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. 2017 Aug 18;10(1):1360629. doi: 10.1080/16549716.2017.1360629

Expected years of life lost through road traffic injuries in Mexico

Efrén Murillo-Zamora a, Oliver Mendoza-Cano b,c, Benjamín Trujillo-Hernández e, José Guzmán-Esquivel f, Alfredo Medina-González g, Miguel Huerta h, Ramón Alberto Sánchez-Piña b, Agustin Lugo-Radillo d,✉
PMCID: PMC5645682  PMID: 28820342

ABSTRACT

Background: Road traffic injuries (RTIs) are a leading cause of premature mortality, mainly in low- and middle-income countries

Objective: To estimate the 2014 burden of RTIs in Mexico calculating years of life lost (YLL) and age-standardized YLL rates (ASYLL), and to evaluate sex, age, and region-related differences in premature mortality.

Methods: Mortality data were obtained from the National Institute of Statistics and Geography and 14,637 deaths of individuals 15 years of age and older were analyzed. The YLL and ASYLL were computed.

Results: The overall burden of RTIs was 332,922 YLL and 82.4% of the deaths occurred in males. Males from 25 to 34 years of age and females from 15 to 24 years of age showed the highest age-adjusted YLL rates (933 and 158 YLL per 100,000 inhabitants, respectively). The national ASYLL rate was 416 per 100,000 inhabitants and the highest state-stratified mortality rates were observed in Tabasco (851), Sinaloa (709), Durango (656), Zacatecas (642), and Baja California Sur (570).

Conclusions: RTIs contributed to the premature mortality rate in the study population. Our findings may be useful from a health policy perspective for designing and prioritizing interventions focused on the prevention of premature loss of life.

KEYWORDS: Traffic accidents, premature mortality, burden of illness, Mexico, health Policy

Background

Road traffic injuries (RTIs) are a leading cause of premature mortality, mainly in low- and middle-income countries [1]. An increase in the related burden has been observed worldwide and pedestrians, cyclists, and two-wheeled motorcycle riders are the most vulnerable populations that contribute to the observed mortality [2,3]. In Mexico, the overall RTI mortality has decreased since 2009; however, an increase in fatal motorcycle mortality has been observed and RTIs are still a main cause of death [4–6].

Traditional mortality rates have been used to evaluate the RTI burden in Mexico, which fail to highlight the premature deaths [7]. In 2015, the country adopted the United Nations Sustainable Development Goal on health (SDG3) and its aim is a 40% reduction of premature deaths by the year 2030. RTIs are one of the targeted causes of premature death [8]. A cost-effectiveness approach is needed to reach the proposed objective and the estimation of standard expected years of life lost (YLL) may be feasible and beneficial.

The YLL measure was developed by the Global Burden of Disease (GBD) study and is a useful analytical tool, from an economic and health policy perspective, for measuring preventable loss of life. The disability-adjusted life year (DALY) is calculated as the sum of the YLL and the years lived with disability (YLD). This health measure incorporates strategies (i.e. time-based discounting) used in cost-effectiveness analyses. In addition, the age-standardized YLL (ASYLL) has been used to compare goals across populations [9] and is a valid measure for identifying demographic or regional subgroups (i.e. the states of a country) with the highest premature mortality rate [10].

To the best of our knowledge, there are no published studies evaluating regional premature mortality due to traffic accidents in Mexico. This approach may be useful to identify high risk subgroups where particular interventions focused on the prevention of premature loss of life may be beneficial. The aim of this study was to estimate the burden of RTIs through YLL and ASYLL in Mexican individuals of 15 years of age and older at death in 2014. In addition, sex, age, and region-related differences were evaluated.

Methods

The YLL for RTIs were calculated following the methods described by the GBD study [11]. First, sex and age-stratified (15–24, 25–34, 35–44, 45–64 and ≥65 years of age) mortality data were obtained from the National Institute of Statistics and Geography of Mexico [12]. The following underlying causes of death (International Classification of Diseases 10th revision, ICD-10) were included: V09.2, V09.3, V12–V14, V19.4–V19.9, V20–V28, V29.4–V29.9, V30–V39, V40–V49,V50–V59, V60–V69,V70–V79,V80.3–V80.5, V81.1, V82.1, V83.0–V83.4,V84.0–V86.4, V87.0–V87.8, V89.2, and V89.9. These events are clustered using code E49B (Mexican List of Causes of Death) for statistical purposes [13].

