For each personal injury accident (i.e. an accident on a road open to public traffic, involving at least one vehicle and involving at least one victim requiring treatment), information describing the accident is seized by the police unit (police, gendarmerie, etc.) which intervened at the scene of the accident. These seizures are collected in a sheet entitled ‘Injury Analysis Bulletin’. All these forms constitute the national register of road traffic injuries, known as the ‘BAAC file’, administered by the National Interministerial Observatory for Road Safety (ONISR).

The databases, extracted from the BAAC file, list all road traffic injuries occurring during a specific year in mainland France, in the overseas departments (Guadeloupe, French Guiana, Martinique, Réunion and Mayotte since 2012) and in the other overseas territories (Saint-Pierre-et-Miquelon, Saint-Barthélemy, Saint-Martin, Wallis and Futuna, French Polynesia and New Caledonia; available only from 2019 in open data) with a simplified description. This includes information on the location of the accident, as provided, as well as information on the characteristics of the accident and its location, the vehicles involved and their victims.

Compared to the aggregated databases 2005-2010 and 2006-2011 currently available on the website www.data.gouv.fr, the databases from 2005 to 2023 are now annual and composed of 4 files (Characteristics – Locations – Vehicles – Users) in csv format.

However, those databases conceal certain specific data relating to users and vehicles and their conduct in so far as disclosure of that data would undermine the protection of the privacy of easily identifiable natural persons or reveal the conduct of such persons, whereas disclosure of that conduct could be detrimental to them (CADA opinion – 2 January 2012).

Warning: Data on the classification of injured persons hospitalised since 2018 cannot be compared to previous years following changes in the seizure process of the police. The indicator ‘injured hospitalised’ has no longer been labelled by the public statistics authority since 2019.

The validity of the statistical operations that can be made from this database depends on the verification methods specific to the field of application of road safety and in particular on a precise knowledge of the definitions relating to each variable used. For any operation, it is important to take note in particular of the structure of the attached BAAC sheet and the guide to using the codification of the road traffic accident analysis bulletin.

It should be noted that a number of indicators from this database are labelled by the public statistics authority (Order of 27 November 2019). The list is available at: https://www.onisr.securite-routiere.gouv.fr/statistical tools/labelled indicators

This table contains information about Dutch residents who died in the given year due to an accident (mainly traffic accidents, accidental falls, drowning or poisoning). The accident may have taken place abroad and/or in the previous year, the date and place of death is the criterion. The data is split by sex and age of the victim. The ICD-10 codes that belong to accidents are V01-W77, W80-X59, Y10-Y19, Y34 and Y85-Y86.

Since 2013 Statistics Netherlands is using IRIS for automatic coding for cause of death. This improved the international comparison of the data. The change in coding did cause a considerable shift in the statistic. Since 2013 the (yearly) ICD-10 updates are applied. For accidents no changes in coding have taken place however.

The persons who died in the MH17 crash in 2014 are not categorized as an accident with an airplane, but as an operation of war (ICD-10 code Y36). These persons are not part of this accidents table.

Data available from 1996 to 2017

Status of the figures: All figures are final.

Changes as of November 20th 2019: This table is stopped. All information can be found in the table 'Deaths; cause of death (extensive list), age and sex' and a great part of it in the table 'Deaths; underlying cause of death (shortlist), sex, age'. The fact that on two points the information is shown in a slightly different way has led to confusion and questions throughout the years. This is why the table has been discontinued. Differences between the discontinued table and the 'short list table' are: - The short list shows accidental poisoning not including intent unknown; - The short list shows suffocation including inhalation and ingestion of food causing obstruction to respiratory tract, while this is not shown in the discontinued table. In paragraph 3 there is a list of links through which substituting information can be found.

When will new figures be published: Does not apply.

Context

For each bodily injury (i.e. an accident on a road open to public traffic, involving at least one vehicle and having made at least one victim requiring treatment), seizures of information describing the accident are carried out by the law enforcement unit (police, gendarmerie, etc.) who intervened at the scene of the accident. These seizures are gathered in a sheet entitled bodily accident analysis report. All of these sheets constitute the national file of traffic accidents known as the “TBAA file” administered by the National Interministerial Road Safety Observatory "ONISR".

