Abstract
Purpose :
Due to the complex nature of interactive factors in glaucoma management, simulation methods have seen growing use in glaucoma therapy economic evaluations. Factors affecting patient health states were evaluated for their effect on health-related quality of life in data collected for a psychometric validation study to improve the precision of health state measurements and utility values generated in economic simulations.
Methods :
The Health Utility for Glaucoma – 5 dimensions (HUG-5) and National Eye Institute Visual Function Questionnaire 25 (NEI-VFQ) instruments were administered to 124 glaucoma patients. Age, gender, ethnicity, education level, marital status, employment status, current cataracts and type of glaucoma were entered in backwards elimination stepwise regressions to identify the magnitude of significant factors influencing health state measured by the NEI-VFQ and the HUG-5. 120 patients were included in the stepwise regression procedure. The mean age (SD) of patients was 66.95 (16.6) with 52 females (43%) and 68 males (57%).
Results :
The first model completed in 6 steps; identifying gender, age, and best-eye VFL as the most important predictors of HUG-5 composite scores (F(3,116) = 35.33, R2adj = 0.464, p < 0.001). The second model completed in 4 steps; identifying the same variables as model 1 with the addition of attending post-secondary education and type of glaucoma (mixed-mechanism) in predicting NEI-VFQ-25 composite scores (F(8,111) = 18.24, R2adj = 0.537, p < 0.001).
Conclusions :
Gender, age, and best-eye visual field loss should be included to moderate patient level health utility values generated within economic simulations to improve precision of cost-effectiveness patient-level simulations.
This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.