PulseNet USA:

1. PulseNet USA: The National Molecular Subtyping Network for Foodborne Disease Surveillance Jennifer A. Kincaid Centers for Disease Control and Prevention May 30, 2008

2. What is PulseNet USA? „Establishedin1996,TheNationalMolecular Subtyping Network for Foodborne Disease Surveillance „Nationalnetworkofstateandlocalpublic health/food regulatory agency laboratories (USDA, FDA) coordinated by CDC and APHL „Performstandardizedmoleculartypingof foodborne disease-causing bacteria by Pulsed-field gel electrophoresis (PFGE) „DynamicdatabasesofDNA “fingerprints”at CDC—available on-demand to participants

3. The National Molecular Subtyping Network for Foodborne Disease Surveillance USDA-APHIS New York Ag. Lab NEW HAMPSHIRE Milwaukee MAINE FDA-ORA VERMONT MI State WASHINGTON MINNESOTA MONTANA NORTH DAKOTA Vet Lab MASSACHUSETTS Philadelphia Ohio Ag. Lab OREGON WISCONSIN RHODE ISLAND NEW YORK CONNECTICUT IDAHO SOUTH DAKOTA FDA-ORA WYOMING New York City IOWA NEW JERSEY OHIO FDA-CVM NEBRASKA NEVADA DELAWARE ILLINOIS FDA-CFSAN UTAH FDA-ORA MARYLAND Washington D.C. FDA-ORA COLORADO VIRGINIA Las KANSAS Vegas KENTUCKY Santa Clara CALIFORNIA MISSOURI County USDA-AMS TENNESSEE SOUTH ARKANSAS OKLAHOMA FDA-ORA ARIZONA CAROLINA NEW MEXICO Los Angeles County USDA- FDA-ORA ARS/FSIS/FERN ALABAMA Orange County GEORGIA FDA-ORA Area Laboratories San Diego TEXAS LOUISIANA County PulseNet Headquarters Florida Ag. Lab ALASKA State/County/City/ Veterinary/ Agricultural Tarrant County Dallas County Laboratories Tampa Houston USDA Laboratories PUERTO RICO HAWAII FDA Laboratories West Mountain South Central North Central Midwest Mid-Atlantic Southeast Northeast

4. PulseNet Objectives „To detectfoodbornediseasescaseclustersthatmay be widespread outbreaks „Assistepidemiologistsininvestigatingoutbreaks „Separateoutbreak-associatedcasesfromothersporadic cases (case definition) „Assistinrapidlyidentifyingthesourceofoutbreaks (culture confirmation) „Actasarapidandeffectivemeansof communication between public health laboratories (rapid alert system) „Attributionanalyses

5. PulseNet: Communication and QA/QC „On-linedatabases „Standardizedprotocols „CDCTeampostings and molecular size standards „Clusterdetection „Outbreakinvestigations „QA/QCManual „Technicalsupport „Standardizedsoftware „Quarterly/AnnualReports and nomenclature from CDC „Trainingworkshops(lab „“PulseNetNews”Newsletter & software) „Tri-annualpublication „Certificationand „PulseNetWebsite (www.cdc.gov/pulsenet) proficiency testing „Annualupdatemeetings

6. The Three Basic Elements of PulseNet 2.Dataanalysis 1.Dataacquisition 3.Dataexchange

7. Pulsed Field Gel Electrophoresis „ PFGE highly discriminatory „ Universal, relatively simple technique that can be used in most laboratories „ Definitive subtyping method, if highly standardized „ Current “Gold Standard” for molecular subtyping

8. PFGE Process Bacterial Suspension Mix with Agarose Plug Mold Chemical Lysis and Washing DNA in Plugs Restriction Enzymes Electrophoresis (PFGE) Documentation (capture gel image) Data Analysis (BioNumerics)

9. PFGE Patterns of E. coli O157:H7 Fragment * * * Sizes (in kilobases) 1135 Kb 452.7 Kb 216.9 Kb 76.8 Kb 33.3 Kb DNA “fingerprints” *Global Reference Standard

10. PulseNet Laboratory Network PulseNet National PFGE Patterns & Participating Labs Databases (CDC) Demographic Data TAT from receipt to upload: ~4 working days D base Management Local Reports Databases

11. Local vs. National Databases Local National „Smaller,resultsfromafew „Larger,resultsfrommany people different labs „Smallerrepresentationof „Largerrepresentationof patterns patterns „Tendstobelessdiverse „Morediverse „Sometimesdifferent „Different “common”patterns “common” patterns than seen than seen locally nationally or in other regions „Seerepresentationfromall „Canonlyseelocal over, different regions representation, no national perspective

