1. Automated Monitoring of Asthma Using the BioSense System
Keydra Phillips, MS1, Sule Mohammed, DVM, MS1,
Gabriel Rainisch, MPH1, and Jerome Tokars, MD, MPH2
1SRA International, Inc, 2CDC
August 27, 2008
National Center for Public Health Informatics
Centers for Disease Control and Prevention
PHIN Conference, Atlanta, GA
Good after noon to all of you, I am Keydra Phillips, I am excited to share with you
Disclaimer: The findings and conclusions in this presentation are those of the author(s) and do not necessarily represent the views of the Centers for Disease Control and Prevention. 1

2. The BioSense System Serves as a national automated surveillance system
Currently receives data from:
540+ civilian hospitals capturing about 11% of all U.S. emergency department (ED) visits
427 send patient chief complaints and 175 send final diagnoses
Median times from patient visit to receipt of ED data at CDC are 8 hours for chief complaints and 5 days for diagnosis codes
355 Department of Defense (DoD) installations and 835 Veterans Affairs (VA) medical facilities 2

3. BioSense System Health Indicators
which contains “493”, “asthma”, “wheezing”, “reactive airway disease”, “bronchospasm”, or “chest tight”
which contains 493.XX
Chief complaint
Free text
Final diagnosis
ICD-9 Codes
BioSense maps chief complaint and diagnosis data to 11 syndromes:
Botulism-like
Fever
Gastrointestinal
Respiratory
Neurological
Cutaneous lesion
Lymphadenitis
Hemorrhagic illness
Rash
Specific infection
Severe illness or Death
and 78 sub-syndromes
Asthma
Respiratory

4. Analysis Rationale
The recent detection of several clusters pertaining to asthma spurred efforts to:
Describe the ability of BioSense data to monitor asthma
Understand what would improve system utility for automated monitoring of asthma
Build collaborative relationships with asthma experts and local public health 4

5. Methods
Retrospectively analyzed civilian hospital ED visits for asthma January 1 to December 31, 2007.
Asthma = visits mapping to asthma subsyndrome
Components of Analysis:
Descriptive
Rates by age group, time period, and city
Associated chief complaints and final diagnoses
Cluster identification and characterization
Time series analysis
Modified version of Early Aberration Reporting System (EARS) C-2 algorithm
Recurrence interval threshold of 500 days
City level unit of analysis 5

6. Descriptive Analysis

7. Results Of 12.4 million ED visits, 278,070 (2.2%) were asthma related
127,095 (46%) were for asthma chief complaint
114,881 (41%) were for an asthma diagnosis
36,149 (13%) had both
These visits represented 239,315 patients (average 1.2 visits per patient) 7

8. Percent of ED Visits with Asthma, by Demographic Characteristics, Jan
Percent of ED Visits with Asthma, by Demographic Characteristics, Jan. 1—Dec. 31, 2007
Age group (yrs)
Asthma Chief Complaint visits n=163,189
Asthma Final Diagnosis visits n=150,975
0-3
23.4
10.8
4-11
18.2
15.3
12-19
9.8
11.8
20-49
33.7
44.6
> 50
14.9
17.5
Total
100.0
Gender
Female
50.8
58.1
Male
49.2
41.9 8

9. Rates and Trends

10. Rate of ED Visits with Asthma, by Demographic Characteristics, Jan
Rate of ED Visits with Asthma, by Demographic Characteristics, Jan. 1—Dec. 31, 2007
Age group (yrs)
Rate of visits with asthma chief complaint
Rate of visits with asthma final diagnosis
0-3
42.7
19.9
4-11
52.0
44.2
12-19
22.8
27.5
20-49
16.9
22.5
> 50
11.3
13.5
Gender
Female
12.0
47.9
Male
14.2
43.1
Rates per 1,000 ED visits
Chief complaint rates= Asthma CC visits/Total CC visits
Final diagnosis rates= Asthma FD visits/Total FD visits 10

11. Rate of Asthma ED Visits by Age and Month, 2007
Chief Complaint
Both graphs follow this same seasonal trend where rates are fairly constant from Jan to May for all age group
Final Diagnosis 11

12. Rate of Asthma ED Visits in Selected U.S. Cities
by Time Period, 2007
Chief Complaint
Final Diagnosis

13. Associated Chief Complaints and Final Diagnoses

14. Most Frequent Subsyndromes
Associated Subsyndromes for Visits with Asthma Chief Complaints, Jan. 1 – Dec. 31, 2007
Among the 163,189 visits
with chief complaint of
asthma:
53,526 (32.8%) also had chief complaints for dyspnea
34,433 (21.1%) also had chief complaint of cough
Most Frequent Subsyndromes %
Dyspnea
32.8
Cough
21.1
Chest pain
17.9
Fever
11.1
Upper respiratory infection
7.9
Nausea and vomiting
Abdominal pain
6.3
Injury, NOS
3.9
Headache
3.8
Bronchitis and bronchilitis
2.2
This table displays the top 10 chief complaints that accompany a chief complaint of asthma
These tables display that chief complaints better capture symptoms of asthma where as final diagnoses better capture perhaps co-morbidites.

