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. 2020 Apr 24;26(Suppl 1):i96–i114. doi: 10.1136/injuryprev-2019-043494

Global injury morbidity and mortality from 1990 to 2017: results from the Global Burden of Disease Study 2017

Spencer L James 1,, Chris D Castle 1, Zachary V Dingels 1, Jack T Fox 1, Erin B Hamilton 1, Zichen Liu 1, Nicholas L S Roberts 1, Dillon O Sylte 1, Nathaniel J Henry 1, Kate E LeGrand 1, Ahmed Abdelalim 2, Amir Abdoli 3, Ibrahim Abdollahpour 4, Rizwan Suliankatchi Abdulkader 5, Aidin Abedi 6, Akine Eshete Abosetugn 7, Abdelrahman I Abushouk 8, Oladimeji M Adebayo 9, Marcela Agudelo-Botero 10, Tauseef Ahmad 11,12, Rushdia Ahmed 13,14, Muktar Beshir Ahmed 15, Miloud Taki Eddine Aichour 16, Fares Alahdab 17, Genet Melak Alamene 18, Fahad Mashhour Alanezi 19, Animut Alebel 20, Niguse Meles Alema 21, Suliman A Alghnam 22, Samar Al-Hajj 23,24, Beriwan Abdulqadir Ali 25,26, Saqib Ali 27, Mahtab Alikhani 28, Cyrus Alinia 29, Vahid Alipour 30,31, Syed Mohamed Aljunid 32,33, Amir Almasi-Hashiani 34, Nihad A Almasri 35, Khalid Altirkawi 36, Yasser Sami Abdeldayem Amer 37,38, Saeed Amini 39, Arianna Maever Loreche Amit 40,41, Catalina Liliana Andrei 42, Alireza Ansari-Moghaddam 43, Carl Abelardo T Antonio 44,45, Seth Christopher Yaw Appiah 46,47, Jalal Arabloo 30, Morteza Arab-Zozani 48, Zohreh Arefi 49, Olatunde Aremu 50, Filippo Ariani 51, Amit Arora 52,53, Malke Asaad 54, Babak Asghari 55, Nefsu Awoke 56, Beatriz Paulina Ayala Quintanilla 57,58, Getinet Ayano 59, Martin Amogre Ayanore 60, Samad Azari 30, Ghasem Azarian 61, Alaa Badawi 62,63, Ashish D Badiye 64, Eleni Bagli 65,66, Atif Amin Baig 67,68, Mohan Bairwa 69,70, Ahad Bakhtiari 71, Arun Balachandran 72,73, Maciej Banach 74,75, Srikanta K Banerjee 76, Palash Chandra Banik 77, Amrit Banstola 78, Suzanne Lyn Barker-Collo 79, Till Winfried Bärnighausen 80,81, Lope H Barrero 82, Akbar Barzegar 83, Mohsen Bayati 84, Bayisa Abdissa Baye 85, Neeraj Bedi 86,87, Masoud Behzadifar 88, Tariku Tesfaye Bekuma 89, Habte Belete 90, Corina Benjet 91, Derrick A Bennett 92, Isabela M Bensenor 93, Kidanemaryam Berhe 94, Pankaj Bhardwaj 95,96, Anusha Ganapati Bhat 97, Krittika Bhattacharyya 98,99, Sadia Bibi 100, Ali Bijani 101, Muhammad Shahdaat Bin Sayeed 102,103, Guilherme Borges 91, Antonio Maria Borzì 104, Soufiane Boufous 105, Alexandra Brazinova 106, Nikolay Ivanovich Briko 107, Shyam S Budhathoki 108, Josip Car 109,110, Rosario Cárdenas 111, Félix Carvalho 112, João Mauricio Castaldelli-Maia 113, Carlos A Castañeda-Orjuela 114,115, Giulio Castelpietra 116,117, Ferrán Catalá-López 118,119, Ester Cerin 120,121, Joht S Chandan 122, Wagaye Fentahun Chanie 123, Soosanna Kumary Chattu 124, Vijay Kumar Chattu 125, Irini Chatziralli 126,127, Neha Chaudhary 128,129, Daniel Youngwhan Cho 130, Mohiuddin Ahsanul Kabir Chowdhury 131,132, Dinh-Toi Chu 133, Samantha M Colquhoun 134, Maria-Magdalena Constantin 135,136, Vera M Costa 112, Giovanni Damiani 137,138, Ahmad Daryani 139, Claudio Alberto Dávila-Cervantes 140, Feleke Mekonnen Demeke 141, Asmamaw Bizuneh Demis 142,143, Gebre Teklemariam Demoz 144,145, Desalegn Getnet Demsie 21, Afshin Derakhshani 146, Kebede Deribe 147,148, Rupak Desai 149, Mostafa Dianati Nasab 150, Diana Dias da Silva 151, Zahra Sadat Dibaji Forooshani 152, Kerrie E Doyle 153, Tim Robert Driscoll 154, Eleonora Dubljanin 155, Bereket Duko Adema 156,157, Arielle Wilder Eagan 158,159, Aziz Eftekhari 160,161, Elham Ehsani-Chimeh 162, Maysaa El Sayed Zaki 163, Demelash Abewa Elemineh 164, Shaimaa I El-Jaafary 2, Ziad El-Khatib 165,166, Christian Lycke Ellingsen 167,168, Mohammad Hassan Emamian 169, Daniel Adane Endalew 170, Sharareh Eskandarieh 171, Pawan Sirwan Faris 172,173, Andre Faro 174, Farshad Farzadfar 175, Yousef Fatahi 176, Wubalem Fekadu 90,177, Tomas Y Ferede 178, Seyed-Mohammad Fereshtehnejad 179,180, Eduarda Fernandes 181, Pietro Ferrara 182, Garumma Tolu Feyissa 183, Irina Filip 184,185, Florian Fischer 186, Morenike Oluwatoyin Folayan 187, Masoud Foroutan 188, Joel Msafiri Francis 189, Richard Charles Franklin 190,191, Takeshi Fukumoto 192,193, Biniyam Sahiledengle Geberemariyam 194, Abadi Kahsu Gebre 195, Ketema Bizuwork Gebremedhin 196, Gebreamlak Gebremedhn Gebremeskel 197,198, Berhe Gebremichael 199, Getnet Azeze Gedefaw 200,201, Birhanu Geta 202, Mansour Ghafourifard 203, Farhad Ghamari 204, Ahmad Ghashghaee 205, Asadollah Gholamian 206,207, Tiffany K Gill 208, Alessandra C Goulart 93,209, Ayman Grada 210, Michal Grivna 211, Mohammed Ibrahim Mohialdeen Gubari 212, Rafael Alves Guimarães 213, Yuming Guo 214,215, Gaurav Gupta 216, Juanita A Haagsma 217, Nima Hafezi-Nejad 218,219, Hassan Haghparast Bidgoli 220, Brian James Hall 221, Randah R Hamadeh 222, Samer Hamidi 223, Josep Maria Haro 224,225, Md Mehedi Hasan 226, Amir Hasanzadeh 227,228, Soheil