Analysis of Census of Fatal Occupational Injuries/Fatality Analysis Reporting System matched data: New insights on work-related motor vehicle crashes. Rosa L Rodríguez-Acosta, Christen Byler, Stephanie G Pratt, Scott Richardson National Institute for Occupational Safety and Health, Morgantown, WV Bureau of Labor Statistics, Washington, DC 2017 Traffic Records Forum New Orleans, Louisiana

Background Why study work-related motor vehicle crashes (MVCs) Leading cause of U.S. work-related deaths. Approximately 1,700 deaths per year from 2003-2015. 1st or 2nd leading cause of death in every major industry group

Background Why match MVC data? Individual sources lack: data on potential risk factors leading to fatal crashes work-relatedness confirmation employment characteristics Matched data = more details: more information on risk factors leading to crashes and circumstances of each event occupational characteristics opportunity to conduct detailed analysis improved prevention and policy recommendations

Methods: Data Sources Census of Fatal Occupational Injuries (CFOI) Federal/State cooperative program conducted by the Bureau of Labor Statistics. Produces comprehensive, accurate, and timely annual counts of all fatal work injuries in the U.S. Cases confirmed using multiple source documents including: Death certificates Reports from: Occupational Safety and Health (OSHA) Administration Coroner/Medical examiner Police Workers’ compensation Media

Methods: Data Sources Collects data on: Employment: industry, occupation, and, ownership Demographic characteristics Injury case characteristics Analyzed the subset of motor vehicle occupant fatalities resulting from roadway incidents involving a motorized land vehicle “those occurring on that part of public highway, street, or road normally used for travel … where at least one vehicle was in regular operation…”

Methods: Data Sources Fatality Analysis Reporting System (FARS) Census of police-reported traffic crashes involving a motor vehicle travelling on traffic way open to the public, and, resulting in the death of a motorist or non-motorist within 30 days of crash Hierarchical data system: crash-, vehicle-, and person-level data. At-work determination based on “injury-at-work” item in death certificate.

Methods: CFOI/FARS Matched Dataset Methods Manuscript

Methods: CFOI/FARS Matched dataset, 2011-2014   Source files Matched cases Data set CFOI Highway All Fatal FARS All Matches N 4,512 98,464 4,060 Description All CFOI records for which event = roadway incident involving motorized land vehicle All FARS person-level records for fatally-injured persons* CFOI Highway merged with All Fatal FARS * Excludes pedestrians. 90% of CFOI roadway cases were also in All Fatal FARS (4,060/4,512) Only 60% of matched cases were identified as at work in FARS (2,442/4,060) Data were generated with restricted access to the CFOI Research file.

Methods: Data Analysis Generated descriptive analysis Crash-level (FARS) Harmful event Manner of collision Speeding and alcohol Vehicle-level (FARS) Vehicle type

Methods: Data Analysis Person-level (all fatalities and drivers only) Demographic characteristics (CFOI) Age Race Gender Occupational characteristics (CFOI) Industry Occupation Drivers’ risk factors/behaviors (FARS)

Results: CFOI/FARS matched dataset Fatal Work-related Motor Vehicle Incidents, CFOI/FARS Matched Data 2011-2014. Crashes Vehicles Fatalities Drivers 3,822 3,879 4,060 3,581 Data were generated with restricted access to the CFOI Research file.

Results: Crash-level 3,822 work-related fatal crashes 3,156 (83%) collisions Type of collision n (%) Motor vehicle in transport 1,769 (56%) Fixed object 1,276 (40%) Non-fixed object/person 111 ( 4%) Manner of Collision n % Angle 606 34 Front-to-Rear 563 32 Front-to-Front 387 22 Sideswipe - Same Direction 94 5 Sideswipe - Opposite Direction 96 Rear-to-Side Not reported, Unknown 18 1 Data were generated with restricted access to the CFOI Research file.

Results: Vehicle-level Vehicles Involved in Fatal Crashes (N=3,879) Vehicle Classification n % Heavy trucks 1,761 45 Pick-up trucks 555 14 Passenger cars 478 12 Medium trucks 317 8 Vans 308 Utility vehicles 155 4 Farming/Construction equipment 104 3 Other/unknown/missing 201 5 Data were generated with restricted access to the CFOI Research file.

Demographic Characteristics All at-work fatalities (N=4,060) Results: Person Level Demographic Characteristics All at-work fatalities (N=4,060) n % Age ≤17 12 18-24 325 8 25-54 2,424 60 55-64 866 21 65+ 433 11 Sex Male 3,752 92 Female 308 Race/ethnicity White 2,916 72 Black 481 Hispanic 530 13 Other 133 4 Data were generated with restricted access to the CFOI Research file.

