1

SPARRA: predicting risk of emergency admission among older

people

Steve KendrickSteve KendrickDelivering for Health Information ProgrammeDelivering for Health Information Programme

ISD ScotlandISD Scotlandwww.isd.scotland.org/dhipwww.isd.scotland.org/dhip

NHS GG&C Public Health Friday SeminarNHS GG&C Public Health Friday SeminarDalian House, 1Dalian House, 1stst December 2006 December 2006

2

Providing information to support ‘Kerr’ and “Delivering for Health” is a key priority for ISD Scotland.

The Delivering for Health Information Programme supports a specific focus of “Delivering for Health”.

3

Public health; healthImprovement; health education

Lower risk:supported self-care

(70-80%)

High risk:disease

Management(15-20%)

Inte

rventio

nsO

utco

mes

Individuals withcomplex needs: case management(3-5%)

Long-term conditions + interface with unscheduled care

Level 1

Level 2

Level 3

Level 4

Emergency admissions

Kerr Unscheduled Care Levels

4

Public health; healthImprovement; health education

Lower risk:supported self-care

(70-80%)

High risk:disease

Management(15-20%)

Inte

rventio

nsO

utco

mes

Individuals withcomplex needs: case management(3-5%)

Level 1

Level 2

Level 3

Level 4

Emergency admissionsDfHIP

Long-term conditions + interface with unscheduled care

Kerr Unscheduled Care Levels

5

DfHIP Priorities around ‘the top of pyramid’

SPARRAHigh risk patients

VHIUsVery high

intensity users

?

End of life care

Care homes

Economics:yield curves

End of life costs

6

SPARRAHigh risk patients

Top 5%bed days

?

End of life care

Care homes

Economics:yield curves

End of life costs

Primary careSPARRA

GP emergencyadmission

rates LTCs/riskstratification

Emergencyadmissions:comparative

trends

Information for CHPs

7

Some old friends … what the world looked like before Kerr

8

Bed days required by emergency inpatients by broad age group. 1981 to 2001. Scotland.

Under 45

45 to 64

65 to 79

0

500000

1000000

1500000

2000000

2500000

3000000

3500000

4000000

4500000

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

Admission year

Bed

days p

er

an

nu

m 80 and over

9

Trends (1981-2001) in emergency admission rates by age group.

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

Admission year

Em

erg

ency

ad

mis

sio

ns

per

100

,000

po

p

0-405-0910-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485+

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Patients experiencing 3 or more emergency admissions w ithin a 1 year period. Scotland 1981 to 2001 by age group.

0

500

1000

1500

2000

2500

300019

81

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

Year of admission

Rat

e p

er 1

00,0

00 p

op

ula

tio

n

0-4

05-9

10-14

15-19

20-24

25-29

30-34

35-39

40-44

45-49

50-54

55-59

60-64

65-69

70-74

75-79

80-84

85 & over

Age group

11

Some more recent trends

12

West Central Belt NHS Boards.

Emergency admissions aged 65+. Standardised for age and sex.

0

5

10

15

20

25

30

1996/97 1997/98 1998/99 1999/00 2000/01 2001/02 2002/03 2003/04 2004/05

Financial year

Sta

nd

ard

ised

rat

e p

er 1

00

po

pu

lati

on

Scotland

Argyll & Clyde

Ayrshire & Arran

Greater Glasgow

Lanarkshire

13

East Central Belt NHS Boards. Emergency admissions aged 65+.

Standardised for age and sex.

0

5

10

15

20

25

1996/97 1997/98 1998/99 1999/00 2000/01 2001/02 2002/03 2003/04 2004/05

Financial year

sta

ndard

isedn r

ate

per

100 p

opula

tion

Scotland

Fife

Forth Valley

Lothian

Tayside

14

West Central Belt NHS Boards. Emergency admissions aged 65+

Standardised for age, sex and deprivation.

0

5

10

15

20

25

30

1996/97 1997/98 1998/99 1999/00 2000/01 2001/02 2002/03 2003/04 2004/05

Financial year

Sta

nd

ard

ised

rat

e p

er 1

00

pop

ula

tion

Scotland

Argyll & Clyde

Ayrshire & Arran

Greater Glasgow

Lanarkshire

15

East Central Belt NHS Boards. Emergency admission rates 65+.

