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1.
Clin Microbiol Infect ; 30 Suppl 1: S14-S25, 2024 Mar.
Article En | MEDLINE | ID: mdl-37802750

BACKGROUND: Antimicrobial resistance is a global threat, which requires novel intervention strategies, for which priority pathogens and settings need to be determined. OBJECTIVES: We evaluated pathogen-specific excess health burden of drug-resistant bloodstream infections (BSIs) in Europe. METHODS: A systematic review and meta-analysis. DATA SOURCES: MEDLINE, Embase, and grey literature for the period January 1990 to May 2022. STUDY ELIGIBILITY CRITERIA: Studies that reported burden data for six key drug-resistant pathogens: carbapenem-resistant (CR) Pseudomonas aeruginosa and Acinetobacter baumannii, third-generation cephalosporin or CR Escherichia coli and Klebsiella pneumoniae, methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus faecium. Excess health outcomes compared with drug-susceptible BSIs or uninfected patients. For MRSA and third-generation cephalosporin E. coli and K. pneumoniae BSIs, five or more European studies were identified. For all others, the search was extended to high-income countries. PARTICIPANTS: Paediatric and adult patients diagnosed with drug-resistant BSI. INTERVENTIONS: Not applicable. ASSESSMENT OF RISK OF BIAS: An adapted version of the Joanna-Briggs Institute assessment tool. METHODS OF DATA SYNTHESIS: Random-effect models were used to pool pathogen-specific burden estimates. RESULTS: We screened 7154 titles, 1078 full-texts and found 56 studies on BSIs. Most studies compared outcomes of drug-resistant to drug-susceptible BSIs (46/56, 82.1%), and reported mortality (55/56 studies, 98.6%). The pooled crude estimate for excess all-cause mortality of drug-resistant versus drug-susceptible BSIs ranged from OR 1.31 (95% CI 1.03-1.68) for CR P. aeruginosa to OR 3.44 (95% CI 1.62-7.32) for CR K. pneumoniae. Pooled crude estimates comparing mortality to uninfected patients were available for vancomycin-resistant Enterococcus and MRSA BSIs (OR of 11.19 [95% CI 6.92-18.09] and OR 6.18 [95% CI 2.10-18.17], respectively). CONCLUSIONS: Drug-resistant BSIs are associated with increased mortality, with the magnitude of the effect influenced by pathogen type and comparator. Future research should address crucial knowledge gaps in pathogen- and infection-specific burdens to guide development of novel interventions.


Bacteremia , Methicillin-Resistant Staphylococcus aureus , Sepsis , Adult , Humans , Child , Bacteremia/drug therapy , Bacteremia/epidemiology , Bacteremia/microbiology , Escherichia coli , Vancomycin/pharmacology , Anti-Bacterial Agents/pharmacology , Anti-Bacterial Agents/therapeutic use , Europe/epidemiology , Sepsis/drug therapy , Cephalosporins/pharmacology , Drug Resistance, Bacterial
2.
Clin Microbiol Infect ; 30 Suppl 1: S26-S36, 2024 Mar.
Article En | MEDLINE | ID: mdl-38128781

