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379 citation(s) found for "Lal, B".

The 2019 Genitourinary Pathology Society (GUPS) White Paper on Contemporary Grading of Prostate Cancer
Epstein, Jonathan I. , Amin, Mahul B. , Fine, Samson W. , Algaba, Ferrán, Aron, Manju , Baydar, Dilek Ertoy , Brimo, Fadi , Cheville, John C. , Colecchia, Maurizio , Cunha, Isabela Werneck da , Delprado, Warick, Giannico, Giovanna A. , Gordetsky, Jennifer , Hansel, Donna E. , Hirsch, Michelle S. , Huang, Jiaoti , Humphrey, Peter A. , Jiménez, Rafael E. , Khani, Francesca , Kong, Qingnuan , Kunju, Lakshmi P., Lal, Priti , Latour, Mathieu, Lotan, Tamara, Mehra, Rohit , Miyamoto, Hiroshi , Montironi, Rodolfo , Netto, George J. , Nguyen, Jane , Osunkoya, Adeboye O. , Robinson, Brian D. , Rubin, Mark A. , So, Jeffrey , Takahashi, Hiroyuki , Tavora, Fabio, Tretiakova, Maria , True, Lawrence D. , Wobker, Sara E. , Yang, Ximing J. , Zhou, Ming , Zynger, Debra L. , Trpkov, Kiril , Távora, Fábio , Gordetsky, Jennifer B , Tretiakova, Maria S , Robinson, Brian D. , Rubin, Mark A , Trpkov, Kiril , López-Beltrán, Antonio , Compérat, Éva , Marzo, Angelo M. De , Guo, Charles Chuanhai , Kryvenko, Oleksandr N. , Maclean, Fiona M. , Magi‐Galluzzi, Cristina , Menon, Santosh , Parwani, Anil Vasdev , Shah, Rajal B.

Archives of Pathology & Laboratory Medicine | June 26, 2020 | Volume: 145 | Issue: 4 | Pages: 461-493

10.5858/arpa.2020-0015-ra | PMID 32589068 Get It

Context.— Controversies and uncertainty persist in prostate cancer grading. Objective.— To update grading recommendations. Data Sources.— Critical review of the literature along with pathology and clinician surveys. Conclusions.— Percent Gleason pattern 4 (%GP4) is as follows: (1) report %GP4 in needle biopsy with Grade Groups (GrGp) 2 and 3, and in needle biopsy on other parts (jars) of lower grade in cases with at least 1 part showing Gleason score (GS) 4 + 4 = 8; and (2) report %GP4: less than 5% or less than 10% and 10% increments thereafter. Tertiary grade patterns are as follows: (1) replace “tertiary grade pattern” in radical prostatectomy (RP) with “minor tertiary pattern 5 (TP5),” and only use in RP with GrGp 2 or 3 with less than 5% Gleason pattern 5; and (2) minor TP5 is noted along with the GS, with the GrGp based on the GS. Global score and magnetic resonance imaging (MRI)-targeted biopsies are as follows: (1) when multiple undesignated cores are taken from a single MRI-targeted lesion, an overall grade for that lesion is given as if all the involved cores were one long core; and (2) if providing a global score, when different scores are found in the standard and the MRI-targeted biopsy, give a single global score (factoring both the systematic standard and the MRI-targeted positive cores). Grade Groups are as follows: (1) Grade Groups (GrGp) is the terminology adopted by major world organizations; and (2) retain GS 3 + 5 = 8 in GrGp 4. Cribriform carcinoma is as follows: (1) report the presence or absence of cribriform glands in biopsy and RP with Gleason pattern 4 carcinoma. Intraductal carcinoma (IDC-P) is as follows: (1) report IDC-P in biopsy and RP; (2) use criteria based on dense cribriform glands (>50% of the gland is composed of epithelium relative to luminal spaces) and/or solid nests and/or marked pleomorphism/necrosis; (3) it is not necessary to perform basal cell immunostains on biopsy and RP to identify IDC-P if the results would not change the overall (highest) GS/GrGp part per case; (4) do not include IDC-P in determining the final GS/GrGp on biopsy and/or RP; and (5) “atypical intraductal proliferation (AIP)” is preferred for an intraductal proliferation of prostatic secretory cells which shows a greater degree of architectural complexity and/or cytological atypia than typical high-grade prostatic intraepithelial neoplasia, yet falling short of the strict diagnostic threshold for IDC-P. Molecular testing is as follows: (1) Ki67 is not ready for routine clinical use; (2) additional studies of active surveillance cohorts are needed to establish the utility of PTEN in this setting; and (3) dedicated studies of RNA-based assays in active surveillance populations are needed to substantiate the utility of these expensive tests in this setting. Artificial intelligence and novel grading schema are as follows: (1) incorporating reactive stromal grade, percent GP4, minor tertiary GP5, and cribriform/intraductal carcinoma are not ready for adoption in current practice.
Intranasal Corticosteroid Therapy: Systematic Review and Meta‐analysis of Reported Safety and Adverse Effects in Adults
Donaldson, Angela M. , Marks, Lisa , Marks, Lisa A., Marks, Lisa A , Choby, Garret , Kim, Daniel Hyeong Seok , Lal, Devyani

