Association between depression and hospital outcomes among older men

Canadian Medical Association Journal (Impact Factor: 5.96). 12/2012; 185(2). DOI: 10.1503/cmaj.121171
Source: PubMed
ABSTRACT
Background:
Studies that have investigated the relation between depression and the type, nature, extent and outcome of general hospital admissions have been limited by their retrospective designs and focus on specific clinical populations. We explored this relation prospectively in a large, community-based sample of older men.

Methods:
A cohort of 5411 men aged 69 years and older enrolled in the Health in Men Study was assessed at baseline for depressive symptoms, defined as a score of 7 or higher on the 15-item Geriatric Depression Scale. Participants were followed for 2 years for occurrence and number of hospital admissions, type of hospital admission, length of hospital stay and inpatient death as recorded in the Western Australian Data Linkage System.

Results:
Of 339 men with depressive symptoms, 152 (44.8%) had at least 1 emergency hospital admission, compared with 1164 of 5072 (22.9%) nondepressed men (p < 0.001). In multivariate analyses, the presence of depressive symptoms was a significant independent predictor of hospital admission (hazard ratio 1.67, 95% confidence interval [CI] 1.38-2.01), number of hospital admissions (incidence rate ratio [IRR] 1.22, 95% CI 1.07-1.39) and total length of hospital stay (IRR 1.65, 95% CI 1.36-2.01).

Interpretation:
Participants with depressive symptoms were at higher risk of hospital admission for nonpsychiatric conditions and were more likely to have longer hospital stays and worse hospital outcomes, compared with nondepressed participants. These results highlight the potential to target this high-risk group to reduce the burden of health care costs in an aging population.

Full-text

Available from: Matthew Prina
O
lder people are the most frequent users of
health services, and the progressive aging
of the worlds population may lead to a
saturation of available services. Therefore, we must
nd ways to reduce preventable admissions to hos-
pital and uncover the factors associated with poten-
tially preventable use of health services. An associ-
ation between depression and hospital admission
for nonpsychiatric conditions has been postulated,
although the data have been limited to specic clin-
ical populations and the interpretation of the results
hampered by the retrospective study design and the
use of self-reported outcomes.
1–8
Consequently,
these ndings cannot be easily generalized or used
to develop data-driven interventions.
We addressed this gap in the literature by
using a community-based population survey
with prospective data linkage to measure impor-
tant health-related outcomes. Our main objective
was to investigate whether community-dwelling
older men with depressive symptoms were more
likely than nondepressed men to be admitted to
general hospitals. Our other aims were to deter-
mine whether the long-term clinical outcomes of
these 2 groups differed in relation to the number
of future hospital admissions, length of hospital
stay and inpatient deaths.
Methods
Patient sample
We identied and selected participants from a
community-derived sample of 5585 men living in
Perth, Western Australia, who collectively com-
pose the Health in Men Study cohort. The Health
in Men Study is a prospective, follow-up study
involving men aged 69 years and older who partic-
ipated in an earlier trial of screening for abdominal
aortic aneurysm. The full details of the cohort,
including assessment procedures and enrolment
are available elsewhere.
9
In brief, 19 352 men aged
65–83 years were randomly selected between Apr.
1, 1996, and Jan. 31, 1999, from the electoral roll
(enrolment is mandatory for Australian citizens)
and invited to participate in the study. A total of
12 203 men completed the full questionnaire,
which covered aspects of their lifestyle and med-
ical history. The surviving men were invited to par-
Association between depression and hospital outcomes
among older men
A. Matthew Prina MPhil, Martijn Huisman PhD, Bu B. Yeap PhD, Graeme J. Hankey MD, Leon Flicker PhD,
Carol Brayne PhD, Osvaldo P. Almeida PhD
Competing interests:
Graeme J. Hankey has
received funds from
Boehringer Ingelheim for
work on a Pradaxa advisory
board and from Bayer for
lectures on new
anticoagulants in atrial
brillation. No other
competing interests
declared.
This article has been peer
reviewed.