The databases, extracted from the TBAA file, list all the bodily accidents of traffic, occurring during a specific year in mainland France, in the departments Overseas (Guadeloupe, Guyana, Martinique, Reunion Island and Mayotte since 2012) and in the others overseas territories (Saint-Pierre-et-Miquelon, Saint-Barthélemy, Saint-Martin, Wallis-et-Futuna, French Polynesia and New Caledonia; available only from 2019 in open data) with a simplified description. This includes accident location information, such as completed as well as information concerning the characteristics of the accident and its location, vehicles involved and their victims.

These databases nevertheless conceal certain specific data relating to users and vehicles. and their behavior insofar as the disclosure of this data would undermine the protection of the privacy of easily identifiable natural persons or would reveal the behavior of such persons when disclosure of such behavior could cause them prejudice (CADA opinion - January 2, 2012). Warning: Data on the qualification of injured hospitalized since 2018 cannot be be compared to previous years following changes in the process for entering the forces of the order. The "hospitalized injured" indicator is no longer labeled by the official statistics authority since 2019. The validity of the statistical analyzes that can be made from this base depends on the modes own checks in the field of road safety application and in particular a precise knowledge of the definitions relating to each variable used. For any exploitation, it

It is important to be aware in particular of the structure of the attached TBAA form as well as of the user guide for the codification of the traffic accident analysis bulletin.

Remember that a number of indicators from this database are subject to labeling, by the official statistics authority (decree of November 27, 2019).

Definitions of the national TBAA data file (Traffic Body Accident Analysis Bulletins)

A bodily accident (fatal and non-fatal) in road traffic reported by the police:

• Involves at least one victim,
• Occurs on a public or private road open to public traffic,
• Involves at least one vehicle.

An injury accident involves a certain number of users. Among these, we distinguish:

• Unharmed people: involved and not dead and whose condition does not require any treatmentmedical due to the accident,
• The victims: involved and not unharmed.
  • people killed: people who die as a result of the accident, immediately or in the thirty days following the accident,
  • injured persons: unkilled victims.
    • The so-called “hospitalized” wounded: victims hospitalized for more than 24 hours
    • Light injuries: victims who received medical treatment but not admitted as patients in hospital for more than 24 hours.

Target

grav

Severity of user injury, injured users are classified into three categories of victims plus unharmed: 1 - Unharmed 2 - Killed 3 - Injured hospitalized 4 - Slightly injured

Database specifications

The Etalab database of traffic accidents in a given year is distributed in 4 headings in the form for each of them of a file in csv format.

1. The CHARACTERISTICS section which describes the general circumstances of the accident
2. The PLACES section which describes the main location of the accident even if it took place at a intersection
3. The VEHICLES involved section
4. The DRIVERS involved section

Each of the variables contained in an item must be able to be linked to the variables of the othersheadings. The accident identifier number (Cf. "Num_Acc") in these 4 sections is used to establish a link between all the variables that describe an accident. When an accident involves several vehicles, it is also necessary to be able to connect each vehicle to its occupants. This link is made by the variable vehicle_id. Most of the variables contained in the four files listed above can contain empty cells or a zero or a period. In all three cases, this is a cell not filled in by law enforcement or not applicable.

Please read the Columns_Description.txt file to learn more about the different features

Source

data.gouv.fr

Content

The Police Service of Northern Ireland (PSNI) compiles data on road traffic collisions (RTCs) that result in injuries. This data is used to monitor and identify trends in the number of individuals killed or injured (either seriously or slightly) due to RTCs on Northern Ireland’s roads.

The PSNI’s injury collision data for Northern Ireland, combined with those for England, Scotland and Wales, provide a comprehensive overview of all such collisions across the UK.

