Remission in major depression: results from a geriatric primary care population.

Abstract

While a recent task force report recommended that remission from major depression be defined according to DSM criteria, most previous work has used depressive symptom rating scales. The current study sought to identify baseline factors associated with treatment outcome in major depression, diagnosed according to DSM-IV criteria.
Data from the Primary Care Research in Substance Abuse and Mental Health for the Elderly (PRISM-E) study were utilized. This analysis focused on 792 geriatric primary care patients with major depression at baseline, which was randomized to services by a mental health professional in primary care or specialty settings. Major depression was diagnosed according to DSM-IV criteria based on a structured interview at baseline and 6 months. The primary outcome was the absence of any DSM-IV depressive disorder at six-month follow-up. Association with baseline demographic characteristics, comorbid anxiety disorder, 'at risk' drinking, number of co-occurring medical conditions, and depressive symptom severity was examined using multiple logistic regression modeling.
Remission occurred in 228 (29%) patients with completed follow-up assessments, while 564 (71%) did not remit. Factors which increased the odds of non-remission included comorbid anxiety (OR=1.60, 95% CI 1.11-2.31), female sex (OR=1.49, 95% CI 1.04-2.15), general medical comorbidity (OR=1.15, 95% CI 1.07-1.24), and increased baseline depressive symptom severity (OR=1.04, 95% CI 1.03-1.06).
The findings underscore the importance of using DSM criteria to define remission from major depression, and suggest that concurrent measurement of depression severity, comorbid anxiety, and medical comorbidity are important in identifying patients requiring targeted interventions to optimize remission from major depression.

Full text

Page 1

Remission in major depression: results from a geriatric
primary care population
Armin R. Azar1,2,3, Mohit P. Chopra2,4,3, Lydia Y. Cho2,3,4, Eugenie Coakley5and James L. Rudolph1,2,3
1Division of Aging, Brigham and Women’s Hospital, Boston, MA, USA
2VA Boston Healthcare System, Boston, MA, USA
3Harvard Medical School, Boston, MA, USA
4Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
5John Snow, Inc., Boston, MA, USA
Correspondence to: Dr J. L. Rudolph, E-mail: jrudolph@partners.org
Objectives: While a recent task force report recommended that remission from major depression be
defined according to DSM criteria, most previous work has used depressive symptom rating scales. The
current study sought to identify baseline factors associated with treatment outcome in major depression,
diagnosed according to DSM-IV criteria.
Methods: Data from the Primary Care Research in Substance Abuse and Mental Health for the Elderly
(PRISM-E) study were utilized. This analysis focused on 792 geriatric primary care patients with major
depression at baseline, which was randomized to services by a mental health professional in primary care
orspecialtysettings.MajordepressionwasdiagnosedaccordingtoDSM-IVcriteriabasedonastructured
interview at baseline and 6 months. The primary outcome was the absence of any DSM-IV depressive
disorder at six-month follow-up. Association with baseline demographic characteristics, comorbid
anxietydisorder,‘atrisk’drinking,numberofco-occurringmedicalconditions,anddepressivesymptom
severity was examined using multiple logistic regression modeling.
Results: Remission occurred in 228 (29%) patients with completed follow-up assessments, while 564
(71%) did not remit. Factors which increased the odds of non-remission included comorbid anxiety
(OR¼1.60, 95% CI 1.11–2.31), female sex (OR¼1.49, 95% CI 1.04–2.15), general medical comorbidity
(OR¼1.15, 95% CI 1.07–1.24), and increased baseline depressive symptom severity (OR¼1.04, 95% CI
1.03–1.06).
Conclusions: The findings underscore the importance of using DSM criteria to define remission from
major depression, and suggest that concurrent measurement of depression severity, comorbid anxiety,
and medical comorbidity are important in identifying patients requiring targeted interventions to
optimize remission from major depression. Copyright # 2010 John Wiley & Sons, Ltd.
Key words: depression; remission; primary care; aged
History: Received 24 September 2009; Accepted 5 January 2010; Published online 14 April 2010 in Wiley Online Library
(wileyonlinelibrary.com).
DOI: 10.1002/gps.2485
Introduction
Depression is a common mental health problem in
older adults, and is linked with increased medical
comorbidity, disability, and mortality, underscoring
the importance of complete treatment of depression to
remission (Schulz et al., 2000; Bruce, 2001; Blazer,
2003). Improvement in major depression that results
in residual symptoms, often referred to as response,
has been shown to be associated with earlier relapse
and recurrence of depression, continued functional
disability, and mortality in the elderly (Ganguli et al.,
2002; Steffens et al., 2003; Lenze et al., 2005). Because
most patients are treated for depression in the primary
care setting, identifying predictors of poor outcome
may provide clinicians with valuable information
RESEARCH ARTICLE
Copyright # 2010 John Wiley & Sons, Ltd.
Int J Geriatr Psychiatry 2011; 26: 48–55.