Correspondence to:
A. Matthew Prina,
amp68@medschl.cam.ac.uk
CMAJ 2013. DOI:10.1503
/cmaj.121171
Research
CMAJ
Background: Studies that have investigated the
relation between depression and the type,
nature, extent and outcome of general hospital
admissions have been limited by their retrospec-
tive designs and focus on specic clinical popula-
tions. We explored this relation prospectively in
a large, community-based sample of older men.
Methods: A cohort of 5411 men aged 69 years
and older enrolled in the Health in Men Study
was assessed at baseline for depressive symp-
toms, dened as a score of 7 or higher on the
15-item Geriatric Depression Scale. Partici-
pants were followed for 2 years for occur-
rence and number of hospital admissions,
type of hospital admission, length of hospital
stay and inpatient death as recorded in the
Western Australian Data Linkage System.
Results: Of 339 men with depressive symp-
toms, 152 (44.8%) had at least 1 emergency
hospital admission, compared with 1164 of
5072 (22.9%) nondepressed men (p < 0.001).
In multivariate analyses, the presence of
depressive symptoms was a signicant inde-
pendent predictor of hospital admission (haz-
ard ratio 1.67, 95% condence interval [CI]
1.382.01), number of hospital admissions
(incidence rate ratio [IRR] 1.22, 95% CI 1.07–
1.39) and total length of hospital stay (IRR
1.65, 95% CI 1.36–2.01).
Interpretation: Participants with depressive
symptoms were at higher risk of hospital
admission for nonpsychiatric conditions and
were more likely to have longer hospital stays
and worse hospital outcomes, compared with
nondepressed participants. These results high-
light the potential to target this high-risk
group to reduce the burden of health care
costs in an aging population.
Abstract
© 2013 Canadian Medical Association or its licensors CMAJ, February 5, 2013, 185(2) 117
Page 1
ticipate in a follow-up study between Oct. 1, 2001,
and Aug. 31, 2004. The 4263 men who attended a
face-to-face follow-up survey and the 1322 who
completed a follow-up questionnaire constitute the
Health in Men Study cohort (n = 5585). Both the
face-to-face survey and the questionnaire included
another health questionnaire and an assessment of
mood status. All the participants who completed
the mood assessment in the Health in Men Study
(5411 [96.9%]) were included in this analysis.
This study was approved by the Department
of Health of Western Australia and the Human
Research Ethics Committee of the University of
Western Australia. All participants provided writ-
ten informed consent.
Data sources
We obtained administrative hospital records from
the Western Australian Data Linkage System,
10
which is a complex multiset system for the cre-
ation, storage, update and retrieval of links between
health- and welfare-related data. The system inte-
grates records from the Western Australian cancer,
death and hospital morbidity registers, as well as
the Mental Health Information System. The hospi-
tal morbidity register records all admissions to pri-
vate and public hospitals since 1980, including
codes for multiple medical diagnoses, admission
and hospital type, and length of stay. The propor-
tion of invalid (false positives) and missed links
(negatives) were both estimated to be 0.11%.
10
Measure of depression
We used the 15-item Geriatric Depression Scale
(GDS-15) to assess depressive symptoms. Partic-
ipants who scored a total of 7 or more points
were dened as having clinically signicant
depressive symptoms. The relatively high cut-off
was chosen a priori to ensure high specicity for
the diagnosis of depression.
11
We used previously
published data to group the severity of depressive
symptoms as follows: no depression (GDS-15
total score of 0), questionable depression (score
of 1–4), mild-to-moderate depression (score of
5–9) and severe depression (score of 10–15).
11
Other measures
Education level was subdivided into categories
and measured as the highest level of education
completed: no schooling, primary school, some
secondary school, completed secondary school,
completed university or other postsecondary
degree. To assess social support, we used the
Duke Social Support Index, a validated scale
that measures individuals’ satisfaction with their
network of relationships.
12
We assessed smoking status by asking men
whether they had ever smoked and whether they
were still smoking at the time of assessment.
Finally, we used the Charlson weighted index,
13
a widely used measure, to assess the presence of
substantial medical comorbidity in our sample.
