Developing a person-based resource allocation formula for general practices

Developing a
person-based
resource allocation
formula for
allocations to general
practices in England
PBRA Team

PBRA Team
University of York
Paul Dixon
Mark Dushieko
Hugh Gravelle
Steve Martin
Nigel Rice
Peter Smith
Centre for Health Economics, University of York, YO10 5DD
Health Dialog
Michael Delorenzo
Nadya Filipova
Ron Russell
Health Dialog, 2 Monument Square, Portland, ME 04101
Nuffield Trust
Martin Bardsley
Jennifer Dixon (project lead/manager)
Elizabeth Eastmure
Theo Georghiou
Adam Steventon
Nuffield Trust, 59 New Cavendish St, London W1G 7LP
New York University
John Billings
Graduate School of Public Service,295 Lafayette St, New York, NY 10012-9604
London School of Hygiene & Tropical Medicine
Colin Sanderson
Health Services Research Unit, London School of Hygiene & Tropical Medicine, Keppel St,
London WC1E 7HT
With thanks to Rob Shaw for advice and support, and Angelique Buhagiar and other Nuffield
Trust colleagues for administrative support.

Contents page
Chapter 1 Background…………………………………………………………………………………… 4
Chapter 2 Developments in resource allocation to 2007……………………………….. 7
Chapter 3 Broad approach taken………………………………………………………….......... 16
Chapter 4 Assembling and linking the main datasets……………………………………. 23
Chapter 5 Assembling and linking key variables…………………………………………... 35
Chapter 6 Modeling strategy……………………………………………………………………….. 51
Chapter 7 Results of national models…………………………………………………………... 60
Chapter 8 Results of SUS models for West Midlands SHA ……………….............. 81
Chapter 9 Calculation of practice allocations……………………………………………….. 84
Chapter 10 Resource implications of alternative models …………………………….. 91
Chapter 11 Issues to consider for PCTs setting budget allocations………………….. 101
Chapter 12 Maternity care…………………………………………………………………………….. 117
Chapter 13 Discussion…………………………………………………………………………………... 125
Chapter 14 Summary…………………………………………………………………………………….. 133
List of appendices………………………………………………………………………........................... 141

PBRA Report 14 10 2009
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Chapter 1 Background
1.1 Introduction
Practice based commissioning allows general practices to commission a range of hospital (inpatient and
outpatient) services for the patients on their registered lists. The budgets to general practices for
commissioning are currently set using a combination of approaches, mainly based on historical
expenditure and a ‘fair shares’ capitation method (1). This method essentially applies the formula for
resource allocations to primary care trusts (PCTs) to practice level populations. PCTs use this method to
set notional budgets for commissioning general practices, and additionally there are a variety of local
arrangements to spread the financial risk of significant over- or under-spending.
It is known that the current method of setting budgets for practice-based commissioning (PBC) is not yet
accurate enough on which to base ‘hard’ as opposed to ‘notional’ budgets. This is because the data on
which current methods are based may not reflect health need at the practice (or person) level
accurately and thus may not be very predictive of likely future expenditure, especially for small
practices. Both factors may result in budgets that expose commissioning general practices and PBC
groups to undue levels of financial risk. Budgets therefore remain largely notional, and the financial
incentives for PBC groups to examine quality and efficiency of the care they commission relatively weak.
Given that it is current policy to encourage the development of practice-based commissioning, plus to
pilot various forms of integrated care (including the integration of primary community and secondary
care) to manage expenditure covering registered populations, it is increasingly pressing to develop
budgets for such entities that are both based on need and accurately predict future spend for the
populations covered.
Recent advances in the availability of information in the NHS, and the technology to link data at
individual level, now allow the potential to exploit much more information on the needs of individuals
which can be used in the development of a resource allocation formula, and to set budgets. Until now,
for example, most of the data used as proxy variables for need for care within resource allocation
formulae have been derived as averages from populations and the average attributed to each individual
living in an area. Now more individual-level information is available, for example on the diagnoses
recorded as an inpatient, and new techniques have been designed to protect the identify of individuals,
fully in accordance with the Data Protection Act, allowing more data to be linked at person-level.
Developments in information also mean that hospital episode statistics data (containing information
recorded on patients when they are treated in hospital as an outpatient or when admitted to hospital)
have been replaced locally with SUS (Secondary Uses Service) data from 2007. The impact of this on
developing a resource allocation formula also needs to be tested.
The NHS in England is not alone in attempting to develop person-based risk adjusted capitation
formulae for resource allocation. Several European countries with social insurance systems have started
to introduce individual-level data into their systems. Sweden has developed an empirical capitation
matrix using linked individual-level demographic, health and socioeconomic data, while the Dutch have
adapted diagnostic cost groups and pharmacy cost groups (groups developed in the US) to adjust the
premium subsidies paid to sickness funds according to the health of the individual. In the US, a risk
adjusted capitation formula is used for Medicare in setting premiums for managed care plans (a task
virtually identical to the one described in this project) using individual level claims data. Other relevant
international developments are discussed further in Chapter 2. In the UK, the existence of universal
coverage, a virtual monopoly payer and widespread computerised records mean that the NHS is at a
distinct advantage compared to other countries in having data to develop more refined allocations at
person-level.

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In the NHS recent advances in individual-level risk adjustment, focusing on diagnostic and utilisation
data from previous healthcare encounters and prescribing, have been used in the NHS to improve
prediction of individual utilisation for case management. Several members of the research team
(Billings, Dixon, Georghiou, Delorenzo, Russell and Filipova) successfully developed predictive risk
models of utilisation of emergency hospital services in England to help support initiatives to reduce
avoidable hospitalisation, now used throughout the NHS in England (2)(3).
This team joined with colleagues with a long track record in developing resource allocation formulae in
England at the University of York (Dixon, Gravelle, Martin, Rice, Smith)(4)(5)(6) and London School of
Hygiene & Tropical Medicine (Sanderson) to use this approach in 2007 to complete a feasibility study to
assess whether a genuinely predictive formula could be developed using person-level data, for
allocations to practice-based commissioning groups. The analysis was unusual in that it used more
sources of individual level than had been exploited before, such as diagnostic data from NHS inpatient,
outpatient and general practice care, and crucially was able to link these data in an innovative way that
protected patient confidentiality. The results were encouraging (see Appendix 1), and on that basis the
further analysis reported here was commissioned by the Department of Health (DH) in July 2008 to
develop a person-based formula to inform budget allocations for commissioning general practices for
2010/11.
1.2 Aims and scope
The specific aims of the project are to:
develop a person-based formula for allocations for commissioning for all general practices in
England for the financial year 2010/11
develop a national formula
develop a formula which promotes equity of access for equal need
provide advice to DH on the resulting method of setting budgets for commissioning general
practices.
The scope for services is to cover NHS-funded:
inpatient care (including critical care and A&E care where these are included in inpatient tariffs)
outpatient care
maternity care (if possible).
Service exclusions from the project include:
mental health care
community health services
primary care
prescribing
privately-funded (non-NHS) patients treated in NHS or non-NHS facilities.
The project excludes any consideration of unavoidable variations in cost in different parts of England –
currently taken into account by the Market Forces Factor supplement to NHS providers in England.
The population covered includes:
the population registered with a general practice located in England. People not registered with
a general practitioner (such as homeless persons, prisoners, members of HM Armed Forces) are
excluded
For the analysis using SUS data only: the population of West Midlands Strategic Health
Authority.

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Previous experience internationally of developing a person-based resource allocation formula points to
optimal conditions for the data on which such a formula is based. These were used as guiding principles
in the project. The data should ideally be:
universally available for all populations and readily updated
of similar quality across areas/institutions regardless of the local needs of the population, in
order not to produce a formula which compounds inequities in health care resource allocation
containing plausible proxy variables for health needs and the predictors of health need
easy, feasible, cost effective and acceptable to collect without undue administrative burden
not subject to perverse incentives or manipulation
readily available and linkable to non-health care data sources at individual level
consistent with data confidentiality requirements
verifiable by independent auditors.
The remaining content in this report covers:
Chapter 2: Developments in resource allocation in NHS England to 2007
Chapter 3: Broad approach taken
Chapter 4: Assembling and linking the main datasets
Chapter 5: Assembling and linking the key variables
Chapter 6: Modeling strategy
Chapter 7: Results of national models
Chapter 8: Results of SUS models for West Midlands Strategic Health Authority
Chapter 9: Calculations of practice allocations
Chapter 10: Resource implications of alternative models
Chapter 11: Issues to consider for PCTs setting budget allocations
Chapter 12: Maternity care
Chapter 13: Discussion
Chapter 14: Summary
References
(1) Practice based commissioning budget setting guidance 2009/10.
http://www.dh.gov.uk/en/Publicationsandstatistics/Publications/PublicationsPolicyAndGuidan
ce/DH_094364
(2) Billings J, Dixon J, Mijanovich T, Wennberg D. Risk stratification in the NHS. BMJ 2006; 333;327-
30
(3) Combined predictive model. http://www.networks.nhs.uk/177
(4) Carr-Hill, R., Hardman, G., Martin, S., Peacock, S., Sheldon, T. A. and Smith, P. A Formula for
Distributing NHS Revenues Based on Small Area Use of Hospital Beds, Centre for Health
Economics, University of York, September. 1994.
(5) Sutton, M., Gravelle, H., Morris, S., Leyland, A., Windmeijer, F., Dibbin, C., Muirhead, M.
Allocation of Resources to English Areas: Individual and Small Area Determinants of Morbidity
and Use of Health Care. Report for Department of Health, December 2002. (AREA Report).
(6) Morris, S., Carr-Hill, R., Dixon, P., Law, M., Rice, N., Sutton, M. and Vallejo-Torres, L. Combining
Age Related and Additional Needs (CARAN) Report. The 2007 Review of the Needs Formulae
for Hospital Services and Prescribing Activity in England. Final Report, 30 November 2007.
ACRA(2007)22.

