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Article

Using a Safe System Framework to Examine the Roadway Mortality Increase Pre-COVID-19 and in the COVID-19 Era in New York State

by
Joyce C. Pressley
1,*,
Zarah Aziz
2,
Emilia Pawlowski
3,
Leah Hines
3,
Aisha Roberts
3,
Jancarlos Guzman
3 and
Michael Bauer
3
1
Department of Epidemiology and Health Policy and Management and the Columbia Center for Injury Science and Prevention, Columbia University, New York, NY 10032, USA
2
Department of Epidemiology, Columbia University, New York, NY 10032, USA
3
New York State, Bureau of Occupational Health and Injury Prevention, Albany, NY 12237, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2025, 22(1), 61; https://doi.org/10.3390/ijerph22010061
Submission received: 11 September 2024 / Revised: 20 December 2024 / Accepted: 28 December 2024 / Published: 3 January 2025
Browse Figures
Versions Notes

Abstract

:
Roadway mortality increased during COVID-19, reversing a multi-decade downward trend. The Fatality Analysis Reporting System (FARS) was used to examine contributing factors pre-COVID-19 and in the COVID-19 era using the five pillars of the Safe System framework: (1) road users; (2) vehicles; (3) roadways; (4) speed; and (5) post-crash care. Two study time periods were matched to control for seasonality differences pre-COVID-19 (n = 1725, 1 April 2018–31 December 2019) and in the COVID-19 era (n = 2010, 1 April 2020–31 December 2021) with a three-month buffer period between the two time frames excluded. Four of the five pillars of the safe system had road safety indicators that worsened during the pandemic. Mortality was 19.7% higher for motor vehicle occupants and 45.1% higher for riders of motorized two-wheeled vehicles. In adjusted analyses, failure to use safety equipment (safety belts/helmets) was associated with 44% higher mortality. Two road user groups, non-motorized bicyclists and pedestrians, did not contribute significantly to higher mortality. Urban roadway crashes were higher compared to rural crashes. Additional scientific inquiry into factors associated with COVID-19-era mortality using the Safe System framework yielded important scientific insights to inform prevention efforts. Motorized two-wheeled vehicles contribute disproportionately to pandemic-era higher mortality and constitute an emerging road safety issue that deserves further attention.

1. Introduction

The arrival of COVID-19 was accompanied by a reversal of the downward trend in motor vehicle mortality that initially caught road scholars by surprise [1]. In the year prior to COVID-19’s appearance, New York State experienced a 3.4% decrease in motor vehicle mortality [2]. Stay-at-home orders; closures of restaurants, bars and entertainment venues; movement to work-from-home; a rise in internet shopping; and a move to virtual education lowered vehicle miles traveled and left city streets, county roads, and sidewalks with fewer travelers [3,4,5,6]. While the scope and magnitude of the mortality changes vary, there is remarkable consistency in the sustained increase across the majority of U.S. states [7,8,9,10,11,12].
While several road user behavioral factors have been implicated, other pillars of the Safe System framework have been neglected with regard to scientific inquiry into their potential contributions. The Safe System’s five pillars are objectives to support Safe System principles that ‘serious road injury is unacceptable, that humans are vulnerable and make mistakes, responsibility is shared, safety is proactive and redundancy is crucial’ [13,14,15]. Previous reports of motor vehicle mortality lend support to our hypotheses that other pillars of the Safe System framework, in addition to road user behavior, deserve further scientific inquiry [16,17,18].
This study compares pre-COVID-19 and COVID-19-era fatal crashes across the five pillars of the Safe System: (1) road users; (2) vehicles; (3) roadways; (4) speed; and (5) post-crash care. Two time periods are selected for comparison that are matched to control for seasonality differences across the pre-COVID-19 and COVID-19-era periods. Additional scientific inquiry into factors associated with higher COVID-19-era mortality could yield important scientific insights to inform future prevention efforts [19,20,21,22].
This paper is an important contribution to the literature as it uses a large sample size, an extended timeframe seasonally and geographically matched with a pre-pandemic control population, and a well-defined injury outcome (death) to explore factors hypothesized to contribute to this continued increase in mortality. Understanding these factors is paramount to returning our roadway mortality to its pre-pandemic trajectory.

2. Materials and Methods

The Fatality Analysis Reporting System (FARS) [23,24,25] is used to investigate factors hypothesized to be associated with the higher pandemic-era motor vehicle mortality in New York State.

2.1. Study Population

The study population included 3735 deaths occurring on a public roadway during two study time frames. A baseline pre-COVID-19 era study included 1725 fatal crash deaths occurring between 1 April 2018 and 31 December 2019. The COVID-19 era included 2010 fatal crash deaths occurring between 1 April 2020 and 31 December 2021. A buffer period from 1 January 2020 to 31 March 2020 was excluded when COVID-19 was beginning [26,27]. The study population included all road user types captured by FARS [23].

2.2. Data Source(s)

FARS is made available by the National Highway Traffic Safety Administration as a deidentified public use data set [23,24]. It contains a census of all fatal crashes on a U.S. roadway and provides information on the person type (driver, passenger, or pedestrian), vehicle type and crash characteristics, roadway features, and post-crash transport. All New York State roadway deaths captured by FARS during the two identified study periods were analyzed.

2.3. Variable Classifications

Key FARS variables were mapped to each of the five pillars of the Safe System framework [16,17,18].
Outcome variable. Mortality that occurred on a public roadway in New York State was compared for the two study periods using binominal multivariable logistic regression.
Covariates. FARS variables mapped across the five pillars of the Safe System framework comprised the independent covariates of the binominal logistic regression model.

