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Background <p>Road traffic deaths (RTDs) are a major global public health concern. Thailand reports the world’s highest fatality rate, at 32.2 deaths per 100,000 population. Despite safety initiatives, evidence on the spatial distribution and determinants of RTDs within Thailand remains limited. This study examined provincial-level spatial patterns of RTDs in 2022 and identified socioeconomic and vehicle-related factors associated with these patterns.</p> Methods <p>A cross-sectional ecological analysis was conducted using secondary provincial-level data. RTD data were sourced from the Thai Road Safety Collaboration Center (ThaiRSC), and sociodemographic and vehicle registration data from the National Statistical Office. Spatial analyses, including autocorrelation and regression modeling, were performed in QGIS and GeoDa.</p> Results <p>Incidence rate of RTDs in 2022 was 22.7 deaths per 100,000 population. High RTD rates clustered in Central and Eastern regions. Bivariate spatial autocorrelation indicated significant positive associations between RTDs and several factors. The spatial lag model (SLM) showed the best fit (R² = 0.50), identifying income and the number of trucks, motorcycles, and sedans per 100,000 population as key predictors.</p> Conclusion <p>Spatial analysis reveals substantial provincial disparities in RTD incidence and highlights socioeconomic and vehicle-related determinants. These findings support geospatial data-driven policymaking for targeted interventions to reduce road traffic fatalities.</p>

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Spatial association and modeling of road traffic deaths in Thailand, 2022

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月30日

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  • Ye Htut Oo,
  • Wongsa Laohasiriwong,
  • Kittipong Sornlorm,
  • Paricha Nippanon

摘要

Background

Road traffic deaths (RTDs) are a major global public health concern. Thailand reports the world’s highest fatality rate, at 32.2 deaths per 100,000 population. Despite safety initiatives, evidence on the spatial distribution and determinants of RTDs within Thailand remains limited. This study examined provincial-level spatial patterns of RTDs in 2022 and identified socioeconomic and vehicle-related factors associated with these patterns.

Methods

A cross-sectional ecological analysis was conducted using secondary provincial-level data. RTD data were sourced from the Thai Road Safety Collaboration Center (ThaiRSC), and sociodemographic and vehicle registration data from the National Statistical Office. Spatial analyses, including autocorrelation and regression modeling, were performed in QGIS and GeoDa.

Results

Incidence rate of RTDs in 2022 was 22.7 deaths per 100,000 population. High RTD rates clustered in Central and Eastern regions. Bivariate spatial autocorrelation indicated significant positive associations between RTDs and several factors. The spatial lag model (SLM) showed the best fit (R² = 0.50), identifying income and the number of trucks, motorcycles, and sedans per 100,000 population as key predictors.

Conclusion

Spatial analysis reveals substantial provincial disparities in RTD incidence and highlights socioeconomic and vehicle-related determinants. These findings support geospatial data-driven policymaking for targeted interventions to reduce road traffic fatalities.