Journal of General Internal Medicine

, Volume 34, Issue 3, pp 372–378 | Cite as

Assessing the Effect of Clinical Inertia on Diabetes Outcomes: a Modeling Approach

  • Maria F. Correa
  • Yan LiEmail author
  • Hye-Chung Kum
  • Mark A. Lawley
Original Research



There are an increasing number of newer and better therapeutic options in the management of diabetes. However, a large proportion of diabetes patients still experience delays in intensification of treatment to achieve appropriate blood glucose targets—a phenomenon called clinical inertia. Despite the high prevalence of clinical inertia, previous research has not examined its long-term effects on diabetes-related health outcomes and mortality.


We sought to examine the impact of clinical inertia on the incidence of diabetes-related complications and death. We also examined how the impact of clinical inertia would vary by the length of treatment delay and population characteristics.


We developed an agent-based model of diabetes and its complications. The model was parameterized and validated by data from health surveys, cohort studies, and trials.


We studied a simulated cohort of patients with diabetes in San Antonio, TX.

Main Measures

We examined 25-year incidences of diabetes-related complications, including retinopathy, neuropathy, nephropathy, and cardiovascular disease.

Key Results

One-year clinical inertia could increase the cumulative incidences of retinopathy, neuropathy, and nephropathy by 7%, 8%, and 18%, respectively. The effects of clinical inertia could be worse for populations who have a longer treatment delay, are aged 65 years or older, or are non-Hispanic whites.


Clinical inertia could result in a substantial increase in the incidence of diabetes-related complications and mortality. A validated agent-based model can be used to study the long-term effect of clinical inertia and, thus, inform clinicians and policymakers to design effective interventions.


diabetes clinical inertia diabetes complications agent-based modeling 


Funding Information

Dr. Li’s role in the research reported in this publication was supported, in part, by the National Heart, Lung, And Blood Institute of the National Institutes of Health under Award Number R01HL141427. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they do not have a conflict of interest.

Supplementary material

11606_2018_4773_MOESM1_ESM.docx (1.6 mb)
ESM 1 (DOCX 1665 kb)


