Current Volume 9
Data-Driven Health Equity: A Proposed Framework for Analytics-Based Health Needs Assessment in Underserved Communities
Abstract
Health equity remains an unfulfilled objective across healthcare systems globally, with conventional facility-based health information systems structurally incapable of measuring health equity in communities with low healthcare utilisation rates where need is paradoxically greatest. This paper proposes a comprehensive framework for data-driven health equity analytics for health needs assessment in underserved communities, drawing on implementation experience across 14 community health programmes in Nigeria Federal Capital Territory. The framework integrates a Python-based mobile survey data collection architecture for low-connectivity environments, an R-based design-based statistical analysis pipeline, a Power BI visualisation layer serving multiple stakeholder audiences, and a community data sovereignty governance structure. Applied across 3,847 household assessments over four survey rounds, the framework identified significant healthcare access disparities. Transferability to NHS Integrated Care System place-based health planning is discussed with a healthcare policy framework comparison table.
Keywords
Health Equity, Underserved Communities, Health Needs Assessment, Community Health Analytics, Python, R, Power BI, Social Determinants Of Health, Integrated Care Systems, Community-Based Participatory Research
Citations
IRE Journals:
Maryann Inimfon Atakpa, Toyosi Abolaji "Data-Driven Health Equity: A Proposed Framework for Analytics-Based Health Needs Assessment in Underserved Communities" Iconic Research And Engineering Journals Volume 4 Issue 5 2020 Page 410-432 https://doi.org/10.64388/IREV4I5-1718410
IEEE:
Maryann Inimfon Atakpa, Toyosi Abolaji
"Data-Driven Health Equity: A Proposed Framework for Analytics-Based Health Needs Assessment in Underserved Communities" Iconic Research And Engineering Journals, 4(5) https://doi.org/10.64388/IREV4I5-1718410
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