The Use of Molecular Epidemiology and Statistical Software to Detect Transmission of Pathogens in the Healthcare Setting Michael Phillips, MD APIC Greater NYC Annual Conference
Disclosures None APIC NYC 2017
Review of the current state of pathogen surveillance Learning objectives Review of the current state of pathogen surveillance How can time-space analysis help? Want is “whole genome sequencing” and “molecular epidemiology” Understand how molecular epidemiology has been used What is the future? What does this mean for the infection preventionist? APIC NYC 2017
The epidemiologic triangle HOST ENVIRONMENT PATHOGEN APIC NYC 2017
Pathogen transmission Contact Water Air APIC NYC 2017
Pathogen transmission Contact Water Air Methicillin-resistant Staph aureus "MRSA" Enterococcus Klebsiella, Enterobacter, E. coli Serratia Acinetobacter ?? Pseudomonas Listeria Clostridium difficile "C diff" Legionella Aspergillus Respiratory virus Tuberculosis Measles APIC NYC 2017
Survival of organisms with contact transmission Bacteria Survival time Clostridium difficile 5 months MRSA 1 to 4 weeks Enterococcus 1 weeks to months Magill, SS. et alN. Engl. J. Med.2014, 370 (13), 1198–1208; Kramer, A.; Schwebke, I.; Kampf, G. BMC Infect. Dis.2006, 6, 130. APIC NYC 2017
Current state APIC NYC 2017
Current state Good Hospital? Bad Hospital? APIC NYC 2017
Geography matters APIC NYC 2017
John Snow – 1854 Cholera outbreak APIC NYC 2017
John Snow – 1854 Cholera outbreak APIC NYC 2017
WHONET and SaTScan WHONET: Developed for the WHO in 1989 Free Management of microbiology data Works with BacLink, a program to convert data into WHONET format SaTScan: Free, developed in 1997 Takes input from WHONET Identifies spatial, space-time disease clusters Uses a Poisson-based model Givens a measure of statistical significance
Prospective surveillance in the community 3 year surveillance period 32 clustered detected, 26 prior to identification by hospital 22 of 26 clusters were “closely related” by PFGE
Retrospective surveillance in the hospital 750 bed hospital 5 year period Compared clusters identified by “rules” vs SaTScan ≥ 3 HA MRSA or VRE on a unit with 2 week period
MRSA and VRE clusters detected by rules based surveillance vs SaTScan. Yes No SaTScan 1 6 7 66 73 VRE Rules based Yes No SaTScan 4 87 APIC NYC 2017
Potential clusters detected by SaTScan Mean cluster size: 6 Median cluster size: 4 21 Gram positive 31 Gram negative 7 Fungi
What about prospective use of SaTScan in Hospitals? APIC NYC 2017
What about prospective use of WHONET SaTScan in Hospitals? APIC NYC 2017
Classification of clusters: high vs low risk Same unit Shared room Same HCW Specimens obtained >72 hours after admission Environmental pathogens Same antibiotic susceptibilities APIC NYC 2017
Classification of clusters: high vs low risk Same unit Shared room Same HCW Specimens obtained >72 hours after admission Environmental pathogens Same antibiotic susceptibilities High risk = 3 or more APIC NYC 2017
High vs low risk clusters, Jan-Sept 2016 (n-168) Characteristic High risk, transmission + High risk, transmission - Low risk # clusters 6 39 123 Median # pts/cluster (range) 2 (2-8) 2 (1-11) Shared room 4 16 27 Shared unit 31 75 Shared HCW 22 44 Culture >72hrs 5 32 72 Same susceptibility 2 25 26 Environmental 8 APIC NYC 2017
Organisms by cluster type, Jan-Sept 2016 (n-168) APIC NYC 2017
SaTScan: surveillance system evaluation Simplicity Flexibility Data quality and acceptability Sensitivity and PPV Timeliness and representativeness Usefulness APIC NYC 2017
Pathogen transmission – food APIC NYC 2017
Pathogen transmission – food BASF Innovent 2017
Pathogen transmission – food APIC NYC 2017
Goal is to identify clusters APIC NYC 2017
Routine typing methods won’t work Clonal Complex 5 (CC5) Clonal Complex 8 (CC8) APIC NYC 2017
DNA review Khan Academy APIC NYC 2017
The Whole Genome Sequencing Process Source: CDC APIC NYC 2017
Sequencing cost curve “next-generation” DNA sequencing Wetterstrand KA. DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP) Available at: www.genome.gov/sequencingcostsdata. Accessed Oct 2017. APIC NYC 2017
How do we know strains are related? GTAC Biotech APIC NYC 2017
Example: MRSA outbreak in the NICU Harris et al, Lancet Infect Dis. 2013 Feb; 13(2):130-136 APIC NYC 2017
Example: WGS allows true measure of hospital transmission Eyre et al. Clin Infect Dis. 2017;65(3):433-41 APIC NYC 2017
Example: Geography and referral patterns matter Williamsburg – cluster 2 Williamsburg – cluster 1
Multidrug resistant MRSA Chlorhexidine resistance Mupirocin resistance APIC NYC 2017
USA 300 SNP Tree Others Others
Other molecular methods: MALDI-TOF Veenemans Eur J Clin Microbiol Infect Dis 2016; 35:829-838 APIC NYC 2017
Linking “molecular” to “epi” Why the NICU? The patients don’t move and LOS is weeks Requires advanced analytics Prospective surveillance Clear and careful messaging Will become a part of our “toolkit” APIC NYC 2017
A few closing thoughts Use of cluster detection methods and “molecular epi” will become routine Sooner than you think! APIC NYC 2017
A few closing thoughts Use of cluster detection methods and “molecular epi” will become routine Sooner than you think! Infection Prevention and Control needs to “own” this space Only IPC can link data and epi to identify potential transmission routes APIC NYC 2017
A few closing thoughts Use of cluster detection methods and “molecular epi” will become routine Sooner than you think! Infection Prevention and Control needs to “own” this space Only IPC can link data and epi to identify potential transmission routes New era of public reporting A new paradigm for IPC and DOH APIC NYC 2017
A few closing thoughts Use of cluster detection methods and “molecular epi” will become routine Sooner than you think! Infection Prevention and Control needs to “own” this space Only IPC can link data and epi to identify potential transmission routes New era of public reporting A new paradigm for IPC and DOH Allow predictive analytics Infection prevention, not just control APIC NYC 2017
Time Period: FY 2016 / Note: Includes Lutheran data