Gwyn Bevan
Mara Airoldi Nikos Argyris Alec Morton Jenifer Smith
Commissioning to Improve Value for Money of Health Services in Hard Times Department of Management London School of Economics & Political Science Houghton Street, London WC2A 2AE
Commissioning to Improve Value for Money of Health Services in Hard Times Gwyn Bevan*, Mara Airoldi, Nikos Argyris, Alec Morton & Jenifer Smith
Department of Management, London School of Economics & Political Science, Houghton Street, London WC2A 2AE 30 August 2011 * Corresponding author: Email R.G.Bevan@lse.ac.uk
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Contents p3
Executive Summary
p13
Chapter 1
Introduction to VfM
p25
Section One Modelling the scale impacts of interventions on population health gain and costs
p27
Chapter 2 VfM of options for the prevention and treatment of stroke for England
p33
Chapter 3 VfM for Selected Interventions for the Population of England
p38
Section Two Decision conferencing in Sheffield and the Isle of Wight
p39
Chapter 4
p45
Chapter 5 VfM for the prevention and treatment of Cancers in Sheffield
p55
Chapter 6
VfM for Dentistry in Sheffield
p61
Chapter 7
VfM across five services in the Isle of Wight
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Chapter 8 Discussion
p79 p87 p88 p89 p111 p123 p126 p129 p131 p151
VfM for the prevention and treatment of eating disorders in Sheffield
End notes Appendix
A Glossary B Tables C Outline of the disease models D Bibliography E Estimating costs and benefits over time F SyMPOSE Decision Conferencing for resource allocation in healthcare – datapack preparation G Commentary on the Sheffield PCT/LSE commissioning workshops facilitated by the sympose research team in 2009 H Outline programme
Executive Summary
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EXECUTIVE SUMMARY
Introduction
1. This report is written in 2011 when the National Health Services (NHSs) of the four countries of the UK face a period of unprecedented austerity. Health services in many countries are in a similar position. These pressures entail generating internal savings by better use of resources to enable development of services. Hence achieving high Value for Money (VfM) must be an overriding objective for those responsible for governing health services in the UK and other countries. This report describes an approach developed in a five-year research programme of research and development (R&D) funded by the Health Foundation that has been designed to enable this objective to be realised. The core idea behind this R&D is that the scarcest resource in governing health services is the management effort and skill required to deliver effective changes to secure VfM. A cardinal principle of good governance is empowering those involved in delivering health care to focus on making the few changes that will have the greatest impact on VfM. This report describes the R&D and application of an approach that identifies such changes. This has become the programme of Systems Modelling for Performance Optimisation and Service Equity (SyMPOSE) in the Department of Management at LSE, which has drawn on methods from economics, epidemiology, psychology and decision sciences.
2. Some of the findings from our work programme are in the form of generalisable evidence. The principal purpose of this report is, however, to show how our methods and approach can be used by those responsible for governing health services so that they can focus on making the few changes that will have the greatest beneficial impact on populations. Furthermore, a distinctive feature of the approach that we have developed is that it is designed to enable key stakeholders to participate to produce evidence in a transparent way to set priorities and understand the VfM of different strategies as compared with continuing with current policies. In this way stakeholders understand the nature of the trade-offs between hard choices and the opportunities foregone by not changing the way care is delivered.
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EXECUTIVE SUMMARY
Background
3. The SyMPOSE programme began as one stream of work of a major five-year, £2.5 million research initiative of The Health Foundation launched in July 2005: the Quest for Quality and Improved Performance (QQUIP). The objectives of QQUIP were to analyse and inform what Leatherman and Sutherland described as the ‘most ambitious, comprehensive, systemic and intentionally funded effort to create predictable and sustainable capacity for improving the quality of a nation’s healthcare system’. The origins of the SyMPOSE programme were to evaluate quality improvements in terms of their impacts on NHS productivity. We developed models that used evidence from the literature on costs and effectiveness, and data on incidence and volumes of types of care. We examined impacts of various types of interventions to develop a generalisable approach designed to answer the question: How might the government maximize population health gain by focusing on a small number of policies? This required developing methods to provide a sound technical basis for estimates of costs and value at the level of populations. The first section of this Report (Chapters 2 and 3) describe this methodological research and give results.
4. Our methodological research gave the basis for developing requisite models to produce evidence for strategies to improve VfM in a socio-technical approach. This was developed through collaborative R&D with organisations responsible for commissioning services. This report describes work with Primary Care Trusts (PCTs) in the Isle of Wight (IoW), in 2008, and Sheffield, in 2009. Through these collaborations we developed our methods so that estimates of the elements of VfM were made visual and transparent to representatives of stakeholders. This enabled them to participate in a series of decision conferences, to generate data and information, assess and compare the VfM of each intervention, and understand the VfM of different strategies. IoW is exceptional in the English NHS in being an integrated organisation responsible for running and strategic purchasing (known as commissioning in England) health services for the Island. In 2008, our collaborative research there developed an agreed strategy for choosing developments to be financed by ‘growth money’. NHS Sheffield is responsible for commissioning secondary care from independent providers (most of these are NHS trusts). In 2009, our collaborative research there explored options for improving VfM within existing resources in the treatment of eating disorders, the prevention and treatment of three cancers (breast, colorectal and lung) and primary and secondary dental care. One outcome was an agreed strategy for eating disorders to reallocate resources to reduce total costs and increase value. These different kinds of collaborative R&D means that we developed an approach that can be
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EXECUTIVE SUMMARY
applied with integrated systems or those with a purchaser / provider split; and to set priorities for spending ‘growth money’ or deciding how to reduce expenditures with least harm. The second section of this Report (Chapters 4, 5, 6 and 7) describe this collaborative research and give results.
Objectives
5.
We sought to develop an approach that could be applied:
a. At a national and local levels to inform strategy and policy;
b. For pathways of care in the prevention and treatment of one disease to inform choices and set priorities;
c. For systems of care to set priorities across the prevention and treatment of different diseases.
The SyMPOSE programme aims to provide:
a. a sound conceptual basis for methods,
b. a way of organising data that these methods require,
c. a way of communicating this information to stakeholders so that it can be easily understood in a participative process, and thus
d. enabling stakeholders to make strategic decisions to change the delivery of services to improve VfM.
Methods
6. We began by developing models to estimate the impacts on value and costs for populations for Stroke, Depression, Coronary Heart Disease (CHD), Type 1 Diabetes and Heart Failure to inform strategy and policy for the population of England.
7. Our R&D with IoW PCT was developed to set priorities across the treatment of different diseases; and with Sheffield PCT to inform choices and set priorities for pathways of care within diseases. This approach required us to develop a visual geometry to make three key concepts transparent:
a. Rectangles of population health gain are based on estimates of numbers who benefit and the degree of benefit for a typical individual. The magnitude of population health gain is the product of these two estimates. Hence, the bigger the rectangle, the greater the population health gain.
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EXECUTIVE SUMMARY
b. Triangles of Value for Money (VfM) with value (the vertical side), costs (the horizontal side), and VfM (slope of the hypotenuse). Value includes population health gain and can incorporate other criteria such as reducing inequalities in health. Costs could include those incurred by the NHS or society. In our studies we have limited these to the former. VfM is the ratio of value to costs. An intervention with high (or low) VfM has a steep (or shallow) slope. If an intervention has a small VfM triangle, with a steep slope, this raises the question of whether this can be expanded. If an intervention has a large VfM triangle, with a shallow slope, this raises the question of whether this can be reduced either in scale or costs.
c. Efficiency frontiers are developed by ordering interventions in terms of VfM. These graph out how value and costs increase for each intervention ordered by their VfM (steepness of their slopes). Each efficiency frontier begins with interventions with the highest VfM (steepest slope) and ends with interventions with the lowest VfM (shallowest slope). This frontier can be set against a budgetary constraint to indicate where reductions can be made to keep spend within the budget with least reduction in value.
8. In this work we used the socio-technical process of decision conferencing, in which groups of stakeholders work with an impartial facilitator, who uses computer-based modelling of data and participants’ judgments, to give continuous visual displays of the model and its results in terms of rectangles of health gain, VfM triangles and the efficiency frontier. Stakeholders include those affected by or, involved in, decisions to change health care: patients, their carers, the public, commissioners (or purchasers) and those working in hospitals, community health services, primary care, the voluntary and independent sectors, and local government. Our collaboration with Sheffield PCT was subjected to an external evaluation by David Collier of Golder Associates, who found this to have been beneficial in improving commissioning across a range of criteria.
9. A particular feature of SyMPOSE approach is that it recognises decision making as a socio-technical process. Decision making ought to be technical in part: if decisions are to be sound and to produce desirable outcomes for local populations they ought to be informed by medical, epidemiological, and economic theory and evidence. Decision making is also a social process, in which evidence has to be interpreted for stakeholders and their values and priorities made explicit in making strategic choices. Decisions depend on commitment by stakeholders and hence the social process of decision making ought to be designed so that stakeholders can see that their views are taken into account
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EXECUTIVE SUMMARY
and can understand the reasons for the hard choices that are agreed. Failure to involve stakeholders constructively can result in vital kinds of tacit knowledge being neglected, the distinctive features of local communities ignored, and plans that appear to be technically sound not being implemented because of lack of support or opposition from key stakeholders.
10. Our approach requires stakeholders to focus remorselessly on ensuring that they have all the data they need to make decisions and to appreciate that it is much better to have approximate estimates than none. Through decision conferencing providers, patients and carers were able to estimate the relative health gains of a typical individual from a series of interventions for mental health, cancers, dental health, cerebrovascular disease, cancers, respiratory disease (and long term conditions), and children. In this way they and other key stakeholders were able to generate rectangles of population health gain and set priorities within and across disease areas using VfM triangles that took account of: NHS costs; and various assessments of value. The estimates of value were always based on population health gain, and in some evaluations took account of other criteria (reduction in health inequalities and the probability of success of the implementation of proposed changes).
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EXECUTIVE SUMMARY
Key findings
11. Limitations in the data that are routinely collected by health services and evidence that is reported in clinical evaluations mean that assumptions have to be made to produce estimates of population health gain. We return to this point below. Subject to that caveat, we summarise the key findings from the different kind of R&D we have done.
12. Our estimates of the impacts on value and costs for populations for Stroke, Depression, Coronary Heart Disease (CHD), Type 1 Diabetes and Heart Failure for the population of England found that the top three interventions in terms of the scale of population health gain were: preventing strokes by reducing dietary intake of salt and improving control of blood pressure in primary care; and seeking to improve the coverage of treatment of depression. The next set of three were: increasing the percentage of patients with a stroke cared for in a stroke unit, implementing a programme of intensive glucose control at the onset of diabetes focusing on adolescents (although this would produce gains in the long run only), and achieving the national suicide prevention strategy. Conversely, policies with only limited potential for national population health gains were: seeking to treat 9% of stroke patients with thrombolysis, implementing the national clinical guidelines for treatment of depression (for those currently being treated), and additional treatment of newly-diagnosed cases of heart failure.
13. Using decision conferencing, in 2008, IoW PCT agreed on priorities for spending ÂŁ1m of growth money; and in 2009, with Sheffield PCT developed efficiency frontiers for the treatment of eating disorders, the prevention and treatment of three cancers (breast, colorectal and lung) and primary and secondary dental care. The analysis of the efficiency frontier for the treatment of eating disorders informed the new strategy and it appears to have reduced the need for intensive care for those who are seriously ill and hence produced more population health gain at reduced total costs. The analysis of cancer did not identify much scope for reallocating resources along the pathway to produce more population health gain at reduced total costs. A key finding for the analysis of the efficiency frontier for primary dental care was that the current system of fees paid to dental practices for their Units of Dental Activity (UDAs) appeared not to have been designed to create incentives for them to provider good VfM. The information generated was used to design a different system that might do so.
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EXECUTIVE SUMMARY
Conclusions and implications
6. We conclude by focussing on the lessons from the socio-technical approach of decision conferencing based on the development of requisite models with IoW and Sheffield PCTs. Both PCTs found that this approach enabled:
a. estimates to be produced that were good enough for strategic choices from a variety of sources of data;
b. these commissioners to demonstrate strong patient, public, professional and provider involvement in planning and prioritising interventions and investment;
c. stakeholders to understand clearly and quantify the expected outcomes from different choices.
7. But both PCTs found this novel approach difficult to implement. This was because of the fundamental problem of the absence of data to assess population health gain of different interventions. But, what is the alternative? We do not see how commissioners could seek to involve patients, carers and the public in setting priorities for interventions without data on their value. Any such exercise would be seen as mockery of a due process of involving stakeholders. The fundamental problem is that these data are not available in a way that enables interventions to be evaluated on a consistent basis: indeed for many interventions there are no data. The most promising way of producing these data (within the limited time available) is, as we have described in this report, by engaging clinicians, patients, carers and the public to agree on the relative value of different interventions. Thus our approach by focusing on estimating value both requires data on value, and is designed to enable engagement of stakeholders to produce these data, and use them in exploring the VfM of different strategies. The alternative to our approach appears to be to set priorities using data that are available which entails using fragmentary inconsistent data on value and hence making it difficult to have a credible exercise that includes clinicians, patients, carers and the public. We see scope to produce data on population health gain for England, which could be scaled for local use and would hence reduce the costs of the SyMPOSE approach. Our experience shows that this approach does require an impartial facilitator, who is skilled in using models to relate estimates generated by decision conferencing to priorities, and subjecting these estimates to sensitivity analysis.
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EXECUTIVE SUMMARY
8. The SyMPOSE research programme has developed new ways of organising the collection of data and the presentation of information designed for deciding on the few changes that can release substantial efficiency savings and gains in value. This approach is of wide generalisibility and has been applied with an integrated system (IoW) and a purchaser / provider split (Sheffield); and to set priorities for spending ‘growth money’ (IoW) or deciding how to reduce expenditures with least harm (Sheffield). We have shown in this report how our approach can inform those responsible for national and local policies, including the design of systems of reimbursement to providers to generate VfM. Furthermore this approach is designed so that stakeholders who do not work for the NHS can contribute to generating the necessary data on value, and understand how estimates of VfM are produced and their implications for choosing between strategies. We see the participation of stakeholders as vital in enabling commissioners win the necessary understanding and support for the hard choices that will have to be made to achieve VFM in hard times. We do not see how a participative process could be credible without data on value, nor how such data could be generated without the processes we have described.
Chapter 1 Introduction
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INTRODUCTION
Purpose
1. This report is written in 2011 when the National Health Services (NHSs) of the four countries of the UK face a period of unprecedented austerity1. Each faces massive challenges to generate internal savings by better use of resources to enable development of services. Hence achieving high Value for Money (VfM). The English NHS faces the ‘Nicholson challenge’ of generating efficiency savings of up to 20 billion of by 20142. Health services in many other countries are under similar budgetary pressures and face similar challenges. This report describes an approach developed in a five-year research programme of research and development (R&D) funded by the Health Foundation that has been designed to enable this objective to be realised. The core idea behind this R&D is that the scarcest resource is the management effort and skill required to deliver effective changes to secure VfM. A cardinal principle of good governance is empowering those involved in delivering health care to focus on making the few changes that will have the greatest beneficial impact. This report describes the R&D and application of an approach that identifies such changes. This has become the programme of Systems Modelling for Performance Optimisation and Service Equity (SyMPOSE) in the Department of Management at LSE, which has drawn on methods from economics, epidemiology, psychology and decision sciences.
2. The SyMPOSE programme is directed to enabling three tasks of commissioning, as it is known in England, which are3:
a. assessing needs of populations, which means estimating the actual or potential impact of different interventions for treatment (including prevention) on population health gain;
b. s etting priorities for populations, which means estimating for each intervention its costs and value (population health gain and other criteria of value such as reducing health inequalities4);
c. finding ways of engaging stakeholders5 so that they can contribute to generating data in the shaping of these priorities and understand the hard choices that have to be made.
We sought to develop an approach that could be applied:
3.
a. at a national and local levels to inform strategy and policy;
b. for pathways of care in the prevention and treatment of one disease to inform choices and set priorities;
4.
c. for systems of care to set priorities across the prevention and treatment of different diseases.
a. a sound conceptual basis for methods,
b. a way of organising the collection of data that these methods require,
c. a way of communicating this information to stakeholders so that it can be easily understood in a participative process, and thus
d. a way of enabling stakeholders to make strategic decisions to change the delivery of services to improve VfM.
The SyMPOSE programme aims to provide:
15
INTRODUCTION
5. The narrative of this report aims to develop understanding of our approach, and illustrate the practical application, of the concepts of rectangles of population health gain, VfM triangles and efficiency frontiers.
Background
6. The SyMPOSE programme began as one stream of work of a major five-year, £2.5 million research initiative of the Health Foundation launched in July 2005: QQUIP (Quest for Quality and Improved Performance. The objectives of QQUIP were to analyse and inform what Leatherman and Sutherland described as the ‘most ambitious, comprehensive, systemic and intentionally funded effort to create predictable and sustainable capacity for improving the quality of a nation’s healthcare system’. The origins of the SyMPOSE programme was to evaluate quality improvements in terms of their impacts on NHS productivity. These models use evidence from the literature on costs and effectiveness, and data on incidence and volumes of types of care. We examined impacts of various types of interventions to develop a generalisable approach designed to inform strategy and policy at a national level by seeking to answer the question: How might the government maximize population health gain by focusing on a small number of policies?
7. The SyMPOSE programme moved from a methodological focus to developing a sociotechnical approach through collaborative R&D to develop strategies with Primary Care Trusts (PCTs) in the Isle of Wight (IoW), in 2008, and Sheffield, in 2009. Our methodological research gave the basis for developing requisite models to produce estimates of the impacts of health care interventions on populations on value and costs. This collaborative R&D provided four extra challenges in developing our methods. We needed to
a. take account of other criteria, such as reducing health inequalities and the likelihood of successful implementation, which we did by using the technical approach of Multi-Criteria Decision Analysis (MCDA);
b. use a process that could involve representatives of stakeholders, which we did by using the socio-technical process of decision conferencing;
c. to develop ways of estimating missing data in a way so that stakeholders can contribute their expertise and understanding; and
d. to develop ways of making these estimates visual and hence transparent for stakeholders so that each participant could understand the VfM of each intervention, compare interventions and understand the VfM of different strategies.
8. Of these four challenges for the first two we drew on well-established methods, the third and fourth are the original contributions of this R&D.
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INTRODUCTION
Estimating VfM for the population of England
9. The Report is organised into two principal sections. Section 1 consists of two Chapters on the development of models of diseases using evidence from the literature and data that are routinely available to estimate VfM for the population of England: for one disease, namely stroke (Chapter 2); and then different interventions for various diseases using methods designed for each disease (Chapter 3). Annex 1 gives an outline of the models and details for the different diseases are given in working papers6. The different interventions were chosen so that we would have to develop an approach that was generalisable and that requiring tackling three methodological problems.
10. First, examining the use of two ways of measuring population health gain: though estimating these in Quality Adjusted Life Years (QALYs) or through the reduction in the Burden of Disease (BoD) in Disability Adjusted Life Years (DALYs)7. Each method uses estimates of trajectories of Quality of Life (QoL) over time of interventions to produce quantitative estimates of their health gains. In QALYs a year of perfect health, with a QoL of 1, generates 1 QALY and is assumed to be equivalent to two years lived with a QoL of 0.5. This calculus provides a common measure of health gains across different interventions and hence their cost-effectiveness to be compared as ratios of costs to QALYs. In contrast DALYs measure the BoD so a year a year of perfect health is a year free of disability and generates zero DALYs. Two years lived with a QoL of 0.5 generates 1 DALY and is assumed to be equivalent to a loss of life for one year. This calculus provides a common measure of the BoD for different diseases. Airoldi and Morton8 (2008) showed that, given consistency in the health status weights of QoL, for a given intervention, the improvement expressed in QALYs equates to a reduction in the BoD expressed in DALYs, provided that a fixed reference age is used9. We report here population health gain in QALYs.
11. Second, we developed methods to produce estimates that are comparable for three types of interventions with differing profiles over time:
a. one-off interventions, for which, after an episode of care, health gains continue (e.g. treatment following a stroke);
b. continuous interventions, for which continuing treatment is necessary for health gains to continue (e.g. prescribing statins); and
c. delayed-impact interventions, for which the health gains accrue a long time after treatment (e.g. improving glucose control in adolescents with type 1 diabetes).
12. Third, accounting for costs and health gains of different interventions incurred at different times. Thus, e.g., someone who has suffered a stroke can, through a single episode of skilled expert care provided immediately afterwards, will benefit for a number of years; an adolescent with type 1 diabetes will only benefit some twenty years later from exercising continuous glucose control. The conventional technical solution to this economic problem is to discount both costs and health gains; for which NICE uses a discount rate of 3.5% a year. Stakeholders can understand why (when there are prospects of economic growth) the future burden of costs of producing a treatment of say £1,000 in a year’s time will be less than now and can see that giving this a present value of £965 is plausible. But it is hard to explain to stakeholders why the value of a year of perfect health (with a current value of 1 QALY), would, in 20 years time, have its present value halved (about 0.5 QALYs). Indeed economists dispute both whether health gains ought to be discounted at all, and, if so, what the discount rate ought to be10. We see discounting health gains as confusing rather than helping stakeholders understand hard choices and so, in the results we present here, we have discounted costs but not health gains.
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INTRODUCTION
13. The research summarised in Section 1 (Chapters 2 and 3) was completed at different times. The estimates are based on the latest information available when those studies were completed, but their findings are unlikely to be changed by information that has been published since. This Section shows show estimates of scale gives a new perspective in setting priorities. An Annex gives an outline of the models and details are given in working papers11. The different interventions were chosen so that we would have to develop an approach that was generalisable and that requiring tackling three methodological problems.
Developing strategies for VfM with the Isle of Wight and NHS Sheffield
14. The second section of our Report describes R&D with two PCTs, IoW and Sheffield, in the development of a socio-technical approach to explore strategic options and agree priorities with stakeholders. This was based on the research described in Section 1 in on methods of estimating impacts of interventions on populations in terms of value and costs. IoW is exceptional in the English NHS in being an integrated organisation responsible for running and strategic purchasing (known as commissioning in England) health services for the Island. In 2008, our collaborative research there developed an agreed strategy for choosing developments to be financed by ‘growth money’. NHS Sheffield is responsible for commissioning secondary care from independent providers (most of these are NHS trusts). In 2009 our collaborative research there sought to develop strategies for prioritisation without real growth. The value of our approach in these different settings shows its generalisibility.
15. Our approach draws on methods from economics, epidemiology, psychology and decision sciences. At the core of our approach is a visual geometry which makes transparent three key concepts:
a. Rectangles of population health gain are based on estimates of numbers who benefit and the degree of health gain for a typical individual. The magnitude of population health gain is the product of these two estimates (see figure 1 [l]). Hence, the bigger the rectangle, the greater the population health gain. These rectangles enable stakeholders to generate the data to estimate the key dimensions of the relative impacts of options in the absence of evidence from the literature
b. Triangles of Value for Money (VfM) with value (the vertical side), costs (the horizontal side), and VfM (slope of the hypotenuse) (see figure 2 [l]). Value includes population health gain and can incorporate other criteria such as reducing inequalities in health. Costs could include those incurred by the NHS or society. In our studies we have limited these to the former. VfM is the ratio of value to costs. An intervention with high (or low) VfM has a steep (or shallow) slope. If an intervention has a small VfM triangle with a steep slope, this raises the question of whether this can be expanded. If an intervention has a large VfM triangle of with a shallow slope this raises the question of whether this can be reduced in scale or costs (see figure 3 [l]).
d. Efficiency frontiers are developed by ordering interventions in terms of VfM. This graphs out how value and costs increase for each intervention in terms of their slopes. It begins with interventions with the highest VfM and ends with interventions with the lowest VfM. This can be set against a budgetary constraint to indicate where reductions can be made to keep spend within the budget with least reduction in value, as illustrated by figure 4 [l] for four options (A, B, C and D) ordered in terms of their VfM. To maximise value within the budget means choosing A and B and perhaps part of C (if this can be implemented in part).
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INTRODUCTION
Figure 1.
Rectangles of population health gain
Health gain/person
Population health gain
Numbers who benefit
Figure 2.
Vf M triangle with value based on population health gain
Early detection and diagnosis
Palliat VfM
Population health gain
Health gain/person
Repatriation of radiotherapy
Numbers who benefit Costs
Early detection and diagnosis
Palliative an
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INTRODUCTION
Figure 3.
Triangles with good and poor VfM
Good VfM VfM Good High population population High health gain gain health
Low costs costs Low Poor VfM VfM Poor
Low population population Low health gain gain health
High costs costs High Figure 4. Efficiency frontier for four options with a budget constraint
Value
D C
B
Early detection detection and and diagnosis diagnosis Early
Budget constraint
Palliative and and EOL EOL Palliative
Value
A
Repatriation of of radiotherapy radiotherapy Repatriation Cost
Cost
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INTRODUCTION
16. In developing and applying these concepts in the Sheffield and IoW PCTs, we have used the socio-technical process of decision conferencing, which combines working in groups helped by an impartial facilitator, on-the-spot computer-based modelling of data and participants’ judgments, and continuous visual display of the model and its results. The analysis uses methods of MultiCriteria Decision Analysis (MCDA), which is an accepted methodology for valuing options across multiple objectives12. We implemented MCDA using the Equity software originally developed at the London School of Economics and Political Science, which generates VfM triangles. Decision conferencing is designed to enable stakeholders to achieve a shared understanding of the issues (though not necessarily consensus), a sense of common purpose (while preserving individual differences of opinion) and a commitment to the way forward (though allowing individual differences in the paths)13. Decision conferencing is based on developing ‘requisite models’14 that are sufficient in form and content to inform the decisions being made.
17. The crucial economic concept that defines costs for priority setting is that of opportunity cost: i.e. the costs of implementing one option are the foregone opportunities of implementing others. But, like value, there are various dimensions to opportunity cost. Money is one crucial dimension from the budget constraint. But another constraint is the limited managerial time available for implementing change: typically it is only possible to implement a small number of changes successfully. Commissioning health services in hard times requires this managerial time to be focused on increasing services with good VfM that achieve substantial population health gain, and reducing expenditure on services with poor VfM to release substantial savings. The relative size of rectangles of population health gain and VfM triangles make the scale of the impacts of different options transparent and hence enable managers and stakeholders to agree on strategies that are likely to have a substantial impact.
18.
The outcomes of the process that we have developed are that stakeholders:
a. appreciate that they are engaged in developing a requisite model for setting priorities in a process that requires essential information only (see Box 1);
b. have confidence in using approximate estimates (when they see that all that matters are orders of magnitude); and
c. understand the rationale for making changes.
19. This approach is applied common sense (as it makes no sense to set priorities without all the steps of Box 1) and offers a new way of organising the collection of the necessary data (which are often not readily available: such as the costs of chemotherapy in the treatment of breast cancer or the numbers of people who would benefit from thrombolysis in treatment of a stroke) and framing the basic information required for priority setting. VfM triangles make transparent for stakeholders the scale of the impact of options (in terms of costs and value) and which options are good and poor VfM.
20.
W e see the strength of our approach being the way it requires stakeholders to focus remorselessly on ensuring that they have all the data they need to make decisions and to appreciate that it is much better to have approximate estimates than none. Our experience shows that some stakeholders (often hospital clinicians) are initially uncomfortable with the way in which our approach mixes data of different provenance. A common view is that unless data are available from Randomised Controlled Trials (or ideally meta-analysis of RCTs) these data ought not to be used. The purpose of our approach is not to challenge Cochrane’s powerful argument for RCTs15, which are indeed the gold standard for evaluating clinical interventions. But commissioners setting priorities are seeking to answer a different question. Their interest is not in comparing different clinical interventions for the same condition, but different kinds of interventions along
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INTRODUCTION
a care pathway for one condition and different kinds of interventions for different conditions. In making these comparisons, even when evidence is available for some interventions from RCTs or meta-analyses, this evidence can only be an approximate guide to the health gains for the local population from those interventions. This is because the actual delivery of care differs from that in the carefully-controlled trials and RCTs are designed to enhance their discriminatory power by excluding various types of patients who would be treated locally16.
21. So our approach does not mix ‘gold’ from RCTs with ‘dross’ of subjective estimates. All data are, to varying degrees, subjective estimates. Assessments of value to be comparable rely on subjective estimates of QoL17. Costs of types of services in primary care and community services are often not reported; and estimates of hospital costs for different services are bedevilled by problems of apportioning common costs18. The key to resolving these problems is through an iterative process in which the various estimates of costs and value are examined for their robustness in the ways in which changes in these estimates would change the ordering of priorities. This is known as sensitivity analysis19. This is a vital part of our approach and we have typically found that the ordering of priorities has been robust to large changes in the agreed initial estimates. Where this is not so, this exercise shows where further analytic effort ought to be targeted on producing better data for guiding the key strategic decisions.
Box 1. Development of a requisite model for setting priorities
i. Identify options. ii. Agree important criteria in assessing value. iii. Estimate performance on each criterion of value, and in particular, population health gain – numbers who benefit and average benefit per individual. iv. Estimate weights of the importance of each criterion of value. v. Identify costs of each option. vi. Estimate VfM: the ratio of value to costs. vii. Determine priorities to increase value at least cost.
22. Two chapters in section 1 describe our concepts of rectangles of population health gain and shows how this information gives a new perspective on setting national and local priorities.
Chapter 2. Stroke for the population of England This Chapter uses evidence from the literature on costs and effectiveness, and data on incidence and volumes of types of care to evaluate prevention (through reduction of salt in the diet or control of blood pressure in primary care) and treatment (by stroke units or thrombolysis) of strokes. The estimates of effectiveness use standardised measures of Quality of Life (QoL) that are comparable with outcomes from other diseases. This Chapter shows, for different kinds of interventions for strokes, how the evidence and data can be transformed into comparable information on rectangles of population health gain in Quality-Adjusted Life Years (QALYs) which gives a new perspective on priorities. Chapter 3. Stroke, Depression, Coronary Heart Disease (CHD), Type 1 Diabetes and Heart Failure for the population of England This Chapter uses evidence from the literature on costs and effectiveness, and data on incidence and volumes of types of care to evaluate prevention or treatment of Stroke, Depression, Coronary Heart Disease (CHD), Type 1 Diabetes and Heart Failure for the population of England.
22
INTRODUCTION
The estimates of effectiveness for each disease use standardised measures of QoL so that the various interventions are comparable. This Chapter shows, for different kinds of interventions for different diseases, how the evidence and data can be transformed into comparable information on rectangles of population health gain in QALYs and might be used to inform deciding a limited set of NHS priorities to achieve maximum health gain at least cost.
23. Section 2 (Chapters 4, 5, 6 and 7) shows how the Sheffield and IoW PCTs in collaborative research with the SyMPOSE research team at LSE used the concepts of rectangles of population health gain, developed VfM triangles and efficiency frontiers to organise the collection of data and evaluate interventions. In Sheffield we analysed care pathways within disease areas, for eating disorders, cancers and dental health. In the IoW we analysed options across five disease areas. This collaborative research relied heavily on support from staff in the PCTs to organise decision conferences for stakeholders (from the PCT, providers, patients, carers, representatives of the local population, voluntary organisations, and local authorities). Each of these Chapters begins by listing the stakeholders who made the decisive contribution to our evaluations. These Chapters describe how, with an impartial facilitator, the stakeholders were able to generate data on estimates of value in various ways, agree an ordering of interventions in terms of their VfM, and in some cases use this information to agree strategic changes.
Chapter 4. Eating disorders in Sheffield This Chapter uses routinely-collected data in Sheffield on costs and volumes of types of care, supplemented by stakeholders’ agreed estimates of effectiveness for different interventions for eating disorders. Stakeholders agreed detailed estimates of the effectiveness in terms of their relative QoL, which means that this information is comparable for the treatment of eating disorders in Sheffield, but not with interventions for treatment of other diseases. This Chapter shows how a PCT can generate the data required to produce, for different kinds of interventions for eating disorders, comparable information on rectangles of population health gain and VfM triangles, and an efficiency frontier. Chapter 5. Cancers in Sheffield This Chapter uses routinely-collected data in Sheffield on costs and volumes of types of care, supplemented by stakeholders’ agreed estimates of effectiveness for each intervention for three types of cancers, breast, colorectal and lung. Stakeholders agreed detailed estimates of effectiveness in terms of relative QoL for cancers, which means that this information is comparable for the treatment of cancers in Sheffield, but not with interventions for treatment of other diseases. This Chapter shows how a PCT can generate the data required to produce, for different kinds of interventions for cancers, comparable information on population health gain and VfM triangles, and efficiency frontiers for each type of cancer and for all three types.
23
INTRODUCTION
Chapter 6. Dental health in Sheffield This Chapter uses routinely-collected data in Sheffield on costs, payments, and volumes for primary and secondary care for dental health, supplemented by stakeholders’ agreed estimates of effectiveness for each intervention. Stakeholders agreed detailed estimates of effectiveness in terms of relative QoL for dental health, which means that this information is not comparable with interventions for treatment of other diseases. This Chapter shows how a PCT can generate the data required to produce, for different kinds of interventions for primary and secondary care for dental health, comparable information on population health gain and VfM triangles, and an efficiency frontier (taking account of the impacts of interventions on reducing health inequalities). This Chapter also shows how this information can be used to relate the system of payments to dental practices to the value they produce, and hence can provide a basis for redesigning a payment system to pay more to those dental practices who produce high value.
Chapter 7. Setting priorities in the Isle of Wight. This Chapter uses routinely-collected data in the IoW on costs, and volumes for 21 interventions across five disease areas, supplemented by stakeholders’ agreed estimates of effectiveness for each intervention. The five priority areas were cerebrovascular disease (Coronary Heart Disease and Stroke), cancers, respiratory disease (and long term conditions), mental health, and children. Stakeholders agreed broad estimates of effectiveness in terms of relative QoL within each priority area and then scaled these so that the interventions could be compared across the different areas. Stakeholders’ estimates of value in terms of population health gain also took into account the impacts of interventions on reducing health inequalities and the probability of the successful implementation of each intervention. This Chapter shows how a PCT can generate the data required to produce, for different kinds of interventions across different diseases, comparable information on population health gain, VfM triangles and an efficiency frontier.
24. The Final Chapter (Chapter 8. Discussion) looks back at lessons from the research we have completed, the external evaluation of the Sheffield project by David Collier of Golder Associates (Collier, 2010), and looks forward to how the approach we have developed can be used to improve VfM in hard times.
Section 1. Modelling the scale impacts of interventions on population health gain and costs
26
SECTION ONE
There are two chapters in Section One which describe the development of methods and give results from modelling the scale impacts of interventions on population health gain and costs. Chapter 2 describes methods and gives results for the treatment and prevention of Stroke. Chapter 3 gives results for the treatment or prevention or both for Stroke, Depression, Coronary Heart Disease (CHD), Type 1 Diabetes and Heart Failure for the population of England. A besetting problem in producing these estimates were inadequacies in the data available in relating evidence from the literature of clinical evaluations to information on populations.
p28
Chapter 2 VfM of options for the prevention and treatment of stroke for England
p33
Chapter 3 VfM for Selected Interventions for the Population of England
27
SECTION ONE
Chapter 2 VfM of options for the prevention and treatment of stroke for England
1. Standard approaches for evaluating VfM for stroke care give information on the ratio of value to costs (the slope of VfM triangles but not their size) and focus on the treatment of strokes. This Chapter illustrates a key principle of our approach by outlining how we estimated of the scale of impacts for interventions to enable comparison of options for the prevention and treatment of stroke. It shows how this new information on scale enables those responsible for policies to know which changes would produce the greatest value for the population of England. Annex 1 gives an outline of the models for stroke and Airoldi et al (2008) gives full details of the evidence used, the assumptions we made (because of lack of data20), the models we developed, and our results.
Types of stroke and options for prevention and treatment
2. There are two main types of strokes: ischaemic21 and hemorrhagic22. Figure 523 is a summary of the most common stroke classification with an indication of the relative frequency (in percentages). Diagnosis of the type of stroke is crucial because, although ischaemic and hemorrhagic stokes have the same symptoms, there are important differences in how they should be treated: anticoagulation drugs (including thrombolysis, which is effective in busting blood clots for ischemic strokes) reduce the clotting ability of the blood might be a good therapy for ischaemic but will increase the risk of death or disability from hemorrhagic strokes.
Figure 5. Stroke classification and the relative frequency relative frequency of different types
Stroke Ischaermic
Atherosclerotic stroke or cerebral thrombosis
Large vessels
Small vessels or lacunar
30%
20%
Hemorrhagic
Embolic stroke or cerebral embolism
Intracerebral
Subarachnoid
30%
13%
7%
FREQUENCY
Life expectancy without treatment 1 year
No treatment
4 years
At time of accessing this service
Radical treatment
28
SECTION ONE
Population health gain from interventions
3. Box 2 gives a summary of the four preventive and two acute interventions we considered for stroke. The principle used in estimating costs and value from interventions throughout this report is to estimate these with reference to the status quo, which provides what economists describe as the ‘counterfactual’. Table 124 gives our estimates of the change in outcomes for stroke cases (in 000s) at one year for each intervention with the pattern of treatment at that time as the counterfactual. Treatment in a Stroke Unit and from Thrombolysis does not prevent strokes, but does result in increases in the numbers who are independent and living at home but dependent, and reductions in the numbers who are living in institutional care and deaths. The preventive measures reduce the numbers who would suffer a stroke and the table shows how these would be distributed as compared with the counterfactual.
4. We assigned weights for the QoL of the different levels of severity25 on a scale in which 100 corresponds to full health and 0 to being dead. This means that an individual living for one year in perfect health (QoL of 100) generates 1 QALY. To estimate QALYs we assumed that people: • in institutional care after stroke have severe impairments (QoL of 38); • people living at home but dependent on carers for daily activities after stroke have mild to moderate impairments (QoL of 38)26; • people who are independent after stroke have no impairment (QoL of 74).
5. Table 2 and Figure 6 gives our estimates, for each intervention, for the population of England: a. the numbers who would benefit (‘000s: on the horizontal axis of Figure 6 );
b. the mean health gain per person who would benefit in QALYs (on the vertical axis of Figure 6); and
c. the population health gain (in QALYs: the area in each rectangle of Figure 6).
6. Premature mortality is the dominant element in the Burden of Disease (BoD) caused by strokes. Stroke units have only a limited impact (in absolute terms) on reducing mortality following a stroke and thrombolysis does not reduce mortality over and above what is achieved by stroke units27. Figure 6 reflects this and shows that:
a. the health gain produced per person would have been low for thrombolysis (we assumed a rate of 9% when the rate attained in the UK was less than a tenth of that);
b. ensuring that all admitted to hospital with a stroke spend most of their time on a stroke unit would have produced limited health gain to nearly 50 000 people; and
c. the greatest health gain per person and in population health gains would have been achieved by the interventions that prevent individuals from having a stroke and hence avoid their premature deaths.
[x] View Table 1.
Stroke in England: Change in outcomes at one year for interventions
29
SECTION ONE
Figure 6.
Stroke in England: rectangles of population health gain
550
7
500 450
4
400 350
5 6
3
300 250
Benefits 0-500
200 150 100 50 0
12 0
1
2
3
4
5
6
Costs (£millions) 0-5218448
[x] View Table 2.
Stroke in England: health gains from interventions Life expectancy without treatment 1 year
Costs and VfM for
No treatment
At time of accessing this service
Radical treatment
prevention and treatment 4 years
7. Tables 3 and 4 give the costs (in £m) in the first year and over the patients’ lifetime for each option (with savings in red). We have used the discount rate used by NICE of 3.5% a year for costs (but have not discounted health gains)28. Our estimates of costs are for those incurred by the NHS only. For each intervention we show the impacts on different elements of costs as compared with the counterfactual: e.g., the stroke unit is estimated to reduce costs of acute and continuing care in the first year (Table 3) but increase these costs over the patients’ lifetime from the treatment required by the increased numbers of survivors. In the first year, three interventions were estimated to reduce total costs: increased treatment in a stroke unit, and the reductions in salt. Over the patients’ lifetime, all interventions, except for the stroke unit, were estimated to reduce NHS costs as a consequence of reductions in acute and continuing care29.
[x] View Table 3.
Stroke in England: impact of interventions on first year costs (£m)
[x] View Table 4.
Stroke in England: impact of interventions for stroke on costs over patients’ lifetime (£m)
30
SECTION ONE
8. Table 5 gives costs (in £m), population health gain (in 000’s QALYs), and VfM (QALY/ cost in £’000s) for the different interventions. Conventionally VfM is the unit of health gain per pound spent. But, for stroke, the ranking begins with the five interventions that produce value and savings to the NHS (and society) which are ranked in terms of savings per QALY gained. The stroke unit is ranked last as this does incur increased costs to the NHS (and society) in caring for the disabled who survive strokes.
9. Table 6 gives ranking and cumulative estimates of value and costs of four of the six interventions. The gains from the two interventions for control of blood pressure and reducing dietary intake of salt are for different levels and cannot be added (to do so would count the health gains from the lower level twice); we chose the less radical of those sets of options (BPhigh and Na2). Although there is a perceived trade-off between prevention and treatment, this shows that for strokes the savings from prevention could finance the extra costs from increasing the availability of stroke units with scope to generate about 176,000 QALYs at no extra cost. Such choices of increasing health and reducing costs to the NHS are always attractive but are particularly so at times of austerity.
[x] View Table 5.
Stroke in England: interventions for stroke ranked by Value-for-Money
[x] View Table 6.
Stroke in England: interventions for stroke with cumulative estimates of value and costs
10. This Chapter has illustrated how the new information generated from estimating the scale of costs and population health gains for the prevention and treatment of stroke gives a different perspective from standard approaches. This suggests a shift in emphasis from treatment to prevention, which would generate savings to finance improved treatment. This information is relevant for the formulation of national and local policies.
31
SECTION ONE
Box 2.
Stroke in England: interventions evaluated for prevention and treatment
Intervention
Description
Observation
SU
Increase the percentage of patients who present to hospital who spend most of their time a stroke unit from 54% to 100%.
The percentage of patients spending most of their time on a stroke unit1 in 2006 was 54% (Clinical Effectiveness & Evaluation Unit 2007).
T
Increase the percentage of stroke patients with Thrombolysis (intravenous tissue Plasmogen Activator or tPA) from 0.8% to 9%.
In April 2008 less than 1 in 3 hospitals in the UK offered, and only about 0.8% of stroke patients received, thrombolysis (Clinical Effectiveness & Evaluation Unit 2007). This was in part due to the strict eligibility criteria, but a leading Australian hospital, using the same strict eligibility criteria, achieved a rate of 9% (National Audit Office 2005).
BPall
Prescribe a first line anti-hypertensive drug to all people aged or older.
A considerable literature claims that a small change in the average levels of blood pressure is likely to have a big impact on cerebrovascular mortality (e.g. Rose 1992; Wald and Law 2003). The rationale for a threshold at 55 years is that about 96% of stroke deaths occur above this age. We estimate benefits for people over the age of 55, who are not currently on antihypertensive treatment, being prescribed a first antihypertensive drug. We assumed a compliance rate of 90%2.
BPhigh
Prescribe a first line antihypertensive drug to all people with blood pressure above 140/90 mmHg who are not currently prescribed any anti-hypertensive
This is prescribing a first line antihypertensive treatment to people who have hypertension (usual blood pressure above 140/90 mmHg), are not currently prescribed, and assumed a 90% compliance.
Na5
Reduce average blood pressure in the population reducing sodium content in diet by 30% (3g salt) through agreement with food industry
Law et al. (Law, Frost et al. 1991) show that a 5mmHg reduction in usual blood pressure can be achieved through dietary manipulation of avoiding salty (sodium) foods and not adding salt at the table or in cooking, that is a reduction of about 3 grams of salt per day or 30% of average daily consumption of salt per person3.
Na2
Reduce average blood pressure in the population reducing sodium content in diet by reducing salt content in bread and cereals through agreement with food industry
Murray et al. (Murray, Lauer et al. 2003) assume that a reduction of about 1.5g of salt per day or 15% reduction in total dietary salt intake could be achieved through a legislation that reduces salt content in processed food and appropriate labelling.
1 2 3
1 T he National Sentinel Audit of stroke defined a stroke unit as “a multidisciplinary team including specialist nursing staff based in a discrete ward which has been designated for stroke patients” (Clinical Effectiveness & Evaluation Unit 2008). The audit focussed on the presence of: a consultant physician with responsibility for stroke; formal links with patient and carer organizations; multidisciplinary meetings at least weekly to plan patient care; provision of information to patients about stroke; and continuing education programmes for staff. 2 D rug therapy with any common antihypertensive drugs (including ‘first line’ drugs such as thiazide-type diuretics and calcium channel blockers recommended by the guidelines of the National Institute for Health and Clinical Excellence (National Institute for Health and Clinical Excellence 2006)) reduces the average systolic blood pressure by 9.1 mmHg and the diastolic one by 5.5mmHg (Law, Wald et al. 2003). We assumed that the drug was suspended if it caused adverse effects (Law, Wald et al. 2003) 3 T his is in line with targets sets by the Food Standards Agency in the UK, which is promoting voluntary salt reduction targets with food manufacturers and retailers toward a target average salt intake of 6 grams per day, that is 3 grams lower than the current average by 2010 (Scientific Advisory Committee on Nutrition 2003; Hoare, Henderson et al. 2004; Food Standard Agency 2005; Food Standards Agency 2006).
32
SECTION ONE
Box 3.
England selected interventions evaluated
Disease
Interventions
Description
Stroke
SU
Increase the percentage of patients who present to hospital to spend most of their time a stroke unit from 54% to 100%.
T
Increase the percentage of stroke patients with Thrombolysis (intravenous tissue Plasmogen Activator or tPA) from 0.8% to 9%.
BPhigh
Prescribe a first line anti-hypertensive drug to all people with blood pressure above 140/90 mmHg who are not currently prescribed any anti-hypertensive.
Na5
Reduce average blood pressure in the population reducing sodium content in diet by 30% (3g salt) through agreement with food industry.
Suicide Prevention
NSPS
Achieve the target of a 20% reduction in suicide rates (for 2010 against a 1997 baseline) through implementation of the National Suicide Prevention Strategy (NSPS) (DH, 2002).
Type-1 diabetes
IGC-short run
Reduce the rates of progression to and through microvascular complications for all patients with Type-1 diabetes from Intensive glucose Control (IGC) (DCCT, 1990, 1993 and 1996). The short-run estimates are based on the average of costs and benefits for a five-year programme.
IGC-long run
Reduce the rates of progression to and through microvascular complications for all patients with Type-1 diabetes from IGC. The long-run estimates are based on a one-year snapshot of a future ‘steady state’ in which patients with Type-1 diabetes have been under continuous IGC from when the disease began.
ED&T
Diagnose and treat cases earlier who are revealed by emergency admission and improve compliance with treatment by ACE inhibitors.
All-incident
Give appropriate treatment for newly-diagnosed cases in primary and outpatient care.
All-prevalent
Give appropriate treatment for all prevalent cases in primary and outpatient care.
Improve-diagnosis
Treat undiagnosed cases with ACE inhibitors.
ACE - LSVD
Extend treatment with ACE inhibitors to diagnosed cases with Left Ventricular Systolic Dysfunction (LSVD).
ACE -compliance
All patients being prescribed with ACE inhibitors comply with treatment.
Coronary Heart Disease
Statins
Improve statin prescription by increasing its coverage, compliance and appropriateness.
Depression
Current-all
Extend the current treatment profile to 100% of the population suffering from depression.
NGD
Change the current care regime to a new treatment profile, as recommended in NICE’s National Clinical Guideline for Depression (NGD) at current treatment rates (NICE, 2004)1.
NGD-all
Extend the NGD recommended treatment profile from 60% to 100% of the population suffering from depression.
Heart failure
1
1 T hese provide a template for evidence-based, high quality care of sufferers with the establishment of Psychological Treatment Centres (PTCs), where trained psychotherapists can deliver evidence-based forms of psychotherapy to sufferers from depression.
33
SECTION ONE
Chapter 3 VfM for Selected Interventions for the Population of England
1. The previous Chapter showed how information on the scale of the impact of interventions, including estimates of population health gain, provided new vital information in setting priorities for the prevention and treatment of stroke. The purpose of this Chapter is further to illustrate the power of information on scale by giving these estimates for selected interventions for different diseases.
2. This Chapter begins by explaining why we chose these different interventions and then gives results. Annex 1 gives an outline of the models and details for the different diseases are given in working papers30. Box 3 gives descriptions of the interventions.
3.
The diseases and interventions we chose and the reasons for this choice are as follows: a. Stroke: Stroke mortality accounted for 80% of the total mortality from all cerebrovascular diseases (which including coronary heart disease and stroke) in the UK31 (Office of National Statistics, 2001-2005) which were the third most common cause of mortality and the leading cause of disability in the UK (Adamson et al., 2004; National Audit Office, 2005)32. Although mortality from stroke had been decreasing in England over the past twenty years, it was still four times higher than in New Zealand and almost twice as high as in the US33 (Leatherman and Sutherland, 2005).
b. Suicide Prevention: Leatherman and Sutherland (2005) reported that suicide accounted for 4,500 deaths per annum in England and was the leading cause of death among men aged from 15 to 24 years, and the second most common cause of death among people under 35 years of age. Within the National Service Framework for mental health34 the only quantified target was that for suicide prevention. Hawton (1998)35 pointed out that a national target for suicide prevention would be the only realistic candidate for a mental health target, which is needed for mental health to avoid slipping backwards.
c. Depression: Depression is a common mental health disorder, with a weekly prevalence of about 2.6% in the UK (Singleton, Bumpstead et al. 2001)36: i.e. in a given week we would expect 2.6% to be clinically depressed. Hollinghurst et al (2000)37 in their analysis of seven options for population health gain found that these were greatest from treatment of depression (the other options examined were prevention and treatment of stroke, heart disease, cataracts, benign prostatic hyperplasia, arthritis of the hip and peptic ulcer).
d. Coronary Heart Disease: CHD was the leading cause of premature mortality in the UK (Peterson, Peto et al. 2005)38 and accounted for 100,000 deaths per year. The reduction of the Burden of Disease (BoD) associated with CHD was a national priority (DH 1999, 2000)39 to be achieved largely by controlling risk factors for CHD. High levels of cholesterol in the blood contributed significantly to CHD incidence as cholesterol clogs the blood vessels that supply the heart and the rest of the body. The best way of controlling blood cholesterol was through the prescription of statins. Leatherman and Sutherland (2005, p 20)40 reported that, in 1999, of six comparator countries, the UK had the second highest mortality rate from CHD for people aged from 35 to 74 years of age41. Some of these deaths could have been avoided by prescribing statins to reduce levels of cholesterol.42 Leatherman and Sutherland (2005, p 32)43 reported that the use of statins across Europe is extensive but variable: in 2000, about 24 Defined Daily Doses (DDDs)44 were prescribed per 1,000 adults
34
SECTION ONE
per day in England; Norway, with the highest rate of statin prescribing, had a rate two and a half times that of England (where there was incomplete coverage) (Majeed et al, 2000)45. There were wide regional variations in England despite large increases in prescribing rates (Healthcare Commission, 2005)46; of those being prescribed statins, about 30 per cent are prescribed a lower dosage than is required for effective prevention, and compliance varies between 80 and 95 per cent (Ward et al, 2005)47. Statin prescribing was identified as the only intervention to have an impact on NHS productivity in terms of population health gain: the other interventions analysed were improved blood pressure control from general practice records and improved one-year survival rates following heart attacks (DH, 2005)48.
e. Heart Failure: The BoD in England due to heart failure was significant (Sutherland et al., 2006)49. Typically, patients with heart failure had poor QoL (Hobbs et al., 2002)50. In addition, prevalence has been increasing (Cowie et al., 1997)51. The Department of Health had made heart failure a priority in the Planning and Priorities Framework for 2003 (DH, 2003)52, setting as a target the improvement of heart failure services in line with the clinical guidance from NICE.
f. Type 1 Diabetes: Leatherman and Sutherland (2005, p 37)53 reported that the levels of control of glycosylated haemaglobin (HbA1c) in diabetic children (aged 0–16 years) in England, Wales and Northern Ireland was poor: in 2004, the National Paediatric Diabetes Audit found that fewer than one in five maintained their blood glucose readings within the recommended level. Good glycaemic control reduces the risks of long-term sequelae of diabetes such as blindness, kidney failure and nerve damage (Diabetes Control and Complications Trial, 1990, 1993, 1996)54. The results of a more recent audit (National Diabetes Audit, 2010)55 howed poor control to be a serious problem for over 80 per cent of the population with Type 1 diabetes aged from 6 to 24 years.
4. Table 7 gives costs (in £m), population health gain (in 000’s QALYs), and VfM (QALY/ cost in £’00s) for the different interventions ranked in terms of their VfM.
[x] View Table 7.
England selected interventions ranked by Value for Money
5. Table 8 and Figure 7 give ranking in terms of the size of health gain. Suppose the government wanted to maximize health gain, but recognized that to have effective implementation would need to focus on at most six policies, how might this information be used? On this basis, the top three interventions would be: preventing strokes by reducing dietary intake of salt and improving control of blood pressure in primary care; and seeking to improve the coverage of treatment of depression. And the next set of three would be: increasing the percentage of patients with a stroke cared for in a stroke unit, implementing a programme of intensive glucose control at the onset of diabetes focusing on adolescents (although this would produce gains in the long run only), and achieving the national suicide prevention strategy. Conversely, policies with only limited potential for national population health gains are: seeking to treat 9% of stroke patients with thrombolysis, implementing the national clinical guidelines for treatment of depression (for those currently being treated), and additional treatment of newly-diagnosed cases of heart failure.
[x] View Table 8.
England selected interventions: ranked by population health gain
6. The purpose of this Chapter is to illustrate further how the new information generated from information on the scale of the impacts of interventions for the prevention and treatment of different diseases gives a different perspective from standard approaches. As we applied this approach for interventions chosen on methodological grounds the findings do not provide a systematic a basis for setting national or local priorities. Our approach
35
SECTION ONE
would need to be extended more widely to provide a firm basis for that. In the selected interventions we have examined, prevention of strokes looks to have potential to produce large gains for the population of England as does treating those with depression.
QALY Gains
Figure 7.
Selected interventions ranked by Health Gain Na2 BPhigh NGD-all Current-all SU IGC-long run NSPS Statins ACE-compliance ED&T ACE-LSVD All-prevallent Improve diagnosis IGC-short run T All-incident NGD 0
Numbers benefit
20
40
60
80
36
SECTION ONE
Box 4.
Eating disorders in Sheffield: interventions evaluated 1
BACK
Intervention
Description
Intensive care
Admission to specialist hospital/residential unit (out of area or in private sector in Sheffield): Recovery programme - working towards a Body Mass Index (BMI) of above 19. Supported environment to regulate eating and prevent purging: though group therapy and structured psychotherapy2. Risk management programme supported re-feeding programme to reach safer BMI (approximately 15).
Private Day
Admission to day service in private sector in Sheffield: Supported environment to regulate eating and prevent purging. Occupational Therapy (OT) support for shopping, eating and activities post discharge. Continuation of psychotherapy where relevant.
SEDS
Sheffield Eating Disorder Service (SEDS): Outpatient appointments with psychiatrist (typically every six weeks) Limited number of dietician appointments to plan meals. Time limited individual therapy3 with general support / case management.
Emergency
Emergency medical admission to Sheffield acute hospital: Medical management of physical risks - may involve re-feeding via nasogastric tube or percutaneous endoscopic gastrostomy (PEG) feed. Input from dietician. Additional psychiatric nursing support occasionally provided.
UniEDOC
University eating disorder primary care clinics Guided self-help CBT informed work delivered by trained nurse specialists. Mainly working with mild / moderate cases, but also some severe.
SYEDA
South Yorkshire Eating Disorders Association (SYEDA - voluntary sector involvement): Self referral system. Provide monthly support groups, psycho-education sessions, courses on topics such as body image and confidence. Limited resource for individual CBT. Work with mild / moderate cases. Some severe
Acute
Admission to acute psychiatric ward
h
footnotes 5-7123
1 W e were unable to evaluate care provided by the community mental health team (the allocation of a care coordinator who can provide support in relation to wider mental health issues, with limited access to individual therapy -- CBT and CAT); and routine primary care (medical monitoring, routine prescriptions, sometimes a role in weighing). 2 T his is usually available (at additional cost) later in the placement, although if the placement is in the Sheffield unit this can sometimes be provided by the Sheffield Eating Disorder Service (SEDS). 3 Cognitive Behavioural Therapy (CBT) or CAT informed) - typically 20 sessions for Bulimia and 40 for Anorexia
37
SECTION ONE
Box 5. Eating disorders in Sheffield: standard definition of severity used in the care pathway
BACK
h
BACK
h
Degree of severity
Description
Mild
Meets all of the following criteria: Frequency of laxative abuse / vomiting is less than 3 times a week; BMI is above 17.5; First episode of illness; Duration of illness less than 6 months.
Moderate
Meets one or more of the following criteria: BMI is between 16 – 17.5; Frequency of laxative abuse / self induced vomiting is more than 3 times a week; Recurrent episodes or duration of illness is more than 6 months; Physical complications, e.g. electrolyte disturbance, amenorrhea.
Severe
Meets one or more of the following criteria: BMI is below 16 Laxative abuse / self induced vomiting more than 5 days per week; Rapid weight loss (loss of 25% of body weight in six months); Meets moderate criteria with has additional health risks (e.g. Diabetes, pregnancy or low potassium); Aged between 16 and 18 years with BMI under 16.5 or under transitional protocol from the Child and Adolescent Mental Health Service (CAMHS).
Box 6. Eating disorders in Sheffield: anchoring points for assessing Quality of Life weights Weight
Description
100
Full health
71
No problems in mobility, usual activities, self-care, no pain or discomfort but some anxiety and depression.
49
No problems in mobility, usual activities, self-care, no pain or discomfort but severe anxiety and depression.
25
Obsessive compulsive disorder with some anxiety and depression.
17
Obsessive compulsive disorder with severe anxiety and depression.
0
Dead.
38
SECTION TWO
Section Two Decision conferencing in Sheffield and the Isle of Wight The previous Section showed how information on the scale of the impacts of interventions gives a valuable new perspective on setting priorities which is relevant at both local and national levels. This Section shows how we used this information in developing a novel approach for setting local priorities through decision conferencing. We report how we did this by R&D in collaboration with PCTs in Sheffield, within three defined services (Chapters 4, 5 and 6), and the IoW across a range of services (Chapter 7). We have developed guidance on the datapack that shows the kinds of data we require (Annex 2). This asks the commissioning body to seek out data for each intervention on annual costs to the NHS, the numbers and types of people benefit from this intervention each year, and for evidence on health gains and other benefits. We found that the two PCTs found it difficult to produce these data. Indeed most of the effort spent by our researchers was on working with staff in the PCTs to generate basic data. The most serious obstacle to assessing VfM was the lack of data on health gains. So these (and other missing data) were generated by decision conferences organised by an impartial facilitator. These involved clinicians and patients (and other key stakeholders) as part of the process of developing rectangles of health gain, VfM triangles and efficiency frontiers. We show how we overcame the problems of the lack of data on health gains in various ways and how we used the data generated produce rectangles of health gain, VfM triangles and efficiency frontiers. An impartial facilitator is vital in organising the generation and interpretation of the necessary data, and this facilitator must be skilled in using a computer model, to relates estimates as input to priorities as output, and sensitivity analysis of how outputs change for changes in the estimated inputs. As our purpose was to identify large gains in VfM from strategic changes, we found that sensitivity analyses showed that our results were typically robust for wide margins of error in the initial estimates.
39
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Chapter 4 VfM for the prevention and treatment of eating disorders in Sheffield
1. This Chapter shows how we developed, for the prevention and treatment of eating disorders in Sheffield, estimates of health gain for different interventions along the pathway of care, and how we then combined these with other data to develop efficiency frontiers. This was a detailed analysis of a relatively small programme within mental health, which was chosen because Tony Nuttall, the lead for Sheffield PCT in Mental Health, believed that there was scope to improve the way in which these services were organised.
2. The stakeholders included four (anonymous) service users, who played a full role in assessing QoL and in supporting the strategic redeployment of services that was agreed. Three were from the South Yorkshire Eating Disorders Association (SYEDA) and one from Sheffield Eating Disorder Service (SEDS) and Riverdale Grange, Eating Disorders Unit. The other stakeholders were (in alphabetical order by surname):
Jenny Allen SYEDA Andy Bragg Assistant Service Director for Recovery, Rehabilitation and Specialist Services. Sheffield Health and Social Care (SHSC) Richard Bulmer Service Director for Acute, Community and Primary Care. SHSC Alan Carter Mental Health Partnership Board Member Gwyneth De Lacey Head of Psychological Health Sheffield. SHSC Sharron Fitzpatrick Deputy Clinic Manager. Riverdale Grange, Eating Disorders Unit Paul Harvey GP and PCT Professional Executive Committee Member Lorraine Hickie Clinic Manager. Riverdale Grange, Eating Disorders Unit Michelle Hinde Occupational Therapist. Riverdale Grange, Eating Disorders Unit Alison James GP University Health Centre Lyndsey Lavender Executive Director. SYEDA Mary Lea Councillor – Sheffield City Council. SCC: Mental Health Partnership Board Member Claire Lockwood Clinical Services Manager. SYEDA Gary McCulloch Health Improvement Principal. NHS Sheffield Sheila Paul Consultant in Public Health. NHS Sheffield Awena Sanders Eating Disorders Consultant. SEDS Steve Todd Commissioning Manager. SCC Amy Wicksteed SEDS Maggie Young SEDS 3. Box 4 gives the list of interventions we evaluated.
4. To assess the health gain at the level of the individual for different interventions it is necessary to have a classification of the severity of eating disorders. Box 5 gives the standard definition of severity used in the care pathway for treating eating disorders in Sheffield.
5. The stakeholders assigned weights of the different levels of severity for QoL by working in four different groups with heterogeneous stakeholders and using anchoring points given in Box 6 (on a scale in which 100 corresponds to the full health and 0 to being dead).
40
SECTION TWO
The groups produced ranges of estimates for the different degrees of severity. This is illustrated by Figure 8 for the moderate degree of severity for eating disorders for which the agreed weight was 50 (with 95% of cases in the interval 20 to 60). These were used to generate units of health gain that are intended to be comparable within eating disorders but not with other services (such as cancers)56. We therefore describe the unit of health gain for eating disorders as QALYsED.
Figure 8. Eating disorders in Sheffield: quality of life weights for patients with moderate severity Full health
Some anxiety or depression
1
Group 1
Group 2
Group 3
0.75
Group 4
0.75
0.6 0.5
0.49
Quality of life
0.4
OCD with some anxiety or depression OCD with severe anxiety or depression Death
0.75
0.71 0.6
Severe anxiety or depression
Agreed
0.25
0.2
0.5
Mean
0.35 0.2
0.17 0
6. We also collected available information on Quality of Care in terms of patient satisfaction for seven interventions: Intensive Care, Private Day Care, Sheffield Eating Disorder Service (SEDS), University Eating Disorders Clinics (UniDOC), South Yorkshire Eating Disorders Association (SYEDA), and admission to an acute ward (Acute). As this information showed similar levels of satisfaction across these interventions it was not used in priority setting as it would not have discriminated between services57.
7. The stakeholders estimated the impacts of each of the seven interventions on the QoL of individuals. We illustrate this for the SEDS service that treats patients with severe eating disorders. Those who provide this service estimated:
a. the proportion of these patients treated in SEDS who would become more severe, stay severe, improve but remain severe, improve to moderate, improve to mild and recover; and
b. their judgement of what would happen for the counterfactual: i.e. if users could not access SEDS and would have to rely on other services (usually GPs, SYEDA, community mental health team and, in case of need, emergency admissions: see Table 9).
41
SECTION TWO
[x] View Table 9.
[x] View Table 10.
Eating disorders in Sheffield: health gain for severe patients from the Specialist Eating Disorder Services (SEDS)
Eating disorders in Sheffield: average health gain per person in QALYsED by service
8. Table 10 gives the results of applying this process to the seven interventions58: the average health gain per person for each intervention is typically the difference between the gain in QoL over time treatment with that intervention and the ‘counterfactual’59. Figures 9 and 10 illustrate, for the seven interventions, the average health gain per person by degree of severity, and the numbers who benefit.
9. Data were collected on the numbers of patients who were treated in the different services so that these could be related to estimates of: their likely health gains (with details of their severity – see Figure 7), the impacts of treatment on health inequalities (from the index of multiple deprivation associated with their postcodes60), and, where services are provided for a number of PCTs, from Sheffield PCT (to relate to costs61). Table 10 summarises this information. Figure 11 gives population health gain for eight interventions. Table 11 gives the interventions ranked in terms of VfM. Figure 13 illustrates this by giving the efficiency frontier for the different options where the VfM triangles are ordered in terms of their slope: starting with the option with the steepest slope and ending with the option with the flattest slope.
Figure 9.