Results: Occupational Characteristics Leading Industries Rank Industry At-work fatalities n % 1 Transportation and Warehousing 1,545 38 2 Construction 435 11 3 Wholesale Trade 257 6 4 Agriculture, Forestry, Fishing and Hunting 235 5 Administrative support, waste management and remediation services 226 Data were generated with restricted access to the CFOI Research file.

Results: Occupational Characteristics Leading Occupations Rank Occupation At-work fatalities N % 1 Transportation and Material Moving 2,330 57 2 Construction and Extraction 396 10 3 Management 228 6 4 Installation, maintenance and repair 195 5 Protective Occupations 188 Data were generated with restricted access to the CFOI Research file.

Results: Driver-related Factors Driver related factors* n (%) Improper lane usage 388 (11%) Overcorrecting 308 ( 9%) Careless driving 154 ( 4%) Failure to yield right-of-way 139 ( 4%) *Leading factors. Data were generated with restricted access to the CFOI Research file.

Results: Driver-related Factors Seat belt use Yes 1,615 (45%) No 1,279 (36%) Drivers Blood Alcohol level* 0.00 g/dl 2,303 (84%) 0.01-0.07 g/dl 73 ( 3%) ≥0.08 g/dl 124 ( 5%) Drivers with BAC ≥ 0.08 g/dl by Vehicle Type Vehicle Type n % Pick-up trucks 33 27 Heavy trucks 26 21 Passenger cars 20 16 Medium trucks 15 12 Vans 9 7 Farm/Construction equipment * 2,727 drivers tested FARS data allowed us to examine SB use. We found that only 45% of deceased drivers were wearing their seat belt at the time of crash. We were also able to examine BAC among deceased drivers. A total of 2,727 drivers were tested for alcohol, out of those: 5% had BAC ≥0.08 g/dl (n=124) Pick-up trucks 33 (27%) Heavy trucks 26 (21%) Passenger cars 20 (16%) Medium trucks 15 (12%) Vans 9 (7%) Farm/construction equipment 9 (7%) Data were generated with restricted access to the CFOI Research file.

Results: Driver-related Factors Driver speeding Yes 726 (21%) No 2,719 (76%) Driver Distracted Yes 348 ( 9%) No 2,411 (67%) Unknown 822 (23%) Distraction n % Unknown source 226 65 Cell phone use 48 14 Inattention/carelessness 44 13 Using/reaching object or device 9 3 Data were generated with restricted access to the CFOI Research file.

Conclusions Vehicles in fatal work-related motor vehicle crashes include a wide range of types beyond heavy and medium trucks. Although the transportation and warehousing industry has the highest number of deceased workers, fatally-injured workers are found across a range of industries.

Conclusions At-work driver fatalities compared to all driver fatalities in FARS: Similar seat belt use. Lower % BAC ≥0.08 g/dl. Lower % speeding. Very low % of speeding drivers who also had BAC ≥0.08 g/dl. Risk Factors CFOI/FARS Matched Dataset FARS National Data¥ Seat belts 45% 46%*; 43%^ BAC ≥0.08 g/dl 5% 21% Speeding ~ 30% Speeding, with BAC≥0.08 g/dl 4% 42% ¥ Data from Traffic Safety Facts Reports *Passenger vehicles and light trucks ^Large trucks and buses. ~ FARS provides data for all occupants.

Strengths Analysis shows the value of matching CFOI/FARS data to advance knowledge and understanding of work-related motor vehicle crashes. Additional analysis of driver’s characteristics and risk factors linked to occupational characteristics will lead to more focused prevention recommendations.

Limitations Definitional differences in CFOI and FARS make complete matches impossible. Cannot address full impact of work-related fatal crashes.

Next Steps Match two additional years of data (2015-2016). Conduct further analysis on seat belt use. Conduct industry-specific analyses. Examine the full impact of work-related fatal crashes by adding data from other road users (not at-work) involved in those.

Questions? Rosa L Rodriguez-Acosta, Ph.D. National Institute for Occupational Safety and Health 304.285.6299 rer3@cdc.gov The findings and conclusions in this presentation are those of the author and do not necessarily represent the views of the National Institute for Occupational Safety and Health. This research was conducted with restricted access to Bureau of Labor Statistics (BLS) data. The views expressed are those of the authors and do not reflect the views of the BLS.