Evidence map›Paper›PMID 36196302›Full record

ArticleBurns & trauma2022

Epidemiology and clinical characteristics of burns in mainland China from 2009 to 2018.

Jie Yang, Guanglei Tian, Jianchao Liu, Huajuan Bai, Shuxu Yang, Mingzi Ran, Hongyu Li, Kui Ma, Siming Yang, Xiaobing Fu

Abstract read
In one paragraph

Article in Burns & trauma, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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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

10 authors.

Jie YangResearch Center for Tissue Repair and Regeneration affiliated to the Medical Innovation Research Department, PLA General Hospital and PLA Medical College, Beijing, P.R. China 100853.
Guanglei TianResearch Center for Tissue Repair and Regeneration affiliated to the Medical Innovation Research Department, PLA General Hospital and PLA Medical College, Beijing, P.R. China 100853.
Jianchao LiuDepartment of Health Service, PLA General Hospital, Beijing, P.R. China 100853.
Huajuan BaiDepartment of Health Service, PLA General Hospital, Beijing, P.R. China 100853.
Shuxu YangResearch Center for Tissue Repair and Regeneration affiliated to the Medical Innovation Research Department, PLA General Hospital and PLA Medical College, Beijing, P.R. China 100853.
Mingzi RanResearch Center for Tissue Repair and Regeneration affiliated to the Medical Innovation Research Department, PLA General Hospital and PLA Medical College, Beijing, P.R. China 100853.
Hongyu LiResearch Center for Tissue Repair and Regeneration affiliated to the Medical Innovation Research Department, PLA General Hospital and PLA Medical College, Beijing, P.R. China 100853.
Kui MaResearch Center for Tissue Repair and Regeneration affiliated to the Medical Innovation Research Department, PLA General Hospital and PLA Medical College, Beijing, P.R. China 100853.
Siming YangResearch Center for Tissue Repair and Regeneration affiliated to the Medical Innovation Research Department, PLA General Hospital and PLA Medical College, Beijing, P.R. China 100853.ORCID https://orcid.org/0000-0001-7766-5412
Xiaobing FuResearch Center for Tissue Repair and Regeneration affiliated to the Medical Innovation Research Department, PLA General Hospital and PLA Medical College, Beijing, P.R. China 100853.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Burn injuries place a heavy burden on the global healthcare system. However, there is still a lack of nationwide studies on the epidemiological characteristics of burn patients in mainland China. The present study aims to accurately analyze the clinical characteristics of burn patients by collecting data in mainland China from 2009 to 2018, which will provide effective strategies for healthcare systems and the government in mainland China. Methods: Patients admitted for burn injuries to 196 hospitals in 31 provinces, autonomous regions and municipalities in mainland China from 2009 to 2018 were included. The data collected included sex, age, month distribution, etiology, region, clinical outcome, injury anatomical location, total burn surface area and mortality. SPSS 19.0 software was used to analyze the data. Results: From 2009 to 2018, the burn patients were 333,995 (0.76%), which included 222,480 (66.61%) males and 111,515 (33.39%) females. From 2009 to 2018, the number of individuals admitted to hospitals for burns showed a downward trend year by year. The burn patients accounted for the highest proportion of inpatients in 0-10 years (38.10%), followed by 40-50 years (13.14%). The highest cure ratio of burn inpatients was in the 20-30 age group (31 394, 71.53%). Among 31 provinces, autonomous regions and municipalities, the province with the highest proportion of total inpatients caused by burns was Inner Mongolia (4.61%), followed by Zhejiang (3.17%), Hainan (2.88%) and Xinjiang (2.64%). Summer (29.16%) was the season with the highest incidence of burn patients admitted to hospitals, followed by spring (25.6%). Scalding (60.19%) was the most frequent kind of burn treated, followed by fire (20.45%). The patients had multiple burn sites (68.89%) most often, followed by burns on the lower limbs (10.91%). From 0% to 10% total body surface area (TBSA) accounted for the highest ratio (37.19%), followed by 90-100% TBSA (21.74%). Conclusions: The present study is the first to describe the associated situation and trends of burn patients in mainland China from 2009 to 2018. Our findings will serve as the latest clinical evidence for healthcare planning and prevention efforts in China and other countries.

Indexed as

BurnsClinical characteristicsEpidemiologyMainland ChinaMulticentre

Identifiers

PMID36196302
PMCPMC9519692

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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.