Otolaryngology–Head and Neck Surgery | June 16, 2020 | Volume: 163 | Issue: 6 | Pages: 1097-1108

10.1177/0194599820931455 | PMID 32539494 Get It

ObjectivesTo address concerns related to the safety profile of both Food and Drug Administration (FDA)–approved and non–FDA‐approved intranasal corticosteroid (INCS) use in the adult population.Data SourceSystematic review of MEDLINE, PubMed, and EMBASE databases using a comprehensive search strategy including all INCS formulations and adverse events. The study design was developed using Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. Additional sources were identified from study references of relevant articles.Review MethodsA structured literature search was conducted. Each study was graded for level of evidence using the Oxford Centre for Evidence‐Based Medicine. Extracted data included population size, study design, drug (dosage, route, and frequency), presence of hypothalamus pituitary axis suppression, ocular symptoms, and treatment‐related adverse events.ResultsA total of 60 studies met inclusion criteria. The studies included use of INCS as metered nasal sprays, drops, injections, aerosols, and irrigations. There were no persistent abnormalities in cortisol level or intraocular pressure change. Meta‐analysis of epistaxis showed a significantly increased risk in the FDA‐approved treatment group in comparison with control (risk ratio 1.56; 95% confidence interval, 1.13‐2.14; P =. 007).ConclusionsOverall, it appears that the use of both FDA and published non‐FDA application of INCS are safe in the adult population. Meta‐analysis demonstrated an increased risk of epistaxis in patients using INCS compared with placebo. Otherwise, there was no significant difference between in adults in the treatment group and placebo group. As an important caveat, the interpretation of safety of nonstandard INCS is restricted to delivery methods and dosages published in the literature.
Rhinologic Practice Special Considerations During COVID‐19: Visit Planning, Personal Protective Equipment, Testing, and Environmental Controls
Howard, Brittany E. , Lal, Devyani

Otolaryngology–Head and Neck Surgery | June 2, 2020 | Volume: 163 | Issue: 4 | Pages: 676-681

10.1177/0194599820933169 | PMID 32484728 Get It

As rhinologists return to practice amid SARS‐CoV‐2, special considerations are warranted given the unique features of their subspecialty. Rhinologist manipulation of nasal tissue, proximity, and frequent aerosol‐generating procedures (AGPs) create high risk for infection transmission. There are 4 areas of special consideration to mitigate risk: (1) previsit planning for risk stratification/mitigation, (2) appropriate personal protective equipment, (3) preprocedural testing, and (4) environmental controls. During previsit planning, risk factors of the patient and procedures are considered. High‐risk AGPs are identified by duration, proximity, manipulation of high–viral load tissue, and use of powered instrumentation. Appropriate personal protective equipment includes selection of respiratory and eye protection. COVID‐19 testing can screen for asymptomatic carriers prior to high‐risk procedures; however, alternative testing methods are required in rhinologic patients not eligible for nasopharyngeal testing due to nasal obstruction or skull base defects. Last, AGPs in rhinologic practices require considerations of room air handling and environmental controls.
Machine learning of biomarkers and clinical observation to predict eosinophilic chronic rhinosinusitis: a pilot study
Thorwarth, Ryan, Scott, Derek W., Lal, Devyani , Marino, Michael J. , Thorwarth, Ryan M, Lal, Devyani , Scott, Derek W.