Eating disorders in Sheffield: average health gain per person in QALYsED from services by severity 0.5
0.4
Average health benefit
0.3
0.2
0.1
T 0.0
Intensive care
Day service
Service
Severe Moderate Mild
0.5
mild
moderate 0.4
0.3 severe
SEDS
Emergency Admisson
Uni EDOC
SYEDA
Acute psych ward
42
SECTION TWO
Figure 10. Eating disorders in Sheffield: numbers who benefit from services by severity 100
80
Number who benefit
60
40
20
0 Intensive care
Day service
SEDS
Emergency Admisson
Uni EDOC
SYEDA
Acute psych ward
Service
Severe Moderate Mild
0.5
mild [x] View Table 11.
Eating disorders 0.4 in Sheffield: spend, value and distribution by class
moderate 0.3 severe
Figure 11. Eating disorders in Sheffield: T population health gain in QALYsED from different services Back to text [h] 0.2 120 0.1
Early detection and diagnosis
100
0
Palliative and EOL
80
BP all
60
Na5 QALYsC / person
40
Na2 SU
20
0
Repatriation of radiotherapy 10
20
30
40
50
0 0
200
400
600
800
1000
1200
Number who benefit
[x] View Table 12.
60
Eating disorders in Sheffield: interventions ranked by Value-for-Money
1400
1600
43
SECTION TWO
Figure 12. Eating disorders in Sheffield: the efficiency frontier for the current care pathway 50
7 8
45 40
4
35
5 6
30 25 20
3
Benefits 0-40
15 10 5 0
2 1 0
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2,000
2,200
Costs 0-2356
10. Figure 12 gives the efficiency frontier for the current care pathway. It shows that the ranking At time of of health gain at the population level per pound spent foraccessing the treatment of eating disorders Life expectancy Radical treatment No treatment this service in Sheffield is as follows (with the numbered vertices of triangles in parentheses): without treatment 1 year
a. b. c. d. e. f. g.
UniEDOC (1 and 2); SYEDA (2 and 3), SEDS (3 and 4), Private (4 and 5), Emergency (5 and 6), Intensive care (6 and 7); and Acute (7 and 8).
4 years
11. Figure 12 shows that, of the total spend of over £2.3m on the treatment of eating disorders, nearly £2m (nearly 90%) is spent on 16 patients who were admitted for intensive care, with a population health gain of 8 QALYsED (and a cost/QALYED of £125,00062). Figure 13 shows the efficiency frontier for the care pathway based on the PCT’s strategy to increase two early interventions (UniEDOC and SEDS) and hence reduce need for, and high expenditure on, intensive care. Figure 13 shows that this strategy has potential to achieve substantial reductions in total costs and gains in value. At the time of the decision conferences, a new eating disorders consultant led change in managing patients in SEDS, providing supporting preliminary evidence for this strategy: the numbers in high-cost intensive care have reduced significantly by April 2010. The PCT is implementing the new strategy from September 2011, by providing resources to expand services commissioned through SEDS and by reducing resources spent in intensive care generating net savings.
2,400
44
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Figure 13. Eating disorders in Sheffield: strategy for achieving greater health gain at less cost 120
Early detection and diagnosis
100
Palliative and EOL
80
60
QALYsC / person
40
Repatriation of radiotherapy 20
0 0
200
400
600
800
1000
1200
1400
1600
Number who benefit
12. This Chapter has shown how we developed our approach with key stakeholders to analyse the treatment of eating disorders along the care pathway in Sheffield. This analysis confirmed the judgement of the lead for mental health in Sheffield that the pattern for delivering care was suboptimal and enabled the stakeholders to identify why this was so and how the care pathway could be improved. In this case this analysis suggested scope to both increase population health gain and reduce total expenditure. The estimates we derived may provide a starting point for work in other Commissioners, but the analysis was organised around the particular configuration of services in Sheffield. The key message of this Chapter is that it gives a powerful illustration of how the approach we developed enabled local stakeholders in one service to agree on a commissioning strategy with good potential to reduce expenditure and increase value.
45
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Chapter 5 VfM for the prevention and treatment of Cancers in Sheffield
1. The previous Chapter illustrated how our approach enabled stakeholders to agree on a reconfiguration of the care pathway on a small component of mental health services: the treatment of eating disorders. This Chapter shows how we developed, for the treatment for three major cancers in Sheffield, for each intervention for each intervention estimates of health gain for different interventions along the pathway of care, and how we then combined these with other data to develop efficiency frontiers. The three major cancers were breast, colorectal cancer and lung. This was an ambitious exercise given the limited time we had available. This meant that we had to examine these pathways across broad categories, which typically included: health promotion, screening, primary care involvement, elective inpatient admissions, outpatient admissions, end of life care, palliative care and emergency admissions. We decided to limit the evaluation to about twenty interventions identified by William Gray, the PCT lead for cancers.
2.
The other stakeholders were (in alphabetical order by surname)63: Shwan Amin Colorectal Cancer Lead Clinician. Sheffield Teaching Hospital (STH) Rachel Barrow Patient Representative(breast cancer) Judith Bird Lead Nurse. North Trent Cancer Network Jo Coy NHS Sheffield Andy Eames NHS Sheffield Kim Fell Director North Trent Cancer Network Patricia Fisher Consultant Clinical Oncologist. Weston Park Hospital Karen Glencross Acting Service Manager / Superintendant Radiographer. STH Anthony Gore General Practitioner. NHS Sheffield Sue Greig Public Health Consultant. NHS Sheffield Gill Guest Cancer Services Director. STH Jennifer Hill Lung Cancer Lead Clinician. STH Heather Hudson Patient Representative (breast & bowel cancer) David Hughes Consultant Histopathologist and Cancer Lead Clinician/ Associate Medical Director for Cancer Services. STH Christine Ingram Lead Breast Clinician. STH Nicky Kenyon Head of Clinical Pathways. NHS Sheffield Provider Services David Levy Lead Clinician. North Trent Cancer Network Terry Lilley Patient Representative - Chair of Sheffield Cancer Voices Martin Salt Lead Nurse. STH Catherine Sorsby Patient Representative (breast cancer) Helena Stanley Macmillan Lung Cancer Support Nurse. Royal Hallamshire Hospital Georgia Thompson Clinical Nurse Specialist Colorectal. STH Jeremy Wight Director of Public Health. NHS Sheffield Makeda Wood Head of Finance STH and Specialised Service. NHS Sheffield
46
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2. Box 7 gives the list of twenty interventions selected for which we could collect information on costs and numbers treated: seven in breast and colorectal cancer, and six in lung cancer64.
Box 7.
Cancers in Sheffield: list of interventions
Breast cancer 1
Health promotion
Raising awareness through campaigns, leaflets, and posters.
2
National Screening
National Breast Cancer Screening: routine screening (all women aged 50-70 should be screened once every 3 years)
3
Elective
Elective inpatient admissions (excluding palliative and End of Life care provided in St Luke and Macmillan)
4
Outpatient
Outpatient admissions
5
EOL
End of Life Care in St Luke and Macmillan
6
Palliative
Palliative care received in the last two years of life as hospital inpatient (e.g. Sheffield teaching hospital)
7
Emergency
Emergency admissions (excluding palliative)
Colorectal cancer Intervention
Notes
1
Screening
Bowel Screening programme: including call and recall
2
Elective
Elective inpatient care (excluding palliative and End of Life care provided in St Luke and Macmillan)
3
Outpatient
Outpatient admissions
4
EOL
End of Life Care in St Luke and Macmillan
5
Palliative
Palliative care received in the last two years of life as hospital inpatient (e.g. Sheffield teaching hospital)
6
Emergency
Emergency admissions (excluding palliative)
7
Primary care
Routine primary care involvement
Lung cancer 1
Health promotion
Raising awareness (campaigns, leaflets, posters) and services to support smoking cessation
2
Elective
Elective inpatient admission: radical treatment, including surgery and possibly radiotherapy (excluding palliative and End of Life care provided in St Luke and Macmillan)
3
Outpatient
Outpatient appointments, usually with a ‘surveillance’ aim
4
EOL
End of Life Care in St Luke and Macmillan
5
Palliative
Palliative care received in the last two years of life as hospital inpatient (e.g. Sheffield teaching hospital)
6
Emergency
Emergency admissions (excluding palliative)
47
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Costs
3. Table 13 lays out how we developed the bases for estimating costs to the PCT for each intervention65 and Table 14 gives the estimated costs66. Our estimates of costs are largely based on financial information (e.g. refunding hospital for their activity according to tariffs for Payment by Results)67. Data on costs were not directly available for key elements and were estimated as follows:
a. costs of imaging (positron emission tomography - computed tomography or PET-CT) were split equally between colorectal and lung cancer treatment and added to their costs of inpatient elective admissions;
b. costs of chemotherapy and radiotherapy were agreed in discussion and added to the cost of Inpatient elective admissions;
c. costs of drugs have been attributed to the treatments for the different cancers68 and added to the costs of Inpatient elective admissions.
[x] View Table 13.
Cancers in Sheffield: bases for estimating costs to the PCT for hospital admissions
[x] View Table 14.
Cancers in Sheffield: PCT cost for each intervention
Population health gain
4. In contrast with the interventions assessed for the eating disorders pathways, it was particularly difficult to identify the average beneficiary from the identified, broad categories of interventions. Participants volunteered tentative estimates in order to understand and evaluate the process and but lacked confidence in their accuracy. We show how we derived these estimates, which need further development.
5. Figures 14, 15, and 16 illustrate how estimates of QoL were derived for lung cancers; and give profiles for QoL for radical treatment, and End Of Life (EOL) care (in the last two years and last few months of life). These three estimates were measured in QALYsC, units of health gain that are intended to be comparable within the three cancers but not with other services (such as for treatment of eating disorders)69.
48
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Figure 14. Cancers in Sheffield: health profiles for inpatient care for lung cancer (Radical treatment) 100 90 80 70
At time of accessing this service Life expectancy without treatment 1 year 4 years
60 50
Life expectancy after surgery 9 years
40
Quality of life
30 20 10
Radical treatment
No treatment
0 0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
13
14
15
16
Time (years)
Figure 15. Cancers in Sheffield: health profiles for end of life care for lung cancer in the last two years of life 100 90 80 70
At time of accessing this service Life expectancy without treatment 1 year 4 years
60 50
Life expectancy after surgery 9 years
40
Quality of life
30 20 10
Radical treatment
No treatment
0 0
1
Time (years)
2
3
4
5
6
7
8
9
10
11
12
49
SECTION TWO
Figure 16. Cancers in Sheffield: health profiles for palliative care for lung cancer in the last few months of life 100
90 80 70 60
At time of accessing this service
Radiotheapy/ chemotherapy Life expectancy without treatment
50 40
Quality of life
30
Life expectancy after radiotherapy/chemotherapy
20 10 0 0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Time (months)
6.
Tables 15 and 16 give estimates of QoL scores for different health states for Breast and Colorectal cancers produced by the decision conferences. We used these in estimating QALYsC for some interventions for Breast and Colorectal cancers using the same approach as for lung cancers.
[x] View Table 15.
Cancers in Sheffield: quality of life scores for different health states in breast cancer
[x] View Table 16.
Cancers in Sheffield: quality of life scores for different health states in colorectal cancer
7. We used estimates derived through detailed analyses for each cancer type as ‘anchors’ from which remaining estimates were assessed for the other interventions. Tables 17, 18 and 19 give, for each cancer, the estimated numbers who benefit from each intervention, the QALYsC and the population health gains (in QALYsC).
[x] View Table 17.
Cancers in Sheffield: population health gain from interventions for treatment of breast cancer
[x] View Table 18.
Cancers in Sheffield: population health gain from interventions for treatment of colorectal cancer
[x] View Table 19.
Cancers in Sheffield: population health gain from interventions for treatment of lung cancer
50
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VfM Triangles
5. Table 20 and Figure 17 give the estimated relative VfM and the efficiency frontier for treatment of breast cancer in Sheffield. Figure 17 shows that the ranking of health gain at the population level per pound spent was as follows (with the numbered vertices of triangles in parentheses): a. Health promotion (1 and 2) b. Screening (2 and 3) c. Elective (3 and 4) d. Outpatient (4 and 5) e. Palliative (5 and 6) f. Emergency (6 and 7) g. EOL (7 and 8)
[x] View Table 20.
Cancers in Sheffield: relative Value-for-Money for treatment of breast cancer
Figure 17. Cancers in Sheffield: the efficiency frontier for the current care pathway for treatment of breast cancer 360
5 6 78 300
4 240
180
Benefits 0-333
120
3
60
0
2 1 0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
11,000
12,000
Costs 0-11664
6. Figure 17 (and Table 20) shows that we estimated that preventive interventions were highly cost-effective for breast cancer. The efficiency frontier of Figure 17 may be compared with Figure 13 (for treatment of eating disorders in Sheffield). The striking difference between At time of these Figures is that although most of the resources used in the treatment of breast cancer accessing Life expectancy Radical treatment No treatment service are spent onwithout electivetreatment inpatient and outpatient care, thesethis were estimated to be cost-effective 1 year and to produce about 80% of the value. Hence, this analysis did not suggest that (in contrast with eating disorders) there was much scope to reduce these costs by earlier treatment. 4 years (Although timely intervention at an early stage is vital this did not form part of our analysis.) What the PCT did learn from this exercise is the scale of expenditure on elective inpatient and outpatient care on drugs (over ÂŁ5m in Table 13 and nearly 50% of total costs in Table 20). 7. One issue that emerged through the discussion of priorities was the different perspectives of patients and clinicians for outpatient care. Clinicians and managers challenged the results of the relatively high VfM of outpatient care and proposed to reduce the frequency of follow-up visits on the grounds that recurrences were usually identified by the patient presenting to her GP, and not by the routine follow up visit. Patients, however, argued that these visits were of great value to them: for providing peace of mind and the feeling of continuing care. This value was captured
51
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in scoring the benefits of interventions and patients could articulate and, by understanding the methods and the results, they could defend their views and initiate a discussion on how the valuable ‘peace of mind’ could have been maintained by spending less (e.g. considering alternatives to being seen by specialist consultants).
8. Table 21 and Figure 18 give the estimated relative VfM and the efficiency frontier for treatment of colorectal cancer in Sheffield. Figure 18 shows that the ranking of health gain at the population level per pound spent is as follows (with the numbered vertices of triangles in parentheses): a. Primary Care (1 and 2) b. Elective (2 and 3) c. Outpatient (3 and 4) d. Screening (4 and 5) e. Palliative (5 and 6) f. Emergency (6 and 7) g. EOL (7 and 8)
[x] View Table 21.
Cancers in Sheffield: relative Value-for-Money for treatment of colorectal cancer
Figure 18. Cancers in Sheffield: the efficiency frontier for the current care pathway for treatment of treatment of colorectal cancer 360
4
300
5
78
6
3
240
180
2 Benefits 0-333
120
60
0
1 0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
Costs 0-9545
9. Figure 18 shows that most of the resources are spent on three interventions of similar cost-effectiveness: primary, inpatient and outpatient care, which together account for At time of about 75% of total costs and 90% of the total value. These results suggest little scope for accessing Life expectancy Radical treatment No treatment this service making changes to treatment improve VfM in the treatment of colorectal cancer in Sheffield. without 1 year
4 years
10,000
52
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10. Table 22 and Figure 19 give the estimated relative VfM and the efficiency frontier for treatment of lung cancer in Sheffield. Figure 19 shows that the ranking of health gain at the population level per pound spent was as follows (with the numbered vertices of triangles in parentheses): a. Health promotion (1 and 2) b. Outpatient (2 and 3) c. EOL (3 and 4) d. Elective (4 and 5) e. Palliative (5 and 6) f. Emergency (6 and 7)
12.
[x] View Table 22.
F igure 19 is the VfM profile for what is for many an incurable disease: health promotion (i.e. smoking cessation) was the only intervention that is cost-effective and produced nearly 90% of the total value for 9% of total costs; and hence treatment produced only 10% of the total value but accounted for 90% of the total costs. This exploration of prevention and treatment of lung cancer in Sheffield suggests that it is worthwhile to examine scope for increasing health promotion through smoking cessation; but there seems to be little scope for reducing the scale of care, which is not cost-effective, by expanding services earlier in the care pathway.
Cancers in Sheffield: relative Value-for-Money for treatment of lung cancer
Figure 19. Cancers in Sheffield: the efficiency frontier for the current care pathway for treatment of lung cancer 360
3
300
6
5
4
7
2
240
180
Benefits 0-333
120
60
0
1 0
500
1,000
1,500
2,000
2,500
3,00
3,500
4,000
4,500
5,000
5,500
Costs 0-9545
13. Table 23 and Figure 20 give the estimated relative VfM and the efficiency frontier for treatment of breast, colorectal and lung cancer in Sheffield. Figure 20 shows At time of that the ranking of health gain at the population level peraccessing pound spent was Life expectancy Radical treatment No treatment this service as follows (with the numbered vertices of triangles in parentheses): without treatment 1 year
a. b. c. d. e.
Lung -Health promotion (1 and 2) Breast- Health promotion (2 and 3) Breast-National Screening (3 and 4) Lung-Outpatient (4 and 5) Colorectal-Primary Care (5 and 6)
4 years
8
53
SECTION TWO
f. g. h. i. j. k. l. m. n. o. p. q. r. s. t.
Colorectal-Elective (6 and 7) Breast-Elective (7 and 8) Breast-Outpatients (8 and 9) Colorectal-Outpatients (9 and 10) Colorectal-Screening (10 and 11) Colorectal-Palliative care (11 and 12) Breast-Palliative (12 and 13) Breast-Emergency (13 and 14) Lung-EOL (14 and 15) Lung-Elective (15 and 16) Lung-Palliative (16 and 17) Breast-EOL (17 and 18) Colorectal-Emergency (18 and 19) Colorectal-EOL (19 and 20) Lung-Emergency (20 and 21).
14. This suggests that: health promotion to prevent lung cancer gave the best VfM, elective treatment for breast cancer was the single intervention with the greatest cost and health gain, and many interventions accounted for limited costs and contributed limited value. These results suggests seeking to expand health promotion to prevent lung cancer and examining scope to maintain the value from elective treatment for breast cancer at reduced costs. This might be done by more detailed examination of the different elements of elective care. This analysis also suggests that given the small scale of many interventions for the three cancers it is not worthwhile directing effort to increase their VfM.
[x] View Table 23.
Cancers in Sheffield: relative Value-for-Money for treatment of breast, colorectal and lung cancer
Figure 20. Cancers in Sheffield: the efficiency frontier for treatment of for breast, colorectal and lung cancer 360
4
300
5
78
6
3
240
180
2 Benefits 0-333
120
60
0
1 0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
Costs 0-9545
Life expectancy without treatment 1 year
No treatment
At time of accessing this service
Radical treatment
10,000
54
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15. This Chapter has described our attempt to examine VfM for prevention and treatment of three cancers in Sheffield. It provides a contrast with the preceding Chapter which analysed treatment of eating disorders: that analysis was specific, detailed and examined a care pathway which the mental health lead (Tony Nuttall) believed to be suboptimal. Our examination of cancers chose three major types using general categories with no initial belief that care was suboptimal. We experienced problems in assessing value for the typical patient. Our tentative results suggested little scope to change the pattern of delivery of care for these pathways. Further work in cancers would need to focus on one type with more detailed analysis of types of care as for the various components of inpatient care for breast cancer. Our analysis of cancers does not mean that using broad categories will necessarily fail to provide useful information in analyses of other types of care.
55
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Chapter 6. VfM for Dentistry in Sheffield
1. The previous two Chapters have illustrated how our approach was applied to pathways for two key elements of medical care: mental health and cancers. This Chapter outlines how our approach was applied to primary and secondary dental care; and how this was developed to analyse fees paid to General Dental Practices and indicate how that might be changed to generate incentives to increase VfM.
2. The lead for dental health in Sheffield was Kate Jones and the data on effectiveness for the different interventions were generated through decision conferences involving other stakeholders (whose names have not been supplied)70: • three managers, one clinician/manager and three secondary care clinicians from NHS Sheffield, • two managers from Sheffield Teaching Hospitals NHS Foundation Trust, • two primary care clinicians as independent contractors, • one patient representative, • one academic in Public Health from the School of Clinical Dentistry, • two public Health consultants from NHS Sheffield.
3. Table 24 gives the list of interventions for dentistry we examined and the numbers who benefit by intervention and their annual costs.
[x] View Table 24.
Dentistry in Sheffield: numbers who benefit by intervention and their annual costs go about here
Population health gain
4. We used routinely collected data to estimate the volume of provided services. The nature of these data mean that these estimates are only approximate. We estimated numbers who benefit for most interventions71 as follows (see Table 26):
a. For primary care (except for orthodontics) from the number of FP17 forms that dentists submit (that record the patient charge collected and the number of units of activity performed), and for orthodontics from the sum of orthodontic treatments started in a year72;
b. For secondary care, from the number of first appointments.
5. We developed estimates of QALYsD: units of health gain that are intended to be comparable within dentistry but not with other services (such as cancers)73. For dental health the QoL ranged from 0 for no health gain to 100 for the health gain from oral surgery or extractions74, which meant that maxillo facial surgery had a QoL score in excess of 100 (133 – see Table 25). We estimated QALYsD, the gain in dental health from each intervention, for five years, which was agreed by the stakeholders to be appropriate. Table 25 gives the estimated QoL and health gain over five years (in QALYsD) per person by intervention (with values used in sensitivity analyses of QoL and QALYsD in parentheses).
6. Table 26 gives for each intervention its estimated population health gain ranked in order for primary and secondary care75. Figures 21 and 22 give these estimates for primary and secondary care and show that: nearly 90% of population health gain in primary care was produced by permanent fillings and extractions; and nearly 70%
56
SECTION TWO
of population health gain in secondary care was produced by oral surgery.
[x] View Table 25.
Dentistry in Sheffield: QALYsD per person by intervention
[x] View Table 26.
Dentistry in Sheffield: population health gain
Primary Care
Figure 21.
Dentistry in Sheffield: population health gain from primary care (‘000s of QALYsD) gain
Permanent fillings Extractions Acrylic dentures Endontics Crowns Bridges fitted Orthodontics (primary care) Inlays Scale and polish Partial metal dentures Fissure sealants Veneers 0
50
100
150
200
‘000s of QALYsD
Secondary care
Figure 22. Dentistry in Sheffield: population health gain from secondary care (‘000s of QALYsD)
Oral Surgery Maxillo facial surgery Restorative dentistry Peadiatric dentistry Dental medicine Paediatric Maxillofacial surgery 0
‘000s of QALYsD
5
10
15
20
25
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VfM
7. Table 27 and Figure 23 give the estimated relative VfM and the efficiency frontier for activity in primary care dentistry in Sheffield. These show that the ranking of health gain at the population level per pound spent was as follows (with the numbered vertices of triangles in parentheses): a. Extractions (1 and 2) b. Endodontic treatments (2 and 3) c. Permanent fillings (3 and 4) d. Bridges (4 and 5) e. Partial metal dentures (5 and 6) f. Acrylic dentures (6 and 7) g. Inlays (7 and 8) h. Crowns (8 and 9) i. Fissure sealants (9 and 10) j. Veneers (10 and 11) k. Orthodontics (11 and 12)and l. Scale and Polish (12 and 13).
8. Figure 23 illustrates extremes of good and poor VfM: permanent fillings and extractions together accounted for less than 40% of total costs and contributed about 90% of population health gain; scale and polish had similar costs but contributed only 0.5% of population health gain.
9. We subjected the estimates of VfM for primary care to sensitivity analysis for Endodontics, Orthodontics, Fissure and Scale and Polish using the ranges generated in the decision conferences. These changes had a minimal effect on the results76.
[x] View Table 27.
Dentistry in Sheffield: VfM for primary dental care
Figure 23.
Dentistry in Sheffield: efficiency frontier for primary dental care 550 500
456
450
78
881 81
12
12
400 350 300 250
Benefits 0-500
200 150
2
3
100 50 0
1 0
5
10
15
20
Costs (ÂŁmillions) 0-24416980
Life expectancy without treatment 1 year
No treatment
At time of accessing this service
Radical treatment
25
30
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10. Table 30 and Figure 25 give the estimated relative VfM and the efficiency frontier for secondary care dentistry in Sheffield. These show that the ranking of health gain at the population level per pound spent was as follows (with the numbered vertices of triangles in parentheses): a. paediatric Maxillo-facial surgery (1 and 2) d. Paediatric dentistry (4 and 5), b. Oral surgery (2 and 3), e. Dental medicine (5 and 6)and c. adult Maxillo-facial surgery (3 and 4), f. gum treatment (6 and 7).
11. Figure 24 again illustrates extremes of good and poor VfM: oral surgery accounted for nearly 40% of costs and contributed nearly 70% of population health gain; gum treatment had similar costs but contributed less than 10% of population health gain. Although participants decided to exclude Orthodontics from the frontier analysis for which they had extremely different views, the sensitivity analysis showed that the interventions had relatively low VfM regardless of the view taken.
[x] View Table 28.
Dentistry in Sheffield: VfM for secondary dental care
Figure 24.
Dentistry in Sheffield: efficiency frontier for secondary dental care 550
7
500 450
4
400 350
5 6
3
300 250
Benefits 0-500
200 150 100 50 0
12 0
1
2
3
4
5
Costs (ÂŁmillions) 0-5218448
Examination of system of paying dental practices
At time of accessing Life expectancy Radical treatment No treatment 12. The examination of VfM for dentistry suggested that the this PCT service was paying about 30% of total without treatment 1 year
costs of primary care for scale and polish that accounted for only 0.5% of population health gain. This raises two issues: Ought the NHS to be4paying for this or ought it to be paid for out years of pocket? And what were the incentives of the current system, in terms of VfM, for setting tariffs for Units of Dental Activity (UDAs) for each of its 84 dental practices? Currently, there is wide variation on the tariffs agreed for each practice, as illustrated by Figure 2577.
6
59
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Figure 25. Dentistry in Sheffield: distribution of tariff per UDA across dental practices 40 35 30 25
Number of Practices
20 15 10 5 0 <20
20-24
25-29
3-34
>35
Current Tariff Ranges (ÂŁ)
0
Figure 26. Dentistry in Sheffield: relationship between value scores and actual tariffs per UDA by dental practice 1Max
Current tariffs
R2=0.08355
Min 0
4
8
12
16
Value scores
13. We used the estimates of QALYsD of interventions in primary care to estimate for each practice, based on the distribution of its work by type of intervention, its estimated dental health gain. Figure 26 shows that there is a weak negative correlation between estimated dental health gain and tariffs per UDA by dental practice: i.e. those practices being paid at higher than average tariffs per UDA tend to produce lower than average dental health gain. Hence the current system of setting tariffs, which is based on what practices were paid in the past, does not appear to be designed to generate incentives for VfM78.
20
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14.
W e used decision conferencing to provide a forum to promote a shared understanding of the weaknesses of the current system of payments and a shared vision how this might be reformed. We explored how a new system might be developed so that dental practices would be rewarded for dental health gain. Figure 27 shows how a redesigned system of tariffs might do so. Such a system could be refined to take account of measures of poor dental health and material deprivation by area in Sheffield to generate a system of tariffs that would also reduce inequalities in health.
Figure 27.
Dentistry in Sheffield: relationship between value scores and proposed tariffs per UDA by dental practice 40 35 30
R2=0.8745
25
Proposed tariffs
20 15 10 0 0
4
8
12
16
20
Value scores
15. This Chapter has shown how a quick analysis using approximate data has raised questions about the VfM of primary and secondary dental care. In primary care, we estimated that permanent fillings and extractions together accounted for less than 40% of total costs and contributed about 90% of population health gain; scale and polish had similar costs but contributed only 0.5% of population health gain; and that the system of fees is not designed to create incentives for VfM. In secondary care, we estimated that oral surgery accounted for nearly 40% of costs and contributed nearly 70% of population health gain; gum treatment had similar costs but contributed less than 10% of population health gain. We see great potential for developing this work further for national and local use. The exploration of setting fees to create incentives for VfM has wider applicability. It has potential to inform the next contract for General Medical Practitioners: we know that the current contract resulted in poor VfM79.
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Chapter 7 VfM across five services in the Isle of Wight
1. The three previous Chapters describe how, in collaboration with stakeholders from Sheffield, we developed assessments of VfM that were comparable within each disease area only. This is because stakeholders’ estimates of QoL were all determined with reference to interventions for the same disease: hence it was meaningful to compare, e.g., estimates of value for end of life care for colorectal cancer with health promotion for lung cancer, but not with extractions in dentistry. Working within disease areas is a natural way of enabling those working in health care to move from deciding how to spend ‘growth money’ into the hard choices of reallocation to release resources. But, it will become necessary in future also to consider reallocation of resources between disease areas (e.g. mental health and cancers). The purpose of this Chapter is to show how our methods can be developed to do this.
2. This Chapter describes a collaboration with the IoW PCT across different diseases, in which different sets of stakeholders assessed QoL and relative population health gain for interventions within each disease in a series of decision conferences, and then in the final decision conference stakeholders agreed how to scale these estimates so that interventions could be compared across diseases. This approach was developed in collaboration with the IoW PCT before our collaboration with Sheffield, and was about prioritising ‘growth money’ that required making trade offs across disease areas. The methods we developed and applied can also be used for reallocating resources between disease areas. Given the need to develop measures of value disease areas we used simpler methods of estimating population health gain within each disease area than in Sheffield (see below). Hence sensitivity analyses to examine the robustness of the priority ordering to changes in these estimates were a key part of our evaluation. These analyses showed a consistent set of interventions produced good VfM.
3. This exercise took place in 2008, when the PCT had £1m of growth money to spend. The Joint Strategic Needs Assessment for the IoW identified five disease areas as priorities: i.e. having claims on growth money for improving health or reducing health inequalities or both80: a. cerebrovascular disease (Coronary Heart Disease and Stroke), b. cancers, c. respiratory disease (and long term conditions), d. mental health, and e. children.
4. A series of workshops explored each disease area81. Stakeholders in these workshops included commissioners, clinicians, patients, nurses and general practitioners, and a member of the PCT’s Executive team. These workshops for each disease area, reviewed the progress from the previous year, identified gaps in services and generated a list of potential strategic initiatives to address the gaps82. Following the workshops the lead commissioners agreed a short-list of 21 initiatives. These are described in Box 8 and were estimated to cost £5.6m in total (see Table 29 below); but there was only £1m of growth money. A final decision conference prioritised these initiatives to agree the strategy for choosing the few interventions that could be afforded. The stakeholders in that final decision conference were:
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Russell Ball Commissioning Manager and Lead of Respiratory conditions strategy Workgroup. IoW PCT Gillian Baker Deputy Director of Commissioning and Chair of Mental Health strategy workgroup. IoW PCT Liz Bell Patient and Public Involvement group (PPI) Nancy Ellacott PPI Graham Gent Practice Based Commissioning. Chair and GP representative. Conal Grier Senior Commissioning Manager and Lead of CVD strategy workgroup (Stroke), IoW PCT Terence Hart Director of Human Resources and Organisational Development. IoW PCT Rachel Hayes Senior Commissioning Manager and Lead of Children strategy Workgroup. IoW PCT Robert Jones PPI Liz MacKenzie Non-Executive Director. IoW PCT Sarah Mitchell Director of Community Services. IoW Council Caroline Morris Senior Commissioning Manager (Primary Care) and colead of Respiratory conditions strategy group. IoW PCT Sheila Paul Chief Operating Officer. IoW PCT Margaret Pratt Chief Executive Director. IoW PCT Mark Price Director of Corporate Affairs. IoW PCT Linda Rann Commissioning Manager and Lead of CVD strategy workgroup (CHD). IoW PCT Eleanor Roddick Assistant Director of Commissioning and Chair of Cancer strategy Workgroup. IoW PCT Helen Shields Director of Commissioning. IoW PCT Jenifer Smith Director of Public Health and Chief Medical Advisor. IoW PCT Ann Ticehurst Commissioning Manager and Lead of Cancer strategy workgroup. IoW PCT Rob Tolfree WCC Assurance team. IoW PCT George Thomson Professional Executive Committee (PEC). IoW PCT Steve Ward Senior Commissioning Manager and Lead of Mental Health strategy Workgroup. IoW PCT Gary Warner Community Pharmacist (PEC). IoW PCT Becky Wastall Assistant Director of Finance and Management Accounting. IoW PCT Saloni Zaveri Public Health trainee. IoW PCT
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Box 8. VfM for IoW: descriptions of twenty-one initiatives in five priority areas Service
CVD
Cancers
Respiratory disease
Mental health
Name
Description
Secondary prevention
This includes increases in clinics for Transient Ischaemic Attack, same day high risk review, imaging and diagnostics, vascular network services, “Patients Voices” (i.e. a programme to collect and share stories of patients, carers and professional to inform patients and raise awareness)
Health promotion
Raising awareness and smoking cessation8.
