cardiff09 - detecting depression in primary & secondary care (may2009)
DESCRIPTION
Lecture for the University of Cardiff Psychiatry programme 2009. Topic is detecting depression - an evidence based approach. 86 slides; most self-explanatory but some slide labels added. Warning! can be a bit statistically heavy!TRANSCRIPT
Alex Mitchell [email protected]
Consultant in Liaison Psychiatry
Detecting Depression in Primary & Secondary Care
Evidence Based At Last?
Cardiff May 2009
Detecting Depression in Primary & Secondary Care
Evidence Based At Last?
2/3rds 1/3rd
25%Psychiatry
10%Medical
1.00
0.64
0.26
0.10
0.00
0.20
0.40
0.60
0.80
1.00
1.20
All visits (N =14,372) Primary care (N =3,605) Psychiatrists (N =293) Medical specialists (N=10,474)
Comment: Slide illustrates added proportion of all depression treated in each setting. Most depression is treated in primary care
% Receiving Any treatment for Depression
10.9 11.3
8.18.8
4.3
5.6
10.9
13.8
6.8
17.9
3.4
5.5
15.4
7.2
0
2
4
6
8
10
12
14
16
18
20
High Inc
omeBelg
ium
France
German
y
Israe
l
Italy
Japa
nNeth
erlan
dsNew
Zeala
nd
Spain USALow
Inco
me
ChinaColom
biaSouth
Afri
caUkra
ine
Wang P et al (2007) Lancet 2007; 370: 841–50
n=84,850 face-to-face interviews
Clinical Questions Evidence
What are the symptoms of depression?Are we looking for depression?If we look, do we detect depression?What tools are available?Do the tools really make a difference?What about acceptability (Ultra-Short Screening)
Depression in medical settings - special?Depression in late-life – special?Implementation of screening - how
What are the symptoms of depression?Are we looking for depression?If we look, do we detect depression?What tools are available?Do the tools really make a difference?What about acceptability (Ultra-Short Screening)
Depression in medical settings - special?Depression in late-life – special?Implementation of screening - how
Clinical Questions Evidence
Loss of confidenceLow motivation / driveWithdrawalAvoidanceSocial isolationWorryFeelings of dreadHelplessnessHopelessness
=> None are official criteria!
Psychic anxietySomatic anxietyAngerIrritabilityLack of reactive moodCognitive ChangeMemory complaintsPerceptual distortion
Which are Criteria for Depression?
YesYesGuilt or self-blame
DSMIVICD10Core Symptoms
YesNoSignificant change in weight
YesYesAgitation or slowing of movements
YesYesSuicidal thoughts or acts
NoYesPoor or increased appetite
NoYesLow self-confidence
YesYesPoor concentration or indecisiveness
YesYesDisturbed sleep
YesYes (core) Fatigue or low energy
Yes (core) Yes (core) Loss of interests or pleasure
Yes (core) Yes (core) Persistent sadness or low mood
Symptom Significance in Depression
(7 or) 8 symptoms (3+4)
(5 or )6 symptoms
4 symptoms (2+2)
2 or 3 symptoms
0 or 1 symptom
ICD10
16 - 21UnspecifiedSevere
12 - 155 symptoms (Mj)
Moderate
8 -112-4 symptoms (minor)
Mild
4 - 71 or No core symptoms
Sub-syndromal
0 - 30 symptomHealthy
HADs D ScoreDSMIVDepression Severity
=> HADS
Useful Symptoms of Depression?
Audience – How useful would the following be?
