: A semi-Markov model was used to compare the cost-effectiveness of the two options to prevent hospital-acquired pressure ulcers among patients admitted to hospital. The time horizon for costs was until patient discharge, up to a maximum of one year. The time horizon for outcomes was the lifetime of the patient. The authors reported that a societal perspective was adopted.
The effectiveness data were extracted from the literature indexed in the MEDLINE database. The main effectiveness estimate was that of the intervention in reducing the incidence of hospital-acquired pressure ulcers. These data were from a published study.
Monetary benefit and utility valuations:
The utilities for the health states were based on European Quality of life (EQ-5D) questionnaire scores. All utilities were divided by 365 to produce quality-adjusted life-days to match the cycle length. The data were from published studies and other sources.
Measure of benefit:
Quality-adjusted life-years (QALYs) were the measure of benefit and they were discounted at an annual rate of 3%.
The direct costs included those of the interventions (including risk assessments, support surfaces, chair cushions, nutrition, repositioning, dealing with moisture and incontinence, and unforeseen costs); the treatment of deep tissue injuries; the treatment of stage I or II hospital-acquired pressure ulcers; and the treatment of stage III or IV hospital-acquired pressure ulcers. The costs of preventing and treating of stage I or II ulcers were from a micro-costing study performed by the authors. Those of stage III or IV ulcers were from published studies. The costs of a deep tissue injury were assumed to equal the costs of daily prevention. The price year was 2009 and all costs were reported in US dollars ($).
Analysis of uncertainty:
One-way sensitivity analyses were conducted to examine the impact of variations in the assumptions on the outcomes. The base-case estimates were varied by ±15%. Threshold analyses were performed for some inputs by changing their values by more than ±15%. A probabilistic sensitivity analysis was undertaken, by applying a distribution to each variable, to assess the uncertainty in all the model parameters simultaneously.