Budget impact of introducing oral semaglutide to the public healthcare benefit package in Saudi Arabia

Abstract

Background

The Kingdom of Saudi Arabia (KSA) has embarked on a Health Sector Transformation Program as part of the Kingdom’s Vision 2030 initiatives with the facilitation of access to healthcare services for the millions in KSA with diabetes an essential part of the Program. Decision-making tools, such as budget impact models, are required to consider the addition of new medications like oral semaglutide that have multifaceted health benefits and address barriers related to therapeutic inertia to reduce diabetes-related complications.

Objective

To determine the financial impact of the introduction of oral semaglutide as a treatment option for people with type 2 diabetes mellitus (T2DM) in KSA.

Methods

From the public payer’s perspective, the budget impact model estimates the costs before and after the introduction of oral semaglutide over a 5-year time horizon. The budget impact of introducing oral semaglutide (primary comparator) compared with three different classes of diabetes medicines: glucagon-like peptide-1 receptor agonists (GLP-1), sodium-glucose transport protein 2 inhibitors (SGLT 2i) and dipeptidyl peptidase 4 inhibitors (DDP-4i) have been calculated based on the projected market shares. The model includes the cost of care through the incorporation of health outcomes that have an impact on the national payer’s budget in Saudi Riyals (SAR).

Results

The budget impact over the five-year time horizon indicates a medication cost increase (17,424,788 SAR), and cost offsets which include a difference in diabetes management costs (-3,625,287 SAR), CV complications costs (-810,733 SAR) and weight loss savings of 453,936 SAR. The cumulative total cost difference is 12,427,858 SAR (0.66%).

Conclusion

The introduction of oral semaglutide 14 mg as a second-line treatment option after metformin is indicated as budget-neutral to slightly budget-inflating for the public pharmaceutical formulary of KSA. The price difference is offset by positive health outcomes and costs. This conclusion was confirmed through a probabilistic sensitivity analysis.

Keywords:

• Type 2 diabetes
• oral semaglutide
• weight loss
• budget impact
• Kingdom of Saudi Arabia

JEL CLASSIFICATION CODES:

• I18
• I1
• I
• I13

Introduction

The Kingdom of Saudi Arabia (KSA) has embarked on a massive Health Sector Transformation Program¹ to restructure the health sector toward a comprehensive, effective, and integrated system based on individual and societal health. The foundation of the Program is value-based care which includes transparency and financial sustainability by promoting public health and preventing diseases, in addition to applying the new model of care related to disease prevention. The program was established in alignment with the Kingdom’s Vision 2030;² among the Program’s first objectives is to facilitate access to healthcare services, with diabetes, a chronic disease affecting millions of people in KSA, central to key health considerations.

KSA is among the top ten countries in the world with the highest prevalence of diabetes mellitus (DM) with a prevalence of ∼18.7% (4.3 million adults) in 2021 with millions more considered to have pre-diabetes.^3,4 By 2030, KSA is expected to be among the top five countries with the highest prevalence of type 2 DM (T2DM).^3,4

T2DM is associated with a high risk of complications including cardiovascular disease (CVD), nephropathy, neuropathy, retinopathy, and peripheral vascular disease. According to Al-Jedai, A. et al. (2022), there is a significant economic burden of T2DM patients with CVD and renal comorbidities.⁵ This is an increasing healthcare resource utilization (HCRU) which includes hospitalizations and outpatient visits, particularly by patients with high CVD risk. For patients with established atherosclerotic cardiovascular disease (ASCVD) a glucagon-like peptide-1 receptor agonist (GLP-1 RA) with proven benefit should be used to reduce major CVD events (MACE). A proven sodium-glucose transport protein 2 inhibitor (SGLT 2i) should be used for patients with heart failure (HF) to reduce MACE and improve kidney outcomes.⁶

The Saudi National Diabetes Centre (SNDC) Guidelines recommend the glycated hemoglobin (HbA1c) target in most adults with T2DM of <7% and consider metformin as the first-line pharmacological treatment,⁷ with the understanding that T2DM is a progressive disease and additional interventions are necessary over time.⁸ Healthcare practitioners and decision-makers are faced with multiple classes of medications at the second-line stage together with barriers to prescribing that produce therapeutic inertia which is a failure to intensify therapy to address poor glycemic control in a timely manner. Presently, only ∼25% of people with T2DM in KSA maintain good glycemic control,^9,10 which puts a significant number of patients at risk for serious complications and potentially threatens the healthcare system with related exorbitant costs. The American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) 2022 consensus statements on the management of hyperglycemia in T2DM recommend a holistic, multifactorial, person-centric approach to T2DM management which continuously addresses CVD risk factor management, cardiorenal protection, glycemic, weight management and patient preference.⁶

Physicians have expressed reluctance to prescribe subcutaneous medication for their patients due to patient non-adherence, patient refusal or elderly patients not having the ability to administer independently, leading to a failure to intensify therapy and ultimately diabetes-related complications.^10–12 Similarly, patients have expressed fear of injections (most common), preference for oral medications over injectables (76.5%), weight gain, hypoglycemia and social impacts related to second-line T2DM therapy.^6,9,10

Oral semaglutide, recently allowed as first-line therapy through a US Food and Drug Administration label update for adults with T2DM, is an intervention that is easier to prescribe and adhere to and may help address some of the physician and patient barriers that prevent improved glycemic control among patients in KSA with T2DM.^9,13

