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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Psychol Med. Author manuscript; available in PMC Feb 1, 2011.
Published in final edited form as:
PMCID: PMC2813515
NIHMSID: NIHMS163261

Age Differences in Major depression: Results from the National Comorbidity Surveys Replication (NCS-R)

Abstract

Background

Although depression appears to decrease in late life, this could be due to misattribution of depressive symptom to physical disorders that increase in late life.

Methods:

We studied age differences in major depressive episodes (MDE) in the National Comorbidity Survey Replication, a national survey of the US household population. DSM-IV MDE was defined without organic exclusions or diagnostic hierarchy rules to facilitate analysis of comorbidity. Physical disorders were assessed with a standard chronic conditions checklist and mental disorders with the WHO Composite International Diagnostic Interview (CIDI) Version 3.0.

Results:

Lifetime and recent DSM-IV/CIDI MDE were significantly less prevalent among respondents ages 65+ than among younger adults. Recent episode severity, but not duration, was also lower among the elderly. Despite prevalence of mental disorders decreasing with age, comorbidity of hierarchy-free MDE with these disorders was either highest among the elderly or unrelated to age. Comorbidity of MDE with physical disorders, in comparison, generally decreased with age despite prevalence of comorbid physical disorders usually increasing. Somewhat more than half of respondents with 12-month MDE received past-year treatment, but the percent in treatment was lowest and most concentrated in the general medical sector among the elderly.

Conclusions:

Given that physical disorders increase with age independent of depression, their lower associations with MDE in old age argue that causal effects of physical disorders on MDE weaken in old age. This result argues against the suggestion that the low estimated prevalence of MDE among the elderly is due to increased confounding with physical disorders.

Keywords: Chronic conditions, Comorbidity, Depression, Elderly, Epidemiology

Community surveys consistently find that elderly people have much less clinical depression than younger people (Blazer & Hybels, 2005; Jorm, 2000). A number of explanations have been proposed for this finding, most focusing on the possibility that depression is underestimated among the elderly. Suggested biases include age-related differentials in recall, mortality, selection out of the household population into nursing homes, willingness to participate in surveys, and willingness to admit psychiatric symptoms in interviews (Schoevers et al., 2008; Snowdon, 1997). However, evidence for these methodological interpretations is weak (Ernst & Angst, 1995), leading some commentators to conclude that the low estimated prevalence of depression among the elderly is genuine (Blazer & Hybels, 2005).

One issue that complicates analysis of late-life depression is that many physical disorders become increasingly prevalent in old age, making boundaries with depression sometimes unclear and raising the possibility that depression is under-estimated because it is confused with the symptoms of physical disorders (Drayer et al., 2005). This issue is complicated by the fact that some somatic disorders that increase with age can induce depression (Bremmer et al., 2008; Salaycik et al., 2007) while late-life depression can increase risk of some physical disorders (Bremmer et al., 2007; Petronijevic et al., 2008).

In an effort to shed light on the possible age-related under-estimation of depression in epidemiological studies due to confounding with physical disorders, we analyzed age-related changes in associations of physical disorders with DSM-IV major depressive episodes (MDE) in the National Comorbidity Survey Replication (NCS-R) (Kessler & Merikangas, 2004). Parallel analysis was carried out with comorbidity between MDE and other DSM-IV mental disorders to determine if the patterns for physical disorders are unique or the same as for mental disorders. MDE was defined without either organic exclusions or diagnostic hierarchy rules to facilitate investigation of comorbidity. Our primary aims were to see if the estimated prevalence of MDE declines with age and to evaluate the extent to which comorbidity of MDE with other disorders changes with age. In studying comorbidity, we focused on associations rather than conditional prevalence, as data on age-related changes in prevalence exist (Blazer et al., 1987; Kennedy et al., 1990) but not data on age-related changes in associations. An examination of these associations is of interest because we would expect them to increase with age if the assessment of MDE is confounded by physical disorders.

As a preliminary to the analysis of comorbidity, we examined basic MDE prevalence by age to show that an inverse age-MDE relationship does, in fact, exist in the NCS-R. We then examined age differences in the ratios of recent (30-day and 12-month) to lifetime prevalence, expecting that recall bias would produce higher ratios among elderly than younger respondents whereas substantive patterns would produce the opposite pattern. We also investigated whether MDE age-of-onset distributions differ by age, expecting that recall bias would result in these distributions being skewed more to the right among older than younger respondents. We then compared age-related differences in median numbers of lifetime MDE episodes among respondents with a history of MDE, expecting that recall bias would result in these values not increasing with age in a substantively plausible way. We then examined age differences in the persistence and severity of 12-month MDE, which we assumed would be less influenced by recall bias than data on lifetime MDE. We are unaware of any previous attempt to investigate these specifications in the context of an analysis of comorbidity between MDE and other disorders.

