Predictive Modeling of Healthcare Costs Using Demographic and Health Data in Nigeria

Purpose: The aim of the study was to analyze the predictive modeling of healthcare costs using demographic and health data in Nigeria. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Predictive modeling of healthcare costs using demographic and health data in Nigeria reveals key predictors such as age, socioeconomic status, and comorbidity burden. These models demonstrate high accuracy in forecasting healthcare expenditures, suggesting potential improvements in resource management and patient care. Integrating predictive analytics into healthcare policy could optimize financial planning and enhance overall healthcare delivery despite existing data challenges and infrastructure limitations. Unique Contribution to Theory, Practice and Policy: Health belief model (HBM), agency theory & complex adaptive systems (CAS) theory may be used to anchor future studies on analyze the predictive modeling of healthcare costs using demographic and health data in Nigeria. Develop tools for risk stratification using predictive models, which can assist healthcare providers and insurers in identifying high-risk individuals who may benefit from targeted interventions. Provide evidence-based insights to inform healthcare policy decisions related to resource allocation, reimbursement models, and healthcare financing.