EVALUATING THE COST-EFFECTIVENESS OF BARIATRIC SURGERY IN THE TREATMENT OF TYPE 2 DIABETES MELLITUS IN NORWAY USING DISCRETE EVENT SIMULATION

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EVALUATING THE COST-EFFECTIVENESS OF BARIATRIC SURGERY IN THE TREATMENT OF TYPE 2 DIABETES MELLITUS

IN NORWAY USING DISCRETE EVENT SIMULATION

Author: Supervisor:

Gloria Yin Kwan Chow Eline Aas

Thesis submitted as a part of the European Master of Health Economics and Management

Department of Health Management and Health Economics Faculty of Medicine

University of Oslo Norway

June 2019

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3 Abbreviations

CEA Cost-effectiveness analysis

CEAC Cost-effectiveness acceptability curve

CHF Congestive Heart Failure

CMA Cost-minimization analysis

CUA Cost-utility analysis

EQ-5D European Quality of Life 5 dimensions

HbA1c Glycosylated haemoglobin

HRQoL Health-related quality of life

ICER Incremental cost-effectiveness ratio

IHD Ischemic Heart Disease

MI Myocardial infarction

NICE National Institute for Health and Care Excellence

NDA Norwegian Diabetes Association

NOK Norwegian crowns (currency)

NoMA Norwegian Medicine Agency

OBGLD Oral blood glucose lowering drugs

PSA Probability sensitivity analysis

RCT Randomized controlled trial

QALY Quality adjusted life year

QoL Quality of Life

SBP Systolic blood pressure

SF-6D Short form-6 Dimension

SG Standard gamble

T2DM Type 2 diabetes mellitus

TTO Time trade off

VAS Visual analogue scale

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Abstract

Aim

To determine the cost-effectiveness of bariatric surgery in overweight and obese (BMI 25 – 35 kg/m²) type 2 diabetes mellitus patients.

Background

The prevalence of type 2 diabetes mellitus (T2DM) has been growing globally and become a burden on healthcare resources in many countries. Many studies have reported a high rate of diabetes remission on obesity patients after performing bariatric surgery, and bariatric surgery has been cost effective for morbidly obese T2DM patients. While studies have suggested that baseline body-mass index (BMI) is not a good predictive factor of the effects of bariatric surgery, the clinical guideline for the intervention remains based on BMI measurement. Therefore, the benefit of bariatric surgery to the society could be restricted due to limited access to it for T2DM patients. In order to provide evidence for loosening of the guideline in Norway, a cost-effectiveness analysis was performed in order to evaluate the cost-effectiveness of bariatric surgery as treatment for T2DM patients who are overweight or obese.

Methods

A discrete event simulation model with the aid of United Kingdom Prospective Diabetes Study Outcomes Model) was developed over lifetime horizon to simulate BMI change, health outcomes and costs of bariatric surgery for overweight and obese (BMI 25 – 35 kg/m²) people with type 2 diabetes mellitus. Population baseline clinical characteristics were obtained from a Norwegian national survey (N=975). Clinical outcomes and costs from pooled trial data were applied in the simulation and Norwegian healthcare perspective was used. Estimates of cost-effectiveness were calculated and represented in terms of incremental cost-effectiveness ratios (ICERs) for different scenarios. Model parameters were obtained from publicly available databases and literature. Costs data were inflated to 2018 values and converted to Euro. ICER falls below to the range of €25,900 to

€103,600 per QALY gained was considered as cost-effective.

Results

The incremental cost-effectiveness ratio for bariatric surgery at lifetime horizon was approximately

€20,613 per quality-adjusted life year (QALY) gained, with incremental cost of €19,583 and incremental QALY of 0.96. The probability of bariatric surgery being cost-effective compared to standard care is 55.3%. ICER is sensitive to changes in BMI, probabilities of surgical complications and surgery costs.

Conclusion

Bariatric surgery for overweight and obese people with T2DM is cost-effective in the Norwegian setting. Probabilities of experiencing post-operation complications and surgery costs are the most determining factors on the cost-effectiveness of bariatric surgery.

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List of Figures

Figure 1 Graphical presentation of the four most standardized techniques of bariatric surgery. ... 17 Figure 2 Flow diagram showing the sequence of the simulation model. ... 31 Figure 3 The tornado plot illustrates the results of one-way sensitivity analyses for various

parameters ... 55 Figure 4 Cost-effectiveness plane ... 56 Figure 5 Cost-effectiveness acceptability curve... 57

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List of Tables

Table 1 Clinical characteristics of T2DM patients in Norway ... 34

Table 2 Average life expectancy of Norwegian general population ... 34

Table 3 Percentage change from baseline for patients with BMI 25-29.9 ... 35

Table 4 Percentage change from baseline for patients with BMI 30-34.9 ... 36

Table 5 Function forms for equations estimating the probability of diabetes-related complications ... 40

Table 6 Functional forms for four equations to estimate the probability of mortality in the current year ... 40

