title: Impact of a reduced error range of SMBG in insulin-treated patients in Germany.
abstract: Modeling approaches demonstrate that improvement in the accuracy of blood glucose (BG) meters may lead to cost savings. An improvement of accuracy of BG meters on the basis of a reduction in error range from 20% to 5% has been reported to be associated with substantial cost savings in Germany. The aim of this study is to analyze potential cost savings related to a reduction in error range from 20% to 15% and 10% of glucose meters in Germany. The health economic analysis included the number of type 1 diabetic and the number of insulin-treated patients in Germany, the costs for glucose monitoring, a model on the effects of the improvement of accuracy on the impact of severe hypoglycemic episodes, HbA1c, and subsequently myocardial infarctions and the costs of diabetes-related complications in Germany. In the model, a reduction of 1% and 3.5% reduction in severe hypoglycemic episodes, and a 0.14% and 0.28% reduction in HbA1c was included. In type 1 diabetes the savings could be equal to a reduction in health care expenditures of more than €1.0 million (20% vs 15% error range) and €3.4 million (20% vs 10% error range). Respectively, potential savings of more than €6.0 million and €20.1 million were calculated for the group of insulin-treated patients. The model demonstrates that a reduction of error range of BG meters from 20% to 15% and 10% may translate into substantial savings for the German health care system.
authors.affiliation: Forschergruppe Diabetes eV, Helmholtz Center Munich, Munich-Neuherberg, Germany Oliver.Schnell@lrz.uni-muenchen.de.
authors.forename: Oliver
authors.initials: O
authors.lastname: Schnell
authors.affiliation: Sciarc Institute, Baierbrunn, Germany.
authors.forename: Michael
authors.initials: M
authors.lastname: Erbach
mesh_terms: D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000187:drug effects* / Q000378:metabolism; D015190:Blood Glucose Self-Monitoring / Q000191:economics* / Q000295:instrumentation*; D017046:Cost Savings; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000175:diagnosis / Q000188:drug therapy* / Q000191:economics*; D016527:Drug Costs; D005858:Germany; D006442:Glycated Hemoglobin / Q000378:metabolism; D017048:Health Care Costs; D005102:Health Expenditures; D006801:Humans; D007003:Hypoglycemia / Q000139:chemically induced / Q000191:economics / Q000517:prevention & control; D007004:Hypoglycemic Agents / Q000009:adverse effects / Q000191:economics* / Q000627:therapeutic use*; D007328:Insulin / Q000009:adverse effects / Q000191:economics* / Q000627:therapeutic use*; D018803:Models, Economic; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D016896:Treatment Outcome
chemical_list: D015415:Biomarkers; D001786:Blood Glucose; D006442:Glycated Hemoglobin; D007004:Hypoglycemic Agents; D007328:Insulin; C517652:hemoglobin A1c protein, human
keywords: accuracy; cost analysis; diabetes; hypoglycemia; self-monitoring of blood glucose