Trends in traffic accidents mortality and social inequalities in Ecuador from 2011 to 2022

Trends in traffic accidents mortality and social inequalities in Ecuador from 2011 to 2022 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trends in traffic accidents mortality and social inequalities in Ecuador from 2011 to 2022 Juan Pablo Holguín-Carvajal, Tamara Otzen, Antonio Sanhueza, Álvaro Castillo, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4254108/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Jul, 2024 Read the published version in BMC Public Health → Version 1 posted 22 You are reading this latest preprint version Abstract Background According to WHO data, traffic accidents caused 1,282,150 deaths globally in 2019, with a projection to become the fifth leading cause of mortality by 2030, highlighting significant public and economic health impacts. This study aimed to describe the trends in traffic accidents mortality in Ecuador between 2011 and 2022, by year, gender, age group, geographic location, type of accident and social inequalities. Methods A population-based study was conducted using national statistics on mortality due to traffic accidents in Ecuador, between 2011 and 2022, obtained from the National Institute of Statistics and Census. Crude mortality rates, adjusted per region per 100,000 inhabitants, were calculated by region, province, gender, and age group. The annual percentage change of the traffic accidents mortality rate and the Absolute Risks were calculated, as well as rate ratios between the groups. Inequalities by per capita income and by illiteracy rate were also calculated. Results The average mortality rate due to traffic accidents in Ecuador (2011–2022) was 19.1. The rates were higher in men (31.3) than in women (7.2), with a rate ratio of 4.3. The rates were higher in the Amazon region (24.0), decreasing in recent years, with a statistically significant negative annual percentage variation of -1.2%, as in the Sierra region and Coast. Santo Domingo de los Tsáchilas presented the highest rate (30.6), while, the highest rate related to age, (27.4) was identified in the 17 to 24 years group. In 2011, the highest rate (22.0) was recorded. The most frequent type of accident was "unspecified" followed by pedestrians. Conclusions There was evidence of an increase (247.7%) of traffic accidents mortality inequalities stratified by per capita income between 2011 and 2019 and a decrease (18.9%) of traffic accidents mortality inequalities stratified by the illiteracy rate between 2014 and 2019. In Ecuador, between 2011 and 2020, transport accident mortality rates are decreasing significantly, showing important disparities by gender, age group, and province. The high frequency of “unspecified” causes denotes the necessity to improve the registration and/or coding system of the causes of death due to traffic accidents in Ecuador. traffic accidents mortality trends Ecuador Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Traffic accidents (TA) encompass any incident involving vehicles designated for transporting individuals or goods between different locations. These incidents are classified under "Chapter XX: External Causes of Morbidity and Mortality" in the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10), facilitating the generation of global statistics via specific codes ( 1 ). TA are coded and summarized as V01-V99 (transport accidents), with subdivisions based on injury type, vehicle involved, accident nature, and other unspecified aspects ( 1 ). The Sustainable Development Goals (SDGs), particularly Goal Three, emphasize Health and Well-being. By 2030, one of the targets is to halve the global number of deaths and injuries resulting from TA ( 2 ). Moreover, the World Health Organization (WHO) underscores the efficacy of measures implemented in numerous countries to enhance road safety ( 3 ). Globally, according to WHO statistics, there were 1,282,150 TA related deaths in 2019, with higher mortality rates observed among men and in low-income countries. Additionally, based on World Bank income groups, TA mortality rates were distributed as follows: low income (28.3/100,000 population), lower-middle income (17.3/100,000 population), upper-middle income (16.8/100,000 population), and high income (8.4/100,000 population) ( 4 ). TA incur substantial economic losses, accounting for approximately 3% of Gross Domestic Product (GDP) in most countries ( 5 ). Trend analyses project that by 2030, traffic accidents will rank as the fifth leading cause of death, a public health issue globally recognized since 2004 ( 6 ). In 2012, TA were the primary cause of death among individuals aged 5–14 in the Americas and the second leading cause among those aged 15–44, resulting in 149,252 fatalities. In South America, particularly the Andean Region, the TA mortality rate was 22.1/100,000 population ( 6 ). In 2019, TA mortality ranked seventh in low-income countries and tenth in lower-middle and upper-middle income countries ( 8 ). A study in Ecuador covering 2000 to 2015 found an average TA mortality rate of 11.4/100,000 population ( 9 ), lower than the Americas' average rate (15.8/100,000 population) ( 6 ). In 2020, Ecuador recorded 16,972 TAs, leading to 2,600 deaths (15.3%). This represents a 31% reduction from 2019, with a mortality rate of 14.8 per 100,000 population. Of the total fatalities, 64% occurred at the accident site, while the remaining 36% died in hospitals or care centers ( 10 ). This study aims to contribute significantly to the existing body of knowledge by providing a comprehensive analysis of the burden posed by TAs in Ecuador, including underlying disparities, and offering recommendations to address this critical public health issue more effectively and equitably. Therefore, our study seeks to fill these gaps by examining the mortality trend attributable to traffic accidents in Ecuador. Specifically, we will analyze distribution patterns across various parameters, with the overarching objective of informing the development of more effective and equitable prevention policies and programs. Methods Aim To describe the trend in TA mortality and inequalities in Ecuador for the period 2011–2022, distributed by year, gender, age group, geographical location, type of accident, and social inequalities. Design An ecological study was conducted using aggregated national level data on traffic accidents in Ecuador during the period from 2011 to 2022. Subjects The population of interest consisted of all individuals who deceased due to TA in Ecuador between 2011 and 2022, obtained from the databases of the National Institute of Statistics and Census (INEC), of the Republic of Ecuador ( 11 ). The general data on the population and live births were obtained from the same database, based on the national population projection distributed by age groups and by provinces, as well as data on the number of vehicles registered at the national level and by province ( 12 ). Study Variables The variables were year (2011 to 2022); geographic region (Coast region: El Oro, Esmeraldas, Guayas, Los Ríos, Manabí, Santo Domingo, Santa Elena; Sierra: Azuay, Bolivar, Cañar, Carchi, Cotopaxi, Chimborazo, Imbabura, Loja, Pichincha, Tungurahua; Amazon: Morona Santiago, Napo, Pastaza, Zamora Chinchipe, Sucumbíos, Orellana; Insular: Galápagos; and Undelimited area); gender (male and female); age group (60 years); and accident type coded according to ICD-10 ( 1 ). Data Analysis Exploratory analyses were conducted using descriptive statistics for percentages, central tendency, and variability. Mortality rates were calculated with the number of deaths as the numerator and population as the denominator, per 100,000 inhabitants. Absolute risks (AR) and rate ratios (RR) were determined by geographic distribution, sex, age group, and accident type. Annual percentage variation (APV) in rates was analyzed using linear regression models with 95% confidence intervals and p-values ( 13 ). Additionally, traffic accident mortality rates (TAMR) per 10,000 vehicles were calculated. Further analyses included examining inequalities in traffic accident mortality as a sole health indicator, alongside annual per capita income (PCI) and literacy rates as socioeconomic stratifiers, and live births per province as the demographic variable. Simple measures of absolute gaps (AG) and relative gaps (RG), as well as complex measures like the Slope Inequality Index (SII), were calculated using simple linear regression models ( 14 ). The analyses were performed using IBM® SPSS® Statistics version 27, Microsoft® Excel for Mac version 16.57, and the EquiGap® macro developed by the EWEC-LAC metrics and monitoring working group. Results General Mortality During the study period, 38,355 individuals died due to traffic accidents in Ecuador, of whom 31,187 (81.3%) were men. The TAMR in this period was 19.4 per 100,000 inhabitants (Table 1). Table 1. Traffic Accident Mortality Rate per 100,000 inhabitants by gender, region, provinces, age range, and male vs female rate ratio with respective 95% CI and p-value. TAMR APV 95% CI p General rate 19.43 -0.42 -1.15 0.31 0.28 Gender Male 31.99 -0.24 -1.00 0.52 0.54 Female 7.19 -1.11 -1.87 -0.35 0.02 Region Sierra 18.96 -0.93 -1.46 -0.41 0.01 Coast 20.35 0.02 -0.85 0.90 0.97 Amazon 24.36 -1.17 -2.29 -0.04 0.07 Insular 7.49 -4.23 -10.29 2.24 0.22 Undelimited area 1.63 -5.29 -9.56 -0.82 0.04 Provinces Azuay 14.15 0.78 -0.92 2.50 0.39 Bolívar 20.64 0.81 -0.79 2.44 0.34 Cañar 23.22 0.62 -0.62 1.87 0.35 Carchi 19.71 -0.30 -1.56 0.97 0.65 Cotopaxi 27.72 -0.95 -2.32 0.43 0.21 Chimborazo 24.76 -0.45 -1.42 0.54 0.40 El Oro 22.16 -0.53 -1.63 0.58 0.37 Esmeraldas 15.69 -0.06 -1.82 1.74 0.95 Guayas 19.24 -0.54 -1.33 0.25 0.21 Imbabura 17.94 -2.67 -3.99 -1.35 <0.01 Loja 12.65 -0.68 -2.38 1.04 0.45 Los Ríos 27.31 0.51 -0.39 1.42 0.29 Manabí 15.65 0.16 -0.60 0.94 0.69 Morona Santiago 23.84 0.88 -1.17 2.97 0.42 Napo 24.15 -1.78 -3.41 -0.12 0.06 Pastaza 17.95 -0.02 -2.70 2.73 0.99 Pichincha 17.83 -0.82 -1.41 -0.24 0.02 Tungurahua 18.68 -0.93 -2.00 0.14 0.12 Zamora Chinchipe 17.10 -1.46 -3.22 0.34 0.14 Galápagos 7.49 -4.23 -10.29 2.24 0.22 Sucumbíos 29.62 -1.93 -3.76 -0.06 0.07 Orellana 27.56 -1.71 -3.24 -0.15 0.06 Santo Domingo 30.63 -0.16 -0.80 0.48 0.63 Santa Elena 13.07 0.59 -0.77 1.97 0.42 Age Range 0 to 16 years 5.10 -2.05 -3.23 -0.86 0.01 17 to 24 years 26.84 -1.02 -1.52 -0.53 <0.01 25 to 40 years 28.57 0.52 -0.45 1.50 0.32 41 to 59 years 21.40 -1.26 -1.87 -0.65 <0.01 60 or more years 31.05 -2.25 -3.07 -1.43 <0.001 1 TAMR: Traffic Accident Mortality Rate; 2 APV: Annual Percentage Variation; 3 CI: Confidence Interval; 4 p: p-value The TAMR between 2011 and 2022 exhibited a decreasing APV of -0.4% (95% CI= -1.15; 0.31; p=0.28) not being statistically significant (Table 1). The years with the highest TAMR were 2011 (22.0 per 100,000 inhabitants) and 2022 (21.7 per 100,000 inhabitants); whereas, the years with the lowest TAMR were 2020 (14.9 per 100,000 inhabitants) and 2016 (18.0 per 100,000 inhabitants) (Table 2). Table 2. Traffic Accident Mortality Rate per 100,000 inhabitants by year, gender, and male vs female rate ratio. Year n TAMR Male Female M/F RR 2011 3368 22.06 36.35 8.01 4.54 2012 3186 20.53 32.88 8.39 3.92 2013 3109 19.71 31.95 7.69 4.15 2014 3323 20.73 33.50 8.20 4.09 2015 3157 19.39 31.85 7.17 4.44 2016 2980 18.03 29.46 6.82 4.32 2017 3079 18.35 29.72 7.20 4.13 2018 3244 19.06 31.22 7.13 4.38 2019 3279 18.99 31.30 6.93 4.52 2020 2600 14.85 24.96 4.94 5.05 2021 3345 19.75 33.67 6.52 5.16 2022 3685 21.75 37.01 7.26 5.10 2011-2022 38355 19.39 31.99 7.19 4.48 1 n: number of deaths; 2 TAMR: Traffic Accident Mortality Rate; 3 M/F: Male/Female; 4 RR: Rate ratio Mortality by Geographic Region According to geographic region, the highest rates in the period were in the Amazon and Coast regions with 24.4 per 100,000 inhabitants and 20.4 per 100,000 inhabitants, respectively (Table 1 and Figure 1). Regarding provinces, the highest TAMR was registered in Santo Domingo (30.6 per 100,000 inhabitants) and Sucumbíos (29.6 per 100,000 inhabitants); while the lowest TAMR was for the undelimited area (1.9 per 100,000 inhabitants) and Galápagos (7.9 per 100,000 inhabitants) (Table 1). The analysis of the trend among geographic regions revealed that the undelimited area and the insular region had the highest APV (-5.3%; 95% CI: -9.56 to -0.82; p=0.04; and -4.2%; 95% CI: -10.29 to 2.24; p=0.22, respectively) (Table 1). There was a 1.2 times higher risk of mortality due to TA in the Amazon compared to the Coast; with a decreasing APV of -1.2% (95% CI: -1.83 to -0.56; p=0.01). It was evident that the greatest difference in rates between the provinces of Santo Domingo de los Tsáchilas and Galapagos (AR=3,7) represented an APV with an annual increase of 4.3% in the rates (95% CI: -0.31 to 9.21; p=0.10) (Table 3). Table 3. Absolute risk and annual percentage variation of absolute risk with 95% CI. AR APV 95% CI p Male/Female 4.48 0.85 0.41 1.29 0.01 Coast/Sierra 1.08 0.98 0.43 1.53 0.01 Amazon/Coast 1.20 -1.20 -1.83 -0.56 0.01 Santo Domingo/Galápagos 3.68 4.34 -0.31 9.21 0.10 Pedestrian/Bus 35.57 -7.71 -12.63 -2.51 0.02 Unspecified Transport/Pedestrian 4.18 6.93 6.01 7.86 <0.001 60 and over/0 to 16 years 6.18 -0.19 -1.40 1.05 0.77 1 AR: Absolute risk; 2 APV: Annual percentage variation; 3 CI: Confidence interval; 4 p : p-value Mortality by Gender Regarding gender, it was identified that men have a higher TAMR than women every year, with the highest TAMR for men in 2022 being 37.0 per 100,000 inhabitants, while for women, it was 8.4 per 100,000 inhabitants in 2012. The lowest TAMR in both men and women was in 2020 with 24.9 per 100,000 inhabitants and 4.9 per 100,000 inhabitants, respectively (Table 2 and Figure 2). Additionally, in 2021, the TAMR was 5.2 times higher in men than in women, while in 2012, it was 3.9 times higher in men than in women. The average male-to-female RR for the 12 years of the study was 4.5 (Table 3). During the study period, women showed a higher APV in TAMR, at -1.11% decreasing (CI=-1.87 to -0.35; p=0.02) (Table 1 and Table 2). For the period 2011-2022, it was confirmed that the absolute risk of TA mortality in men compared to women was 4.5 times higher (AR=4.5); indicating an annual increase, associated with an APV of 0.9% (CI=0.41-1.29; p=0.01) (Table 1 and Table 3). Mortality by Age Group Analyzing the TAMR according to age groups, the highest mortality was observed in the ≥60 years group (31.0 per 100,000 inhabitants) and the 25 to 40 years group (28.6 per 100,000 inhabitants); while the lowest rate was in the 0 to 16 years group (5.1 per 100,000 inhabitants). This trend remained consistent throughout the study period except for 2020, when the ≥60 years group exhibited the lowest rate (19.9 per 100,000 inhabitants) (Table 1). Year General Male Female x SD Min Max x SD Min Max x SD Min Max Diff 2011 37.11 20.86 1 99 36.94 19.86 1 99 37.86 24.82 1 99 0.9 2012 37.03 20.58 1 99 36.73 19.59 1 99 38.17 24.00 1 99 1.4 2013 37.33 20.63 1 103 36.63 19.43 1 99 40.19 24.72 1 103 3.6 2014 37.91 20.58 1 100 37.5 19.54 1 100 39.55 24.28 1 99 2.1 2015 37.75 20.79 1 101 36.76 19.30 1 101 42.08 25.91 1 93 5.3 2016 38.83 20.80 1 98 38.37 19.61 1 95 40.79 25.14 1 98 2.4 2017 38.74 20.89 1 102 38.23 19.55 1 98 40.81 25.51 1 102 2.6 2018 38.65 20.70 1 101 37.98 19.73 1 100 41.56 24.22 1 101 3.6 2019 38.68 20.44 1 102 38.06 19.54 1 99 41.43 23.85 1 102 3.4 2020 37.16 18.69 1 109 36.85 17.92 1 109 38.71 22.09 1 99 1.9 2021 37.17 18.46 1 110 36.73 17.43 1 110 39.31 22.77 1 96 2.6 2022 36.99 17.97 1 99 36.3 16.88 1 99 40.34 22.22 1 98 4 2011-2022 37.78 20.12 1 101,9 37.26 19.04 1 100.7 40.07 24.13 1 99.08 2.82 1 x: Average age; 2 SD: Standard Deviation; 2 Min: Minimum age; 3 Max: Maximun age; 4 Diff: Average age difference The overall average age of fatalities due to TA was 37.8 years (Standard Deviation (SD)=20.1). For men, it was 37.3 (SD = 19.0) and for women, 40.0 (SD = 24.1). The year with the lowest average age was 2022 (36.9 years, SD = 17.9), and the highest was 2017 (38.7 years). The differences for each year remained constant (APV=0.03%; p=0.72; 95% CI: -0.12 to 0.17) (Table 4). Table 4. Averages of ages of fatalities due to Traffic Accidents. Year General Male Female x SD Min Max x SD Min Max x SD Min Max Diff 2011 37.11 20.86 1 99 36.94 19.86 1 99 37.86 24.82 1 99 0.9 2012 37.03 20.58 1 99 36.73 19.59 1 99 38.17 24.00 1 99 1.4 2013 37.33 20.63 1 103 36.63 19.43 1 99 40.19 24.72 1 103 3.6 2014 37.91 20.58 1 100 37.5 19.54 1 100 39.55 24.28 1 99 2.1 2015 37.75 20.79 1 101 36.76 19.30 1 101 42.08 25.91 1 93 5.3 2016 38.83 20.80 1 98 38.37 19.61 1 95 40.79 25.14 1 98 2.4 2017 38.74 20.89 1 102 38.23 19.55 1 98 40.81 25.51 1 102 2.6 2018 38.65 20.70 1 101 37.98 19.73 1 100 41.56 24.22 1 101 3.6 2019 38.68 20.44 1 102 38.06 19.54 1 99 41.43 23.85 1 102 3.4 2020 37.16 18.69 1 109 36.85 17.92 1 109 38.71 22.09 1 99 1.9 2021 37.17 18.46 1 110 36.73 17.43 1 110 39.31 22.77 1 96 2.6 2022 36.99 17.97 1 99 36.3 16.88 1 99 40.34 22.22 1 98 4 2011-2022 37.78 20.12 1 101,9 37.26 19.04 1 100.7 40.07 24.13 1 99.08 2.82 1 x: Average age; 2 SD: Standard Deviation; 2 Min: Minimum age; 3 Max: Maximun age; 4 Diff: Average age difference Regarding the rate ratio in the ≥60 years