Session Type: 1-hour Oral Session
Session Title: 1-hour Oral Session
Authors(s): P. Pinho Da Silva (1), F. Arldt Da Silva (2), C.A. Santos Rodrigues (2), L. Passos Souza (2), E. Martins De Lima (2), M.H. Barcelos Pereira (2), C. Neder Candella (2), M. Zenaide De Oliveira Alves (2), N. Dias Lourenço (2), W. Souza Tassinari (3), C. Barcellos (4), M. Zenaide Ribeiro Gomes (1)
Authors Affiliations(s): (1) Oswaldo Cruz Institute, FIOCRUZ, Brazil, (2) Hospital Federal dos Servidores do Estado, Ministry of Health, Brazil, (3) Univesidade Federal Rural do Rio de Janeiro, Brazil, (4) Institute of Scientific and Technological Communication and Information in Health, FIOCRUZ, Brazil
Third Party Affiliation: Nucleus of Hospital Research study collaborators
Background:
The emergence and spread of antimicrobial resistance (AMR) and infectious agents have challenged hospitals in recent decades. Currently, the transmission of SARS-COV-2 in health services is a major concern. Our aim was to investigate the circulation of target infectious agents by Geographic Information System (GIS) and spatial-temporal statistics to improve surveillance and control of hospital-acquired infection (HAI) and of AMR, using Klebsiella pneumoniae complex as a model.
Methods:Retrospective study carried out in a 450-bed federal, tertiary hospital, located in Rio de Janeiro. We investigated, in the hospital’s microbiology laboratory database, all isolates of K. pneumoniae complex identified by the Vitek-2 system (BioMérieux) from clinical and surveillance samples of hospitalized patients from 2014 to 2016. 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 profile and patients with carbapenem-resistant K. pneumoniae (CRKp) complex were georeferenced by intensive and nonintensive care wards. Space-time permutation probability scan test was used for cluster signals detection.
Results:
Of the total of 759 studied isolates, a significant increase in the resistance profile of K. pneumoniae complex was detected during the studied years. We also identified 2 space-time clusters affecting adult and paediatric patients harbouring CRKp complex on different floors (Figures 1 and 2, A: Pre-cluster period, B: Cluster period, C: Cluster represented in heat map layer in red tones, the higher the colours tone the higher the occurrences), unnoticed by regular AMR surveillance. Patient flow maps (Figure 3) show the epidemiological link between clustered patients and clearly demonstrated that the adult ICU (ward # 100) was involved in both clusters, but in the second cluster, the coronary ICU (ward #351) was the hot spot affecting neighbouring wards.
In-hospital GIS with space-time statistical analysis can be applied in hospital. The GIS methodology was useful for detecting clusters even in different wards and floors. It has the potential to expand and facilitate early detection of hospital outbreaks, and may become a new tool in combating AMR. The surveillance and control of any HAI may benefit from this methodology, including diseases such as COVID-19.
Keyword(s): Geographic information system, Spatial-temporal statistics, Hospital surveillanceSession Type: 1-hour Oral Session
Session Title: 1-hour Oral Session
Authors(s): P. Pinho Da Silva (1), F. Arldt Da Silva (2), C.A. Santos Rodrigues (2), L. Passos Souza (2), E. Martins De Lima (2), M.H. Barcelos Pereira (2), C. Neder Candella (2), M. Zenaide De Oliveira Alves (2), N. Dias Lourenço (2), W. Souza Tassinari (3), C. Barcellos (4), M. Zenaide Ribeiro Gomes (1)
Authors Affiliations(s): (1) Oswaldo Cruz Institute, FIOCRUZ, Brazil, (2) Hospital Federal dos Servidores do Estado, Ministry of Health, Brazil, (3) Univesidade Federal Rural do Rio de Janeiro, Brazil, (4) Institute of Scientific and Technological Communication and Information in Health, FIOCRUZ, Brazil
Third Party Affiliation: Nucleus of Hospital Research study collaborators
Background:
The emergence and spread of antimicrobial resistance (AMR) and infectious agents have challenged hospitals in recent decades. Currently, the transmission of SARS-COV-2 in health services is a major concern. Our aim was to investigate the circulation of target infectious agents by Geographic Information System (GIS) and spatial-temporal statistics to improve surveillance and control of hospital-acquired infection (HAI) and of AMR, using Klebsiella pneumoniae complex as a model.