This dataset is retrieved from data.gov.uk, a website that is built and maintained by the Government Digital Service. It contains four csv files: 1. casualty2017-2022.csv: The casualties involved in collisions, including casualty information (whether the casualty was a driver, passenger, pedestrian or cyclist) and vehicle involved. 2. collision2017-2022.csv: The conditions under which the collisions occurred, including collision severity, number of vehicles and casualties involved, time, location, weather, road conditions and carriageway hazards. 3. vehicle2017-2022.csv: The vehicles involved in each collision, including type of vehicle, operation at the time of the collision, object involved and driver information. 4. vehicle-index.csv: A list of variables and values for vehicle2017-2022.csv.

Possible Explorations

• Assess how environmental conditions affect accident likelihood and severity.
• Examine the influence of time (day and hour) on accident occurrence and severity.
• Show differences in casualty demographics.
• Identify regional variations in road traffic collisions.
• Analyze trends in traffic accidents and fatality rates.

Important Note

The following variables are documented for collisions resulting in fatal and serious injuries only:

Collision Records
1. a_jdet - Junction Detail 2. a_jcont - Junction Control 3. a_pedphys - Pedestrian Crossing Facilities - Physical 4. a_pedhum - Pedestrian Crossing Facilities - Human 5. a_light - Light Conditions 6. a_weat - Weather Conditions 7. a_roadsc - Road Surface Conditions 8. a_speccs - Special Conditions at Site 9. a_chaz - Carriageway Hazard 10. a_scene - Did a Police Officer Attend the Scene of the Collision

Casualty Records
1. c_loc - Pedestrian Location 2. c_move - Pedestrian Movement 3. c_pcv - Bus or Coach Passenger 4. c_pedinj - Pedestrian casualty injured in the course of on the road work

Vehicle Records
1. v_junc - Junction Location of Vehicle at Time of Impact 2. v_skid - Skidding / Overturning 3. v_hit - First Object Hit in Carriageway 4. v_leave - Vehicle Leaving Carriageway 5. v_hitoff - First Object Hit off Carriageway 6. v_forreg - Foreign Registered Vehicle

Licence

UK Open Government Licence (OGL)

Acknowledgements

The data were publicly visible and published by the Police Service of Northern Ireland.

The database ARIA (Analysis, Research and Information on Accidents), published by the BARPI (Bureau d’Analyse des Risques et Pollutions Industriels), office of the DGPR, lists technological incidents or accidents which have, or could have, adversely affected public health or safety or the environment. ARIA has recorded more than 60 000 accidents or incidents in France or abroad. In accordance with: - Article L312-1-1 of the Code of Relations between the Public and the Administration (CRPA) which provides that administrations have the obligation to publish online "data, updated regularly, the publication of which is of economic, social, health or environmental interest"; - Article L300-4 of the RCAP adds that the data must be published "in an open standard, easily reusable and exploitable by an automated processing system"; BARPI provides here the export of its entire database. Note: There is always a gap of 4 months between the knowledge of an event, its entry, analysis, quality control and its posting in batches every 2 months or so. The re-use of the data is allowed only for non-commercial uses and provided that the source of the data, BARPI, is cited.

Context

It is data of a collision accident between a person and a motor vehicle that occurred in New York in 2021. Only cases of injury or death over $1,000 were filtered out. The date and time of the incident, the location of the injury, and the police description of the incident are summarized.

