Automated Detection of Infectious Disease Outbreaks in Hospitals: A Retrospective Cohort Study
Background:
Detection of outbreaks of hospital-acquired infections is often based on simple rules, such as the occurrence of three new cases of a single pathogen in two weeks on the same ward. These rules typically focus on only a few pathogens, and they do not account for the pathogens' underlying prevalence, the normal random variation in rates, and clusters that may occur beyond a single ward, such as those associated with specialty services. Ideally, outbreak detection programs should evaluate many pathogens, using a wide array of data sources.
Methods and Findings:
We applied a space-time permutation scan statistic to microbiology data from patients admitted to a 750-bed academic medical center in 2002–2006, using WHONET-SaTScan laboratory information software from the World Health Organization (WHO) Collaborating Centre for Surveillance of Antimicrobial Resistance. We evaluated patients' first isolates for each potential pathogenic species. In order to evaluate hospital-associated infections, only pathogens first isolated >2 d after admission were included. Clusters were sought daily across the entire hospital, as well as in hospital wards, specialty services, and using similar antimicrobial susceptibility profiles. We assessed clusters that had a likelihood of occurring by chance less than once per year. For methicillin-resistant Staphylococcus aureus (MRSA) or vancomycin-resistant enterococci (VRE), WHONET-SaTScan–generated clusters were compared to those previously identified by the Infection Control program, which were based on a rule-based criterion of three occurrences in two weeks in the same ward. Two hospital epidemiologists independently classified each cluster's importance. From 2002 to 2006, WHONET-SaTScan found 59 clusters involving 2–27 patients (median 4). Clusters were identified by antimicrobial resistance profile (41%), wards (29%), service (13%), and hospital-wide assessments (17%). WHONET-SaTScan rapidly detected the two previously known gram-negative pathogen clusters. Compared to rule-based thresholds, WHONET-SaTScan considered only one of 73 previously designated MRSA clusters and 0 of 87 VRE clusters as episodes statistically unlikely to have occurred by chance. WHONET-SaTScan identified six MRSA and four VRE clusters that were previously unknown. Epidemiologists considered more than 95% of the 59 detected clusters to merit consideration, with 27% warranting active investigation or intervention.
Conclusions:
Automated statistical software identified hospital clusters that had escaped routine detection. It also classified many previously identified clusters as events likely to occur because of normal random fluctuations. This automated method has the potential to provide valuable real-time guidance both by identifying otherwise unrecognized outbreaks and by preventing the unnecessary implementation of resource-intensive infection control measures that interfere with regular patient care.
: Please see later in the article for the Editors' Summary
Vyšlo v časopise:
Automated Detection of Infectious Disease Outbreaks in Hospitals: A Retrospective Cohort Study. PLoS Med 7(2): e32767. doi:10.1371/journal.pmed.1000238
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pmed.1000238
Souhrn
Background:
Detection of outbreaks of hospital-acquired infections is often based on simple rules, such as the occurrence of three new cases of a single pathogen in two weeks on the same ward. These rules typically focus on only a few pathogens, and they do not account for the pathogens' underlying prevalence, the normal random variation in rates, and clusters that may occur beyond a single ward, such as those associated with specialty services. Ideally, outbreak detection programs should evaluate many pathogens, using a wide array of data sources.