Rehabilitation
Improving cardiac rehabilitation after acute care9.
Stroke emergency
Including ambulances ‘blue lighting’ all stroke patients, initial assessment in A&E, awareness of “FAST” (i.e. that Facial weakness, Arm or leg weakness and Speech problems mean it is Time to contact an ambulance), Diagnostics, imaging and out-of-hours, access to Hyper-acute unit, thrombolysis (and telemedicine).
CHD acute
Includes thrombolysis, Primary PCI and Acute Coronary Syndrome and better transfer to mainland
Early detection
Includes screening (particularly for bowel and increasing uptake for colorectal) and rapid access to diagnostics.
EOL
End of Life (EoL) includes strategy coordination, psychological support, and appointing a second consultant in palliative care.
Repatriation of radiotherapy
Includes improving access to radiotherapy and reducing use of acute beds (in length of stay and admissions).
Pneumonia
Improving the care pathway for frail elderly people presenting with respiratory conditions.
Dementia
Further development of services for dementia.
Prison
Increase input in psychiatry in prisons.
Psychological therapies
Includes increased access to psychological therapies for people with physical illness and long-term care and support for range of therapies (including those provided by the third sector) .
Dementia
Further development of services for dementia.
Prison
Increase input in psychiatry in prisons.
Psychological therapies
Includes increased access to psychological therapies for people with physical illness and long-term care and support for range of therapies (including those provided by the third sector)10.
Social inclusion
Social inclusion and self-directed care includes: implementing a ‘recovery approach’ (a stepped model which outlines a journey to recovery based on the 12-step programme developed by the Alcoholics Anonymous); advocacy service (i.e. support to patients and carers to make informed decisions and to access services); support for carers and representation of users of services; support, coordination and training for third sector; support for those in employment and keeping people in employment.
Alcohol misuse
Includes: developing alcohol services across mental health and general services and support for all services.
Workforce development
Includes: training, partnership working and recruitment.
Obesity Primary Care
Includes about 20% of a new dietician to act as an advisor to Primary Care
School
Includes health promotion and primary care
CAMHS
Child and Adolescent Mental Health (CAMHS): includes counselling in schools on alcohol, risky behaviour and self esteem.
Primary prevention
Includes: health visitor, nursery nurses, school nurses, programmes on advice on parenting, sexual health and reducing teenage pregnancies.
Dental
Increase access to dental through early intervention.
Obesity counselling
Includes 80% of a new dietician and support funding from pilot run by the Rural Community Council (RCC).
Children
footnotes 8,9,10
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5.
The approach we developed was based on
a. assessing for interventions within each disease area the degree of health gain for a typical individual, the impact on health inequalities, and the probability of successful implementation; and b. using MCDA to produce consistent estimates of value across the five disease areas.
This Chapter is organised into sections that describe how we:
6.
a. estimated costs and value across three criteria for cancers; b. put our estimates of values of each criterion on a common scale across the five areas; c. prioritised interventions.
Estimating costs and units of value for cancers across three criteria
7.
Box 9.
Box 9 gives the basis of cost estimates for cancers.
VfM for IoW: cost estimates for cancers Intervention Early detection
Total 2 yr cost (£’000s) 300
Screening
100
Rapid access to diagnostics
200
Repatriation of radiotherapy
50
Access to radiotherapy
50
Support & surveillance11 End of life care
0 760
Acute Psychologist
180
Second Palliative Care Consultant
280
Educational strategy co-ordination
300
8. Stakeholders developed assessments of value using three criteria: population health gain, reduction in inequalities, and probability of success which were estimated as follows.
9. Population health gain was defined as degree of health gain for a typical individual (including value to carers and family where this was deemed relevant and material) and the numbers who benefit. We considered one disease area at a time and one criterion at a time. For each priority area, we followed a systematic process with groups of stakeholders:
a. For each priority area, the commissioning lead described the nature of the interventions to be evaluated with their estimated costs over the next two years; b. For each intervention in that area, the group discussed how many people on the Island were likely to benefit83, agreed a common picture of the ’typical’ patient84 whom the initiative would target and the likely health gain of that ’typical’ patient from the intervention compared to current care.
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10.
We illustrate the second step for cancers, for which there were three interventions: a. Early detection and diagnosis (through improved screening and rapid access to diagnostics), b. Palliative and End of Life (EOL) care (by implementation of the EOL national strategy), c. Repatriation of radiotherapy (from mainland providers to IoW).
11. The discussion began by describing what difference the intervention would make to the health gain (change in trajectories of QoL over time) for the typical patients and their family/ carers (see Box 10). Based on shared views of these typical patients the stakeholders were asked to assume that these three patients were the only people on the Island and to consider the following question: ‘if our only objective was to improve health and quality of life, and we would be able to fund only one of the strategic initiatives in this area (assuming success) which is the one we would fund?’. Stakeholders agreed the answer was Early detection and diagnosis, which was given a health gain score of 100%. Stakeholders agreed that the typical patient receiving the improved Palliative and End of Life (EOL) care had a relative health gain of 75% as compared with Early detection and diagnosis and that for Repatriation of radiotherapy the relative health gain would be 25% (see Box 10).
12. Figure 28 rectangles of population health gains in QALYsC (with the numbers who benefit on the horizontal axis and the average health gain per person on the vertical axis). Table 29 (below) gives the values for interventions for cancers for number expected to benefit, health gain for typical individual, and population health gain (QALYsC) which is the product of the two preceding columns86.
85
Box 10.
VfM for IoW: assessing population health gain for cancers
Intervention
Part A
Part B
Number who benefit
Typical patient
Health gain for typical patient (assuming success)
Health gain score for typical patient
Early detection
200
Mid-60s More likely to be female From “hard to reach” groups in society
Earlier diagnosis will result in improved outcomes.
100
Repatriation of radiotherapy
300
Mid-60s More likely to be female Sick ++
Relocation of treatment to IoW: More convenient Reduced time &money spent on travel & mainland accommodation Less stress from travel
25
EOL
1500
End of life care Late 70s Life limiting long term health condition From across all socioeconomic groups
Benefits to carers, family & friends. Benefits to patient Will not add years to life but will added life to years.
75
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Figure 28. VfM for IoW: population health gain for interventions for cancers
Back to text [h]
120
Early detection and diagnosis
100
Palliative and EOL
80
60
QALYsC / person
40
Repatriation of radiotherapy 20
0 0
200
400
600
800
1000
1200
1400
1600
Number who benefit
13. Reduction of health inequalities was defined in terms of access and health outcomes (across geographical areas, between men and women, and special groups). We illustrate this for cancers for which stakeholders were asked to consider the following question: â&#x20AC;&#x2DC;If our only objective were to reduce health inequalities on the Island and we could provide one and only one of these three initiatives (assuming the initiatives were successful), which one of them would we prefer to provide?â&#x20AC;&#x2122;. Stakeholders agreed this applied to Early detection. They also agreed that Repatriation of radiotherapy would have no effect on reducing health inequalities (see Box 11). Hence we gave Early detection and diagnosis a reference score of 100 Units of Equality (UEs) and Repatriation of radiotherapy a score of 0 UEs. Palliative & EOL care was judged about halfway between these two extremes with 50 UEs (see Box 11).
14. The third criterion of value was probability of success (with 100% meaning certain success and 0% meaning no success) in achieving the estimated population health gain and the reduction in health inequality (if the initiative were funded). The estimate of the probabilities of success aimed to take account of: ease of implementation, availability of workforce, acceptability to stakeholders (e.g. willingness to make this change happen), process complexity (e.g. number of steps required). For cancers probabilities of success of implementation of each policy as agreed in decision conferences were estimated to be 95% for Early detection and diagnosis; 70% for Palliative &EOL care and 10% for Repatriation of radiotherapy (see Box 12).
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Box 11. VfM for IoW: assessing impacts on reduction in health inequalities for cancers Intervention
Describe how this intervention may affect health inequalities on the Island
Score
Early detection
This will have the greatest impact out of the three intervention areas BUT if not targeted His may widen: must be targeted in order to achieve the highest score
100
Repatriation of radiotherapy
Benefits over next 2 years likely to be small
EOL care
Benefits across all socio-economic groups therefore many people will benefit. Needs targeting for maximum impact on health inequalities.
0 50
Box 12. VfM for IoW: assessing probabilities of success of interventions for cancers Intervention
Thinking about what could go wrong in implementing this intervention
Early detection
High probability of success and high confidence that intervention can be implemented.
95
Repatriation of radiotherapy
Much difficulty anticipated in implementing.
10
EOL care
Recruitment issues likely and would need cultural & behavioural shift within NHS workforce for this to be successfully implemented.
70
Probability score
15. Table 29 gives estimates of costs and value for interventions within each priority area. The estimates of costs and of probabilities of success are comparable across areas; but the estimates of population health gain and reductions in health inequalities are not (they are comparable within each area only). The next section describes how we used MCDA to develop a common unit of value to compare interventions across areas for population health gain and reductions in health inequalities.
[x] View Table 29.
VfM for IoW: estimates of costs and value for interventions within each priority area
Estimating common values of value across the five areas for the three criteria
16.
We derived estimated value on a common scale in three steps.
17. First, we â&#x20AC;&#x2DC;normalisedâ&#x20AC;&#x2122; scores on each criterion of value for each area, from zero to 100 for the total cumulative improvement from all projects being fully successful. This is illustrated for cancers in Table 30 for the criterion of population health gain: the second column gives the cumulative sum of the scores (QALYsC), the third column gives the normalised scores.
[x] View Table 30.
VfM for IoW: from QALYsC to normalized scores for cancers
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18. Second, we estimated relative weights for each priority area to reflect its importance (withincriterion weights)87. The group began the weighting process by considering population health gain and were asked, â&#x20AC;&#x2DC;If you only cared about population health gain and you could implement all the interventions in one of the five areas (Cancer, CVD, Respiratory, Mental Health and Children), which area would you choose?â&#x20AC;&#x2122;. Stakeholders agreed on CVD, which was given a weight of 100. They agreed that after CVD, Mental Health and Children were the next highest priorities and of equal importance. The swing in preference from doing nothing to doing all the projects listed was compared between two areas for population health gain. Stakeholders agreed that: going from doing nothing to doing all the interventions in mental health and children was worth 90% of going from doing nothing to doing all the interventions for CVD.
19. Thus, the weights for the preference scales are in the ratio of 90 (for Mental Health and Children) to 100 (for CVD). This is illustrated by Figure 29 for CVD and Mental Health. Continuing this process gave weights for Cancers of 80%, and Respiratory disease of 40%. These weights were checked for consistency and revised. The process was then repeated for the remaining criteria. The results for the within-criterion weights are given for population health gain and health inequality in the top five rows of Table 31.
[x] View Table 31.
Figure 29.
VfM for IoW: weights used in modelling
20. Third, we weighted the relative importance of the two criteria of population health gain and reductions in health inequalities (across-criteria weighting). Stakeholders compared the value of 100 (achieved by all interventions in the CVD area) in population health gain with reducing health inequalities. The group judged these to be of equal importance had no preference between the two and the weights were set to 50 for both criteria (which is given in the bottom row of Table 31).
VfM for IoW: illustrating swing weights for mental health and CVD Population
Population
100
100
This swing in preference
...of this swing in preference
Mental health
CVD
0
0
Weights: 90
Weights: 100
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Figure 30. VfM for IoW: with value based on population health gain, reduction of health inequalities and probability of successful implementation
Inequalities
Value Score
VfM
Population health gain
Probability of successful implementation
Costs
Numbers who benefit
21. Figure 30 illustrates how the various criteria of value and costs are combined to produce VfM triangle using MCDA for value based on the three criteria (population health gain, reduction of health inequalities and probability of successful implementation). For each intervention, on the vertical axis of value, the mean value of normalised scores on population health gain and reducing health inequalities was derived (as these criteria had equal weight), and that mean value was Early detection and diagnosis multiplied by the probability of success. This gave for each intervention its expected value on a common scale (based on population health gain, reduction in health inequalities and probability Palliative and EOL of success). This is illustrated for cancers by Table 3288. Thus for Early detection and diagnosis:
a. The total population health gain from all interventions in cancers was 140,000 QALYsC (with a total normalised score of 100) so the normalised score (20,000 QALYsC for Early detection) was 1489. Repatriation of radiotherapy
b. The total population health gain from all interventions in cancers was weighted at 80% of the total population health gain from all interventions in CVD; so the value of the weighted normalised score for population health gain for Early detection and diagnosis was 1190.
c. The total score for the reduction in health inequalities from all interventions in cancers was 150, so the normalised score for Early detection (100) was 6791. The score for reducing health inequalities from all interventions in cancers was weighted at 50% of score from all interventions in CVD, so the value of the weighted normalised score for reduction in health inequalities for Early detection (67) was 3392.
d. The scores for population health gain (11) and reduction in health inequalities (33) were equally weighted at 0.5 giving a total of 2293. This was then weighted by the probability of success (95%) to give an expected weighted value (EWV) of 2194 (this assumes that failure produces no value).
e. The VfM is the ratio of EWV (21) to costs (ÂŁ300,000) and is 0.07 EWBVs per ÂŁ1,000.
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[x] View Table 32.
VfM for IoW: weighted scores for cancers based on MCDA
22. The 21 interventions evaluated in the final decision conference are given and described in Box 8. Estimates for costs and value (number expected to benefit, health gain for typical individual, population health gain, reduction of health inequalities, and probability of success) for the 21 interventions are illustrated by:
a. Figure 31, which gives estimated weighted normalised score for population health gain;
b. Figure 32, which gives estimated weighted normalised scores for the reduction of health inequalities; and
c. Figure 33, which gives estimated probabilities of success.
Initiatives
Figure 31. VfM for IoW: estimated weighted normalised scores for population health gain for the twenty-one initiatives 86 85
Prevention Palliative and EOL Punemonia (frail elderly) Dementia Services Alcohol misuse svc Primary prevention TIA and secondary prevention Access to dental CAMHS School Early detect diagnosis Obesity training Workforce development Prison MH Psych therapies Obesity 1:1 Social inclusion Stroke med emerg CHD acute Repatriation of radiotherapy CAMHS 1:1 Cardiac Rehab
53 49 34 32 25 23 21 15 14 14 13 12 11 11 11 7 6 3 1 0
10
20
30
40
50
60
70
80
90
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Initiatives
Figure 32. VfM for IoW: estimated weighted normalised scores for impact on reducing health inequalities for the twenty-one initiatives Pneumonia (frail eld) Prevention Early detect diagn TIA & 2ndary prev Prison MH Social inclusion Stroke med emerg Psych therapies Primary prevention Palliative & EOL Access to dental Alcohol misuse svc Workforce developm Cardiac Rehab CAMHS 1:1 CAMHS School Obesity 1:1 Obesity training Dementia services CHD acute Repatriation of radiotherapy
79 52 44 39 32 31 26 23 22 22 16 16 16 13 11 11 9 6 3 3 â&#x20AC;&#x201C; 0
10
20
30
40
50
Initiatives
Figure 33. VfM for IoW: estimated scores for probability of success for the twenty-one initiatives
Dementia services Prison MH None of above Early detect diagn Workforce developm Stroke med emerg Cardiac Rehab pneumonia(frail eld) Social inclusion CAMHS 1:1 Psych therapies Obesity training TIA & 2ndary prev CHD acute Palliative & EOL CAMHS School Primary prevention Access to dental Prevention Alcohol misuse svc Obesity 1:1 Repatriation of radiotherapy 0
10
20
30
40
50
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Prioritising interventions
23. Three scenarios were examined to give expected value (population health gains and reduction in inequalities) using different values for the probabilities of complete success and gains from the alternative. This was done by assuming that each intervention:
a. would achieve complete success (with probability of 100%); b. would achieve complete success with 80% probability and the alternative (with 20% probability) would produce 50% of the population health gain of complete success ; and c. would achieve complete success with 80% probability and the alternative (with 20% probability) would produce zero population health gain (0).
24. Rankings based on VfM across the three scenarios identified a consistent set of six initiatives with good and poor VfM (although there were slight variations in the ordering). The top six produced a value score of about 300 points at a cost of £600,000 over two years. Rankings based on Value only across the three scenarios identified a consistent set of ten initiatives with high and six with poor population health gain (although there were slight variations in the ordering). Box 12 gives the initiatives identified as having good and poor VfM and Value. 25. Tables 33 and 34 give the twenty-one interventions ranked by VfM and value. Figure 34 gives the efficiency frontier based on VfM and a value frontier in which the interventions are ranked in terms of the scale of value only. Figure 34 assumes scenario 7c: that if the initiative were funded but not successful, it would deliver only 50% of the value. Figure 35 summarises the process used to generate the VfM and the value frontier. Thus if IoW had £1m to spend over the next two years, using the rankings by:
a. VfM, IoW would fund eight interventions, Pneumonia (for the frail elderly), Dementia services, TIA & Secondary Prevention, Prison MH, Obesity training, Workforce develop, Psychological Therapies, and Early Detection and Diagnosis; and b. Value only would identify two interventions only, Pneumonia (for the frail elderly) and prevention (health promotion initiatives, including smoking cessation).
Figure 34.
VfM for IoW: Twenty-one interventions ranked by VfM and value 1000 900 800
VfM ordering
700 600
Cumulative benefit score
500
Total benefit ordering
400 300
Lower bound
200 100 0 0
£1,000
£2,000
£3,000
£5,000
£6,000
Cost in £ ‘000
2
Life expectancy
At time of accessing
Radical treatment
£7,000
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Figure 35.
Process flowchart to generate VfM and value frontiers Scoring interventions
on criteria: Population health gains (rectangles) Reduction of health inequalities Probability of successful implementation
Weighting Critera
Within criterion weights Across criterion weights
Value of interventions
Cost of interventions
Weighted scores
VfM Triangles
VfM Frontier frontier
Ranking interventions according to the slope of the VfM triangles
Value frontier
Ranked interventions from high to lowest value
6. The PCT Board decided to fund the seven interventions by VfM (a) and found Figure 34 was particularly helpful in enabling them to visualize and understand the implications of its funding decisions. This showed that is, with the same amount of resources, they could deliver about 25% more value by setting priorities in terms of VfM than Value alone. The Board considered the socio-technical approach discussed here an appropriate process to inform and justify this difficult decision.
Box 13.
VfM for IoW: Interventions identified as having good and poor VfM and Value
Criterion
VfM
Value only
Good
Poor
Pneumonia
CHD acute
Dementia
Stroke emergency
Secondary prevention (CVD)
Repatriation of radiotherapy (Cancers)
Obesity Primary Care
Dental
Prison
Primary prevention (Children)
Psychological therapies
Obesity counselling
Pneumonia
Repatriation of radiotherapy (Cancers)
EOL
CHD acute
Secondary prevention (CVD)
Obesity counselling
Early detection (Cancers)
CAMHS
Dementia
Rehabilitation (CVD)
Prison
Obesity training
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Table 33 gives estimates of costs and value for interventions within each priority area
[x] View Table 33.
VfM for IoW: twenty-one interventions ranked by VfM
[x] View Table 34.
VfM for IoW: twenty-one interventions ranked by value
27. The approach we developed in collaboration with IoW used much simpler ways of estimating QALY gains within each area than the later collaboration with Sheffield, but was also more complex as it entailed weighting value across for different disease areas using multiple criteria. The stakeholders valued this innovative and challenging way of exploring and deciding priorities and the PCT board were so confident in the outcomes that this process was used to set their strategic priorities.
28. This Chapter has illustrated how our approach can be used to assess VfM of interventions across different disease areas. In moving from setting priorities for â&#x20AC;&#x2DC;growth moneyâ&#x20AC;&#x2122; to making saving, we would see assessing VfM of interventions across different disease areas being developed after having worked to make savings within a number of disease areas (as in Sheffield).
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Chapter 8 Discussion
1. This Chapter aims to pull together what we see as implications for commissioning health services to improve VFM in hard times from the various strands of our R&D in the development of a socio-technical process. In this concluding Chapter we begin by looking back at what we see as the main points of that development and then move on to consider the costs and advantages of the SyMPOSE approach for commissioners of health services seeking to achieve better VfM for their populations in hard times.
Looking back
2. Our initial focus was on development of technical methods using conventional sources of data. Deriving estimates of population health gains required conceptual development, judgements over the degree of detail in modelling, and assumptions over missing or inadequate data. We developed a series of models to estimate the impacts of different options for prevention or treatment on population health gain. This new information on scale suggests a different priority ordering than the standard methods of marginal analysis (of costs/QALY).
3. These analyses of VfM for the treatment and prevention of Stroke estimated that prevention by reducing dietary intake of salt and improving control of blood pressure in primary care would both produce the greatest population health gains and savings (to the NHS and society). The evaluations of selected interventions in a number of disease areas also estimated that preventing strokes in those ways and improving the coverage of treatment of depression would both produce the greatest population health gains. Analysis of these results for England showed that these results could easily be scaled for local use and there was no need to apply model at the level of each PCT.
4. The more fundamental point is, however, that information on the scale of the impacts of interventions is critically important for making savings in some areas to generate resources to develop others. This is because, as such reallocations are so difficult to implement, managers need to focus such strategic shifts on interventions that can release large savings with little sacrifice in population health gain, to finance the development of interventions with large population health gains.
5. The next phase of R&D was collaborative research with commissioners seeking to improve VfM for their populations. This built on our methodological work in estimating population health gain as this information is fundamental to assessing VfM for populations. But the emphasis of this R&D was on developing the social process of engaging key stakeholders. To enable this social process, we developed visual displays of rectangles of population health gain, VfM triangles and efficiency frontiers. These have enabled stakeholders to participate in generating missing data to assess value, to contribute to the shaping of priorities and to understand and justify the choices that have to be made for strategic changes.
6. Our accounts of the collaboration with PCTs have emphasised the crucial role that stakeholders have played and we have been explicit about the constraints that always apply to the decisions PCTs have to make: on the time spent by stakeholders in meetings and on the data that are and are not available. Our contribution has been one of providing tools for stakeholders to use in collecting and generating data and translating these into
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information for priority setting through a systematic, transparent and auditable process.
7. Our collaborative R&D began in the IoW and enabled stakeholders to develop a strategic overview of the VfM of 21 interventions across five disease areas and agree on how to prioritise spend of ‘growth money’. The estimates of value were based on three criteria: population health gain, reducing inequalities in health, and the probabilities of successful implementation. Subjecting the results to sensitivity analysis suggested that our methods did produce requisite models in terms of the adequacy of the results in deciding which interventions ought to be prioritised95. The PCT based their strategy on that analysis.
8. Our collaborative R&D in Sheffield enabled stakeholders to develop a strategic overview within three disease areas: to identify whether there was or was not scope to reallocate resources to improve population health gain, and if so, what the nature of that reallocation ought to be. In outline the outcomes for the three disease areas were as follows.
a. Eating disorders: we estimated that intensive care for the acutely ill accounted for 70% of total costs but produced poor VfM. Stakeholders agreed to expand the scale of the earlier interventions on the care pathway, that were estimated to produce high VfM, believing that this would reduce the need for care for the acutely ill. This strategy has been implemented and appears to have worked.
b. Cancers: structuring options in broad categories was necessary to examine breast, colorectal and lung cancers in the time that was available but using these categories we were unable to found no similar scope for strategic shifts of resources.
c. Dental care: in primary care, we estimated that permanent fillings and extractions together accounted for less than 40% of total costs and contributed about 90% of population health gain; scale and polish had similar costs but contributed only 0.5% of population health gain; and that the system of fees is not designed to create incentives for VfM. In secondary care, we estimated that oral surgery accounted for nearly 40% of costs and contributed nearly 70% of population health gain; gum treatment had similar costs but contributed less than 10% of population health gain.
9. The collaboration with Sheffield was subjected to an external evaluation by David Collier of Golder Associates (Annex 3). He considered the following questions:
10. His summary judgement was that the answers to all these questions were positive although there were difficulties caused by the exercise being rushed and the problems of assembling basic information. Its success was due to the ability of key members of their project teams (Mara Airoldi, Nikos Argyris, and Alec Morton) and the willingness of those involved to give
• • • • • • • • • • •
as the PCT’s selection of the LSE approach sensibly managed? W Was the initial scoping process appropriate and well managed? Was the basic approach appropriate? Were the events well organised and run? Were the scope and methods used in the workshops appropriate? Was the scoring and weighting process implemented effectively? Was the evidence base assembled appropriately? Did the process provide for stakeholder engagement? Was the event reporting satisfactory? Did the process integrate with the wider decision context? Was the process followed-up?
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the process a chanceâ&#x20AC;&#x2122;. David Collier is expert in decision conferencing in areas outside the NHS, especially environmental issues. He noted the abundance of data routinely available in the NHS but was surprised that much of the data collected is not what is needed to inform decisions.
Looking forward
11. We see great potential for developing this work further for national and local use. The development of methods to assess population health gain for England that showed value from preventing strokes have implications for national and local policies: reducing the salt content in food and better control of blood pressure in primary care. The approach used to analyse the system of paying General Dental Practitioners in Sheffield could be developed to inform the next contract for General Medical Practitioners.
12. Our development of decision conferencing is of generic applicability as shown by the analyses in the earlier chapters: the methods of assessing the health gain of interventions can be based on detailed estimates of changes in QoL over time (as in Sheffield) or broad assessments (as in IoW); the assessments of value can be based on a single criterion of population health gain (as in Sheffield), or multiple criteria (as in IoW). Our approach has proved useful for IoW PCT, which is an integrated organisation, in deciding how to prioritise â&#x20AC;&#x2DC;growth moneyâ&#x20AC;&#x2122;; and Sheffield PCT, which contracts with providers, in deciding how to make savings with least harm. In developing our approach we have also worked with Lambeth PCT, on health promotion; the Ministry of Health and Long Term Care of Ontario, on primary care; and have with MeSLAB of the Scuola Superiore di Sant Anna, who have worked with two districts of the region of Tuscany, on heart failure. In looking forward to how health services can do more with less, we begin with three observations based on our R&D.
13. First, systems of health care are not organised to produce the data required for assessing VfM for populations. The most serious obstacle is the lack of data on value, and in particular the absence of the consistent data required to estimate health gains of patients from prevention and treatment. In developing models of diseases we experienced difficulties in relating the findings from clinical evaluations are to epidemiological information . For the decisions commissioner have to make in setting priorities the data that are routinely-collected are organised to produce information neither on the numbers of patients treated , nor their health gains , nor costs . We see the best way of generating missing data on the value produced by health care is by involving patients, carers and the general public developing estimates of health gain in the ways we have described in this report.
14. Second, we began our research programme by developing quite detailed models of various diseases with some trepidation, as there are experts who spend their whole careers in developing models much more complex than ours. We discovered, however, that for comparing interventions in terms of population health gain our models were requisite in that their findings were robust when subjected to sensitivity analyses. This gave us confidence to use decision conferences to elicit from providers and users of services their assessments of individual health gain for priority setting in the Isle of Wight, where again the chosen priorities were robust to sensitivity analysis. The analyses of the relative scale of costs and value along care pathways in Sheffield were again robust in identifying which interventions account for most of the costs and produce most value and made transparent to all stakeholders through the rectangles of population health gain and VfM triangles.
15. Third, we see enormous advantages in involving stakeholders in examining where services ought to expand and contract as they can give information that reflects and makes explicit the distinctive features of the locality. An advantage of including representatives of all providers who contribute to a care pathway is that they meet each other and can understand the nature
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of their different contributions. Some of our decision conferences were the first time that the different providers had all been in one room at the same time. Involving patients, carers and the public creates the appropriate forum for making hard choices and hence provides a good foundation for public consultation. This is because this involvement produces good understanding of the concerns of patients, carers and the general public and means that the proposals have involved these stakeholders. We believe that it is better to involve stakeholders in the development of options rather than exclude them from that process and rely instead on public consultation after strategies have been decided by those working within the NHS .
9. We conclude by considering the questions of the costs of our approach as compared with alternatives and how our approach might be developed. There are three fundamental characteristics of the approach that we have developed. First, it is based on the organising concept of VfM with value defined in terms of populations. Second, it requires requisite models to generate the necessary data (that are good enough for strategic decisions) on costs and value on a consistent basis. Third, it requires an impartial facilitator who is skilled in using models to relate estimates generated by decision conferencing to priorities, and subjecting these estimates to sensitivity analysis. This is because the stakeholders can then see that the generating of data in developing the various estimates is guided by an individual who is disinterested in the outcomes in terms of priorities.
16. Both PCTs found our approach difficult to implement because of problems with the data required. But what is the alternative? We do not see how commissioners could seek to involve patients, carers and the public in setting priorities without data on the value of health care. Any such exercise would be seen as mockery of a due process of involving stakeholders. We see the most promising way of producing these data is, as we have described in this report, in engaging clinicians with patients, carers and the public to agree on the relative value of different interventions. This also creates a framework which, because it necessitates strong clinical input from across the health system will, more likely, secure concordance and compliance from the professionals who, in practice, commit those resources. Thus our approach by focusing on estimating value both requires and is designed to enable engagement of stakeholders. So it seems to us, that the alternative to our approach is to set priorities without systematic assessments of value or by excluding clinicians patients, carers and the public.
17. Most of the costs of the process we have developed are on the organisation and generation of data to be able to estimate VfM for populations. So, if better data were available, our approach would have low costs. Exploratory work we have done suggests we could reduce the costs of our approach by producing estimates of population health gain for England and scaling these to local population: such estimates would be good enough for strategic decisions. We believe that the benefits of a participative process in both generating data on value, and understanding different stakeholdersâ&#x20AC;&#x2122; views on priorities far outweigh its costs. It enables patients and the public understand the hard choices have to be made when the answer is not more resources. Hence we see our approach as excellent way for commissioners to develop ownership of the best way to improve health and health services for the population they serve.
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End notes [h] 1. Appleby J. (2011) What’s happening to NHS spending across the UK? BMJ 342: d2982 [h] 2. House of Commons Health Committee (2010) Second Report of Session 2010–11, Public Expenditure. HC 512. London: The Stationery Office. http://www.publications.parliament.uk/pa/cm201011/cmselect/cmhealth/512/512.pdf
[h] 3. See Department of Health (2007) World Class Commissioning: Vision Summary. London, Department of Health. Department of Health (2008) Commissioning Assurance Handbook. London, Department of Health.
[h] 4. or at least trying to prevent or slow down the way in which these have widened [h] 5. Stakeholders include those affected by or, involved in, decisions to change health care: patients, their carers, the public, and those working in hospitals, community health services, primary care, the voluntary and independent sectors, and local government.
[h] 6. Airoldi, M., Bevan, G., Morton, A., Oliveira, M., Smith, J. (2008) Requisite models for strategic commissioning: the example of type 1 diabetes. Healthcare Management Science, 11: pp 89-100. Airoldi, M., Bevan, G., Morton, A., Oliveira, M., Smith, J. (2008). Estimating the health gains and cost impact of selected interventions to reduce stroke mortality and morbidity in England. QQUIP report, The Health Foundation, London, UK. Morton, A., Airoldi,M., Bevan, G., Oliveira, M., Smith, J. (2006). Estimating health gains in England from the National Suicide Prevention Strategy. QQUIP report, The Health Foundation, London, UK. Morton, A., Airoldi,M., Bevan, G., Oliveira, M., Smith, J. (2008). Estimating the health gains and cost impact of treatment for depression in England. QQUIP report, The Health Foundation, London, UK. Oliveira, M., Bevan, G., Airoldi, M., Morton, A. Smith, A. (2006) Estimating health and productivity gains from improving prescribing statins to lower the burden of Coronary Heart Disease, QQUIP report, The Health Foundation , London, UK. Oliveira, M., Bevan, G., Airoldi, M., Morton, A. Smith, A. (2010) Estimating the impacts on health gains and costs from improving diagnosis and treatment of Heart Failure in England, QQUIP report, The Health Foundation, London, UK.
[h] 7. Each is based on an individual’s health profile in terms of mortality and morbidity, uses a Quality-of-Life (QoL) scale as a common denominator, and combines the morbidity and mortality components in a single measure. It is assumed that throughout time an individual’s health can vary within a quality of life continuum anchored between the states ‘death’ and ‘perfect health’. The two anchor states are associated with a weight of zero and one for QALYs; and one and zero for DALYs (see below). The weights for all intermediate health states assume unique values within this range. The calculation of QALYs and DALYs involves multiplying the weights for each health state by the time spent in the state.
[h] 8. Airoldi and Morton (2008) Adjusting life for quality or disability: stylistic difference or substantial dispute? Health Economics, 18(11): 1237–1247.