METHODS

Sample

The NCS-R was a face-to-face household survey conducted between February 2001 and December 2002 in a nationally representative sample of the US adult (ages 18+) household population. The response rate was 73.0%. The interview was in two parts. Part I, administered to all 9,282 respondents, assessed core DSM-IV mental disorders. Part II, administered to all Part I respondents who screened positive for any Part I disorder (n = 4,235) plus a probability sub-sample of other Part I respondents (n = 1,457), assessed additional disorders and correlates. The Part I sample was weighted to adjust for differential probabilities of selection. The Part II sample was additionally weighted to adjust for the under-sampling of respondents with no Part I disorder. A final post-stratification weight was used to match the Part II sample with the 2000 Census on a variety of socio-demographic and geographic variables. NCS-R sampling, field, and weighting procedures are discussed in more detail elsewhere (Kessler et al., 2004).

Measures

Mental disorders

DSM-IV mental disorders were assessed with Version 3.0 of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI) (Kessler & Üstün, 2004), a fully-structured lay-administered interview that generates diagnoses for common mental disorders. The disorders considered in addition to MDE are other mood disorders (dysthymic disorder, bipolar disorder), anxiety disorders (generalized anxiety disorder, panic disorder, agoraphobia without panic disorder, specific phobia, social phobia, post-traumatic stress disorder, adult separation anxiety disorder), intermittent explosive disorder, and substance disorders (alcohol and drug abuse with and without dependence). Good concordance was found between CIDI diagnoses and blinded clinical assessments in an NCS-R clinical reappraisal study (Haro et al., 2006). All disorders were defined without organic exclusions and without diagnostic hierarchy rules for purposes of the current report in order to facilitate analysis of comorbidity.

We examined information about MDE course by analyzing retrospectively reported CIDI data on age-of-onset, number of lifetime episodes, and, among 12-month cases, number of weeks in episode in the past 12 months. Clinical severity was assessed among 12-month cases using the Quick Inventory of Depressive Symptoms Self-Report (QIDS) (Rush et al., 2003), a validated measure of depression clinical severity. Standard QIDS cut-points were used to define episodes as severe (including original QIDS ratings of very severe), moderate, mild, or not clinically significant. Role impairment among 12-month cases was assessed with the Sheehan Disability Scales (SDS) (Leon et al., 1997), a validated disorder-specific measure of role impairment. Respondents were asked to rate separately how much these symptoms interfered with home management, work, social life, and personal relationships using a 0-10 visual analogue scale of none (0), mild (1-3), moderate (4-6), severe (7-9), and very severe (10). Severe and very severe were combined and we focused on the role domain with the highest impairment. Respondents with 12-month MDE were also asked to estimate the number of days out of 365 in the past year they were totally unable to work or carry out their other usual activities because of their depression.

Comorbid physical disorders

Physical disorders were assessed with a chronic conditions checklist based on the list in the US National Health Interview Survey (Schoenborn et al., 2003). Such checklists are widely used in epidemiological studies and yield more accurate reports than estimates derived from open-ended questions (Knight et al., 2001). Methodological studies have documented good concordance between checklist condition reports and medical records (Baker et al., 2001; Edwards et al., 1994; Revicki et al., 2004). The prevalence estimates of these conditions in the NCS-R are in accordance with those in other large-scale community surveys (Schoenborn et al., 2003). Five broad classes of physical disorders are considered: cardiovascular (heart attack, hypertension, other heart disease, stroke), musculoskeletal (arthritis/rheumatism, chronic back/neck problems), respiratory (seasonal allergies, asthma, other chronic lung disease such as COPD and tuberculosis), pain conditions (frequent/severe headaches, other chronic pain conditions), and other disorders (Cancer, diabetes, ulcers). Only disorders present in the past 12 months are considered.

Depression treatment

Treatment was assessed by asking respondents about treatment in the past 12 months for any problem with emotions, nerves, or substance use by a psychiatrist, other mental health professional (e.g., clinical psychologist, psychiatric social worker), general medical provider, human services professional (e.g., religious counselor, social worker in a social services agency), and in the complementary-alternative medicine (CAM) sector (either a self-help group or treatment by a CAM professional).We examine treatment separately in the specialty (psychiatrist or other mental health professional), general medical, healthcare (either specialty or general medical), human services, and CAM sectors.

Analysis methods

Age differences in prevalence, course, severity, and treatment of MDE were examined using cross-tabulations and mean comparisons across age groups. Multiple regression analysis was used to study comorbidity between MDE and other disorders. Both linear and logistic link functions (Hosmer & Lemeshow, 2001) were used in parallel to estimate the regression equations because these approaches sometimes yield different results due to their differing assumptions about functional form of associations (difference in prevalence of comorbid disorders in linear regression and relative-odds of comorbid disorders in logistic regression). All regression equations were estimated separately in four age groups (18-34, 35-49, 50-64, 65+) and the total sample controlling for sex, education and marital status. The total-sample equations also included dummy predictor variables for age groups and interactions of the other predictors with these age group dummies. Wald χ2 tests (logistic) and F-tests (linear regression) were used to evaluate the significance of interactions. We also evaluated whether the conditional prevalence of comorbid disorders among people with MDE varies with age, again using Wald χ2 tests and F-tests. Logistic regression equations were also used to study socio-demographic correlates of MDE and patterns of treatment for MDE. Because the NCS-R data are weighted and clustered, the Taylor series linearization method (Wolter, 1985) implemented in the SUDAAN software system (Research Triangle Institute, 2002) was used to estimate statistical significance. Significance was consistently evaluated using .05-level two-sided tests.