group, a value of 6.4 times higher TAMR compared to the 0 to 16 years group was identified, with an annual decrease of -0.19% (95% CI: -1.40; 1.05; p=0.77) (Table 3). Mortality by Type of Accident Regarding the TAMR, it was reported that between 2011 and 2022, there were 6,698 (17.5%) fatalities due to "pedestrian injured in transport accidents (ICD-10 V01-V09)" and 22,121 (57.7%) fatalities due to "other unspecified transport accidents (ICD-10 V089)" (Table 5). These causes remained constant as the most frequent throughout each year; from 2018 onwards, there was an increase in fatalities among "Motorcyclists or occupants of motorized 3-wheeled vehicles (ICD-10 V20-V39)" as follows: 2018 (15.0%), 2019 (15.3%), 2020 (13.2%), 2021 (16.8%), 2022 (13.2%), and a decrease in fatalities among "pedestrians injured in transport accidents (ICD-10 V01-V09)" as follows: 2018 (14.6%), 2019 (13.7%), 2020 (11.2%), 2021 (9.3%), 2022 (7.0%). Conducting an analysis of the types of annual TA deaths between 2011 and 2022, mortality among "pedestrians" showed the greatest variation with a tendency to decrease (APV= -5.7%; 95% CI: -6.45 to -4.91; p<0.001) (Table 5). Table 5. Types of traffic accidents with their respective percentages and annual percentage rates variation with 95% CI. Types of traffic accidents n % APV CI 95% p Pedestrian 6698 17.46 -5.68 -6.45 -4.91 <0.001 Cyclist 416 1.08 0.89 -1.60 3.44 0.50 Motorcyclist 5430 14.16 1.91 -0.41 4.29 0.14 Vehicle Occupant 998 2.60 -4.63 -7.20 -1.98 0.01 Van Occupant 485 1.26 2.40 -2.14 7.15 0.33 Heavy Vehicle Occupant 289 0.75 3.21 -1.02 7.62 0.17 Bus Occupant 387 1.01 2.25 -3.40 8.23 0.46 Other Transport 1133 2.95 -1.00 -7.26 5.69 0.77 Unspecified 22121 57.67 0.84 0.03 1.67 0.07 Maritime, Aerial, Space 398 1.04 -4.54 -10.20 1.47 0.17 1 n: number of death; 2 %: Percentage of fatalities; 2 APV: Annual Percentage Variation; 3 CI: Confidence Interval; 4 p : p-value Regarding the differences in mortality risk by type of TA, it was evident that "Other unspecified transport" had 4.2 times more TAMR than that occurring in "pedestrians", with an APV of 6.9 (95% CI: 6.01 to 7.86; p<0.001); while the highest risk of mortality from traffic accident was among "pedestrians", 35.6 times more than "bus occupants", with an annual decrease of -7.7% (95% CI: -12.63 to -2.51; p=0.02). Additionally, the mortality rate per 10,000 vehicles from 2011 to 2022 was calculated, resulting in 15.8 per 10,000 vehicles. It was also observed that the year with the highest mortality was 2011 (22.6 per 10,000 vehicles); and the lowest rate was in 2020 (11.0 per 10,000 vehicles). The APV of TA mortality per 10,000 registered vehicles for the entire period was a decreasing -2.4% (95% CI: -2.98 to -1.75; p<0.001). Inequality Analysis It was identified that in 2011, there were 0.4 more deaths (AG) per 100,000 live births due to TA in the group of provinces with the lowest PCI compared to the group of provinces with the highest PCI; whereas, in 2019, there were 2.9 more deaths (AG) per 100,000 live births due to traffic accidents in the provinces with the lowest PCI compared to those with the highest PCI, representing a 500% increase in the AG between 2011 and 2019 (Figure 3). The risk of mortality due to TA in 2011 for the group of provinces with the lowest PCI was 1.0 times higher (RG) than for the group of provinces with the highest PCI; while in 2019, the risk of mortality due to TA in the group of provinces with the lowest PCI was 1.1 times higher (RG) than in the group of provinces with the highest PCI, indicating a 14.5 percentage point increase in the RR between 2011 and 2019 (Figure 3). Upon calculating the SII in TA mortality in provinces stratified by PCI, it was found that inequality increased by 247.7% between 2011 and 2019. The analysis of the TAMR per 100,000 live births (mortality rates) in the years 2011 and 2019, when compared with the equity stratifier (PCI) and categorized by quintiles (Q1 to Q4, ranging from least advantageous condition to most advantageous condition), reveals that the highest mortality rate is predominantly observed in Q1, and the lowest in Q4 for both years. Concerning simple metrics, it is noted that the equity stratifier (PCI) registered a value of 0.48 (95% CI: -17.01 to 17.96) in BA in 2011, and 2.98 (95% CI: -14.57 to 20.53) in 2019; along with a value of 1.02 (95% CI: 0.49 to 2.12) in BR in 2011 and 1.17 (95% CI: 0.47 to 2.92) in 2019. These figures indicate the most significant departure from the condition of equity, reflecting the greatest degree of inequality concentrated among populations with the most and least social advantage, respectively (Figure 3). In 2014, there were 3.0 more deaths (AG) per 100,000 live births due to TA in the group of provinces with lower literacy levels compared to the group with higher literacy levels; whereas, in 2019, there were 2.7 more deaths (AG) per 100,000 live births due to TA in the group of provinces with lower literacy levels compared to those with higher literacy levels, signifying a 10.5% decrease in the AG between 2014 and 2019 (Figure 4). The risk of mortality due to TA in 2014 in the group of provinces with lower literacy levels was 1.1 times higher (RG) than in the group of provinces with higher literacy levels; a value very similar to 2019 (1.1 times higher (RG)), representing a 0.5% decrease in the RG (Figure 4). The analysis of the TAMR per 100,000 live births (mortality rates) in the years 2014 and 2019, when compared with the equity stratifier (illiteracy rate percentage) and categorized by quintiles (Q1 to Q4, ranging from least advantageous condition to most advantageous condition), indicates that the highest mortality rate is concentrated in Q1, while the lowest is in Q4 for both years. In terms of simple metrics, the equity stratifier (illiteracy rate percentage) demonstrated a value of -3.08 (95% CI: -18.3 to 12.14) in BA in 2014, and -2.76 (95% CI: -17.47 to 11.95) in 2019; coupled with a value of 0.861 (95% CI: 0.4 to 1.84) in BR in 2014 and 0.86 (95% CI: 0.39 to 1.91) in 2019. These values represent the most significant deviation from the condition of equity, reflecting the highest degree of inequality concentrated among populations with the most and least social advantage, respectively (Figure 4). When considering the SII in TA mortality in provinces stratified by the illiteracy rate percentage, it was found that inequality decreased by 18.0%. Regarding complex metrics, it is observed that the IPC stratifiers with values of 247.7 in IDP and illiteracy rate with values of 18.0 in IDP, describe the values furthest from the conditions of equity. The condition of inequality is focused among populations with the most and least social advantage, respectively, over the years. Discussion Considering the thorough examination conducted in this study, it is important to acknowledge the potential vulnerability to ecological fallacy, an inherent risk when interpreting data aggregated at the group level, which may not fully capture individual level nuances. However, it is crucial to emphasize that despite this inherent limitation, the integrity and robustness of the research findings remain steady and unaffected. The methodological rigor employed, alongside the data analysis techniques applied, ensures that the conclusions drawn provide a contribution to the understanding of the subject matter. Thus, while acknowledging this potential limitation, the study's outcomes stand as valuable insights into the prevailing trends and patterns, bolstering the scientific discourse on the topic. Regarding the differences in TA mortality rates between men and women, men exhibit higher rates (4 to 6 times more), as found in studies across Latin America and globally ( 15 – 18 ), as well as in our study. The most impacted age group was the one over 60 years, paralleling findings in Colombia where patients in this age range faced double the mortality risk from traffic accident compared to younger patients ( 19 ), however, findings from another study conducted in Colombia using data from 2019 revealed that the age group with the highest mortality rate was between 25 and 34 years old ( 18 ). A systematic review encompassing primary studies from the United States, Iran, Brazil, Egypt, China, Canada, and others highlighted increased mortality risk in the over 60 age group [OR = 2.57, CI 95% 1.2–5.4] ( 20 ). The 17 to 24-year age group ranked second in mortality rates, mirroring patterns in Argentina, Brazil, Chile in Latin America, and Poland in Europe, where this age group is most affected, attributed to riskier behaviors like speeding and non-helmet use ( 16 , 21 – 24 ). Our findings indicate a modest decrease of 0.42%, though it lacks statistical significance, notably in Sierra, compared to Brazil, Paraguay, Pakistan, Mongolia, and North Korea, where trends are stable or increasing ( 25 , 26 ). This underscores the need to sustain or enhance public policies for road safety as a public health priority, as mandated by Ecuador's law on terrestrial transport, transit, and road safety ( 27 ). Significant issues with underreporting and misclassification of TA types leading to mortality complicate the understanding of the problem's magnitude and limit regional data comparison. In our study, 54.9% were classified as "(V89) Accident in another type of unspecified transport," impacting data analysis precision ( 28 ). Despite initiatives by PAHO and the Latin American and Caribbean Network for Strengthening Health Systems (RELACSIS) to train health personnel in proper death certificate completion as per WHO standards ( 28 ). In the analysis of inequalities, it was evidenced that the level of illiteracy and per capita income pose greater risks in provinces with lower literacy and income rates compared to the quartile with better social conditions, echoing findings from a study in Quito where the highest mortality rate was prevalent in populous areas ( 29 ). A study in Canada revealed a higher incidence of pedestrians, cyclists, and vehicle occupants injured in poorer areas compared to wealthier ones ( 30 ). This mirrors results from a study in Norway, which showed increased mortality from TA in the 16 to 20-year age group in correlation with rising levels of social disadvantage and declining parental education levels ( 31 ). Similarly, in the United States, it was indicated that there is a strong socioeconomic pattern associated with traffic accident mortality, where groups with higher education levels exhibited a greater decrease in mortality over time ( 32 ). The year 2020, marking the onset of the COVID-19 pandemic, brought changes in global traffic accident mortality trends. Our study identified a significant decrease in TA mortality rates compared to 2019 (a variation of 4.1 points), a trend consistent across all provinces and age groups. Notably, in the over 60 age group, there was a major reduction in traffic accident mortality rates from 30.8 per 100,000 inhabitants in 2019 to 19.9 per 100,000 inhabitants in 2020 (a variation of 10.99 points) ( 33 ). An TA analysis in Ecuador during the pandemic's early stages showed a 67.4% reduction in fatalities due to confinement and mobility restrictions ( 33 ). Research in Peru reported that TA mortality experienced the greatest decline among external causes of death, particularly during the first 40 days of confinement, before returning to its previous trend towards the end of 2020 ( 34 ). Most reports and studies conducted in Ecuador have primarily provided descriptive data without thoroughly exploring the relationship between traffic accident mortality rates and other variables. In 2013, a study specifically conducted in Quito aimed to elucidate the distribution of fatalities and their determinants. This study highlighted that a small proportion of the deceased (0.49%) belonged to a high socioeconomic status, while the majority (76%) belonged to a lower socioeconomic status. Moreover, it identified that several male adult fatalities occurred due to pedestrian accidents while under the influence of alcohol. These findings underscore the importance of considering socioeconomic factors and alcohol consumption in further analyses stemming from this study ( 29 ). Lastly, factors associated with road safety in Ecuador are mandatory, including speed reduction, use of seat belts, child seats, helmets for motorcyclists, and refraining from driving under the influence of alcohol and drugs ( 6 , 27 ). However, it is imperative to acknowledge the nuanced complexity of adherence to these measures across diverse demographic groups within the population. This variability underscores a substantial limitation in our study, as we did not assess these factors comprehensively. Driving under the influence of alcohol is a common risk behavior, as observed in a study across several Latin American countries, where 1 in 6 injured patients admitted to the Emergency Department had consumed alcohol up to 6 hours before the accident. The probability of having a traffic accident after drinking was 5 times higher than in those who did not drink (with each alcoholic drink, the risk increased by 13%) ( 35 ). Therefore, future research should analyze these variables and their influence on traffic accident mortality in the Ecuadorian population. The limitations encompass various aspects, including the reliance on secondary data sources to ascertain both mortality rates stemming from traffic related injuries and the multifaceted elements associated with helmet and seat belt usage, driving under the influence of alcohol and drugs, and speed regulation, which warrants a more thorough examination. Moreover, the incomplete data regarding the age distribution of the vehicle fleet, as well as the state and safety features of vehicles and motorcycles, present notable hurdles. Additionally, the lack of comprehensive information concerning the equipment available in ambulances for patient care, along with inadequate insights into the condition of Ecuador's road infrastructure, underscores the necessity for more extensive scrutiny. Mitigating these constraints is pivotal for propelling the ongoing research endeavors within this domain forward. Conclusions This study is one of the few conducted in Ecuador aimed at describing and analyzing the trends in mortality due to TA. It has found an annual decrease in mortality rates during the study period, with a more significant decline among men, in the Amazon and Coast regions, and in the age group of 60 years and older. There is evidence of substantial underreporting in the causes of death. After excluding the main cause ((V89) Accident in another type of unspecified transport), the pedestrian group is the most affected. However, there has been a decrease in recent years, where motorcyclists exhibit higher mortality despite reforms to traffic laws made over these 10 years. Furthermore, this is the first study on inequalities in TA in Ecuador, in which we have conducted an analysis combining descriptive, associative, and inequality measures. We hope to provide the necessary information for decision makers to prioritize this public health issue. A particularly interesting finding relates to vulnerable road users over 60 years of age, who show the highest mortality rates. Although the overall rate in this group tends to decrease over the entire 12-year period, their vulnerability deserves special attention. There are socioeconomic inequalities in mortality rates, but it is necessary to study them more deeply to achieve the desired impact on reducing mortality due to traffic accidents. From these data, public policies could be generated. Abbreviations AG - Absolute gaps AR - Absolute risks APV - Annual percentage variation ICD-10 - 10th revision of the International Statistical Classification of Diseases and Related Health Problems GDP - Gross Domestic Product RELACSIS - Latin American and Caribbean Network for Strengthening Health Systems INEC - National Institute of Statistics and Census, of the Republic of Ecuador PCI - per capita income RR - rate ratios RG - Relative gaps SII - Slope Inequality Index SDGs - Sustainable Development Goals TAMR - traffic accident mortality rates TA - Traffic accidents WHO - World Health Organization Declarations Ethics Not applicable, as the study used official statistics and publicly accessible unnamed data from the Republic of Ecuador. Consent for publication Not applicable Availability of data and materials The data that are presented in this study are available on request from the corresponding author. The data are not publicly available due to maintaining privacy data of the participants such as e-mail addresses. Competing interests The authors declare no conflict of interest. Funding This research received no external funding. Author Contributions Conceptualization, J.P.H-C. and T.O.; methodology, J.P.H-C. and T.O.; soft-ware, J.P.H-C. and T.O.; validation, J.P.H-C. and T.O.; formal analysis, J.P.H-C. and T.O.; investigation, J.P.H-C. and T.O.; resources, J.P.H-C. and T.O.; data curation, J.P.H-C. and T.O.; writing—original draft preparation, J.P.H-C. and T.O.; writing—review and editing, J.P.H-C., T.O., A.S., A.C., C.M., G.M., F.G-A., and F.S-C.; supervision, T.O.; project administration J.P.H-C. and T.O.. All authors have read and agreed to the published version of the manuscript. Acknowledgments ANID—MILENIO—NCS2021_013 and ANID + SUBVENCIÓN A INSTALACIÓN EN LA ACADEMIA CONVOCATORIA AÑO 2022 + 85220114 References World Health Organization. ICD-10 Version: 2019. 