Methods:Retrospective study carried out in a 450-bed federal, tertiary hospital, located in Rio de Janeiro. We investigated, in the hospital’s microbiology laboratory database, all isolates of K. pneumoniae complex identified by the Vitek-2 system (BioMérieux) from clinical and surveillance samples of hospitalized patients from 2014 to 2016. 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 profile and patients with carbapenem-resistant K. pneumoniae (CRKp) complex were georeferenced by intensive and nonintensive care wards. Space-time permutation probability scan test was used for cluster signals detection.
Results:
Of the total of 759 studied isolates, a significant increase in the resistance profile of K. pneumoniae complex was detected during the studied years. We also identified 2 space-time clusters affecting adult and paediatric patients harbouring CRKp complex on different floors (Figures 1 and 2, A: Pre-cluster period, B: Cluster period, C: Cluster represented in heat map layer in red tones, the higher the colours tone the higher the occurrences), unnoticed by regular AMR surveillance. Patient flow maps (Figure 3) show the epidemiological link between clustered patients and clearly demonstrated that the adult ICU (ward # 100) was involved in both clusters, but in the second cluster, the coronary ICU (ward #351) was the hot spot affecting neighbouring wards.
In-hospital GIS with space-time statistical analysis can be applied in hospital. The GIS methodology was useful for detecting clusters even in different wards and floors. It has the potential to expand and facilitate early detection of hospital outbreaks, and may become a new tool in combating AMR. The surveillance and control of any HAI may benefit from this methodology, including diseases such as COVID-19.
Keyword(s): Geographic information system, Spatial-temporal statistics, Hospital surveillance
Session Type: 1-hour Oral Session
Session Title: 1-hour Oral Session
Authors(s): P. Pinho Da Silva (1), F. Arldt Da Silva (2), C.A. Santos Rodrigues (2), L. Passos Souza (2), E. Martins De Lima (2), M.H. Barcelos Pereira (2), C. Neder Candella (2), M. Zenaide De Oliveira Alves (2), N. Dias Lourenço (2), W. Souza Tassinari (3), C. Barcellos (4), M. Zenaide Ribeiro Gomes (1)
Authors Affiliations(s): (1) Oswaldo Cruz Institute, FIOCRUZ, Brazil, (2) Hospital Federal dos Servidores do Estado, Ministry of Health, Brazil, (3) Univesidade Federal Rural do Rio de Janeiro, Brazil, (4) Institute of Scientific and Technological Communication and Information in Health, FIOCRUZ, Brazil
Third Party Affiliation: Nucleus of Hospital Research study collaborators
Background:
The emergence and spread of antimicrobial resistance (AMR) and infectious agents have challenged hospitals in recent decades. Currently, the transmission of SARS-COV-2 in health services is a major concern. Our aim was to investigate the circulation of target infectious agents by Geographic Information System (GIS) and spatial-temporal statistics to improve surveillance and control of hospital-acquired infection (HAI) and of AMR, using Klebsiella pneumoniae complex as a model.
Methods:Retrospective study carried out in a 450-bed federal, tertiary hospital, located in Rio de Janeiro. We investigated, in the hospital’s microbiology laboratory database, all isolates of K. pneumoniae complex identified by the Vitek-2 system (BioMérieux) from clinical and surveillance samples of hospitalized patients from 2014 to 2016. 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 profile and patients with carbapenem-resistant K. pneumoniae (CRKp) complex were georeferenced by intensive and nonintensive care wards. Space-time permutation probability scan test was used for cluster signals detection.
