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The impact of the Marie Curie Nursing

Service on place of death and hospital

use at the end of life

Xavier Chitnis, Michael Cooke

Predictive Risk Conference

8th July 2013

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Background: End of Life Care at home

• Spending time with loved ones, pain control, and being cared from in their preferred place are among the top priorities for people at the end of life (Engelberg et al. 2005)

• Surveys of the general public and dying people suggest the majority would prefer to die at home (Gomes et al. 2013)

• 51% of deaths in hospital in England in 2011, with only 22% of deaths at home

• The 2011 VOICES survey asked bereaved relatives whether the person who died had enough choice about where they died: Hospital 29% yes, care home 53%, hospice 70%, home 88%.

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Evaluation: The Marie Curie Nursing Service (MCNS)

• Service model: home-based end-of-life nursing care delivered my registered nurses and healthcare assistants, most often 9 hour shifts of overnight care. 30,000 patients per year across UK.

• Longstanding belief: MCNS enables more people to die at home and reduces the amount of time they spend in hospital

• Evidence gaps:

• How many MCNS patients die at home? Service data has gaps.

• How much time do MCNS patients spend in hospital?

• What is a fair comparison? We know ≠ ‘general’ dying population

• Aim: To evaluate the impact of Marie Curie Nursing Service care on place of death, hospital use and costs at the end of life

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Matched control studies – broad aim

>1M individuals - died Jan 2009 to Nov 2011, did not

receive service

(everyone else)

30,000 individuals - died Jan 2009 to Nov 2011 &

received Marie Curie nursing service before death

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Matched control studies – broad aim

>1M individuals - died Jan 2009 to Nov 2011, did not

receive service

(everyone else)

Aim to find 30,000 individuals who match

almost exactly on a broad range of

characteristics

Use these people as study control group

30,000 individuals - died Jan 2009 to Nov 2011 &

received Marie Curie nursing service before death

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Final datasets available for analysis

Nuffield trust

Identifiers:

HESID on all

ONS deaths Hospital inpatient, outpatient, AE MC data - desensitised

Use all this

info to carry

out matched

control

analysis

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Measure Mean (sd)

Age (years) 75.2 (12.1)

Female 47.60%

History of cancer 76.90%

Number of different cancers (in preceding three years) 1.6 (1.3)

Number of chronic conditions 1.5 (1.5)

Median number of days from first MCNS visit to death 8

Characteristics of Marie Curie Cohort (n=31,107)

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Stages in matching process

• Construct longitudinal histories for agreed time periods and estimate costs

• Match on selected variables eg time to death; age; gender; deprivation health conditions/diagnoses; cancer type and history, prior hospital use (IP, OP and A&E)

• Select cases with the best match

• Compare hospital activity and costs

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Period of interest

23rd May 2010

9th March 2010

Date of death Time (days)

First Marie Curie Nursing

Service visit 9th May 2010

Index date 23rd

February 2010

Marie Curie case

Matched control

14 days

Figure 1 - Process for calculating the index date

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Control group – how well matched?

0%

5%

10%

15%

20%

25%

30%

35%

40%

19-44 45-54 55-64 65-74 75-84 85+

% o

f e

ach

gro

up

Age band

Age

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

% White % Black % Asian % Other

Ethnicity

Ethnicity

0%

2%

4%

6%

8%

10%

12%

14%

1 2 3 4 5 6 7 8 9 10

Decile of deprivation

Deprivation

0%

5%

10%

15%

20%

25%

30%

35%

40%

19-44 45-54 55-64 65-74 75-84 85+

% o

f e

ach

gro

up

Age band

Age

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

% White % Black % Asian % Other

Ethnicity

Ethnicity

0%

2%

4%

6%

8%

10%

12%

14%

1 2 3 4 5 6 7 8 9 10

Decile of deprivation

Deprivation

Marie Curie Controls

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0%

10%

20%

30%

40%

50%

Comorbidities

0%

5%

10%

15%

20%

25%

30%

35%

Cancer diagnoses

Control group – how well matched? Diagnostic history

0%

10%

20%

30%

40%

50%

Comorbidities

0%

5%

10%

15%

20%

25%

30%

35%

Cancer diagnoses

Marie Curie Controls

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Place of death for Marie Curie patients and matched

controls

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Mean (sd) activity per person

Activity type Marie Curie

Matched

controls

Crude

difference IRR P value

Emergency

admissions 0.15 (0.48) 0.44 (0.73) -0.29 0.34 <.0001

Elective admissions 0.06 (0.78) 0.14 (1.16) -0.08 0.47 <.0001

Outpatient

attendances 0.25 (1.65) 0.52 (2.01) -0.27 0.46 <.0001

A&E attendances 0.10 (0.38) 0.34 (0.63) -0.24 0.28 <.0001

Emergency bed-days 1.32 (5.59) 3.60 (8.97) -2.28 0.37 <.0001

Elective bed-days 0.25 (2.38) 0.45 (3.35) -0.2 0.58 <.0001

Hospital activity for Marie Curie patients and matched controls

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Number of emergency admissions per 1,000 people by

day over the last three months of life

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Number of emergency admissions per 1,000 people by

day over the last three months of life (continued)

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Post-index date average hospital costs for Marie Curie

patients and matched controls

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Proportion of deaths at home for Marie Curie patients

and matched controls, by history of cancer

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Difference in adjusted hospital costs per person between

Marie Curie patients and controls, by history of cancer

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Conclusions

• Evaluation of large-scale, existing end-of-life care service using

well-matched controls

• Those who received home-based nursing care:

• Much more likely to die at home

• Lower use of hospital care

• Lower hospital costs

• Impact of MCNS care greater for those without cancer –

surprising finding, although literature limited

• Results confirm previous work on benefits of home-based

support but added significance from numbers studied and the

breadth of service provision

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Conclusions (continued)

• Caveats:

• Other costs – reduction in hospital costs considered against

other costs (including MCNS care), and possible increased

used of other services (e.g. GPs, community services & social

care)

• Unobserved confounders – although groups well-matched,

there may have been unobserved factors not recorded in

routine data influencing suitability for home-based end-of-life

care, e.g. personal preferences, availability of family/carer

support etc.

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Impact for Marie Curie

Understanding our patients

• Much richer understanding of demographic profile, clinical

characteristics and outcomes for MCNS patients

• Outcomes for patients with and without cancer of great interest

Commissioning

• Can demonstrate, for the first time, that service reduces hospital

costs and increases the number of home deaths

• Breakdowns by service type enable us to build much more

robust business cases for the potential impact of new services

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Impact for Marie Curie

‘The report clearly shows that specialist nurses support patient choice, reduce unwanted hospital admission and also appear to realise significant savings.

Nurses see too many instances of patients at the end of their life having to come into hospital, often at night and against their best wishes. This obviously causes distress to individuals and families.

The NHS needs to sustain investment in this specialist, out of hours care, so that wherever and whenever a person dies, they can be given excellent pain-relief, dignity and care.’

Dr Peter Carter, RCN Chief Executive and General Secretary

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