Advancing a National Cost-Effective Prevention Initiative for the Prediabetic Population

Main Article Content

Timothy M Dall Michael O'Grady Michael V Storm Karin B Gillespie Jerry Franz William P Iacobucci James C Capretta

Abstract

Background: Progression from prediabetes to type 2 diabetes can be prevented or slowed with cost-effective lifestyle interventions targeting weight loss through improved diet and increased physical activity. This study provides estimates of the potential long term economic implications to society and federal budget implications if implemented on a national scale in the U.S.

Methods and Findings: Using an epidemiologically-based microsimulation model, we analyzed the potential health and economic implications if a national lifestyle intervention patterned after the National Diabetes Prevention Program were implemented among the estimated 37.9 million overweight or obese adults age 40 to 70 whose prediabetes is either already diagnosed or whose diabetes might be detected under the recent national screening recommendations. Cumulative over ten years, the average medical savings ranged from $10,970 for adults ages 40-49 at time of intervention, to $15,250 for adults ages 65-70 at time of intervention. Cumulative over 20 years, population medical savings were highest ($21,840) for the age 40-49 population and lowest ($8,030) for the age 65-70 population reflecting that lifestyle intervention increases longevity which in turn increases lifetime medical expenditures. If one quarter of these 37.9 million adults (9.5 million) completed the intervention, then cumulative over 10 years there could be $121 billion lower medical expenditures, $219 billion higher economic output, 2.5 million fewer cases of diabetes, and 800,000 fewer deaths. Over 20 years societal economic benefits continue to grow, though Medicare and Social Security expenditures for the additional 1.5 million people alive offset almost all the cumulative Medicare savings and additional federal tax revenues.

Conclusions: A large-scale program to provide access to lifestyle intervention to millions of adults with prediabetes could be highly cost effective. The health and economic rewards to society extend beyond the 10-year window used for calculating federal budget implications.

Article Details

How to Cite
DALL, Timothy M et al. Advancing a National Cost-Effective Prevention Initiative for the Prediabetic Population. Medical Research Archives, [S.l.], n. 3, july 2016. ISSN 2375-1924. Available at: <https://journals.ke-i.org/index.php/mra/article/view/532>. Date accessed: 08 dec. 2019.
Keywords
diabetes; prediabetes; prevention; microsimulation
Section
Research Articles

References

References

American Diabetes Association (2013). Economic costs of diabetes in the U.S. in 2012. Diabetes care. 36(4):1033-1046. http://care.diabetesjournals.org/content/early/2013/03/05/dc12-2625.full.pdf+html

Boards of Trustees of the Federal Hospital Insurance and Federal Supplementary Medical Insurance Trust Funds (2012). 2012 Annual Report of the Boards of Trustees of the Federal Hospital Insurance and Federal Supplementary Medical Insurance Trust Funds. https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/ReportsTrustFunds/downloads/tr2012.pdf

Centers for Disease Control and Prevention (CDC) (2014a). National Diabetes Statistics Report, 2014. Centers for Disease Control and Prevention. http://www.cdc.gov/diabetes/pubs/statsreport14/national-diabetes-report-web.pdf

Centers for Disease Control and Prevention (CDC) (2014b). Prediabetes. Centers for Disease Control and Prevention. http://www.cdc.gov/diabetes/consumer/prediabetes.htm

Centers for Disease Control and Prevention, N. C. H. S. (2013). Underlying Cause of Death 1999-2010 on CDC WONDER Online Database, released 2012. Centers for Disease Control and Prevention, National Center for Health Statistics.

Clarke, P. M., Gray, A. M., Briggs, A., Farmer, A. J., Fenn, P., Stevens, R. J. et al. (2004). A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68). Diabetologia, 47, 1747-1759.

Community Preventive Services Task Force (6-10-2015). Diabetes Prevention and Control: Combined Diet and Physical Activity Promotion Programs to Prevent Type 2 Diabetes Among People at Increased Risk. http://www.thecommunityguide.org/diabetes/combineddietandpa.html.

Dall, T. M., Storm, M. V., Semilla, A. P., Wintfeld, N., O'Grady, M., & Venkat Narayan, K. M. (2015). Value of Lifestyle Intervention to Prevent Diabetes and Sequelae. American journal of preventive medicine, 48, 271-280.

Diabetes Prevention Program Research Group (2015). Long-term effects of lifestyle intervention or metformin on diabetes development and microvascular complications over 15-year follow-up: the Diabetes Prevention Program Outcomes Study. Lancet Diabetes Endocrinol., 3, 866-875.

