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!

1. Detecting Depression in Primary & Secondary Care
Evidence Based At Last?
Alex Mitchell alex.mitchell@leicspart.nhs.uk
Consultant in Liaison Psychiatry
Cardiff May 2009

2. Detecting Depression in Primary & Secondary Care
Evidence Based At Last?
2/3rds 1/3rd
10% 25%
Medical Psychiatry

3. Comment: Slide illustrates added proportion of all
depression treated in each setting. Most depression
is treated in primary care
1.20
1.00
1.00
0.80
0.64
0.60
0.40
0.26
0.20
0.10
0.00
All visits (N =14,372) Primary care (N =3,605) Psychiatrists (N =293) Medical specialists (N
=10,474)

4. % Receiving Any treatment for Depression
20
17.9
18 n=84,850 face-to-face interviews
16 15.4
13.8
14
12 11.3
10.9 10.9
10
8.8
8.1
8 7.2
6.8
6 5.6 5.5
4.3
4 3.4
2
0
SA
in
n
ly
na
m
ca
e
a
l
y
ne
ce
e
nd
e
s
m
bi
pa
an
m
It a
a
nd
ra
u
hi
i
an
U
ai
la
Sp
fr
co
om
co
gi
Ja
m
Is
C
kr
rla
A
a
Fr
el
In
er
In
Ze
ol
U
h
B
he
G
w
ut
h
C
ew
et
ig
Lo
So
H
N
N
Wang P et al (2007) Lancet 2007; 370: 841–50

5. 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

6. 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

7. Which are Criteria for Depression?
Loss of confidence Psychic anxiety
Low motivation / drive Somatic anxiety
Withdrawal Anger
Avoidance Irritability
Social isolation Lack of reactive mood
Worry Cognitive Change
Feelings of dread Memory complaints
Helplessness Perceptual distortion
Hopelessness
=> None are official criteria!

8. Core Symptoms ICD10 DSMIV
Persistent sadness or low mood Yes (core)‫‏‬ Yes (core)‫‏‬
Loss of interests or pleasure Yes (core)‫‏‬ Yes (core)‫‏‬
Fatigue or low energy Yes (core)‫‏‬ Yes
Disturbed sleep Yes Yes
Poor concentration or Yes Yes
indecisiveness
Low self-confidence Yes No
Poor or increased appetite Yes No
Suicidal thoughts or acts Yes Yes
Agitation or slowing of Yes Yes
movements
Guilt or self-blame Yes Yes
Significant change in weight No Yes

9. Symptom Significance in Depression
Depression ICD10 DSMIV HADs D Score
Severity
Healthy 0 or 1 0 symptom 0-3
symptom
Sub-syndromal 2 or 3 1 or No core 4-7
symptoms symptoms
Mild 4 symptoms 2-4 symptoms 8 -11
(2+2)‫‏‬ (minor)‫‏‬
Moderate (5 or )6 5 symptoms 12 - 15
symptoms (Mj)‫‏‬
Severe (7 or) 8 Unspecified 16 - 21
symptoms
(3+4)‫‏‬
=> HADS

10. Useful Symptoms of Depression?
Audience – How useful would the following be?
Depressed Non-Depressed
Low mood 100% 0%
Insomnia 50% 25%
Weight gain 5% 8%
Diagnosis => Occurrence (se) & discrimination (ppv)‫‏‬
=> illustration

11. Graphical – single discriminating symptom
Comment: Slide illustrates the concept of
discrimination using one symptom severity of “low
mood”
Non-Depressed
Depressed
#
of
Individuals
With symptom Point of Rarity
Severity of Low Mood

12. Graphical – single symptom
Non-Depressed
Depressed
# ?Point of Rarity
of
Individuals
With symptom
Severity of
Low Mood

13. Pooled
Comment: Slide illustrates added hypothetical
distribution of mood scores in a population with
hidden depression
Non-Depressed
Depressed
#
of
Individuals
With symptom
Severity of Low Mood

14. Comment: Slide illustrates added actual distribution
of mood scores on the HADS in a cancer
population with hidden depression from the
Edinburgh cancer centre

