Alex Mitchell www.psycho-oncology.info

Department of Cancer & Molecular Medicine, Leicester Royal Infirmary

Department of Liaison Psychiatry, Leicester General Hospital

Portugal 2010Portugal 2010

WORKSHOP Day 1

Science of Screening:Definitions, analysis, screening tools, case-finding tools, prevalence, link with physical concerns

WORKSHOP Day 1

Science of Screening:Definitions, analysis, screening tools, case-finding tools, prevalence, link with physical concerns

Schedule Day 1Schedule Day 1

930-10.00 – Introduction, groups and issues

10.00-11.00 – T1 Basic science of screening

Break

11.30 – 12.30 – Group task #1

Lunch

1.30-2.30 – T2 Symptoms, Burden, Help, Needs in Cancer

Break

3.00 – 4.00 – Evaluation of a screening paper

10 Questions10 Questions1. How do we understand screening studies2. Can we design a screening study3. Which instrument works best4. Which is the most popular tool5. How good are clinicians alone6. Can the DT be improved7. Is screening effective in clinical practice8. What are the barriers to successful implementation9. How can screening be improved10. Do somatic symptoms interfere with the diagnosis

Incidence > Prevalence > SuvivorshipIncidence > Prevalence > Suvivorship

Changing landscape of epidemiology

10.9million incident cases (1mi breast, lung colorectal); 25mi prevalent cases

23

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3

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4

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8

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14

16

25

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15UK Rank

(12th)

(10th)

(5th)

(4th)

(6th)

(15th)

(8th)

(3rd)

(1st)

(2nd)

All others

Lip, oral cavity

Leukaemia

NHL

Bladder

Oesophagus

Liver

Stomach

Colorectum

Prostate

Lung

world (%) uk (%)27

3

3

4

4

5

6

9

9

9

23

27

3

1

5

1

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2

12

2

12

31UK Rank

(7th)

(18th)

(5th)

(19th)

(4th)

(13th)

(3rd)

(11th)

(2nd)

(1st)

All others

NHL

Thyroid

Ovary

Liver

Uterus

Stomach

Lung

Cervix

Colorectum

Breast

world(%) uk(%)

Males

Most commonly diagnosed cancers worldwide

Females

Cancer Death Rates* Among Men, US,1930-2005

*Age-adjusted to the 2000 US standard population.Source: US Mortality Data 1960-2005, US Mortality Volumes 1930-1959,National Center for Health Statistics, Centers for Disease Control and Prevention, 2008.

0

20

40

60

80

10019

30

1935

1940

1945

1950

1955

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

Lung & bronchus

Colon & rectum

Stomach

Rate Per 100,000

Prostate

Pancreas

LiverLeukemia

Cancer Death Rates* Among Women, US,1930-2005

*Age-adjusted to the 2000 US standard population.Source: US Mortality Data 1960-2005, US Mortality Volumes 1930-1959,National Center for Health Statistics, Centers for Disease Control and Prevention, 2008.

0

20

40

60

80

10019

30

1935

1940

1945

1950

1955

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

Lung & bronchus

Colon & rectum

Uterus

Stomach

Breast

Ovary

Pancreas

Rate Per 100,000

0

10

20

30

40

50

60

70

80

90

100

Melanom

aBrea

st (fe

male)

Urinary

bladde

r

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e

Colon

All site

s

Rectum

Non-H

odgkin

lymph

oma

Ovary

Leuk

emiaLu

ng and

bron

chus

Pancre

as

1975-19771984-19861996-2004Change

5 Year Survival in US Cancers

Distress Thermometer – PooledProportion

18 .4 %

12 .9 %

11.2 %12 .3 %

8 .1%

11.9 %

5.0 %

2 .8 % 2 .6 %

7.7% 7.2 %

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

20.0%

Zero One Two Three Four Five Six Seven Eight Nine Ten

Insignificant SevereModerateMildMinimal

p124

50%

94.2%

37.4%

8 yrs N= 9282 NCS‐R

N=23 studies; 50% some treatment 33% minimal treatment N=19 studies; 30% 1 in 1/12; 10% 3 in 3 months