Depressed Non-DepressedLow mood 100% 0%
Insomnia 50% 25%
Weight gain 5% 8%
Diagnosis => Occurrence (se) & discrimination (ppv)
=> illustration
Graphical – single discriminating symptom
Non-Depressed
Depressed# ofIndividualsWith symptom
Severity of Low Mood
Point of Rarity
Comment: Slide illustrates the concept of discrimination using one symptom severity of “low mood”
Graphical – single symptom
Non-Depressed
Depressed# ofIndividualsWith symptom
Severity ofLow Mood
?Point of Rarity
Pooled
Non-Depressed
Depressed# ofIndividualsWith symptom
Severity of Low Mood
Comment: Slide illustrates added hypothetical distribution of mood scores in a population with hidden depression
Comment: Slide illustrates added actual distribution of mood scores on the HADS in a cancer population with hidden depression from the Edinburgh cancer centre
“Common” Symptoms of Depression
0.120.56Thoughts of death
0.330.59Psychic anxiety
0.120.61Worthlessness
0.420.69Anxiety
0.270.70Insomnia
0.120.81Diminished interest/pleasure
0.240.82Diminished concentration
0.320.83Sleep disturbance
0.270.87Concentration/indecision
0.320.87Loss of energy
0.300.88Diminished drive
0.180.93Depressed mood
Non-Depressed FrqDepressed FrqItem
Mitchell, Zimmerman et al MIDAS Database. Psychol Med 2009
“Uncommon” Symptoms
0.060.16Increased weight
0.060.19Hypersomnia
0.070.19Increased appetite
0.060.22Lack of reactive mood
0.060.23Decreased weight
0.040.28Psychomotor retardation
0.090.34Psychomotor agitation
0.260.44Anger
0.110.45Decreased appetite
0.250.46Somatic anxiety
Non-Depressed ProportionDepressed ProportionItem
Mitchell, Zimmerman et al MIDAS Database. Psychol Med 2009
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
Loss
of e
nerg
yDi
min
ishe
d dr
ive
Slee
p di
stur
banc
eCo
ncen
trat
ion/
inde
cisi
onDe
pres
sed
moo
d
Anxi
ety
Dim
inis
hed
conc
entr
atio
n
Inso
mni
aDi
min
ishe
d in
tere
st/p
leas
ure
Psyc
hic
anxi
ety
Help
less
ness
Wor
thle
ssne
ssHo
pele
ssne
ssSo
mat
ic a
nxie
tyTh
ough
ts o
f dea
th
Ange
rEx
cess
ive
guilt
Psyc
hom
otor
cha
nge
Inde
cisiv
enes
sDe
crea
sed
appe
tite
Psyc
hom
otor
agi
tatio
nPs
ycho
mot
or re
tard
atio
nDe
crea
sed
wei
ght
Lack
of r
eact
ive
moo
dIn
crea
sed
appe
tite
Hype
rsom
nia
Incr
ease
d w
eigh
t
All Case ProportionDepressed ProportionNon-Depressed Proportion
n=1523
Comment: Slide illustrates sensitivity and specificity of each mood symptom
-0.10
0.00
0.10
0.20
0.30
0.40
0.50A
nger
Anx
iety
Dec
reas
ed a
ppet
ite
Dec
reas
ed w
eigh
t
Dep
ress
ed m
ood
Dim
inis
hed
conc
entr
atio
n
Dim
inis
hed
driv
eD
imin
ishe
d in
tere
st/p
leas
ure
Exce
ssiv
e gu
ilt
Hel
ple
ssne
ss
Hop
eles
snes
s
Hyp
erso
mni
a
Incr
ease
d ap
peti
te
Incr
ease
d w
eigh
t
Inde
cisi
vene
ss
Inso
mni
aLa
ck o
f re
acti
ve m
ood
Loss
of
ener
gy
Psyc
hic
anxi
ety
Psyc
hom
otor
agi
tati
on
Psyc
hom
otor
cha
nge
Psyc
hom
otor
ret
arda
tion
Slee
p di
stur
banc
e
Som
atic
anx
iety
Thou
ghts
of
deat
h
Wor
thle
ssne
ss
Rule-In Added Value (PPV-Prev)Rule-Out Added Value (NPV-Prev)
Comment: Slide illustrates added value of each symptom when diagnosing depression and when identifying non-depressed
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Depressed Mood
Diminished drive
Diminished interest/pleasure
Loss of energy
Sleep disturbance
Diminished concentration
Sensitivity
1 - Specificity
n=1523
Comment: Slide illustrates summary ROC curve sensitivity/1-specficity plot for each mood symptom
What are the symptoms of depression?Are we looking for depression?If we look, do we detect depression?What tools are available?Do the tools really make a difference?What about acceptability (Ultra-Short Screening)
Depression in medical settings - special?Depression in late-life – special?Implementation of screening - how
Clinical Questions Evidence
Audience
Which method do you prefer?