Results:
Of the total of 759 studied isolates, a significant increase in the resistance profile of K. pneumoniae complex was detected during the studied years. We also identified 2 space-time clusters affecting adult and paediatric patients harbouring CRKp complex on different floors (Figures 1 and 2, A: Pre-cluster period, B: Cluster period, C: Cluster represented in heat map layer in red tones, the higher the colours tone the higher the occurrences), unnoticed by regular AMR surveillance. Patient flow maps (Figure 3) show the epidemiological link between clustered patients and clearly demonstrated that the adult ICU (ward # 100) was involved in both clusters, but in the second cluster, the coronary ICU (ward #351) was the hot spot affecting neighbouring wards.
In-hospital GIS with space-time statistical analysis can be applied in hospital. The GIS methodology was useful for detecting clusters even in different wards and floors. It has the potential to expand and facilitate early detection of hospital outbreaks, and may become a new tool in combating AMR. The surveillance and control of any HAI may benefit from this methodology, including diseases such as COVID-19.
Keyword(s): Geographic information system, Spatial-temporal statistics, Hospital surveillanceSession Type: 1-hour Oral Session
Session Title: 1-hour Oral Session
Authors(s): P. Pinho Da Silva (1), F. Arldt Da Silva (2), C.A. Santos Rodrigues (2), L. Passos Souza (2), E. Martins De Lima (2), M.H. Barcelos Pereira (2), C. Neder Candella (2), M. Zenaide De Oliveira Alves (2), N. Dias Lourenço (2), W. Souza Tassinari (3), C. Barcellos (4), M. Zenaide Ribeiro Gomes (1)
Authors Affiliations(s): (1) Oswaldo Cruz Institute, FIOCRUZ, Brazil, (2) Hospital Federal dos Servidores do Estado, Ministry of Health, Brazil, (3) Univesidade Federal Rural do Rio de Janeiro, Brazil, (4) Institute of Scientific and Technological Communication and Information in Health, FIOCRUZ, Brazil
Third Party Affiliation: Nucleus of Hospital Research study collaborators
Background:
The emergence and spread of antimicrobial resistance (AMR) and infectious agents have challenged hospitals in recent decades. Currently, the transmission of SARS-COV-2 in health services is a major concern. Our aim was to investigate the circulation of target infectious agents by Geographic Information System (GIS) and spatial-temporal statistics to improve surveillance and control of hospital-acquired infection (HAI) and of AMR, using Klebsiella pneumoniae complex as a model.
Methods:Retrospective study carried out in a 450-bed federal, tertiary hospital, located in Rio de Janeiro. We investigated, in the hospital’s microbiology laboratory database, all isolates of K. pneumoniae complex identified by the Vitek-2 system (BioMérieux) from clinical and surveillance samples of hospitalized patients from 2014 to 2016. 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 profile and patients with carbapenem-resistant K. pneumoniae (CRKp) complex were georeferenced by intensive and nonintensive care wards. Space-time permutation probability scan test was used for cluster signals detection.
Results:
Of the total of 759 studied isolates, a significant increase in the resistance profile of K. pneumoniae complex was detected during the studied years. We also identified 2 space-time clusters affecting adult and paediatric patients harbouring CRKp complex on different floors (Figures 1 and 2, A: Pre-cluster period, B: Cluster period, C: Cluster represented in heat map layer in red tones, the higher the colours tone the higher the occurrences), unnoticed by regular AMR surveillance. Patient flow maps (Figure 3) show the epidemiological link between clustered patients and clearly demonstrated that the adult ICU (ward # 100) was involved in both clusters, but in the second cluster, the coronary ICU (ward #351) was the hot spot affecting neighbouring wards.
In-hospital GIS with space-time statistical analysis can be applied in hospital. The GIS methodology was useful for detecting clusters even in different wards and floors. It has the potential to expand and facilitate early detection of hospital outbreaks, and may become a new tool in combating AMR. The surveillance and control of any HAI may benefit from this methodology, including diseases such as COVID-19.
Keyword(s): Geographic information system, Spatial-temporal statistics, Hospital surveillance