TerryPendo/Global_Health_and-Poverty-Analysis

Data analysis project on HIV and multidimensional poverty in East Africa. Built using R, R Shiny, and Excel datasets. # Global Health, Poverty and Mortality Analysis ## Objective To analyse global HIV prevalence and its relationship with multidimensional poverty, and to visualise child mortality trends across East African Community (EAC) countries. This project combines data cleaning, descriptive analytics, statistical modelling and geospatial mapping. ## Dataset Sources - **HIV data 2000–2023** – WHO / UNAIDS data on people living with HIV - **Multidimensional poverty data** – World Bank / UN data on poverty headcount and deprivation factors - **Child mortality data** – UN Inter-agency Group for Child Mortality Estimation (U5 and neonatal rates) - **Shapefiles** – GADM (administrative boundaries for EAC countries) ## Workflow 1. **Data Cleaning** - Extracted numeric HIV estimates from text values - Removed “No data” and `<` estimates - Harmonised poverty dataset column names 2. **Exploratory Analysis** - Calculated total HIV burden per country and cumulative percentages - Identified countries contributing to 75% of the global burden and within each WHO region - Plotted time-series trends by country and region 3. **Statistical Modelling** - Merged HIV data with multidimensional poverty indicators - Fitted a mixed-effects model to examine the relationship between HIV prevalence and poverty factors over time 4. **Mortality Mapping (EAC)** - Filtered under-five and neonatal mortality for 8 EAC countries - Computed latest median estimates - Produced choropleth maps and trend lines for mortality rates 5. **Visualization**: - Line plots for HIV trends (2000–2023) and mortality rate trends in East African Countries. - Maps showing spatial distribution of under-five and neo-natal mortality rates. - Interactive Shiny dashboard for dynamic filtering. 6. **Interactive Dashboard** - Built a Shiny dashboard for dynamic exploration of the cleaned datasets - Integrated plots and maps into one interface for end-users ## Tools Used - R (tidyverse, dplyr, ggplot2, lme4, sf, plotly, viridis, sca …