Exploring Trends in HIV Prevalence and Neonatal Mortality in Sub-Saharan Africa (2000–2023)

This project presents a longitudinal, cross-country analysis of two critical public health indicators: HIV burden and neonatal mortality across Sub-Saharan Africa from 2000 to 2023. The study combines publicly available datasets from the World Health Organization and UNICEF/UN IGME, using Python and R to conduct in-depth trend analysis, visualize regional disparities, and explore relationships between disease burden and child survival outcomes. Goals and Scope: Analyze the temporal trends of people living with HIV across African nations, with emphasis on high-burden regions Examine neonatal mortality rate (NMR) trends across wealth quintiles, years, and sexes Explore possible associations between HIV prevalence and neonatal mortality rates using side-by-side analysis and correlation-based exploration Data Sources: HIV Dataset (2000–2023): Indicator: "Estimated number of people (all ages) living with HIV" Country-level data, disaggregated by year and region Extracted from WHO global datasets Neonatal Mortality Dataset (UN IGME Estimates): Indicator: "Neonatal mortality rate per 1,000 live births" Dimensions: Sex, Wealth Quintile, Year Covers Sub-Saharan Africa, sourced from UNICEF and UN IGME Technologies and Methods: Tools: Python (pandas, seaborn, matplotlib), R (ggplot2, tidyverse) Processes: Data wrangling and unification Time-series analysis Grouped summaries by country, region, sex, and socioeconomic group Dual-axis plotting and heatmap visualizations Optional regression or correlation analysis to explore interdependence Key Insights: HIV burden remained critically high in select Sub-Saharan countries despite global treatment efforts; progress varied across nations Neonatal mortality showed an overall decline, but inequities persist across wealth quintiles and demographic groups Preliminary findings suggest a geospatial and developmental correlation between regions with higher HIV burden and persistent neonatal mortality, especially in under-resourced health systems Impact and Value: Builds a scalable, data-driven template for health systems analysis Provides a dual-outcome lens for policymakers, NGOs, and healthcare leaders to understand compound public health vulnerabilities Demonstrates multi-source data integration, health analytics, and longitudinal analysis skills critical for public health data scientists