1. Geographical information system and spatial–temporal statistics for monitoring infectious agents in hospital: a model using Klebsiella pneumoniae complex.
- Author
-
da Silva, Priscila Pinho, da Silva, Fabiola A., Rodrigues, Caio Augusto Santos, Souza, Leonardo Passos, de Lima, Elisangela Martins, Pereira, Maria Helena B., Candella, Claudio Neder, de Oliveira Alves, Marcio Zenaide, Lourenço, Newton D., Tassinari, Wagner S., Barcellos, Christovam, Gomes, Marisa Zenaide Ribeiro, on behalf of Nucleus of Hospital Research Study Collaborators, Dutra, Vitoria Pinson Ruggi, da Silva, Maxuel Cassiano, Tonhá, João Pedro Silva, de Mello, Luciana Sênos, Castro, Murillo Marçal, Mathuiy, Yann Rodrigues, and da Silva Machado, Amanda Aparecida
- Subjects
KLEBSIELLA pneumoniae ,GEOGRAPHIC information systems ,HOSPITAL utilization ,CHILD patients ,NOSOCOMIAL infections ,DRUG resistance in microorganisms ,HOSPITAL statistics - Abstract
Background: The emergence and spread of antimicrobial resistance and infectious agents have challenged hospitals in recent decades. Our aim was to investigate the circulation of target infectious agents using Geographic Information System (GIS) and spatial–temporal statistics to improve surveillance and control of healthcare-associated infection and of antimicrobial resistance (AMR), using Klebsiella pneumoniae complex as a model. Methods: A retrospective study carried out in a 450-bed federal, tertiary hospital, located in Rio de Janeiro. All isolates of K. pneumoniae complex from clinical and surveillance cultures of hospitalized patients between 2014 and 2016, identified by the use of Vitek-2 system (BioMérieux), were extracted from the hospital's microbiology laboratory database. A basic scaled map of the hospital's physical structure was created in AutoCAD and converted to QGis software (version 2.18). Thereafter, bacteria according to resistance profiles and patients with carbapenem-resistant K. pneumoniae (CRKp) complex were georeferenced by intensive and nonintensive care wards. Space–time permutation probability scan tests were used for cluster signals detection. Results: Of the total 759 studied isolates, a significant increase in the resistance profile of K. pneumoniae complex was detected during the studied years. We also identified two space–time clusters affecting adult and paediatric patients harbouring CRKp complex on different floors, unnoticed by regular antimicrobial resistance surveillance. Conclusions: In-hospital GIS with space–time statistical analysis can be applied in hospitals. This spatial methodology has the potential to expand and facilitate early detection of hospital outbreaks and may become a new tool in combating AMR or hospital-acquired infection. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF