RESUMEN
Stay-at-home orders - imposed to prevent the spread of COVID-19 - drastically changed the way highways operate. Despite lower traffic volumes during these times, the rate of fatal and serious injury crashes increased significantly across the United States due to increased speeding on roads with less traffic congestion and lower levels of speed enforcement. This paper uses a mixed effect binomial regression model to investigate the impact of stay-at-home orders on odds of speeding on urban limited access highway segments in Maine and Connecticut. This paper also establishes a link between traffic density and the odds of speeding. For this purpose, hourly speed and volume probe data were collected on limited access highway segments for the U.S. states of Maine and Connecticut to estimate the traffic density. The traffic density was then combined with the roadway geometric characteristics, speed limit, as well as dummy variables denoting the time of the week, time of the day, COVID-19 phases (before, during and after stay-at-home order), and the interactions between them. Density, represented in the model as Level of Service, was found to be associated with the odds of speeding, with better levels of service such as A, or B (low density) resulting in the higher odds that drivers would speed. We also found that narrower shoulder width could result in lower odds of speeding. Furthermore, we found that during the stay-at-home order, the odds of speeding by more than 10, 15, and 20 mph increased respectively by 54%, 71% and 85% in Connecticut, and by 15%, 36%, and 65% in Maine during evening peak hours. Additionally, one year after the onset of the pandemic, during evening peak hours, the odds of speeding greater than 10, 15, and 20 mph were still 35%, 29%, and 19% greater in Connecticut and 35% 35% and 20% greater in Maine compared to before pandemic.
Asunto(s)
Conducción de Automóvil , COVID-19 , Humanos , Accidentes de Tránsito/prevención & control , Pandemias , COVID-19/epidemiología , COVID-19/prevención & control , Modelos Estadísticos , Connecticut/epidemiologíaRESUMEN
The COVID-19 pandemic caused a significant change in traffic operations and safety. For instance, various U.S. states reported an increase in the rate of fatal and severe injury crashes over this duration. In April and May of 2020, comprehensive stay-at-home orders were issued across the country, including in Maine. These orders resulted in drastic reductions in traffic volume. Additionally, there is anecdotal evidence that speed enforcement had been reduced during pandemic. Drivers responded to these changes by increasing their speed. More importantly, data show that speeding continues to occur, even one year after the onset of the pandemic. This study develops statistical models to quantify the impact of the pandemic on speeding in Maine. We developed models for three rural facility types (i.e., major collectors, minor arterials, and principal arterials) using a mixed effect Binomial regression model and short duration speed and traffic count data collected at continuous count stations in Maine. Our results show that the odds of speeding by more than 15 mph increased by 34% for rural major collectors, 32% for rural minor arterials, and 51% for rural principal arterials (non-Interstates) during the stay-at-home order in April and May of 2020 compared to the same months in 2019. In addition, the odds of speeding by more than 15 mph, in April and May of 2021, one year after the order, were still 27% higher on rural major collectors and 17% higher on rural principal arterials compared to the same months in 2019.
Asunto(s)
Conducción de Automóvil , COVID-19 , Accidentes de Tránsito/prevención & control , COVID-19/epidemiología , COVID-19/prevención & control , Humanos , Maine/epidemiología , Pandemias , Población RuralRESUMEN
Previous work has hypothesised that cows in low body condition become lame. We tested this in a prospective longitudinal study. Body condition score (BCS), causes of lameness and milk yield were collected from a 600-cow herd over 44-months. Mixed effect binomial models and a continuous outcome model were used to investigate the associations between lameness, BCS and milk yield. In total, 14,320 risk periods were obtained from 1137 cows. There were 1510 lameness treatments: the most common causes of lameness were sole ulcer (SU) (39%), sole haemorrhage (SH) (13%), digital dermatitis (DD) (10%) and white line disease (WLD) (8%). These varied by year and year quarter. Body condition was scored at 60-day intervals. BCS ranged from 1 to 5 with a mean of 2.5, scores were higher in very early lactation but varied widely throughout lactation; approximately 45% of scores were <2.5. The key finding was that BCS<2.5 was associated with an increased risk of treatment for lameness in the following 0-2 months and >2-4 months for all causes of lameness and also specifically for SU/WLD lameness. BCS<2.5 was associated with an increased risk of treatment for SH in the following 0-2 months but not >2-4 months. There was no such association with DD. All lameness, SU/WLD, SH and DD were significantly more likely to occur in cows that had been lame previously, but the effect of BCS was present even when all repeat cases of lameness were excluded from the analysis. Milk yield was significantly higher and fell in the month before treatment in cows lame with SU/WLD but it was not significantly higher for cows that were treated for DD compared with non-lame cows. These findings support the hypothesis that low BCS contributes to the development of horn related claw lameness but not infectious claw diseases in dairy cows. One link between low BCS and lameness is a thin digital cushion which has been proposed as a trigger for claw horn disease. Cows with BCS 2 produced more milk than cows with BCS 2.5, however, this was only approximately 100 kg difference in yield over a 305-day lactation. Given the increased risk of lameness in cows with BCS 2, the direct costs of lameness and the small variability in milk yield by BCS, preventing cows from falling to BCS<2.5 would improve cow welfare and be economically beneficial.