Page 2

on those patients requiring close follow-up and
monitoring (Schulberg et al., 1998).
Some of the factors shown to be associated with
non-response and non-remission in previous treat-
ment studies include comorbid anxiety symptoms
or disorder and medical comorbidity (Oslin et al.,
2002; Alexopoulos et al., 2005; Steffens and McQuoid,
2005; Reynolds et al., 2006; Andreescu et al., 2007).
However, most of these studies examined response
(Oslin et al., 2002; Lenze et al., 2003; Alexopoulos
et al., 2005; Steffens and McQuoid, 2005; Reynolds
et al., 2006; Andreescu et al., 2007), as defined by
an improvement in depressive symptoms using a
threshold on a depression rating scale, rather than
remission (i.e., the absence of any DSM depressive
disorder).
A recent report by the American College of
Neuropsychopharmacology (ACNP) Task Force on
response and remission in major depression high-
lighted two points: (1) the target outcome for
treatment of major depression isremission, as opposed
toresponse;and(2)thedefinitionsofremissionshould
include all nine of the DSM-IV TR (American
Psychiatric Association, 2000) core symptoms used
to diagnose a major depressive episode (Rush et al.,
2006). Further, the Task Force noted concerns in
utilizing rating scales where certain criterion symp-
toms may not be included (e.g., concentration/
decision making). Given these concerns and the noted
clinical differences in remission versus response,
the present study sought to examine factors related
to treatment outcome of older adults with major
depression in primary care, where the absence or
presence of a depressive disorder was based on DSM
criteria using a structured diagnostic psychiatric
interview.
The Primary Care Research in Substance Abuse and
Mental Health for the Elderly (PRISM-E) study was a
randomized clinical trial that compared enhanced
referral mental health services to an integrated mental
health and substance abuse model of care for older
primary care patients with depression, anxiety, or at-
risk alcohol use (Levkoff et al., 2004). The primary
outcome of the study found that older patients,
including those with a depressive disorder, were more
likely to use collaborative mental health treatment
within a primary care clinic (Bartels et al., 2004). That
said, remission rates in all depressive disorders,
including major depression, did not differ across
treatment models (Krahn et al., 2006). However,
patients with major depression enrolled in the
enhanced referral model arm did demonstrate a
greater reduction in depression severity as measured
by a depression rating scale. Importantly, the prior
analyses used reduction in symptom severity, rather
than DSM criteria, as the primary outcome. Using
the PRISM-E, our study sought to identify factors
associated with major depression outcome (according
to DSM-IV diagnostic criteria) at 6 months. We
hypothesized that the presence of comorbid anxiety
disorders, increasing general medical illness burden as
wellasdepressivesymptomseveritywouldberelatedto
non-remission in this study-sample. In examining
demographic and clinical factors related to outcome in
major depression, we hope to identity those patients at
greatest risk for a chronic course.
Methods
A secondary analysis of patients with major depression
from the PRISM-E study was conducted. Details
regarding this sample, data collection, and consent
procedures are described elsewhere (Bartels et al.,
2004; Levkoff et al., 2004; Krahn et al., 2006). Briefly,
primarycareclinicsfromwhichpatientswererecruited
included five VA Medical Centers, three community
health centers, and two hospital networks. Patients
were randomly assigned to one of two models of
care. In the integrated model, mental health services,
substance abuse services, or both were provided in a
primarycareclinicbyamentalhealthprofessional.The
enhanced specialty referral model provided mental
health services, substance abuse services, or both in a
specialty setting that was physically separate from the
primary care clinic and designated as a mental health
or substance abuse clinic. Modalities of treatment
across integrated and enhanced specialty referral sites
predominately included individual therapy and phar-
macotherapy. Exclusion criteria included the presence
of significant cognitive impairment, psychotic dis-
order, or receiving formal treatment for a mental
health disorder at the time of enrollment. All study
participantsprovidedwritten
according to local institutional review board (IRB)
approval.
informed consent
Sample
Baseline data from the overall sample was available
for 2243 primary care patients aged 65 and older
(Figure 1). Inclusion criteria for the current analysis
included: baseline major depression diagnosis and
randomization into one of the two treatment models
upon conclusion of the baseline evaluation. As such,
Copyright # 2010 John Wiley & Sons, Ltd.
Int J Geriatr Psychiatry 2011; 26: 48–55.
Remission in late-life major depression
49

