Estimated undertreatment of carbapenem-resistant Gram-negative bacterial infections in eight low-income and middle-income countries: a modelling study
Notice bibliographique
Résumé
BACKGROUND: Carbapenem-resistant Gram-negative (CRGN) bacterial infections are an urgent health threat, especially in low-income and middle-income countries (LMICs), where they are rarely detected and might not be treated appropriately given inadequate health system capacity. To understand this treatment gap, we estimated the total number of CRGN bacterial infections requiring an active agent and the number of individuals potentially initiated on appropriate treatment in eight large LMICs. METHODS: For eight selected countries (Bangladesh, Brazil, Egypt, India, Kenya, Mexico, Pakistan, and South Africa), we estimated deaths associated with CRGN bacterial infections (that were not susceptible to other antibiotics) in 2019 using data from the Global Burden of Disease 2021 study on antimicrobial resistance. We used estimates from the literature to establish infection type-specific case fatality rates and an overall case fatality rate for CRGN bacterial infections. The total number of CRGN bacterial infections requiring an active agent could then be calculated by dividing the total number of CRGN bacterial infection-related deaths by the overall case fatality rate. We estimated the treatment gap (ie, the number of individuals with CRGN bacterial infections who were not appropriately treated) by subtracting from the total number of infections the number of individuals who initiated appropriate treatment, which was estimated using 2019 IQVIA sales data for six antibiotics active against CRGN bacteria, corrected to account for IQVIA's partial data coverage for each country and dose-adjusted by age. FINDINGS: In 2019, in the eight selected countries, we estimated that there were 1 496 219 CRGN bacterial infections (95% CI 1 365 392-1 627 047) but that only 103 647 treatment courses were procured. The resulting treatment gap (1 392 572 cases [95% CI 1 261 745-1 523 400]) meant that only 6·9% of patients were treated appropriately. The treatment gap persisted even when we used more restrictive assumptions. The most-procured antibiotic was tigecycline (intravenous; 47 531 [45·9%] of 103 647 courses). India procured most of the treatment courses (83 468 [80·5%] courses), with 7·8% of infections treated appropriately (treatment gap 982 848 cases [95% CI 909 291-1 056 405]). The rates of appropriate treatment coverage were highest in Mexico (5634 [5·4%] courses procured; treatment gap 32 141 cases [30 416-33 867]) and Egypt (7572 [7·3%] courses procured; treatment gap 43 258 cases [38 742-47 774]), both with 14·9% of infections treated appropriately. INTERPRETATION: Infections caused by CRGN bacteria are likely to be significantly undertreated in LMICs. To close this treatment gap, improved access to diagnostics and antibiotics, strengthening of health systems, and research to identify gaps in the treatment pathway are needed. FUNDING: Global Antibiotic Research and Development Partnership, supported by the Governments of Canada, Germany, Japan, Monaco, the Netherlands, Switzerland, and the UK, and by the Canton of Geneva, the EU, the Bill & Melinda Gates Foundation, Global Health EDCTP3, GSK, the RIGHT Foundation, the South African Medical Research Council, and Wellcome.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 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 ».