peller03/Primary-Health-Care-Across-Nigeria

This project presents an end-to-end Primary Health Center (PHC) analytics dashboard for Nigeria, built with Power BI. It explores patient outcomes, disease burden, facility functionality, and workforce distribution to uncover healthcare access gaps and support data-driven public health decisions. # Primary-Health-Center-Analytics-Nigeria --- ## Table of Contents - Project Scope - Data Sources - Tool Used - Data Cleaning & Preparation - Exploratory Data Analysis Performed - EDA Visual Insights - Data Analysis - Results / Findings - Recommendations - Limitations ### Project Scope This project analyzes **Primary Health Center (PHC) performance across Nigeria** using simulated healthcare data. The objective is to evaluate **patient outcomes, disease burden, facility distribution, and workforce capacity** to understand how well PHCs are positioned to meet public health demands. The dashboard is structured into four analytical views: - Executive Overview - Disease Analysis - Patient Analysis - Facility Analysis The analysis focuses on identifying **healthcare access gaps, workload imbalances, and disease pressure points** across states. --- ### Data Sources The dataset used for this project is **simulated PHC data** designed to reflect healthcare delivery patterns across Nigeria. Key data domains include: - Patient records - Disease reports - Facility operational status - Health workforce distribution - State-level healthcare summaries - Time-based reporting (Year, Month) The simulation mirrors real-world public health reporting structures commonly used in PHC systems. --- ### Tool Used - Microsoft Power BI – Data modeling, DAX calculations, and dashboard design - Power Query – Data transformation, data quality checks, and normalization - DAX – KPI creation, ratios, and performance indicators --- ### Data Cleaning & Preparation 1. Standardized numerical fields for patient counts, disease cases, and workforce figures. 2. Ensured consistent state and facility naming conventions. 3. Created calculated measures for recovery rate, mortality burden ratio, and patient load. 4. Validated time-based fields (Year, Month Name) for trend analysis. 5. Grouped age bands and disease categories for meaningful aggregation. 6. Verified operational status classificatio …