Published online Feb 10, 2017. doi: 10.5306/wjco.v8.i1.75
Peer-review started: August 23, 2016
First decision: October 21, 2016
Revised: December 1, 2016
Accepted: December 13, 2016
Article in press: December 14, 2016
Published online: February 10, 2017
Processing time: 171 Days and 12.1 Hours
To identify unique clusters of patients based on their concerns in using analgesia for cancer pain and predictors of the cluster membership.
This was a 3-mo prospective observational study (n = 207). Patients were included if they were adults (≥ 18 years), diagnosed with solid tumors or multiple myelomas, and had at least one prescription of around-the-clock pain medication for cancer or cancer-treatment-related pain. Patients were recruited from two outpatient medical oncology clinics within a large health system in Philadelphia. A choice-based conjoint (CBC) analysis experiment was used to elicit analgesic treatment preferences (utilities). Patients employed trade-offs based on five analgesic attributes (percent relief from analgesics, type of analgesic, type of side-effects, severity of side-effects, out of pocket cost). Patients were clustered based on CBC utilities using novel adaptive statistical methods. Multiple logistic regression was used to identify predictors of cluster membership.
The analyses found 4 unique clusters: Most patients made trade-offs based on the expectation of pain relief (cluster 1, 41%). For a subset, the main underlying concern was type of analgesic prescribed, i.e., opioid vs non-opioid (cluster 2, 11%) and type of analgesic side effects (cluster 4, 21%), respectively. About one in four made trade-offs based on multiple concerns simultaneously including pain relief, type of side effects, and severity of side effects (cluster 3, 28%). In multivariable analysis, to identify predictors of cluster membership, clinical and socioeconomic factors (education, health literacy, income, social support) rather than analgesic attitudes and beliefs were found important; only the belief, i.e., pain medications can mask changes in health or keep you from knowing what is going on in your body was found significant in predicting two of the four clusters [cluster 1 (-); cluster 4 (+)].
Most patients appear to be driven by a single salient concern in using analgesia for cancer pain. Addressing these concerns, perhaps through real time clinical assessments, may improve patients’ analgesic adherence patterns and cancer pain outcomes.
Core tip: Lack of adherence to analgesia for cancer pain is a prevalent clinical problem. The 2016 Centers for Disease Control and Prevention guidelines provide recommendations to clinicians for opioid prescription. However, this focus will be incomplete without understanding what concerns anchor patients’ decisions to use analgesia for cancer pain. We used a trade-off analysis technique and novel adaptive methods to first show that unique clusters of patients exist based on the main concerns that anchor their preferences for analgesia for cancer pain. We then identified factors that predict membership in each preference cluster. We found that socioeconomic factors, including education, health literacy, income (rather than attitudes and beliefs about analgesics) played a role in predicting three out of four clusters. Most analgesic beliefs and concerns, including the widely indicated addiction concerns, did not predict cluster membership.