Table 7 Annual probabilities of experiencing surgical complications ... 41

Table 8 Cost Breakdown for Bariatric surgery in public hospital in Norway ... 42

Table 9 Costs of Bariatric Surgery in Different Hospitals in Norway ... 43

Table 10 Unit costs of surgical complications ... 43

Table 11 Direct medical costs by main resource category per patient per year ... 45

Table 12 Breakdown of costs for standard day-based intensive care ... 46

Table 13 Breakdown of costs for standard day-based lifestyle changing course with follow-up group ... 46

Table 14 Costs of diabetes-related complications per event ... 47

Table 15 Utility of T2DM patients and disutility of various T2DM-related complication events. ... 49

Table 16 Cost-effectiveness results for 1000 T2DM patients from healthcare perspective compared to standard care ... 50

Table 17 Disaggregated results of costs ... 50

Table 18 Number of years with normal BMI (below 30 kg/m²) and HbA1c level (below 7 %) experienced by patients with and without performing bariatric surgery ... 51

Table 19 Testing values of probabilities for getting surgical complications ... 52

Table 20 The means and 95% confidence intervals of disutility caused by various complications ... 53

Table 21 Results of Deterministic sensitivity analysis ... 53

Table 22 Statistical outcomes of costs and QALYs in each patient group and the resulting incremental costs and QALY ... 56

Table 23 Variable used in prediction of diabetes-related complications and mortality ... 73

Table 24 Functional form, parameters and coefficients (SEs) for equations estimating the probability of diabetes-associated macrovascular complications ... 75

Table 25 Functional form, parameters and coefficients (SEs) for equations predicting the pribability of diabetes-associated microvascular complications ... 76

Table 26 Functional form, parameters, and coefficients (SEs) for four equations to estimate the probability of mortality in the current year ... 77

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Introduction

The global prevalence of diabetes mellitus among adults over 18 years old has been growing in the last two decades and become a major public health challenge. A report from World Health Organization has recorded a rise of disease rate from 4.7% in 1980 to 8.5% in 2014 and it also predicted that diabetes mellitus will be the seventh leading cause of death in 2030 (Mathers & Loncar, 2006).

Although the disease rate in Norway in generally lower than the global prevalence, the prevalence of T2DM has been increased from 4.9% to 6.1% from 2009 to 2014 despite a decreasing incidence of the disease probably due to diagnosis at a younger age and increase longevity. (Ruiz et al., 2018).

Type 2 diabetes mellitus (T2DM) is a chronic medical condition characterized with hyperglycaemia caused by impaired insulin secretion and/ or resistance to insulin. With uncontrolled T2DM, patients are susceptible to prolonged elevated blood glucose level, which may in turn cause severe damages in various body systems and organs, causing blindness, kidney failure, lower limb amputation and cardiovascular events. Such complication events would greatly affect the quality of life of patients and impose a huge economic burden to society (Papatheodorou, Papanas, Banach, Papazoglou, &

Edmonds, 2016).

Due to the chronic nature and high global prevalence, the disease itself and its management imposes a huge economic burden on the global healthcare system and the global economy in both direct and indirect ways, including medical cost, costs associated with productivity loss, premature mortality and maybe negative impacts on national GDP (Baudot, Aguade, Barnay, Gastaldi-Menager, & Fagot- Campagna, 2019; Ramzan, Timmins, Hasan, & Babar, 2018).

Bariatric surgery has been shown to be an effective treatment for sustainable weight loss and improvement in quality of life, as well as reducing the risk of obesity-related comorbidities (Adams et al., 2007; Mingrone et al., 2015). While T2DM is the most commonly studied metabolic disorder associated with obesity, few studies have demonstrated improvement in diabetic conditions and high remission rate of T2DM in obesity patients compared to conventional medical therapy (Kashyap, Bhatt,

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11 Schauer, & Investigators, 2010; Mingrone et al., 2015). Therefore, bariatric surgery has a rising popularity to be included in the treatment regimens as part of the management of T2DM and obesity.

Despite the current BMI-based guideline, few studies have showed that baseline BMI is not a major determinant on the effect of bariatric surgery, such as remission of T2DM (Carlsson et al., 2012), cardiovascular disease (Sjostrom et al., 2012), mortality (Sjostrom et al., 2007) and cancer incidence (Sjostrom et al., 2009). Hence, patients that are not currently eligible for bariatric surgery in the guidelines may possibly benefit from the procedures. Several clinical studies reported patients with a wide range of BMI experienced a high remission rate or improvement of T2DM after bariatric surgery compared to patients taken conventional medical therapy (Buchwald et al., 2004; Dixon et al., 2008;

Hall, Pellen, Sedman, & Jain, 2010; Huang et al., 2011; Schauer et al., 2012). Therefore, some experts proposed an amendment to the guidelines, as strict BMI cut-offs could not help prioritizing the use of bariatric surgery if the ultimate goal is to reduce diabetes incidence and cardiovascular risk factors (Sjoholm et al., 2013).

Apart from the clinical effectiveness of bariatric surgery for T2DM patients, the economic aspect of revising the current clinical guidelines plays an important role in the decision-making process.