Page 3

1195participantswithoutamajordepressiondiagnosis
were excluded. Of the remaining participants, those
not randomized at baseline (n¼93) were also
excluded. Thirty participants were excluded due to
missing data (i.e., incomplete interview), as were those
with a diagnosis of hypomania (n¼6) and psychotic
syndrome (n¼1). Further, participants already receiv-
ing mental health or substance abuse treatment at the
time of the baseline evaluation (n¼18) were also
excluded. In an effort to increase generalizability to
older primary care patients, those with a comorbid
anxiety disorder (n¼309) or at-risk alcohol use
(n¼70) were not excluded from the final sample.
The current study’s final sample consisted of 900
elderly primary care patients with baseline diagnosis of
major depressive disorder and randomization into one
of the study interventions (i.e., integrated, enhanced
specialty referral), of which 108 (12%) were lost to all
follow-up. Compared with the final analytic sample
(n¼792), those lost to follow-up were more often
male and African-American (results not shown).
Measures
Psychiatric disorder. The Mini-International Neurop-
sychiatric Interview (MINI; Sheehan et al., 1998), a
structured diagnostic psychiatry interview for DSM-IV
and ICD-10 psychiatric disorders, was used to establish
the following diagnoses of interest: major depression,
dysthymia, minor depression, depression NOS, panic
disorder, generalized anxiety disorder (GAD), anxiety
NOS,and‘atrisk’drinking.Ofparticularnote,theMINI
assessedallninecriteriasymptomsforamajordepressive
episode according to DSM-IV diagnostic criteria. The
MINI further follows DSM-IV diagnostic criteria for
major depression by evaluating symptoms in the past
2 weeks. For dysthymia, symptoms occurring for the
last 2 years were evaluated according to DSM criteria.
Duration of symptoms for depression NOS and minor
depression was at least 2 and 4 weeks, respectively. At-
risk alcohol use was defined as any of the following:
(a) ?14 drinks in the previous week for men and ?12
drinksinthepreviousweekforwomen,(b)?4bingesin
the previous 3 months, in which a binge was defined as
?4 drinks in 1 day, or (c) use of alcohol-targeted
medication and ?7 drinks in the previous week for men
and women. The MINI, administered in-person at all
assessment points, has been shown to be a valid and
reliable measure of psychiatric disorders (Sheehan et al.,
1997). Based on the administration protocol of the
MINI, there was no attempt to establish the primacy of
either major depressive disorder or another psychiatric
disorder at enrollment.
Depression severity. The Center for Epidemiological
Studies Depression Scale (CES-D; Radloff, 1977) is a
20-item self-report measure of depressive symptoms in
thepastweek.Withapossiblerangeof0–60,higherscores
are suggestive of greater depressive symptom severity.
Generalmedicalillness. Assessment of medicalcomor-
bidity was based on self-report of 20 medical
conditions (e.g., diabetes, arthritis/rheumatism, liver
trouble/jaundice, and cancer). The presence or absence
of the medical conditions was summed to form a
composite score, with higher scores indicating greater
medical burden. Previous research has found good
agreement between self-reports and medical records
for select medical conditions (Bush et al., 1989).
Outcome
Theprimaryoutcomeforthisanalysiswasremissionof
major depression at the 6-month PRISM-E follow-up,
Enrolled and consented
(N=2,243)
Excluded (N=1,195)
No depressive syndrome
diagnosis (N=558)
No major depression
diagnosis (N=637)
Patients with baseline MDD
(N=1,048)
Excluded (N=148)
Not randomized (N=93)
Missing data (N=30)
Hypomanic or psychotic
syndrome (N=7)
Currently receiving mental health
or substance abuse treatment
(N=18)
Baseline Sample
(N=900)
Excluded (N=108)
Lost to all follow-up
Final Analytic Sample
(N=792)
Figure 1 Study participant progression.
Copyright # 2010 John Wiley & Sons, Ltd.
Int J Geriatr Psychiatry 2011; 26: 48–55.
50 A. R. Azar et al.