New medications are being introduced that are considering additional health outcomes beyond the control of HbA1c, including CVD, and most recently, weight management. With the prevalence of obesity in KSA at 35.4% and overweight prevalence among the highest in the world at 69.7% in 2016, reducing weight is a crucial step for proper and sustained plasma glucose control.^7,14,15 Oral semaglutide for patients with T2DM offers glycemic control, weight control and CV, renal and additional benefits, in line with treatment guidelines that recommend a patient-centred approach including a review of individual risk for diabetes-related complications, BMI, and potential patient adherence issues.¹⁶ Studies indicate that oral semaglutide, compared to other oral antidiabetic drugs empagliflozin and sitagliptin, provides superior HbA1c reductions in patients with T2DM (up to 1.8%) and provides superior weight reduction (up to 6.4 kg).⁶ Further, almost 80% of patients reported good or very good treatment satisfaction with oral semaglutide.⁶

The introduction of new healthcare interventions requires careful decision-making on the part of the budget holder. Analysis over time with the incorporation of multiple aspects of the health system and patient health outcomes is becoming an increasingly important tool. The Health Sector Transformation Program in KSA incorporates a focus on health technology assessment (HTA),¹⁷ with a recently established HTA entity to contribute to the decision-making process. This focus aligns with an interest in health economic evaluation wherein reforms and infrastructure improvements required to build capacity are underway.¹⁸ As of 2022, economic analysis of health technologies is limited in KSA and challenged by lack of data and especially country-specific data.¹⁸ The development of a budget impact analysis for the introduction of oral semaglutide seeks to offer findings from this important tool to decision-makers in KSA to augment HTA program information and contribute to the growing area of health economic analysis in KSA with the objective to determine the financial impact of the introduction of oral semaglutide as a treatment option for people with T2DM from a Saudi public payer perspective.

Methods

Population

The population included in the budget impact analysis includes patients with T2DM who have failed to achieve their HbA1c targets with metformin. Based on the SNDC Guidelines, these patients should receive second-line treatment which may include combination therapy of metformin plus a GLP-1 RA including semaglutide subcutaneous (SC), dulaglutide or liraglutide; oral semaglutide; a sodium-glucose transport protein 2 inhibitor (SGLT 2i) including empagliflozin, dapagliflozin; or a dipeptidyl peptidase 4 inhibitor (DPP-4i) including sitagliptin, vildagliptin, or linagliptin.⁷ A patient-centric approach to treatment selection is recommended with a review of comorbidities including obesity. For patients with established atherosclerotic cardiovascular disease (ASCVD) or chronic kidney disease (CKD), a GLP-1 RA or SGLT 2i is recommended as second-line therapy; for patients with a greater need for glucose-lowering effect from their medication a GLP-1 RA is recommended, and for patients with HF, a SGLT 2i is preferred. Further, the ADA/EASD consensus statements indicate that semaglutide is recommended as a first-line GLP-1 RA with proven CVD benefit with a significant impact on reducing the risk of MACE, including non-fatal stroke, in high-risk patients with T2DM.⁶ For people with T2DM who are uncontrolled on metformin and living with obesity, the SNDC Guidelines recommended a GLP-1 RA or SGLT-2i for their good effect on weight loss.⁷ Semaglutide therapy for patients with T2DM offers high efficacy in both body weight reduction and glucose control.

The patient flow diagram (Figure 1) illustrates the T2DM population and the comparators of the budget impact analysis (second-line therapy).

Figure 1. Patient flow diagram.

The affected patient population was derived from the starting total population size of Saudi Arabia through the application of epidemiological data on the Saudi population, public and private coverage, prevalence of DM (18%), the prevalence of T2DM (95%), the incidence of T2DM (1.0%), age (45–69 years), patients uncontrolled on metformin (40%) and BMI ranges greater than 30 kg/m2.^19–21 (Table 1 and Table A1) The target population age was based on the baseline characteristics in the Pioneer Program wherein Pioneer (2)²⁷ the population age was 57 years (standard deviation (SD) =10 years), and in Pioneer (3)²⁸ the population age was 58 years (SD = 10 years). Data availability regarding the relative risk reduction associated with the mean weight loss derived from available local literature limited the upper limit of the age range.²⁹ The specialty senior population was not intended to be included in this study.

Table 1. Basic model Parameters: Population Breakdown.

Total population in Saudi Arabia	32,175,244	^22
Total Saudi population (%)	58.4%	^22
Total number of Saudi population aged 45–69	3,006,762	^23
Total population with private coverage aged 45–69	2,477,343	^24
Prevalence of DM (%)	18%	^19
Prevalence of T2DM aged 45–69 (%)	95%	^19
Patients uncontrolled on metformin (%)	40%	^20^,^21
Prevalence of patients with BMI ≥ 30 kg/m^2 (%)	32.6%	^25
Total number of T2DM patients aged 45–69 with public coverage uncontrolled on metformin with BMI≥ 30 kg/m^2	47,049	^26

Comparators

The budget impact analysis was conducted from the perspective of the KSA public health payer. The classes of medications included GLP-1 RAs, SGLT 2i and DPP-4i. The model included the direct medical costs of care through the incorporation of different health outcomes that have an impact on the national payer’s budget: proportion of patients achieving HbA1c% target (<7); cardiovascular risk reduction as a cost offset; budget savings as a result from weight loss (impact of GLP-1 RAs). These direct medical costs correlate to the patient profiles for appropriate allocation of treatment from the Saudi Diabetes Clinical Practice Guidelines including HbA1c target achievement, BMI, comorbidities including HF, ASCVD, and CKD.⁷ Indirect costs were not included as they are not relevant to the payer’s perspective. Unit costs were derived from local economic evidence and inflated to the current prices using the Saudi Consumer Price Indices (CPI) and the public tender price was used for the medication costs. The model was designed to incorporate chosen health outcomes to produce the budget impact results.