T1. Basic Science of ScreeningT1. Basic Science of Screening

Definitions

Graphics

Diagnostic Testing……by application (who)Diagnostic Testing……by application (who)

Routine Screening

The systematic application of a test or inquiry, to all individuals who may have (or who have not sought medical help for that disorder)

Targeted (High Risk)

The highly selected application of a test or inquiry, to identify individuals at high risk of a specific disorder by virtue of known risk factors

Adapted from Department of Health. Annual report of the national screening committee. London: DoH, 1997.

Diagnostic Testing……by aim (why)Diagnostic Testing……by aim (why)

ScreeningRule out those without the disorder with high accuracy (high

NPV)

Case-FindingRule in those with the disorder with high accuracy (high PPV)

Diagnostic Testing……by method (how)Diagnostic Testing……by method (how)

ScreeningA simple tool with high acceptability but good NPV

Case-FindingAn accurate tool with high PPV and NPV

RatingSimple, patient rated, correl. With QoL and other outcomes

Defining Diagnostic Testing…by comparatorDefining Diagnostic Testing…by comparator

Accuracy (aka convergent validity)The degree of approximation (veracity) to a robust comparator

Validity (aka criterion validity)The degree of approximation (veracity) to a criterion reference

PrecisionThe degree of predictability (low SD) in the measure

Stage Type Purpose Description

Pre-clinical Development Development of the proposed tool or test

Here the aim is to develop a screening method that is likely to help in the detection of the underlying disorder, either in a specific setting or in all setting. Issues of acceptability of the tool to both patients and staff must be considered in order for implementation to be successful.

Phase I_screen

Diagnostic validity Early diagnostic validity testing in a selected sample and refinement of tool

The aim is to evaluate the early design of the screening method against a known (ideally accurate) standard known as the criterion reference. In early testing the tool may be refined, selecting most useful aspects and deleting redundant aspects in order to make the tool as efficient (brief) as possible whilst retaining its value.

Phase II_screen

Diagnostic validity Diagnostic validity in a representative sample

The aim is to assess the refined tool against a criterion (gold standard) in a real world sample where the comparator subjects may comprise several competing condition which may otherwise cause difficulty regarding differential diagnosis.

Phase III_screen

Implementation Screening RCT; clinicians using vs not using a screening tool

This is an important step in which the tool is evaluated clinically in one group with access to the new method compared to a second group (ideally selected in a randomized fashion) who make assessments without the tool.

Phase IV_screen

Implementation Screening implementation studies using real-world outcomes

In this last step the screening tool /method is introduced clinically but monitored to discover the effect on important patient outcomes such as new identifications, new cases treated and new cases entering remission.

Development of Diagnostic Tests

Concepts: Se Sp PPV NPVConcepts: Se Sp PPV NPV

Accuracy 2x2 TableAccuracy 2x2 Table

Depression

PRESENT

Depression

ABSENT

Test +ve True +ve False +ve PPV

Test ‐ve False ‐Ve True ‐Ve NPV

Sensitivity Specificity Prevalence

Reference StandardDisorder Present

Reference StandardNo Disorder

Test+ve A B

A/A + BPPV

Test-ve C D

D/C + DNPV

Total A / A + CSn

D / B + DSp

Basic Measures of AccuracyBasic Measures of Accuracy

Sensitivity (Se) a/(a + c) TP / (TP + FN)

A measure of accuracy defined the proportion of patients with disease in whom the test result is positive: a/(a + c)