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Chapter 2 Developments in resource allocation to 2007
This section outlines the development of resource allocation in the NHS in England and summarises
recent international experience in using person-based data in resource allocation. The broad approach
to our analysis is outlined.
2.1 Resource allocation in the NHS in England for hospital and community health services (HCHS)
A brief history
The main aim of the development of a weighted capitation formula for resource allocation in the NHS in
England has been to provide equal opportunity of access to health care for people at equal risk. Since
1999 a second aim has been to contribute to the reduction in avoidable health inequalities.
The weighted capitation formula was first developed in 1976 and sought to allocate NHS resources for
hospital and community health services on the basis of population size, age, sex and various indicators
intended to reflect health needs. The method of allocations for other forms of expenditure, such as
prescribing, is not discussed here. The unit of resource allocation has varied over time following various
administrative reorganizations within the NHS, but generally has been for populations no smaller than
100,000.
Since 1976 the technical method of analysis has developed but four significant and related challenges
have remained:
(a) there is no gold standard measure of health needs;
(b) it has therefore been difficult to develop a formula based on need that is unaffected
by utilisation or supply;
(c) there is a paucity of data at individual level;
(d) theory as to the relationship between need, use and supply.
(a) No gold standard measure of health need
There has been very little information directly measuring the health needs of the population, therefore
proxy measures have generally been used. Typically the dependent variable in statistical analyses has
been NHS utilisation of care by a population in an area (or resulting expenditure), and researchers have
sought to find the factors which explain, or are associated, with these levels of utilisation. Typically
these factors are grouped into ‘need’ and ‘non-need’ variables. ‘Need’ variables are those which policy
makers have decided ought to affect allocations. ‘Non-need’ variables are those which have been
decided should not affect allocations; these include ‘supply’ variables and some socio-economic
variables (which in analysis are shown to have counter-intuitive effects which would affect allocations in
unacceptable ways). The decision as to which variables are ‘need’ and non-need’ is essentially a
subjective one, albeit based on some evidence, although it is recognised that both sets have overlapping
effects (need driven by supply and vice versa).
The data available on health needs (for example ill health) in the English population have not been
available at person level (beyond age and sex) so have been gathered at area or population level. The
types of information used in models have included:
census questions on health
occasional surveys of health on a sample of the populations (e.g. Health Survey for England)
standardized mortality rates
unemployment rates
socioeconomic deprivation scores.

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These are so-called ‘proxy’ variables and have been selected on the basis of their face validity, coverage
of multiple domains of health (such as morbidity, social circumstances, education, ethnicity), parsimony,
and on the basis that they predict expenditure on hospital and community health services (HCHS) in a
statistically significant manner at small area level.
There are obvious problems in using these area-based proxy indicators of need, for example proxy
variables may not always be legitimate indicators of health need, or may reflect illegitimate factors, for
example historic patterns in the supply of health care. In practice it is difficult to untangle one from the
other. Socio-demographic variables from census and health survey data are updated infrequently, so
may be out of date.
The morbidity indicators measured in this way do not cover all health conditions for which people may
have health care needs. Furthermore morbidity data from surveys do not yield enough information to
provide robust small area estimates, and thus, more pertinently for this study, estimates at practice
level for budget-setting.
(b) The difficulty of developing a formula based on need that is unaffected by utilisation, or supply of
health services
The exploitation of utilisation patterns as an approximate indicator for the cost of meeting health need
assumes that all current health needs are manifested in current healthcare utilisation patterns. Certain
disadvantaged groups who tend to use healthcare services less, may have unmet needs which are
missed by this methodology. Supply can also induce demand and vice versa. Utilisation, need and supply
of health services thus have overlapping effects, and it has been difficult to disentangle these in analysis.
This has been the fundamental challenge in developing current resource allocation formulae, which
remains without a gold standard measure of health need.
(c) The paucity of data at individual level
Proxy needs variables have traditionally been measured at area level, for example using census data.
However, these data are not available for analysis at individual level, leading to ‘ecological fallacy’,
whereby individuals within a geographical area may have characteristics very different to the area
average. This creates significant difficulties if the unit at which the variables are measured is larger than
the unit at which resources are allocated.
(d) The relationship between need, use and supply
The relationships amongst variables labelled need, use and supply have long been contested by
economists (1)(2) and health services researchers (3).The multivariate modelling approach used in
developing recent formulae has typically selected independent variables that most influence health
need (measured by utilisation, or expenditure on care), such as area-based attributed data on need
(such as deprivation) or ‘supply’ of care (such as the number of hospital beds available). The proxy
needs variables are then taken account of in the resource allocation process, but the ‘supply’ variables
are not (eg they are set at some national average (‘frozen’) to remove local differences in the supply of
services). Even though the supply of care may be influenced by need, a decision is taken in resource
allocation to designate some variables as ‘need’ (and take account of them in resource allocation) and
some as ‘supply’ (and ignore them in resource allocation). Yet it is known that these three factors are
integrally linked, labelling them as either ‘need’ or ‘supply’ masks important issues, and ‘freezing’ the
supply variables might also freeze out some need effects.
In developing resource allocation formulae in the NHS, more often than not, a wide range of variables
reflecting health need and health care supply, or both, are tested in a multivariate model. Incorporation
of variables into the ultimate model selected has been largely on the basis of strength of the variable in
explaining variations in the dependent variable (typically expenditures in health care) rather than any a
priori theory.
Much of the development over time has been to do with developing new modelling techniques or
incorporating new data sources. The PBRA approach described in this paper is no different in that

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respect. There is an obvious gap at the heart of these resource allocation approaches, the lack of an
adequate theory as to how health needs, supply of health services, and utilisation of care interact, and
which variables is it legitimate to include in a resource allocation formula with the objective of allocating
resources according to need.
This is a very complex area and, whilst there has been much written on the subject, it is perhaps not
surprising that there has not been more headway in the last couple of decades in a theoretical base,
which draws in a range of academic disciplines. Yet it is worth further thinking and analysis to see if
there cannot be more progress rather than the ‘kitchen sink’ approach typically taken in modelling. This
point is returned to in Chapter 13.
There have been periodic revisions to the weighted capitation formula as shown in Table 2.1. A useful
description and analysis of these revisions by Bevan (4)(5) is not repeated here.
Table 2.1
Reviews of resource allocation in English NHS for HCHS, 1976- today
Drawn from Bevan (2) and Bevan and Van der Ven (16)
Note: RAWP = Resource Allocation Working Party
RoR = Review of RAWP
AREA = Allocation of Resources to English Areas
CARAN = Combining Age Related Additional Needs (9)
Since 1976 this work has effectively resulted in a differential growth in resources for HCHS away from
areas which have been historically well endowed with health care facilities (such as London), towards
the north of England.
2.2 Resource allocation to general practices commissioning hospital-based care
Brief history
In 1991 volunteer general practices were allowed to hold a budget to commission elective inpatient
care, outpatient care, A&E care, and prescribing, under the GP fundholding scheme. The budgets for GP
fundholders were largely set on a historic basis and negotiated locally. While there was some evidence
of disproportionate allocation to GP fundholding practices relative to other practices (6), it was also
recognized that the data available at the time plus the small populations covered by budgets, meant
that developing a resource allocation formula based on need was infeasible (7)(8).
In 2007, the team from Brunel University that developed the CARAN formula was asked to extend the
formula for PCT allocations to allow it to be used to set practice budgets. This could have been done by
applying the ‘need’ coefficients in the CARAN formula derived from the small area regressions in the PCT
model to the practice population similar to the ‘fair shares’ solution (a top-down approach), or by
Year Name Scope Unavoidable
costs
included?
Allocations
to
Approximate
population
size
Years applied
1976 RAWP HCHS Y 14 RHAs 3m 77/78 – 90/91
1980 RoR HCHS Y 14 RHAs 3m 91/92 – 94/95
1993 University of
York
HS Y 14 RHAs
192 DHAs
3m
250,000
95/96-
2001 AREA HCHS Y 303 PCTs 175,000 02/03 -06/07
2006 CARAN HCHS N 152 PCTs 350,000 07/08 - ongoing

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estimating individual-level capitations and using them to produce a practice budget (a ‘bottom-up’
approach).
The CARAN team used the second approach and re estimated the coefficients for the health needs
variables at individual level (9). While CARAN team had some information on the needs of individuals
who had had an inpatient encounter (such as age and sex, area of residence and recorded diagnoses)
they had very little information on non-users - people who had not been admitted to hospital over the
period of scrutiny. This was in part because the CARAN team was not able to link HES data with NSTS
data at individual-level.
Table 2.2
Resource allocation to general practices for commissioning HCHS services in English NHS, 1991
2.3 International experience of developing capitation formulae
This section briefly summarizes recent international empirical approaches to developing capitation
payments when individual level data are available. It seeks to explain the approach to developing a
PBRA formula we have taken, and in particular why we sought to include individual-level morbidity
variables such as ICD-10 diagnoses recorded on HES, and why we tested in the prior feasibility study
three diagnosis groupings – Hierarchical Cost Categories (publicly available), ambulatory clinical groups
(ACGs) and diagnostic cost groups (DCGs) (both proprietary).
The intention of risk adjustment at individual-level is usually to develop unbiased estimates of the
expected expenditure on an individual over a fixed period (say one year), given their measurable
personal characteristics. The driving force behind the development of many person-based capitation
systems has often been the need to attach capitation payments to individuals when they are free to
choose competing insurers. However, the principles underlying the use of person-based capitation
formulae remain valid when one is dealing with non-competitive insurers or with public health care
systems with central funds allocated to lower level commissioning bodies such as NHS PCTs or general
practices on the basis of their registered populations.
In its simplest form, the person-based approach might entail a simple contingency table of the expected
expenditure by age and sex, as routinely used in many resource allocation formulae. Indeed, the formula
used in the NHS in England has traditionally used a small number of age categories as the building block
of its capitation payments. Age and sex adjustment is the starting point for most individual based
capitation formulae. With (say) 8 age categories and 2 sex categories, this would require estimation of
just 8x2 = 16 distinct capitation payments, a manageable task.
Year Name Scope Unavoidable
costs included?
Allocations to Average
practice
size
Years applied
1991 GP
Fundholding
Elective
Outpatient
Prescribing
Community
Services
N Volunteer
fundholding
practices
6,500 90/91-97/08
2001 AREA HCHS
Prescribing
N 8200 general
practices
6,500 04/05 -06/07
2006 CARAN HCHS
Prescribing
N 8200 general
practices
6,500 07/08 - ongoing

