Contribution of nosocomial transmission to Klebsiella pneumoniae neonatal sepsis in Africa and South Asia: analysis of infection clusters inferred from pathogen genomics and temporal data
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Abstract
Background
Klebsiella pneumoniae is the leading cause of sepsis among neonates in low- and middle-income countries (LMICs) in Africa and Asia, contributing substantially to the overall burden of antimicrobial resistant (AMR) infections and mortality among neonates globally. Pathogen sequencing has been used to investigate case clusters and confirm nosocomial transmission in a small number of neonatal units. Here we utilise pathogen sequence data to estimate the fraction of K. pneumoniae neonatal sepsis attributable to nosocomial transmission in African and South Asian countries.
Methods and Findings
We estimated the proportion of invasive K. pneumoniae disease involved in nosocomial transmission clusters in a given neonatal unit, using single-linkage clustering based on pairwise temporal and genetic distances estimated from bacterial whole-genome sequences aggregated from 10 contributing studies. Analysing 1,523 K. pneumoniae isolates from 27 units in 13 countries in Africa and South Asia between 2013 and 2023, we inferred 156 nosocomial transmission clusters, ranging from 2 to 188 neonates each (83 of the clusters comprised ≥3 cases). Overall, we estimated that 1,035 neonatal infections (68.0%) were part of nosocomial transmission clusters. Excluding the first infection in each cluster as a potential index case, we estimate at least 879 (57.7%) infections were acquired via nosocomial transmission. Sensitivity analyses showed that results were robust to the choice of genetic distance estimation methods and thresholds used to define clusters, and cluster estimates were stable over temporal distance thresholds ranging from 2 to 8 weeks. Isolates were mostly extended-spectrum beta-lactamase (ESBL) producers (90.9%) and included 172 multi-locus sequence types (STs). Fourteen STs, including several globally recognised multidrug-resistant lineages, were associated with transmission clusters at multiple units and these were collectively responsible for two-thirds of all infections. Carriage of carbapenemase genes (adjusted odds ratio, aOR = 2.08 [95% confidence interval, CI: 1.04–4.14], p=0.02) and ESBL genes (aOR = 2.48 [95% CI: 1.26–4.90] p=0.006) were significantly positively associated with transmission.
Conclusions
Nosocomial transmission contributes to a substantial proportion of K. pneumoniae sepsis in neonatal care units in Africa and South Asia. Reducing transmission within these settings through improved infection prevention and control and other measures could substantially reduce the neonatal sepsis burden. A high burden of transmission clusters is associated with the same drug-resistant lineages that are recognised as high-risk clones associated with hospital outbreaks in high-income countries, indicating global connectivity of the AMR pathogen population.
Author Summary
Why Was This Study Done?
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Klebsiella pneumoniae is the leading cause of sepsis among neonates in low- and middle-income countries (LMICs) in Africa and Asia, and the infections are difficult to treat due to rising rates of antimicrobial resistance.
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Invasive bacterial diseases are typically transmitted to neonates from their mothers before, during or soon after birth (vertical transmission) or from the hospital environment and healthcare workers (horizontal transmission).
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The fraction of K. pneumoniae neonatal sepsis cases attributable to horizontal transmission is unknown, but this information is important to understand the role of infection prevention and control (IPC) measures in lowering disease burden.
What Did the Researchers Do and Find?
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We developed a simple method to detect transmission clusters from genetic and temporal distance data and found the method to be robust to the choice of genetic and temporal distance thresholds.
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We applied this method to detect transmission clusters among 1,523 K. pneumoniae neonatal sepsis cases from 10 studies and 27 hospitals across Africa and South Asia.
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We estimate over half of sepsis cases (68.0%) were part of a transmission cluster, and by excluding the hypothetical index case for each cluster we estimate at least 57.7% of infections were acquired via nosocomial transmission.
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Most of the isolates (90.9%) were extended-spectrum beta-lactamase (ESBL) producers (conferring resistance to third-generation cephalosporin antibiotics), and carriage of ESBL and carbapenemase genes (conferring resistance to carbapenem antibiotics) were positively associated with transmission.
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Fourteen genetic lineages were associated with clusters in multiple neonatal units, together accounting for two-thirds of all infections.
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Many of these same lineages are common causes of drug-resistant hospital outbreaks in high-income countries.
What Do These Findings Mean?
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A substantial proportion of K. pneumoniae neonatal sepsis cases are potentially preventable with improvements in IPC in neonatal units.
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Further work is needed to identify and better understand transmission routes and risk factors for transmission to support the implementation of effective and scalable IPC solutions.
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Our findings highlight the importance of genomic surveillance to support IPC interventions for K. pneumoniae and other pathogens, and reveal many of the same ‘drug-resistant problem clones’ are responsible for hospital outbreaks across high-and low-income countries.