Information about co-occurring medical condi-
tions during the 10 years before assessment for
the Health in Men Study was derived from the
Western Australian Data Linkage System for all
participants. We followed the procedures de -
scribed by Quan and colleagues
14
for the coding
of algorithms, and we used Staggs Stata routine
to calculate Charlson index scores.
Outcomes measures
We investigated hospital admission (0 = not admit-
ted, 1 = admitted), number of hospital admissions,
Research
118 CMAJ, February 5, 2013, 185(2)
Table 1: Baseline characteristics of 5411 men with valid 15-item Geriatric
Depression Scale ratings, by depression status*
Group, no. (%) of participants†
Characteristic
No depression
n = 5072
Depression
n = 339 p value
Age group, yr
< 0.001‡
69–74 1804 (35.6) 85 (25.1)
75–79 2185 (43.1) 150 (44.2)
80–84 873 (17.2) 78 (23.0)
85
208 (4.1) 26 (7.7)
Missing values 0 (0.0) 0 (0.0)
Education
< 0.001‡
None 21 (0.4) 4 (1.2)
Primary 766 (15.1) 83 (24.5)
Some secondary 1915 (37.8) 128 (37.8)
Secondary 1329 (26.2) 71 (20.9)
Postsecondary 1037 (20.5) 53 (15.6)
Missing values 2 (0.0) 0 (0.0)
Smoking
< 0.001‡
Never 1682 (33.2) 69 (20.4)
Past 3132 (61.8) 238 (70.4)
Current 253 (5.0) 31 (9.2)
Missing values 3 (0.1) 1 (0.3)
Duke Social Support Index tertiles
< 0.001‡
Highest 2259 (44.7) 30 (9.0)
Middle 1608 (31.8) 71 (21.3)
Lowest 1181 (23.4) 232 (69.7)
Missing values 22 (0.4) 6 (1.8)
Charlson index (weighted),
mean (95% CI)
1.17
(1.12–1.22)
2.21
(1.94–2.48)
< 0.001
§
Missing values 571 (11.3) 14 (4.1)
Note: CI = confidence interval, GDS-15 = 15-item Geriatric Depression Scale.
*No clinically significant depression = GDS-15 score < 7; clinically significant depression =
GDS-15 score 7.
†Unless stated otherwise.
χ
2
test.
§Student t test.
Page 2
mean total length of stay across hospital admis-
sions, median length of stay, type of admission
(elective v. emergency), overnight admission and
inpatient death. For all analyses, we included only
emergency (not elective) ad missions, because they
are more indicative of acute health problems.
We retrieved participants’ hospital records for
24 months following assessment for the Health
in Men Study. We chose this follow-up period a
priori because we assessed depression only once
and because we wished to generate data that
would be comparable to other studies.
15–17
Statistical analysis
We identied potential confounding variables by
comparing baseline characteristics of partici-
pants with and without depression. We analyzed
24-month outcomes separately for all admissions
and overnight admissions, and we stratied out-
comes by depression status. We reported p val-
ues from χ
2
tests for categorical variables. Stu-
dent t test was used to compare the number of
hospital admissions among men with and with-
out depression. We used Mann–Whitney tests to
compare length of hospital stay.
We reported incidence rate ratios (IRRs) with
95% condence intervals (CIs) after performing
zero-inated negative binomial regressions
18,19
to
account for overdispersed count outcome vari-
ables (mean length of stay, total length of stay,
number of hospital admissions) with excess
zeros. We used the Vuong nonnested test to
assess the t of the models.
20
We plotted adjusted Kaplan–Meier curves,
together with log-ranked test results, to compare
cumulative admission rates of men with and with-
out depression. We included age group, education
level, Duke Social Support Index tertiles, smoking
status and weighted Charlson index in the models
as confounding variables. We estimated hazard
ratios (HRs) by performing Cox regressions after
checking that the proportional hazard assumption
held. Finally, to investigate the association between
depression and high-cost use of health services, we
used Poisson regression analysis to determine
mutually adjusted prevalence ratios with 95% CIs.