12. PulseNet Activity* *as of May 15, 2008 Over 325,000 PFGE patterns or DNA “fingerprints” submitted to PulseNet databases since 1996 Database Entries Patterns submitted Submitted 1st Enzyme 2nd Enzyme Campylobacter 5,453 5,400 1,834 E. coli 31,634 30,262 16,674 Listeria 9,226 9,007 8,002 Salmonella 190,298 188,241 28,998 Shigella 32,922 32,222 2,156 V. cholerae 303 282 272 V. parahaemolyticus 37 37 37 Y. pestis 1,976 1,975 33

13. PulseNet Activity, 1996-2007 PFGE patterns submitted to PulseNet Databases 70000 60000 50000 40000 30000 20000 10000 0 1996 1997 1998 1999 2000 2001* 2002 2003 2004 2005 2006 2007

14. PulseNet is a cluster detection tool, not an outbreak detection system „A PulseNetCLUSTERisagroupof patterns that are found indistinguishable by PFGE „CLUSTERSofcasesidentifiedby PulseNet are investigated by epidemiologists „Ifepidemiologiclinksarefoundbetween cases, the cluster is classified as an OUTBREAK

15. PulseNetDataAnalysis: The Cluster Search •Patterns submitted electronically • 60- or 120-day cluster search performed •Visually compare indistinguishable •Patterns and clusters are named by CDC •Clusters communicated to the foodborne epidemiologists Cluster of indistinguishable patterns

16. Cluster Detection in PulseNet: PulseNet Workspace on CDC Team Subject:0802LACJPX-1c (JPXX01.0088)_LAC_Typhimurium LAC has a cluster of 3 Typhimurium isolates that are indistinguisable by XbaI and BlnI. The collection dates are from 12/10/2007 to 1/10/2008. Epi under investigation. The isolate numbers are: LAC_Z19766 2 year old boy, no travel LAC_Z19999, 43 year old female, no travel LAC_Z20072, 60 year old female, no travel We have seen this pattern before in LAC. Our epi contact is (name and phone) LAC08008PN.BDL Posted: 05 March 2008 09:52 AM

17. Cluster Detection „Local „National „Performclustersearch „Performclustersearch within local database withinnationaldatabase or respond to local CDC „Comparetonational Team posting database „Verifyisolateinformation „PostmessagetoCDC „Namepatterns Team „Checkbandmarkings „Beginepiinvestigation „Assignclustercode „Lookatpattern frequencies/trends

18. A large outbreak in one place may be obvious Detected and investigated locally

19. A dispersed outbreak in many places may be difficult to detect, unless… „ Detect outbreaks centrally (or locally) through surveillance (widely dispersed, organism too common to notice small increase, identify related cases) „ Investigation coordinated centrally „ Distinguish from concurrent sporadic cases „ Provide microbiological evidence of sources of outbreaks

20. Common CDC Epi Requests „Pasttrendsorpastassociationof cluster/outbreak pattern „PatternFrequencygraphs „PulseNetClusterLog „Cluster/Outbreakcodes „Updatedlinelists „Oftendailyforveryactiveorhighprofileoutbreaks „Additionalinformationforrumorlogsand conference calls

21. CDC Epi Requests: Additional Info „Secondenzymeresults „VetNetmatches „USDA-ARSdatabase „Connectionto VetNetnetworkestablishedin2007 „Allclusterpatternsidentifiedby PulseNetareroutinelycheckedagainst VetNet „Internationalmatches „Connectionto PulseNetCanadadatabasesestablishedin2007 „Allclusterpatternsidentifiedby PulseNetareroutinelycheckedagainst PulseNet Canada databases „PulseNetparticipantsabroad(PulseNetLatinAmerica,Europe,Asia Pacific, and Middle East) „Emailalertsto PulseNetInternational

22. Interpreting Patterns within Outbreaks „Forsurveillance(clusterdetection),allowno differences for possible matches „One-banddifferencesaresocommoninlargedatabases that virtually all submissions would be part of a cluster „Foroutbreaks(casedefinition),differencesmaybe acceptable „Maybenecessaryforshigellosisinfections(personto person) „Iftheisolateswithdifferentpatternshaveepidemiologic links, assign different pattern names but include in reports (give same outbreak code)

23. Additional Restriction Enzymes „Addstothediscriminationofallorganisms when using PulseNet protocols „makesclusterdetectionmoreprecise „Canlimittheneedforepifollow-upoflikely unrelated cases „AlthoughitaddstothecostofPulseNet surveillance, running fingerprint profiles of both enzymes on the initial gel is often cost-effective and saves time

24. Additional Restriction Enzymes Example Salmonella Montevideo Indistinguishable 1st enzyme patterns: need 2nd enzyme results to distinguish clusters

25. Examples of PulseNet Involvement in Foodborne Outbreaks

26. E. Coli O157:H7 Outbreak in Colorado, July 2002 „Outbreakpatternwasrare „Closelyrelatedtoaverycommonpattern „34casesin11states „Outbreakstrainfoundingroundbeeffrommeat processing plant by USDA/FSIS „Outbreakstoppedafterrecallof18.4million pounds of ground beef products from a plant