Your own skills (no assistance)
Start with 1 or 2 questions
Limit to 7 questions
20 questions!
Phone a friend!
1,2 or 3 Simple QQ15%
Clinical Skills Alone73%
ICD10/DSMIV0%
Short QQ3%
Other/Uncertain9% Other/Uncertain
2%
Use a QQ15%
ICD10/DSMIV13%
Clinical Skills Alone55%
1,2 or 3 Simple QQ15%
Cancer StaffCurrent Method (n=226)
Psychiatrists
Comment: Slide illustrates preferences of cancer clinicians for detecting depression in a national survey
1,2 or 3 Simple QQ24%
Clinical Skills Alone20%
ICD10/DSMIV24%
Short QQ24%
Long QQ8%
Algorithm26%
Short QQ23%
ICD10/DSMIV0%
Clinical Skills Alone17%
1,2 or 3 Simple QQ34%
Cancer StaffIdeal Method (n=226)
Psychiatrists
Effective?
Comment: Slide illustrates “ideal” preferences of cancer clinicians for detecting depression in a national survey
1,2 or 3 Simple QQ15%
Clinical Skills Alone73%
ICD10/DSMIV0%
Short QQ3%
Other/Uncertain9% Other/Uncertain
2%
Use a QQ15%
ICD10/DSMIV13%
Clinical Skills Alone55%
1,2 or 3 Simple QQ15%
Cancer Staff Psychiatrists
Current MethodComment: Slide illustrates preferences of cancer clinicians vs psychiatrists for detecting depression
1,2 or 3 Simple QQ24%
Clinical Skills Alone20%
ICD10/DSMIV24%
Short QQ24%
Long QQ8%
Algorithm26%
Short QQ23%
ICD10/DSMIV0%
Clinical Skills Alone17%
1,2 or 3 Simple QQ34%
Cancer Staff Psychiatrists
Ideal MethodComment: Slide illustrates “ideal” preferences of cancer clinicians vs psychiatrists for detecting depression
Do Clinicians Look for Depression Often?
1,2 or 3 Simple QQ15%
Clinical Skills Alone73%
ICD10/DSMIV0%
Short QQ3%
Other/Uncertain9%
Methods to Evaluate Depression
Unassisted Clinician Conventional Scales
Verbal Questions Visual-Analogue Test
PHQ2
WHO-5
Whooley/NICE
Distress Thermometer
Depression Thermometer
Ultra-Short (<5) Short (5-10) Long (10+)Untrained Trained
1,2 or 3 Simple QQ15%
Clinical Skills Alone73%
ICD10/DSMIV0%
Short QQ3%
Other/Uncertain9%
1,2 or 3 Simple QQ15%
Clinical Skills Alone73%
ICD10/DSMIV0%
Short QQ3%
Other/Uncertain9%
What are the symptoms of depression?Are we looking for depression?If we look, do we detect depression?What tools are available?Do the tools really make a difference?What about acceptability (Ultra-Short Screening)
Depression in medical settings - special?Depression in late-life – special?Implementation of screening - how
Clinical Questions Evidence
GP Detection of Depression – Meta-analysis
Mitchell, Vaze, Rao
Methods100 studies of GP recognition rate => 35 with Se Sp9x DSM7x ICD109x HADS4x CES-D; 4x PHQ2x BDI
Mitchell, Vaze, Rao (2009) in press Lancet
Accuracy 2x2 Table
PrevalenceSpecificitySensitivity
NPVTrue -VeFalse -VeTest -ve
PPVFalse +veTrue +veTest +ve
DepressionABSENT
DepressionPRESENT
Accuracy of GP’s Diagnoses
955927,6406553
667825,1254050GP -ve