Hassanipour 229, Hadi Hassankhani 230,231, Hamid Yimam Hassen 232,233, Rasmus Havmoeller 234, Khezar Hayat 235,236, Delia Hendrie 59, Fatemeh Heydarpour 237, Martha Híjar 238,239, Hung Chak Ho 240, Chi Linh Hoang 241, Michael K Hole 242, Ramesh Holla 243, Naznin Hossain 244,245, Mehdi Hosseinzadeh 246,247, Sorin Hostiuc 248,249, Guoqing Hu 250, Segun Emmanuel Ibitoye 251, Olayinka Stephen Ilesanmi 252, Irena Ilic 155, Milena D Ilic 253, Leeberk Raja Inbaraj 254, Endang Indriasih 255, Seyed Sina Naghibi Irvani 256, Sheikh Mohammed Shariful Islam 257,258, M Mofizul Islam 259, Rebecca Q Ivers 260, Kathryn H Jacobsen 261, Mohammad Ali Jahani 262, Nader Jahanmehr 263,264, Mihajlo Jakovljevic 265, Farzad Jalilian 266, Sudha Jayaraman 267, Achala Upendra Jayatilleke 268,269, Ravi Prakash Jha 270, Yetunde O John-Akinola 251, Jost B Jonas 271,272, Nitin Joseph 273, Farahnaz Joukar 229, Jacek Jerzy Jozwiak 274, Suresh Banayya Jungari 275, Mikk Jürisson 276, Ali Kabir 277, Rajendra Kadel 278, Amaha Kahsay 94, Leila R Kalankesh 279, Rohollah Kalhor 280,281, Teshome Abegaz Kamil 282, Tanuj Kanchan 283, Neeti Kapoor 64, Manoochehr Karami 284, Amir Kasaeian 285,286, Hagazi Gebremedhin Kassaye 21, Taras Kavetskyy 287,288, Hafte Kahsay Kebede 289, Peter Njenga Keiyoro 290, Abraham Getachew Kelbore 291, Bayew Kelkay 292, Yousef Saleh Khader 293, Morteza Abdullatif Khafaie 294, Nauman Khalid 295, Ibrahim A Khalil 296, Rovshan Khalilov 297, Mohammad Khammarnia 298, Ejaz Ahmad Khan 299, Maseer Khan 300, Tripti Khanna 301,302, Habibolah Khazaie 303, Fatemeh Khosravi Shadmani 304, Roba Khundkar 305, Daniel N Kiirithio 306, Young-Eun Kim 307, Daniel Kim 308, Yun Jin Kim 309, Adnan Kisa 310, Sezer Kisa 311, Hamidreza Komaki 312,313, Shivakumar K M Kondlahalli 314, Vladimir Andreevich Korshunov 107, Ai Koyanagi 315,316, Moritz U G Kraemer 317,318, Kewal Krishan 319, Burcu Kucuk Bicer 320,321, Nuworza Kugbey 322,323, Vivek Kumar 324, Nithin Kumar 273, G Anil Kumar 325, Manasi Kumar 326,327, Girikumar Kumaresh 328, Om P Kurmi 327,329, Oluwatosin Kuti 330, Carlo La Vecchia 331, Faris Hasan Lami 332, Prabhat Lamichhane 333, Justin J Lang 334, Van C Lansingh 335,336, Dennis Odai Laryea 337, Savita Lasrado 338, Arman Latifi 339, Paolo Lauriola 340, Janet L Leasher 341, Shaun Wen Huey Lee 342,343, Tsegaye Lolaso Lenjebo 344, Miriam Levi 51,345, Shanshan Li 214, Shai Linn 346, Xuefeng Liu 347, Alan D Lopez 1,348,349, Paulo A Lotufo 350, Raimundas Lunevicius 351,352, Ronan A Lyons 353, Mohammed Madadin 354, Muhammed Magdy Abd El Razek 355, Narayan Bahadur Mahotra 356, Marek Majdan 357, Azeem Majeed 358, Jeadran N Malagon-Rojas 359,360, Venkatesh Maled 361,362, Reza Malekzadeh 363,364, Deborah Carvalho Malta 365, Navid Manafi 366,367, Amir Manafi 368, Ana-Laura Manda 369, Narayana Manjunatha 370, Fariborz Mansour-Ghanaei 229, Borhan Mansouri 371, Mohammad Ali Mansournia 372, Joemer C Maravilla 373, Lyn M March 374, Amanda J Mason-Jones 375, Seyedeh Zahra Masoumi 376, Benjamin Ballard Massenburg 130, Pallab K Maulik 377,378, Gebrekiros Gebremichael Meles 379, Addisu Melese 141, Zeleke Aschalew Melketsedik 380, Peter T N Memiah 381, Walter Mendoza 382, Ritesh G Menezes 383, Meresa Berwo Mengesha 384, Melkamu Merid Mengesha 385, Tuomo J Meretoja 386,387, Atte Meretoja 388,389, Hayimro Edemealem Merie 164, Tomislav Mestrovic 390,391, Bartosz Miazgowski 392, Tomasz Miazgowski 393, Ted R Miller 59,394, GK Mini 395,396, Andreea Mirica 397,398, Erkin M Mirrakhimov 399,400, Mehdi Mirzaei-Alavijeh 266, Prasanna Mithra 273, Babak Moazen 401,402, Masoud Moghadaszadeh 403,404, Efat Mohamadi 405, Yousef Mohammad 406, Karzan Abdulmuhsin Mohammad 407,408, Aso Mohammad Darwesh 409, Naser Mohammad Gholi Mezerji 410, Abdollah Mohammadian-Hafshejani 411, Milad Mohammadoo-Khorasani 412, Reza Mohammadpourhodki 413, Shafiu Mohammed 80,414, Jemal Abdu Mohammed 415, Farnam Mohebi 175,416, Mariam Molokhia 417, Lorenzo Monasta 418, Yoshan Moodley 419, Mahmood Moosazadeh 420, Masoud Moradi 421, Ghobad Moradi 422,423, Maziar Moradi-Lakeh 424, Farhad Moradpour 422, Lidia Morawska 425, Ilais Moreno Velásquez 426, Naho Morisaki 427, Shane Douglas Morrison 130, Tilahun Belete Mossie 90, Atalay Goshu Muluneh 428, Srinivas Murthy 429, Kamarul Imran Musa 430, Ghulam Mustafa 431,432, Ashraf F Nabhan 433,434, Ahamarshan Jayaraman Nagarajan 435,436, Gurudatta Naik 437, Mukhammad David Naimzada 438,439, Farid Najafi 440, Vinay Nangia 441, Bruno Ramos Nascimento 442, Morteza Naserbakht 424,443, Vinod Nayak 444, Duduzile Edith Ndwandwe 445, Ionut Negoi 446,447, Josephine W Ngunjiri 448, Cuong Tat Nguyen 449, Huong Lan Thi Nguyen 449, Rajan Nikbakhsh 450,451, Dina Nur Anggraini Ningrum 452,453, Chukwudi A Nnaji 445,454, Peter S Nyasulu 455, Felix Akpojene Ogbo 112, Onome Bright Oghenetega 456, In-Hwan Oh 457, Emmanuel Wandera