2019. https://icd.who.int/browse10/2019/en . Accessed 12 Feb 2022. World Health Organization. Road traffic mortality. 2023. https://www.who.int/data/gho/data/themes/topics/sdg-target-3_6-road-traffic-injuries . Accessed 12 Feb 2023. World Health Organization. Global status report on road safety 2018 summary. 2018. http://apps.who.int/bookorders . Accessed 4 Mar 2023. World Health Organization. Road traffic deaths - Data by WHO region. 2022. https://apps.who.int/gho/data/view.main.RoadTrafficDeathREG?lang=en . Accessed 12 Feb 2023. World Health Organization. Traumatismos causados por el tránsito. 2021. https://www.who.int/es/news-room/fact-sheets/detail/road-traffic-injuries . Accessed 12 Feb 2023. Organización Panamericana de la Salud. Datos sobre la seguridad vial en la Región de las Américas. 2016. https://iris.paho.org/bitstream/handle/10665.2/28565/9789275319123-spa.pdf?sequence=6 . Accessed 23 Aug 2023. Thomas P, Muhlrad N, Hill J, Yannis G, Dupont E, Martensen H et al. Road Safety Data, Collection, Transfer and Analysis DaCota - Final Project Report. 2013. http://www.dacota-project.eu/Deliverables/DaCoTA_Final_Report.pdf . Accessed 23 Aug 2023. World Health Organization. The top 10 causes of death. 2020. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death . Accessed 12 Feb 12 2023. Algora-Buenafé AF, Russo-Puga M, Suasnavas-Bermúdez PR, Merino-Salazar P, Gómez-García AR. Tendencias de los accidentes de tránsito en Ecuador: 2000–2015. Revista Gerencia y Politicas de Salud. 2017;16(33):52–8. 10.11144/Javeriana.rgps16-33.tate . Instituto Nacional de Estadística y Censos (INEC). Anuario de Estadísticas de Transporte 2020. 2021. https://www.ecuadorencifras.gob.ec/documentos/web-inec/Estadisticas_Economicas/Estadistica de Transporte/2020/2020_ANET_PPT.pdf. Accessed 23 Aug 2023. Instituto Nacional de Estadística y Censos (INEC). Transporte. 2022. https://www.ecuadorencifras.gob.ec/transporte/ . Accessed 28 Feb 2023. Instituto Nacional de Estadística y Censos (INEC). Defunciones Generales. 2022. https://www.ecuadorencifras.gob.ec/defunciones-generales/ . Accessed 20 Aug 2023. Sanhueza A, Otzen T, Manterola C, Araneda N, Sanhueza A. Statistical Approaches for Analyzing a Continuous Outcome in Experimental Studies. Int J Morphol. 2014;32(1):339–50. 10.4067/S0717-95022014000100054 . Every Woman Every Child Latin America and Caribbean (EWEC LAC). Guía paso a paso para el cálculo de métricas de desigualdad en salud. 2015. https://www.everywomaneverychild-lac.org/wp-content/uploads/2017/06/Guia-paso-a-paso-versión-final.pdf . Accessed 28 Feb 2023. Ladeira RM, Malta DC, De Morais Neto OL, De Mesquita Silva Montenegro M, Filho AMS, Vasconcelos CH, et al. Acidentes de transporte terrestre: Estudo Carga Global de Doenças, Brasil e unidades federadas, 1990 e 2015. Revista Brasileira de Epi-demiologia. 2017;20:157–70. 10.1590/1980-5497201700050013 . Genowska A, Jamiołkowski J, Szafraniec K, Fryc J, Pajak A. Health care resources and 24,910 deaths due to traffic accidents: An ecological mortality study in Poland. Int J Environ Res Public Health. 2021;18(11). 10.3390/ijerph18115561 . Bahadorimonfared A, Soori H, Mehrabi Y, Delpisheh A, Esmaili A, Salehi M, et al. Trends of Fatal Road Traffic Injuries in Iran (2004–2011). PLoS ONE. 2013;8(5):1–5. 10.1371/journal.pone.0065198 . Murillo-Hoyos J, García-Moreno LM, Tinjacá N, Jaramillo C. Mortalidad por lesiones de tránsito y desigualdades sociales en Colombia, 2019. Revista Panamericana de Salud Pública 2023. 10.26633/RPSP.2023.121 . Segura Cardona ÁM, Cardona Arango D, Berbesí Fernández DY, Agudelo Martínez A. Mortalidad por accidente de tránsito en el adulto mayor en Colombia. Rev Saude Publica. 2017;51:1–8. 10.1590/S1518-8787.2017051006405 . Azami-Aghdash S, Aghaei MH, Sadeghi-Bazarghani H. Epidemiology of Road Traffic Injuries among Elderly People; A Systematic Review and Meta-Analysis. Bull Emerg Trauma. 2018;6(4):279–91. 10.29252/beat-060403 . Escanés G. Evolución de la mortalidad por atropellos y colisiones de tránsito en Argentina entre 2001 y 2010. Rev Bras Estud Popul. 2015;32(1):49–71. 10.1590/S0102-30982015000000004 . De Abreu OM, De Souza Menezes DR, De Freitas Mathias E. Impact of the brazilian traffic code and the law against drinking and driving on mortality from motor vehicle accidents. Cad Saude Publica. 2018;34(8). 10.1590/0102-311X00122117 . Otzen T, Sanhueza A, Manterola C, Hetz M, Melnik T. Transport accident mortality in Chile: Trends from 2000 to 2012. Ciencia e Saude Coletiva. 2016;21(12):3711–8. 10.1590/1413-812320152112.12652016 . Simán V, Rubio M, Talavera I, González K, Tinjacá N. Informe de juventud y siniestralidad vial. La relevancia de las juventudes para el logro de la meta del ODS 3.6, en materia de reducción de siniestralidad vial. 2021. https://www.paho.org/es/documentos/informe-juventud-siniestralidad-vial . Accessed 28 Feb 2023. Moreira MR, Ribeiro JM, Motta CT, Motta JIJ. Mortality by road traffic accidents in adolescents and young people, Brazil, 1996–2015: Will we achieve SDG 3.6? Ciencia e Saude Coletiva. 2018;23(9):2785–96. 10.1590/1413-81232018239.17082018 . James SL, Lucchesi LR, Bisignano C, Castle CD, Dingels ZV, Fox JT, et al. Morbidity and mortality from road injuries: Results from the Global Burden of Disease Study 2017. Inj Prev. 2020;26(Supp 1):i46–56. 10.1136/injuryprev-2019-043302 . Asamblea Nacional. Ley Orgánica Reformatoria De La Ley Orgánica De Transporte Terrestre, Tránsito Y Seguridad Vial [Internet]. Quinto Suplemento No 512-Registro Oficial Ecuador; 2021 p. 149. https://www.comisiontransito.gob.ec/wp-content/uploads/downloads/2021/08/LEY-ORGANICA-REFORMATORIA-DE-LA-LEY-ORGANICA-DE-TRANSPORTE-TERRESTRE-TRANSITO-Y-SEGURIDAD-VIAL.pdf . Accessed 28 Feb 2023. Pan American Health Organization (PAHO). Curso virtual sobre el correcto llenado del Certificado de Defunción – RELACSIS. https://www3.paho.org/relacsis/index.php/es/areas-de-trabajo/registro-adecuado-de-causas-de-muerte/curso-virtual-certificado-defuncion/ . Accessed 9 Mar 2023. Montero-Moretta GE. Revista Facultad Nac de Salud Pública. 2018;36(3):31–42. 10.17533/udea.rfnsp.v36n3a04 . Determinación social de la mortalidad por accidentes de tránsito en el distrito metropolitano de Quito, año 2013. Morency P, Gauvin L, Plante C, Fournier M, Morency C. Neighborhood Social Inequalities in Road Traffic Injuries: The Influence of Traffic Volume and Road Design. 2012;102(6):1112–9. 10.2105/AJPH.2011.300528 . Kristensen P, Kristiansen T, Rehn M, Gravseth HM, Bjerkedal T. Social inequalities in road traffic deaths at age 16–20 years among all 611 654 Norwegians born between 1967 and 1976: a multilevel analysis. Inj Prev. 2012;18(1):3–9. 10.1136/ip.2011.031682 . Harper S, Charters TJ, Strumpf EC. Trends in socioeconomic inequalities in motor vehicle accident deaths in the United States, 1995–2010. Am J Epidemiol. 2015;182(7):606–14. 10.1093/aje/kwv099 . Gómez-García A, Escobar-Segovia K, Cajías-Vasco P. Impacto del COVID-19 en la mortalidad por accidentes de tránsito en provincias de la República. de Ecuador CienciAmérica. 2021;10(1):24. https://cienciamerica.edu.ec/index.php/uti/article/view/355/695 . Calderon-Anyosa RJC, Bilal U, Kaufman JS. Variation in non-external and external causes of death in Peru in relation to the COVID-19 lockdown. Yale J Biology Med. 2021;94(1):23–40. PMID: 33795980. Borges G, Monteiro M, Cherpitel CJ, Orozco R, Ye Y, Poznyak V, et al. Alcohol and Road Traffic Injuries in Latin America and the Caribbean: A Case-Crossover Study. Alcohol Clin Exp Res. 2017;41(10):1731–7. 10.1111/acer.13467 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Jul, 2024 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 24 Jun, 2024 Reviews received at journal 23 Jun, 2024 Reviews received at journal 22 Jun, 2024 Reviews received at journal 22 Jun, 2024 Reviews received at journal 21 Jun, 2024 Reviews received at journal 20 Jun, 2024 Reviewers agreed at journal 17 Jun, 2024 Reviewers agreed at journal 16 Jun, 2024 Reviews received at journal 14 Jun, 2024 Reviewers agreed at journal 14 Jun, 2024 Reviewers agreed at journal 14 Jun, 2024 Reviewers agreed at journal 14 Jun, 2024 Reviewers agreed at journal 14 Jun, 2024 Reviewers agreed at journal 13 Jun, 2024 Reviewers agreed at journal 12 Jun, 2024 Reviewers agreed at journal 12 Jun, 2024 Reviewers agreed at journal 12 Jun, 2024 Reviewers invited by journal 12 Jun, 2024 Editor invited by journal 22 Apr, 2024 Editor assigned by journal 11 Apr, 2024 Submission checks completed at journal 11 Apr, 2024 First submitted to journal 11 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4254108","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290291771,"identity":"c5ac9612-4da9-4517-a291-77b0edbda78d","order_by":0,"name":"Juan Pablo Holguín-Carvajal","email":"","orcid":"","institution":"University of La Frontera","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"Pablo","lastName":"Holguín-Carvajal","suffix":""},{"id":290291772,"identity":"12de4c5b-fc06-40a7-a584-f41d4e5089de","order_by":1,"name":"Tamara Otzen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYNCDDwxyCA4fDkU8yAzGGQzGQIoZIsJGjBZmHmK02LOfTvxcUcMgZ89/+Nhj2zaDfH7p/oOPgSLyOG3hyd0seeYYgzEPw7F049w2A8uZcw4zGwJFDNtwOix3g2QDG0NiD2OPmXRu2x8DgxvJbCCRBJy28L/d/LPhH1ALM/83acs2A5AWdpAIbi0SudskG9uAWth42KQZIVrYGIEiuLXceLvNsrFPwpjnDJuZZM85AwPJGcnGkkARnH5h78/dfLPhm40ce//hZxI/ygwM+CUSH34Eisjz49ACBRJEiIyCUTAKRsEoIB4AAPu6Shte5OUkAAAAAElFTkSuQmCC","orcid":"","institution":"University of La Frontera","correspondingAuthor":true,"prefix":"","firstName":"Tamara","middleName":"","lastName":"Otzen","suffix":""},{"id":290291774,"identity":"2f077f66-ccd1-47a1-bb11-f8c5c7b260a5","order_by":2,"name":"Antonio Sanhueza","email":"","orcid":"","institution":"Organización Panamericana de la Salud","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"","lastName":"Sanhueza","suffix":""},{"id":290291776,"identity":"31c699a3-8381-48e9-8d8a-d7bb095466e3","order_by":3,"name":"Álvaro Castillo","email":"","orcid":"","institution":"Núcleo Milenio de Sociomedicina","correspondingAuthor":false,"prefix":"","firstName":"Álvaro","middleName":"","lastName":"Castillo","suffix":""},{"id":290291778,"identity":"c01837a1-5aee-43bd-8b58-041ee77119cf","order_by":4,"name":"Carlos Manterola","email":"","orcid":"","institution":"University of La Frontera","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Manterola","suffix":""},{"id":290291780,"identity":"3b22311a-5992-4982-b8ae-e20916312136","order_by":5,"name":"Georgina Muñoz","email":"","orcid":"","institution":"University of La Frontera","correspondingAuthor":false,"prefix":"","firstName":"Georgina","middleName":"","lastName":"Muñoz","suffix":""},{"id":290291781,"identity":"91b8f763-f3cc-425f-bdcd-9cd034779fb2","order_by":6,"name":"Fernanda García-Aguilera","email":"","orcid":"","institution":"University of La Frontera","correspondingAuthor":false,"prefix":"","firstName":"Fernanda","middleName":"","lastName":"García-Aguilera","suffix":""},{"id":290291782,"identity":"f7127d25-3a09-4d4b-b028-657bd1a1a4a2","order_by":7,"name":"Fernanda Salgado-Castillo","email":"","orcid":"","institution":"University of Azuay","correspondingAuthor":false,"prefix":"","firstName":"Fernanda","middleName":"","lastName":"Salgado-Castillo","suffix":""}],"badges":[],"createdAt":"2024-04-11 19:14:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4254108/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4254108/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-024-19494-7","type":"published","date":"2024-07-21T17:47:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54745270,"identity":"a8ab082f-5e57-4ba3-83c5-68adc51796b4","added_by":"auto","created_at":"2024-04-16 07:07:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":23826,"visible":true,"origin":"","legend":"\u003cp\u003eTraffic Accident Mortality Rate per 100,000 inhabitants by region.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4254108/v1/92d8a6b1e3e37086468659b0.png"},{"id":54745900,"identity":"98b43671-b949-4190-9d52-524092ccbfc2","added_by":"auto","created_at":"2024-04-16 07:15:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25165,"visible":true,"origin":"","legend":"\u003cp\u003eTraffic Accident Mortality Rate per 100,000 inhabitants, by gender.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4254108/v1/762487faced0e9358acc9303.png"},{"id":54745271,"identity":"052788fa-163d-450b-b3bf-ddd7aa94e842","added_by":"auto","created_at":"2024-04-16 07:07:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53965,"visible":true,"origin":"","legend":"\u003cp\u003eChange in the absolute gap in Traffic Accident Mortality between 2011 and 2019.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4254108/v1/0aec064ae41fdbad0b6d08d0.png"},{"id":54745273,"identity":"cc5f8c17-f81a-43cc-9427-d3582b81671b","added_by":"auto","created_at":"2024-04-16 07:07:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":45324,"visible":true,"origin":"","legend":"\u003cp\u003eChange in the Absolute Gap in Traffic Accident Mortality between 2014 and 2019.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4254108/v1/9c82b994b7b778662ed4e325.png"},{"id":60775376,"identity":"1583a08a-bbef-4b68-8875-f65b2b8fec77","added_by":"auto","created_at":"2024-07-21 17:47:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1130777,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4254108/v1/da09e375-a7f1-4ecc-b913-9142887db2ac.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trends in traffic accidents mortality and social inequalities in Ecuador from 2011 to 2022","fulltext":[{"header":"Background","content":"\u003cp\u003eTraffic accidents (TA) encompass any incident involving vehicles designated for transporting individuals or goods between different locations. These incidents are classified under \"Chapter XX: External Causes of Morbidity and Mortality\" in the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10), facilitating the generation of global statistics via specific codes (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). TA are coded and summarized as V01-V99 (transport accidents), with subdivisions based on injury type, vehicle involved, accident nature, and other unspecified aspects (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Sustainable Development Goals (SDGs), particularly Goal Three, emphasize Health and Well-being. By 2030, one of the targets is to halve the global number of deaths and injuries resulting from TA (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Moreover, the World Health Organization (WHO) underscores the efficacy of measures implemented in numerous countries to enhance road safety (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlobally, according to WHO statistics, there were 1,282,150 TA related deaths in 2019, with higher mortality rates observed among men and in low-income countries. Additionally, based on World Bank income groups, TA mortality rates were distributed as follows: low income (28.3/100,000 population), lower-middle income (17.3/100,000 population), upper-middle income (16.8/100,000 population), and high income (8.4/100,000 population) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTA incur substantial economic losses, accounting for approximately 3% of Gross Domestic Product (GDP) in most countries (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Trend analyses project that by 2030, traffic accidents will rank as the fifth leading cause of death, a public health issue globally recognized since 2004 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2012, TA were the primary cause of death among individuals aged 5\u0026ndash;14 in the Americas and the second leading cause among those aged 15\u0026ndash;44, resulting in 149,252 fatalities. In South America, particularly the Andean Region, the TA mortality rate was 22.1/100,000 population (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2019, TA mortality ranked seventh in low-income countries and tenth in lower-middle and upper-middle income countries (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA study in Ecuador covering 2000 to 2015 found an average TA mortality rate of 11.4/100,000 population (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), lower than the Americas' average rate (15.8/100,000 population) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2020, Ecuador recorded 16,972 TAs, leading to 2,600 deaths (15.3%). This represents a 31% reduction from 2019, with a mortality rate of 14.8 per 100,000 population. Of the total fatalities, 64% occurred at the accident site, while the remaining 36% died in hospitals or care centers (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study aims to contribute significantly to the existing body of knowledge by providing a comprehensive analysis of the burden posed by TAs in Ecuador, including underlying disparities, and offering recommendations to address this critical public health issue more effectively and equitably. Therefore, our study seeks to fill these gaps by examining the mortality trend attributable to traffic accidents in Ecuador. Specifically, we will analyze distribution patterns across various parameters, with the overarching objective of informing the development of more effective and equitable prevention policies and programs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eTo describe the trend in TA mortality and inequalities in Ecuador for the period 2011\u0026ndash;2022, distributed by year, gender, age group, geographical location, type of accident, and social inequalities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDesign\u003c/h2\u003e \u003cp\u003eAn ecological study was conducted using aggregated national level data on traffic accidents in Ecuador during the period from 2011 to 2022.