Cardiff09 - Detecting Depression in Primary & Secondary Care (May2009)

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




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


Severity ofLow Mood

?Point of Rarity

Pooled

Non-Depressed


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




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%



Cancer StaffCurrent Method (n=226)‏

Psychiatrists

Comment: Slide illustrates preferences of cancer clinicians for detecting depression in a national survey



ICD10/DSMIV24%

Short QQ24%

Long QQ8%

Algorithm26%

Short QQ23%

ICD10/DSMIV0%



Cancer StaffIdeal Method (n=226)‏

Psychiatrists

Effective?

Comment: Slide illustrates “ideal” preferences of cancer clinicians for detecting depression in a national survey



ICD10/DSMIV0%

Short QQ3%

Other/Uncertain9% Other/Uncertain

2%

Use a QQ15%

ICD10/DSMIV13%



Cancer Staff Psychiatrists

Current MethodComment: Slide illustrates preferences of cancer clinicians vs psychiatrists for detecting depression



ICD10/DSMIV24%

Short QQ24%

Long QQ8%

Algorithm26%

Short QQ23%

ICD10/DSMIV0%



Cancer Staff Psychiatrists

Ideal MethodComment: Slide illustrates “ideal” preferences of cancer clinicians vs psychiatrists for detecting depression

Do Clinicians Look for Depression Often?



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



ICD10/DSMIV0%

Short QQ3%

Other/Uncertain9%



ICD10/DSMIV0%

Short QQ3%

Other/Uncertain9%




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

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

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

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


Post

-test

Pro

babi

lity


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


Post

-test

Pro

babi

lity

Distress+

Distress-


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

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


Pos

t-tes

t Pro

babi

lity

GP+GP-Baseline ProbabilityNurse+Nurse-Oncologist+Oncologists-




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




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

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


Post

-test

Pro

babi

lity

Clinician Positive (Fallowfield et al, 2001)

Clinician Negative (Fallowfield et al, 2001)


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

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


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%

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


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




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




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




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


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




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

Health & Medicine

Cardiff09 - Detecting Depression in Primary & Secondary Care (May2009)