[h] 9. But the conventional method used to estimate DALYs uses residual life expectancy at deaths using life tables; this avoids a problem when using a fixed reference point (e.g. age 75) of there being assumed to be no BoD when the age of death is higher than the fixed reference point (e.g. age 77). Airoldi and Morton (2008) also showed, however, that using life tables to estimate the BoD to estimate the impacts of interventions in reducing the BoD is also problematic as this produces bizarre results; and that both problems can be avoided by using high reference age beyond normal life expectancy (e.g., 100 years).
[h] 10. See Broome J (1994) Discounting the Future, Philosophy and Public Affairs 23(2): 128–156 and Stern N (2008) The Economics of Climate Change, American Economic Review 98(2): 1-37.
[h] 11. Airoldi, M., Bevan, G., Morton, A., Oliveira, M., Smith, J. (2008) Requisite models for strategic commissioning: the example of type 1 diabetes. Healthcare Management Science, 11: pp 89-100. Airoldi, M., Bevan, G., Morton, A., Oliveira, M., Smith, J. (2008). Estimating the health gains and cost impact of selected interventions to
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reduce stroke mortality and morbidity in England. QQUIP report, The Health Foundation, London, UK. Morton, A., Airoldi,M., Bevan, G., Oliveira, M., Smith, J. (2006). Estimating health gains in England from the National Suicide Prevention Strategy. QQUIP report, The Health Foundation, London, UK. Morton, A., Airoldi,M., Bevan, G., Oliveira, M., Smith, J. (2008). Estimating the health gains and cost impact of treatment for depression in England. QQUIP report, The Health Foundation, London, UK. Oliveira, M., Bevan, G., Airoldi, M., Morton, A. Smith, A. (2006) Estimating health and productivity gains from improving prescribing statins to lower the burden of Coronary Heart Disease, QQUIP report, The Health Foundation , London, UK. Oliveira, M., Bevan, G., Airoldi, M., Morton, A. Smith, A. (2010) Estimating the impacts on health gains and costs from improving diagnosis and treatment of Heart Failure in England, QQUIP report, The Health Foundation, London, UK.
[h] 12. See Keeney, R. L. and H. Raiffa (1976). Decisions With Multiple Objectives: Preferences and Value Tradeoffs. New York, John Wiley; “Decisions involving multiple objectives” (Chapter 2) and “Resource allocation and negotiation problems” (Chapter 12) in Goodwin, P. and G. Wright (1998). Decision Analysis for Management Judgment, 2nd edition. Chichester, John Wiley.
[h] 13. See Phillips, L. D. (2007). Decision Conferencing. Chapter 19 in W. Edwards, R.F. Miles Jr, D. von Winterfeldt (Eds) Advances in Decision Analysis. From Foundations to Applications. New York: Cambridge University Press; p375-99
[h] 14. See Phillips, L. D. (1984). A theory of requisite decision models. Acta Psychologica 56: 29-48. [h] 15. Cochrane AL (1972) Effectiveness and efficiency: random reflections on health services. London Nuffield Provincial Hospitals Trust.
[h] 16. This is a complex issue see e.g., Peppercorn JM, Weeks JC, Cook EF, Joffe S (2004) Comparison of outcomes in cancer patients treated within and outside clinical trials: conceptual framework and structured review. Lancet, 363(9405): 263-70.
[h] 17. Nord E (1992 ) Methods for quality adjustment of life years. Social Science and Medicine 34: 559-69 [h] 18. Finkler SA, Ward DM, Baker JJ (2007) Essentials of cost accounting for health care organizations. Sudbury: Jones and Bartlett.
[h] 19. Kuhn also emphasised the iterative nature of development between theory and measurement in the natural sciences. See Kuhn TS (1961 ) The function of measurement in modern physical science. Isis, 52(2): 161-171 .
[h] 20. Such as the crucial distinction between ischaemic and hemorrhagic strokes which is not made in the data routinely collected for 70% of strokes in GPs’ registers, see Hippisley-Cox, J., M. Pringle, and R. Ryan (2004) Stroke: prevalence, incidence and care in general practices 2002 to 2004. Final report to the National Stroke Audit team, Royal College of Physicians. QResearch.
[h] 21. Caused by insufficient flow of blood to the brain: most by atherosclerotic plaques in the arteries of the brain and some by a blood clot generated inside the heart or in an artery in the body that breaks off and travels to the brain causing the blockage.
[h] 22. Caused by the rupture of a brain artery, causing blood to flow in the brain and damaging its cells with high blood pressure being the main risk factor.
[h] 23. Incidence from http://www.strokecenter.org/education/ais_stroke_types/stroke_types.htm (last accessed 24.01.2008)
[h] 24. In Table 1, treatment in a Stroke Unit and from Thrombolysis does not prevent stroke [h] 25. We reviewed the literature on QoL weights for stroke patients. In the base model, we used values from Dorman, P., M. Dennis, et al. (2000), “Are the modified “simple questions” a valid and reliable measure of health related quality of life after stroke?” Journal of Neurology, Neurosurgery and Psychiatry 69: 487-493. We tested the
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robustness of the model results to this assumption through sensitivity analysis by using different sets of weights derived from the literature.
[h] 26. In the base model we made the conservative assumption that the key outcome for stroke is to recover independence and use the average QoL weight of 0.38 both for severe and moderate impairments. In sensitivity analysis we tested the robustness of the model assuming different sets of weights depending of the degree of the impairment.
[h] 27. Our analysis makes the vitally important distinction between whether patients survive or die; but the conventional way of reporting results from trials of Thrombolysis does not (with some notable exception, e.g. Hacke, W., M. Kaste, et al. (2008). Thrombolysis with Alteplase 3 to 4.5 Hours after Acute Ischemic Stroke. The New England Journal of Medicine 359(13): 1317-29.). Instead patients who die and survive, but are dependent, are grouped together as having ‘poor functional outcomes’. This grouping makes the extraordinary assumption that patients who survived a stroke, required some help to look after their own affairs, but were still able to walk without assistance, might as well have been dead.
[h] 28. We explained the reasons for this in Section 2. [h] 29. Furthermore these savings were an underestimate as they excluded what can be the considerable social costs of informal care.
[h] 30. Airoldi, M., Bevan, G., Morton, A., Oliveira, M., Smith, J. (2008) Requisite models for strategic commissioning: the example of type 1 diabetes. Healthcare Management Science, 11: pp 89-100. Airoldi, M., Bevan, G., Morton, A., Oliveira, M., Smith, J. (2008). Estimating the health gains and cost impact of selected interventions to reduce stroke mortality and morbidity in England. QQUIP report, The Health Foundation, London, UK. Morton, A., Airoldi,M., Bevan, G., Oliveira, M., Smith, J. (2006). Estimating health gains in England from the National Suicide Prevention Strategy. QQUIP report, The Health Foundation, London, UK. Morton, A., Airoldi,M., Bevan, G., Oliveira, M., Smith, J. (2008). Estimating the health gains and cost impact of treatment for depression in England. QQUIP report, The Health Foundation, London, UK. Oliveira, M., Bevan, G., Airoldi, M., Morton, A. Smith, A. (2006) Estimating health and productivity gains from improving prescribing statins to lower the burden of Coronary Heart Disease, QQUIP report, The Health Foundation , London, UK. Oliveira, M., Bevan, G., Airoldi, M., Morton, A. Smith, A. (2010) Estimating the impacts on health gains and costs from improving diagnosis and treatment of Heart Failure in England, QQUIP report, The Health Foundation, London, UK.
[h] 31. Office for National Statistics (2001) Mortality statistics. Cause. Series DH2, no.28. Office for National Statistics (2002) Mortality statistics. Cause. Series DH2, no.29. Office for National Statistics. (2003). Mortality statistics. Cause. Series DH2, no.30. Office for National Statistics (2004) Mortality statistics. Cause. Series DH2, no.31. Office for National Statistics (2005) Mortality statistics. Cause. Series DH2, no.32.
[h] 32. Adamson, J., A. Beswick, et al. (2004). Is Stroke the Most Common Cause of Disability? Journal of Stroke and Cerebrovascular Diseases 13(4): 171-177. National Audit Office (2005) Reducing brain Damage: Faster access to better stroke care. HC 452 Session 2005-2006. London: The Stationery Office.
[h] 33. Leatherman, S. and K. Sutherland (2005). The Quest for Quality in the NHS. A chartbook on quality of care in the UK. London: The Nuffield Trust.
[h] 34. Secretary of State for Health (1999) National service framework for mental health: modern standards and service models. London: Department of Health, p. 78. <http://www.dh.gov.uk/en/Publicationsandstatistics/ Publications/PublicationsPolicyAndGuidance/DH_4009598>
[h] 35. Hawton, K. (1998). A national target for reducing suicide. BMJ, 317: 156-157. [h] 36. Singleton, N., Bumpstead, R., O’Brien, M., Lee, A., Meitzer, H. (2001) Psychiatric morbidity among adults living in private households. London, HMSO.
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[h] 37. Hollinghurst, S., G. Bevan, et al. (2000). Estimating the “avoidable” burden of disease by Disability Adjusted Life Years (DALYs). Health Care Management Science 3(1): 9–21.
[h] 38. Petersen, S., V. Peto, Scarborough, P. and Rayner, M. (2005). Coronary heart disease statistics. British Health Foundation, London.
[h] 39. Department of Health (1999) Saving Lives: Our Healthier Nation. London: Department of Health. Department of Health (2000). National Service Framework for Coronary Heart Disease -Modern Standard and Service Models. London: Department of Health.
[h] 40. Leatherman S, Sutherland K. (2005) The quest for quality in the NHS: A chartbook on quality of care in the UK. Oxford: Radcliffe Publishing. p 20.
[h] 41. The United States had marginally higher rates, but the UK rates were much higher than in Sweden, Germany, Australia and France; compared to France, UK rates were four and three times higher for males and females.
[h] 42. For example, a long-term change of 0.6mmol/l concentration among middle-aged men corresponds to a coronary risk change of at least 25 per cent and hence has the potential to decrease mortality and morbidity from CHD by 30 per cent. See Beaglehole, R. and A. Dobson (2005). Contributions to change: Major risk prevention factors and the potential for prevention. Coronary Heart Disease Epidemiology. In From Aetiology to Public Health. M. Marmot and P. Elliot. Oxford: Oxford University Press, pp. 174-186.
[h] 43. Leatherman S, Sutherland K. (2005) The quest for quality in the NHS: A chartbook on quality of care in the UK. Oxford: Radcliffe Publishing, p 32.
[h] 44. A DDD is the average maintenance dose per day for a drug’s main indication in adults. [h] 45. Majeed, A., K. Moser, et al. (2000) Age, sex and practice variations in the use of statins in general practice in England and Wales. Journal of Public Health Medicine 22(3): 275-279.
[h] 46. HealthCare Commission (2005) National Service Framework Report: Getting to the heart of it, Coronary heart disease in England: A review of progress towards national standards (Summary report). London: Commission for Healthcare Audit and Inspection. Although strategic health authorities with the highest levels of CHD tend to have the highest prescribing rates as those with low prescribing rates have not had the greatest increases, there has been little progress in reducing variations in these rates. See Boyle R.(2004) Meeting the challenge of cardiovascular care in the new National Health Service. Heart 90(iv)3.
[h] 47. Ward, S., L. Jones, et al. (2005). Technology assessment report commissioned by the HTA Programme on behalf of The National Institute for Clinical Excellence. Statins for the Prevention of Coronary Events. London: National Institute for Clinical Excellence.
[h] 48. Department of Health (2005) Healthcare output and productivity: Accounting for quality change. London, Department of Health. Department of Health (2005). Measurement of Healthcare Output and Productivity Use of Statins and Calculation of Value Weight. Technical Paper Accounting for Quality Change. London, Department of Health.
[h] 49. Sutherland, K., E. R. Brody, et al. (2007). Quest for Quality and Improved Performance: Quality Enhancing Interventions -Healthcare delivery models for heart failure. London, Health Foundation UK.
[h] 50. Hobbs, F. D. R., J. E. Kenkre, et al. (2002). Impact of Heart failure and left ventricular systolic dysfunction on quality of life. European Heart Journal 23: 1867-1876.
[h] 51. Cowie, M. R., A. Mosterd, et al. (1997). The epidemiology of heart failure. European Heart Journal 18: 208-225.
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[h] 52. Department of Health (2003) Improvement, Expansion and Reform: the next 3 years, Planning and Priorities Framework 2003-2006. London: Department of Health.
[h] 53. Leatherman S, Sutherland K. (2005) The quest for quality in the NHS: A chartbook on quality of care in the UK. Oxford: Radcliffe Publishing, p 37.
[h] 54. Diabetes Control and Complications Trial (1990) Diabetes Control and Complication Trial Update, Diabetes Care, 13(4): 427-433. Diabetes Control and Complications Trial (1993) The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus, New England Journal of Medicine, 329 (14): 977-986. Diabetes Control and Complications Trial,. (1996) Diabetes Control and Complications Trial, 1996. Lifetime benefits and costs of intensive therapy as practiced in the Diabetes Control and Complications Trial, JAMA 276(17): 1409-15.
[h] 55. National Diabetes Paediatric Report 2008-2009. <http://www.ic.nhs.uk/webfiles/Services/NCASP/audits%20 and%20reports/NDA_Paediatric_Report_2008_2009.pdf>
[h] 56. Chapter 7 illustrates how such comparisons can be made. [h] 57. During the Decision Conference, however, a service user highlighted that her admission to a General ward was of poor quality because staff was not aware of the special need of a person with Eating Disorders. This would suggest that Emergency admissions may be associated with a low Quality of Care compared to other interventions.
[h] 58. It has not been possible to collect sufficient information on the Community Mental Health team either before or during the meeting. The information on routine GP involvement was available only for a very small sample of practices and it was agreed not to assess this intervention.
[h] 59. The definition of the ‘counterfactual’ for Specialist hospital/Residential units and Private Day services was that in the view of the experts that the patient would have access to no service. This means that the estimated benefits attributable to Specialist hospital/Residential units and Private Day services are probably overestimates as compared with those for other services.
[h] 60. We collected these data but did not include them in the formal analyisis.This analysis is unlikely to affect the overall results presented in this report because the socio-economic background of people accessing the services was similar across intervention: the health gain adjusted for health inequality would approximately change in equal proportion across intervention maintaining the current VfM ordering.
[h] 61. Some participants highlighted that there was a significant difference between the cost of providing the service and the funding that was contributed by the PCT. For instance, University GP clinics re-invested part of the QOF money to run their Eating Disorder services. Similarly, there are elements of the Specialist Eating Disorder Service which were not specifically commissioned by the PCT. Also, the voluntary sector (SYEDA) relied on several funding sources beyond the PCT. Although the methodology of assessing VfM used in the Decision Conference can be used to assess the VfM of the service overall, in the meeting we focussed on the VfM of the PCT spend.
[h] 62. The conventional threshold for an intervention to be deemed cost-effective is £30,000 per QALY. [h] 63. The LSE representative taking part were Mara Airoldi, Nikos Argyris, Alec Morton and Cornelius Schaub (LSE/ Decision Institute); and also David Collier from Golder Associates who conducted the external evaluation.
[h] 64. It was not possible to collect sufficient information on five selected interventions: routine primary care involvement in Breast Cancer; raising awareness in Lung cancer; health promotion and Community support in Colorectal cancer.
[h] 65. Most of these data were provided by Makeda Wood (PCT Finance lead) and Andy Eames (PCT Information
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END NOTES
manager) from routinely collected data. Makeda Wood and Will Gray proposed the proportion of inpatient cost (excluding St Luke and Macmillan) attributed to palliative care is: 15% of costs of total annual bed-days in breast cancer; 20% of costs of total annual bed-days in colorectal cancer; and 75% of costs of total annual bed-days in lung cancer.
[h] 66. Joanne Coy of the Public Health Directorate estimated the cost of health promotion and awareness raising campaigns. The costs of primary care routine involvement for colorectal cancer were based on expert judgment of Dr Anthony Gore, a practising GP in Sheffield, who provided his clinical expertise in the assessment of interventions for this type of cancer.
[h] 67. We recognise that there are limitations in these data as good measures of costs. But our analysis also indicates where it is worth spending more effort to derive better estimates of costs.
[h] 68. These estimates are based on data provided by Makeda Wood. [h] 69. Chapter 7 illustrates how such comparisons can be made. [h] 70. The LSE representative taking part were Mara Airoldi, Nikos Argyris, Alec Morton and Cornelius Schaub (LSE/ Decision Institute); and also David Collier from Golder Associates who conducted the external evaluation. Some participants noted that too few of the invited clinicians were able to attend the meetings.
[h] 71. We did not consider examination only and radiography because we assumed that their benefits were included in the treatments which might follow them; and, fluoride varnish, and scale and polish for patients with periodontal disease because of lack of agreement on their benefits.
[h] 72. This includes: removable upper, removable lower, fixed upper and fixed lower. [h] 73. Chapter 7 illustrates how such comparisons can be made. [h] 74. We do not give details of how we derived these estimates here. [h] 75. We were unable to assess health gain for: examination only, radiography, fluoride varnish, referral for advance mandatory services (primary care) and orthodontics (secondary care).
[h] 76. The only change occurred when the estimated VfM of Orthodontics in primary care was reduced to the level of routine Scale and Polish.
[h] 77. We note that we had to exclude some dental practice contracts from the analysis due to insufficient data on clinical activities. Our analysis of tariffs and the results reported here are based on this ‘reduced’ dataset.
[h] 78. Note that this may be sensitive to the use of different QALY weights, as discussed earlier [h] 79. House of Commons Committee of Public Accounts (2008) NHS Pay Modernisation: New contracts for General Practice services in England. Forty–first Report of Session 2007–08 (HC 463). London: The Stationery Office Limited.
[h] 80. Although life expectancy on the Island is relatively high compared to England and South-East England, there are marked inequalities in health as indicated by variations in standardised mortality rates.
[h] 81. We organised one workshops for each disease area except for cerebrovascular diseases, for which we organised two workshops on CHD and Stroke.
[h] 82. Some consistency in approach across the workshops was achieved by all those facilitating attending at least one other workshop.
85
END NOTES
[h] 83. We excluded carers from this count as we were interested in the number of affected ‘patients’. [h] 84. e.g. age, gender, socio-economic background, the relationship between the condition and the patient’s carers/ family/work environment
[h] 85. These will not be comparable with the QALYsC we derived in Sheffield. [h] 86. We checked our intuitive judgment for these results for the average beneficiary and the total number of beneficiaries: e.g. that these were for Early detection almost three times as valuable as for Active treatment (i.e. a score of 20,000 compared to a score of 7,500).
[h] 87. The swing in preference is affected not only by the difference between the least and most preferred scores, but also by how much that difference matters. The swing weight does not reflect the absolute importance associated with the scale.
[h] 88. Table 34 gives the results from normalizing the weights for the three interventions for Cancers. Table 35 gives the normalization over 21 interventions so the VfM ratios for the cancer interventions are not the same in these two tables, but ratios of VfM for the for the three interventions for Cancers are the same in both tables (the first is twice as much as the one and the third one about a tenth of the second).
[h] 89. 20,000*100/140,000. [h] 90. 14*0.8 [h] 91. 100/150 [h] 92. 67*0.5 [h] 93. (11 + 33)*0.5 [h] 94. 22*.95 [h] 95. By developing two frontiers based on VfM and scale of benefit, the PCT Board was clear of the implications of their investment decisions: choices based on VfM appeared to offer up to 25% additional benefit.
Appendix
A Glossary B Tables C Outline of the disease models D Bibliography E Estimating costs and benefits over time F SyMPOSE Decision Conferencing for resource allocation in healthcare â&#x20AC;&#x201C; datapack preparation G Commentary on the Sheffield PCT/LSE commissioning workshops facilitated by the sympose research team in 2009 H Outline programme
88
APPENDIX ANNEX A
Glossary Burden of Disease: the impact of a disease on the health of a population, taking into account prevalence, mortality rates and the extent of disability. Counterfactual scenario: we used this as a description of what would have happened or what would happen in the absence of a policy. Hence, in assessing the value of a new policy, this is the status quo. DALY (Disability Adjusted Life Years): a measure of population ill-health that combines years lost due to premature mortality and years lived in states of less than full health. One DALY is the equivalent of one year lost due to disability or premature mortality. Decision conferencing: a participative process led by an impartial facilitator to iteratively build a requisite model of a problem with key stakeholders. Discounting: a process to determine the present value of costs or benefits occurring in the future. Note that in this report we have discounted costs only Efficiency frontier: a graph mapping different levels of inputs (e.g. financial resources) to the highest level of output they are expected to generate. MCDA (Multi-Criteria Decision Analysis): a branch of decision analysis that values the outcomes of decisions across multiple criteria. Opportunity cost: the cost of an action in terms of the value of the best alternative forgone. Population health gain: the sum of individual health gains in a population, from an intervention or policy.
Quality of Life (Health-related Quality of Life): a valuation of a health state on a scale in which (typically) 1 corresponds to the value of full health and 0 to the value of death. QALY (Quality Adjusted Life Years): a measure of health that combines life expectancy and the quality of life during years lived. One QALY is the equivalent of one year lived in full health. Requisite model: a model that is sufficient to represent a problem, including beliefs about uncertainty and about preferences, such that additional refinements would not generate further insights in the problem. Person Trade Off (PTO): a technique to assess the value of different health interventions by considering how many outcomes of one type (e.g. how many patients diagnosed early for breast cancer) are equivalent in value to one outcome of a different type (e.g. chemotherapy to one patient). Sensitivity analysis: the study of the robustness of a model results to changes in its inputs. Socio-technical process: a process which simultaneously considers the social dimension of a problem, e.g. to engage stakeholders in defining and solving a problem, and its technical dimension, e.g. the rational-analytic model which can be used to represent and solve a problem.
89
APPENDIX – TABLES ANNEX B
Table 1.
Stroke in England: Change in outcomes at one year for interventions1
Back to text [h]
SU
T
Stroke cases prevented ('000s)
BPall
BPhigh
Na5
Na2
-16.8
-8.2
-18.8
-7.9
Outcomes at 1 year ('000s): Independent
1.6
1.0
-6.5
-3.1
-7.2
-3.1
Living at home but dependent
1.1
-0.6
-3.1
-1.5
-3.5
-1.5
Living in institutional care
-0.7
-0.4
-2.5
-1.2
-2.7
-1.2
Dead
-2.0
-4.8
-2.3
-5.3
-2.2
Table 2. Stroke in England: health gains from interventions
Back to text [h]
Numbers who benefit ('000s) Mean gain / person who benefits (QALYs) Total gain ('000s QALYs)
SU
T
BPall
BPhigh
Na5
Na2
48.8
10.7
16.8
8.2
18.8
7.9
0.8
0.21
7.76
7.18
9.43
9.53
39.0
2.3
130.3
58.9
177.2
75.3
Table 3. Stroke in England: impact of interventions on first year costs (£m)2
Back to text [h]
Assessed interventions Impact on elements of NHS costs
SU
T
BP all
BP high
Na5
Na2
Cost of acute stroke care
-7
-2
-104
-50
-116
-49
5.1
-0.02
-0.01
-0.02
-0.01
224
98
Cost of thrombolysis Cost of anti-hypertensives Cost of continuing care (excluding informal care)
-3.6
-3
-25
-12
-28
-12
Total costs
-11
0.3
95
35
-144
-61
Notes – Tables 1-3 1 N egative and positive numbers are reductions (in red) and increases (in black) in the numbers of cases. The reductions for preventative interventions are reported twice. In the first line we report the total number of prevented strokes; in the following lines we specify the expected severity of the prevented strokes in terms of what the first year outcome would have been, had the strokes not been prevented. 2 Where ‘costs’ are in red, this indicates savings.
90
APPENDIX – TABLES
Table 4. Stroke in England: impact of interventions for stroke on costs over patients’ lifetime (£m)1
Back to text [h]
Assessed interventions Impact on elements of NHS costs
SU
T
BP all
BP high
Na5
Na2
Cost of acute stroke care
6
-2
-120
-58
-134
-56
Cost of thrombolysis
-
5.1
-0.02
-0.01
-0.02
-0.01
Cost of anti-hypertensives
-
-
224
98
-
-
Cost of continuing care (excluding informal care)
239
-38
-244
-119
-274
-116
Total costs
245
-35
-139
-79
-408
-172
Table 5. Stroke in England: Interventions for stroke ranked by Value-for-Money2
Back to text [h]
Intervention
Costs* (£m)
VfM (QALY/ Cost £’000s)
(‘000s QALYs)
BPall
-139
130
-0.94
BPhigh
-79
59
-0.75
Na2
-172
75
-0.44
Na5
-408
177
-0.43
T
-35
2
-0.07
SU
245
39
0.16
SU
245
39
0.16
Notes – Tables 4, 5 1 Where ‘costs’ are in red, this indicates savings. 2 Where ‘costs’ are negative and hence savings these are given in red (for the first six interventions).
91
APPENDIX – TABLES
Table 6. Stroke in England: interventions for stroke with cumulative estimates of value and costs3
Back to text [h]
Intervention
Cost (£m)
Cumulative costs (£m)
Value (‘000s QALYs)
Cumulative benefit (‘000s QALYs)
VfM QALY / Cost (£’000s)
-79
-79
59
59
-0.75
-172
-251
75
134
-0.44
T
-35
-286
2
137
-0.07
SU
245
-41
39
176
0.16
T
-35
2
-0.07
SU
245
39
0.16
SU
245
39
0.16
BPhigh Na2
Table 7. England selected interventions ranked by Value for Money Back to text [h] Disease / Intervention
Gains ('000s QALYS)
Cumulative Gains ('000s QALYs)
Annual Costs/ Savings (£m)
Cumulative Annual Costs/ Savings (£m)
VfM (QALY/ Cost in £000s)
Stroke
BPhigh
59
59
-79
-79
-0.75
Heart Failure
ACE-compliance
11
70
-18
-97
-0.61
Stroke
Na2
75
145
-172
-269
-0.44
Stroke
T
2
147
-35
-304
-0.06
Heart Failure
ED&T
10
157
9
-295
1.11
Suicide
NSPS
20
177
20
-275
1.00
Depression
Current-all
42
219
84
-191
0.50
CHD
Statins
19
238
57
-134
0.33
Depression
NGD-all
46
284
212
78
0.22
Heart Failure
All-prevalent
7
291
34
112
0.21
Heart Failure
All-incident
2
293
10
122
0.20
Heart Failure
ACE - LSVD
10
303
51
173
0.20
Diabetes
IGC-long run
24
327
183
356
0.13
Stroke
SU
39
366
245
601
0.12
Heart Failure
Improve diagnosis
3
369
34
635
0.09
Depression
NGD
2
371
77
712
0.03
Diabetes
IGC-short run
3
374
227
939
0.01
Notes – Table 6 3 Where ‘costs’ are negative and hence savings these are given in red (for the first three interventions)...
92
APPENDIX – TABLES
Table 8. Disease / Intervention
England selected interventions ranked by Value for Money4 Back to text [h] Gains ('000s QALYS)
Cumulative Gains ('000s QALYs)
Annual Costs/ Savings (£m)
Cumulative Annual Costs/Savings (£m)
VfM (QALY/ Cost in £000s)
Stroke
Na2
75
75
-172
-172
-0.44
Stroke
BPhigh
59
134
-79
-251
-0.75
Depression
NGD-all
46
180
212
-39
0.22
Depression
Current-all
42
222
84
45
0.50
Stroke
SU
29
251
245
290
0.12
Diabetes
IGC-long run
24
275
183
473
0.13
Suicide
NSPS
20
295
20
493
1.00
CHD
Statins
19
314
57
550
0.33
Heart Failure
ACE-compliance
11
325
-18
532
-0.61
Heart Failure
ED&T
10
335
9
541
1.11
Heart Failure
ACE - LSVD
10
345
51
592
0.20
Heart Failure
All-prevalent
7
352
34
626
0.21
Heart Failure
Improve diagnosis
3
355
34
660
0.09
Diabetes
IGC-short run
3
358
227
887
0.01
Stroke
T
2
360
-35
852
-0.06
Heart Failure
All-incident
2
362
10
862
0.20
Depression
NGD
2
364
77
939
0.03
Table 9. Eating disorders in Sheffield: health gain for severe patients from the Specialist Eating Disorder Services (SEDS) Back to text [h] SEDS Health after one year
Counterfactual QoL
Health after one year
QoL
2% become more severe
10
15% become more severe
10
10% stay the same
12
30% stay the same
12
20% improve but remain severe
15
35% improve but remain severe
15
38% improve to moderate
50
10% improve to moderate
50
20% improve to mild
71
5% improve to mild
71
10% recover Average health gain in QALYs
100 0.48
Notes – Table 8 4 Where ‘costs’ are in red, this indicates savings
5% recover
100 0.24
93
APPENDIX – TABLES
Table 10. Eating disorders in Sheffield: average health gain per person by service
Back to text [h]
Intervention
Care
Counterfactual
Average health gain per person (QALYsED)
Intensive care
0.610
0.145
0.465
Private day care
0.615
0.239
0.376
SEDS
0.476
0.239
0.237
Emergency
0.080
0.020
0.060
UniEDOC
0.605
0.449
0.156
SYEDA
0.699
0.353
0.345
Acute
0.120
0.100
0.020
Table 11. Eating disorders in Sheffield: spend, value and distribution by class Intervention
Back to text [h]
Annual Cost (£’000s)
Number who benefit
Annual Cost to PCT (£/000)
Number who benefit from PCT spend
Class I
Class II
Class III
Class IV
Class V
Health gain score per person (QALYsED)
971
16
971
16
10
0
1
2
3
0.465
48
4
48
4
2
0
0
0
2
0.376
SEDS
341
150
214
150
33
14
21
12
31
0.237
Emergency
200
14
64
4
0
1
5
4
0.060
30
127
12
51
80
6
13
7
21
0.156
SYEDA
132
143
30
32
24
37
37
37
15
0.345
Acute
46
2
46
2
1
1
Intensive care Private day
UniEDOC
0.020
94
APPENDIX – TABLES
Table 12. Eating disorders in Sheffield: Interventions ranked by Value-for-Money Intervention
Back to text [h]
Cost to PCT (£’000s)
Cumulative cost (£’000s)
Value (QALYsED)
Cumulative benefit (QALYsED)
VfM (QALYED /£’000s)
1
none
2
UniEDOC
12
12
7.91
7.91
0.659
3
SYEDA
30
42
11.22
19.14
0.374
4
SEDS
214
256
21.74
40.87
0.102
5
Private day
48
304
1.50
42.38
0.031
6
Emergency
64
368
0.85
43.22
0.013
7
Intensive care
971
1,339
7.44
50.66
0.008
8
Acute
46
1,385
0.04
50.70
0.001
95
APPENDIX – TABLES
Table 13.
Cancers in Sheffield: Bases for estimating costs to the PCT for hospital admissions
Back to text [h]
Total annual costs (£’000s) Number who benefit / year (2009/10)
Average number admissions / person
Bed-days
Drugs
Radiotherapy & chemo therapy
Elective
669
4
1,690
5,422
450
Emergency
116
1
251
251
Palliative (elective)
126
4
298
298
22
1
44
44
PET & CT
Totals
Breast cancer
Palliative (emergency) Totals
2,283
5,422
450
288
200
7,562
8,155
Colorectal cancer Elective
436
2
1,524
Emergency
194
1
1,115
1,115
Palliative (elective)
163
2
421
421
73
1
279
279
Palliative (emergency) Totals
325
2,337
3,339
288
200
325
4,152
318
88
325
1,044
Lung cancer Elective
61
3
313
Emergency
50
1
243
243
Palliative (elective)
299
3
1,203
1,203
Palliative (emergency)
248
1
730
730
Totals
2,489
318
88
325
3,219
96
APPENDIX – TABLES
Table 14.
