Economic evaluation
Economic evaluation has played a vital role in decision-making process, especially in healthcare industry. Generally, economic evaluation can be used to inform wide-range of decisions in a systematic approach, so that the decision would be made based on scientific evidence and the likely effects with proper accountability (Drummond, Sculpher, Claxton, Stoddart, & Torrance, 2015).
Technically speaking, economic evaluation is a tool linking costs and outcomes, aiming to provide evidence of the decision.
Due to the scarce healthcare resources, choices among alternatives were often made. Therefore, it is important to compare appropriate alternatives in the evaluation in terms of the costs and possible outcomes. With such perspective, decision-makers will be informed what will be given up and the expected benefits if an intervention is launched; this would allow efficient allocation of resources, including capital, and human resources.
Depending on the nature of outcomes, three different techniques can be used in economic evaluation:
(1) cost-benefit analysis (CBA); (2) cost-utility analysis (CUA); (3) cost-effectiveness analysis (CEA). To
22 simplify, cost-benefit analysis measures health outcomes in monetary units, whereas cost-utility analysis accounts for healthy years or as known as quality-adjusted life-years (QALYs) as health outcomes. Cost-effectiveness analysis, the most common form, used the natural units of possible health outcomes, such as life-years gained, reduction in blood glucose level, etc. (Drummond et al., 2015) The use of cost-utility analysis is more common in the industry as the standardization of outcomes allows a fair comparison between analyses among various interventions in health care system, for example, a drug for cervical cancer vs a screening program for cervical cancer.
Methods for economic evaluation
Traditional methods
Decision trees and Markov models have been the most common and traditional methods used in economic evaluations. Decision trees is simple and easy to use and thus it was once applied successfully in analyses; however, the use of decision trees has been criticized for its limitation of structure. A rigid structure and mutually exclusive outcomes are needed for economic evaluation using decision trees.
Therefore, Markov model was employed as an alternative approach. Unlike decision trees, Markov model do not base on mutually exclusive outcomes but mutually exclusive ‘health states’ and transitions among them. In order to estimate the cost and outcomes associated with disease progression, different aspects of the disease, including continuous outcomes, have to be turned into various discrete ‘states’, such as severity of the disease. Thus, in some situations, there could be
almost hundreds of states if the disease is recurring. Besides, patients simulated in Markov model can be only in one state at a time (Drummond, 2015). All of these may lead to over-simplification of the disease.
Discrete event simulation
Discrete event simulation (DES) is a more flexible alternative for modelling in economic evaluation (Karnon, 2003), especially when data is basically clinical parameters. Attributes (e.g. age, sex, duration
23 of disease) would be simulated for each individual patient, with pre-defined values. These attributes could be updated every cycle or at particular time points while the time and way of update can be determined by analysts. Moreover, disease progression and the occurrence of events can be specified by the values of attributes. (Caro, 2005) Events occurred are not necessarily to be a change in patient’s state; they could be discontinuation of treatment, discharge from hospital, readmission, etc. Unlike Markov model, events in discrete event simulation can occur simultaneously; and the rate of events occurred can depend on any attributes or parameters and related functions. (Caro, 2005) Therefore, discrete event simulation can relax some of the assumptions and limitations in Markov models or decision trees; and hence, give more flexibility and precision to the resulting cost-effectiveness.
Health outcomes
Owing to the universal nature of quality-adjusted life-years (QALYs), the use of QALYs as measurement of health outcome is preferable in economic evaluations. This approach is also approved by the Norwegian Medicines Agency (NoMA). QALYs is basically a generic measure of health-related quality of life (HRQoL), as its calculation is the product of HRQoL and life-years gained. Therefore, the use of cost-utility analysis allows decision maker to understand the opportunity costs forgone and compare the intervention to others in both healthcare and other sectors.
Health-related quality of life, which is also called utility, ranges from 0 (worst possible health status) to 1 (best possible health status). HRQoL can be measured by various generic and specific utility instruments (Dowie, 2002). Generic instruments usually include several aspects of well-being. For example, EuroQol-5 Dimension (EQ-5D) assess health states through five dimensions, i.e. mobility, self-care, usual activities, pain/ discomfort, and anxiety/ depression (Balestroni & Bertolotti, 2012).
Another frequently used instrument is Medical Outcomes Study Short Form Six-Dimension (SF-6D), including 6 dimensions: physical functioning, role limitations, pain, vitality, social functioning, and metal health (Brazier, Roberts, & Deverill, 2002). Different instruments weigh health dimensions differently. Some may argue that disease-specific instruments should be used in order to reflect the
24 efficacy of interventions and treatments (Assari, Lankarani, Montazeri, Soroush, & Mousavi, 2009).
