AIMS: The aim of this study was to determine the mean costs and outcomes associated with modifiable risk factors in patients with type 2 diabetes and to determine equivalent changes to these risk factors in terms of financial costs and health outcomes. METHODS: The Cardiff Stochastic Simulation Cost-Utility Model (DiabForecaster), which evolved from the Eastman model, was used to follow a cohort of 10 000 patients over 20 years. RESULTS: Costs were affected most significantly by changes in the total cholesterol to HDL cholesterol (Total-C:HDL-C) ratio and in HbA(1c). Unit increases in Total-C:HDL-C increased discounted costs by pound 200 per patient; for ratios > 8 units, unit increases led to cost increases of pound 300 per patient. Unit increases in HbA(1c) increased per patient discounted costs from pound 200 (5-6%) up to pound 2900 (10-11%). Similar patterns were observed for QALYs. Estimates of equivalence showed that a 1% reduction in HbA(1c) was equivalent to an 0.4 increment in QALYs, which was equivalent to a reduction of 44 mmHg in SBP, 18.2 mg/dL in HDL, 100 mg/dL in total cholesterol or 1.8 units of Total-C:HDL-C ratio. A 1% reduction in HbA(1c) was also equivalent to pound 108 less cost, which was equivalent to a 13.0 mmHg decrease in SBP or a 0.57 unit decrease in the Total-C:HDL-C ratio. CONCLUSIONS: This model provides reliable utility estimates for diabetic complications and may eliminate uncertainty in cost-effectiveness analyses of treatment. These data also provide a novel way of comparing the value of treatments that have multiple effects.
AIMS: The aim of this study was to determine the mean costs and outcomes associated with modifiable risk factors in patients with type 2 diabetes and to determine equivalent changes to these risk factors in terms of financial costs and health outcomes. METHODS: The Cardiff Stochastic Simulation Cost-Utility Model (DiabForecaster), which evolved from the Eastman model, was used to follow a cohort of 10 000 patients over 20 years. RESULTS: Costs were affected most significantly by changes in the total cholesterol to HDL cholesterol (Total-C:HDL-C) ratio and in HbA(1c). Unit increases in Total-C:HDL-C increased discounted costs by pound 200 per patient; for ratios > 8 units, unit increases led to cost increases of pound 300 per patient. Unit increases in HbA(1c) increased per patient discounted costs from pound 200 (5-6%) up to pound 2900 (10-11%). Similar patterns were observed for QALYs. Estimates of equivalence showed that a 1% reduction in HbA(1c) was equivalent to an 0.4 increment in QALYs, which was equivalent to a reduction of 44 mmHg in SBP, 18.2 mg/dL in HDL, 100 mg/dL in total cholesterol or 1.8 units of Total-C:HDL-C ratio. A 1% reduction in HbA(1c) was also equivalent to pound 108 less cost, which was equivalent to a 13.0 mmHg decrease in SBP or a 0.57 unit decrease in the Total-C:HDL-C ratio. CONCLUSIONS: This model provides reliable utility estimates for diabetic complications and may eliminate uncertainty in cost-effectiveness analyses of treatment. These data also provide a novel way of comparing the value of treatments that have multiple effects.
Authors: Verughese Jacob; Sajal K Chattopadhyay; Anilkrishna B Thota; Krista K Proia; Gibril Njie; David P Hopkins; Ramona K C Finnie; Nicolaas P Pronk; Thomas E Kottke Journal: Am J Prev Med Date: 2015-11 Impact factor: 5.043
Authors: Verughese Jacob; Sajal K Chattopadhyay; David P Hopkins; Jeffrey A Reynolds; Ka Zang Xiong; Christopher D Jones; Betsy J Rodriguez; Krista K Proia; Nicolaas P Pronk; John M Clymer; Ron Z Goetzel Journal: Am J Prev Med Date: 2019-03 Impact factor: 5.043
Authors: Verughese Jacob; Sajal K Chattopadhyay; Krista K Proia; David P Hopkins; Jeffrey Reynolds; Anilkrishna B Thota; Christopher D Jones; Daniel T Lackland; Kimberly J Rask; Nicolaas P Pronk; John M Clymer; Ron Z Goetzel Journal: Am J Prev Med Date: 2017-08-14 Impact factor: 5.043
Authors: Shien Guo; Duygu Bozkaya; Alexandra Ward; Judith A O'Brien; Khajak Ishak; Randy Bennett; Ahmad Al-Sabbagh; Dennis M Meletiche Journal: Pharmacoeconomics Date: 2009 Impact factor: 4.981