501825152503GP +ve
DepressionABSENT
DepressionPRESENT
Sensitivity48%
PPV 42.8%
Specificity80.1%
NPV 85.1%
Prevalence 19%
N=35 studies
Mitchell, Vaze, Rao Lancet in Press
Unassisted Accuracy
Non-Depressed
Depressed# ofIndividuals
TestResult
Cut-off value
False +veFalse -ve
True -ve True +ve
Unassisted Accuracy - Prospective
Non-Depressedn=80
Depressedn=20#
ofIndividuals
TestResult
Cut-off value
False +ve16
False -ve10
True -ve64
True +ve10
Comment: Slide illustrates detection of depression (incl false + false –) for each 100 consecutive patients in primary care if prospective cases are recorded
Unassisted Accuracy - Retrospective
Non-Depressedn=80
Depressedn=20#
ofIndividuals
TestResult
Cut-off value
False +ve7
False -ve13
True -ve73
True +ve7
Comment: Slide illustrates detection of depression (incl false + false –) for each 100 consecutive patients in primary care if GPs opinions are gathers from notes
Some Predictors of Detection
Giving sufficient timeAsking about depressionLooking for symptomsRecognizing symptomsHigh and low risk samplesMild Moderate Severe
GP Recognition of Individual symptomProportion of Individual Symptoms Recognised by GPs
76.1
36.4 34.631.6
21.616.7
13.39.1 8.3 8.3
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
Low m
ood
Insomnia
Hypoc
hondri
asis
Loss
of in
terest
Tearfu
lness
Anxiety
Loss
of en
ergy
Pessim
ism
Anorex
ia
Not Copin
g
O’Conner et al (2001) Depression in primary care.Int Psychogeriatr 13(3) 367-374.
86.8
55.6 54.4
43.3
36
29.826.2 25.6 25.2 23.8 24
21.4 21.2
13.9 12.89.5
7.2 7 7 5.9 4.8 4.1 2.6 1.8 1.8 1.3 0.9 0.4 0.40
10
20
30
40
50
60
70
80
90
100Sl
eep
distu
rban
ces;
inso
mni
a; e
arly
wake
ning
Loss
of a
ppet
ite; o
vere
ating
; wei
ght c
hang
es
Depr
esse
d m
ood;
hop
eles
snes
s; sa
d; g
loom
y
Apat
hy; l
etha
rgy;
tired
ness
; las
situd
e
Loss
of i
nter
est;
with
draw
al; in
diffe
renc
e; lo
nelin
ess
Loss
of e
nerg
y; lo
ss o
f driv
e; b
urnt
out
Loss
of l
ibido
; los
s of
sex
driv
e; im
pote
nce
Tear
s; we
eping
; cry
ing
Anxio
us; a
gita
ted;
irrit
able
; res
tless
, ten
se; s
tress
ed
Feeli
ng w
orth
less
; guil
ty; la
ck o
f sel
f este
em
Som
atic;
vege
tativ
e sy
mpt
oms;
mala
ise; m
ultip
le co
nsult
ation
s
Suici
de th
ough
ts; th
ough
t of s
elf in
jury
Loss
of c
once
ntra
tion;
poo
r mem
ory,
poo
r thi
nkin
g
Dim
inish
ed p
erfo
rman
ce; i
nabi
lity to
cop
e
Emot
ional
labil
ity; m
ood
swing
s
Loss
of a
ffect
; flat
affe
ct; lo
ss o
f em
otion
Loss
of e
njoym
ent o
r ple
asur
e; la
ck o
f hum
or
Beha
viour
al pr
oble
ms;
agg
ress
ivene
ss; b
ehav
iour
al ch
ange
s
Pess
imism
; neg
ative
atti
tude
s, w
orry
ing
Psyc
hom
otor
reta
rdat
ion;
slow
ness
Head
ache
s; d
izzin
ess
Appe
aran
ce; s
peec
h; e
xces
sive
smilin
g; va
guen
ess,
etc.