Besides, different techniques for measurements can be used in these instruments, naming visual analogue scale (VAS), time trade-off (TTO) and standard gambling (SG).
Perspective
Apart from health gain measurement, the perspective used in the study also has a significant role.
Societal and healthcare perspectives are typical perspectives used in health economics. Societal perspective provides an insight into the impact of the intervention to the society, including the productivity loss due to absence from work, informal care provided by caregivers, transportation costs;
whereas healthcare perspective only concern the costs imposed to the healthcare system (Drummond et al., 2015). Generally, societal perspective is recommended, however, healthcare perspective is also acceptable when corresponding data is lacking.
Cost-utility analysis
Decision rules in economic evaluation often involve the calculation of differences in costs and effects between the intervention and its comparators. When an intervention is said to be dominant over others and cost-effective, it costs less and brings more effects than other comparators. However, in reality, it is more often to have an intervention offering more health benefits but also costing more.
In the case of CUA, a standard reference, incremental cost-effectiveness ratio (ICER), has been developed in order to compare the extent of cost-effectiveness. ICER is expressed as the additional cost for extra unit of effect, e.g. QALYs. The calculation of ICER for bariatric surgery compared to intensive medical therapy can be expressed as the following formula:
𝐼𝐶𝐸𝑅 = 𝐶𝑜𝑠𝑡𝑠 𝑜𝑓 𝑏𝑎𝑟𝑖𝑎𝑡𝑟𝑖𝑐 𝑠𝑢𝑟𝑔𝑒𝑟𝑦 − 𝑐𝑜𝑠𝑡𝑠 𝑜𝑓 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑣𝑒 𝑚𝑒𝑑𝑖𝑐𝑎𝑙 𝑡ℎ𝑒𝑟𝑎𝑝𝑦 𝑄𝐴𝐿𝑌𝑠 𝑎𝑓𝑡𝑒𝑟 𝑏𝑎𝑟𝑖𝑎𝑡𝑟𝑖𝑐 𝑠𝑢𝑟𝑔𝑒𝑟𝑦 − 𝑄𝐴𝐿𝑌𝑠 𝑎𝑓𝑡𝑒𝑟 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑣𝑒 𝑚𝑒𝑑𝑖𝑐𝑎𝑙 𝑡ℎ𝑒𝑟𝑎𝑝𝑦
During interpretation of ICERs, one must be careful to identify the dominant and dominated intervention. An intervention is said dominant when it yielded more health gains with lower costs, whereas a dominated intervention increases cost but is less effective (D. J. Cohen & Reynolds, 2008).
25 Some countries have set an ICER threshold as a reference for decision making process based on the willingness-to-pay (WTP) of the country. For example, the National Institute for Health and Clinical Excellence (NICE) in the United Kingdom has been using a ICER threshold ranging from 20,000 GBP to 30,000 GBP from 2001 to 2008 (McCabe, Claxton, & Culyer, 2008). In the current framework for priority setting in Norway, multiple thresholds, ranging from 250,000 to 1,000,000 NOK (≈ €25,900 to
€103,600), were recommended depending on categorisation of health loss experienced by the patient group (Ottersen et al., 2016).
Sensitivity analyses
When it comes to the reality, there is wide range of uncertainty and variability associated with the decisions, especially when the use of clinical trials as a vehicle of economic evaluation becomes well-accepted. The uncertainty around the sample data, e.g. the variability within different populations, shall be evaluated and reported in economic evaluation as sensitivity analysis so as to enhance the generalisability of the study.
Deterministic sensitivity analysis (DSA) is the most common form of sensitivity analyses, where one or more input parameters is varied across a reasonable range (A. Briggs, Sculpher, & Buxton, 1994). The value of parameter concerned, such as utility parameters, costs of each health states and the transition probabilities among health states, is changed manually and the separate effect of each parameter on outcome, i.e. ICER, can be established and illustrated graphically on a tornado diagram.
This type of sensitivity analyses is also called one-way analysis when only one variable is simulated.
However, variables could interact with each other during simulation. Hence, multivariate sensitivity analysis shall be applied in order to study the effect of two or more parameters on outcomes (Drummond, 2015). The ranges used shall be obtained from previous studies or explained, instead of arbitrary range. Sometimes, it is referred as scenario analysis.
