figure 2). This interaction with age indicated that women experienced more negative affect in early adulthood, but men increased more in older adulthood. The trajectories of men and women converged by old age, such that after about age 70 years there were no longer differences in depressed affect between the sexes. There was no effect of sex on the slopes of the other 2 subscales.

Place holder to copy figure label and caption
Figure 2.
Depressed Affect Scores by Sex

Estimated trajectory of depressed affect scores by sex.

Graphic Jump Location

Modest effects emerged for educational level and ethnicity. Education was associated with fewer symptoms of depression, particularly somatic complaints and interpersonal problems. African Americans and participants of other ethnicities had slightly higher mean levels of interpersonal problems, but scores for interpersonal problems increased less in older age for participants of other ethnicities than for white participants. Finally, scores for somatic complaints did not increase as much in older age in African Americans and participants of other ethnicities as in white participants (Tables 3 and 4).

Antidepressant Use

Not surprisingly, antidepressant medication use was associated with the intercept and trajectory of depressive symptoms (Tables 3 and 4). Participants who took antidepressants reported more depressive symptoms than those who did not and their depressive symptoms declined less across adulthood. The association of antidepressant use and the 3 subscales was similar to that for the overall CES-D score. As a supplementary analysis, we reanalyzed all models after excluding participants who reported ever taking antidepressant medication. The estimates were virtually identical to those for the entire sample. We also tested whether there was a difference in the trajectory of the 570 participants who had ever experienced severe depressive symptoms (CES-D score, ≥16) at any point in the study. Indeed, these participants had an amplified curve compared with those who had not experienced severe depressive symptoms. That is, they reported more depressive symptoms in early adulthood and had a steeper decline across middle adulthood and a steeper increase in old age (eFigure 2 in the Supplement).

Morbidity

We next tested whether disease burden or functional limitations could account for the uptick in depressive symptoms in old age (Tables 3 and 4). Morbidity was primarily associated with the intercept of depressive symptoms: participants with greater disease burden and more functional limitations reported more depressive symptoms than those with less morbidity, particularly depressed affect and somatic complaints. Disease burden was also associated with a greater increase in depressed affect in old age. The IADLs had a negative effect on the slope of the total CES-D and somatic complaints, such that depressive symptom scores increased less with age in participants with more limitations. This effect was driven by the effect of IADLs on the intercept. That is, those with functional limitations reported more depressive symptoms, but over time those who did not report IADLs increased significantly more in depressive symptoms so that they caught up to those reporting IADLs (eFigure 3 in the Supplement). Because of the reduced sample size and assessments of ADLs and IADLs, there was not enough power to test whether functional limitations were associated with the quadratic slope of depressive symptoms. Accounting for disease burden and functional limitations did not eliminate the increase in depressive symptoms in old age (table available from the corresponding author).

Mortality

We tested the effect of mortality on the trajectory of depressive symptoms in several ways. First, we entered a dummy-coded variable into the model that contrasted participants who died during the study with those who were still living at the time of analysis as a level 2 predictor of the intercept and slope (Tables 3 and 4). For the overall scale score, death was associated with a higher intercept but was unrelated to the slopes. The opposite pattern, however, emerged for somatic complaints: death was unrelated to the intercept of somatic complaints, but it predicted a steeper slope at older ages (Figure 3). Death was unrelated to interpersonal problems and depressed affect. Of note, the increase in depressive symptoms in late life remained after death was accounted for (table available from the corresponding author).

Place holder to copy figure label and caption
Figure 3.
Somatic Complaint Scores in Old Age

Estimated trajectory of somatic complaint scores in old age in participants still living and participants who died.

Graphic Jump Location

Second, we repeated the HLM analyses after excluding all CES-D assessments within 5 years of death. The linear slope estimates were slightly smaller, but the pattern of estimates was virtually identical to that of the total sample (table available from the corresponding author).

Third, we tested whether there was a terminal increase in depressive symptoms with proximity to death. We reanalyzed the data from 728 participants who had died  during the study, using time to death as the metric rather than time since birth (ie, chronological age).44 We controlled for sex, ethnicity, and educational level, and we included age at each assessment as a time-varying covariate. In this case, the intercept represents the estimated depressive symptoms at the time of death, and the slope represents the estimated change in depressive symptoms every year before death. Thus, a negative slope indicates an increase in depressive symptoms for every year approaching death (ie, each successive year before death had fewer depressive symptoms). The linear slopes of the CES-D and 2 of the 3 subscales (depressed affect and somatic complaints) were significant, which indicated an increase in depressive symptoms with approaching death (Table 5). The quadratic slope of the CES-D was also significant, but this was due to the positively worded items. The quadratic slope was not significant for any of the subscales, nor was it significant for the CES-D total score without the positively valenced items (π2 = 0.00 [SE = 0.00]; P = .99). These results suggested that although there was an increase in depressive symptoms with approaching death, the increase was not exponential.

