In financial year 2018, around 23.4 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.

Motor-vehicle deaths in the United States have decreased greatly since the 1970s and 1980s. In 2022, there were around 13.8 deaths from motor vehicles per 100,000 population, compared to a rate of 26.8 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 6 and 9 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 inexperienced drivers. As of 2017, around 5.5 percent of high school students reported they had driven while under the influence of alcohol. Drinking and driving is more common among males than females and Hispanic males reported drinking and driving more than other races or ethnicities.

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.

VITAL SIGNS INDICATOR
Fatalities From Crashes (EN4)

FULL MEASURE NAME
Fatalities from Crashes (traffic collisions)

LAST UPDATED
October 2022

DESCRIPTION
Fatalities from crashes refers to deaths as a result of fatalities sustained in collisions. The California Highway Patrol includes deaths within 30 days of the collision that are a result of fatalities sustained as part of this metric. This total fatalities dataset includes fatality counts for the region and counties, as well as individual collision data and metropolitan area data.

DATA SOURCE
National Highway Safety Administration: Fatality Analysis Reporting System - https://www.nhtsa.gov/file-downloads?p=nhtsa/downloads/FARS/
1990-2020

Caltrans: Highway Performance Monitoring System (HPMS) - https://dot.ca.gov/programs/research-innovation-system-information/highway-performance-monitoring-system
Annual Vehicle Miles Traveled (VMT)
2001-2020

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
1990-2020

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html
1990-2020

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Fatalities from crashes data is reported to the National Highway Traffic Safety Administration through the Fatality Analysis Reporting System (FARS) program. Data for individual collisions is reported by the California Highway Patrol (CHP) to the Statewide Integrated Traffic Records System (SWITRS). The data was tabulated using provided categories specifying injury level, individuals involved, causes of collision and location/jurisdiction of collision (for more information refer to the SWITRS codebook - http://tims.berkeley.edu/help/files/switrs_codebook.doc). For case data, latitude and longitude information for each accident is geocoded by SafeTREC’s Transportation Injury Mapping System (TIMS). Fatalities were normalized over historic population data from the US Census Bureau’s population estimates and vehicle miles traveled (VMT) data from the Federal Highway Administration.

The crash data only include crashes that involved a motor vehicle. Bicyclist and pedestrian fatalities that did not involve a motor vehicle, such as a bicyclist and pedestrian collision or a bicycle crash due to a pothole, are not included in the data.

For more regarding reporting procedures and injury classification, refer to the CHP Manual - https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/ca_chp555_manual_2_2003_ch1-13.pdf.

The number of traffic accident fatalities showed a decreasing trend in Finland over the period from 2013 to 2023. In 2023, 173 road casualties were reported in Finland. 2023 was the lowest figure with 173 casualties during the shown period.

In 2023, more than 1.8 thousand individuals were killed in motor vehicle accidents in Poland. An overall decrease in the number of road deaths was observed within the period shown. The highest number of road fatalities in this timeframe occurred in 2007, with figures nearly twice as high as those reported in 2020. Car occupants killed most oftenCar occupants were those most commonly affected by traffic accidents. In 2020, 47 percent of those who had been killed on the road were car drivers or passengers. Also at high risk were pedestrians. Road fatality costs at 6.03bn PLNThe most recent evaluation of road accident related expenses found that fatalities had cost 6.85 billion Polish złoty in 2018. In total, road accidents came with a price tag of 37.48 billion Polish złoty.

"Road Accidents Dataset":

Description: This comprehensive dataset provides detailed information on road accidents reported over multiple years. The dataset encompasses various attributes related to accident status, vehicle and casualty references, demographics, and severity of casualties. It includes essential factors such as pedestrian details, casualty types, road maintenance worker involvement, and the Index of Multiple Deprivation (IMD) decile for casualties' home areas.

Columns: 1. Status: The status of the accident (e.g., reported, under investigation). 2. Accident_Index: A unique identifier for each reported accident. 3. Accident_Year: The year in which the accident occurred. 4. Accident_Reference: A reference number associated with the accident. 5. Vehicle_Reference: A reference number for the involved vehicle in the accident. 6. Casualty_Reference: A reference number for the casualty involved in the accident. 7. Casualty_Class: Indicates the class of the casualty (e.g., driver, passenger, pedestrian). 8. Sex_of_Casualty: The gender of the casualty (male or female). 9. Age_of_Casualty: The age of the casualty. 10. Age_Band_of_Casualty: Age group to which the casualty belongs (e.g., 0-5, 6-10, 11-15). 11. Casualty_Severity: The severity of the casualty's injuries (e.g., fatal, serious, slight). 12. Pedestrian_Location: The location of the pedestrian at the time of the accident. 13. Pedestrian_Movement: The movement of the pedestrian during the accident. 14. Car_Passenger: Indicates whether the casualty was a car passenger at the time of the accident (yes or no). 15. Bus_or_Coach_Passenger: Indicates whether the casualty was a bus or coach passenger (yes or no). 16. Pedestrian_Road_Maintenance_Worker: Indicates whether the casualty was a road maintenance worker (yes or no). 17. Casualty_Type: The type of casualty (e.g., driver/rider, passenger, pedestrian). 18. Casualty_Home_Area_Type: The type of area in which the casualty resides (e.g., urban, rural). 19. Casualty_IMD_Decile: The IMD decile of the area where the casualty resides (a measure of deprivation). 20. LSOA_of_Casualty: The Lower Layer Super Output Area (LSOA) associated with the casualty's location.

