Lessons Learned from the National Syndromic

Surveillance Conference

Sponsored by the Centers for Disease Control and Prevention

NYC Department of Health and Mental HygieneNew York Academy of Medicine

September 23-24, 2002

New York City

What is Syndromic Surveillance?

“Passive” Systems Minimal burden Designed to detect and monitor large #

usual/mild illnesses “Active” Systems-

Educational Outreach Tool Designed to detect and report small #

unusual/severe syndromes

Public Health PracticeLocal health officers shall exercise due diligence in ascertaining the existence of outbreaks of illness or the unusual prevalence of diseases, and shall immediately investigate the causes of same

New York State Sanitary Code, 10 NYCRR Chapter 1, Section 2.16(a)

Research & DevelopmentNon-traditional data sourcesAcademia (training) & contractorsAuthorized agents of public health departments

Legal Mandate:Who Should be Doing This?

Privacy and Confidentiality

Health departments have strong tradition of maintaining security of confidentiality information Public health provisions in HIPAA

Data collected under auspices of bioterrorism surveillance de-linked from any identifiers for non-BT surveillance

Goals

Early detection of large outbreaks Characterization of size, spread, and

tempo of outbreaks once detected

Monitoring of disease trends

Day 1- feels fine Day 2- headaches, fever- buys Tylenol Day 3- develops cough- calls nurse hotline Day 4- Sees private doctor: “flu” Day 5- Worsens- calls ambulance

seen in ED Day 6- Admitted- “pneumonia” Day 7- Critically ill- ICU Day 8- Expires- “respiratory failure”

Potential Syndromic Surveillance Data Sources

Day 1- feels fine Day 2- headaches, fever- buys Tylenol Day 3- develops cough- calls nurse hotline Day 4- Sees private doctor: “flu” Day 5- Worsens- calls ambulance

seen in ED Day 6- Admitted- “pneumonia” Day 7- Critically ill- ICU Day 8- Expires- “respiratory failure”

Pharmaceutical Sales

Nurse’s Hotline

Managed Care Org

Ambulance Dispatch (EMS)

ED Logs

Absenteeism

Potential Syndromic Surveillance Data Sources

Traditional Surveillance

Data Transfer

2 4 2 (TX)

FTP server (NJ)

Email server at DOH (NYC) Stand-alone PC

at DOH

Data available for analysis

HHC

manualautomatic

2 4 2 (TX)

FTP server (NJ)

Email server at DOH (NYC) Stand-alone PC

at DOH

Data available for analysis

HHC

manualautomatic

Emergency DepartmentEMS

FTP Server

Inside Firewall

Data available

FTP Server

Inside Firewall

Data available

Pharmacy

Data requirements Core variables

Hospital name Date of visit Time of visit Age Sex Chief complaint (free text) Home zip code +/- Unique identifier

Discharge diagnosis not generally available in timely manner

Need to consider response protocols – patient identification, logistics

Electronic coding of chief complaints into clinical

syndromes Performed in SAS Text-string recognition Mutually exclusive vs. overlapping Hierarchy of coding Iterative refinement of syndrome definition Entire dataset can be recoded easily –

allows for changes in definition and addition of new syndromes

Electronic ED logs AGE SEX TIME CHIEF COMPLAINT ZIP 15 M 01:04 ASSAULTED YESTERDAY, RT EYE REDDENED.11691 1 M 01:17 FEVER 104 AS PER MOTHER. 11455 42 F 03:20 11220 4 F 01:45 FEVER, COUGH, LABORED BREATHING. 11507 62 F 22:51 ASTHMA ATTACK. 10013 48 M 13:04 SOB AT HOME. 10027 26 M 06:02 C/O DIFFICULTY BREATHING. 66 M 17:01 PT. MOTTLED AND CYANOTIC. 10031

Text Recognition with SAS IF index(cc,"FEV")>0 or index(cc,"HIGH TEMP")>0 or index(cc,"NIGHT SWEAT")>0 or (index(cc,"CHILL")>0 and index(cc,"ACHILLES")=0) or index(cc,"780.6") etc. then FEVER=1;