  1. 1.
    Shaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract 2010;87(1):4–14.CrossRefGoogle Scholar
  2. 2.
    Boyle JP, Thompson TJ, Gregg EW, Barker LE, Williamson DF. Projection of the year 2050 burden of diabetes in the US adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence. Popul Health Metrics 2010;8(1):29.CrossRefGoogle Scholar
  3. 3.
    Cowie CC, Rust KF, Byrd-Holt DD, et al. Prevalence of diabetes and impaired fasting glucose in adults in the US population National Health and Nutrition Examination Survey 1999–2002. Diabetes Care 2006;29(6):1263–1268.CrossRefGoogle Scholar
  4. 4.
    Centers for Disease Control and Prevention. Diabetes Data & Statistics. Atlanta, GA; 2018. Accessed August 31, 2018.
  5. 5.
    Li R, Bilik D, Brown MB, et al. Medical costs associated with type 2 diabetes complications and comorbidities. Am J Manag Care 2013;19(5):421.Google Scholar
  6. 6.
    Nathan DM, DCCT Research Group. The diabetes control and complications trial/epidemiology of diabetes interventions and complications study at 30 years: overview. Diabetes Care 2014;37(1):9–16.CrossRefGoogle Scholar
  7. 7.
    Lovshin JA, Zinman B. Diabetes: clinical inertia—a barrier to effective management of T2DM. Nat Rev Endocrinol 2013;9(11):635–636.CrossRefGoogle Scholar
  8. 8.
    Stone MA, Charpentier G, Doggen K, et al. Quality of care of people with type 2 diabetes in eight European countries. Diabetes Care 2013;36(9):2628–2638.CrossRefGoogle Scholar
  9. 9.
    Khunti K, Wolden ML, Thorsted BL, Andersen M, Davies MJ. Clinical inertia in people with type 2 diabetes. Diabetes Care 2013;36(11):3411–3417.CrossRefGoogle Scholar
  10. 10.
    Strain WD, Blüher M, Paldánius P. Clinical inertia in individualising care for diabetes: is there time to do more in type 2 diabetes?, Diabetes Ther 2014;5(2):347–354.CrossRefGoogle Scholar
  11. 11.
    Osataphan S, Chalermchai T, Ngaosuwan K. Clinical inertia causing new or progression of diabetic retinopathy in type 2 diabetes: A retrospective cohort study. J Diabetes 2017;9(3):267–274.CrossRefGoogle Scholar
  12. 12.
    Li Y, Lawley MA, Siscovick DS, Zhang D, Pagán JA. Agent-Based Modeling of Chronic Diseases: A Narrative Review and Future Research Directions. Prev Chronic Dis 2016;13:150561.CrossRefGoogle Scholar
  13. 13.
    Nianogo RA, Arah OA. Agent-Based Modeling of Noncommunicable Diseases: A Systematic Review. Am J Public Health 2015;105(3):e20–e31.CrossRefGoogle Scholar
  14. 14.
    Epstein JM. Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton University Press, Princeton, NJ; 2006.Google Scholar
  15. 15.
    Rigotti NA, Wallace RB. Using Agent-Based Models to Address “Wicked Problems” Like Tobacco Use: A Report from the Institute of Medicine. Ann Intern Med 2015;163(6):469–471.CrossRefGoogle Scholar
  16. 16.
    Li Y, Kong N, Lawley M, Weiss L, Pagán JA. Advancing the Use of Evidence-Based Decision-Making in Local Health Departments With Systems Science Methodologies. Am J Public Health 2015;105(S2):S217–S222.CrossRefGoogle Scholar
  17. 17.
    Li Y, Kong N, Lawley MA, Pagán JA. Using Systems Science for Population Health Management in Primary Care. J Prim Care Community Health 2014;5(4):242–246.CrossRefGoogle Scholar
  18. 18.
    Li Y, Kong N, Lawley M, Pagán JA. Assessing lifestyle interventions to improve cardiovascular health using an agent-based model. In: Proceedings of the 2014 Winter Simulation Conference. IEEE Press; 2014:1221–1232.Google Scholar
  19. 19.
    Hoerger TJ, Bethke AD, Richter A, et al. Cost-effectiveness of intensive glycemic control, intensified hypertension control, and serum cholesterol level reduction for type 2 diabetes. JAMA 2002;287(19):2542–2551.CrossRefGoogle Scholar
  20. 20.
    Zhou H, Isaman DJ, Messinger S, et al. A computer simulation model of diabetes progression, quality of life, and cost. Diabetes Care 2005;28(12):2856–2863.CrossRefGoogle Scholar
  21. 21.
    Stevens RJ, Coleman RL, Adler AI, Stratton IM, Matthews DR, Holman RR. Risk factors for myocardial infarction case fatality and stroke case fatality in type 2 diabetes UKPDS 66. Diabetes Care 2004;27(1):201–207.CrossRefGoogle Scholar
  22. 22.
    American Diabetes Association. Standards of medical care in diabetes—2016 abridged for primary care providers. Clin Diabetes 2016;34(1):3–21.CrossRefGoogle Scholar
  23. 23.
    Fagiolo G, Moneta A, Windrum P. A critical guide to empirical validation of agent-based models in economics: Methodologies, procedures, and open problems. Comput Econ 2007;30(3):195–226.CrossRefGoogle Scholar
  24. 24.
    Eddy DM, Hollingworth W, Caro JJ, Tsevat J, McDonald KM, Wong JB. Model transparency and validation a report of the ISPOR-SMDM Modeling Good Research Practices Task Force–7. Med Decis Mak 2012;32(5):733–743.CrossRefGoogle Scholar
  25. 25.
    Adler AI, Stevens RJ, Manley SE, et al. Development and progression of nephropathy in type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS 64). Kidney Int 2003;63(1):225–232.CrossRefGoogle Scholar
  26. 26.
    The Action to Control Cardiovascular Risk in Diabetes Study Group. Effects of intensive glucose lowering in type 2 diabetes. N Engl J Med 2008;2008(358):2545–2559.Google Scholar
  27. 27.
    UK Prospective Diabetes Study (UKPDS) Group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet 1998;352(9131):837–853.CrossRefGoogle Scholar
  28. 28.
    Li G, Zhang P, Wang J, et al. The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study. Lancet 2008;371(9626):1783–1789.CrossRefGoogle Scholar
  29. 29.
    Partanen J, Niskanen L, Lehtinen J, Mervaala E, Siitonen O, Uusitupa M. Natural history of peripheral neuropathy in patients with non-insulin-dependent diabetes mellitus. N Engl J Med 1995;333(2):89–94.CrossRefGoogle Scholar
  30. 30.
    Robert Wood Johnson Foundation, University of Wisconsin Population Health Institute. County Health Rankings & Roadmaps. Accessed August 31, 2018.
  31. 31.
    Hoerger TJ, Hicks KA, Sorensen SW, et al. Cost-effectiveness of screening for pre-diabetes among overweight and obese US adults. Diabetes Care 2007;30(11):2874–2879.CrossRefGoogle Scholar
  32. 32.
    Paul SK, Klein K, Thorsted BL, Wolden ML, Khunti K. Delay in treatment intensification increases the risks of cardiovascular events in patients with type 2 diabetes. Cardiovasc Diabetol 2015;14(1):100.CrossRefGoogle Scholar

Copyright information

© Society of General Internal Medicine 2018

Authors and Affiliations

  • Maria F. Correa
    • 1
  • Yan Li
    • 2
    • 3
    Email author
  • Hye-Chung Kum
    • 4
    • 5
    • 6
  • Mark A. Lawley
    • 5
    • 6
  1. 1.Department of PsychiatryThe University of Texas at Austin Dell Medical SchoolAustinUSA
  2. 2.Center for Health InnovationThe New York Academy of MedicineNew YorkUSA
  3. 3.Department of Population Health Science and PolicyIcahn School of Medicine at Mount SinaiNew YorkUSA
  4. 4.Department of Health Policy and ManagementTexas A&M UniversityCollege StationUSA
  5. 5.Center for Remote Health Technologies and SystemsTexas A&M UniversityCollege StationUSA
  6. 6.Department of Industrial and Systems EngineeringTexas A&M UniversityCollege StationUSA

Personalised recommendations