Browsing by Author "Grange Z"

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    Extended-spectrum β-lactamase- and AmpC β-lactamase-producing Enterobacterales associated with urinary tract infections in the New Zealand community: a case-control study
    (Elsevier Ltd on behalf of International Society for Infectious Diseases, 2023-03) Toombs-Ruane LJ; Marshall JC; Benschop J; Drinković D; Midwinter AC; Biggs PJ; Grange Z; Baker MG; Douwes J; Roberts MG; French NP; Burgess SA
    OBJECTIVES: To assess whether having a pet in the home is a risk factor for community-acquired urinary tract infections associated with extended-spectrum β-lactamase (ESBL)- or AmpC β-lactamase (ACBL)- producing Enterobacterales. METHODS: An unmatched case-control study was conducted between August 2015 and September 2017. Cases (n = 141) were people with community-acquired urinary tract infection (UTI) caused by ESBL- or ACBL-producing Enterobacterales. Controls (n = 525) were recruited from the community. A telephone questionnaire on pet ownership and other factors was administered, and associations were assessed using logistic regression. RESULTS: Pet ownership was not associated with ESBL- or ACBL-producing Enterobacterales-related human UTIs. A positive association was observed for recent antimicrobial treatment, travel to Asia in the previous year, and a doctor's visit in the last 6 months. Among isolates with an ESBL-/ACBL-producing phenotype, 126/134 (94%) were Escherichia coli, with sequence type 131 being the most common (47/126). CONCLUSIONS: Companion animals in the home were not found to be associated with ESBL- or ACBL-producing Enterobacterales-related community-acquired UTIs in New Zealand. Risk factors included overseas travel, recent antibiotic use, and doctor visits.
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    The future of zoonotic risk prediction
    (The Royal Society, 2021-11-08) Carlson CJ; Farrell MJ; Grange Z; Han BA; Mollentze N; Phelan AL; Rasmussen AL; Albery GF; Bett B; Brett-Major DM; Cohen LE; Dallas T; Eskew EA; Fagre AC; Forbes KM; Gibb R; Halabi S; Hammer CC; Katz R; Kindrachuk J; Muylaert RL; Nutter FB; Ogola J; Olival KJ; Rourke M; Ryan SJ; Ross N; Seifert SN; Sironen T; Standley CJ; Taylor K; Venter M; Webala PW
    In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges?