HealthcareAssociated Infection

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Healthcare-Associated Infection Influenza
Surveillance System
Gina Oda, MS, CIC Associate Director, Office of
Public Health Surveillance and Research
Michaele Mahoney OIT Project Manager, Albany OIFO
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Course Outline

• Background
• Goals and development of HAIISS
• Functionality and validation
• National deployment
• HAIISS in action examples

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Impact of HAI

• According to CDC, 1.7 million people per year
  acquire HAI in US hospitals
• 99,000 deaths
• HAIs cost on average 25,000 per infection
• More than 70 of bacteria causing HAI are
  resistant to at least one commonly used
  antibiotic

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HAI Types and Proportions
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Seasonal Influenza

• Each year in the U.S. 200,000 people are
  hospitalized, and 36,000 die from the flu
• This is in a good year
• Prevention Vaccination

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Pandemic Influenza

• Occurs 2-3 times per century
• Previous 3 pandemics
• 1968-69
• 1957-58
• 1918-19
• No way to predict when it will happen again, nor
  how severe
• No vaccine available

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1918 Influenza Pandemic

• 1/3 of the worlds population infected
• 25,000,000 died worldwide
• More died than in WWI, WWII, Korea Vietnam Wars
• 195,000 Americans died in October 1918 the
  deadliest month in US history

www.pandemicflu.gov www.cdc.gov/ncidod/EID/vol12n
o01/05-0979.htm
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Pandemic Flu

• Occurs due to a major change in the genetic
  makeup of the influenza virus
• Antigenic shift vs. drift
• Pandemic is possible when 3 conditions met
• New genetic variant introduced into human
  population
• Causes serious illness in humans
• Spreads easily from person to person

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Recent Pandemic Concerns

• H5N1 Avian Influenza meets the first 2 conditions
• Now endemic in domestic poultry in some parts of
  the world
• Little or no immunity to H5N1 strain among human
  population

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Benefit of Surveillance

• Landmark Study on the Efficacy of Nosocomial
  Infection Control (SENIC Project, 1970s-80s)
  demonstrated components of effective infection
  control program
• Surveillance
• Trained professionals (ICPs)
• Reporting mechanism

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Benefit of Surveillance

• Antimicrobial usage monitoring
• Reduce cost
• Improve care
• Decrease resistance

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Bioterrorism and Naturally Emerging Pathogens

• Early detection and situational awareness
  Syndromic Surveillance
• Naturally-occurring West Nile Virus, SARS,
  Salmonella in peanut butter
• Bioterrorism Anthrax post-9/11, Salmonella in
  salad bars in Oregon

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Evolution of Surveillance Systems in US

• National Nosocomial Infections Surveillance
  (NNIS) System
• The first national, aggregated HAI surveillance
  system
• Established in 1970
• 300 hospitals
• National benchmark
• Manual case finding, data entry
• Strict criteria to ensure standardization
• Voluntary/anonymous participation

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National Healthcare Safety Network (NHSN)

• Next generation of NNIS
• Secure, HIPPAA compliant, web-based reporting
• 950 facilities
• Includes facilities reporting under state-mandate
• Includes facilities only reporting one type of
  infection

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HAI Surveillance in VA Today

• Only 13 VA hospitals participate in NHSN
• Comprehensive, aggregate data for all HAI is not
  available
• Opportunities exist to
• Provide accurate data for frontline staff
• Identify best practices for infection prevention
• Monitor results of control strategies implemented
  across VA
• Establish VA as a major source of cutting edge
  research in HAI prevention

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HAIs in VA NHSN Data

• Retrospective review of VA hospital infection
  data
• Ventilator associated pneumonia rates
• 13 VA vs. 300 non-VA medical centers

Oda G, et al., ICAAC, Chicago, IL 9/07 (Abstract
K-1066)
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HAIs in VA NHSN Data

• Surgical Site Infection, Coronary Artery Bypass
  Grafts (CABG) 1992-2004

Oda G, et al., ICAAC, Chicago, IL 9/07 (Abstract
K-1066)
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National Influenza Surveillance
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VA/DoD Influenza-Like Illness Data
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Mandatory Reporting of HAIs

• As of March 2008, 24 states have mandated public
  reporting of HAI
• VA hospitals exempt (VHA Directive 2006-016)
• VHA MRSA Prevention Initiative
• Mandatory surveillance and reporting within VHA
• Mandated prevention/control measures

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Problems with Current Surveillance Systems

