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Priority-setting in health is risky and challenging, particularly in resource-constrained settings. It is not simply a narrow technical exercise, and involves the mobilisation of a wide range of capacities among stakeholders – not only the technical capacity to “do” research in economic evaluations. Using the Individuals, Nodes, Networks and Environment (INNE) framework, we identify those stakeholders, whose capacity needs will vary along the evidence-to-policy continuum. Policymakers and healthcare managers require the capacity to commission and use relevant evidence (including evidence of clinical and cost-effectiveness, and of social values); academics need to understand and respond to decision-makers’ needs to produce relevant research. The health system at all levels will need institutional capacity building to incentivise routine generation and use of evidence. Knowledge brokers, including priority-setting agencies (such as England’s National Institute for Health and Care Excellence, and Health Interventions and Technology Assessment Program, Thailand) and the media can play an important role in facilitating engagement and knowledge transfer between the various actors. Especially at the outset but at every step, it is critical that patients and the public understand that trade-offs are inherent in priority-setting, and careful efforts should be made to engage them, and to hear their views throughout the process. There is thus no single approach to capacity building; rather a spectrum of activities that recognises the roles and skills of all stakeholders. A range of methods, including formal and informal training, networking and engagement, and support through collaboration on projects, should be flexibly employed (and tailored to specific needs of each country) to support institutionalisation of evidence-informed priority-setting. Finally, capacity building should be a two-way process; those who build capacity should also attend to their own capacity development in order to sustain and improve impact.

Background: Resource allocation in health is universally challenging, but especially so in resource-constrained contexts in the Global South. Pursuing a strategy of evidence-based decision-making and using tools such as Health Technology Assessment (HTA), can help address issues relating to both affordability and equity when allocating resources. Three BRICS and Global South countries, China, India and South Africa have committed to strengthening HTA capacity and developing their domestic HTA systems, with the goal of getting evidence translated into policy. Through assessing and comparing the HTA journey of each country it may be possible to identify common problems and shareable insights.Objectives: This collaborative paper aimed to share knowledge on strengthening HTA systems to enable enhanced evidence-based decision-making in the Global South by: Identifying common barriers and enablers in three BRICS countries in the Global South; and Exploring how South-South collaboration can strengthen HTA capacity and utilisation for better healthcare decision-making.Methods: A descriptive and explorative comparative analysis was conducted comprising a Within-Case analysis to produce a narrative of the HTA journey in each country and an Across-Case analysis to explore both knowledge that could be shared and any potential knowledge gaps.Results: Analyses revealed that China, India and South Africa share many barriers to strengthening and developing HTA systems such as: (1) Minimal HTA expertise; (2) Weak health data infrastructure; (3) Rising healthcare costs; (4) Fragmented healthcare systems; and (5) Significant growth in non-communicable diseases. Stakeholder engagement and institutionalisation of HTA were identified as two conducive factors for strengthening HTA systems.Conclusion: China, India and South Africa have all committed to establishing robust HTA systems to inform evidence-based priority setting and have experienced similar challenges. Engagement among countries of the Global South can provide a supportive platform to share knowledge that is more applicable and pragmatic.

The purpose of this paper is to identify the treatment patterns and corresponding costs of healthcare resource use associated with palliative care for different types of advanced cancer patients, from the time they started strong opioid treatment until death. This was a modelling study performed from the perspective of the UK's National Health Service (NHS). A data set was created comprising 547 patients in the DIN-Link database who had a Read code for malignant neoplasms with a specific tumour-type diagnosis and who received their first strong opioid between 1 January 1998 and 30 September 2000 and died during that period. Palliative care-related resource utilization data were obtained from the DIN-Link database. Unit costs at 2000/2001 prices were applied to the resource use estimates to determine the mean cost of palliative care from the start of treatment until death. There were significant differences in age between patients with different cancer types and in patients' survival from diagnosis, time to the start of palliative care and duration of palliative care. The mean duration from cancer diagnosis to the start of strong opioid treatment ranged from 0.7 to 5.4 years in patients with lung and breast cancer respectively. Moreover, the length of palliative care ranged from 180 to 372 days in patients with these cancer types respectively. There were also statistically significant differences in resource use between patients with different cancer types, but this reflected, in part, the varying durations of palliative care. Nevertheless, there were also differences in the monthly number of primary care visits reflecting the different number of monthly prescriptions. There was no apparent relationship between the length and corresponding cost of palliative care which ranged from 1816 pounds sterling for colon cancer to 4789 pounds sterling for ovarian cancer. Additionally, on average, only a third of all patients also received 4-hourly morphine as part of their initial strong opioid treatment. The total cost of palliative care varied between cancer type and reflects, at least in part, the distinct clinical features associated with different tumours and the varying lengths of survival following the start of strong opioid treatment. Nevertheless, no apparent relationship was found between length of palliative care and corresponding costs. This analysis provides data on palliative care resource use for a variety of cancers and could provide useful input when planning local healthcare strategies and building service commissioning models.