A. Introduction to Health Economics Dr Alan Haycox Reader in Health Economics Health Economics Unit University of Liverpool Management School
Introduction to Health Economics Programme The programme will be broken down into four sections: 1. Introduction to health economics 2. Economics modelling: Theory & Practice 3. Value of new drugs including new cancer drugs: Scottish Medicines Consortia (SMC) Scotland 4. Value of new drugs including new cancer drugs: NICE (England)
Methods of economic evaluation and other techniques The four types of health economic evaluation are: CMA CEA CUA CBA We will also cover measuring health related quality of life as well as economic modelling
The Four Methods of economic evaluation Cost Minimisation Analysis (CMA) Cost Effectiveness Analysis (CEA) Cost Utility Analysis (CUA) Cost Benefit Analysis (CBA)
Cost Minimisation Analysis (CMA) Simplest of all methods of economic evaluation Does not mean benefits are ignored they have to be proven to be equivalent Once benefits have been proven to be equivalent, analysis needs only to consider costs
Example - CMA of generic formulations and different treatments Two drugs with exactly the same pharmaceutical components with differing costs, e.g. different formulations of paclitaxel Two approaches to cancer surgery with similar outcomes but different costs, i.e. one approach more invasive requiring more extensive analgesia
Cost-Effectiveness Analysis (CEA) Health benefits are measured in natural units reflecting a single dominant therapeutic goal Reduction in blood pressure (treatment) Increase in cases detected (screening) CEA is only useful and undertaken if a single dimension dominates the health outcome to be compared
Example - CEA of alternative Approaches to cervical screening How much more does the more effective screening system cost? (incremental costs) How many more cases are detected by the more effective screening system? (incremental effectiveness) What is the incremental cost-effectiveness ratio (ICER)? ICER = incremental cost/incremental effectiveness
(-) Incremental costs (+) The Cost-effectiveness Plane Existing technology dominates Costeffectiveness ratio (additional cost per additional success) Costeffectiveness ratio (cost saved per reduced success) New technology dominates (-) Incremental effectiveness (+)
Probability cost-effective Incorporating cost-effectiveness thresholds (CEAC s) for decision-making 1 0.8 0.5 0.3 0 0 30,000 50,000 20,000 Ceiling ratio
Cost Utility Analysis (CUA) Incorporates the effects on morbidity (quality of life) and mortality (quantity of life) The most commonly used index is the qualityadjusted life-year (QALY) A QALY is calculated by aggregating the number of years gained from a health care intervention, weighted by the relative value attached to each future health state Issues underlying outcome analysis for CUA are explored in detail in the next session
Measuring Quality of Life (QoL) QoL weights reflect the subjective level of wellbeing experienced in different health states; the more preferable a health state the higher will be its associated value Perfect health = 1 Death = 0 We will return shortly to the methods used to help determine QoL. In the meantime, give a brief overview to determine QALY gains
Preference elicitation methods There are three main methods for direct measurement used in cost utility analysis. Visual Analogue Scale (VAS) Standard Gamble (SG) Time Trade-off (TTO)
Visual Analogue Scale (VAS) Individuals are asked to indicate where on the line between the best and the worst imaginable health states they would rate a pre-defined health state
VAS We would like you to indicate on this scale how good or bad is your health today, in your opinion. Please do this by drawing a line from the box below to wherever point on the scale indicates how good or bad your current health state is 100 90 80 70 60 50 40 30 Your own health state today 20 10 0
Standard Gamble Alternative 1: Health state H 1 with certainty Choice With probability p: Full health, H 2 Alternative 2: Gamble With probability (1-p): Death, H 3
Standard Gamble board Choice A 90 10 % Chance % Chance PERFECT HEALTH DEATH Choice B Some problems in moving about No problems with self-care No problems with usual activities Moderate pain or discomfort Not anxious or depressed
The time trade-off method QOL A =1 Value of health QOL B 0 LOL A LOL B Years QALY A = QALY B
Cost-benefit analysis This requires all costs and benefits to be measured in the same unit money In cost-benefit analysis an activity should be undertaken if the sum of the benefits are greater than the sum of the costs The difficulties of converting all benefits (pain, anxiety, disability, death) to a monetary equivalence implies that CBA is rarely used in health economic analyses
Summary Which tool for which analysis? What is the context of the analysis? What is the nature of the comparison being made? What is the nature of the outcome arising from the competing options?