27. 1993 Western States E. coli O157 Outbreak 70 60 outbreak detected 1993 726 ill, 4 deaths 50 40 30 39 d 20 10 0 1 8 15 22 29 36 43 50 57 64 71 Day of Outbreak 2002 Colorado E. coli O157 Outbreak 70 60 50 outbreak detected 2002 44 ill, no deaths 40 30 20 18 d 10 0 1 8 15 22 29 36 43 50 57 64 71 Day of Outbreak

28. S. Tennessee outbreak numbers (April 11, 2007) „567cases „47statesinvolved „Nointernationalcases „Medianage51years(<1-95years) „73%females „Nodeaths,20%hospitalized „Amongfemales:1/3UTI

29. Salmonella Tennessee Cases by State, (N=563) 1-9 cases 10-19 cases 20+ cases *Slide courtesy of EIS officer Anandi Sheth

30. Peanut Butter Samples „ 34 positive samples of peanut butter „ Production dates from contaminated jars: 7/27/06 - 1/29/07 „ 2 of the 3 outbreak patterns were found in the peanut butter

31. Notable Outbreaks „SalmonellaWandsworthand Typhimurium: Veggie Booty „SalmonellaHeidelberg:Hummus „SalmonellaI4,5,12:i:-:Potpies „SalmonellaEnteritidis:ChickenCordonBleu „E.coliO157:groundbeef „E.coliO157:frozenpepperonipizza „Listeriamonocytogenes:milk

32. Examples of Other Database Uses

33. Database Uses: Pattern Frequencies Long-term surveillance to aide in active surveillance—is this a true increase?

34. Database Uses: Pattern Trends Long-term surveillance to see if a pattern is still seen as commonly in the database

35. Database Uses: Attribution Analysis Long-term surveillance to see if certain patterns may be associated with specific foods Legend CDS 1 CDS 2 CDS 3 CDS 4 CDS 5

36. PulseNet S. Newport Attribution Analysis Figure 13: Proportional Distribution of Illnesses by food commodities using three attribution analysis methods 30.0% 25.0% 20.0% Method 1 15.0% Method 2 Method 3 10.0% 5.0% 0.0% Food Commodities Method 1: Considering three major clusters Method 2: Considering major clusters and sub-clusters Method 3: Considering sub-clusters with no non-human isolates of unknown

37. PulseNet International… A Family of Networks

38. Why are we going international with PulseNet? „Weliveinaglobalcommunity „Foodsproducedononecontinentmaybe consumed and cause disease in another - a global issue „Needaneffectiveglobalearlywarningsystem „Globalnetworkingtoutilizescarcepublichealth resources effectively „Respondtootheremerginginfectiousdiseasesor acts of bioterrorism

39. PulseNet International Collaborations „Outbreakinvestigations „LaboratoryandAnalysisTroubleshooting „Protocoldevelopmentandvalidation „Vibriocholerae „Vibrioparahaemolyticus „Development,EvaluationandValidationof Next Generation Typing Methods „Canada,China,HongKong,JapanandU.K.MLVA subtypingprotocolfor E.coliO157

40. PulseNet Challenges and Future Improvements „Enhancesupportforoutbreakinvestigations „Newfastermethods „Reducethetimeittakesforisolatestogofrom clinical lab to the state/local public health lab „ReducethetimeforPFGEtestingofisolates „Expectation:~4daysfromreceiptofisolatetouploadto the national database(s) „Achievereal-timedetectionofclusters „Improvecommunicationbetweenlaboratoriansand epidemiologists at the state and national levels

41. PulseNet Challenges and Future Improvements „Strengthencollaborationswithfoodindustry „VetNet(USDA-ARS) „Currentlycollecting Salmonellaand CampylobacterNARMS isolates „Attributionanalysis „Protocols „Vibrioparahaemolyticus „Protocolhasbeendevelopedanddatabasescriptswere included in the latest version of the MasterScripts (March 2007) „Thenumberofsubmissionsto PulseNet continues to rise…..

42. New Subtyping Methods „PFGEdataiscomplex „Needforsimple,non-imagebasedas discriminatory supplementary methods „Sequencingbasedmethods „Multi Locus Variablenumberoftandemrepeats Analysis(MLVA) „SNPanalysis

43. MLVA Analysis „Sequence-basedsubtyping „CanfurtherdiscriminatecommonPFGE patterns through highly variable target sequences „Datamaybeepidemiologicallymorerelevant than PFGE data „Resultsmorestraightforward „Don’thavethesameinterpretationissuesaswith PFGE band marking

44. Thank you for your attention The findings and conclusions in this presentation are those of the author and do not necessarily represent the views of the Centers for Disease Control and Prevention.