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Such demographic adjustment is manifestly inadequate, as it ignores the many influences on expected
expenditure other than age and sex. Most fundamentally, patients require different volume and
intensity of health care, depending on their underlying health and social circumstances. For this reason,
all capitation schemes seek to augment basic demographic risk factors with additional personal
characteristics. Ideally these characteristics should be readily measurable, verifiable indicators of the
person’s health status, particularly of long-term conditions such as diabetes. In addition, risk factors
such as smoking status might be considered important potential indicators.
However, the indicators in any capitation formula need to satisfy many criteria. As noted in Chapter 1,
they should be: feasible, with low administrative cost; consistently, reliably, verifiably and universally
recorded; not vulnerable to manipulation or fraud; legitimate predictors of health care expenditure;
encouraging efficient delivery of public services, and be free from perverse incentives; respect
confidentiality requirements; and parsimonious and plausible. In practice, this severely limits the choice
of variables, as in most contexts only very restricted information on the characteristics of individuals
conforms to such criteria. In particular, adequate, universally recorded health status data are rarely
available in any country.
As a result, two broad approaches towards the choice of person-based risk adjusters have developed.
The first uses general indicators of socioeconomic disadvantage, in the belief that these are strongly
related to health care utilisation needs, and that more direct indicators of health status are either not
available, or vulnerable to manipulation. Thus, in countries such as Sweden and the Netherlands the
existence of universal registers of citizens has made it possible to augment demographic variables with
variables such as disability status and employment status in the capitation formula. Some states in
Canada and Australia have also been able to include ethnicity in the person-based elements of the
formulae they use. These methods are feasible only if there exists a universal register that records the
relevant social circumstances of all citizens, and they are therefore infeasible in the UK.
The second approach seeks more directly to use indicators of health status arising from previous
encounters with the health system. This is especially attractive in systems with comprehensive billing
arrangements for paying providers, as financial information systems must often include indicators of the
patient’s diagnosis, such as drugs prescribed, diagnosis related group (DRG) status, procedures
undertaken, or even direct diagnoses by clinicians. These methods therefore use past encounters or
utilisation as a predictor for contemporary use, and often offer the most powerful statistical explanatory
power (anything up to 18% of the variation in spending is explained at individual level). Their main
limitation is that they could induce perverse responses amongst providers (increased utilisation in order
to attract higher capitation payments in the future), and they may be conservative (they necessarily
result in low capitation payments for the categories of patients who have failed to secure needed access
to health care in the past).
Whichever approach is adopted, the introduction of more risk factors to the rudimentary demographic
set can quickly lead to an unmanageable proliferation of contingency table cells and the associated
capitations to be estimated if it is assumed that the effects of one risk factor (such as age) depend on
the level of other risk factors (such as gender, social class). For example, if we add to age (8 categories)
and sex (x2) new risk factors such as social class (x5), employment status (x3), housing tenure (x2) and
marital status (x2), the number of payments to be estimated might in principle be 8x2x5x3x2x2=2,160.
The technical task becomes huge, the main technical challenge being to collapse the large number of
potential cells into a feasible set. Table 2.3 gives an example of a ‘reduced’ contingency table, developed
for Stockholm County.
A cruder but more practical approach to addressing the ‘curse of dimensionality’ is to assume that risk
factors operate independently and to quantify the independent marginal contribution to the capitation
payment of each additional needs factor. That is, an individual’s risk factors are simply added up to
create a capitation payment, and no interactions between risk factors – or only a small number of
interactions - are considered. This has been the basis of many practical systems of person-based
capitation formulae developed since the mid-1990s. The additivity assumption makes it easier to use

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multiple regression methods to quantify the effects of potential risk factors by reducing the number of
coefficients to be estimated.
1
The regression approach has been used in the US since 1982 for Medicare, the statutory federal
insurance scheme for elderly people (aged 65 and over). Early approaches use the person’s age, sex,
county of residence, welfare status and whether or not they live in a nursing home (10). Since then the
method has been steadily refined. In 2000 a method known as Principal Inpatient Diagnostic Cost Group
(PIP-DCG) was introduced, which included about 15 indicators of disease severity, based on inpatient
utilisation during the preceding year (11).
The PIP-DCG model was a clear improvement, in that it acknowledged some aspects of the person’s
sickness level, and offered a dramatic improvement in predictive power of future health care costs (10).
However, its reliance on inpatient diagnosis was highly selective, and introduced potentially serious
adverse incentives, by encouraging inpatient care in preference to potentially more cost-effective care
in other settings.
Table 2.3
The abridged Stockholm health care capitation matrix, Swedish krona per month, 1994
Medical & Surgical Psychiatric
Age Owner occupier Rented Owner occupier Rented
<1 7200 0 0
1-24 1900 2100 400 600
25-64 cohabiting
Higher non-manual 3100 3600 400 800
Other non-manual 3700 4300 600 900
Manual 4000 4400 900 1300
Not employed 5300 6400 1400 2400
25-64 living alone
Higher non-manual 3600 3900 900 1600
Other non-manual 3600 4200 1000 2400
Manual 3900 4600 1400 3800
Not employed 5100 5400 4900 12700
65-84
Cohabiting 13500 16500 500 1000
Living alone 15400 18200 1100 2100
>84
Cohabiting 27600 29800 300 1000
Living alone 24200 29400 500 1000
Source: (Diderichsen, Varde and Whitehead, 1997)(12)
A more comprehensive scheme known as the hierarchical condition categories (HCC) model was
therefore introduced in 2004. The essence of the HCC approach is unchanged from the preceding PIP-
DCG model. Capitation payments are adjusted for the severity of a beneficiary’s sickness level, as
indicated by previous health care diagnosis. However, the new diagnosis cost groups (about 70 in
number) were based on both ambulatory and inpatient diagnoses, and were very much more refined
than the PIP categories (13). Furthermore, although related diagnoses within a HCC are counted only
once towards the capitation payment, if a patient has more than one unrelated diagnosis, then both can
contribute to the payment calculation.
1
It would be possible to estimate the average expenditures in the cells in the multiplicative cell-matrix
approach by multiple regressions so that the essential distinction between the two approaches is not
the method of estimation but the underlying assumptions about how risk factors affect expenditure.

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Beyond Medicare, a number of proprietary diagnosis schemes have emerged as a result of the need to
refine capitation payments for other US insurers, such as the state-run Medicaid schemes for the poor,
and private healthcare maintenance organisations. Two of the leading examples are the Adjusted
Clinical Group (ACG) and Diagnostic Cost Group (DCG) methods, which seek to incorporate increasingly
refined indicators of diagnosis to be used in patient classification, including pharmaceutical use and
ambulatory care, as well as hospital care. The major technical contribution of these methods is to
collapse a wealth of diagnostic information derived from patient encounters into a manageable number
of patient classifications.
Although the US has led the development of person-based capitation formulae, increasingly refined
mechanisms, often based on the US methods, are now being implemented in countries with competitive
social insurance schemes, such as Belgium, Germany, Israel, the Netherlands, and Switzerland (14)(15).
They are also being tested in countries with non-competitive public sector purchasers, such as Canada.
The major contribution of diagnosis-based capitation methods is the greatly increased predictive power
they offer over other methods, often explaining up to 17% of an individual’s future health care
expenditure. When individuals are aggregated into purchaser risk pools, such as PCTs or general
practices, the precision of the purchaser’s budget is very much higher than under cruder capitation
methods.
The major limitations of the methods are their considerable information requirements, the risk that they
will stimulate unnecessary healthcare use, the incentives they offer (in systems with competing insurers)
to ‘cream skim’ patients for whom expected expenditure is less than the capitation payment, and the
risk that they underestimate the spending needs of patients who suffer from unmet need (a problem
they share with most empirical methods).
2.4 Implications for this project
The project reported here was preceded by a feasibility study by the same team in 2007 which tested
the inclusion in models of several morbidity markers measured at person level:
1. Full ICD-10 diagnoses from inpatient data
2. Data recorded in general practice from GP consultations (diagnostic data and biomedical data
such as blood pressure, smoking status, and body mass index)
3. ICD-10 subchapters
4. Prescription drugs prescribed (from data recorded in general practice)
5. Hierarchical condition categories (developed for capitation payments under Medicare). (See
Chapter 5 for further information on these).
6. Adjusted Clinical Groups (ACG)
7. Diagnostic Cost Groups (DCGs).
Of these, information on 1-5 are in the public domain, the grouping algorithms used in 6 and 7 are
owned by private companies and available under license only. The feasibility study suggested that the
performance of models would be enhanced using all markers except 2, in particular groups 5-7. The full
rationale, analysis, results and conclusions are shown in Appendix 1.
In this study, the objective was to develop a national person-based formula that could be applied to
practices. We relied upon data that were available nationally that could be used in developing a
formula. Data from general practices are not yet available nationally, only in a small number of PCTs, so
person-based morbidity markers from group 2 and 4 could not be used. Given available data, the
approach taken was to include in the analysis, person based information for groups 3 and 5 – ie using
publicly available information. Group 1 included too much information and the feasibility study showed
that the diagnoses could be more usefully grouped in the modeling without loss of performance.