Conclusion - What Health Economics aims to achieve Efficiency: Does the allocation of scarce resources maximise the achievement of health outcomes? Equity: Is the sharing of health care resources fair between people? The manner in which we are attempting to achieve these aims is explored in the following presentations
What is Health Related Quality of Life? A multi-dimensional concept that encompasses the physical, emotional and social components associated with an illness or its treatment
Measuring Health-Related Quality of Life (HRQoL) Pain P 4 B P 3 P 2 A = P 0 D 0 = Normal health P 1 P 0 A D 0 D 1 D 2 D Disability B = P 4 D 3 = Total disability & severe pain
What are Q of L weights? Such weights reflect the subjective level of wellbeing experienced in different health states; the more preferable a health state the higher will be its associated weight. Perfect health = 1 Death = 0
Measuring Health Gain in Theory 1 Prognosis with intervention QUALITY OF LIFE = Health gain Prognosis without intervention 0 ONSET OF ILLNESS INTERVENTION DEATH TIME
Two types of Measure Generic instruments Designed to have broad application across a wide range of disease states eg sickness impact profile, Nottingham health profile, EuroQol Disease specific instruments designed to assess the impact of specific disease states eg arthritis impact measurement scale, back pain disability questionnaire
Calculating QALYs A Simple Example Survival and associated health states With treatment X Without treatment X 10 years in improved health 8 years in poorer health Preference weights for health states With treatment X 0.7 Without treatment X 0.5
QALY Analysis for Treatment X With treatment X Survival = 10 years Q of L = 0.7 Without treatment X Survival = 8 years Q of L = 0.5 QALYs = (10 X 0.7) = 7.0 QALY = (8 X 0.5) = 4.0 QALY gain = 3.0 Q.A.L.Y s (7.0-4.0) Cost of intervention = 45,000 Cost per QALY = 15,000
QALYs For and against their use in health economic evaluations For Generic multi-dimensional Easy to apply Provides practical guidance in allocating health care resources between very different therapeutic interventions Against Too superficial to measure the full benefits from health care? Insufficiently sensitive to capture small changes in the patient s Q of L Can we really measure quality of life in only five questions?
Measuring gains from different types of intervention = Health gain Quality of life Improved survival (increased length of life) only Time
Measuring gains from different types of intervention = Health gain Quality of life Improved quality of life only Time
Measuring gains from different types of intervention = Health gain Quality of life Improved survival and improved quality of life Time
Measuring gains from different types of intervention = Health gain = Health loss Quality of life Improved survival at expense of decreased quality of life Time
Conclusion of this section Accurate health outcome measurement is vital in determining the value and hence priority that should be placed on competing healthcare interventions. For cancer, this includes screening, initial management (adjuvant treatment, surgery, radiotherapy), management of advanced disease and end of life The need for sensitivity and practicality may pull in different directions QALYs assume that all health interventions aim either to make us live longer (quantity) or live better (quality)
B. Economic Modelling Theory & Practice
Therapeutic interventions are messy and complex Limited understanding of how things work Disease/Treatments/Services Limited evidence of effectiveness A better treatment? How much better and is it better for all patients? Evidence limited in time and place Are RCTs valid for other situations and in other countries? Variable quality and limited availability of evidence How to fill gaps? What is the comparative value of RCTs, observational data and expert opinion?
