Longitudinal Analysis of Diabetic Foot Disease in Kiambu County: A PhD-Level Mixed-Methods Study

This project outlines a comprehensive data science workflow developed to support a PhD-level clinical study on Diabetic Foot Disease (DFD) across intervention and control sites in Kiambu County, Kenya. It demonstrates robust statistical design and implementation using R programming, integrating baseline and endline assessments, with advanced modeling to evaluate the impact of educational interventions on patient outcomes. Project Goals: Evaluate DFD prevalence and predictors at baseline Assess the impact of a targeted education intervention Quantify the incidence of new DFD cases over time Support evidence-based public health decision-making through reproducible analytics Data Structure: 4 datasets: Baseline and Endline for both Intervention and Control groups Variables include: blood pressure, HbA1c, eGFR, comorbidities, demographic & behavioral factors Analytical Workflow and Techniques: 1. Data Cleaning and Preparation Unified import and structure standardization in R Labeled categorical variables for clarity (e.g., sex, education, residence) Derived key metrics like binary DFD status, CKD stages, and comorbidity indicators 2. Data Integration Merged data on unique patient ID Constructed time and group indicators for repeated measures analysis Ensured matched records for before–after comparison 3. Exploratory Data Analysis (EDA) Summary stats by group and timepoint Visualizations: boxplots, density plots, cross-tabulations Initial identification of risk trends 4. Research Question-Specific Modeling RQ1: DFD Prevalence at Baseline → Cross-sectional analysis + chi-square tests RQ2: Predictors of DFD → Logistic regression (adjusted ORs, interaction terms, multicollinearity checks) RQ3: Effectiveness of Education Intervention → Difference-in-Differences (DID) model → Paired t-tests/Wilcoxon tests → Linear mixed models for clustering effects RQ4: Incidence of New DFD → Poisson/Log-binomial regression for relative risk 5. Advanced Extensions (PhD-Level Depth) Latent Class Analysis for symptom clustering Propensity Score Matching for baseline adjustment Multilevel modeling (site-level effects) 6. Reporting and Documentation High-quality tables with gtsummary, visualizations via ggplot2 Full reproducibility with R Markdown / Quarto Recommendation to publish in peer-reviewed journals Why R Over SPSS? Supports complex longitudinal and multilevel models Allows automation, reproducibility, and customization Better suited for publication-ready graphics and robust workflows Skills and Value Demonstrated: Designed and executed a full analytical pipeline for clinical data Applied causal inference and advanced regression techniques Leveraged open-source tools for reproducible public health research Delivered a scalable template for future intervention evaluations