15. Most Frequent Subsyndromes
Associated Subsyndromes for Visits with Asthma Final Diagnoses Jan. 1 – Dec. 31, 2007
Among the 150,975 visits
with final diagnosis of
asthma:
22,948 (15.2%) also received final diagnosis for hypertension
19,627 (12.9%) also received final diagnosis of upper respiratory infection
Most Frequent Subsyndromes %
Hypertension
15.2
Upper respiratory infection
12.9
Dyspnea
9.7
Bronchitis and bronchilitis
8.8
Chest pain
8.2
Diabetes mellitus
8.0
Cough
6.1
Abdominal pain
5.5
Pneumonia / lung abscess
4.4

16. Cluster Identification and Characterization

17. Top 5 Asthma Clusters, Jan. 1 – Dec. 31, 2007
City
# of Hospitals
Type
# of Cluster Days
Peak Day
Obs
Exp
Obs/Exp
Stds
San Diego, CA 6
Complaint 3
10/23/07 26 7
3.9 10
City A 15 1
09/16/07 46
3.0
8.4
City B 41 2
09/02/07 82
2.0
7.4
City C 25
Diagnosis 4
10/02/07 76 32
2.4
04/26/07 71 38
1.9
*Obs=observed count, Exp=expected count, Stds=standard deviations above expected

18. fires start; some evacuations ordered
ED Visits by Chief Complaint and Final Diagnosis of Asthma –San Diego, CA, October 22-24, 2007
Physician diagnosis of asthma
Chief complaint of asthma
Days with significantly high (p<0.01)§
number of visits for asthma
Witch Creek and Harris
fires start; some evacuations ordered
Wind changes from
easterly to westerly
Most evacuation orders retracted
Fires contained
Month and day
No. of visits
Sep
Oct
Nov 60 55 50 45 40 35 30 25 20 15 10 5
CDC. Monitoring Health Effects of Wildfires Using the BioSense System --- San Diego County, California, October MMWR 2008;57:

19. Asthma Cluster, City A September 16, 2007

20. Asthma Cluster, City B September 1-2, 2007

21. Asthma Cluster, City C October 1-4, 2007

22. Asthma Cluster, City C April 23-26, 2007

23. Summary
Using data in the BioSense system, we were able to effectively identify secular trends in asthma ED visits; rates decreased during the summer months and increased during the fall season.
Asthma ED visit rates were higher for final diagnoses and highest among the 4-11 age group.
The most frequent co-morbidities were dyspnea, chest pain, and cough for chief complaints visits, and hypertension, dyspnea, upper respiratory infection for final diagnosis visits.
Seasonal trends, demographic trends, and geographic clusters found in BioSense data are confirmed in other data sources and asthma studies.

24. Conclusions
Asthma is one of the limited number of diseases that can be effectively monitored using chief complaint and final diagnosis data.
The potential utility of the BioSense system for automated monitoring of asthma includes the timeliness of the data, variety of data types, and the wide national coverage.
The results of this analysis suggest that BioSense may be an important and timely source of information on asthma that could be used to track morbidity and inform public health interventions for leveraging prevention efforts. 24

25. Next Steps
Engage subject matter experts to study the utility of BioSense in supplementing current asthma surveillance and research efforts.
Identify correlations between atmospheric trace elements and asthma seasonal trends.
Use SatScan software to perform spatial data analyses for each month at city, multistate, or regional levels.

26. Acknowledgment Hospital Emergency Dept Data Sources
Alegent Health
Aurora Health Care Corporate Office
Banner Health
Baylor Health Care System
BJC Healthcare
CHRISTUS Health
Cook Childrens Health Care System
Cook County Bureau of Health
Cook County Dept of Public Health
Gwinnett Health System
Indiana State Department of Health
Individual Hospitals
MedStar Health
Methodist Healthcare
Michigan Dept of Community Health
Missouri Dept of Health/Senior Services
Mount Carmel Health System
North Carolina (NC Detect)
Ohio Department of Health
Saint Luke's Health System
Sentara Healthcare
Sharp HealthCare
Sierra Providence Health Network
Tarrant County Advanced Practice Center
Tenet Healthcare Corporation
U of California San Diego Healthcare
Beth Israel Deaconess Medical Center
Children's Hospital Boston
Children's Hospital Los Angeles
Denver Health and Hospital Authority
Johns Hopkins Hospital and Health System
Oregon Health Sciences University
Thomas Jefferson University Hospital
University Medical Center Las Vegas

27. Thank You 27