Abstract
Monitoring and understanding the trends in motor vehicle traffic (MVT) mortality is crucial for developing effective interventions and tracking progress in reducing deaths related to MVT. This study aimed to assess the trends in MVT mortality in New York City from 1999 through 2020. Publicly available de-identifiable mortality data were abstracted from the Centers for Disease Control and Prevention's Wide-ranging Online Data for Epidemiologic Research. MVT deaths were identified using the International Classification of Diseases Codes, 10th Revision: V02–V04 (.1, .9), V09.2, V12–V14 (.3–.9), V19 (.4–.6), V20–V28 (.3–.9), V29–V79 (.4–.9), V80 (.3–.5), V81.1, V82.1, V83–V86 (.0–.3), V87 (.0–.8), and V89.2. Age adjusted mortality rates (AAMR) were abstracted by county (Bronx; Kings; Queens; New York), age (in years) (< 25; 25–44; 45–64; ≥ 65), sex (male; female), race/ethnicity (Non-Hispanic Black; Non-Hispanic White; Asian/Pacific Islander; Hispanic), and road user type (motor vehicle occupant; motorcyclist; pedal cyclist; pedestrian). Joinpoint regression models were fitted to estimate the annual percentage change (APC) and average annual percentage change (AAPC) in AAMR during the study period. The Parametric Method was used to compute 95% confidence intervals (CI). Between 1999 and 2020, a total of 8,011 MVT deaths were recorded in New York City. Mortality rates were highest among males (age adjusted mortality rate (AAMR) = 6.4 per 100,000; 95% CI: 6.2, 6.5), Non-Hispanic Blacks (AAMR = 4.8; 95% CI: 4.6, 5.0), older adults (AAMR = 8.9; 95% CI: 8.6, 9.3), and persons from Richmond County (AAMR = 5.2; 95% CI: 4.8, 5.7). MVT death rates, overall, have declined by 3% per year (95% CI: −3.6, −2.3) from 1999 to 2020. The rates have fallen or stabilized by race/ethnicity, county of residence, road user type, and age group. In contrast, rates have increased by 18.1% per year among females and by 17.4% per year in Kings County from 2017 to 2020.The results of this study draw attention to the worsening trends in MVT mortality among females and in Kings County, New York City. Further investigation is needed to determine the underlying behavioral, social, and environmental factors contributing to this increase, such as polysubstance or alcohol abuse, psychosocial stressors, access to medical and emergency care, and compliance with traffic laws. These findings emphasize the importance of developing targeted interventions to prevent MVT deaths and ensure the health and safety of the community.
Keywords: Motor vehicle traffic, Mortality, New York, Unintentional injuries, Accidents, Vision Zero, United States
Introduction
Accidents have failed to gain deserved notoriety and attention in public health and health policy interventions for reasons ranging from public and professional misperceptions to insufficient attention to scientific approaches in the study of accidents [1]. Current statistical estimates consider accidents, specifically from motor vehicle traffic (MVT), among the most common mechanisms of unintentional injury [2, 3].
Like many other health outcomes, most, if not all, risks associated with increased MVT mortality are potentially modifiable, but a few others, notably race and age, are non-modifiable [4]. While the risk of injury is higher in the young, mortality is increased among the elderly [5–7], and males [5, 6]. Other demographic groups experiencing increased mortality from MVT injuries are Black and Hispanic subpopulations, who tend to live and work in less safe neighborhoods [5]. Besides sociodemographic factors, variables in the physical environment have also been implicated, such as trends in weather-related fatalities, which tend to be higher between November and April, especially when the roads are wet and less frequent when icy [8]. Some authors found that the highest fatalities occurred between July and August, on weekends, and during holidays [9].
Individual and/or behavioral factors, such as alcohol consumption, alone or in combination with other substances of abuse, are consistently associated with increased injury and mortality [2, 6, 10, 11]. A study found that about 60% of fatal injuries from MVT were associated with drivers' use of at least one drug of abuse [10]. Some authors have demonstrated that while the epidemiological assessment of polysubstance use, which is defined as a driver testing positive for at least two substances and alcohol, is limited in the literature, a small proportion of drivers have been found, in roadside surveys, to test positive for a mixture of controlled substances. In some cases, some authors have demonstrated rates as high as 20% [10]. While alcohol is a risk factor for increased injury risk, it is also an independent risk factor for increased MVT mortality, negatively impacting survivability [6].
New York State has one of the lowest MVT mortality rates in the United States; yet MVT injuries are still a leading cause of mortality in the state [12]. Examining temporal trends is essential to ensuring an evidence-based approach to monitoring MVT mortality and targeting interventions. This study aimed to assess MVT mortality trends in New York City from 1999 through 2020.
