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Vol 21, No. 04, April 2017   |   Issue PDF view/purchase
Use of Modelling for Better Diabetes Care

75 million people in Southeast Asia have diabetes mellitus (DM), a group of metabolic diseases caused by defects in insulin secretion or action. DM patients face higher risk of long-term complications such as damage to their vital organs – heart, eyes and kidneys – and nerve damage (neuropathy) [1]. The burden of this disease is getting heavier as the number of people in Southeast Asia with DM is expected to rise to 123 million by 2035. [2]

The direct global costs of the disease –mainly medical costs – are significant, estimated at US$825 billion in 2014. Indirect costs due to productivity loss are substantial, not only to the diabetic patients themselves, but also the health sector and national economies. For example, in Singapore, 42 percent of total economic costs were attributed to direct medical costs of diabetes, while 58 percent were indirect costs. [3] In the near future, these costs are likely to surge in Asia, hence increasing the incumbent burden of the disease.

Because of DM’s health and economic impact, prioritising competing inventions through the analysis of costs and benefits via complex disease modelling can yield significant benefits. One such model that has been widely applied is the QuintilesIMS CORE Diabetes Model (CDM).

QuintilesIMS CORE Diabetes Model

The CDM is a microsimulation model that consists of several Markov sub-models. It predicts the long-term health outcomes and costs associated with the management of Type I and II diabetes (see Figure 1). [4] The CDM has been used extensively to evaluate the cost-effectiveness of all drug classes in the treatment of diabetes [5,6] to inform reimbursement decisions, and also to clarify public health issues and identify optimal patient management strategies [7,8].

To carry out this ‘value for money’ assessment, the CDM combines costs and health consequences associated with therapy choice and disease progression to provide robust estimates of cost-effectiveness.

This model translates the impact of interventions on surrogate parameters as HbA1c, BMI, lipid parameters and blood pressure into hard outcomes, such as the occurrence of micro- and macrovascular complications and mortality, by using validated risk equations. All those hard outcomes are linked with direct and indirect costs as well as an impact on utility. Other effects related to treatment, such as hypoglycaemia or other adverse effects are also taken into consideration because they also result in costs and impact on quality of life.

The data used to populate the model is taken from clinical trials, local unit costs (official sources), epidemiological data and de-identified and aggregated patient level data. The model’s outcomes can be viewed in both short and long term. Impacting at short term are mainly the costs of the intervention and the occurrence of hypoglycaemia. In the long run the impact of micro- and macrovascular complications becomes more imminent. Results are presented as total costs, quality-adjusted life year (QALY), and incremental cost-effectiveness ratio. Breakdown of costs per type of event is available, as well as the number of events that happened.

With that, the model can potentially serve as a roadmap for payers and/or providers to select preventive and treatment interventions that can effectively reduce costs or improve patient outcomes in the long run.

For instance, the QuintilesIMS CDM has been used to demonstrate the long-term cost-effectiveness of several interventions for diabetes in more than 200 studies since 2003, such as the comparative effectiveness of various insulins. This insightful data of insulin comparisons and other studies were used by the government and third-party payers in some countries to evaluate the cost-effectiveness of diabetes patient management in low-income populations.

The insights generated by CDM are helpful in overcoming the hurdles of performing costly, long-term clinical studies to evaluate hard outcomes. Through the model simulation, stakeholders can better understand the burden of diabetes care and where it might go in the future. They can make data-driven decisions about how to achieve maximum value for money and improve patient outcomes.

Application of the CDM

The CDM was used to measure the clinical outcomes of Type II diabetes patients supported by a new glucose control system called the Internet-Based Glucose Monitoring System (IBGMS), in addition to existing treatment [9]. IBGMS was implemented in South Korea at a diabetes centre for which interactive communication can be carried out between physicians and patients.

The model was used to simulate the prevalence of diabetic complications over a 35-year period and compare the results with and without the usage of IBGMS. In this simulation, results showed the beneficial effect of IBGMS on reducing the diabetic complications when added to existing treatment. Complications that could be reduced included diabetic retinopathy, diabetic neuropathy, diabetic nephropathy, and diabetic foot ulcer. This equated to better quality of life and cost-savings for the patients.

In addition to simulating potential clinical impact, researchers also are able to calculate the return on investment of a new innovation or technology prior to making a business decision to introduce it more widely.

