Short-term effects of ambient temperature and road traffic accident injuries in Dalian, Northern China: A distributed lag non-linear analysis

Abstract

Background: Although traffic accidents cause considerable economic losses and injuries to individuals, families, and communities, little is known about the impact of meteorological factors on the incidence of traffic accident injuries (TAIs). Therefore, a time-series study was conducted to explore the effect of meteorological variables on TAIs in Dalian, Northern China.

Methods: Poisson generalized linear models (PGLM) combined with distributed lag nonlinear models (DLNM) were used to estimate the association between daily TAIs and ambient temperature in Dalian, China, 2015-2017. The injury data collected by Dalian national injury surveillance hospitals, and meteorological data were extracted and accumulated from the National Meteorological Information Center. Modified the model with variables such as pressure, humidity, precipitation, PM_2.5, SO₂, O₃, day of the week, seasonality, and time trend. In the subgroup analysis, the modification effects of gender and age were also examined.

Results: Both high temperatures (RR = 1.198, 95％CI：1.017-1.411) and low temperatures (RR = 1.017, 95％CI：1.001-1.035) increased the risk of TAIs. The cumulative lag effect would last until after the 7th day. While the 40-59 years subgroup seemed to be more vulnerable in high temperature environments, those who are more than 60 years showed higher TAIs in low temperatures for both single-day and cumulative TAI risks.

Conclusions: Identifying the association between ambient temperature and traffic injuries could provide needed scientific evidence for relevant public health actions.

Keywords: Ambient temperature; Distributed lag nonlinear model; Injury; Meteorological factors; Time series analysis; Traffic accident injury.