Impact of the pandemic on traffic injuries in Macao: an analysis of interrupted time-series data.

Abstract

Objective: With social restrictions serving as a mitigating factor against the severe consequences of public health emergencies, this study investigates the impact of episodic travel restrictions on road traffic injuries (TIs) in Macao during the pandemic, employing Interrupted Time Series Analysis (ITSA) models.

Methods: We used ITSA models, including Bayesian Structured Time Series and Seasonal Autoregressive Integrated Moving Average models, to assess traffic outcomes, particularly focusing on total road traffic crashes (RTCs) and TIs. Predictive models were developed for traffic fatalities, fatal RTCs, RTCs involving injuries and vehicles sustaining damage.

Results: From 2014 to 2020, Macao recorded a total of 99 541 RTCs. Over the study period, there were 32 562 reported injuries. After the outbreak of the epidemic, traffic volume decreased by 53.03%, leading to a 25.54% reduction in RTCs. The severity of crashes also declined, with TIs decreasing by 20.35% compared with the same period in 2019, and fatalities and damaged vehicles decreasing by 37.50% and 26.62%, respectively. Analysis of the interrupted time-series data revealed that the actual number of RTCs after COVID-19 in 2020 was 20% (95% CI: 14% to 26%) lower than expected, and TIs were reduced by 11% (95% CI: 3% to 19%).

Conclusion: This study demonstrates that the implementation of episodic travel restrictions significantly reduced TIs and crashes in Macao, providing crucial insights for traffic management and resource allocation during pandemics. These findings contribute to understanding the dynamic relationship between travel restrictions and road traffic outcomes.