15. “Common” Symptoms of Depression
Item Depressed Frq Non-Depressed Frq
Depressed mood 0.93 0.18
Diminished drive 0.88 0.30
Loss of energy 0.87 0.32
Concentration/indecision 0.87 0.27
Sleep disturbance 0.83 0.32
Diminished concentration 0.82 0.24
Diminished interest/pleasure 0.81 0.12
Insomnia 0.70 0.27
Anxiety 0.69 0.42
Worthlessness 0.61 0.12
Psychic anxiety 0.59 0.33
Thoughts of death 0.56 0.12
Mitchell, Zimmerman et al MIDAS Database. Psychol Med 2009

16. “Uncommon” Symptoms
Non-Depressed
Item Depressed Proportion Proportion
Somatic anxiety 0.46 0.25
Decreased appetite 0.45 0.11
Anger 0.44 0.26
Psychomotor agitation 0.34 0.09
Psychomotor retardation 0.28 0.04
Decreased weight 0.23 0.06
Lack of reactive mood 0.22 0.06
Increased appetite 0.19 0.07
Hypersomnia 0.19 0.06
Increased weight 0.16 0.06
Mitchell, Zimmerman et al MIDAS Database. Psychol Med 2009

17. 0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
L os
s of
ene
rg y
Dim
inis
he dd
r ive
Sl e
ep
dis t
C on urb
anc
c en
tr at e
ion
/i n
dec
n=1523
is io
n
D ep
res
sed
mo
od
Dim A nx
inis iet y
hed
c onc
ent
r at
ion
Dim Ins o
inis
he m nia
d in
t er
est
/p l
e asu
re
Ps y
chi
ca nx i
e ty
Hel
p less
ss ne
Wo
r th
les s
nes
s
Hop
e les s
nes
s
Som
ati c
anx
iety
Tho
ug hts
of dea
th
specificity of each mood symptom
A ng
er
Exc
ess
Comment: Slide illustrates sensitivity and
ive
guil
Ps y t
cho
mo
t or
c ha
ng e
Ind
ec i
siv e
nes
D ec s
rea
s ed
app
eti t
Ps y
cho e
mo
t or
agi
Ps y tati
cho on
mo
t or
ret
ard
atio
n
D ec
rea
s ed
wei
Lac g ht
ko
f re
act
ive
mo
od
Inc
rea
sed
app
et it
e
Hy p
erso
mn
ia
All Case Proportion
Inc
rea
Depressed Proportion
sed
we
ight
Non-Depressed Proportion

18. -0.10
0.00
0.10
0.20
0.30
0.40
0.50
A nge
r
A nxie
ty
Decr
ea s e
d app
eti te
Decr
ea s e
d we
ig ht
Depr
es sed
m ood
Dimin
is hed
c onc
entr a
t ion
identifying non-depressed
Dimin
is hed
dr ive
Dimin
is hed
int er
est /p
leasu
re
Exc e
ss ive
guilt
Help
less n
Comment: Slide illustrates added value of each
ess
symptom when diagnosing depression and when
Hope
le s snes
s
Hy pe
rsom
ni a
Inc re
ased
appe
t ite
Inc re
ased
w eig
ht
Indec
isiv e
ne ss
Ins om
nia
L ac k
of re
act iv
e mo
od
L os s
o f en
erg y
Ps ych
i c anx
iety
Ps ych
o mot o
r agi ta
tion
Ps ych
o mot o
r c han
ge
Ps ych
o mot o
r ret ar
datio
n
Sl eep
dis tu
rban
ce
Soma
ti c a
n x iety
Rule-In Added Value (PPV-Prev)
Thou
gh
Rule-Out Added Value (NPV-Prev)
ts of
deat
h
Wor t
hle s snes
s

19. 1 Depressed Mood
S Diminished interest/pleasure
e
0.9 Diminished drive
n
s Loss of energy
i Sleep disturbance
0.8
t
Diminished concentration
i
0.7 v
i
t
0.6 y
0.5
0.4
Comment: Slide illustrates summary ROC curve
0.3 sensitivity/1-specficity plot for each mood
symptom
0.2
0.1
1 - Specificity
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
n=1523

20. 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

21. 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!