Therefore, an economic evaluation of the intervention could be a strong evidence for effective resource allocation and facilitate the decision-making by providing information of costs and health outcomes. A few economic evaluations taking settings of Australia, United States and United Kingdom, have demonstrated bariatric surgery to be cost-saving and bring health benefits to T2DM patients with BMI over 35 kg/m²(Henteleff, Birch, Hallowell, & Group, 2013; Hoerger et al., 2010; Keating et al., 2009; Picot et al., 2009). However, the cost-effectiveness of bariatric surgery for overweight or mildly obese T2DM patients are seldom studied. Only one study has evaluated the cost-effectiveness of bariatric surgery concerning overweight T2DM patients and concluded that gastric band surgery was cost effective in the U.S. setting with an ICER of USD 22,000 at 15-year time horizon (2017 value) (Wentworth et al., 2017). Although a few clinical trials have been conducted on the target diabetic

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12 population, the bridge linking these clinical data and economic values was still lacking. This thesis would hopefully fill the knowledge gap of this area.

The objective of this thesis was to assess the cost-effectiveness of bariatric surgery as a treatment algorithm for T2DM who are overweight and obese compared to conventional intervention (medical therapies and lifestyle-based program) in the Norway setting.

Structure of the thesis

The paper has been divided into the following chapters: Introduction, Theoretical Framework, Methodology, Input & Material, Results, and Discussion. In the Background chapter, the aetiology, prevalence, complications and treatment algorithms of type 2 diabetes mellitus would be mentioned.

Besides, the background of bariatric surgery, various surgical techniques and the determining factors of health outcomes are discussed.

In Theoretical Framework chapter, theory in economic evaluation and health economics applied in this study are reviewed, and the commonly used terminology and its definition were introduced.

Methodology chapter provides an outline of how the computer simulation model was built, including the input parameters and their sources, and risk equations utilized for complication risk. The uncertainty in parameters is explored in terms of probabilistic sensitivity analysis (PSA). The Result chapter illustrates the main findings from the cost-effectiveness analysis on the ICER and other secondary outcomes, as well as the result of probabilistic sensitivity analysis. The next chapter, Discussion, discusses the validity and limitations of the study, implications and direction for further study. Last but not least, conclusion is made from the findings.

Background

Diabetes mellitus

Diabetes mellitus, a non-communicable metabolic disease, is characterized by a chronic hyperglycaemic condition due to insufficient action of insulin. Impaired insulin secretion and insulin

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13 resistance jointly contribute to the development of the disease, in particular type 2 diabetes mellitus.

Impaired insulin secretion can be identified at the pre-diabetic phrase as hyperglycaemia after meal resulting from impaired glucose tolerance (IGT). If the disease is left untreated, the condition will progressively worsen and lead to glucose toxicity and lipo-toxicity. At the same time, insulin resistance develops so that the body and the corresponding target organs, specifically muscles and liver, cannot response to the elevated blood glucose concentration properly. When insulin resistance and reduced insulin secretion continue to develop, the elevation of blood glucose would become permanent. (Kahn, Cooper, & Del Prato, 2014)

Aetiology of Type 2 Diabetes Mellitus

Type 2 diabetes mellitus (T2DM) is caused by a combination of genetic and environmental factors.

Genetic factors could be the cause of impaired insulin secretion and insulin resistance while environmental factors are obesity, lack of physical activities, stress and aging. The disease indeed is resulted from complex interaction of multiple genes and environmental factors. The increasing prevalence of type 2 diabetes mellitus is believed to be a consequence of lifestyle changes. (Kahn et al., 2014)

Prevalence of Type 2 Diabetes Mellitus

The global prevalence of diabetes mellitus among adults over 18 years old has been growing in the last two decades and become a major public health challenge. A report from World Health Organization has recorded a rise of disease rate from 4.7% in 1980 to 8.5% in 2014 and it also projects that diabetes mellitus will be the seventh leading cause of death in 2030 (Mathers & Loncar, 2006).

Another study has predicted the global prevalence of T2DM would rise from 171 million in 2000 to 366 million in 2030 (Wild, Roglic, Green, Sicree, & King, 2004). Although the disease seems to threaten global public health, there is a high degree of variability for prevalence globally (Skyler et al., 2017). In Norway, the disease rate is generally lower than the global prevalence, the prevalence of T2DM has been increased from 4.9% to 6.1% from 2009 to 2014 despite a decreasing incidence of the disease

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14 (Ruiz et al., 2018). It was estimated to be around 195,000 T2DM patients in Norway, and at the same time, it is believed around 25% of T2DM patients remain undiagnosed (Sorensen, Arneberg, Line, &

Berg, 2016).

Complications caused by Type 2 Diabetes Mellitus

Untreated T2DM may lead to various conditions, such as excessive urination (polyuria), urinary-tract infections, prolonged healing time for wounds due to high possibility of infections, abnormal thirst and drowsiness. Prolonged elevated blood glucose level can lead to several numbers of complications, which can be categorised as macrovascular and microvascular events. Retinopathy, neuropathy and nephropathy are classified as microvascular complications, and blindness, amputation and renal failure are the most severe consequences of them. Macrovascular complications are caused by the impaired blood supply to the heart, brain and lower extremities. Therefore, patients may experience congestive heart failure, ischemic heart disease, myocardial infarction, and stroke. Apart from diabetes-related complications, T2DM patients are also susceptible to frequent episodes of hypoglycaemia induced by treatment and fear of hypoglycaemia. All of the complications could affect the quality of life of T2DM patients.