Page 4

as assessed by the MINI. In particular, remission was
defined as the absence of a DSM-IV depression
diagnosis (i.e., major depression, dysthymia, minor
depression or depression NOS) at the 6-month follow-
up.Assuch, non-remission wasdefinedasthepresence
of any DSM-IV depression diagnosis at the final
assessment point. For patients who did not complete
the 6-month assessment, the 3-month follow-up was
used (n¼72). These 72 patients, when compared to
those with 6-month data, were more often men and
endorsed lower, but still clinically significant, levels
of baseline depressive symptoms (CES-D: 3 month
completers¼25.57?9.95, 6 month completers¼
28.60?9.89; p¼0.014).
Statistical analysis
Bivariable
clinical characteristics of participants attaining remis-
sion at follow-up to those who did not remiss using
x2tests for dichotomous variables and t-tests for
continuous measures. Demographic variables of
interest included: age, gender, race/ethnicity, and
education. Clinical characteristics included: anxiety
disorder diagnosis, ‘at risk’ drinking, depressive
symptom severity, and general medical illness burden
at baseline.
Variables on which the two groups differed signifi-
cantly in bivariable analyses (p<0.05) were selected for
entry into a multivariable logistic regression model
analyses compareddemographic and
using a likelihood-ratio (LR) backward elimination
approach. This model allowed for the removal of
variables failing to make significant independent
contributions to the prediction model (exclusion
criteria p¼0.15). Despite PRISM-E not demonstrating
a difference in rates of remission between the treatment
arms, the randomization group (i.e., integrated,
enhanced referral) was included in multivariable
modeling as subgroup analysis found that patients with
major depression randomized into enhanced referral
care had improved depressive symptom severity (Krahn
et al., 2006). To improve clinical utility of the findings,
we also examined percent remission by baseline
depression severity quartile.
Results
Of the 792 patients with major depression included in
the final analytic sample, 29% (n¼228) attained
remission (i.e., progressed from major depression to
no depression diagnosis), while 71% (n¼564) were
non-remitters (i.e., maintained a DSM-IV depression
diagnosis).
Baseline demographic and clinical characteristics for
those with and without DSM-IV depression at follow-
up are presented in Table 1. Sex, education, comorbid
anxietydisorder,baselinedepressivesymptomseverity,
and general medical comorbidity were found to
significantly differ between those who had remitted
at follow-up and those who maintained a depression
Table 1 Baseline demographic and clinical characteristics stratified by depression statusa
CharacteristicRemission (n¼228) Non-remission (n¼564)p-value Included in multi-variable
selection model
Age, mean (SD)
Male, n (%)
Race, n (%)
Caucasian
African American
Hispanic
Other
Education, n (%)
<12 years
?12 years
CES-D, mean (SD)b
Anxiety diagnosis, n (% yes)
‘At risk’ drinking, n (% yes)
GMC, mean (SD)c
Model of Care, n (%)
Integrated
Referral
73.62 (6.19)
169 (74%)
73.64 (6.23)
366 (65%)
0.966
0.012X
86 (38%)
63 (28%)
60 (26%)
18 (8%)
209 (37%)
134 (24%)
144 (26%)
75 (13%)
0.166
X
113 (50%)
113 (50%)
24.90 (10.00)
56 (25%)
15 (7%)
3.58 (2.10)
327 (58%)
234 (42%)
29.69 (9.58)
221 (39%)
48 (9%)
4.32 (2.32)
0.034
<0.001
<0.001
0.360
<0.001
X
X
X
X
110 (48%)
118 (52%)
295 (52%)
269 (48%)
0.301
aThe table does not include 108 patients who were lost to follow-up. These patients were more often male and African-American.
bCenter for Epidemiological Studies Depression Scale (CES-D). Range; 60¼worst.
cGeneral medical comorbidity (GMC). Range; 20¼highest comorbidity.
Copyright # 2010 John Wiley & Sons, Ltd.
Int J Geriatr Psychiatry 2011; 26: 48–55.
Remission in late-life major depression
51