Probabilistic sensitivity analysis (PSA) is another type of sensitivity analysis which addresses uncertainty with specific ranges and distributions. For each parameter, a distribution was specified
26 based on its mean estimate and standard error. Beta distribution was used for utility parameters, probabilities of complications and percentage change from baseline. Gammas distribution was applied to costs and patients’ baseline characteristics as values of these parameters should be all positive (A.
H. Briggs, Claxton, & Sculpher, 2006). Therefore, the likeliness of various scenarios to occur is also taken into account. Monte Carlo simulation is a one of the applications of PSA; which randomly generates values from each of the defined distributions for each input parameters and simulate and record the outcomes. In this case, a large number of hypothetical patients is simulated, i.e. 1000 patients for each therapy group, by Excel VBA Macro. (A. Briggs et al., 1994). The resulting ICERs are plotted on the cost-effectiveness plane. Besides, a cost-effectiveness acceptability curve (CEAC) will be generated by using different willingness-to-pay (WTP) thresholds. The probability of the intervention being cost-effective over conventional therapy will be shown on CEAC.
Half-cycle correction
Half-cycle correction is commonly applied in economic evaluations so as to reflect the costs and outcomes closer to the real-world scenario. In modelling, transitions among health states are assumed to happen either at the start or the end of cycle. However, most of the transitions are normally happened during the cycle. Therefore, the estimation of costs and outcomes would be either underestimated or overestimated. The adaption of half-cycle correction can avoid such discrepancies of both costs and health outcomes and reduce the approximation error. (O'Mahony, Newall, & van Rosmalen, 2015)
Discount rate
According to positive time preference, people tend to enjoy good things now rather than in the future.
Hence, the present value of both future costs or health outcomes is less than that in the future (Severens & Milne, 2004). In order to obtain the present value of future outcomes, discounting is implemented in economic evaluation based on country-specific guidelines. (O'Mahony et al., 2015)
27 Therefore, both costs and health outcomes obtained 1 year after surgery or equivalent for conventional therapy group in this study were discounted at 4% per annum, according to the recommendation of NoMA and Norwegian Ministry of Finance. (legemiddelverk, 2018)
Methods
Overview
Target population
The population contains both sexes aged between 20-70 years old, with a mean of 60.8 for females and 59.1 for males. Patients who are aged above 70 years old are excluded because this group of patients is more susceptible to post-operation complications and longer recovery time; therefore, it is not common to perform bariatric surgery on these patients. All patients are assumed to have T2DM (defined as HbA1c level reaching 7% or above) for a mean of 6 years and have been overweight or obese (BMI within 25-35 kg/m2). Other baseline characteristics, e.g. systolic blood pressure, total cholesterol, LDL cholesterol, HDL cholesterol and triglycerides level, are based on a nationwide survey conducted in Norway (Jenssen, Tonstad, Claudi, Midthjell, & Cooper, 2008)
Intervention
The intervention in this study is bariatric or metabolic surgery. There are a few surgical techniques for performing bariatric surgery, such as gastric bypass, sleeve gastrectomy, mini gastric bypass and duodenal switch.
Comparator
The comparator is this CEA is the standard care received by T2DM patients, which includes lifestyle-based intervention and medical therapies, such as oral antidiabetic agents and insulin-lifestyle-based medications, except bariatric surgery. Due to limited clinical data, these techniques would be evaluated as one intervention.
28 Health outcomes
The primary health outcome of this study is quality-adjusted life years (QALYs), which is based on the life years gained by patients and the utility that patients experienced. In addition, T2DM-free years after bariatric surgery is evaluated as well.
Perspective
The perspective used in this cost-utility analysis of bariatric surgery for treating T2DM in Norwegian population is healthcare perspective, which is based on Norwegian Directorate of Health’s guideline.
With this perspective, only the costs related to health care service provided to target population and health outcomes experienced by patients would be taken into consideration. Therefore, other costs to patients, such as out-of-pocket co-payments, productivity loss, transportation to health care facilities or costs of informal caregivers, are not included in this study.
Time horizon
In order to ensure all important future differences in costs and consequences between the alternatives are identified, a lifelong horizon is applied in this analysis.