Second, the YLL measure was computed for males and females by multiplying the number of RTI deaths by the number of years of expected remaining life at the respective age interval according to the 2013 Tables of Life (Global Health Observatory) of Mexico [14]. The total population was obtained from the 2010 National Census of Population and Housing [15]. The average age of death, per age interval, was calculated using national data from the Statistical and Epidemiological Death Registration System [16]. The following parameters were fixed: discount rate (r) = 0.03, age-weighting (β) = 0.04, adjustment constant for age-weights (C) = 0.1658, and age-weighting modulation (K) = 0. Templates (Microsoft® Excel®) from the GBD study were used to compute the YLL and the summary statistics were estimated using Stata® MP 13.0 (StataCorp LP). Finally, the ASYLL rates per 100,000 inhabitants were estimated using the World Standard Population (2000–2025).

The Local Health Research and Ethics Committee of the Mexican Institute of Social Security approved the present study.

Results

The number of RTI-associated deaths in 2014 was 11,944 in males and 2693 in females (Table 1), representing 2.5% of all deaths occurring in Mexico within the same period. The unadjusted mortality rates per 100,000 inhabitants were 31.7 in males (ranging from 18.4 to 66.0) and 19.0 in females (ranging from 4.2 to 12.1).

Table 1.

Deaths by road traffic injuries, Mexico 2014.

  Males
 Females
 Overall
State n % Rate n % Rate n % Rate
Aguascalientes 170 (5.9) 44.2 44 (1.9) 10.4 214 (4.1) 26.5
Baja California 250 (2.7) 22.4 89 (1.5) 8.1 339 (2.2) 15.3
Baja California Sur 91 (5.8) 39.6 25 (2.4) 11.4 116 (4.4) 25.8
Campeche 100 (4.5) 35.2 29 (1.7) 9.8 129 (3.3) 22.3
Coahuila 366 (4.3) 38.9 92 (1.4) 9.5 458 (3.0) 23.9
Colima 90 (4.3) 39.4 16 (1.1) 6.8 106 (3.0) 22.8
Chiapas 455 (3.8) 30.5 83 (0.8) 5.2 538 (2.4) 17.4
Chihuahua 364 (3.1) 31.9 123 (1.4) 10.4 487 (2.4) 21.0
Mexico City 580 (2.0) 18.4 209 (0.8) 5.9 789 (1.4) 11.7
Durango 265 (5.5) 49.3 66 (1.9) 11.6 331 (4.0) 29.9
Guanajuato 642 (4.2) 36.5 144 (1.2) 7.2 786 (2.9) 21.0
Guerrero 309 (3.4) 29.0 89 (1.3) 7.5 398 (2.5) 17.7
Hidalgo 276 (3.8) 31.6 85 (1.4) 8.7 361 (2.7) 19.5
Jalisco 875 (4.0) 35.4 209 (1.2) 7.9 1084 (2.7) 21.1
Mexico 1557 (4.0) 30.5 359 (1.1) 6.5 1916 (2.7) 18.0
Michoacan 370 (2.8) 26.1 89 (0.9) 5.6 459 (1.9) 15.3
Morelos 129 (2.4) 21.6 40 (0.9) 6.0 169 (1.7) 13.4
Nayarit 146 (4.5) 38.8 36 (1.5) 9.3 182 (3.2) 23.9
Nuevo León 400 (3.1) 24.3 123 (1.2) 7.3 523 (2.2) 15.7
Oaxaca 354 (3.1) 29.3 86 (0.9) 6.2 440 (2.1) 17.0
Puebla 541 (3.2) 29.6 116 (0.8) 5.5 657 (2.1) 16.7
Queretaro 241 (5.2) 39.9 56 (1.4) 8.4 297 (3.5) 23.4
Quintana Roo 131 (4.8) 27.9 21 (1.1) 4.6 152 (3.3) 16.4
San Luis Potosi 359 (4.7) 42.2 83 (1.4) 9.0 442 (3.2) 24.9
Sinaloa 533 (6.3) 55.0 98 (1.7) 9.8 631 (4.5) 32.0
Sonora 371 (4.1) 39.6 92 (1.5) 9.8 463 (3.1) 24.7
Tabasco 494 (7.8) 66.0 96 (2.0) 12.1 590 (5.3) 38.2
Tamaulipas 383 (3.9) 34.6 123 (1.6) 10.6 506 (2.9) 22.3
Tlaxcala 127 (4.4) 33.3 26 (1.0) 6.1 153 (2.8) 19.0
Veracruz 515 (2.0) 20.1 121 (0.6) 4.2 636 (1.4) 11.7
Yucatan 214 (3.4) 31.2 46 (0.9) 6.4 260 (2.3) 18.5
Zacatecas 246 (5.5) 50.3 49 (1.3) 9.2 295 (3.6) 28.9
Overall 11,944 (3.6) 31.7 2963 (1.1) 7.3 14,907 (2.5) 19.0
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The absolute frequencies (n), proportions (%) from the total number of registered deaths, and unadjusted mortality rates per 100,000 inhabitants are presented.

