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      Neonatal Colonization With Antibiotic-Resistant Pathogens in Low- and Middle-Income Countries : A Systematic Review and Meta-Analysis

      research-article
      , MD 1 , 2 , , , MD, PhD 3 , , MD, PhD 4 , 5 , 6 , 7 , , MD, PhD 1 , 2
      JAMA Network Open
      American Medical Association

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          Abstract

          This systematic review and meta-analysis investigates the prevalence of colonization with antibiotic-resistant bacterial pathogens in the first 3 months of life in low- and middle-income countries (LMICs).

          Key Points

          Question

          What are the prevalence of and factors associated with colonization with third-generation cephalosporin–resistant Enterobacterales (3GCRE), carbapenem-resistant Enterobacterales (CRE), and methicillin-resistant Staphylococcus aureus (MRSA) during the first 3 months of life in low- and middle-income countries (LMICs)?

          Findings

          In this systematic review and meta-analysis of 67 studies including 17 152 individuals, the pooled prevalence of 3GCRE colonization was 30% compared with 3% for CRE and 3% for MRSA colonization. Hospital birth, previous neonatal antibiotic use, and prolonged rupture of membranes were associated with increased risk of 3GCRE colonization.

          Meaning

          The findings suggest substantial prevalence of antibiotic-resistant pathogen carriage in neonates and infants in LMICs.

          Abstract

          Importance

          In low- and middle-income countries (LMICs), neonatal bacterial infections are mainly caused by Enterobacterales species and Staphylococcus aureus, which are also the leading causes of mortality directly attributable to antimicrobial resistance. As bacterial colonization often precedes infection, better knowledge of colonization is crucial to prevent antibiotic-resistant neonatal sepsis.

          Objective

          To synthesize current evidence on the prevalence of and factors associated with colonization with third-generation cephalosporin–resistant Enterobacterales (3GCRE), carbapenem-resistant Enterobacterales (CRE), and methicillin-resistant S aureus (MRSA) during the first 3 months of life in LMICs.

          Data Sources

          PubMed, Scopus, Web of Science, and the World Health Organization Global Index Medicus were searched for articles published from January 1, 2000, through July 29, 2024.

          Study Selection

          Included studies were conducted in LMICs and reported prevalence rates or factors associated with colonization with 3GCRE, CRE, or MRSA in neonates and infants up to 3 months of age. Outbreak reports were excluded.

          Data Extraction and Synthesis

          Data extraction and risk-of-bias assessment using a Joanna Briggs Institute tool were performed by 2 independent reviewers. Pooled prevalence for each pathogen was computed using a random-effects model. Reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline.

          Main Outcomes and Measures

          Prevalence of and factors associated with 3GCRE, CRE, and MRSA colonization.

          Results

          Of the 3147 articles identified in the search, 67 studies (51 for 3GCRE and CRE and 16 for MRSA) including 17 152 individuals were eligible. The pooled prevalence of 3GCRE colonization was 30.2% (95% CI, 21.4%-40.7%; τ 2 = 1.48; I 2 = 95.1%), varying from 18.2% (95% CI, 10.8%-29.1%) in nonhospitalized individuals to 48.2% (95% CI, 36.4%-60.2%) in hospitalized individuals. The prevalence of CRE colonization was 2.6% (95% CI, 0.7%-8.8%; τ 2 = 7.79; I 2 = 95.6%), while it was 2.7% (95% CI, 1.0%-6.7%; τ 2 = 2.58; I 2 = 93.5%) for MRSA. Increased risk of colonization with 3GCRE was associated with hospital birth (odds ratio [OR], 1.87; 95% CI, 1.33-2.64), neonatal antibiotic use (OR, 2.96; 95% CI, 1.43-6.11), and prolonged rupture of membranes (OR, 3.86; 95% CI, 2.19-6.84).