Table Graphic Jump LocationTable 5.  Hierarchical Linear Modeling Coefficients and Variance Estimates of Intercept, Linear, and Quadratic Equations Predicting Depressive Symptoms in 728 Participants, With Distance to Death (in Years) Used as the Time Metric1

To put the effect of death into context, we examined the corresponding change in depressive symptoms for long-lived participants. We selected 59 participants aged 90 years or older at their last CES-D assessment and estimated their trajectories. The mean age at death was 85 years, so we compared the increase in depressive symptoms across the last decade of life with the increase in depressive symptoms among the long-lived participants between the ages of 75 and 85 years. From the estimates based on the distance to death analysis, depressive symptom scores increased about 2.0 points in the decade before participants died. By comparison, scores in the long-lived participants increased about 1.9 points between the ages of 75 and 85 years. Somatic complaints, which might be expected to increase the most before death, increased by about 1.0 point in the last decade of life, whereas they increased by 1.7 points in the long-lived group. Thus, although there seems to be an increase in depressive symptoms with the approach of death, the increase is roughly similar to that seen with age among the most long-lived (and thus presumably healthier) participants.

Using repeated assessments of the CES-D for 30 years, we estimated the trajectory of depressive symptoms across the adult life span. At about age 66 years, participants scored approximately 5.78 on the CES-D scale, which is in the range of scores for other large samples of similar age (eg, the mean CES-D score was 5.81 at a mean age of about 65 years in the Rotterdam Study45). Significant linear and quadratic slopes indicated that symptoms of depression tend to be highest in young adulthood, decrease across middle adulthood, and increase again in older age, with the most prominent change seen for those who had ever experienced severe depressive symptoms. Individual differences in the slopes of the trajectories suggested that not everyone is changing in the same way. The use of antidepressant medication had the largest association with the slope of depressive symptoms; the effects of the demographic factors, disease burden, and functional limitations were small to moderate by comparison. Finally, disease burden, functional limitations, and impending death explained only part of the increase in depressive symptoms in older adulthood.

Psychological health across the life span has been addressed in several ways, including age-related changes in the prevalence of major depression and other mood disorders, assessments of depressive symptoms, and various indexes of well-being. Large-scale studies have consistently documented declines in the 12-month prevalence of mood disorders across adulthood, including a further decline in older age.4648 Consistent with smaller studies, our findings parallel these age trends until older adulthood, when, starting in about the seventh decade of life, depressive symptoms begin to rise. This uptick toward the end of life differs from age-related changes in prevalence, but the increase in old age is similar to findings in smaller longitudinal studies of depressive symptoms limited to older adulthood,8,10 findings in subthreshold depression,49 and the trajectory of related constructs, such as neuroticism.50,51 In the present study, the increase in depressive symptoms toward the end of life was relatively modest, with an estimated mean increase of 1.4 per decade points in the CES-D total scale score between the ages of 60 and 90 years. In contrast to clinical mood disorders, the increase in depressive symptoms in old age may be a general phenomenon that tends to occur across a broad segment of the population, not just in a few cases that cross the clinical threshold. Thus, although the prevalence of extreme depressive symptoms may decline, the mean number of depressive symptoms in the population may increase.

Symptoms of depression tend to be more severe among women,12,13 ethnic minorities,14,19,20 and persons with lower education.15,16 Our findings generally mirror these mean-level trends, except that, because women reported higher well-being as well as more depressed affect, sex was unrelated to the intercept of the CES-D total scale score. With age, there may17 or may not7,8 be sex differences in the trajectory of depressive symptoms. We found support for both positions: sex was associated with the slope of depressed affect but not with any other symptoms of depression. The convergence between men and women in older age was due mainly to a steeper increase in symptoms reported by men starting in their mid-60s.

In charting the trajectory of depressive symptoms into old age, it is important to distinguish between somatic and nonsomatic aspects of depressive symptoms. Our analysis at the subscale level indicated that the uptick in the CES-D total scale score in older adulthood was not due exclusively to somatic complaints. In addition to the increase in somatic complaints, which may be due in part to declines in physical health, older age was also associated with an increase in depressed affect. Although pain and other physical conditions increase substantially with age, the comorbid association between depression and physical ailment may not.47 Functional limitations may be associated with increases in depressive symptoms, but declines in physical health with aging do not account for all of the increases in depressive symptoms.8,47 Similarly, in the present study, neither disease burden nor functional limitations could completely explain the increase in depressive symptoms at older ages. More detailed assessments of physical functioning, however, are needed before ruling out that the increase in depressive symptoms with age is not due solely to declines in physical health.

