Global Burden of Parkinson's Disease from 1990 to 2021 with Forecasts to 2035

Global, regional, and national data on Parkinson's disease burden from 1990 to 2021, with projections to 2035. The dataset, authored by Sibo Huang and last updated in March 2026, integrates prevalence, mortality, and disability-adjusted life years (DALY) metrics from the Global Burden of Disease 2021 study. It employs Bayesian age-period-cohort modeling and decomposition analysis to assess drivers of disease burden.

Use Cases

• Forecasting Parkinson's disease prevalence trends to 2035 based on historical data and the Bayesian age-period-cohort model.
• Analyzing regional disparities in disease burden based on Socio-Demographic Index (SDI) levels mentioned in the description.
• Decomposing the drivers of increasing PD burden, such as population aging and growth, using the described decomposition analysis.
• Projecting future mortality trends for Parkinson's disease based on the 1990-2021 time series.

Strengths

• Covers a 31-year time series from 1990 to 2021 with forecasts extending to 2035.
• Integrates multiple burden metrics: prevalence, mortality, and disability-adjusted life years (DALY).
• Includes regional and national-level analysis stratified by Socio-Demographic Index (SDI).
• Uses established modeling frameworks: the Global Burden of Disease 2021 data and Bayesian age-period-cohort (BAPC) model.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data is provided in DOCX and TIF formats, which may require conversion for typical tabular analysis.

Provenance

Source

Global Burden of Disease (GBD) 2021 study.

Collection Method

Systematic analysis and modeling using Bayesian age-period-cohort (BAPC) and decomposition methods.

Time Range

1990 to 2021, with projections to 2035.

Freshness

Last updated 2026-03-17 14:45:21; freshness should be verified.

Geography

Global, regional, and national levels.