Ogombo-collins/Analysing-Healthcare-Facilities-in-Kenya

This project provides a detailed analysis of the distribution, capacity, and accessibility of health facilities across Kenya. # Kenya’s Healthcare Facilities Analysis (2025) This project provides a detailed analysis of the distribution, capacity, and accessibility of health facilities across Kenya. Using publicly available datasets, including national health facility registries and 2019 county population census data. The notebook explores disparities, infrastructure gaps, and county-level differences affecting healthcare access. The goal of the project: Offer **data-driven insights** that can support policymakers, health economists, county planners, development partners, and researchers working in Kenya’s health sector. --- ## 📊 Key Objectives 1. **Understand geographic/healthcare services distribution of healthcare facilities** - By county - By facility type (dispensary, health centre, clinic, etc.) - By ownership (public, private, etc) 2. **Assess healthcare access relative to population** - Facilities per 10,000 residents - Most underserved counties - Counties with over-concentration or facility gaps 3. **Identify infrastructure disparities across counties** - Urban vs rural mismatches - Population density impact - Service availability gaps 4. **Provide practical insights for resource allocation and planning** --- ## 🧵 Data Sources 1. **Kenya Health Facilities Registry** - Retrieved from OpenAfrica’s Health Facilities in Kenya dataset. It's updated monthly. - Includes facility type, ownership, geolocation, and operational status. - Access data here: Health facilities in Kenya 2. **Kenya Population & Housing Census (2019)** - Contains population counts per county. Used to calculate population-adjusted metrics such as facility ratios. - Access data here: Counties population --- ## 🛠️ Methodology & Analysis ### 1. Initial Data Cleaning & Transformation These steps were applied to the raw health facilities dataset (`df`): - **Handle Missing Capacity Data:** - Missing values in **`Beds`** and **`Cots`** were filled with **0**. - Both columns were converted to integer typ …