RESULTS

Prevalence estimates

The estimated lifetime prevalence of MDE in the total sample is 19.2%. (Table 1) This estimate varies significantly across the four age groups considered here (χ23 = 70.4, p < .001) due to a much lower estimated prevalence among respondents in the oldest age group (65+; 9.8%) than in younger age groups (18-34, 35-49, 50-64; 19.4-22.7%). The same general pattern holds for 12-month and 30-day prevalence estimates (8.3% and 3.1%, respectively) in the total sample, where estimates vary significantly across the age groups (χ23 = 46.9-103.5, p < .001) due largely to much lower prevalence estimates among respondents ages 65+ (2.6% and 1.0%, respectively) than in younger age groups (7.7-10.4% and 3.0-3.7%, respectively). Very similar age differences are obtained separately for women and men despite prevalence estimates being consistently higher among women than men.

Table 1
Thirty-day, 12-month, and lifetime prevalence estimates of DSM-IV/CIDI MDE by age and gender

It is noteworthy that the ratio of the prevalence estimates among 65+ year olds compared to the total sample is lower for recent (30-day and 12-month) prevalence (31-32%) than for lifetime prevalence (51%). The same pattern holds separately among women (36-38% vs. 57%) and men (9-19% vs. 35.1%). This gradient suggests that recall error is not responsible for the lower prevalence estimates among 65+ year olds, as recall failure would produce an opposite pattern, with high estimates in the shortest time frame and low estimates in lifetime prevalence. A related observation is that the ratio of 12-month prevalence to lifetime prevalence is consistently lower among respondents ages 65+ (22-28%) than younger respondents (37-57%). This is an expectable pattern on substantive grounds, but would be reversed if elderly recall failure drove differences in estimates..

Age-of-onset, lifetime course, and 12-month severity

Retrospectively reported age-of-onset (AOO) of MDE varies significantly across age groups in the total sample (χ23 = 696.8, p < .001). A monotonic increase exists in mean AOO from the youngest to oldest age groups: 17.8 (ages 18-34), 25.5 (ages 35-49), 33.1 (ages 50-64), and 43.0 (ages 65+). (Table 2, Part I) The difference between the median age-at-interview in each age group and the mean AOO of MDE in that age group increases with increasing age. For example, in the youngest age group (ages 18-34 at interview), mean AOO is approximately 8 years earlier than the median age-at-interview (i.e., 17.8 vs. 26) compared to 16, 24, and over 30 years in the successively older age groups. This pattern is what we would expect based on substantive processes and, like the patterns in Table 1, argues against a methodological interpretation of the age differences. As would be expected based on these inter-cohort differences, the mean number of lifetime episodes of MDE reported by respondents with a lifetime history increases monotonically with age-at-interview, from a low of 15.4 among respondents in the 18-34 age group to 30.2 in the 65+ age group.

Table 2
Dimensions of DSM-IV/CIDI MDE lifetime onset, course, and 12-month persistence and severity by age among NCS-R respondents with MDE

The mean self-reported duration of depressive episodes in the 12 months before interview, which was retrospectively reported to be 27.5 weeks, does not vary significantly across age groups (F3,692 = 1.6, p = .21). (Table 2, Part II) However, symptom severity of these episodes, as assessed by the QIDS, does vary significantly with age, with the proportion of cases classified clinically mild higher in the 65+ age group (21.8%) than in younger age groups (6.8-10.3%) and the percent classified clinically severe lower in the 65+ age group (37.8%) than in the younger age groups (45.8-53.0%). Consistent with these results, the proportion of 12-month cases rating their depression as causing severe role impairments varies significantly with age (χ23 = 9.9, p = .020) and is lowest in the 65+ age group (56.5% vs. 61.3-72.2%). The mean number of days out of role in the past year due to depression among 12-month cases also varies significantly with age (F3,692 = 8.3, p < .001) and is again lowest in the 65+ age group (18.8 vs. 28.3-58.4 days).