Results
A total of 5411 (96.9%) men provided valid
GDS-15 ratings and were included in the analy-
sis. The mean age of participants was 76.8 (stan-
dard deviation 3.7) years, and 339 (6.3%) had a
Research
CMAJ, February 5, 2013, 185(2) 119
Table 2: Hospital outcomes among 5411 men with valid 15-item Geriatric
Depression Scale ratings, by depression status*
Outcome
Group, mean (95% CI)†
p value
No depression
n = 5072
Depression
n = 339
Emergency admission,
no. (%) of participants
< 0.001
No 3908 (77.0) 187 (55.2)
Yes 1164 (22.9) 152 (44.8)
No. of emergency admissions 0.4 (0.4–0.4) 1.0 (0.8–1.2) < 0.001
Total days in hospital 11.9 (10.9–12.9) 21.0 (15.9–26.0) < 0.001
Length of stay, d 0.009
< 2 382 (32.8) 43 (28.3)
3–5 360 (30.9) 33 (21.7)
6–12 281 (24.1) 49 (32.2)
12
141 (12.1) 27 (17.8)
Length of stay, d, median
(IQR)
4.0 (2.0–8.0) 5.8 (2.4–11.0) 0.002
Note: CI = confidence interval, GDS-15 = 15-item Geriatric Depression Scale,
IQR = interquartile range.
*No clinically significant depression = GDS-15 score < 7; clinically significant depression =
GDS-15 score 7.
†Unless stated otherwise.
Table 3: Univariate and multivariate effects of depression on 2-year outcomes
Variable
Mean length of stay,
IRR (95% CI)*
Total length of stay,
IRR (95% CI)
No. of hospital admissions,
IRR (95% CI)
Univariate Multivariate Univariate Multivariate Univariate Multivariate
Depression† 1.32 (1.13–1.53) 1.25 (1.06–1.48) 1.76 (1.47–2.12) 1.65 (1.36–2.01) 1.30 (1.15–1.47) 1.22 (1.071.39)
Age 1.16 (1.10–1.23) 1.27 (1.18–1.36) 1.07 (1.02–1.13)
Education level 0.99 (0.94–1.04) 0.96 (0.91–1.02) 0.96 (0.92–1.01)
Duke Social Support Index
tertiles
0.98 (0.92–1.04) 1.01 (0.94–1.09) 1.01 (0.96–1.07)
Smoking 0.94 (0.84–1.07) 0.82 (0.72–0.94) 0.88 (0.80–0.98)
Charlson index (weighted) 1.02 (0.99–1.04) 1.08 (1.05–1.11) 1.05 (1.03–1.07)
Note: CI = confidence interval, GDS-15 = 15-item Geriatric Depression Scale, IRR = incidence rate ratio.
*Incidence rate ratios gained from zero-inflated negative binomial regressions.
†No clinically significant depression = GDS-15 score < 7; clinically significant depression = GDS-15 score 7.
Page 3
GDS-15 score of 7 or greater. Compared to men
without depression, those with depression were
older, less educated and more likely to be current
smokers, and they had a higher number of
comorbidities (Table 1).
At the end of the study period, there had been a
total of 2426 emergency admissions, most of
which (2170) involved overnight stays; there were
8283 elective admissions. Of the 339 men with
depression, 152 (44.8%) had at least 1 emergency
admission, compared with 1164 (22.9%) of the
5072 men without depression (χ
2
= 82.7, p <
0.001). Men with depression had a twofold
increase in the mean number of hospital admis-
sions, and these lasted on average twice as long as
for men without depression (Table 2). Overnight
admissions were more frequent among men with
depression (depression: 93.2%; no depression:
88.8%; χ
2
= 6.08, p = 0.01) as were inpatient
deaths (depression: 4.1%; no depression: 1.5%;
χ
2
= 19.82, p < 0.001).
Length of stay and number of hospital admis-
sions had very skewed distributions with an
excess number of zeros. Potential confounders
were therefore investigated by running zero-
inated negative binomial regressions (Table 3).
Length of stay was longer and number of hospi-
tal admissions was higher among men with
depressive symptoms compared with those with-
out, even after adjustment. The adjusted IRR was
1.25 (95% CI 1.06–1.48) for mean length of stay,
1.65 (95% CI 1.36–2.01) for total length of stay
and 1.22 (95% CI 1.07–1.39) for number of hos-
pital admissions. Vuong tests were signicant,
conrming that the use of zero-inated models
was indicated.
We investigated probability of hospital admis-
sion in the 2 groups by plotting Kaplan–Meier
curves adjusted for age, education level, smoking
status, Duke Social Support Index tertiles and
comorbidities (Figure 1). We performed Cox
regression analyses after computationally and
graphically conrming the proportional hazards
assumption (Table 4). In the fully adjusted
model, the presence of depressive symptoms
increased the hazard for hospital admission (HR
1.67 [95% CI 1.38–2.01] and inpatient death
(HR 1.81 [95% CI 0.82–4.04]), though this was
not statistically signicant.
Increasing scores in the GDS-15 were also
associated with higher HRs for both hospital
admission and death, with men scoring 10 to 15
points at baseline having almost twofold higher
HRs compared with men who scored 5 to 9
points (mild to moderate depression; Table 5).
A sensitivity analysis using a cut-off score of
5 points on the GDS-15 was also carried out. The
ndings were consistent and were not affected by
this different cut-off (data not shown).
Interpretation
In this study, the presence of clinically signi-
cant symptoms of depression in older men was
associated with increased risk of hospital admis-
sion, higher number of readmissions and longer
use of services. These associations remained sta-
tistically signicant after adjustment for several
confounding variables.
Few studies have investigated the effect of
clinically signicant depressive symptoms on
hospital admission and outcomes in people liv-
ing in the community. A Danish group explored
Research
120 CMAJ, February 5, 2013, 185(2)
0.00
0.25
0.50
0.75
1.00
Probability of hospital admission
0 200 400 600 800
Follow-up, d
0.00
0.25
0.50
0.75
1.00
Probability of hospital admission
0 200 400 600 800
Follow-up, d
Log rank test p < 0.001
Log rank test p < 0.001
Score = 0
Score = 1–4
Score = 5–9
Score = 10–15
Score < 7
Score ≥ 7
A
B
Figure 1: Kaplan–Meier estimates of hospital admissions by (A) depression status
and (B) severity of symptoms. Adjusted for age, education level, smoking status,
Duke Social Support Index tertiles and weighted Charlson index. Severity of symp-
toms: 15-item Geriatric Depression Scale score = 0 (no depression), 1–4 (question-
able depression), 5–9 (mild-to-moderate depression) and 10–15 (severe depression).
Page 4
this association in a general population
21
by fol-
lowing a group of 75-year-old adults over a 5-
year period. The group found a weak association
between depression and subsequent hospital
admission among women, and no association
among men. This result is possibly explained by
the relatively small sample size and the use of a
depression scale not designed for older adults.
Wong and colleagues
22
found a relation
between depression and increased length of hospi-
tal stay and number of admissions in an older
population in southern China, although the magni-
tude of the association was smaller than in our
study. This may be because of a lower prevalence
of depression at baseline and the use of self-report
measures when recording comorbidities. Similar
ndings in diverse populations were described by
von Ammon Cavanaugh and colleagues,
23
who
reported that a diagnosis of major depressive dis-
order and a history of depression independently
predicted inpatient death. Finally, in a similar
study by Prina and colleagues
15
involving an older
Dutch population, longer length of hospital stay
and higher rates of admission and inpatient death
were reported among depressed patients. How-
ever, only length of stay was associated with
depression after adjustment for sociodemographic
variables and comorbidities.
Several potential reasons can be proposed to
explain the higher risk of hospital admission
among older men with depression. Treatment
adherence is known to be poor among patients
with mood disorders.