Methods.
Data Sources
We abstracted mortality data from the Underlying Cause of Death files in the Centers for Disease Control and Prevention's Wide-ranging Online Data for Epidemiologic Research (WONDER) database [13]. International Classification of Diseases Codes, 10th Revision (ICD-10)- V02–V04 (0.1, 0.9), V09.2, V12–V14 (0.3–0.9), V19 (0.4–0.6), V20–V28 (0.3–0.9), V29–V79 (0.4–0.9), V80 (0.3–0.5), V81.1, V82.1, V83–V86 (0.0–0.3), V87 (0.0–0.8), and V89.2 were used to identify MVT decedents in New York City from 1999 through 2020. Additionally, we identified decedents based on road user type: Motor vehicle occupants (ICD-10 codes: V30–V79 (0.4–0.9), V83–V86 (0.0–0.3), V87 (0.0–0.8), and V89.2); motorcyclists (ICD-10 codes: V20–V28 (0.3–0.9) and V29 (0.4–0.9)); pedal cyclists (ICD-10 codes: V12–V14 (0.3–0.9) and V19 (0.4–0.6)), and; pedestrians (ICD-10 codes: V02–V04 (0.1, 0.9) and V09.2).
Crude and age adjusted mortality rates (AAMR) were also abstracted by county (Bronx, Kings, Queens, New York), age (in years) (< 25, 25–44, 45–64, 65), sex (male, female), and race/ethnicity (Non-Hispanic Black, Non-Hispanic White, Asian/Pacific Islander, Hispanic).
Statistical Analysis
Temporal trends in MVT mortality rates were assessed by fitting a Joinpoint regression model. The iterative model-building process begins by assuming a linear trend in AAMR- i.e. with no joinpoint [14]. Subsequently, a joinpoint is included, signifying a change in trend, and a statistical test is conducted to assess the significance of this inclusion relative to the null model. Additional joinpoints are included and the process is repeated until an optimum number of joinpoints is derived using Monte Carlo permutation. An annual percentage change (APC) is computed to quantify the change the in rate of AAMR between two consecutive- piecewise- joinpoints. An average annual percentage change (AAPC) estimates the average change in trend over the entire study period. 95% confidence intervals (CI) were derived using the Parametric Method.
Model Specification/Characteristics and Parameter Settings
The dependent variable was the log-transformed AAMR; the dependent variable was 'year of death.' Interval type was configured as 'Annual.' Considering the homoscedasticity of the standard errors, the constant variance option was selected. Default settings were used for: Method (Grid Search), Model Selection Method (Permutation Test), AAPC Segment Range (Entire Range), and APC/AAPC/Tau Confidence Intervals (Parametric Method).
All statistical analyses were conducted using Stata® (College Station, Texas) and the Joinpoint Regression Program® (Calverton, Maryland).
Results
Between 1999 and 2020, a total of 8,011 MVT deaths (AAMR = 4.3 per 100,000; 95% CI: 4.2–4.4) were recorded in New York City. Mortality rates were highest among males (AAMR = 6.4; 95% CI: 6.2, 6.5), Non-Hispanic Blacks (AAMR = 4.8; 95% CI: 4.6, 5.0), older adults (AAMR = 8.9; 95% CI: 8.6, 9.3), and persons from Richmond County (AAMR = 5.2; 95% CI: 4.8, 5.7). (Table 1).
Table 1.
