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. 2018 Nov 1;6(1):1527654.
doi: 10.1080/20016689.2018.1527654. eCollection 2018.

Budget impact of antimicrobial wound dressings in the treatment of venous leg ulcers in the German outpatient care sector: a budget impact analysis

Affiliations

Budget impact of antimicrobial wound dressings in the treatment of venous leg ulcers in the German outpatient care sector: a budget impact analysis

Maria Gueltzow et al. J Mark Access Health Policy. .

Abstract

Background: Hard-to-heal wounds are associated with high treatment costs and, in Germany, are mostly treated in the outpatient care sector. Wound dressings are the main cost-drivers in venous leg ulcer (VLU) care which prescription is budget-restricted. Objective: To determine to what extent the choice of antimicrobial dressing affects the spending in outpatient care by investigating the budget impact of the bioburden-reducing dressing Cutimed Sorbact. Methods: The budget impact analysis was performed comparing three different scenarios of the intervention mix of antimicrobial dressings. A Markov model was used to estimate the VLU progression during one year. The budget impact was determined by comparing the dressing and medicine resource use and costs of the three scenarios. Results: This analysis confirms the high treatment costs of VLU care. ScenarioA leads to a decreased resource use of antimicrobial dressings and results in 20.86% lower treatment costs after 12 months. The increased use of Cutimed Sorbact has a positive budget impact. Conclusion: This analysis indicates that the treatment of VLU patients may result in an exceedance of the budget per patient that is available to the treating practitioner. The choice of wound dressing, however, may positively affect the prescribers' budget spending in outpatient care.

Keywords: Budget impact; Cutimed Sorbact; DACC; Health economics; antimicrobial wound dressings; chronic wounds; cost-of-illness; outpatient care; silver dressings; venous leg ulcer.

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Figures

Figure 1.
Figure 1.
Identification of the base-case population. *D: Dermatologist
Figure 2.
Figure 2.
Markov Model with transition probabilities. Monthly cycles. D: DACC Dressing [7], Si: Silver Dressing [8], P: PHMB Dressing [9].
Figure 3.
Figure 3.
Percentage of healed and active (not healed) VLU per scenario after 12 months.
Figure 4.
Figure 4.
Annual dressing resource use per patient.
Figure 5.
Figure 5.
Relative budget impact per scenario (compared with base-case scenario).
Figure 6.
Figure 6.
Univariate sensitivity analysis: Tornado chart. Baseline budget impact (SA-SBase): −6,345,272.61€. IH: infection healing, MS: market share, DC: number of weekly dressing changes.
None

References

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