Treatment Algorithm for Type 2 Diabetes Mellitus

The objective of T2DM is to control the blood glucose level and thus prevent the onset and progression of vascular complications, so that the quality of life and lifespan could be comparable to those of healthy people. The treatment algorithm can be generally divided as lifestyle improvement programs, oral anti-diabetic drugs, such as metformin and sulfonylurea m, and insulin-based medications (Kahn et al., 2014). All diabetic therapy aims at regulating blood glucose level within normal range, decreasing the risk of cardiovascular complications or improving insulin sensitivity (Kahn et al., 2014).

Impact of Type 2 Diabetes Mellitus

The disease itself and its management imposes a huge economic burden on the global healthcare system and the global economy in both direct and indirect ways. Burden includes medical cost, costs

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15 associated with productivity loss, premature mortality and maybe negative impacts on national GDP (Baudot et al., 2019; Ramzan et al., 2018). Apart from the costs of medication, a wide range of health professionals is required for the care and treatment, such as physicians, nurses, dieticians, obstetricians, ophthalmologists, vascular surgeon and physiotherapists. A study estimated both the direct and indirect cost of diabetes (type 1 and type 2) in Norway to be €516 million in 2011 (Sorensen et al., 2016), which was estimated as €293 million in 2005 (Solli, Jenssen, & Kristiansen, 2010). Of which, more than 20% was spent on drugs, including blood glucose lowering drugs and insulin, blood pressure lowering drugs, and cholesterol lowering drugs.

Bariatric Surgery as Alternative T2DM Treatment Strategy – Metabolic Surgery

Obesity is a major independent risk factor for T2DM as it contributes to the development of insulin resistance (Mokdad et al., 2003), and the risk of T2DM is positively related with BMI (Colditz, Willett, Rotnitzky, & Manson, 1995). Weight management is considered as a priority for diabetes management and prevention of comorbidities and obesity-associated complications. The current medical approach for weight management includes lifestyle changing programs, anti-diabetic drugs for reduction of insulin resistance and improvement of insulin secretion. However, the result of such strategies is somehow unsatisfying. Sometimes the weight loss after behavioural changing intervention is temporary while patients are exposed to higher risk of hypoglycaemia after taking anti-diabetic mediation. Besides, a high proportion of T2DM patients failed to achieve and maintain the goal of glycaemic control, i.e. haemoglobin A1c level below 7% according to the American Diabetes Association, with pharmacological anti-diabetic therapy and weight loss programs. (Kashyap, Gatmaitan, Brethauer, & Schauer, 2010)

While the outcomes of anti-diabetic drugs and insulin therapy is unsatisfying, growing experimental and clinical evidence have shown that weight loss surgery, known as bariatric surgery, with rearrangement of gastrointestinal (GI) tracts may results in remission of T2DM. The manipulation of

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16 gastrointestinal tracts, which serves as an alternative treatment regimen for T2DM through metabolic regulation, is also called metabolic surgery. (Cefalu, Rubino, & Cummings, 2016)

In addition to weight loss, the reduction of insulin resistance and improvement of incretin secretion also contribute to the better glucose homeostasis (Rubino, Schauer, Kaplan, & Cummings, 2010).

Although the mechanism of metabolic surgery in T2DM remission remains unclear, many have proposed the possible mechanisms of action, including changes in gut hormones, bile acid metabolism, intestinal nutrient sensing and metabolism, and modification of gut microbiota (Cefalu et al., 2016).

In spite of that, various studies, including prospective study and meta-analysis, consistently revealed the superior efficacy of weight loss, T2DM remission, improved lipid profiles, enhanced quality of life, reduction in use of medications and reduction of cardiovascular risk factors associated with bariatric surgery in obese T2DM patients compared to various conventional medical or lifestyle interventions (Buchwald et al., 2009; Gloy et al., 2013; Schauer et al., 2003; Sjostrom et al., 2012).

Description of Surgical Procedures

Bariatric surgery can be distinguished by their mechanism of action, categorised as purely restrictive, malabsorptive or combination techniques. Many different techniques have been applied to manipulate the length and arrangement of the upper gastrointestinal tract, and the following four procedures are the most studied in terms of safety and efficacy as weight loss surgery or metabolic surgery.