Methods and Findings:
We applied a space-time permutation scan statistic to microbiology data from patients admitted to a 750-bed academic medical center in 2002–2006, using WHONET-SaTScan laboratory information software from the World Health Organization (WHO) Collaborating Centre for Surveillance of Antimicrobial Resistance. We evaluated patients' first isolates for each potential pathogenic species. In order to evaluate hospital-associated infections, only pathogens first isolated >2 d after admission were included. Clusters were sought daily across the entire hospital, as well as in hospital wards, specialty services, and using similar antimicrobial susceptibility profiles. We assessed clusters that had a likelihood of occurring by chance less than once per year. For methicillin-resistant Staphylococcus aureus (MRSA) or vancomycin-resistant enterococci (VRE), WHONET-SaTScan–generated clusters were compared to those previously identified by the Infection Control program, which were based on a rule-based criterion of three occurrences in two weeks in the same ward. Two hospital epidemiologists independently classified each cluster's importance. From 2002 to 2006, WHONET-SaTScan found 59 clusters involving 2–27 patients (median 4). Clusters were identified by antimicrobial resistance profile (41%), wards (29%), service (13%), and hospital-wide assessments (17%). WHONET-SaTScan rapidly detected the two previously known gram-negative pathogen clusters. Compared to rule-based thresholds, WHONET-SaTScan considered only one of 73 previously designated MRSA clusters and 0 of 87 VRE clusters as episodes statistically unlikely to have occurred by chance. WHONET-SaTScan identified six MRSA and four VRE clusters that were previously unknown. Epidemiologists considered more than 95% of the 59 detected clusters to merit consideration, with 27% warranting active investigation or intervention.
Conclusions:
Automated statistical software identified hospital clusters that had escaped routine detection. It also classified many previously identified clusters as events likely to occur because of normal random fluctuations. This automated method has the potential to provide valuable real-time guidance both by identifying otherwise unrecognized outbreaks and by preventing the unnecessary implementation of resource-intensive infection control measures that interfere with regular patient care.
: Please see later in the article for the Editors' Summary
Zdroje
1. ZazaS
JarvisWR
1996 Hospital epidemiology and infection control.
MayhallCG
Baltimore Williams & Wilkins
2. HaleyRW
TenneyJH
LindseyJO
GarnerJS
BennettJV
1985 How frequent are clusters of nosocomial infection in community hospitals? Infect Control 6 233 236
3. WenzelRP
ThompsonRL
LandrySM
RussellBS
MillerPJ
1983 Hospital acquired infections in intensive care unit patients: an overview with emphasis on epidemics. Infect Control 4 371 375
4. GastmeierP
Stamm-BalderjahnS
HansenS
ZuschneidI
SohrD
2006 Where should one search when confronted with clusters of nosocomial infection? Am J Infect Control 34 603 605
5. MellmannA
FriedrichAW
RosenkotterN
RothgangerJ
KarchH
2006 Automated DNA sequence-based early warning system for the detection of methicillin-resistant Staphylococcus aureus outbreaks. PLoS Medicine 3 e33 doi:10.1371/journal.pmed.0030033
6. Clinical and Laboratory Standards Institute 2007 Performance standards for antimicrobial susceptibility testing; seventeenth informational supplement. M100-S17 Wayne, Pennsylvania CLSI
7. WHONET Software 2009 from the World Health Organization (WHO) Collaborating Centre for Surveillance of Antimicrobial Resistance. Available: http://www.who.int/drugresistance/whonetsoftware. Accessed 30 June 2009
8. SatScan 2005 SatScan. Available: http://www.satscan.org/. Accessed 16 July 2009
9. KulldorffM
1997 A spatial scan statistic. Commun Stat Theory Methods 26 1481 1496
10. KulldorffM
AthasW
FeuerE
MillerB
KeyC
1998 Evaluating cluster alarms: a space-time scan statistic and brain cancer in Los Alamos. Am J Public Health 88 1377 1380
11. KulldorffM
2001 Prospective time-periodic geographical disease surveillance using a scan statistic. J R Stat Soc Ser A 164 61 72
12. KulldorffM
HeffernanR
HartmanJ
AssunçãoR
MostashariF
2005 A space-time per-mutation scan statistic for disease outbreak detection. PLoS Med 2 e59 doi:10.1371/journal.pmed.0020059
13. KleinmanK
LazarusR
PlattR
2004 A generalized linear mixed models approach for detecting incident clusters of disease in small areas, with an application to biological terrorism. Am J Epidemiol 159 217 224
14. KlompasM
YokoeD
2009 Automated surveillance of health care-associated infections. Clin Infect Dis 48 1268 1275
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