14 | P a g e
Depending on the results, we might suggest further testing of proprietary diagnostic groupers 6 and 7 at
a later stage (see Chapter 13).
2.5 Conclusion
While there have been advances in the development of resource allocation formulae in the NHS in
England since 1976 significant challenges have remained, in particular the complex relationship between
need, supply and utilisation of care, and the limited sources of data on which to base analysis. However,
the availability of more data at individual level which can be linked, has the potential to improve the
accuracy of allocations at practice-level for commissioning inpatient and outpatient care.
One conclusion from our analysis of international experience was that there are distinct advantages of
developing a person-level formula within a publicly funded system like the NHS, because of better
access than most countries to person-level data, and with few incentives for commissioning groups (in
this case commissioning general practices on indeed PCTs) to compete for patients on the basis of
health risks. Under these circumstances, the advantages are likely to outweigh the drawbacks.
The second main conclusion was that measures of disease severity, based on recorded diagnoses could
be useful to test in developing person-based resource allocation formulae in the NHS. However it was
recognised that a diagnosis was dependent upon prior utilisation of NHS care, yet diagnoses are a rich
and important source of information on health need.
Third, individual-level predictions of expenditure one year ahead are at best of the order of 17%.
References
(1) Culyer, A. and Wagstaff, A. (1993). Equity and equality in health and health care. Journal of
Health Economics. 12, 431-457.
(2) Gravelle, H., Sutton, M., Morris, S., Windmeijer, F., Leyland, A., Dibben, C. and Muirhead, M.
(2003) “Modeling supply and demand influences on the use of health care: implications for
deriving a needs-based capitation formula. Health Economics, 12, 985-1004
(3) Paul-Shaheen P, Clark JD, Williams D. Small area analysis: a review and analysis of the North
American literature. J Health Polit Policy Law. 1987 Winter;12(4):741–809.
(4) Bevan RG. The search for a proportionate care law by formula funding in the English NHS.
Financial Accountability and Management (forthcoming)
(5) Review of the Weighted Capitation Formula. A report submitted to the Secretary of State for
Health [RARP 33] & Supplement to the Review of the Weighted Capitation Formula. [Supplement
to RARP 33] (Department of Health, London)
http://www.dh.gov.uk/en/Managingyourorganisation/Financeandplanning/Allocations/DH_4108515
<http://www.dh.gov.uk/en/Managingyourorganisation/Financeandplanning/Allocations/DH_4108515>
(6) Dixon J et al. Distribution of NHS funds between fundholding and non-fundholding practices.
British Medical Journal 1994;306:30-4
(7) Martin S, Rice P, Smith PC. (1998) Risk and the general practitioner budget holder. Social Science
and Medicine 47, 10, 1547-54
(8) M T A Sheldon, P Smith, M Borowitz, S Martin, and R C Hill. Attempt at deriving a formula for
setting general practitioner fundholding budgets BMJ 1994; 309: 1059 - 1064

15 | P a g e
Age Related and Additional Needs (CARAN) Report. The 2007 Review of the Needs Formulae for
Hospital Services and Prescribing Activity in England. Final Report, 30 November 2007.
ACRA(2007)22.
(10) Health Care Financing Administration (1998). Announcement of calendar year 1999
Medicare+Choice payment rates. Washington, Health Care Financing Administration.
(11) Iezzoni, L. I., J. Z. Ayanian, D. W. Bates and H. R. Burstin (1998). "Paying more fairly for Medicare
capitated care." New England Journal of Medicine 339(26), 1933-1937.
(12) Diderichsen, F., E. Varde and M. Whitehead (1997). "Resource allocation to health authorities: the
quest for an equitable formula in Britain and Sweden." British Medical Journal 315, 875-878.
(13) Ash, A., R. Ellis, G. Pope, J. Ayanian, D. Bates, H. Burstin, L. Iezzoni, E. MacKay and W. Yu (2000).
"Using Diagnoses to Describe Populations and Predict Costs." Health Care Financing Review 21(3),
7-28.
(14) Pope, G., J. Kautter, R. Ellis, A. Ash, J. Ayanian, L. Iezzoni, M. Ingber, J. Levy and J. Robst
(2004)."Risk Adjustment of Medicare Capitation Payments Using the CMS-HCC Model." Health
Care Financing Review 25(4), 119-141.
(15) Van de Ven, W. P. M. M., F. Beck, D. Buchner, L. Chernichovsky, A. Gardiol and A. Holly (2003).
"Risk adjustment and risk selection in the sickness fund insurance market in five European
countries." Health Policy 65(1), 75-98.
(16) Bevan RG, Van der Ven W. Purchaser competition in the NHS: Lessons from The Netherlands.
Forthcoming, Nuffield Trust, 2010.

16 | P a g e
Chapter 3 Broad approach taken
3.1 What our approach adds
The approach by the PBRA team is similar to that taken by the York, AREA and CARAN teams and others
in that it attempts to explain expenditure on health care using proxy information on health needs and
supply. The main departures with previous work are to:
use individual-level data on both users and non-users
use data from past NHS encounters to measure past morbidity directly
predict future health expenditures.
We use a bottom-up approach: to predict individual level expenditures based on both individual and
area based attributed need and supply characteristics and aggregate up to practices. We then calculate
per capita average needs for 38 age gender groups for every practice in England (see Chapter 5). This is
applied to individuals registered in each practice (according to their age and gender group) and summed
by practice to provide a weighted need for each practice which can then be used to calculate the
practice share of the PCT budget. We use the latest available data at each stage of the process.
We link data at person level:
by using person level data from the National Strategic Tracing Service (NSTS) which lists basic
information on people registered in general practices in England (ie gives person level
information both users and non-users)
by using person level data from several NHS sources (inpatient, outpatient) and linking them
over time and with the NSTS data using a new pseudonymisation process (described below)
that protects the identities of individuals in accordance with the Data Protection Act.
We use person-level diagnostic information for previous years (where a person has had diagnoses
recorded as an inpatient). In the feasibility study (see Appendix 1) the added value of grouping ICD-10
diagnosis codes was shown, and various groupings were tested.
We compare the model developed with HES data with the same model built using SUS data for a
strategic health authority (SHA), West Midlands SHA. This analysis was done because SUS data may be
available more recently than HES data, and because of concerns about the quality of SUS data.
3.2 Data issues
Data sources
The main model to be built had the following broad elements:
Expenditure(individual) = f (needs(individual),needs(attributed),supply (attributed), ….)
Where expenditure refers to year t and the needs and supply variables refer to previous years (t-1, t-2)
The main sources of data used to build the model are shown in Figure 3. 1.

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Figure 3.1
Main sources of data used to build the person-based resource allocation model
Notes:
HES = Hospital episode statistics (provided by NHS Information Centre via Northgate)
SUS = Secondary uses service (provided by West Midlands’ Strategic Health Authority)
NSTS = NHS National Strategic Tracing Service (provided by ATOS Origin)
QoF = Quality and Outcomes Framework (provided by DH)
GMS = General Medical Services data (provided by DH).
The main sources of NHS data (at person level) we use in the project are:
activity data from hospital episode statistics (HES): covering inpatient and outpatient activity
2002/3 - 2007/8 for all residents in England. The data were obtained from the NHS Information
Centre via Northgate. This dataset included :
o information on individual health needs: age, sex, ICD 10 diagnoses recorded
o information to link attributed needs from population data: area of residence
o information to allow individual costs to be calculated :Healthcare Resource Group
(HRG), specialty of care and (for inpatients) length of stay.
activity data from the secondary uses service (SUS): covering inpatient, outpatient and A&E
activity for 2005/6, 2006/7, 2007/8 for all residents of one strategic health authority (West
Midlands). The data were obtained from West Midlands CBSA (Shared Services Agency). This
dataset included :
o information on individual health needs: age, sex, ICD 10 diagnoses recorded
o information to link attributed needs from population data: area of residence
o information to allow individual costs to be calculated :Healthcare Resource Group
(HRG), specialty of care and (for inpatients) length of stay.
a ‘member file’ obtained from the National Strategic Tracing Service (NSTS): showing all
residents in England who were registered with a practice 2002/3 to 2007/8. This dataset
included:
o information on health needs: age, sex
o information to link attributed needs and supply from population data: area of
residence; GP practice of registration
o information useful for producing budget allocations: length of time registered with any
practice over the time period

18 | P a g e
We use practice population data from NSTS because ONS do not supply population information at
practice level. The last census was 2001, and updated projections only available at local authority level.
GP registration data from NSTS is updated at least quarterly.
The main sources of attributed needs and supply variables are described in Chapter 4.
3 Linking data sets
The NHS number (pseudonymised) provided the link between the main datasets, allowing GP
registration data (from NSTS) to be linked to HES and SUS data and attributed data. Using information
on HES and SUS, individual cost data were added. This is shown schematically in Figure 3.2.
Figure 3.2
Linkages between main datasets
Pseudonymised
NHS number
NSTS
HESSUS
Attributed data
Costs Costs
A critical part of the project was to link NSTS data with SUS and HES data as shown. This required the
following steps:
obtaining necessary permissions to use and link NHS data at person level using pseudonymised
NHS number
liaising with the data suppliers and NHS Information Centre to pseudonymise the data using
one encryption key to encrypt each individual NHS number in the three data sets (NSTS, HES,
SUS)
linking the three datasets at person-level using encrypted NHS number.
The attributed data (containing information relating to health needs of populations and the supply of
health services available to populations) was linked to NSTS data described via information on the area
of residence of the individual, and the GP practice the individual was registered with.

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3.3 Quality
We tested the quality of the three main datasets and the success of the data linkage (See Chapter 4 and
related appendices).
3.4 Costing
HES inpatient and outpatient data at individual level was costed in the expenditure years analysed. For
inpatient data the basic steps required:
Linking episodes of care into ‘spells’ (ie discrete admissions in an individual)
Excluding spells not covered by the scope of the project (such as in psychiatry specialties) or
incomplete spells
Applying national tariff costs using the 2008/09 tariff
Calculating costs for spells where national tariffs do not apply using an approach agreed with
DH.
3.5 Approach to building models
Broad approach
The basic approach used was standard – to build a statistical model in which expenditure is the
dependent variable and needs and supply variables are independent or explanatory variables. However
we have extended previous work by using a very large individual level data set which enabled us to have
a development sample and two separate (patient and practice level) validation samples (described
below).
Figure 3.3
The broad model
Expenditurei
Needs i supplya
Needsa Other variablesa, , ,( (
Where:
Expenditurei = Expenditure of an individual i
Needsi = health proxy needs of individual i
Needsa = attributed health needs of individual i
Supplya – attributed supply of health services/facilities to individual i
Other variablesa – attribution of other variables to individual i