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17 Laparoscopic adjustable gastric banding (LAGB) is a reversible procedure which involves the restriction of the upper part of the stomach with a silicone adjustable band. The size of the band is adjustable by adding or removing fluid in a balloon around the band, and hence the weight loss effect. (Franco, Ruiz, Palermo, & Gagner, 2011)

Roux-en-Y gastric bypass (RYGB) is both a restrictive and malabsorptive irreversible procedure which aim at bypass duodenum by connecting the stomach and jejunum. In a RYGB surgery, a small gastric pouch, around 15 – 30 mL, is created on the lesser gastric curvature. And, the jejunum is divided 50 – 100 cm distal to the ligament of Treitz. Then, the newly created stomach pouch is anastomosed to the distal limb of jejunum, which is also called the alimentary Roux limb. The proximal duodenum or

Figure 1 Graphical presentation of the four most standardized techniques of bariatric surgery. A Laparoscopic adjustable gastric banding, B Vertical sleeve gastrectomy, C Roux-en-Y gastric bypass, and D Biliopancreatic diversion (From Gagnon, C., & Schafer, A. L. (2018). Bone Health After Bariatric Surgery. JBMR Plus, 2(3), 121-133. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/30283897. doi:10.1002/jbm4.10048)

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18 biliopancreatic limb is anastomosed to the alimentary limb through a jejunojejunostomy 100 – 150 cm distal to the gastrojejunostomy. (Franco et al., 2011)

Sleeve gastrectomy (SG) at first serves as the first step of two-stage RYGB or BPD/DS, but the clinical outcome is so satisfactory that clinicians started to use the technique alone. SG is a restrictive irreversible procedure. Surgeons would create a gastric sleeve tube with the aid of bougies. (Franco et al., 2011)

Biliopancreatic diversion (BPD) is a malabsorptive irreversible procedure and can be performed with or without duodenal switch (DS). The stomach is removed horizontally, and the rest of the stomach is connected to the distal 250 cm of the small intestine. The duodenum, jejunum and part of the proximal ileum are bypassed and connected to the alimentary limb 50 cm proximal to the ileocecal valve.

(Koliaki, Liatis, le Roux, & Kokkinos, 2017)

Almost all operations are performed laparoscopically so as to reduce the operation duration and lower complication rates and faster recovery. Due to the limited efficacy, LAGB has been faded out in some countries. In the meantime, RYGB and SG become dominant techniques for bariatric surgery in most of the countries. (Angrisani et al., 2015)

Clinical and biological determinants of T2DM remission after bariatric surgery

Many researchers have tried to find the best clinical and biological predictors of T2DM remission after bariatric surgery. However, the definitions of T2DM remission among all studies are not standardised.

In this study, complete T2DM remission is defined as HbA1c < 6% for at least 1 year after surgery.

Partial T2DM remission is defined as HbA1c < 6.5% for at least 1 year. T2DM relapse is defined as HbA1c > 6.5% after initial post-operation remission. A retrospective cohort study of T2DM patients showed that around 68.2% of patients are observed to be remitted from T2DM after RYGB surgery, however, 35.1% of them experienced T2DM relapse in later years (Arterburn et al., 2013).

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19 While surgically-induced weight loss seems to favour T2DM remission, a meta-analysis of RCTs, controlled clinical studies and cohort studies demonstrated T2DM remission rate is independent of baseline BMI (Panunzi, De Gaetano, Carnicelli, & Mingrone, 2015). Alternatively, the weight loss itself has a predictive power for T2DM remission (Arterburn et al., 2013).

Apart from weight loss, other metabolic parameters seem to play a role in T2DM remission rate and risk of relapse. These parameters include baseline T2DM duration, preoperative use of intensive insulin therapy, and the degree of glycaemic control (Panunzi et al., 2016; Panunzi et al., 2015). A study concluded that lower baseline waist circumference and lower HbA1c baseline level can lead to better glycaemic control (Panunzi et al., 2016). But even if patient did not experience T2DM remission after surgery, patients may still enjoy improvements in metabolic control and reduction in morbidity and mortality, especially those with a longer T2DM duration and poorer glycaemic control.

Current Guideline for Bariatric Surgery Patient Selection

The US National Institute of Health (NIH) has published a statement in 1991 regarding the use of bariatric surgery and patient selection criteria for the surgery ("NIH conference. Gastrointestinal surgery for severe obesity. Consensus Development Conference Panel," 1991). The statement restricts the patient selection criteria to the following two groups: (1) patients with BMI greater than 40 kg/m², (2) patients greater than 35 kg/m² with at least one comorbidity related to obesity, i.e. T2DM, sleep apnoea, heart disease, etc.

However, these recommendations were written more than two decades ago, while minimally invasive surgical techniques had not been developed. At that time, the most common bariatric surgeries were vertical banded gastroplasty and gastric bypass. Since the advancement in surgical technology, the post-operation mortality has been greatly reduced (Cummings & Cohen, 2014). In the last two decades, the emergence of laparoscopic surgery has been remarkable and more entirely new procedures, such as sleeve gastrectomy, have been developed; in the meanwhile, vertical banded gastroplasty has been faded out. Besides, the aforementioned NIH statement did not address the dramatic effects of

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20 bariatric surgery on T2DM patients beyond caloric restriction and weight loss (Buchwald et al., 2009;

Pories et al., 1995). While NIH admitted the 1991 recommendations were outdated and limited the use of bariatric surgery, they have no intention to review the recommendations, even more biological and clinical evidences of the health benefits brought by bariatric surgery become available.