Page 5

diagnosis. ‘At risk’ drinking was not found to
significantly differentiate depression status. Results
did not significantly change when study participants
with ‘at risk’ drinking (n¼70) were excluded from all
analyses (results not shown). Compared to Caucasian
patients, African-American
(p¼0.950), and Other (i.e., Native American, Asian,
and Other) patients (p¼0.063) were no more likely to
remiss at the study’s conclusion.
Logistic regression analysis, presented in Table 2,
found the following to be significant predictors of
non-remission: baseline anxiety disorder diagnosis,
sex, general medical comorbidity, and depressive
symptom severity at baseline: final model, X2¼62.83,
df¼4, p<0.001, overall correct classification¼71.6%.
The presence of a comorbid anxiety disorder increased
the odds of non-remission by a factor of 1.6 (95% CI
1.11–2.31, p¼0.012). Similarly, women had 1.5
greater odds of non-remission than men in this
study-sample (95% CI 1.04–2.15, p¼0.032). General
medical comorbidity predicted non-remission, such
that the presence of each additional medical condition
was associated with increased odds of non-remission
by a factor of 1.2 (95% CI 1.07–1.24, p<0.001). Odds
of non-remission increased by a factor of 1.0 (95% CI
1.03–1.06, p<0.001) for each point increase on the
CES-D, the self-report measure of depressive symptom
severity. Underscoring the significant relationship
between depressive symptom severity and outcome
in major depression, CES-D quartiles scores at
baseline were calculated and compared on rates of
remission. As presented in Figure 2, rates of remission
were found to decrease linearly as a function of
increasing baseline CES-D quartile scores (X2¼36.54,
df¼3, p<0.001). Model of treatment (i.e., integrated,
specialty referral) and education were not found to
independently predict outcome in the final logistic
regression model.
(p¼0.504), Hispanic
The mean (? SD) follow-up CES-D score for those
who reached remission was 7.40 (?4.41), which was
significantly lower than the meanscore (24.73?10.39)
of the group that did not achieve remission at follow-
up (t (2, 772)¼?23.54, p<0.001).
Given the final analyses included 72 study partici-
pants with only 3-month data and no 6-month
assessment, bivariable and multivariable analyses were
re-run excluding those patients with incomplete data
at 6 months. In the bivariable analyses, results were
unchanged with the exception of education, which
no longer reached statistical significance (p¼0.068).
Further, results remained unchanged in the final
logistic regression model, where baseline anxiety
disorder diagnosis, sex, general medical comorbidity,
and depressive symptom severity at baseline continued
to predict 6-month depression status (results not
shown). Finally, the two groups (3 and 6 month
Table 2 Multivariable logistic regression predicting non-remission in major depression (N¼772)
VariableParameter estimateStandard errorOdds ratio (95% CI)p-value
Baseline Model (Model 1)
Sex
Education
Anxiety disorder
General medical comorbidity
Depressive symptom severity
Treatment randomization
Final Model (Model 3)
Sex
Anxiety disorder
General medical comorbidity
Depressive symptom severity
0.36
?0.23
0.49
0.14
0.04
?0.20
0.40
0.47
0.14
0.04
0.19
0.17
0.19
0.04
0.01
0.17
1.43 (0.99–2.07)
0.79 (0.57–1.11)
1.63 (1.13–2.37)
1.15 (1.06–1.24)
1.04 (1.03–1.06)
0.82 (0.59–1.14)
0.057
0.173
0.010
<0.001
<0.001
0.238
0.19
0.19
0.04
0.01
1.49 (1.04–2.15)
1.60 (1.11–2.31)
1.15 (1.07–1.24)
1.04 (1.03–1.06)
0.032
0.012
<0.001
<0.001
Figure 2 Percent remitted by baseline CES-D quartile scores.
Copyright # 2010 John Wiley & Sons, Ltd.
Int J Geriatr Psychiatry 2011; 26: 48–55.
52A. R. Azar et al.