• NHSN
• Time consuming
• Case finding
• Application of criteria
• Data entry
• Limited to small subset of infections
• Criteria are subjective/not compatible with
  electronic systems
• Not adequately validated

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Problems with Current Surveillance Systems

• National Flu Surveillance
• Only data available is aggregated
• Sentinel provider system representative of small
  fraction of US providers
• Not a lot of lead time in predicting/identifying
  outbreaks
• Mandated systems
• Questionable data integrity

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Ideal Surveillance System

• Local data as well as aggregate
• Standardized
• Comprehensive
• Valid
• Automated

Electronic
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Office of Public Health Surveillance and Research

• Established in 2006, within Office of Public
  Health and Environmental Hazards
• HAIISS project goals established
• Key partnerships established CDC, Johns Hopkins
  University (ESSENCE)

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HAIISS Program Goals

• VA should be the Nations leader in
  healthcare-associated infection control and
  quality improvement
• Jonathan Perlin, MD, former VHA Undersecretary
  for Health
• Leverage VHAs advanced electronic medical record
  and national, integrated healthcare system to
  establish a comprehensive electronic surveillance
  system to monitor
• Healthcare-associated infections and resistance
  trends
• Influenza and other emerging infectious diseases
  or syndromes associated with bioterrorist activity

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4 Surveillance Categories/Pillars

• Category 1
• CDCs National Healthcare Safety Network (NHSN)
  infection surveillance
• Category 2
• Syndromic surveillance
• Category 3
• Organisms/infections of epidemiologic
  significance
• Category 4
• Antimicrobial usage and decision support

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HAIISS Build it or Buy it?

• BOTH Work with HAI software vendor to modify
  COTS product to meet VA needs utilize
  ESSENCE/Biosense, already established in VA
• Request for proposals April 2006
• Multiphase review process whittled vendor
  selection down to 3
• Feasibility studies/technical solutions
  submittedNov/Dec 2006

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Contract Award

• OPHSR and OIT evaluation team convened
• Onsite review of 3 vendors products
• Contract awarded to Vecna Technologies, February
  2007
• Prototype development begunrequirements for HAI
  detection algorithms

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HAIISS Prototype Development

• Prototype released at Palo Alto alpha test site
  December 2007
• Ongoing development and validation testing of
  software
• Ongoing work with OIT
• HL7 messaging for key data elements
  microbiology, pharmacy, ADT
• Enterprise architecture for deployment to
  multiple sites

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Determination of HAIISS Architecture

• Reliable, fast, accessible is a MUST
• Where will enterprise hardware/software reside?
• What will be the role of HDR?

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Next Steps Validation

• Testing of application functionality
• User interface
• System bugs
• Does the system do what its supposed to?
• Validity of data
• Compare to VistA data extractions
• Completeness and accuracy
• Investigate discrepancies
• Compare to gold standard manual surveillance

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Next Steps Deployment

• Completion of HL7 Messaging and Architecture
• Step-wise deployment, beginning Fall 2008
• 4 Pilot Sites
• Continue with completion of installation in all
  12 VA NHSN sites
• Familiar with surveillance methodology
• Strong stakeholder interest
• Enhancement of CDC partnership
• National deployment153 VA hospitals

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HAIISS in Action Examples
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Dashboard Infection Alerts
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Dashboard Saved Reports
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Create a Report
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Create a Report
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Category 1 NHSN Infections
Medical Device-Associated Infections

• Central line-associated bloodstream infection
  (CLABSI)
• Catheter-associated urinary tract infection
  (CAUTI)
• Ventilator-associated pneumonia (VAP)
• Surgical site infection (SSI)

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Category 1 NHSN Infections

• Complex decision process
• Detection algorithms must apply established NHSN
  infection definitions in a consistent manner
• Microbiology data
• Admission data
• Clinical factors

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Bloodstream Infection Algorithm
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BSI Surveillance Example
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BSI Surveillance Example
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BSI Surveillance Example
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BSI Surveillance Example
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HAI Detection Algorithm Validity

• Evaluate cases detected by Pathfinder
• Do alerts trigger when they should?
• False positives
• False negatives
• Does data entered into Dynamic Case Form trigger
  the appropriate decision by algorithm?
• Utilize historic data extraction to allow
  evaluation of large number of samples
• Future validation studies multi-center studies
  in collaboration with CDC EpiCenter Program