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The high rates of ESBL gene carriage among isolates in this study indicates that empirical treatment based on the current WHO guidelines may result in high rates of treatment failure.
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The limitations of this study include the lack of sufficient clinical data to allow high-resolution investigation of transmission dynamics, as well as facility-level data to investigate contributors to the observed differences in transmission burden across sites.
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This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/20673449.
Major Issues
The word "transmission" should be used cautiously throughout. The clusters are inferred from genetic relatedness and collection dates, not observed transmission chains. This is reasonable, but the paper sometimes moves from "clustered infections" to "nosocomial transmission" quite strongly. The authors should consistently state that these are inferred nosocomial transmission clusters and that alternative explanations, such as repeated exposure to a persistent environmental reservoir or repeated introductions of highly prevalent clones, cannot always be separated.
Single-linkage clustering can create large chained clusters. The authors clearly explain that not all pairs within …
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/20673449.
Major Issues
The word "transmission" should be used cautiously throughout. The clusters are inferred from genetic relatedness and collection dates, not observed transmission chains. This is reasonable, but the paper sometimes moves from "clustered infections" to "nosocomial transmission" quite strongly. The authors should consistently state that these are inferred nosocomial transmission clusters and that alternative explanations, such as repeated exposure to a persistent environmental reservoir or repeated introductions of highly prevalent clones, cannot always be separated.
Single-linkage clustering can create large chained clusters. The authors clearly explain that not all pairs within a cluster need to be within the genetic or temporal threshold. This matters especially for very large clusters, such as the ST307 cluster involving 188 neonates. The manuscript would be strengthened by reporting within-cluster maximum pairwise genetic distance, maximum temporal span, and perhaps a sensitivity analysis using complete-linkage or stricter cluster definitions for the largest clusters.
Sampling and denominator differences could bias site comparisons. The included studies differ in duration, culture practices, isolate storage/revival, sequencing success, clinical inclusion criteria, and whether CSF isolates were included. These factors can affect the probability of detecting clusters and the denominator of sequenced K. pneumoniae infections. The authors discuss this, but should more explicitly caution against ranking sites or regions by transmission burden without standardized sampling intensity.
Transmission proportion may be both conservative and biased in complex ways. Excluding one index case per cluster is conservative, but missed cases, colonization-only transmission, repeat isolates, and variation in blood-culture sensitivity could alter estimates in either direction. The paper should avoid presenting 57.7% as a precise attributable fraction and instead frame it as a lower-bound estimate under explicit assumptions.
Facility-level associations are underpowered and should be treated as exploratory. The observations about piped water availability and neonatal surgical facilities are important, but they are based on limited, post hoc facility-level data and small numbers of sites. These findings should be described as hypothesis-generating rather than evidence of specific determinants.
AMR-transmission associations are difficult to interpret causally. ESBL and carbapenemase genes are associated with clustered infections, but this could reflect lineage structure, treatment selection, blood-culture detection probability, hospital persistence, or true transmissibility. The authors discuss this well, but the abstract and conclusions should avoid implying that resistance itself directly increases transmission without qualification.
Clinical metadata limitations constrain interpretation. The absence of standardized admission dates, birth dates, disease-onset timing, ward movement, bed location, colonization screening, maternal isolates, environmental isolates, and IPC exposure data prevents resolution of transmission routes. The discussion should more clearly separate what this study can estimate, clustered invasive disease burden, from what it cannot estimate, exact route or source.
Minor Issues
Page 1 appears crowded, with the affiliation block overlapping the medRxiv footer in the rendered PDF. The title page should be reformatted.
Figures 1-3 are very informative but dense. Larger fonts or simplified main panels with detailed tables moved to supplements would improve readability.
Clarify early in the Results that the 68.0% and 57.7% are overall isolate-level estimates, while 53.3% and 33.5% are median site-level estimates.
Define "introduction" clearly when used for clusters and singleton cases, since it is an analytical unit rather than a directly observed importation event.
Consider presenting a short table of the largest clusters, including ST, site, duration, number of cases, and maximum pairwise SNV distance.
The manuscript should clarify how missing patient identifiers in the MLW Biobank may affect repeat-isolate exclusion and cluster size estimates.
The discussion of WHO empirical therapy implications is important, but it should note that genotype-predicted resistance does not fully replace phenotypic susceptibility testing.
Please ensure all supplementary tables and figures are easy to locate from the main text, since many key assumptions are in supplements.
Competing interests
The author declares that they have no competing interests.
Use of Artificial Intelligence (AI)
The author declares that they did not use generative AI to come up with new ideas for their review.
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