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Research Report
A simulation model to estimate cost-offsets for a disease-management program for chronic kidney disease Expert Rev. Pharmacoecon. Outcomes Res. 15(2), 341–347 (2015)
Afschin Gandjour*1, Ulrich Tschulena2, Sonja Steppan2 and Emanuele Gatti3 1 Frankfurt School of Finance and Management, Frankfurt, Germany 2 Fresenius Medical Care Deutschland GmbH, Bad Homburg v.d.H., Germany 3 Donau-Universita¨t Krems, Krems, Austria *Author for correspondence: Tel.: +49 069 154 008 832 Fax: +49 069 154 008 4832 [email protected]
Aim: The aim of this paper is to develop a simulation model that analyzes cost-offsets of a hypothetical disease management program (DMP) for patients with chronic kidney disease (CKD) in Germany compared to no such program. Methods: A lifetime Markov model with simulated 65-year-old patients with CKD was developed using published data on costs and health status and simulating the progression to end-stage renal disease (ESRD), cardiovascular disease and death. A statutory health insurance perspective was adopted. Results: This modeling study shows considerable potential for cost-offsets from a DMP for patients with CKD. The potential for cost-offsets increases with relative risk reduction by the DMP and baseline glomerular filtration rate. Results are most sensitive to the cost of dialysis treatment. Conclusion: This paper presents a general ‘prototype’ simulation model for the prevention of ESRD. The model allows for further modification and adaptation in future applications. KEYWORDS: chronic kidney disease • disease management • simulation model
Chronic kidney disease (CKD) is a major public health problem worldwide. Data from the US National Health and Nutrition Examination Surveys suggest that at least 13% of adults aged 20 years or older have CKD [1]. CKD is associated with increased risks of death, cardiovascular events and hospitalization [2]. The definition of CKD is based on the presence of kidney damage (i.e., albuminuria) or decreased kidney function (i.e., glomerular filtration rate [GFR]
Research Report
A simulation model to estimate cost-offsets for a disease-management program for chronic kidney disease Expert Rev. Pharmacoecon. Outcomes Res. 15(2), 341–347 (2015)
Afschin Gandjour*1, Ulrich Tschulena2, Sonja Steppan2 and Emanuele Gatti3 1 Frankfurt School of Finance and Management, Frankfurt, Germany 2 Fresenius Medical Care Deutschland GmbH, Bad Homburg v.d.H., Germany 3 Donau-Universita¨t Krems, Krems, Austria *Author for correspondence: Tel.: +49 069 154 008 832 Fax: +49 069 154 008 4832 [email protected]
Aim: The aim of this paper is to develop a simulation model that analyzes cost-offsets of a hypothetical disease management program (DMP) for patients with chronic kidney disease (CKD) in Germany compared to no such program. Methods: A lifetime Markov model with simulated 65-year-old patients with CKD was developed using published data on costs and health status and simulating the progression to end-stage renal disease (ESRD), cardiovascular disease and death. A statutory health insurance perspective was adopted. Results: This modeling study shows considerable potential for cost-offsets from a DMP for patients with CKD. The potential for cost-offsets increases with relative risk reduction by the DMP and baseline glomerular filtration rate. Results are most sensitive to the cost of dialysis treatment. Conclusion: This paper presents a general ‘prototype’ simulation model for the prevention of ESRD. The model allows for further modification and adaptation in future applications. KEYWORDS: chronic kidney disease • disease management • simulation model
Chronic kidney disease (CKD) is a major public health problem worldwide. Data from the US National Health and Nutrition Examination Surveys suggest that at least 13% of adults aged 20 years or older have CKD [1]. CKD is associated with increased risks of death, cardiovascular events and hospitalization [2]. The definition of CKD is based on the presence of kidney damage (i.e., albuminuria) or decreased kidney function (i.e., glomerular filtration rate [GFR]
