The mixed effects of precipitation on traffic crashes.
OBJECTIVE
This paper investigates the relationship between precipitation and traffic crashes in the US during the period 1975-2000. Traffic crashes represent the leading cause of death and injury for young adults in the US, and the ninth leading cause of death for the overall population. Prior studies have found that precipitation raises the risk of traffic crashes significantly.


METHODS
A negative binomial regression approach is employed. Two different units of analysis are examined: state-months and state-days. The sample includes all 48 contiguous states.


RESULTS
A surprising negative and significant relationship between monthly precipitation and monthly fatal crashes is found. However, in the daily level analysis, a strong positive relationship is estimated, as in prior studies. The source of the contrasting results appears to be a substantial negative lagged effect of precipitation across days within a state-month. In other words, if it rained a lot yesterday, then on average, today there are fewer crashes. Additional analysis shows that the risk imposed by precipitation increases dramatically as the time since last precipitation increases. For example, 1cm of precipitation increases the fatal crash rate for a state-day by about 3% if exactly 2 days have passed since the last precipitation and by about 9% if more than 20 days have passed. This basic pattern holds for non-fatal crashes as well.


CONCLUSIONS
The lagged effects of precipitation across days may be explained by the clearing of oil that accumulates on roads during dry periods or by the conditioning of people to drive more safely in wet conditions. Either way, policy interventions that prepare drivers more adequately for the risks of precipitation following dry periods are likely to be beneficial.
Eisenberg
Daniel
D
School of Public Health, University of California, Berkeley, CA, USA. daniel7@uclink.berkley.edu
D000063:Accidents, Traffic / Q000517:prevention & control / Q000706:statistics & numerical data*; D006801:Humans; D010507:Periodicity; D016012:Poisson Distribution; D011891:Rain; D012044:Regression Analysis; D012914:Snow; D014481:United States