Development of mathematical models for Practice based Commissioning budgets for adult mental health in the UK
Submitting Institution
University of ExeterUnit of Assessment
Mathematical SciencesSummary Impact Type
HealthResearch Subject Area(s)
Medical and Health Sciences: Public Health and Health Services
Economics: Applied Economics
Summary of the impact
Professor Trevor Bailey of the University of Exeter led the
methodological and computational
development of new improved mathematical models to more fairly allocate
resources, and
particularly mental health resources, to GP practices in the UK within an
interdisciplinary research
team from the universities of Plymouth, Southampton and St Andrews. The
mental health services
component of NHS Practice based commissioning (PBC) introduced by the
Department of Health
(DoH) from 2007 onwards, deals with resource allocation for specialist
healthcare for some
400,000 patients with severe mental illness. From 2009 to 2011, the team's
mental health
estimates, based upon the modelling efforts of Bailey, were used to set
practice-level PBC budgets
accounting for around £8 billion of NHS funding, the DoH describing this
as a `step-change
improvement' in how mental health needs are modelled.
Underpinning research
Resource allocation in the UK NHS since 2007 has been progressively
determined via Practice
based commissioning (PBC), a reform introduced by the Department of Health
(DoH) aimed at
more engagement of GP practices, clinicians and health professionals in
local funding decisions.
PBC groups now determine and design effective responses to local patient
needs and allocate
resources against competing service priorities. However, at a national
level, the setting of PBC
budgets must still accord with the core NHS principle that `individuals in
equal need should have
equal access to care, irrespective of where they live'. This is
problematic because sufficient
information is not collected to directly measure need for healthcare in
different local areas for
different conditions (as opposed to demand or utilisation of those
services). Traditionally, the basic
assumption underlying NHS resource allocation has therefore been that
utilisation of healthcare
services can be used to determine need indirectly by modelling the
relationship between utilisation
and socio-economic variables whilst correcting for supply factors.
Information on the level of such
socio-economic variables in small areas (such as practices) can then be
used to allocate resources
in a way that reflects needs.
However, despite the sophistication of some of the modelling involved,
the use of local utilisation
data as a proxy for need clearly runs the risk of reflecting and
reinforcing existing inequalities in the
relationship between underlying healthcare needs and the resources
allocated to address them.
Various published studies, including work [2,3] by Dr Alex Gibson
(University of Exeter until 2005),
have presented evidence for such inequalities in NHS resource allocation
in England and
questioned whether existing utilisation is an appropriate basis upon which
to set fair target
allocations commensurate with `equal opportunity of access for equal
needs'.
Against this background and building on Gibson's earlier work at Exeter,
in 2007 Trevor Bailey
(Professor of Computational Statistics, University of Exeter since 1986)
collaborated with
Plymouths Professor Sheena Asthana (Health Policy), Dr Alex Gibson and Dr
Paul Hewson (also
University of Exeter 2000-2005), and others from the Universities of
Southampton and St Andrews,
to pioneer an innovative approach to resource allocation based on
morbidity (epidemiological
evidence) rather than solely utilisation data. Bailey and his Exeter team
led on formulation and
development of the statistical modelling and computational aspects
underpinning the work and
Asthana and others involved in the team led on coordinating the health
policy and data handling
dimensions.
This team successfully won a tender in 2007 by DoH and National Institute
for Health Services
Research (NIHR) to undertake a feasibility study examining whether direct
epidemiological
evidence could be used as a basis for setting health care capitations [4].
A key advantage of their
novel approach to Person Based Resource Allocation (PBRA) was that it
proposed directly
modelling the relationship between morbidity and personal characteristics
such as age, gender,
ethnicity and socio-economic status, so largely circumventing the
difficulty faced by utilisation-based
approaches of having to disentangle legitimate need factors from
illegitimate drivers of need
associated with unmet need and supply-side factors. The need-based
approach to PBRA
developed during the feasibility study rested on a modelling framework
formulated by Bailey which
merged two separate methodologies. First, Bayesian modelling and
population micro simulation
techniques were used to generate estimates of the number of individuals
experiencing designated
categories of morbidity within the units to which resources are to be
allocated. Second, estimates
of the resource required by each of those individuals in order to meet
their health care needs were
developed either on the basis of national average historic use, or
normative tariffs. A probabilistic
approach was adopted throughout. Thus, all estimates produced were
expressed not in terms of
"averages" but in terms of 95% or 99% "Credible Intervals" whereby all of
the uncertainty in the
modelling was retained and made explicit. More detail on the results is
given in Section 4.