Specificity (Sp) d/(b + d) TN / (TN + FP)A measure of accuracy defined as the proportion of patients without disease in

whom the test result is negative

Positive Predictive Value a/(a+b) TP / (TP + FP)A measure of rule‐in accuracy defined as the proportion of true positives in

those that screen positive screening result, as follows

Negative Predictive Value c/(c+d) TN / (TN + FN)A measure of rule‐out accuracy defined as the proportion of true negatives in

those that screen negative screening result, as follows

Concepts => FiguresConcepts => Figures

Graphical – Screening principles

Non-Depressed

Depressed

# ofIndividuals

# ofIndividuals

Severity of Depression

Graphical – Screening principles

Non-Depressed

Depressed

# ofIndividuals

Cut-Off

# ofIndividuals

Severity of Depression

HighLow

Graphical – Screening principles

Non-Depressed

Depressed

# ofIndividuals

Cut-Off

# ofIndividuals

Severity of Depression

HighLow

High Sensitivity >>>>

<<<< high Specificity

Graphical – Screening principles

Non-Depressed

Depressed

# ofIndividuals

Cut-Off

# ofIndividuals

Severity of Depression

HighLow

High Sensitivity >>>>

<<<< low Specificity

Can This Help establish a syndrome?Can This Help establish a syndrome?

Example: A Clear Disease [#1]Example: A Clear Disease [#1]

Disorder

Number ofIndividuals

False +veFalse +ve

True -veTrue -ve

Point of Partial Rarity

Test Result

No Disorder

False -veFalse -ve

True +veTrue +ve

Example: A Probable Syndrome [#2]Example: A Probable Syndrome [#2]

Disorder

Number ofIndividuals

False +veFalse +ve False -veFalse -ve

True -veTrue -ve

True +veTrue +ve

MMSE Cognitive Score

No Disorder

Example: A Normally Distributed Trait [#3]Example: A Normally Distributed Trait [#3]

Disorder

Number ofIndividuals

False +veFalse +ve False -veFalse -ve

True -veTrue -ve

True +veTrue +ve

MMSE Cognitive Score

No Disorder

Example: DementiaExample: Dementia

Disease?Syndrome?Trait?

Hubbert et al (2005) BMC GeriatricsHubbert et al (2005) BMC Geriatrics

MMSE scores for dementia (n=72)and non-dementia (n=2735)

Huppert et al BMC Geriatrc 2005

Example: DepressionExample: Depression

DiseaseSyndromeTrait

Thompson et al (2001) n=18,414 HADS-DThompson et al (2001) n=18,414 HADS-D

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PHQ9 Linear distribution

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PHQ9 (Major Depression)PHQ9 (Minor Depression)PHQ9 (Non-Depressed)

Baker-Glen, Mitchell et al (2008)

T1. Science ExamplesT1. Science Examples

2x2 tables

workshop

Accuracy in wordsAccuracy in words

Sensitivity The chance of testing positive among those with the condition

The chance of rejecting the null hypothesis among those that do not satisfy the null hypothesis

SpecificityThe chance of testing negative among those without the condition

The chance of accepting the null hypothesis among those that satisfy the null hypothesis

Positive Predictive Value The chance of having the condition among those that test positive

The chance of not satisfying the null hypothesis among those that reject the null hypothesis

Negative Predictive Value The chance of not having the condition among those that test negative

The chance of satisfying the null hypothesis among those that accept the null hypothesis

Type I Error or α (alpha) or p-Value or false positive rate The chance of testing positive among those without the condition

The chance of rejecting the null hypothesis among those that satisfy the null hypothesis

Type II Error or β (beta) or false negative rate The chance of testing negative among those with the condition

The chance of accepting the null hypothesis among those that do not satisfy the null hypothesis

False Discovery Rate or q-Value The chance of not having the condition among those that test positive

The chance of satisfying the null hypothesis among those that reject the null hypothesis

False Omission Rate The chance of having the condition among those that test negative

The chance of not satisfying the null hypothesis among those that accept the null hypothesis

Rule-in AccuracyRule-in Accuracy

DepressionPRESENT

DepressionABSENT

Test +ve True +ve False +ve(type I error)

PPV(discrimination)

Test -ve False –Ve

(type II error)

True -Ve NPV

Sensitivity(occurrence)

Specificity Prevalence

Rule-Out AccuracyRule-Out Accuracy

DepressionPRESENT

DepressionABSENT

Test +ve True +ve False +ve PPV

Test -ve False –Ve(type II error)