Page 6

completers) were not found to significantly differ in
rates of remission (p¼0.150).
Discussion
The current project sought to identify baseline factors
related to major depression outcome in older primary
care patients enrolled in mental health treatment.
Results indicated that baseline anxiety disorder, female
sex, increased medical comorbidity, and baseline
depressive symptom severity independently predicted
non-remission at 6 months. Clinically, these findings
aresignificantastheypointtowardtheaddedchallenge
in treating depression within primary care when
accompanied by psychiatric and medical comorbidity.
Giventhenegativehealthconsequencesassociatedwith
depression,failuretoattainremissionfollowingmental
healthtreatmentplacesthesepatientsatanevengreater
risk for additional morbidity and possible mortality.
This study expands the previous literature by
defining remission in major depression as the absence
of DSM-IV diagnostic criteria for any depressive
disorder. Classifying remission according to criterion
symptoms is clinically significant as even residual
symptoms of depression have been linked with shorter
time to relapse and impaired functioning, even in the
elderly (Chopra et al., 2005; Cui et al., 2008). This
criterion is not only more stringent, but serves as a
good goal for treatment. However, our finding that
depression severity was an independent predictor of
remission suggests that a baseline measure of
depression severity is beneficial in determining the
possibility of remission. Thus, while only using DSM-
IV criteria for the diagnosis of major depression in
older primary care patients may describe who has
depression, it will likely miss the opportunity to
identify patients who will not remiss and require
additional care due to a more severe depression.
Additionally, our finding linking comorbid anxiety
and non-remission in major depression is consistent
with some (Steffens and McQuoid, 2005), but not all
previous research (Lenze et al., 2003). In a naturalistic
studyofolderadultsreceivingtreatmentfordepression
and comorbid symptoms of generalized anxiety
disorder, Steffens and McQuoid (2005) found the
presence of generalized anxiety disorder (GAD)
symptoms to be associated with a longer time-to-
remission, when compared with depression alone.
Similarly, in a separate study, older adults receiving
pharmacotherapy and interpersonal psychotherapy for
the treatment of major depression were found to have
slower rates of response when also presenting with
elevated levels of anxiety (Andreescu et al., 2007).
Participants with comorbid anxiety were also more
likely to have a recurrence of depression within 2 years.
While the aforementioned studies found comorbid
anxiety to be linked with poorer outcomes in older
adultswithdepression,Lenzeetal.(2003)failedtofind
differences in rates of response in older adults with
and without comorbid anxiety receiving treatment for
depression. While these mixed findings point toward
the need for additional research in comorbid anxiety
and depression, particularly in later life, our results
suggestthatthepresenceofanxietyneedstobeassessed
at baseline in patients who are being initiated on
treatment for major depression.
These findings need to be highlighted in the
demographic and ethnic diversity of the enrolled
population. While 63% of the study sample was non-
Caucasian, we found no differences in remission from
DSM-IV depression with respect to ethnicity. This is
consistent witha priorPRISM-Eprojectthat examined
differences in service use and outcome, as defined by
CES-D scores (Area ´n et al., 2008). Our finding is novel
and extends this previous work by examining race
differences in DSM-IV major depression outcome
within a diverse sample. The current project’s finding
that women were less likely to achieve remission than
men is consistent with some studies (Thase et al.,
2005), though others have failed to identify gender
differences in treatment response (Quitkin et al.,
2002). Our results are surprising given that previous
research has shown older men to utilize less mental
health care than women in the treatment of depression
(Unu ¨tzer et al., 2003). That said, the finding of female
sex as a predictor of non-remission needs to be
interpretedcautiouslyasfemalesexandeducation(i.e.,
less than 12 years), which was found to differentiate
remission from non-remission in bivariable analyses,
were highly correlated.
This study has several strengths. First, the study
utilized a large, ethnically and geographically diverse
sample of older primary care patients in the PRISM-E
dataset, thereby making the results more generalizable.
Second, the use of the MINI provided a standardized
structured diagnostic psychiatricinterview to assessfor
major depression and additional psychiatric disorders
including anxiety and other depressive disorders.
Further, in using the MINI to define outcome rather
than a percentage decline on the CES-D, we adhered
to DSM criteria while also extending the work of
previous PRISM-E projects. Finally, while enrollment
was restricted to those not in treatment at baseline,
only 18 of 1048 (1.7%) patients with major depression
at enrollment were receiving current mental health or
Copyright # 2010 John Wiley & Sons, Ltd.
Int J Geriatr Psychiatry 2011; 26: 48–55.
Remission in late-life major depression
53

