Identifying interactions among factors related to death occurred at the scene of traffic accidents: Application of "logic regression" method.

Aim: Traffic accidents are caused by several interacting risk factors. This study aimed to investigate the interactions among risk factors associated with death at the accident scene (DATAS) as an indicator of the crash severity, for pedestrians, passengers, and drivers by adopting "Logic Regression" as a novel approach in the traffic field. Method: A case-control study was designed based on the police data from the Road Traffic Injury Registry in northwest of Iran during 2014-2016. For each of the pedestrians, passengers, and drivers' datasets, logic regression with "logit" link function was fitted and interactions were identified using Annealing algorithm. Model selection was performed using the cross-validation and the null model randomization procedure. Results: regarding pedestrians, "The occurrence of the accident outside a city in a situation where there was insufficient light" (OR = 6.87, P-value<0.001) and "the age over 65 years" (OR = 2.97, P-value<0.001) increased the chance of DATAS. "Accidents happening in residential inner-city areas with a light vehicle, and presence of the pedestrians in the safe zone or on the non-separate two-way road" combination lowered the chance of DATAS (OR = 0.14, P-value<0.001). For passengers, "Accidents happening in outside the city or overturn of the vehicle" combination (OR = 8.55, P-value<0.001), and "accidents happening on defective roads" (OR = 2.18, P-value<0.001) increased the odds of DATAS; When "driver was not injured or the vehicle was two-wheeled", chance of DATAS decreased for passengers (OR = 0.25, p-value<0.001). The odds of DATAS were higher for "drivers who had a head-on accident, or drove a two-wheeler vehicle, or overturned the vehicle" (OR = 4.03, P-value<0.001). "Accident on the roads other than runway or the absence of a multi-car accident or an accident in a non-residential area" (OR = 6.04, P-value<0.001), as well "the accident which occurred outside the city or on defective roads, and the drivers were male" had a higher risk of DATAS for drivers (OR = 5.40, P-value<0.001). Conclusion: By focusing on identifying interaction effects among risk factors associated with DATAS through logic regression, this study contributes to the understanding of the complex nature of traffic accidents and the potential for reducing their occurrence rate or severity. According to the results, the simultaneous presence of some risk factors such as the quality of roads, skill of drivers, physical ability of pedestrians, and compliance with traffic rules play an important role in the severity of the accident. The revealed interactions have practical significance and can play a significant role in the problem-solving process and facilitate breaking the chain of combinations among the risk factors. Therefore, practical suggestions of this study are to control at least one of the risk factors present in each of the identified combinations in order to break the combination to reduce the severity of accidents. This may have, in turn, help the policy-makers, road users, and healthcare professionals to promote road safety through prioritizing interventions focusing on effect size of simultaneous coexistence of crash severity determinants and not just the main effects of single risk factors or their simple two-way interactions.

Predictors of Pediatric COVID-19 vaccination: a case-control study in Tabriz, Iran.

INTRODUCTION: COVID-19 vaccination of children can help reduce the severity of the infection and the death rate caused by it and also helps achieve herd immunity. The level of acceptance and high vaccination coverage is the main elements in the success of immunization programs. Children's vaccination is dependent on their parent's decision. This study aims to identify predictors of the children's COVID-19 vaccination accomplishment by their parents. METHOD: In this case-control study, 577 vaccinated children as cases and 366 un-vaccinated children as controls were randomly selected from the general population of Tabriz, Iran 2022, and their data were collected by telephone calls and interviews with the children's parents. Cases and controls were compared in terms of clinical and demographic factors of the child as well as the socioeconomic status (SES) of their parents by using a multivariable mixed-effect logistic regression model. RESULTS: According to the results of the multivariable logistic regression, the age of the child (OR = 1.26 95% CI (1.14, 1.40), p-value < 0.001), previous COVID-19 infection of the child (OR = 1.92, 95% CI (1.21, 3.04), p-value < 0.001), having no underlying disease in the child (OR = 1.76, 95% CI (1.02, 3.02), p-value = 0.04), the dwelling place of the household (the high-level dwelling in compared to a low level (OR = 3.34, 95% CI (1.6, 6.64), p-value = 0.001), the middle level of dwelling compared with low level (OR = 4.87, 95% CI (2.46, 9.51), p-value < 0.001)), and Father's job (Employee and technician Fathers compared to worker fathers (OR = 2.99, 95% CI (1.55, 5.77), p-value = 0.001)) were significant independent predictors of children COVID-19 vaccination. CONCLUSION: Several demographic and socioeconomic factors were associated with children's vaccination. Older children, children without any underlying disease, children with a history of COVID-19 infection, and children of parents with higher levels of SES were more likely to receive the COVID-19 vaccine. This finding can be considered in children's vaccination policymaking.

A Fuzzy Clustering Approach to Identify Pedestrians' Traffic Behavior Patterns.

BACKGROUND: Pattern recognition of pedestrians' traffic behavior can enhance the management efficiency of interested groups by targeting access to them and facilitating planning via more specific surveys. This study aimed to evaluate the pedestrians' traffic behavior pattern by fuzzy clustering algorithm and assess the factors related to higher-risk traffic behavior of pedestrians. Study Design: This study is a secondary methodological study based on the data from a cross-sectional study. METHODS: The fuzzy c-means (FCM), as a machine learning clustering method, was conducted to identify the pattern of traffic behaviors by collecting data from 600 pedestrians in Urmia, Iran via "the Pedestrian Behavior Questionnaire" (PBQ) and using 5 domains of PBQ. Multiple logistic regression was fitted to identify risk factors of traffic behaviors. RESULTS: Results revealed two clusters consisting of lower-risk and higher-risk behaviors. The majority of pedestrians (64.33%) were in the lower-risk cluster. Subjects≤33 years old (Odds ratio [OR]=1.92, P<0.001), subjects with≤6 years of education (OR=1.74, P=0.010), males (OR=1.90, P=0.001), unmarried pedestrians (OR=3.61, P=0.007), and users of public transportation (OR=2.01, P=0.002) were more likely to have higher-risk traffic behavior. CONCLUSION: We identified traffic behavior patterns of Urmia pedestrians with lower-risk and higher-risk behaviors via FCM. The findings from this study would be helpful for policymakers to promote safety measures and train pedestrians.