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Enregistrement W2991564522 · doi:10.1001/jamaoncol.2018.2706

Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2016

2018· review· en· W2991564522 sur OpenAlexaff
Christina Fitzmaurice, Tomi Akinyemiju, Faris Lami, Shazia Alam, Reza Alizadeh‐Navaei, Christine A. Allen, Ubai Alsharif, Nelson Alvis‐Guzmán, Erfan Amini, Benjamin O. Anderson, Olatunde Aremu, Al Artaman, Solomon Weldegebreal Asgedom, Reza Assadi, Tesfay Mehari Atey, Leticia Ávila‐Burgos, Ashish Awasthi, Huda Omer Ba Saleem, Aleksandra Barać, James R. Bennett, Isabela M. Benseñor, Nickhill Bhakta, Hermann Brenner, Lucero Cahuana-Hurtado, Carlos A Castañeda-Orjuela, Ferrán Catalá-López, Jee-Young J Choi, Devasahayam Jesudas Christopher, Sheng‐Chia Chung, María Paula Curado, Lalit Dandona, Rakhi Dandona, José das Neves, Subhojit Dey, Samath Dhamminda Dharmaratne, David Teye Doku, Tim Driscoll, Manisha Dubey, Hedyeh Ebrahimi, Dumessa Edessa, Ziad El‐Khatib, Aman Yesuf Endries, Florian Fischer, Lisa M Force, Kyle J Foreman, Solomon Weldemariam Gebrehiwot, Sameer Vali Gopalani, Giuseppe Grosso, Rahul Gupta, Bishal Gyawali, Randah R Hamadeh, Samer Hamidi, James Harvey, Hamid Yimam Hassen, Roderick J. Hay, Simon I Hay, Behzad Heibati, Molla Kahssay Hiluf, Nobuyuki Horita, Hung Chak Ho, Olayinka Stephen Ilesanmi, Kaire Innos, Farhad Islami, Mihajlo Jakovljević, Sarah Charlotte Johnson, Jost B Jonas, Amir Kasaeian, Tesfaye Kassa, Yousef Khader, Ejaz Ahmad Khan, Gulfaraz Khan, Young‐Ho Khang, Mohammad Hossein Khosravi, Jagdish Khubchandani, Jacek A Kopec, G Anil Kumar, Michael Kutz, Deepesh Lad, Alessandra Lafranconi, Qing Lan, Yirga Legesse, James Leigh, Shai Linn, Raimundas Lunevičius, Azeem Majeed, Reza Malekzadeh, Déborah Carvalho Malta, LG Mantovani, Brian J. McMahon, Toni Meier, Yohannes Adama Melaku, Mulugeta Melku, Peter Memiah, Walter Mendoza, Tuomo J Meretoja, Haftay Berhane Mezgebe, Ted R. Miller, Shafiu Mohammed, Ali H. Mokdad, Mahmood Moosazadeh, Paula Moraga, Seyyed Meysam Mousavi, Vinay Nangia, Cuong Tat Nguyen, Vuong Minh Nong, Felix Akpojene Ogbo, Andrew T Olagunju, P A Mahesh, Eun‐Kee Park, Tejas Patel, David M. Pereira, Farhad Pishgar, Maarten J. Postma, Farshad Pourmalek, Mostafa Qorbani, Anwar Rafay, Salman Rawaf, David Laith Rawaf, Gholamreza Roshandel, Saeid Safiri, Hamideh Salimzadeh, Juan Sanabria, Milena M Santric-Milicevic, Benn Sartorius, Maheswar Satpathy, Sadaf G Sepanlou, Katya Anne Shackelford, Masood Ali Shaikh, Mahdi Sharif-Alhoseini, Jun She, Min‐Jeong Shin, Ivy Shiue, Mark G. Shrime, Abiy H Sinke, Mekonnen Sisay, Amber Sligar, Mu’awiyyah Babale Sufiyan, Bryan L. Sykes, Rafael Tabarés‐Seisdedos, Gizachew Assefa Tessema, Roman Topór-Mądry, Tung Thanh Tran, Bach Xuan Tran, Kingsley Nnanna Ukwaja, Vasily Vlassov, Elisabete Weiderpass, Hywel C Williams, Nigus Bililign Yimer, Naohiro Yonemoto, Mustafa Z Younis, Christopher J L Murray, Mohsen Naghavi