Okunga 458, Andrew T Olagunju 459,460, Tinuke O Olagunju 461, Ahmed Omar Bali 462, Obinna E Onwujekwe 463, Kwaku Oppong Asante 464,465, Heather M Orpana 466,467, Erika Ota 468, Nikita Otstavnov 438,469, Stanislav S Otstavnov 438,470, Mahesh P A 471, Jagadish Rao Padubidri 472, Smita Pakhale 473, Keyvan Pakshir 474, Songhomitra Panda-Jonas 475, Eun-Kee Park 476, Sangram Kishor Patel 477,478, Ashish Pathak 165,479, Sanghamitra Pati 480, George C Patton 481,482, Kebreab Paulos 483, Amy E Peden 191,484, Veincent Christian Filipino Pepito 485, Jeevan Pereira 486, Hai Quang Pham 449, Michael R Phillips 487,488, Marina Pinheiro 489, Roman V Polibin 490, Suzanne Polinder 217, Hossein Poustchi 363, Swayam Prakash 491, Dimas Ria Angga Pribadi 492, Parul Puri 493, Zahiruddin Quazi Syed 96, Mohammad Rabiee 494, Navid Rabiee 495, Amir Radfar 496,497, Anwar Rafay 498, Ata Rafiee 499, Alireza Rafiei 500,501, Fakher Rahim 502,503, Siavash Rahimi 504, Vafa Rahimi-Movaghar 505, Muhammad Aziz Rahman 506,507, Ali Rajabpour-Sanati 508, Fatemeh Rajati 421, Ivo Rakovac 509, Kavitha Ranganathan 510, Sowmya J Rao 511, Vahid Rashedi 512, Prateek Rastogi 513, Priya Rathi 514, Salman Rawaf 358,515, Lal Rawal 516, Reza Rawassizadeh 517, Vishnu Renjith 518, Andre M N Renzaho 519,520, Serge Resnikoff 521, Aziz Rezapour 522, Ana Isabel Ribeiro 523, Jennifer Rickard 524,525, Carlos Miguel Rios González 526,527, Luca Ronfani 418, Gholamreza Roshandel 363,528, Anas M Saad 529, Yogesh Damodar Sabde 530, Siamak Sabour 531, Basema Saddik 532, Saeed Safari 533, Roya Safari-Faramani 534, Hamid Safarpour 535, Mahdi Safdarian 505,536, S Mohammad Sajadi 537, Payman Salamati 505, Farkhonde Salehi 538, Saleh Salehi Zahabi 539,540, Marwa R Rashad Salem 541, Hosni Salem 542, Omar Salman 543,544, Inbal Salz 545, Abdallah M Samy 546, Juan Sanabria 547,548, Lidia Sanchez Riera 549,550, Milena M Santric Milicevic 551,552, Abdur Razzaque Sarker 553, Arash Sarveazad 554, Brijesh Sathian 555,556, Monika Sawhney 557, Susan M Sawyer 558,559, Sonia Saxena 560, Mehdi Sayyah 561, David C Schwebel 562, Soraya Seedat 563, Subramanian Senthilkumaran 564, Sadaf G Sepanlou 363,364, Seyedmojtaba Seyedmousavi 565, Feng Sha 566, Faramarz Shaahmadi 567, Saeed Shahabi 568, Masood Ali Shaikh 569, Mehran Shams-Beyranvand 570, Morteza Shamsizadeh 571, Mahdi Sharif-Alhoseini 505, Hamid Sharifi 572, Aziz Sheikh 573,574, Mika Shigematsu 575, Jae Il Shin 576,577, Rahman Shiri 578, Soraya Siabani 579,580, Inga Dora Sigfusdottir 581,582, Pankaj Kumar Singh 583, Jasvinder A Singh 584,585, Dhirendra Narain Sinha 586,587, Catalin-Gabriel Smarandache 588,589, Emma U R Smith 590,591, Amin Soheili 592,593, Bija Soleymani 237, Ali Reza Soltanian 594, Joan B Soriano 595,596, Muluken Bekele Sorrie 597, Ireneous N Soyiri 598,599, Dan J Stein 600,601, Mark A Stokes 602, Mu'awiyyah Babale Sufiyan 603, Hafiz Ansar Rasul Suleria 604, Bryan L Sykes 605, Rafael Tabarés-Seisdedos 606,607, Karen M Tabb 608, Biruk Wogayehu Taddele 609, Degena Bahrey Tadesse 197,610, Animut Tagele Tamiru 611, Ingan Ukur Tarigan 255, Yonatal Mesfin Tefera 612,613, Arash Tehrani-Banihashemi 424,614, Merhawi Gebremedhin Tekle 199, Gebretsadkan Hintsa Tekulu 615, Ayenew Kassie Tesema 616, Berhe Etsay Tesfay 617, Rekha Thapar 273, Asres Bedaso Tilahune 618, Kenean Getaneh Tlaye 142, Hamid Reza Tohidinik 372,572, Roman Topor-Madry 619,620, Bach Xuan Tran 621, Khanh Bao Tran 622,623, Jaya Prasad Tripathy 624, Alexander C Tsai 625,626, Lorainne Tudor Car 627, Saif Ullah 628, Irfan Ullah 629,630, Maida Umar 631, Bhaskaran Unnikrishnan 273, Era Upadhyay 632, Olalekan A Uthman 633, Pascual R Valdez 634,635, Tommi Juhani Vasankari 636, Narayanaswamy Venketasubramanian 637,638, Francesco S Violante 639,640, Vasily Vlassov 641, Yasir Waheed 642, Girmay Teklay Weldesamuel 197, Andrea Werdecker 643,644, Taweewat Wiangkham 645, Haileab Fekadu Wolde 428, Dawit Habte Woldeyes 646, Dawit Zewdu Wondafrash 647,648, Temesgen Gebeyehu Wondmeneh 415, Adam Belay Wondmieneh 196,649, Ai-Min Wu 650, Rajaram Yadav 493, Ali Yadollahpour 651, Yuichiro Yano 652, Sanni Yaya 653, Vahid Yazdi-Feyzabadi 654,655, Paul Yip 656,657, Engida Yisma 658, Naohiro Yonemoto 659, Seok-Jun Yoon 307, Yoosik Youm 660, Mustafa Z Younis 661,662, Zabihollah Yousefi 663,664, Yong Yu 665, Chuanhua Yu 666,667, Hasan Yusefzadeh 29, Telma Zahirian Moghadam 30,668, Zoubida Zaidi 669, Sojib Bin Zaman 131,670, Mohammad Zamani 671, Maryam Zamanian 34, Hamed Zandian 668,672, Ahmad Zarei 673, Fatemeh Zare 674, Zhi-Jiang Zhang 675, Yunquan Zhang 676,677, Sanjay Zodpey 678, Lalit Dandona 1,325,349, Rakhi Dandona 1,325, Louisa Degenhardt 1,679, Samath Dhamminda Dharmaratne 1,349,680, Simon I Hay 1,349, Ali H Mokdad 1,349, Robert C Reiner Jr 1,349, Benn Sartorius 349,681, Theo Vos 1,349
PMCID: PMC7571366  PMID: 32332142