This dataset provides valuable insights for analyzing road accidents, identifying trends, and implementing safety measures to reduce casualties and enhance road safety. Researchers, policymakers, and analysts can leverage this dataset for evidence-based decision-making and improving overall road transportation systems.

Data sets that have been prepared from the Open Data Portal of the Queensland Government (2022) available from https://www.data.qld.gov.au/dataset/crash-data-from-queensland-roads.

Provides information from 2011 - 2021 on all police reported fatalities and hospitalisations that have occurred during this time period, showing road conditions and driver demographics.

Frequencies and rates of driver deaths by substances detected through postmortem toxicology, vehicle type, year, demographics.

This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

In 2023, the crude death rate of the ethnic Indian in Malaysia was 8.3 deaths for every 1,000 people, the highest among other ethnic groups. By comparison, the crude death rate of the Bumiputera, the largest ethnic group in Malaysia, was at 5.8 deaths per 1,000 people.

This dataset contains traffic accident records from Addis Ababa City, Ethiopia, spanning the years 2016 to 2022. The dataset includes 13,064 rows and 31 features related to various factors influencing road traffic accident severity. The target variable is categorized into three severity levels: slight, serious, and fatal injuries.The dataset aims to facilitate the analysis and prediction of road traffic accident severity using machine learning algorithms. It was initially collected by the Addis Ababa Police Department and contains a rich set of variables, including weather conditions, collision type, driver demographics, road conditions, and time of accident, among others. This comprehensive dataset serves as a foundation for developing predictive models for accident severity, which can be valuable for urban planning, traffic safety research, and policy development.Key features in the dataset include:Accident Severity (Target Variable): Categorical variable indicating the severity of the accident (slight, serious, fatal).Weather Conditions: Describes the weather at the time of the accident (e.g., clear, rainy, foggy).Collision Type: The type of collision (e.g., rear-end, side-impact).Driver Demographics: Features like driver age, driver sex, and experience that may affect accident outcomes.Location: Various aspects of the accident location, including junction type, road type, and alignment.Temporal Features: Time-related variables such as day of the week, time of day, and seasonal trends.Vehicle Information: Includes vehicle type, vehicle defect, vehicle movement, and the relationship between the vehicle owner and the driver.Casualty Information: Includes age, sex, and fitness of the casualty.

This statistic displays the total number of road fatalities in the Netherlands from 1996 to 2022. It shows that between 1996 and 2022, the number of road fatalities in the Netherlands fluctuated. In 2022, 737 people died in traffic-related accidents, an increase in comparison to the previous year.

Participant demographics (N = 421).

This statistic displays the total number of road fatalities in the Netherlands in 2022, by mode of transportation. It shows that in 2022, the largest number of victims died in bicycle accidents with 291 casualties, followed by car accidents with 225 casualties.

Socio-demographics characteristics of the study sample (N = 568).

In 2022, the number of deaths due to road accidents in India among victims between 25 to 35 years amounted to nearly 42.6 thousand, the most compared to other age groups. That year, there were over 169 thousand accidental fatalities across the south Asian country. Over-speeding was the leading contributor of accidents. Combined, state and national highways recorded around 258 thousand road accidents in 2022. This number had dropped significantly in 2016, before increasing again in recent years.

Accident demographics

The Indian subcontinent ranked first in terms of road accident deaths according to the World Road Statistics which comprised of 199 countries. A majority of victims were two-wheeler commuters. Additionally, pedestrians made up a high share of victims as well, reflecting the lack of infrastructure, be it improper footpaths and the lack of foot-over bridges or negligence of traffic rules. About 70 percent of the road accidents in India accounted for about six percent of the global road traffic accidents.

Accident prevention

Poor enforcement of fines, in addition to mild punishments and corruption encourages drivers, especially among young Indians, to engage in rash driving. Accident awareness programs were initiated by the government among the motorists, along with the National Road Safety Policy to encourage safe transport, strict enforcement of safety laws and fines and establishment of road safety database.

This dataset contains counts of deaths for California counties based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

In 2018, approximately 30 percent of people with a disability living in households in Australia whose main condition was caused by an accident reported that the accident occurred on the street, road, or highway. Other leading accident locations included at work or in the home.

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 63.78 gun homicides per 100,000 of the population. In comparison, there were only 2.58 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 57 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 15,000 homicides in 2022, almost half 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.