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Enregistrement W2889562406 · doi:10.1016/s1473-3099(18)30310-4

Estimates of the global, regional, and national morbidity, mortality, and aetiologies of lower respiratory infections in 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

2018· article· en· W2889562406 sur OpenAlexfundno aff
Christopher Troeger, Brigette F. Blacker, Puja C Rao, Stephanie R M Zimsen, Samuel B Albertson, Aniruddha Deshpande, Tamer H. Farag, Abebe Zegeye, Ifedayo Adetifa, Tara Ballav Adhikari, Faris Lami, Ayman Al‐Eyadhy, Nelson Alvis‐Guzmán, Azmeraw T. Amare, Yaw Ampem Amoako, Carl Abelardo T. Antonio, Olatunde Aremu, Ephrem Tsegay Asfaw, Solomon Weldegebreal Asgedom, Tesfay Mehari Atey, Engi F. Attia, Euripide Avokpaho, Henok Tadesse Ayele, Ayuk Betrand Tambe, Kalpana Balakrishnan, Aleksandra Barać, Quique Bassat, Masoud Behzadifar, Meysam Behzadifar, Soumyadeep Bhaumik, Zulfiqar A Bhutta, Alexandria Brown, Paulo Augusto Moreira Camargos, Carlos A Castañeda-Orjuela, Danny V. Colombara, Sara Conti, Abel Fekadu Dadi, Lalit Dandona, Rakhi Dandona, Huyen Phuc, Dumessa Edessa, Hajer Elkout, Daniel Obadare Fijabi, Kyle J Foreman, Mohammad H. Forouzanfar, Nancy Fullman, Alberto L Garcia-Basteiro, Rahul Gupta, Gessessew Bugssa Hailu, Hamid Yimam Hassen, Mohammad Taghi Hedayati, Mohsen Heidari, Desalegn Tsegaw Hibstu, Nobuyuki Horita, Olayinka Stephen Ilesanmi, Mihajlo Jakovljević, Amr Jamal, Amaha Kahsay, Amir Kasaeian, Dessalegn H Kassa, Md Nuruzzaman Khan, Yun Jin Kim, Niranjan Kissoon, Luke D. Knibbs, Sonali Kochhar, G Anil Kumar, Rakesh Lodha, Hassan Magdy Abd El Razek, Déborah Carvalho Malta, Joseph L. Mathew, Desalegn Tadese Mengistu, Haftay Berhane Mezgebe, Karzan Abdulmuhsin Mohammad, Fatemeh Momeniha, Cuong Tat Nguyen, Katie R. Nielsen, Dina Nur Anggraini Ningrum, Yirga Legesse Nirayo, Eyal Oren, Justin R. Ortiz, Maarten J. Postma, Reginald Quansah, Chhabi Lal Ranabhat, Mohammad Sadegh Rezai, George Mugambage Ruhago, Joshua A. Salomon, Benn Sartorius, Miloje Savic, Monika Sawhney, Aziz Sheikh, Mika Shigematsu, Jasvinder A. Singh, Ranjani Somayaji, Mu’awiyyah Babale Sufiyan, Getachew Redae Taffere, Mohamad‐Hani Temsah, Matthew Thompson, Ruoyan Tobe-Gai, Roman Topór-Mądry, Bach Xuan Tran, Tung Thanh Tran, Kald Beshir Tuem, Kingsley Nnanna Ukwaja, Judd L. Walson, Fitsum Weldegebreal, Andrea Werdecker, T. Eoin West, Naohiro Yonemoto, Maysaa El Sayed Zaki, Lei Zhou, Sanjay Zodpey, Theo Vos, Mohsen Naghavi, Stephen S Lim, Ali H. Mokdad, Christopher J L Murray, Simon I Hay, Robert C. Reiner

