Evidence map›Paper›PMID 40602809›Full record

ArticleBMJ (Clinical research ed.)2025

Global, regional, and national characteristics of the main causes of increased disease burden due to the covid-19 pandemic: time-series modelling analysis of global burden of disease study 2021.

Can Chen, Wenkai Zhou, Yifan Cui, Kexin Cao, Mengsha Chen, Rongrong Qu, Jiani Miao, Jiaxing Qi, Xiaoyue Wu, Jiaxin Chen and 7 more

Abstract read
In one paragraph

Article in BMJ (Clinical research ed.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–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

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  6. 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

17 authors.

Can ChenDepartment of Epidemiology, School of Public Health, Department of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China.
Wenkai ZhouDepartment of Epidemiology, School of Public Health, Department of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China.
Yifan CuiCenter for Data Science, Zhejiang University, Hangzhou 310058, China.
Kexin CaoDepartment of Epidemiology, School of Public Health, Department of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China.
Mengsha ChenDepartment of Epidemiology, School of Public Health, Department of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China.
Rongrong QuDepartment of Epidemiology, School of Public Health, Department of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China.
Jiani MiaoDepartment of Epidemiology, School of Public Health, Department of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China.
Jiaxing QiDepartment of Epidemiology, School of Public Health, Department of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China.
Xiaoyue WuDepartment of Epidemiology, School of Public Health, Department of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China.
Jiaxin ChenDepartment of Epidemiology, School of Public Health, Department of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China.
Huihui ZhangDepartment of Epidemiology, School of Public Health, Department of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China.
Anqi DaiDepartment of Epidemiology, School of Public Health, Department of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China.
Qianqian FengDepartment of Epidemiology, School of Public Health, Department of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China.
Yi YangDepartment of Epidemiology, School of Public Health, Department of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China yangshigui@zju.edu.cn.
Jingtong ZhouDepartment of Epidemiology, School of Public Health, Department of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China.
Ning DongDepartment of Epidemiology, School of Public Health, Department of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China.
Shigui YangDepartment of Epidemiology, School of Public Health, Department of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China yangshigui@zju.edu.cn.ORCID 0000-0002-0147-297X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo quantify and identify the main causes of increased disease burden due to coronavirus disease 2019 (covid-19) pandemic.

designTime-series modelling study. DATA SOURCE: Global Burden of Disease Study 2021.

main outcome measuresAbsolute and relative rate differences were calculated, along with their 95% confidence intervals (95% CIs), between the observed and expected rates for 174 causes of increases in incidence, prevalence, disability adjusted life years (DALYs) and deaths in 2020-2021. A statistically significant increase was indicated if the 95% CIs of the rate differences were above 0.

resultsGlobally, the rate of age standardised DALYs increased in absolute difference per 100 000 and relative rate difference by 97.9 (95% CI 46.9 to 148.9) and 12.2% (95% CI 5.8% to 18.5%) for malaria, 83.0 (79.2 to 86.8) and 12.2% (11.7% to 12.8%) for depressive disorders, and 73.8 (72.2 to 75.4) and 14.3% (14.0% to 14.7%) for anxiety disorders, which were prominent and statistically significant, followed by stroke, tuberculosis, and ischaemic heart disease. Additionally, the age standardised incidence and prevalence per 100 000 significantly increased for depressive disorders (618.0 (95% CI 589.3 to 646.8) and 414.2 (394.6 to 433.9)) and anxiety disorders (102.4 (101.3 to 103.6) and 628.1 (614.5 to 641.7)), as well as notable rises in age standardised prevalence for ischaemic heart disease (11.3 (5.8 to 16.7)) and stroke (3.0 (1.1 to 4.8)). Furthermore, age standardised mortality due to malaria significantly increased (1.3 (0.5 to 2.1) per 100 000). Depressive and anxiety disorders were the most predominant causes of increased DALY burden globally, especially among females; while malaria had the most severe increased DALY burden in the African region, typically affecting children younger than five years; and stroke and ischaemic heart disease in the European region and in individuals aged 70 and older.

conclusionThe covid-19 pandemic significantly increased the burden of several non-covid conditions, particularly mental health disorders, malaria in young children in the African region, and stroke and ischaemic heart disease in older adults, with notable disparities across age and sex. These findings underscore the urgent need to strengthen health system resilience, enhance integrated surveillance, and adopt syndemic-informed strategies to support equitable preparedness for future public health emergencies.

Indexed as

COVID-19Global Burden of DiseaseAdolescentAdultAgedCost of IllnessDisability-Adjusted Life YearsFemaleGlobal HealthHumansIncidenceMalariaMaleMiddle AgedPandemicsPrevalence

Identifiers

PMID40602809
PMCPMC12216812

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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.