Estimated undertreatment of carbapenem-resistant Gram-negative bacterial infections in eight low-income and middle-income countries: a modelling study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".