Heianza, Y., Arase, Y., Fujihara, K., Hsieh, S. D., Saito, K., Tsuji, H. et al. (2012). Longitudinal Trajectories of HbA1c and Fasting Plasma Glucose Levels During the Development of Type 2 Diabetes The Toranomon Hospital Health Management Center Study 7 (TOPICS 7). Diabetes care, 35, 1050-1052.

Hippisley-Cox, J. & Coupland, C. (2010). Predicting the risk of Chronic Kidney Disease in Men and Women in England and Wales: prospective derivation and external validation of the QKidney-Scores. BMC family practice, 11, 49.

Knowler, W. C., Barrett-Connor, E., Fowler, S. E., Hamman, R. F., Lachin, J. M., Walker, E. A. et al. (2002). Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N.Engl.J Med, 346, 393-403.

Knowler, W. C., Fowler, S. E., Hamman, R. F., Christophi, C. A., Hoffman, H. J., Brenneman, A. T. et al. (2009). 10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. Lancet, 374, 1677-1686.

Lawlor, M. S., Blackwell, C. S., Isom, S. P., Katula, J. A., Vitolins, M. Z., Morgan, T. M. et al. (2013). Cost of a group translation of the Diabetes Prevention Program: Healthy Living Partnerships to Prevent Diabetes. Am.J Prev.Med, 44, S381-S389.

LeFevre, M. L. (2014). Behavioral counseling to promote a healthful diet and physical activity for cardiovascular disease prevention in adults with cardiovascular risk factors: U.S. preventive services task force recommendation statement. Ann.Intern Med, 161, 587-593.

Neter, J. E., Stam, B. E., Kok, F. J., Grobbee, D. E., & Geleijnse, J. M. (2003). Influence of weight reduction on blood pressure a meta-analysis of randomized controlled trials. Hypertension, 42, 878-884.

Norman J (2015). Half of Overweight in U.S. Not Seriously Trying to Lose Weight. http://www.gallup.com/poll/187580/half-overweight-not-seriously-trying-lose-weight.aspx.

Riley, G. F. & Lubitz, J. D. (2010). Long-term trends in Medicare payments in the last year of life. Health Serv.Res., 45, 565-576.

Semilla, A. P., Chen, F., & Dall, T. M. (15 A.D.). Reductions in Mortality Among Medicare Beneficiaries Following the Implementation of Medicare Part D. American Journal of Managed Care, 21, S165-S172.

Sheehan, T. J., DuBrava, S., DeChello, L. M., & Fang, Z. (2003). Rates of weight change for black and white Americans over a twenty year period. Int.J Obes.Relat Metab Disord., 27, 498-504.

Siu, A. L. (2015). Screening for Abnormal Blood Glucose and Type 2 Diabetes Mellitus: U.S. Preventive Services Task Force Recommendation Statement. Ann.Intern.Med., 163, 861-868.

Su, W., Chen, F., Iacobucci, W., Dall, T. M., & Perreault, L. (2016). Return on Investment for Digital Behavioral Counseling in Patients with Prediabetes and Cardiovascular Disease. Prev.Chronic.Dis. 13; 150357. http://www.cdc.gov/pcd/issues/2016/15_0357.htm

Su, W., Huang, J., Chen, F., Iacobucci, W., Mocarski, M., Dall, T. M. et al. (2015). Modeling the clinical and economic implications of obesity using microsimulation. J.Med.Econ., 1-12. http://informahealthcare.com/doi/abs/10.3111/13696998.2015.1058805

Sullivan, P. W., Lawrence, W. F., & Ghushchyan, V. (2005). A national catalog of preference-based scores for chronic conditions in the United States. Medical care, 43, 736-749.

U.S.Department of Health and Human Services, P. H. S. N. I. o. H. (4-13-2009). The Framingham Study: An Epidemiological Intervention of Cardiovascular Diseases: Section 34: Some Risk Factors Related to the Annual Incidence of Cardiovascular Disease and Death Using Pooled Repeated Biennial Measurements: Framingham Heart Study, 30 Year Followup.

Wilson, P. W., Bozeman, S. R., Burton, T. M., Hoaglin, D. C., Ben-Joseph, R., & Pashos, C. L. (2008). Prediction of first events of coronary heart disease and stroke with consideration of adiposity. Circulation, 118, 124-130.

Yan Feng Li, Geiss Linda S, Burrows Nilka R, Rolka Deborah B, & Albright Ann (2013). Awareness of Prediabetes--United States, 2005-2010. Morbidity and Mortality Weekly Report, 62, 209-212.

Zhang, P., Brown, M. B., Bilik, D., Ackermann, R. T., Li, R., & Herman, W. H. (2012). Health Utility Scores for People With Type 2 Diabetes in US Managed Care Health Plans Results from Translating Research Into Action for Diabetes (TRIAD). Diabetes care, 35, 2250-2256.

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.