Data SummaryED PharmacyEMS

Syndromic Grouping

Call-Type Chief Complaint

Drug Class

Geographic Grouping

Pickup Zip Home Zip

Hospital

Store Zip

Other Information

Age

Gender

Follow-up Possible

Yes

Data SummaryED PharmacyEMS

Daily Volume ~ 3,000

calls

~6,500 visits

~6,000 Rx

~26,000 OTC

Coverage >95% 65-70% ~30%

Prospective Data Collection

March 1998 October 2001

August 2002

Analytic Methods

Cyclical Regression

Scan Statistic

CUSUM

Scan Statistic

In development

Data Summary

EDEMS

Syndromes “ILI” Respiratory

Febrile

GI

Detection Limit

(city-wide)

~50 calls ~100 visits

Detection Limit (localized)

~10 calls 10-20 visits

Denominator Surveillance is Less Sensitive than Syndromic

100

1000

10000

Date

Nu

mb

er

of

ED

Vis

its

Total Visits

Fever/Respiratory

GI/ Vomiting

0

5000

10000

15000

20000

25000

7/5/

1997

9/27

/199

7

12/2

0/19

97

3/14

/199

8

6/6/

1998

8/29

/199

8

11/2

1/19

98

2/13

/199

9

5/8/

1999

7/31

/199

9

10/2

3/19

99

1/15

/200

0

4/8/

2000

7/1/

2000

9/23

/200

0

12/1

6/20

00

3/10

/200

1

6/2/

2001

8/25

/200

1

11/1

7/20

01

2/9/

2002

5/4/

2002

7/27

/200

2

Week Ending

Un

its

per

100

,000

pre

scri

pti

on

s

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Resp

Flu

Selected Antibiotic and Antiviral Prescriptions1997-2002

ED Respiratory Visits, Nov-May

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

1-Nov 1-Dec 1-Jan 1-Feb 1-Mar 1-Apr 1-May

res

pir

ato

ry /

oth

er

Temporal scanCUSUM (C3)

Influenza A B

EMS calls

Subway worker- “flu”ED respiratory visits

Pharmacy Antiviral RxPrescription Data

0.0%

0.1%

0.2%

0.3%

0.4%

0.5%

0.6%

0.7%

0.8%

0.9%

1.0%

11/4

/200

1

11/11

/200

1

11/1

8/20

01

11/2

5/20

01

12/2

/200

1

12/9

/200

1

12/1

6/20

01

12/2

3/20

01

12/3

0/20

01

1/6/

2002

1/13

/200

2

1/20

/200

2

1/27

/200

2

2/3/

2002

2/10

/200

2

2/17

/200

2

2/24

/200

2

3/3/

2002

3/10

/200

2

3/17

/200

2

Week Beginning

Infl

ue

nza

Pre

sc

rip

tio

ns

as

% o

f To

tal

West Nile Virus ActivityThrough September 2001

Tabletop Drills

REDEX (2001)REDEX (2001) Test of 911-EMS System

SANDBOX (2002)SANDBOX (2002) Test of ED System

Nov 12 9.17 am Flight AA 587 Crashes in Rockaways

Respiratory Zip Code Signal (7 zips) 27 Observed / 10 Expected p<0.001

Hospital Signal 31 Observed/ 16 Expected p<0.05

0

5

10

15

20

25

30

35

40

10/2

5/20

01

10/2

7/20

01

10/2

9/20

01

10/3

1/20

01

11/2

/200

1

11/4

/200

1

11/6

/200

1

11/8

/200

1

11/1

0/20

01

11/1

2/20

01

11/1

4/20

01

11/1

6/20

01

11/1

8/20

01

11/2

0/20

01

11/2

2/20

01

11/2

4/20

01

11/2

6/20

01

11/2

8/20

01

11/3

0/20

01

12/2

/200

1

12/4

/200

1

12/6

/200

1

12/8

/200

1

12/1

0/20

01

12/1

2/20

01

12/1

4/20

01

12/1

6/20

01

12/1

8/20

01

Date

Res

p/N

on

e S

ynd

rom

es

Rockaways

Rest of City

Investigation Key Questions

True increase or natural variability? Bioterrorism or self-limited illness?