International Forum of Allergy & Rhinology | May 30, 2020 | Volume: 11 | Issue: 1 | Pages: 8-15

10.1002/alr.22632 | PMID 32472743 Get It

BackgroundSubtyping chronic rhinosinusitis (CRS) by tissue eosinophilia has prognostic and therapeutic implications, and is difficult to predict using peripheral eosinophil counts or polyp status alone. The objective of this study was to test machine learning for prediction of eosinophilic CRS (eCRS).MethodsInput variables were defined as peripheral eosinophil count, urinary leukotriene E4 (uLTE4) level, and polyp status. The output was diagnosis of eCRS, defined as tissue eosinophil count >10 per high‐power field. Patients undergoing surgery for CRS were retrospectively reviewed for complete datasets. Univariate analysis was performed for each input as a predictor of eCRS. Logistic regression and artificial neural network (ANN) machine learning models were developed using random and surgeon‐specific training/test datasets.ResultsA total of 80 patients met inclusion criteria. In univariate analysis, area under the receiver operator characteristic curve (AUC) for peripheral eosinophil count and uLTE4 were 0.738 (95% confidence interval [CI], 0.616 to 0.840) and 0.728 (95% CI, 0.605 to 0.822), respectively. Presence of polyps was 94.1% sensitive, but 51.7% specific. Logistic regression models using random and surgeon specific datasets resulted in AUC of 0.882 (95% CI, 0.665 to 0.970) and 0.945 (95% CI, 0.755 to 0.995), respectively. ANN models resulted in AUC of 0.918 (95% CI, 0.756 to 0.975) and 0.956 (95% CI, 0.828 to 0.999) using random and surgeon‐specific datasets, respectively. Model comparison of logistic regression and ANN was not statistically different. All machine learning models had AUC greater than univariate analyses (all p < 0.003).ConclusionMachine learning of 3 clinical inputs has the potential to predict eCRS with high sensitivity and specificity in this patient population. Prospective investigation using larger and more diverse populations is warranted.
Faculty Opinions recommendation of Pathogenesis of chronic rhinosinusitis with nasal polyps: role of IL-6 in airway epithelial cell dysfunction.
Bajpai, Shilpika , Lal, Devyani

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature | May 27, 2020 | Volume: No volume available | Issue: No issue available | Pages: No page available

10.3410/f.737631789.793574485 Get It

Faculty Opinions recommendation of Association of chemosensory dysfunction and COVID-19 in patients presenting with influenza-like symptoms.
Bajpai, Shilpika , Lal, Devyani

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature | May 15, 2020 | Volume: No volume available | Issue: No issue available | Pages: No page available

10.3410/f.737735466.793574487 Get It

AWE-10 An international survey on the diagnosis and management of severe gastrointestinal dysmotility
Cuerda, Cristina , Joly, Francisca , Kelly, Darlene G., Paine, Peter , Staun, Michael, Szczepanek, Kinga , Gossum, A. Van , Wanten, Geert, Lal, Simon , Vasant, Dipesh H. , Pironi, Loris , Bozzetti, Federico

Abstracts of Distinction | May 8, 2020 | Volume: No volume available | Issue: No issue available | Pages: A207.1-A207

10.1136/gutjnl-2019-bsgabstracts.395 Get It

Re: From the frontlines of COVID–19—how prepared are we as obstetricians? A commentary
Sahu, Kamal Kant , Lal, Amos , Lal, Amos , Mishra, Ajay Kumar

BJOG: An International Journal of Obstetrics & Gynaecology | May 6, 2020 | Volume: 127 | Issue: 8 | Pages: 1041-1041

10.1111/1471-0528.16257 | PMID 32372542 Get It

The CREST-2 experience with the evolving challenges of COVID-19
Meschia, James F. , Barrett, Kevin M. , Brown, Robert D. , Turan, Tanya N. , Howard, Virginia J. , Voeks, Jenifer H. , Lal, Brajesh K. , Brott, Thomas G. , Brott, Thomas G , Meschia, James F , Howard, Virginia J , Howard, George

