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Epidemiology and excess mortality of antimicrobial resistance in bacteraemias among cancer patients: a cohort study using routinely collected health data from regional hospital trusts in Oxford and Oslo, 2008-2018.
OBJECTIVES: We investigated the epidemiology and impact on mortality of antimicrobial resistance (AMR) in cancer patients with bacteraemia at Oxford University Hospitals (OxUH), UK, and Oslo University Hospital (OsUH), Norway, during 2008-2018. DESIGN: Historical cohort study. SETTING: Regional hospital trusts with multiple sites in OxUH and OsUH. METHODS: Patients with cancer and blood cultures positive for one of six pathogen groups during a hospital stay within 3 years following their first cancer diagnosis were followed for 30 days after their first bacteraemia episode. We determined the number of cases and the proportion of infections with an AMR phenotype. Excess mortality and the population-attributable fraction (PAF) due to AMR were estimated by contrasting observed mortality at the end of follow-up with an estimated counterfactual scenario where AMR was absent from all bacteraemias, using inverse probability weighting. MAIN OUTCOME MEASURE: 30-day all-cause mortality following the first bacteraemia episode. MAIN EXPOSURE MEASURE: A resistant phenotype of the causative pathogen. RESULTS: The study included 1929 patients at OxUH and 1640 patients at OsUH. The highest resistance proportions were found for vancomycin resistance in enterococci (85/314, 27.1%) and carbapenem-resistance in Pseudomonas aeruginosa (63/260, 24.2%) at OxUH, and third-generation cephalosporin resistance in Escherichia coli (62/743, 8.3%) and Klebsiella pneumoniae (14/223, 6.3%) at OsUH. Observed mortality for all infections was 26.4% at OxUH, with an estimated counterfactual mortality without AMR of 24.7%, yielding an excess mortality of 1.7% (95% CI: 0.8 to 2.5%). The PAF was 6.3% (95% CI: 2.9 to 9.6%), meaning an estimated 32 of 509 deaths could be attributed to AMR. Limited events at OsUH precluded a similar estimate. CONCLUSIONS: Despite estimating modest excess mortality, the mortality attributable to resistance in these two high-income, low-prevalence settings highlights the potential for escalation if global resistance trends continue to worsen.
Reemergence of Cosmopolitan Genotype Dengue Virus Serotype 2, Southern Vietnam.
We performed phylogenetic analysis on dengue virus serotype 2 Cosmopolitan genotype in Ho Chi Minh City, Vietnam. We document virus emergence, probable routes of introduction, and timeline of events. Our findings highlight the need for continuous, systematic genomic surveillance to manage outbreaks and forecast future epidemics.
Estimating epidemic dynamics with genomic and time series data.
Accurately estimating the prevalence and transmissibility of an infectious disease is an important task in genetic infectious disease epidemiology. However, generating accurate estimates of these quantities, that make use of both epidemic time series and pathogen genome sequence data, is a challenging problem. Phylogenetic birth-death processes are a popular choice for modelling the transmission of infectious diseases, but it is difficult to estimate the prevalence of infection with them. Here, we extended our approximate likelihood approach, which combines phylogenetic information from sampled pathogen genomes and epidemiological information from a time series of case counts, to estimate historical prevalence in addition to the effective reproduction number. We implement this new method in a BEAST2 package called Timtam. In a simulation study our approximation is seen to be well-calibrated and recovers the parameters of simulated data. To demonstrate how Timtam can be applied to real datasets, we carried out empirical analyses of data from two infectious disease outbreaks: the outbreak of SARS-CoV-2 onboard the Diamond Princess cruise ship in early 2020 and poliomyelitis in Tajikistan in 2010. In both cases we recover estimates consistent with previous analyses.