Heav
y use
of a
lcoho
l, tob
acco
or d
rugs
Delu
sions
; hall
ucin
atio
ns; c
onfu
sion
Reac
tion
to p
roba
ble
caus
es o
r life
eve
nts
Fam
ily o
r pas
t hist
ory
of d
epre
ssio
n
Obs
essiv
e id
eatio
n; p
hobia
sLa
ck o
f ins
ight
Perio
d of
life
(men
opau
se)
Comment: Slide illustrates which symptoms are asked about by GPS looking for depression
Effect of Prevalence
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0.9
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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Pre-test Probability
Post
-test
Pro
babi
lity
Baseline Probability
Depression+
Depression-
Comment: Slide illustrates Bayesian curve – pre-test post test probability for every possible prevalence
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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Pre-test Probability
Post
-test
Pro
babi
lity
Baseline Probability
Depression+
Depression-
PPV
NPV
Effect of Severity
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Pre-test Probability
Post
-test
Pro
babi
lity
Distress+
Distress-
Baseline Probability
Depression+
Depression-
Comment: Slide illustrates GP diagnosis of depression is more successful than their diagnosis of milder “distress”
GPs vs Oncologists vs Nurses
Who is better?
Bayesian analysis
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0.90
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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Pre-test Probability
Pos
t-tes
t Pro
babi
lity
GP+GP-Baseline ProbabilityNurse+Nurse-Oncologist+Oncologists-
What are the symptoms of depression?Are we looking for depression?If we look, do we detect depression?What tools are available?Do the tools really make a difference?What about acceptability (Ultra-Short Screening)
Depression in medical settings - special?Depression in late-life – special?Implementation of screening - how
Clinical Questions Evidence
20 Instruments for Depression
SDS (20)EPDS (10)
GDS (30,15)MADRAS (10)
DEPS (10)DADS (7)
Zung (20)DSMIV (9)
CES-D (20,10)PHQ9 (9) Distress Therm (1)
BSI (53)MOS-D (8)WHO-5 (5)
BDI (21,13) BDI (7) PHQ2 (2)
HAM-D (21) PHQ1HADS (7)
Long > 10Short > 5 < 11Ultra-short <6
Addition: Comparison of Scale Scores
47–4939–4133–35262665–6763-654
45–4637–3832252562–2460-624
42–4435–3630–31242459–6157-594
40–4133–3429232356–5854-564
393228222254–5552-534
35–3829–3126–27212149–5347-514 (v Severe)
342825202048463
332724191946–4744-453
31–322623181844–4542-433
29–3024–2521–22171741–4340-413
303527–282320161639–4037-393 (severe)
2934262218–19151537–38362
24–2520–2118–19141434–3633-352
22–2318–1917131331–3330-322
20–211716121229–3028-292
1920191614–15111126–2824-272 (Moderate)
181917–1814–1513101024–25231
15–1613129922–2321-221
13–1411–12118819–2118-201
12109–107717–1816-171
10710–11986614–1612-151 (Mild)
968–97–875512–13110
6–75–65–6449–119-100
544337–87-80
3–43322660
221–2114–54-50
000–10–10000–30-30 (None)
BDIMADRSHRSD24HRSD21HRSD17QIDS-SR16QIDS-C16IDS-SR30IDS-C30Severity1
=> Symptoms
What are the symptoms of depression?Are we looking for depression?If we look, do we detect depression?What tools are available?Do the tools really make a difference?What about acceptability (Ultra-Short Screening)
Depression in medical settings - special?Depression in late-life – special?Implementation of screening - how
Clinical Questions Evidence
Screening Evidence - Yes
USPSTF
good evidence that screening improves the accurate identification of depressed patients in primary care settings and that treatment of depressed adults identified in primary care settings decreases clinical morbidity.
