uthmanolanase/Analysis-of-Drug-Resistant_Tuberculosis-in-Nigeria
Domaine:
healthcareType de record:
paper
Créateur:
uth
Hôte:
Independent R-based analysis of MDR/RR-TB trends in Nigeria (2015–2023).
# 📊 Analysis of Drug-Resistant Tuberculosis in Nigeria
### 🧠 Author: Uthman Al-Ameen Olalekan
*MPH, University of Ibadan, Nigeria*
*PhD Applicant in Epidemiology*
## 📌 Overview
This project presents an independent R-based analysis of the burden and trends of multidrug/rifampicin-resistant tuberculosis (MDR/RR-TB) in Nigeria from 2015 to 2023. Using publicly available WHO Global TB surveillance data, the analysis includes descriptive statistics, data visualization, correlation analysis, and linear regression to explore patterns and predictors of treatment initiation.
This manuscript is currently under review at BMC Infectious Diseases (Springer Nature).
> 📄 **Preprint Notice:**
> This study was previously shared as a preprint on *Research Square*:
> **Uthman Olalekan Al-Ameen (2025).** *Assessment of the Tuberculosis Care Cascade in Nigeria: A 9-Year Analysis of MDR/RR- TB Detection and Treatment (2015–2023).*
> 🔗 DOI:
doi.org
> 📜 *This is a preprint and is currently under peer review.*
> Licensed under a **CC BY 4.0 License**.
> This repository provides all code, datasets, and outputs to promote transparency and reproducibility
## 🧪 Objective
To examine the patterns and trends of **drug-resistant tuberculosis (TB) in Nigeria (2015–2023)**, with particular focus on:
- 📊 Incidence of Drug-Resistant TB
- ✅ Treatment Success Rates
- 🔄 Resistance Trends across MDR-TB and RR-TB cases
## Methods
- Descriptive statistics to examine temporal patterns
- Line and scatter plots using `ggplot2` for visual representation
- Correlation analysis to assess relationships between case estimates and treatment data
- Linear regression to identify potential predictors of treatment coverage
## Tools & Packages
- R Markdown (for reproducible reporting)
- ggplot2, dplyr, readr (for data wrangling and visualization)
- flextable, officer (for automated reporting)
## 📂 Data Sources
- World Health Organization (WHO)
## 📈 Sample Visualizations …
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