          Conclusions and Relevance

          In this systematic review and meta-analysis of antibiotic-resistant pathogen carriage in individuals aged 0 to 3 months, the pooled prevalence was substantial despite a limited exposure period. Although high heterogeneity between studies limited extrapolation of results, the findings highlight the need for further investigation to identify transmission routes and to design targeted and effective preventive measures.

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          Most cited references96

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          Quantifying heterogeneity in a meta-analysis.

          The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
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            Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis

            (2022)
            Summary Background Antimicrobial resistance (AMR) poses a major threat to human health around the world. Previous publications have estimated the effect of AMR on incidence, deaths, hospital length of stay, and health-care costs for specific pathogen–drug combinations in select locations. To our knowledge, this study presents the most comprehensive estimates of AMR burden to date. Methods We estimated deaths and disability-adjusted life-years (DALYs) attributable to and associated with bacterial AMR for 23 pathogens and 88 pathogen–drug combinations in 204 countries and territories in 2019. We obtained data from systematic literature reviews, hospital systems, surveillance systems, and other sources, covering 471 million individual records or isolates and 7585 study-location-years. We used predictive statistical modelling to produce estimates of AMR burden for all locations, including for locations with no data. Our approach can be divided into five broad components: number of deaths where infection played a role, proportion of infectious deaths attributable to a given infectious syndrome, proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of a given pathogen resistant to an antibiotic of interest, and the excess risk of death or duration of an infection associated with this resistance. Using these components, we estimated disease burden based on two counterfactuals: deaths attributable to AMR (based on an alternative scenario in which all drug-resistant infections were replaced by drug-susceptible infections), and deaths associated with AMR (based on an alternative scenario in which all drug-resistant infections were replaced by no infection). We generated 95% uncertainty intervals (UIs) for final estimates as the 25th and 975th ordered values across 1000 posterior draws, and models were cross-validated for out-of-sample predictive validity. We present final estimates aggregated to the global and regional level. Findings On the basis of our predictive statistical models, there were an estimated 4·95 million (3·62–6·57) deaths associated with bacterial AMR in 2019, including 1·27 million (95% UI 0·911–1·71) deaths attributable to bacterial AMR. At the regional level, we estimated the all-age death rate attributable to resistance to be highest in western sub-Saharan Africa, at 27·3 deaths per 100 000 (20·9–35·3), and lowest in Australasia, at 6·5 deaths (4·3–9·4) per 100 000. Lower respiratory infections accounted for more than 1·5 million deaths associated with resistance in 2019, making it the most burdensome infectious syndrome. The six leading pathogens for deaths associated with resistance (Escherichia coli, followed by Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa) were responsible for 929 000 (660 000–1 270 000) deaths attributable to AMR and 3·57 million (2·62–4·78) deaths associated with AMR in 2019. One pathogen–drug combination, meticillin-resistant S aureus, caused more than 100 000 deaths attributable to AMR in 2019, while six more each caused 50 000–100 000 deaths: multidrug-resistant excluding extensively drug-resistant tuberculosis, third-generation cephalosporin-resistant E coli, carbapenem-resistant A baumannii, fluoroquinolone-resistant E coli, carbapenem-resistant K pneumoniae, and third-generation cephalosporin-resistant K pneumoniae. Interpretation To our knowledge, this study provides the first comprehensive assessment of the global burden of AMR, as well as an evaluation of the availability of data. AMR is a leading cause of death around the world, with the highest burdens in low-resource settings. Understanding the burden of AMR and the leading pathogen–drug combinations contributing to it is crucial to making informed and location-specific policy decisions, particularly about infection prevention and control programmes, access to essential antibiotics, and research and development of new vaccines and antibiotics. There are serious data gaps in many low-income settings, emphasising the need to expand microbiology laboratory capacity and data collection systems to improve our understanding of this important human health threat. Funding Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care using UK aid funding managed by the Fleming Fund.
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              Human gut microbiome viewed across age and geography

