Building an ROI Tool for Diabetes Prevention Programs in the Kingdom of Saudi Arabia
Author(s)
Abdulrahman Alsheikh, MD, MPH1, Abdullah Althemery, BEc, PhD2, Mohammed Alsalamh, BSc1, Shatha Alshaibi, BSc, MSc1;
1Lean Business Services, Riyadh, Saudi Arabia, 2Prince Sattam bin Abdulaziz University, Riyadh, Saudi Arabia
1Lean Business Services, Riyadh, Saudi Arabia, 2Prince Sattam bin Abdulaziz University, Riyadh, Saudi Arabia
Presentation Documents
OBJECTIVES: Saudi Arabia ranks among the highest globally in diabetes incidence, underscoring the urgent need for effective diabetes prevention initiatives, especially among prediabetic individuals. This study aimed to develop and validate a return-on-investment (ROI) tool for diabetes prevention programs, tailored to the Saudi healthcare system.
METHODS: A comprehensive calculator was built based on both clinical and economic inputs. Clinical data, such as the prevalence of prediabetes, incidence of diabetes, and associated complications were derived from the population health platform at the Ministry of Health, a robust population health management platform covering all Saudi residents and mapping demographic data to geographic locations. Economic inputs applied a direct costing technique using the attributable fraction method, and cost equations were adjusted for the first year of diagnosis as well as for long-term diabetes complications. The model’s outputs included the projected number of new diabetes cases, net costs, incremental cost-effectiveness ratios (ICERs), and years of diabetes averted. Statistical analyses were conducted using Python 3.13.1, and AnyLogic 8.9.3 (The AnyLogic Company).
RESULTS: The model was tested using a hypothetical diabetes prevention program costing USD 210 per participant. In a 10-year simulation, medical cost savings per participant increased steadily over time, with a breakeven point reached in the fifth year. Over the full 10-year horizon, the cumulative cost saving was USD 756.35, yielding a net saving of USD 169.03 per participant. In addition, the program conferred significant health benefits, resulting in a cumulative gain of 0.147 QALYs per participant.
CONCLUSIONS: The newly developed ROI tool demonstrates robust capacity for the economic evaluation of diabetes prevention programs. Leveraging region-specific data from Yamamah enabled accurate, context-relevant estimates of cost savings and health outcomes. This tool can inform policymakers and other stakeholders in designing and prioritizing diabetes prevention strategies across the Kingdom of Saudi Arabia.
METHODS: A comprehensive calculator was built based on both clinical and economic inputs. Clinical data, such as the prevalence of prediabetes, incidence of diabetes, and associated complications were derived from the population health platform at the Ministry of Health, a robust population health management platform covering all Saudi residents and mapping demographic data to geographic locations. Economic inputs applied a direct costing technique using the attributable fraction method, and cost equations were adjusted for the first year of diagnosis as well as for long-term diabetes complications. The model’s outputs included the projected number of new diabetes cases, net costs, incremental cost-effectiveness ratios (ICERs), and years of diabetes averted. Statistical analyses were conducted using Python 3.13.1, and AnyLogic 8.9.3 (The AnyLogic Company).
RESULTS: The model was tested using a hypothetical diabetes prevention program costing USD 210 per participant. In a 10-year simulation, medical cost savings per participant increased steadily over time, with a breakeven point reached in the fifth year. Over the full 10-year horizon, the cumulative cost saving was USD 756.35, yielding a net saving of USD 169.03 per participant. In addition, the program conferred significant health benefits, resulting in a cumulative gain of 0.147 QALYs per participant.
CONCLUSIONS: The newly developed ROI tool demonstrates robust capacity for the economic evaluation of diabetes prevention programs. Leveraging region-specific data from Yamamah enabled accurate, context-relevant estimates of cost savings and health outcomes. This tool can inform policymakers and other stakeholders in designing and prioritizing diabetes prevention strategies across the Kingdom of Saudi Arabia.
Conference/Value in Health Info
2025-05, ISPOR 2025, Montréal, Quebec, CA
Value in Health, Volume 28, Issue S1
Code
EE1
Topic
Economic Evaluation
Disease
No Additional Disease & Conditions/Specialized Treatment Areas, SDC: Diabetes/Endocrine/Metabolic Disorders (including obesity)