Abstract

Background

Past research in population health trends has shown that injuries form a substantial burden of population health loss. Regular updates to injury burden assessments are critical. We report Global Burden of Disease (GBD) 2017 Study estimates on morbidity and mortality for all injuries.

Methods

We reviewed results for injuries from the GBD 2017 study. GBD 2017 measured injury-specific mortality and years of life lost (YLLs) using the Cause of Death Ensemble model. To measure non-fatal injuries, GBD 2017 modelled injury-specific incidence and converted this to prevalence and years lived with disability (YLDs). YLLs and YLDs were summed to calculate disability-adjusted life years (DALYs).

Findings

In 1990, there were 4 260 493 (4 085 700 to 4 396 138) injury deaths, which increased to 4 484 722 (4 332 010 to 4 585 554) deaths in 2017, while age-standardised mortality decreased from 1079 (1073 to 1086) to 738 (730 to 745) per 100 000. In 1990, there were 354 064 302 (95% uncertainty interval: 338 174 876 to 371 610 802) new cases of injury globally, which increased to 520 710 288 (493 430 247 to 547 988 635) new cases in 2017. During this time, age-standardised incidence decreased non-significantly from 6824 (6534 to 7147) to 6763 (6412 to 7118) per 100 000. Between 1990 and 2017, age-standardised DALYs decreased from 4947 (4655 to 5233) per 100 000 to 3267 (3058 to 3505).

Interpretation

Injuries are an important cause of health loss globally, though mortality has declined between 1990 and 2017. Future research in injury burden should focus on prevention in high-burden populations, improving data collection and ensuring access to medical care.

Keywords: burden of disease, global, descriptive epidemiology

Introduction

Injury burden assessments are a critical component of population health measurement. Across the global landscape of population health research, injuries are unique in that they are almost universally avertable yet can cause death or disability at any age. Even common injuries such as concussion resulting from falls, violence or road injuries may cause longer term sequelae, and injuries such as spinal cord injuries or limb amputations can cause long-term disability.1 As a result, injuries are recognised as being a source of lost health and human capital that could be averted with improved safety and prevention programmes as well as ensuring access to care resources.2 Across geographies, certain injuries such as envenomation may be relevant in specific locations where venomous creatures live, while injuries such as those occurring from adverse medical events are an increasing area of research in higher income areas of the world.3–5 Bolstering such programmes, however, requires detailed measurement of when, where and to whom injuries are occurring, necessitating focused research studies to add insight and context to broader geographical trends. Across all domains of injury prevention research, it is important to measure the causes of injury, such as road injuries, and the resulting disability, such as fractures, burns or traumatic brain injury, that can occur as a result. Such detailed measurement lends perspective for understanding burden and anticipating resources needed to care for and hopefully prevent future injury burden. Detailed measurements and assessments of this nature are critical for empowering policy makers and health system planners to appropriately plan and invest for mitigating future health loss from injuries. Reducing injury burden is an important component in global efforts such as the Sustainable Development Goal 3 to ‘ensure healthy lives and promote well-being for all at all ages’.6

While some research has focused on a certain type of injury or outcome from injury or specific area of the world,7–10 it has become important in an era of more sophisticated population health measurement to measure health loss from injuries comprehensively with detailed fatal and non-fatal estimates for different ages, sexes, across time periods and accounting for multiple different types of morbidity that can occur in an injury. Previously published literature on global injury burden through 2015 has provided comprehensive measurements of health loss due to injuries but still require regular updates to help inform research and policy, as new years of estimates are added and as new injuries and injury outcomes are incorporated.11 Comprehensive research of this nature shows how injury burden varies dynamically by age, sex, year, area of the world and type of injury, and hence, it is important to maintain close monitoring of injury burden every year in all parts of the world. In addition, as new datasets and statistical modelling methods become available, producing regular updates to burden estimation also ensures that results are as accurate as possible.

While the burden of injuries is widely studied and monitored through various methods of research, the Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study is the only study framework that routinely provides estimates of morbidity and mortality from an exhaustive list of injuries in all areas of the world across ages and sexes. The most recent update to GBD was published in 2018 and provided morbidity and mortality estimates for 30 mutually exclusive causes of injury for 195 countries from 1990 to 2017.12–17 As part of this regular update, new datasets on cause of death and incidence are incorporated into the study, and additional geographical detail is added to better measure heterogeneity in burden estimates at a subnational level. In addition, updates such as reporting both nature of injury and cause of injury (described in more detail below) are incorporated. In this study, we describe key components in the GBD injury methodology and provide results from key trends in injury burden in terms of incidence, prevalence, years lived with disability (YLDs), cause-specific mortality, years of life lost (YLLs) and disability-adjusted life years (DALYs) by country, age groups, sex, year and injury type.

Methods

The methods and results in this study are the same as are provided in GBD capstone publications, and a detailed description of GBD data and methods used for all processes related to GBD 2017 is provided in associated studies.12–17 Overall, GBD methods are also summarised in online supplementary appendix 1. Below, we summarise the specific methods used for measurement of injuries morbidity and mortality in GBD 2017.

Supplementary data

injuryprev-2019-043494supp001.pdf (64.6KB, pdf)

Key components of GBD study design

The GBD study incorporates several key components to allow for internally consistent estimates across all burden measures and metrics. First, population is measured to ensure consistent denominators for all population-level measurement. Second, all-cause mortality is measured using demographic methods. Third, cause-specific mortality for a mutually exclusive, collectively exhaustive hierarchy of diseases and injuries is measured, such that every death has one underlying cause of death and such that estimates for every possible cause of death are included, which requires the use of residual causes like ‘other transport injuries’. This results in the sum of cause-specific mortality equalling total all-cause mortality. Fourth, non-fatal health loss is measured for individuals living with a disease or injury that detracts from their full health status. Fifth, a composite measure of mortality and morbidity is computed. These steps are conducted within an age, sex and location hierarchy constructed such that demographic detail is available but where all estimates are internally consistent with all other estimates. GBD produces estimates for all causes, ages, sexes, years and locations. Risk factors and attributable burden for different are also measured, but those results are not included in this study.

Case definition and cause hierarchy

The GBD case definition for an injury death is a death where the injury was the underlying cause of death. For example, if an individual falls on ice and sustains an epidural haematoma and dies after a seizure, the fall is the underlying cause. If an individual sustains a myocardial infarction and then falls and sustains the same epidural haematoma, then the myocardial infarction is the underlying cause of death. For non-fatal injuries, we define a case as an injury that warranted medical care. For example, if an individual slips and falls but does not sustain any bodily injury, it is not considered an injury. Online supplementary appendix table 1 provides the International Classification of Disease (ICD) codes used to identify causes of injury.

