Enhanced Surveillance Methods and Applications:

A Local PerspectiveDani Arnold, M.S.

Bioterrorism/Infectious Disease EpidemiologistWinnebago County Health Department

What is Syndromic Surveillance?

• “The term ‘syndromic surveillance’ applies to surveillance using health-related data that precede diagnosis and signal a sufficient probability of case or an outbreak to warrant further public health response,” (CDC).

Why Syndromic Surveillance?

• By designing surveillance systems that target initial symptoms of disease, rather than waiting days or weeks to detect illness, steps can be taken earlier to treat ill persons and possibly prevent others from becoming ill.

00.10.20.30.40.50.60.70.80.9

1

0 24 48 72 96 120 144 168

Incubation Period (Hours)

Dis

ease

Det

ectio

n

Effective Treatment Period

Gain of 2 days

Early Detection Traditional DiseaseDetection

Phase IIAcute Illness

Phase IInitial Symptoms

Need for Early Detection

Source: http://www.cs.umbc.edu

Syndromic Surveillance Systems

Santa Clara County Public Health Department

Syndromal Surveillance Tally Sheet

Santa Clara County’s Tally SheetE a r ly W a r n in g S y s te m

S y n d r o m a l S u r v e illa n c e T a lly S h e e t F a c ili ty : _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ D a te : _ _ /_ _ /_ _ S h if t (c h e c k o n e ): D a y s

E v e n in g s N ig h ts

F o r e a c h p a tie n t th a t y o u e v a lu a te in tr ia g e , p le a se re c o rd w h e th e r th e y fa ll in to o n e (o r m o re ) o f th e c a te g o rie s l is te d b e lo w , o r “n o n e o f th e a b o v e .” A t th e e n d o f th e sh if t e n te r th e to ta ls a n d fa x th e in fo rm a tio n to 4 0 8 -8 8 5 -3 7 0 9 . T h a n k y o u fo r y o u r c o o p e ra tio n .

S y n d r o m e T a lly S h if t to ta l F lu - lik e sy m p to m s

F e v e r w ith m e n ta l s ta tu s c h a n g e

F e v e r w ith sk in ra sh

D ia rrh e a w ith d e h y d ra tio n

V isu a l o r sw a llo w in g d iff ic u lt ie s , d ro o p in g e y e lid s , s lu rre d sp e e c h o r d ry m o u th

A c u te re sp ira to ry d is tre s s

E x p o su re to “su sp ic io u s” i te m /su b s ta n c e

N o n e o f th e a b o v e

F A X to 4 0 8 .8 8 5 .3 7 0 9 S ig n a tu r e _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

Santa Clara County Public Health Department

• Faxes are collected several times a day by staff who manually enter the data into a surveillance database

• Graphical displays of the prior days’counts are manually generated and emailed to the communicable disease officer for Santa Clara County

Syndromal Surveillance Tally Sheet

RODS

RODS• Real-time Outbreak and Disease Surveillance

• Funding through the CDC, Defense Advanced Research Projects Agency (DARPA), and the Pennsylvania Department of Health

• Work is centered on development of NEDSS-compliant detection systems offered for free to qualified public health departments

RODS• Examines data routinely collected by

clinical and other information systems automatically and in real-time

• Currently in operation in Pennsylvania and in Utah, and in the works in Chicago

RODS

• RODS software comprises three main components:

1. A Health-Level 7 (HL7) listener that receives HL7 messages in real-time through TCP/IP socket connections from hospitals• Registration data is extracted from the HL7

messages and stored in a database for analysis.

RODS• RODS software comprises three main

components: (Continued)2. A time-series detection algorithm that

monitors data identifies deviations from expected levels

3. Servlets for use in web applications that generate temporal and spatial views of registration data and the results of the detection algorithm.

Real-time Outbreak Detection System (RODS)

EARS

EARS• Early Aberration Reporting System

• Developed by the Centers for Disease Control and Prevention (CDC)

• Purpose: Provide national, state, and local health departments with several alternative aberration detection methods that have been developed for syndromic surveillance

EARS• A group of SAS® programs for data

analysis

• Following Sept. 11 EARS was modified into a standard surveillance system for the New York City and Washington, DC Departments of Health

EARS

• Allows for selection of validated aberration detection methods

• Can be used with any data source

EARS• Implemented throughout the United

States in several state and local health departments and in health departments in several other countries.

• Used for syndromic surveillance at several large public events:– Democratic National Convention of 2000– 2001 Super Bowl – 2001 World Series

EARS• Detection comprises 2 broad categories:

– Case definition methods • Defines an “event of interest” tracking those

syndromes considered of greatest importance

– Pattern recognition methods• Identifies symptoms (or sets of symptoms) that

deviate from an expected baseline

EARS• Long-term surveillance

– Lasts longer than 30 days

• Short-term surveillance– “Drop-in” surveillance– Large public events

Challenges Confronting Syndromic Surveillance

Questions to Answer

Reingold A. If syndromic surveillance is the answer, what is thequestion? Biosecurity and Bioterrorism: Biodefense Strategy, Practice, and Science 2003; 1(2): 1-5.

