The Patient - Patient-Centered Outcomes Research

, Volume 10, Issue 5, pp 643–651 | Cite as

Patient Preferences for Pain Management in Advanced Cancer: Results from a Discrete Choice Experiment

  • David M. Meads
  • John L. O’Dwyer
  • Claire T. Hulme
  • Phani Chintakayala
  • Karen Vinall-Collier
  • Michael I. Bennett
Original Research Article

Abstract

Background

Pain from advanced cancer remains prevalent, severe and often under-treated.

Aim

The aim of this study was to conduct a discrete choice experiment with patients to understand their preferences for pain management services and inform service development.

Methods

Focus groups were used to develop the attributes and levels of the discrete choice experiment. The attributes were: waiting time, type of healthcare professional, out-of-pocket costs, side-effect control, quality of communication, quality of information and pain control. Patients completed the discrete choice experiment along with clinical and health-related quality of life questions. Conditional and mixed logit models were used to analyse the data.

Results

Patients with cancer pain (n = 221) and within palliative care services completed the survey (45% were female, mean age 64.6 years; age range 21–92 years). The most important aspects of pain management were: good pain control, zero out-of-pocket costs and good side-effect control. Poor or moderate pain control and £30 costs drew the highest negative preferences. Respondents preferred control of side effects and provision of better information and communication, over access to certain healthcare professionals. Those with lower health-related quality of life were less willing to wait for treatment and willing to incur higher costs. The presence of a carer influenced preferences.

Conclusions

Outcome attributes were more important than process attributes but the latter were still valued. Thus, supporting self-management, for example by providing better information on pain may be a worthwhile endeavour. However, service provision may need to account for individual characteristics given the heterogeneity in preferences.

Supplementary material

40271_2017_236_MOESM1_ESM.doc (44 kb)
Supplementary material 1 (DOC 44 kb)
40271_2017_236_MOESM2_ESM.doc (34 kb)
Supplementary material 2 (DOC 34 kb)
40271_2017_236_MOESM3_ESM.doc (42 kb)
Supplementary material 3 (DOC 42 kb)

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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • David M. Meads
    • 1
  • John L. O’Dwyer
    • 1
  • Claire T. Hulme
    • 1
  • Phani Chintakayala
    • 2
  • Karen Vinall-Collier
    • 1
    • 3
  • Michael I. Bennett
    • 4
  1. 1.Academic Unit of Health Economics, Leeds Institute of Health SciencesUniversity of LeedsLeedsUK
  2. 2.Leeds University Business School and Leeds Institute for Data AnalyticsUniversity of LeedsLeedsUK
  3. 3.School of DentistryUniversity of LeedsLeedsUK
  4. 4.Academic Unit of Palliative Care, Leeds Institute of Health SciencesUniversity of LeedsLeedsUK

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