22. Cancer Staff Psychiatrists
Current Method (n=226)‫‏‬
Other/Uncertain
9% Other/Uncertain
ICD10/DSMIV 2%
0% ICD10/DSMIV
13%
Short QQ
3%
1,2 or 3 Sim ple
QQ
15%
Clinical Skills
Use a QQ Alone
15% 55%
Clinical Skills
Alone
73% 1,2 or 3 Sim ple
QQ
15%
Comment: Slide illustrates preferences of cancer
clinicians for detecting depression in a national
survey

23. Cancer Staff Psychiatrists
Ideal Method (n=226)‫‏‬
Effective?
Long QQ
8%
Clinical Skills Clinical Skills
Alone Alone
Algorithm 20%
17%
26%
ICD10/DSMIV
24%
ICD10/DSMIV 1,2 or 3 Sim ple
0% 1,2 or 3 Sim ple QQ
QQ 24%
Short QQ 34%
23%
Short QQ
24%
Comment: Slide illustrates “ideal” preferences of
cancer clinicians for detecting depression in a
national survey

24. Cancer Staff Psychiatrists
Other/Uncertain
9% Other/Uncertain
ICD10/DSMIV 2%
0% ICD10/DSMIV
13%
Short QQ
3%
1,2 or 3 Sim ple
QQ
15%
Clinical Skills
Use a QQ Alone
15% 55%
Clinical Skills
Alone
73% 1,2 or 3 Sim ple
QQ
15%
Comment: Slide illustrates preferences of cancer
clinicians vs psychiatrists for detecting
Current Method
depression

25. Cancer Staff Psychiatrists
Long QQ
8%
Clinical Skills Clinical Skills
Alone Alone
Algorithm 20%
17%
26%
ICD10/DSMIV
24%
CD10/DSMIV 1,2 or 3 Sim ple
0% 1,2 or 3 Sim ple QQ
QQ 24%
Short QQ 34%
23%
Short QQ
24%
Comment: Slide illustrates “ideal” preferences of
cancer clinicians vs psychiatrists for detecting
Ideal Method depression

26. Do Clinicians Look for Depression Often?

27. Methods to Evaluate Depression
Unassisted Clinician Conventional Scales
Untrained Trained Short (5-10)‫ ‏‬Long (10+)‫‏‬
Othe r/Uncertain
Ultra-Short (<5)‫‏‬
9%
ICD10/DSMIV
0%
Short QQ
3% Other/Uncertain Other/Uncertain
9% 9%
ICD10/DSMIV ICD10/DSMIV
0% 0%
Short QQ Short QQ
1,2 or 3 Sim ple
3% 3%
QQ
15%
Clinical Sk ills 1,2 or 3 Sim ple 1,2 or 3 Sim ple
Alone QQ QQ
73% 15% 15%
Clinical Skills Clinical Skills
Alone Alone
73% 73%
Verbal Questions Visual-Analogue Test
PHQ2 Distress Thermometer
WHO-5 Depression Thermometer
Whooley/NICE

28. 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

29. GP Detection of Depression – Meta-analysis
Mitchell, Vaze, Rao
Methods
100 studies of GP recognition rate => 35 with Se Sp
9x DSM
7x ICD10
9x HADS
4x CES-D; 4x PHQ
2x BDI
Mitchell, Vaze, Rao (2009) in press Lancet

30. Accuracy 2x2 Table
Depression Depression
PRESENT ABSENT
Test +ve True +ve False +ve PPV
Test -ve False -Ve True -Ve NPV
Sensitivity Specificity Prevalence

31. N=35 studies
Accuracy of GP’s Diagnoses
Depression Depression
PRESENT ABSENT
GP +ve 2503 2515 5018
PPV 42.8%
GP -ve 4050 25,125 6678
NPV 85.1%
6553 27,640 9559
Sensitivity Specificity
Prevalence 19%
48% 80.1%
Mitchell, Vaze, Rao Lancet in Press

