Suffolk - Detecting Depression Primary Vs Secondary Care (Nov09)

Alex Mitchell [email protected]

Consultant in Liaison Psychiatry

Detecting Depression in Primary & Secondary Care

Evidence Based Comparison

Bury St Edmonds - No Physical Health Without Mental Health 2009

Introduction to Physical/Mental Comorbidity

No Physical Health Without Mental Health

• Awareness of the link between physical and mental health

• Liaison Mental Health Services

• Engaging Patients and Carers

• Re-organisation, Quality & Commissioning

• Training and Education

Quality of medical care

Quality of preventive care

Quality of cardiac care

Quality of Care MI vs No MI27 examined receipt of medical care in those with and without mental illness

19/27 showed deficits in care

10 examined medical care in those with and without substance use disorder (or dual-diagnosis

10/10 showed deficits in care

Quality of Medical Treatment i ProceduresSummary meta-analysis plot [random effects]

0.1 0.2 0.5 1 2

combined 0.89 (0.82, 0.96)

Kisely 2007 [Revascularisation] 0.92 (0.86, 1.07)

Petersen 2003 [Revascularisation] 0.89 (0.79, 0.98)

Lawrence 2003 [Revascularisation Women] 0.34 (0.18, 0.64)

Lawrence 2003 [Revascularisation Men] 0.31 (0.21, 0.45)

Druss 2001 [Revascularisation] 0.74 (0.56, 0.95)

Plomondon 2007 [PCI] 1.06 (0.97, 1.15)

Druss 2000 [PTCA] 0.96 (0.91, 1.02)

Jones 2005 [PTCA] 1.04 (0.98, 1.10)

Druss 2000 [Cath] 0.74 (0.70, 0.78)

Plomondon 2007 [Cath] 1.05 (0.98, 1.13)

Druss 2000 [CABG] 0.90 (0.85, 0.96)

Plomondon 2007 [CABG] 1.02 (0.99, 1.06)

Jones 2005 [CABG] 0.91 (0.75, 1.09)

Petersen 2003 [Angiography] 0.90 (0.83, 0.98)

relative risk (95% confidence interval)

HR =0.89 p<0.004

Quality of Medical Treatment ii Medication

Summary meta-analysis plot [random effects]

0.5 1 2 5 10 100

combined 0.92 (0.85, 1.00)

HAART (Himelhoch2007) 0.85 (0.71, 1.23)

HAART (Himelhoch2004) 2.28 (1.24, 32.50)

HAART (Mijch) 1.28 (1.04, 1.57)

BBlockers (Petersen) 0.78 (0.69, 0.92)

Bblocker (Plomondon) 1.11 (0.97, 1.28)

Bblocker (Druss2001) 0.85 (0.72, 0.98)

Aspirin (Plomondon) 0.93 (0.83, 1.04)

Aspirin (Petersen) 0.96 (0.81, 1.15)

Aspirin (Druss2001) 0.81 (0.65, 0.98)

ACE-I or ARBb (Plomondon) 0.93 (0.84, 1.01)

ACE (Petersen) 0.92 (0.79, 1.09)

ACE (Druss2001) 0.81 (0.65, 0.98)

odds ratio (95% confidence interval)


0.1 0.2 0.5 1 2 5

combined 0.79 (0.66, 0.95)

Statin (Weiss) 0.54 (0.36, 0.51)

Statin (Kreyenbuhl) 0.29 (0.11, 0.77)

Statin (Hippisley-Cox) 0.85 (0.80, 0.91)

Osteoporosis (Bishop) 0.38 (0.15, 0.97)

Insulin (Weiss) 1.44 (0.96, 2.16)

Cholesterol (Weiss) 1.85 (1.11, 3.09)

Cholesterol (Desai) 1.01 (0.37, 2.77)

Bblocker (Weiss) 0.96 (0.54, 1.71)

Bblocker (Hippisley-Cox) 0.96 (0.88, 1.06)

Bblocker (Desai) 0.70 (0.43, 1.15)

Aspirin (Weiss) 0.89 (0.64, 1.24)

Aspirin (Hippisley-Cox) 1.00 (0.97, 1.04)

Aspirin (Desai) 1.07 (0.49, 2.30)

Arthritis (Redelmeier) 0.59 (0.57, 0.62)