Available Methods “Drill down” Query clinicians/ laboratories Chart reviews Patient followup Increased diagnostic testing

Investigation Checked same-day logs at 2 hospitals

Increase not sustained

Chart review in one hospital (9 cases) Smoke Inhalation (1 case) Atypical Chest Pain/ Anxious (2 cases) Shortness of Breath- “Psych” (1 case) Asthma Exacerbation (3 cases) URI/LRI (2 cases)

Future Directions

Research Agenda More evaluations- Simulation models and “spiked”

validation datasets Better cluster detection software Signal Integration Optimizing response protocols- Inexpensive (and

accurate) rapid diagnostics

Emergency Department Surveillance Chief Complaint and/or Discharge Diagnosis HL7 Standards Need standard cc->syndrome coder (SAS)

Is It Worth the Effort? Costs

Implementation costs can be modest Operational costs=time of public health staff,

investigations

Benefits Possibility of huge benefit if early detection Characterization Strengthening traditional surveillance Dual Use

“Dual Use”

Opportunity to use new syndromic surveillance infrastructure other public health activities as well as for bioterror events

Can enhance all public health efforts Sets higher standard for all surveillance

(e.g., laboratory)

Cipro and Doxycycline Prescriptions

0

50000

100000

150000

200000

250000

7/1/2001 7/29/2001 8/26/2001 9/23/2001 10/21/2001 11/18/2001 12/16/2001 1/13/2002

0

5000

10000

15000

20000

25000

30000

35000

40000

Cipro Doxycycline

9/11

First anthrax case reported, 10/4/01.

CDC recommends doxycyline 10/28/01.

Drug Overdose Epidemiology of drug overdoses Detection of outbreaks

0

20

40

60

80

100

120

140

1 2 3 4 5 6 7

Total

Drop Page Fields Here

Drop More Series Fields Here

SatFri

Total

60

70

80

90

100

110

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Total

Day of Month

Day of Week

New Year’s

Suicidal Ideation/AttemptsNov. 2001 to Sept. 19, 2002

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

11/1

/200

1

11/1

5/20

01

11/2

9/20

01

12/1

3/20

01

12/2

7/20

01

1/10

/200

2

1/24

/200

2

2/7/

2002

2/21

/200

2

3/7/

2002

3/21

/200

2

4/4/

2002

4/18

/200

2

5/2/

2002

5/16

/200

2

5/30

/200

2

6/13

/200

2

6/27

/200

2

7/11

/200

2

7/25

/200

2

8/8/

2002

8/22

/200

2

9/5/

2002

9/19

/200

2

Asthma ED Visits and EMS Calls

0

2000

4000

6000

8000

10000

120007/

5/19

97

9/5/

1997

11/5

/199

7

1/5/

1998

3/5/

1998

5/5/

1998

7/5/

1998

9/5/

1998

11/5

/199

8

1/5/

1999

3/5/

1999

5/5/

1999

7/5/

1999

9/5/

1999

11/5

/199

9

1/5/

2000

3/5/

2000

5/5/

2000

7/5/

2000

9/5/

2000

11/5

/200

0

1/5/

2001

3/5/

2001

5/5/

2001

7/5/

2001

9/5/

2001

11/5

/200

1

1/5/

2002

3/5/

2002

5/5/

2002

7/5/

2002

Acute Therapy

Chronic Therapy

Improvement in Asthma Treatment

Tobacco cessation aids sold at a large pharmacy chain

0

50

100

150

200

250

300

350

400

Week Ending

Un

its

per

100

,000

pre

scri

pti

on

s

NRT

$0.39 increase in

State tax

$1.42 increase

in City tax

So What?

Strengthened surveillance systems in place Potential to better monitor all public health

situations Even if there are no more bioterror attacks,

preparation can strengthen our public health infrastructure and ability to respond

“Syndromic” surveillance vs. better surveillance

Acknowledgements

NYCDOH Syndromic Surveillance Team:

Joel AckelsbergSharon Balter Katie BornschlegelBryan Cherry Hyunok ChoiDebjani DasJessica HartmanRick Heffernan Adam Karpati

Marci Layton Jennifer LengKaren LevinMike PhillipsSudha ReddyRich RosselliPolly ThomasDon Weiss

Field teamsMIS staff

Spatial ScanStatistic

Developed by Martin Kulldorff Flexible windows in time and space Probability through Monte Carlo

simulations Controls for multiple comparisons Modified for infectious disease

surveillance