2.4. Pillar 1: Road User-Level Characteristics

2.4.1. Demographics and Person Type

Driver ages were categorized as shown in Table 1. For descriptive purposes, all motor vehicle occupants were characterized as: child (0–12), teen (13–19), younger adult (20–44), middle-aged adult (45–64), and older adult (65 and older). Sex was captured in FARS as a dichotomous variable.
Person type. Fatalities were categorized as: (1) driver or passenger of a four-wheeled motor vehicle; (2) a motorcycle, moped or other motorized two- and three-wheeled vehicles; (3) bicycle, not motorized; (4) pedestrian; or (5) other/unknown.

2.4.2. Driver/Occupant Behavior

Use of Protective Gear

Occupant restraint was categorized as a dichotomous variable, restrained or not restrained, with use of any type of restraint categorized as restrained.
Helmet use. For motorized two- and three-wheeled vehicles, the use of a helmet was classified as present or absent. Helmet wearing was not available for non-motorized bicyclists.
Driver license. License was considered valid if the driver had a valid learner’s permit, intermediate or full license, or a temporary license. Invalid included no license or a revoked, expired, suspended, or canceled license.
History of risky driving behavior. Risky driving behavior was categorized as several dichotomous variables (Table S1) and as a composite variable consisting of a count of the total and of the types of risky driving behaviors within the last three years (Table 1). Drivers were classified as having a risky driving history if, within the three years prior to the fatal crash, their record demonstrated a previous citation for (1) driving under the influence/driving impaired; (2) speeding; (3) driving with a suspended, revoked, canceled, or expired driver license; or another moving violation.
Driver drug or alcohol status. This variable was categorized as negative, positive, or not tested. A positive status included any of the following: (1) police reported alcohol involvement; (2) driver blood alcohol concentration over the legal limit; or (3) another positive drug or alcohol test.

2.5. Pillar 2: Vehicle and Vehicle Crash Level Characteristics

Vehicle model type. Vehicle type was categorized as passenger car, utility vehicle (SUV), van, small or large pickup truck, motorized two-wheeled vehicle and other. Vehicles such as large trucks, farm equipment, and buses were classified as “other”.
Motorized two- or three-wheeled vehicle. This variable was examined as a total and by specific type of vehicle, including two-wheeled motorcycles (n = 621), three-wheeled motorcycles (n = 8), off-road motorcycles (n = 18), and moped/motor scooter/minibikes (n = 51).
Single or multiple vehicle collisions. Collisions were characterized as single (one vehicle), two vehicles, or more than two vehicles.
Collision type. Collision types were categorized as head-on, angle, sideswipe, rear-end, or other. Non-collision was defined as crashes that did not involve collision with a motor vehicle in transport.
Vehicle maneuver at time of crash. Vehicle action at the time of crash was collapsed due to small cell sizes: (1) going straight; (2) turning (left, right or U); (3) negotiating a curve; (4) passing or overtaking; and (5) other/unknown.
Vehicle rollover and ejection. Rollover was classified as dichotomous (rollover and no rollover). Ejected was collapsed into (1) not ejected and (2) ejected, partial or full.

2.6. Pillar 3: Roadway Characteristics

New York State Regions. Three regions of New York State were examined. New York City comprises five boroughs (counties): New York, Bronx, Queens, Kings, and Richmond (Staten Island). Long Island comprises the Suffolk and Nassau counties. Upstate comprises the 55 remaining counties [28].
Roadway features. Several roadway characteristics were examined including urban/rural roadway, number of lanes, divided roadway, intersection, roadway surface, weather, traffic control devices, and lighting conditions.

2.7. Pillar 4: Speeding

Speed was assessed using two variables: speed-related crash and history of ticketing for speeding in the last 3 years. Actual miles per hour traveled was not available for analysis [23].

2.8. Pillar 5: Post Crash Care

Post crash care was examined for the following: (1) died at the crash scene, not transported; (2) died en route; and (3) alive at emergency department arrival. Times examined were from crash to emergency medical service arrival and time from crash to hospital arrival. Persons who died at the scene were excluded from crash time to hospital arrival. Time crash occurred. This variable was used in conjunction with two other variables as the basis for estimating time from crash to emergency medical service arrival (out-of-hospital care) and time from crash to hospital arrival. All calculated time variables are in minutes. Time from crash to emergency medical service arrival at the scene (minutes). This constructed variable is a calculation of the difference between the time of crash occurrence and time of emergency medical service arrival on the scene. This variable is reported for the total population emergency medical service responses regardless of whether they were transported and is also stratified by: (1) died at the crash scene; (2) died en route to the hospital; and (3) did not die in route or at the scene. Time from crash to arrival at hospital (minutes). This constructed variable is calculated for transported crash victims from the time of crash occurrence to the time of emergency medical service arrival at the hospital. Persons who died en route are included, but persons considered dead on arrival at the scene were excluded from crash time to hospital arrival calculations.

2.9. Statistical Analysis

FARS data variables were mapped to the five pillars of the Safe System framework. Two time periods were constructed from these four years of data that allowed us to control seasonality by matching the month of fatal crash in the two time periods. The pre-COVID period included 1 April 2018–31 December 2019 and the COVID-19 era was 1 April 2020–31 December 2021. A transition period of 3 months (1 January 2020 to 31 March 2020) was excluded as a transition period that included the time frame when COVID-19 was beginning to emerge as an infectious disease. April 2020 was the first full month of data after the COVID-19-related restaurant and entertainment venues had been shuttered in New York State and comprised the beginning of Time Period 2, the COVID-19 era. Descriptive statistics were examined for variables of interest representing each of the five pillars. All data were examined for numerical characteristics using bivariable analyses before being used in multivariable models. Chi square, logistic regression and multi-level logistic regression were used to analyze data [29]. Our goal was to investigate characteristics of fatal crashes across the two time periods immediately before COVID-19 and compared to the COVID-19 era. To examine differences in mortality risk factors between the Pre-COVID-19 and the COVID-19 eras, unadjusted and adjusted binominal logistic regression models were constructed with mortality in the two periods as the outcome variable and variables mapped across the five pillars as the independent covariates. All statistical analyses were two-sided and a p-value < 0.05 was considered statistically significant. All analyses were performed in R [30,31].

