Data-Driven Health Equity: A Proposed Framework for Analytics-Based Health Needs Assessment in Underserved Communities

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@article{1718410,
      author = {Maryann Inimfon Atakpa, Toyosi Abolaji},
      title = {Data-Driven Health Equity: A Proposed Framework for Analytics-Based Health Needs Assessment in Underserved Communities},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {4},
      number = {5},
      pages = {410-432},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1718410.pdf},
      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},
      month = {November},
      doi = {https://doi.org/10.64388/IREV4I5-1718410}
  }