Gaps in Healthcare and Demographic Data in African Countries: A proposed solution

Accurate demographic and mortality data remain scarce for many African nations, leading to unreliable population projections and challenges in epidemiological modeling. Major demographic databases, such as the United Nations World Population Prospects and the Global Burden of Disease (GBD) study, highlight significant gaps in fertility, mortality, and life expectancy data. Existing estimation methods often rely on statistical imputation, borrowing data from neighboring countries or applying generalized adjustments. However, these approaches frequently lack a transparent, systematic rationale for selecting reference nations, reducing the credibility and accuracy of demographic estimates. Methods: To address this issue, we propose the National Demographic, Economic, and Social Similarity (NDECOS) Index, a structured and replicable framework for imputing missing demographic and health data. NDECOS employs a clustering algorithm that groups countries based on quantifiable metrics such as economic development, public health infrastructure, and governance stability. This method allows for a principled selection of reference countries, ensuring that demographic estimates are derived from nations with verifiable similarities rather than arbitrary selection. Expected Contributions: The NDECOS framework improves upon existing estimation practices by providing a transparent and theoretically grounded similarity metric for demographic imputation. By applying this methodology to mortality estimation, disease burden modeling, and public health research, we anticipate more accurate and credible projections for African countries with limited or incomplete national datasets. Conclusion: By leveraging a systematic similarity metric, NDECOS enhances the reliability of demographic and health estimates, enabling policymakers, researchers, and humanitarian organizations to make more informed decisions regarding public health, policy, and resource allocation. This framework offers a scalable and adaptable solution for addressing data gaps in global health research and demographic forecasting. Keywords: similarity theory, imputation, healthcare data, Africa