3. Results

New York State experienced a total of 3735 fatalities during the 42 months comprising the two periods of this pre-COVID-19 and COVID-19 era study (Table 1). Total mortality was 16.5% higher during the COVID-19 era compared to the pre-COVID-19 baseline time frame with the proportion of increase differing significantly by crash category and person type. Motor vehicle occupants accounted for approximately half of all fatalities and pedestrian injuries for one-quarter. The remaining deaths were from motorized two- and three-wheeled vehicles (19%) and non-motorized bicycles (4%). Total mortality and mortality by sex are shown by month for the two time periods, as shown in Figure 1a,b. Males exhibited higher mortality and greater seasonal variation than females, but sex differences were not significantly different between the two study periods.

3.1. Pillar 1: Road User Characteristics

3.1.1. Road User Type

Motor vehicle occupant mortality was 19.7% higher with driver mortality higher by 18.0% and accounting for nearly 76% of all occupant mortality. Two road user groups, non-motorized bicyclists, and pedestrians, accounted for an insignificant proportion of the higher pandemic-associated mortality. Motorcycles and motorized two-wheeled vehicle mortality was 45.1% higher. This was primarily due to drivers of motorcycles/motorized two-wheeled vehicles, who had 46.5% higher mortality, while passenger mortality for these vehicles was 20.0% higher in the pandemic era. Non-motorized bicycle mortality showed a smaller mortality change during the COVID-19 era (4.2%) while pedestrian deaths were 6.6% lower during the pandemic. Passengers of motor vehicles accounted for a smaller proportion of occupants and had 25.0% more deaths during the pandemic.
Although pandemic mortality associated with having a history of several risky driving behaviors in the COVID-19 era tended to be higher across several categories (Table S1), only history of prior speeding violation (66.7% higher) was statistically significant (Table 1).

3.1.2. Motor Vehicle Occupants

There were significant differences in age between the two study periods with more mortality occurring in drivers aged 30–34 and 35–39 years (Table 1). Drivers aged 45 years and older exhibited comparatively small increases while mortality in drivers aged 65 years and over declined slightly. The proportion of drivers who died unrestrained during the pandemic was 40% higher compared to a 5.7% higher mortality in those who were restrained (Table 1).

3.1.3. Motorized Two- and Three-Wheeled Vehicles

Mortality in motorized two- and three-wheeled vehicles comprised 18.8% of total traffic mortality in this study population, with 95% of those deaths being drivers of the vehicles (Table 1). Overall, motorized two- and three-wheeled vehicle mortality increased by 45% during the COVID-19 era. The proportion of deaths involving helmetless riders increased from 6.3% pre-COVID-19 to 14.4% during the pandemic. While mortality in helmeted riders increased by only 35%, the increase was 235% in the helmetless (Table 1).

3.1.4. Pedestrians

Overall pedestrian deaths declined during the COVID-19 era, but this decrease was concentrated in the early COVID era (Figure S1) and was uneven across the age span (Table 1). The overall decline was driven by a more than 20% drop in the 65 years and older age group (Table 1). Males comprised 60% of pedestrian deaths in both study periods. Mortality declines were seen for both males and females, but the sharpest decline was observed for males (9.0% vs. 2.9%). Two groups, ages 45–64 and 65 and over comprised nearly two-thirds of all pedestrian deaths and experienced a 7.9% and 22.7% decline, respectively.

3.2. Pillar 2: Vehicle Characteristics

There were significant differences in the types of vehicles involved in fatal crashes between the two study periods (Table 2). In addition to the increase in motorcycle deaths, the most notable change was the increase in roadway deaths involving motorized mopeds, scooters, minibikes, and off-road motorcycles. The absolute number of deaths involving these vehicles increased between the two periods (n = 15 to 54, p < 0.001) with a percent change between the two periods of more than 200%. Mortality associated with other off-road vehicles, such as ATVs and UTVs, was 42.3% higher. Smaller increases were observed for cars, SUVs, vans, and pickup trucks (Table 2).
Vehicle passing or overtaking accounted for approximately 3% of pre-COVID-19 vehicle crashes involving a fatality, but nearly doubled during the COVID-19 era (Table S2). The largest percent change in vehicle collisions involved multi-vehicle crashes with more than two vehicles (56.2%) (Table 2). Two-vehicle collisions increased by more than 20% while single-vehicle collisions showed the smallest increase. Vehicle sideswipes and rear end collisions contributed significantly to the increased mortality (Table 2).

3.3. Pillar 3: Roadway Characteristics

Several roadway characteristics were associated with an increase in fatalities during the COVID-19 era.

Urbanization

Mortality increased more on urban than rural roadways. More than 70% of fatalities occurred on urban roadways, which saw a 23.2% increase in fatalities during the pandemic (Table 2) (Figure 2a).
All regions of New York State experienced an increase in road mortality during the COVID-19 era, but the percent increase was highest in Upstate and New York City, with Long Island experiencing smaller increases (Table 2) (Figure 2b). Upstate experienced greater seasonal variation in mortality during both study periods than did either New York City or Long Island.
Four-way intersections had a very modest increase (1.9%) while Y and T intersections had a significant decline. Fatalities categorized as not at an intersection (n = 2500) had the largest increase in the absolute percentage of fatalities (61.2% to 70.9%). Fatal crashes associated with driving in inclement weather were lower during the pandemic (Table 2).