Neurology | May 2, 2020 | Volume: 95 | Issue: 1 | Pages: 29-36

10.1212/wnl.0000000000009698 | PMID 32358216 Get It

Global injury morbidity and mortality from 1990 to 2017: results from the Global Burden of Disease Study 2017
James, Spencer L , Castle, Chris D, Dingels, Zachary V, Fox, Jack T, Hamilton, Erin B, Sylte, Dillon O , Henry, Nathaniel J , LeGrand, Kate E , Abdelalim, Ahmed , Abdulkader, Rizwan Suliankatchi , Abedi, Aidin , Eshete, Akine , Abushouk, Abdelrahman Ibrahim , Adebayo, Oladimeji , Agudelo‐Botero, Marcela , Ahmad, Tauseef , Aichour, Miloud Taki Eddine, Alahdab, Fares , Alamene, Genet Melak, Alanezi, Fahad , Alebel, Animut , Alema, Niguse Meles , Ali, Beriwan Abdulqadir, Alikhani, Mahtab , Alipour, Vahid, Almasri, Nihad A. , Altirkawi, Khalid A , Amer, Yasser Sami , Amini, Saeed, Amit, Arianna Maever L. , Andrei, Cătălina Liliana , Ansari-Moghaddam, Alireza, Antonio, Carl Abelardo T, Appiah, Seth Christopher Yaw , Arabloo, Jalal , Arefi, Zohreh , Aremu, Olatunde , Ariani, Filippo , Arora, Amit , Asghari, Babak , Awoke, Nefsu , Quintanilla, Beatriz Paulina Ayala , Ayano, Getinet , Ayanore, Martin Amogre , Azari, Samad , Azarian, Ghasem , Badawi, Alaa, Badiye, Ashish , Bagli, Eleni , Baig, Atif Amin , Bairwa, Mohan , Bakhtiari, Ahad , Banach, Maciej , Banerjee, Srikanta , Banik, Palash Chandra , Banstola, Amrit , Barker‐Collo, Suzanne , Bärnighausen, Till , Barrero, Lope H. , Barzegar, Akbar , Baye, Bayisa Abdissa , Bekuma, Tariku Tesfaye , Belete, Habte , Bennett, Derrick , Berhe, Kidanemaryam , Bhardwaj, Pankaj , Bhat, Anusha Ganapati , Bhattacharyya, Krittika , Bijani, Ali , Sayeed, Muhammad Shahdaat Bin , Borzì, Antonio Maria , Bražinová, Alexandra, Брико, Н. И. , Budhathoki, Shyam Sundar , Car, Josip , Cárdenas, Rosario , Carvalho, Félix , Castaldelli-Maia, João Maurício , Castañeda-Orjuela, Carlos A , Castelpietra, Giulio , Catalá-López, Ferrán , Cerin, Ester , Chandan, Joht Singh , Chanie, Wagaye Fentahun, Chattu, Soosanna Kumary , Chattu, Vijay Kumar , Chaudhary, Neha , Cho, Daniel , Chowdhury, Mohiuddin Ahsanul Kabir , Chu, Dinh‐Toi , Colquhoun, Samantha , Costa, Vera Marisa , Damiani, Giovanni , Dávila-Cervantes, Claudio Alberto , Demoz, Gebre Teklemariam , Demsie, Desalegn Getnet , Derakhshani, Afshin , Deribe, Kebede , Desai, Rupak , Nasab, Mostafa Dianati, Silva, Diana Dias da , Forooshani, Zahra Sadat Dibaji, Doyle, Kerrie , Driscoll, Tim , Dubljanin, Eleonora , Adema, Bereket Duko , Eagan, Arielle Wilder , Eftekhari, Aziz , Ehsani‐Chimeh, Elham , Elemineh, Demelash Abewa , El-Jaafary, Shaimaa I , El‐Khatib, Ziad , Ellingsen, Christian Lycke , Emamian, Mohammad Hassan , Endalew, Daniel Adane , Faris, Pawan , Faro, André , Farzadfar, Farshad , Fatahi, Yousef , Fekadu, Wubalem , Ferede, Tomas Y, Fereshtehnejad, Seyed‐Mohammad , Fernandes, Eduarda , Ferrara, Pietro , Feyissa, Garumma Tolu , Fischer, Florian , Foláyan, Morẹ́nikẹ́ Oluwátóyìn , Francis, Joel M , Fukumoto, Takeshi , Geberemariyam, Biniyam Sahiledengle, Gebre, Abadi