Given the accumulating evidence of the superior efficacy of bariatric surgery on remission of T2DM and other metabolic diseases, endocrinologists and surgeons have been trying to extend the use of bariatric surgery beyond weight loss, and therefore, the term “metabolic surgery” is preferable. While

the debate on whether bariatric surgery should be performed on patients who are not eligible according to the NIH guideline, many researchers have proved that these operations are safe and effective as a treatment algorithm for T2DM patients with BMI lower than 35 kg/m² (R. V. Cohen et al., 2012; Reis, Alvarez-Leite, Bressan, & Alfenas, 2012)

Cost-effectiveness of extending the guideline to overweight or mildly obese T2DM patients

Despite the clinical effectiveness of bariatric surgery for T2DM patients and the urge to include ineligible patients into guideline, the economic aspect of revising the current clinical guidelines plays an important role in the decision-making process. With the clinical data of costs and health outcomes, an economic evaluation of the intervention would be a strong evidence for effective resource allocation and facilitate the decision-making process. A few economic evaluations in Australia, United States and United Kingdom, have demonstrated bariatric surgery to be cost-saving and provide health benefits to type 2 diabetes patients with BMI over 35 kg/m²(Henteleff et al., 2013; Hoerger et al., 2010; Keating et al., 2009; Picot et al., 2009). However, the cost-effectiveness of bariatric surgery for overweight or mildly obese (i.e. BMI within 25 – 35 kg/m²) type 2 diabetes patients are seldom studied.

One study concerning overweight T2DM patients has revealed that incremental cost effectiveness ratio (ICERs) drops when the study time horizon extended in the U.S. setting (Wentworth et al., 2017).

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21 In order to evaluate the cost-effectiveness of bariatric surgery as a treatment algorithm for T2DM among overweight and obese compared to conventional medical treatment in the Norway setting will be explored in this study.

The Research Question(s) and Objectives

The aim of this study was to estimate, in Norwegian perspective, the lifetime health and economic effects of bariatric surgery as a treatment for type 2 diabetes mellitus patients in overweight or class I obese patients (with BMI 25 – 35 kg/m2) compared to conventional medical therapy, in the form of cost-utility analysis.

Theoretical framework

Theory overview

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

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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

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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

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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).

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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

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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)

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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-based medications, except bariatric surgery. Due to limited clinical data, these techniques would be evaluated as one intervention.

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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

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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

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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.

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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

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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

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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.

Baseline Risk Factors

In order to simulate the effect of bariatric surgery at patient level, patients’ baseline clinical risk factors were defined before they chose to get surgery or not. All patients, no matter if they would have bariatric surgery or not, shared the same statistical variability of various clinical parameters, which is obtained from a national wide survey in Norway so that the study can be performed with Norwegian perspective. Table 1 shows the mean and standard errors of the different clinical characteristics of the patients.

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34 Table 1 Clinical characteristics of T2DM patients in Norway

Male Female Distribution

Demographics

Sex (%) 54.8% 45.2% NA

Age (years) 59.1 ± 10.8 60.8 ± 10.7 Gamma

Complication risk factors

Diabetes duration (years) 6.7 ± 5.9 6.9 ± 6.4 Gamma

Smokers (%) 26.8 27.7 Gamma

BMI (kg/m²) 29.3 ± 4.7 30.9 ± 6.0 Gamma

HbA1c (%) 7.3 ± 1.4 7.1 ±1.3 Gamma

SBP 135.3 ± 16.9 138.7 ± 17.9 Gamma

Total cholesterol (mmol/L) 5.0 ± 1.0 5.4 ± 1.0 Gamma LDL cholesterol (mmol/L) 3.0 ± 0.9 3.3 ± 0.9 Gamma HDL cholesterol (mmol/L) 1.1 ± 0.3 1.3 ± 0.4 Gamma

Triglycerides (mmol/L) 1.8 ± 1.3 1.7 ± 0.9 Gamma

Data are means ± S.D. BMI – Body mass index

Mortality

Norway-specific life expectancy were obtained from the Statistics Norway (StatistiskSentralbyrå, 2019). As T2DM specific survival data for Norway is not available, gender-specific survival data for Norwegian population were applied in the model. Mortality was assumed to be unaffected by treatment strategies, except mortality due to T2DM-related complications. If patients survived from complications throughout all the years, the simulation would be put to a stop when the life expectancy has been reached. The gender-specific life expectancies of Norwegian population are listed in Table 5.

Table 2 Average life expectancy of Norwegian general population

Male Female Distribution

Life expectancy 83.30 86.28 Normal

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35

Changes of Clinical Risk Factors throughout lifetime

According to the annual report of Scandinavian Obesity Surgery Registry (SOReg) for Norway and Sweden, around 43.7% of cases were gastric bypass while 52.3% for sleeve gastrectomy in Norway (Koliaki et al., 2017). However, data of bariatric surgery for T2DM patients with BMI within 25 – 30 kg/m2 is limited; therefore, data of other uncommon procedures, e.g. laparoscopic adjustable gastric banding (LAGB), duodenal-jejunal bypass (DJB), and biliopancreatic diversion with duodenal switch, were also used.