20 | P a g e
Data lags
We used a prospective model in which expenditure in the budget year of interest was to be predicted
using data from two previous years. For a formula to predict expenditure in 2010/11, ideally explanatory
variables from 2009/10 and 2008/09 would be used. But, to build and test the model using known cost
data, 2007/08 and 2008/09 data would have to be used to predict expenditures in 2009/10 (as shown
below in Figure 3.4).
Figure 3.4
Ideal data for producing allocations
2008/09 2009/10 2010/11
Explanatory variables
Prediction variable
2007/08 2008/09 2009/10
Ideal data for model for 2010/11 allocations
Explanatory variables
Ideal data to build and test model for 2010/11 allocations
Prediction variable
However, NHS HES data are available only with a considerable lag. Until April 2009 we had HES data
only up to 2006/7. From May 2009 onwards we were able to use HES data for 2008/9 in modeling.
Hence the most recent year for which data were available on expenditure at the time of study was
2007/08, so that data from 2005/06 and 2006/07 were used to build the model and predict these
2007/8 expenditures.
Two potentially important and related issues arose. First, if 2005/06 -2007/08 data were used to build
the model, and the model used to predict expenditures for 2010/11, what was the potential impact of
this data lag on performance of the model? Second, the most important predictor of future expenditure
in a general practice is the population size. Given the movements in practice populations over time, how
could the model be used to apply to the population registration figures closest to budget setting year
(eg April 2010)?
On the first issue, the impact of data lags was assessed (see Chapter 7). We found that the longer the
data lag between the explanatory variables and expenditure the lower the performance in predicting
both individual level and practice level expenditure. On the second, the approach eventually taken was
to calculate practice allocations in three steps which made use of the most recently available data at
each step:
Step 1: was to estimate a model for 2007/8 expenditure for individuals in a practice at 1 April
2007 using explanatories from 2005/6 and 2006/7.
Step 2: was to apply the coefficients from this model to the average of the explanatory
variables for 2006/7, 2007/8 for each age/gender group in each practice where the averages
were based on the patients registered in the practice at 1 April 2008. This enabled us to use

21 | P a g e
the most up to date information on explanatories to calculate need per capita for every
practice in England.
Step 3: was to calculate total practice need by multiplying the per capita need by the practice
list size which could be measured just before practice budgets are set using the latest available
list data (which has a much shorter time lag than HES data).
This approach removed the need to apply unique individual level predictions to those moving practices,
and therefore removed the need to track specific individuals when they moved. The approach also
reduced the impact on model performance of data lags because the age-gender group average needs
index would be more stable over time than individual level needs variables (and therefore
expenditures). The implication however is that (a) the age/gender average needs index in each practice
generated from lagged data is an adequate representation of the age/gender average needs index at
the beginning of the budget year (April 2010) and (b) that the health needs of leavers and joiners to a
particular practice are similar to the average for that age/gender group. We found that practice
calculated allocations were highly robust to lags in the calculation. These issues are discussed further in
Chapter 11.
3.6 Modelling
The distribution of individual expenditure is very skewed because a high proportion of individuals have
no inpatient expenditure. We compared alternative methods of analysing the data (separate models to
explain having zero expenditure and to explain the amount of expenditure for those with any
expenditure): linear models; transformations of the cost variable to reduce skewness; and generalised
linear models. We found that simple ordinary least squares (OLS) regression using cost, rather than log
of cost, outperformed other methods in predicting expenditure. See Appendix 12.
Having established that OLS was the preferred method of estimation, we then embarked on a search for
the best model to explain individual expenditure. We did this in three stages.
Initially we examined models which explained 2006/7 expenditure of those on practice lists at 1
April 2005 using explanatories from 2003/4 and 2004/5 ie with a one year lag between the
explanatories and expenditure.
We then estimated models to explain 2006/7 expenditure of those on practice lists at 1 April
2006 using explanatories from 2004/5 and 2005/6. This had the advantage of reducing the lag
between explanatories and expenditure and meant that we did not have to consider how to
deal with patients who moved onto or off patient lists between the date the sample was drawn
(1 April 2005) and the start of the expenditure year 2006/7.
From May 2009 we had access to HES data for 2007/8 and estimated models to o explain
2007/8 expenditure of those on practice lists at 1 April 2007 using explanatories from 2005/6
and 2006/7.
In all stages we started with full models with a large number of explanatory variables - both morbidity
variables from past HES records and attributed need and supply variables. We kept morbidity variables,
age and gender, and PCT dummy variables in all models and selected the set of attributed need and
supply variables on the basis of their contribution to model fit. We used alternative sets of morbidity
markers derived from the ICD10 information in past HES records but found that a relatively
straightforward categorisation with 152 morbidity groups performed as well as more complicated
groupings. Our final parsimonious models typically contained only a few attributed needs and supply
variables. The past individual level morbidity variables were by far the most powerful in explaining
future expenditure.

22 | P a g e
3.7 Approach to budget setting
The models were intended to be used to allocate PCT funds to practices for hospital services – ie sub-
PCT allocations. The models generated for each practice (approximately 8200) in England a practice-
specific average needs index for 38 age and gender bands. This index is used to define the share of
allocations that to go to practices, based on the demography of the registered population.
As a result of building and testing models, a number of issues came to light relevant to setting budgets
for commissioning practices. These included:
How to handle high cost patients and treatments? We tested models which excluded high cost
patients over certain thresholds of costs, and discussed risk sharing in detail.
How to adjust for population movements and practice closures
Border issues: individuals travelling to Scotland, Wales and N Ireland for care.
How to deal with maternity cases and babies?
These issues are discussed further in the following chapters.
3.8 Comparison with existing applications
The results for practice allocations using our preferred models were compared with allocations using the
existing ‘fair shares’ method, which in turn is based upon the CARAN formula (1).
3.9 Conclusion
This chapter outlined the broad approach to analysis, which will be discussed further in Chapters 6,7,8
and 9 with accompanying appendices as indicated.
References
Age Related and Additional Needs (CARAN) Report. The 2007 Review of the Needs Formulae
for Hospital Services and Prescribing Activity in England. Final Report, 30 November 2007.
ACRA(2007)22.

23 | P a g e
Chapter 4 Assembling and linking the main datasets
4.1 Main sources
The main sources of NHS data (at person-level) to be used and linked in the project were:
o activity data from hospital episode statistics (HES): covering individual level inpatient and
outpatient activity 2002/3 - 2007/8 for all residents in England. The data were obtained from
the NHS Information Centre. Data on A&E attendances were not nationally available at the time
that the data were requested, and furthermore there are known and significant problems with
their quality and coverage.
o activity data from the secondary uses service (SUS): covering individual level inpatient,
outpatient and A&E activity for 2005/6, 2006/7, 2007/8 for all residents of one strategic health
authority (West Midlands). The data were obtained from West Midlands CBSA (Commissioning
Support Business Agency)
o a ‘member file’ from the National Strategic Tracing Service (NSTS): showing all residents in
England registered with a practice 2002/3 to 2007/8.
These data sets were linked at person-level using unique, person-level NHS numbers that had been
encrypted using a pseudonymisation process as described below.
To these basic datasets were added information on costs, and attributed data from a number of sources
(see Figure 3.2). This chapter describes in brief the process of obtaining and linking the data, and the
results from investigations of the quality of the data.
4.2 The three main datasets
The main tasks to assemble and link the datasets were to:
o obtain necessary permissions to use and link NHS data at person level using the pseudonymised
NHS number
o obtain and link the data using pseudonymised NHS number.
Obtaining HES, SUS and NSTS
(a) Hospital episode statistics (HES) data
Permission to obtain HES data for all residents in England for the period 2002/3 to 2007/08 was granted
from DH and supplied by Northgate. Some of the HES data were already held by the PBRA team.
(b) Secondary Uses Service (SUS) data
Permission to obtain SUS data was granted from one strategic health authority (West Midlands) to be
supplied by the West Midlands’ Commissioning Business Support Agency. The PBRA team had access to
complete inpatient and outpatient data sets for the financial years 05/06, 06/07 and 07/08 and partial
data for financial year 08/09. The data referred to all residents of 17 PCTs who were registered in a
general practice within the SHA, wherever they were treated.
(c) National Strategic Tracing Service (NSTS) data – the ‘member file’
Permission to use data was obtained from the NSTS, the data were supplied by Atos Origin. The dataset
contained information on all persons registered with a general practice in England over the period 1
April 2002 to 31 March 2008. The dataset included NHS number (subsequently pseudonymised – see
below), information on age, gender, area of residence (census lower super output area), dates of joining
and leaving different practices and date of death.

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Linking data at person level
Encrypting NHS numbers
The PBRA team was granted permission from the Data Monitoring Security Group at the Department of
Health to link individual level records via encrypted (pseudonymised) NHS numbers.
The unique person-level NHS number on SUS, HES and NSTS datasets was encrypted prior to the team
receiving the data. As the datasets were to be linked, the NHS number on all of the datasets had to be
encrypted in the same way, using the same software and encryption key. The PBRA team designed the
encryption process across the HES, NSTS and SUS data suppliers within specifications set by Connecting
for Health. In order to comply with our permission requirements, it was essential that we were not
aware of the encryption key used at any stage. We explored the option of the NHS Information Centre
taking on the role of broker, organising the software and agreeing and sharing the encryption key. While
the NHS Information Centre intends to take this role on in the future, it was not able to be a broker in
this case.
Each of the data providers used the latest version of encryption software provided by Health Dialog
(which met standards set by Connecting for Health) to encrypt the NHS number in the data sets. The
three data providers agreed a common encryption key and shared it amongst themselves. For the HES
data, a look-up table was produced linking a unique HES data field ‘HESID’ to the encrypted NHS ID
number. This enabled the HES data held by the PBRA team, which had a full set of fields including HESID
(but not NHS number), to be linked with the anonymised NSTS data on patients in practices.
Linkage
How all the datasets were linked is shown pictorially in Figure 4.1.
Figure 4.1
Linking the datasets for analysis

25 | P a g e
Adding cost information
Individual-level costs of inpatient and outpatient care were added to the dataset using the information
contained on person-level utilisation from HES and SUS data and combining with information on costs
obtained from the DH. How this was done is described further in Chapter 5.
Adding attributed data
A large variety of attributed variables (data available at an area or institution level) were tested in the
models as indicated in Figure 3.1. The area/institution level for which they were available, and how they
were linked to the three main datasets is shown in the table below. The construction and linkage of
these variables is described in more detail in Chapter 5.
Table 4.1
Linkage of attributed needs and supply variables
Name Source Brief description Year Linked via
Practice level
QOF DH, NPCRDC Prevalence and
performance scores
2004/5 –
2006/7
GP practice code
Area level
CARAN CARAN team Needs (61) and supply 2006 MSOA, LSOA
(NSTS)
ONS ONS Neighbourhood
Statistics
Socio-economic 2001-
2007
MSOA, LSOA
(NSTS)
GMS DH Practice
characteristics (eg GP
numbers)
2004-
2006
GP practice code
Hospital level DH Supply (beds, MRI
etc)
2006-
2007
GP practice code,
LSOA
4.3 Assessing the quality of the three main NHS datasets
HES
(a) inpatient
Hospital Episode Statistics (HES) contain details of all individual admissions to NHS hospitals in England.
The raw data include private patients treated in NHS hospitals, patients who were resident outside
England and NHS-funded care delivered non-NHS facilities.
On admission to hospital each patient is assigned to the care of a particular consultant and HES records
a new consultant episode. When the patient is discharged from hospital or dies or is transferred from
the care of one consultant to another while still in the same hospital, the record is closed and becomes a
finished consultant episode (FCE). The period between admission to and discharge from the hospital is
known as a spell of care and several FCEs might be recorded on a patient within a single spell of care. In
this project, inpatient spells were identified by grouping together episodes for the same person with the
same admission and discharge dates.

