Literature DB >> 12627983

A simulation model for estimating direct costs of type 1 diabetes prevention.

Jarmo Hahl1, Tuula Simell, Antti Kupila, Päivi Keskinen, Mikael Knip, Jorma Ilonen, Olli Simell.   

Abstract

BACKGROUND: The ongoing Type 1 Diabetes Prediction and Prevention Project in Finland (DIPP) is based on screening of genetic type 1 diabetes mellitus susceptibility, subsequent autoantibody follow-up and experimental preventive treatment with nasal insulin.
OBJECTIVE: To analyse direct costs of type 1 diabetes prevention therapy with nasal insulin as it is now being studied in the DIPP project, and as it might be used as a part of routine healthcare in Finland. DATA AND METHODS: For the purposes of cost analysis, two different diabetes prevention models were constructed. The research-oriented model followed accurately the DIPP protocol and the practice-oriented model was based on the estimates of a panel of experts on how the prevention would be conducted as a part of the routine healthcare in Finland. To take into account the uncertainty and variability attached to the use of resources, a Monte Carlo simulation model was utilised. The costs of the two models comprising 500 iterations each were simulated using the Monte Carlo model. STUDY PERSPECTIVE: This study was performed from the healthcare provider's viewpoint.
RESULTS: The total direct costs per person of the research-oriented model were 2102 and 1676 euros (EUR) in the first and second year and those of the practice-oriented model EUR827 and EUR675, respectively (EUR1 approximately dollars US1.1; 2002 values). Subsequently, the costs rose only as a result of the increased use of insulin as the children grew older. After the 15th year, when the age structure of the population in the study had stabilised, the annual direct costs per person were EUR1798 (research-oriented model) and EUR797 (practice-oriented model).
CONCLUSIONS: The costs of prevention with nasal insulin are low when compared with estimates of the annual healthcare costs of type 1 diabetes. This study suggests, with some critical assumptions (in particular, that nasal insulin is effective in the prevention of type I diabetes), that a 2 to 3-year delay in the disease onset may make prevention according to the practice-oriented model cost saving.

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Year:  2003        PMID: 12627983     DOI: 10.2165/00019053-200321050-00001

Source DB:  PubMed          Journal:  Pharmacoeconomics        ISSN: 1170-7690            Impact factor:   4.981


  10 in total

1.  Population-based genetic screening for the estimation of Type 1 diabetes mellitus risk in Finland: selective genotyping of markers in the HLA-DQB1, HLA-DQA1 and HLA-DRB1 loci.

Authors:  S Nejentsev; M Sjöroos; T Soukka; M Knip; O Simell; T Lövgren; J Ilonen
Journal:  Diabet Med       Date:  1999-12       Impact factor: 4.359

2.  The friction cost method for measuring indirect costs of disease.

Authors:  M A Koopmanschap; F F Rutten; B M van Ineveld; L van Roijen
Journal:  J Health Econ       Date:  1995-06       Impact factor: 3.883

3.  Estimating costs in the economic evaluation of medical technologies.

Authors:  B R Luce; A Elixhauser
Journal:  Int J Technol Assess Health Care       Date:  1990       Impact factor: 2.188

4.  Indirect cost in economic evaluation: the opportunity cost of unpaid inputs.

Authors:  J Posnett; S Jan
Journal:  Health Econ       Date:  1996 Jan-Feb       Impact factor: 3.046

Review 5.  The impact of indirect costs on outcomes of health care programs.

Authors:  M A Koopmanschap; F F Rutten
Journal:  Health Econ       Date:  1994 Nov-Dec       Impact factor: 3.046

6.  Feasibility of genetic and immunological prediction of type I diabetes in a population-based birth cohort.

Authors:  A Kupila; P Muona; T Simell; P Arvilommi; H Savolainen; A M Hämäläinen; S Korhonen; T Kimpimäki; M Sjöroos; J Ilonen; M Knip; O Simell
Journal:  Diabetologia       Date:  2001-03       Impact factor: 10.122

7.  Costs and cost-minimisation analysis.

Authors:  R Robinson
Journal:  BMJ       Date:  1993-09-18

8.  Costs of predicting IDDM.

Authors:  J Hahl; T Simell; J Ilonen; M Knip; O Simell
Journal:  Diabetologia       Date:  1998-01       Impact factor: 10.122

9.  Newborn screening for HLA markers associated with IDDM: diabetes autoimmunity study in the young (DAISY).

Authors:  M Rewers; T L Bugawan; J M Norris; A Blair; B Beaty; M Hoffman; R S McDuffie; R F Hamman; G Klingensmith; G S Eisenbarth; H A Erlich
Journal:  Diabetologia       Date:  1996-07       Impact factor: 10.122

Review 10.  Prevention of diabetes in the NOD mouse: implications for therapeutic intervention in human disease.

Authors:  M A Bowman; E H Leiter; M A Atkinson
Journal:  Immunol Today       Date:  1994-03
  10 in total

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