Content

• CRASH_DATE
• CRASH_TIME
• PERSON_INJURY : Injured, killed, unspecified
• PERSON_AGE : Automatically calculated based on date of birth
• BODILY_INJURY : Injured body area (i.e. head, face, neck, etc.)
• SAFETY_EQUIPMENT : Safety equipment being used (i.e. lap belt, harness, child restraint, air bag, etc.)
• PERSON_SEX
• PERSON_TYPE : Bicyclist, Motor Vehicle Occupant, Pedestrian
• PED_LOCATION : Location of the pedestrian (i.e. at intersection, not at intersection)
• CONTRIBUTING_FACTOR_2 : Factors contributing to the collision for designated vehicle
• EJECTION : Indicates the following: Not ejected, partially ejected, or ejected from the vehicle
• COMPLAINT : Type of physical complaint (ex. Concussion, severe burn, severe bleeding, etc.)
• EMOTIONAL_STATUS : Apparent death, unconscious, semiconscious, etc.
• VEHICLE_ID : Unique vehicle record associated with person. Foreign Key to the vehicle table
• PERSON_ID : Person identification code assigned by system
• CONTRIBUTING_FACTOR_1 : Factors contributing to the collision for designated vehicle
• POSITION_IN_VEHICLE : Seating position #1-#8 (i.e. driver, front passenger, etc.)
• PED_ROLE : Pedestrian, witness, in-line skater, other, etc.
• UNIQUE_ID : Unique record code generated by system. Primary Key for Person table.
• PED_ACTION : What the pedestrian was doing at time of crash (i.e., walking with the signal, against the signal, etc.)
• COLLISION_ID : Crash identification code. Foreign Key, matches unique_id from the Crash table.

Inspiration

Through EDA, you can check the characteristics of each traffic incident by date and by type of incident.

###

Source

https://data.cityofnewyork.us/

Abstract The study aimed to analyze and study the prevalence and circumstances of ophthalmic emergencies and urgencies in the Emergency Room of the Hospital de Clínicas de Uberlândia and the Central Ambulatory (Amélio Marques) of the Federal University of Uberlândia (HCU-UFU) from August 2016 to August 2017. This is a necessary study since ophthalmic emergencies are important causes of morbidities in society(1). In addition, this study will help to develop prevention policies and to make greater training of professionals based on knowledge of the main causes of eye emergencies. The proposed methodology included the collection of data from the medical record, with the information of sex, age and occupation/profession being collected. This is an observational, descriptive, transversal, exploratory epidemiological study. The study showed that males were the most affected and the most incident age group was between 19 and 45 years old. Eye trauma due to a foreign body is the most common cause of demand for ophthalmologic emergency care with a close relationship with work activities (mechanics and ironmongers). In this way, the data will be an important resource to assist in understanding the epidemiological profile of the ophthalmology emergency room in order to optimize its administration and to encourage the adoption of public prevention policies within the scope of occupational health.

Netherlands NL: Road Fatalities: Per One Million Vehicle-km data was reported at 4.940 Ratio in 2023. This records a decrease from the previous number of 5.557 Ratio for 2022. Netherlands NL: Road Fatalities: Per One Million Vehicle-km data is updated yearly, averaging 4.764 Ratio from Dec 1994 (Median) to 2023, with 20 observations. The data reached an all-time high of 12.110 Ratio in 1995 and a record low of 4.312 Ratio in 2014. Netherlands NL: Road Fatalities: Per One Million Vehicle-km data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Netherlands – Table NL.OECD.ITF: Road Traffic and Road Accident Fatalities: OECD Member: Annual. [COVERAGE] ROAD FATALITIES A road fatality is any person killed immediately or dying within 30 days as a result of an injury accident, excluding suicides. A killed person is excluded if the competent authority declares the cause of death to be suicide, i.e. a deliberate act to injure oneself resulting in death. For countries that do not apply the threshold of 30 days, conversion coefficients are estimated so that comparison on the basis of the 30-day definition can be made. ROAD TRAFFIC Road traffic is any movement of a road vehicle on a given road network. When a road vehicle is being carried on another vehicle, only the movement of the carrying (active mode) is considered. [COVERAGE] ROAD TRAFFIC Data refer to road motor vehicle traffic of national and foreign vehicles on the national territory. [STAT_CONC_DEF] ROAD FATALITIES In 1996, there was a change in the methodology, that creates a break in the series.