The primary comparator was the newly introduced oral semaglutide 14 mg against three classes of diabetes medicines. These include three groups of comparators: injectable GLP-1 RAs (semaglutide SC, dulaglutide 1.5 mg, liraglutide 1.8 mg); SGLT 2i (empagliflozin, dapagliflozin); and DPP-4i (sitagliptin, vildagliptin, linagliptin) (Table 2). The inclusion of each comparator and comparator grouping is separately modifiable in the model. Groupings were determined by class of medication with the values determined through a weighted average based on the market share derived from the IQVIA report.³⁰

Table 2. Budget impact model comparators.

	Comparator name	Value MAT MS%
Primary comparator	oral semaglutide 14 mg
Comparator 1	GLP-1 RAs	38%
	Semaglutide SC.	48%
	Dulaglutide 1.5 mg	39%
	Liraglutide 1.8 mg	13%
Comparator 2	SGLT 2i	15%
	Empagliflozin	78%
	Dapagliflozin	22%
Comparator 3	DPP-4i	47%
	Sitagliptin	79%
	Vildagliptin	5%
	Linagliptin	16%

Abbreviations. MAT, moving annual total; MS, market share; GLP-1 RA, glucagon-like peptide-1 receptor agonist; SGLT 2i, sodium-glucose transport protein 2 inhibitor; DPP-4i, dipeptidyl peptidase 4 inhibitor; mg, milligram.

KSA Ministry of Health decision-makers identified the relevant comparators at the scoping stage of the study based on clinical practice in the KSA public sector and local diabetes guidelines in the development of the model. Though insulin is considered one of the strongest glucose-lowering agents, according to the consensus report by the ADA and EASD,⁶ insulin is the preferred option for patients whose HbA1c is more than 10% and associated with symptoms like weight loss or ketonuria/ketosis and with acute glycemic dysregulation, which is not the case for the model target population. Increasing evidence supports the utilization of other regimens e.g. GLP-1 RA, SGLT-2i in particular patient profiles (CV and renal complications and overweight) which is in alignment with the SNDC Guidelines.⁷

Inputs

Basic model Parameters

The basic model parameters involved standard inputs such as population size, time horizon, prevalence and incidence information, age and BMI ranges (Table 3).

Table 3. Basic model parameters.

Starting year	2023
Time horizon	5 years
Diabetes attributable mortality	0.75%	^19
Discount rate	3.5%
Currency	SAR
Prevalence of diabetes mellitus	18%	^19
Incidence of T2DM	1.0%	^19
Age group	45−69 years
Patients uncontrolled on metformin	40%	^20^,^21
BMI range	32−35

Abbreviations. T2DM, type 2 diabetes mellitus; BMI, body mass index.

The five-year time horizon was the requested period by public payers in the Kingdom of Saudi Arabia, with a year-to-year budget impact also included in the results.

Market mix

The market mix scenarios were considered with (new scenario) and without (current scenario) the inclusion of oral semaglutide 14 mg over a five-year time horizon. The market mix includes GLP-1 RAs (primary competitor), SGLT 2i and DPP-4i (secondary comparator to accommodate for the attrition impact) with market percentages determined through IQVIA market intelligence reports³⁰ (Table 4).

Table 4. Market mix*.

	2023	2024	2025	2026	2027
Current scenario (without oral semaglutide 14 mg)
oral semaglutide 14 mg	0%	0%	0%	0%	0%
GLP-1 RAs	18%	21%	23%	26%	27%
SGLT 2i	20%	22%	25%	28%	30%
DPP-4i	62%	57%	52%	46%	43%
	100%	100%	100%	100%	100%
New Scenario (with oral semaglutide 14 mg)
oral semaglutide 14 mg	1%	3%	5%	8%	10%
GLP-1 RAs	17%	19%	20%	21%	21%
SGLT 2i	20%	22%	24%	26%	28%
DPP-4i	62%	56%	51%	45%	41%
	100%	100%	100%	100%	100%

Abbreviations. GLP-1 RA, glucagon-like peptide-1 receptor agonist; SGLT 2i, sodium-glucose transport protein 2 inhibitor; DPP-4i, dipeptidyl peptidase 4 inhibitor; mg, milligram.

*projections were developed using the moving annual total (MAT) value market shares [30].

Model input Parameters

The clinical outcomes incorporated into the model (Tables 5–8) reflect available data on comorbidities associated with T2DM. These included cardiovascular (CV) risk reduction related to T2DM treatments, specifically the 3-point major CV event outcome (3 P-MACE), CV death, non-fatal stroke and non-fatal myocardial infarction (MI); the proportion of patients with uncontrolled HbA1c (>7.0%), together with HbA1c reduction; weight reduction as a health outcome (derived from Value of Weight Loss Tool (VOWL), Saudi Version);²⁹ and weight loss saving impact on health outcomes and risk reduction related to CKD, HF, hypertension and dyslipidemia.

Table 5. Model inputs—CV risk reduction*.