Standardised for age, sex and deprivation.

0

5

10

15

20

25

30

1996/97 1997/98 1998/99 1999/00 2000/01 2001/02 2002/03 2003/04 2004/05

Financial year

Sta

ndard

ised r

ate

per

100 p

opula

tion

Scotland

Fife

Forth Valley

Lothian

Tayside

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Aged 85+ expereincing 3 or more emergency admissions in a single year.Per 100,000 population. Unstandardised.

0

500

1,000

1,500

2,000

2,500

3,000

3,500

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Financial year ending

Pe

r 1

00

,00

0 p

op

ula

tio

n.

Scotland

WestDunbartonshire

Edinburgh City

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SPARRA …….

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SPARRA stands for…

SScottish

PPatients

AAt

RRisk of

RReadmission and

AAdmission

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Purpose of SPARRA

• Identify those people at greatest risk of Identify those people at greatest risk of emergency inpatient admissionemergency inpatient admission

• Current cohort: people aged 65 and Current cohort: people aged 65 and over with at least one emergency over with at least one emergency admission in the previous three yearsadmission in the previous three years

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Steps in implementing model

• Develop predictive modelDevelop predictive model (logistic (logistic regression) regression) based on patients for whom we based on patients for whom we do know the outcome – historic datado know the outcome – historic data

• Identify what determines the likelihood of Identify what determines the likelihood of future emergency admissionfuture emergency admission

• Apply model to patients for whom we Apply model to patients for whom we don’t know the outcomedon’t know the outcome

• Calculate individual risks• Feed back results to front lineFeed back results to front line

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1st January 2004

Predictor variablesOutcome year

Developing the predictive model

Time Period

2001 2002 2003 2004

Cohort includes all aged 65+with an emergency admissionin previous three years(around 25% of 65+ pop.)

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The shoulders upon which we stand

• Substantial American literature see e.g. Substantial American literature see e.g. King’s Fund literature reviewKing’s Fund literature review

• King’s Fund: John BillingsKing’s Fund: John Billings

• NHS Tayside/University of Dundee model NHS Tayside/University of Dundee model – Peter Donnan– Peter Donnan

• Highland; Lanarkshire; Ayrshire and Highland; Lanarkshire; Ayrshire and ArranArran

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Our approach

• No ‘black boxes’No ‘black boxes’

• Transparent – understand what’s under Transparent – understand what’s under the bonnetthe bonnet

• CollaborativeCollaborative

• EvolutionaryEvolutionary

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Independent variables

• Number of previous emergency, elective, day Number of previous emergency, elective, day case admissions; total bed days case admissions; total bed days

• Time since most recent emergency admissionTime since most recent emergency admission• Age/genderAge/gender• DeprivationDeprivation• Most recent admission diagnosis, number of Most recent admission diagnosis, number of

different diagnosis groupsdifferent diagnosis groups. . • NHS BoardNHS Board

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Results: major factors emerging as predictors

• Number of previous emergency admissionsNumber of previous emergency admissions• Time since most recent admissionTime since most recent admission• AgeAge• Interaction between age and previous Interaction between age and previous

emergency admissionsemergency admissions• DeprivationDeprivation• Number of diagnosesNumber of diagnoses• Most recent diagnosis – especially COPDMost recent diagnosis – especially COPD

• NB. NHS Board not significantNB. NHS Board not significant

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SPARRA Odds ratios for main effects of previous emergency admissions on probability of admission in next twelve months

0

1

2

3

4

5

6

1 2 3 4 5 6 or more

Number of previous emergency admissions

Od

ds

ra

tio

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Percentage admitted as emergency in next year by number of previous admissions and age. SPARRA cohort.

0%

10%

20%

30%

40%

50%

60%

70%

80%

One Two Three Four Five 6 or more

Number of emergency admissions in previous three years

Per

cen

tag

e ad

mit

ted

65 to 69

70 to 74

75 to 79

80 to 84

85 to 89

90 and over

Age group

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Example: individual with very highvery high predicted probability of admission

• Predicted probability of admission Predicted probability of admission 86%86%• MaleMale aged aged 65 to 6965 to 69• Less than Less than one monthone month since most recent since most recent

admissionadmission• 6+6+ previous emergency admissions previous emergency admissions• Glasgow – Glasgow – most deprived decilemost deprived decile• Most recent admission diagnosis: Most recent admission diagnosis: COPDCOPD• Outcome: admitted as emergencyOutcome: admitted as emergency

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1st April 2006

Predictor variablesOutcome year

Applying the predictive model

Time Period

April 2003 to March 2006April 2006-March 2007

Based on previous 3 years of hospital admissions

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SPARRA cohort and risk categories in population context. Scotland: predicted probabilities from April 2006.