For each personal injury accident (i.e. an accident on a road open to public traffic, involving at least one vehicle and involving at least one victim requiring treatment), information describing the accident is seized by the police unit (police, gendarmerie, etc.) which intervened at the scene of the accident. These seizures are collected in a sheet entitled ‘Injury Analysis Bulletin’. All these forms constitute the national register of road traffic injuries, known as the BAAC file, which is administered by the National Interministerial Observatory for Road Safety (ONISR). The databases, extracted from the BAAC file, list all road traffic injuries occurring during a specific year in mainland France and the overseas departments (Guadeloupe, French Guiana, Martinique, Réunion and Mayotte since 2012) with a simplified description. This includes information on the location of the accident, as provided, as well as information on the characteristics of the accident and its location, the vehicles involved and their victims. Compared to the aggregated databases 2005-2010 and 2006-2011 currently available on the website www.data.gouv.fr, the databases from 2005 to 2019 are now annual and composed of 4 files (Characteristics – Locations – Vehicles – Users) in csv format. However, those databases conceal certain specific data relating to users and vehicles and their conduct in so far as disclosure of that data would undermine the protection of the privacy of easily identifiable natural persons or reveal the conduct of such persons, whereas disclosure of that conduct could be detrimental to them (CADA opinion – 2 January 2012). This dataset refers to accidents from 2012 to 2019.

ABSTRACT Objective: To draw an epidemiological and occupational profile of eye trauma at a Brazilian referral center, make comparisons with the literature and provide subsidies for the adoption of adequate prevention and enforcement measures. Methods: Descriptive and prospective epidemiological study using a standardized questionnaire to collect data from 60 patients presenting with eye trauma at an ophthalmology service (HUCAM) between 1 april 2013 and 1 october 2013. Results: The male gender was predominant (80%). Ages ranged from 8 to 60 years. Most accidents (56.7%) occurred in the workplace, followed by the home (28.3%). Most injuries were closed, predominantly contusions, followed by foreign body on the external eye. Importantly, 82.9% of the victims of work-related trauma wore no eye protection at the time of the accident. Conclusions: Eye trauma in the workplace and elsewhere is an important problem of public health as it affects primarily the economically active population and may have serious consequences. A considerable proportion of eye trauma is easily avoidable by using personal protective equipment. To minimize the incidence of eye trauma, more attention should be given to instruction in and enforcement of the use of such equipment, supported by frequent prevention campaigns.

Context

This public dataset contains data concerning the public and private insurance companies provided by IRDAI(Insurance Regulatory and Development Authority of India) from 2013-2022. This is a multi-index data and can be a great practice to hone manipulation of pandas multi-index dataframes. Mainly, the business of the companies (total premiums and number of policies), subscription information(number of people subscribed), Claims incurred and the Network hospitals enrolled by Third Party Administrators are attributes focused by the dataset.

Content

The Excel file contains the following data | Table No.| Contents| | --- | --- | |**A**|**III.A: HEALTH INSURANCE BUSINESS OF GENERAL AND HEALTH INSURERS**| |62| Health Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |63| Personal Accident Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |64| Overseas Travel Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |65| Domestic Travel Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |66| Health Insurance - Net Premium Earned, Incurred Claims and Incurred Claims Ratio| |67| Personal Accident Insurance - Net Premium Earned, Incurred Claims and Incurred Claims Ratio| |68| Overseas Travel Insurance - Net Earned Premium, Incurred Claims and Incurred Claims Ratio| |69| Domestic Travel Insurance - Net Earned Premium, Incurred Claims and Incurred Claims Ratio| |70| Details of Claims Development and Aging - Health Insurance Business| |71| State-wise Health Insurance Business| |72| State-wise Individual Health Insurance Business| |73| State-wise Personal Accident Insurance Business| |74| State-wise Overseas Insurance Business| |75| State-wise Domestic Insurance Business| |76| State-wise Claims Settlement under Health Insurance Business| |**B**|**III.B: HEALTH INSURANCE BUSINESS OF LIFE INSURERS**| |77| Health Insurance Business in respect of Products offered by Life Insurers - New Busienss| |78| Health Insurance Business in respect of Products offered by Life insurers - Renewal Business| |79| Health Insurance Business in respect of Riders attached to Life Insurance Products - New Business| |80| Health Insurance Business in respect of Riders attached to Life Insurance Products - Renewal Business| |**C**|**III.C: OTHERS**| |81| Network Hospital Enrolled by TPAs| |82| State-wise Details on Number of Network Providers |