In addition to disease burden, proximity to death may partially contribute to the end-of-life increase in depressive symptoms. Previous research has found that well-being declines exponentially with impending death25,44; there may be a corresponding increase in depressive symptoms. We found partial support for this hypothesis. There was a small increase in depressive symptoms with approaching death when either age or distance to death was used as the time metric. There was not, however, any evidence of an exponential increase in depressive symptoms, and the increase was comparable to that of age-related changes estimated from the most long-lived participants in the sample. Taken together, previous research on well-being and the present study on depressive symptoms suggest that as death approaches, individuals may become less happy rather than experience more sadness. Similarly to disease burden and functional limitations, impending death did not fully account for all of the increase in depressive symptoms in old age.

Factors other than those tested in this study may contribute to the increase in depressive symptoms in older age. With age comes loss, and the loss of loved ones,52 social support networks,10,53 employment,52 and income54 can contribute to increases in depressive symptoms. Psychological factors, such as changes in time perspective,10 feelings of obsolescence, and loss of personal control,52 as well as changes in coping styles and beliefs10 also may contribute to the increase in depressive symptoms toward the end of life. Thus, life circumstances and psychological processes may explain the increase in depressive symptoms in old age that is not accounted for by deteriorating physical health.

This study had several strengths, including a large sample with more than 30 years of assessments of one of the most commonly used measures of depressive symptoms in epidemiology across a broad age range. Despite these strengths, some limitations should be considered. For example, our sample was more educated than the general population. Our findings, however, were broadly consistent with cross-sectional studies of age-related changes in depressive symptoms.7 In addition, our analyses of impending death might be limited in 2 ways. First, because participants with a terminal illness may have missed assessments because they were too sick to continue participation in the study, we may have missed a critical time before death. Second, because our sample was fairly privileged, participants may have remained healthier longer, enduring a relatively shorter decline toward death. Thus, the increase in depressive symptoms with impending death may have been more modest than in more representative samples.

Despite these limitations, the present research provides useful information on changes in depressive symptoms across the adult life span. The overall trajectory was consistent with the clinical literature until older adulthood, which suggests that older adults are susceptible to increased distress. These seemingly modest effects are nonetheless clinically meaningful. Previous research on subthreshold depression, for example, has suggested that scoring just 6 points on the CES-D is associated with a significant increase in functional limitations 3 to 4 years later55 and with more disability days and lower self-rated health and social support.56 Mild depressive symptoms also have been associated with slower physical and cognitive functioning.57 Thus, seemingly modest depressive symptoms may have a significant effect on many aspects of an individual’s life in older adulthood. The divergence with clinical depression and the effect of even modest depressive symptoms on physical and cognitive functioning underscore the importance of assessing distress that does not pass a clinical threshold.

Submitted for Publication: August 7, 2012; final revision received November 29, 2012; accepted November 30, 2012.

Corresponding Author: Angelina R. Sutin, PhD, Department of Medical Humanities and Social Sciences, Florida State University College of Medicine, 1115 W Call St, Tallahassee, FL 32306 (angelina.sutin@med.fsu.edu)

Published Online: June 12, 2013. doi:10.1001/jamapsychiatry.2013.193.

Author Contributions: Dr Sutin had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Sutin, Terracciano, and Zonderman.

Acquisition of data: Ferrucci and Zonderman.

Analysis and interpretation of data: Sutin, Terracciano, Milaneschi, and An.

Drafting of the manuscript: Sutin and An.

Critical revision of the manuscript for important intellectual content: Terracciano, Milaneschi, An, Ferrucci, and Zonderman.

Statistical analysis: Sutin, Terracciano, Milaneschi, An, and Zonderman.

Administrative, technical, and material support: An.

Study supervision: Ferrucci and Zonderman.

Conflict of Interest Disclosures: None reported.

Funding/Support: This research was supported in part by the Intramural Research Program of the National Institute on Aging, National Institutes of Health.

Role of the Sponsor: The funder had no role in the design and conduct of the study; in the collection, analysis, and interpretation of the data; or in the preparation or review of the manuscript or the decision to submit it for publication.