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSubjects\u003c/h2\u003e \u003cp\u003eThe population of interest consisted of all individuals who deceased due to TA in Ecuador between 2011 and 2022, obtained from the databases of the National Institute of Statistics and Census (INEC), of the Republic of Ecuador (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The general data on the population and live births were obtained from the same database, based on the national population projection distributed by age groups and by provinces, as well as data on the number of vehicles registered at the national level and by province (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStudy Variables\u003c/h2\u003e \u003cp\u003eThe variables were year (2011 to 2022); geographic region (Coast region: El Oro, Esmeraldas, Guayas, Los R\u0026iacute;os, Manab\u0026iacute;, Santo Domingo, Santa Elena; Sierra: Azuay, Bolivar, Ca\u0026ntilde;ar, Carchi, Cotopaxi, Chimborazo, Imbabura, Loja, Pichincha, Tungurahua; Amazon: Morona Santiago, Napo, Pastaza, Zamora Chinchipe, Sucumb\u0026iacute;os, Orellana; Insular: Gal\u0026aacute;pagos; and Undelimited area); gender (male and female); age group (\u0026lt;\u0026thinsp;16 years; 17\u0026ndash;24 years; 25\u0026ndash;40 years; 41\u0026ndash;59 years; \u0026gt;60 years); and accident type coded according to ICD-10 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eExploratory analyses were conducted using descriptive statistics for percentages, central tendency, and variability. Mortality rates were calculated with the number of deaths as the numerator and population as the denominator, per 100,000 inhabitants. Absolute risks (AR) and rate ratios (RR) were determined by geographic distribution, sex, age group, and accident type. Annual percentage variation (APV) in rates was analyzed using linear regression models with 95% confidence intervals and p-values (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Additionally, traffic accident mortality rates (TAMR) per 10,000 vehicles were calculated.\u003c/p\u003e \u003cp\u003eFurther analyses included examining inequalities in traffic accident mortality as a sole health indicator, alongside annual per capita income (PCI) and literacy rates as socioeconomic stratifiers, and live births per province as the demographic variable. Simple measures of absolute gaps (AG) and relative gaps (RG), as well as complex measures like the Slope Inequality Index (SII), were calculated using simple linear regression models (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe analyses were performed using IBM\u0026reg; SPSS\u0026reg; Statistics version 27, Microsoft\u0026reg; Excel for Mac version 16.57, and the EquiGap\u0026reg; macro developed by the EWEC-LAC metrics and monitoring working group.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eGeneral Mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the study period, 38,355 individuals died due to traffic accidents in Ecuador, of whom 31,187 (81.3%) were men. The TAMR in this period was 19.4 per 100,000 inhabitants (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eTraffic Accident Mortality Rate per 100,000 inhabitants by gender, region, provinces, age range, and male vs female rate ratio with respective 95% CI and p-value.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.678776290630974%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.931166347992352%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.045889101338432%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTAMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.413001912045889%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.164435946462714%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.766730401529637%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneral rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e\u003cstrong\u003e19.43\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.42\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.31\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.678776290630974%\" rowspan=\"2\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.931166347992352%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.045889101338432%\"\u003e\n \u003cp\u003e31.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.413001912045889%\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.94263862332696%\"\u003e\n \u003cp\u003e-1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.221797323135755%\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.766730401529637%\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e7.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e-1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e-0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.678776290630974%\" rowspan=\"5\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.931166347992352%\"\u003e\n \u003cp\u003eSierra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.045889101338432%\"\u003e\n \u003cp\u003e18.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.413001912045889%\"\u003e\n \u003cp\u003e-0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.94263862332696%\"\u003e\n \u003cp\u003e-1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.221797323135755%\"\u003e\n \u003cp\u003e-0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.766730401529637%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eCoast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e20.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eAmazon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e24.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e-1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eInsular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e7.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e-4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-10.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eUndelimited area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e-5.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-9.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e-0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.678776290630974%\" rowspan=\"24\"\u003e\n \u003cp\u003eProvinces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.931166347992352%\"\u003e\n \u003cp\u003eAzuay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.045889101338432%\"\u003e\n \u003cp\u003e14.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.413001912045889%\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.94263862332696%\"\u003e\n \u003cp\u003e-0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.221797323135755%\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.766730401529637%\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eBol\u0026iacute;var\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e20.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eCa\u0026ntilde;ar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e23.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eCarchi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e19.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e-0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eCotopaxi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e27.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e-0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eChimborazo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e24.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eEl Oro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e22.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e-0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eEsmeraldas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e15.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eGuayas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e19.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e-0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eImbabura\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e17.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e-2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-3.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e-1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eLoja\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e12.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e-0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eLos R\u0026iacute;os\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e27.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eManab\u0026iacute;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e15.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eMorona Santiago\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e23.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eNapo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e24.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e-1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003ePastaza\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e17.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003ePichincha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e17.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e-0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eTungurahua\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e18.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e-0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eZamora Chinchipe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e17.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e-1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eGal\u0026aacute;pagos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e7.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e-4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-10.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eSucumb\u0026iacute;os\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e29.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e-1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eOrellana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e27.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e-1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eSanto Domingo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e30.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003eSanta Elena\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e13.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\"\u003e\n \u003cp\u003e-0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.678776290630974%\" rowspan=\"5\"\u003e\n \u003cp\u003eAge Range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.931166347992352%\"\u003e\n \u003cp\u003e0 to 16 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.045889101338432%\"\u003e\n \u003cp\u003e5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.413001912045889%\" valign=\"bottom\"\u003e\n \u003cp\u003e-2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.94263862332696%\" valign=\"bottom\"\u003e\n \u003cp\u003e-3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.221797323135755%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.766730401529637%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003e17 to 24 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e26.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\"\u003e\n \u003cp\u003e-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\"\u003e\n \u003cp\u003e-0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003e25 to 40 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e28.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003e41 to 59 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e21.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.868480725623584%\"\u003e\n \u003cp\u003e60 or more years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e31.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.977324263038549%\" valign=\"bottom\"\u003e\n \u003cp\u003e-2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791383219954648%\" valign=\"bottom\"\u003e\n \u003cp\u003e-3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.750566893424036%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eTAMR: Traffic Accident Mortality Rate; \u003csup\u003e2\u003c/sup\u003eAPV: Annual Percentage Variation; \u003csup\u003e3\u003c/sup\u003eCI: Confidence Interval; \u003csup\u003e4\u003c/sup\u003ep: p-value\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe TAMR between 2011 and 2022 exhibited a decreasing APV of -0.4% (95% CI= -1.15; 0.31; p=0.28) not being statistically significant (Table 1).\u003c/p\u003e\n\u003cp\u003eThe years with the highest TAMR were 2011 (22.0 per 100,000 inhabitants) and 2022 (21.7 per 100,000 inhabitants); whereas, the years with the lowest TAMR were 2020 (14.9 per 100,000 inhabitants) and 2016 (18.0 per 100,000 inhabitants) (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eTraffic Accident Mortality Rate per 100,000 inhabitants by year, gender, and male vs female rate ratio.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.606118546845124%\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.766730401529637%\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.252390057361378%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTAMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001912045889101%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.208413001912046%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.164435946462714%\"\u003e\n \u003cp\u003e\u003cstrong\u003eM/F RR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.606118546845124%\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.766730401529637%\"\u003e\n \u003cp\u003e3368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.252390057361378%\"\u003e\n \u003cp\u003e22.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001912045889101%\"\u003e\n \u003cp\u003e36.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.208413001912046%\"\u003e\n \u003cp\u003e8.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.164435946462714%\"\u003e\n \u003cp\u003e4.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.606118546845124%\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.766730401529637%\"\u003e\n \u003cp\u003e3186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.252390057361378%\"\u003e\n \u003cp\u003e20.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001912045889101%\"\u003e\n \u003cp\u003e32.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.208413001912046%\"\u003e\n \u003cp\u003e8.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.164435946462714%\"\u003e\n \u003cp\u003e3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.606118546845124%\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.766730401529637%\"\u003e\n \u003cp\u003e3109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.252390057361378%\"\u003e\n \u003cp\u003e19.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001912045889101%\"\u003e\n \u003cp\u003e31.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.208413001912046%\"\u003e\n \u003cp\u003e7.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.164435946462714%\"\u003e\n \u003cp\u003e4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.606118546845124%\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.766730401529637%\"\u003e\n \u003cp\u003e3323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.252390057361378%\"\u003e\n \u003cp\u003e20.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001912045889101%\"\u003e\n \u003cp\u003e33.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.208413001912046%\"\u003e\n \u003cp\u003e8.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.164435946462714%\"\u003e\n \u003cp\u003e4.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.606118546845124%\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.766730401529637%\"\u003e\n \u003cp\u003e3157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.252390057361378%\"\u003e\n \u003cp\u003e19.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001912045889101%\"\u003e\n \u003cp\u003e31.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.208413001912046%\"\u003e\n \u003cp\u003e7.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.164435946462714%\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.606118546845124%\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.766730401529637%\"\u003e\n \u003cp\u003e2980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.252390057361378%\"\u003e\n \u003cp\u003e18.