24
This could result in
patients arriving in hospital at more acute or
severe stages of their illness, potentially increas-
ing length of stay and risk of death during admis-
sion. Our data show a higher number of emer-
gency admissions than elective admissions among
participants with depression, which is consistent
with this hypothesis. Depression is also an inter-
Research
CMAJ, February 5, 2013, 185(2) 121
Table 4: Cox regression analyses with dichotomous depression variable
Variable
Hospital admissions, HR (95% CI) Inpatient death, HR (95% CI)
Univariate Multivariate Univariate Multivariate
Depression* 2.46 (2.08–2.92) 1.67 (1.38–2.01) 3.82 (1.85–7.9) 1.81 (0.82–4.04)
Age 1.34 (1.25–1.43) 2.42 (1.78–3.31)
Education level 0.91 (0.86–0.97) 1.14 (0.87–1.50)
Duke Social Support Index
tertiles
0.97 (0.90–1.04) 0.84 (0.58–1.20)
Smoking 0.76 (0.67–0.86) 0.56 (0.29–1.19)
Charlson index (weighted) 1.17 (1.14–1.20) 1.19 (1.081.25)
Note: CI = confidence interval, GDS-15 = 15-item Geriatric Depression Scale, HR = hazard ratio.
*No clinically significant depression = GDS-15 score < 7; clinically significant depression = GDS-15 score 7.
Table 5: Cox regression analyses with grouped depression variable
Variable
Hospital admissions, HR (95% CI) Inpatient death, HR (95% CI)
Univariate Multivariate Univariate Multivariate
Depression*†
GDS-15 score = 1–4 2.06 (1.83–2.31) 1.70 (1.50–1.92) 3.72 (2.0–6.89) 2.45 (1.26–4.75)
GDS-15 score = 5–9 2.98 (2.45–3.61) 2.08 (1.68–2.58) 6.11 (2.53–14.8) 3.05 (1.15–8.13)
GDS-15 score = 10–15 4.66 (3.236.72) 3.06 (2.10–4.46) 11.00 (2.54–47.7) 4.38 (0.89–21.4)
Age 1.29 (1.21–1.38) 2.28 (1.67–3.12)
Education level 0.93 (0.87–0.98) 1.17 (0.88–1.54)
Duke Social Support Index
tertiles
1.03 (0.96–1.10) 0.93 (0.65–1.33)
Smoking 0.78 (0.69–0.89) 0.60 (0.30–1.22)
Charlson index (weighted) 1.15 (1.13–1.18) 1.16 (1.05–1.29)
Note: CI = confidence interval, GDS-15 = 15-item Geriatric Depression Scale, HR = hazard ratio.
*No clinically significant depression = GDS-15 score < 7; clinically significant depression = GDS-15 score 7.
†Reference GDS-15 score = 0.
Page 5
nalizing disorder that could potentially hamper
effective communication with health care profes-
sionals, delaying a potential diagnosis and conse-
quent treatment. Depressive symptoms in older
adults could aggravate chronic diseases and dis-
ability.
25
This could unfavourably inuence older
people’s ability to look after themselves, leading
to poorer self-perceived health, an increase in
unexplained physical symptoms and, conse-
quently, a rise in medical admissions. Further-
more, there is an association between the number
of physical conditions and depression, and a
dose–response relation has been described.
26
In the current study, we have found that, even
after adjustment for a robust measure of comor-
bidity (Charlson index), depression was a strong
independent risk factor for hospital admission,
longer hospital stays and worse hospital out-
comes. This suggests that the association between
depression and comorbidity, disability and hospi-
tal admission is complex and cannot be attributed
solely to age, prevalent clinical morbidity, social
support, education or smoking. However, even
after adjustment for comorbidities, it is difcult
to know to what extent depression may be a man-
ifestation of early stages of diseases. We cannot
therefore exclude the possibility that the ndings
may partially reect depression as an epiphenom-
enon for other diseases.
We found a dose–response relation between
depression severity and hospital admission,
which suggests that reducing the symptoms may
potentially improve hospital outcomes. However,
subthreshold symptoms should not be underesti-
mated, because they still have an impact on hos-
pital admission and associated outcomes.
Limitations
Our study population was limited to men aged
69 years and older. We do not know whether our
ndings are generalizable to younger adults,
women and people living outside Australia,
although there is no obvious reason that this
would not be the case, particularly in other
developed countries. Although the GDS-15 has
been proven to be a valid instrument for screen-
ing for major depressive disorder, it does not
have the potential to differentiate between symp-
toms of major depressive disorder and depressive
symptoms caused by other psychiatric diagnoses
(e.g., dementia, psychosis), which could affect
the interpretation of our results. We measured
depressive symptoms only at baseline, and this
exposure could have changed during the follow-
up period. Hence, we were unable to determine
whether change in depressive status could affect
hospital outcomes.