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Van de Velde  S, Bracke  P, Levecque  K, Meuleman  B.  Gender differences in depression in 25 European countries after eliminating measurement bias in the CES-D 8. Soc Sci Res. 2010;39:396-404.
Link to Article
Bromberger  JT, Harlow  S, Avis  N, Kravitz  HM, Cordal  A.  Racial/ethnic differences in the prevalence of depressive symptoms among middle-aged women: the Study of Women’s Health Across the Nation (SWAN). Am J Public Health. 2004;94(8):1378-1385.
PubMed   |  Link to Article
Kim  J, Durden  E.  Socioeconomic status and age trajectories of health. Soc Sci Med. 2007;65(12):2489-2502.
PubMed   |  Link to Article
Miech  RA, Shanahan  MJ.  Socioeconomic status and depression over the life course. J Health Soc Behav. 2000;41:162-176.
Link to Article
Barefoot  JC, Mortensen  EL, Helms  MJ, Avlund  K, Schroll  M.  A longitudinal study of gender differences in depressive symptoms from age 50 to 80. Psychol Aging. 2001;16(2):342-345.
PubMed   |  Link to Article
Skarupski  KA, Mendes de Leon  CF, Bienias  JL,  et al.  Black-white differences in depressive symptoms among older adults over time. J Gerontol B Psychol Sci Soc Sci. 2005;60(3):136-142.
PubMed   |  Link to Article
Walsemann  KM, Gee  GC, Geronimus  AT.  Ethnic differences in trajectories of depressive symptoms: disadvantage in family background, high school experiences, and adult characteristics. J Health Soc Behav. 2009;50(1):82-98.
PubMed   |  Link to Article
Xiao Xu, Liang  J, Bennett  JM, Quiñones  AR, Wen Ye.  Ethnic differences in the dynamics of depressive symptoms in middle-aged and older Americans. J Aging Health. 2010;22(5):631-652.
PubMed   |  Link to Article
Wells  KB, Stewart  A, Hays  RD,  et al.  The functioning and well-being of depressed patients: results from the Medical Outcomes Study. JAMA. 1989;262(7):914-919.
PubMed   |  Link to Article
Katon  W, Lin  EHB, Kroenke  K.  The association of depression and anxiety with medical symptom burden in patients with chronic medical illness. Gen Hosp Psychiatry. 2007;29(2):147-155.
PubMed   |  Link to Article
Frasure-Smith  N, Lespérance  F, Talajic  M.  Depression and 18-month prognosis after myocardial infarction. Circulation. 1995;91(4):999-1005.
PubMed   |  Link to Article
Dunne  E, Wrosch  C, Miller  GE.  Goal disengagement, functional disability, and depressive symptoms in old age. Health Psychol. 2011;30(6):763-770.
PubMed   |  Link to Article
Gerstorf  D, Ram  N, Mayraz  G,  et al.  Late-life decline in well-being across adulthood in Germany, the United Kingdom, and the United States: something is seriously wrong at the end of life. Psychol Aging. 2010;25(2):477-485.
PubMed   |  Link to Article
Fonda  SJ, Herzog  AR.  Patterns and risk factors of change in somatic and mood symptoms among older adults. Ann Epidemiol. 2001;11(6):361-368.
PubMed   |  Link to Article
Khan  A, Brodhead  AE, Kolts  RL, Brown  WA.  Severity of depressive symptoms and response to antidepressants and placebo in antidepressant trials. J Psychiatr Res. 2005;39(2):145-150.
PubMed   |  Link to Article
Radloff  LS.  The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1:385-401.
Link to Article
Beekman  ATF, Deeg  DJH, Van Limbeek  J, Braam  AW, De Vries  MZ, Van Tilburg  W.  Criterion validity of the Center for Epidemiologic Studies Depression scale (CES-D): results from a community-based sample of older subjects in the Netherlands. Psychol Med. 1997;27(1):231-235.
PubMed   |  Link to Article
Hertzog  C, Van Alstine  J, Usala  PD, Hultsch  DF, Dixon  R.  Measurement properties of the Center for Epidemiological Studies Depression scale (CES-D) in older populations. Psychol Assess. 1990;2:64-72. doi:10.1037/1040-3590.2.1.64.
Link to Article
Charlson  ME, Pompei  P, Ales  KL, MacKenzie  CR.  A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-383.
PubMed   |  Link to Article
Deyo  RA, Cherkin  DC, Ciol  MA.  Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613-619.
PubMed   |  Link to Article
Schneeweiss  S, Wang  PS, Avorn  J, Maclure  M, Levin  R, Glynn  RJ.  Consistency of performance ranking of comorbidity adjustment scores in Canadian and U.S. utilization data. J Gen Intern Med. 2004;19(5, pt 1):444-450.
PubMed   |  Link to Article
Yan  Y, Birman-Deych  E, Radford  MJ, Nilasena  DS, Gage  BF.  Comorbidity indices to predict mortality from Medicare data: results from the National Registry of Atrial Fibrillation. Med Care. 2005;43(11):1073-1077.
PubMed   |  Link to Article
Katz  S, Ford  AB, Moskowitz  RW, Jackson  BA, Jaffe  MW.  Studies of illness in the aged: the index of ADL: a standardized measure if biological and psychosocial function. JAMA. 1963;185:914-919.