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001912045889101%\"\u003e\n \u003cp\u003e29.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.208413001912046%\"\u003e\n \u003cp\u003e6.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.164435946462714%\"\u003e\n \u003cp\u003e4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.606118546845124%\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.766730401529637%\"\u003e\n \u003cp\u003e3079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.252390057361378%\"\u003e\n \u003cp\u003e18.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001912045889101%\"\u003e\n \u003cp\u003e29.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.208413001912046%\"\u003e\n \u003cp\u003e7.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.164435946462714%\"\u003e\n \u003cp\u003e4.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.606118546845124%\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.766730401529637%\"\u003e\n \u003cp\u003e3244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.252390057361378%\"\u003e\n \u003cp\u003e19.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001912045889101%\"\u003e\n \u003cp\u003e31.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.208413001912046%\"\u003e\n \u003cp\u003e7.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.164435946462714%\"\u003e\n \u003cp\u003e4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.606118546845124%\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.766730401529637%\"\u003e\n \u003cp\u003e3279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.252390057361378%\"\u003e\n \u003cp\u003e18.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001912045889101%\"\u003e\n \u003cp\u003e31.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.208413001912046%\"\u003e\n \u003cp\u003e6.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.164435946462714%\"\u003e\n \u003cp\u003e4.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.606118546845124%\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.766730401529637%\"\u003e\n \u003cp\u003e2600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.252390057361378%\"\u003e\n \u003cp\u003e14.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001912045889101%\"\u003e\n \u003cp\u003e24.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.208413001912046%\"\u003e\n \u003cp\u003e4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.164435946462714%\"\u003e\n \u003cp\u003e5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.606118546845124%\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.766730401529637%\"\u003e\n \u003cp\u003e3345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.252390057361378%\"\u003e\n \u003cp\u003e19.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001912045889101%\"\u003e\n \u003cp\u003e33.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.208413001912046%\"\u003e\n \u003cp\u003e6.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.164435946462714%\"\u003e\n \u003cp\u003e5.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.606118546845124%\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.766730401529637%\"\u003e\n \u003cp\u003e3685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.252390057361378%\"\u003e\n \u003cp\u003e21.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001912045889101%\"\u003e\n \u003cp\u003e37.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.208413001912046%\"\u003e\n \u003cp\u003e7.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.164435946462714%\"\u003e\n \u003cp\u003e5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.606118546845124%\"\u003e\n \u003cp\u003e2011-2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.766730401529637%\"\u003e\n \u003cp\u003e38355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.252390057361378%\"\u003e\n \u003cp\u003e19.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001912045889101%\"\u003e\n \u003cp\u003e31.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.208413001912046%\"\u003e\n \u003cp\u003e7.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.164435946462714%\"\u003e\n \u003cp\u003e4.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003en: number of deaths; \u003csup\u003e2\u003c/sup\u003eTAMR: Traffic Accident Mortality Rate; \u003csup\u003e3\u003c/sup\u003eM/F: Male/Female; \u003csup\u003e4\u003c/sup\u003eRR: Rate ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eMortality by Geographic Region\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to geographic region, the highest rates in the period were in the Amazon and Coast regions with 24.4 per 100,000 inhabitants and 20.4 per 100,000 inhabitants, respectively (Table 1 and Figure 1).\u003c/p\u003e\n\u003cp\u003eRegarding provinces, the highest TAMR was registered in Santo Domingo (30.6 per 100,000 inhabitants) and Sucumb\u0026iacute;os (29.6 per 100,000 inhabitants); while the lowest TAMR was for the undelimited area (1.9 per 100,000 inhabitants) and Gal\u0026aacute;pagos (7.9 per 100,000 inhabitants) (Table 1).\u003c/p\u003e\n\u003cp\u003eThe analysis of the trend among geographic regions revealed that the undelimited area and the insular region had the highest APV (-5.3%; 95% CI: -9.56 to -0.82; p=0.04; and -4.2%; 95% CI: -10.29 to 2.24; p=0.22, respectively) (Table 1).\u003c/p\u003e\n\u003cp\u003eThere was a 1.2 times higher risk of mortality due to TA in the Amazon compared to the Coast; with a decreasing APV of -1.2% (95% CI: -1.83 to -0.56; p=0.01). It was evident that the greatest difference in rates between the provinces of Santo Domingo de los Ts\u0026aacute;chilas and Galapagos (AR=3,7) represented an APV with an annual increase of 4.3% in the rates (95% CI: -0.31 to 9.21; p=0.10) (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eAbsolute risk and annual percentage variation of absolute risk with 95% CI.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.465648854961835%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.419847328244273%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.603053435114504%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.465648854961835%\"\u003e\n \u003cp\u003eMale/Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e4.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.068702290076336%\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.603053435114504%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.465648854961835%\"\u003e\n \u003cp\u003eCoast/Sierra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.068702290076336%\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.603053435114504%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.465648854961835%\"\u003e\n \u003cp\u003eAmazon/Coast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\"\u003e\n \u003cp\u003e-1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.068702290076336%\"\u003e\n \u003cp\u003e-1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e-0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.603053435114504%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.465648854961835%\"\u003e\n \u003cp\u003eSanto Domingo/Gal\u0026aacute;pagos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\"\u003e\n \u003cp\u003e4.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.068702290076336%\"\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e9.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.603053435114504%\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.465648854961835%\"\u003e\n \u003cp\u003ePedestrian/Bus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e35.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\"\u003e\n \u003cp\u003e-7.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.068702290076336%\"\u003e\n \u003cp\u003e-12.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e-2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.603053435114504%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.465648854961835%\"\u003e\n \u003cp\u003eUnspecified Transport/Pedestrian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\"\u003e\n \u003cp\u003e6.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.068702290076336%\"\u003e\n \u003cp\u003e6.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e7.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.603053435114504%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.465648854961835%\"\u003e\n \u003cp\u003e60 and over/0 to 16 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e6.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.068702290076336%\"\u003e\n \u003cp\u003e-1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.603053435114504%\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eAR: Absolute risk; \u003csup\u003e2\u003c/sup\u003eAPV: Annual percentage variation; \u003csup\u003e3\u003c/sup\u003eCI: Confidence interval; \u003csup\u003e4\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e: p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMortality by Gender\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegarding gender, it was identified that men have a higher TAMR than women every year, with the highest TAMR for men in 2022 being 37.0 per 100,000 inhabitants, while for women, it was 8.4 per 100,000 inhabitants in 2012. The lowest TAMR in both men and women was in 2020 with 24.9 per 100,000 inhabitants and 4.9 per 100,000 inhabitants, respectively (Table 2 and Figure 2).\u003c/p\u003e\n\u003cp\u003eAdditionally, in 2021, the TAMR was 5.2 times higher in men than in women, while in 2012, it was 3.9 times higher in men than in women. The average male-to-female RR for the 12 years of the study was 4.5 (Table 3).\u003c/p\u003e\n\u003cp\u003eDuring the study period, women showed a higher APV in TAMR, at -1.11% decreasing (CI=-1.87 to -0.35; p=0.02) (Table 1 and Table 2).\u003c/p\u003e\n\u003cp\u003eFor the period 2011-2022, it was confirmed that the absolute risk of TA mortality in men compared to women was 4.5 times higher (AR=4.5); indicating an annual increase, associated with an APV of 0.9% (CI=0.41-1.29; p=0.01) (Table 1 and Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMortality by Age Group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalyzing the TAMR according to age groups, the highest mortality was observed in the \u0026ge;60 years group (31.0 per 100,000 inhabitants) and the 25 to 40 years group (28.6 per 100,000\u0026nbsp;\u003c/p\u003e\n\u003cp\u003einhabitants); while the lowest rate was in the 0 to 16 years group (5.1 per 100,000 inhabitants). This trend remained consistent throughout the study period except for 2020, when the \u0026ge;60 years group exhibited the lowest rate (19.9 per 100,000 inhabitants) (Table 1).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"516\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.077519379844961%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.968992248062015%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneral\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.58139534883721%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.26356589147287%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.108527131782946%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.423326133909287%\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.207343412526997%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.047516198704104%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.047516198704104%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.047516198704104%\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.207343412526997%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.207343412526997%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.047516198704104%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.207343412526997%\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311015118790497%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.047516198704104%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.047516198704104%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.151187904967603%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiff\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e37.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e20.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e36.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e19.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e37.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e24.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e37.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e20.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e36.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e19.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e38.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e24.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e37.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e20.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e36.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e19.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e40.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e24.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e37.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e20.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e19.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e39.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e24.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e37.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e20.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e36.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e19.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e42.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e25.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e38.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e20.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e38.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e19.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e40.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e25.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e38.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e20.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e38.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e19.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e40.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e25.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e38.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e20.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e37.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e19.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e41.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e24.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e38.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e20.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e38.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e19.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e41.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e23.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e37.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e18.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e36.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e17.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e38.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e22.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e37.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e18.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e36.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e17.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e39.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e22.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e36.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e17.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e36.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e16.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e40.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e22.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2011-2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e37.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e20.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e101,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e37.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e19.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e100.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e40.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e24.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"14\"\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003ex: Average age; \u003csup\u003e2\u003c/sup\u003eSD: Standard Deviation; \u003csup\u003e2\u003c/sup\u003eMin: Minimum age;\u003csup\u003e\u0026nbsp;3\u003c/sup\u003eMax: Maximun age; \u003csup\u003e4\u003c/sup\u003eDiff: Average age difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe overall average age of fatalities due to TA was 37.8 years (Standard Deviation (SD)=20.1). For men, it was 37.3 (SD = 19.0) and for women, 40.0 (SD = 24.1). The year with the lowest average age was 2022 (36.9 years, SD = 17.9), and the highest was 2017 (38.7 years). The differences for each year remained constant (APV=0.03%; p=0.72; 95% CI: -0.12 to 0.17) (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eAverages of ages of fatalities due to Traffic Accidents.