Data source: Main causes of mortality by place of residence, age and sex, 2014; National Institute of Statistics and Geography.

Table 2 shows the YLL by sex, age group, and state of residence. The YLL was higher in males in all age groups. The overall YLL in males was 274,451 and the highest age-adjusted rate (933 per 100,000 inhabitants) was observed in individuals from 25 to 34 years of age at death. The YLL in females was 58,470 and the highest rates were observed in young females (15–24 years of age, 158 YLL per 100,000 inhabitants).

Table 2.

Years of life lost by road traffic injuries and age-adjusted rates per 100,000 inhabitants, Mexico 2014.

  15–24 yr.
 25–34 yr.
 35–44 yr.
 45–64 yr.
 ≥65 yr.
State YLL ASYLL YLL ASYLL YLL ASYLL YLL ASYLL YLL ASYLL
Males                    
 Aguascalientes 1161 1036 772 892 829 1106 811 971 211 769
 Baja California 1501 506 1252 470 1463 594 1444 603 144 219
 Baja California Sur 481 802 746 1278 341 703 455 915 76 571
 Campeche 651 825 746 1173 463 844 455 717 59 252
 Coahuila 2237 902 2476 1177 1536 809 1464 671 482 654
 Colima 680 1114 612 1211 268 610 435 817 85 438
 Chiapas 3172 675 3036 926 1974 747 2275 727 279 239
 Chihuahua 2549 835 2476 989 1609 675 1583 618 296 324
 Mexico City 3002 408 3648 530 2608 409 2730 339 778 275
 Durango 1614 1045 1571 1401 1194 1190 1345 1124 271 528
 Guanajuato 5551 1083 3142 818 2511 756 2512 666 828 539
 Guerrero 3427 1062 1411 654 1170 638 1226 524 211 193
 Hidalgo 2124 883 2050 1126 1194 725 1147 559 144 176
 Jalisco 6202 900 5272 957 3437 728 3758 687 1074 505
 Mexico 8496 606 15,390 1329 6459 618 5420 469 1184 353
 Michoacan 2832 691 2530 869 1511 599 1503 475 313 210
 Morelos 821 504 879 719 609 547 514 359 135 237
 Nayarit 935 914 666 843 780 1102 772 894 144 376
 Nuevo León 1756 430 1997 520 2096 598 2354 623 490 387
 Oaxaca 2407 697 2130 886 1853 888 1286 464 406 298
 Puebla 4701 870 2689 678 2243 672 2572 648 440 270
 Queretaro 1558 897 1411 1005 1341 1122 890 695 279 658
 Quintana Roo 736 550 879 696 731 721 593 674 101 504
 San Luis Potosi 2379 988 1891 1094 1511 977 1741 902 457 513
 Sinaloa 3568 1368 3222 1582 2194 1163 2552 1121 566 637
 Sonora 1898 775 1864 898 1901 1012 2176 982 389 514
 Tabasco 3314 1563 3036 1796 2486 1725 2334 1407 364 637
 Tamaulipas 1954 687 2423 968 1828 807 2018 794 389 429
 Tlaxcala 623 565 772 909 731 1010 593 729 135 416
 Veracruz 3682 534 3462 684 2291 477 2394 379 338 130
 Yucatan 1161 619 985 650 1073 849 1128 726 296 454
 Zacatecas 2152 1569 1331 1336 1146 1265 910 848 228 421
 Overall 79,325 768 76,766 933 53,382 730 53,389 623 11,590 362
Females                    
 Aguascalientes 137 119 179 187 116 138 185 198 128 389
 Baja California 467 162 512 195 256 110 463 192 120 161
 Baja California Sur 165 292 128 231 47 102 130 271 38 271
 Campeche 165 205 179 258 47 81 148 229 45 196
 Coahuila 439 180 487 225 163 82 611 265 128 155
 Colima 110 179 51 97 70 151 37 67 38 178
 Chiapas 384 77 461 123 488 168 315 98 98 83
 Chihuahua 824 272 384 150 465 190 407 149 271 266
 Mexico City 934 126 615 83 419 59 1296 135 474 117
 Durango 494 318 307 254 233 213 222 169 105 196
 Guanajuato 741 134 692 154 488 128 630 147 263 148
 Guerrero 604 178 384 157 116 56 352 135 211 168
 Hidalgo 439 172 333 154 302 160 296 132 203 215
 Jalisco 1675 241 871 148 628 123 907 148 286 115
 Mexico 2197 154 1665 131 1209 105 1778 140 497 121
 Michoacan 412 94 564 169 279 98 426 120 128 76
 Morelos 192 114 231 165 93 72 111 69 105 157
 Nayarit 165 162 179 217 186 258 93 103 75 192
 Nuevo León 714 179 538 140 349 99 704 177 173 117
 Oaxaca 330 88 589 205 279 114 370 117 143 89
 Puebla 494 86 333 72 349 90 630 136 271 135
 Queretaro 247 136 333 211 93 70 315 223 98 192
 Quintana Roo 28 21 128 103 47 48 148 177 38 194
 San Luis Potosi 522 210 564 287 372 215 204 97 113 118
 Sinaloa 961 373 435 206 372 190 259 107 120 128
 Sonora 412 175 435 212 256 137 389 172 211 255
 Tabasco 494 226 461 241 442 282 407 240 143 242
 Tamaulipas 494 175 922 350 233 98 574 213 211 201
 Tlaxcala 192 169 102 103 93 112 93 101 45 121
 Veracruz 659 93 461 78 488 89 389 56 278 93
 Yucatan 275 146 179 112 163 120 259 154 60 86
 Zacatecas 357 247 256 226 163 163 148 126 83 146
 Overall 16,721 158 13,958 155 9299 116 13,294 141 5198 139
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YLL, years of life lost; ASYLL, age-standardized years of life lost; yr., years old.