Cardiovascular Risk Reduction (3P-MACE)	CV Death	Reference	Non-fatal stroke	Reference	Non-fatal MI	Reference
Base rate for T2DM patients per PY	0.02	^31	0.00	^32	0.01	^33
Oral semaglutide 14 mg	0.49	^34	0.76	^34	1.18	^34
GLP-1 RAs
Semaglutide SC.	0.98	^35	0.61	^35	0.74	^35
Dulaglutide 1.5 mg	0.91	^36	0.76	^36	0.96	^36
Liraglutide 1.8 mg	0.78	^37	0.89	^37	0.88	^37
SGLT 2i
Empagliflozin	0.62	^38	1.24	^38	0.87	^38
Dapagliflozin	0.98	^39	1.01	^39	0.89	^39
DPP-4i
Sitagliptin	1.03	^40	0.95	^40	0.97	^40
Vildagliptin	0.93	^41	0.96	^41	0.79	^41
Linagliptin	1.12	^42	0.96	^42	1.53	^42

Abbreviations. 3P MACE, 3-point major CV event outcome; CV, cardiovascular; MI, myocardial infarction; T2DM, type 2 diabetes mellitus; PY, per year; GLP-1 RA, glucagon-like peptide-1 receptor agonist; SGLT 2i, sodium-glucose transport protein 2 inhibitor; DPP-4i, dipeptidyl peptidase 4 inhibitor; mg, milligram.

*hazard ratios.

Table 6. Model inputs—proportion of patients uncontrolled HbA1c.

Proportion of patients uncontrolled*	HbA1c < 7.0% (26 weeks)	Reference	HbA1c < 7.0% (52 weeks)
Comparator
oral semaglutide 14 mg	69.7%	^16	90.8%
GLP-1 RAs
Semaglutide SC.	79.8%	^16	95.9%
Dulaglutide 1.5 mg	64.7%	^16	87.5%
Liraglutide 1.8 mg	64.6%	^16	87.5%
SGLT 2i
Empagliflozin	41%	^27	64.8%
Dapagliflozin	41%	^43	64.8%
DPP-4i
Sitagliptin	32%	^28	53.8%
Vildagliptin	22%	^44	39.2%
Linagliptin	26%	^45	47.9%

*Uncontrolled patients defined as the proportion of T2DM patients with HbA_1c% >7.0%.

Abbreviations. HbA1c, glycated hemoglobin; GLP-1 RA, glucagon-like peptide-1 receptor agonist; SGLT 2i, sodium-glucose transport protein 2 inhibitor; DPP-4i, dipeptidyl peptidase 4 inhibitor; mg, milligram.

Table 7. Model inputs—weight reduction.

Weight reduction as a health outcome*	Mean weight reduction %	Reference
Comparator
oral semaglutide 14 mg	5%	^27
GLP-1 RAs
Semaglutide SC.	7%	^46
Dulaglutide 1.5 mg	3%	^46
Liraglutide 1.8 mg	3%	^46
SGLT 2i
Empagliflozin	4%	^27
Dapagliflozin	4%	^43
DPP-4i
Sitagliptin	1%	^28
Vildagliptin	1%	^44
Linagliptin	1%	^45

*Risk reductions associated with weight loss will be derived from the Value of weight loss tool (Saudi version) [29].

Abbreviations. GLP-1 RA, glucagon-like peptide-1 receptor agonist; SGLT 2i, sodium-glucose transport protein 2 inhibitor; DPP-4i, dipeptidyl peptidase 4 inhibitor; mg, milligram.

Table 8. Model inputs: weight-loss saving impact.

Weight loss saving impact	Prevalence	Reference	RR for 7% weight loss	RR for 5% weight loss	RR for 4% weight loss	RR for 3% weight loss	RR for 1% weight loss	Reference
Chronic kidney disease (CKD)	24%	^47	0.01	0.01	0.01	0.00	0.00	^29
Heart failure	2%	^47	0.05	0.04	0.03	0.03	0.01	^29
Hypertension	63%	^48	0.02	0.01	0.01	0.01	0.00	^29
Dyslipidemia	67%	^49	0.01	0.01	0.01	0.00	0.00	^29

Diabetes management costs (including laboratory tests, hospitalization, outpatient visits, specialists, dietary education, and others) were incorporated for patients with T2DM with HbA1c less than 7%, between 7% and 9% and greater than 9% (Table A2). Further, costs related to T2DM comorbidities were considered based on costs per patient and converted into SAR and inflated to current values using the Saudi consumer price indices (Table A3).

The model input for proportion of patients with uncontrolled HbA1c will be transformed to annual values using standardized transformation techniques.

Sensitivity analyses

Probabilistic and deterministic sensitivity analyses were conducted by attributing the option of Beta, Lognormal, or Gamma distributions to each input parameter in the series as detailed in Table A4. The probabilistic sensitivity analysis assessed the robustness of the BIM and determined the probability of budget inflation greater than 5, 10 and 15% and the probability of budget savings greater than 5, 10 and 15%. Deterministic sensitivity analyses were conducted to investigate the sensitivity of the outcomes based on the impact of the input parameters on the results of the model.

Model outcomes

The results from the BIM are intended to determine the budget impact year over year and of introducing oral semaglutide to the KSA public pharmaceutical formulary together with the impact on the overall budget with a five-year time horizon. Results will additionally reveal the impact on health outcomes including the number of CV events, the number of patients with T2DM achieving HbA1c target, and weight loss savings per scenario.