Germany DE: Road Traffic: Vehicle-km per One Thousand Units of Current USD GDP data was reported at 169.015 Ratio in 2022. This records an increase from the previous number of 160.336 Ratio for 2021. Germany DE: Road Traffic: Vehicle-km per One Thousand Units of Current USD GDP data is updated yearly, averaging 221.652 Ratio from Dec 1994 (Median) to 2022, with 29 observations. The data reached an all-time high of 350.880 Ratio in 2001 and a record low of 160.336 Ratio in 2021. Germany DE: Road Traffic: Vehicle-km per One Thousand Units of Current USD GDP data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Germany – Table DE.OECD.ITF: Road Traffic and Road Accident Fatalities: OECD Member: Annual. [COVERAGE] ROAD TRAFFIC Road traffic is any movement of a road vehicle on a given road network. When a road vehicle is being carried on another vehicle, only the movement of the carrying (active mode) is considered. [COVERAGE] ROAD TRAFFIC Data refer to road motor vehicle traffic of national and foreign motor vehicles on the road network of the Federal Republic of Germany. Data do not include road motor vehicle traffic of motor vehicles owned by the German Federal Armed Forces, the Federal Border Police or the foreign military forces. [STAT_CONC_DEF] ROAD TRAFFIC Data are calculated using automatic and manual roadside traffic counts adjusted by incorporating information on fuel sales.

For each personal injury accident (i.e. an accident on a road open to public traffic, involving at least one vehicle and involving at least one victim requiring treatment), information describing the accident is seized by the police unit (police, gendarmerie, etc.) which intervened at the scene of the accident. These seizures are collected in a sheet entitled ‘Injury Analysis Bulletin’. All these forms constitute the national register of road traffic injuries, known as the BAAC file, which is administered by the National Interministerial Observatory for Road Safety (ONISR). The databases, extracted from the BAAC file, list all road traffic injuries occurring during a specific year in mainland France and the overseas departments (Guadeloupe, French Guiana, Martinique, Réunion and Mayotte since 2012) with a simplified description. This includes information on the location of the accident, as provided, as well as information on the characteristics of the accident and its location, the vehicles involved and their victims. Compared to the aggregated databases 2005-2010 and 2006-2011 currently available on the website www.data.gouv.fr, the databases from 2005 to 2015 are now annual and consist of 4 files (Characteristics – Locations – Vehicles – Users ) in csv format. However, those databases conceal certain specific data relating to users and vehicles and their conduct in so far as disclosure of that data would undermine the protection of the privacy of easily identifiable natural persons or reveal the conduct of such persons, whereas disclosure of that conduct could be detrimental to them (CADA opinion – 2 January 2012).

Automobile insurance subscription information is data that comprehensively provides major contract statistics related to automobile insurance, information on damage status, and information on the status of automobile accident victims. The data consists of the following three operations. ① Automobile insurance contract information inquiry: Function to search contract statistics such as insurance type, collateral type, gender, age, foreign products, vehicle type, number of subscriptions, and earned insurance premiums. ② Automobile insurance loss status information inquiry: Function to search insurance type, collateral type, vehicle type, amount of damage, number of injuries/partial losses, number of deaths/total losses, etc. ③ Automobile insurance victim information inquiry: Function to provide statistical information on death injury type, disability status, injury grade, disability grade, and number of people.

As a result of the continued annual growth in global air traffic passenger demand, the number of airplanes that were involved in accidents is on the increase. Although the United States is ranked among the 20 countries with the highest quality of air infrastructure, the U.S. reports the highest number of civil airliner accidents worldwide. 2020 was the year with more plane crashes victims, despite fewer flights The number of people killed in accidents involving large commercial aircraft has risen globally in 2020, even though the number of commercial flights performed last year dropped by 57 percent to 16.4 million. More than half of the total number of deaths were recorded in January 2020, when an Ukrainian plane was shot down in Iranian airspace, a tragedy that killed 176 people. The second fatal incident took place in May, when a Pakistani airliner crashed, killing 97 people. Changes in aviation safety In terms of fatal accidents, it seems that aviation safety experienced some decline on a couple of parameters. For example, there were 0.37 jet hull losses per one million flights in 2016. In 2017, passenger flights recorded the safest year in world history, with only 0.11 jet hull losses per one million flights. In 2020, the region with the highest hull loss rate was the Commonwealth of Independent States. These figures do not take into account accidents involving military, training, private, cargo and helicopter flights.