Small benefits have been observed in studies that simply feed back screeningresults to clinicians.
Larger benefits have been observed in studies in which the communication of screening results is coordinated with effective follow-up and treatment.
Pignone, M. P., Gaynes, B. N., Rushton, J. L., et al (2002) Screening for depression in adults: a summary of the evidence for the U.S. Preventive Services Task Force. Annals of Internal Medicine, 136, 765-776. => Gilbody
Screening Evidence - No
Gilbody, S. M., House, A. O. & Sheldon, T. A. (2001) Routinely administered questionnaires for depression and anxiety: systematic review. BMJ, 322 (7283), 406-409. => NICE
Do Tools Work?
Clinician rate vs tool rate (both against SCID)
Clinician with vs without tool
Tool vs SCID
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0.40
0.50
0.60
0.70
0.80
0.90
1.00
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Pre-test Probability
Post
-test
Pro
babi
lity
Clinician Positive (Fallowfield et al, 2001)
Clinician Negative (Fallowfield et al, 2001)
Baseline Probability
HADS-D Positive (Mata-analysis)
HADS-D Negative (Meta-analysis)
Comment: Slide illustrates Bayesian curve comparison from indirect studies of clinician and HADS
This illustrates POTENTIAL gain from screening
Comment: Slide illustrates actual gain in meta-analysis of screening implementation in primary care
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0.60
0.70
0.80
0.90
1.00
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Pre-test Probability
Pos
t-tes
t Pro
babi
lity
Clinical+Clinical-Baseline ProbabilityScreen+Screen-
Comment: Slide illustrates Bayesian curve comparison from RCT studies of clinician with and without screening
This illustrates ACTUAL gain from screening
HADS Validity vs Structured Interview
METHODSAgainst depression 9x studies of the HADS-D; 5x of the HADS-T and 2x of the HADS-A were identified.
RESULTSHADS-T = HADS-D = HADS-AThe clinical utility index (UI+, UI-) was 0.214 and 0.789 for the HADS-D.
Sensitivity Specificity PPV NPV FCHADS-D 51.4% 86.9% 41.6% 90.8% 81.4% HADS-A 82.4% 81.7% 35.9% 97.4% 81.8%
HADS-T 77.7% 84.3% 44.5% 95.9% 83.4%
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0.20
0.30
0.40
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0.70
0.80
0.90
1.00
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Pre-test Probability
Pos
t-tes
t Pro
babi
lity
HADS-T Positive (N=5)HADS-T Negative (N=5)Baseline ProbabilityHADS-A Positive (N=2)HADS-A Negative (N=2)HADS-D Positive (N=9)HADS-D Negative (N=9)
Comment: Slide illustrates Bayesian curve comparison of HADS in detection of depression in cancer settings.
Against expectations HADS-A was most successful
What are the symptoms of depression?Are we looking for depression?If we look, do we detect depression?What tools are available?Do the tools really make a difference?What about acceptability (Ultra-Short Screening)
Depression in medical settings - special?Depression in late-life – special?Implementation of screening - how
Clinical Questions Evidence
Test Duration
Ultra-short screening tools were defined as those with 1-4 items taking less than 2 minutes to complete.Short screening tools were defined as those with 5-14 items, taking between 2 and five minutes to complete.Standard screening tools were defined as those with 15 or more items, taking more than five minutes to complete.
=> Tools table
NICE Screening: How?
Step 1: Recognition
• Use two screening questions, such as:– “During the last month, have you often been bothered by feeling down, depressed or hopeless?”
– “During the last month, have you often been bothered by having little interest or pleasure in doing things?”
