theo georghiou and dr jessica sheringham: data and colorectal cancer, 30 june 2014

© Nuffield Trust June 2014

What can linked data tell us about GPs’

role in diagnosing colorectal cancer?

30 June 2014

Jessica Sheringham & Theo Georghiou

© Nuffield Trust

Outline

Background: Why colorectal cancer?

What we did

• Aims & setting

• Linkage

• Constructing & examining routes to diagnosis

Illustrative findings

Discussion

• Colorectal cancer

• Wider applications

© Nuffield Trust 16 July 2014 © Nuffield Trust

Background

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Why colorectal cancer?

4th most common cancer in UK

Incidence increasing

Most common in older people

55% overall survive 5 years after diagnosis

Survival much better if diagnosed at an early stage:

• 5-year survival: early stage (“Dukes Stage A”) = 93%

• 5-year survival: late stage (“Dukes Stage D”) = 6.6%

References: CRUK, 2014; NCIN data briefing, 2009

© Nuffield Trust

Reference: Coleman et al. Lancet 2011

Colorectal cancer: Age-standardised 1-year and 5-year

relative survival trends 1995–2007, by cancer and country

© Nuffield Trust

Improving outcomes for colorectal cancer: points for

intervention

Screening

Symptom awareness

Patients & public Prevention

© Nuffield Trust


intervention

Prevention

Screening

Symptom awareness

Patients & public Secondary care

Access to effective

treatment

Diagnosis

© Nuffield Trust


intervention

Diagnostic referrals

Primary care

• 2-week wait referral pathway underpinned by NICE guidance

• Decision support tools e.g. RATs(Hamilton 2013), Qrisk (H-Cox 2012, Collins 2012)

BUT

• Only 24% diagnosed on 2-week wait (2WW) pathway, 24% diagnosed

as emergencies(Thorne et al. 2006)

• Existing monitoring strategies, e.g. audit, reliant on GP/practice

participation – could underestimate variation

Access to effective

treatment

Patients & public Secondary care Prevention

Screening

Symptom awareness

Diagnosis


The project

© Nuffield Trust

Project

Aim: Explore the feasibility of examining quality of diagnostic process across the patient pathway using routinely available data

Objectives

1. Establish whether linkage of three datasets (primary care, secondary care and cancer registry) possible

2. Apply chosen candidate indicator(s) of quality to examine variations in diagnostic process to identify points for intervention at patient or population level

© Nuffield Trust

Time-based

• Patient interval: symptoms to

presentation

• Primary care interval:

presentation to diagnosis

• Secondary care interval:

diagnosis to treatment

Event-based

• Stage at diagnosis

• Route: emergency diagnosis

• Short-term survival

Candidate indicators: How measure the quality of the

diagnostic process?

Reference: Lyratzopoulos, 2014


Methods development

© Nuffield Trust

Project setting: Outer North East London

1m population & 4 diverse boroughs

RB WF

B&D

HV

Havering

(HV)

Waltham

Forest (WF)

Reference: borough profiles, www.london.gov.uk

% Population over 65 (2011)

Income support claimants (2013)

Redbridge

(RB)

RB WF

B&D

HV

Barking &

Dagenham

(B&D)

http://www.london.gov.uk/

© Nuffield Trust

Datasets and linkage

Key data:

Date of cancer diagnosis

Stage of cancer

Colorectal cancer

diagnoses

Four CCGs

2009 – 2011

N = 1,367

Cancer registry data from Public Health England

© Nuffield Trust


Key data:

Date of cancer diagnosis

Stage of cancer

Colorectal cancer

diagnoses

Four CCGs

2009 – 2011

N = 1,367

All cancer

diagnoses

2005 – 2010


© Nuffield Trust


Identify and remove prior

cancers

Colorectal cancer

diagnoses

N = 1,367

All cancer

diagnoses


© Nuffield Trust


Colorectal cancer

Diagnoses

2009-2011

N = 1,150


Colorectal cancer diagnosis,

no prior cancer

© Nuffield Trust


Colorectal cancer

diagnoses

2009-2011

N = 1,150

GP and Hospital data from CCGs

For population with recorded colorectal cancer

diagnosis during 2007-2012

GP data

Four CCGs (registered)