              Gut microbial communities represent one source of human genetic and metabolic diversity. To examine how gut microbiomes differ between human populations when viewed from the perspective of component microbial lineages, encoded metabolic functions, stage of postnatal development, and environmental exposures, we characterized bacterial species present in fecal samples obtained from 531 individuals representing healthy Amerindians from the Amazonas of Venezuela, residents of rural Malawian communities, and inhabitants of USA metropolitan areas, as well as the gene content of 110 of their microbiomes. This cohort encompassed infants, children, teenagers and adults, parents and offspring, and included mono- and dizygotic twins. Shared features of the functional maturation of the gut microbiome were identified during the first three years of life in all three populations, including age-associated changes in the representation of genes involved in vitamin biosynthesis and metabolism. Pronounced differences in bacterial species assemblages and functional gene repertoires were noted between individuals residing in the USA compared to the other two countries. These distinctive features are evident in early infancy as well as adulthood. In addition, the similarity of fecal microbiomes among family members extends across cultures. These findings underscore the need to consider the microbiome when evaluating human development, nutritional needs, physiological variations, and the impact of Westernization.
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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                5 November 2024
                November 2024
                5 November 2024
                : 7
                : 11
                : e2441596
                Affiliations
                [1 ]Anti-Infective Evasion and Pharmacoepidemiology Team, Center for Epidemiology and Population Health, Université Paris-Saclay, UVSQ, INSERM, Montigny-le-Bretonneux, France
                [2 ]Epidemiology and Modelling of Antibiotic Evasion, Institut Pasteur, Université Paris Cité, Paris, France
                [3 ]Department of Neonatal Medicine, AP-HP, Hôpital Necker-Enfants Malades, Université Paris Cité, Paris, France
                [4 ]Clinical Microbiology Group, Institut Pasteur of Guadeloupe, Les Abymes, France
                [5 ]Clinical Microbiology Laboratory, University Hospital of Guadeloupe, Pointe-à-Pitre, Guadeloupe, France
                [6 ]Clinical Investigation Center Antilles–Guyane, Unit 1424, INSERM, Pointe-à-Pitre, Guadeloupe, France
                [7 ]Pathogenesis and Control of Chronic and Emerging Infections, University of Montpellier, INSERM, Établissement français du sang, University of Antilles, Pointe-à-Pitre, France
                Author notes
                Article Information
                Accepted for Publication: September 3, 2024.
                Published: November 5, 2024. doi:10.1001/jamanetworkopen.2024.41596
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2024 Beaumont AL et al. JAMA Network Open.
                Corresponding Author: Anne-Lise Beaumont, Epidemiology and Modelling of Antibiotic Evasion, Institut Pasteur, Université Paris Cité, 25 rue du Dr Roux, 75015 Paris, France ( anne-lise.beaumont@ 123456pasteur.fr ).
                Author Contributions: Dr Beaumont had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Beaumont, Huynh.
                Acquisition, analysis, or interpretation of data: All authors.
                Drafting of the manuscript: Beaumont, Breurec, Huynh.
                Critical review of the manuscript for important intellectual content: All authors.
                Statistical analysis: Beaumont.
                Obtained funding: Huynh.
                Supervision: Breurec, Huynh.
                Conflict of Interest Disclosures: Dr Beaumont reported receiving financial support from the Fondation Pour la Recherche Médicale and the Collège des Maladies Infectieuses et Tropicales. Prof Kermorvant-Duchemin reported receiving grants from the Fondation Université Paris Cité and Agence Nationale de la Recherche and personal fees from AstraZeneca outside the submitted work. No other disclosures were reported.
                Data Sharing Statement: See Supplement 2.
                Additional Contributions: Catherine Cécilio, librarian at the Institut Pasteur, provided assistance with the literature research strategy without compensation.
                Article
                zoi241199
                10.1001/jamanetworkopen.2024.41596
                11581591
                39499519
                7f8bebc1-e5f8-4e83-82c5-b1fac2997ac0
                Copyright 2024 Beaumont AL et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 17 April 2024
                : 3 September 2024
                Categories
                Research
                Original Investigation
                Online Only
                Pediatrics

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