All the previously listed clinical parameters would change over time. As the available data from clinical trials are rather short-term, both groups would adopt the percentage change from baseline for the first three cycles (i.e. T=1 to T=3) and then all clinical parameters were assumed to be unchanged afterwards. Standard deviation was set as 10% of the values when data is not available.

Table 3 Percentage change from baseline for patients with BMI 25-29.9 Patient with BMI of 25-29.9 kg/m²

Bariatric surgery Intensive Medical Therapy

Parameters %

change

Range for one-way sensitivity analysis

% change

Distribution

BMI

1-year -9.075 -1.15 to -17 -1.85 -3.69 to 0 Normal

2-year -8.1 -1.2 to - 15 -1.23 -1.72 to – 0.74 Normal

HbA1c

1-year -20 -10 to -30 -4.75 -2.27 to -7.23 Normal

2-year -17.8 -11.6 to -24 -5.64 0 to -11.28 Normal

Total Cholesterol

1-year -14.5 -3 to -26 +1.35 -7.81 to +10.5 Normal

2-year -14.5 -6 to -23 -1.86 -9.80 to +6.08 Normal

HDL Cholesterol

1-year -11.34 -6.38 to -16.3 -2.34 -6.98 to +2.31 Normal

2-year +1.25 -22.5 to +25 +3.66 +3.15 to +4.17 Normal

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36 LDL Cholesterol

1-year -10 0 to -20 -7.5 -0.15 to 0 Normal

2-year -10 -5 to -24 -9.375 -18.75 to 0 Normal

Triglycerides

1-year +31.52 +3.03 to +60 +3.36 -6.32 to +13.0 Normal

2-year +14.38 -31.25 to +60 -20.03 -17.34 to -22.7 Normal

SBP

1-year -5 -10 to 0 -3.2 -5 to 0 Normal

2-year -5 -10 to 0 -1.53 -3 to 0 Normal

BMI – Body mass index; HbA1c – Glycated haemoglobin; HDL – High-density lipoprotein; LDL – Low- density lipoprotein; SBP – Systolic blood pressure

Table 4 Percentage change from baseline for patients with BMI 30-34.9 Patient with BMI of 30-34.9 kg/m²

Bariatric surgery Intensive Medical Therapy

Parameters %

change

% change

Distribution

BMI

1-year -23.6 -19.6 to -27.6 -0.45 -1.21 to 0.30 Normal

2-year -20.9 -17.2 to - 24.6 -3.95 -8.51 to – 0.61 Normal

3-year -20.97 -15.0 to -25.0 -0.61 -1.21 to 0 Normal

HbA1c

1-year -34.1 -25.2 to -43.0 -8.24 -25.18 to +8.70 Normal

2-year -24.1 -10.2 to -37.9 -12.23 -18.28 to -6.17 Normal

3-year -27.1 -26.3 to -28.0 -3.34 -6.67 to 0 Normal

Total Cholesterol

1-year -22.0 -4.0 to -40.0 -7.50 -20.10 to +5.10 Normal

2-year -15.32 -12.6 to -18.0 -5.64 -22.01 to +10.74 Normal

3-year -16.06 -12.1 to -20.0 -10.53 -21.05 to 0 Normal

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37 HDL Cholesterol

1-year +33.5 +5.0 to +62.0 +3.72 -6.82 to +14.25 Normal

2-year +19.5 +5.0 to +34.0 +8.95 -1.85 to +19.75 Normal

3-year +20.0 +5.0 to +35.0 +10.67 +1.84 to 19.5 Normal

LDL Cholesterol

1-year -17.65 -48.70 to +13.40 +1.49 -2.28 to +5.26 Normal

2-year -9.11 -29.80 to +11.58 -7.53 -15.06 to 0 Normal

3-year +6.50 -2.00 to +15.0 -0.60 -1.19 to 0 Normal

Triglycerides

1-year -39.0 -25.0 to -53.0 -21.64 -43.56 to +0.29 Normal

2-year -50.5 -30.0 to -71.0 -15.07 -38.67 to +8.54 Normal

3-year -37.5 -25.0 to -50.0 -19.11 -38.22 to 0 Normal

SBP

1-year -8.17 -21.33 to +5.0 -9.16 -2.88 to -15.45 Normal

2-year +4.01 -4.54 to +12.58 -4.65 -7.11 to 2.19 Normal

3-year +8.0 -10.0 to +15.0 -0.26 0 to +0.52 Normal

BMI – Body mass index; HbA1c – Glycated haemoglobin; HDL – High-density lipoprotein; LDL – Low- density lipoprotein; SBP – Systolic blood pressure

Mortality

The death of the patient cohort was determined by three ways in the model: (1) life expectancy of Norwegian population; (2) 30-day post-operation mortality; and (3) mortality of T2DM patients after complication event(s). The life expectancy data of Norwegian population was derived from Statistics Norway while 30-day post-operation mortality was taken from observational studies or randomized clinical trials. Mortality of T2DM patients was calculated with four risk equations applied in the UKPDS Outcomes Model. Both costs and utilities would be summed once patient is dead.