Cancers in Sheffield: PCT cost for each intervention Intervention
Back to text [h]
Annual Cost to PCT (£’000s)
Breast Cancer Health promotion
20
National Screening
950
Elective
7,562
Outpatient
2,380
EOL
159
Palliative
342
Emergency
251
Total spend considered in Breast Cancer
11,663
Colorectal Cancer Screening
448
Elective
2,337
Outpatient
1,869
EOL
216
Palliative
700
Emergency
1,115
Primary care
2,860
Total spend considered in Colorectal Cancer
9,545
Lung Cancer 7 Health promotion
483
Elective inpatient
1,044
Outpatients
975
EOL
516
Palliative Emergency Total spend considered in Lung Cancer
1,932 243 5,193
Total spend considered in Breast, Colorectal and Lung Cancer 26,402
Notes – Table 8 5 We were unable to estimate costs for primary care
97
APPENDIX – TABLES
Table 15. Cancers in Sheffield: Quality of life scores for different health states in breast cancer Back to text [h] Health status
Description Of Health State
Full health
No problem walking about No problem with self care No problems with usual activities No pain or discomfort No anxiety or depression
Diagnostic and primary surgery
Average For 80% of cases: No problem walking about No problem with self care No problems with usual activities No pain or discomfort Moderate anxiety or depression For 20% of cases: No problem walking about No problem with self care No problems with usual activities Moderate pain or discomfort Moderate anxiety or depression
67
Radiotherapy
As above but less anxiety and more physical discomfort
67
Clinically disease free during recovery phase
Average 75% of cases 25% of cases
76 85 48
Clinically disease free beyond recovery phase
Average 90% of cases 10% of cases (develop a lymphodema)8
Disseminated/metastatic
Assessed with reference to the other scores as benchmarks
Terminal – successfully supported
Terminal – not supported
QoL Score
Some problem walking about Some problem with self care Unable to perform usual activities Moderate pain or discomfort Moderate anxiety or depression Assessed with reference to the other scores as benchmarks
Notes – Table 15 6 N o problem walking, some problem in self care, some problems with usual activities, extreme pain and discomfort and either moderate or severe anxiety and depression)
100
71 53
82 100 1724 20 15
6
98
APPENDIX – TABLES
Table 16. Cancers in Sheffield: quality of life scores for different health states in colorectal cancer7
Back to text [h]
Health status
Description Of Health State
QoL Score
Full Health
No problems in walking about No problems with self care No problems with performing usual activities No pain or discomfort No anxiety or depression
Undergoing investigation or recently diagnosed
Average 10% (late diagnosis) 30% (intermediate diagnosis) 60% (early diagnosis)
60 14 53 71
Having or recently had potentially curative therapy (surgery / chemotherapy / radiotherapy)
Average 10% 70% 10% 10%
20 43 21 4 3
Therapy Completed – Early Survivorship
Average 60% 30% 10%
38 53 21 3
Recurrent disease – receiving palliative or life extending treatment
Average 60% 30% 10%
17 21 13 3
Patients in last year of life
Average 50% 30% 20%
31 53 21 3
Table 17. Cancers in Sheffield: population health gain from interventions for treatment of breast cancer Group of activities/ interventions
Number of people who benefit
100
Back to text [h]
Health gain per person (QALYsC)
Population health gain (QALYsC)
Health promotion
3,400
0.08
272
National screening
15,082
0.1
1,508
669
8.32
5,566
1,043
1.25
1,304
Elective Outpatient EOL
53
0.4
21.2
Palliative
148
0.7
103.6
Emergency
116
0.54
62.6
Notes – Table 16 7 Adapted by Dr Anthony Gore
99
APPENDIX â&#x20AC;&#x201C; TABLES
Table 18. Cancers in Sheffield: population health gain from interventions for treatment of colorectal cancer Intervention Screening
Number of people who benefit
Back to text [h]
Health gain per person (QALYsC)
50
3.3
Elective
436
4.08
Outpatient
975
0.9
72
0.2
EOL
Population health gain (QALYsC) 165 1,778 877 14.40
Palliative
236
1
Emergency
194
0.5
97
3,000
0.9
2,700
Primary care
Table 19. Cancers in Sheffield: population health gain from interventions for treatment of lung cancer Group of activities/ interventions Health Promotion Elective
Number of people who benefit 848
236
Back to text [h]
Health gain per person (QALYsC) 13
Population health gain (QALYsC) 11,024
61
2.9
176
Outpatient
653
1.5
979
EOL
172
0.5210
89
Palliative
547
0.52
284
50
0.25
13
Emergency Primary care
3,000
0.9
2,700
100
APPENDIX – TABLES
Table 20. Cancers in Sheffield: relative Value-for-Money of interventions for breast cancer Intervention
Back to text [h]
Cost to PCT (£’000s)
Cumulative cost (£’000s)
Population Health Gain (QALYsC)
Cumulative Population Health Gain (QALYsC)
VfM (QALYsC / £’000s)
20
20
272
272
13.60
950
970
1,508
1,780
1.59
1
None
2
Health promotion
3
Screening
4
Elective
7,562
8,532
5,566
7,346
0.74
5
Outpatient
2,380
10,912
1,304
8,650
0.55
6
Palliative
342
11,254
104
8,754
0.30
7
Emergency
251
11,505
63
8,817
0.25
8
EOL
159
11,664
21
8,838
0.13
Table 21. Cancers in Sheffield: relative Value-for-Money of interventions for breast cancer Intervention
Back to text [h]
Cost to PCT (£’000s)
Cumulative cost (£’000s)
Population Health Gain (QALYsC)
Cumulative Population Health Gain (QALYsC)
VfM (QALYsC / £’000s)
1
None
2
Primary Care
2,860
2,860
2,700
2,700
0.94
3
Elective
2,337
5,197
1,778
4,478
0.76
4
Outpatient
1,869
7,066
877
5,355
0.47
5
Screening
448
7,514
165
5,520
0.37
6
Palliative
700
8,214
235
5,755
0.34
7
Emergency
1,115
9,329
97
5,852
0.09
8
EoL
216
9,545
14
5,866
0.06
101
APPENDIX – TABLES
Table 22. Cancers in Sheffield: relative Value-for-Money of interventions for Lung Cancer
Back to text [h]
Intervention
Cost to PCT (£’000s)
Cumulative cost (£’000s)
Population Health Gain (QALYsC)
Cumulative Population Health Gain (QALYsC)
VfM (QALYsC / £’000s)
1
None
2
Health promotion
483
483
11,024
11,024
22.82
3
Outpatient
975
1,458
979
12,003
1.00
4
EOL
516
1,974
89
12,092
0.17
5
Elective
1,044
3,018
176
12,268
0.17
6
Palliative
1,932
4,950
284
12,552
0.15
7
Emergency
243
5,193
13
12,565
0.05
8
EoL
216
9,545
14
5,866
0.06
102
APPENDIX – TABLES
Table 23. Cancers in Sheffield: relative Value-for-Money of interventions for breast, colorectal and lung cancer Cancer site
Intervention
Lung
Back to text [h]
Cost to PCT (£’000s)
Cumulative cost (£’000s)
Population Health Gain (QALYsC)
Cumulative Population Health Gain (QALYsC)
VfM (QALYsC / £’000s)
Health promotion
483
483
11,024
11,024
22.82
Breast
Health promotion
20
503
272
11,296
13.60
Breast
National Screening
950
1,453
1,508
12,804
1.59
Lung
Outpatient
975
2,428
979
13,783
1.00
Colorectal
Primary Care
2,860
5,288
2,700
16,483
0.94
Colorectal
Elective
2,337
7,625
1,778
18,261
0.76
Breast
Elective
7,562
15,187
5,566
23,827
0.74
Breast
Outpatients
2,380
17,567
1,304
25,131
0.55
Colorectal
Outpatients
1,869
19,436
877
26,008
0.47
Colorectal
Screening
448
19,884
165
26,173
0.37
Colorectal
Palliative care
700
20,584
235
26,408
0.34
Breast
Palliative
342
20,926
104
26,512
0.30
Breast
Emergency
251
21,177
63
26,575
0.25
Lung
EOL
516
21,693
89
26,664
0.17
Lung
Elective
1,044
22,737
176
26,840
0.17
Lung
Palliative
1,932
24,669
284
27,124
0.15
Breast
EOL
159
24,828
21
27,145
0.13
Colorectal
Emergency
1,115
25,943
97
27,242
0.09
Colorectal
EOL
216
26,159
14
27,256
0.06
Lung
Emergency
243
26,402
13
27,269
0.05
103
APPENDIX – TABLES
Table 24. Dentistry in Sheffield: numbers who benefit by intervention and their annual costs Primary care
Back to text [h] Number of treatments (‘000s)
Annual Cost (£’000s)
Examination only
61.3
1,774
Scale and Polish
222.6
8,932
Fluoride Varnish
7.4
308
Fissure Sealants
2.9
153
64.1
3,387
5.2
436
119.1
7,481
29.0
2,002
Inlays
0.8
169
Crowns
5.8
1,207
Dentures-Acrylic
9.3
1,896
Dentures-Metal
0.5
92
Veneers applied
0.5
105
Bridges Fitted
0.8
171
Referral for advance mandatory services
1.4
59
Orthodontics
2.5
2,747
533.1
30,917
Dental Medicine Specialties
0.9
177
Maxillo-Facial Surgery
1.2
473
Oral Surgery
7.3
1,978
Orthodontics
0.8
2,217
Paediatric Dentistry
1.8
578
Paediatric Maxillo-Facial Surgery
0.1
24
Restorative Dentistry
2.9
1,988
15.0
7,435
Radiographs Endodontic Treatment Permanent Fillings and Sealant Restorations Extractions
Total Secondary care
Total
38,352
104
APPENDIX â&#x20AC;&#x201C; TABLES
Table 25. Dentistry in Sheffield: QALYsD per person by intervention8 Intervention
Back to text [h]
QoL12
QALYsD13 per person over 5 years
Maxillo facial surgery
133
3.82
Paediatric Maxillo-facial surgery
133
3.82
Oral Surgery
100
2.88
Extractions
100
2.88
81.5 (47.5)
2.34 (1.37)
Bridges fitted
76
2.19
Partial metal dentures
60
1.73
Acrylic dentures
58
1.67
Permanent fillings
55
1.58
Inlays
55
1.58
Crowns
50
1.44
42.2
1.21
35
1.01
23.5 (41.7 and 5.2)
0.68 (1.2 and 0.15)
15.1
0.43
10
0.29
Fissure sealants
9 (12)
0.26 (0.35)
Scale and polish
0.25 (0.1 and 0.4)
0.007 (0.003 and 0.012)
From 60.5 to 44.85
From 1.74 to 1.29
Endodontics
Paediatric dentistry Gum treatment Orthodontics (primary care) Dental medicine Veneers
Orthodontics (secondary care)
Notes â&#x20AC;&#x201C; Table 25 8 W e were unable to assess QoL and hence QALYsD for: examination only, radiography, fluoride varnish, and referral for advance mandatory services 9 0 =no benefit; 100=as much benefit as oral surgery or extractions scale. Numbers in parentheses were used in sensitivity analysis. 10 Numbers in parentheses were used in sensitivity analysis.
105
APPENDIX – TABLES
Table 26. Care setting
Primary care
Dentistry in Sheffield: population health gain Intervention
QALYsD per person
Numbers who benefit (‘000s)
Population Health Gain (‘000s QALYsD)
Permanent fillings
1.58
119.1
188.4
Extractions
2.88
29.0
83.3
Acrylic dentures
1.67
9.3
15.5
Endodontics
2.34
5.2
12.2
Crowns
1.44
5.8
8.4
Bridges fitted
2.19
0.8
1.8
Orthodontics (primary care)
0.68
2.5
1.7
Inlays
1.58
0.8
1.3
Scale and polish
0.0072
222.6
1.6
Partial metal dentures
1.73
0.5
0.8
Fissure sealants
0.26
2.9
0.8
Veneers
0.29
0.5
0.1
399.0
315.9
Totals for primary care Secondary care
Back to text [h]
Oral Surgery
2.88
7.3
21.0
Maxillo facial surgery
3.82
1.2
4.6
Gum treatment
1.01
2.9
2.9
Paediatric dentistry
1.21
1.8
2.2
Dental medicine
0.43
0.9
0.4
Paediatric Maxillo-facial surgery
3.82
0.1
0.4
14.2
31.4
413.2
347.3
Totals for secondary care Totals for primary & secondary care
106
APPENDIX – TABLES
Table 27. Intervention
Dentistry in Sheffield: VfM for primary care
Back to text [h]
Costs (£’000s)
Cumulative cost (£’000s)
Population Health Gain (‘000s QALYsD)
Cumulative Population Health Gain (‘000s QALYsD)
VfM (QALYsD / £)
2,002
2,002
2,897
2,897
1.45
436
2,438
424
3,321
0.97
7,481
9,919
6,552
9,873
0.88
171
10,090
64
9,937
0.37
92
10,182
27
9,964
0.29
1,896
12,077
542
10,506
0.29
169
12,246
46
10,552
0.28
1,207
13,452
291
10,843
0.24
1
none
2
Extractions
3
Endodontics
4
Permanent fillings
5
Bridges fitted
6
Partial metal dentures
7
Acrylic dentures
8
Inlays
9
Crowns
10
Fissure sealants
153
13,606
26
10,869
0.17
11
Veneers
105
13,711
5
10,874
0.05
12
Orthodontics (primary care)
1,774
15,485
59
10,933
0.03
13
Scale and polish
8,932
24,424
56
10,988
0.006
Table 28. Intervention
Dentistry in Sheffield: VfM for secondary care
Back to text [h]
Costs (£s)
Cumulative cost (£s)
Population Health Gain
Cumulative Population Health Gain
VfM
24
24
14
14
0.56
1,978
2,002
731
745
0.37
1
none
2
Paediatric Maxillofacial surgery
3
Oral Surgery
4
Maxillo facial surgery
473
2,475
159
904
0.34
5
Paediatric dentistry
578
3,054
75
978
0.13
6
Dental medicine
177
3,230
14
992
0.08
7
Gum treatment
1,988
5,218
100
1,093
0.05
107
APPENDIX – TABLES
Table 29. VfM for IoW: estimates of costs and value for interventions within each priority area Priority Area
Cancers
Intervention
Costs (£’000s)
Number expected to benefit
Health gain r average individual
Population health gain (‘000s of UCBs)
Health inequality (UEs)
Probability of Success (%)
Early detection
300
200
100
20
100
95
EOL
760
1,500
75
113
50
70
50
300
25
8
0
10
Secondary prevention
130
500
90
45
75
70
Health promotion
650
1,600
100
160
100
50
Rehabilitation
100
50
50
3
25
100
Stroke emergency
600
320
60
19
50
85
CHD acute
300
150
80
12
5
70
Pneumonia
75
2,500
100
250
100
80
Dementia
50
2,000
40
80
10
100
Prison
150
300
70
21
100
100
Psychological therapies
120
200
100
20
70
75
Social inclusion
300
200
90
18
95
80
Alcohol misuse
300
700
80
56
50
50
Workforce development
100
24,000
2
48
70
90
Repatriation of radiotherapy
CVD
Respiratory
Mental Health
Children
Back to text [h]
Table 30. VfM for IoW: from QALYsC to normalized scores for cancers Intervention
Population Health Gain (‘000s QALYsC)
None of the above Early detection EOL Repatriation of radiotherapy
Back to text [h] Cumulative Score (‘000s QALYsC)
Normalized Scores
0.0
0.0
0
20.0
20.0
14
112.5
132.5
95
7.5
140
Notes – Table 29 1 These are units of benefit within each area on a scale of 0 to 100 and are not comparable across areas.
100
108
APPENDIX – TABLES
Table 31.
VfM for IoW: weights used in modelling
Back to text [h]
Population Health Gain
Health inequality
100
100
Cancer
80
50
Respiratory
40
60
Mental health
90
80
Children
90
70
Across Weights:
50
50
Cvd
Table 32. VfM for IoW: Weighted scores for cancers based on MCDA
Back to text [h]
Population health gain Intervention
Early detection EOL Repatriation of radiotherapy Totals
Health inequality
QALYsC (’000s)
Normalised score
Weight for cancers (%)
Weighted score
Raw Score
Normalised score
Weight for cancers
Weighted score
20
14
80
11
100
67
50
33
112.5
80
80
64
50
33
50
17
7.5
5
80
4
0
0
50
0
140
100
80
150
100
50
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APPENDIX
Table 33. VfM for IoW: Twenty-one interventions ranked by VfM1 Priority area
Strategic Intervention
1
Respiratory
2
Back to text [h]
Incremental Cost in £’000
Cumulative Cost in £’000
Incremental Benefit
Cumulative Benefit
Value for Money
pneumonia
75
75
118
118
1.58
Cvd
Prevention
650
725
105
223
0.16
3
Cancer
Palliative & EOL
760
1485
91
314
0.1
4
Cancer
Early detect - diagnosis
300
1785
57
371
0.19
5
Cvd
TIA & 2ndary prevention
130
1915
54
425
0.42
6
Mental health
Dementia services
50
1965
52
477
1.04
7
Children
Primary prevention
600
2565
46
523
0.08
8
Mental health
Prison MH
150
2715
45
568
0.30
9
Mental health
Alcohol misuse svc
300
3015
38
606
0.13
10
Mental health
Social inclusion
300
3315
38
643
0.13
11
Cvd
Stroke medical emergency
600
3915
34
677
0.06
12
Children
Access to dental
480
4395
32
710
0.07
13
Mental health
Psych therapies
120
4515
30
740
0.25
14
Children
Workforce development
100
4615
28
768
0.28
15
Children
CAMHS School
160
4775
28
795
0.17
16
Children
Obesity training
60
4835
17
813
0.29
17
Cvd
Cardiac Rehab
100
4935
13
826
0.13
18
Children
CAMHS 1:1
80
5015
13
838
0.16
19
Children
Obesity 1:1
140
5155
12
850
0.09
20
Cvd
CHD acute
300
5455
8
858
0.03
21
Cancer
Active Treatment
50
5505
3
861
0.06
Notes – Table 33 1 T his ranking assumes that if the initiative were not fully successful it would deliver no benefits. Different shadings highlight initiatives costing up to £1m, £2m and £3m.
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APPENDIX
Table 34. VfM for IoW: Twenty-one interventions ranked by benefit2 Rank
Priority area
Intervention
1
Respiratory
Pneumonia
2
Cancer
3
Back to text [h]
Cost £’000
Cumulative cost (£’000s)
Benefit
Cumulative Benefit
75
75
105
105
1.4
EOL
760
835
75
180
0.1
Cvd
Prevention
650
1,485
70
250
0.11
4
Cancer
Early detection
300
1,785
56
306
0.19
5
Mental health
Dementia
50
1,835
52
358
1.04
6
Mental health
Prison
150
1,985
45
403
0.3
7
Cvd
Secondary prevention
130
2,115
44
447
0.34
8
Children
Primary prevention
600
2,715
38
485
0.06
9
Mental health
Social inclusion
300
3,015
33
518
0.11
10
Cvd
Stroke emergency
600
3,615
31
549
0.05
11
Children
Workforce development
100
3,715
26
575
0.26
12
Mental health
Psych therapies
120
3,835
26
601
0.22
13
Mental health
Alcohol misuse
300
4,135
25
626
0.08
14
Children
Dental
480
4,615
24
650
0.05
15
Children
CAMHS School
160
4,775
23
673
0.14
16
Children
Obesity Primary Care
60
4,835
15
688
0.25
17
Cvd
Rehabilitation
4,935
14
702
0.14
18
Children
CAMHS
80
5,015
11
713
0.14
19
Cvd
CHD acute
300
5,315
6
719
0.02
20
Children
Obesity counselling
140
5,455
4
723
0.03
21
Cancer
Repatriation of radiotherapy
50
5,505
1
724
0.01
Notes – Table 34 2 T his ranking assumes that if the initiative is not fully successful it has a zero benefit score. Different shadings highlight initiatives costing up to £1m, £2m and £3m.
Value for Money
111
APPENDIX ANNEX C
Outline of the disease models Introduction This Annex outlines six models for • stroke, • suicide prevention, • depression, • coronary heart disease, • heart failure, and • type 1 diabetes. It begins by describing the framework we developed for these models and the methods of computation. It then outlines the nature of each model.
Model structure In modelling we need to determine a ‘model structure’ for the distribution of populations with disease being treated across different health states over time. One way to determine the transition of individuals between health-states is through assigning appropriate transition probabilities. We discuss other options for this in the second part of this chapter. We provide an illustration of a general model structure in figure A1. In this example the QoL scale has been made discrete (i.e. not continuous) by defining four separate health states. We assume that the order of these, in terms of QoL weights, is S1, S2, S3, S4, D, where state D represents death. This structure defines a scenario in which the health of individuals can only deteriorate over time, possibly due to a degenerative condition.
Figure A1
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APPENDIX
Computation The initial run of each model is for a scenario where none of the interventions considered is implemented. This will provide us with counterfactual of a population health baseline which can determine the effectiveness of all interventions under consideration. Subsequently, the model is run again for each of the intervention scenarios in order to obtain corresponding estimates of population health. Evaluating these against the baseline provides effectiveness estimates for each of the possible interventions. The model is run in steps, defined by the time units we use (usually one year). The total number of steps is referred to as the â&#x20AC;&#x2DC;running timeâ&#x20AC;&#x2122; of the model. At the end of every step we obtain a different distribution of our population across all possible health states. The total number of individuals in each health state at the end of every step can also be interpreted as number of life-years lived in a certain health state (one life-year per individual). As a result the cumulative totals for each health state across our time horizon determine the cumulative number of life years lived in these states on our populations. These cumulative life years can then be adjusted by QoL weights in order to obtain QALY estimates for a particular intervention scenario (see figure A2).
Figure A2
Model Development and Implementation Basic Steps 1. Determine a collection of health states and associated QoL (Disability) weights 2. Determine the time units for the model and the time-horizon 3. Determine the model population 4. Develop model structure based on disease and policy objectives 5. Run the model for each possible scenario including the baseline, by altering the parameters accordingly 6. Use the results to calculate effectiveness estimates for each healthcare intervention under consideration
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APPENDIX
Prevention and treatment of Stroke The model for prevention and treatment of Stroke described here is that of Airoldi et al. (2008). This model estimates the impact of four interventions on the mortality and morbidity attributable to stroke in the population. The four interventions considered are of two types: a) acute care interventions (increase in patients receiving thrombolysis and those treated in stroke units), and b) preventive interventions (increased prescription of antihypertensive drugs and reduction of sodium in processed food): 1. BPall: reducing average usual blood pressure in the population from prescribing a first line anti-hypertensive drug to the population of 55-years old or older who are not currently prescribed anti-hypertensive medication. 2. BPallhigh: reducing average usual blood pressure in the population from prescribing a first line anti-hypertensive drug to all people with blood pressure 140/90 mmHg who are not currently prescribed anti-hypertensive medication. 3. Na2: reducing average usual blood pressure in the population from reducing the average daily consumption of salt by 30% through agreement with food industry. 4. Na5: reducing average usual blood pressure in the population by 5mmHg, from reducing the average daily consumption of through agreement with food industry. 5. SU: treating 100% of all hospitalised stroke patients in stroke units. 6. T: providing thrombolysis to 9% of stroke cases within three hours of stroke onset
Model description Time: The model uses a one-year period as its time-unit. In addition, a one-year implementation period is considered for all interventions. Subsequently the benefits of this short-term implementation can be calculated for the first year after the intervention and over the patientsâ&#x20AC;&#x2122; lifetime, in order to assess both the short-term and long-term effects of the interventions. Population: An incidence-based model is used, which generates a one-year cohort of stroke patients and their expected health profile over their lifetime. The incident population can be calculated using age and gender specific stroke incidence rates according to the Oxford Vascular Study (Rothwell, et al., 2005). This incident population (denoted N) is used to calculate the baseline scenario and the impact of acute interventions. Preventive interventions result in a reduction of the incident population in the beginning of the simulation. Failing to generate expected QALYs for these individuals (denoted R) would result in a significant underestimation of the impact of preventive interventions. As a result, we need to estimate their expected health profiles as well. It is important to note that for the case of preventive interventions, we need to additionally estimate the expected health profiles for individuals that do not experience. Health states: The following health states were defined to assess the impact of different interventions. These apply to the incident stroke-population. a Living at home and fully recovered (HI) b Living at home but not independent (HD) c In institutional care (IC) d Dead (D) No specific health states were used directly when measuring the BoD in the population that avoids stroke as a result of preventive interventions. Instead, the model assumes
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APPENDIX
that this group is subject to â&#x20AC;&#x2DC;averageâ&#x20AC;&#x2122; mortality and disability rates throughout their lifetime according to age and gender. These can be calculated with use of EuroQoL tariffs based on the Health Survey for England (Department of Health, 1996). Model structure: Data limitations do not allow for specifying a well-defined structure, in the sense that it is impossible to specify transition probabilities across health states. The distribution of the incident population over different health states can be calculated for the first year following a stroke. From the second year onwards, assuming a constant mortality rate, we can derive an exponential survival curve for the proportions of the surviving population (A(t)). In a similar fashion, we can derive survival curves for the proportions of the population living at home (H(t)) and the independent survivors (I(t)). Note that a different set of survival curves needs to be estimated for every implementation scenario including the baseline. An example of these survival curves is given in figure A3. These correspond to the thrombolysis intervention. The overall model structure is illustrated in figure A4.
Figure A3
Figure A4
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APPENDIX
Suicide prevention The model for prevention of suicide is described here is that of Morton et al (2006). This model was designed to obtain estimates of the ‘avoidable’ BoD from reducing suicide rates according to the Government’s National Suicide Prevention Strategy for 2010 (DH, 2002): NSPS: Meeting the target of the National Suicide Prevention Strategy 20% reduction in suicides against a 1997 baseline (i.e. a reduction of 600 suicides each year) .
Model description Time: A one-year period is used as the basic time-unit for the model. The model run-time is a one year period. The ‘avoidable’ deaths within the run-time period are then used to calculate ‘avoidable’ DALYs without imposing a time-horizon, i.e. until the fixed-reference age of 120 years. Population: The model was applied to the population of England. It generates 100 one-year age cohorts of a population and estimates the changes in the current population due to suicide-related mortality. Health states: The model seeks to identify ‘avoidable’ deaths (and associated ‘avoidable’ YLLs) from suicide prevention. It does not account for different levels of QoL (or disability). As a result, only two health states are needed: for those who are death (D) and alive (A). Health states: For each cohort j=1,…,n. The YLLs due to suicides are estimated according to the equations below. The two intervention scenarios defined above can be modelled by altering the value of λ in both equations such that the value of 1-λ defines the target mortality rate of each scenario. 1
‘Avoidable’
2
‘Avoidable’
where the model parameters are as follows
• • • •
j indexes the age cohorts μj is the current age-specific suicide rate Nj is the population of the jth age cohort Lj is the residual life expectancy of the jth age cohort
The overall model structure is illustrated in figure A5 below.
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APPENDIX
Figure A5
Treatment of depression The models described of the treatment of depression here is that of the short-term model of Morton et al (2008). This model was designed to estimate the impact of treatment for depression, more specifically the reduction in the BoD from the implementation of the NGD. The interventions considered by the short-term model are as follows: 1 Current-all: extending the current treatment profile to 100% of the population suffering from depression. 2 NGD: changing the current care regime to a new treatment profile, as recommended in NICEâ&#x20AC;&#x2122;s National Clinical Guideline for Depression (NGD) at current treatment rates (NICE, 2004). 3 NGD-all: extending the NGD recommended treatment profile from 60% to 100% of the population suffering from depression.
Model Description Time: The short-term model has a one-year run-time designed to capture the effect of implementing the NICE guidelines within a single year. More specifically it considers the BoD (measured by YLDs) associated with the episodes of everyone who becomes depressed in a given year. The time-horizon extends further than the one-year period such that the full number of YLDs associated with any depression episode can be captured. The time-horizon for the long-term model is an individualâ&#x20AC;&#x2122;s entire lifecycle (defined by the life expectancy at birth). The basic time unit is the one-year period. Population: The short-term model was applied to the population in England, but is designed, as usual, to be applied on any local population, subject to data availability. The long-term model simulates a single individual over their lifetime. Health States: In the short-term model, the health states are defined according two different classifications of depression in the NGD, with respect to the severity and the duration/recurrence of episodes. Overall, there are twelve depression types where each
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APPENDIX
type can be mild/ moderate/ severe and at the same time ordinary/ recurrent/ treatmentresistant/ chronic. We note that there is no health state defined for death. In the long-term model two health states are used indicating whether the simulated individual is suffering from depression at any particular time. In addition there is an ‘end’ health state for death.
Model Structure: The run-time period of one year is used to calculate the incidence of depression episodes of different types within that year, which have their own disability weight. These are then combined with the mean duration of depression episodes for different treatments in order to calculate YLDs. The sum of YLDs over all depression types provides the overall BoD generated by depression episodes occurring within the one-year run-time period. The equation below defines this calculation:
Where: • • • • • • • • •
d(s,t) is the mean duration of an episode of depression of type s with treatment t n is the population p(s) is the proportion of sufferers receiving treatment w(s) is the disability weight associated with having depression of type s s is an index of depression type which can be (mild/ moderate/ severe) and (transitory/ recurrent/ treatment resistant/ chronic) t is an index of treatment type which can be AD/ STPT/ LTPT only; or a combination therapy consisting of AD and STPT or LTPT; or none of the above ( ) is the incidence of depression of type s is the proportion of those treated who are treated with treatment t is the proportion of those with depression type s complying with treatment t
Figure A6 below illustrates the structure of the term model.
Figure A6
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Prevention of Coronary Heart Disease The model for prevention of Coronary Heart Disease described here is that of Oliveira et al. (2006). This model was designed to estimate the effects of improving statin prescription for reducing high cholesterol and risk. The â&#x20AC;&#x2DC;improvementâ&#x20AC;&#x2122; under consideration concerns the increase in coverage, compliance and appropriateness of treatment with statins: CHD-risk: reducing cholesterol in the population through a risk-based approach, as in the CHD National Service Framework (NSF) (DH, 2000), which aims to reduce levels of cholesterol in individuals who are assessed as high risk cases.
Model description Time: The usual one-year period is used as a time unit. The simulation considers two separate situations. In the first, a five-year implementation is considered period but no time-horizon for calculating DALYs is imposed. The second case considers the impact on the population in the long run, defined by a pre-determined point in the future. Population: A population based model is used which can be applied to any population. The short-run case uses a closed population model. The long-run scenario assumes that the size and age distribution of the population is stable over time by replacing individuals that die at a particular year with new entrants of the same age. Health States: The following health states were defined in order to assess the impact of improvement in the prescriptions of statins: 1 Alive but with no previous history of CHD 2 Alive, but with a history of CHD 3 Dead Because the model is concerned with the impact on the incidence of CHD, only the last two states were assigned disability weights, equal to 0.1 and 1 respectively. Model structure: A state transition simulation model structure is used. The model splits the population into the following groups at every year t of the simulation: LRt: population with low risk for developing CHD HRt: population with high CHD risk (due to cholesterol, associated with other risk factors) that are untreated HRTt: population with high CHD risk (due to cholesterol, associated with other risk factors) that are treated CHDt: population with CHD history but untreated CHDTt: population with CHD history under effective treatment Dt: Deaths from CHD YLLs are obviously generated from Dt. YLDs are generated according to the incidence of CHD every year t which is defined as: Incidence: It = CHDt + CHDTt - CHDt-1 + CHDTt-1
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The transition between model states and the calculation of the BoD is summarised in figure A7 below.
Figure A7
Glucose control in Type-1 Diabetes The model of glucose control in Type-1 diabetes described here is that of Airoldi et al. (2008). This model is designed to estimate the impact of intensive glucose control (IGC) on the development of renal complications in the population of type-1 diabetes patients. This impact is measured by calculating the â&#x20AC;&#x2DC;avoidableâ&#x20AC;&#x2122; BoD (DALYS averted) though IGC interventions: 1 IGC-short run: this intervention scenario explores the impacts of reducing the rates of progression to and through microvascular complications (DCCT, 1990;1993;1996) from an implementing IGC l for all Diabetes Type-1 patients in England. 2 IGC-long run: this explores the impact of IGC in a long run scenario where all patients receive intensive treatment at the onset of the disease.