Demographic Characteristics of Motor Vehicle Traffic Decedents, New York City, 1999–2020
| Variable | Motor Vehicle Traffic Death n (%) |
Age Adjusted Annual Rate Per 100,000 Persons (95% CI) |
|---|---|---|
| Overall Population | 8,011 |
4.3 (4.2–4.4) |
| Sex | ||
| Female | 2,552 (31.9) | 2.5 (2.4–2.6) |
| Male | 5,459 (68.1) | 6.4 (6.2–6.5) |
| a Race/Ethnicity | ||
| Non-Hispanic White | 2,915 (36.4) | 4.2 (4.0–4.3) |
| Non-Hispanic Black | 2,087 (26.1) | 4.8 (4.6–5.0) |
| Asian/Pacific Islander | 871 (10.9) | 3.7 (3.5–4.0) |
| Hispanic | 1,993 (24.9) | 4.0 (3.8–4.1) |
| County | ||
| Bronx | 1,315 (16.4) | 4.3 (4.1–4.6) |
| Kings | 2,546 (31.8) | 4.5 (4.4–4.7) |
| New York | 1,189 (14.8) | 3.1 (2.9–3.3) |
| Queens | 2,423 (30.2) | 4.7 (4.5–4.9) |
| Richmond | 538 (6.7) | 5.2 (4.8–5.7) |
| Age (Years) | ||
| < 25 | 1,602 (20.0) | 2.7 (2.5–2.8) |
| 25–44 | 2,467 (30.8) | 4.2 (4.0–4.3) |
| 45–64 | 1,892 (23.6) | 4.4 (4.2–4.6) |
| ≥ 65 | 2,049 (25.6) | 8.9 (8.6–9.3) |
| Road User Type | ||
| Motor vehicle occupant | 3,379 (42.2) | 1.8 (1.8–1.9) |
| Motor cyclist | 823 (10.3) | 0.4 (0.4–0.5) |
| Pedal cyclist | 308 (3.8) | 0.2 (0.2–0.2) |
| Pedestrian | 3,501 (43.7) | 1.9 (1.8–1.9) |
aAmerican Indians/Alaska Natives were excluded due to low and unreliable death counts
Temporal Trends Overall and by Race/Ethnicity
MVT death rates, overall, declined by 3% per year (95% CI: −3.6, −2.3) from 1999 to 2020. Stratification by race and ethnicity revealed that AAMR declined by 3.9% per year (95% CI: −4.7, −3.2) among Non-Hispanic Whites; by 2.4% per year (95% CI: −3.7, −1.2) among Non-Hispanic Blacks; by 3.3% per year (95% CI: −4.2, −2.3) among Asians/Pacific Islanders, and; by 1.6% per year (95% CI: −2.4, −0.7) among Hispanics (Table 2; Fig. 1).
Table 2.
Annual Percentage Changes (APC) and Average Annual Percentage Changes (AAPC) in Motor Vehicle Traffic Deaths, United States, 1999–2020
| Variable | Trend Segment | Segment Endpoints | APC (95% CI) | AAPC | |
|---|---|---|---|---|---|
| Lower | Upper | ||||
| Overall | 1 | 1999 | 2020 | *−3.0 (−3.6, −2.3) | *−3.0 (−3.6, −2.3) |
| Race/Ethnicity | |||||
| Non-Hispanic White | 1 | 1999 | 2020 | *−3.9 (−4.7, −3.2) | *−3.9 (−4.7, −3.2) |
| Non-Hispanic Black | 1 | 1999 | 2020 | *−2.4 (−3.7, −1.2) | *−2.4 (−3.7, −1.2) |
| Asian/Pacific Islander | 1 | 1999 | 2020 | *−3.3 (−4.2, −2.3) | *−3.3 (−4.2, −2.3) |
| Hispanic | 1 | 1999 | 2020 | *−1.6 (−2.4, −0.7) | *−1.6 (−2.4, −0.7) |
| Sex | |||||
| Male | 1 | 1999 | 2020 | *−2.7 (−3.4, −2.0) | *−2.7 (−3.4, −2.0) |
| Female | 1 | 1999 | 2014 | *−2.7 (−3.7, −1.8) | −2.4 (−5.6, 1.0) |
| 2 | 2014 | 2017 | −17.7 (−34.3, 3.1) | ||
| 3 | 2017 | 2020 | *18.1 (5.5, 32.2) | ||
| Age (Years) | |||||
| < 25 | 1 | 1999 | 2020 | *−4.0 (−5.6, −2.3) | *−4.0 (−5.6, −2.3) |
| 25–44 | 1 | 1999 | 2018 | *−3.3 (−4.2, −2.5) | −0.9 (−3.5, 1.9) |
| 2 | 2018 | 2020 | 25.8 (−6.4, 69.1) | ||
| 45–64 | 1 | 1999 | 2020 | *−2.2 (−2.9, −1.5) | *−2.2 (−2.9, −1.5) |
| ≥ 65 | 1 | 1999 | 2020 | *−3.7 (−4.5, −2.9) | *−3.7 (−4.5, −2.9) |
| a County | |||||
| Bronx | 1 | 1999 | 2020 | *−2.2 (−3.4, −0.9) | *−2.2 (−3.4, −0.9) |
| Kings | 1 | 1999 | 2006 | −0.7 (−3.9, 2.7) | −1.7 (−6.1, 2.8) |
| 2 | 2006 | 2010 | −11.6 (−21.9, 0.0) | ||
| 3 | 2010 | 2014 | 7.1 (−5.4, 21.3) | ||
| 4 | 2014 | 2017 | −17.7 (−35.8, 5.5) | ||
| 5 | 2017 | 2020 | *17.4 (3.8, 33.0) | ||
| New York | 1 | 1999 | 2012 | *−2.6 (−4.1, −1.0) | −2.4 (−5.6, 0.8) |
| 2 | 2012 | 2018 | *−9.3 (−15.2, −2.9) | ||
| 3 | 2018 | 2020 | 22.4 (−9.7, 65.9) | ||
| Queens | 1 | 1999 | 2020 | *−2.5 (−3.2, −1.9) | *−2.5 (−3.2, −1.9) |
| b Road User Type | |||||
| Motor vehicle occupant | 1 | 1999 | 2018 | *−5.3 (−6.3, −4.2) | −2.4 (−5.7, 1.0) |
| 2 | 2018 | 2020 | 29.4 (−11.1, 88.2) | ||
| Motor cyclist | 1 | 1999 | 2020 | −0.1 (−1.9, 1.8) | 0.1 (−1.9, 1.8) |
| Pedestrian | 1 | 1999 | 2020 | *−2.6 (−3.4, −1.8) | *−2.6 (−3.4, −1.8) |
*significant at p < 0.05; confidence interval does not include zero
aRichmond County was excluded due to low and unreliable death counts
bPedal cyclist was excluded due to low and unreliable death counts
Fig. 1.