Distribution of DT ScoresRansom (2006) PO (n=491)
13.814.7
15.7
13.2
10.4
8.47.7 7.3
3.7 3.3
1.8
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
Score 0 Score 1 Score 2 Score 3 Score 4 Score 5 Score 6 Score 7 Score 8 Score 9 Score 10
Gessler, Lowe Psycho-oncology (in press 2008)
SCAN, SCID, PSE, CIDI, MINI
BDI, MADRAS, Hamilton
HADS, EPDS, PHQ9, CES-D
LONG
PHQ2, NICE, DT
SHORT
High NPVLow PPV
High NPVMed PPV
High NPVHigh PPV
MEDIUM
What are the symptoms of depression?Are we looking for depression?If we look, do we detect depression?What tools are available?Do the tools really make a difference?What about acceptability (Ultra-Short Screening)
Depression in medical settings - special?Depression in late-life – special?Implementation of screening - how
Clinical Questions Evidence
Approaches to Somatic Symptoms of DepressionInclusiveUses all of the symptoms of depression, regardless of whether they may or may not be secondary to a physical illness. This approach is used in the Schedule for Affective Disorders and Schizophrenia (SADS) and the Research Diagnostic Criteria.
ExclusiveEliminates somatic symptoms but without substitution. There is concern that this might lower sensitivity. with an increased likelihood of missed cases (false negatives)
EtiologicAssesses the origin of each symptom and only counts a symptom ofdepression if it is clearly not the result of the physical illness. This is proposed by the Structured Clinical Interview for DSM and Diagnostic Interview Schedule (DIS), as well as the DSM-III-R/IV).
SubstitutiveAssumes somatic symptoms are a contaminant and replaces these additional cognitive symptoms. However it is not clear what specific symptoms should be substituted
Somatic Bias in Mood Scales
Medically Unwell
Primary Depression
Secondary Depression
Comment: Slide illustrates concept of phenomenology of depressions in medical disease
Study: Coyne Thombs Mitchell
N= 1200 – 4500Pooled database studyAll comparative studies
Co-morbid Depression vs Primary Depression
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Agitatio
n (Com
orbid)
Agitatio
n (Prim
ary)
Anxiety
(Com
orbid)
Anxiety
(Prim
ary)
Appetite
(Comorb
id)
Appetite
(Prim
ary)
Concen
tratio
n (Comorb
id)
Concen
tratio
n (Prim
ary)
Fatigu
e (Comorb
id)
Fatigu
e (Prim
ary)
Guilt (
Comorbid)
Guilt (
Primar
y)
Hopeles
snes
s (Comorb
id)
Hopeles
snes
s (Prim
ary)
Insomnia
(Comor
bid)
Insomnia
(Prim
ary)
Loss In
teres
t (Comorb
id)
Loss In
teres
t (Prim
ary)
Low Mood (C
omorbid)
Low Mood (P
rimary
)
Retard
ation (
Comorbid)
Retard
ation (
Primary)
Suicide (
Comorbid)
Suicide (
Primar
y)
Weight L
oss (C
omorbid)
Weight L
oss (P
rimary
)
*
*
*
*
*
**
*
*
Comorbid Depression
Primary Depression
n=4069 vs 4982Comment: Slide illustrates similar symptoms profile in comorbid vsprimary depression
Co-morbid Depression vs Medical Illness Alone
n= 4069 vs 1217
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Anxiety
(Com
orbid)
Anxiety
(Med
ical)
Concen
tratio
n (Comorb
id)
Concen
tratio
n (Med
ical)
Fatigu
e (Comorb
id)Fati
gue (
Medica
l)
Hopeles
snes
s (Comorb
id)
Hopeles
snes
s (Med
ical)
Insomnia
(any t
ype)
(Comorb
id)
Insomnia
(any t
ype)
(Med
ical)
Loss In
teres
t (Comorb
id)
Loss In
teres
t (Med
ical)
Low Mood (C
omorbid)
Low Mood (M
edical)
Retard
ation (
Comorbid)
Retard
ation (
Medica
l)
Suicide (
Comorbid)
Suicide (
Medica
l)
Weight L
oss (C