PubMed   |  Link to Article
Lawton  MP, Brody  EM.  Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist. 1969;9(3):179-186.
PubMed   |  Link to Article
Raudenbush  SW, Bryk  AS. Hierarchical Linear Models: Applications and Data Analysis Methods.2nd ed. Thousand Oaks, CA: Sage; 2002.
Singer  JD, Willett  JB. Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. New York, NY: Oxford University Press; 2003.
Gueorguieva  R, Krystal  JH.  Move over ANOVA: progress in analyzing repeated-measures data and its reflection in papers published in the Archives of General PsychiatryArch Gen Psychiatry. 2004;61(3):310-317.
PubMed   |  Link to Article
HLM [computer program]. Version 6. Lincolnwood, IL: Scientific Software International; 2004.
Drøyvold  WB, Nilsen  TIL, Krüger  Ø,  et al.  Change in height, weight and body mass index: longitudinal data from the HUNT Study in Norway. Int J Obes (Lond). 2006;30(6):935-939.
PubMed   |  Link to Article
Rissanen  A, Heliövaara  M, Aromaa  A.  Overweight and anthropometric changes in adulthood: a prospective study of 17,000 Finns. Int J Obes. 1988;12(5):391-401.
PubMed
Sutin  AR, Terracciano  A, Milaneschi  Y, An  Y, Ferrucci  L, Zonderman  AB.  The effect of birth cohort on well-being: the legacy of economic hard times. Psychol Sci. 2013;24(3):379-385.
PubMed   |  Link to Article
Gerstorf  D, Ram  N, Estabrook  R, Schupp  J, Wagner  GG, Lindenberger  U.  Life satisfaction shows terminal decline in old age: longitudinal evidence from the German Socio-Economic Panel Study (SOEP). Dev Psychol. 2008;44(4):1148-1159.
PubMed   |  Link to Article
Hek  K, Demirkan  A, Lahti  J,  et al.  A genome-wide association study of depressive symptoms. Biol Psychiatry. 2013;73(7):667-678.
PubMed   |  Link to Article
Kessler  RC, McGonagle  KA, Zhao  S,  et al.  Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States: results from the National Comorbidity Survey. Arch Gen Psychiatry. 1994;51(1):8-19.
PubMed   |  Link to Article
Scott  KM, Von Korff  M, Alonso  J,  et al.  Age patterns in the prevalence of DSM-IV depressive/anxiety disorders with and without physical co-morbidity. Psychol Med. 2008;38(11):1659-1669.
PubMed   |  Link to Article
Byers  AL, Yaffe  K, Covinsky  KE, Friedman  MB, Bruce  ML.  High occurrence of mood and anxiety disorders among older adults: the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2010;67(5):489-496.
PubMed   |  Link to Article
Meeks  TW, Vahia  IV, Lavretsky  H, Kulkarni  G, Jeste  DV.  A tune in “a minor” can “b major”: a review of epidemiology, illness course, and public health implications of subthreshold depression in older adults. J Affect Disord. 2011;129(1-3):126-142.
PubMed   |  Link to Article
Terracciano  A, McCrae  RR, Brant  LJ, Costa  PT  Jr.  Hierarchical linear modeling analyses of the NEO-PI-R scales in the Baltimore Longitudinal Study of Aging. Psychol Aging. 2005;20(3):493-506.
PubMed   |  Link to Article
Small  BJ, Hertzog  C, Hultsch  DF, Dixon  RA; Victoria Longitudinal Study.  Stability and change in adult personality over 6 years: findings from the Victoria Longitudinal Study. J Gerontol B Psychol Sci Soc Sci. 2003;58(3):166-176.
PubMed   |  Link to Article
Mirowsky  J, Ross  CE.  Age and depression. J Health Soc Behav. 1992;33(3):187-212.
PubMed   |  Link to Article
Lynch  SM, George  LK.  Interlocking trajectories of loss-related events and depressive symptoms among elders. J Gerontol B Psychol Sci Soc Sci. 2002;57(2):S117-S125.
PubMed   |  Link to Article
Blazer  D, Burchett  B, Service  C, George  LK.  The association of age and depression among the elderly: an epidemiologic exploration. J Gerontol. 1991;46(6):M210-M215.
PubMed   |  Link to Article
Hybels  CF, Pieper  CF, Blazer  DG.  The complex relationship between depressive symptoms and functional limitations in community-dwelling older adults: the impact of subthreshold depression. Psychol Med. 2009;39(10):1677-1688.
PubMed   |  Link to Article
Hybels  CF, Blazer  DG, Pieper  CF.  Toward a threshold for subthreshold depression: an analysis of correlates of depression by severity of symptoms using data from an elderly community sample. Gerontologist. 2001;41(3):357-365.
PubMed   |  Link to Article
Albert  SM, Bear-Lehman  J, Burkhardt  A.  Mild depressive symptoms, self-reported disability, and slowing across multiple functional domains. Int Psychogeriatr. 2012;24(2):253-260.
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure 1.
Scores for the Center for Epidemiologic Studies Depression Total Scale and 3 Subscales Across Adulthood

