Burden of disease in francophone Africa, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017
Notice bibliographique
Résumé
BACKGROUND: Peer-reviewed literature on health is almost exclusively published in English, limiting the uptake of research for decision making in francophone African countries. We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 to assess the burden of disease in francophone Africa and inform health professionals and their partners in the region. METHODS: We assessed the burden of disease in the 21 francophone African countries and compared the results with those for their non-francophone counterparts in three economic communities: the Economic Community of West African States, the Economic Community of Central African States, and the Southern African Development Community. GBD 2017 employed a variety of statistical models to determine the number of deaths from each cause, through the Cause of Death Ensemble model algorithm, using CoDCorrect to ensure that the number of deaths per cause did not exceed the total number of estimated deaths. After producing estimates for the number of deaths from each of the 282 fatal outcomes included in the GBD 2017 list of causes, the years of life lost (YLLs) due to premature death were calculated. Years lived with disability (YLDs) were estimated as the product of prevalence and a disability weight for all mutually exclusive sequelae. Disability-adjusted life-years (DALYs) were calculated as the sum of YLLs and YLDs. All calculations are presented with 95% uncertainty intervals (UIs). A sample of 1000 draws was taken from the posterior distribution of each estimation step; aggregation of uncertainty across age, sex, and location was done on each draw, assuming independence of uncertainty. The lower and upper UIs represent the ordinal 25th and 975th draws of each quantity and attempt to describe modelling as well as sampling error. FINDINGS: In 2017, 779 deaths (95% UI 750-809) per 100 000 population occurred in francophone Africa, a decrease of 45·3% since 1990. Malaria, lower respiratory infections, neonatal disorders, diarrhoeal diseases, and tuberculosis were the top five Level 3 causes of death. These five causes were found among the six leading causes of death in most francophone countries. In 2017, francophone Africa experienced 53 570 DALYs (50 164-57 361) per 100 000 population, distributed between 43 708 YLLs (41 673-45 742) and 9862 YLDs (7331-12 749) per 100 000 population. In 2017, YLLs constituted the majority of DALYs in the 21 countries of francophone Africa. Age-specific and cause-specific mortality and population ageing were responsible for most of the reductions in disease burden, whereas population growth was responsible for most of the increases. INTERPRETATION: Francophone Africa still carries a high burden of communicable and neonatal diseases, probably due to the weakness of health-care systems and services, as evidenced by the almost complete attribution of DALYs to YLLs. To cope with this burden of disease, francophone Africa should define its priorities and invest more resources in health-system strengthening and in the quality and quantity of health-care services, especially in rural and remote areas. The region could also be prioritised in terms of technical and financial assistance focused on achieving these goals, as much as on demographic investments including education and family planning. 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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».