Having shown their need-based PBRA approach to be feasible, the team was
then commissioned
in a subsequently funded research project to use their casemix modelling
approach on more recent
data to develop the PBC formula for the difficult area of Mental Health
[5]. In a highly computational
study using multi-processor MCMC algorithms devised by Bailey and Hewson,
the probability of
individuals falling into six mental health casemix groups was estimated
from multinomial, multi-level
Bayesian models fitted to some 40,000 HSfE data records and then applied
to several million
micro-simulated sub-populations within some 8,500 GP practices nationally
The resulting needs
estimates were then combined with resource needs for each group and
aggregated to give the
resource allocation for adult mental health for each individual practice.
These results were then used by the DoH in national resource allocation
for mental health services
in 2009-2011 as described more fully in Section 4. An account of the work
was published in the
Journal of Health Services Research Policy in 2011 [1].
References to the research
Evidence of the quality of the research that underpins this case study is
provided through the
following peer-reviewed publications and grants secured through
competitive funding sources.
[1]**Asthana S, Gibson A, Hewson P, Bailey T, Dibben C. (2011).
General practitioner
commissioning consortia and budgetary risk: evidence from the modelling of
'fair share'
practice budgets for mental health, J Health Serv Res Policy, vol.
16, no. 2, 95-101.
[2]**Asthana S, Gibson A, Moon G, Brigham P. (2003). Allocating
resources for health and
social care: the significance of rurality, Health and Social Care in the
Community, vol. 11, no 6,
486-493.
[3]**Asthana S, Gibson A, Parsons E. (1999). The geography of
fundholding in southwest
England: implications for the evolution of primary care groups, Health
& Place, vol. 5, 271-278.
Key Supporting Grants
[4] S. Asthana, A. Gibson, T. Bailey, C. Dibbens. The
feasibility of developing an approach to
Person Based Resource Allocation (PBRA) based on epidemiological data.
National Institute
for Health Research (Policy Research Programme), 2007, £121,269.
[5] S. Asthana, A. Gibson, T. Bailey, C. Dibbens. Developing
a resource allocation formula at
General Practice level based on individual patient characteristics
(Person-Based Resource
Allocation): Mental Health. National Institute for Health Research
(Policy Research
Programme), 2008, £191,216.
** Papers that best indicate quality of underpinning research.
Details of the impact
Reporting directly to the DoH Advisory Committee on Resource Allocation
(ACRA), Bailey and
team successfully developed and implemented two different modelling
frameworks during the
feasibility study [4] referred to in Section 2. The first was aimed at
acute specialities and illustrated
in the project report [see evidence item a] through generation of
GP practice based estimates of
resource needs across England for the treatment of each of cardiovascular
disease,
endocrine/metabolic disease and diabetes. It involved modelling the
log-odds of self-reported
longstanding illness (LSI) in specific illness categories from individuals
included in the Health
Survey for England (HSfE) over a number of years. To do this Binomial
multi-level Bayesian
models including random effects and a range of individual,
socio-demographic, socio-economic
and geographical variables were employed. A computationally intensive
micro-simulation of sub
populations in each GP practice nationally was then generated using
iterative proportional fitting
applied to available OPCS small area census data tables. An
age/sex/illness specific resource
need distribution was then sampled for each sub-population from national
historic costs drawn from
the `Hospital Episodes Statistics' (HES) dataset. Results were combined to
derive an estimate of
the total resource required to treat each condition within each GP
practice.
The second approach pioneered by Bailey and the modelling team in the
feasibility project study
report [see evidence item b], and then subsequently refined in the
second research project [5]
referred to in Section 2 and its associated report [c], was aimed
specifically at the difficult area of
adult mental health. It sought to determine the probability that
particular individuals will fall into one
of a number of case mix categories based upon a classification of adult
mental health care
combining clinically agreed and coherent treatment pathways with
iso-resource patient groups (i.e.
groups of patients that make similar resource demands on the NHS). In
order to implement this
approach, adults in the HSfE are classified as having needs in five
casemix mental health groups.
Then, paralleling the modelling of the approach for acute specialties
(except that a multinomial
rather than binomial Bayesian hierarchical model is fitted), individuals'
socio-demographic and
other characteristics are related to their casemix category. This
multinomial model is then applied
to micro simulated sub-populations to obtain estimates of the number of
people in each casemix
category in each practice. Relative resource needs are attached to
practices on the basis of
national per capita costs within the casemix.
In 2009/10 primary care trusts in the UK spent £8.08 billion on
secondary care mental health and
an additional £8.37 billion in 2010/11 through PCB. The mental
health component of PCB includes
the Community Health Services budget for adult mental illness, child and
adolescent psychiatry,
forensic psychiatry and old age psychiatry. Services provided under the
mental illness component
include; continuing care, crisis teams, early psychosis intervention and
hospitalisation.