Supplementary data

injuryprev-2019-043494supp002.pdf (82.7KB, pdf)

Cause-specific mortality estimation

Cause-specific mortality from injuries is measured using the Cause of Death Ensemble model (CODEm). CODEm is described in more detail elsewhere; a summary of its use for injuries is as follows.18 First, all available data that can be used for cause of death estimation are identified. For injuries, this includes vital registration, verbal autopsy, police records, mortuary data and census data. These data are processed for use in the GBD cause and demographic hierarchy via a series of data processing steps including a process whereby ill-defined causes of death are reassigned to true underlying causes of death, which is described in more detail elsewhere but essentially is the process by which ill-defined causes of death are reclassified to causes of death in the GBD cause hierarchy.19 20 Next, a cause-specific mortality model is developed for each one of the 30 different causes of injury. For example, falls are modelled differently than road injuries, though both use the same CODEm modelling architecture. For each cause of injury, covariates that may be associated with the cause are identified and added as candidate covariates. CODEm runs different combinations of models using different covariates and outcome variables, specifically cause fraction models and cause-specific mortality rate models. Ensembles of models are also conducted to test performance of overall models formed from submodels. Once all models have been run, the top-performing models are selected based on out-of-sample predictive validity, wherein the model makes predictions on data that were not included in developing the model. The top-performing models are then weighted according to performance, and the final estimates form the penultimate estimate for cause-specific mortality from that injury. Those estimates are then adjusted to fit within the all-cause mortality estimate, so that cause-specific deaths sum up to the overall mortality estimate for each population and demographic. YLLs are computed as the cause-specific mortality rate at a given age multiplied by the residual life expectancy at that age, which is based on the observed maximum global life expectancy.

Non-fatal injury estimation

Non-fatal injury estimation is also described in more detail in GBD literature. Key components in this process are as follows. First, data on incidence of non-fatal injury causes (eg, road injuries) is obtained from the GBD collaborator network and other injury research groups and researchers around the world. Data are cleaned and organised according to GBD study guidelines. Next, incidence of each cause of injury is modelled in DisMod-MR 2.1, which is a Bayesian meta-analysis tool used extensively in GBD research. Incidence estimates of injuries requiring medical care for each cause of injury then stream through an analytical pipeline. During this process, injury incidence is split into inpatient and outpatient to account for the different severity that is expected to occur. The coefficient that determines this split is derived from locations where both inpatient and outpatient data are available. After this, we measure the proportion of each cause of injury that leads to one of 47 different natures of injury using clinical data where both cause and nature are coded as well a Dirichlet statistical modelling process. Based on these steps, the incidence of each cause is also split into incidence of each cause-nature, which is the proportion of a given cause’s incidence leading to some specific nature of injury being the most severe injury sustained as estimated by the Dirichlet regression. These estimates are then converted to short-term and long-term injuries based on probability of each injury becoming long term, as determined by long-term follow-up injury surveys.21–27 For short-term injuries, incidence is converted to prevalence based on multiplying incidence by an expected duration of injury as determined by physicians and injury experts involved in the GBD study. For long-term injuries, incidence is converted to prevalence using differential equations that take into account the increased mortality for certain types of injury, for example, traumatic brain injury.1 Disability weights as derived elsewhere in the GBD study are then used to measure disability based on nature of injury.28 These measures are then summed across natures of injury for each cause to calculate YLDs. Each of these steps is conducted for every cause, age, sex, year and location in the GBD study design. Associated literature provides more detail on each of these steps.12–17

DALY measurement

DALYs are calculated by summing YLLs and YLDs for each cause, age, sex, year and location.

Uncertainty measurement

Uncertainty is measured at each step of the analytical process based on the sample size, SE or original uncertainty interval (UI) from each input to the study. Uncertainty is propagated through each step of the analysis by maintaining distributions of 1000 draws on which each analytical step is conducted. Final 95% UIs are determined based on the 25th and 975th values of the ordered values across draws.

Code and results

Steps of the analytical process were conducted in Python version 2.7, Stata V.13.1 or R version 3.3. All steps of the analytical process are available online at ghdx.healthdata.org. This study reports a subset of measures and metrics for every cause of injury. All results and results with additional detail by age, sex, year and location can be downloaded at ghdx.healthdata.org.

Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) statement

This study is adherent with guidelines from the GATHER (described in more detail in online supplementary appendix 2).29

Supplementary data

injuryprev-2019-043494supp003.pdf (87.6KB, pdf)

Results

Online supplementary appendix table 2 shows age-standardised incidence, prevalence, YLDs, deaths, YLLs and DALYs in 2017 by country as well as percentage change and UI from 1990 for each metric. Online supplementary appendix table 3 shows all-age numbers (ie, not divided by population) of incidence, prevalence, YLDs, deaths, YLLs and DALYs in 2017 by country as well as percentage change from 1990 and UI for each metric. In some instances, the UI for the per cent change crosses zero, meaning that statistically there was no significant difference. Online supplementary appendix figures 1–6, show the incidence and mortality from transport injuries, unintentional injuries, and interpersonal violence and self-harm by country for 2017 as well as the percentage change for both incidence and mortality between 1990 and 2017. All other results including age-specific and sex-specific results can be viewed and downloaded via freely and publicly available tools at ghdx.healthdata.org.

Supplementary data

injuryprev-2019-043494supp004.pdf (147.8KB, pdf)

Supplementary data

injuryprev-2019-043494supp005.pdf (167.9KB, pdf)

Supplementary data

injuryprev-2019-043494supp006.pdf (17MB, pdf)

Supplementary data

injuryprev-2019-043494supp007.pdf (17MB, pdf)

Supplementary data

injuryprev-2019-043494supp008.pdf (17MB, pdf)

Supplementary data

injuryprev-2019-043494supp009.pdf (17MB, pdf)

Supplementary data

injuryprev-2019-043494supp010.pdf (17MB, pdf)

Supplementary data

injuryprev-2019-043494supp011.pdf (17MB, pdf)

Global trends in overall injury burden

In terms of fatal outcomes, deaths due to all injuries increased from 4 260 493 (4 085 700 to 4 396 138) in 1990 to 4 484 722 (4 332 010 to 4 585 554) in 2017, while YLLs decreased from 232 104 206 (219 920 058 to 241 973 733) to 195 231 148 (188 807 653 to 199 825 464) and age-standardised mortality rates decreased from 1079 (1073 to 1086) to 738 (730 to 745) per 100 000. In terms of non-fatal outcomes, all-injury incidence (new cases) increased from 354 064 302 (338 174 876 to 371 610 802) in 1990 to 520 710 288 (493 430 247 to 547 988 635) in 2017, and YLDs increased from 37 452 031 (27 805 854 to 49 010 103) to 57 174 469 (42 073 855 to 75 427 036), while age-standardised incidence rates decreased non-significantly from 6824 (6534 to 7147) to 6763 (6412 to 7118) per 100 000. In terms of DALYs, age-standardised DALY rates decreased from 4947 (4655 to 5233) per 100 000 in 1990 to 3267 (3058 to 3505) in 2017.