Finally, the Charlson weighted index was
originally created to estimate death and may not
take into account all of the diagnoses that may
increase hospital admission. Future research
could involve a more comprehensive index to
account for comorbidities.
Conclusion
Our study emphasizes the independent associa-
tion between the presence of depressive symp-
toms in older men living in the community and
hospital admissions, highlighting a possible tar-
get to identify men with potentially preventable
admissions. Larger studies may be able to inves-
tigate effect modication, to determine more
clearly what factors, if any, mediate the relation
between depression and hospital outcomes. It is
not clear whether reducing depressive symptoms
would result in fewer hospital admissions, and
further research is required to clarify this issue.
Our data extend previous ndings on the associa-
tion between depression and hospital admission,
with focus on the general population and admis-
sion frequency, length of stay and outcomes.
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Afliations: From the Department of Public
Health and Primary Care (Prina, Brayne), Cam-
bridge Institute of Public Health, Cambridge Uni-
versity, Cambridge, UK; the Western Australia
Centre for Health and Ageing (Prina, Hankey,
Flicker, Almeida), Centre for Medical Research,
University of Western Australia; the School of
Psychiatry and Clinical Neurosciences (Almeida),
University of Western Australia, Crawley, Aus-
tralia; the Department of Psychiatry (Almeida),
Royal Perth Hospital, Perth, Australia; the Depart-
ment of Neurology (Hankey), Royal Perth Hospi-
tal, Perth, Australia; the School of Medicine and
Pharmacology (Yeap, Flicker), University of
Western Australia, Crawley, Australia; the Depart-
ment of Endocrinology and Diabetes (Yeap), Fre-
mantle Hospital, Fremantle, Western Australia;
the Department of Geriatric Medicine (Flicker),
Royal Perth Hospital, Perth, Australia; the Depart-
ments of Epidemiology and Biostatistics, and of
Sociology, and the EMGO Institute for Health and
Care Research (Huisman), VU University, Ams-
terdam, The Netherlands.
Contributors: The study was designed by A.
Matthew Prina, Martijn Huisman, Carol Brayne and
Osvaldo Almeida. The analyses were conducted by
A. Matthew Prina, who drafted the article. All of the
authors interpreted the data and revised the manu-
script. All of the authors approved the nal version
submitted for publication.
Acknowledgement: A. Matthew Prina was sup-
ported by an Endeavour Research Fellowship. No
other specic funding was obtained for this project.
Research
CMAJ, February 5, 2013, 185(2) 123
Page 7
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    • "The significant association of depressive symptom severity with " any community HSU " is in line with some community-based general population studies [10,14] but not others [13]. The absence of a similar association with hospital admissions contradicts results from a study involving older men with elevated depressive symptoms living in Australia [12] but corroborates results from a study involving older people registered with a health maintenance organisation in USA [14]. The significant association of depressive symptom severity with " any community HSU " yielded a small effect size (PR = 1.02), similar to the findings of a previous meta-analysis of the association of these symptoms with hospital admissions which also yielded a small effect size (RR = 1.36) [11]. "
    [Show abstract] [Hide abstract] ABSTRACT: Background: Comprehensive understanding of the determinants of health service use (HSU) by older people with depression is essential for health service planning for an ageing global population. This study aimed to determine the extent to which depressive symptom severity and functioning are associated with HSU by older people with depression in low and middle income countries (LMICs). Methods: A cross-sectional analysis of the 10/66 Dementia Research Group population-based surveys dataset. Participants (n = 4590) were those aged 65 or older, in the clinical range for depressive symptoms (defined as scoring four or more on the EURO-D), living in 13 urban and/or rural catchment areas in nine LMICs. Associations were calculated using Poisson regression and random-effects meta-analysis. Results: After adjustment for confounding variables, (EURO-D) depressive symptom severity was significantly associated with "any community HSU" (Pooled Prevalence Ratios = 1.02; 95% CI = 1.01-1.03) but not hospital admission. Conversely, after adjustment, (WHODAS-II) functioning was significantly associated with hospital admission (Pooled PR = 1.14; 95% CI = 1.02-1.26) but not "any community HSU". Conclusions: Depressive symptom severity does not explain a large proportion of the variance in HSU by older people with depression in LMICs. The association of functioning with this HSU is worthy of further investigation. In LMICs, variables related to accessibility may be more important correlates of HSU than variables directly related to health problems.
