Health Utility Values Measured by EQ-5D in Breast Cancer From Low- and Middle-Income Countries: A Systematic Review...

Health utility values (HUVs) are fundamental parameters in economic evaluations used to assess the value of healthcare interventions. However, patients living with breast cancer in low- and middle-income countries (LMICs) face distinct burdens compared with those in high-income countries (HICs), driven by profound disparities in resources, infrastructure, and cultural context. High reliance on HUVs from HICs risks misrepresenting the specific disease burden in LMICs, introducing uncertainty in local healthcare resource allocation. This protocol outlines the methods for a planned systematic review and meta-analysis to synthesize available evidence from LMICs to generate pooled EuroQol-5 Dimensions HUV estimates that are contextually appropriate for local decision making.

By following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, this protocol is registered in PROSPERO (CRD420251208371). The initial literature search will be conducted in Cochrane CENTRAL, PubMed, Web of Science core collection and Scopus using keywords such as 'health-related quality of life', 'EuroQol-5 Dimensions', 'breast cancer' and 'low-middle-income countries'. Risk-of-bias assessment will be conducted using the Cochrane Risk-of-Bias Tool (RoB 2) and Newcastle-Ottawa Scale (NOS) depending on study designs. R packages (metaprop) will be used to pool the mean HUVs of people living with breast cancer using fixed-effect (inverse variance method) and random-effect models (Der Simonian-Laird method). Subgroup analysis will be conducted based on different categories of interest such as EQ-5D versions, country income level and clinical stagings.

This protocol provides a systematic guide to synthesize EQ-5D utility values specific to breast cancer in LMICs. The anticipated findings will establish a critical evidence base for decision making in resource-constrained settings. By generating contextually relevant estimates, this will minimize parameter uncertainty and support more equitable resource allocation. Hence, it will contribute to health systems that are responsive not only to the clinical burden of the disease but also to the multidimensional well-being of the patient and families it affects.