Reduce Morbidity/Mortality Following Bioterrorist Event

• Difficult to determine with certainty– Attack must involve area under surveillance– Need comparison group

• Easier to determine sensitivity/specificity in detecting clusters of naturally occurring illness– Useful for comparing SSS, but no substitute

for real-world operation

Detect Bioterrorist Events of Given Type/Size

• Must be looking for the right syndrome

• Size of attack must be sufficient to be detected– Dependent on sensitivity of system and

numbers/rates used to signal possible clusters

Challenges of Implementation

Challenges of Implementation

• Resource Utilization

• User Acceptance

• Technological consideration– Constraints– Opportunities

Methodology Questions• How to differentiate from similar non-

bio-weapon illnesses?

• How does choice of elements correspond to sensitivity and specificity of model?

• How can you model probability abstractions?

Winnebago County Health Department System: BEANS

B.E.A.N.S.

• B.E.A.N.S. (Bioterrorism Epidemic Advanced Notification System)

• System has been “live” for over a year and a half

• Designed to detect moderate to large-scale bioterrorism events in Winnebago County where Category A agents are disseminated

Biological Agents/Diseases• Category A

– Anthrax, botulism, plague, smallpox, tularemia, viral hemorrhagic fevers (filoviruses & arenaviruses)

• Category B– Brucellosis, epsilon toxin of Clostridium perfringens,

food safety threats, glanders, melioidosis, psittacosis, Q fever, ricin toxin, Staphylococcal enterotoxin B, typhus fever, viral encephalitis & water safety threats

• Category C– Emerging infectious diseases such as hantavirus

Agents Targeted by Surveillance System• Anthrax• Bacterial gastroenteritis• Botulism• Chickenpox • Encephalitis• Measles• Plague• Sepsis• Smallpox• Tularemia• Viral hemorrhagic fever

Syndromic Surveillance System

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Syndromic Surveillance Apparatus

B.E.A.N.S.• Submissions are

captured in real-time• Minimal impact on

workload for triage nurses

• Symptoms are detected at earliest point in patient visit enabling for early detection of outbreaks

Agents Targeted by Each Syndrome• Fever with shortness of breath (C1):

– Smallpox, anthrax, plague, tularemia, botulism, viral hemorrhagic fever, influenza

• Fever with mental status change (C2):– Encephalitis or sepsis

• Fever with either rash or blisters (C3): – Smallpox, measles, chickenpox

• Diarrhea or vomiting (age >6yrs) (C4):– Bacterial gastroenteritis

• Bilateral weakness (face or limbs) (C5):– Botulism

• None of the above (C6):– Captures total number of patients seen allowing for analyses

both by absolute number and percent – allows for better statistical comparison

What we've seen so far…Fever with Shortness of Breath

0

2

4

6

8

10

12

247

273

299

325

351 12 38 64 91 117

145

171

197

223

249

276

302

328

354 14 40 66 92 118

144

170

196

222

248

274

300

326

352 13 39

Day of the year

Num

ber o

f peo

ple

pres

entin

g w

ith C

1

C1

What we've seen so far…Diarrhea or Vomiting (age >6 years)

0

2

4

6

8

10

12

14

16

18

247

273

299

325

351 12 38 64 91 117

145

171

197

223

249

276

302

328

354 14 40 66 92 118

144

170

196

222

248

274

300

326

352 13 39

Day of the year

Num

ber o

f peo

ple

pres

entin

g w

ith C

4

C4

Percentage of all Visits to ED Triage by Syndrome

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

1 13 25 37 49 61 73 85 97 109

121

133

145

157

169

181

193

205

217

229

241

253

265

277

289

301

313

325

337

349

361

Day of the Year

Perc

ent o

f Tot

al V

olum

e

C4

What we’ve seen so far…Diarrhea or vomiting (age >6yrs)

Small food borne outbreak

Instance where there were many unrelated cases in one day

A Comparison of Pneumonia & Influenza Mortality, School Absenteeism, Visits for Pneumonia, ILI and Specified Syndromic Surveillance Rates for the 2005-2006 Influenza

Seasons

0

5

10

15

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38 41 44 47 50 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 3 6 9 12

CDC Week

Rat

e

Smoothed Pneumonia Visits Absenteeism Rate ILI Composite RatesFever w/SOB Rate Diarrhea/Vomiting (age > 6 yrs) Smoothed P&I Mortality Rate

POD Tabletop Exercise

• 80 paper cases simulating patients presenting at local ERs

• “Symptoms” assessed and entered into BEANS system as though patient was being triaged at ER

Surveillance screen before adding paper cases

Surveillance screen after adding table-top paper cases

Other Surveillance Activities

• Influenza• Pneumonia• Pollen• I-NEDSS Enteric Module• School Absenteeism

Limitations

Limitations

• Not capturing a complete triage picture– Some patients are not getting entered (i.e.

ambulance bay)• Relies on human input

– Not well integrated into hospital systems• Continuous maintenance and upkeep of

computer operating systems and data transferring

Limitations

• Lack of steady stream of funding

• Compatibility across jurisdictions– Not all of these systems discussed today will

work in each county; systems are chosen based on population size and community need

Recommendations

• Syndromic Surveillance will become commonplace at state and national levels within the next 5 years

• IDPH should facilitate local efforts to develop systems and provide guidance to ensure that State standards are met

Recommendations

Continued efforts in: • Collection• Analysis• Interpretation• Dissemination of data, linked to

Public health practice