Evidence map›Paper›PMID 41815271›Full record

ArticleInternational journal of public health2026

The Burden of Parkinson's Disease Based on the GBD 2021.

Huiqun Yang, Liyuan Pu, Tian Zhao, Lifang Pan, Yuanbo Jiang, Liyuan Han, Qiongfeng Guan

Abstract read
In one paragraph

Article in International journal of public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Huiqun YangDepartment of Clinical Epidemiology, Ningbo 2 Hospital, Ningbo, Zhejiang, China.
Liyuan PuDepartment of Clinical Epidemiology, Ningbo 2 Hospital, Ningbo, Zhejiang, China.
Tian ZhaoDepartment of Clinical Epidemiology, Ningbo 2 Hospital, Ningbo, Zhejiang, China.
Lifang PanDepartment of Clinical Epidemiology, Ningbo 2 Hospital, Ningbo, Zhejiang, China.
Yuanbo JiangDepartment of Clinical Epidemiology, Ningbo 2 Hospital, Ningbo, Zhejiang, China.
Liyuan HanDepartment of Clinical Epidemiology, Ningbo 2 Hospital, Ningbo, Zhejiang, China.
Qiongfeng GuanNingbo No. 2 Hospital, Ningbo, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To examine global temporal trends in Parkinson's disease (PD) prevalence from 1992 to 2021, providing a foundation for targeted prevention and control strategies for neurodegenerative disorder. Methods: Data from the Global Burden of Disease Study 2021 were analyzed using an age-period-cohort model to assess temporal trends in PD at global levels. Decomposition analysis evaluated the influence of population aging, growth and epidemiological transitions on disease burden. Results: From 1992 to 2021, the global number of PD cases increased from 3,471,682.09 to 11,756,618.58. The age-standardized prevalence rose from 191.38 per 100,000 to 292.93 per 100,000 population, with a global net drift value of 1.42%. Globally, PD prevalence increased and then declined with age, though some regions showed continuous rise. Period and cohort effects suggested increasing relative risk worldwide and in several regions. Decomposition analysis identified population growth as the primary driver of the increasing global PD burden. Conclusion: PD prevalence and age-standardized rates increased globally and regionally, with a marked surge among individuals aged 60+. Underscoring the need for region-tailored strategies aligned with World Health Organization's objectives for 2030.

Indexed as

Global Burden of DiseaseGlobal HealthParkinson DiseaseAgedAged, 80 and overCost of IllnessFemaleHumansMaleMiddle AgedPrevalenceage-period-cohortdecomposition analysisParkinson’s diseaseprevalancetemporal trends

Identifiers

PMID41815271
PMCPMC12971533

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.