Estimated trajectory of scores for the Center for Epidemiologic Studies Depression (CES-D) total scale and 3 subscales across adulthood. Raw scored were z-transformed so that all scales could be plotted on the same axis. (eFigure 1 in the Supplement shows the estimated trajectories of each scale in the original metric.)

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.
Depressed Affect Scores by Sex

Estimated trajectory of depressed affect scores by sex.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 3.
Somatic Complaint Scores in Old Age

Estimated trajectory of somatic complaint scores in old age in participants still living and participants who died.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1.  Mean Follow-up and Number of Assessments by Baseline Age in 2320 Participants
Table Graphic Jump LocationTable 2.  HLM Coefficients and Variance Estimates of Intercept, Linear, and Quadratic Equations Predicting Depressive Symptoms From Age and Age Squared (in Decades) in 2320 Participants1
Table Graphic Jump LocationTable 3.  Effect of Demographic Factors, Antidepressant Medication Use, Disease Burden, and Death on the Intercepts and Slopes of Depressive Symptoms Overall and Depressed Affect in 2320 Participants
Table Graphic Jump LocationTable 4.  Effect of Demographic Factors, Antidepressant Medication Use, Disease Burden, and Death on the Intercepts and Slopes of Somatic Complaints and Interpersonal Problems in 2320 Participants
Table Graphic Jump LocationTable 5.  Hierarchical Linear Modeling Coefficients and Variance Estimates of Intercept, Linear, and Quadratic Equations Predicting Depressive Symptoms in 728 Participants, With Distance to Death (in Years) Used as the Time Metric1