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"516\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.077519379844961%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.968992248062015%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneral\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.58139534883721%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.26356589147287%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.108527131782946%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.423326133909287%\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.207343412526997%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.047516198704104%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.047516198704104%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.047516198704104%\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.207343412526997%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.207343412526997%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.047516198704104%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.207343412526997%\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311015118790497%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.047516198704104%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.047516198704104%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.151187904967603%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiff\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e37.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e20.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e36.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e19.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e37.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e24.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e37.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e20.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e36.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e19.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e38.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e24.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e37.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e20.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e36.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e19.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e40.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e24.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e37.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e20.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e19.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e39.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e24.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e37.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e20.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e36.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e19.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e42.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e25.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e38.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e20.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e38.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e19.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e40.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e25.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e38.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e20.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e38.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e19.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e40.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e25.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e38.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e20.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e37.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e19.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e41.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e24.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e38.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e20.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e38.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e19.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e41.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e23.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e37.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e18.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e36.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e17.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e38.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e22.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e37.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e18.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e36.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e17.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e39.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e22.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e36.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e17.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e36.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e16.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e40.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e22.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.097087378640778%\"\u003e\n \u003cp\u003e2011-2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.572815533980583%\"\u003e\n \u003cp\u003e37.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e20.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e101,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e37.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e19.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e100.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.378640776699029%\"\u003e\n \u003cp\u003e40.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\"\u003e\n \u003cp\u003e24.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436893203883495%\"\u003e\n \u003cp\u003e99.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12621359223301%\"\u003e\n \u003cp\u003e2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"14\"\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003ex: Average age; \u003csup\u003e2\u003c/sup\u003eSD: Standard Deviation; \u003csup\u003e2\u003c/sup\u003eMin: Minimum age;\u003csup\u003e\u0026nbsp;3\u003c/sup\u003eMax: Maximun age; \u003csup\u003e4\u003c/sup\u003eDiff: Average age difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Regarding the rate ratio in the \u0026ge;60 years group, a value of 6.4 times higher TAMR compared to the 0 to 16 years group was identified, with an annual decrease of -0.19% (95% CI: -1.40; 1.05; p=0.77) (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMortality by Type of Accident\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegarding the TAMR, it was reported that between 2011 and 2022, there were 6,698 (17.5%) fatalities due to \u0026quot;pedestrian injured in transport accidents (ICD-10 V01-V09)\u0026quot; and 22,121 (57.7%) fatalities due to \u0026quot;other unspecified transport accidents (ICD-10 V089)\u0026quot; (Table 5).\u003c/p\u003e\n\u003cp\u003eThese causes remained constant as the most frequent throughout each year; from 2018 onwards, there was an increase in fatalities among \u0026quot;Motorcyclists or occupants of motorized 3-wheeled vehicles (ICD-10 V20-V39)\u0026quot; as follows: 2018 (15.0%), 2019 (15.3%), 2020 (13.2%), 2021 (16.8%), 2022 (13.2%), and a decrease in fatalities among \u0026quot;pedestrians injured in transport accidents (ICD-10 V01-V09)\u0026quot; as follows: 2018 (14.6%), 2019 (13.7%), 2020 (11.2%), 2021 (9.3%), 2022 (7.0%).\u003c/p\u003e\n\u003cp\u003eConducting an analysis of the types of annual TA deaths between 2011 and 2022, mortality among \u0026quot;pedestrians\u0026quot; showed the greatest variation with a tendency to decrease (APV= -5.7%; 95% CI: -6.45 to -4.91; p\u0026lt;0.001) (Table 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u0026nbsp;\u003c/strong\u003eTypes of traffic accidents with their respective percentages and annual percentage rates variation with 95% CI.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.458015267175576%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Types of traffic accidents\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.114503816793894%\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.84732824427481%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI 95%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.068702290076336%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.458015267175576%\"\u003e\n \u003cp\u003ePedestrian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.114503816793894%\"\u003e\n \u003cp\u003e6698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e17.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"bottom\"\u003e\n \u003cp\u003e-5.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.49618320610687%\"\u003e\n \u003cp\u003e-6.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e-4.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.068702290076336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.458015267175576%\"\u003e\n \u003cp\u003eCyclist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.114503816793894%\"\u003e\n \u003cp\u003e416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.49618320610687%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.068702290076336%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.458015267175576%\"\u003e\n \u003cp\u003eMotorcyclist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.114503816793894%\"\u003e\n \u003cp\u003e5430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e14.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.49618320610687%\"\u003e\n \u003cp\u003e-0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e4.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.068702290076336%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.458015267175576%\"\u003e\n \u003cp\u003eVehicle Occupant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.114503816793894%\"\u003e\n \u003cp\u003e998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\"\u003e\n \u003cp\u003e-4.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.49618320610687%\"\u003e\n \u003cp\u003e-7.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e-1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.068702290076336%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.458015267175576%\"\u003e\n \u003cp\u003eVan Occupant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.114503816793894%\"\u003e\n \u003cp\u003e485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.49618320610687%\"\u003e\n \u003cp\u003e-2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e7.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.068702290076336%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.458015267175576%\"\u003e\n \u003cp\u003eHeavy Vehicle Occupant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.114503816793894%\"\u003e\n \u003cp\u003e289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\"\u003e\n \u003cp\u003e3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.49618320610687%\"\u003e\n \u003cp\u003e-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e7.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.068702290076336%\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.458015267175576%\"\u003e\n \u003cp\u003eBus Occupant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.114503816793894%\"\u003e\n \u003cp\u003e387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.49618320610687%\"\u003e\n \u003cp\u003e-3.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e8.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.068702290076336%\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.458015267175576%\"\u003e\n \u003cp\u003eOther Transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.114503816793894%\"\u003e\n \u003cp\u003e1133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\"\u003e\n \u003cp\u003e-1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.49618320610687%\"\u003e\n \u003cp\u003e-7.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e5.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.068702290076336%\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.458015267175576%\"\u003e\n \u003cp\u003eUnspecified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.114503816793894%\"\u003e\n \u003cp\u003e22121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e57.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.49618320610687%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.068702290076336%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.458015267175576%\"\u003e\n \u003cp\u003eMaritime, Aerial, Space\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.114503816793894%\"\u003e\n \u003cp\u003e398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16030534351145%\"\u003e\n \u003cp\u003e-4.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.49618320610687%\"\u003e\n \u003cp\u003e-10.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.351145038167939%\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.068702290076336%\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003en: number of death; \u003csup\u003e2\u003c/sup\u003e%: Percentage of fatalities; \u003csup\u003e2\u003c/sup\u003eAPV: Annual Percentage Variation; \u003csup\u003e3\u003c/sup\u003eCI: Confidence Interval; \u003csup\u003e4\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e: p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eRegarding the differences in mortality risk by type of TA, it was evident that \u0026quot;Other unspecified transport\u0026quot; had 4.2 times more TAMR than that occurring in \u0026quot;pedestrians\u0026quot;, with an APV of 6.9 (95% CI: 6.01 to 7.86; p\u0026lt;0.001); while the highest risk of mortality from traffic accident was among \u0026quot;pedestrians\u0026quot;, 35.6 times more than \u0026quot;bus occupants\u0026quot;, with an annual decrease of -7.7% (95% CI: -12.63 to -2.51; p=0.02).\u003c/p\u003e\n\u003cp\u003eAdditionally, the mortality rate per 10,000 vehicles from 2011 to 2022 was calculated, resulting in 15.8 per 10,000 vehicles. It was also observed that the year with the highest mortality was 2011 (22.6 per 10,000 vehicles); and the lowest rate was in 2020 (11.0 per 10,000 vehicles). The APV of TA mortality per 10,000 registered vehicles for the entire period was a decreasing -2.4% (95% CI: -2.98 to -1.75; p\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInequality Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt was identified that in 2011, there were 0.4 more deaths (AG) per 100,000 live births due to TA in the group of provinces with the lowest PCI compared to the group of provinces with the highest PCI; whereas, in 2019, there were 2.9 more deaths (AG) per 100,000 live births due to traffic accidents in the provinces with the lowest PCI compared to those with the highest PCI, representing a 500% increase in the AG between 2011 and 2019 (Figure 3).\u003c/p\u003e\n\u003cp\u003eThe risk of mortality due to TA in 2011 for the group of provinces with the lowest PCI was 1.0 times higher (RG) than for the group of provinces with the highest PCI; while in 2019, the risk of mortality due to TA in the group of provinces with the lowest PCI was 1.1 times higher (RG) than in the group of provinces with the highest PCI, indicating a 14.5 percentage point increase in the RR between 2011 and 2019 (Figure 3).\u003c/p\u003e\n\u003cp\u003eUpon calculating the SII in TA mortality in provinces stratified by PCI, it was found that inequality increased by 247.7% between 2011 and 2019.\u003c/p\u003e\n\u003cp\u003eThe analysis of the TAMR per 100,000 live births (mortality rates) in the years 2011 and 2019, when compared with the equity stratifier (PCI) and categorized by quintiles (Q1 to Q4, ranging from least advantageous condition to most advantageous condition), reveals that the highest mortality rate is predominantly observed in Q1, and the lowest in Q4 for both years. Concerning simple metrics, it is noted that the equity stratifier (PCI) registered a value of 0.48 (95% CI: -17.01 to 17.96) in BA in 2011, and 2.98 (95% CI: -14.57 to 20.53) in 2019; along with a value of 1.02 (95% CI: 0.49 to 2.12) in BR in 2011 and 1.17 (95% CI: 0.47 to 2.92) in 2019. These figures indicate the most significant departure from the condition of equity, reflecting the greatest degree of inequality concentrated among populations with the most and least social advantage, respectively (Figure 3).