In summary, modelling provides a framework for synthesising data from a variety of sources and allows simulation to bring benefit to patients via prevention, treatment choices and self-care. Furthermore, applying disease modelling such as CDM is valuable approach to provide more evidence to substantiate a value-for-money argument thus able to maximise patient outcomes with a value-based healthcare concept.

Continuous Glucose Monitoring (CGM) offering

In June 2016, QuintilesIMS announced the launch of its Continuous Glucose Monitoring (CGM) service offering to improve diabetes outcomes. The CGM offering provides investigators in clinical research a myriad of customised data analytics and performance reports through Infosario® technology platform. Compared to traditional diabetes monitoring models, these glycemic data sets can be analysed more rapidly and efficiently to uncover trends such as non-compliance and potential safety triggers.

“With diabetes diagnoses on the rise, the need for innovative and accessible treatments also increases. CGM can provide a variety of in-depth measures from diabetes patients – information that can be used to enhance the way patients are treated for diabetes and why this offering is so critical,” said Margaret Keegan, president of Data Sciences, Safety and Regulatory Services at QuintilesIMS.

Through the offering, continuous glucose measurements will be captured by a wearable sensor and wirelessly sent to a receiver, recording readings throughout the day. In the clinical trial setting, this comprehensive and timely glucose visibility has the potential to improve patient safety and accelerate the drug developmental phases.

Innovative wearable technology is combined with the therapeutic and analytical expertise from the company’s Diabetes Centre of Excellence, to help enhance the efficiency and quality of diabetes-focused clinical trials, in order to improve diabetes care to patients worldwide.


  1. Diseases and Conditions: Diabetes. Complications. Available at: https://www.mayoclinic.org/diseases-conditions/diabetes/basics/complications/con-20033091 Accessed 3 April 2017
  2. IDF. IDF Diabetes Atlas. Sixth edition. 2014 [Accessed 2016 Aug 08]. Available at: https://www.idf.org/sites/default/files/DA-regional-factsheets-2014_FINAL.pdf
  3. Png ME, Yoong J, Phan TP, Wee HL. Current and future economic burden of diabetes among working-age adults in Asia: conservative estimates for Singapore from 2010-2050. BMC Public Health. 2016;16:153.
  4. Palmer AJ, Roze S, Valentine WJ, Minshall ME, Foos V, LuratiFM, et al. The CORE Diabetes Model: Projecting long-term clinical outcomes, costs and cost-effectiveness of interventions in diabetes mellitus (types 1 and 2) to support clinical and reimbursement decision-making. Curr Med Res Opin. 2004;20 Suppl 1:S5-26.
  5. Palmer JL, Beaudet A, White J, Plun-Favreau J, Smith-Palmer J. Cost-effectiveness of biphasic insulin aspart versus insulin glargine in patients with type 2 diabetes in China. Adv Ther. 2010;27(11):814-27.
  6. Palmer AJ, Roze S, Valentine WJ, Smith I, Wittrup-Jensen KU. Cost-effectiveness of detemir-based basal/bolus therapy versus NPH-based basal/bolus therapy for type 1 diabetes in a UK setting: an economic analysis based on meta-analysis results of four clinical trials. Curr Med Res Opin. 2004;20(11):1729-46.
  7. Palmer AJ, Roze S, Valentine WJ, Minshall ME, Hayes C, Oglesby A, et al. Impact of changes in HbA1c, lipids and blood pressure on long-term outcomes in type 2 diabetes patients: an analysis using the CORE Diabetes Model. Curr Med Res Opin. 2004;20 Suppl 1:S53-8.
  8. Watkins JB, Minshall ME, Sullivan SD. Application of economic analyses in U.S. managed care formulary decisions: a private payer’s experience. J Manag Care Pharm. 2006;12(9):726-35.
  9. Cho JH, Lee JH, Oh JA, Kang MJ, Choi YH, Kwon HS, et al. Complication reducing effect of the Information Technology-Based Diabetes Management System on subjects with Type 2 Diabetes. Journal of Diabetes Science and Technology. Jan 2008;2(1):76-81

Editor’s note:

The content of the article is based on the white paper “Advancing Value-Based Healthcare in Asia,” a report by the QuintilesIMS Institute in Asia. To learn more about QuintilesIMS, visit www.quintilesims.com.

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