Comorbidity of 12-month MDE with other 12-month DSM-IV disorders

All 14 DSM-IV disorders considered here are significantly and positively associated with MDE in the total sample in both linear (results not shown, but available on request) and logistic regression models. (Table 3) ORs are in the range 3.6-161.9. Consistent with previously-reported NCS-R results (Kessler et al., 2005), the conditional 12-month prevalence of comorbid disorders among respondents with MDE is lowest among those 65+ years old for 10 of the 14 comorbid disorders (6 significantly so at the .05 level). Associations (odds-ratios) of MDE with comorbid mental disorders vary significantly with age for six of the 14 comorbid disorders. In three of these six the OR is highest in the 65+ age group (bipolar disorder, panic disorder, generalized anxiety disorder). In the other three, which involve substance disorders, the OR is missing in the 65+ age group because no elderly respondents (with or without MDE) have the comorbid disorder but the OR is highest in the next oldest age group (50-64). The OR is also highest in the 65+ age group for three other disorders (social phobia, PTSD, intermittent explosive disorder) even though the age difference is not statistically significant.

Table 3
Twelve-month comorbidity (odds-ratios) of DSM-IV/CIDI MDE with other 12-month DSM-IV/CIDI disorders by age1

As noted above in the section on analysis methods, ORs describe multiplicative relationships, which mean ORs can be large even when the proportions of depressed and non-depressed people who have a particular comorbid disorder are small. For example, when the conditional prevalence of a comorbid disorder is 3% among people with depression and 2% among people without depression, the difference is small (1%) but the odds-ratio is large (3.1). It is consequently useful to examine patterns of comorbidity based on linear regression equations. When we did this (detailed results are available on request), we found that age-related variations in prevalence differences for comorbid disorders are less consistent than age-related variations in ORs. In four cases the prevalence difference is highest among the elderly (dysthymia, PTSD, alcohol abuse, drug dependence, the latter two involving respondents in the 50-64 age group because the comorbid disorder did not occur among those in the 65+ age group) and in five other cases lowest among the young (bipolar disorder, panic disorder, adult separation anxiety, drug abuse, alcohol dependence, with the last three referring to the 50-64 age group), but only two of these nine are statistically significant (drug abuse and dependence). In two other cases the prevalence difference is lowest among the elderly (agoraphobia, specific phobia), while in the remaining three cases there is no clear age trend in the prevalence differences.

Comorbidity of 12-month MDE with chronic physical disorders

As with the comorbid mental disorders, all 14 physical disorders are significantly comorbid with MDE in linear models in the total sample. In 11 of these 14 the comorbid disorder is more prevalent among people with than without MDE and in the other three less prevalent. (Detailed results are available on request.) Only 6 of the 14, in comparison, are significantly comorbid with MDE in logistic regression models. (Table 4) All six ORs are positive (1.4-3.8). The association between age and the conditional prevalence of the comorbid physical disorders is also for the most part positive, with conditional prevalence either increasing monotonically with age (hypertension, stroke, arthritis), being highest in the two oldest age groups (cancer, heart disease, chronic lung disease, chronic pain), or being lowest in the youngest age group (low back pain, ulcers, heart attack, diabetes). However, the age gradient is not consistent, as age is significantly related to prevalence for only eight of the 14 disorders and prevalence is highest in the 65+ age group for only three of these eight (hypertension, stroke, arthritis). Prevalence is highest in the next oldest age group (50-64) for the majority of comorbid disorders. Nonetheless, the proportion of depressed respondents with one or more comorbid physical disorders is considerably higher in the 65+ age group (94.6%) than in any of the younger age groups (69.8-89.6%).

Table 4
Twelve-month comorbidity (odds-ratios) of DSM-IV/CIDI MDE with chronic physical disorders by age1

Associations of MDE with comorbid disorders have a general tendency to decrease with age. In the logistic regression models, the ORs decrease with age for six disorders (heart attack, heart disease, asthma, lung disease, cancer, diabetes), are lowest in the elderly for two others (allergies, ulcers), and are highest in the young for one other (hypertension), although only three of these nine are statistically significant (heart attack, lung disease, hypertension). In the linear regression models, the MDE prevalence difference deceases monotonically with age for seven comorbid disorders and is lowest among the elderly for five others (low back pain, headaches, chronic pain, diabetes, ulcers). Seven of these 12 age trends are statistically significant. An age trend is absent for only the two remaining disorders (hypertension, stroke).

Age-related differences in treatment of 12-month MDE

Somewhat more than half (57.7%) of NCS-R respondents with 12-month MDE reported treatment for emotional problems in the year before interview. (Table 5, Part I) The proportion in treatment is lowest in the 65+ age group (48.3% vs. 51.2-64.0) due to low proportions of the elderly than younger respondents in specialty (18.3% vs. 29.9-38.2%) and CAM (3.9% vs. 7.3-12.3%) treatment. More detailed analysis shows that these low treatment proportions are not due to the lower severity of MDE, as the proportion in treatment among the severely depressed also varies significantly with age (χ23 = 9.5, p = .023) and is lowest among the 65+ years olds (33.6% vs. 63.3-69.9%). Proportional treatment in the general medical (GM) sector also varies significantly with age, as depressed patients in the 65+ age group receive a higher proportion of their treatment in the GM sector (78.8%) than do younger respondents (47.8-69.5%).

