Background: In 2013, New Delhi metallo-β-lactamase (NDM)-producing Escherichia coli, a type of carbapenem-resistant Enterobacteriaceae uncommon in the United States, was identified in a tertiary care hospital (hospital A) in northeastern Illinois. The outbreak was traced to a contaminated duodenoscope. Patient-sharing patterns can be described through social network analysis and ego networks, which could be used to identify hospitals most likely to accept patients from a hospital with an outbreak. Methods: Using Illinois' hospital discharge data and the Illinois extensively drug-resistant organism (XDRO) registry, we constructed an ego network around hospital A. We identified which facilities NDM outbreak patients subsequently visited and whether the facilities reported NDM cases. Results: Of the 31 outbreak cases entered into the XDRO registry who visited hospital A, 19 (61%) were subsequently admitted to 13 other hospitals during the following 12 months. Of the 13 hospitals, the majority (n = 9; 69%) were in our defined ego network, and 5 of those 9 hospitals consequently reported at least 1 additional NDM case. Ego network facilities were more likely to identify cases compared to a geographically defined group of facilities (9/22 vs 10/66; P = .01); only 1 reported case fell outside of the ego network. Conclusions: The outbreak hospital's ego network accurately predicted which hospitals the outbreak patients would visit. Many of these hospitals reported additional NDM cases. Prior knowledge of this ego network could have efficiently focused public health resources on these high-risk facilities.
Background: In 2013, New Delhi metallo-β-lactamase (NDM)-producing Escherichia coli, a type of carbapenem-resistant Enterobacteriaceae uncommon in the United States, was identified in a tertiary care hospital (hospital A) in northeastern Illinois. The outbreak was traced to a contaminated duodenoscope. Patient-sharing patterns can be described through social network analysis and ego networks, which could be used to identify hospitals most likely to accept patients from a hospital with an outbreak. Methods: Using Illinois' hospital discharge data and the Illinois extensively drug-resistant organism (XDRO) registry, we constructed an ego network around hospital A. We identified which facilities NDM outbreak patients subsequently visited and whether the facilities reported NDM cases. Results: Of the 31 outbreak cases entered into the XDRO registry who visited hospital A, 19 (61%) were subsequently admitted to 13 other hospitals during the following 12 months. Of the 13 hospitals, the majority (n = 9; 69%) were in our defined ego network, and 5 of those 9 hospitals consequently reported at least 1 additional NDM case. Ego network facilities were more likely to identify cases compared to a geographically defined group of facilities (9/22 vs 10/66; P = .01); only 1 reported case fell outside of the ego network. Conclusions: The outbreak hospital's ego network accurately predicted which hospitals the outbreak patients would visit. Many of these hospitals reported additional NDM cases. Prior knowledge of this ego network could have efficiently focused public health resources on these high-risk facilities.
Authors: Daniel J Diekema; Po-Ren Hsueh; Rodrigo E Mendes; Michael A Pfaller; Kenneth V Rolston; Helio S Sader; Ronald N Jones Journal: Antimicrob Agents Chemother Date: 2019-06-24 Impact factor: 5.191
Authors: Cori L Ofstead; Brandy L Buro; Krystina M Hopkins; John E Eiland; Harry P Wetzler; David R Lichtenstein Journal: Endosc Int Open Date: 2020-11-17
Authors: Monika J Piotrowska; Konrad Sakowski; André Karch; Hannan Tahir; Johannes Horn; Mirjam E Kretzschmar; Rafael T Mikolajczyk Journal: PLoS Comput Biol Date: 2020-11-30 Impact factor: 4.475
Authors: Bruce Y Lee; Sarah M Bartsch; Michael Y Lin; Lindsey Asti; Joel Welling; Leslie E Mueller; Jim Leonard; Shawn T Brown; Kruti Doshi; Sarah K Kemble; Elizabeth A Mitgang; Robert A Weinstein; William E Trick; Mary K Hayden Journal: Am J Epidemiol Date: 2021-02-01 Impact factor: 4.897