Hence we need to model in order to 1. Extrapolate beyond the results of a trial 2. Link intermediate clinical endpoints to final outcomes 3. Generalise to alternative settings 4. Synthesise head-to-head comparisons where relevant trials do not exist
1. Extrapolating beyond the results of a trial Economic evaluations require long term analyses to comprehensively assess the costs and benefits arising from an intervention Techniques A range of techniques are available to extrapolate outcome data into the future e.g. constant benefits or linear extrapolation
2. Linking intermediate endpoints to final outcomes where necessary Where RCTs only report intermediate clinical endpoints e.g. Hypercholesterolaemia (changes in HDL/LDL) Response rates to length of survival Disease free progression to length of survival Economic evaluations in comparing cost-effectiveness attempt to consider harder outcomes Life-years gained Techniques Logistic equations and other methods are used to try and determine impact on length of survival
3. Generalising to alternative settings Costs Costs differ from one setting (e.g. country) to another Techniques Adapt analyses to take account of local unit costs, comparators and patterns of care Efficacy Patients are carefully selected in clinical trials Compliance in trials is artificially high Techniques Develop an impact model that identifies factors underlying the success of a healthcare intervention and dichotomise between locally specific and generalisable
4. Synthesising head-to-head comparisons RCTs do often compare an active drug vs. Placebo; alternatively an add-on drug to an existing regimen and not a replacement. Clinicians need to know whether a new drug is superior to existing therapeutic interventions not as an add on especially when scarce resources Techniques Modelling allows for the results of more than one trial to be incorporated thus facilitating indirect comparisons between drugs
Stages in developing an economic model 1. Define the problem and your objective 2. Identify all relevant factors and how they inter-relate 3. Search for data/information to quantify those relationships 4. Choose an appropriate methodology/structure 5. Construct and calibrate the model 6. Test/validate model 7. Revise/correct model (return to stage 5 as required) 8. Apply model results to problem/decision
Knowledge requirements for modelling Epidemiological: Population at risk, mortality, effects Medical: Nature of the disease and how well do the treatment and comparators work? Economic Resources consumed at each stage of the treatment process
Data requirements for modelling Parameter estimates for each possible outcome or health state Probabilities of occurrence of each outcome or health state Cost for each resource consumed during the process of care provision
Types of model 1. Decision Tree Model all possible treatment paths and outcomes Each alternative is shown as a branch Each branch is connected by a decision (choice) node Outcomes are connected to branches by probability (chance) nodes Terminal health states / outcomes totalled for costs & benefits
Types of model 2. Markov Chain Based on movements between defined health states caused by events Individuals may enter the system at one or more source states Individuals progress from one state to another according to a set of transition probabilities Transitions occur at predetermined intervals (cycle period) Model may include one or more sink or terminal states (no exit)
Example of a simple Markov Model 1-p 1 -p 3 1-p 2 1 Patient Asymptomatic disease p 1 Progressive disease p 2 Death p 3 p n = transitional probability
How robust are health economic analyses? Issue to be addressed: Do limitations in either the quality or availability of evidence affect the recommended decision? If the decision is not altered despite reasonable variations in key assumptions/parameters, then the analysis can be considered to be robust Two types of uncertainty: Structural (is the model design correct?) Parameter (are the values correct?)