In the estimation by region (Table 3), the overall YLL in males ranged from 88.7% (Quintana Roo) to 76.1% (Baja California). The national estimate was 82.4%. The highest ASYLL rates per 100,000 inhabitants were registered in individuals from 25 to 34 years of age (526), followed by those from 15 to 24 years of age (459) and from 35 to 44 years of age (410). The overall ASYLL rate was 416 per 100,000 inhabitants and the highest state-stratified mortality rates were observed in Tabasco (851), Sinaloa (709), Durango (656), Zacatecas, (642) and Baja California Sur (570).

Table 3.

Age-standardized years of life lost by road traffic injuries, Mexico 2014.

State YLL, n (%) % Males in YLL ASYLLa
15–24 yr.. 25–34 yr.. 35–44 yr.. 45–64 yr.. ≥65 yr. Overall
1 Tabasco 13,480 (4.0) 85.6 883 971 973 817 436 851
2 Sinaloa 14,250 (4.3) 84.9 874 882 668 600 375 709
3 Durango 7357 (2.2) 81.5 681 806 680 624 358 656
4 Zacatecas 6774 (2.0) 85.1 891 744 688 470 280 642
5 Baja California Sur 2606 (0.8) 80.6 555 769 411 600 418 570
6 Aguascalientes 4530 (1.4) 83.5 570 522 593 563 562 561
7 San Luis Potosi 9752 (2.9) 81.8 593 664 574 481 308 543
8 Sonora 9930 (3.0) 82.9 481 557 575 572 378 527
9 Nayarit 3993 (1.2) 82.5 539 523 677 489 283 519
10 Coahuila 10,022 (3.1) 81.8 544 695 436 462 390 517
11 Queretaro 6564 (2.1) 83.5 507 584 567 447 403 507
12 Colima 2385 (0.7) 87.2 645 640 374 436 303 500
13 Campeche 2958 (0.9) 80.3 512 695 453 471 224 497
14 Tamaulipas 11,045 (3.3) 78.0 432 652 444 495 307 483
15 Jalisco 24,110 (7.2) 81.9 569 539 414 402 295 459
16 Chihuahua 10,863 (3.2) 78.4 555 565 430 375 293 457
17 Guanajuato 17,358 (5.2) 83.8 591 460 421 389 329 449
18 Hidalgo 8234 (2.5) 80.9 517 599 423 335 197 433
19 Tlaxcala 3381 (1.0) 84.5 364 475 530 397 259 415
20 Mexico 44,294 (13.3) 83.4 378 701 349 296 225 402
21 Chiapas 12,482 (3.7) 86.0 367 498 444 408 160 397
22 Yucatan 5578 (1.7) 83.2 383 374 472 428 264 396
23 Guerrero 9113 (2.7) 81.7 608 389 330 319 180 385
24 Quintana Roo 3429 (1.0) 88.7 289 401 393 432 352 378
25 Oaxaca 9792 (2.9) 82.5 380 515 471 279 185 377
26 Puebla 14,720 (4.4) 85.9 466 351 358 373 195 367
27 Michoacan 10,497 (3.2) 82.8 383 495 333 287 139 345
28 Baja California 7621 (2.3) 76.1 336 333 359 397 188 339
29 Nuevo León 11,170 (3.4) 77.8 306 330 347 394 242 335
30 Morelos 3691 (1.1) 80.2 306 423 292 206 194 289
31 Veracruz 14,441 (4.3) 84.2 310 357 270 210 110 263
32 Mexico City 16,502 (5.0) 77.4 267 298 224 228 182 245
  Overall 332,922   82.4 459 526 410 370 242 416
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YLL, years of life lost; ASYLL, age-standardized years of life lost; yr., years old.

