Evidence map›Paper›PMID 42812183›Full record

ArticleFrontiers in public health2026

Injury mechanisms and occurrence locations among hospital-recorded cases in Lanshan District, Linyi, China, 2021-2025.

Yong Ding, Yu Liu, Zhaoxia Wang, Qi Cao, Weiqing Song

Abstract read
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Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Yong DingKey Laboratory of Birth Defect Prevention and Control, Women and Children's Hospital of Linyi, Linyi, Shandong, China.
Yu LiuLinyi Lanshan District Center for Disease Control and Prevention, Linyi, Shandong, China.
Zhaoxia WangLinyi Lanshan District Center for Disease Control and Prevention, Linyi, Shandong, China.
Qi CaoKey Laboratory of Birth Defect Prevention and Control, Women and Children's Hospital of Linyi, Linyi, Shandong, China.
Weiqing SongDepartment of Public Health, Linyi Traditional Chinese Medicine Hospital, Linyi, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To describe the distribution of injury mechanisms and occurrence locations among hospital-recorded injury cases and assess their associations with recorded demographic and contextual characteristics. Methods: In this retrospective study, we analyzed routinely collected injury-surveillance records from 22 reporting units in Lanshan District, Linyi, China. Records from 2021 through 25 November 2025 were eligible. After excluding four records with sex coded as unknown/other, one structurally malformed record, and one exact duplicate, 68,311 records remained. Mechanism and occurrence location were classified into seven and eight mutually exclusive categories, respectively. Multinomial logistic regression estimated relative risk ratios (RRRs) for category comparisons, adjusted for sex, age, occupation, calendar year, season, injury intent, and documented alcohol and psychotropic-substance status. Leave-one-reporting-unit-out analyses assessed the stability of the main comparisons. Modified Poisson models with reporting-unit-clustered standard errors and Cramer's Results: Males contributed 40,433 records (59.19%). Blunt injuries were the most common mechanism (52,468; 76.81%); residences (29,529; 43.23%), roads/streets (22,346; 32.71%), and industrial/construction locations (10,829; 15.85%) were the most common locations. The principal demographic comparisons were broadly consistent across reporting-unit deletion analyses. Males had higher sharp-versus-blunt (RRR 2.02, 95% CI 1.45-2.82) and industrial/construction-versus-residence RRRs (RRR 2.68, 95% CI 2.27-3.18), and a lower road/street-versus-residence RRR (RRR 0.87, 95% CI 0.83-0.92). Compared with adults aged 35-64 years, those aged 15-34 years had higher sharp-versus-blunt (RRR 1.61, 95% CI 1.02-2.53) and road/street-versus-residence RRRs (RRR 1.41, 95% CI 1.16-1.70). Alcohol and psychotropic-substance estimates were imprecise and sensitive to reporting-unit removal. Conclusion: Sex and age were associated with the composition of mechanisms and locations among hospital-recorded injury cases. The findings describe the surveillance case series and do not estimate population incidence or causal effects. Behavioral indicators should be interpreted as exploratory because positive records were sparse and disproportionately concentrated in the largest reporting unit.

Indexed as

Wounds and InjuriesAdolescentAdultAgedChildChild, PreschoolChinaFemaleHumansInfantMaleMiddle AgedRetrospective StudiesYoung Adultcluster influencehospital-recorded injury visitsinjury mechanisminjury surveillancemultinomial logistic regressionoccurrence location

Identifiers

PMID42812183
PMCPMC13619393

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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.