32. Unassisted Accuracy
Cut-off value
Non-Depressed
Depressed
#
of
Individuals True -ve True +ve
False -ve False +ve
Test
Result

33. Unassisted Accuracy - Prospective
Comment: Slide illustrates detection of
depression (incl false + false –) for each
100 consecutive patients in primary care
if prospective cases are recorded
Cut-off value
Non-Depressed
n=80
Depressed
#
n=20
of
Individuals True -ve True +ve
64 10
False -ve False +ve
10 16 Test
Result

34. Unassisted Accuracy - Retrospective
Comment: Slide illustrates detection of
depression (incl false + false –) for each
100 consecutive patients in primary care
if GPs opinions are gathers from notes
Cut-off value
Non-Depressed
n=80
Depressed
#
n=20
of
Individuals True -ve True +ve
73 7
False -ve False +ve
13 7 Test
Result

35. Some Predictors of Detection
Giving sufficient time
Asking about depression
Looking for symptoms
Recognizing symptoms
High and low risk samples
Mild Moderate Severe

36. GP Recognition of Individual symptom
Proportion of Individual Symptoms Recognised by GPs
80.0 76.1
70.0
60.0
50.0
40.0 36.4
34.6
31.6
30.0
21.6
20.0 16.7
13.3
9.1 8.3 8.3
10.0
0.0
s
ng
a
d
gy
s
ia
st
ty
ism
es
oo
si
ni
ex
re
xie
pi
er
ia
m
ln
m
m
te
Co
or
en
dr
An
so
fu
in
i
An
w
ss
on
ar
In
t
of
Lo
No
of
Pe
Te
ch
ss
ss
po
Lo
Lo
Hy
O’Conner et al (2001) Depression in primary care.
Int Psychogeriatr 13(3) 367-374.

37. Sl e
ep
di s
tur
ban
Los ces
so ; in
fa s om
ppe ni a
De ti te ; ea
0
10
20
30
40
50
60
70
80
90
100
pre ; ov
s se ere r ly
dm a tin wa
g; w ke n
ood
e ig ing
; ho
86.8
Los pe ht c
Apa les han
so thy sne ges
f in ; le s s;
ter tha s ad
es t r gy
;w ; tir ; gl
ithd edn oom
raw
al ; es s y
55.6 54.4
Los in d ; la
so iffe s si
fe ren tud
Los ner c e; e
43.3
so gy; lo n
f lib l os eli n
ido so ess
; lo f dr
36
ss i ve
Anx of s ; bu
io u ex rnt
ou
s; a d ri v
e; i t
29.8
g ita mp
ted Te ote
; irr ars
Som Fe i tab ;w nce
atic eli n l e; eep
; ve gw res ing
get orth tl es ; cr
yi n
ativ
es l es
s; g
s, t
ens g
ym uil t e; s
pt o y; l t re
ms ac k s se
;m of s d
ala
26.2 25.6 25.2
Sui i se el f
Los ci d ;m est
ee m
so e th ulti
f co ou ple
23.8
nc e ght con
ntr a s; t s ul
tio n hou ta t
ion
ght s
24
Dim ; po of s
or el f
ini s me inj u
hed mo ry
per ry ,
f or poo
ma r th
nce i nk
Em ; in i ng
21.4 21.2
Los otio abi
so na li ty
f af l la to
Be fec bil i cop
h av Los
so t; f
lat
ty ;
mo e
i ou fe a ff od
ral njo ect sw
pro ym ; lo ing
bl e
ms
ent ss s
13.9 12.8
or of e
; ag pl e mo
gr e as u tion
ss iv re ;
9.5
ene lac
Pe s s; ko
s si be f hu
mi s hav mo
m; i ou r
7.2
ne ral
gat c ha
Ps iv e
yc h atti nge
s
7
App om tud
ear oto es ,
anc r re wo
e; tar r ry
ing
7
spe dat
ec h i on
; sl
; ex He
ada ow
ces nes
si v c he s
5.9
He es s; d
av y mi l iz zi
i ng nes
us e ; va s
4.8
of a gue
l co nes
De hol s, e
l us , to tc .
i on bac
4.1
Re s; h co
ac t all u or
ion ci n dru
to p atio gs
2.6
r ob ns;
abl c on
Fa ec fus
mil aus ion
yo es
1.8
r pa or
life
looking for depression
st h eve
i sto
ry n ts
1.8
Ob of d
ses epr
si ve es s
i de i on
1.3
ati o
n; p
ho
bia
Comment: Slide illustrates which
s
symptoms are asked about by GPS
0.9
Per Lac
i od ko
f in
of l s ig
ife ht
0.4
(me
no p
aus
e)
0.4