References

Murray  CJL, Lopez  AD.  Global mortality, disability, and the contribution of risk factors: Global Burden of Disease Study. Lancet. 1997;349(9063):1436-1442.
PubMed   |  Link to Article
Moussavi  S, Chatterji  S, Verdes  E, Tandon  A, Patel  V, Ustun  B.  Depression, chronic diseases, and decrements in health: results from the World Health Surveys. Lancet. 2007;370(9590):851-858.
PubMed   |  Link to Article
Coryell  W, Scheftner  W, Keller  M, Endicott  J, Maser  J, Klerman  GL.  The enduring psychosocial consequences of mania and depression. Am J Psychiatry. 1993;150(5):720-727.
PubMed
Greenberg  PE, Kessler  RC, Birnbaum  HG,  et al.  The economic burden of depression in the United States: how did it change between 1990 and 2000? J Clin Psychiatry. 2003;64(12):1465-1475.
PubMed   |  Link to Article
Kessler  RC, Birnbaum  H, Bromet  E, Hwang  I, Sampson  N, Shahly  V.  Age differences in major depression: results from the National Comorbidity Survey Replication (NCS-R). Psychol Med. 2010;40(2):225-237.
PubMed   |  Link to Article
Blazer  DG, Landerman  LR, Hays  JC, Simonsick  EM, Saunders  WB.  Symptoms of depression among community-dwelling elderly African-American and white older adults. Psychol Med. 1998;28(6):1311-1320.
PubMed   |  Link to Article
Kessler  RC, Foster  C, Webster  PS, House  JS.  The relationship between age and depressive symptoms in two national surveys. Psychol Aging. 1992;7(1):119-126.
PubMed   |  Link to Article
Fiske  A, Gatz  M, Pedersen  NL.  Depressive symptoms and aging: the effects of illness and non-health-related events. J Gerontol B Psychol Sci Soc Sci. 2003;58(6):320-328.
PubMed   |  Link to Article
Needham  BL, Epel  ES, Adler  NE, Kiefe  C.  Trajectories of change in obesity and symptoms of depression: the CARDIA study. Am J Public Health. 2010;100(6):1040-1046.
PubMed   |  Link to Article
Rothermund  K, Brandtstädter  J.  Depression in later life: cross-sequential patterns and possible determinants. Psychol Aging. 2003;18(1):80-90.
PubMed   |  Link to Article
Hankin  BL, Abramson  LY, Moffitt  TE, Silva  PA, McGee  R, Angell  KE.  Development of depression from preadolescence to young adulthood: emerging gender differences in a 10-year longitudinal study. J Abnorm Psychol. 1998;107(1):128-140.
PubMed   |  Link to Article
Kessler  RC.  Epidemiology of women and depression. J Affect Disord. 2003;74(1):5-13.
PubMed   |  Link to Article
Van de Velde  S, Bracke  P, Levecque  K, Meuleman  B.  Gender differences in depression in 25 European countries after eliminating measurement bias in the CES-D 8. Soc Sci Res. 2010;39:396-404.
Link to Article
Bromberger  JT, Harlow  S, Avis  N, Kravitz  HM, Cordal  A.  Racial/ethnic differences in the prevalence of depressive symptoms among middle-aged women: the Study of Women’s Health Across the Nation (SWAN). Am J Public Health. 2004;94(8):1378-1385.
PubMed   |  Link to Article
Kim  J, Durden  E.  Socioeconomic status and age trajectories of health. Soc Sci Med. 2007;65(12):2489-2502.
PubMed   |  Link to Article
Miech  RA, Shanahan  MJ.  Socioeconomic status and depression over the life course. J Health Soc Behav. 2000;41:162-176.
Link to Article
Barefoot  JC, Mortensen  EL, Helms  MJ, Avlund  K, Schroll  M.  A longitudinal study of gender differences in depressive symptoms from age 50 to 80. Psychol Aging. 2001;16(2):342-345.
PubMed   |  Link to Article
Skarupski  KA, Mendes de Leon  CF, Bienias  JL,  et al.  Black-white differences in depressive symptoms among older adults over time. J Gerontol B Psychol Sci Soc Sci. 2005;60(3):136-142.
PubMed   |  Link to Article
Walsemann  KM, Gee  GC, Geronimus  AT.  Ethnic differences in trajectories of depressive symptoms: disadvantage in family background, high school experiences, and adult characteristics. J Health Soc Behav. 2009;50(1):82-98.
PubMed   |  Link to Article
Xiao Xu, Liang  J, Bennett  JM, Quiñones  AR, Wen Ye.  Ethnic differences in the dynamics of depressive symptoms in middle-aged and older Americans. J Aging Health. 2010;22(5):631-652.
PubMed   |  Link to Article
Wells  KB, Stewart  A, Hays  RD,  et al.  The functioning and well-being of depressed patients: results from the Medical Outcomes Study. JAMA. 1989;262(7):914-919.
PubMed   |  Link to Article
Katon  W, Lin  EHB, Kroenke  K.  The association of depression and anxiety with medical symptom burden in patients with chronic medical illness. Gen Hosp Psychiatry. 2007;29(2):147-155.
PubMed   |  Link to Article
Frasure-Smith  N, Lespérance  F, Talajic  M.  Depression and 18-month prognosis after myocardial infarction. Circulation. 1995;91(4):999-1005.
PubMed   |  Link to Article
Dunne  E, Wrosch  C, Miller  GE.  Goal disengagement, functional disability, and depressive symptoms in old age. Health Psychol. 2011;30(6):763-770.
PubMed   |  Link to Article
Gerstorf  D, Ram  N, Mayraz  G,  et al.  Late-life decline in well-being across adulthood in Germany, the United Kingdom, and the United States: something is seriously wrong at the end of life. Psychol Aging. 2010;25(2):477-485.
PubMed   |  Link to Article
Fonda  SJ, Herzog  AR.  Patterns and risk factors of change in somatic and mood symptoms among older adults. Ann Epidemiol. 2001;11(6):361-368.
PubMed   |  Link to Article
Khan  A, Brodhead  AE, Kolts  RL, Brown  WA.  Severity of depressive symptoms and response to antidepressants and placebo in antidepressant trials. J Psychiatr Res. 2005;39(2):145-150.
PubMed   |  Link to Article
Radloff  LS.  The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1:385-401.
Link to Article
Beekman  ATF, Deeg  DJH, Van Limbeek  J, Braam  AW, De Vries  MZ, Van Tilburg  W.  Criterion validity of the Center for Epidemiologic Studies Depression scale (CES-D): results from a community-based sample of older subjects in the Netherlands. Psychol Med. 1997;27(1):231-235.
PubMed   |  Link to Article
Hertzog  C, Van Alstine  J, Usala  PD, Hultsch  DF, Dixon  R.  Measurement properties of the Center for Epidemiological Studies Depression scale (CES-D) in older populations. Psychol Assess. 1990;2:64-72. doi:10.1037/1040-3590.2.1.64.