Kahsu , Gebremedhin, Ketema Bizuwork , Gebremeskel, Gebreamlak Gebremedhn , Gebremichael, Berhe , Gedefaw, Getnet , Geta, Birhanu, Ghafourifard, Mansour , Ghamari, Farhad , Ghashghaee, Ahmad , Gholamian, Asadollah , Gill, Tiffany K, Goulart, Alessandra C. , Grada, Ayman , Gubari, Mohammed Ibrahim Mohialdeen , Guo, Yuming , Haagsma, Juanita A. , Hafezi‐Nejad, Nima , Hall, Brian J. , Hamadeh, Randah R , Hasan, Md. Mehedi , Hassen, Hamid Yimam , Havmoeller, Rasmus, Heydarpour, Fatemeh , Hı́jar, Martha , Ho, Hung Chak , Hoang, Chi Linh , Hole, Michael K., Hossain, Naznin , Hosseinzadeh, Mehdi , Hostiuc, Sorin , Ibitoye, Segun Emmanuel , Ilić, Irena , Ilić, Milena , Inbaraj, Leeberk Raja , Indriasih, Endang , Irvani, Seyed Sina Naghibi , Islam, Sheikh Mohammed Shariful , Islam, M. Mofizul , Ivers, Rebecca , Jacobsen, Kathryn H. , Jahani, Mohammad Ali, Jahanmehr, Nader , Jakovljević, Mihajlo , Jayaraman, Sudha , Jha, Ravi Prakash , John-Akinola, Yetunde O. , Joseph, Nitin , Joukar, Farahnaz, Jozwiak, Jacek Jerzy , Jürisson, Mikk , Kadel, Rajendra , Kahsay, Amaha , Kalankesh, Leila R , Kalhor, Rohollah , Kamil, Teshome Abegaz, Kapoor, Neeti , Karami, Manoochehr , Kasaeian, Amir , Kassaye, Hagazi Gebremedhin, Kavetskyy, Taras , Kebede, Hafte Kahsay , Keiyoro, Peter Njenga, Kelbore, Abraham Getachew , Kelkay, Bayew, Khader, Yousef , Khafaie, Morteza Abdullatif , Khalid, Nauman , Khalil, Ibrahim A, Khalilov, Rovshan , Khan, Ejaz Ahmad , Khan, Maseer , Khanna, Tripti, Shadmani, Fatemeh Khosravi , Khundkar, Roba , Kiirithio, Daniel N, Kim, Yun Jin , Kısa, Adnan , Kısa, Sezer , Komaki, Hamidreza , Kondlahalli, Shivakumar K M, Korshunov, Vladimir Andreevich , Kraemer, Moritz U. G. , Krishan, Kewal , Biçer, Burcu Küçük , Kugbey, Nuworza , Kumar, Vivek , Kumar, G Anil , Kumar, Manasi , Kumaresh, Girikumar, Kurmi, Om , Kuti, Oluwatosin, Lami, Faris , Lamichhane, Prabhat , Lang, Justin J. , Lasrado, Savita , Leasher, Janet L , Lee, Shaun Wen Huey , Lenjebo, Tsegaye Lolaso , Li, Shanshan , Linn, Shai , Liu, Xuefeng , Lunevičius, Raimundas , Lyons, Ronan A , Madadin, Mohammed , Razek, Muhammed Magdy Abd El, Mahotra, Narayan Bahadur , Majdán, Marek , Malagón-Rojas, Jeadran , Maled, Venkatesh , Manafi, Navid , Manafi, Amir , Manda, Ana-Laura, Mansour-Ghanaei, Fariborz, Mansouri, Borhan , March, Lyn , Mason‐Jones, Amanda J. , Masoumi, Seyedeh Zahra , Massenburg, Benjamin B. , Maulik, Pallab K , Meles, Gebrekiros Gebremichael , Melketsedik, Zeleke Aschalew , Memiah, Peter , Mendoza, Walter, Menezes, Ritesh G. , Mengesha, Melkamu Merid , Meretoja, Tuomo J , Meretoja, Atte , Merie, Hayimro Edemealem , Miazgowski, Bartosz , Miazgowski, Tomasz , Miller, Ted R. , Mini, GK , Мirrakhimov, Erkin М , Mirzaei-Alavijeh, Mehdi , Mithra, Prasanna , Moazen, Babak , Moghadaszadeh, Masoud, Mohammad, Yousef , Mohammad, Karzan Abdulmuhsin , Mezerji, Naser Mohammad Gholi, Mohammadian-Hafshejani, Abdollah , Mohammadoo-Khorasani, Milad, Mohammed, Shafiu , Mohammed, Jemal , Mohebi, Farnam , Molokhia, Mariam , Monasta, Lorenzo , Moodley, Yoshan , Moosazadeh, Mahmood , Moradi, Masoud , Moradi, Ghobad , Moradpour, Farhad , Morawska, Lídia , Velásquez, Ilais Moreno , Mossie, Tilahun Belete , Murthy, Srinivas , Musa, Kamarul Imran , Mustafa, Ghulam , Nabhan, Ashraf , Nagarajan, Ahamarshan Jayaraman , Naik, Gurudatta , Naimzada, Mukhammad David , Nangia, Vinay, Nascimento, Bruno Ramos , Naserbakht, Morteza , Ndwandwe, Duduzile , Negoi, Ionuţ , Ngunjiri, Josephine W, Nguyen, Cuong Tat , Nikbakhsh, Rajan , Ningrum, Dina Nur Anggraini , Nnaji, Chukwudi A , Nyasulu, Peter S. , Ogbo, Felix Akpojene , Oghenetega, Onome Bright , Okunga, Emmanuel , Olagunju, Andrew T , Olagunju, Tinuke O , Bali, Ahmed Omar , Onwujekwe, Obinna , Asante, Kwaku Oppong , Orpana, Heather , Otstavnov, Nikita , Otstavnov, Stanislav S , Mahesh, P A , Padubidri, Jagadish Rao , Pakhalé, Smita , Pakshir, Keyvan , Panda‐Jonas, Songhomitra, Park, Eun‐Kee , Patel, Sangram Kishor , Pathak, Ashish , Pati, Sanghamitra , Paulos, Kebreab , Pepito, Veincent Christian Filipino , Pham, Hai Quang , Pinheiro, Marina , Полибин, Р. В. , Polinder, Suzanne, Poustchi, Hossein, Prakash, Swayam , Pribadi, Dimas Ria Angga , Puri, Parul , Syed, Zahiruddin Quazi, Rabiee, Navid , Radfar, Amir , Rafay, Anwar, Rafiee, Ata , Rafiei, Alireza , Rahim, Fakher , Rahimi, Siavash , Rahimi‐Movaghar, Vafa , Rahman, Muhammad Aziz , Rajabpour-Sanati, Ali, Rajati, Fatemeh , Rakovac, Ivo , Ranganathan, Kavitha , Rao, Sowmya J, Rashedi, Vahid , Rastogi, Prateek , Rathi, Priya , Rawal, Lal , Rawassizadeh, Reza , Renjith, Vishnu , Renzaho, André M. N. , Resnikoff, Serge , Rezapour, Aziz, Rickard, Jennifer , Ríos-González, Carlos Miguel , Sabde, Yogesh , Saddik, Basema , Safari, Saeed , Safarpour, Hamid , Safdarian, Mahdi , Sajadi, S. Mohammad , Salamati, Payman, Salehi, Farkhonde , Zahabi, Saleh Salehi , Salem, Marwa Rashad , Salem, Hosni , Salman, Omar , Salz, Inbal, Samy, Abdallah M , Sanabria, Juan , Sànchez-Riera, Lídia , Santric-Milicevic, Milena M , Sarveazad, Arash , Sathian, Brijesh , Sawhney, Monika, Sawyer, Susan M. , Saxena, Sonia , Schwebel, David C. , Seedat, Soraya , Senthilkumaran, Subramanian , Seyedmousavi, Seyedmojtaba , Sha, Feng , Shaahmadi, Faramarz , Shaikh, Masood Ali , Shams-Beyranvand, Mehran , Shamsizadeh, Morteza , Sharif-Alhoseini, Mahdi , Sharifi, Hamid , Sheikh, Aziz , Shigematsu, Mika, Shin, Jae Il , Shiri, Rahman , Sigfúsdóttir, Inga Dóra, Singh, Pankaj Kumar , Sinha, Dhirendra Narain , Smarandache, Catalin-Gabriel , Soleymani, Bija, Soriano, Joan B. , Soyiri, Ireneous N , Stein, Dan J. , Sufiyan, Mu’awiyyah Babale , Suleria, Hafiz Ansar Rasul , Sykes, Bryan L. , Tabarés‐Seisdedos, Rafael , Tabb, Karen M. , Wogayehu, Biruk , Tamiru, Animut Tagele , Tarigan, Ingan Ukur , Tefera, Yibekal Manaye , Tekle, Merhawi Gebremedhin , Tekulu, Gebretsadkan Hintsa , Kassie, Ayenew , Bedaso, Asres , Tlaye, Kenean Getaneh , Tohidinik, Hamid Reza , Topór-Mądry, Roman , Tripathy, Jaya Prasad , Tsai, Alexander