aASYLL rates per 100,000 inhabitants. The World Standard Population 2000–2025 (World Health Organization) was used.

Discussion

Our findings suggest that the 2014 RTI burden in Mexico was 332,922 YLL and the mortality rate was higher for males (274,451 YLL, 82.4%). We identified sex, age, and region-related patterns that may be useful in designing and prioritizing intervention policies focused on the prevention of premature loss of life.

The illness burden of RTIs in Mexico was previously estimated at the national level [1] and the regional (per state) burden was not evaluated. To the best of our knowledge, this is the first study evaluating regional differences in the burden using the YLL and ASYLL rates as health measures.

The disparities between the sexes observed in our study are a common global finding and sex-related stereotypes about risk-taking and risk perception while driving have an influence on this phenomenon [17,18]. In urban areas of Mexico, male automobile drivers are more likely to be involved in alcohol-impaired driving and less likely to use seatbelts [19,20]. A higher RTI-related mortality among males has also been described in developed countries, mainly in younger individuals [21].

Nearly 75% of analyzed deaths occurred in individuals ≤44 years of age and the highest ASYLL rate (526 per 100,000 inhabitants) was observed in adults from 25 to 34 years of age. The 25–34 year age group is more likely to be employed, compared with individuals 24 years of age and younger [22], thus increasing the economic burden.

Heterogeneous state-specific ASYLL rates were found. The highest rate was observed in Tabasco (southeastern region of Mexico), where 5.3% of all deaths registered in 2014 were due to RTIs. This is double the number of the national estimate (2.5%) within the same period. Interestingly, the motorization rate of Tabasco is lower than the average rate (273.1 vs. 310.3 per 100,000 inhabitants, respectively), but the number of accidents occurring on federal highways (17.6%) and the total number of deaths involving motorcycle riders (36.5%) were higher than the national estimates (5.4% and 12.4%, respectively). These events may have determined the scenario observed.

The overall ASYLL rates were low in wealthy (i.e. Nuevo León) and highly motorized (i.e. Mexico City) states, perhaps due to the quality of emergency care provided and the exposure of populations particularly vulnerable to traffic [23]. Within-country disparities in RTI burden have been described in other populations [21,24,25]. This fact may be secondary to multiple risk exposures, including infrastructural characteristics, health care facilities and alcohol drinking prevalence [25].

