Yan Li1,2, Norma A Padrón3, Anil T Mangla4, Pamela G Russo5, Thomas Schlenker6, José A Pagán1,7,8. 1. 1 Center for Health Innovation, The New York Academy of Medicine, New York, NY, USA. 2. 2 Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 3. 3 College of Population Health, Thomas Jefferson University, Philadelphia, PA, USA. 4. 4 School of Osteopathic Medicine, University of the Incarnate Word, San Antonio, TX, USA. 5. 5 Robert Wood Johnson Foundation, Princeton, NJ, USA. 6. 6 Interlex, San Antonio, TX, USA. 7. 7 Department of Public Health Policy and Management, College of Global Public Health, New York University, New York, NY, USA. 8. 8 Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA.
Abstract
OBJECTIVES: Because of state and federal health care reform, local health departments play an increasingly prominent role leading and coordinating disease prevention programs in the United States. This case study shows how a local health department working in chronic disease prevention and management can use systems science and evidence-based decision making to inform program selection, implementation, and assessment; enhance engagement with local health systems and organizations; and possibly optimize health care delivery and population health. METHODS: The authors built a systems-science agent-based simulation model of diabetes progression for the San Antonio Metropolitan Health District, a local health department, to simulate health and cost outcomes for the population of San Antonio for a 20-year period (2015-2034) using 2 scenarios: 1 in which hemoglobin A1c (HbA1c) values for a population were similar to the current distribution of values in San Antonio, and the other with a hypothetical 1-percentage-point reduction in HbA1c values. RESULTS: They projected that a 1-percentage-point reduction in HbA1c would lead to a decrease in the 20-year prevalence of end-stage renal disease from 1.7% to 0.9%, lower extremity amputation from 4.6% to 2.9%, blindness from 15.1% to 10.7%, myocardial infarction from 23.8% to 17.9%, and stroke from 9.8% to 7.2%. They estimated annual direct medical cost savings (in 2015 US dollars) from reducing HbA1c by 1 percentage point ranging from $6842 (myocardial infarction) to $39 800 (end-stage renal disease) for each averted case of diabetes complications. CONCLUSIONS: Local health departments could benefit from the use of systems science and evidence-based decision making to estimate public health program effectiveness and costs, calculate return on investment, and develop a business case for adopting programs.
OBJECTIVES: Because of state and federal health care reform, local health departments play an increasingly prominent role leading and coordinating disease prevention programs in the United States. This case study shows how a local health department working in chronic disease prevention and management can use systems science and evidence-based decision making to inform program selection, implementation, and assessment; enhance engagement with local health systems and organizations; and possibly optimize health care delivery and population health. METHODS: The authors built a systems-science agent-based simulation model of diabetes progression for the San Antonio Metropolitan Health District, a local health department, to simulate health and cost outcomes for the population of San Antonio for a 20-year period (2015-2034) using 2 scenarios: 1 in which hemoglobin A1c (HbA1c) values for a population were similar to the current distribution of values in San Antonio, and the other with a hypothetical 1-percentage-point reduction in HbA1c values. RESULTS: They projected that a 1-percentage-point reduction in HbA1c would lead to a decrease in the 20-year prevalence of end-stage renal disease from 1.7% to 0.9%, lower extremity amputation from 4.6% to 2.9%, blindness from 15.1% to 10.7%, myocardial infarction from 23.8% to 17.9%, and stroke from 9.8% to 7.2%. They estimated annual direct medical cost savings (in 2015 US dollars) from reducing HbA1c by 1 percentage point ranging from $6842 (myocardial infarction) to $39 800 (end-stage renal disease) for each averted case of diabetes complications. CONCLUSIONS: Local health departments could benefit from the use of systems science and evidence-based decision making to estimate public health program effectiveness and costs, calculate return on investment, and develop a business case for adopting programs.
Entities:
Keywords:
agent-based modeling; diabetes; local health departments; population health; systems science
Authors: Russell L Rothman; Robb Malone; Betsy Bryant; Ayumi K Shintani; Britton Crigler; Darren A Dewalt; Robert S Dittus; Morris Weinberger; Michael P Pignone Journal: Am J Med Date: 2005-03 Impact factor: 4.965
Authors: Honghong Zhou; Deanna J M Isaman; Shari Messinger; Morton B Brown; Ronald Klein; Michael Brandle; William H Herman Journal: Diabetes Care Date: 2005-12 Impact factor: 19.112
Authors: Rui Li; Dori Bilik; Morton B Brown; Ping Zhang; Susan L Ettner; Ronald T Ackermann; Jesse C Crosson; William H Herman Journal: Am J Manag Care Date: 2013-05 Impact factor: 2.229
Authors: Magdalena Cerdá; Mohammad S Jalali; Ava D Hamilton; Catherine DiGennaro; Ayaz Hyder; Julian Santaella-Tenorio; Navdep Kaur; Christina Wang; Katherine M Keyes Journal: Epidemiol Rev Date: 2022-01-14 Impact factor: 6.222