Natalia Olchanski1, David van Klaveren2, Joshua T Cohen2, John B Wong2,3, Robin Ruthazer2, David M Kent2. 1. Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, 800 Washington Street #63, Boston, MA, 02111, USA. nolchanski@tuftsmedicalcenter.org. 2. Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, 800 Washington Street #63, Boston, MA, 02111, USA. 3. Division of Clinical Decision Making, Tufts Medical Center, Boston, MA, USA.
Abstract
OBJECTIVE: Approximately 84 million people in the USA have pre-diabetes, but only a fraction of them receive proven effective therapies to prevent type 2 diabetes. We estimated the value of prioritizing individuals at highest risk of progression to diabetes for treatment, compared to non-targeted treatment of individuals meeting inclusion criteria for the Diabetes Prevention Program (DPP). METHODS: Using microsimulation to project outcomes in the DPP trial population, we compared two interventions to usual care: (1) lifestyle modification and (2) metformin administration. For each intervention, we compared targeted and non-targeted strategies, assuming either limited or unlimited program capacity. We modeled the individualized risk of developing diabetes and projected diabetic outcomes to yield lifetime costs and quality-adjusted life expectancy, from which we estimated net monetary benefits (NMB) for both lifestyle and metformin versus usual care. RESULTS: Compared to usual care, lifestyle modification conferred positive benefits and reduced lifetime costs for all eligible individuals. Metformin's NMB was negative for the lowest population risk quintile. By avoiding use when costs outweighed benefits, targeted administration of metformin conferred a benefit of $500 per person. If only 20% of the population could receive treatment, when prioritizing individuals based on diabetes risk, rather than treating a 20% random sample, the difference in NMB ranged from $14,000 to $20,000 per person. CONCLUSIONS: Targeting active diabetes prevention to patients at highest risk could improve health outcomes and reduce costs compared to providing the same intervention to a similar number of patients with pre-diabetes without targeted selection.
OBJECTIVE: Approximately 84 million people in the USA have pre-diabetes, but only a fraction of them receive proven effective therapies to prevent type 2 diabetes. We estimated the value of prioritizing individuals at highest risk of progression to diabetes for treatment, compared to non-targeted treatment of individuals meeting inclusion criteria for the Diabetes Prevention Program (DPP). METHODS: Using microsimulation to project outcomes in the DPP trial population, we compared two interventions to usual care: (1) lifestyle modification and (2) metformin administration. For each intervention, we compared targeted and non-targeted strategies, assuming either limited or unlimited program capacity. We modeled the individualized risk of developing diabetes and projected diabetic outcomes to yield lifetime costs and quality-adjusted life expectancy, from which we estimated net monetary benefits (NMB) for both lifestyle and metformin versus usual care. RESULTS: Compared to usual care, lifestyle modification conferred positive benefits and reduced lifetime costs for all eligible individuals. Metformin's NMB was negative for the lowest population risk quintile. By avoiding use when costs outweighed benefits, targeted administration of metformin conferred a benefit of $500 per person. If only 20% of the population could receive treatment, when prioritizing individuals based on diabetes risk, rather than treating a 20% random sample, the difference in NMB ranged from $14,000 to $20,000 per person. CONCLUSIONS: Targeting active diabetes prevention to patients at highest risk could improve health outcomes and reduce costs compared to providing the same intervention to a similar number of patients with pre-diabetes without targeted selection.
Entities:
Keywords:
Diabetes prevention; Economic analysis; Heterogeneity of treatment effect; Lifestyle modification; Risk based; Type 2 diabetes; Value
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