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Estimating Health State Utility Values for Comorbidities

Author

Listed:
  • Roberta Ara

    (University of Sheffield)

  • John Brazier

    (University of Sheffield)

Abstract

A comorbidity is defined as the presence of at least one additional health condition co-occurring with a primary health condition. Decision analytic models in healthcare depict the typical clinical pathway of patients in general clinical practice and frequently include health states defined to represent comorbidities such as sequelae or adverse events. Health state utility values (HSUVs) are often not available for these and analysts generally estimate them. This article provides a summary of the methodological literature on estimating methods frequently used together with worked examples. The three main methods used (minimum, multiplicative and additive) can produce a wide range in the values estimated. In general, the minimum method overestimates observed HSUVs and the magnitude of error tends to increase as the observed values decrease. Conversely, the additive and multiplicative methods generally underestimate observed values and the magnitude of the errors is generally greater for the additive method. HSUVs estimated using the multiplicative method tend to decrease for lower HSUVs and the largest errors are in observed HSUVs >0.6. Differences in estimated values can produce substantial differences in the resulting incremental cost effectiveness ratio. Based on the current evidence, the multiplicative method is advocated but additional research is required to determine appropriate methods when estimating values for additional comorbidities.

Suggested Citation

  • Roberta Ara & John Brazier, 2017. "Estimating Health State Utility Values for Comorbidities," PharmacoEconomics, Springer, vol. 35(1), pages 89-94, December.
  • Handle: RePEc:spr:pharme:v:35:y:2017:i:1:d:10.1007_s40273-017-0551-z
    DOI: 10.1007/s40273-017-0551-z
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    Cited by:

    1. Roberta Ara & John Brazier & Ismail Azzabi Zouraq, 2017. "The Use of Health State Utility Values in Decision Models," PharmacoEconomics, Springer, vol. 35(1), pages 77-88, December.
    2. Tristan M Snowsill & Neil A J Ryan & Emma J Crosbie & Ian M Frayling & D Gareth Evans & Chris J Hyde, 2019. "Cost-effectiveness analysis of reflex testing for Lynch syndrome in women with endometrial cancer in the UK setting," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-18, August.

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