In 2006, the department of health (DoH) released the PBC toolkit which
was recommended for use
in 2007 and is used by GPs and medical professionals to allocate resources
to practices in the UK.
In the period 2007-2009 results of the research described above
were presented by Bailey and
Asthana to the DoH Advisory Committee on Resource Allocation (ACRA) and
influenced thinking in
this group. ACRA was established in September 1997 as the successor body
to the Resource
Allocation Group (RAG). ACRA directly advises the Secretary of State for
Health on the
distribution of resources across primary and secondary care to ensure that
these fully reflect local
population need and operate as fairly as possible. Members include
academics, NHS senior
managers and GPs.
In 2009/10 the DoH implemented new changes to the toolkit [a]
which included an entirely new
methodology for the mental health component of the toolkit developed by
Bailey and Asthana [see
evidence item b and evidence item c]. It was described by
the DoH in their PBC budget guidance
for 2009/10 as `The new methodology has undergone extensive
testing by researchers and the
department of health and we believe it provides a step change
improvement in the way we model
mental health need'. In 2009/10 the model was used by PCTs,
GPs and medical professionals
responsible for the practice based resource allocation to distribute £8.08
billion pounds worth of
services for mental health.
In 2010/11 an 'improved' version of the methodology was
implemented in the Practice-based
commissioning budget guidance for 2010/11 [evidence item d]
which utilises a full multilevel model
that separately captures individual and area-level effects. The new
methodology was extensively
tested by the Department of Health and was overseen by the advisory
committee on Resource
Allocations. According to the NHS `Capturing both these effects makes
the estimates more
responsive to the needs of each practices population'. Similarly in
2010/11, the model was used to
distribute £8.37 billion of primary care services for mental health. The
National Audit Office's
Cross-government landscape: Formula funding of local public services
references the project
[evidence item e] as having `so far informed practice-based
commissioning, and may also form the
basis for allocations to clinical commissioning groups in the future'.
According to the Chief Economist/Deputy Chief Analyst of the Department
of Health, who is a
member of ACRA [evidence item f]:
`It was Trevor Bailey who helped operationalize the new approach that
did not rely on past
utilization of service. This was particularly important for mental
health where utilization data was
patchy and represented the "old model" of service provision with over
reliance on hospital
inpatients. Trevor Bailey implemented an innovative approach based on
directly observed
morbidity indicators at individual level, rather than utilization at
area level. This was known as
Person Based resource Allocation (PBRA) and the technique was piloted in
2007 and successfully
implemented in 2007/8. The highly computational technique using 40,000
Health Survey for
England data records led to a new formula being used to set target
allocations for £8bn of funding
for mental health services for General Practice in 2009/10 and 2010/11,
The new method,
implemented by Trevor, proved to be practicable and had the added
advantage that the resulting
estimates of need included "confidence intervals" for different sized
populations.'
Sources to corroborate the impact
a. Department of Health Practice-based commissioning budget
guidance for 2009/10
`Methodological changes and toolkit guide' References the research p.10.
http://www.dh.gov.uk/en/Publicationsandstatistics/Publications/PublicationsPolicyAndGuidance/DH_094364
b. Asthana, S., Gibson, A., Bailey, T., Dibben, C., Hewson, P.,
Economou, T., Batchelor, D.,
Eastham, J., Craig, R., Scholes, S., Flowers, J., Jenner, D. Person
Based Resource Allocation
(PBRA): The Feasibility of Developing a Need-Based Approach to PBRA.
Report to the
Department of Health (Policy Research Programme). 2008. University of
Plymouth. 118pp.
c. Asthana, S., Gibson, A., Bailey, T., Dibben, C., Hewson, P.
Developing a Person Based
Resource Allocation Formula for Setting Practice Level Mental Health
Budgets: 2009/10 and
2010/11. Final Report April 2009. Universities of Plymouth, Exeter and St
Andrews.
d. Department of Health Practice-based commissioning budget
guidance for 2010/11
`Methodological changes and toolkit guide' References the research p.10
http://www.dh.gov.uk/en/Publicationsandstatistics/Publications/PublicationsPolicyAndGuidance/DH_111057
e. The National Audit Office. Cross-government landscape: Formula
funding of local public
services, July 2010 references research p.31 http://www.official-documents.gov.uk/document/hc1012/hc10/1090/1090.pdf
f. Letter of corroboration from Chief Economist/Deputy Chief
Analyst, Department of Health who
is a member of ACRA.