Link to Article
Charlson  ME, Pompei  P, Ales  KL, MacKenzie  CR.  A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-383.
PubMed   |  Link to Article
Deyo  RA, Cherkin  DC, Ciol  MA.  Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613-619.
PubMed   |  Link to Article
Schneeweiss  S, Wang  PS, Avorn  J, Maclure  M, Levin  R, Glynn  RJ.  Consistency of performance ranking of comorbidity adjustment scores in Canadian and U.S. utilization data. J Gen Intern Med. 2004;19(5, pt 1):444-450.
PubMed   |  Link to Article
Yan  Y, Birman-Deych  E, Radford  MJ, Nilasena  DS, Gage  BF.  Comorbidity indices to predict mortality from Medicare data: results from the National Registry of Atrial Fibrillation. Med Care. 2005;43(11):1073-1077.
PubMed   |  Link to Article
Katz  S, Ford  AB, Moskowitz  RW, Jackson  BA, Jaffe  MW.  Studies of illness in the aged: the index of ADL: a standardized measure if biological and psychosocial function. JAMA. 1963;185:914-919.
PubMed   |  Link to Article
Lawton  MP, Brody  EM.  Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist. 1969;9(3):179-186.
PubMed   |  Link to Article
Raudenbush  SW, Bryk  AS. Hierarchical Linear Models: Applications and Data Analysis Methods.2nd ed. Thousand Oaks, CA: Sage; 2002.
Singer  JD, Willett  JB. Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. New York, NY: Oxford University Press; 2003.
Gueorguieva  R, Krystal  JH.  Move over ANOVA: progress in analyzing repeated-measures data and its reflection in papers published in the Archives of General PsychiatryArch Gen Psychiatry. 2004;61(3):310-317.
PubMed   |  Link to Article
HLM [computer program]. Version 6. Lincolnwood, IL: Scientific Software International; 2004.
Drøyvold  WB, Nilsen  TIL, Krüger  Ø,  et al.  Change in height, weight and body mass index: longitudinal data from the HUNT Study in Norway. Int J Obes (Lond). 2006;30(6):935-939.
PubMed   |  Link to Article
Rissanen  A, Heliövaara  M, Aromaa  A.  Overweight and anthropometric changes in adulthood: a prospective study of 17,000 Finns. Int J Obes. 1988;12(5):391-401.
PubMed
Sutin  AR, Terracciano  A, Milaneschi  Y, An  Y, Ferrucci  L, Zonderman  AB.  The effect of birth cohort on well-being: the legacy of economic hard times. Psychol Sci. 2013;24(3):379-385.
PubMed   |  Link to Article
Gerstorf  D, Ram  N, Estabrook  R, Schupp  J, Wagner  GG, Lindenberger  U.  Life satisfaction shows terminal decline in old age: longitudinal evidence from the German Socio-Economic Panel Study (SOEP). Dev Psychol. 2008;44(4):1148-1159.
PubMed   |  Link to Article
Hek  K, Demirkan  A, Lahti  J,  et al.  A genome-wide association study of depressive symptoms. Biol Psychiatry. 2013;73(7):667-678.
PubMed   |  Link to Article
Kessler  RC, McGonagle  KA, Zhao  S,  et al.  Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States: results from the National Comorbidity Survey. Arch Gen Psychiatry. 1994;51(1):8-19.
PubMed   |  Link to Article
Scott  KM, Von Korff  M, Alonso  J,  et al.  Age patterns in the prevalence of DSM-IV depressive/anxiety disorders with and without physical co-morbidity. Psychol Med. 2008;38(11):1659-1669.
PubMed   |  Link to Article
Byers  AL, Yaffe  K, Covinsky  KE, Friedman  MB, Bruce  ML.  High occurrence of mood and anxiety disorders among older adults: the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2010;67(5):489-496.
PubMed   |  Link to Article
Meeks  TW, Vahia  IV, Lavretsky  H, Kulkarni  G, Jeste  DV.  A tune in “a minor” can “b major”: a review of epidemiology, illness course, and public health implications of subthreshold depression in older adults. J Affect Disord. 2011;129(1-3):126-142.
PubMed   |  Link to Article
Terracciano  A, McCrae  RR, Brant  LJ, Costa  PT  Jr.  Hierarchical linear modeling analyses of the NEO-PI-R scales in the Baltimore Longitudinal Study of Aging. Psychol Aging. 2005;20(3):493-506.
PubMed   |  Link to Article
Small  BJ, Hertzog  C, Hultsch  DF, Dixon  RA; Victoria Longitudinal Study.  Stability and change in adult personality over 6 years: findings from the Victoria Longitudinal Study. J Gerontol B Psychol Sci Soc Sci. 2003;58(3):166-176.
PubMed   |  Link to Article
Mirowsky  J, Ross  CE.  Age and depression. J Health Soc Behav. 1992;33(3):187-212.
PubMed   |  Link to Article
Lynch  SM, George  LK.  Interlocking trajectories of loss-related events and depressive symptoms among elders. J Gerontol B Psychol Sci Soc Sci. 2002;57(2):S117-S125.
PubMed   |  Link to Article
Blazer  D, Burchett  B, Service  C, George  LK.  The association of age and depression among the elderly: an epidemiologic exploration. J Gerontol. 1991;46(6):M210-M215.
PubMed   |  Link to Article
Hybels  CF, Pieper  CF, Blazer  DG.  The complex relationship between depressive symptoms and functional limitations in community-dwelling older adults: the impact of subthreshold depression. Psychol Med. 2009;39(10):1677-1688.
PubMed   |  Link to Article
Hybels  CF, Blazer  DG, Pieper  CF.  Toward a threshold for subthreshold depression: an analysis of correlates of depression by severity of symptoms using data from an elderly community sample. Gerontologist. 2001;41(3):357-365.
PubMed   |  Link to Article
Albert  SM, Bear-Lehman  J, Burkhardt  A.  Mild depressive symptoms, self-reported disability, and slowing across multiple functional domains. Int Psychogeriatr. 2012;24(2):253-260.
PubMed   |  Link to Article

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Supplement.

Supplemental Text. Additional Information About the BLSA

eTable. Deviance Statistic for the Baseline Model and Models With Age (Linear Slope) and Age Squared (Quadratic Slope)

eFigure 1. Spaghetti Plots of the Raw Data.

eFigure 2. Estimated Trajectory of the CES-D by Ever Experiencing Severe Depressive Symptoms (CES-D =16) During the Study Period.

eFigure 3. Estimated Trajectory of the CES-D by IADLs.

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