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BSI Algorithm Validation

• Preliminary study
• 118 positive blood cultures reviewed
• Compared to routine manual surveillance by ICPs,
  the HAIISS electronic algorithm compared very
  well
• 90 sensitivity
• 91 specificity
• 84 positive predictive value
• 95 negative predictive value

Oda G, et al. Abstract submitted for ICAAC/IDSA
conference, October 2008
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Category 2 Syndromic Surveillance

• 7 Syndrome Groups
• Respiratory cough, pneumonia, upper-respiratory
  infection, Influenza-Like Illness (ILI)
• Gastrointestinal vomiting, diarrhea
• Neurological meningitis, botulism-like
• Dermatologic 1 hemorrhagic
• Dermatologic 2 vesicular (smallpox-like)
• Fever, Malaise, Sepsis
• Coma/Sudden Death

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Category 2 Syndromic Surveillance

• Outpatient-based
• Emphasis on community-based outbreaks/events
• Utilizes ICD9 codes, chief complaints to detect
  early evidence of infectious activity
• Sensitive, but not very specific
• Can combine multiple data sources to better
  characterize events

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Evaluation of ICD-9 Coding Abnormalities using
ESSENCE

• Frequent Hemorrhagic Illness syndrome alerts
  due to overuse of ICD9 code 286.9, coagulation
  defect, not otherwise specified.
• Result of widespread miscoding of patients
  undergoing anticoagulation therapy.

Jan Mar 2007
Oda G, et al. 6th ISDS Conference, Indianapolis,
IN 10/07 (abstract 80).
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Multi-State Salmonella Outbreak

• February 14, 2007 CDC and FDA issued a recall
  of peanut butter manufactured at Plant A due to
  contamination with Salmonella serotype Tennessee
• 628 persons infected with outbreak strain

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Multi-State Salmonella Outbreak

• VA Data from ESSENCE system

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ESSENCE Data 2005-2006 Influenza Season
CDC utilizes the U.S. Sentinel Providers
Surveillance Network, a nationwide system. ILI
defined as 'fever (temperature of gt100F) PLUS
either a cough or a sore throat'
VHA utilizes ESSENCE ILI ICD-9 codes generated
for each outpatient visit
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ILI Surveillance Example
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ILI Surveillance Example
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ILI Surveillance Example
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Category 2 Implementation

• Establishment of Palo Alto ESSENCE Test Bed
• Enhance influenza detection model with overlay of
  additional data elements
• Lay groundwork for novel applications Telephone
  Care and Nursing Home syndromic surveillance
  models
• Link to ESSENCE to be included on HAIISS Dashboard

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Category 3 Organisms and Infections of
Epidemiologic Significance

• Multi-drug resistant organisms (MDRO)
• MRSA, VRE, multi-resistant gram negatives
• Clustering of organisms of epidemiologic
  significance
• C. difficile, Legionella, Tuberculosis
• Infectious diseases/conditions reportable to the
  local, state, and national public health
  authorities
• Other significant infections and outbreaks
  nursing home, dialysis, etc.

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Category 3 Examples
Clostridium difficile
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Category 3 Examples
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Outbreak Investigation
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Outbreak Investigation
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Outbreak Investigation
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Outbreak Investigation

• Save report criteria for quick reference
• Click enable monitoring to create a
  surveillance monitor which will create an alert
  every time the selected event occurs
• Organism isolated on selected ward(s), with
  selected antibiogram, etc.
• Can also set to alert whenever a particular
  threshold of isolates occurs (via control chart)

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Category 4 Antibiotic Usage and Decision Support

• Antimicrobial usage trends, by drug, and
  locations within facility
• Antimicrobial resistance patterns, by organism,
  and locations within facility
• Drug/bug mismatch identification (antibiotic
  prescribed does not correlate with resistance
  pattern of organism isolated)send alert to
  designated staff
• Improve prescribing practices and prevent
  antimicrobial resistance

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Resistant Bacteria Antimicrobial Usage Examples
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Resistant Bacteria Antimicrobial Usage Examples
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Resistant Bacteria Antimicrobial Usage Examples
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Summary

• HAIISS incorporates multi-faceted surveillance
  methodologies
• Many opportunities to collaborate and advance
  science of infection surveillance
• Implementation and validation a complex and
  ongoing process, requiring coordination and
  cooperation of multiple partners/stakeholders

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Summary

• HAIISS is poised to provide VA with
  enterprise-wide electronic HAI and syndromic
  surveillance system
• Success will establish VA as leader, innovator,
  and key partner in field of healthcare
  epidemiology