26 | P a g e
For each episode a number of data items are collected on each person, including age, gender, date of
admission and discharge, specialty; whether an emergency or elective admission; primary and
secondary diagnoses; treatments undertaken; Healthcare Resource Group (HRG) (a code reflecting the
cost of the diagnoses and treatments given).
We had HES data on inpatient activity from 2003/4 to 2007/8, with more than 15 million new records
added each year, and outpatient attendance data from 2003/04 to 2007/8 onwards, with more than 60
million new records added each year.
The total number of records obtained for each year is shown in Table 4.2.
Table 4.2
Summary of records on HES (inpatient), 2004/05 to 2007/08
Year Spells FCEs
2004/05 12,592,250 14,458,833
2005/06 13,296,645 15,294,852
2006/07 13,712,374 15,777,368
2007/08 14,317,088 16,435,181
(b) Outpatient
HES also contain details of all NHS outpatient attendances in England. The data are recorded for each
attendance and include age, gender, specialty of attendance, and consultant under whose care the
patient was. Data used for the project was for the years 2003/04 to 2007/08. The total number of
records per year from 2004/05 is shown in Table 4.3.
Table 4.3
Summary of records on HES (outpatient), 2004/05 to 2007/08
Year NHS Attendances
2004/05 54,420,813
2005/06 60,608,403
2006/07 63,217,226
2007/08 66,649,484
HES data are provided by NHS acute and Foundation Trusts, and non-NHS facilities providing NHS-
funded care, to Northgate. Northgate apply various routines to clean the data, for example by removing
duplicate records submitted by health care providers. No further action was taken in this project to
identify any other duplicate episodes and attendances.
(c) HES data from non-NHS providers
There have been concerns about the quality of inpatient and outpatient HES data recorded on NHS-
funded patients from non-NHS providers: both the number of attendances and admissions/day cases
recorded and the diagnostic data recorded (1).
Department of Health commissioning statistics (2) provide information on independent sector care
commissioned by PCTs and contracted out by NHS hospitals. The data contain volumes and costs
associated with care commissioned and contracted out by PCTs and NHS hospitals respectively.
According to these data, approximately 1% of inpatient NHS treatments occur in non-NHS providers.

27 | P a g e
In 2007/8, around 76% of independent sector inpatient activity was commissioned directly by PCTs, with
the remainder being outsourced from NHS Trusts. In total, 145,000 NHS-funded inpatient episodes
were provided by the independent sector in 2007/8 in comparison to 103,000 recorded on HES. This
suggests that the HES figures are not complete. It is unclear whether care outsourced from Trusts to the
independent sector will feature in HES under the Trust provider code or the independent sector
provider code.
The table below shows the number and proportion of NHS funded inpatient and day case episodes and
outpatient attendances in non-NHS providers, as recorded on HES.
Table 4.4
Number and proportion of NHS funded inpatient and day case episodes and outpatient attendances in
non-NHS providers, as recorded on HES, 2004/05 to 2007/08
2004/5 2005/6 2006/7 2007/8
Number of inpatient episodes in
independent sector providers
18,000 26,000 74,000 103,000
as percentage of all episodes 0.1% 0.2% 0.5% 0.6%
Number of outpatient attendances at
independent sector providers
18,000 54,000 268,000
as percentage of all attendances 0.0% 0.1% 0.4%
Many HES inpatient episodes have missing diagnostic and treatment code data, which are used to assign
HRG codes. Outpatient data do not contain diagnostic codes – specialty codes are used to cost each
attendance.
Table 4.5 below shows the extent of missing information in HES data used to cost activity comparing all
providers with non-NHS providers.
Table 4.5
Proportion of inpatient episodes/outpatient attendances with missing information used to cost the
activity 2004/05 to 2007/08
2004/5 2005/6 2006/7 2007/8
Proportion of inpatient episodes with missing HRGs
Independent sector providers 71% 80% 56% 39%
All providers 3% 1% 1% 1%
Proportion of outpatient attendances with missing specialty codes
Independent sector providers 0% 24% 11%
All providers 2% 2% 0%
So while the data from the private sector are incomplete, at most about 1% of NHS-funded inpatient
and outpatient activity is carried out in non-NHS providers, even allowing for shortcomings in recording
data on HES. This figure may vary considerably among commissioning general practices. We identified
39 practices for which the non-NHS (independent) sector provided more than 10% of inpatient episodes
of care for the practice in one year – 31 in Kent and the Medway PCT, and 8 in Somerset PCT. The
independent sector provided more than 5% inpatient episodes for 176 practices. For outpatient care, 58
practices had more than 5% outpatient attendances provided by the independent sector; these
practices were concentrated in South Staffordshire, East Sussex, Bradford, Derbyshire and Somerset.

28 | P a g e
The practices are generally located close to independent treatment centres. Appendix 3 contains more
detail including maps showing the geographical location of practices most affected.
The project used data on NHS-funded care to develop a resource allocation formula in two ways: to
calculate the yearly hospital cost of each individual (the predictor variable); and as an indicator of prior
health need of the individual whose costs are being explained (the explanatory variable). Therefore
missing data from the independent sector will affect both sides of the model shown earlier in Figure 3.3.
Since data on NHS-funded care in the independent sector has improved considerably particularly in
2007/08, any data shortfall would affect the explanatory variables more than the predictor variables.
However since a national formula was developed in which the proportion of NHS-funded care in the
independent sector was at most 1%, the overall impact on the estimated model coefficients is likely to
be small.
SUS
More detailed analysis of the quality of inpatient and outpatient data on SUS records covering residents
of West Midlands Strategic Health Authority was carried out was carried out in two main respects:
Quality of the SUS data – discussed in this section.
Comparisons between SUS and HES – discussed in section 4.4 below.
The West Midlands CBSA provided SUS data for services commissioned by the 17 West Midlands
primary care trusts (PCTs) in fiscal years 05/06, 06/07, and 07/08. The PBRA team provided a national
extract of HES records for the same fiscal years. Both data were filtered by commissioner code limiting
records to those commissioned by PCTs within the West Midlands SHA, and for patients residing within
these PCTs.
(a) Inpatient data
In general the quality of the coding of SUS inpatient data appeared to be good. Key observations
included:
2% of the discharge method codes were missing for one PCT, meaning that it is not known what
happened to patients when they were discharged.
For 8% of one PCT’s records and 6% of another, it was not possible to identify which hospital
(provider) the patient attended.
The percentage of records where the primary procedure is populated gives an indication of the
percentage of admissions for which a procedure is performed (procedures include operations,
treatments like chemotherapy or investigative tests like MRIs). This varied significantly across
the PCTs – from 63% to 45%.
In one PCT the most frequently occurring primary diagnosis for admitted patients was ‘Persons
encountering health services in other circumstances’.
There was a significant increase in the number of daycase admissions in 2007/08 compared to
2008/09 across all PCTs.
The percent of records where the discharge method is ‘Spell not yet finished’ varied across the
PCT. Because all the spells have a discharge date, meaning the patient has been discharged, the
coding of this variable appears unreliable. In one PCT for 2007/08, 10% of spells were coded
with this discharge method, which accounted for 19% of the total cost.
‘Compensation for renal failure’ was the most prevalent primary procedure for two PCTs for
2006/07, accounting for 19% and 20% of their procedures respectively. There were very few
records for this procedure in any other year.
The procedure ‘Continuous Infusion of therapeutic substance’ was highly prevalent in the
2007/08 data but not for any other year.

29 | P a g e
The average length of stay (LOS) for an elective admission was highly variable: 10 days for two
PCTs in financial years 2005/06, 2007/08 and 2008/09. The average for other PCTs was around
4 days.
Appendix 6 has full details.
(b) Outpatient data
In total 18,514,193 outpatient attendances were received from CBSA. Key observations included:
There was a marked increase in the number of records at the start of the financial year 2008/09
The outcome of attendance (i.e. whether or not the patient was discharged from the
Consultant’s care following the appointment) was blank for around 50% of records for two PCTs
The primary diagnosis field was largely not populated except in one PCT where 85% of records
were coded
Most of the primary procedures in two PCTs were coded X999, which is an invalid code
• Nursing Episode was the most frequently occurring Treatment Function code in one PCT.
Trauma and Orthopaedics and General Medicine were the most frequent for all the other PCTs
• The percentage of attendances that were a first attendance varied from 25% to 41% for
2007/08
• In one PCT the number of Consultant Referrals outnumbered GP Referrals
• The percentage of appointments resulting in the patient being discharged varied from 2% to
47% in 2007/08
• Females aged 17-44 were the largest consumers of outpatient appointments. Overall, females
accounted for 57% of outpatient records.
In conclusion, overall quality of SUS data was thought to be satisfactory on which to build models.
NSTS
(a) Basic information
The data on GP registrations come from the National Strategic Tracing Service, which was established in
1999 as a database of all people who were born, or who have been registered with a GP, in England and
Wales. The NHS number was developed in the last decade specifically to support unique patient
identification within the NHS. It is a unique 10-digit number. The first nine digits are the identifier and
the tenth is a check digit used to confirm the number's validity. New entries to NSTS are created as a
result of weekly submissions of new births from the Registrars of Births & Deaths and for new
registrations with GPs (such as immigrants).
The NSTS dataset (the ‘member file’) contained information on all persons registered with a general
practice in England over the period 1 April 2002 to August 2008. The dataset was delivered in three files:
(i) file 1: included the individual's year of birth, gender, and month and year of death;
(ii) file 2: included the LSOA identifier for the individual's home address, the date that the person
moved to the address, and the date that the person left the address;
(iii) file 3: included a PCT code, a practice identifier, the date the person registered with the
practice, and the date that the person ceased to be registered with the practice
Each file also included the individual's encrypted NHS number so that we were able to merge the three
NSTS data sets. In addition, the NSTS also supplied a HESID-to-encrypted-NHS-number look-up table.
This enabled us to attach an NHS identifier to each HESID and thus to combine the cost data from HES
with the patient registration data from NSTS. The three files from NSTS were almost (but not