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38

T2DM-associated Complication Risk - Risk Equations from the UKPDS Outcomes Model

The UKPDS Outcomes Model and its Risk Equations

Due to limited availability of Norwegian data for diabetes population and time constraints of this master thesis, it is impossible to develop a diabetes model specific for Norwegian population.

Therefore, the UKPDS Outcomes Model was chosen because of its availability and general recognition.

The model was developed based on a large-scale observational study in UK and it can be performed in Microsoft Excel.

The United Kingdom Prospective Diabetes Study (UKPDS) was a multi-centre, prospective, randomized trial which has recruited 5,100 newly diagnosed T2DM patients. This study was the largest clinical study on T2DM and provided a lot of data for future research. As a result, more than 80 publications were published using UKPDS data alone.

A simulation diabetes model, which is as known as the UKPDS Outcomes Model, was developed with the aim of predicting the likely occurrence of common diabetes-associated complications and death over lifetime based on UKPDS data (Hayes et al., 2013). The simulations are based on probabilistic discrete-time illness-death model. With the corresponding risk equation, the annual probability of death or experiencing one or more complications is calculated for each patient based on clinical parameters. Then, a random number is generated from a uniform distribution ranging from 0 to 1 and the number is compared to the probabilities calculated aforementioned so as to determine if an event happened during the cycle. The simulation was repeated for every cycle with updated clinical risk factors until the patient was dead.

Thirteen risk equations were implemented to determine the first occurrence of eight diabetes- associated complications (MI, IHD, CHF, stroke, amputation, ulcer, blindness and renal failure) and second event equations for MI, stroke and amputation. Various factors were included as covariates in risk equations, for example, time invariant factors (e.g. gender, age at diagnoses of diabetes), time varying clinical risk factors (e.g. HbA1c, SBP) and time varying co-morbidities (e.g. history of MI). On

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39 the other hand, four mutually exclusive equations were used to forecast the occurrence of death in the current year. Except ulcer and blindness, it is believed that patient could probably die after the occurrence of complications. The risk equations for mortality have taken the complications previously occurred and complications occurred in the current cycle into consideration. Two Gompertz proportional hazard survival models were used to estimate the hazard of death with no events happened in current annual cycle; with and without a history of complications. The probability of death for patients experienced one or more complications in the current cycle is determined by two logistic equations, one for patients with no complication history and one for patients with complication history.

To model the effect of changes in clinical parameters on the probabilities of getting various diabetes- related complications for this study, the aforementioned risk equations were used in the simulation.

These risk equations take patients’ clinical parameters into account gives the probabilities of having events and mortality under different conditions.

Risk Equations for T2DM-related complications

The risk of getting T2DM-related complications, both microvascular and macrovascular, is simulated by using the risk equations obtained from the UKPDS study. A set of clinical risk factors were used in the equations as predicator variables. These risk factors include systolic blood pressure (SBP), haemoglobin A1c (HbA1c), HDL and LDL cholesterol, BMI, peripheral vascular disease (PVD), and atrial fibrillation. Due to limited availability of clinical data after bariatric surgery, only the following clinical variables were applied to the risk equations: BMI, HbA1c, Total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, and SBP.

There were thirteen risk equations to forecast the probability of occurrence of eight diabetes complications, including myocardial infarction, stroke, congestive heart failure, ischemic heart disease, amputation, blindness, renal failure and ulcer. For MI, stroke and amputation, there are additional

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40 equations for second event. The details of risk equations and its function form were listed as follows.

The parameters and coefficients of all risk equations were attached as appendix.

Table 5 Function forms for equations estimating the probability of diabetes-related complications (Hayes et al., 2013)

T2DM Risk Complications Function forms

First MI (Male) Exponential

First MI (Female) Weibull

Second MI Exponential

First Stroke Weibull

Second Stroke Weibull

CHF Weibull

IHD Weibull

First amputation (no prior ulcer) Weibull First amputation (prior ulcer) Exponential Second amputation Exponential

Blindness Exponential

Renal failure Exponential

Ulcer Logistic

Risk Equations for Mortality Caused by Complications

There are four mutually exclusive equations for estimating the probability of death in different years when patients’ complication status varied. Gompertz proportional hazards survival models were used for years in which no complication events occurred while logistics models were applied to predict the mortality in the years of a complication event. Apart from ulcer and blindness, other complications were expected to associate with mortality. Two equations using logistic regression models were employed to estimate the mortality of patients in the year of event(s) and the subsequent year(s) of events. The two remaining Gompertz proportional hazards survival models are to estimate the death in years with no events, with and without history. The functional form and conditions of the four equations are stated in the following table.

Table 6 Functional forms for four equations to estimate the probability of mortality in the current year (Hayes et al., 2013)

Conditions Function forms

Death in years with no history or events Gompertz Death in 1^st year of event(s) Logistic Death in years with history but not events Gompertz Death in subsequent year(s) of event(s) Logistic

EVALUATING THE COST-EFFECTIVENESS OF BARIATRIC SURGERY IN THE TREATMENT OF TYPE 2 DIABETES MELLITUS IN NORWAY USING DISCRETE EVENT SIMULATION