3.4. Pillar 4: Speeding

The only speed-related variable associated with a significant change in mortality during the pandemic was having a history of being ticketed during the last three years for speeding (Table 1). Speeding at the time of the fatal crash increased from 32.7% of fatalities pre-COVID-19 to 36.4% during the COVID-19 era (Table 2). Speed-related crashes exhibited a 31.3% higher mortality compared to a 11.7% higher mortality in crashes not noted to be speed-related.

3.5. Pillar 5: Post Crash Care

3.5.1. Mode of Transport

Post crash transport differed significantly with the COVID-19 era being associated with a 30% higher proportion who were not transported and a 9.4% higher proportion transported by ground ambulance (Table 2). There was a tendency of shorter time frames between crash and arrival at the hospital during the COVID-19 era. There were 14.8% fewer transports with times of one hour or longer between the crash and facility arrival and 36.1% fewer transports that were 90 min or longer (Table S2).

3.5.2. Independent Risk Factors for Mortality for Persons in or on Motorized Vehicles

Unadjusted and adjusted independent risk factors for mortality during the COVID period compared to the pre-COVID period for occupants of four-wheeled passenger vehicles, and motorized two- and three-wheeled vehicles are shown in Table 3.
In adjusted analyses, which examined variables across all five pillars of the Safe System framework, occupants of motorized two- and three-wheeled vehicles had 27% higher mortality than four-wheeled motor vehicle occupants (Table 3). Urban crash mortality was 46.7% higher. Failure to use safety equipment was associated with 44% higher mortality. Crash types and locations varied between the two study periods, with angle and sideswipe collisions and non-intersection crashes being significantly higher (Table 3). In the COVID era, more crash fatalities occurred at the scene, and thus the passengers were not transported for treatment (Table 3).

4. Discussion

This study identified multiple factors associated with New York State’s COVID-19-era reversal of declining mortality trends. Among the fatally injured, there was a significant degradation in safety equipment use (restraints and helmets), an increase in deaths for vulnerable vehicle types (motorized two-wheeled), an increase in deaths of those aged 30–39 years, a geographical shift toward more urban deaths, more multi-vehicle crashes, an increase in non-intersection crashes, more sideswipe and angle crashes, and more deaths on the scene that were not transported for post-crash care. Response times tended to be shorter during the pandemic, possibly due to less congested roads, but on-the-scene mortality was higher, potentially reflecting more severe injury associated with the less use of safety equipment.
While non-motorized bicycles and pedestrians contributed little to higher mortality during the examined time frame, a significant increase in motorized two-wheeled vehicle deaths contributed disproportionately—almost half of the excess increase in pandemic mortality. Mortality increases were almost seven-fold higher in unhelmeted compared to helmeted riders. This suggests a need to carefully examine ongoing federal funding policies aimed at increasing access to shared motorized micro-mobility vehicles [32], without concomitant policies that increase access to protective gear, such as helmets, which have been demonstrated to be an effective countermeasure for injury [33].
The majority of COVID-19-era pedestrian declines occurred early in the pandemic when restaurants, bars and entertainment venues were closed or operating with modified hours and to older adults who were more susceptible to adverse COVID-19 outcomes [34,35,36]. As the pandemic progressed, pedestrian deaths began to rise to pre-pandemic levels suggesting the need for further surveillance of factors associated with pedestrian deaths [37].
In unadjusted analyses, a higher proportion of drivers in the pandemic era had a history of speeding, but speeding during the pandemic era was not different. High visibility enforcement, associated with lower mortality pre-pandemic, was suspended during the pandemic and enforcement outside of this time frame was also lower [38,39,40,41]. State police traffic ticketing rebounded more quickly than local police jurisdictions. Pandemic-era enforcement was lower in incorporated urban areas where officers were charged with also addressing an increase in other criminal behaviors [39].
There are several important contributions to the literature that address travel patterns, as well as crash and traffic flow issues associated with COVID-19 lockdowns, closures and work-from-home mandates [10,11,17]. However, there is a paucity of COVID-related motor vehicle traffic injury outcomes that have a sample size adequate enough to examine the wide array of potentially contributing factors. Mortality remains higher than the pre-pandemic era. This is not fully explained by risky driving behavior. Thus, this paper is an important contribution to the literature as it uses a large sample size, an extended timeframe exactly matched with a pre-pandemic control population, and a well-defined injury outcome (death) to explore factors that were hypothesized to contribute to this continued increase in mortality. Understanding these factors is paramount to returning our roadway mortality to its pre-pandemic trajectory.
This study has limitations. Although New York State is very geographically heterogenous and experienced similar roadway mortality increases observed in other parts of the country, it was the epicenter of the pandemic and may not be representative of the U.S. [42,43]. Population-level denominators were unstable for producing rates due to pandemic shifts in travel patterns and population dynamics that left many high-rise housing complexes at one-third occupancy [27,44,45]. FARS data have well-established limitations with regard to drug data collection [25,46,47]. Lowered law enforcement roadway ticketing violations and overburdened medical examiners may have contributed to undetected COVID-19-era driver speeding and impairment [48,49,50].