Results

Healthcare budget impact

The healthcare budget impact realized with the introduction of oral semaglutide 14 mg was reported year by year and over five years and included medication costs, diabetes management costs, CV complication costs, weight loss savings and total cost impact. In year 2024, the addition of oral semaglutide increased medication costs by 1,973,048 SAR, decreased diabetes management costs (-460,924 SAR) and CV complications costs (-65,766 SAR) and provided weight loss savings (75,401 SAR). In year 2025, the addition of oral semaglutide increased medication costs by 3,540,287 SAR, decreased diabetes management costs (-748,411 SAR) and CV complication costs (-144,761 SAR) and realized weight loss savings (92,451 SAR). In year 2026, medication costs increased by 5,114,810 SAR, diabetes management costs (-1,010,780 SAR) and CV complication costs (-230,078 SAR) decreased, and cost savings were realized for weight loss (109,849 SAR). By year 2027, the introduction of oral semaglutide will increase medication costs by 6,692,173 SAR, CV complications costs decrease (-403,734 SAR), and diabetes management costs decrease (-1,414,715 SAR), and weight loss cost savings were realized (174,760 SAR) (Table A5).

The budget impact over the complete time horizon (cumulative five years) indicates a medication cost increase (17,424,788 SAR), and cost offsets which includes a difference in diabetes management costs (-3,625,287 SAR), CV complications costs (-810,733 SAR) and weight loss savings of 453,936 SAR. The total cost differential is 0.66% (12,427,858 SAR) (Table 9). Detailed comparative costs of each medication is included in the appendix (Tables A6 and A7).

Table 9. Budget impact over full time horizon.

Full time horizon	Medication costs	Diabetes management costs	SHE costs	CV. Complications costs	Weight loss savings	Total costs
Current scenario (without oral semaglutide 14 mg)	327,081,501 SAR	1,069,078,015 SAR	2,426,404 SAR	494,625,5087 SAR	23,548,406 SAR	1,869,663,022 SAR
New Scenario (with oral semaglutide 14 mg)	344,506,289 SAR	1,065,452,729 SAR	2,319,430 SAR	493,814,755 SAR	24,002,342 SAR	1,882,090,881 SAR
Difference (value)	17,424,788 SAR	−3,625,287 SAR	−106,974 SAR	−810,733 SAR	453,936 SAR	12,427,858 SAR
Difference (%)	5%	−0.34%	−4%	−0.16%	2%	0.66%

Abbreviations. SHE, severe hypoglycemic event; CV, cardiovascular.

Health outcomes impact

The impact on health outcomes, as they relate to people with T2DM when oral semaglutide is introduced is evident. The number of patients achieving HbA1c target (<7%) increased by ∼1,500 patients; the number of CV deaths is reduced by 95 patients; the number of non-fatal strokes is reduced by four events; the number of non-fatal MI events is increased by 67; and the number of severe hypoglycemia episodes is reduced by 71.

Sensitivity analysis

The probabilistic sensitivity analysis considered 115 variables by attributing different distributions to the input parameters. These included multi-faceted prevalence data, prices, health outcomes by therapy, and costs associated with comorbid events. The analysis supported the results of the BIM, with the probability of inflating the healthcare budget by more than 2% being less than 0.01 (Figure 2).

Figure 2. Probabilistic sensitivity analysis.

The deterministic sensitivity analysis considered 82 variables revealing top input parameters including oral and injectable semaglutide, dulaglutide price per pack and oral semaglutide proportion of patients achieving HbA1C% target were the top input parameters that impacted the results of the model (Figure 3).

Figure 3. Deterministic sensitivity analysis.

Discussion

The objective of this analysis was to determine the budget impact of introducing oral semaglutide as a treatment option for patients with T2DM in KSA. The results reported with high confidence indicated an increase in medication costs offset by a reduction in DM management costs, CV complications costs and savings realized through patient weight loss. The findings indicated that the introduction of oral semaglutide is budget neutral to cost saving, allowing for a focus on other considerations related to the treatment of patients with T2DM in KSA. These may include factors influencing therapeutic inertia, medication adherence and the modern realities of the KSA population that include high T2DM and obesity rates reflected in the strategic objectives of the KSA Health Sector Transformation Program.¹

Understanding the cost implications of this new therapeutic option for people with T2DM is a vital contribution to decision-making and healthcare budget planning in KSA. Results derived from randomized controlled trials further confirmed positive health outcomes for patients with T2DM in terms of improved HbA1c results, fewer hypoglycemic events, and weight loss benefits.⁶ These findings together allow for consideration of this new intervention and how it will address challenges among the growing T2DM population in KSA, assured that it will not overwhelm the resources of the healthcare budget.

Insight into the barriers to second-line therapy for patients with T2DM provides an important patient/prescriber perspective. Consideration of KSA-specific healthcare costs offers a clear understanding of the budget impact, and analysis of KSA-specific health outcomes provides a comprehensive understanding of the healthcare environment. The results of the budget impact analysis of the main comparator, oral semaglutide, to be considered against the available treatment modalities in KSA from the public payer’s perspective, offer insights that the introduction of oral semaglutide as a second-line intervention will have a positive impact on health outcomes and not affect the healthcare budget negatively.