-

50,000

100,000

150,000

200,000

250,000

300,000

65 to 69 70 to 74 75 to 79 80 to 84 85 to 89 90 and over

Age group

Po

pu

lati

on Not in SPARRA cohort

Under 30% risk

30-50% risk

50% plus risk

SPARRA cohort: admitted as emergency in previous 3 years

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Scotland: SPARRA cohort of over 65s admitted as emergencyin previous three years. Distribution of Predicted Probabilities

1287

58088

68692

44135

22696

10942

54562751

0

10000

20000

30000

40000

50000

60000

70000

80000

Under 10% 10 to 20% 20 to 30% 30 to 40% 40 to 50% 50% to 60% 60% to 70% 70% plus

Nu

mb

er o

f ca

ses

3.8% 60%+

SPARRA cohort encompasses around a quarter of the population aged 65 and over

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SPARRA. Over 65s. Distribution of Predicted Probabilities

10

1,084

1,557

1,189

587

269

11350

11 --

200

400

600

800

1,000

1,200

1,400

1,600

1,800

0-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90% & Over

Risk probability group

Nu

mb

er o

f ca

ses

(S03000021) South East Glasgow Community Health & Care Partner

60% and over: 3.57%

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SPARRA. Over 65s. Distribution of Predicted Probabilities

7

1,106

1,535

1,266

641

341

15486

10 --

200

400

600

800

1,000

1,200

1,400

1,600

1,800

0-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90% & Over

Risk probability group

Nu

mb

er o

f ca

ses

(S03000022) South West Glasgow Community Health & Care Partner

60% and over: 4.85%

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SPARRA. Over 65s. Distribution of Predicted Probabilities

6

770

1,149

884

430

255

187

7420 -

-

200

400

600

800

1,000

1,200

1,400

0-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90% & Over

Risk probability group

Num

ber

of c

ases

(S03000019) North Glasgow Community Health & Care Partnership

60% and over: 7.44%

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Age group make up of SPARRA risk categories

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90% andover

Risk probability group

Per

cent

age

(%)

90+

85-89

80-84

75-79

70-74

65-69

Age Group

(S03000022) South West Glasgow Community Health & Care Partner

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Make up of SPARRA risk categories in terms of SIMD decile

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90% andover

Risk probability group

Per

cen

tag

e (%

)

10

9

8

7

6

5

4

3

2

1

SIMD Decile

(S03000022) South West Glasgow Community Health & Care Partner

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Make up of SPARRA risk categories in terms of SIMD decile

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90% andover

Risk probability group

Per

cen

tag

e (%

)

10

9

8

7

6

5

4

3

2

1

SIMD Decile

(S03000019) North Glasgow Community Health & Care Partnership

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Edinburgh City. Make up of SPARRA risk categories in terms of SIMD decile.

0%

20%

40%

60%

80%

100%

0-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90%

Probability of emergency admission

10

9

8

7

6

5

4

3

2

1

SIMDdecile

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SPARRA risk categories by most recent diagnosis groupSouth West Glasgow CHCP

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90% andoverRisk probability group

Per

cen

tag

e (%

)

Other

Injuries, etc.