Techniques for handling uncertainty Structural: scenario analysis Re-run the analysis with alternate assumptions and model structures Parameter: sensitivity analysis (SA) Re-run the analysis with different parameter values One-way SA, Multi-way SA, Extreme values SA, Probabilistic SA
Incremental Cost Presentation of results of sensitivity analysis 1. Cost-Effectiveness Plane 3000 2000 1000 0-0.05 0-1000 0.05 0.1 0.15-2000 -3000-4000 -5000 Incremental QALY
Probability cost-effective Presentation of results of sensitivity analysis 2. CE Acceptability Curve 1 0.8 0.6 0.4 0.2 0 0 10,000 20,000 30,000 40,000 50,000 60,000 Value of ceiling ratio
Using the results of modelling A model simply provides a structure (good or bad) that organises complex relationships and data enabling them to be interpreted and manipulated By predicting and comparing costs and outcomes of competing interventions, it enables decision-makers to address problems in a more systematic manner
Good economic modelling practice A good model provides a structure that allows data to be interpreted and used. However, to maximise the value of the model, certain principles should be followed: Keep analyses simple Keep analyses transparent Make explicit the quality of the underlying data Keep a focus on uncertainty Compare the results obtained in your model to others
Conclusion - converting numbers to knowledge Remember: Numbers are meaningless Data = numbers with meaning and a source of integrity Information = data interpreted Knowledge = information in action
C. Value of new drugs including new cancer drugs: Scottish Medicines Consortia (SMC), Scotland
Only limited number of new products having reasonable health gain SMC recently analysed their guidance for 281 new products and indications (all drug classes) issued between April 2002 and September 2008 Data extracted from base case QALY gain estimates provided by the manufacturers showed: Overall median health gain - 0.1 QALY Mean health gain - 0.5 QALYs (standard deviation 1.72) This broken down as: 22% offered no benefit 28% offered >0 0.1 QALY 25% offered >0.1-0.5 QALY 13% offered >0.5-1.0 QALY 12% offered >1 QALY Ref: Andrew Walker and Ailsa Brown EACPT 2009
Recent examples of new drugs not recommended by SMC as economic concerns Drug Disease Reason for rejection Cost/ QALY Sunitinib (SUTENT) GIST and mrcc Economic case not proven 34000-81000 Pemetrexed (ALIMTA) Metastatic NSCL cancer Economic case not proven Up to 53,000 AVASTIN and ERBITUX Metastatic ca colon/ rectum Economic case not proven 24000 93000 Rimonabant (ACCOMPLIA) Obesity Economic case not proven Not assessed - no comparator Aliskiren (RASILEZ) Essential hypertension High costs with comparable efficacy 11-14/ year (generic ACEi) vs. 257-309 Ref: SMC website
SMC and new anti-cancer medicines recently reviewed 61 cancer medicines reviewed 36 for advanced/metastatic cancer 25 for earlier/adjuvant treatment Median QALY gain (over current treatment) 0.38 for advanced cancer 0.30 for earlier/adjuvant treatment Mean QALY gain (over current treatment) 0.52 for both groups
What do these mean and median QALY gains imply in reality? Median health gain 6 months with quality of life 70% of normal Mean health gain 8-9 months with QoL 70% Only 6 drugs (10%) offered 1 QALY 22 drugs (36%) offered 0.2 QALY = 3 months at 70% of normal QoL Overall
Some individual cancer drugs had considerable health gain Some of the greatest health-gains are with really innovative drugs: Trastuzumab 2.4 QALYs Nilotinib 2.1 QALYs Bortezomib 1.1 QALYs Even if these are expensive, they may offer good value-for-money The issue subsequently becomes affordability and opportunity costs (workshop)
Health gain with cancer drugs similar to other disease area Anti-cancer drugs are much like new drugs for other disease areas Musculoskeletal (11) 0.66 QALY Infections (33) 0.11 QALY Endocrine (24) 0.07 QALY Cardiovascular (33) 0.05 QALY CNS and pain (55) 0.04 QALY Overall new drugs in general do not appear to be as valuable as many would like to think!
D. Value of new drugs including new cancer drugs: NICE (England)
What does NICE mean by cost-effective? More effective and less costly More effective and more costly AND additional effect is worth the extra cost Less effective and less costly AND the cost saving is large enough to compensate for the loss of effect What is the cost-effectiveness threshold for acceptance? NICE does not use a precise ICER threshold above which a technology would automatically be defined as not cost effective or below which it would
Why do NICE use a costeffectiveness threshold? The appropriate threshold to be used is that of the opportunity cost of programmes displaced by new, more costly technologies If most plausible estimate is below 20,000 per QALY gained: cost effective use of NHS resources Above 20,000: are there benefits not captured by the QALY? Has quality of life aspect been adequately measured? Above 30,000 need to identify an increasingly stronger case for supporting the technology as an effective use of NHS resources
End of life care: The NICE criteria Introduced 5 January 2009, revised July 2009 Three criteria in order to qualify: The treatment is indicated for patients with a short life expectancy, normally <24 months There is sufficient evidence to indicate that the treatment offers an extension to life, normally of at least an additional 3 months, compared to current NHS treatment The treatment is licensed or otherwise indicated for small patient populations
End of life care: The NICE process For eligible treatments, the Committee will consider: The impact of giving greater weight to QALYs achieved in the later stages of terminal diseases, using the assumption that the extended survival period is experienced at the full quality of life anticipated for a healthy individual of the same age The magnitude of the additional weight that would need to be assigned to the QALY benefits in this patient group for the cost-effectiveness of the technology to fall within the current threshold range Committee requires that the assumptions used in the reference case economic modelling are plausible, objective and robust
End of life care: Specifying the comparator The comparator for the technology being assessed is very important because the choice to a large extent determines the incremental costs and incremental effects (and thus the cost per QALY) Relevant comparators might include: Therapies routinely used in the NHS Current best practice What is expected to be replaced (SMC) Do nothing (e.g. best supportive care)
End of life care: Measurement of health benefit The incremental QALYs as a result of a treatment have two components: Changes in survival Changes in health-related quality of life The main challenge with estimating changes in survival arises because the data on clinical effectiveness typically means that long-term overall survival must be extrapolated from short-term progression-free survival data Two challenges recur with quality of life data 1. The absence of data 2. Unsatisfactory measure of quality of life
Case study: Cetuximab for locally advanced squamous cell cancer of head and neck Cetuximab with radiotherapy versus radiotherapy alone in patients considered unsuitable for chemotherapy RCT showed significant improvement in duration of locoregional control, overall and progression-free survival, and overall response rate for the combination than for radiotherapy alone (Bonner et al, NEJM 2006) Manufacturer estimated a cost per QALY of 6,390 Committee rejected the submission highlighting uncertainties regarding the clinical evidence (e.g. RT regimens used in trial not typical of UK current practice, high proportion of patients in trial suitable for chemotherapy, and no clinical benefit demonstrated in patients with poor performance status)
End of life care: The importance of sub-groups Cost-effectiveness generally varies across sub-groups Important because ICER for entire patient group may be above the threshold but there may be sub-groups for whom the intervention is cost-effective Similarly, an ICER below the threshold for the patient group as a whole may hide ICERs for particular subgroups above the cost-effectiveness threshold RCTs often under-powered to assess treatment effects in sub-groups
Additional analysis presented following appeal Karnofsky performance status Hazard Rate Confidence Interval 100 0.61 0.28 to 1.31 13,200 90 0.58 0.39 to 0.88 4,500 80 1.11 0.69 to 1.77 58,200 Cost effectiveness 70 1.22 0.53 to 2.78 RT dominant <70 3.41 0.65 to 17.7 37,000
NICE recommendation (June 2008) The Committee concluded that Cetuximab in combination with radiotherapy is clinically and cost-effective in patients with locally advanced squamous cell cancer of the head and neck who have a Karnofsky performance status score of 90% or greater and for whom platinum-based chemoradiotherapy treatment is contraindicated
NICE evaluation: A summary ICERs and cost-effectiveness Understanding the economic model Key elements to watch out for: Appropriate comparators Relevant sub-groups Measurement of health benefit Analysis of uncertainty
The importance of HTA: Conclusion No health system can afford to fund all new healthcare interventions so we inevitably have to prioritise and choose HTA simply attempts to identify the healthcare interventions that provide sufficient clinical benefit to justify their cost HTA enables health systems to optimise the amount of patient benefit obtained from the limited resources available to the healthcare system HTA also enables an informed debate to be undertaken with the industry concerning the importance of linking drug pricing to drug effectiveness