Figure 1 shows age-standardised DALY rates by country for 2017. While certain countries—specifically, Syria, Central African Republic and Iraq—have much higher DALY rates than most other countries, there still exists considerable heterogeneity across countries that are not among these countries with the highest burden. South Sudan, Somalia and Yemen have much higher injury burden than much of the rest of the world, for example, with age-standardised DALY rates of 7391.51 per 100 000 (6536.44 to 8440.14), 7364.66 per 100 000 (6143.11 to 8960.58) and 7297.88 per 100 000 (6525.7 to 8438.15), respectively. Papua New Guinea also demonstrates high all-injury burden with 6803.33 DALYs per 100 000 (5652.2 to 8040.89) in 2017.

Figure 1.

Figure 1

Age-standardised DALY rates by country, 2017. DALYs, disability-adjusted life years.

Figure 2 presents deaths as a stacked graph for overall injury groups and population from 1990 to 2017 with labelled fatal discontinuities, defined as changes in deaths due to sudden, unexpected spikes in mortality that depart from the underlying mortality trend.13 Although population has steadily increased in the 28 years of the study, deaths per year due to injuries have remained relatively consistent over time. Natural disasters, such as earthquakes, have caused pronounced spikes in unintentional injuries deaths, while conflict and genocide have caused spikes in deaths in the interpersonal violence injury category.

Figure 2.

Figure 2

Global deaths for level 2 injuries and population from 1990 to 2017 with labelled fatal discontinuities.

All-injury YLDs and YLLs by country in 2017

Figure 3 shows the percentage of total all-age, combined-sex YLDs by country in 2017. This figure shows several geographical patterns that help depict the non-fatal burden of injuries globally in terms of their relative contribution to overall disability. First, the percentage of total disability caused by injuries varies widely by country. Mauritius experiences only 3.04% (2.79% to 3.29%) of non-fatal burden from injuries, while Slovenia experiences 19.11% (17.11% to 21.27%) of non-fatal burden from injuries. In other words, if all disability in these two populations is combined in 2017, there is over sixfold variation in how much of this disability was caused by injuries. These patterns also reflect burden from non-injury conditions, since locations with higher burden from communicable disease may have correspondingly lower proportion due to injuries. As an extension of these geographical trends, this map makes it evident that there are striking regional patterns in non-fatal injury burden. Eastern and Central Europe and Central Asia as well as Australasia have a notably higher percentage of total non-fatal burden from injuries than countries in other regions, while these percentages are relatively lower in most areas of Africa, the Americas and areas of South, East and Southeast Asia. To some extent, this map also reflects the underlying burden from non-injury causes, too, since areas of the world with high non-fatal disability from conditions such as anaemia, communicable diseases and other types of health loss could have correspondingly higher percentages of disability from these conditions instead of injuries. This map also shows examples of positive deviations from global trends; Indonesia, for example, has a relatively low percentage of non-fatal health loss due to injuries compared with many other countries.

Figure 3.

Figure 3

Percentage of YLDs in all ages due to injuries in 2017. YLDs, years lived with disability.

Figure 4 similarly shows the percentage of total all-age, combined-sex YLLs by country in 2017. This figure interestingly shows how mortality patterns demonstrate different geographical trends than the non-fatal burden, as depicted in figure 2, though it should be noted that YLLs will also be disproportionately higher in younger populations, all else being equal. In particular, the locations with the highest percentage of YLLs due to injuries are in certain countries in North Africa and the Middle East, including Syria, where 59.51% (56.59% to 62.35%) of YLLs were due to injuries in 2017, and Iraq, where 41.34% of YLLs were due to injuries in 2017. Areas of Latin America including Venezuela, Honduras and Belize also have a relatively high percentage of total YLLs due to injuries. Conversely, certain areas of the world also demonstrate a relatively low percentage of total YLLs due to injuries, specifically, certain countries in Africa such as Nigeria and Madagascar have relatively lower percentages, though this also reflects relatively higher mortality from other non-injury causes in these countries.

Figure 4.

Figure 4

Percentage of YLLs in all ages due to injuries in 2017. YLLs, years of life lost.

Cause-specific DALY rates by sex

Figure 5 shows cause-specific DALY rates by sex for 17 injuries in 2017 as well as percentage change from 1990 to 2017 by cause and sex. The black and dark blue bars show causes with greater relative improvement over the time period of this study, while lighter blue, orange and red show injuries that have had lesser improvements, no improvements or increasing burden over time.

Figure 5.

Figure 5

Age-standardised DALY rates by sex for injuries in level 3 of the GBD cause hierarchy in 2017 and percentage change from 1990 to 2017. DALY, disability-adjusted life year; GBD, Global Burden of Disease.

In 2017, men experienced higher age-standardised DALY rates than women for all injuries except fire, heat and hot substances. The most marked differences, where DALY rates for men are more than double those of women, can be seen in self-harm, interpersonal violence, road injuries, other transport injuries, exposure to mechanical forces, environmental heat and cold exposure, and executions and police violence. Road injuries (1272 (1209 to 1331) per 100 000), self-harm (577 (525 to 604)) and falls (550 (462 to 653)) were the causes with the highest DALY rates for men in 2017. Women had the highest DALY rates due to the same injuries, but at a lesser magnitude, with rates of 467 (432 to 502) per 100 000 for road injuries, 367 (304 to 442) for falls and 282 (268 to 293) for self-harm.

The causes with the largest decreases in DALY rates for men from 1990 to 2017 were exposure to forces of nature (72.4% (63.8% to 79.1%)), drowning (62.7% (58.8% to 65.4%)) and fire, heat and hot substances (43.6% (26.4% to 49.9%)). For women, exposure to forces of nature (72.8% (63.8% to 79.6%)), drowning (65.8% (58.6% to 69.2%)) and self-harm (50.8% (48.2% to 55.9%)) had the largest decreases in DALY rates. The only increases in DALY rates were seen in executions and police conflict for both women (298.0% (257.1% to 389.0%)) and men (46.4% (31.2% to 173.0%)).

Comparative regional DALY rates in 2017

Figure 6 shows a heatmap of the number of standard deviations (SD) above or below the mean of a row (ie, a Z-score) of age-standardised DALY rates for select injuries by GBD region in 2017. For example, the figure shows that the rate of age-standardised DALYs in Eastern Europe is approximately three SD higher than the across mean age-standardised DALY rates of environmental heat and cold exposure across all regions. Poisonings is also a cause with an age-standardised DALY rate that is approximately three SD higher than in other regions. Positive deviance is seen in high-income Asia Pacific for road injuries, where age-standardised DALYs are one SD lower than the mean across regions. Conversely, Central sub-Saharan Africa has age-standardised DALY rates that are two SD higher than the mean across regions. This figure also demonstrates how certain causes have relatively less variation across regions, for example, most regions do not deviate from the mean age-standardised DALY rates across regions for exposure to forces of nature, with the exception of the Caribbean, which had an age-standardised DALY rate that was approximately four SD above the mean across regions in 2017. Oceania and Eastern Europe stand out as having higher DALY rates for select injuries than other regions, while East Asia, high-income Asia Pacific, high-income North America, Western Europe and Southern Latin America experienced less than average burden of injuries in 2017.

Figure 6.

