In financial year 2018, around **** thousand new road accident claims were lodged to the ACC by European people in New Zealand. The ACC provides financial support and compensation to citizens, residents and temporary visitors in New Zealand if they have suffered accidental personal injuries.

Age-adjusted rate of deaths from motor vehicle traffic injuries by sex, race/ethnicity, age; trends if available. Source: Santa Clara County Public Health Department, VRBIS, 2007-2016. Data as of 05/26/2017; U.S. Census Bureau; 2010 Census, Tables PCT12, PCT12H, PCT12I, PCT12J, PCT12K, PCT12L, PCT12M; generated by Baath M.; using American FactFinder; Accessed June 20, 2017. METADATA:Notes (String): Lists table title, notes and sourcesYear (String): Year of data; presented as single year or pooled years (2012 to 2016)Category (String): Lists the category representing the data: Santa Clara County is for total population, sex: Male and Female, race/ethnicity: African American, Asian/Pacific Islander, Latino and White (non-Hispanic White only); age categories as follows: 0 to 17, 18 to 44, 45 to 64, 65+; <1, 1 to 4, 5 to 14, 15 to 24, 25 to 34, 35 to 44, 45 to 54, 55 to 64, 65 to 74, 75 to 84, 85+; United States and Healthy People 2020 targetRate per 100,000 people (Numeric): Rate of deaths from motor vehicle traffic injuries. Rates for age groups are reported as age-specific rates per 100,000 people. All other rates are age-adjusted rates per 100,000 people.

In financial year 2018, the cost of active road related claims lodged to the ACC by European people in New Zealand amounted to over *** million New Zealand dollars. The ACC provides financial support and compensation to citizens, residents and temporary visitors in New Zealand if they have suffered accidental personal injuries.

This study documents disparities on the basis of nationality, ethnicity, and gender in court awards regarding the loss of future earnings in road accident cases in Israel. We analyze a random selection of 236 court decisions in road accident cases that reached final decisions on their merits between 1978 and 2018, in which the nationality, ethnicity, and gender of victims were identifiable (via first and last names). We show that although in Israel the reliance on sex and race based statistical data to calculate damages in tort cases is a prohibited practice, courts tend to reach lower estimates of the future lost earnings of Mizrahi Jews, Arabs, and women than the future lost earnings of otherwise similarly situated Ashkenazi Jewish men. In the analyses, we hold injured persons’ earnings at the time of the accident and occupations constant. The effects we observe are significant in magnitude. The results of our study are particularly noteworthy, given the fact that we document disparities that correspond with the already existing labor force inequalities and discrimination in hiring, salary, and promotion on the basis of nationality, ethnicity, and gender in Israel.

Motor-vehicle deaths in the United States have decreased greatly since the 1970s and 1980s. In 2023, there were around **** deaths from motor vehicles per 100,000 population, compared to a rate of **** deaths per 100,000 in 1970. Laws requiring drivers and passengers to wear safety belts and advancements in safety technology in vehicles are major drivers for these reductions. Motor-vehicle accidents in the U.S. Americans spend a significant amount of time behind the wheel. Many cities lack convenient and reliable public transportation and, especially in rural areas, cars are a necessary means of transportation. In 2020, August was the month with the highest number of fatal crashes, followed by September and June. The deadliest time of day for fatal vehicle crashes is between * and * p.m., most likely due to the after-work rush hour and more people who are under the influence of alcohol. Drinking and driving among youth Drinking and driving remains a relevant problem across the United States and can be especially problematic among younger people. In 2023, around *** percent of those aged 21 to 25 years in the United States reported driving under the influence of alcohol in the preceding year. Furthermore, around ***** percent of those aged 16 to 20 drove after drinking within the past year.

