Open-Source R Shiny Dashboard (Global Burden of Disease Analysis Suite) for Multimethod Burden of Disease Research: Development and Evaluation Study.

backgroundThe Global Burden of Disease (GBD) study provides the most comprehensive global burden estimates available, but translating GBD data downloads into structured analyses requires an integrated, multimethod workflow spanning trend estimation, changepoint detection, demographic decomposition, frontier benchmarking, and health inequality measurement. Existing tools cover portions of this workflow, but none integrate it end-to-end in a code-free interface, creating a substantial technical barrier, particularly for investigators in resource-limited settings.

objectiveThis study aimed to develop and release the GBD Analysis Suite, an open-source R Shiny dashboard that consolidates 11 commonly used analytical modules for GBD-style burden-of-disease research into a single, code-free, web interface supporting user-uploaded data, and to describe its underlying architecture, analytical methodology, and intended applications.

methodsThe application was built using R 4.5.3 and the Shiny framework, integrating established statistical packages, including forecast, segmented, openxlsx, ggplot2, plotly, and others, into modules covering data import, choropleth mapping, estimated annual percentage change, joinpoint regression, Kitagawa and Das Gupta decomposition, multimodel time-series forecasting, descriptive visualization, sociodemographic index-based frontier analysis, descriptive age-period-cohort visualization, slope and relative indices of inequality, and batch export. The application accepts Global Health Data Exchange (GHDx)-formatted CSV or Excel files, automatically detects standard variable names, and enforces internal consistency through built-in validation checks. Kitagawa decomposition uses the symmetric mid-point formulation to ensure exact additivity. The three ensemble forecast models (autoregressive integrated moving average [ARIMA], exponential smoothing state-space [ETS], and a feed-forward neural network [NNETAR]) apply Box-Cox log transformation, with an explicit nonnegativity floor applied to the point forecast and prediction intervals of every model.

resultsThe application is publicly deployed and can be accessed through any modern web browser without requiring local installation. On the bundled 196-country, 34-year demonstration dataset, exporting the full set of outputs completes in approximately 12 seconds at a peak memory footprint of approximately 600 MB. A feature comparison against 5 existing tools (GBD Compare, GBD Foresight, NCI Joinpoint, BAPC, and NordPred) across 14 capability dimensions confirms that the GBD Analysis Suite is the only reviewed platform combining user-uploaded data input with code-free access to all 6 core analytical methods of modern GBD papers. Source code is publicly available under the MIT license.

conclusionsThe GBD Analysis Suite lowers the technical barrier to reproducible, multimethod burden of disease analyses. It is designed particularly for researchers in low- and middle-income countries where biostatistical support, commercial software licenses, and high-performance computing are often unavailable. The modular architecture supports flexibility across disease areas, geographies, and time windows, subject to input data compatibility. Community contributions, feature requests, and collaborative extensions are welcomed through the project's public GitHub repository.