Exploring the landscape of essential health data science skills and research challenges: a survey of stakeholders in Africa, Asia, and Latin America and the Caribbean

Abstract

Background: Data science approaches have been pivotal in addressing public health challenges. However, there has been limited focus on identifying essential data science skills for health researchers, gaps in capacity building provision, barriers to access, and potential solutions. Objectives: This review aims to identify essential data science skills for health researchers and key stakeholders in Africa, Asia, and Latin America and the Caribbean (LAC), as well as to explore gaps and barriers in data science capacity building and share potential solutions, including any regional variations. Methods: An online survey was conducted in English, French, Spanish and Portuguese, gathering both quantitative and qualitative responses. Descriptive analysis was performed in R V4.3, and a thematic workshop approach facilitated qualitative analysis. Findings: From 262 responses from individuals across 54 low- and middle-income countries (LMICs), representing various institutions and roles, we summarised essential data science skills globally and by region. Thematic analysis revealed key gaps and barriers in capacity building, including limited training resources, lack of mentoring, challenges with data quality, infrastructure and privacy issues, and the absence of a conducive research environment. Conclusion and future directions: Respondents' consensus on essential data science skills suggests the need for a standardised framework for capacity building, adaptable to regional contexts. Greater investment, coupled with expanded collaboration and networking, would help address gaps and barriers, fostering a robust data science ecosystem and advancing insights into global health challenges.

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