BACKGROUND: Quantifying the resource use and cost of antimicrobial resistance establishes the magnitude of the problem and drives action. OBJECTIVES: Assessment of resource use and cost associated with infections with six key drug-resistant pathogens in Europe. METHODS: A systematic review and Bayesian meta-analysis. DATA SOURCES: MEDLINE (Ovid), Embase (Ovid), Econlit databases, and grey literature for the period 1 January 1990, to 21 June 2022. STUDY ELIGIBILITY CRITERIA: Resource use and cost outcomes (including excess length of stay, overall costs, and other excess in or outpatient costs) were compared between patients with defined antibiotic-resistant infections caused by carbapenem-resistant (CR) Pseudomonas aeruginosa and Acinetobacter baumannii, CR or third-generation cephalosporin Escherichia coli (3GCREC) and Klebsiella pneumoniae, methicillin-resistant Staphylococcus aureus, and vancomycin-resistant Enterococcus faecium, and patients with drug-susceptible or no infection. PARTICIPANTS: All patients diagnosed with drug-resistant bloodstream infections (BSIs). INTERVENTIONS: NA. ASSESSMENT OF RISK OF BIAS: An adapted version of the Joanna Briggs Institute assessment tool, incorporating case-control, cohort, and economic assessment frameworks. METHODS OF DATA SYNTHESIS: Hierarchical Bayesian meta-analyses were used to assess pathogen-specific resource use estimates. RESULTS: Of 5969 screened publications, 37 were included in the review. Data were sparse and heterogeneous. Most studies estimated the attributable burden by, comparing resistant and susceptible pathogens (32/37). Four studies analysed the excess cost of hospitalization attributable to 3GCREC BSIs, ranging from -€ 2465.50 to € 6402.81. Eight studies presented adjusted excess length of hospital stay estimates for methicillin-resistant S. aureus and 3GCREC BSIs (4 each) allowing for Bayesian hierarchical analysis, estimating means of 1.26 (95% credible interval [CrI], -0.72 to 4.17) and 1.78 (95% CrI, -0.02 to 3.38) days, respectively. CONCLUSIONS: Evidence on most cost and resource use outcomes and across most pathogen-resistance combinations was severely lacking. Given the importance of this evidence for rational policymaking, further research is urgently needed.


Anti-Infective Agents , Methicillin-Resistant Staphylococcus aureus , Humans , Bayes Theorem , Anti-Bacterial Agents/pharmacology , Anti-Bacterial Agents/therapeutic use , Escherichia coli , Pseudomonas aeruginosa , Drug Resistance, Bacterial
3.
Viruses ; 14(1)2022 01 17.
Article En | MEDLINE | ID: mdl-35062366

Arboviruses remain a significant cause of morbidity, mortality and economic cost across the global human population. Epidemics of arboviral disease, such as Zika and dengue, also cause significant disruption to health services at local and national levels. This study examined 2014-2016 Zika and dengue epidemic data at the sub-national level to characterise transmission across the Dominican Republic. For each municipality, spatio-temporal mapping was used to characterise disease burden, while data were age and sex standardised to quantify burden distributions among the population. In separate analyses, time-ordered data were combined with the underlying disease migration interval distribution to produce a network of likely transmission chain events, displayed using transmission chain likelihood matrices. Finally, municipal-specific reproduction numbers (Rm) were established using a Wallinga-Teunis matrix. Dengue and Zika epidemics peaked during weeks 39-52 of 2015 and weeks 14-27 of 2016, respectively. At the provincial level, dengue attack rates were high in Hermanas Mirabal and San José de Ocoa (58.1 and 49.2 cases per 10,000 population, respectively), compared with the Zika burden, which was highest in Independencia and San José de Ocoa (21.2 and 13.4 cases per 10,000 population, respectively). Across municipalities, high disease burden was observed in Cotuí (622 dengue cases per 10,000 population) and Jimani (32 Zika cases per 10,000 population). Municipal infector-infectee transmission likelihood matrices identified seven 0% likelihood transmission events throughout the dengue epidemic and two 0% likelihood transmission events during the Zika epidemic. Municipality reproduction numbers (Rm) were consistently higher, and persisted for a greater duration, during the Zika epidemic (Rm = 1.0) than during the dengue epidemic (Rm < 1.0). This research highlights the importance of disease surveillance in land border municipalities as an early warning for infectious disease transmission. It also demonstrates that a high number of importation events are required to sustain transmission in endemic settings, and vice versa for newly emerged diseases. The inception of a novel epidemiological metric, Rm, reports transmission risk using standardised spatial units, and can be used to identify high transmission risk municipalities to better focus public health interventions for dengue, Zika and other infectious diseases.


Dengue/epidemiology , Epidemics/statistics & numerical data , Public Health/methods , Zika Virus Infection/epidemiology , Cities/statistics & numerical data , Datasets as Topic , Dengue/prevention & control , Dengue Virus/pathogenicity , Dominican Republic/epidemiology , Epidemics/prevention & control , Humans , Models, Statistical , Zika Virus/pathogenicity , Zika Virus Infection/prevention & control
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