Model description Time: The basic time unit for the model is the usual one year period and the model runtime is five years. Two separate situations are considered in order provide both the short and long-term estimates of the impact of IGC on the population. The first situation considers the impact of a five-year long implementation of IGC in diabetes patients. The second situation considers with one-year snapshot of the future situation in 100 years from now, assuming all diabetes patients have been under continuous IGC. In other words, the first scenario considers present benefits without using a time-horizon whereas the second considers future benefits with use of a one-year time horizon in the future. Population: The model can be applied to any local population provided we can estimate the number of type-1 diabetes patients within that population. The largest population used was 170,000, corresponding to the estimated number of diabetes type-1 patients in England. Health states: The model uses the concentration of the protein albumin in the urine as a guide and accordingly defines four separate health states, namely Normo-
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albuminuria, Microalbuminuria, Macroalbuminuria and End-stage Renal Disease (ESRD). The four states are denoted S1,…,S4 respectively. The disability weights used are those developed by the Dutch Disability weight study (Stouthard et al, 1997). Model structure: It is assumed that a patient’s health can only deteriorate over time. Within one year a patient’s can either remain unchanged or deteriorate to the immediately ‘worse’ health state. The model utilises a Markov chain structure. The transition between states does not depend on a patient’s patient history and is determined by a list of transition probabilities. In order to allow for variation in the transition probabilities the population was split into five different age-groups, each with a distinct set of transition probabilities. These are denoted P(j,Si,Sk) where j is the age group and Si, Sk are health states. The mortality rate for each health state and age group is denoted M(j,Si). The different transition probabilities and mortality rates can be defined according to results of clinical trials published in the literature. See figure A8 for a visual representation of the model structure. We now describe how to obtain estimates for the five-year implementation scenario. After running the model for five years we obtain the cumulative life years for each health state, denoted A(s). Adjusting these by the disability weights, denoted w(s) leads to the total number of YLDs in our population. We can calculate the YLLs in a similar fashion. The cumulative number of deaths within the model run-time (five years) is denoted A(D). This also provides the YLLs within the five year period (recall that the disability weight for death is equal to one). Since there are no more deaths after the fifth year, every additional year until the end of our horizon (the fixed reference age of 120) the number of YLLs will increase by A(D), i.e. an overall increase of 115*A(D). As a result the overall number of YLLs is 116*A(D) and the BoD is DALYs=YLLs+YLDs= 116*A(D) + ∑ A(s)*w(s).
Figure A8
Diagnosis and Treatment of Heart Failure The model described here is that of Oliveira et al. (2010). It was used to assess the impact of earlier diagnosis and additional coverage in treatment in the BoD associated with heart failure. In particular, the implementation of the following interventions were considered: 1. All-incident: extending the current treatment to cover 100% of incident cases.
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2.
E arly diagnosis: earlier diagnosis and treatment for 80% of the incident population diagnosed through hospitalisation.
3. All-prevalent: extending treatment to 100% of the prevalent population with Heart Failure through follow up in the primary and outpatient care. 4. Improve diagnosis: increasing diagnosis rate to 80% of currently undiagnosed patients. 5. ACE: prescribing ACE inhibitors to all diagnosed patients with HF and Left Ventricular Systolic Dysfunction (LSVD). 6.
A CE compliance: as above but with ensuring 100% compliance with treatment.
Model description Time: The usual one-year period is used as a time unit. The model considers a five-year implementation scenario and calculates the expected health profile of the population over this period. Population: A population-based model is used. Overall, four cohorts of patients are generated, corresponding to the treated and untreated cases in the prevalent and incident populations. These are generated for the first year on the basis of the total population. The evolution of these cohorts (and sub-cohorts) over time is calculated by the model. Health states: The health states for the model were defined according to the most common classification of heart failure, by the New York Heart Association (NYHA), which classifies classes by specific symptoms. The states (classes) are as follows: 1 No physical limitations 2 Slight limitation of physical activity 3 Marked limitation of physical activity 4 Inability to carry on an physical activity without discomfort
Model structure A state transition simulation model structure is used. It is assumed that patient between the prevalent and incident groups do not mix. Between two consecutive years, an individualâ&#x20AC;&#x2122;s health can remain unchanged unless they do not survive (due to heart failure or other causes). The exception to this is the transition from year 3 to year 4, where all individuals of the first three functional classes in all untreated cohorts register a decrement in their functional class. The estimates for the population in all sub-cohorts for the first five years, and the deaths due to heart failure within the implementation horizon can be used to generate the BoD attributable to heart failure for each of the intervention scenarios and the baseline. The model can provide a population DALY estimate for each year, denoted DALYs(t), for t =1,â&#x20AC;Ś5. The overall DALYs for the five years are the sum of the individual year DALY estimates and this constitutes the observable BoD. Figure A9 below illustrates the structure of the model.
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Figure A9
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APPENDIX ANNEX D
Bibliography Adamson J, Beswick A and Ebrahim S. (2004). Is Stroke the Most Common Cause of Disability? Journal of Stroke and Cerebrovascular Diseases, 13, pp 171-177 Airoldi, M., Bevan, G., Morton, A., Oliveira, M., Smith, J. (2008) Requisite models for strategic commissioning: the example of type 1 diabetes. Healthcare Management Science, 11. pp 89-100. Airoldi, M., Bevan, G., Morton, A., Oliveira, M., Smith, J. (2008). Estimating the health gains and cost impact of selected interventions to reduce stroke mortality and morbidity in England. (2008). QQUIP report, The Health Foundation, London, UK. Bevan, G., Airoldi, M., Morton, A., Oliveira, M., Smith, J. (2007). Estimating health and productivity gains in England from selected interventions. (2008). QQUIP report, The Health Foundation, London, UK. Birch, S., Gafni, A. (1993). Changing the problem to fit the solution: Johannesson and Weinstein’s (mis)application of economics to real world problems, Journal of Health Economics 12, pp. 469–476. Brooks, R. with the EuroQol group. (1996). EuroQol: the current state of play. Health Policy, 37, pp 53-72. Cowie, M. R., A. Mosterd, et al. (1997). “The epidemiology of heart failure.” European Heart Journal 18: 208-225. DCCT. (1990) Diabetes Control and Complication Trial Update, Diabetes Care, 13(4): 427-433. DCCT. (1993) The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus, New England Journal of Medicine, 329 (14): 977-986. DCCT. (1996) Diabetes Control and Complications Trial, 1996. Lifetime benefits and costs of intensive therapy as practiced in the Diabetes Control and Complications Trial, JAMA 276(17): 1409-15. Department of Health. (1999). National service framework for mental health: modern standards and service models. London, Department of Health. Department of Health (1999). Saving Lives: Our Healthier Nation. Department of Health, London. Department of Health (2000). National Service Framework for Coronary Heart Disease -Modern Standard and Service Models. Department of Health, London. Department of Health. (2002). National Suicide Prevention Strategy. London: Department of Health. Department of Health (2003). Improvement, Expansion and Reform: the next 3 years, Planning and Priorities Framework 2003-2006. London, Department of Health.
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Department of Health. (2007). World Class Commissioning: Vision Summary. London, Department of Health. Department of Health. (2008). Commissioning Assurance Handbook. London, Department of Health. Drummond, M.F., Sculpher, M.J., Torrance, W.J., O’Brien, B.J., Stoddart, G.L. (2005). Methods for the Economic Evaluation of Healthcare Programmes. Oxford University Press. Oxford, UK. Forouhi NG, Merrick D. Goyder E, Ferguson BA, Abbas J, Lachowycz K, Wild SH.(2006) Diabetes prevalence in England, 2001 -- estimates from an epidemiological model. Diabetic Medicine, 23 (2):189-97 Garber, A., Phelps, C.E. (1997). Economic foundations of cost-effectiveness analysis, Journal of Health Economics, 16 (1), pp 1-31. Gold, M.R., Siegel, J.E., Russell, L.B., Weinstein, M.C. (1996). CostEffectiveness in Healt and Medicine. Oxford University Press USA. Hawton, K. (1998). A national target for reducing suicide. British Medical Journal, 317, pp 156-157. Hobbs, F. D. R., J. E. Kenkre, et al. (2002). “Impact of Heart failure and left ventricular systolic dysfunction on quality of life.” European Heart Journal 23: 1867-1876. Kaplan, R.M., Anderson, J. (1988). A general health policy mode: Updaye and applications. Health Services Research, 23, pp 20.-235. Leatherman, S., Sutherland, K. (2003). The Quest for Quality in the NHS: A MidTerm Evaluation of the Ten-Year Quality Agenda, Nuffield Trust. Leatherman, S., Sutherland, K. (2004). “Quality of care in the NHS of England.” British Medical Journal USA 4: 144. Leatherman S, Sutherland K. (2005) The quest for quality in the NHS: A chartbook on quality of care in the UK. Oxford: Radcliffe Publishing. Morton, A., Airoldi,M., Bevan, G., Oliveira, M., Smith, J. (2006). Estimating health gains in England from the National Suicide Prevention Strategy. QQUIP report, The Health Foundation, London, UK. Morton, A., Airoldi,M., Bevan, G., Oliveira, M., Smith, J. (2008). Estimating the health gains and cost impact of treatment for depression in England. QQUIP report, The Health Foundation, London, UK. National Audit Office (2005). Reducing brain Damage: Faster access to better stroke care. National Institute for Clinical Excellence. (2004). Depression: management of depression in primary and secondary care. National Institute for Clinical Excellence, London. National Institute for Clinical Excellence. (2008). Guide to the Methods of Technology Appraisal. National Institute for Clinical Excellence, London.
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Office for National Statistics. (2001). Mortality statistics. Cause. Series DH2, no.28. Office for National Statistics. (2002). Mortality statistics. Cause. Series DH2, no.29. Office for National Statistics. (2003a). Mortality statistics. Cause. Series DH2, no.30. Office of National Statistics. (2003b). Mortality statistics, Series DH1, no. 36. 2003. Office for National Statistics. (2004). Mortality statistics. Cause. Series DH2, no.31. Office for National Statistics. (2005). Mortality statistics. Cause. Series DH2, no.32. Oliveira, M., Bevan, G., Airoldi, M., Morton, A. Smith, A. (2006) Estimating health and productivity gains from improving prescribing statins to lower the burden of Coronary Heart Disease, QQUIP report, The Health Foundation , London, UK. Oliveira, M., Bevan, G., Airoldi, M., Morton, A. Smith, A. (2010) Estimating the impacts on health gains and costs from improving diagnosis and treatment of Heart Failure in England, QQUIP report, The Health Foundation, London, UK. Petersen, S., V. Peto, Scarborough, P. and Rayner, M. (2005). Coronary heart disease statistics. British Health Foundation, London. Rothwell P.M., Coull A., Silver L., Fairhead J., Giles M., Lovelock C., Redgrave J., Bull L., Welch S., Cuthbertson F., Binney L., Gutnikov S., Anslow P., Banning A., Mant, D. Mehta, Z. (2005) Population-based study of event-rate, incidence, case fatality, and mortality for all aclute vascular events in al arterial territories (Oxford Vascular Study). The Lancet, 366, pp 1773-1783. Singleton, N., Bumpstead, R., Oâ&#x20AC;&#x2122;Brien, M., Lee, A., Meitzer, H. (2001). Psychiatric morbidity among adults living in private households. London, HMSO. Stinnett, A.A., Paltiel, A.D. (1996) Mathematical programming for the efficient allocation of health care resources, Journal of Health Economics, 15 (2), pp 641-653. Tan-Tores Edejer, T.. Baltussem, R., Adam, T., Hutubessy, R., Acharya, A., Evans, D.B., Murray, C.J.L. (2003). Making Choices in Health: WHO Guide to CostEffectiveness Analysis. World Health Organization, Switzerland. Stouthard MEA, Essink-Bot ML et al. (1997). Disability weights for diseases in the Netherlands. Rotterdam, Erasmus University, Department of Public Health. Sutherland, K., E. R. Brody, et al. (2007). Quest for Quality and Improved Performance: Quality Enhancing Interventions -Healthcare delivery models for heart failure. London, Health Foundation UK. Ward, S., L. Jones, et al. (2005). Statins for the Prevention of Coronary Events. Technology assessment report commissioned by the HTA Programme on behalf of The National Institute for Clinical Excellence, National Institute for Clinical Excellence, London.
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Estimating costs and benefits over time This Appendix explores issues in estimating costs and benefits over time. There are a number of reasons why limited time-horizons might be used: 1. Simplicity. It is difficult to develop lifetime disease models. This means that it is impractical to attempt this for the many interventions considered in setting priorities for commissioning. Hence, it is essential to model for short time-horizons only. 2. Realism. Making effective planning decisions requires realistic estimates of the effects of different healthcare interventions on population health. Modelling for long time horizons pushes predictions further into an increasingly uncertain future because of e.g. the development of a new drug or treatment. 3. Suitability. The main aim for evaluating health benefits over time is to inform policy decisions. This entails using time-horizons that are suitable for the scope of the proposed policies which will typically be concerned with the short term. Figure 18 illustrates how to define time horizons to match benefits and costs. We assume that an intervention is implemented at point t1 which extends the individualâ&#x20AC;&#x2122;s life to the end of our time horizon and possibly beyond. There are three ways this could be modelled, by: i. Making a simplifying assumption that the individualâ&#x20AC;&#x2122;s health profile at time t* is maintained until the end of life (defined by the fixed reference age); or ii. Making an alternative simplifying assumption that the individual dies at t* (and so there are no QALY gains beyond then); or iii. Projecting the individualâ&#x20AC;&#x2122;s health profile, which decreases steadily up to point t3.
Figure 19
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We illustrate ways of projecting costs and benefits for three types of interventions with differing profiles over time: a One-off interventions, for which the costs are incurred early on but the flow of benefits continues for a much longer period. b Continuous interventions, where the flow of benefits requires a continuous flow of costs c Future-Impact interventions, for which the benefits accrue after a long sustained flow of costs. Figure 19 illustrates benefits (top panel) and costs (bottom panel) over time for one-off interventions. Consider a scenario where an intervention is implemented up to point t1 (where the cost reduces to zero) for which the associated health gains end at time t2. This is an intervention for which all costs are incurred early on but benefits accrue over a much longer time period (e.g. hip-replacement) as in category a) above. If a short time horizon is used, such as H1, we are capturing all costs but only a small proportion of the benefits attributable to the intervention. As a result the intervention appears significantly less costeffective than it actually is. The cost-effectiveness increases as longer time-horizons are used as an increasing proportion of health gains can be captured. A similar situation occurs if we assume that the distribution of health benefits is given by the curve ending at point t5. Such substantial health gains are typically associated with acute interventions that prevent death (e.g. treatment in a stroke unit which results in preventing death from a stroke, followed by a possible short period of assisted rehabilitation). In this case using short time horizons provides significantly distorted estimates on the interventionâ&#x20AC;&#x2122;s cost-effectiveness. We now consider a continuous intervention for which the cost profile is given by the dotted curve ending at t3 and the benefit profile by the dotted line ending at t4. In this case both benefits and costs are distributed relatively evenly which is typical for conditions for which continuous treatment (e.g. using statins for lowering cholesterol) is required to sustain benefits . Shortly after treatment ends (as indicated by costs), so do the benefits. Due to the relative stability in the benefit and cost profiles, the use of long or short time-horizons does not seem to have an important impact on the interventionâ&#x20AC;&#x2122;s costeffectiveness. Overall, the use of shorter time horizons is better suited for interventions with relatively similar cost and benefit profiles whereas the need for using longer timehorizons increases when the distributions for these profiles are notably different. Figure 20 illustrates benefits (top panel) and costs (bottom panel) over time for FutureImpact interventions Costs remain relatively static but there is a long delay before there are any benefits, As e.g. for type 1 diabetes. In this scenario it is the location of the time-horizon that has the greatest influence on our estimates. Using short or long time-horizons early considerably reduces the appeal of an intervention which can have a significant impact on the populationâ&#x20AC;&#x2122;s health in the future. Instead, it is more useful to take snapshots of the future by using time horizons that include periods where the intervention has reached its true potential. As before, the duration of time-horizons can be short or long depending on the similarity of the cost and benefit profiles in the future.
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Figure 20
Figure 21
Time Horizons (basic guidelines) Short time-horizons
Long time-horizons
Future Snapshots
cross
tick
cross
Continuous interventions
tick
–
–
Future-impact interventions
tick
–
tick
One-off interventions
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SyMPOSE Decision Conferencing for resource allocation in healthcare – Datapack preparation In this document we provide a checklist of information which may be helpful to inform the discussion on priority setting and resource allocation and a simple standard template to summarise key information. The aim of this document is to start a discussion with the SyMPOSE team about the available information and to help the team to familiarise with the particular disease area. Information checklist • list of candidate treatments, ideally with associated clinical thresholds/ diagnostic criteria - for example relevant NICE guidance • any information on effectiveness of said treatments, either individual studies or meta-analyses • geographical variations in consumption of treatments • health economic studies on treatments (if any) • information on condition-specific Quality of Life (QoL) scales (if any) • internal data on costs (and some indication of how derived, so we know what is inside the costing and also what is missing) • epidemiological data on population prevalence of condition • audits of populations undergoing treatments against clinical thresholds to check for appropriateness of treatment - would be particularly useful if we envisage withdrawing or restricting treatment • any relevant internal data on health improvement arising from treatment (e.g. changes in admission rates; routinely captured QoL data) • any information on resource consumption - e.g. number of outpatient appointments per patient episode. We should recognise that there may be no data collected on some use of resources, e.g. GPs, diagnostics • any relevant info comparing with other providers
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Standard summary template for priority setting Please use a separate table for each intervention. Note: the template is designed to set priorities for interventions for one financial year. If the intervention requires more than one year investment, please indicate the timescale, costs and benefits over time in the Comments. For more information on completing this template, contact the SyMPOSE team please contact the team Mara Airoldi (m.airoldi@lse.ac.uk) or Alec Morton (a.morton@lse.ac.uk). Intervention
Please provide a name for the intervention
Description of the intervention
Please give a description of the intervention from a clinical perspective (what does the intervention consist of?) and organizational (in particular the necessary staff involved in its delivery)
Cost p.a.
Please provide the annual cost of providing the intervention for the local population (if possible indicate fixed costs –e.g. training or a x-ray machine - and variable costs –e.g. material used for each patient - separately).
No. people who benefit
Please indicate how many people benefit from this intervention every year (indicate the number of new patients separately if relevant).
Information about the people who benefit
Please provide a description of the beneficiaries in terms of demographic information and severity.
Clinical effectiveness
Please provide information about the effectiveness of the intervention from the literature or based on your expertise. Please specify what comparator you used (e.g. ‘compared to no intervention’). These benefits might be over a lifetime (e.g. the benefits of an appendectomy) or may require recurrent investments to be sustained (e.g. statin prescribing). Please consider only the effect which will follow from providing the intervention for one year.
Benefits beyond the patient
If relevant, please indicate benefits beyond the patients, e.g. to carers and family.
Comments
Please provide any other information which you think should be considered in assessing the intervention.
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Commentary on the Sheffield PCT/ LSE Commissioning Workshops Facilitated by the SyMPOSE Research team in 20091 Introduction This report comprises a commentary from David Collier 2 on the implementation of a new framework to support commissioning by Sheffield Primary Care Trust (PCT) and the London School of Economics and Political Science’s (LSE) Operational Research Group. It offers conclusions – summarised in Section 7 - and identifies some unresolved questions that the PCT and LSE might like to consider. It is a personal view contributed on an expenses-only basis at the invitation of the LSE and PCT teams. It was based on the author’s observation of one set of workshops and semi-structured telephone interviews with a cross-section of PCT and LSE staff over the course of the project, supplemented by discussions with other participants. The purpose was to provide some additional insights to help consider how the approach piloted here might be used in the future. It was not a systematic evaluation against declared objectives, and should not be interpreted as such. The author is grateful for the help of the interviewees, but the conclusions and comments in this report are his alone and may not accord with those of any other party. He cannot claim to be speaking for everyone and this report needs to be considered alongside participants’ own feedback. All interviews were non-attributable, but if direct quotes have been included they are shown in italics.
Inception Was the PCT’s selection of the LSE approach sensibly managed? The LSE team had previously developed a decision conference methodology for prioritising investments in health initiatives and successfully applied it to investment options for the Isle of Wight NHS Primary Care Trust. The use of this type of approach is more common in other sectors but multi-criteria decision analysis (MCDA) type methodologies have been used elsewhere to support PCT commissioning in public health applications. In developing their approach for the Isle of Wight pilot, the LSE team were therefore able to draw on experience elsewhere, particularly (we understand) on work in Oregon, in the UK with NICE, and around the world with a related approach known as Program Budgeting and Marginal Analysis (PBMA)3.
1 This external evaluation was by David Collier of Golder Associates 2 david@davidcollier.me.uk 3 Airoldi, Smith, Bevan, Morton, and Argyris (in preparation), “Maintaining quality in hard times: engaging key stakeholders in a transparent prioritysetting process with Decision Conferencing”.
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The Isle of Wight work was reported at a Health Foundation Round Table event in February 20094. It supported decision-making through a combination of social process and analytical work. The MCDA looked at potential investments across five key areas (Cardiovascular Disease; Cancer; Children; Mental Health; Respiratory) using as criteria: health benefit; health inequalities; feasibility; and cost. Members of the PCT executive team were at that event and invited LSE to present their approach to a wider Sheffield PCT audience and suggest how it might be applied within their commissioning framework. That meeting took place on 14th May 2009 and the PCT subsequently invited LSE to pilot a modified process in Sheffield. The ‘holy grail’ might be a commissioning programme based very largely on health economics principles, but everyone we talked to had more realistic expectations. Inevitably, people within the PCT and its stakeholder groups had different ideas of what might be achieved, but the prevailing senior management view seems to have been that it was an example of an approach with a number of attractive features that made it a good candidate for exploratory application. It potentially brought a degree of rigour and evidence-based decision-making that meant decisions could not only be made but also justified. Structured processes like this one tend to be more credible and transparent. The stakeholder / service user participation and collaborative decisionmaking elements would contribute to meeting World Class Commissioning competencies5. The LSE’s third-party facilitation might help if relationships turned defensive. Management did not seem to be looking to the LSE approach to solve all their prioritisation problems, or even necessarily to provide them with a finished tool, but rather to take the PCT through a process that would leave it in a much better position to develop its approach to decision-making and an overall commissioning programme. The PCT’s 5-year strategy is set out in Achieving Balanced Health, which is refreshed each year. The 2009 refresh was close to issue at the time of writing, and made mention of the project in the context of prioritising initiatives for 2010 onwards. However, we are not yet clear where the underlying thinking on commissioning strategy is or will be documented. There is a ‘Commissioning for Quality’ strategy, but that has a different focus. The PCT seemed aware that there has often been poor uptake of results from economic evaluations at the local level in the NHS and of some of the reasons why this tended to be the case, including a lack of clear strategic context. Proponents of Program Budgeting and Marginal Analysis in particular argue that their approach integrates into wider management processes based on the same principles, but the challenge is common to all analytical methodologies that work at this level. Unless they are conceived as part of a properly resourced and thought-through management process into which results from economic evaluations and other evidence feed, it seems to us that even successful trial applications of MCDA/CEA (cost effectiveness analysis) methodologies will struggle to leave a lasting legacy. Coming across the LSE work as the PCT did might seem serendipitous, but senior management were
4 P resentation downloaded from http://www.yhpho.org.uk/resource/view.aspx?RID=69756 in March 2010. 5 The project was included in the commissioning competencies paper presented to the Board in February 2010 (Sections C5 and C11).
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actively looking for something like this – preferably with some academic rigour and respectability behind it - and had in the past discussed the options with other university teams. The fact that the external costs were covered by funding from the Health Foundation was a contributing factor in the choice of approach and enabled the project to go ahead as it did, but it was not the only reason. Whether the PCT would have found something better or established a more coherent picture of its priorities in terms of evidence-based commissioning methodologies if it had had the resources to conduct a needs analysis and a structured review of experience with other methodologies is a reasonable question, but again not one that we are in a position to answer. Since it involved stakeholders without any non-disclosure agreement, may have resulted in public and media comment. It therefore had an element of risk for those involved as well as being a different way of doing things in several respects. The approach was being trialled but nevertheless had the potential to affect commissioning decisions. Some PCT staff were therefore noticeably cautious at the start as to how ’real’ the Sheffield trial would be and expectations varied about the extent to which it would make a significant contribution to forthcoming commissioning decisions. This was reflected in some of the statements made in at least one of the early workshops where participants were told (in response to comments on data validity) that the purpose was to test the methodology rather than come up with the ‘right’ result. This may have been true at the time but by the end there seemed to be a sense that the insights gained were proving genuinely valuable and consistent with existing understanding.
Project Scoping Was the initial scoping process appropriate and well managed? Before this sort of approach can be usefully applied, there needs to be an acceptance of the need for prioritisation and of the possibility that resources may be reduced as well as increased. People are naturally often reluctant to accept loss of funding and so similar initiatives elsewhere have moved more slowly to get the maximum ‘buy in’ to the project. Those with commissioning responsibilities in pilot areas therefore needed to be persuaded of the potential value of the approach and the trial and to clearly understand the basis for proposing ‘their’ area and the implications for the commissioning process. However, we understand that there were constraints that meant that the current project had to be delivered against tight timescales and it is not clear that the timetable allowed for buy-in and understanding to be reliably developed. Also, perhaps not everyone appreciated the extent to which a pressurised timetable risked key clinicians (for example) being unavailable, or choosing not to attend because there had been insufficient time to engage them. The briefing documentation was, at best, basic.
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The decision about the trial areas was taken by the Director of Strategy, the Director of Public Health and the CEO. The Directors of Finance & Healthcare Procurement and of Standards and Engagement were less directly involved on this occasion, which might be taken as an indicator that this was an opportunistic project rather than the consequence of strategic analysis but we do not have the information to form a view. Three key commissioning areas were selected for the Sheffield trial: Cancer; Dentistry; and Mental Health. We understand this was on the basis that they were areas where spending in Sheffield seemed proportionately higher than might be expected from national statistics. The leads from the Public Health Directorate for this project were: Specialised Services Strategy & Specification Manager (covering Cancer); Director of Dental Public Health; and the Mental Health Strategy and Specification Manager. The PCT then set up a Steering Group to guide and monitor the progress of the work. The Steering Group consisted of: the Director of Strategy, the Director of Public Health, the three leads from the Public Health Directorate directly engaged in the pilots, the Heads of Finance and Healthcare Procurement and of Standards and Engagement, and the lead analyst. The rationale for the choice of the three areas which the work would focus on seems to have been clearly understood from the start by those involved in making the choice but we have less evidence that it was explored with or communicated to the three project leads and the implementation requirements systematically thought through. If the original idea was to cover a high proportion of each area, it was an unrealistic goal but the project leads seem to have managed to focus the project onto better defined aspects of ‘their’ services. For each commissioning area, the intent was as follows. • •
• • •
eet the project lead and scope the activity breakdown and data requirements, and then M prepare a workshop data pack using data from the PCT and from LSE sources. Run a pair of workshops involving managers, stakeholders and clinicians to prioritise the different activities on the basis of the cost and benefit data. The first workshop was intended to explain the process and review the cost data, the second was intended to focus on constructing the benefit scales and considering the results. Draw up a draft report for each area for review by the PCT’s area project lead and then workshop participants before issue. Each PCT area lead would organise follow-up analysis and discussion as appropriate. Hold a workshop at the end of the process for all the parties involved, to consider what has been learned about the use of this sort of methodology and how it might be taken forwards.
This basic framework seemed sensible to us at the time and still does, although inevitably (especially given the compressed timescales) there were obstacles to overcome in delivering it. The outline programme agreed by the Steering Group is attached as Appendix A. The dates are those when the events ran rather than when they were originally scheduled. The project as a whole took about twelve months from start to finish. It seems to us that the project leads responded positively to what might have seemed (we deduce) a slightly haphazard inception and the prospect of a process which they knew little about but which might have potentially difficult conclusions for their areas. They had limited time available,
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but were supportive of the LSE team and subsequently looked for opportunities to take the conclusions forward. Reasons the people most involved gave us for being supportive or at least participating included: • • • •
L ong standing interest in health economics; Concern about what might happen in their absence; A general sense that something better than the current approach was needed; Recognition of what was offered by this approach.
The project leads probably already had a good idea of where they would look to increase or decrease resources to optimise patient benefit and would originally have been looking to the process less for new insights and maybe more to help them build a case for change and persuade those affected. However, our impression is that once they saw how it worked many of those involved became more optimistic that it would deliver something new and not just tell them what they already understood. In summary, the motivations for the programme and the intended benefits as perceived by our interviewees included: • • • • • • •
etter analysis, though the use of a structured method based on health economics B principles (in particular Cost Effectiveness Analysis); Better analysis through the use of an agreed evidence-base; Better analysis and NHS ownership though collaborative decision-making processes; Better analysis, transparency, and wider ‘ownership’ of outcomes through external stakeholder involvement; Meeting targets for involvement; Helping participants adopt a ‘constructive mindset’ through external facilitation and workshop approach to shared analysis; Supporting the decision-making that follows, by providing information and by ensuring that the ‘front end’ of the decision-making demonstrates the attributes (collaboration, rigour, transparency etc.) required of the process as a whole.
MCDA / Cost Effectiveness Analysis Was the basic approach appropriate? The Multi-Criteria Decision Analysis approach is designed to support the development of a portfolio of projects or investments by comparing alternatives on a range of criteria, such that trade-offs can be explored by applying different weighting to their importance. Where resources are limited, this allows projects to be ranked according to the ratio of the sum of those criteria that constitute a detriment (usually cost) to the sum of those that constitute a benefit. Projects are then added in order to the portfolio until the total cost reaches the budget limit. Sensitivity and trade-off analysis would usually follow, with iteration as necessary. In summary, the main steps planned were as follows, although as the process evolved the requirement for cross-attribute weighting was superseded as described in Section 5.3 below.
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1. Establish the decision context 2. Identify the areas and the options to be appraised for each area 3. Identify objectives and criteria 4. Scoring: assess preference scores for the options 5. Weighting: assess swing weights for criteria 6. Combine weights and scores; calculate the priority index for each option 7. Examine results: efficient frontier and affordable portfolio 8. Conduct sensitivity and trade-off analyses 9. Iterate In the Isle of Wight experience, the detriment was cost and the benefit criteria were health benefits and health inequality benefit. Feasibility was taken into account when estimating expected benefit scores and explored extensively in sensitivity analysis. A range of relatively self-contained options for spending additional funding were ranked and selected for further consideration according to detriment/benefit ratio (in practice, a cost/benefit or value for money ratio). The Sheffield context was quite different. Here, the aim was to explore techniques for reallocating resources between existing activities within a commissioning area, and potentially between commissioning areas. For some activities spending could be adjusted incrementally e.g. some kinds of spending on health promotion. For others changing spending outside certain limits might mean opening or closing a specialist unit. This would introduce step changes in cost or might result in a service not being available at all. In the Isle of Wight, the workshops were looking at new funding opportunities, whereas in Sheffield the budget is fixed (or maybe even reducing) so decisions to increase in one area mean a reduction in another. This affects the use of the MCDA methodology but more importantly greatly affects the stakeholder dynamics and gives people a motivation to defend ‘their’ budget. Also, although there was some initial consideration and data collection relating to health justice issues, it proved impractical to build it into the MCDA framework in the time available and so it was decided to consider it within the ‘integration’ phase that would follow, where MCDA outputs and other information would be brought together in the decision-making process. The MCDA in the Sheffield context therefore became a more conventional cost effectiveness study. If the costs are measured in money terms and the benefits in terms of health impact, the ratio between cost and benefit provides an indication of the value for money of that option. This was expressed graphically in the workshops as a triangle with one axis representing cost and one value. The slope of the triangle is a measure of the cost/benefit ratio and therefore ‘stacking’ triangles clearly shows the options in order of value for money, the so-called ‘efficient frontier’. This approach to CEA is numerically-based but not overly focused on ‘number-crunching’ or seeking (what usually turns out to be false) precision. It handles complexity well if the analysts are not trying to work at too detailed a level. However, it might take several commissioning cycles before even one area can be comprehensively modelled. To deliver real benefit in a PCT context, conventional wisdom is therefore that it has to be used iteratively and applied consistently across a number of commissioning cycles, as it has been on the Isle of Wight. Each iteration builds up more understanding of the original commissioning areas. If the scope is progressively widened, the approach can be extended to related areas so that it can
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also be used between instead of just within them. Resolution can also be improved through iteration, perhaps using in-house resources and better data to look at discrete issues. This may not have been fully appreciated, and the Isle of White process was incorrectly perceived by some as having been a ‘one pass’ exercise. We note, however, that health justice issues and an improved value-for-money analysis across different commissioning areas were discussed at a meeting between the LSE team, the CEO and the Steering Group on December 3rd 2010. MCDA/CEA can be a desk top exercise; there is no inevitable requirement for stakeholder involvement. However, a broad range of stakeholder inputs to data validation, service options, scales and weightings is widely held to improve both the quality of the decision-making and commitment to the outcome, and leaves a greater legacy in terms of understanding and enthusiasm for future collaboration. Where the collaborative element is emphasised and the aim is to reach a conclusion, as was the case in Sheffield, it is often referred to as decision conferencing, but decision conferencing in fact encompasses a broader range of methodologies and is not limited to MCDA/CEA applications. Indeed, stakeholder involvement in a structured workshop is sometime even more important in contexts not particularly well suited to numerical MCDA/CEA. For instance, many elements of MCDA/CEA inputs, outputs and logic can in principle be reviewed and readily understood ‘offline’ whereas the equivalents for more deliberative formats may not be. In this instance, the opportunities for stakeholder involvement in the process in general seemed sensible and appropriate. The events fulfilled the requirements of a decision conference in this respect and the ‘social dimension’ was the key to the relative success of this trial. In referring to decision conferences, however, we have to be aware that what is generally being sought is a conclusion from a study, but that this conclusion is rarely going to be the final decision. It is axiomatic in MCDA/CEA that the eventual decision almost always has to take into account a range of factors that cannot readily be included within its scope (or are better assessed in some other way). This is certainly the case for the Isle of Wight and Sheffield implementations of the LSE’s approach, which were followed by further analysis and ‘integration’ with other types of information, constraints, and strategic objectives etc. It is not clear to us that the MCDA/CEA and integration activities were initially appreciated as being two linked parts of one process, or that the integration phase was structured and allowed for within the programme. Exercises of this sort are labour intensive, for the project team but also for PCT management, for those providing the data, and for the clinicians and stakeholders involved. The LSE team’s costs were covered by a grant but the other costs were real and born by the PCT. To make a sensible judgement about the future use of this or comparable approaches, the LSE team and PCT would have needed to make arrangements in advance to collect cost data and to evaluate it afterwards. We do not have the information to say whether this was done, but the internal cost must have been significant. No one we spoke to believed it was without its difficulties, but it was a pilot study put together at considerable speed and our assessment is that the CEA aspects of this approach generally seemed to meet the expectations of the Steering Group and probably exceeded the expectations of the
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commissioning area project leads and the stakeholders involved, both in terms of what it did and in terms of how comprehensible it was. There are some more detailed comments on implementation later in this note but our judgment is that in general terms the LSE approach and the MCDA/CEA methodology used here were appropriate (though we do not have the knowledge to say that they were necessarily the most appropriate).