\u003c/p\u003e\n\u003cp\u003eIn 2014, there were 3.0 more deaths (AG) per 100,000 live births due to TA in the group of provinces with lower literacy levels compared to the group with higher literacy levels; whereas, in 2019, there were 2.7 more deaths (AG) per 100,000 live births due to TA in the group of provinces with lower literacy levels compared to those with higher literacy levels, signifying a 10.5% decrease in the AG between 2014 and 2019 (Figure 4).\u003c/p\u003e\n\u003cp\u003eThe risk of mortality due to TA in 2014 in the group of provinces with lower literacy levels was 1.1 times higher (RG) than in the group of provinces with higher literacy levels; a value very similar to 2019 (1.1 times higher (RG)), representing a 0.5% decrease in the RG (Figure 4).\u003c/p\u003e\n\u003cp\u003eThe analysis of the TAMR per 100,000 live births (mortality rates) in the years 2014 and 2019, when compared with the equity stratifier (illiteracy rate percentage) and categorized by quintiles (Q1 to Q4, ranging from least advantageous condition to most advantageous condition), indicates that the highest mortality rate is concentrated in Q1, while the lowest is in Q4 for both years. In terms of simple metrics, the equity stratifier (illiteracy rate percentage) demonstrated a value of -3.08 (95% CI: -18.3 to 12.14) in BA in 2014, and -2.76 (95% CI: -17.47 to 11.95) in 2019; coupled with a value of 0.861 (95% CI: 0.4 to 1.84) in BR in 2014 and 0.86 (95% CI: 0.39 to 1.91) in 2019. These values represent the most significant deviation from the condition of equity, reflecting the highest degree of inequality concentrated among populations with the most and least social advantage, respectively (Figure 4).\u003c/p\u003e\n\u003cp\u003eWhen considering the SII in TA mortality in provinces stratified by the illiteracy rate percentage, it was found that inequality decreased by 18.0%.\u003c/p\u003e\n\u003cp\u003eRegarding complex metrics, it is observed that the IPC stratifiers with values of 247.7 in IDP and illiteracy rate with values of 18.0 in IDP, describe the values furthest from the conditions of equity. The condition of inequality is focused among populations with the most and least social advantage, respectively, over the years.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eConsidering the thorough examination conducted in this study, it is important to acknowledge the potential vulnerability to ecological fallacy, an inherent risk when interpreting data aggregated at the group level, which may not fully capture individual level nuances. However, it is crucial to emphasize that despite this inherent limitation, the integrity and robustness of the research findings remain steady and unaffected. The methodological rigor employed, alongside the data analysis techniques applied, ensures that the conclusions drawn provide a contribution to the understanding of the subject matter. Thus, while acknowledging this potential limitation, the study's outcomes stand as valuable insights into the prevailing trends and patterns, bolstering the scientific discourse on the topic.\u003c/p\u003e \u003cp\u003eRegarding the differences in TA mortality rates between men and women, men exhibit higher rates (4 to 6 times more), as found in studies across Latin America and globally (\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), as well as in our study. The most impacted age group was the one over 60 years, paralleling findings in Colombia where patients in this age range faced double the mortality risk from traffic accident compared to younger patients (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), however, findings from another study conducted in Colombia using data from 2019 revealed that the age group with the highest mortality rate was between 25 and 34 years old (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). A systematic review encompassing primary studies from the United States, Iran, Brazil, Egypt, China, Canada, and others highlighted increased mortality risk in the over 60 age group [OR\u0026thinsp;=\u0026thinsp;2.57, CI 95% 1.2\u0026ndash;5.4] (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe 17 to 24-year age group ranked second in mortality rates, mirroring patterns in Argentina, Brazil, Chile in Latin America, and Poland in Europe, where this age group is most affected, attributed to riskier behaviors like speeding and non-helmet use (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Our findings indicate a modest decrease of 0.42%, though it lacks statistical significance, notably in Sierra, compared to Brazil, Paraguay, Pakistan, Mongolia, and North Korea, where trends are stable or increasing (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). This underscores the need to sustain or enhance public policies for road safety as a public health priority, as mandated by Ecuador's law on terrestrial transport, transit, and road safety (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSignificant issues with underreporting and misclassification of TA types leading to mortality complicate the understanding of the problem's magnitude and limit regional data comparison. In our study, 54.9% were classified as \"(V89) Accident in another type of unspecified transport,\" impacting data analysis precision (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Despite initiatives by PAHO and the Latin American and Caribbean Network for Strengthening Health Systems (RELACSIS) to train health personnel in proper death certificate completion as per WHO standards (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the analysis of inequalities, it was evidenced that the level of illiteracy and per capita income pose greater risks in provinces with lower literacy and income rates compared to the quartile with better social conditions, echoing findings from a study in Quito where the highest mortality rate was prevalent in populous areas (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). A study in Canada revealed a higher incidence of pedestrians, cyclists, and vehicle occupants injured in poorer areas compared to wealthier ones (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). This mirrors results from a study in Norway, which showed increased mortality from TA in the 16 to 20-year age group in correlation with rising levels of social disadvantage and declining parental education levels (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Similarly, in the United States, it was indicated that there is a strong socioeconomic pattern associated with traffic accident mortality, where groups with higher education levels exhibited a greater decrease in mortality over time (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe year 2020, marking the onset of the COVID-19 pandemic, brought changes in global traffic accident mortality trends. Our study identified a significant decrease in TA mortality rates compared to 2019 (a variation of 4.1 points), a trend consistent across all provinces and age groups. Notably, in the over 60 age group, there was a major reduction in traffic accident mortality rates from 30.8 per 100,000 inhabitants in 2019 to 19.9 per 100,000 inhabitants in 2020 (a variation of 10.99 points) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). An TA analysis in Ecuador during the pandemic's early stages showed a 67.4% reduction in fatalities due to confinement and mobility restrictions (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Research in Peru reported that TA mortality experienced the greatest decline among external causes of death, particularly during the first 40 days of confinement, before returning to its previous trend towards the end of 2020 (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMost reports and studies conducted in Ecuador have primarily provided descriptive data without thoroughly exploring the relationship between traffic accident mortality rates and other variables. In 2013, a study specifically conducted in Quito aimed to elucidate the distribution of fatalities and their determinants. This study highlighted that a small proportion of the deceased (0.49%) belonged to a high socioeconomic status, while the majority (76%) belonged to a lower socioeconomic status. Moreover, it identified that several male adult fatalities occurred due to pedestrian accidents while under the influence of alcohol. These findings underscore the importance of considering socioeconomic factors and alcohol consumption in further analyses stemming from this study (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLastly, factors associated with road safety in Ecuador are mandatory, including speed reduction, use of seat belts, child seats, helmets for motorcyclists, and refraining from driving under the influence of alcohol and drugs (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). However, it is imperative to acknowledge the nuanced complexity of adherence to these measures across diverse demographic groups within the population. This variability underscores a substantial limitation in our study, as we did not assess these factors comprehensively. Driving under the influence of alcohol is a common risk behavior, as observed in a study across several Latin American countries, where 1 in 6 injured patients admitted to the Emergency Department had consumed alcohol up to 6 hours before the accident. The probability of having a traffic accident after drinking was 5 times higher than in those who did not drink (with each alcoholic drink, the risk increased by 13%) (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Therefore, future research should analyze these variables and their influence on traffic accident mortality in the Ecuadorian population.\u003c/p\u003e \u003cp\u003eThe limitations encompass various aspects, including the reliance on secondary data sources to ascertain both mortality rates stemming from traffic related injuries and the multifaceted elements associated with helmet and seat belt usage, driving under the influence of alcohol and drugs, and speed regulation, which warrants a more thorough examination. Moreover, the incomplete data regarding the age distribution of the vehicle fleet, as well as the state and safety features of vehicles and motorcycles, present notable hurdles. Additionally, the lack of comprehensive information concerning the equipment available in ambulances for patient care, along with inadequate insights into the condition of Ecuador's road infrastructure, underscores the necessity for more extensive scrutiny. Mitigating these constraints is pivotal for propelling the ongoing research endeavors within this domain forward.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study is one of the few conducted in Ecuador aimed at describing and analyzing the trends in mortality due to TA. It has found an annual decrease in mortality rates during the study period, with a more significant decline among men, in the Amazon and Coast regions, and in the age group of 60 years and older. There is evidence of substantial underreporting in the causes of death. After excluding the main cause ((V89) Accident in another type of unspecified transport), the pedestrian group is the most affected. However, there has been a decrease in recent years, where motorcyclists exhibit higher mortality despite reforms to traffic laws made over these 10 years.\u003c/p\u003e \u003cp\u003eFurthermore, this is the first study on inequalities in TA in Ecuador, in which we have conducted an analysis combining descriptive, associative, and inequality measures. We hope to provide the necessary information for decision makers to prioritize this public health issue.\u003c/p\u003e \u003cp\u003eA particularly interesting finding relates to vulnerable road users over 60 years of age, who show the highest mortality rates. Although the overall rate in this group tends to decrease over the entire 12-year period, their vulnerability deserves special attention.\u003c/p\u003e \u003cp\u003eThere are socioeconomic inequalities in mortality rates, but it is necessary to study them more deeply to achieve the desired impact on reducing mortality due to traffic accidents. From these data, public policies could be generated.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAG - Absolute gaps\u003c/p\u003e\n\u003cp\u003eAR - Absolute risks\u003c/p\u003e\n\u003cp\u003eAPV - Annual percentage variation\u003c/p\u003e\n\u003cp\u003eICD-10 - 10th revision of the International Statistical Classification of Diseases and Related Health Problems\u003c/p\u003e\n\u003cp\u003eGDP - Gross Domestic Product\u003c/p\u003e\n\u003cp\u003eRELACSIS - Latin American and Caribbean Network for Strengthening Health Systems\u003c/p\u003e\n\u003cp\u003eINEC - National Institute of Statistics and Census, of the Republic of Ecuador\u003c/p\u003e\n\u003cp\u003ePCI - per capita income\u003c/p\u003e\n\u003cp\u003eRR - rate ratios\u003c/p\u003e\n\u003cp\u003eRG - Relative gaps\u003c/p\u003e\n\u003cp\u003eSII - Slope Inequality Index\u003c/p\u003e\n\u003cp\u003eSDGs - Sustainable Development Goals\u003c/p\u003e\n\u003cp\u003eTAMR - traffic accident mortality rates\u003c/p\u003e\n\u003cp\u003eTA - Traffic accidents\u003c/p\u003e\n\u003cp\u003eWHO - World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable, as the study used official statistics and publicly accessible unnamed data from the Republic of Ecuador.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that are presented in this study are available on request from the corresponding author. The data are not publicly available due to maintaining privacy data of the participants such as e-mail addresses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, J.P.H-C. and T.O.; methodology, J.P.H-C. and T.O.; soft-ware, J.P.H-C. and T.O.; validation, J.P.H-C. and T.O.; formal analysis, J.P.H-C. and T.O.; investigation, J.P.H-C. and T.O.; resources, J.P.H-C. and T.O.; data curation, J.P.H-C. and T.O.; writing\u0026mdash;original draft preparation, J.P.H-C. and T.O.; writing\u0026mdash;review and editing, J.P.H-C., T.O., A.S., A.C., C.M., G.M., F.G-A., and F.S-C.; supervision, T.O.; project administration J.P.H-C. and T.O.. All authors have read and agreed to the published version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eANID\u0026mdash;MILENIO\u0026mdash;NCS2021_013 and ANID + SUBVENCI\u0026Oacute;N A INSTALACI\u0026Oacute;N EN LA ACADEMIA CONVOCATORIA A\u0026Ntilde;O 2022 + 85220114\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. ICD-10 Version: 2019. 2019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://icd.who.int/browse10/2019/en\u003c/span\u003e\u003cspan address=\"https://icd.who.int/browse10/2019/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 12 Feb 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Road traffic mortality. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/data/gho/data/themes/topics/sdg-target-3_6-road-traffic-injuries\u003c/span\u003e\u003cspan address=\"https://www.who.int/data/gho/data/themes/topics/sdg-target-3_6-road-traffic-injuries\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 12 Feb 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Global status report on road safety 2018 summary. 2018. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://apps.who.int/bookorders\u003c/span\u003e\u003cspan address=\"http://apps.who.int/bookorders\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 4 Mar 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Road traffic deaths - Data by WHO region. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://apps.who.int/gho/data/view.main.RoadTrafficDeathREG?lang=en\u003c/span\u003e\u003cspan address=\"https://apps.who.int/gho/data/view.main.RoadTrafficDeathREG?lang=en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 12 Feb 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Traumatismos causados por el tr\u0026aacute;nsito. 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/es/news-room/fact-sheets/detail/road-traffic-injuries\u003c/span\u003e\u003cspan address=\"https://www.who.int/es/news-room/fact-sheets/detail/road-traffic-injuries\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 12 Feb 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganizaci\u0026oacute;n Panamericana de la Salud. Datos sobre la seguridad vial en la Regi\u0026oacute;n de las Am\u0026eacute;ricas. 2016. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iris.paho.org/bitstream/handle/10665.2/28565/9789275319123-spa.pdf?sequence=6\u003c/span\u003e\u003cspan address=\"https://iris.paho.org/bitstream/handle/10665.2/28565/9789275319123-spa.pdf?sequence=6\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 23 Aug 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas P, Muhlrad N, Hill J, Yannis G, Dupont E, Martensen H et al. Road Safety Data, Collection, Transfer and Analysis DaCota - Final Project Report. 2013. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.dacota-project.eu/Deliverables/DaCoTA_Final_Report.pdf\u003c/span\u003e\u003cspan address=\"http://www.dacota-project.eu/Deliverables/DaCoTA_Final_Report.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 23 Aug 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. The top 10 causes of death. 