The conceptual framework of traffic injuries is complex. They result from the interaction of road users, vehicles, and infrastructure [26]. The published data regarding interventions to reduce the related burden in low- and middle-income countries is limited [27]. The World Report on RTI prevention highlights the need for an integrated effort focusing on improved information systems, response capacity strengthening, and reduced exposure to modifiable risk factors plus the availability of resources for targeting them [17].

Several risks factors associated with increased RTI risk and death have been documented in the Mexican population. These factors include those influencing the occurrence of the crash (speeding, alcohol consumption, and hand-held mobile phones) and injury severity (non-use of crash helmets by two-wheeled vehicle users and non-use of retention devices, such as seat belts and child safety seats) [28–30].

Most of the cost-effectiveness strategies to reduce the RTI burden in low- and middle-income countries are linked to legislative interventions [27]. The current Mexican road legislation is permissive and poorly applied [31]. An integrated juridical effort is needed that includes the regulation of alcohol-impaired driving, prohibition of talking on hand-held phones while driving, verification of the use of retention devices by drivers and occupants, and high penalties for offenders. Susceptible populations must be included [32].

The population mobility patterns have changed and a constant growth in motorcycle users has been observed since 2002 [33]. A simultaneous increase in fatal motorcycle injuries has been documented [6]. In addition, also in Mexico, high mortality rates secondary to intentional injuries (i.e. homicide and suicide) are observed [34].

The potential limitations of our study must be cited. First of all, despite the fact that official mortality data were used to compute the YLL and ASYLL rates, the burden of disease may be underestimated, given that approximately 30% of fatal RTIs are misclassified [35]. Second, data to estimate the RTI-associated YLD are not systematically collected in Mexico and therefore we were unable to compute the DALY. However, the YLL and ASYLL rates are valid stand-alone indicators for quantifying premature mortality due to specific events [36–38]. Third, a clustered analysis was performed and no specific populations involved in those injuries were identified. Governmental data indicate that the highest number of victims of fatal RTIs in 2013 were pedestrians (51.5%), followed by occupants of four-wheeled automobiles (34.4%), motorcycle riders (12.3%), and bicyclists (1.8%) [39].

Conclusions

Our findings provide quantitative evidence of the burden of RTIs in Mexico. These are preventable events and efforts to reduce the associated economic and social burden must be made. Sex, age, and region-related patterns were highlighted in our study and they may be useful in improving the impact of public policies focused on the prevention of premature loss of life secondary to traffic injuries.

Acknowledgments

None.

Biography

EMZ designed the study and performed data collection andanalysis; wrote the manuscript. OMC and ALR designed the study, analyzed data and wrote the manuscript. BTH analyzed data and wrote the manuscript. JGE wrote the manuscript. AMG wrote the manuscript. MH wrote the manuscript. RASP wrote the manuscript.

Responsible Editor Siddhivinayak Hirve, Organisation Mondiale de la Sante, Switzerland

Funding Statement

None.

Disclosure statement

No potential conflict of interest was reported by the authors.

Ethics and consent

The Local Health Research and Ethics Committee of the Mexican Institute of Social Security approved the present study. The informed consent was waived due to the particular design of this study.

Paper context

The road traffic injuries-related mortality in Mexico has been evaluated using traditional rates that fail to highlight the premature deaths. The years of life lost due to these injuries during 2014 were estimated in this study and sex, age, and region-related differences were evaluated. The obtained estimators may useful to improve the impact of public policies focused on the prevention of these events.

