Research

How do Zimbabweans value health states?

Jennifer Jelsma¹ , Kristian Hansen² , Willy de Weerdt³ , Paul de Cock⁴ and Paul Kind⁵

¹  Division of Physiotherapy, University of Cape Town, Anzio Road, Observatory, South Africa

²  Department of Health Services Research, University of Copenhagen

³  Faculteit Lichamelijke Opvoeding en Kinesitherapie, Katholieke Universiteit Leuven, Belgium

⁴  Centrum voor Ontwikkelingsstoornissen, Faculteit Geneeskunde, Katholieke Universiteit, Leuven, Belgium

⁵  University of York; Department of Preventive Medicine, University of Wisconsin

author email corresponding author email

Population Health Metrics 2003, 1:11doi:10.1186/1478-7954-1-11

Published:	16 December 2003

Abstract

Background

Quality of life weights based on valuations of health states are often used in cost utility analysis and population health measures. This paper reports on an attempt to develop quality of life weights within the Zimbabwe context.

Methods

2,384 residents in randomly selected small residential plots of land in a high-density suburb of Harare valued descriptors of 38 health states based on different combinations of the five domains of the EQ-5D (mobility, self-care, usual activities, pain or discomfort and anxiety or depression). The English version of the EQ-5D was used. The time trade-off method was used to determine the values, and 19,020 individual preferences for health states were analysed. A residual maximum likelihood linear mixed model was used to estimate a function for predicting the values of all possible combinations of levels on the five domains. The model was fit to a random subset of two-thirds of the observations, with the remaining observations reserved for analysis of predictive validity. The results were compared to a similar study undertaken in the United Kingdom.

Results

A credible model was developed to predict the values of states that were not valued directly. In the subset of observations reserved for validation, the mean absolute difference between predicted and observed values was 0.045. All domains of the EQ-5D were found to contribute significantly to the model, both at the moderate and severe levels. Severe pain was found to have the largest negative coefficient, followed by the inability to wash and dress oneself.

Conclusion

Despite a generally lower education level than their European counterparts, urban Zimbabweans appear to value health states in a consistent manner, and the determination of a global method of establishing quality of life weights may be feasible and valid. However, as the relative weightings of the different domains, although correlated, differed from the standard set of weights recommended by the EuroQol Group, the locally determined coefficients should be used within the Zimbabwean context.