30 | P a g e
completely) linkable at individual level and they contained a small proportion of persons residing across
the border in Wales.
West Midlands SHA also supplied a member file for the SHA population showing all patients registered
with general practices in the SHA as of the middle of 2008.
(b) Imperfections with NHS numbers
For the purposes of this project, the NHS number would ideally be a unique individual key: each NHS
number would have only one person and one GP registration attached to it, and each person would
have only one NHS number. While the NHS number is expected to be unique in the vast majority of
cases, there are four potential imperfections in the way in which NHS numbers are assigned:
(i) A single NHS number may have more than one GP registration attached to it, for example
during the period that a patient's new GP registration is processed and a previous GP
registration is closed.
The period of double GP registration may sometimes be prolonged through error and sometimes never
corrected. 0.6% of patients had duplicate practice records on NSTS as at 1 April 2008 (Table 4.6).
Table 4.6
Percent of patients with duplicate practice records on 1 April, 2005-2008
2005 2006 2007 2008
Percent of patients with duplicate practice
records on 1 April
0.66% 0.62% 0.62% 0.58%
The dataset received from NSTS contained the registration history of 66.1m individuals. We cleaned the
data in two ways. First, there were 130m records, but many pairs of these records referred to
consecutive apparently uninterrupted periods in the same general practice. Reducing these paired cases
down to single records, with appropriate start and end dates, the number of records reduced to 103m.
Second, end dates in a practice in 22% of moves lagged behind a person’s registration with a new
practice or were missing. To avoid counting individuals as being registered in more than one practice at
any one time, we changed ‘end dates’ to coincide with the start date of a new GP registration.
(ii) A single NHS number may have more than one name or address attached to it, for example
because somebody has changed their name or address, or because the name or address
entered was erroneous and has now been corrected.
This will not a problem for this project where the most recent address was used to identify an
individual’s area of residence. However while potentially erroneous cases (so-called ‘stop-noted’
records) are being investigated, they were excluded from the NSTS dataset supplied to us. In September
2007 there were 369,000 of these records, and the majority were NHS numbers that had been allocated
in error or had become confused with other records. Information is not publicly available on the
geographical or practice distribution of these stop-noted records.
(iii) A single individual may have more than one NHS number, for example because he or she has
been registered without a previous registration being picked up (perhaps because they have
registered under a different name).
Some of these cases will be the result of fraud by an individual wanting to register with several GPs
concurrently, perhaps to obtain drug supplies.

31 | P a g e
(iv) Some episodes may have no NHS number attached to them.
In HES, around 3.4% of inpatient episodes in 2006/7 did not have an NHS number attached to them,
representing around 3.3% of total expenditure. This was a reduction compared to previous years (Table
4.7). Outpatient attendances are less likely than inpatient episodes to be missing an NHS number.
Table 4.7
Percent of inpatient episodes with missing NHS numbers 2003/04 – 2007/08
2003/4 2004/5 2005/6 2006/7 2007/8
Percentage of inpatient episodes with
missing NHS numbers
6.23% 4.48% 4.12% 3.43% 3.10%
Percentage of outpatient attendances with
missing NHS numbers
6.13% 4.36% 3.45% 2.84% 2.24%
Of these four issues, we were able to reduce the impact of (i) in the analysis, and we were able to
include in the analysis some information on (iv) - patients with episodes with no NHS number (provided
they had a practice recorded on HES – see Chapter 7). Individuals in group (ii) were already excluded
from the dataset by the data suppliers. We had no means of allowing for individuals with duplicate NHS
numbers (iii) but it seems unlikely to be a major problem for our estimated cost models.
Appendices 2 (quality of NSTS and linkage with HES) and 14 (irregularities with NHS number) have
further detail.
4.4 Linking datasets
Linking HES and NSTS data
The HES and NSTS datasets were linked using the encrypted NHS numbers for the years 2003/04 to
2007/08. A small percentage of HES data contained records with missing NHS numbers which
could therefore not be matched to the NSTS data (see Table 4.7). The number of records in HES with a
valid NHS number that could not be found in the NSTS member file was negligible. Appendix 2 has more
details.
Linking SUS and HES
The rationale for wanting to link SUS data from West Midlands SHA and HES data for West Midlands
SHA was to be able to apply and compare the results of models on a national sample of individuals
(using HES data), to individuals in West Midlands using SUS data. The comparison were thought
important because (a) there have been concerns about the quality of data recorded on SUS, and (b) SUS
data will become more available than HES in future, and will be available much closer to real time than
HES (which is currently available in January to March of the following year – ie lagged by a minimum of
9-12months). Thus in theory SUS may be preferable to use in future for use in person-based resource
allocation to commissioning general practices.
SUS and HES data (both referring to the population of for West Midland SHA) were linked via
pseudonymised NHS number obtained from the member file from West Midlands SHA. The data were
compared to determine if SUS and HES records contained consistent information that would allow the
application of a national model developed on HES data to be applied to SUS files.

32 | P a g e
(a) Inpatient data
Table 4.8 gives a summary of the linkage and comparisons.
Table 4.8
Comparison HES and SUS linked records
2005/06 2006/07
HES SUS HES SUS
Number of patients 713,770 736,312 737,829 739,233
Number of matched patients 698,294 719,841
Percent patients matched 98% 95% 98% 97%
Number of patients not
matching
15,476 38,018 17,988 19,392
Number of records 1,447,065 1,416,677 1,512,604 1,436,351
Number of episodes 1,446,970 1,416,668 1,512,538 1,436,341
Number of matched episodes 1,337,651 1,288,046
Number of matched episodes
with all diagnosis equal
1,226,909 (92%) 1,170,407 (91%)
After removing duplicate entries, SUS inpatient data contained approximately 2% fewer episodes and
3% more patients in 05/06 than HES data. SUS data contained 5% fewer episodes, but nearly the same
number of patients in 06/07. In the 05/06 data, 98 percent of HES patients and 95 percent of SUS
patients were matched. For 06/07, 98 percent of HES patients and 97 percent of SUS patients were
matched. For episodes, 92 percent of HES episodes and 94 percent of SUS episodes were matched for
05/06. For 06/07, only 85 percent of HES episodes and 90 percent of SUS episodes could be matched. All
remaining comparisons are based on matched episodes.
For matched episodes, initial comparison of the 14 diagnosis fields resulted in perfect agreement of 92
percent of matched episodes in 05/06 and 91 percent of episodes in 06/07. Examination of the
disagreeing records suggested that most of the disagreement came from the 4
th
and 5
th
positions of the
diagnosis. Expecting that the discrepancy could be the result of data cleansing and understanding that
application of morbidity groupers we intended to use (see Chapter 5) was based on the first three
characters of the diagnosis, we repeated the comparison using only the first 3 characters. This resulted
in 99 percent agreement in all matched episodes. Appendix 4(a)(b)(c) gives more details.
In conclusion, there was satisfactory linkage, and very good agreement in the first three characters of
the diagnosis fields between SUS and HES inpatient data.

33 | P a g e
Outpatient data
Table 4.9 contains information about record matching.
Table 4.9.
Comparison of SUS and HES outpatient records
2005/06 2006/07
HES SUS HES SUS
Number of patients 1,624,673 1,670,810 1,631,205 1,662,112
Number of matched patients 1,603,278 1,605,659
Percent patients matched 99% 96% 98% 97%
Number of patients not
matching
21,395 67,532 25,546 56,453
Number of records 5,378,098 5,251,200 5,574,752 5,410,194
Number of visits 4,934,857 4,905,983 5,139,276 5,044,158
Number of matched visits 4,692,629 4,337,736
Number of visits with all
diagnosis equal 615,410 (13%) 608,165 (14%)
After removing duplicate entries, SUS outpatient data contained approximately the same number of
attendances and 3% more patients in 05/06 than HES data. SUS data contained nearly 2% fewer
attendances and 2% more patients in 06/07. In the 05/06 data, 99% of HES patients and 96% of SUS
patients were matched. For 06/07, 98% of HES patients and 97% of SUS patients were matched. After
matching visits, 95% of HES visits and 96% of SUS visits were matched in 05/06. For 06/07, only 84% of
HES visits and 86% of SUS visits were matched. All remaining comparisons are made based on matched
visits.
Initial comparison of the diagnosis field resulted in agreement for only 13% of matched visits in both
fiscal years. Limiting the comparison to the first three characters did not improve the match. Applying
the 152 ICD-10 morbidity grouper (see Chapter 5) produced perfect agreement for only 11% of patients.
Further investigation determined that nearly all of the disagreement occurred in the category “unknown
& unspecified causes of morbidity”. After removing this category from the comparison, the morbidity
groupers were in perfect agreement for nearly 100% of patients. Further detail is shown in Appendix
4(a)(b)(c).
In conclusion, with the exception of “unknown & unspecified causes of morbidity”, there was very good
agreement in diagnosis between SUS and HES outpatient data.
4.5 Conclusion
Despite some discrepancies, overall the quality of the HES and SUS data, and the linkage between the
three main datasets was found to be satisfactory to support further statistical analysis.
We were not able to assess in further detail whether the quality of data and linkage were worse for
certain practices or geographical areas over others.