2007-2012

Hospital data:

inpatient, outpatient, A&E

Key data:

Socio demographic information (e.g. age, gender, deprivation)

Hospital contacts & procedures

GP contacts & Read codes (GP recorded symptoms and activities)

© Nuffield Trust


Colorectal cancer

diagnoses

N = 1,150

GP data

Hospital data:


GP and Hospital data from CCGs

© Nuffield Trust


Colorectal cancer

diagnoses

N = 1,150

GP data

Hospital data:


Not all individuals with diagnosis found in CCG data

© Nuffield Trust

Colorectal cancer

diagnoses

2009-2011

N = 943


GP data

At least 21 months prior to diagnosis

Hospital data:

inpatient, outpatient, A&E 82% of Registry

records ‘matched’

local data

‘Unmatched’: high

% missing stage

and higher % of

patients over 90

years

© Nuffield Trust

Assigning a ‘route’ to diagnosis

1. Looked back at patient records 6 months

(starting from the hospital episode closest to date of diagnosis)

Reference: Elliss-Brookes et al, 2012

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(starting from the hospital episode closest to date of diagnosis)

2. Examined previous activity and referral source

(refined to exclude activity NOT connected with colorectal cancer)


© Nuffield Trust

Referral source

= “GP 2WW”

© Nuffield Trust



starting from the hospital episode closest to date of diagnosis

2. Examined referral source and previous activity

Refined to exclude activity NOT connected with colorectal cancer

3. Assigned each patient to one of four routes to diagnosis:

Emergency

GP – urgent/2WW

GP – routine/unknown

Consultant, other, unknown


© Nuffield Trust

Analysis at population and individual levels

1. POPULATION: Logistic regression to identify factors associated with

increased chance of emergency presentations

• Cancer stage at diagnosis: early, vs late/missing

• Consultation characteristics:

• no. GP visits

• relevant symptoms (using Read Codes in GP records: anaemia, rectal

bleeding, diarrhoea, constipation, abdominal pain, other, incl. weight loss,

fatigue other altered bowel)

• Patient demographics: age, gender

• Area: borough, deprivation

2. INDIVIDUAL: Characteristics of pathways within each route



1. Cohort

2. Population level

3. Individual level

© Nuffield Trust

Diagnostic route in our cohort vs. other estimates

31

52

19

26 24

24 24

0%

20%

40%

60%

80%

100%

Cohort Thorne et al

Emergency

GP urgent/2WW

Alternative route(Consultant/other/unknown)

Alternative route (GProutine/unknown)

Cohort, n=943 Thorne et al (2006)



1. Cohort

2. Population level

3. Individual level

© Nuffield Trust

Characteristics of emergency presentation vs. other routes

Symptoms

Ref:

no symptom

Stage

Ref: early

Age

Ref: 60-69y

Borough

Ref: “2”

Area

deprivation

Ref: Most

deprived 20%

Ad

juste

d o

dd

s r

atio

0.01

0.1

1

10

"Late

"/M

issin

g

Tota

l n

o. G

P v

isits (

12

m b

efo

redia

gnosis

) Abdo

min

al

Co

nstip

ation

Re

cta

l

20

-59 y

70

-79 y

80

+ y 1 3 4

20

-40%

40

-60%

60

-80%

20

% le

ast de

prived

Mis

sin

g

Logistic regression, adjusted for stage, symptoms, age, borough, deprivation and clustering between practices