ABSTRACT OBJECTIVE To estimate the potential years of life lost by road traffic injuries three years after the beginning of the Decade of Action for Traffic Safety. METHODS We analyzed the data of the Sistema de Informações sobre Mortalidade (SIM – Mortality Information System) related to road traffic injuries, in 2013. We estimated the crude and standardized mortality rates for Brazil and geographic regions. We calculated, for the Country, the proportional mortality according to age groups, education level, race/skin color, and type or quality of the victim while user of the public highway. We estimated the potential years of life lost according to sex. RESULTS The mortality rate in 2013 was of 21.0 deaths per 100,000 inhabitants for the Country. The Midwest region presented the highest rate (29.9 deaths per 100,000 inhabitants). Most of the deaths by road traffic injuries took place with males (34.9 deaths per 100,000 males). More than half of the people who have died because of road traffic injuries were of black race/skin color, young adults (24.2%), individuals with low schooling (24.0%), and motorcyclists (28.5%). The mortality rate in the triennium 2011-2013 decreased 4.1%, but increased among motorcyclists. Across the Country, more than a million of potential years of life were lost, in 2013, because of road traffic injuries, especially in the age group of 20 to 29 years. CONCLUSIONS The impact of the high mortality rate is of over a million of potential years of life lost by road traffic injuries, especially among adults in productive age (early mortality), in only one year, representing extreme social cost arising from a cause of death that could be prevented. Despite the reduction of mortality by road traffic injuries from 2011 to 2013, the mortality rates increased among motorcyclists.

Yearly citation counts for the publication titled "Medical complications and deaths in 21 and 56 km road race runners: a 4-year prospective study in 65 865 runners—SAFER study I".

Prevalence of risky behaviors associated with traffic crashes in the 14 Brazilian capitals participating in the life in traffic project and adjusted prevalence ratio by age, sex, education, race and type of road user, 2019.

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This data set maps the locations of crashes involving pedestrians in the Chapel Hill Region of North Carolina.

The data comes from police-reported bicycle-motor vehicle and pedestrian-motor vehicle collisions that occurred on the public roadway network, public vehicular areas and private properties (if reported) from January 2007 through December 2013. Users are able to click and view information specific to each crash. Information for each crash includes: County, City, Crash Date, Crash Day, Crash Group, Crash Location, Crash Time, Crash Severity, Bike/Pedestrian Age Group, Bike/Pedestrian Alcohol Detected, Bike Direction, Bike/Pedestrian Injury, Bike/Pedestrian Position, Bike/Pedestrian Race, Bike/Pedestrian Sex, Ambulance Response, Driver Age Group, Driver Estimated Speed, Speed Limit, Driver Alcohol Detected, Driver Injury, Driver Race, Driver Sex, Driver Vehicle Type, Hit and Run, Development, Light Condition, Locality, Number of Lanes, Road Characteristics/Class/Condition/Configuration, Road Defects/Features, Traffic Control, Crash Type, and/or Weather. Crash identification numbers have been removed from the data for protection of privacy. Crash records were obtained NCDOTs Traffic Engineering Accident Analysis System (TEAAS).

This data set maps the locations of crashes involving pedestrians in the Chapel Hill Region of North Carolina.

The data comes from police-reported bicycle-motor vehicle and pedestrian-motor vehicle collisions that occurred on the public roadway network, public vehicular areas and private properties (if reported) from January 2007 through December 2013. Users are able to click and view information specific to each crash. Information for each crash includes: County, City, Crash Date, Crash Day, Crash Group, Crash Location, Crash Time, Crash Severity, Bike/Pedestrian Age Group, Bike/Pedestrian Alcohol Detected, Bike Direction, Bike/Pedestrian Injury, Bike/Pedestrian Position, Bike/Pedestrian Race, Bike/Pedestrian Sex, Ambulance Response, Driver Age Group, Driver Estimated Speed, Speed Limit, Driver Alcohol Detected, Driver Injury, Driver Race, Driver Sex, Driver Vehicle Type, Hit and Run, Development, Light Condition, Locality, Number of Lanes, Road Characteristics/Class/Condition/Configuration, Road Defects/Features, Traffic Control, Crash Type, and/or Weather. Crash identification numbers have been removed from the data for protection of privacy. Crash records were obtained NCDOTs Traffic Engineering Accident Analysis System (TEAAS).

