1. Allocating Healthcare Budgets to
General Practices
Peter C. Smith on behalf of PBRA team
Imperial College Business School &
Centre for Health Policy
http://www.nuffieldtrust.org.uk/projects/index.aspx?id=338

2. The Person-based resource allocation
(PBRA) project
• Led by Jennifer Dixon (Nuffield Trust) from
2007
• Initial purpose was to develop budgets for
practice based commissioning based on
individual patient data
• Coverage: secondary care, prescribing,
community health services

3. Reviews of resource allocation in English NHS
Hospital and Community Health Services , 1976- today
Year Name Allocations Approximate Years applied
to population
size
1976 RAWP 14 RHAs 3m 77/78 – 90/91
1980 RoR 14 RHAs 3m 91/92 – 94/95
1993 University of 14 RHAs 3m 95/96 – 01/02
York 192 DHAs 250,000
2001 AREA 303 PCTs 175,000 02/03 – 06/07
2006 CARAN 152 PCTs 350,000 07/08 –
Drawn from Bevan, and Bevan and Van der Ven
Note: RAWP = Resource Allocation Working Party
RoR = Review of RAWP
AREA = Allocation of Resources to English Areas
CARAN = Combining Age Related Additional Needs (9)
3

4. PBRA modelling principles
• Use of individual-level data on both users and
non-users of health care services (entire English
population)
• Use of data from past NHS encounters to
measure morbidity directly (via ICD chapters)
• Predict future expenditure at an individual level.
• Developed on samples of 5 million patients
registered within GP practices – models validated
on separate sample of 5 million patients.
• Models further assessed by performance of
predictions at practice level

5. Linking the data sets for analysis
5

6. Modelling principles
Explanatory variables Prediction variable
2005/06 2006/07 2007/08
Samples drawn from patients registered 1 April 2007
6

7. Modelling
• Hospital-based expenditure excluding maternity and
mental illness
• Modelled hospital expenditure in year t as a function
of:
– Age and sex (36)
– Diagnostic categories from hospital utilization in years t-1
and t-2 (152)
– Attributed GP and small area needs characteristics (135)
– Attributed small area supply characteristics (63)
– PCT (152)
• Note: did not consider variables with potentially
adverse incentive effects, eg number of encounters

8. Summary results of a set of five models, predicting
costs for 2007/08 using data from 2005/06 & 2006/07
MODEL R2 individual R2 practice
Model 1: age and gender 0.0366 0.3444
Model 2 - ADD:
152 morbidity markers 0.1223 0.6084
Model 3 - ADD:
152 PCT dummies 0.1227 0.7437
Model 4 - ADD:
135 attributed needs & 63 supply 0.1230 0.7851
Model 5 - REDUCE TO:
7 attributed needs & 3 supply 0.1229 0.7735

9. Type of Variable name
variable
Individual • Age and gender
• 157 ICD-10 groups
Attributed • Persons in social rented housing
needs
• All disability allowance claimants
• Persons aged 16-74 with no qualifications (age standardised)
• Mature city professionals
• Proportion of students in the population
• Whether the person had a privately funded inpatient episode of care
provided by the NHS in previous two years
• Asthma prevalence rate
Attributed • Quality of stroke care (primary and secondary care), by weighted
supply population
• Accessibility to MRI scanner
• Catchment population of the hospital trust that supplied the practice
with the largest number of inpatient admissions

10. Using the formula to allocate to
practices
• ‘Freeze’ supply variables at national levels
• For each individual, calculate predicted NHS
hospital costs
• For each practice calculate average costs in each
age/sex category
• Assign age/sex specific averages to all individuals
in practice
– To address data lags and changes in registration
• Share out PCT budget according to practices’ total
predicted expenditure

11. Distance from target and practice size
for the new model and practices with more than 500 patients
2
DFT index: relative to England mean
0 .5 1 1.5
0 10000 20000 30000 40000
practice size: number of patients
Excludes the 16 practices with a DFT index > 2.