Figure 6

Heatmap showing the Z-score of age-standardised mean DALY rates for select injuries by GBD region in 2017. GBD, Global Burden of Disease.

Discussion

Measuring, understanding and acting on the global burden of injuries should be considered a foundational component of population health research. While this study has reviewed injury burden trends from GBD 2017, it is also evident that these trends are sufficiently different by injury type and geography that it becomes difficult to succinctly generalise the findings in this study. Nevertheless, this study reveals themes and principles germane to the state of global injury burden in 2017 that are relevant to injury burden and prevention research.

First, it should be recognised that despite global population growth with increases in injury cases and deaths, age-standardised death rates from injuries declined from 1990 to 2017. More research into successful improvements for specific injuries in specific countries should be more investigated to help guide efforts towards future improvements. In general terms, the reduction in injury mortality likely represent the combined effects of improvements in healthcare systems, investments in injury prevention programmes and, in certain circumstances, safety improvement such as vehicle safety testing, helmet, seatbelt and drinking and driving laws. While burden trends across all diseases and injuries vary by geography and time, these improvements in injury burden are generally consistent with reporting of communicable and non-communicable disease trends reported in GBD 2017.

Despite improvements in terms of rates, however, it is important to consider the impact of absolute injury burden in younger and adult ages on the social capital and workforce in a country. Second, in reviewing temporal trends in figure 2, it becomes evident that war and conflict and environmental disasters can cause profound increases in deaths over a short period of time. This unfortunate and tragic reality should be made more broadly visible as issues such as war, conflict and climate change continue to threaten the populations of the 21st century. Third, sex differentials in the burden of different injury types are large, with men experiencing significantly higher burden from the four leading causes of injury DALYs in 2017. Preventive research and focused interventions into why this is occurring in road injuries, falls, self-harm, interpersonal violence and drowning is critical. It is also critical to address injuries such as fire, heat and hot substance and sexual violence where females experience greater burden and to better understand the factors that drive sex differences. As a fourth theme, we observed that there are cases of both positive and negative deviance from cross-region trends for each injury, as shown in figure 6, which appear to occur even outside of expected differences by income group. For example, understanding why high-income Asia Pacific and Western Europe are performing better than high-income North America in road injury burden could help improve road injury burden even in this higher income setting.

Beyond these four themes, there are evidently a great deal of nuances and specific outcomes to measure and understand in future injury research. While every cause of health loss in a population is important to measure and understand, injuries are unique in that understanding burden requires investigation of an array of circumstances such as infrastructure, the built environment, rates of interpersonal violence in a population and individual behaviours such as alcohol intoxication or drug use. The findings in this paper also demonstrate how it is critical to measure and understand the spectrum of health loss due to injuries ranging from relatively silent injuries to injuries that profoundly affect functional status. An incident as elemental as a trip and fall can lead to profoundly disabling health consequences such as spinal cord injury, which can have lifelong disability. The disability caused by shorter term injuries, such as an arm fracture, in addition to causing suffering and disability, can cause loss of human capital.30 While this study focused more on the causes of injury as defined in the GBD cause hierarchy, future GBD studies should focus also on depicting the distribution of nature of injury results to better understand how these types of disability affect an individual’s functional status. Such analyses become increasingly meaningful as research emerges on, for example, the increased risk of dementia that traumatic brain injury patients may experience.31 The findings in this paper also demonstrate how measuring injury burden necessitates review of the population factors that affect injury risk. For example, an event as disastrous as an earthquake may have radically different impacts on a population depending on infrastructure and access to care resources. Understanding how populations can protect themselves against future, unanticipated catastrophe could lead to averted death and disability in the future. As was shown in figure 2, catastrophic events both in terms of natural disasters and war and conflict can significantly add to the death and disability experienced by a population in a short period of time.

The geographical trends shown in this paper are also critical to review and understand by the broader global health community. As shown in figure 6, considerable heterogeneity exists across regions for certain causes. While vehicles were driven in nearly every populated area of earth in 2017, this study shows that different regions of the world have markedly different rates of death and disability resulting from road injuries, underscoring the importance of measuring and understanding the effects of specific factors on injury burden.32 It is not necessarily surprising to observe that countries or regions with relatively lower healthcare access and quality, less road safety infrastructure and lower utilisation of vehicles with modern safety standards would have higher rates of road injuries DALYs. The question that extends from this observation, however, is the extent to which burden from this type of injury cause could be avoided were every country to have the safety and prevention factors available in higher income settings. The injury and safety research communities should consider future investigation of counterfactual analyses to better measure and understand the impact that road safety legislation, modernisation of roads and vehicles and improving first response medical care could have on road injury burden, as an example, though parallel examples can be developed for other injury causes as well. This research could help cost-effectiveness analyses and guide investment in safer infrastructure.

These observations converge on a common theme: much of the injury burden may be largely preventable and understanding the success or failure of different prevention efforts should be a prioritised area of health research. Moreover, it is critical for there to be continued engagement across different areas of the world for the purposes of discussing effective and ineffective injury prevention strategies. Dialogue focused on findings across injury prevention efforts via forums such as global safety conferences as well as studies published in research journals should continue to help policy makers and public health planners make strategic investments for preventing future injury burden.33 In addition, more research into the cause of injury and resulting bodily injury and environmental and contextual features where injuries occur such type of road in a road injury or fires in factories versus in residences may provide further insight into preventing future injury burden.

Known limitations of injury burden estimation in the GBD framework have been reported previously in peer-reviewed literature.1 11 13 16 Generally, identified limitations include data sparsity and correspondingly greater uncertainty in certain geographies, limited geographical coverage of data informing long-term disability estimates and cause–nature relationships, and potential reporting biases for injuries such as self-harm and interpersonal violence. These limitations have been discussed in the aforementioned literature, and this overview study was additionally limited in scope due to the extensive size of the GBD cause hierarchy and location hierarchy. Indeed, over 1400 different cause–nature combinations are available for reporting in the GBD cause hierarchy, and future research would benefit from examining results in the detailed cause hierarchy and across the detailed location, age and sex hierarchy. The GBD Study platform and collaborator network provide a constructive collaborative platform on which future assessments can be conducted and published.

Conclusion

Injury burden is complex but foundational in formulating global health loss. We have identified four broad trends in global injury burden that converge on the principle that injuries should be considered largely preventable but that detailed burden estimates through recent years are a critical global resource to inform meaningful policy. It will be important accurate measurement to continue into the future to guide injury prevention policy.

What is already known on the subject.

  • Injury burden globally varies across many dimensions but remains as an important component of global health loss. Regular updates in injury burden measurement are critical.

  • Injuries can be largely preventable, but prevention efforts must be guided by up-to-date estimates of injury burden that can be used on an age-specific, sex-specific, year-specific, location-specific and injury-specific basis.

What this study adds.

  • This study incorporates updated data and methods that were used in Global Burden of Disease 2017 with updated burden estimates for the year 2017, as well as newly available results in terms of nature of injury.

  • Global age-standardised mortality and disability-adjusted life years decreased between 1990 and 2017. Decreases in age-standardised incidence were not statistically significant.