Purpose: To examine the epidemiologic and clinical characteristics of ocular injuries and their association with socioeconomic status (SES).

Material and Methods: All cases of ocular injuries hospitalized in Department of Ophthalmology of University Clinical Center Tuzla, Bosnia and Herzegovina, from January 2009 to December 2012 and January 2017 and December 2019 were prospectively followed. The injuries were classified according to Ocular Trauma Classification System (OTCS) and Birmingham Eye Trauma Terminology (BETT).

Results: This study included a total of 420 eyes from 396 patients. There were 162 (38.57%; 95%CI:32.86–44.99) open globe injuries (OGI) and 258 (61.43%; 95%CI: 54.16–69.4) closed globe injuries (CGI). The decrease in incidence of ocular trauma requiring hospitalization was noted from 16.7 per 100 000 (95%CI: 13.11–20.97) in 2009 to 9.25 per 100 000 (95%CI: 6.64–12.55) in 2019 (p=0.006). Most injuries occurred in males 341 (81.19%; 95%CI: 72.8–90.28), active working patients 258 (61.43%, 95%CI:54.16–69.4), and patients with rural residence 285 (67.86%; 95%CI: 60.21–76.21). Almost all ocular injuries 418 (99.52%; 95%CI: 90.21–109.54) occurred in patients with middle and lower SES categories, and home was the most prevalent place of injury in 258 (61.43%, 95%CI: 54.16–69.4) patients. The total of 289 (70.49%; 95%CI: 62.59–79.1) patients had good final best corrected visual acuity (BCVA). Poor final BCVA was associated with lower ocular trauma score (OTS) (p=0.000), poor initial BCVA (p=0.000), penetrating injuries of cornea (p=0.004) and sclera (p=0.001), Zone III injuries (p=0.000), intraocular foreign body presence (p=0.000), cataract (p=0.002), retinal detachment (p=0.001), endophthalmitis (p=0.000) and vitreous hemorrhage (p=0.010).

Conclusion: This study provides a detailed insight into epidemiology and socio-economic characteristics of patients hospitalized for ocular injuries.

Used Car Price Prediction Dataset is a comprehensive collection of automotive information extracted from the popular automotive marketplace website, https://www.cars.com. This dataset comprises 4,009 data points, each representing a unique vehicle listing, and includes nine distinct features providing valuable insights into the world of automobiles.

• Brand & Model: Identify the brand or company name along with the specific model of each vehicle.
• Model Year: Discover the manufacturing year of the vehicles, crucial for assessing depreciation and technology advancements.
• Mileage: Obtain the mileage of each vehicle, a key indicator of wear and tear and potential maintenance requirements.
• Fuel Type: Learn about the type of fuel the vehicles run on, whether it's gasoline, diesel, electric, or hybrid.
• Engine Type: Understand the engine specifications, shedding light on performance and efficiency.
• Transmission: Determine the transmission type, whether automatic, manual, or another variant.
• Exterior & Interior Colors: Explore the aesthetic aspects of the vehicles, including exterior and interior color options.
• Accident History: Discover whether a vehicle has a prior history of accidents or damage, crucial for informed decision-making.
• Clean Title: Evaluate the availability of a clean title, which can impact the vehicle's resale value and legal status.
• Price: Access the listed prices for each vehicle, aiding in price comparison and budgeting.

This dataset is a valuable resource for automotive enthusiasts, buyers, and researchers interested in analyzing trends, making informed purchasing decisions or conducting studies related to the automotive industry and consumer preferences. Whether you are a data analyst, car buyer, or researcher, this dataset offers a wealth of information to explore and analyze.