Strengths and limitations

Strengths of the budget impact analysis include the consideration of costs at a population level so that the impact on the public healthcare budget as a whole may be determined. The comprehensive sensitivity analysis offers confidence in the probabilities presented in the findings. The analytical approach selected through the unique BIM is an appropriate means to determine the budget impact of the introduction of oral semaglutide 14 mg in KSA. These important connections in the development of this work allowed for key opinion leaders to populate the model pertaining to model input costs due to the scarcity of this information and the complexity of the care required (e.g. testing, specialist support, emergency room, nursing, hospital stay, etc.).

Overarching limitations of the budget impact analysis were the lack of KSA-specific data, an underlying challenge of all economic analyses of health technologies.¹⁸

Specifically related to cardiovascular risk-reduction, the BIA was limited by the lack of meta-analysis that included all comparators. For GLP-1 (the main comparators) indirect treatment comparison was used and cardiovascular outcome trials comparing the medication versus placebo were used for DPP-4i and SGLT-2i; though quite similar in methodology, this is considered a limitation due to the heterogeneous population.

Conclusion

The introduction of oral semaglutide 14 mg as a second-line treatment after metformin is budget-neutral to slightly budget-inflating for the public pharmaceutical formulary of KSA. The price difference is offset by positive health outcomes including more patients in KSA achieving HbA1c targets, fewer CV events and impactful weight-loss cost savings. This conclusion was confirmed through a probabilistic sensitivity analysis.

Transparency

 

Author contributions

All authors were involved in the development of the model, analysis and contributed to the final manuscript.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Acknowledgements

The authors would like to acknowledge Janet Crain (KTE Bridge Consulting, Canada) for medical writing assistance in the preparation of this manuscript, funded by NovoNordisk, KSA.

Declaration of funding

This work was funded by NovoNordisk, KSA.

Declaration of financial/other relationships

MA has received speaker’s honoraria from NovoNordisk, KSA.

The other authors have no competing interests to declare.

Appendices

Table A1. Basic model parameters: population breakdown—detailed calculation.

		Calculation
Total number of Saudi population	18,792,262	58.4% of Total Population
Total percentage of Saudi population aged 45–69	16%	% of Total Saudi Population
Total percentage of Saudi population with private coverage aged 45–69	36.2%	% of Total Saudi Population with Private Coverage
Total number of Saudi population with private coverage aged 45–69	896,798	Applying the % to the Total Saudi Population with Private Coverage
Total number of population with public coverage aged 45–69	2,109,964	Saudi Population (45–69) with private insurance less the total population (45–69)
Total number of DM patients with public coverage aged 45–69	379,793	Prevalence of DM applied to Saudi population with public coverage (45–69)
Total number of T2DM patients with public coverage aged 45–69	360,804	Prevalence of T2DM applied to Saudi population with public coverage (45–69)
Total number of T2DM patients aged 45–69 with public coverage uncontrolled on metformin	144,322	% of patient uncontrolled on Metformin applied to T2DM patents with public coverage (45–69)

Table A2. Model inputs—annual non-medication costs per patient.

Annual non-medication costs per patient (2011)	HbA1c < 7%		HbA1C 7-9%		HbA1c > 9%
Laboratory tests cost	654.1 SAR	^51	747.7 SAR	^51	1,131.9 SAR	^51
Hospitalization cost	935.0 SAR	^51	1,466.4 SAR	^51	2,550.0 SAR	^51
Outpatients visit cost	580.0 SAR	^51	584.6 SAR	^51	594.1 SAR	^51
Medical specialty cost	580.0 SAR	^51	592.7 SAR	^51	572.1 SAR	^51
Dietary education cost	55.0 SAR	^51	40.3 SAR	^51	115.0 SAR	^51
Other costs*	352.1 SAR	^51	722.8 SAR	^51	1,587.9 SAR	^51
Total (2011 prices)	3,156.2 SAR	^51	4,154.5 SAR	^51	6,551.0 SAR	^51
Total (2022 prices)	3,819.0 SAR		5,027.0 SAR		7,926.8 SAR	^51
HbA1c <7%	3,819.0 SAR
HbA1c >7%	6,476.9 SAR

Abbreviation. HbA1c, glycated hemoglobin.

Table A3. Model inputs—prevalence of comorbidities in T2DM population.

Prevalence of comorbidities in T2DM population	2018 prices (USD)		Prices in SAR	2022 Prices	S
CV. death	$6,000	^52	SAR 22,560.00	SAR 23,888.79	Inflated
Non-fatal stroke	$29,576	^52	SAR111,205.76	SAR117,755.81	Inflated
Acute stroke rehabilitation (year)	$14,627	^52	SAR 54,997.52	SAR 58,236.89	Inflated
Non-fatal MI	$8,960	^53	SAR 33,689.60	SAR 35,673.93	Inflated
Severe hypoglycemia (SHE)				SAR 1,650	^54
Chronic kidney disease (CKD)				SAR 17,945	*
Heart failure				SAR 26,900	^55
Hypertension				SAR 8,437	*
Dyslipidemia				SAR 15,896	*

Abbreviation. T2DM, type 2 diabetes mellitus; MI, myocardial infarction.

*derived from VOWL tool: Data on file.

Table A4. Probabilistic sensitivity analyses distribution.