ACE-I or ARBb (Weiss) 0.83 (0.61, 1.14)

ACE (Kreyenbuhl) 0.23 (0.12, 0.44)


OR =0.92 OR =0.79


0.01 0.1 0.2 0.5 1 2 5 10 100

combined 0.72 (0.51, 1.00)

Statin (Kreyenbuhl) 0.14 (0.05, 0.44)

Statin (Hippisley-Cox) 1.15 (0.80, 1.95)

HAART (Yun) 1.43 (1.18, 1.74)

HAART (Tegger) 0.36 (0.25, 0.50)

Cholesterol (Hippisley-Cox) 0.86 (0.70, 12.30)

Cholesterol (Desai) 1.31 (0.57, 3.00)

Chemotherapy (Goodwin) 0.65 (0.43, 1.00)

Bblocker (Wang) 0.55 (0.45, 0.55)

Bblocker (Hippisley-Cox) 1.18 (0.94, 1.56)

Bblocker (Desai) 0.70 (0.48, 1.03)

Aspirin (Desai) 0.75 (0.39, 1.43)

ACE (Kreyenbuhl) 0.46 (0.18, 1.19)


OR =0.72

SMI Schz Affective

Detecting Depression in Primary & Secondary Care

Evidence Based Update

2/3rds 1/3rd

25%Psychiatry

10%Medical

Primary Care

cg90cg42

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

Clinical Questions Evidence

Detecting depression RoutinelyPC vs SC; International Differences?

Symptoms of DepressionToo complex? Distress?

Depression in medical settingsSpecial? Somatic symptoms?

Depression in late-lifeDifferent?

Enhanced DetectionWhich tool?Do they work?

Recognition in Routine Care

Is “diagnosis as usual” sufficient?

Audience

Which method do you prefer?

Your own skills (first assessment)

Start with 1 or2 questions

Limit to 7 or9 questions

20 questions

Phone a friend!

Audience

Which method do you prefer?

Your own skills (first assessment) 50% ‏

Start with 1 or 32%2 questions 73%

Limit to 7 or 75%9 questions 80%

20 questions 85%

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/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/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


Clinical Skills Alone

73%

ICD10/DSMIV0%

Short QQ3%

Other/Uncertain9%


Clinical Skills Alone

73%

ICD10/DSMIV0%

Short QQ3%

Other/Uncertain9%

GP Detection of Depression – Meta-analysis

Methods– 140 studies of GP recognition

rate =>

– 90 depression– 40 interview– 19 se sp (+2)– 10 countries

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

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 – Medical Notes

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

% 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

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

100

Slee

p di

stur

banc

es; in

som

nia;

ear

ly w

aken

ing

Loss

of a

ppet

ite; o

vere

atin

g; w

eigh

t cha

nges

Dep

ress

ed m

ood;

hop

eles

snes

s; s

ad; g

loom

y

Apat

hy; l

etha

rgy;

tire

dnes

s; la

ssitu

de

Loss

of i

nter

est;

with

draw

al; i

ndiff

eren

ce; l

onel

ines

s

Loss

of e

nerg

y; lo

ss o

f driv

e; b

urnt

out

Loss

of l

ibid

o; lo

ss o

f sex

driv

e; im

pote

nce

Tear

s; w

eepi

ng; c

ryin

g

Anxi

ous;

agi

tate

d; ir

ritab

le; r

estle

ss, t

ense

; stre

ssed

Feel

ing

wor

thle

ss; g

uilty

; lac

k of

sel

f est

eem

Som

atic

; veg

etat

ive

sym

ptom

s; m

alai

se; m

ultip

le c

onsu

ltatio

ns

Suic

ide

thou

ghts;

thou

ght o

f sel

f inj

ury

Loss

of c

once

ntra

tion;

poo

r mem

ory,

poo

r thi

nkin

g

Dim

inis

hed

perfo

rman

ce; i

nabi

lity

to c

ope

Emot

iona

l labi

lity;

moo

d sw

ings

Loss

of a

ffect

; fla

t affe

ct; l

oss

of e

mot

ion

Loss

of e

njoy

men

t or p

leas

ure;

lack

of h

umor

Beha

viou

ral p

robl

ems;

agg

ress

iven

ess;

beha

viou

ral c

hang

es

Pess

imis

m; n

egat

ive

attit

udes

, wor

ryin

g

Psyc

hom

otor

reta

rdat

ion;

slow

ness

Hea

dach

es; d

izzi

ness

Appe

aran

ce; s

peec

h; e

xces

sive

sm

iling

; vag

uene

ss, e

tc.

