ACCESS TO TREATMENT AND QUALITY OF LIFE

B. Herczeg¹, M. Polgarne hoschek², I. Mucsi³, M.Z. Molnar⁴, Z. Kalo^5
1Faculty Of Pharmacy, Department Of Pharmacy Administration, Semmelweis University, Budapest/HUNGARY, ²Faculty Of Economics, University of West Hungary, Sopron/HUNGARY, ³Medicine, Semmelweis University, Budapest/HUNGARY, ⁴Department Of Transplantation And Surgery, Semmelweis University, Budapest/HUNGARY, ⁵Health Economics Research Centre, Eotvos Lorand University, Budapest/HUNGARY

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Introduction: In evidence based health policy and public reimbursement decisions QALY (quality adjusted life year) has become a standard measure to express patient benefits in order to ensure the standard aggregation and comparability of health gain related to different medical technologies. KDQoL-SF is a widely used disease specific instrument to measure the quality of life of patients with end-stage renal disease. KDQoL-SF is based on the generic SF-36 health survey, the instrument is also appropriate for calculating utility scores (the quality component of QALYs). Our objective was to explore the determinants of utility measured by KDQoL-SF among patients with renal transplantation. Method: Clinical and socio-demographic status of 833 Hungarian patients with renal transplantation were recorded together with the KDQoL-SF in a cross-sectional survey. KDQoL-SF results were converted to SF-6D utility values by employing the Brazier algorithm. We conducted univariate analyses to investigate the correlation between socio-demographic, clinical variables and utility of patients. To quantify different determinants of utility we built multivariate linear regression models in a stepwise hierarchical manner. Results: SF-6D index of patients was 0.737 (SD = 0.3712). Elderly patients and females reported decreased utility. Higher number of comorbidities, lower socioeconomic class, longer time spent on dialysis and worse kidney function also resulted in lower SF6D score. Family relationship and higher educational status positively influenced the utility of patients. Age, gender, socioeconomic class, family and educational status, number of comorbidities, time spent on dialysis, creatine clearance remained significant explanatory variables in the multivariate regression model. Variables in the final model could explain only 23-24% of SF-6D scores. Conclusion: As expected, in addition to clinical variables socio-demographic factors also influence the utility of renal transplanted patients. Therefore the capacity of patients to benefit from renal transplantation depends also on their demographic and socioeconomic status.

Disclosure: All authors have declared no conflicts of interest.