5. Conclusions

The Safe System framework identified several previously unreported and potentially modifiable areas associated with increased pandemic era mortality. Four of the five pillars of the safe system had road safety indicators that worsened significantly during the pandemic era including: (1) urban compared to rural roadway mortality; (2) motorized two- and three-wheeled vehicles emerging as a significant contributor to pandemic era mortality increases while non-motorized bicyclists and pedestrians contributed little to the increase during the timeframe of this study; (3) lack of safety equipment use; and (4) an increase in more roadside, non-transported deaths. Lack of safety belts/helmets was associated with an increase in mortality with helmetless motorized two-wheeled rider deaths being nearly seven-fold higher than those who were helmeted. For risky behavior in motor vehicle occupants, countermeasures, such as the Click-It-Or-Ticket high visibility enforcement program, has been demonstrated to be effective at reducing mortality through addressing multiple risky driving behaviors [41]. The rapid growth of shared motorized two-wheeled vehicles has outpaced our currently available prevention approaches for safe transport on these vehicles. Further study is needed on effective approaches related to developing and enforcing rules of the road that specifically govern safety for all road users, offering road safety education, and mechanisms to provide safety equipment in the context of the rapid growth of shared motorized two- and three-wheeled vehicles. The identification of motorized two-wheeled vehicles contributing disproportionately to pandemic mortality is an emerging issue that threatens our vision to lower roadway deaths and needs further study [51]. There were troubling trends emerging regarding pedestrian mortality as the state began to re-open that deserve continued monitoring. Thus, this study both yielded important new scientific findings that can be used to focus on future prevention efforts and identified areas where additional study is needed to address emerging issues contributing to the sustained pandemic-era mortality increase.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph22010061/s1, Figure S1: Total Pedestrian Mortality New York State Pre-COVID and in the COVID Era; Table S1: Fatal Crashes Pre-COVID and in the COVID Era in New York State by Road User Characteristics, FARS 2018–2021; Table S2: Fatal Crashes Pre-COVID and in the COVID Era in New York State by Roadway, Vehicle, Crash and Post-Crash Characteristics, FARS 2018–2021.

Author Contributions

Conceptualization, methodology, original draft, and figure preparation J.C.P.; Data analysis and table preparation, Z.A.; Writing—Original draft preparation, J.C.P.; Study design J.C.P. and M.B. Writing—Manuscript review, discussion, and comment, M.B., J.G., E.P., L.H. and A.R.; Funding Acquisition, M.B. All authors have read and agreed to the published version of the manuscript.

Funding

This epidemiological research was funded as follows: Dr. Joyce Pressley receives partial salary support from New York State Governor’s Traffic Safety Committee grant: HS1-2024-HRI/NYS DOH-00194-(088) and the National Center for Injury Prevention and Control of the Centers for Disease Control and Prevention (Grants 1R49CE002096 and 1R49CE003555-01) and The New York State Department of Health staff receive grant support from the National Center for Injury Prevention and Control of the Centers for Disease Control and Prevention (NU17CE010055); New York State Governor’s Traffic Safety Committee grants: HS1-2025-HRI/NYS DOH-00132-(088) and HS1-2025-HRI/NYS DOH-00131-(088).

Institutional Review Board Statement

Not applicable. Under the federal regulations for human subjects (45 CFR Part 46), research involving publicly available data sets without personal identifiers, such as FARS, do not require IRB review.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are available on the Fatality Analysis Reporting System (FARS) website, https://www.nhtsa.gov/research-data/fatality-analysis-reporting-system-fars (accessed on 20 December 2024), distributed for free by the NHTSA.

Conflicts of Interest

The authors declare no conflicts of interest.