Sym tom s, signs and ill-defined conditions

Mental disorders anddiseases of the nervoussystem Diseases of thedigestive and urinarysystemCOPD

Other disorders ofrespiratory system

Other disorders ofcirculatory system

Heart Disease

Cancer

Most re ce nt Dia gnosis group

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SPARRA risk categories: number of diagnosis groups in 3 year period

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90% andover

Risk probability group

Per

cen

tag

e (%

)

6+

5

4

3

2

1

Diagnosis grouping

s

(S03000022) South West Glasgow Community Health & Care Partner

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How well does the model perform

• Reasonable area under the ROC. 0.69 Reasonable area under the ROC. 0.69 compared with c0.8 when e.g. primary compared with c0.8 when e.g. primary care variables included (c.f 0.685 King’s care variables included (c.f 0.685 King’s Fund hospital-based model)Fund hospital-based model)

• Likely to be identifying the great bulk of Likely to be identifying the great bulk of the high risk patients out there in the the high risk patients out there in the community c 75-90%community c 75-90%

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1st April 2006

Predictor variablesOutcome year

Applying the predictive model

Time Period

April 2003 to March 2006

April 2006 to March 2007

Now

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Usually 6 months until SMR01 data complete enough: how much of an issue?• What might have happened in 6 monthsWhat might have happened in 6 months• Patient may havePatient may have

a) died – must check via local systemsb) been admitted – increase in future riskc) not been admitted – decline in future

risk

It is an issue, not a showstopper – but It is an issue, not a showstopper – but not satisfactorynot satisfactory

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Forms of feedback

• Identifiable details of high-risk patientsIdentifiable details of high-risk patients– fed back on CD on receipt of

confidentiality form– values of model variables as well as

ID and probabilities• Local distributions of risk levels Local distributions of risk levels

– how many people at all levels of risk– By Board, CHP, practice

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The role of SPARRA?

Original conception – fairly narrow, mechanicalOriginal conception – fairly narrow, mechanical

SPARRA identifies a pool of high-risk patientsSPARRA identifies a pool of high-risk patients

Further local assessment identifies those for Further local assessment identifies those for whom e.g. case management is appropriatewhom e.g. case management is appropriate

Full stopFull stop

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Emerging functions: SPARRA as a focus for integration

• ““international research suggests that international research suggests that integration is most needed and works integration is most needed and works best when it focuses on a specifiable best when it focuses on a specifiable group of people with complex needsgroup of people with complex needs, , and where the system is clear and readily and where the system is clear and readily understood by service users (and understood by service users (and preferably designed with them as full preferably designed with them as full partners)” partners)” Integrated Care: A Guide, Integrated Care: A Guide, Integrated Care NetworkIntegrated Care Network

(cited by David Colin-Thome)(cited by David Colin-Thome)

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Emerging functions: SPARRA as a seed

• Local teams often use SPARRA in Local teams often use SPARRA in combination with other sources of local combination with other sources of local information (e.g. GP registers)information (e.g. GP registers)

• SPARRA may become just one component SPARRA may become just one component of a dynamic, multi-source locally owned of a dynamic, multi-source locally owned register of vulnerable peopleregister of vulnerable people

• cf Exeter. Wide range of sources for up-to-cf Exeter. Wide range of sources for up-to-date list which ‘keeps tabs on’ vulnerable date list which ‘keeps tabs on’ vulnerable people. No high tech/IT. Based on people. No high tech/IT. Based on commitment and case managementcommitment and case management

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Further development of model

• Move to incorporate real-time data: via SystemWatchMove to incorporate real-time data: via SystemWatch • Incorporating primary care data. Incorporating primary care data. Needs to be led locallyNeeds to be led locally

• Relation with social care data c.f. Highland – needs to be Relation with social care data c.f. Highland – needs to be done locally.done locally.

• Economic aspects – what could be the pay-off?Economic aspects – what could be the pay-off?

• Evaluation – SPARRA to help evaluate impact of models of Evaluation – SPARRA to help evaluate impact of models of anticipatory careanticipatory care

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Current take up of SPARRA

• Around 4 Boards motoringAround 4 Boards motoring

• 6-10 Boards/CHPs – very keen – have 6-10 Boards/CHPs – very keen – have received data received data (i.e. around half of CHPs (i.e. around half of CHPs have data either directly or indirectly)have data either directly or indirectly)

• Most of rest – in discussionMost of rest – in discussion

• A very few – still to start a conversationA very few – still to start a conversation

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The response to SPARRA output

• Starting to get feedback: the results Starting to get feedback: the results seem to be making reasonable senseseem to be making reasonable sense

• Major frustration: based on out-of-date Major frustration: based on out-of-date datadata

• This is primary use of healthcare This is primary use of healthcare information: information: helping determine how to helping determine how to deliver the best care to real peopledeliver the best care to real people

• Only the beginningOnly the beginning