Page 7

substance abuse treatment, thereby precluding them
from study participation. The PRISM-E, however, did
successfully target and improve access to treatment for
a vulnerable group that historically did not receive
mental health care (i.e., older adults). As a result,
theinitialtreatment(ofeitherarm)waslikelytoimprove
the baseline mood disorder in this study sample.
The findings should be interpreted in light of the
study’s limitations, which stem primarily from its
design of re-examining data from a project that has
already been conducted. First, given that remission
wasdefinedaccordingtodepressionstatusatonepoint
only (6 months), it is possible that patients reaching
remission prior to 6 months may have relapsed by the
assessment period, thereby not being considered in
remission for purposes of the current study. Similarly,
some patients categorized as non-remitters may vary
from other non-remitters based on a cyclical pattern of
depression status. Further, the study was unable to
investigate the role of cognition, particularly executive
function, on depression outcome, as the PRISM-E
screened out those with cognitive impairment. This is
particularly important given that executive dysfunc-
tion has been previously linked with depression,
medical comorbidity, and treatment response in
depression (Alexopoulos et al., 2000; Lockwood
et al., 2002). Another limitation is the use of self-
report to quantify general medical illness burden,
though previous research has demonstrated good
agreement between self-report and medical record
review of general medical illness (Bush et al., 1989).
Because those who did not follow up were more likely
to be male and African-American, our final cohort
may not be representative of this subgroup. Further, it
may be that those lost to follow up were less sick and
therefore less likely to follow through with treatment.
Finally, it is entirely possible that other unstudied
factors (e.g., social support, socioeconomic status,
access to health care) may influence the association
with non-remission in major depression than the
variables examined in the current project.
Conclusion
To conclude, the current study identified demographic
and clinical characteristics that predict change in
depression status at 6 months in older primary
care patients enrolled in mental health treatment. The
findings offer additional support for the close monitor-
ing and follow-up of depressed older adults with
comorbid psychiatric and medical conditions. Future
studies could explore treatment approaches specifically
targetedatpopulations identifiedthroughresearchsuch
as the current investigation, who are at special risk for
limited response to treatment for depression.
Conflict of interest
The authors have no potential conflicts of interests to
disclose.
Acknowledgements
Dr Azar was supported by the Kaplen Fellowship on
Depression through the Department of Psychiatry,
HarvardMedicalSchool.DrRudolphissupportedbya
VA Rehabilitation Research and Development Career
DevelopmentAward.Additionalsupportwasprovided
by NIH grant 5-R03-AG029861-02. This paper is the
result of work supported with resources and the use of
facilities at the VA Boston Healthcare System. The
authors retained full independence in the conduct of
this research.
PRISM-E is a collaborative research study funded by
the Substance Abuse and Mental Health Services
Administration (SAMHSA), including its three cen-
ters: Center for Mental Health Services (CMHS),
Center for Substance Abuse Treatment (CSAT), and
the Center for Substance Abuse and Prevention
(CSAP). The Department of Veterans Affairs (VA),
the Health Resources and Services Administration
(HRSA), and the Centers for Medicare and Medicaid
Services (CMS) provided additional support and
funding (PI, PRISM-E Data Coordinating Center,
Sue E. Levkoff, UD1SM52229-04).
References
Alexopoulos GS, Katz IR, Bruce ML, et al. 2005. Remission in depressed geriatric
primarycarepatients:areportfromthePROSPECTstudy.AmJPsychiatr162:718–
724.
Alexopoulos GS, Meyers BS, Young RC, et al. 2000. Executive dysfunction and long-
term outcomes of geriatric depression. Arch Gen Psychiatr 57: 285–290.
American Psychiatric Association. 2000. Diagnostic and Statistical Manual of Mental
Disorders, 4th edn. (DSM-IV-TR) American Psychiatric Press: Washington.
Key points
? Seventy-one percent of older primary care
patients with DSM-IV major depression did
not remit at 6 months.
? Non-remission in late-life major depression was
associated with psychiatric and medical factors.
Copyright # 2010 John Wiley & Sons, Ltd.
Int J Geriatr Psychiatry 2011; 26: 48–55.
54 A. R. Azar et al.