True -Ve NPV(discrimination)

Sensitivity Specificity(occurrence)

Prevalence

Accuracy 2x2 TableAccuracy 2x2 Table

DepressionPRESENT

DepressionABSENT

Test +ve True +ve False +ve PPV

Test -ve False -Ve True -Ve NPV

Sensitivity Specificity Prevalence

Test vs Major DepressionTest vs Major Depression

DepressionPRESENT

DepressionABSENT

Test +ve 500 1500 2000

Test -ve 500 4500 5000

1000 6000 7000

Sensitivity50%

PPV 25%

Specificity75%

NPV 90%

Prevalence 14%

Test vs Major + Min DepressionTest vs Major + Min Depression

DepressionPRESENT

DepressionABSENT

Test +ve 500 1500 2000

Test -ve 500 500 1000

1000 2000 3000

Sensitivity50%

PPV 25%

Specificity33%

NPV 50%

Prevalence 33%

T2. Advanced TechniquesT2. Advanced Techniques

Combined tests

Added Value

Cut-Offs

Prevalence adjustments

Summary MeasuresSummary Measures

Youden's JSensitivity + Specificity – 1

Predictive Summary IndexPPV + NPV – 1

Overall accuracy (fraction correct)TP+TN / TP+FP+TN+FN

Added ValueAdded Value

Definition 1:The additional ability of a test to rule‐in or rule‐out

compared with the baseline ratePPV minus PrevalenceNPV minus prevalence

Definition 2:The additional of a test to rule‐in or rule‐out compared

with the unassisted ratePPV test minus PPV no test (assuming equal prevalence)

LR+ test minus LR+ no test

AUC test minus AUC no test

Reciprocal MeasuresReciprocal Measures

Number Needed to Diagnose (NND)1 / (Youden's J)

Number Needed to Predict (NNP)1 / (PSI)

Number Needed to Screen (NNS)1/(FC‐FiC)

-0.10

0.00

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nger

Anx

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Dec

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Dep

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Dim

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Psyc

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Psyc

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Psyc

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Slee

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Som

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anx

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Thou

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of

deat

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Wor

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Rule-In Added Value (PPV-Prev)Rule-Out Added Value (NPV-Prev)

Accuracy of Tests: Visual Post-test ProbabilitiesAccuracy of Tests: Visual Post-test Probabilities

0% 100%25% 75%

Very unlikely Very likelylikelyunlikely

2 Questions

Overall

PHQ-2

WHO5 (1+3)

1 Question3% - (37) - 63% = 60%

3% - (16) - 32% = 29%

3% - (16) - 32% = 29%

10% - (22) -50% = 54%

32% - (37) - 96% = 64%

Henckel et al (2004) Eur Arch Psychiatry Clin Neuros

CIDI (computer) Any Depression

Henckel et al (2004) Eur Arch Psychiatry Clin Neuros

CIDI (computer) Any Depression

Arroll B et al (2003) BMJ

CIDI (computer) Mj Depression

CIDI (computer) Mj Depression

Murphy JM, Berwick DM, Weinstein MC, Borus JF, Budman SH, Klerman GL 1987 : Performance of screening and diagnostic tests: Application of Receiver Operating Characteristic ROC analysis. Arch Gen Psychiatry 44:550-555

Receiver Operating Characteristic

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Pre-test Probability

Post

-test

Pro

babi

lity

Depression Present (Routine)

Depression Absent (Routine)

Depression Scales +ve (Median)

Depression Scales -ve (Median)