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Ijerph 22 00061 g001
Figure 1. (a) Total mortality on New York State roadways: pre-COVID-19 (dashed lines) and in the COVID-19 era (solid lines). (b) Mortality on New York State roadways by sex: pre-COVID-19 (dashed lines) and in the COVID-19 era (solid lines).
Figure 1. (a) Total mortality on New York State roadways: pre-COVID-19 (dashed lines) and in the COVID-19 era (solid lines). (b) Mortality on New York State roadways by sex: pre-COVID-19 (dashed lines) and in the COVID-19 era (solid lines).
Ijerph 22 00061 g001
Ijerph 22 00061 g002aIjerph 22 00061 g002b
Figure 2. (a) Mortality on New York State roadways for urban (purple) and rural (green) areas: pre-COVID-19 (dashed lines) and in the COVID-19 era (solid lines). (b) Total mortality by month for Upstate, Long Island and New York City: pre-COVID-19 (dashed lines) and in the COVID-19 era (solid lines).
Figure 2. (a) Mortality on New York State roadways for urban (purple) and rural (green) areas: pre-COVID-19 (dashed lines) and in the COVID-19 era (solid lines). (b) Total mortality by month for Upstate, Long Island and New York City: pre-COVID-19 (dashed lines) and in the COVID-19 era (solid lines).
Ijerph 22 00061 g002aIjerph 22 00061 g002b
Table 1. Fatal Crashes pre-COVID and in the COVID era in New York State by road user characteristics, FARS 2018–2021.
Table 1. Fatal Crashes pre-COVID and in the COVID era in New York State by road user characteristics, FARS 2018–2021.
Pre-COVIDCOVID EraTotal
Demographic and Crash
Characteristics		% Change1 April 2018–31 December 20191 April 2020–31 December 20211 April 2018–31 December 2021Chi-Square
n (%)X² (p-Value)
n (%) n = 1725n = 2010n = 3735
Road user type  16.5217252010373523.770 (<0.001)
   Motor vehicle occupants19.70868 (50.3)1039 (51.7)1907 (51.1)
   Motorized 2-wheeled MV occupants45.10286 (16.6)415 (20.6)701 (18.8)
   Bicycle, not motorized4.1772 (4.2)75 (3.7)147 (3.9)
   Pedestrian−6.57487 (28.2)455 (22.6)942 (25.2)
   Other/unknown116.6712 (0.7)26 (1.3)38 (1.0)
Age, collapsed (all occupants) 11531450260311.291 (0.023)
   0–1250.016 (1.4)24 (1.7)40 (1.5)
   13–1916.274 (6.4)86 (5.9)160 (6.1)
   20–4441.8521 (45.2)739 (51.0)1260 (48.4)
   45–6418.7299 (25.9)355 (24.5)654 (25.1)
   65+1.2243 (21.1)246 (17.0)489 (18.8)
Safety equipment use 16411909355086.659 (<0.001)
   Unrestrained/not helmeted52.7364 (22.2)556 (29.1)920 (25.9)
   Restrained or helmeted21.4740 (45.1)898 (47.0)1638 (46.1)
   Not applicable (i.e., pedestrians)−6.6487 (29.7)455 (23.8)942 (26.5)
Motor Vehicles (four-wheeled), Drivers
Age 663782144518.734 (0.009)
   ≤190.039 (5.9)39 (5.0)78 (5.4)
   20–240.073 (11.0)73 (9.3)146 (10.1)
   25–2941.363 (9.5)89 (11.4)152 (10.5)
   30–3497.846 (6.9)91 (11.6)137 (9.5)
   35–3975.032 (4.8)56 (7.2)88 (6.1)
   40–4440.035 (5.3)49 (6.3)84 (5.8)
   45–645.6198 (29.9)209 (26.7)407 (28.2)
   65 and over−0.6177 (26.7)176 (22.5)353 (24.4)
Sex 66478414480.058 (0.810)
   Male18.9512 (77.1)609 (77.8)1121 (77.5)
   Female14.5152 (22.9)174 (22.2)326 (22.5)
Impaired, alcohol 0.764 (0.382)
   No21.5488 (73.5)593 (75.6)1081 (74.7)
   Yes8.5176 (26.5)191 (24.4)367 (25.3)
Restraint Status
   Driver of MV18.166478414485.627 (0.018)
      Unrestrained39.8201 (30.3)281 (35.8)482 (33.3)
      Restrained5.7407 (61.3)430 (54.8)837 (57.8)
      Not reported30.456 (8.4)73 (9.3)129 (8.9)
Motorized two-wheeled Vehicle, Drivers
Age 27139666715.433 (0.031)
   <=1933.39 (3.3)12 (3.0)21 (3.1)
   20–2457.633 (12.2)52 (13.1)85 (12.7)
   25–29−14.363 (23.2)54 (13.6)117 (17.5)
   30–34106.332 (11.8)66 (16.7)98 (14.7)
   35–39138.121 (7.7)50 (12.6)71 (10.6)
   40–4445.020 (7.4)29 (7.3)49 (7.3)
   45–6435.174 (27.3)100 (25.3)174 (26.1)
   65 and over73.719 (7.0)33 (8.3)52 (7.8)
Sex 2713976680.064 (0.800)
   Male47.5261 (96.3)385 (97.0)646 (96.7)
   Female20.010 (3.7)12 (3.0)22 (3.3)
Helmet wearing 2683976659.616 (0.002)
   No235.317 (6.3)57 (14.4)74 (11.1)
   Yes35.1242 (90.3)327 (82.4)569 (85.6)
   Unknown44.49 (3.4)13 (3.3)22 (3.3)
Impaired, alcohol 2713976681.936 (0.164)
   No57.0193 (71.2)303 (76.3)496 (74.3)
   Yes20.578 (28.8)94 (23.7)172 (25.7)
Pedestrians
Age 4834489314.792 (0.309)
   0–1233.312 (2.5)16 (3.6)28 (3.0)
   13–1910.010 (2.1)11 (2.5)21 (2.3)
   20–447.4135 (28.0)145 (32.4)280 (30.1)
   45–64−7.9151 (31.3)139 (31.0)290 (31.1)
   65+−21.7175 (36.2)137 (30.6)312 (33.5)
Sex 4874559420.209 (0.648)
   Male−9.0312 (64.1)284 (62.4)596 (63.3)
   Female−2.3175 (35.9)171 (37.6)346 (36.7)
Bicyclists, Non Motorized
Age 72751472.836 (0.586)
   0–12−66.73 (4.2)1 (1.3)4 (2.7)