Implementation In this section, we have collated the more significant of our observations on the delivery of the approach and process discussed above, drawing also on the insights of the PCT’s Steering Group members, commissioning area project leads and other participants.
Workshop Events Were the events well organised and run? Our impression is that the LSE academic team delivered what in other circumstances would be a challenging professional consultancy job very well indeed. Our only point in relation to this is that most consultancy teams would probably not have agreed to the timescales because of the risk of failure, especially given the dependency on data that had to be supplied by hard-pressed people and the lack of prior commitment from key internal stakeholders. They would also probably have sought to narrow the scope e.g. by ‘parking’ health justice issues at the start. A major contributor to a successful conclusion in these circumstances was the quality of the project team’s work during the events and the good will and support they received from the PCT staff and stakeholders involved. All seemed to pull together, despite any misgivings. The standard of facilitation was excellent and remarked on positively by all we spoke to, particularly the lead facilitator but also the support team: ‘academics with social skills’; ‘it was not a research project, it was real and useful’; ‘the team seemed enthusiastic and creative and always able to come up with a change of direction when needed’. A question raised in one of our interviews was, how much domain knowledge does the facilitator need? There is no one right answer, but our impression is that on this project the facilitators had to understand the problem and the context in some detail, especially when the process is evolving and the workshop plan is being modified as the day progresses. It is the combination of process, supporting evidence and an expert/stakeholder group that makes workshops like these effective, but the facilitator must understand the significance of what is being said. The project team was well briefed and seemed to have quite a deep understanding, which is an achievement given that three areas were being progressed in parallel. The key to getting that knowledge was that the lead facilitator was also the project leader and immersed in every aspect of the work. Their skills and domain knowledge allowed the team to make progress when others might have stalled, and the goodwill meant that participants were willing to backtrack or forgive shortcomings in data or detailed preparation.
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This might not have been the case with another team, or with other participants. And, whilst entering data and running sensitivity exercises with alternative data sets is normal, live work during workshops on spreadsheets with complex formulae is not. Unless a similarly skilled and knowledgeable team can be fielded, a more tightly structured event with more detailed preparation would be needed. Although there were some shortcomings, the workshops were decently organised, the venue was suitable, and administration and housekeeping generally did what was required. The balance of responsibilities between the lead facilitator and the rest of the team seemed to work well enough. We only saw one member of the team leading a plenary session for any length of time so we cannot comment on whether the others would have done as well, but they managed table discussions effectively. We have not inquired about contingency arrangements and the procedures should key members of the team or participants become unavailable or a workshop need to be cancelled, but we note that it is good practice to have a project risk register and to agree the detail of who would do what in different circumstances. As already discussed, the timetable was tight. Busy clinicians especially, but also stakeholders, have schedules to keep and many other commitments to juggle. They therefore need good notice of events and effective briefing packs. The timescales meant that particularly for the first workshop in each series people really did not have much warning and often arrived with a relatively poor grasp of what was happening and why this area had been chosen, and with little chance to consider and comment on data. Interviewees noted that ‘some obvious people’ were missing from workshops, either because they were not invited in time, because they were missed off the invitation list, or maybe because they chose not to come. In Dentistry there was a low take up of invites initially but it was much stronger for the second workshop, perhaps when people realised the significance. Where it can be achieved, consistent attendance is important across workshops. People with no previous MCDA insight would have had a problem at the start of the second workshop and time therefore had to be spent getting everyone up to speed. Although this was managed by the team, it did contribute to agenda pressure in the second round of workshops and there was less time than there should have been to test alternatives. People did not challenge data and models as they might have done, because of the need to get through the task and perhaps also out of a sense of common purpose with the team and other participants. We were left with a strong impression of a very positive and supportive group, to such an extent compared to other workshops we have attended that we wondered whether participants had been selected for their willingness to go with the process even if that meant that some key players were not invited. That would be understandable if this were a trial with relatively few expectations of being able to apply the conclusions directly, but it could be a problem if it were ‘for real’. Overall therefore, our assessment is that the team did very well but in several respects the project ‘got away with it’ and should not take it for granted that the outcome would be successful next time without time for more thorough preparation.
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Workshop Scope and Methods Were the scope and methods used in the workshops appropriate? The delivery of the methodology in the workshops was broadly based on that used in the Isle of Wight, but it was heavily modified for the Sheffield context. It was facilitated rather than chaired and our impression is that although the basic structure was planned, the individual elements and timings were not tied down. This of course provides flexibility and was probably the only option given the speed at which the process was developed, and it worked well enough here, but it was nevertheless rather risky. A prior ‘full dress rehearsal’ workshop would have made a lot of sense. In the workshops we attended, the principles seemed well explained. The conceptual framework was well translated and communicated. People from a wide range of backgrounds who would not normally have contact with such approaches seemed to understand it. Health economics divides clinicians as much as it does patient groups but – perhaps surprisingly - all seemed reasonably comfortable with it at the events we attended. Such methodologies are sometimes characterised as ’cold’ and unable to factor in ethical issues (see next section), but here there seemed to be recognition that they offer (for instance) a way to help ‘the system’ place fair weight on support and aftercare services in circumstances where there might be a tendency to focus on major interventions. Some stakeholders clearly feel there are services that they are important to patients but not rated by clinicians. Especially where distinct options can be identified, additional impacts such as health equality can be factored into the analysis by using a multi-attribute framework that more explicitly allows trade-offs to be explored e.g. between aggregate heath benefit and health justice. However, although the analysis was later extended at a Steering Group level meeting to compare value for money across different diseases, CEA as applied here generally concentrated on the aggregate health benefit. It is doubtful whether it would actually have been practical to include consideration of health equalities - especially given that what was being considered were incremental changes to existing services rather than new project options – so maybe it should have been ruled out early or addressed through some separate planned process. Although CEA is capable of handling complexity, it is always easier to apply where the boundaries – organisational, commissioning cycle, treatment, disease definition etc. – are clearer. CEA often seems to conclude that interventions further up the chain (e.g. prevention or awarenessraising) may be more cost-effective, but unfortunately the further up one goes the less likely there is to be hard data and organisational and funding boundaries become constraints. For instance, in this case health promotion and thus disease avoidance may be a cost-effective alternative to investment in treatment facilities with better overall health outcomes, but if it is not funded by the PCT it is not actually an alternative in this context. Some related issues emerged in discussion. For instance, getting people to present earlier saves both lives and money quickly, whereas a benefit through lifestyle change does not work through into the population for some time and there is a potentially lengthy period where the costs of treatment and of prevention have to be carried simultaneously. Furthermore, these sorts of intervention may have benefits across a range of different illnesses, which means the costs and benefits must somehow be partitioned across commissioning areas – which may not be easy to
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achieve. Not all areas were at the same stage in strategy development or review and so there were limits on what was potentially ‘up for grabs’. Targets have to be met as well. Issues like these need careful handling and judgments have to be made as to whether the complexities are best dealt with inside or outside the MCDA/CEA part of the process. Finally, our impression is that there are areas where staff know the PCT and its supply chain have to be more efficient and can see that there are many ways in which it can be, but are frustrated by the fact that progress is almost impossible because that is ‘just the way the system is set up’. Moving on to the mechanics of the workshops, the software seemed to work well enough and the team were familiar with it. The presentation of the results was sensible, although sometimes a bit hurried. The graphical representations were helpful. The facilitators were mindful of the potential for the results to be misrepresented as being more objective and quantified than could actually be justified. There were still many subjective elements to the process and opportunities for individual perspectives to skew results. Were two workshops for each commissioning area enough? The usual 80/20 probably applies here as it does elsewhere, and especially as this was to some extent experimental our conclusion is that splitting it over three days would not have been justified – although scheduling a structured follow-up workshop probably would have been. More investment in workshops might not have delivered much more insight but more investment in preparation would have.
Use of Benefit Scales & Weightings Was the scoring and weighting process implemented effectively? Benefit scales were developed interactively during the workshop, based on some prior analysis and experience from the other commissioning areas. The basic intent was to use a patient experience / quality of life measure integrated over time, essentially a QALY (Quality-Adjusted Life Year) approach. The use of QALYs is widespread but still hotly contested by some health practitioners, academics, and other stakeholders. Many defend the approach but others have argued that cost benefit/effectiveness analysis gives inadequate guidance on what courses of action are right or just. Available justice frameworks are variously said by critics to be conceptually and/or ethically unsatisfying. Certainly there are limitations, but QALYs seemed appropriate here and no one actually suggested to us that an alternative might have been preferable. Some participants seemed to feel that the LSE implementation of the QALY concept was not transparently tied back to other work that has been done within the NHS to try and put commissioning on a common footing, although we understand that it was in fact quite thoroughly researched. Participants went along with the approach without question in the workshops we witnessed, though perhaps not everyone was mentally weighting the ‘Quality’ and ‘Years’ parts of the equation equally. We stand to be corrected, but it seemed to us that clinicians were implicitly weighting the Years part higher whereas some patient groups emphasised the Quality side more. MCDA approaches conventionally include scoring and weighting to indicate the relative importance of attributes, and some participants seemed to think there was to be a weighting step here. With only one attribute to balance against cost this would have had no purpose, although conceivably one
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might consider ‘decomposing’ and weighting the components of a QALY score. We observed only the cancer workshops. In these, the LSE team emphasised the patient’s experience as a means of understanding the Quality scales and relative scores during diagnosis, treatment, and recovery. The approach used was to ask participants to ‘put yourself in their place, how do you feel?’ This would be more intuitively appropriate for some participants than others and some diseases than other. It also begs the question, are we trying to cure people or make them feel better. Would it weigh against emergency services that keep people alive or prevent deterioration rather than those that improve conditions? The aim is to capture the participants views, and this is clearly valid and a fundamental part of such processes. The risk, though, is that if there is too much reliance on ‘feeling’ without enough reference to supporting objective measures or more broadly based generic (though still of course subjective) QALY assessments, the conclusions can be too driven by personalities – especially if the scoring is all done in open session without any ‘Delphi’ element – or emotional reactions to some conditions. How patients feel varies considerably, and how well workshop participants can visualise how they or others would feel also varies considerably, even given the same level of medical or social insight (which participants may well not have). It then becomes difficult to achieve consistency and compare results across workshops or more widely across commissioning areas. The LSE team were clearly aware of this issue and we understand that during the analysis they also made some systematic use of QALY weights proposed in the literature as a benchmark in Cancer and in Eating Disorders. In Dentistry, they explained that they “developed a life profile and used the QALY framework directly together with decision trees to deal with patients’ heterogeneity”. It is not realistic to ask commissioning area leads and other colleagues to comment on data issues live on the day, although it may be that the PCT leads could have led more of the data pack discussion rather than the facilitator doing it. The facilitators were neutral and quite well informed, but was it reasonable to expect them to understand the nuances? In one workshop we witnessed, there was a related discussion on weighting the views of different participants. The workshop format effectively gave everyone an equal voice in discussions from which a consensus generally emerged. However, should the perceptions of a psychiatrist working with cancer patients or a patient group coordinator count for more than someone perhaps with strong views but is present for another reason? Alternative weighting sets are a solution where weighting is part of the process but this was not an option here. As it turned out, this group worked with the facilitator to arrive at a common sense outcome and those with less patient contact generally deferred to those that had more, and clinicians and patient group representatives genuinely seemed to value each other’s perspectives. However, it will always be a potential issue, particularly in organisations with some hierarchical heritage. The approach was offered as a basis for discussion and not imposed, it supported a broader vision of the treatment pathway, and at the workshops we witnessed it was accepted and applied without serious question. The scales therefore worked and the interactive discussion of alternatives that led to the final agreement was well managed. But, our view remains that leaving so much to do ‘live’ on the day was an unnecessary risk. What if there had been different views that people wanted to pursue more seriously? Time would have run out. We accept that it happened this way because of programme constraints, but do recommend that consideration be given to working up proposed scales at a preliminary meeting.
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Evidence Base Was the evidence base assembled appropriately? A major benefit of this type of process is that the organisation has to assemble an evidence pack for each service area. Collaborative review of data against a purpose is always valuable and a robust evidence base is needed for any commissioning process so this has real benefits, revealing what is actually known and quantifiable and where gaps in data exist. Data typically needs to be analysed and processed into useful information for use in commissioning processes. Organisations conducting option assessments are often faced with a mass of data but recognise that processing is needed to produce insights at the right level of detail for the task. They try to get beyond ‘emptying the box of data on the floor to see what we have’. Rather, they try to work out what higher level information they need and then either process existing data, collect new data, or look for information from similar contexts that might work as a proxy. They also tend to organise information by process. Our interpretation is that the PCT had these as objectives, but the timescales were just too tight. Participants clearly recognised that a major benefit of the process was that it encouraged people to think in terms of commissioning pathways rather than buying treatments, and that information could be much more valuable if it were organised in this way. There was just not enough time and resources to do as much as they would have wanted. Overall, especially given the time available to prepare them, the evidence packs seem reasonably comprehensive and adequate for the workshops. However, the PCT resources required for preparation of evidence packs may not have been fully appreciated and the baseline information was therefore not always of consistent quality and detail. More time would also have allowed more focused data-gathering. If you are asking people to collate data for you, you do need to specify in advance what is required. The management of the data collection process reflected the time pressures everyone was under. Given a longer lead-in time, a scoping phase could have looked at each pathway, thought about scales, and followed up with an ‘evidence pack group’ which could have been convened – working by email if necessary - to map out what was available and provide the data in a consistent format. This may seem like a large investment but is probably required if good quality data is to be provided and is cheaper than spending workshop time on the task. It would also drive future data collection improvements. Participants remarked on the fact that they did not receive the evidence packs in time to verify or add to the information from their own knowledge. There was not, therefore, the ‘pooling’ of data that there might have been and there may well have been significant omissions. In theory, the process provided an audit trail that allowed uncertainty in data to be tracked through to uncertainty in the conclusions and thereby help participants keep a sense of what was certain and what was assertion or hypothesis, but we do wonder whether a more systematic approach to associating data items and confidence could be applied. Some workshop approaches place more emphasis than was practicable here on capturing the decision logic and tying it back at key points to specific information and data that answers the question ‘how do you know?’ We suggest that this is an area where the LSE team might consider enhancements to their methodology.
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The PCT was generally responsible for data provision. We are not clear whether senior PCT management had a clearer picture, but the LSE team seem to have assumed that more information was available in usable form than there actually was. Workshop participants felt that, even given ample time to prepare, it was not particularly good for any commissioning area. Outcome, cost and benefit relationships are clearly fundamental to any evidence-based commissioning programme, and indeed to many other aspects of management decision-making, so this is a significant problem. It is not, however, within our remit to explore the issue further. Preventative and health precaution work seemed less well served than hospital-based activities. It was revealing that proxy data for clinical outcomes often had to be used instead of direct data on outcomes. Outcome prediction was clearly much easier in some areas. If a cancer goes untreated, participants could make a good guess as to what the outcome would typically be, but what about mental illness? Benefit data was similarly easier to arrive at in some areas than others. How does one estimate the benefit of e.g. residential care? Information on community care is an issue for several reasons e.g. support is provided from a range of agencies. As in other areas, as part of the follow up it would be worth comparing what would normally have been done with what was done during this project, and to build on experience of what worked well and what was less successful. Whereas the group we witnessed was very tolerant of data uncertainty (or even error) and could see that broad conclusions were not affected, others might not be and might argue that the conclusions were invalid because the inputs were not all correct (as opposed to sufficiently robust). Alternative decision-making methods may be even worse of course, because that data should underpin any analysis method, but we do believe that on another day the workshops could well have run into serious difficulties because of problems with the data. This is another reason for ensuring participants have a chance to review it in advance and so try to achieve prior consensus that it is adequate for the purpose. Perhaps surprisingly to outsiders, even basic cost data sometimes seemed an eye-opener for many stakeholders, for instance the impact on the budget of some cancer drugs. Irrespective of tradeoffs, this alone seemed to make people think about where efficiency savings might have the biggest payoff. This is already done within the PCT of course, but not necessarily in this collaborative environment. The LSE contribution to data and benchmarking appeared valuable and valued, but trying to extract, organise and review management data from a distance was always going to be difficult. It required good access to PCT information and key staff â&#x20AC;&#x201C; some of whom were clearly very busy and not in a position to put much time into organising data selection. The time and cost for the LSE team to go to meetings and work with PCT staff on the data were covered by a grant on this occasion but would be a substantial budget element if this were a consultancy project. A vast amount of work on health economics has been done by NHS and others, especially (it was said) on cancer, but it is unclear to what extent data or conclusions culled from the literature was fed into the process and whether insights and analysis were more generally summarised and taken into account. At least some workshop participants believed it had been but we have not seen any documentation. Consistent data across a range of service providers is presumably never going to be easy to compile, but is a necessary objective to support any kind of structured evidence-based commissioning analysis. The insights gained here can hopefully be taken forward by those with responsibilities in this area.
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Engagement Did the process provide for stakeholder engagement? The approach adopted was designed to be consistent with a broader desire for collaborative analysis and decision-making within the PCT, and for wider engagement with providers and with external stakeholder groups or constituencies (e.g. service users). It actually brought service users and experts together and the interaction through discussion of data and priorities will have spread mutual understanding. There are subtle differences here: collaborative analysis and decision-making involving the whole pathway supply chain is one strand; the engagement of stakeholders who provide additional insights and whose engagement provides transparency is another. The process as delivered seemed to strike a good balance, but these two do need to be thought about separately as well as in conjunction. The sort of event that is optimised for one combination may not be optimal for the other. For instance, independent facilitation may be more important where the wider community is involved and, realistically, some things discussed between the PCT and supply chain would not be said – or at least not said in the same way - in front of patient groups. In the events we witnessed, everyone was very ‘rational’ when discussing the allocation of resources, but what if the direction the evidence was suggesting was to take resources out of cancer instead of prioritising within it? The vehemence of the debate over NICE’s verdicts on cancer drug priorities gives an indication of what might happen – would the PCT be willing to persevere with an approach that occasionally ‘went wrong’ and generated passions that spilled over into the local media rather than staying behind closed doors? It arguably should, but it needs to understand that engagement is a worthwhile but sometimes rocky road. Although the format may have been different, stakeholders and service users have been involved in prioritisation before and ranking exercises have been done in some areas. However, external stakeholders and service users seemed to find the current process particularly useful and felt they were being listened to more than they had been previously. The extent to which people could engage varied of course and some LINKS/ patient group stakeholders worried about whether they had made sufficient useful input. However, our impression was that they did make good points and did take part in discussion that helped mutual understanding. A few seemed to think they might be taking the place of someone more important, but this was not the case and we saw nothing that might have reinforced that impression. Irrespective of the external involvement, the value of bringing people together just from within the PCT and supply chain seemed significant. For managers, this may be seen as part of the job. For the health professionals, some may initially have seen it as a distraction from their primary role but valued it more as time went on. It may be that the presence of external stakeholders acted as a brake, along with independent facilitation, on defensiveness or falling back on negotiating positions. The process as a whole did seem a useful vehicle for collaboration and engagement, and experience in follow up events suggests that the change of dynamic was a factor in promoting openness. It was the mix of the social and technical that worked well, and attention does have to be paid to forming the group at the start of workshops and making everyone feel welcome and comfortable.
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The facilitation team have a role here, but also (perhaps particularly) the commissioning area leads and PCT staff. There is no relationship with the facilitators that needs to be ‘reset’ or preconceptions that need to be dispelled, whereas there might be between other people. Our impression is that the PCT participants played the role of hosts well and most people did not feel shy about contributing, though we recognise that support from the facilitators and perhaps pre-selection for people who would speak up were also significant factors.
Reporting Was the event reporting satisfactory? The workshop reports are important records and are supporting evidence to the conclusions. They need to be a complete record, but not so detailed that they are unreadable. Good executive summaries can be passed on to colleagues. There were some comments on the challenges facing the authors because they needed knowledge not just of the approach and of treatment pathway issues etc. but also of the local context in Sheffield. The commissioning contexts in the three areas chosen were also different. The reports contain detailed analysis. Obviously, both the data and analysis must both be (a) correct and (b) reported correctly. Iteration, comment and data checks should not be a problem, but time and resources have to be allowed for iteration and detailed comment – both calendar time and facilitation team/NHS staff time. How much time was perhaps not really appreciated beforehand. Reports should be kept simple, as they usually were here. They should be written with their audience in mind and the level of detail on the process and data should be appropriate to their purpose. Numerical data and other evidence should not be included just for the sake of completeness. Reference can be made back to the evidence packs for the detail and for evidence that was not used – there may be a charge of selective use of information if the event reports only include what was used and do not link to the full data set. Feedback on this occasion suggests that the workshop reports were readable by non-specialists and captured the key points well. There seems to have been some concern that they would be ‘academic’ reports that concentrated on methodologies, but the opinion afterwards was that the team succeeded not only in documenting the process in a usable form but also the emerging insights into cost-effectiveness. The intent was to circulate event reports to all participants for comment before finalising them. This seems important, and we think commitments were given, but we are not clear that it actually happened in each case.
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Decision-Making Wider Decision Context Did the process integrate with the wider decision context? The MCDA makes a contribution to evidence-backed decision-making and transparency, but decision making will have to draw on a much broader spectrum of information and processes if it is to be robust. Analysis generates and gives information on strategy options but certainly does not make the decisions. Some at the workshops were clearly concerned that the MCDA would be a decision-making process, and therefore would need more people involved, but when they understood it was a prioritisation / data gathering process feeding into decision-making they seemed more comfortable. However, if it better informs decisions, it begs the question, common in MCDA applications, of what this wider decision-making process is. Typically, MCDA outputs are only one input to the eventual decision, albeit often a major one. Benefits such as transparency and rigour will therefore only extend to the decision-making process as a whole if they are also embodied within the other elements of that decision-making. A transparent process for ranking does not make for a transparent decision if the output from an MCDA goes in to a ‘black box’ discussion whose logic is not properly captured and structured, and where assertions are accepted without underpinning evidence. Structure and transparency are therefore required across the commissioning process as a whole before it can be claimed to have these attributes. As we have already intimated, our impression is that the PCT has not yet come to a consistent view as to the best way to move from this analysis to decision-making and individual commissioning areas have more or less developed their own follow-up process, building on insight into the cost effectiveness of different parts of the pathways, thinking through implications, and then factoring them into decisions on commissioning or decommissioning services. We recommend that any write up of the process always makes this clear, and also that the wider logic is explored in a structured manner and documented, otherwise (a) significant drivers for the decision-making will be missed and (b) undue weight may be placed (or presume to have been placed) on the CEA. Legal challenge is always a possibility and the conclusions must not be seen to rest solely on things within the CEA that are uncertain or subject to alternative interpretation. It is usually easy to try to discredit even the best CEA/MCDA analyses through attacking the detail of the methodology or data, even though it might not change the outcome. Some have suggested that Scenario Analysis potentially complements the type of CEA carried out and provides a bridge between the analysis and the decision-making. The wider implications of alternatives and a more complete representation of the decision logic can be explored and notional commissioning strategies assembled against for one or more preferred scenarios. Our understanding is that something along these lines was done to follow up the CEA work on eating disorders but perhaps both the LSE and the PCT might think more systematically about ‘what happens next’.
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Follow-up Was the process followed-up? We understand that there was an internal workshop in December 2009 to consider what had been learned in process terms and the emerging commissions for the commissioning strategies. This will be followed by a workshop in April 2010 to communicate the outcomes more generally to stakeholders and update them on follow up work. The draft of this evaluation report will also be presented. We are aware that there has been follow-up in each of the three commissioning areas, organised by the PCT, and we have been told that in at least one case parts of the analysis have been submitted separately to the Department of Health. This is positive news. Without follow up, the PCT would not be getting the most, now and over successive commissioning cycles, for its investment. Failure to follow-up as promised would also damage the credibility of commissioning process generally with internal and external stakeholders. We do not have the details of the follow-up work and it is not within our remit to comment on its effectiveness but feedback from interviewees does seem to indicate that the CEA insights were genuinely useful and that follow-up work did find it a good base on which to build. The same conclusions might have been reached by other means but the evidence-based CEA approach and the engagement contributed greatly and peoplesâ&#x20AC;&#x2122; minds seemed much clearer. Although it differed from area to area, it was in each case led by the project commissioning area lead. This is probably the right level within the organisation and those involved seemed committed to follow-through, with the caveat that some (notably cancer) had greater pressures on their time than others. We understand that the LSE team have been involved in some of this follow-up work e.g. in scenario development with Eating Disorders and Cancer, and potentially also Dentistry. It is understandable that LSE involvement should reduce given workload and cost constraints, the need to gain in-house experience, and the need for clear ownership. Much of the follow-up presumably did not require their direct input. However, there may nevertheless have been value in maintaining some continuity during the transition, if for no other reason than to help carry the participative behaviours over into the wider decision-making process. Anecdotally, in some cases once the independent analysis and facilitation team were no longer driving the process the dynamics at events changed and some stakeholders began to revert to a less collaborative style. There is genuine interest in persevering with the approach and perhaps looking for opportunities to do something similar in the future, either in the same or different commissioning areas. However, the test will be to see what really endures, and whether use of this sort of methodology continues. A cross-organisational evidence-based commissioning champion might be needed, preferably someone who has a broad enough awareness of structured methods and the enthusiasm needed to push forward against inevitable opposition. Once the workshop programme was over, there seems to have been a loss of momentum in the administration, and communications with stakeholders and between area teams became less coordinated and less reliable. Perhaps this is inevitable but stakeholder processes, even in the follow-up phase, do rely on good ongoing communications and on maintaining networks. More generally, if the delivery capacity is not there, it will be very hard to expand the scope or to
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follow through on what has already been done. It is clear that to sustainably embed this type of approach in the commissioning process the PCT cannot rely solely on external expertise. Many people in the PCT have facilitation skills and external facilitators can be brought in as required. However, we would suggest that to get best value for money the PCT will need to provide some of its people with the right knowledge and skills to generate scenarios, run the simpler analyses, and support events during the follow-up phase. This is even truer if the PCT is seeking to move towards a health economics and evidence-based approach as a strategic objective.
Summary and Conclusions Our main conclusions are set out below. We would like to stress again that they represent a personal commentary rather than a systematic evaluation against declared objectives, and should not be interpreted as such. We posed a series of evaluation questions which when taken together were intended to cover the development and management of the project, the choice and development of the methodology, its implementation, and the PCT’s follow up and integration into its wider decision-making. • • • • • • • • • • •
as the PCT’s selection of the LSE approach sensibly managed? W Was the initial scoping process appropriate and well managed? Was the basic approach appropriate? Were the events well organised and run? Were the scope and methods used in the workshops appropriate? Was the scoring and weighting process implemented effectively? Was the evidence base assembled appropriately? Did the process provide for stakeholder engagement? Was the event reporting satisfactory? Did the process integrate with the wider decision context? Was the process followed-up?
It each case, the answer was broadly affirmative, and therefore our overall judgment is that this was a successful project that has the potential to help shape the PCT’s future approach to commissioning as well as making a valuable contribution to current decisions. But in each case we also had some reservations and observations that do need to be taken into account when considering the lessons learned. Under slightly different circumstances or on another day the outcome may have been quite different. The basic approach was well founded and engaged stakeholders, and expectations were generally realistic. The workshops were well facilitated and reported. However, in several areas our impression is that both the PCT and LSE ‘got away with it’, primarily because of the ability of key members of their project teams and the willingness of those involved to give the process a chance. We acknowledge the advantages of hindsight, but it should perhaps have been clear that the timescales and scope of what was being attempted were too ambitious. This had consequences for scoping and workshop development, and did not allow enough time to make sure all the key players fully understood and were signed on to the process. Also mainly as a consequence of this, the evidence pack development was a somewhat fraught
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affair. In the event, they proved adequate for the trial because participants were willing to keep shortcomings in perspective. However, to properly support any sustained commitment to evidence-based commissioning more time would need to be allowed and evidence pack development would need to be better planned and resourced. LSE team members seem to have assumed that more information was available in usable form than there actually was and our impression is that workshop participants felt that, even given ample time to prepare, it was not particularly good for any commissioning area. More pre-work would have allowed the teams to focus on obtaining the data of greatest relevance and validating and processing into useful information, but good quality information is obviously a pre-requisite for any evidence-based commissioning methodology. Although its assessment is outside our remit, we note that there has been follow up work in each of the three areas and the LSE has provided scenario support. Our impression is that the PCT has not yet come to a consistent view as to the best way to move from this analysis to decisionmaking, and individual commissioning areas have to date developed their own approaches. One of the aims of the April 2010 workshop is presumably to consider whether more coordination would be helpful or not. The process offered improvements in transparency, rationality, and rigour and the response of stakeholders to these characteristics seemed very positive. This sort of analysis is, however, only ever one input to the final decision-making, so to claim credit for these qualities the PCT will have to show that they are also attributes of the decision-making process as a whole. To gain the best value in the longer term, the PCT will have to use processes like this one within a broader evidence-based commissioning strategic framework, probably in conjunction with other methods as part of a ‘tool kit’. There may be value in repeating and extending this year’s work in future commissioning cycles, but it does need to part of a wider programme. In conclusion then, our judgement is that the planning and development may have been rushed and somewhat ad-hoc but it nevertheless seems to have been a valuable piece of work. It was well received by stakeholders and produced useful insights, both for services in the three areas being considered and for the PCT’s thinking about strategic frameworks, information requirements, and toolkits for evidence-based commissioning.
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APPENDIX ANNEX H
Outline Programme LSE presentation to PCT
14th May 2009
Steering Group established
11th June 2009
Programme agreed
8th July 2009
Mental Health planning meeting
Fortnightly Steering Group meetings in June and August; conference calls during same period between LSE team and Tony Nuttall
Mental Health data pack
Circulated on 5th August 2009 (for first workshop) and 9th September 2009 (for second workshop)
2 x Mental health workshops
6th August 2009 and 17th September 2009
Mental Health event report
Summary report post first event: 31st August 2009 Report second event: 15th October 2009
Mental Health Follow-up meeting
21st October 2009
Mental Health final report
16th November 2009 (including scenario analysis developed during follow up meeting)
Dentistry planning meeting
Fortnightly Steering Group meetings in August and September, conference calls during same period between LSE team and Kate Jones
Dentistry data pack
12th October 2009
2 x Dentistry workshops
16th October 2009 and 3rd November 2009
Dentistry event report
Summary report post first event: 30th October 2009 Report of second event: 16th November 2009 (still under revision and to be circulated)
Cancer planning meeting
Fortnightly Steering Group meetings in August and September, conference calls during same period between LSE team, Will Gray, Makeeda Wood and Andy Eames
Cancer data pack
For first event: 21st September 2009 For second event: 30th September 2009
2 x Cancer workshops
22nd September 2009 and 6th October 2009
Cancer event report
Summary report of first event: 12th October 2009 Final report: 16th November 2009
Cancer follow-up meeting
3rd February 2010
Process report to CEO (included health inequality and allocation across area considerations)
3rd December 2009
Independent commentary (This report)
March 2010
Process review workshop
April 20th 2010