Prior Probability

PPV=0.41

NPV=0. 97

Prevalence of 0.15

Group Work #1Group Work #1

930-10.00 – Introduction, groups and issues

10.00-11.00 – T1 Basic science of screening

Break

11.30 – 12.30 – Group task #1

Lunch

1.30-2.30 – T2 Symptoms, Burden, Help, Needs in Cancer

Break

3.00 – 4.00 – Evaluation of a screening paper

Cancer Mj Depression vs NonMjCancer Mj Depression vs NonMj

Clinicians diagnosis using DSMIV vs SCAN/PSE

50 people with depression

200 without depression

Clinicians using DSMIVClinicians using DSMIV

IF: Clinicians diagnosed 50 cases with depressionIF: Their specificity was 95%

Q. What was the sensitivity?Q. What was the prevalence?Q. What was the PPV?Q. What was overall accuracy

Test vs Major DepressionTest vs Major Depression

DepressionOn SCAN

DepressionABSENT

Test +ve(Clinician)

40 10 50

Test -ve 10 190

50 200

Sensitivity80%

PPV 80%

Specificity95%

NPV 95%

Prevalence 0.20%

0.00

0.10

0.20

0.30

0.40

0.50

0.60

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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

NH NAs+

NH NAs-

Baseline Probability

Cancer Mj+Mn Depression vs Non Cancer Mj+Mn Depression vs Non

Clinicians diagnosis using DSMIV vs SCAN/PSE

50 people with depression

200 without depression => 50 had minor depression

=> Answer 2=> Answer 2

Test vs Major DepressionTest vs Major Depression

DepressionOn SCAN

DepressionABSENT

Test +ve(Clinician)

50 0 50

Test -ve 50 150 200

100 150

Sensitivity50%

SN-OUT

PPV 100%

Specificity100%

SP-IN

NPV 40%

Prevalence 66.7%

0.00

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Pre-test Probability

Post

-test

Pro

babi

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NH NAs+

NH NAs-

Baseline Probability

Likelihood RatiosLikelihood Ratios

Likelihood Ratio for Positive Tests The chance of testing positive among those with the condition; divided by the chance of testing positive among those without the condition Sensitivity / (1 - Specificity) [ TP / (TP + FN) ] / [ FP / (FP + TN) ]

= PPV / Prevalence

Likelihood Ratio for Negative Tests The chance of testing negative among those with the condition; divided by the chance of testing negative among those without the condition Specificity / (1 – Sensitivity)[ FN / (FN + TP) ] / [ TN / (TN + FP) ]

= NPV / Prevalence

T3. Symptoms, Help, Needs in CancerT3. Symptoms, Help, Needs in Cancer

Clinician Opinion

Patient Opinion

Psychosocial

Complications

Help

Seeking

Symptoms

Recognized

Intervention

Offered

Help

Accepted

Complication

Resolves

Lag

time

Lag

time

Lag

time

Lag

time

Lag

time

years months weeks weeks days

Cancer

OnsetCancer

Progress

Lessons?

462 (42%)Meetable Needs

1093 (100%)Population

388 (84%)Aware of Need

172 (44%)Requested Help

80 (47%)Needs Met

462 needs

17.3%

322 DSMIV

25%

T4. How Common is Distress?T4. How Common is Distress?

Clinician Opinion

Patient Opinion

Requires depressed mood for most of the day, for most days (by subjective account or observation) for at least 2 years

The symptoms cause clinically significant distress OR impairment in social, occupational, or other important areas of functioning.

Requires persistently low mood two (or more) of the following six symptoms:

(1) poor appetite or overeating (2) Insomnia or hypersomnia(3) low energy or fatigue (4) low self-esteem (5) poor concentration or difficulty

making decisions (6) feelings of hopelessness

DSM-IV Dysthymic disorder

Acute: if the disturbance lasts less than 6 months Chronic: if the disturbance lasts for 6 months

These symptoms cause marked distress that is in excess of what would be expected from exposure to the stressor OR significant impairment in social or occupational (academic) functioning

Requires the development of emotional or behavioral symptoms in response to an identifiable stressor(s) occurring within 3 months of the onset of the stressor(s). Once the stressor has terminated, the symptoms do not persist for more than an additional 6 months.

DSM-IV Adjustment disorder

2 weeksThese symptoms cause clinically important distress OR impair work, social or personal functioning.

Requires two to four out of nine symptoms with at least at least one from the first two (depressed mood and loss of interest).