Table 5
Past year treatment of emotional problems among respondent with 12-month DSM-IV/CIDI MDE by age

Age-related socio-demographic correlates of MDE prevalence and treatment

We examined four socio-demographic correlates of MDE and treatment: sex, family income, employment status (employed/self-employed, student, homemaker, retired, disables/unemployed), and marital status (married, previously married, never married). All four are significantly related to MDE in the total sample. Elevated risk of MDE is related to being female, poor, disabled/unemployed, and unmarried, with ORs of 1.7-2.8. (Detailed results are available on request.) The strength of these associations (ORs), though, varies significantly with age. The higher odds of MDE among women than men is especially pronounced among respondents 65+ years old (3.2 vs.1.5-20). The elevated odds of MDE associated with being poor (in the lowest quartile of the income-per-family-member distribution) is also especially pronounced among respondents 65+ years old (3.9 vs. 1.2-2.7). Employment status, in comparison, is unrelated to MDE among the elderly although it is significant at earlier ages. The elevated odds of MDE among the previously married, finally, is somewhat higher among respondents 65+ years old than those 50-64 years old (2.3 vs. 1.7). Sex and marital status are the only two socio-demographic variables studied that are significantly related to 12-month treatment of emotional problems among respondents with 12-month MDE. Women have higher odds of treatment than men (1.5) and the previously married have higher odds of treatment than the married (1.6). The association of sex with treatment is significantly weaker among depressed people who are 65+ years old (1.0) than younger (1.3-2.2). (Detailed results are available on request.)

DISCUSSION

The above results are limited by several sampling and measurement problems. Regarding sampling, results could be influenced by truncation of the severity spectrum of physical disorders if people with extreme physical disorders are less likely to be interviewed. This bias would lead to under-estimation of the association between physical disorders and MDE, possibly more among the elderly. Regarding measurement, physical disorders were assessed with a checklist, while mental disorders were assessed with a diagnostic interview. This difference could have led to artificial overlap between estimated diagnoses of mental and physical disorders, as core symptoms of some physical conditions (e.g., lethargy, insomnia) are also symptoms of mental disorders. If present, this bias presumably led to an artificial increase in the estimated associations of MDE with physical disorders.

Within the context of these limitations, our results are consistent with previous research in finding less MDE among older than younger respondents (Blazer & Hybels, 2005; Weissman et al., 1991). The findings of higher mean AOO and longer time lag between AOO and current age among the depressed elderly, while substantively plausible, are inconsistent with the findings of Simon and Von Korff (1992) in the ECA study. Simon and Von Korff used this substantively implausible pattern to argue that the seemingly higher lifetime prevalence of depression in younger cohorts is due to recall bias among the elderly. The absence of this implausible pattern in the NCS-R is presumably due to the use of an innovative AOO probing technique developed for the baseline NCS that reduces recall bias (Knäuper et al., 1999). The fact that the age difference in MDE persists despite this methodological innovation argues against recall bias accounting for the age-MDE relationship. It is still possible, though, that sample selection bias is at work, with depressed people more likely than others to die at an early age, to be outside the household population (e.g., in nursing homes), to refuse to be interviewed, or to deny depression when they are interviewed. The results reported here shed no light on these possibilities, which could account for the evidence of cohort effects found in previous epidemiological studies of depression (Kessler et al., 2003).

The finding that retrospectively reported number of lifetime episodes increases with age among respondents with a history of MDE is substantively plausible. We are aware of no previous studies of this association. The finding that age is unrelated to duration of 12-month depressive symptoms among 12-month cases is consistent with previous research (Mitchell & Subramaniam, 2005; Sargeant et al., 1990). The findings that symptom severity, severity of role impairment, and mean number of days out of role due to 12-month depression are all significantly lower among elderly than younger depressed people are also consistent with the small number of direct (Koenig et al., 1991) and indirect (Ernst & Angst, 1995) studies of this association in the literature.

As noted in the introduction, previous studies have consistently found that recent prevalence of most mental disorders decreases with age (Blazer et al., 1987) and that prevalence of many physical disorders increases with age (Kennedy et al., 1990). Our replication of these findings is consequently unremarkable. However, we are aware of virtually no research on age differences in the associations between depression and comorbid disorders. Although there is considerable research on comorbidity of late-life depression (Alexopoulos et al., 2002; Taylor et al., 2004), this research fails to make comparisons with earlier ages. Our finding that the associations of MDE with comorbid mental disorders generally increase with age while the associations of MDE with comorbid physical disorders generally decrease with age are consequently unique. We found no studies in the literature that examined age differences in the association between depression and comorbid mental disorders and only one that examined age differences in the association of depression with comorbid physical disorders (Fiske et al., 2003). The latter study found that the association between a composite measure of physical disorders and scores on a screening scale of depressive symptoms did not change over the life course in a sample of Swedish twins.