2020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 12 Feb 12 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlgora-Buenaf\u0026eacute; AF, Russo-Puga M, Suasnavas-Berm\u0026uacute;dez PR, Merino-Salazar P, G\u0026oacute;mez-Garc\u0026iacute;a AR. Tendencias de los accidentes de tr\u0026aacute;nsito en Ecuador: 2000\u0026ndash;2015. Revista Gerencia y Politicas de Salud. 2017;16(33):52\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.11144/Javeriana.rgps16-33.tate\u003c/span\u003e\u003cspan address=\"10.11144/Javeriana.rgps16-33.tate\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInstituto Nacional de Estad\u0026iacute;stica y Censos (INEC). Anuario de Estad\u0026iacute;sticas de Transporte 2020. 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ecuadorencifras.gob.ec/documentos/web-inec/Estadisticas_Economicas/Estadistica\u003c/span\u003e\u003cspan address=\"https://www.ecuadorencifras.gob.ec/documentos/web-inec/Estadisticas_Economicas/Estadistica\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e de Transporte/2020/2020_ANET_PPT.pdf. Accessed 23 Aug 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInstituto Nacional de Estad\u0026iacute;stica y Censos (INEC). Transporte. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ecuadorencifras.gob.ec/transporte/\u003c/span\u003e\u003cspan address=\"https://www.ecuadorencifras.gob.ec/transporte/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 28 Feb 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInstituto Nacional de Estad\u0026iacute;stica y Censos (INEC). Defunciones Generales. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ecuadorencifras.gob.ec/defunciones-generales/\u003c/span\u003e\u003cspan address=\"https://www.ecuadorencifras.gob.ec/defunciones-generales/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 20 Aug 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanhueza A, Otzen T, Manterola C, Araneda N, Sanhueza A. Statistical Approaches for Analyzing a Continuous Outcome in Experimental Studies. Int J Morphol. 2014;32(1):339\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4067/S0717-95022014000100054\u003c/span\u003e\u003cspan address=\"10.4067/S0717-95022014000100054\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvery Woman Every Child Latin America and Caribbean (EWEC LAC). Gu\u0026iacute;a paso a paso para el c\u0026aacute;lculo de m\u0026eacute;tricas de desigualdad en salud. 2015. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.everywomaneverychild-lac.org/wp-content/uploads/2017/06/Guia-paso-a-paso-versi\u0026oacute;n-final.pdf\u003c/span\u003e\u003cspan address=\"https://www.everywomaneverychild-lac.org/wp-content/uploads/2017/06/Guia-paso-a-paso-versi\u0026oacute;n-final.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 28 Feb 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLadeira RM, Malta DC, De Morais Neto OL, De Mesquita Silva Montenegro M, Filho AMS, Vasconcelos CH, et al. Acidentes de transporte terrestre: Estudo Carga Global de Doen\u0026ccedil;as, Brasil e unidades federadas, 1990 e 2015. Revista Brasileira de Epi-demiologia. 2017;20:157\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1590/1980-5497201700050013\u003c/span\u003e\u003cspan address=\"10.1590/1980-5497201700050013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGenowska A, Jamiołkowski J, Szafraniec K, Fryc J, Pajak A. Health care resources and 24,910 deaths due to traffic accidents: An ecological mortality study in Poland. Int J Environ Res Public Health. 2021;18(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph18115561\u003c/span\u003e\u003cspan address=\"10.3390/ijerph18115561\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBahadorimonfared A, Soori H, Mehrabi Y, Delpisheh A, Esmaili A, Salehi M, et al. Trends of Fatal Road Traffic Injuries in Iran (2004\u0026ndash;2011). PLoS ONE. 2013;8(5):1\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0065198\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0065198\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurillo-Hoyos J, Garc\u0026iacute;a-Moreno LM, Tinjac\u0026aacute; N, Jaramillo C. Mortalidad por lesiones de tr\u0026aacute;nsito y desigualdades sociales en Colombia, 2019. Revista Panamericana de Salud P\u0026uacute;blica 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.26633/RPSP.2023.121\u003c/span\u003e\u003cspan address=\"10.26633/RPSP.2023.121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSegura Cardona \u0026Aacute;M, Cardona Arango D, Berbes\u0026iacute; Fern\u0026aacute;ndez DY, Agudelo Mart\u0026iacute;nez A. Mortalidad por accidente de tr\u0026aacute;nsito en el adulto mayor en Colombia. Rev Saude Publica. 2017;51:1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1590/S1518-8787.2017051006405\u003c/span\u003e\u003cspan address=\"10.1590/S1518-8787.2017051006405\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzami-Aghdash S, Aghaei MH, Sadeghi-Bazarghani H. Epidemiology of Road Traffic Injuries among Elderly People; A Systematic Review and Meta-Analysis. Bull Emerg Trauma. 2018;6(4):279\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.29252/beat-060403\u003c/span\u003e\u003cspan address=\"10.29252/beat-060403\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEscan\u0026eacute;s G. Evoluci\u0026oacute;n de la mortalidad por atropellos y colisiones de tr\u0026aacute;nsito en Argentina entre 2001 y 2010. Rev Bras Estud Popul. 2015;32(1):49\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1590/S0102-30982015000000004\u003c/span\u003e\u003cspan address=\"10.1590/S0102-30982015000000004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Abreu OM, De Souza Menezes DR, De Freitas Mathias E. Impact of the brazilian traffic code and the law against drinking and driving on mortality from motor vehicle accidents. Cad Saude Publica. 2018;34(8). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1590/0102-311X00122117\u003c/span\u003e\u003cspan address=\"10.1590/0102-311X00122117\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOtzen T, Sanhueza A, Manterola C, Hetz M, Melnik T. Transport accident mortality in Chile: Trends from 2000 to 2012. Ciencia e Saude Coletiva. 2016;21(12):3711\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1590/1413-812320152112.12652016\u003c/span\u003e\u003cspan address=\"10.1590/1413-812320152112.12652016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSim\u0026aacute;n V, Rubio M, Talavera I, Gonz\u0026aacute;lez K, Tinjac\u0026aacute; N. Informe de juventud y siniestralidad vial. La relevancia de las juventudes para el logro de la meta del ODS 3.6, en materia de reducci\u0026oacute;n de siniestralidad vial. 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.paho.org/es/documentos/informe-juventud-siniestralidad-vial\u003c/span\u003e\u003cspan address=\"https://www.paho.org/es/documentos/informe-juventud-siniestralidad-vial\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 28 Feb 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoreira MR, Ribeiro JM, Motta CT, Motta JIJ. Mortality by road traffic accidents in adolescents and young people, Brazil, 1996\u0026ndash;2015: Will we achieve SDG 3.6? Ciencia e Saude Coletiva. 2018;23(9):2785\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1590/1413-81232018239.17082018\u003c/span\u003e\u003cspan address=\"10.1590/1413-81232018239.17082018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJames SL, Lucchesi LR, Bisignano C, Castle CD, Dingels ZV, Fox JT, et al. Morbidity and mortality from road injuries: Results from the Global Burden of Disease Study 2017. Inj Prev. 2020;26(Supp 1):i46\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/injuryprev-2019-043302\u003c/span\u003e\u003cspan address=\"10.1136/injuryprev-2019-043302\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsamblea Nacional. Ley Org\u0026aacute;nica Reformatoria De La Ley Org\u0026aacute;nica De Transporte Terrestre, Tr\u0026aacute;nsito Y Seguridad Vial [Internet]. Quinto Suplemento No 512-Registro Oficial Ecuador; 2021 p. 149. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.comisiontransito.gob.ec/wp-content/uploads/downloads/2021/08/LEY-ORGANICA-REFORMATORIA-DE-LA-LEY-ORGANICA-DE-TRANSPORTE-TERRESTRE-TRANSITO-Y-SEGURIDAD-VIAL.pdf\u003c/span\u003e\u003cspan address=\"https://www.comisiontransito.gob.ec/wp-content/uploads/downloads/2021/08/LEY-ORGANICA-REFORMATORIA-DE-LA-LEY-ORGANICA-DE-TRANSPORTE-TERRESTRE-TRANSITO-Y-SEGURIDAD-VIAL.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 28 Feb 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan American Health Organization (PAHO). Curso virtual sobre el correcto llenado del Certificado de Defunci\u0026oacute;n \u0026ndash; RELACSIS. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www3.paho.org/relacsis/index.php/es/areas-de-trabajo/registro-adecuado-de-causas-de-muerte/curso-virtual-certificado-defuncion/\u003c/span\u003e\u003cspan address=\"https://www3.paho.org/relacsis/index.php/es/areas-de-trabajo/registro-adecuado-de-causas-de-muerte/curso-virtual-certificado-defuncion/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 9 Mar 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontero-Moretta GE. Revista Facultad Nac de Salud P\u0026uacute;blica. 2018;36(3):31\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.17533/udea.rfnsp.v36n3a04\u003c/span\u003e\u003cspan address=\"10.17533/udea.rfnsp.v36n3a04\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Determinaci\u0026oacute;n social de la mortalidad por accidentes de tr\u0026aacute;nsito en el distrito metropolitano de Quito, a\u0026ntilde;o 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorency P, Gauvin L, Plante C, Fournier M, Morency C. Neighborhood Social Inequalities in Road Traffic Injuries: The Influence of Traffic Volume and Road Design. 2012;102(6):1112\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2105/AJPH.2011.300528\u003c/span\u003e\u003cspan address=\"10.2105/AJPH.2011.300528\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKristensen P, Kristiansen T, Rehn M, Gravseth HM, Bjerkedal T. Social inequalities in road traffic deaths at age 16\u0026ndash;20 years among all 611 654 Norwegians born between 1967 and 1976: a multilevel analysis. Inj Prev. 2012;18(1):3\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/ip.2011.031682\u003c/span\u003e\u003cspan address=\"10.1136/ip.2011.031682\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarper S, Charters TJ, Strumpf EC. Trends in socioeconomic inequalities in motor vehicle accident deaths in the United States, 1995\u0026ndash;2010. Am J Epidemiol. 2015;182(7):606\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/aje/kwv099\u003c/span\u003e\u003cspan address=\"10.1093/aje/kwv099\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026oacute;mez-Garc\u0026iacute;a A, Escobar-Segovia K, Caj\u0026iacute;as-Vasco P. Impacto del COVID-19 en la mortalidad por accidentes de tr\u0026aacute;nsito en provincias de la Rep\u0026uacute;blica. de Ecuador CienciAm\u0026eacute;rica. 2021;10(1):24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cienciamerica.edu.ec/index.php/uti/article/view/355/695\u003c/span\u003e\u003cspan address=\"https://cienciamerica.edu.ec/index.php/uti/article/view/355/695\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalderon-Anyosa RJC, Bilal U, Kaufman JS. Variation in non-external and external causes of death in Peru in relation to the COVID-19 lockdown. Yale J Biology Med. 2021;94(1):23\u0026ndash;40. PMID: 33795980.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorges G, Monteiro M, Cherpitel CJ, Orozco R, Ye Y, Poznyak V, et al. Alcohol and Road Traffic Injuries in Latin America and the Caribbean: A Case-Crossover Study. Alcohol Clin Exp Res. 2017;41(10):1731\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/acer.13467\u003c/span\u003e\u003cspan address=\"10.1111/acer.13467\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"traffic accidents, mortality, trends, Ecuador","lastPublishedDoi":"10.21203/rs.3.rs-4254108/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4254108/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAccording to WHO data, traffic accidents caused 1,282,150 deaths globally in 2019, with a projection to become the fifth leading cause of mortality by 2030, highlighting significant public and economic health impacts. This study aimed to describe the trends in traffic accidents mortality in Ecuador between 2011 and 2022, by year, gender, age group, geographic location, type of accident and social inequalities.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA population-based study was conducted using national statistics on mortality due to traffic accidents in Ecuador, between 2011 and 2022, obtained from the National Institute of Statistics and Census. Crude mortality rates, adjusted per region per 100,000 inhabitants, were calculated by region, province, gender, and age group. The annual percentage change of the traffic accidents mortality rate and the Absolute Risks were calculated, as well as rate ratios between the groups. Inequalities by per capita income and by illiteracy rate were also calculated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe average mortality rate due to traffic accidents in Ecuador (2011\u0026ndash;2022) was 19.1. The rates were higher in men (31.3) than in women (7.2), with a rate ratio of 4.3. The rates were higher in the Amazon region (24.0), decreasing in recent years, with a statistically significant negative annual percentage variation of -1.2%, as in the Sierra region and Coast. Santo Domingo de los Ts\u0026aacute;chilas presented the highest rate (30.6), while, the highest rate related to age, (27.4) was identified in the 17 to 24 years group. In 2011, the highest rate (22.0) was recorded. The most frequent type of accident was \"unspecified\" followed by pedestrians.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThere was evidence of an increase (247.7%) of traffic accidents mortality inequalities stratified by per capita income between 2011 and 2019 and a decrease (18.9%) of traffic accidents mortality inequalities stratified by the illiteracy rate between 2014 and 2019. In Ecuador, between 2011 and 2020, transport accident mortality rates are decreasing significantly, showing important disparities by gender, age group, and province. The high frequency of \u0026ldquo;unspecified\u0026rdquo; causes denotes the necessity to improve the registration and/or coding system of the causes of death due to traffic accidents in Ecuador.\u003c/p\u003e","manuscriptTitle":"Trends in traffic accidents mortality and social inequalities in Ecuador from 2011 to 2022","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-16 07:07:16","doi":"10.21203/rs.3.rs-4254108/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-24T05:00:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-23T11:54:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-23T01:05:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-22T21:43:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-21T17:50:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-20T14:33:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223516136350258239850295043117561873466","date":"2024-06-18T01:25:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126254359825251988208804586673649629544","date":"2024-06-16T17:09:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-15T02:31:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303922026064339557725231056609878480699","date":"2024-06-15T01:35:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"286442713445252407853560978508993098220","date":"2024-06-14T16:32:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74254259184887900044110479919716843051","date":"2024-06-14T13:55:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277679134430296979108654676530438957158","date":"2024-06-14T08:56:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113479122045440042907712363954081084702","date":"2024-06-13T20:07:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"258553770661020748161055342967710909725","date":"2024-06-12T19:09:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89750079509119351877100203733707479200","date":"2024-06-12T18:10:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99087794940992822841277919963193072241","date":"2024-06-12T15:24:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-12T15:04:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-22T07:08:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-12T00:25:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-12T00:25:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-04-11T18:59:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3d409ec4-5512-498e-8e85-9af2761f7a64","owner":[],"postedDate":"April 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-07-21T17:47:35+00:00","versionOfRecord":{"articleIdentity":"rs-4254108","link":"https://doi.org/10.1186/s12889-024-19494-7","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2024-07-21 17:47:35","publishedOnDateReadable":"July 21st, 2024"},"versionCreatedAt":"2024-04-16 07:07:16","video":"","vorDoi":"10.1186/s12889-024-19494-7","vorDoiUrl":"https://doi.org/10.1186/s12889-024-19494-7","workflowStages":[]},"version":"v1","identity":"rs-4254108","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4254108","identity":"rs-4254108","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}