DSM-IV Minor Depressive Disorder

2 weeksThese symptoms cause clinically important distress OR impair work, social or personal functioning.

Requires five or more out of nine symptoms with at least at least one from the first two (depressed mood and loss of interest).

DSM-IV Major Depressive Disorder

2 weeks unless symptoms are unusually severe or of rapid onset).

At least some difficulty in continuing with ordinary work and social activities

Requires two of the first three symptoms (depressed mood, loss of interest in everyday activities, reduction in energy) plus at least two of the remaining seven symptoms (minimum of four symptoms)

ICD-10 Depressive Episode

DurationClinical SignificanceSymptoms

Depression

13%

20%

57%

48%

38%

18%

Anxiety

Adjustment Disorder

N=11N=4

N=10

Comment: Slide illustrates meta-analytic rates of mood disorder

Prevalence of depression in Palliative settings

20 studies involving 2655 individuals

16.9% (95% CI = 13.2% to 21.0%)

13.0% (95% CI = 11.6% to 14.5%) for MDD

p572

Proportion meta-analysis plot [random effects]

0.0 0.2 0.4 0.6

combined 0.17 (0.13, 0.21)

Maguire et al (1999) 0.05 (0.01, 0.14)

Akechi et al (2004) 0.07 (0.04, 0.11)

Kadan-Lottich et al (2005) 0.07 (0.04, 0.11)

Love et al (2004) 0.07 (0.04, 0.11)

Wilson et al (2004) 0.12 (0.05, 0.22)

Chochinov et al (1997) 0.12 (0.08, 0.18)

Wilson et al (2007) 0.13 (0.10, 0.17)

Kelly et al (2004) 0.14 (0.06, 0.26)

Chochinov et al (1994) 0.17 (0.11, 0.24)

Le Fevre et al (1999) 0.18 (0.10, 0.28)

Breitbart et al (2000) 0.18 (0.11, 0.28)

Meyer et al (2003) 0.20 (0.10, 0.35)

Minagawa et al (1996) 0.20 (0.11, 0.34)

Lloyd-Williams et al (2001) 0.22 (0.14, 0.31)

Hopwood et al (1991) 0.25 (0.16, 0.36)

Desai et al (1999) [late] 0.25 (0.10, 0.47)

Payne et al (2007) 0.26 (0.19, 0.33)

Lloyd-Williams et al (2003) 0.27 (0.17, 0.39)

Jen et al (2006) 0.27 (0.19, 0.36)

Lloyd-Williams et al (2007) 0.30 (0.24, 0.36)

proportion (95% confidence interval)

Prevalence of depression in Oncology settings

57 studies involving 9195 individuals across 12 countries.

The prevalence of depression was 17.3% (95% CI = 13.8% to 21.2%),

13.0% (95% CI = 11.6% to 14.5%) for MDD

p572

Proportion meta-analysis plot [random effects]

0.0 0.3 0.6 0.9

combined 0.1730 (0.1375, 0.2116)

Colon et al (1991) 0.0100 (0.0003, 0.0545)

Massie and Holland (1987) 0.0147 (0.0063, 0.0287)

Hardman et al (1989) 0.0317 (0.0087, 0.0793)

Derogatis et al (1983) 0.0372 (0.0162, 0.0720)

Lansky et al (1985) 0.0455 (0.0291, 0.0676)

Mehnert et al (2007) 0.0472 (0.0175, 0.1000)

Katz et al (2004) 0.0500 (0.0104, 0.1392)

Singer et al (2008) 0.0519 (0.0300, 0.0830)

Sneeuw et al (1994) 0.0540 (0.0367, 0.0761)

Pasacreta et al (1997) 0.0633 (0.0209, 0.1416)

Lee et al (1992) 0.0660 (0.0356, 0.1102)

Reuter and Hart (2001) 0.0761 (0.0422, 0.1244)

Grassi et al (2009) 0.0826 (0.0385, 0.1510)

Grassi et al (1993) 0.0828 (0.0448, 0.1374)

Walker et al (2007) 0.0831 (0.0568, 0.1165)