Notice bibliographique

RevueJAMA Oncology · 2018
Typereview
Langueen
DomaineMedicine
ThématiqueGlobal Cancer Incidence and Screening
Établissements canadiensUniversity of British ColumbiaOttawa HospitalUniversity of Manitoba
Organismes subventionnairesMedical Research CouncilCollege of Medicine, Seoul National UniversityUniversity of California, IrvineLaboratório Associado para a Química VerdeWestern Sydney UniversityAlborz University of Medical SciencesFakultet Medicinskih Nauka, Univerziteta U KragujevcuUniversity of PeradeniyaHelsingin YliopistoNational Research University Higher School of EconomicsChristian Medical College, VelloreTampereen YliopistoUniversity of HaifaUniversitair Medisch Centrum GroningenHaramaya UniversityInyuvesi Yakwazulu-NataliUniversity of GondarUniversidade Federal de Minas GeraisGolestan University of Medical SciencesTehran University of Medical Sciences and Health ServicesMazandaran University of Medical SciencesSamara UniversityUnited Nations Population FundKarolinska InstitutetUniversidade do PortoSeoul National UniversityRijksuniversiteit GroningenNational Institute for Health and Care ResearchJordan University of Science and TechnologyUniversity of OxfordSamfundet FolkhälsanMaragheh University of Medical SciencesFudan UniversityAhmadu Bello UniversityUniwersytet Medyczny im. Piastów Slaskich we WroclawiuTrường Đại học Duy TânIran University of Medical SciencesRede de Química e TecnologiaUniversity of West FloridaNational Cancer InstituteUniversity College LondonMekelle UniversityArabian Gulf UniversityCase Western Reserve UniversityUniversitetet i TromsøBaqiyatallah University of Medical SciencesImperial College LondonJohns Hopkins UniversityCurtin University of TechnologyUniversity of WashingtonAarhus UniversitetUnited Arab Emirates UniversityJackson State UniversityKorea UniversitySouth African Medical Research CouncilKing's College LondonPublic Health Foundation of IndiaUniversität BielefeldKosin UniversityBall State UniversityUniwersytet Jagielloński Collegium Medicum
Mots-clésMedicineYears of potential life lostCancerPopulationDisease burdenGlobal healthDemographyEpidemiologyIncidence (geometry)Environmental healthGerontologyPublic healthLife expectancyPathologyInternal medicine

Résumé

récupéré en direct d'OpenAlex

Importance: The increasing burden due to cancer and other noncommunicable diseases poses a threat to human development, which has resulted in global political commitments reflected in the Sustainable Development Goals as well as the World Health Organization (WHO) Global Action Plan on Non-Communicable Diseases. To determine if these commitments have resulted in improved cancer control, quantitative assessments of the cancer burden are required. Objective: To assess the burden for 29 cancer groups over time to provide a framework for policy discussion, resource allocation, and research focus. Evidence Review: Cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life-years (DALYs) were evaluated for 195 countries and territories by age and sex using the Global Burden of Disease study estimation methods. Levels and trends were analyzed over time, as well as by the Sociodemographic Index (SDI). Changes in incident cases were categorized by changes due to epidemiological vs demographic transition. Findings: In 2016, there were 17.2 million cancer cases worldwide and 8.9 million deaths. Cancer cases increased by 28% between 2006 and 2016. The smallest increase was seen in high SDI countries. Globally, population aging contributed 17%; population growth, 12%; and changes in age-specific rates, -1% to this change. The most common incident cancer globally for men was prostate cancer (1.4 million cases). The leading cause of cancer deaths and DALYs was tracheal, bronchus, and lung cancer (1.2 million deaths and 25.4 million DALYs). For women, the most common incident cancer and the leading cause of cancer deaths and DALYs was breast cancer (1.7 million incident cases, 535 000 deaths, and 14.9 million DALYs). In 2016, cancer caused 213.2 million DALYs globally for both sexes combined. Between 2006 and 2016, the average annual age-standardized incidence rates for all cancers combined increased in 130 of 195 countries or territories, and the average annual age-standardized death rates decreased within that timeframe in 143 of 195 countries or territories. Conclusions and Relevance: Large disparities exist between countries in cancer incidence, deaths, and associated disability. Scaling up cancer prevention and ensuring universal access to cancer care are required for health equity and to fulfill the global commitments for noncommunicable disease and cancer control.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,011
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,040
Score d'incertitude au seuil0,079

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,011
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,003
Bibliométrie0,0060,011
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,187
Tête enseignante GPT0,449
Écart entre enseignants0,262 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1 542
Publié2018
Routes d'admission1
Résumé présentoui

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