   13–19−10.010 (13.9)9 (12.0)19 (12.9)
   20–4412.524 (33.3)27 (36.0)51 (34.7)
   45–6433.321 (29.2)28 (37.3)49 (33.3)
   65+−28.614 (19.4)10 (13.3)24 (16.3)
Sex 72751471.948 (0.163)
   Male18.255 (76.4)65 (86.7)120 (81.6)
   Female−41.217 (23.6)10 (13.3)27 (18.4)
Note: Motor vehicle is abbreviated as MV for table readability. Unless otherwise stated, Chi square excludes unknowns.
Table 2. Fatal crashes pre-COVID and in the COVID era in New York State by roadway, vehicle, crash and post-crash characteristics, FARS 2018–2021.
Table 2. Fatal crashes pre-COVID and in the COVID era in New York State by roadway, vehicle, crash and post-crash characteristics, FARS 2018–2021.
% ChangePre-COVIDCOVID EraTotal
1 April 2018–31 December 20191 April 2020–31 December 20211 April 2018–31 December 2021Chi-Square
n (%)X² (p-Value)
n = 1725n = 2010n = 3735
Pillar II: Vehicle Characteristics
Number of Vehicles 17252010373510.308 (0.006)
   Single8.441031 (59.8)1118 (55.6)2149 (57.5)
   Two22.16564 (32.7)689 (34.3)1253 (33.5)
   Multiple (2+)56.15130 (7.5)203 (10.1)333 (8.9)
Motorized Vehicle Type 11551461261625.856 (0.007)
   Car22.43486 (42.1)595 (40.7)1081 (41.3)
   SUV19.69193 (16.7)231 (15.8)424 (16.2)
   Van18.1844 (3.8)52 (3.6)96 (3.7)
   Light/Pickup Trucks12.6679 (6.8)89 (6.1)168 (6.4)
   Medium/Heavy Trucks11.5426 (2.3)29 (2.0)55 (2.1)
   Other Motorized Vehicles
      Two-Wheel Motorcycles31.34268 (23.2)352 (24.1)620 (23.7)
      Three-Wheel Motorcycles200.002 (0.2)6 (0.4)8 (0.3)
      Moped/Motor Scooters/Minibikes263.6411 (0.1)40 (2.7)51 (1.9)
      Off-Road Motorcycles250.004 (0.3)14 (0.1)18 (0.7)
   Other Off-Road Vehicles42.3126 (2.3)37 (2.5)63 (2.4)
   Other Vehicle Types−72.7311 (1.0)3 (0.2)14 (0.5)
   Unknown160.005 (0.4)13 (0.9)18 (0.7)
Collision type 17192008372733.229 (<0.001))
   Not a collision with MV in transport7.401162 (67.6)1248 (62.2)2410 (64.7)
   Angle24.14261 (15.2)324 (16.1)585 (15.7)
   Head-on22.65181 (10.5)222 (11.1)403 (10.8)
   Rear-end67.0588 (5.1)147 (7.3)235 (6.3)
   Sideswipe225.0020 (1.2)65 (3.2)85 (2.3)
Pillar III: Roadway Characteristics
Urbanization 1725201037357.670 (0.006)
   Rural0.0494 (28.6)494 (24.6)988 (26.5)
   Urban23.21230 (71.3)1515 (75.4)2745 (73.5)
RUCC Rankings 1725201037358.218 (0.016)
   Metropolitan20.291464 (84.9)1761 (87.6)3225 (86.3)
   Non-metropolitan, adjacent−9.48232 (13.4)210 (10.4)442 (11.8)
   Non-metropolitan, non-adjacent34.4829 (1.7)39 (1.9)68 (1.8)
NY State Geographical Breakdown 1698197536732.171 (0.338)
   NYC19.6373 (22.0)446 (22.6)819 (22.3)
   Long Island5.6359 (21.1)379 (19.2)738 (20.1)
   Upstate19.0966 (56.9)1150 (58.2)2116 (57.6)
Number of lanes 1140142025609.260 (0.026)
   One13.5137 (3.2)42 (3.0)79 (3.1)
   Two or more, one-way traffic8.3324 (2.1)26 (1.8)50 (2.0)
   Two or more, two-way traffic, divided52.59270 (23.7)412 (29.0)682 (26.6)
   Two or more, two-way traffic, not divided16.19809 (71.0)940 (66.2)1749 (68.3)
Intersection type 17242009373353.601 (<0.001)
   Not an intersection34.981055 (61.2)1424 (70.9)2479 (66.4)
   Four-way intersection1.87374 (21.7)381 (19.0)755 (20.2)
   T and Y intersections−32.53292 (16.9)197 (9.8)489 (13.1)
   Other133.333 (0.2)7 (0.3)10 (0.3)
Roadway Driving Conditions 17252010373517.619 (0.002)
   Clear conditions21.681098 (63.7)1336 (66.5)2434 (65.2)
   Rain−14.53179 (10.4)153 (7.6)332 (8.9)
   Cloudy20.88388 (22.5)469 (23.3)857 (22.9)
   Sleet/Hail, Snow, Freezing Rain/Drizzle−48.4833 (1.9)17 (0.8)50 (1.3)
   Other, Fog/Smog/Smoke, Severe Crosswinds45.4511 (0.6)16 (0.8)27 (0.7)
   Unknown/Not Reported18.7516 (0.9)19 (0.9)35 (0.9)
Traffic control devices 11541460261412.725 (0.048)
   No Controls25.12868 (75.2)1086 (74.4)1954 (74.8)
   Traffic Control Signal33.09139 (12.0)185 (12.7)324 (12.4)
   Stop Sign−7.6978 (6.8)72 (4.9)150 (5.7)
   Yield Sign300.001 (0.1)4 (0.3)5 (0.2)
   Railway Crossing−50.004 (0.3)2 (0.1)6 (0.2)
   Other Signs/Signalsn/a0 (0.0)4 (0.3)4 (0.2)
   Unknown/Not Reported67.1964 (5.5)107 (7.3)171 (6.5)
Lighting conditions 1725201037357.692 (0.174)
   Daylight10.88864 (50.1)958 (47.7)1822 (48.8)
   Dark, not lighted26.64259 (15.0)328 (16.3)587 (15.7)
   Dark, lighted18.82510 (29.6)606 (30.1)1116 (29.9)
   Dawn0.0042 (2.4)42 (2.1)84 (2.2)
   Dusk37.5048 (2.8)66 (3.3)114 (3.1)
   Unknown/Not Reported400.002 (0.1)10 (0.5)12 (0.3)
Pillar IV: Speed
Speed Related, Motor Vehicle Drivers 66478414481.973 (0.160)
   No11.7446 (67.2)498 (63.5)944 (65.2)
   Yes31.3217 (32.7)285 (36.4)502 (34.7)
   Unknown0.01 (0.2)1 (0.1)2 (0.1)
Speed Related, Motorized 2-wheeled MV Drivers 2713976680.001 (0.981)