Kawase et al (2006) 0.0851 (0.0553, 0.1240)

Coyne et al (2004) 0.0885 (0.0433, 0.1567)

Alexander et al (2010) 0.0900 (0.0542, 0.1385)

Love et al (2002) 0.0957 (0.0650, 0.1346)

Ozalp et al (2008) 0.0971 (0.0576, 0.1510)

Morasso et al (2001) 0.0985 (0.0535, 0.1625)

Costantini et al (1999) 0.0985 (0.0535, 0.1625)

Silberfarb et al (1980) 0.1027 (0.0587, 0.1638)

Desai et al (1999) [early] 0.1111 (0.0371, 0.2405)

Morasso et al (1996) 0.1121 (0.0593, 0.1877)

Prieto et al (2002) 0.1227 (0.0825, 0.1735)

Ibbotson et al (1994) 0.1242 (0.0776, 0.1853)

Payne et al (1999) 0.1290 (0.0363, 0.2983)

Kugaya et al (1998) 0.1328 (0.0793, 0.2041)

Alexander et al (1993) 0.1333 (0.0594, 0.2459)

Gandubert et al (2009) 0.1597 (0.1040, 0.2300)

Razavi et al (1990) 0.1667 (0.1189, 0.2241)

Akizuki et al (2005) 0.1797 (0.1376, 0.2283)

Leopold et al (1998) 0.1887 (0.0944, 0.3197)

Devlen et al (1987) 0.1889 (0.1141, 0.2851)

Berard et al (1998) 0.1900 (0.1184, 0.2807)

Joffe et al (1986) 0.1905 (0.0545, 0.4191)

Berard et al (1998) 0.2100 (0.1349, 0.3029)

Maunsell et al (1992) 0.2146 (0.1605, 0.2772)

Grandi et al (1987) 0.2222 (0.0641, 0.4764)

Evans et al (1986) 0.2289 (0.1438, 0.3342)

Spiegel et al (1984) 0.2292 (0.1495, 0.3261)

Golden et al (1991) 0.2308 (0.1353, 0.3519)

Fallowfield et al (1990) 0.2565 (0.2054, 0.3131)

Hosaka and Aoki (1996) 0.2800 (0.1623, 0.4249)

Kathol et al (1990) 0.2961 (0.2248, 0.3754)

Green et al (1998) 0.3125 (0.2417, 0.3904)

Jenkins et al (1991) 0.3182 (0.1386, 0.5487)

Burgess et al (2005) 0.3317 (0.2672, 0.4012)

Hall et al (1999) 0.3722 (0.3139, 0.4333)

Morton et al (1984) 0.3958 (0.2577, 0.5473)

Baile et al (1992) 0.4000 (0.2570, 0.5567)

Passik et al (2001) 0.4167 (0.2907, 0.5512)

Bukberg et al (1984) 0.4194 (0.2951, 0.5515)

Massie et al (1979) 0.4850 (0.4303, 0.5401)

Ciaramella and Poli (2001) 0.4900 (0.3886, 0.5920)

Levine et al (1978) 0.5600 (0.4572, 0.6592)

Plumb & Holland (1981) 0.7750 (0.6679, 0.8609)

proportion (95% confidence interval)