  • Trends over time vary depending on the specific injury, sex and location.

  • Injury burden in a population can be radically affected by war, civil conflict and natural disasters.

Acknowledgments

Syed Aljunid acknowledges the Department of Health Policy and Management, Faculty of Public Health, Kuwait University and International Centre for Casemix and Clinical Coding, Faculty of Medicine, National University of Malaysia and for the approval and support to participate in this research project. Alaa Badawi acknowledges support from the Public Health Agency of Canada. Till Bärnighausen acknowledges support from the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the German Federal Ministry of Education and Research. Felix Carvalho acknowledges UID/MULTI/04378/2019 support with funding from FCT/MCTES through national funds. Vera M Costa acknowledges her grant (SFRH/BHD/110001/2015), received by Portuguese national funds through Fundação para a Ciência e Tecnologia (FCT), IP, under the Norma Transitória DL57/2016/CP1334/CT0006. Kebede Deribe acknowledges support from a grant from the Wellcome Trust [grant number 201900] as part of his International Intermediate Fellowship. Tim Driscoll acknowledges the work on occupational risk factors was partially supported by funds from the World Health Organization. Eduarda Fernandes acknowledges UID/QUI/50006/2019 support with funding from FCT/MCTES through national funds. Yuming Guo acknowledges support from Career Development Fellowships of the Australian National Health and Medical Research Council (numbers APP1107107 and APP1163693). Sheikh Mohammed Shariful Islam acknowledges funding by a Fellowship from National Heart Foundation of Australia and Institute for Physical Activity and Nutrition, Deakin University. Mihajlo Jakovljevic acknowledges support by the Ministry of Education Science and Technological Development of the Republic of Serbia through the Grant number OI175014; publication of results was not contingent upon Ministry's censorship or approval. Sudha Jayaraman acknowledges support from: NIH R21: 1R21TW010439-01A1 (PI); Rotary Foundation Global Grant #GG1749568 (PI); NIH P20: 1P20CA210284-01A1 (Co-PI); DOD grant W81XWH-16-2-0040 (Co-I) during the submitted work. Yun Jin Kim acknowledges support from a grant from the Research Management Centre, Xiamen University Malaysia [grant number: XMUMRF/2018-C2/ITCM/0001]. Kewal Krishan acknowledges support by UGC Centre of Advanced Study (CAS II) awarded to the Department of Anthropology, Panjab University, Chandigarh, India. Manasi Kumar acknowledges FIC/NIH funding from grant K43 1K43MH114320-01. Amanda Mason-Jones acknowledges institutional support from the University of York. Walter Mendoza is currently Program Analyst Population and Development at the Peru Country Office of the United Nations Population Fund-UNFPA, which not necessarily endorses this study. Mariam Molokhia acknowledges support from the National Institute for Health Research Biomedical Research Center at Guy’s and St Thomas’ National Health Service Foundation Trust and King’s College London. Ilais Moreno Velásquez acknowledges support by the Sistema Nacional de Investigación (SNI, Senacyt, Panama). Mukhammad David Naimzada acknowledges support from Government of the Russian Federation (Agreement No – 075-02-2019-967). Stanislav S. Otstavnov acknowledges the support from the Government of the Russian Federation (Agreement No – 075-02-2019-967). Ashish Pathak acknowledges support from the Indian Council of Medical Research (ICMR), New Delhi, India (Grant number 2013-1253). Michael R Phillips acknowledges support in part by a grant from the National Science Foundation of China (No. 81761128031). Marina Pinheiro acknowledges FCT for funding support through program DL 57/2016-Norma transitória. Abdallah M. Samy acknowledges support from a fellowship from the Egyptian Fulbright Mission Program. Milena Santric Milicevic acknowledges the support from the Ministry of Education, Science and Technological Development, the Republic of Serbia (Contract No. 175087). Seyedmojtaba Seyedmousavi acknowledges support from the Intramural Research Program of the National Institutes of Health, Clinical Center, Department of Laboratory Medicine, Bethesda, MD, USA. Rafael Tabarés-Seisdedos acknowledges support in part by the national grant PI17/00719 from ISCIII-FEDER. Sojib Bin Zaman acknowledges support from an "Australian Government Research Training Program (RTP) Scholarship." Louisa Degenhardt acknowledges support from an Australian National Health and Medical Research Council (NHMRC) Senior Principal Research Fellowship (#1135991) and by a National Institute of Health (NIH) National Institute on Drug Abuse (NIDA) grant (R01DA1104470).

Footnotes

Funding: Bill and Melinda Gates Foundation OPP1152504.

Map disclaimer: The depiction of boundaries on the map(s) in this article do not imply the expression of any opinion whatsoever on the part of BMJ (or any member of its group) concerning the legal status of any country, territory, jurisdiction or area or of its authorities. The map(s) are provided without any warranty of any kind, either express or implied.

Competing interests: Dr. James reports grants from Sanofi Pasteur, outside the submitted work. Dr. Driscoll reports grants from World Health Organization, during the conduct of the study. Dr. Ivers reports grants from National Health and Medical Research Council of Australia, during the conduct of the study. Dr. Jozwiak reports personal fees from TEVA, personal fees from ALAB, personal fees from BOEHRINGER INGELHEIM, personal fees from SYNEXUS, non-financial support from SERVIER, non-financial support from MICROLIFE, non-financial support from MEDICOVER, outside the submitted work. Dr. Rakovac reports grants from World Health Organization, during the conduct of the study. Dr Shariful Islam is funded by National Heart Foundation of Australia and Institute for Physical Activity and Nutrition, Deakin University. Dr. Sheikh reports grants from Health Data Research UK, outside the submitted work. Dr. Singh reports personal fees from Crealta/Horizon, Medisys, Fidia, UBM LLC, Trio health, Medscape, WebMD, Clinical Care options, Clearview healthcare partners, Putnam associates, Spherix, Practice Point communications, the National Institutes of Health and the American College of Rheumatology, and Speaker’s bureau of Simply Speaking, owns stock options in Amarin pharmaceuticals and Viking pharmaceuticals, serves on the steering committee of OMERACT, an international organization that develops measures for clinical trials and receives arm’s length funding from 12 pharmaceutical companies, serves on the FDA Arthritis Advisory Committee, is a member of the Veterans Affairs Rheumatology Field Advisory Committee, and is the editor and the Director of the UAB Cochrane Musculoskeletal Group Satellite Center on Network Meta-analysis, outside the submitted work. Dr. Stein reports personal fees from Lundbeck, personal fees from Sun, outside the submitted work. Dr. Degenhardt reports grants from Indivior, Seqirus, Reckitt Benckiser, outside the submitted work.

Patient consent for publication: Not required.

Provenance and peer review: Not commissioned; externally peer reviewed.

Data availability statement: Availability of input data depends on original source. Select data are available in a public, open access repository. Select data are available on reasonable request. Select data may be obtained from a third party and are not publicly available. All results relevant to the study are included in the article or uploaded as supplementary information or are available online.

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Supplementary Materials

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injuryprev-2019-043494supp011.pdf (17MB, pdf)


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