OBJECTIVES: Multiple pieces of evidence are required ahead of launching a product in a country. Part of this includes healthcare resource use (HRU) data, especially in markets where assessing the budget impact of the asset is essential to payers. In many low- and middle-income countries (LMICs), sourcing such data remains difficult given the lack of infrastructure, data collection initiatives, or frequency of data collection at the market level^1-3. As such, HRU data can often be treated as unavailable or outdated, which could subsequently impact the reliability and power of financial outcomes. This study therefore aims to explore the barriers of HRU data collection in LMICs, with solutions to support pharmaceutical companies in identifying HRU input data.

METHODS: Targeted secondary research was undertaken to identify barriers in collecting HRU cost and utilization data across LMICs. Key terms searched included “challenges to HRU data collection in LMICs”, “barriers to HRU data collection in LMICs”, and “managing missing HRU data in LMICs”.

RESULTS: The findings identified multiple data collection barriers in LMICs, including poor infrastructure, poor quality of data, lack of state funding, and local red tape^1,2,3,4. Poor infrastructure, for example, is observed at a technical and human level⁴. LMICs often have a lack of well organized regional/nationwide databases for HRU and poorly trained staff in data collection⁴. Additionally, researchers reported the impact of red tape, with permission being required from chiefs or street communities before data collection can begin⁵. However, solutions exist for handling missing data including exclusion, interpolation, and using sensitivity analyses to assess data points that have been validated by clinicians and payers through primary research interview¹.

CONCLUSIONS: Barriers to data collection leads to missing HRU data across disease areas, subsequently impacting the power of resourcing analyses in LMICs. Solutions including payer/clinician validated data and sensitivity analysis can support analyses with missing HRU data.