Trends in Motor Vehicle Traffic Deaths by Race and Ethnicity, US, 1999–2020.Non−Hispanic Whites: (Annual Percentage Change [APC] = −3.9; 95% CI:−4.7, −3.2); Non-Hispanic Blacks: (APC = −2.4; 95% CI: −3.7, −1.2); Asian/Pacific Islander: (APC = −3.3; 95% CI: −4.2, −2.3); Hispanics: (APC = −1.6; 95% CI: −2.4, −0.7). The APC values and their 95% Confidence Intervals (CIs) were estimated using Joinpoint regression analysis, a statistical method that identifies changes in trends over time. Negative APC values indicate a decreasing trend in mortality rates, while positive APC values indicate an increasing trend. The 95% CI provides a range of values within which the true APC is likely to fall with 95% probability. Non-Hispanic Whites, non-Hispanic Blacks, Asian/Pacific Islanders, and Hispanics are mutually exclusive racial and ethnic groups as defined by the US Census Bureau
Trends by Sex
AAMR among male decedents declined consistently at an annual rate of 2.7% (95% CI: −3.4, −2.0) from 1999 to 2020. Among females, three significant trend segments were noted- a significant decline from 1999 to 2014 (APC = −2.7; 95% CI: −3.7, −1.8); a stationary trend from 2014 to 2017 (APC = −17.7; 95% CI: −34.3, 3.1), and; an ascending trend, by 18.1% per year (95% CI: 5.5, 32.2) from 2017 to 2020.
Trends by Age
During 1999 to 2019, AAMR decreased by 4% per year (95% CI: −5.6, −2.3) among persons aged less than 25 years; by 2.2% per year (95% CI: −2.9, −1.5) among persons aged 45–64 years, and; by 3.7% per year (95% CI: −4.5, −2.9) among older adults, aged more than 65 years. The rates among 25–44 year-olds declined by 3.3% per year (95% CI: −4.2, −2.5) from 1999 to 2018, and then stabilized from 2018 to 2020 (APC = 25.8; 95% CI: −6.4, 69.1).
Trends by Road User Type
AAMR among motor vehicle occupants initially declined at an annual rate of 5.3% from 1999 to 2018 (95% CI: −6.3, −4.2), but stabilized from 2018 to 2020 (APC = 29.4; 95% CI: −11.1, 88.2). The rate remained stationary (APC = −0.1; 95% CI: −1.9, 1.8) among motorcyclist from 1999 to 2020. Among pedestrians, AAMR dropped by 2.6% per year (95% CI: −3.4, −1.8) from 1999 to 2020 (Table 2).
Trends by County
AAMR in Bronx County declined at an annual rate of 2.2% (95% CI: −3.4, −0.9) from 1999 to 2020. The trend in Kings County was initially stationary from 1999 to 2017 but increased by 17.4% per year (95% CI: 3.8, 33.0) from 2017 to 2020. In New York County, AAMR declined from 1999 to 2012 (APC = −2.6; 95% CI: −4.1, −1.0) and from 2012 to 2018 (APC = −9.3; 95% CI: −15.2, −2.9). The rate stabilized afterwards, from 2018 to 2020 (APC = 22.4; 95% CI: −9.7, 65.9). AAMR in Queens County declined by 2.5% per year from 1999 to 2020.