Input parameter	Distribution
Prevalance_Diabetes	Beta
Prevalance_T2DM	Lognormal
Incidence_T2DM	Beta
Diabetes_attributable_mortality	Beta
PTs_uncontrolled_Met.	Lognormal
GLP_MS%_MNAID	Beta
Semaglutide_MS% _GLP	Beta
Dula_MS%_GLP	Beta
Lira_MS% _GLP	Beta
DPP_MS% _MNAID	Beta
SITA_MS%_DPP4	Beta
VILDA_MS%_DPP4	Beta
LINA_MS%_DPP4	Beta
SGLT_MS%_MNAID	Beta
Empa_MS%_SGLT	Beta
Cana_MS%_SGLT	Beta
Dapa_MS%_SGLT	Beta
Sema_O_pack_price	Gamma
Sema_SC_pack_price	Gamma
Dula_pack_price	Gamma
Lira_pack_price	Gamma
Empa_pack_price	Gamma
Cana_pack_price	Gamma
Dapa_pack_price	Gamma
SITA_pack_price	Gamma
VILDA_pack_price	Gamma
LINA_pack_price	Gamma
PTs_below_7%_SemaO%	Beta
PTs_below_7%_Sema_SC.	Beta
PTs_below_7%_Dula	Beta
PTs_below_7%_Lira	Beta
PTs_below_7%_Empa	Beta
PTs_below_7%_Cana	Beta
PTs_below_7%_Dapa	Beta
PTs_below_7%_SITA	Beta
PTs_below_7%_VILDA	Beta
PTs_below_7%_LINA	Beta
IR_PER_PY_Stroke	Beta
IR_PER_PY_MI	Beta
IR_PER_PY_CV_death	Beta
HR_stroke_SemaO	Lognormal
HR_stroke_Sema	Lognormal
HR_stroke_Dula	Beta
HR_stroke_Lira	Lognormal
HR_stroke_Empa	Lognormal
HR_stroke_Cana	Lognormal
HR_stroke_dapa	Lognormal
HR_stroke_Sita	Lognormal
HR_stroke_Vilda	Lognormal
HR_stroke_Lina	Lognormal
HR_MI_SemaO	Lognormal
HR_MI_Sema	Lognormal
HR_MI_Dula	Lognormal
HR_MI_Lira	Lognormal
HR_MI_Empa	Lognormal
HR_MI_Cana	Lognormal
HR_MI_dapa	Lognormal
HR_MI_Sita	Lognormal
HR_MI_Vilda	Lognormal
HR_MI_Lina	Lognormal
HR_CV_death_SemaO	Beta
HR_CV_death_Sema	Lognormal
HR_CV_death_Dula	Lognormal
HR_CV_death_Lira	Beta
HR_CV_death_Empa	Beta
HR_CV_death_Cana	Lognormal
HR_CV_death_dapa	Lognormal
HR_CV_death_Sita	Lognormal
HR_CV_death_Vilda	Lognormal
HR_CV_death_Lina	Lognormal
IR_SHE_SemaO	Beta
IR_SHE_Sema	Beta
IR_SHE_Dula	Beta
IR_SHE_Lira	Beta
IR_SHE_Empa	Beta
IR_SHE_Cana	Beta
IR_SHE_dapa	Beta
IR_SHE_Sita	Beta
IR_SHE_Vilda	Beta
IR_SHE_Lina	Beta
Prevalence_CKD_T2D	Beta
Prevalence_HF_T2D	Beta
Prevalence_HTN_T2D	Beta
Prevalence_Dyslipidaemia_T2D	Beta
7%WL_RR_CKD_T2D	Beta
7%WL_RR_HF_T2D	Beta
7%WL_RR_HTN_T2D	Beta
7%WL_RR_Dyslipidaemia_T2D	Beta
5%WL_RR_CKD_T2D	Beta
5%WL_RR_HF_T2D	Beta
5%WL_RR_HTN_T2D	Beta
5%WL_RR_Dyslipidaemia_T2D	Beta
4%WL_RR_CKD_T2D	Beta
4%WL_RR_HF_T2D	Beta
4%WL_RR_HTN_T2D	Beta
4%WL_RR_Dyslipidaemia_T2D	Beta
3%WL_RR_CKD_T2D	Beta
3%WL_RR_HF_T2D	Beta
3%WL_RR_HTN_T2D	Beta
3%WL_RR_Dyslipidaemia_T2D	Beta
1%WL_RR_CKD_T2D	Beta
1%WL_RR_HF_T2D	Beta
1%WL_RR_HTN_T2D	Beta
1%WL_RR_Dyslipidaemia_T2D	Beta
Cost_CV_death	Gamma
Cost_Non-fatal stroke	Gamma
Cost_Acute stroke rehabilitation (year)	Gamma
Cost_Non-fatal MI	Gamma
Cost_SHE	Gamma
Cost_CKD	Gamma
Cost_HF	Gamma
Cost_Hypertension	Gamma
Cost_Dyslipidaemia	Gamma
Cost_T2D_HbA1c <7%	Gamma
Cost_T2D_HbA1c >7%	Gamma

Table A5. Year over year healthcare budget impact.