© Nuffield Trust

Characteristics of emergency presentation (EP) vs. other

routes

Ad

juste

d o

dd

s r

atio

0.01

0.1

1

10

"Late

"/M

issin

g

Tota

l n

o. G

P v

isits (

12

m b

efo

redia

gnosis

) Abdo

min

al

Co

nstip

ation

Re

cta

l

20

-59 y

70

-79 y

80

+ y 1 3 4

20

-40%

40

-60%

60

-80%

20

% le

ast de

prived

Mis

sin

g

Symptoms

Ref:

no symptom

Age

Ref: 60-69y

Borough

Ref: “2”

Area

deprivation

Ref: Most

deprived 20%

Stage

Ref: early

Higher odds of emergency presentation for late stage

cancers is consistent with:

- theory of EP as a marker of diagnostic delay

- other literature (McPhail 2013, Downing 2012)

© Nuffield Trust


Symptoms

Ref:

no symptom

Stage

Ref: early

Age

Ref: 60-69y

Borough

Ref: “2”

Area

deprivation

Ref: Most

deprived 20%

Ad

juste

d o

dd

s r

atio

0.01

0.1

1

10

"Late

"/M

issin

g

Tota

l n

o. G

P v

isits (

12

m b

efo

redia

gnosis

) Abdo

min

al

Co

nstip

ation

Re

cta

l

20

-59 y

70

-79 y

80

+ y 1 3 4

20

-40%

40

-60%

60

-80%

20

% le

ast de

prived

Mis

sin

g

Fewer GP visits → EP

Abdominal pain & constipation → EP

more common

Rectal bleeding → EP less likely

?clinical manifestation of emergency

cases different?

© Nuffield Trust


Age

Ref: 60-69y

Borough

Ref: “2”

Area

deprivation

Ref: Most

deprived 20%

Adju

ste

d o

dds r

atio

0.01

0.1

1

10

"Late

"/M

issin

g

Tota

l n

o. G

P v

isits (

12

m b

efo

redia

gnosis

) Abdo

min

al

Co

nstip

ation

Re

cta

l

20

-59 y

70

-79 y

80

+ y 1 3 4

20

-40%

40

-60%

60

-80%

20

% le

ast de

prived

Mis

sin

g

Symptoms

Ref:

no symptom

Age

Ref: 60-69y

Borough

Ref: “2”

Area

deprivation

Ref: Most

deprived 20%

Significant differences by

borough

No significant deprivation

associations

?? Healthcare system

factors??



1. Cohort

2. Population level

3. Individual level


Summary and discussion

points

© Nuffield Trust

Summary

1. Linkage:

• feasible (not quick – cancer data was rate limiting step)

• relatively complete set, (cf 82% cancer cases vs 17% audit participation) BUT

• important biases to consider

2. Routes to diagnosis:

• distinguishing activity from pathways

• POPULATION: important differences between patients, clinical characteristics and boroughs by route to diagnosis

• INDIVIDUAL: diversity of healthcare use can identify cases for indepth audit

© Nuffield Trust

Discussion points and next steps

Variations in colorectal cancer diagnostic pathways can be identified using routine data:

• Identifies a) local targets for intervention b) specific cases for indepth audit

Next steps

• Refine measures/criteria to identify cases for indepth audit

Transferable methods, approaches to other clinical areas

• Challenges of defining diagnostic interval

• Pros and cons of pathways analysis

© Nuffield Trust

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Acknowledgements:

• Xavier Chitnis, The Royal Marsden NHS Foundation Trust

• Dr Martin Bardsley, Nuffield Trust

• Knowledge & Intelligence Team (London), Public Health England: Neil

Hanchett and Ashu Sehgal

• Rob Meaker, Phil Kozcan, Outer North East London CCGs

• Stuart Bond, Health Analytics

Acknowledgements

theo georghiou and dr jessica sheringham: data and colorectal cancer, 30 june 2014

Health & Medicine

nuffield trust datasets

nuffield trust reference

nuffield trust background

common cancer

nuffield trust assignin

diagnosis hospital data

gp data hospital data

nuffield trust16