It is difficult to offer a clear causal interpretation of the generally increasing ORs of MDE with comorbid mental disorders with age because both MDE and most comorbid mental disorders have decreasing prevalence with age. Given that the prevalence differences decrease with age, the most plausible interpretation is that the subset of comorbid cases has a more persistent course than other cases due to causal effects that might involve MDE influencing the onset or course of comorbid mental disorders, comorbid mental disorders influencing the onset or course of comorbid MDE, common causes influencing the onset or course of both disorders, or some combination of these processes.

The generally decreasing ORs of MDE with comorbid physical disorders are more interpretable because the age patterns in prevalence are different for MDE (decreasing prevalence with age) and most comorbid physical disorders (increasing prevalence with age). In a situation of this sort, it is likely that the decreasing ORs are at least partially due to the causal effects of physical disorders on MDE decreasing with age. Whether or not causal effects of MDE on comorbid physical disorders also decrease with age is difficult to say because the implications of such a decrease on the prevalence of physical disorders would be negligible in light of the low prevalence of MDE. This argues against the suggestion that the low estimated prevalence of MDE among the elderly is due to increased confounding with physical disorders.

Our results shed no light on why it is that physical disorders have decreasing effects on MDE among the elderly. One possibility suggested in the literature is that elderly people are more accepting than younger people of the inevitability of physical illness, resulting in the otherwise adverse effects of physical disorders on depression being buffered (Ernst & Angst, 1995). Although we are aware of no direct test of this hypothesis, elderly people have been shown to be more likely than younger people to cope with adverse situations by using strategies that accept and adapt rather than try to change these situations (Diehl et al., 1996) and that disengage from stressful situations in ways that reduce adverse emotional effects (Charles & Carstensen, 2008). Other research has shown similar age differences in coping with physical illness (Felton & Revenson, 1987), but has not investigated whether these differences attenuate the impact of physical disorders on depression. The investigation of this buffering effect is an important next research step that, while beyond the scope of the present study, may help delineate positive patterns of response to increasing age and infirmity.

Acknowledgements

The National Comorbidity Survey Replication (NCS-R) is supported by the National Institute of Mental Health (NIMH; U01-MH60220) with supplemental support from the National Institute on Drug Abuse (NIDA), the Substance Abuse and Mental Health Services Administration (SAMHSA), the Robert Wood Johnson Foundation (RWJF; Grant 044780), and the John W. Alden Trust. Preparation of this report was additionally supported by an unrestricted educational grant from sanofi-aventis. Collaborating NCS-R investigators include Ronald C. Kessler (Principal Investigator, Harvard Medical School), Kathleen Merikangas (Co-Principal Investigator, NIMH), James Anthony (Michigan State University), William Eaton (The Johns Hopkins University), Meyer Glantz (NIDA), Doreen Koretz (Harvard University), Jane McLeod (Indiana University), Mark Olfson (New York State Psychiatric Institute, College of Physicians and Surgeons of Columbia University), Harold Pincus (University of Pittsburgh), Greg Simon (Group Health Cooperative), Michael Von Korff (Group Health Cooperative), Philip S. Wang (NIMH), Kenneth Wells (UCLA), Elaine Wethington (Cornell University), and Hans-Ulrich Wittchen (Max Planck Institute of Psychiatry; Technical University of Dresden). The views and opinions expressed in this report are those of the authors and should not be construed to represent the views of any of the sponsoring organizations, agencies, or U.S. Government. A complete list of NCS publications and the full text of all NCS-R instruments can be found at http://www.hcp.med.harvard.edu/ncs. Send correspondence to ude.dravrah.dem.pch@scn.

The NCS-R is carried out in conjunction with the World Health Organization World Mental Health (WMH) Survey Initiative. We thank the staff of the WMH Data Collection and Data Analysis Coordination Centres for assistance with instrumentation, fieldwork, and consultation on data analysis. These activities were supported by the National Institute of Mental Health (R01 MH070884), the John D. and Catherine T. MacArthur Foundation, the Pfizer Foundation, the US Public Health Service (R13-MH066849, R01-MH069864, and R01 DA016558), the Fogarty International Center (FIRCA R03-TW006481), the Pan American Health Organization, Eli Lilly and Company, Ortho-McNeil Pharmaceutical, Inc., GlaxoSmithKline, and Bristol-Myers Squibb. A complete list of WMH publications can be found at http://www.hcp.med.harvard.edu/wmh/. The authors thank Kathleen Merikangas, the staff of sanofi-aventis, and Hans-Ulrich Wittchen for comments on an earlier draft of the paper.

Footnotes

Declaration of interests: Dr. Kessler has been a consultant for GlaxoSmithKline Inc., Kaiser Permanente, Pfizer Inc., sanofi-aventis, Shire Pharmaceuticals, and Wyeth-Ayerst; has served on advisory boards for Eli Lilly & Company and Wyeth-Ayerst; and has had research support for his epidemiological studies from Bristol-Myers Squibb, Eli Lilly & Company, GlaxoSmithKline, Johnson & Johnson Pharmaceuticals, Ortho-McNeil Pharmaceuticals Inc., Pfizer Inc., and sanofi-aventis. Dr. Birnbaum was supported in this research by funding from sanofi-aventis research and development, Paris, France. The remaining authors have no declaration of interests.