12. Distance from target
Percentage of practices more than
x% away from target
> +/- 5% > +/- 10% > +/- 20%
DFT relative to PCT
mean 61.1 34.6 14.0
DFT relative to national
mean 72.5 48.9 20.9
12

13. Phase III Objectives: in progress
• Refresh existing PBRA model using more
recent data (for allocations 2011/12)
• Develop improved PBRA model (for allocations
2012/13)
• Model a variety of risk sharing arrangements
(to inform shadow GP Consortia and NHS
Commissioning Board)
• Develop a final PBRA formula (for allocations
2013/14)

14. Basic model
Explanatory variables Prediction variable
2007/08 2008/09 2009/10

15. Data lag
2007/08 2008/09 2009/10 2010/11 2011/12 2012/13

16. GP budgets and risk:
we’ve been here before
• GP fundholding c.1991
• Total fundholding c.1995
• ‘Primary Care Groups’ c.1998
• Practice based commissioning c.2002
Martin, S., Rice, N. and Smith, P. (1998), “Risk and the general
practitioner budget holder”, Social Science and Medicine, 47(10),
1547-1554.
Smith, P. (1999), “Setting budgets for general practice in the New NHS”,
British Medical Journal, 318, 776-779.

17. Fundholding
• Relatively generous budgets
• Limited set of elective conditions plus
prescribing covered
• Per patient limit £6000
• Overspends largely borne by Health Authority
• Underspends kept by practice for patient
services
• A very ‘soft’ budget

18. Decomposing the variation in practice
expenditure
• The formula captures average clinical responses
to measured patient and area characteristics.
Therefore any variation from the formula will be
due to:
– Variations in clinical practice;
– Variations in the prices of treatments used by the
practice;
– Imperfections in the formula caused by known patient
characteristics that are not captured in the formula;
– Random (chance) variations in levels of sickness
within the practice population.

19. High cost cases
Number of practices
Percentage of cases over £20K per person per year 19

20. Sampled from patients (10m) within a 20% random sample of all patients
100 replications for each consortium size
Consortium size increased in units of 10,000
40 Consortia risk profile
Consortium risk per capita(£)
Upper 95% C.I.
20
14
Average risk
0
-13.5
-20
Lower 95% C.I.
-40
0 100000 200000 300000 400000 500000
Consortium list size
Average risk Lower CI
Upper CI
Simulations from all data
Risk smoothed over time - predicted versus actual expenditure

21. Consortia risk profile
Consortium size Consortium size
10000 100000
.6
.6
.4
.4
.2
.2
0
0
Probability
0 2 4 6 8 10 0 2 4 6 8 10
Consortium size Consortium size
300000 500000
.6
.6
.4
.4
.2
.2
0
0
0 2 4 6 8 10 0 2 4 6 8 10
Percentage Variation
Simulations from all data
Probability of more than an X percent variation from annual budget
Acknowledgement: Nigel Rice and Hugh Gravelle

22. Consortia risk profile
Consortium size Consortium size
10000 50000
.6
.6
.4
.4
.2
.2
0
0
Probability
0 2 4 6 8 10 0 2 4 6 8 10
Consortium size Consortium size
100000 150000
.6
.6
.4
.4
.2
.2
0
0
0 2 4 6 8 10 0 2 4 6 8 10
Percentage Variation
Omit £100k Omit £150k
Probability of more than an X percent variation from annual budget
Simulations omitting high cost patients from practice lists
Acknowledgement: Nigel Rice and Hugh Gravelle

23. Some possible consequences of ‘hard’
budget constraints
• Practices that perceive that their expenditure will fall below their
budget may “spend up” in order to protect their budgetary position
in future years;
• Practices that perceive that their expenditure will exceed their
budget may be thrown into crisis as they seek to conform to the
budget;
• Patients may be treated inequitably. Different practices will be
under different budgetary pressures, and so may adopt different
treatment practices.
• Within a practice, choice of treatment may vary over the course of
a year if the practice’s perception of its budgetary position changes.
• General practices may adopt a variety of defensive stratagems, such
as cream skimming patients they perceive to be healthier than
implied by their capitation payment.

24. Some budgetary risk management
strategies
• Pooling practices
• Pooling years
• Excluding predictably expensive patients
• ‘Carving out’ certain procedures or services
• Analysis of reasons for variations from budgets
• Allowing some reinsurance of risk
– Limiting liability on individual episode
– Limiting liability on individual patient
– Risk sharing
– Retention of a contingency fund
– Etc
• Making sanctions and rewards proportionate