Evidence map›Paper›PMID 41953395›Full record

ArticleGlobal health research and policy2026

Developing an approach to estimate the number of road traffic crashes at the national level using media-reported data.

Min Zhao, Peixia Cheng, David C Schwebel, Wangxin Xiao, Lei Yang, Shuying Zhao, Li Li, Peishan Ning, Zhenzhen Rao, Guoqing Hu

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Article in Global health research and policy, 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

What it found

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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

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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

10 authors.

Min ZhaoDepartment of Epidemiology and Health Statistics, Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University, Changsha, China.
Peixia ChengDepartment of Child, Adolescent and Women's Health, School of Public Health, Capital Medical University, Beijing, China.
David C SchwebelOffice of the Vice President for Research, University of Iowa, Iowa City, IA, United States of America.
Wangxin XiaoSchool of Intelligent Biomedical Engineering, Hunan University of Technology and Business, Changsha, China.
Lei YangDepartment of Epidemiology and Health Statistics, Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University, Changsha, China.
Shuying ZhaoDepartment of Epidemiology and Health Statistics, Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University, Changsha, China.
Li LiDepartment of Epidemiology and Health Statistics, Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University, Changsha, China.
Peishan NingDepartment of Epidemiology and Health Statistics, Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University, Changsha, China.
Zhenzhen RaoDepartment of Epidemiology and Health Statistics, Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University, Changsha, China.
Guoqing HuDepartment of Epidemiology and Health Statistics, Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Road traffic injury is a significant global health challenge, and timely available data are significant to monitor this trend. We aimed to develop a cost-effective approach in resource-limited settings to estimate the number of road traffic crashes at the national level by utilizing media-reported data. Methods: Media-reported data about road traffic crashes were extracted from the Automated Road Traffic Crash Data Platform (ARTCDP) and augmented based on the available police reports with limited free-access. Besides, crash data were approximated according to the national disease surveillance point (DSP). We then fitted four common machine learning models (linear regression, artificial neural network, support vector machine, classification and regression tree) with six predictors to determine the best predictive model for road traffic crashes in China and correct underestimation of police-reported data. Results: Of the 50,850 media outlets indexed by the ARTCDP, 379 media outlets reporting road traffic crash news quarterly were determined as the most reliable media-reported data sources. Of the four machine learning methods, artificial neural network performed best, yielding an Conclusions: Our approach based on ARTCDP-collected media outlets demonstrated excellent predictive performance and has potential to be used for estimating national road traffic crash statistics in resource-limited locations where official statistics are absent, not freely accessible, not reliable, or not yet released.

Indexed as

Accidents, TrafficMass MediaChinaHumansMachine LearningNeural Networks, ComputerPredictive Learning ModelsChinaData augmentationMedia reportsRoad traffic crash

Identifiers

PMID41953395
PMCPMC13053696

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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 OpenQuestion graph.