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App README

Uganda Child Health & Nutrition Analytics Dashboard

Overview

This comprehensive R Shiny dashboard supports World Vision's mission to improve child health outcomes in Uganda through advanced statistical analysis and data-driven insights.

Features

🌍 Multi-Focus Areas

Addressing World Vision's key focus areas:

  • —Child Health & Mortality: Track and predict under-5 and infant mortality rates
  • —Nutrition: Monitor stunting, wasting, and underweight prevalence
  • —WASH: Analyze water and sanitation access impact on health
  • —Disease Prevention: Track malaria, HIV, and tuberculosis indicators
  • —Maternal Health: Evaluate antenatal care and skilled birth attendance

📊 Advanced Statistical Techniques

  1. 1.Generalized Linear Models (GLM)
  2. 2.Multiple regression analysis for health determinants
  3. 3.Ridge and LASSO regression for feature selection
  4. 4.Model diagnostics and validation
  1. 1.Causal Inference with Propensity Score Matching
  2. 2.Evaluate intervention effectiveness
  3. 3.Control for confounding variables
  4. 4.Estimate average treatment effects
  1. 1.Machine Learning with Random Forests
  2. 2.Predict health outcomes using ensemble methods
  3. 3.Feature importance analysis
  4. 4.Model performance evaluation
  1. 1.Intervention Simulation
  2. 2.Design multi-sector health programs
  3. 3.Budget optimization
  4. 4.Cost-effectiveness analysis
  5. 5.Lives saved estimation

🎯 Key Capabilities

  • —Interactive Visualizations: Dynamic Plotly charts for data exploration
  • —Real-time Analysis: Instant model updates based on user inputs
  • —Regional Comparison: Identify disparities across Uganda's regions
  • —Temporal Trends: Track progress over time (2010-2023)
  • —Predictive Modeling: Forecast future health outcomes

Data Sources

The dashboard integrates multiple Uganda health indicators:

  • —Child mortality rates (under-5 and infant)
  • —Nutrition indicators (stunting, wasting, underweight)
  • —Health system performance metrics
  • —Disease burden statistics
  • —WASH access indicators
  • —Maternal and reproductive health data

Technical Implementation

Statistical Methods

  • —GLM: For identifying risk factors and protective factors
  • —Ridge/LASSO: For handling multicollinearity and feature selection
  • —PSM: For quasi-experimental evaluation of interventions
  • —Random Forest: For non-linear relationships and predictions

Technology Stack

  • —R Shiny: Interactive web application framework
  • —shinydashboard: Professional dashboard layout
  • —plotly: Interactive visualizations
  • —tidyverse: Data manipulation
  • —randomForest: Machine learning
  • —MatchIt: Propensity score matching
  • —glmnet: Regularized regression

Usage Instructions

Navigation

  1. 1.Overview: View current health status and trends
  2. 2.Statistical Modeling: Run GLM, Ridge, or LASSO regression
  3. 3.Causal Inference: Evaluate program effectiveness with PSM
  4. 4.Intervention Simulator: Design and test health interventions
  5. 5.Machine Learning: Train predictive models

Example Workflow

  1. 1.Start with Overview to understand current health status
  2. 2.Use Statistical Modeling to identify key risk factors
  3. 3.Apply Causal Inference to evaluate existing programs
  4. 4.Design new interventions in the Simulator
  5. 5.Validate predictions with Machine Learning

Impact

This tool enables World Vision teams to:

  • —Make evidence-based decisions on program priorities
  • —Identify high-risk populations for targeted interventions
  • —Optimize resource allocation across multiple sectors
  • —Track progress toward health targets
  • —Predict future health outcomes for proactive planning

Data Privacy

The dashboard uses synthetic data that mirrors real Uganda health patterns while protecting individual privacy. In production, it can be connected to actual health surveillance systems with appropriate data protection measures.

Contributing

We welcome contributions to improve this tool for humanitarian impact. Please submit issues or pull requests on GitHub.

Acknowledgments

Developed to support World Vision's commitment to transforming children's lives through evidence-based health interventions in Uganda.

"Our vision for every child, life in all its fullness"