Evidence map›Paper›PMID 41675669›Full record

ArticleFrontiers in psychiatry2025

Global burden of schizophrenia in 204 countries and regions from 1990 to 2021 and machine learning-based projections to 2036: an analysis of the 2021 global burden of disease study.

Zhenyu Feng, Xuesong Shan, Baowen Fan, Ling Yang, Fang Gong

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
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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

5 authors.

Zhenyu FengDepartment of General Practice, The Affiliated Yongchuan Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China.
Xuesong ShanDepartment of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Baowen FanDepartment of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Ling YangDepartment of Neurology, The Affiliated Yongchuan Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China.
Fang GongThe Affiliated Yongchuan Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Schizophrenia is a chronic mental disorder and one of the greatest contributors to the global burden of disease. This study aimed to analyze the global burden of schizophrenia from 1990 to 2021 and predict its future trends. Methods: Data from the 2021 Global Burden of Disease (GBD) study were used. Trend analysis was conducted using the estimated annual percentage change (EAPC), Joinpoint regression, and age-period-cohort analysis. Future projections were generated using the Bayesian age-period-cohort (BAPC) model and time-series machine learning (ML) models. Results: 2021, schizophrenia was estimated to affect approximately 23.6 million individuals worldwide, with 1.22 million new cases reported globally. This disorder accounted for 14.82 million disability-adjusted life years (DALYs) lost. From 1990 to 2021, the age-standardized incidence rate (ASIR) exhibited a declining trend with an EAPC of -0.04% (95% UI: -0.04% to -0.03%), whereas the age-standardized prevalence rate (ASPR) and age-standardized disability-adjusted life years rate (ASDR) demonstrated upward trajectories, showing EAPCs of 0.03% (95% UI: 0.02% to 0.04%) and 0.04%(95% UI: -0.03% to -0.05%), respectively. The peak age of onset was 20-24 years, while prevalence and DALYs peaked at 30-34 years, with males exhibiting a higher disease burden. The ARIMA, PROPHET, and Elastic Net models demonstrated superior predictive performance for forecasting future trends of global ASIR, ASPR, and ASDR. Projections suggest a continued global decline in ASIR, while ASPR and ASDR are expected to rise in the future. Conclusion: Our research indicates that the ASIR, ASPR, and ASDR of schizophrenia exhibit significant correlations with gender, socio-demographic index (SDI) and regions. From 2022 to 2036, while the global ASIR of schizophrenia may decline, both the ASPR and ASDR are projected to rise. The escalating disease burden of schizophrenia poses a significant challenge for countries across all development levels.

Indexed as

disability-adjusted life yearsglobal burden of diseaseincidencepredictionschizophrenia

Identifiers

PMID41675669
PMCPMC12887702

What Socratic holds

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Registered trials

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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 Socratic graph.