38. Effect of Prevalence

39. 1
Post-test Probability
0.9 Comment: Slide illustrates Bayesian
curve – pre-test post test probability for
every possible prevalence
0.8
0.7
0.6
0.5
0.4
0.3 Baseline Probability
Depression+
0.2
Depression-
0.1
Pre-test Probability
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

40. 1
Post-test Probability
0.9
0.8
0.7
0.6
0.5
PPV
0.4
0.3 Baseline Probability
Depression+
0.2
NPV
Depression-
0.1
Pre-test Probability
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

41. Effect of Severity

42. 1.00
Post-test Probability
0.90 Comment: Slide illustrates GP diagnosis
of depression is more successful than
their diagnosis of milder “distress”
0.80
0.70
0.60
0.50
0.40
Distress+
0.30
Distress-
Baseline Probability
0.20
Depression+
Depression-
0.10
Pre-test Probability
0.00
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

43. GPs vs Oncologists vs Nurses
Who is better?
Bayesian analysis

44. 1.00
Post-test Probability
GP+
GP-
0.90 Baseline Probability
Nurse+
Nurse-
0.80 Oncologist+
Oncologists-
0.70
0.60
0.50
0.40
0.30
0.20
0.10
Pre-test Probability
0.00
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

45. 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

46. 20 Instruments for Depression
Ultra-short <6 Short > 5 < 11 Long > 10
PHQ1 HADS (7)‫‏‬ HAM-D (21)‫‏‬
PHQ2 (2)‫‏‬ BDI (7)‫‏‬ BDI (21,13)‫‏‬
WHO-5 (5)‫‏‬ MOS-D (8)‫‏‬ BSI (53)‫‏‬
Distress Therm (1)‫‏‬ PHQ9 (9)‫‏‬ CES-D (20,10)‫‏‬
DSMIV (9)‫‏‬ Zung (20)‫‏‬
MADRAS (10)‫‏‬ GDS (30,15)‫‏‬
EPDS (10)‫‏‬ SDS (20)‫‏‬
DADS (7)‫‏‬ DEPS (10)‫‏‬

48. Severity1 IDS-C30 IDS-SR30 QIDS-C16 QIDS-SR16 HRSD17 HRSD21 HRSD24 MADRS BDI
Addition: Comparison of Scale Scores
0 (None)‫‏‬
0
0-3
4-5
0–3
4–5
0
1
0
1
0
1–2
0–1
2
0–1
2
0 0
0 6 6 2 2 3 3 3–4
0 7-8 7–8 3 3 4 4 5
0 9-10 9–11 4 4 5–6 5–6 6–7
0 11 12–13 5 5 7 7–8 8–9 6 9
1 (Mild)‫‏‬ 12-15 14–16 6 6 8 9 10–11 7 10
1 16-17 17–18 7 7 9–10 10 12
1 18-20 19–21 8 8 11 11–12 13–14
1 21-22 22–23 9 9 12 13 15–16
1 23 24–25 10 10 13 14–15 17–18 19 18
2 (Moderate)‫‏‬ 24-27 26–28 11 11 14–15 16 19 20 19
2 28-29 29–30 12 12 16 17 20–21
2 30-32 31–33 13 13 17 18–19 22–23
2 33-35 34–36 14 14 18–19 20–21 24–25
2 36 37–38 15 15 18–19 22 26 34 29
3 (severe)‫‏‬ 37-39 39–40 16 16 20 23 27–28 35 30
3 40-41 41–43 17 17 21–22 24–25 29–30
3 42-43 44–45 18 18 23 26 31–32
3 44-45 46–47 19 19 24 27 33
3 46 48 20 20 25 28 34
4 (v Severe)‫‏‬ 47-51 49–53 21 21 26–27 29–31 35–38
4 52-53 54–55 22 22 28 32 39
4 54-56 56–58 23 23 29 33–34 40–41
4 57-59 59–61 24 24 30–31 35–36 42–44
4 60-62 62–24 25 25 32 37–38 45–46
4 63-65 65–67 26 26 33–35 39–41 47–49