C. , Car, Lorainne Tudor , Ullah, Saif , Ullah, Irfan , Umar, Maida, Unnikrishnan, Bhaskaran , Upadhyay, Era , Valdéz, Pascual , Violante, Francesco Saverio , Waheed, Yasir , Teklay, Girmay , Werdecker, Andrea , Wiangkham, Taweewat , Wolde, Haileab Fekadu , Woldeyes, Dawit Habte , Yadav, Rajaram , Yadollahpour, Ali , Yano, Yuichiro , Yaya, Sanni , Yazdi‐Feyzabadi, Vahid , Yisma, Engida , Yoon, Seok‐Jun , Youm, Yoosik , Younis, Mustafa Z , Yousefi, Zabihollah , Yu, Yong , Yusefzadeh, Hasan, Moghadam, Telma Zahirian , Zaman, Sojib Bin , Zamanian, Maryam , Zhang, Zhi-Jiang , Zhang, Yunquan , Zodpey, Sanjay , Dandona, Lalit , Dandona, Rakhi , Hay, Simon I , Mokdad, Ali H. , Reiner, Robert C. , Sartorius, Benn , Vos, Theo , Topór‐Mądry, Roman , Zahiruddin, Quazi Syed , Dianatinasab, Mostafa , Sanati, Ali Rajabpour , Baig, Atif Amin , Kumar, Vivek , Stein, Dan J , Castaldelli-Maia, João M , Farzadfar, Farshad , Shin, Jae I L , Islam, Sheikh Mohammed Shariful , Rahman, Muhammad Aziz , Belete, T , Liu, Zichen, Roberts, Nicholas L S , Abdoli, Amir , Abdollahpour, Ibrahim , Ahmed, Rushdiá , Ahmed, Muktar Beshir , Alghnam, Suliman Abdullah , Al‐Hajj, Samar , Ali, Saqib , Alinia, Cyrus , Aljunid, Syed Mohamed , Almasi‐Hashiani, Amir , Amini, Saeed, Antonio, Carl Abelardo T. , Arab-Zozani, Morteza , Asaad, Malke , Balachandran, Arun , Bayati, Mohsen , Bedi, Neeraj , Behzadifar, Masoud , Benjet, Corina L. , Benseñor, Isabela M. , Bibi, Sadia , Borges, Guilherme , Boufous, Soufiane , Chatziralli, Irini , Constantin, María Magdalena , Daryani, Ahmad , Demeke, Feleke Mekonnen , Demis, Asmamaw , Forooshani, Zahra Sadat Dibaji, Duko, Bereket , Zaki, Maysaa El Sayed , Adane, Daniel, Eskandarieh, Sharareh , Yeheyis, Tomas Y. , Filip, Irina , Foroutan, Masoud , Franklin, Richard C. , Gill, Tiffany K. , Grivna, Michal , Guimarães, Rafael Alves , Gupta, Gaurav Kumar , Haghparast‐Bidgoli, Hassan , Hamidi, Samer , Haro, Josep María , Hasanzadeh, Amir , Hassanipour, Soheil , Hassankhani, Hadi , Hayat, Khezar , Hendrie, Delia V. , Holla, Ramesh , Hu, Guoqing , Ilesanmi, Olayinka Stephen , Jalilian, Farzad , Jayatilleke, Achala Upendra , Jonas, Jost B , Jungari, Suresh Banayya , Kabir, Ali , Kanchan, Tanuj , Rade, Bayew Kelkay , Khalil, Ibrahim A, Khammarnia, Mohammad , Khazaie, Habibolah , Kim, Young‐Eun , Kim, Daniel , Shivakumar, KM , Koyanagi, Ai , Kumar, Nithin , Vecchia, Carlo La , Lansingh, Van Charles , Laryea, Dennis Odai , Latifi, Arman , Lauriola, Paolo , Levi, Miriam , Lopez, Alan D , Lotufo, Paulo A. , Majeed, Azeem , Malekzadeh, Reza , Malta, Déborah Carvalho , Manjunatha, Narayana , Mansour‐Ghanaei, Fariborz , Mansournia, Mohammad Alì , Maravilla, Joemer Calderon , Melese, Addisu Dagnaw , Aschalew, Zeleke , Mengesha, Meresa Berwo , 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Injury Prevention | April 24, 2020 | Volume: 26 | Issue: Suppl 2 | Pages: i96-i114

10.1136/injuryprev-2019-043494 | PMID 32332142 Get It

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.