Notice bibliographique

RevueThe Lancet Infectious Diseases · 2018
Typearticle
Langueen
DomaineMedicine
ThématiqueRespiratory viral infections research
Établissements canadiensnon disponible
Organismes subventionnairesMedical Research CouncilCollege of Medicine, Seoul National UniversityUniversity of Health and Allied SciencesCanadian Institutes of Health ResearchMuhimbili University of Health and Allied SciencesDebre Markos UniversityDamietta UniversityAlborz University of Medical SciencesAlfaisal UniversityHormozgan University of Medical SciencesFakultet Medicinskih Nauka, Univerziteta U KragujevcuMansoura UniversityAlberta InnovatesXiamen UniversityUniversidade Federal de Minas GeraisHospital for Sick ChildrenUniversitat de BarcelonaUniversitair Medisch Centrum GroningenHaramaya UniversityTaipei Medical UniversityNational Institutes of HealthTehran University of Medical Sciences and Health ServicesBabol University of Medical SciencesMazandaran University of Medical SciencesLorestan University of Medical SciencesMinistry of Education, Science and TechnologyUniversity of OxfordJordan University of Science and TechnologyGeorg-August-Universität GöttingenUniversidad Nacional de ColombiaInvasive Fungi Research Center, Mazandaran University of Medical SciencesSeoul National UniversityMinistarstvo Prosvete, Nauke i Tehnološkog RazvojaPublic Health Foundation of IndiaInyuvesi Yakwazulu-NataliIndian Institute of Technology KanpurHawassa UniversityCystic Fibrosis CanadaUniversity of EdinburghMaragheh University of Medical SciencesMeso Scale DiagnosticsFudan UniversityUniversitas Negeri SemarangAhmadu Bello UniversitySouth African Medical Research CouncilBayer FundUniversity of GhanaSaint Paul's Hospital Millennium Medical CollegeUniwersytet Medyczny im. Piastów Slaskich we WroclawiuWellcome TrustTrường Đại học Duy TânIran University of Medical SciencesYonsei UniversityUniversity of MemphisPfizerBill and Melinda Gates FoundationPostgraduate Institute of Medical Education and Research, ChandigarhGeorge Institute for Global HealthTakeda Pharmaceutical CompanyBrandeis UniversityNational Center for Child Health and DevelopmentCystic Fibrosis FoundationFlinders UniversityHarvard UniversityHorizon PharmaceuticalsImperial College LondonGlaxoSmithKlineSanofiSan Diego State UniversityWorld Health OrganizationUniwersytet Jagielloński Collegium MedicumRijksuniversiteit GroningenBristol-Myers SquibbErasmus Universitair Medisch Centrum RotterdamAstraZenecaAstellas Pharma US
Mots-clésMedicineBurden of diseaseDiseaseIntensive care medicineEnvironmental healthDemographyInternal medicine

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Lower respiratory infections are a leading cause of morbidity and mortality around the world. The Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study 2016, provides an up-to-date analysis of the burden of lower respiratory infections in 195 countries. This study assesses cases, deaths, and aetiologies spanning the past 26 years and shows how the burden of lower respiratory infection has changed in people of all ages. METHODS: We used three separate modelling strategies for lower respiratory infections in GBD 2016: a Bayesian hierarchical ensemble modelling platform (Cause of Death Ensemble model), which uses vital registration, verbal autopsy data, and surveillance system data to predict mortality due to lower respiratory infections; a compartmental meta-regression tool (DisMod-MR), which uses scientific literature, population representative surveys, and health-care data to predict incidence, prevalence, and mortality; and modelling of counterfactual estimates of the population attributable fraction of lower respiratory infection episodes due to Streptococcus pneumoniae, Haemophilus influenzae type b, influenza, and respiratory syncytial virus. We calculated each modelled estimate for each age, sex, year, and location. We modelled the exposure level in a population for a given risk factor using DisMod-MR and a spatio-temporal Gaussian process regression, and assessed the effectiveness of targeted interventions for each risk factor in children younger than 5 years. We also did a decomposition analysis of the change in LRI deaths from 2000-16 using the risk factors associated with LRI in GBD 2016. FINDINGS: In 2016, lower respiratory infections caused 652 572 deaths (95% uncertainty interval [UI] 586 475-720 612) in children younger than 5 years (under-5s), 1 080 958 deaths (943 749-1 170 638) in adults older than 70 years, and 2 377 697 deaths (2 145 584-2 512 809) in people of all ages, worldwide. Streptococcus pneumoniae was the leading cause of lower respiratory infection morbidity and mortality globally, contributing to more deaths than all other aetiologies combined in 2016 (1 189 937 deaths, 95% UI 690 445-1 770 660). Childhood wasting remains the leading risk factor for lower respiratory infection mortality among children younger than 5 years, responsible for 61·4% of lower respiratory infection deaths in 2016 (95% UI 45·7-69·6). Interventions to improve wasting, household air pollution, ambient particulate matter pollution, and expanded antibiotic use could avert one under-5 death due to lower respiratory infection for every 4000 children treated in the countries with the highest lower respiratory infection burden. INTERPRETATION: Our findings show substantial progress in the reduction of lower respiratory infection burden, but this progress has not been equal across locations, has been driven by decreases in several primary risk factors, and might require more effort among elderly adults. By highlighting regions and populations with the highest burden, and the risk factors that could have the greatest effect, funders, policy makers, and programme implementers can more effectively reduce lower respiratory infections among the world's most susceptible populations. FUNDING: Bill & Melinda Gates Foundation.

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)0,021
score de la tête « metaresearch » (Gemma)0,039
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Méta-analyse · Signal consensuel: Méta-analyse
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,026
Score d'incertitude au seuil0,110

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0210,039
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0040,022
Bibliométrie0,0070,010
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0010,002
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 Opus0,105
Tête enseignante GPT0,417
Écart entre enseignants0,311 · 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'étudeMéta-analyse
Domainenon disponible
GenreEmpirique

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

En bref

Citations2 043
Publié2018
Routes d'admission1
Résumé présentoui

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