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. 2022 May 20;17(5):e0268190.
doi: 10.1371/journal.pone.0268190. eCollection 2022.

Relationship between mobility and road traffic injuries during COVID-19 pandemic-The role of attendant factors

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Relationship between mobility and road traffic injuries during COVID-19 pandemic-The role of attendant factors

Kandaswamy Paramasivan et al. PLoS One. .

Abstract

This study investigates the important role of attendant factors, such as road traffic victims' access to trauma centres, the robustness of health infrastructure, and the responsiveness of police and emergency services in the incidence of Road Traffic Injuries (RTI) during the pandemic-induced COVID-19 lockdowns. The differential effects of the first and second waves of the pandemic concerning perceived health risk and legal restrictions provide us with a natural experiment that helps us differentiate between the impact of attendant factors and the standard relationship between mobility and Road Traffic Injuries. The authors use the auto-regressive recurrent neural network method on two population levels-Tamil Nadu (TN), a predominantly rural state, and Chennai, the most significant metropolitan city of the state, to draw causal inference through counterfactual predictions on daily counts of road traffic deaths and Road Traffic Injuries. During the first wave of the pandemic, which was less severe than the second wave, the traffic flow was correlated to Road Traffic Death/Road Traffic Injury. In the second wave's partial and post lockdown phases, an unprecedented fall of over 70% in Road Traffic Injury-Grievous as against Road Traffic Injury-Minor was recorded. Attendant factors, such as the ability of the victim to approach relief centres, the capability of health and other allied infrastructures, transportation and medical treatment of road traffic crash victims, and minimal access to other emergency services, including police, assumed greater significance than overall traffic flow in the incidence of Road Traffic Injury in the more severe second wave. These findings highlight the significant role these attendant factors play in producing the discrepancy between the actual road traffic incident rate and the officially registered rate. Thus, our study enables practitioners to observe the mobility-adjusted actual incidence rate devoid of factors related to reporting and registration of accidents.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1
A schematic representation of the DeepAR model during (a) Training phase (b) Testing phase.
Fig 2
Fig 2. Graph of daily number of infections (Y-axis–Left in thousands) and fatalities (Y-axis-Right) during two waves of pandemic (X-axis day timeline).
Fig 3
Fig 3. Graph of percentage change in mobility from baselines in six spatial domains during the first and second waves of the pandemic in 2020 and 2021.
Source: Google mobility index.
Fig 4
Fig 4. Plot of daily number of road traffic deaths (RTD) in TN actual versus predicted during various phases of wave one and wave two in 2020 and 2021.
Fig 5
Fig 5. Plot of the daily number of grievous Road Traffic Injuries (RTI-Grievous) in TN actual versus predicted during various phases of wave one and wave two in 2020 and 2021.
Fig 6
Fig 6. Plot of the daily number of minor Road Traffic Injuries (RTI-Minor) in TN actual versus predicted during various phases of wave one and wave two in 2020 and 2021.

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