ArticleBMC public health2024
A spatial autocorrelation analysis of road traffic accidents by severity using Moran's I spatial statistics: a study from Nepal 2019-2022.
Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Spatial association and modeling of road traffic deaths in Thailand, 2022.Population health metrics · 2026Article
- North-South asymmetry of Parkinson's disease mortality in Brazil between 2009 and 2023: a spatial analysis.Scientific reports · 2026Article
- Integrating migration into cancer geography: spatial clustering of five digestive cancer burden in China.Frontiers in public health · 2026Article
- Spatiotemporal dynamics of low-carbon technology collaboration networks and regional public health governance implications: evidence from China's Yangtze River Delta.Frontiers in public health · 2026Article
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6 authors.
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Abstract
backgroundRoad traffic accidents (RTAs) are a significant global public health issue, leading to injuries, fatalities, and substantial economic losses. In Nepal, RTAs are a major concern, with notable regional variations in incidence and severity. This study analyzed the spatial distribution and types of road traffic injuries in Nepal from Fiscal Year (FY) 2019-2020 to 2021-2022, focusing on the impact of road types and geographic factors.
methodsThe study covers all 77 districts of Nepal, incorporating diverse geographic and climatic regions. Data on RTAs, including fatal and non-fatal injuries, were obtained from the Kathmandu Valley Traffic Police Office, Nepal. Spatial analysis was conducted using Quantum GIS (QGIS) and GeoDa software. Univariate and bivariate risk factor analyses were performed using Moran's I statistics to detect spatial autocorrelation in RTA severity.
resultsThe overall fatality rate increased from 7.70 to 9.89 per 100,000 population from FY 2019-2020 to 2021-2022. However, spatial clustering of crashes showed a decline over the years. In FY 2019-2020 (Moran's I; 0.276, p-value; 0.001), moderate clustering was observed, which weakened in the subsequent years (Moran's I; 0.127, p-value; 0.002), with a near-random distribution by FY 2021-2022 (Moran's I; -0.015, p-value; 0.457). The analysis of crash severity revealed significant variations across districts, with high fatality rates in remote areas like Mustang and Mugu, and low rates in districts such as Manang. ROAD NETWORK ANALYSIS: The study examined the impact of different road types on RTA severity. Bitumen (BT) roads showed a negative correlation with RTA rates, while Earthen Roads (ER) were positively associated with higher RTA rates. Hot-spot and cold-spot clusters were identified for each road type, highlighting higher or lower RTA severity areas.
conclusionThis study provides valuable insights into the spatial patterns of RTAs in Nepal and the influence of road types on accident severity. The findings emphasize the need for targeted road safety interventions and infrastructure improvements, particularly in high-risk areas. By understanding spatial distributions and road network impacts, policymakers can better address road safety challenges and reduce the incidence of RTAs in Nepal.
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