This study investigated whether the North Carolina State Highway Patrol (NCSHP) practiced racial profiling. The NCSHP provided data on all vehicular stops (Parts 1 and 2), written warnings (Part 3), and citations (Part 4) its officers issued in 2000. This included data on what the stops or tickets were for, the race, sex, and age of the driver, and the make, model, and year of the car being driven. Data on accidents in 2000 (Part 5), also obtained from the NCSHP, were used to examine whether there were racial disparities in unsafe driving practices. These data included information about what caused the accident and the race, sex, and age of the driver. The NCSHP also supplied data on all officers who worked for the NCSHP in 2000 (Part 6), including their race, age, and rank. The data in Part 6 can be linked to the data in Parts 3 and 4. In addition, two surveys of North Carolina drivers were conducted to gather information on reported typical driving behaviors that may influence the probability of being stopped, and to gather information about stops conducted by law enforcement agencies across the state. One was conducted using a sample of North Carolina drivers who had recently renewed their licenses (Part 7), and the other used a sample of North Carolina drivers who were ticketed for speeding between June 1, 1999, and June 1, 2000 (Part 8).

This multi-scale map shows life expectancy - a widely-used measure of health and mortality. From the 2020 County Health Rankings page about Life Expectancy:"Life Expectancy is an AverageLife Expectancy measures the average number of years from birth a person can expect to live, according to the current mortality experience (age-specific death rates) of the population. Life Expectancy takes into account the number of deaths in a given time period and the average number of people at risk of dying during that period, allowing us to compare data across counties with different population sizes.Life Expectancy is Age-AdjustedAge is a non-modifiable risk factor, and as age increases, poor health outcomes are more likely. Life Expectancy is age-adjusted in order to fairly compare counties with differing age structures.What Deaths Count Toward Life Expectancy?Deaths are counted in the county where the individual lived. So, even if an individual dies in a car crash on the other side of the state, that death is attributed to his/her home county.Some Data are SuppressedA missing value is reported for counties with fewer than 5,000 population-years-at-risk in the time frame.Measure LimitationsLife Expectancy includes mortality of all age groups in a population instead of focusing just on premature deaths and thus can be dominated by deaths of the elderly.[1] This could draw attention to areas with higher mortality rates among the oldest segment of the population, where there may be little that can be done to change chronic health problems that have developed over many years. However, this captures the burden of chronic disease in a population better than premature death measures.[2]Furthermore, the calculation of life expectancy is complex and not easy to communicate. Methodologically, it can produce misleading results caused by hidden differences in age structure, is sensitive to infant and child mortality, and tends to be overestimated in small populations."Click on the map to see a breakdown by race/ethnicity in the pop-up: Full details about this measureThere are many factors that play into life expectancy: rates of noncommunicable diseases such as cancer, diabetes, and obesity, prevalence of tobacco use, prevalence of domestic violence, and many more.Data from County Health Rankings 2020 (in this layer and referenced below), available for nation, state, and county, and available in ArcGIS Living Atlas of the World

The Ontario health survey is designed to provide baseline statistical data on the health of the Ontario population, with meaningful information at the health unit/district level. The objectives of the survey were: measure the health status of the population collect data on the determinants (risk factors) of the major causes of illness and death in Ontario collect data related to the social, economic, demographic and geographic variations in health measure awareness of the risk behaviours related to smoking, alcohol, nutrition and exercise collect measures of the use of health services provide planning data for each of the 42 public health units and 28 district health councils across the province collect data comparable to measure in the Canadian and Quebec health surveys Part one of the survey, completed by the person most knowledgeable, focused on recent or current health problems of members of the household, disability days, accidents and injuries, health status, chronic health problems, the use of health services, and demographic information such as income and education. Part two of the survey, completed by each member of the household, covered self-rated health, the use of medicines and drugs, smoking, alcohol use, family relationships, social support, psychological/emotional well-being, suicide, dental health, driving and road safety, women's reproductive health, sexual health, occupational health, physical activities, and nutrition.