34 | P a g e
References
(1) Independent sector treatment centres. The evidence so far. Healthcare Commission, July 2007.
http://www.cqc.org.uk/_db/_documents/Independent_sector_treatment_centres_The_eviden
se_so_far.pdf
The ‘commissioning statistics’ are more detailed versions of Appendix NSRC5 to the National
Schedule of Reference Costs.
http://www.dh.gov.uk/en/Publicationsandstatistics/Publications/PublicationsPolicyAndGuidan
ce/DH_082571

35 | P a g e
Chapter 5 Assembling and linking key variables
Figure 3.1 summarises the key variables. This chapter contains more information on how these were
constructed, sourced and linked.
5.1 Dependent variable: patient expenditure/cost
HES
(a) Inpatient expenditure
(i) Exclusions
Costs were applied to all four years of HES inpatient data: 2004/5, 2005/6, 2006/7 and 2007/8. Three
groups of records on HES were treated differently: incomplete spells; mental health spells; and privately
funded spells.
We assumed that commissioning practices would be charged (actually or notionally) with costs when
the spell of hospital inpatient care ended. Hence the costs of spells which were incomplete at the end
of a financial year (ie carried on across different data years) were not attributed to that year but to the
year in which the spell ended.
Mental health spells were not costed because expenditure for this care is not in the scope of this
project. Because the aim is to devise a formula for allocating NHS resources, the costs of spells for
private patients treated in NHS hospitals were excluded. However we did use the morbidity information
from HES records for both mental health and private spells since they conveyed morbidity information
about patients which could predict their future use of the NHS. For more detail on the criteria for these
see Appendix 5.
Maternity inpatient costs (both mother and baby) are included in the total patient costs for years up to
2006/7. Maternity costs were not included in the total patient costs for 2007/8, but calculated and
assessed separately (see Chapter 13).
Table 5.1
Number and percentage of spells not complete in each data year 2004/05-2007/08
Year Number of
spells
Percent of costed spells
2004/5 130667 1.0%
2005/6 178329 1.3%
2006/7 169961 1.2%
2007/8 See note 1 See note 1
Note 1: comparable data are not available for 2007/8 as unfinished episodes were not requested for that year
Table 5.2
Numbers of mental health spells excluded 2004/05-2007/08
Year Mental health spells
2004/05 261,454
2005/06 253,500
2006/07 250,687
2007/08 239,406

36 | P a g e
Table 5.3
Numbers of privately funded finished consultant episodes (FCEs) 2004/05-2007/08
Year Privately funded FCEs
2004/05 119,163
2005/06 121,103
2006/07 123,693
2007/08 124,478
Table 5.4
Numbers of maternity spells in HES inpatient data, 2006/7 -2007/08
Maternity HRG Number of spells
(2006/7)
Percent of all
maternity spells
(2006/7)
N spells
(2007/8)
Percent of all
maternity spells
(2007/8)
Total baby 587,575 32.72 628,076 32.72
Total mother 1,207,971 67.28 1,291,735 67.28
(ii) Assigning costs
We costed spells using the most recent unit costs available at the time of costing (autumn 2008).
Information was used from within each HES spell (admission) to assign a ‘healthcare resource group’
(HRG) for which a national tariff is available. Spells of care were identified and costed on the basis of the
most resource intensive episode within the spell. As agreed with the DH, the costs broadly follow the
guidance for the 2008/9 tariffs (1).
The 2008/9 tariffs did not cover all types of hospital care. Methods were agreed with the Department of
Health on how to cost spells which included episodes for which there were no tariff available. In general,
where there was no tariff for a Health Care Resource Group (HRG), we looked for a suitable Reference
Cost in the 2005/6 set (2). The 2005/6 figures were chosen because they were the basis of the 2008/9
tariffs. Specialty average costs are applied when there was insufficient detail on the episode to attach
either a tariff or Reference Cost. Both the tariffs and Reference Costs relate to version 3.5 of the HRGs
(3). Version 4 is currently being introduced, but would not have been available to cost most of the HES
data used in this project.
The methods used to cost the HES inpatient data are summarised in the following table – which also
shows the typical percentage of spells handled by each method (for 2005/6). Appendix 5 provides
further information.

37 | P a g e
Table 5.5
Summary of methods for cost HES inpatient activity data, 2005/06
Sources of cost data for constituent
episodes
Percent of all
spells in 05/06
Method for applying costs
All covered by tariffs 96.466 Use tariffs to apply costs to entire spell
All covered by reference costs 1.400
Use ref costs to cost each episode – apply inflation
adjustment and trimpoints specific to Ref Costs
All without costable HRG 1.855
Use specialty spell average cost derived from spells
that can be entirely costed with tariffs
Some tariff and some reference costs 0.046 Cost conventionally: apply tariff to spell
Some tariff and some uncostable 0.203 Cost conventionally: apply tariff to spell
Some ref costs and some uncostable 0.030
Use specialty spell average cost derived from spells
that can be entirely costed with tariffs
Some tariff, reference costs and
uncostable 0.001
Cost conventionally: apply tariff to spell
Total 100.000
The table shows that more than 96% of spells were costed with a tariff. Of the 1.4% that required the
use of reference costs, 94.5% were mental health care for which costs were not included in the scope of
this project. For the 1.85% without costable HRGs, the DH agreed these should be costed (with specialty
average costs) for the present exercise.
(iii) Adjustments to costs
Several adjustments to the tariff are described in the guidance (1) and were implemented in this project:
Supplements for augmented care are added, based on the total number of days and types of
care implied by the relevant HES fields.
Adjustments for stays exceeding the trimpoint for the spell tariff. The number of excess days is
computed by subtracting the number of days in the trimpoint from the total length of the spell,
having first subtracted the length of any periods of augmented care.
Adjustments for short stay emergency spells. The guidance describes adjustments to reduce
the tariff by set amounts for spells that are substantially shorter than the trimpoint. The rules in
the guidance are followed to apply these reductions.
Differential tariffs for emergency spells were not applied, following discussions with DH. The
rationale for the lower tariff for emergency spells is mainly to manage the financial risk
between providers and commissioners, rather than an adjustment to reflect different volumes
of activity.
Specialist top-up costs. These were applied according to the guidance.
Reference costs were adjusted upwards to take account of inflation since 2005 and downwards to
account for the market forces factors (MFF) (that are removed from the tariffs, but not from the
reference costs).
Further details on costing inpatient care and a descriptive analysis of costs is in Appendix 5.
(b) Outpatient expenditure
Costs were applied to all four years of HES outpatient data: 2004/5, 2005/6, 2006/7 and 2007/8. The
unit of analysis here is attendance to outpatients rather than a spell. Two groups of records were not
costed: attendances in mental health specialties; and attendances that were privately funded.

38 | P a g e
Table 5.6
The number and percentage of outpatient attendances not costed in mental health and privately
funded care in NHS, 2006/07-2007/08
Year Mental health
NHS attendances
Privately funded
attendances
2006/07 2,484,868 181,214
2007/08 2,429,745 246,792
Again, the approach follows the 2008/9 tariff guidance. Both mandatory and indicative tariffs were
used. The variables and criteria used to attach tariffs to the outpatient attendances were:
whether the patient is a child (aged 16 and under);
whether the attendance is a first or follow-up session;
the treatment specialty function code (when there is no valid treatment function code, the
main specialty function tariff is used).
Two sets of attendances cannot be costed in this way:
those that are not covered by tariffs
those that are missing codes for both treatment and main specialty function.
The number of attendances that can and cannot be costed by tariffs is reported in Table 5.7.
The 2008/9 tariffs could be matched to approximately 93% of attendances (see Table 5.7). Across all
four years approximately 4% of attendances had HRGs for which there are reference costs but no tariffs.
However, most of these related to attendances for mental health care and were excluded from the
expenditures predicted. Reference costs were used for those that remained. The 2-3% of attendances
with invalid codes for either treatment or main specialty function were assigned the average cost of the
attendances that could be costed with tariffs.
Table 5.7
Costing Methods Applied to Outpatient Attendances
2004/5 2005/6 2006/7 2007/8
Children Adults Children Adults Children Adults Children Adults
Costed
with
tariffs N 5,627,314 44,821,262 6,151,129 49,906,918 6,241,735 52,273,852 6,463,084 55,301,414
% 93.53% 92.60% 93.20% 92.50% 92.52% 92.65% 92.14 92.83
Costed
with ref
costs N 245,980 2,474,336 293,044 2,769,735 309,551 2,687,169 328,107 2,550,257
% 4.09% 5.11% 4.44% 5.13% 4.59% 4.76% 4.68 4.28
Average
cost
used N 143,442 1,108,479 155,527 1,275,358 194,749 1,460,952 223,142 1,720,663
% 2.38% 2.29% 2.36% 2.36% 2.89% 2.59% 3.18 2.89
Total N 6,016,736 48,404,077 6,599,700 53,952,011 6,746,035 56,421,973 7,014,333 59,572,334
Further details on costing outpatient attendances and descriptive analysis of costs are in Appendix 5.

39 | P a g e
Table 5.8 gives summary statistics for the costs per inpatient spell from 2004/5 to 2007/8 (excluding
mental health and including maternity). The mean cost per spell fell in 2007/8. We attribute this to
reduced recording of intensive or critical care and a reduction in the length of stay (see Chapter 11). The
effect is to reduce the number of very high cost spells and thus to reduce the variation in costs across
patients. The median cost per spell is constant over the period because the median spell type accounted
for a significant proportion of total spells.
Table 5.8
Costs per spell (all inpatients)
N Mean Median Std Dev
2004/5 7328350 2497 1124 4925
2005/6 7593531 2595 1124 5688
2006/7 7691928 2660 1124 6611
2007/8 7940965 2423 1124 4721
Figure 5.1 gives the distribution of the total hospital cost per patient for patients costing less than
£10,000 for 2007/08. It shows the usual feature of expenditure data: a highly right skewed distribution
with a long thin tail of high cost patients.
Figure 5.1
Cost £ per inpatient 2007/08 for inpatients costing less than £10,000
0
2.0e-044.0e-046.0e-048.0e-04
.001
Density
0 2000 4000 6000 8000 10000
cost £ per inpatient
for inpatients costing < £10000
Cost £ per inpatient 2007/08
Note: The distribution is histogram is overlaid with an appropriately scaled normal density plot (the normal will
have the same mean and standard deviation as the data) together with an appropriately scaled kernel density.
SUS
CBSA supplied data through the secondary uses service (SUS) for 17 West Midlands SHA Primary Care
Trusts in West Midlands SHA. The data supplied covered inpatient, outpatient and accident and
emergency activity, for the period 1st
April 2005 to 31st
July 2008.

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