omorbid)
Weight L
oss (M
edical)
Worthles
snes
s (Comor
bid)
Worthles
snes
s (Med
ical)
Medical Illness Alone
Comorbid Depression
**
*
*
*
*
*
*
*
Comment: Slide illustrates distinct symptoms profile in comorbid depression vs medical illness alone
Medically Unwell
Primary Depression
Secondary Depression
Comment: Slide illustrates actual phenomenology of depressions in medical disease
What are the symptoms of depression?Are we looking for depression?If we look, do we detect depression?What tools are available?Do the tools really make a difference?What about acceptability (Ultra-Short Screening)
Depression in medical settings - special?Depression in late-life – special?Implementation of screening - how
Clinical Questions Evidence
Are Certain Symptoms Common in Older People
-0.25
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
Hel
ples
snes
s
Hop
eles
snes
s
Wor
thle
ssne
ss
Anx
iety
(Som
atic
anx
iety
)
Ang
er
Inde
cisi
vene
ss
Thou
ghts
of D
eath
Dim
inis
hed
Con
cent
ratio
n
Anx
iety
(Com
bine
d)
Incr
ease
d A
ppet
ite
Slee
p D
istu
rban
ce (H
yper
som
nia)
Slee
p D
istu
rban
ce (C
ombi
ned)
Incr
ease
d W
eigh
t
Loss
of E
nerg
y
Psyc
hom
otor
Agi
tatio
n
Anx
iety
(Psy
chic
anx
iety
)
Exce
ssiv
e G
uilt
Dim
inis
hed
Inte
rest
Slee
p D
istu
rban
ce (I
nsom
nia)
Dec
reas
ed A
ppet
ite
Dep
ress
ed M
ood
Psyc
hom
otor
Ret
arda
tion
Dec
reas
ed W
eigh
t
More common in late-life depression
More common in early-life depression
Comment: Slide illustrates simple frequency of symptoms in late life vsmid-life depression
Accuracy - Comparative
Accuracy Old vs Young
Accuracy O%%Older
Accuracy Y%%Younger
DepressionABSENT
DepressionPRESENT
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
Anger
Anxiety
(Com
bined)
Anxiety
(Psy
chic
anxie
ty)
Anxiety
(Somatic
anxiet
y)
Decre
ased
App
etite
Decre
ased
Weig
ht
Depres
sed M
ood
Diminish
ed C
oncentra
tion
Diminish
ed In
teres
tExc
essiv
e Guilt
Helples
snes
sHope
lessn
ess
Increas
ed A
ppetite
Increas
ed W
eight
Indecisi
venes
sLoss
of Ene
rgy
Psych
omotor Agita
tion
Psych
omotor Retar
datio
n
Sleep D
isturban
ce (C
ombined)
Sleep D
isturban
ce (H
ypers
omnia)
Sleep D
isturban
ce (In
somnia)
Thoughts
of Dea
thWorth
lessn
ess
<55>54>59>64
*
*
*
*
*
**
*
Comment: Slide illustrates diagnostic value of symptoms in late life vs mid-life depression – few have special significance
Clinicians Detection
Is it Influenced by differences?
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Pre-test Probability
Pos
t-tes
t Pro
babi
lity
Routine Case-Finding Late-LifeRoutine Exclusion Late-lifeBaseline ProbabilityRoutine Case-Finding MixedRoutine Exclusion MixedRoutine Case-Finding YoungerRoutine Exclusion Younger
Comment: Slide illustrates detection of late life vs mid-life depression in primary care – GPs are least successful with late-life depression
What are the symptoms of depression?Are we looking for depression?If we look, do we detect depression?What tools are available?Do the tools really make a difference?What about acceptability (Ultra-Short Screening)
Depression in medical settings - special?Depression in late-life – special?Implementation of screening - how
Clinical Questions Evidence
FURTHER READING:
Screening for Depression in Clinical Practice An Evidence-Based guideAlex J Mitchell & James C Coyne
ISBN13: 9780195380194ISBN10: 0195380193
Paperback, 416 pagesNov 2009Price:$49.95 / £39.99