References

  • [1]. Dalal K, Lin Z, Gifford M, et al. Economics of global burden of road traffic injuries and their relationship with health system variables. Int J Prev Med. 2013;4(12):1442–7. [PMC free article] [PubMed] [Google Scholar]
  • [2]. Mathers CD, Loncar D.. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006;3(11):e442. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [3]. Ameratunga S, Híjar M, Norton R. Road-traffic injuries: confronting disparities to address a global-health problem. Lancet. 2006;367(9521):1533–1540. [DOI] [PubMed] [Google Scholar]
  • [4]. Híjar M, Chandran A, Pérez-Núñez R, et al. Quantifying the underestimated burden of road traffic mortality in Mexico: a comparison of three approaches. Traffic Inj Prev. 2012;13(Suppl 1):5–10. [DOI] [PubMed] [Google Scholar]
  • [5]. Arreola-Rissa C, Santos-Guzmán J, Esquivel-Guzmán A, et al. Traffic related deaths in Nuevo León, Mexico: causes and associated factors. Salud Publica Mex. 2008;50 Suppl 1:S48–S54. [DOI] [PubMed] [Google Scholar]
  • [6]. Rodrigues EM, Villaveces A, Sanhueza A, et al. Trends in fatal motorcycle injuries in the Americas, 1998–2010. Int J Inj Contr Saf Promot. 2014;21(2):170–180. [DOI] [PubMed] [Google Scholar]
  • [7]. Thacker SB, Stroup DF, Carande-Kulis V, et al. Measuring the public’s health. Public Health Rep. 2006;121(1):14–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [8]. González-Pier E, Barraza-Lloréns M, Beyeler N, et al. Mexico’s path towards the sustainable development goal for health: an assessment of the feasibility of reducing premature mortality by 40% by 2030. Lancet Glob Health. 2016;4(10):e714–e725. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [9]. Murillo-Zamora E, García-Ceballos R, Delgado-Enciso I, et al. Regional-level estimation of expected years of life lost attributable to overweight and obesity among Mexican adults. Glob Health Action. 2016;9:31642. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [10]. Marshall RJ. Standard expected years of life lost as a measure of mortality: norms and reference to New Zealand data. Aust N Z J Public Health. 2004;28(5):452–457. [DOI] [PubMed] [Google Scholar]
  • [11]. Murray CJ, López AD, editors. The global burden of disease: a comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020. Boston (MA): Harvard University Press, World Health Organization, Harvard School of Public Health, World Bank; 1996. [Google Scholar]
  • [12]. Instituto Nacional de Estadística y Geografía Principales causas de mortalidad por lugar de residencia, edad y sexo: 2014. 2016. [cited 2016 October 2]. Available from: http://www.inegi.org.mx/est/contenidos/proyectos/registros/vitales/mortalidad/tabulados/PC.asp?t=14&c=11817 [Google Scholar]
  • [13]. Instituto Nacional de Estadística y Geografía Lista mexicana de causas de muerte. 2013. [cited 2016 October 8]. Available from: www3.inegi.org.mx/rnm/index.php/catalog/57/download/3131 [Google Scholar]
  • [14]. Global Health Observatory Life tables by country: Mexico. 2015. [cited 2015 September 19]. Available from: http://apps.who.int/gho/data/?theme=main&vid=61060 [Google Scholar]
  • [15]. Instituto Nacional de Estadístia y Geografía Censo Nacional de población y Vivienda 2010. 2015. [Google Scholar]
  • [16]. Dirección General de Epidemiología Manual de procedimientos estandarizados del Sistema Estadístico y Epidemiológico de las Defunciones. 2013. [Google Scholar]
  • [17]. World Health Organization Global status report on road safety 2015. Geneva: World Healh Organization; 2015. [Google Scholar]
  • [18]. Granié M-A, Papafava E. Gender stereotypes associated with vehicle driving among French preadolescents and adolescents. Transport Res Part F. 2011;14(5):341–353. [Google Scholar]
  • [19]. Cervantes Trejo A, Leenen I. [The use of seatbelts and child seats in drivers and passengers of motor vehicles in four metropolitan areas in Mexico]. Gac Med Mex. 2015;151(1):54–65. [PubMed] [Google Scholar]
  • [20]. Cervantes Trejo A, Leenen I. [If you drink, don’t drive: drunk drivers in Guadalajara and Leon, Mexico]. Gac Med Mex. 2014;150(6):552–562. [PubMed] [Google Scholar]
  • [21]. Dibben C, Popham F. Are health inequalities evident at all ages? An ecological study of English mortality records. Eur J Public Health. 2013;23(1):39–45. [DOI] [PubMed] [Google Scholar]
