Summary of Current Bioterrorism/Outbreak/Emerging Public Health Threat Surveillance Activities



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Summary of Current Bioterrorism/Outbreak/Emerging Public Health Threat Surveillance Activities

  • Matthew Groenewold, MSPH


Bioterrorism Surveillance

  • The Louisville Metro Health Department currently surveils community patterns of both acute morbidity and mortality in an effort to detect outbreaks of disease that might be associated with covert bioterror attacks or other emerging infectious or noninfectious public health threats.



Data Sources

  • Hospital Emergency Departments

    • Five sentinel hospitals
  • Ambulance dispatch records

    • Louisville Fire and Rescue
  • Jefferson County Coroner’s Office

    • Daily count of cases


Bioterrorism Surveillance

  • These surveillance activities are intended to provide sufficient advanced warning of an event to allow for early intervention that could reduce consequent morbidity and mortality.



Bioterrorism Surveillance

  • Increased concern over the possibility of large-scale bioterrorism since Anthrax attacks of October 2001.

  • In response, public health community seeks to develop and deploy systems to provide early warning of an event.



Bioterrorism Surveillance

  • Given the absence of a widespread, effective system for the environmental detection of potential bioterrorism agents, the symptomatic presentation of affected people to healthcare providers may be the first detectable indication of a covert attack.



Syndromic Surveillance

  • Although largely untested, syndromic surveillance has emerged as perhaps the most promising epidemiological method for detecting the intentional, covert release of a pathogen on a large scale.



Syndromic Surveillance Systems

  • Use health-related data that precede diagnosis but signal a sufficient probability of an outbreak to warrant further public health response.

    • These systems assess the number, not of specific diagnoses, but of cases occurring in predefined categories of symptomology referred to as “syndromes.”
      • The syndromes are intended to represent the majority of presentations associated with bioterrorism agents.


Syndromic Surveillance Systems

  • Seen as more likely to detect bioterror events than traditional, diagnosis-based disease-reporting systems for a number of reasons:

    • Practitioner awareness of potential bioterror agents and emerging or re-emerging infectious diseases is limited.
    • Second, many of these diseases have nonspecific prodromes similar to those of other, more common illnesses.
  • Consequently, affected people presenting to emergency departments or other healthcare facilities may not be recognized as victims of bioterrorism.



Disease-Reporting Systems

  • Rely on a diagnosis

    • Often requires laboratory confirmation.
    • Some lab tests or cultures require days or even weeks to become positive.
  • Data often incomplete.

    • Practitioners often do not report even traditionally reportable diseases.
    • Practitioners often do not order the tests required for laboratory confirmation
      • Do not suspect the disease
      • Test deemed unnecessary in the context of the clinical care of an individual patient.


Acute Morbidity

  • Syndromic surveillance of Emergency Department visits

    • Five sentinel hospitals spread across county
    • Triage nurses assign patients to one of seven syndromes
    • Data entry structurally enforced
    • Data files automatically emailed to Health Department for analysis each morning


Acute Morbidity

  • Syndromic surveillance of ambulance dispatch data

    • Louisville Fire and Rescue EMS division
    • Dispatcher assigns one of 26 “nature of call” dispatch codes for each ambulance run
    • Dispatch codes used as syndromes
    • Data files emailed to Health Department each morning for analysis


Mortality

  • Surveillance of Coroner’s cases

    • Coroner’s cases considered proxy measure for unexpected or suspicious deaths
    • Currently, Coroner’s Office Reports Daily number of cases by phone
    • The Real-Time Mortality Surveillance System (RTMS), funded by the Center for the Deterrence of Biowarfare and Bioterrorism, is being implemented




Analysis

  • Statistical methods—Historical

    • In the past, the Health Department surveilled the aggregate daily volume of 16 predetermined dispatch categories of Louisville Fire & Rescue ambulance runs, deemed to be potentially indicative of infectious disease. The number of specified ambulance runs occurring on each day was compared to the year-to-date mean of daily run counts. Counts that were greater than two standard deviations above the mean were considered surveillance significant.


Analysis

  • Statistical methods—Historical

    • Statistical methods historically used to surveil ambulance runs would, by definition, recognize approximately 2.5% of all observations as “high.”
      • Nine false positives or “false alarms” per year


Analysis

  • Statistical methods—Historical

    • Also unable to detect gradual increases in the data because the “running average” of runs would be “dragged” upwards along with the slowly increasing volume of runs.
    • Unable to detect prolonged but sub-threshold increases in the number of runs


Analysis

  • Current Statistical Methods

    • CUSUM
      • modification of a technique that was originally developed for quality control monitoring of industrial processes
      • relies on the monitoring of cumulative differences between observed and expected data in a time window when compared to a threshold


Analysis

  • Current Statistical Methods

    • CUSUM
      • predetermined reference value is subtracted from a second value, derived from the observed number of cases
      • resulting sum is then added to the sum for the previous month and so on
      • If the cumulative sum of these values, the “cusum,” exceeds a warning value, further follow-up is indicated


Analysis

  • Current Statistical Methods

    • CUSUM
      • Because we are interested only in detecting increases in the mean disease frequency, not decreases, the cusum is not allowed to fall below zero.
      • If a negative value is obtained, the cusum is set to zero.
      • This method is designed to indicate false positives only once in a 500-day period.














Definitions

  • SNR

    • Syndrome to None Ratio. The number of cases occurring in a given syndrome per 100 cases occurring in the “Other” syndrome. The SNR, rather than the raw number of cases, is used in used in the CUSUM calculations to control for the effect of daily fluctuations in total ED visit volume .
  • Expected

  • Alert Level

    • The CUSUM threshold above which an alert is signaled and further investigative follow-up is indicated.


Analysis

  • Problematic issues

    • The baseline
      • Expected numbers
        • Level of aggregation
        • Time period covered
        • Summary measure/statistic used
      • Example
        • Expected numbers (2 year arithmetic mean) for ambulance run syndromes seasonally (quarterly) adjusted




Analysis

  • Problematic issues

    • The baseline
      • A better idea?
        • Time series analyses/Forecasting
          • Fourier (Spectral or Harmonic) analysis
          • Exponential or other types of smoothing
          • ARIMA (Box-Jenkins) method or SARIMA


Analysis

  • Problematic issues

    • The baseline
      • Time series analysis
        • Application to a particular problem in the RTMS








Analysis

  • Problematic issues

    • Validity of syndromes
      • Definitions
      • Interrater reliability study


Data Analysis



Data Analysis





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