Discussion
We used a large publicly available dataset and assessed the trends in MVT mortality in New York City from 1999 to 2020. The mortality rate, overall, decreased throughout the study period, as is the trend among all racial and ethnic subgroups. Recent trends have declined or stabilized by age and road user type. However, rates have increased among females and persons from Kings County, from 2017 to 2020.
The findings of declining or stationary trends in MVT mortality are similar to those reported nationwide [15]. New York was the first state in the United States to implement a law prohibiting the use of a handheld cell phone while driving [16]. There is evidence that the ban has had a large and lasting impact on MVT mortality [16–18]. [17] examined the mean annual rate of crashes in New York State in the period before the ban (1997–2001) and after the ban (2002–2007). The authors found a significant reduction in crash mortality statewide and in 46 of 62 counties, including New York City. Another reason for the improved MVT mortality trends could be the Vision Zero initiative implemented in New York City in 2014, which expanded enforcement against dangerous moving violations [19]. For example, the City's camera program has been associated with a 60% reduction in speeding around public schools [19]. The initiative has also improved street designs and safety configurations and expanded public outreach and communications, amongst other developments [19]. More recently, New York City Mayor- Eric Adams launched a $4 million campaign- 'Speeding ruins lives. Slow down,' to improve traffic violence associated with the COVID-19 Pandemic [20]. The campaign is designed to feature video advertisements and other media-related content in nine languages. However, the current study could not have captured the impact of this campaign, considering that the mortality data were from 1999 to 2020. Additional reasons for the improved mortality trends could include strengthening road traffic surveillance and sanctions (e.g., around seat belt use and driving under the influence), the driver violation point system, motor vehicle inspections, and other traffic and road safety regulations [19]. Further research should identify the determinants of the improved trends reported in this study to ensure a sustainable decline in motor vehicle mortality and the attainment of the Vision Zero objective in New York City.
Despite the declining or stabilizing mortality trend, overall, and by age, race/ethnicity, and road user type, MVT mortality rates have increased in females and residents of Kings County. The reasons that underpin these findings are unclear and warrant further research. Previous studies have demonstrated that males have experienced higher mortality rates from motor vehicle accidents than females, which might be attributed to their gender roles [21]. However, as time has progressed, the prominence of gender roles has decreased, thus, narrowing the gap in MVT fatality rates- the Convergence Hypothesis [22]. Future studies should investigate the predictors of the rising mortality rate among females and residents of Kings County. Age distribution, polysubstance or alcohol abuse, psychosocial stressors, changes in access to public transportation, drivers education policy, access to emergency care, and differential compliance with traffic laws, among other factors, might explain the rising trend in this demographic.
This study is subject to several limitations. Mortality rates for American Indians/Alaska Natives, decedents from Richmond County, and pedal cyclists were excluded from the analysis because the counts were too small or deemed unreliable by the WONDER database. Future studies could explore alternative data sources to address these missing categories and improve the accuracy of mortality estimates. Second, the race and ethnicity of decedents were primarily obtained from death certificates and subject to potential misclassification, as previously reported in other studies that used WONDER data [23, 24]. Third, this study focused only on mortality events from motor vehicle traffic accidents and did not include non-fatal crashes. Future research could expand the scope to encompass non-fatal events and potentially provide a more comprehensive picture of traffic safety outcomes.
Conclusions
The results of this study show a generally positive trend in MVT mortality in New York City, with decreases seen across various demographic groups, such as age, race/ethnicity, and type of road user. This improvement may be attributed to the implementation of effective road traffic surveillance measures and sanctions, road safety campaigns (such as the Vision Zero initiative), motor vehicle inspections, and other traffic and road safety regulations. However, the study also identifies a concerning increase in MVT mortality among females and residents of Kings County from 2017 to 2020. Further investigation is necessary to understand the potential impact of factors such as polysubstance or alcohol abuse, psychosocial stressors, access to medical and emergency care, and compliance with traffic laws on this observed trend. These findings emphasize the importance of continued efforts to prevent MVT deaths and promote community health and safety.
Acknowledgements
None.
Funding
The authors did not receive support from any organization for the submitted work.
Declarations
Conflict of interest
The authors of this manuscript have no relevant financial or non-financial competing interests to disclose.
Footnotes
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Contributor Information
Ibraheem M. Karaye, Email: Ibraheem.m.karaye@hofstra.edu
Temitope Olokunlade, Email: adegbayi@tamu.edu.
Alyssa Cevetello, Email: acevetello1@pride.hofstra.edu.
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