Contributor Information

Ronald C. Kessler, Department of Health Care Policy, Harvard Medical School, Boston, MA, USA.

Howard Birnbaum, The Analysis Group, Boston, MA, USA.

Evelyn Bromet, Department of Psychiatry and Behavioral Sciences, State University of New York at Stony Brook, Stony Brook, NY, USA.

Irving Hwang, Department of Health Care Policy, Harvard Medical School, Boston, MA, USA.

Nancy Sampson, Department of Health Care Policy, Harvard Medical School, Boston, MA, USA.

Victoria Shahly, Boston Psychoanalytic Society and Institute, Boston, MA, USA.

REFERENCES

  • Alexopoulos GS, Buckwalter K, Olin J, Martinez R, Wainscott C, Krishnan KR. Comorbidity of late life depression: an opportunity for research on mechanisms and treatment. Biological Psychiatry. 2002;52:543–558. [PubMed]
  • Baker M, Stabile M, Deri C. What do self-reported, objective measures of health measure? NBER Working Paper. 2001:8419.
  • Blazer D, Hughes DC, George LK. The epidemiology of depression in an elderly community population. Gerontologist. 1987;27:281–287. [PubMed]
  • Blazer DG, 2nd, Hybels CF. Origins of depression in later life. Psychological Medicine. 2005;35:1241–1252. [PubMed]
  • Bremmer MA, Beekman AT, Deeg DJ, Penninx BW, Dik MG, Hack CE, Hoogendijk WJ. Inflammatory markers in late-life depression: results from a population-based study. Journal of Affective Disorders. 2008;106:249–255. [PubMed]
  • Bremmer MA, Deeg DJ, Beekman AT, Penninx BW, Lips P, Hoogendijk WJ. Major depression in late life is associated with both hypo- and hypercortisolemia. Biological Psychiatry. 2007;62:479–486. [PubMed]
  • Charles ST, Carstensen LL. Unpleasant situations elicit different emotional responses in younger and older adults. Psychology and Aging. 2008;23:495–504. [PMC free article] [PubMed]
  • Diehl M, Coyle N, Labouvie-Vief G. Age and sex differences in strategies of coping and defense across the life span. Psychology and Aging. 1996;11:127–139. [PubMed]
  • Drayer RA, Mulsant BH, Lenze EJ, Rollman BL, Dew MA, Kelleher K, Karp JF, Begley A, Schulberg HC, Reynolds CF., 3rd Somatic symptoms of depression in elderly patients with medical comorbidities. International Journal of Geriatric Psychiatry. 2005;20:973–982. [PubMed]
  • Edwards WS, Winn DM, Kurlantzick V, Sheridan S, Berk ML, Retchin S, Collins JG. Evaluation of National Health Interview Survey Diagnostic Reporting. National Center for Health Statistics. Vital and Health Statistics. 1994;2:120.
  • Ernst C, Angst J. Depression in old age. Is there a real decrease in prevalence? A review. European Archives of Psychiatry and Clinical Neuroscience. 1995;245:272–287. [PubMed]
  • Felton BJ, Revenson TA. Age differences in coping with chronic illness. Psychology and Aging. 1987;2:164–170. [PubMed]
  • Fiske A, Gatz M, Pedersen NL. Depressive Symptoms and Aging: The Effects of Illness and Non-Health-Related Events. Journals of Gerontology Series B: Psychological Sciences and Social Sciences. 2003;58:320–328. [PubMed]
  • Haro JM, Arbabzadeh-Bouchez S, Brugha TS, de Girolamo G, Guyer ME, Jin R, Lepine JP, Mazzi F, Reneses B, Vilagut G, Sampson NA, Kessler RC. Concordance of the Composite International Diagnostic Interview Version 3.0 (CIDI 3.0) with standardized clinical assessments in the WHO World Mental Health surveys. International Journal of Methods in Psychiatric Research. 2006;15:167–180. [PubMed]
  • Hosmer DW, Lemeshow S. Applied Logistic Regression. Second Edition Wiley & Son; New York, NY: 2001.
  • Jorm AF. Does old age reduce the risk of anxiety and depression? A review of epidemiological studies across the adult life span. Psychological Medicine. 2000;30:11–22. [PubMed]
  • Kennedy GJ, Kelman HR, Thomas C. The emergence of depressive symptoms in late life: the importance of declining health and increasing disability. Journal of Community Health. 1990;15:93–104. [PubMed]
  • Kessler RC, Berglund P, Demler O, Jin R, Koretz D, Merikangas KR, Rush AJ, Walters EE, Wang PS. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R) Journal of the American Medical Association. 2003;289:3095–3105. [PubMed]
  • Kessler RC, Berglund P, Chiu WT, Demler O, Heeringa S, Hiripi E, Jin R, Pennell BE, Walters EE, Zaslavsky A, Zheng H. The US National Comorbidity Survey Replication (NCS-R): design and field procedures. International Journal of Methods in Psychiatric Research. 2004;13:69–92. [PubMed]