   No45.5154 (56.8)224 (56.4)378 (56.6)
   Yes47.9117 (43.2)173 (43.6)290 (43.4)
Pillar V: Post Crash Care
DOA 1725201037355.803 (0.016)
   Not dead at scene or en route9.901111 (64.4)1221 (60.7)2332 (62.4)
   Yes, DOA29.77608 (35.2)789 (39.3)1397 (37.4)
      Dead at scene29.90602 (99.0)782 (99.1)1384 (99.1)
      Dead en route16.676 (1.0)7 (0.9)13 (0.9)
   Unknown−100.006 (0.3)0 (0.0)6 (0.2)
Mode of transport 17252010373512.860 (0.012)
   Not transported30.21609 (35.3)793 (39.5)1402 (37.5)
   Ambulance, ground9.421083 (62.8)1185 (59.0)2268 (60.7)
   Ambulance, air3.8526 (1.5)27 (1.3)53 (1.4)
   Fire/policen/a0 (0.0)3 (0.1)3 (0.1)
   Other/Unknown/Not Reported−71.437 (0.4)2 (0.1)9 (0.2)
Time from crash to hospital arrival 56458511497.817 (0.020)
   0–29 min13.33195 (34.6)221 (37.8)416 (36.2)
   30–59 min13.64220 (39.0)250 (42.7)470 (40.9)
   60+ min−23.49149 (26.4)114 (19.5)263 (22.9)
Notes: MV is abbreviated as “MV” for table readability. Unless otherwise stated, Chi square excludes unknowns. For additional Safe System variables mapped onto the FARS data set, see supplemental Tables S1 and S2.
Table 3. Unadjusted and adjusted multivariable risk factors for mortality during the COVID-19 era compared to pre-COVID-19, FARS 2018–2021.
Table 3. Unadjusted and adjusted multivariable risk factors for mortality during the COVID-19 era compared to pre-COVID-19, FARS 2018–2021.
Unadjusted OR (95% CI)Adjusted Multivariable OR (95% CI)
Pillar I
   Age
      <=19RefRef
      20–241.110 (0.692, 1.779)1.227 (0.752, 2.003)
      25–291.068 (0.673, 1.695)1.101 (0.680, 1.781)
      30–341.894 (1.174, 3.062)1.891 (1.150, 3.116)
      35–391.882 (1.127, 3.156)2.025 (1.186, 3.474)
      40–441.335 (0.791, 2.258)1.612 (0.931, 2.798)
      45–641.069 (0.697, 1.639)1.267 (0.808, 1.985)
      65 and over1.004 (0.646, 1.558)1.390 (0.869, 2.225)
   Sex
      FemaleRefRef
      Male1.120 (0.889, 1.410)1.035 (0.803, 1.332)
   Safety Equipment
      Restrained/helmetedRefRef
      Unrestrained/not helmeted1.353 (1.123, 1.633)1.442 (1.170, 1.780)
Pillar II
   Vehicle Type, collapsed
      Motor vehicles, four-wheeledRefRef
      Motorized two- and three-wheeled vehicles1.229 (1.020, 1.482)1.270 (1.018, 1.585)
   Collision Type
      Not a collision with MV in transportRefRef
      Angle1.016 (0.816, 1.265)1.365 (1.033, 1.809)
      Head-on1.033 (0.808, 1.322)1.128 (0.863, 1.476)
      Rear-end1.252 (0.911, 1.729)1.262 (0.895, 1.791)
      Sideswipe2.848 (1.593, 5.422)2.665 (1.464, 5.152)
      Other0.277 (0.040, 1.208)0.276 (0.040, 1.226)
Pillar III
   Urbanization
      RuralRefRef
      Urban1.374 (1.146, 1.646)1.467 (1.201, 1.793)
   Intersection Type
      IntersectionRefRef
      Not an intersection1.393 (1.149, 1.689)1.670 (1.312, 2.128)
Pillar IV
   Speeding-related Crash
      NoRefRef
      Yes1.140 (0.954, 1.362)1.099 (0.889, 1.361)
Pillar V
   Post Crash Care
      Not transportedRefRef
      Transported by ground0.859 (0.720, 1.024)0.773 (0.636, 0.939)
      Transported by air0.838 (0.447, 1.584)0.823 (0.433, 1.577)
OR: odds ratio with 95% confidence interval.
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Pressley, J.C.; Aziz, Z.; Pawlowski, E.; Hines, L.; Roberts, A.; Guzman, J.; Bauer, M. Using a Safe System Framework to Examine the Roadway Mortality Increase Pre-COVID-19 and in the COVID-19 Era in New York State. Int. J. Environ. Res. Public Health 2025, 22, 61. https://doi.org/10.3390/ijerph22010061

AMA Style

Pressley JC, Aziz Z, Pawlowski E, Hines L, Roberts A, Guzman J, Bauer M. Using a Safe System Framework to Examine the Roadway Mortality Increase Pre-COVID-19 and in the COVID-19 Era in New York State. International Journal of Environmental Research and Public Health. 2025; 22(1):61. https://doi.org/10.3390/ijerph22010061

Chicago/Turabian Style

Pressley, Joyce C., Zarah Aziz, Emilia Pawlowski, Leah Hines, Aisha Roberts, Jancarlos Guzman, and Michael Bauer. 2025. "Using a Safe System Framework to Examine the Roadway Mortality Increase Pre-COVID-19 and in the COVID-19 Era in New York State" International Journal of Environmental Research and Public Health 22, no. 1: 61. https://doi.org/10.3390/ijerph22010061

APA Style

Pressley, J. C., Aziz, Z., Pawlowski, E., Hines, L., Roberts, A., Guzman, J., & Bauer, M. (2025). Using a Safe System Framework to Examine the Roadway Mortality Increase Pre-COVID-19 and in the COVID-19 Era in New York State. International Journal of Environmental Research and Public Health, 22(1), 61. https://doi.org/10.3390/ijerph22010061

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