  • [22]. Bureau of Labor Statistics Economic news release: employee tenure in 2016 (USDL-16-1867). 2016. [cited 2016 October 23]. Available from: http://www.bls.gov/news.release/pdf/tenure.pdf [Google Scholar]
  • [23]. LaGrone L, Riggle K, Joshipura M, et al. Uptake of the World Health Organization’s trauma care guidelines: a systematic review. Bull World Health Organ. 2016;94(8):585–598C. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [24]. Bhatti JA, Khoso AK, Waseem H, et al. Variations in sub-national road traffic fatality trends in a low-income country. Int J Crit Illn Inj Sci. 2013;3(1):25–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [25]. La Torre G, Van Beeck E, Quaranta G, et al. Determinants of within-country variation in traffic accident mortality in Italy: a geographical analysis. Int J Health Geogr. 2007;6(1):49. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [26]. Prieto L, Sacristán JA. Problems and solutions in calculating quality-adjusted life years (QALYs). Health Qual Life Outcomes. 2003;1:80. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [27]. Staton C, Vissoci J, Gong E, et al. Road traffic injury prevention initiatives: a systematic review and metasummary of effectiveness in low and middle income countries. PLoS One. 2016;11(1):e0144971. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [28]. Espitia-Hardeman V, Vélez L, Muñoz E, et al. [Impact of interventions directed toward motorcyclist death prevention in Cali, Colombia: 1993–2001]. Salud Publica Mex. 2008;50(Suppl 1):S69–S77. [DOI] [PubMed] [Google Scholar]
  • [29]. Ishigami Y, Klein RM. Is a hands-free phone safer than a handheld phone? J Safety Res. 2009;40(2):157–164. [DOI] [PubMed] [Google Scholar]
  • [30]. Gómez-García L, Pérez-Núñez R, Hidalgo-Solórzano E. [Short-term impact of changes in drinking-and-driving legislation in Guadalajara and Zapopan, Jalisco, Mexico]. Cad Saude Publica. 2014;30(6):1281–1292. [DOI] [PubMed] [Google Scholar]
  • [31]. Organización Panamericana de la salud Informe sobre el Estado de la Seguridad Vial en la Región de las América. 2009. [cited 2016 Oct 30]. Available from: http://www.who.int/violence_injury_prevention/road_safety_status/2009/gsrrs_paho.pdf
  • [32]. Pérez-Núñez R, Híjar M, Celis A, et al. [Road traffic injuries in Mexico: evidences to strengthen the Mexican road safety strategy]. Cad Saude Publica. 2014;30(5):911–925. [DOI] [PubMed] [Google Scholar]
  • [33]. Instituto Nacional de Geografía y Estadística Vehículos de motor registrados en circulación: 1980–2015. 2016. [cited 2016 June 4]. Available from: http://www.inegi.org.mx/sistemas/olap/Proyectos/bd/continuas/transporte/vehiculos.asp?s=est?c=13158 [Google Scholar]
  • [34]. Norheim OF, Jha P, Admasu K, et al. Avoiding 40% of the premature deaths in each country, 2010–30: review of national mortality trends to help quantify the UN sustainable development goal for health. Lancet. 2015;385(9964):239–252. [DOI] [PubMed] [Google Scholar]
  • [35]. Shahraz S, Bhalla K, Lozano R, et al. Improving the quality of road injury statistics by using regression models to redistribute ill-defined events. Inj Prev. 2013;19(1):1–5. [DOI] [PubMed] [Google Scholar]
  • [36]. Aragón TJ, Lichtensztajn DY, Katcher BS, et al. Calculating expected years of life lost for assessing local ethnic disparities in causes of premature death. BMC Public Health. 2008;8:116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [37]. Katcher BS, Reiter RB, Aragón TJ. Estimating alcohol-related premature mortality in San Francisco: use of population-attributable fractions from the global burden of disease study. BMC Public Health. 2010;10:682. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [38]. Lukaschek K, Erazo N, Baumert J, et al. Suicide mortality in comparison to traffic accidents and homicides as causes of unnatural death. An analysis of 14,441 cases in Germany in the year 2010. Int J Environ Res Public Health. 2012;9(3):924–931. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [39]. Consejo Nacional para la Prevención de Accidentes Perfil nacional y estatal de seguridad vial 2014. 2016. [cited 2016 November 2]. Available from: http://www.conapra.salud.gob.mx/Interior/Perfil_Accidentes_Estado.html [Google Scholar]

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