    Full-text · Article · Apr 2015 · International Journal of Environmental Research and Public Health
  • Source
    • "Effective communication with health professional could also be affected in people with depressed mood. This could lead to a delay in a diagnosis and treatment [12]. Depressive symptoms may also impair a person's motivation towards recovery and their response to rehabilitation. "
    [Show abstract] [Hide abstract] ABSTRACT: Objectives This paper aims to systematically review observational studies that have analysed whether depressive symptoms in the community are associated with higher general hospital admissions, longer hospital stays and increased risk of re-admission. Methods We identified prospective studies that looked at depressive symptoms in the community as a risk factor for non-psychiatric general hospital admissions, length of stay or risk of re-admission. The search was carried out on MEDLINE, PsycINFO, Cochrane Library Database, and followed up with contact with authors and scanning of reference lists. Results Eleven studies fulfilled our inclusion and exclusion criteria, and all were deemed to be of moderate to high quality. Meta-analysis of seven studies with relevant data suggested that depressive symptoms may be a predictor of subsequent admission to a general hospital in unadjusted analyses (RR = 1.36, 95% CI: 1.28–1.44), but findings after adjustment for confounding variables were inconsistent. The narrative synthesis also reported depressive symptoms to be independently associated with longer length of stay, and higher re-admission risk. Conclusions Depressive symptoms are associated with a higher risk of hospitalisation, longer length of stay and a higher re-admission risk. Some of these associations may be mediated by other factors, and should be explored in more details.
    Full-text · Article · Nov 2014 · Journal of psychosomatic research
  • Source
    • "In health care, much of this literature has dealt with the impact upon disease-specific HRQoL and the influence of specific disease states (Ampon, Williamson , Correll, & Marks, 2005). There is also a large literature documenting the impact of depression and depressive symptoms on mortality both within the general population (Blazer, Hybels, & Pieper, 2001) and in populations with a variety of diseases (Barth, Schumacher, & Herrmann- Lingen, 2004; Covinsky, Fortinsky, Palmer, Kresevic, & Landefeld, 1997; Moraska et al., 2013; Prina et al., 2013; Satin, Linden, & Phillips, 2009; Saz & Dewey, 2001). Blazer and colleagues' work suggests that this effect is complex, likely operating through multiple causal pathways . "
    [Show abstract] [Hide abstract] ABSTRACT: Objectives: Depression and depressive symptoms predict death, but it is less clear if more general measures of life satisfaction (LS) predict death. Our objectives were to determine: (1) if LS predicts mortality over a five-year period in community-living older adults; and (2) which aspects of LS predict death. Method: 1751 adults over the age of 65 who were living in the community were sampled from a representative population sampling frame in 1991/1992 and followed five years later. Age, gender, and education were self-reported. An index of multimorbidity and the Older American Resource Survey measured health and functional status, and the Terrible-Delightful Scale assessed overall LS as well as satisfaction with: health, finances, family, friends, housing, recreation, self-esteem, religion, and transportation. Cox proportional hazards models examined the influence of LS on time to death. Results: 417 participants died during the five-year study period. Overall LS and all aspects of LS except finances, religion, and self-esteem predicted death in unadjusted analyses. In fully adjusted analyses, LS with health, housing, and recreation predicted death. Other aspects of LS did not predict death after accounting for functional status and multimorbidity. Conclusion: LS predicted death, but certain aspects of LS are more strongly associated with death. The effect of LS is complex and may be mediated or confounded by health and functional status. It is important to consider different domains of LS when considering the impact of this important emotional indicator on mortality among older adults.
    Full-text · Article · Jul 2014 · Aging and Mental Health
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