Heav

y us

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alc

ohol

, tob

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or d

rugs

Del

usio

ns; h

allu

cina

tions

; con

fusi

on

Rea

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pro

babl

e ca

uses

or l

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vent

s

Fam

ily o

r pas

t his

tory

of d

epre

ssio

n

Obs

essi

ve id

eatio

n; p

hobi

asLa

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

GP Asks about:

GP Recognizes:Proportion 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.

Predictors of Recognition

Prevalence10% rural 15% mean 20% urban 20% (oncology 25%)

Severity70% mild 20% moderate 10% severe

InternationalLow in developing but in Western:Italy > Netherlands >Australia > UK > US

Cumulative77% single 89% 3-6 months

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

Pos

t-tes

t Pro

babi

lity

Baseline Probability

Depression+

Depression-

PPV

NPV

Comment: Slide illustrates Bayesian curve – pre-test post test probability for every possible prevalence

Recognition from WHO PPGHC Study (Ustun, Goldberg et al)

7470 69.6

61.5 59.656.7 56.7 55.6 54.2

45.7 43.939.7

28.4

22.2 21 19.3

0

10

20

30

40

50

60

70

80

Santia

go

Verona

Manch

ester

Paris

Groningen

Berlin

Seattle

Mainz

TOTALBangalo

reRio de J

aneir

o

Ibadan

Ankara

Athen

sShan

ghaiNagas

aki

Symptoms of Depression…usual suspects

Reminder of DSMIV and ICD

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

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

0

500

1000

1500

2000

2500

3000

Zero One

TwoThree

Four

Five SixSev

eneig

htNine

TenElevenTwelv

eTh

irtee

nFourte

enFif

teen

SixteenSeve

nteen

Eighteen

0

0.05

0.1

0.15

0.2

0.25

0.3

Eight

Nine Ten

Eleven

Twelv

eTh

irtee

nFo

urtee

n

Fiftee

nSixt

een

Seven

teen

Eighteen

Ninetee

n

Twen

tyTw

enty-

one

Proportion MissedProportion Recognized

Symptoms of Depression…time for change

Are the classical symptoms evidence based?

“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 n=2300

“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

-0.10

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

Depression in Medical Settings

Are the symptoms (phenomenology) unique?

Is it harder to detect?

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

Detection in Hospital Settings

CNS in oncology; n=350

Bayesian analysis

13.1

16.7

28.6 28.6

41.443.5 43.5

56.5

83.3

62.5

71.4

0

10

20

30

40

50

60

70

80

90

Zero One Two Three Four Five Six Seven Eight Nine Ten

Series1Series2

Comment: Slide illustrates diagnostic accuracy according to score on DT

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

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

Comment: Doctors appear to be more successful at ruling-in or giving a diagnosis, nurses more successful at ruling out

Depression in Older People

Does it go unrecognized?

Are Somatic Symptoms Common in Older People?

0.00

0.10

0.20

0.30

0.40

0.50

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Post

-test

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

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0

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Hel

ples

snes

s

Hop

eles

snes

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Wor

thle

ssne

ss

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iety

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atic

anx

iety

)

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er

Inde

cisi

vene

ss

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ghts

of D

eath

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inis

hed

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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

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Anger

Anxiety

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bined)

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chic

anxie

ty)

Anxiety

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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

Enhanced Detection Options

Do scales and tools make a difference?

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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 in Study from Christensen

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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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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

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?”

Summary

Depression is modestly common & easily missed5% have depression as their main reason for presentation

Most depression is comorbid50% adults 80% elderly have physical illness

All health profressionals struggle with diagnosisSymptom approach

Routine screening modestly effectiveHigh risk, targeted and algorithm approaches

Dimensional approach developingTrials in cardiac care and oncology and neurology of ET

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

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Suffolk - Detecting Depression Primary Vs Secondary Care (Nov09)

Health & Medicine

Transcript of Suffolk - Detecting Depression Primary Vs Secondary Care (Nov09)