Evidence map›Paper›PMID 34039295›Full record

ArticleBMC infectious diseases2021

The differential demographic pattern of coronavirus disease 2019 fatality outside Hubei and from six hospitals in Hubei, China: a descriptive analysis.

Qing-Bin Lu, Hai-Yang Zhang, Tian-Le Che, Han Zhao, Xi Chen, Rui Li, Wan-Li Jiang, Hao-Long Zeng, Xiao-Ai Zhang, Hui Long and 8 more

Abstract read
In one paragraph

Article in BMC infectious diseases, 2021. 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. Review
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

18 authors.

Qing-Bin Lu *Department of Laboratorial Science and Technology, School of Public Health, Peking University, Beijing, 100191, P. R. China.
Hai-Yang Zhang *State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, 100071, P. R. China.
Tian-Le CheState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, 100071, P. R. China.
Han ZhaoSchool of Mathematical Sciences, Beijing Normal University, Beijing, 100875, P.R. China.
Xi ChenDepartment of Thoracic and Vascular Surgery, Wuhan No. 1 Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, P. R. China.
Rui LiDepartment of Healthcare Management, School of Health Sciences, Wuhan University, Wuhan, 430071, P. R. China.
Wan-Li JiangDepartment of Thoracic Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, P. R. China.
Hao-Long ZengDepartment of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Xiao-Ai ZhangState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, 100071, P. R. China.
Hui LongTianyou Hospital affiliated to Wuhan University of Science and Technology, Wuhan, 430064, P. R. China.
Qiang WangInstitute of Infection, Immunology and Tumor Microenvironent, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Medical College, Wuhan University of Science and Technology, Wuhan, 430065, P. R. China.
Ming-Qing WuTianyou Hospital affiliated to Wuhan University of Science and Technology, Wuhan, 430064, P. R. China.
Michael P WardSydney School of Veterinary Science, The University of Sydney, Camden, NSW, Australia.
Yue ChenDepartment of Epidemiology and Community Medicine, Faculty of Medicine, University of Ottawa, 451 Smyth Rd, Ottawa, Ontario, Canada.
Zhi-Jie ZhangDepartment of Epidemiology and Health Statistics, School of Public Health, Fudan University, Shanghai, P. R. China. epistat@gmail.com.
Yang YangDepartment of Biostatistics, College of Public Health and Health Professions, and Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA. yangyang@ufl.edu.
Li-Qun FangState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, 100071, P. R. China. fang_lq@163.com.
Wei LiuState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, 100071, P. R. China. lwbime@163.com.

Funding

China Mega-Project on Infectious Disease Prevention 2018ZX10201-001;2018ZX10713002;2018ZX10101003;2017ZX10103004National Natural Science Foundation of China 81825019
6 · The paper itself

Abstract

backgroundThe coronavirus disease 2019 (COVID-19) epidemic has been largely controlled in China, to the point where case fatality rate (CFR) data can be comprehensively evaluated.

methodsData on confirmed patients, with a final outcome reported as of 29 March 2020, were obtained from official websites and other internet sources. The hospitalized CFR (HCFR) was estimated, epidemiological features described, and risk factors for a fatal outcome identified.

resultsThe overall HCFR in China was estimated to be 4.6% (95% CI 4.5-4.8%, P < 0.001). It increased with age and was higher in males than females. Although the highest HCFR observed was in male patients ≥70 years old, the relative risks for death outcome by sex varied across age groups, and the greatest HCFR risk ratio for males vs. females was shown in the age group of 50-60 years, higher than age groups of 60-70 and ≥ 70 years. Differential age/sex HCFR patterns across geographical regions were found: the age effect on HCFR was greater in other provinces outside Hubei than in Wuhan. An effect of longer interval from symptom onset to admission was only observed outside Hubei, not in Wuhan. By performing multivariate analysis and survival analysis, the higher HCFR was associated with older age (both P < 0.001), and male sex (both P < 0.001). Only in regions outside Hubei, longer interval from symptom onset to admission, were associated with higher HCFR.

conclusionsThis up-to-date and comprehensive picture of COVID-19 HCFR and its drivers will help healthcare givers target limited medical resources to patients with high risk of fatality.

Indexed as

HospitalizationHospital MortalitySARS-CoV-2AdultAgedAge FactorsChinaCOVID-19FemaleHumansMaleMiddle AgedRisk FactorsSex FactorsTime-to-TreatmentCase fatality rateChinaCOVID-19

Identifiers

PMID34039295
PMCPMC8153527

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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.