Description

This dataset contains traffic violation information from electronic traffic violations issued in the US. Any information that can be used to uniquely identify the vehicle, the vehicle owner or the officer issuing the violation is not included. Some features were removed from the original dataset and all remaining character features were recoded as nominal factor variables. All punctuation characters were removed from factor levels. The variable 'Violation.Type' is used as target by default. The smaller target categories 'SERO' and 'ESERO' were collapsed into one category labeled 'SERO'. Unused factor levels and a few almost constant features were dropped.

Features

• Description: Text description of the specific charge
• Belts: If seat belts were in use in accident cases or not?
• Personal Injury: If traffic violation involved Personal Injury or not?
• Property Damage: If traffic violation involved Property Damage or not?
• Commercial License: If the driver holds a Commercial Drivers License or not?
• Commercial Vehicle: If the vehicle committing the traffic violation is a commercial vehicle or not?
• State: State issuing the vehicle registration
• VehicleType: Type of vehicle (Examples: Automobile, Station Wagon, Heavy Duty Truck, etc.)
• Year: Year the vehicle was made
• Make: Manufacturer of the vehicle (Examples: Ford, Chevy, Honda, Toyota, etc.)
• Model: Model of the vehicle
• Color: Color of the vehicle
• Charge: Alphanumeric code for the specific charge
• Contributed To Accident: If the traffic violation was a contributing factor in an accident or not?
• Race: Race of the driver (Example: Asian, Black, White, Other, etc.)
• Gender: Gender of the driver (F = Female, M = Male)
• Driver City: City of the driver’s home address
• Driver State: State of the driver’s home address
• DL State: State issuing the Driver’s License
• Arrest Type: Type of Arrest (A = Marked, B = Unmarked, etc.)
• Violation Type: Type of Violation (Examples: Warning, Citation, SERO)

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Location of death by urbanization level and race with number of deaths and percentage in parentheses. NaN =  missing or unidentified data due to less than 20 sample size.

In the United States, Black people have higher rates of gun homicide than White people across all age groups. As of 2022, gun homicide rates were highest among Black people aged between 15 and 24 years, at ***** gun homicides per 100,000 of the population. In comparison, there were only **** gun homicides per 100,000 of the White population within this age range. However, the risk for gun homicide was greatest among all adolescents and adults between the ages of 15 to 44 in that year. The impact of guns on young Americans In the last few years, firearms have become the leading cause of death for American children and teenagers aged one to 19 years old, accounting for more deaths than car crashes and diseases. School shootings also remain on the rise recently, with the U.S. recording ** times as many school shootings than other high-income nations from 2009 to 2018. Black students in particular experience a disproportionately high number of school shootings relative to their population, and K-12 teachers at schools made up mostly of students of color are more likely to report feeling afraid that they or their students would be a victim of attack or harm. The right to bear arms Despite increasingly high rates of gun-related violence, gun ownership remains a significant part of American culture, largely due to the fact that the right to bear arms is written into the U.S. Constitution. Although firearms are the most common murder weapon used in the U.S., accounting for approximately ****** homicides in 2022, almost **** of American households have at least one firearm in their possession. Consequently, it is evident that firearms remain easily accessible nationwide, even though gun laws may vary from state to state. However, the topic of gun control still causes political controversy, as the majority of Republicans agree that it is more important to protect the right of Americans to own guns, while Democrats are more inclined to believe that it is more important to limit gun ownership.

Men above the age of ** were more likely to be injured in a drink-driving-related road accident in 2019, especially those in the age group ** to **, in which male casualties were more than double the number of female casualties. Both *** boys and girls aged 15 years old or younger were injured in drink-driving accidents.