Modelling
Model Overview
To allow for patient level simulation modelling, discrete event simulation was used as modelling technique to study the cost-effectiveness of bariatric surgery as a treatment for T2DM versus no surgery. With this technique, heterogeneity in disease progression and other outcomes could be captured and continuous changes of clinical parameters could be tracked. Figure 1 shows the flow diagram of the simulation model. This model simulates various risk factors of a single patient, such as HbA1c, BMI, lipid profile and SBP, throughout the lifetime. In each cycle, which is set as one year, the changes of different clinical parameters were simulated and recorded. Based on these risk factors, common diabetes-related complication risks and mortality under different situations are simulated
29 using risk equations and algorithm published in United Kingdom Prospective Diabetes Study (UKPDS 82) (Hayes, Leal, Gray, Holman, & Clarke, 2013) and quality of life (QoL) decrements caused by incidence of complication events were also recorded. In addition, current BMI and HbA1c were used to categorize into obesity grades and remission of diabetes in order to provide supplementary information of obesity-free years and T2DM-free years. All accumulated costs and health outcomes were recorded by VBA macro.
Before the simulation starts (T=0), patient level data is simulated from distributions with defined means and standard errors of a series of clinical characteristics. These data include demographic factors (current age, duration of diabetes, and gender), risk factors (smoker, haemoglobin A1c level, BMI, LDL, HDL and SBP), and adverse event history (CHF, IHD, MI, stroke, blindness, ulcer, amputation and renal failure). Patients are assumed to have no events history at the beginning of the model.
For patients in the surgery arm, 30-day post-operation mortality and post-operation complications were considered. The simulation would generate a random number and patient would move to the next cycle (i.e. T=2) if the randomly generated number is greater than the 30-day mortality. Post-operation complications have been categorized depending on the time of occurrence and the severity.
The costs due to complications and disutility caused by surgery were added.
In the first 3 years (T=1 to T=3), all patients’ clinical parameters followed the percentage changes from
baseline, which were obtained from observational studies or randomized clinical trials. In order to ensure statistical variability of each individual, values of changes were also randomly drawn from defined distribution with means and standard errors. From the fourth year (T=4) until death, patients’
clinical characteristics were assumed to remain unchanged. The medical costs for T2DM and utility related to T2DM and obesity were accumulated in the model.
Random numbers were generated by Microsoft Excel and patients were considered to experience an event when the random number is smaller than the probability calculated from risk equations. If an event occurs, event history would be updated, and the costs and disutility caused by the event would
30 be recorded. If the patient survived from complication events, the simulation will move to the next cycle, both age and diabetes duration would be updated at the same time. The cycle would be repeated until patient’s death.
As Monte Carlo simulation was applied in this model, the simulation was repeated for 1000 times to exploit the possible outcomes. When the relevant is not available, assumptions were made to minimize the introduction of bias into the model.
31 Figure 2 Flow diagram showing the sequence of the simulation model. Oval represents “Start” or
“Stop”; parallelogram represents “Data input/update”; rhombus represents “Decision” and rectangle represents “Process of data”; arrows indicates the flow of the simulation
32 Key assumptions
Several assumptions were made for the model structure and model inputs in this cost-effectiveness analysis.
Model structure
In the setting of the model, the intermediate outcomes of a single patient were assumed to fluctuate within defined ranges in different years; and 10 years after surgery, the change of risk factors was assumed to be stable. The risk of complications was calculated from the clinical risk factors with the aid of a foreign model; but for some complications, like MI, stroke and amputation, assumptions is made that at most two times of these complications could be happened in a lifetime. Once an event of complication occurred, except an event of ulcer, the subsequent costs and utility decrement were assumed to be recurred in the rest of lifetime.
PSA distribution for transitional probabilities
When statistical variability (standard errors of means) of data was unknown, a percentage of mean was assumed to be standard errors.
Input and material
In order to collect data from currently available sources as input to the model, systematic literature searches were done in PubMed, Cochrane Library, Embase and Google Scholar databases. The search was set with specific keywords for the population (type 2 diabetes mellitus), and the intervention (bariatric surgery). The search results were screened and only health economics literature with relevant titles and abstract were selected; without any filter on neither publication year nor language.
Systematic reviews were first screened, and a Swedish HTA published in 2016 was found (Fändriks L, 2016). Therefore, the update search after the aforementioned HTA was conducted. Clinical trials were excluded if the BMI of study group was over 35 kg/m2 or the surgical techniques were not commonly used in Norwegian healthcare institution. Therefore, only clinical trials, which involved type 2 diabetes
33 mellitus patients with BMI below 35 kg/m2 and used mainly Roux-en-Y gastric bypass and sleeve gastrectomy as main intervention, were kept for data extraction. As there is not much data about the effects of bariatric surgery on overweight T2DM patients, surgical techniques other than RYGB and SG were also considered. Apart from that, external transferability of costs and utilities data from available economic evaluation studies to Norwegian setting was also concerned.