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01-12-2013 | Uitgave 10/2013

Quality of Life Research 10/2013

A reference set of health utilities for long-term survivors of prostate cancer: population-based data from Ontario, Canada

Tijdschrift:
Quality of Life Research > Uitgave 10/2013
Auteurs:
Murray D. Krahn, Karen E. Bremner, Shabbir M. H. Alibhai, Andy Ni, George Tomlinson, Audrey Laporte, Gary Naglie

Abstract

Purpose

To measure quality of life (QOL) and utilities for prostate cancer (PC) patients and determine their predictors.

Methods

A population-based, community-dwelling, geographically diverse sample of long-term PC survivors in Ontario, Canada, was identified from the Ontario Cancer Registry and contacted through their referring physician. Consenting patients completed questionnaires by mail: Health Utilities Index (HUI 2/3), Patient Oriented Prostate Utility Scale PORPUS-U (utility), PORPUS-P (health profile), Functional Assessment of Cancer Therapy-Prostate (FACT-P), and Prostate Cancer Index (PCI). Clinical data were obtained from chart reviews. Regression models determined the effects of a series of variables on QOL and utility.

Results

We received questionnaires and reviewed charts for 585 patients (mean age 72.6, 2–13 years postdiagnosis). Mean utility scores were as follows: PORPUS-U = 0.92, HUI2 = 0.85, and HUI3 = 0.78. Mean health profile scores were as follows: PORPUS-P = 71.7, PCI sexual, urinary, and bowel function = 23.7, 79.1, and 84.6, respectively (0 = worst, 100 = best), and FACT-P = 125.1 (0 = worst, 156 = best). In multiple regression analyses, comorbidity and PCI urinary, sexual, and bowel function were significant predictors of other QOL measures. With all variables, 32–50 % of the variance in utilities was explained.

Conclusions

Many variables affect global QOL of PC survivors; only prostate symptoms and comorbidity have independent effects. Our model allows estimation of the effects of multiple factors on utilities. These utilities for long-term outcomes of PC and its treatment are valuable for decision/cost-effectiveness models of PC treatment.

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