Page 8

Andreescu C, Lenze EJ, Dew MA, et al. 2007. Effect of comorbid anxiety on treatment
response and relapse riskin late-life depression:controlledstudy. Br JPsychiatr190:
344–349.
Area ´n PA, Ayalon L, Jin C, et al. 2008. Integrated specialty mental health care among
olderminorities improvesaccessbutnotoutcomes: resultsofthePRISMe study.Int
J Geriatr Psychiatr 23: 1086–1092.
Bartels SJ, Coakley EH, Zubritsky C, et al. 2004. Improving access to geriatric mental
health services: a randomized trial comparing treatment engagement with inte-
grated versus enhanced referral care for depression, anxiety, and at-risk alcohol use.
Am J Psychiatr 161: 1455–1462.
Blazer DG. 2003. Depression in late-life: review and commentary. J Gerontol A Biol Sci
Med Sci 58: 249–265.
Bruce ML. 2001. Depression and disability in late life: directions for future research.
Am J Geriatr Psychiatr 9: 102–112.
Bush TL, Miller SR, Golden AL, et al. 1989. Self-report and medical record report
agreement of selected medical conditions in the elderly. Am J Public Health 79:
1554–1556.
ChopraMP,Zubritsky C,KnottK,etal.2005.Importanceofsubsyndromalsymptoms
of depression in elderly patients. Am J Geriatr Psychiatr 13: 597–606.
Cui X, Lyness JM, Tang W, et al. 2008. Outcomes and predictors of late-life depression
trajectories in older primary care patients. Am J Geriatr Psychiatr 16: 406–415.
Ganguli M, Dodge HH, Mulsant BH. 2002. Rates and predictors of mortality in an
aging, rural, community-based cohort: the role of depression. Arch Gen Psychiatr
59: 1046–1052.
Krahn DD, Bartels SJ, Coakley E, et al. 2006. PRISM-E: comparison of integrated care and
enhanced specialty referral models in depression outcomes. Psychiatr Serv 57: 946–953.
Lenze EJ, Mulsant BH, Dew MA, et al. 2003. Good treatment outcomes in late-life
depression with comorbid anxiety. J Affect Disord 77: 247–254.
Lenze EJ, Schulz R, Martire LM, et al. 2005. The course of functional decline in older
people with persistently elevated depressive symptoms: longitudinal findings from
the Cardiovascular Health Study. J Am Geriatr Soc 53: 569–575.
Levkoff SE, Chen H, Coakley E, et al. 2004. Design and sample characteristics of the
PRISM-E multisite randomized trial to improve behavioral health care for the
elderly. J Aging Health 16: 3–27.
Lockwood KA, Alexopoulos GS, van Gorp WG. 2002. Executive dysfunction in
geriatric depression. Am J Psychiatr 159: 1119–1126.
Oslin DW, Datto CJ, Kallan MJ, et al. 2002. Association between medical comorbidity
and treatment outcomes in late-life depression. J Am Geriatr Soc 50: 823–828.
Quitkin FM, Stewart JW, McGrath PJ, et al. 2002. Are there differences between
women’s and men’s antidepressant responses? Am J Psychiatr 159: 1848–1854.
Radloff LS. 1977. The CES-D scale: a self-report depression scale for research in the
general population. Appl Psychol Meas 1: 385–401.
Reynolds CF, Dew MA, Pollock BG, et al. 2006. Maintenance treatment of major
depression in old age. N Engl J Med 354: 1130–1138.
Rush AJ, Kraemer HC, Sackeim HA, et al. 2006. Report by the ACNP Task Force on
response and remission in major depressive disorder. Neuropsychopharmacology 31:
1841–1853.
Schulberg HC, Katon W, Simon GE, et al. 1998. Treating major depression in
primary care practice: an update of the Agency for Health Care Policy and
Research Practice Guidelines. Arch Gen Psychiatr 55: 1121–1127.
Schulz R, Beach SR, Ives DG, et al. 2000. Association between depression and
mortality in older adults: the Cardiovascular Health Study. Arch Intern Med
160: 1761–1768.
Sheehan DV, Lecrubier Y, Harnett-Sheehan K, et al. 1998. The Mini International
Neuropsychiatric Interview (M.I.N.I.): the development and validation of a struc-
tured diagnostic psychiatric interview. J Clin Psychiat 59: 22–33.
Sheehan DV, Lecrubier Y, Sheehan KH, et al. 1997. The validity of the Mini
International Neuropsychiatric Interview (MINI) according to the SCID-P and
its reliability. Eur Psychiatr 12: 232–241.
Steffens DC, McQuoid DR. 2005. Impact of symptoms of generalized anxiety disorder
on the course of late-life depression. Am J Geriatr Psychiar 13: 40–47.
Steffens DC, McQuoid DR, Krishnan KR. 2003. Partial response as a predictor of
outcome in geriatric depression. Am J Geriatr Psychiatr 11: 340–348.
Thase ME, Entsuah R, Cantillon M, et al. 2005. Relative antidepressant efficacy of
venlafaxine and SSRIs: sex-age interactions. J Womens Health 14: 609–616.
Unu ¨tzer J, Wayne K, Callahan CM, et al. 2003. Depression treatment in a
sample of 1,801 depressed older adults in primary care. J Am Geriatr Soc 51:
505–514.
Copyright # 2010 John Wiley & Sons, Ltd.
Int J Geriatr Psychiatry 2011; 26: 48–55.
Remission in late-life major depression
55