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

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

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

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

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

Descriptive Analysis

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

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

Rates and Trends

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

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

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

Associated Chief Complaints and Final Diagnoses

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.

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

Cluster Identification and Characterization

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

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 24 26 28 30 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 3 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 2007. MMWR 2008;57:27--741-747 .

Asthma Cluster, City A September 16, 2007

Asthma Cluster, City B September 1-2, 2007

Asthma Cluster, City C October 1-4, 2007

Asthma Cluster, City C April 23-26, 2007

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.

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

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.

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

Thank You www.cdc.gov/biosense BioSenseHelp@CDC.GOV 27