49. => Symptoms

50. 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

54. 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 screening
results 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

55. 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

56. Do Tools Work?
Clinician rate vs tool rate (both against SCID)‫‏‬
Clinician with vs without tool
Tool vs SCID

57. 1.00 Comment: Slide illustrates Bayesian
Post-test Probability
curve comparison from indirect studies
of clinician and HADS
0.90
This illustrates POTENTIAL gain from
screening
0.80
0.70
0.60
0.50
0.40
Clinician Positive (Fallowfield et al, 2001)
0.30
Clinician Negative (Fallowfield et al, 2001)
Baseline Probability
0.20
HADS-D Positive (Mata-analysis)
HADS-D Negative (Meta-analysis)
0.10
Pre-test Probability
0.00
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

58. Comment: Slide illustrates actual gain in
meta-analysis of screening
implementation in primary care

59. 1.00
Post-test Probability
Clinical+
Clinical-
0.90 Baseline Probability
Screen+
Screen-
0.80
0.70
0.60
0.50
0.40
Comment: Slide illustrates Bayesian
0.30 curve comparison from RCT studies of
clinician with and without screening
0.20 This illustrates ACTUAL gain from
screening
0.10
Pre-test Probability
0.00
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

60. HADS Validity vs Structured Interview
METHODS
Against depression 9x studies of the HADS-D; 5x of the
HADS-T and 2x of the HADS-A were identified.
RESULTS
HADS-T = HADS-D = HADS-A
The clinical utility index (UI+, UI-) was 0.214 and 0.789 for the
HADS-D.
Sensitivity Specificity PPV NPV FC
HADS-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%

61. 1.00
Comment: Slide illustrates Bayesian
Post-test Probability
curve comparison of HADS in detection
of depression in cancer settings.
0.90
Against expectations HADS-A was most
successful
0.80
0.70
0.60
0.50
0.40
HADS-T Positive (N=5)
HADS-T Negative (N=5)
0.30
Baseline Probability
HADS-A Positive (N=2)
HADS-A Negative (N=2)
0.20
HADS-D Positive (N=9)
HADS-D Negative (N=9)
0.10
Pre-test Probability
0.00
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

62. 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

63. 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

64. 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?”

65. Distribution of DT Scores
Ransom (2006) PO (n=491)
18.0
15.7
16.0
14.7
13.8
14.0 13.2
12.0
10.4
10.0
8.4
7.7
8.0 7.3
6.0
3.7
4.0 3.3
1.8
2.0
0.0
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)‫‏‬

68. SCAN, SCID, PSE, CIDI, MINI
LONG
BDI, MADRAS, Hamilton
High NPV MEDIUM
High PPV
SHORT
High NPV HADS, EPDS, PHQ9, CES-D
Med PPV
High NPV
PHQ2, NICE, DT
Low PPV

69. 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

70. Approaches to Somatic Symptoms of Depression
Inclusive
Uses 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.
Exclusive
Eliminates somatic symptoms but without substitution. There is concern that
this might lower sensitivity. with an increased likelihood of missed cases (false
negatives)‫‏‬
Etiologic
Assesses the origin of each symptom and only counts a symptom of
depression 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).
Substitutive
Assumes somatic symptoms are a contaminant and replaces these additional
cognitive symptoms. However it is not clear what specific symptoms should be
substituted

71. Somatic Bias in Mood Scales

72. Comment: Slide illustrates concept of
phenomenology of depressions in
medical disease
Primary Depression
Medically Unwell Secondary
Depression

73. Study: Coyne Thombs Mitchell
N= 1200 – 4500
Pooled database study
All comparative studies