  • Kessler RC, Chiu WT, Demler O, Merikangas KR, Walters EE. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry. 2005;62:617–627. [PMC free article] [PubMed]
  • Kessler RC, Merikangas KR. The National Comorbidity Survey Replication (NCS-R): background and aims. International Journal of Methods in Psychiatric Research. 2004;13:60–68. [PubMed]
  • Kessler RC, Üstün TB. The World Mental Health (WMH) Survey Initiative Version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI) International Journal of Methods in Psychiatric Research. 2004;13:93–121. [PubMed]
  • Knäuper B, Cannell CF, Schwarz N, Bruce ML, Kessler RC. Improving accuracy of major depression age-of-onset reports in the US National Comorbidity Survey. International Journal of Methods in Psychiatric Research. 1999;8:39–48.
  • Knight M, Stewart-Brown S, Fletcher L. Estimating health needs: the impact of a checklist of conditions and quality of life measurement on health information derived from community surveys. Journal of Public Health Medicine. 2001;23:179–186. [PubMed]
  • Koenig HG, Meador KG, Shelp F, Goli V, Cohen HJ, Blazer DG. Major depressive disorder in hospitalized medically ill patients: an examination of young and elderly male veterans. Journal of the American Geriatric Society. 1991;39:881–890. [PubMed]
  • Leon AC, Olfson M, Portera L, Farber L, Sheehan DV. Assessing psychiatric impairment in primary care with the Sheehan Disability Scale. International Journal of Psychiatry in Medicine. 1997;27:93–105. [PubMed]
  • Mitchell AJ, Subramaniam H. Prognosis of depression in old age compared to middle age: a systematic review of comparative studies. American Journal of Psychiatry. 2005;162:1588–1601. [PubMed]
  • Petronijevic M, Petronijevic N, Ivkovic M, Stefanovic D, Radonjic N, Glisic B, Ristic G, Damjanovic A, Paunovic V. Low bone mineral density and high bone metabolism turnover in premenopausal women with unipolar depression. Bone. 2008;42:582–590. [PubMed]
  • Research Triangle Institute . SUDAAN: Professional Software for Survey Data Analysis [computer program] Research Triangle Institute; Research Triangle Park, NC: 2002.
  • Revicki DA, Rentz AM, Dubois D, Kahrilas P, Stanghellini V, Talley NJ, Tack J. Gastroparesis Cardinal Symptom Index (GCSI): development and validation of a patient reported assessment of severity of gastroparesis symptoms. Quality of Life Research. 2004;13:833–844. [PubMed]
  • Rush AJ, Trivedi MH, Ibrahim HM, Carmody TJ, Arnow B, Klein DN, Markowitz JC, Ninan PT, Kornstein S, Manber R, Thase ME, Kocsis JH, Keller MB. The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biological Psychiatry. 2003;54:573–583. [PubMed]
  • Salaycik KJ, Kelly-Hayes M, Beiser A, Nguyen AH, Brady SM, Kase CS, Wolf PA. Depressive symptoms and risk of stroke: the Framingham Study. Stroke. 2007;38:16–21. [PubMed]
  • Sargeant JK, Bruce ML, Florio LP, Weissman MM. Factors associated with 1-year outcome of major depression in the community. Archives of General Psychiatry. 1990;47:519–526. [PubMed]
  • Schoenborn CA, Adams PF, Schiller JS. Summary health statistics for the U.S. population: National Health Interview Survey, 2000. Vital and Health Statistics. 2003;10:1–83. [PubMed]
  • Schoevers RA, Geerlings MI, Deeg DJ, Holwerda TJ, Jonker C, Beekman AT. Depression and excess mortality: evidence for a dose response relation in community living elderly. International Journal of Geriatric Psychiatry. 2008 [PubMed]
  • Simon GE, VonKorff M. Reevaluation of secular trends in depression rates. American Journal of Epidemiology. 1992;135:1411–1422. [PubMed]
  • Snowdon J. Depression in old age: questions concerning prevalence studies. International Journal of Geriatric Psychiatry. 1997;12:1043–1045. [PubMed]
  • Taylor WD, McQuoid DR, Krishnan KR. Medical comorbidity in late-life depression. International Journal of Geriatric Psychiatry. 2004;19:935–943. [PubMed]
  • Weissman MM, Bruce ML, Leaf PJ, Florio LP, Holzer C. Affective disorders. In: Robins LN, Regier DA, editors. Psychiatric Disorders in America: The Epidemiologic Catchment Area Study. Free Press; New York: 1991. pp. 53–801.
  • Wolter KM. Introduction to Variance Estimation. Springer-Verlag; New York, NY: 1985.
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