Optimizing cancer pain management in resource-limited settings

Adequate cancer pain management (CPM) is challenging in resource-limited settings, where current international guideline recommendations are difficult to implement owing to constraints such as inadequate availability and accessibility of opioids, limited awareness of appropriate opioid use among patients and clinicians, and lack of guidance on how to translate the best evidence into clinical practice. The multinational and multidisciplinary CAncer Pain managEment in Resource-limited settings (CAPER) Working Group proposes a two-step initiative to bridge clinical practice gaps in CPM in resource-limited settings.

A thorough review of the literature, a steering committee meeting in February 2017, and post-meeting teleconference discussions contributed to the development of this initiative. As a first step, we developed practical evidence-based CPM algorithms to support healthcare providers (HCPs) in tailoring treatment according to availability of and access to resources. The second part of the initiative proposes a framework to support an effective implementation of the CPM algorithms that includes an educational program, a pilot implementation, and an advocacy plan.

We developed CPM algorithms for first-line use, breakthrough cancer pain, opioid rotation, and refractory cancer pain based on the National Comprehensive Cancer Network guidelines and expert consensus. Our proposed educational program emphasizes the practical elements and illustrates how HCPs can provide optimal CPM according to evidence-based guidelines despite varied resource limitations. Pilot studies are proposed to demonstrate the effectiveness of the algorithms and the educational program, as well as for providing evidence to support a draft advocacy document, to lobby policymakers to improve availability and accessibility of analgesics in resource-limited settings.

These practical evidence-informed algorithms and the implementation framework represent the first multinational step towards achieving optimal CPM in resource-limited settings.