2023	Medication costs	Diabetes management costs	SHE costs	CV. Complications costs	Weight loss savings	Total costs
Current scenario (without oral semaglutide 14 mg)	59,712,236 SAR	227,339,484 SAR	531,980 SAR	83,123,377 SAR	4,346,486 SAR	366,360,592 SAR
New Scenario (with oral semaglutide 14 mg)	59,816,575 SAR	227,349,015 SAR	528,070 SAR	83,156,941 SAR	4,347,958 SAR	366,502,643 SAR
Difference (value)	104,339 SAR	9,531 SAR	−3,910 SAR	33,564 SAR	1,473 SAR	142,051 SAR
Difference (%)	0%	0%	−1%	0%	0%	0.0%
2024	Medication costs	Diabetes management costs	SHE costs	CV. Complications costs	Weight loss savings	Total costs
Current scenario (without oral semaglutide 14 mg)	63,375,976 SAR	219,084,300 SAR	506,751 SAR	91,390,644 SAR	4,541,331 SAR	369,816,340 SAR
New Scenario (with oral semaglutide 14 mg)	65,344,076 SAR	218,650,608 SAR	493,401 SAR	91,325,051 SAR	4,616,544 SAR	371,196,592 SAR
Difference (value)	1,968,100 SAR	−433,692 SAR	−13,350 SAR	−65,593 SAR	75,212 SAR	1,380,252 SAR
Difference (%)	3%	0%	−3%	0%	2%	0.4%
2025	Medication costs	Diabetes management costs	SHE costs	CV. Complications costs	Weight loss savings	Total costs
Current scenario (without oral semaglutide 14 mg)	65,357,352 SAR	211,433,680 SAR	482,547 SAR	99,157,756 SAR	4,704,901 SAR	371,726,434 SAR
New Scenario (with oral semaglutide 14 mg)	688,884,330 SAR	210,713,709	462,203 SAR	99,013,543 SAR	4,797,004 SAR	374,276,781 SAR
Difference (value)	3,526,978 SAR	−719,971 SAR	−20,344 SAR	−144,212 SAR	92,104 SAR	2,550,347 SAR
Difference (%)	5%	0%	−4%	0%	2%	0.7%
2026	Medication costs	Diabetes management costs	SHE costs	CV. Complications costs	Weight loss savings	Total costs
Current scenario (without oral semaglutide 14 mg)	68,739,191 SAR	203,607,582 SAR	457,473 SAR	106,208,870 SAR	4,922,008 SAR	374,091,109 SAR
New Scenario (with oral semaglutide 14 mg)	73,828,379 SAR	202,627,048 SAR	426,973 SAR	105,979,881 SAR	5,031,307 SAR	377,830,974 SAR
Difference (value)	5,089,188 SAR	−980,534 SAR	−30,500 SAR	−228,989 SAR	109,299 SAR	3,739,866 SAR
Difference (%)	7%	0%	−7%	0%	2%	1.0%
2027	Medication costs	Diabetes management costs	SHE costs	CV. Complications costs	Weight loss savings	Total costs
Current scenario (without oral semaglutide 14 mg)	68,637,463 SAR	197,026,243 SAR	438,824 SAR	113,053,180 SAR	4,943,140 SAR	374,212,570 SAR
New Scenario (with oral semaglutide 14 mg)	75,287,758 SAR	195,668,702 SAR	400,464 SAR	112,651,742 SAR	5,116,806 SAR	378,891,861 SAR
Difference (value)	6,650,295 SAR	−1,357,541 SAR	−38,360 SAR	−401,438 SAR	173,666 SAR	4,679,290 SAR
Difference (%)	10%	−1%	−9%	0%	4%	1.3%

Abbreviations. SHE, severe hypoglycemic event; CV –, cardiovascular; mg -, milligram.

Table A6. Current scenario (without oral semaglutide 14 mg).

Cost offset	Oral semaglutide 14mg	GLP-1 RAs	SGLT-2i	DPP-4i	Total
Medication costs	0 SAR	212,358,853 SAR	53,598,633 SAR	61,124,015 SAR	327,081,501 SAR
Diabetes management costs (non-medication)	0 SAR	207,016,942 SAR	264,561,153 SAR	597,499,920 SAR	1,069,078,015 SAR
Cost of SHE	0 SAR	440,360 SAR	479,651 SAR	1,506,393 SAR	2,426,404 SAR
Cost of CV	0 SAR	91,336,780 SAR	127,988,521 SAR	275,300,207 SAR	494,625,508 SAR
Weight-loss saving impact	0 SAR	9,986,181 SAR	8,817,648 SAR	4,744,577 SAR	23,548,406 SAR
Total	0 SAR	501,166,754 SAR	437,810,311 SAR	930,685,958 SAR	1,869,663,022 SAR

Abbreviations. SHE, severe hypoglycemic event; CV, cardiovascular; mg, milligram.

Table A7. New scenario (with oral semaglutide 14 mg).

Cost offset	Oral semaglutide 14 mg	GLP-1 RAs	SGLT-2i	DPP-4i	Total
Medication costs	51,541,803 SAR	181,468,325 SAR	51,515,010 SAR	59,981,151 SAR	344,506,289 SAR
Diabetes management costs (non-medication)	47,944,616 SAR	176,903,469 SAR	254,276,456 SAR	586,328,187 SAR	1,065,452,729 SAR
Cost of SHE	3,894 SAR	376,304 SAR	461,004 SAR	1,478,227 SAR	2,319,430 SAR
Cost of CV	20,755,510 SAR	78,769,664 SAR	123,641,174 SAR	270,648,426 SAR	493,814,775 SAR
Weight-loss saving impact	2,338,058 SAR	8,533,553 SAR	8,474,866 SAR	4,655,865 SAR	24,002,342 SAR
Total	117,907,766 SAR	428,984,209 SAR	421,418,780 SAR	913,780,126 SAR	1,882,090,881 SAR

Abbreviations. SHE, severe hypoglycemic event; CV, cardiovascular; mg, milligram.