Distress Thermometer

Distress Thermometer – Pooled Table

ScoreRansom 2006

Tuinman2008

Mitchell 2009

Lord 2010

Hoffman 2004

Gessler2009

Clover 2009

Jacobsen 2005 Sum

Proportion

Zero 68 38 61 123 14 27 65 71 467 18.4%

One 72 31 42 68 5 26 39 46 329 12.9%

Two 77 22 35 44 5 18 30 54 285 11.2%

Three 65 37 42 46 8 23 45 46 312 12.3%

Four 51 29 29 30 8 7 21 31 206 8.1%

Five 41 46 62 40 11 13 41 48 302 11.9%

Six 38 32 23 28 2 16 26 31 196 7.7%

Seven 36 21 23 38 2 15 32 16 183 7.2%

Eight 18 12 18 29 6 9 19 15 126 5.0%

Nine 16 5 8 14 3 3 13 9 71 2.8%

Ten 9 4 7 20 4 0 9 13 66 2.6%

Sum 491 277 350 480 68 157 340 380 2543

Proportion 19.3% 10.9% 13.8% 18.9% 2.7% 6.2% 13.4% 14.9%

Proportion

18 .4 %

12 .9 %

11.2 %12 .3 %

8 .1%

11.9 %

5.0 %

2 .8 % 2 .6 %

7.7% 7.2 %

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

20.0%

Zero One Two Three Four Five Six Seven Eight Nine Ten

Insignificant SevereModerateMildMinimal

p124

50%

T5. Getting HelpT5. Getting Help

Clinician Opinion

Patient Opinion

Arrol et al (2005) BMJArrol et al (2005) BMJ

Setting 19 general practitioners in six clinics in New Zealand. Participants 1025 consecutive patients receiving no psychotropic drugs.

After screening “is this something you would like help with?

The help question alone had a sensitivity of 75% and a specificity of 94%

The general practitioner with PHQ2 diagnosis had a sensitivity of 79% and a specificity of 94%

Arrol (2005) – Mj DepressionArrol (2005) – Mj Depression

47 CIDI cases with Major depression

25 (53%) wanted help

10 (21%) wanted the option of help

12 (25%) did not want help

Arrol (2005) – No DepressionArrol (2005) – No Depression

889 CIDI cases with Major depression

27 (3%) wanted help

24 (3%) wanted the option of help

838 (94%) did not want help

2x2 Help Table2x2 Help Table

Clinician thinks:Help Needed

Clinician thinks:Help Not Needed

Patient Says:Help Wanted

=> Intervention => Refuse?

Patient Says:Help Not Wanted

=> Delay =>Agree discharge

MethodologyMethodology

Study I: Baker-Glen, Symonds, Granger “ET Validation”(a) n=129 chemotherapy attendees(b) n=86 chemotherapy f/u

Study II: Sampson, Symonds, Granger “ET Extension”(c) n=250 chemotherapy + late

Study III: Lord, Symonds, Granger “Coping”(d) n=250

Study IV: Mitchell, Symonds, Steward “SMI RCT”(e) n=300

Help – Who Wants Help?Help – Who Wants Help?

20% said they wanted professional help for psychosocial issues.

Only 36% of those distressed on the DT wanted help.

Help – Do They Need It?Help – Do They Need It?

27% had major depression

62% had major or minor depression

88% had some distress (HADS, PHQ, DT)

Are Those Not Wanting Help OK?Are Those Not Wanting Help OK?

41/104 (39%) of decliners had no identifiable condition

=> 61% of those refusing help actually have a potentially serious psychosocial condition.

What Kind of Help is Wanted?What Kind of Help is Wanted?

19% wanted medication (eg antidepressants)

31% want self help guidelines

31% wanted group therapy

56% wanted illness information.

58% complementary therapies

62% face-to-face psychological support

Help – Who From?Help – Who From?

Nurse specialists (54%)

Family and friends (21%)

Spiritual advisor (8%)

Psychiatrist (4%).

Why Not Needed?Why Not Needed?

“getting help elsewhere” (57%)

“feel well” (41%)

“coping on my own” (31%)

“fear of stigma”, “fear of side effects”, “not likely to be effective for me”, and “don’t like to talk about problems” (all less than 10%)

4. Is Help a Predictor?4. Is Help a Predictor?

Help as a Predictor of Depression?Help as a Predictor of Depression?

Outcome Predictor Sensitivity Specificity PPV NPV

DSMIV Major Depression Help QQ Alone 0.47 0.83 0.27 0.92

DSMIV Mj + Minor Depression Help QQ Alone 0.36 0.88 0.39 0.86

DSMIV Mj + Minor Depression Help QQ AND PHQ2 0.36 0.99 0.88 0.88

Can This Be Used Clinically?Can This Be Used Clinically?