NYC Syndromic Surveillance IFH HIT Meaningful Use Workshop 10/1/2010 Marlena Plagianos, MS NYCDOHMH

What is Biosurveillance? “Collection and integration of timely health- related information for public health action achieved through the early detection, characterization, and situation awareness of exposures and acute human health events of public health significance.” Aaron T Fleischauer, PhD; Pamela S Diaz, MD; Daniel M Sosin MD. Biosurveillance: A Definition, Scope and Description of Current Capability for a National Strategy. Advances in Disease Surveillance 2008;5:175 2

Traditional Surveillance Case definitions Historically low compliance Laboratory confirmation can be slow Still important (e.g. H1N1 in NYC) 3

Laboratory Confirmation Making firm diagnosis commonly relies on lab result Limited in-house testing in outpatient setting (minutes) Commercial laboratory testing takes time (days- weeks) 4

Traditional Reporting is Labor Intensive 5

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Syndromic Surveillance Pre-diagnostic indicators of disease Readiness scenarios: bioterrorism, pandemics Objectives: –Timely, sensitive, specific surveillance –Detect outbreak before ‘astute clinician’ Typical Process 7 Collect data Process & code data Establish baseline Identify outbreak Sound alarm

New and Exciting Data Types 8 Data sourceLevel of data Data typeSettingCare phase Medication salesAggregateDrug categoryPre-clinicalPre-diagnostic School absencesAggregateFrequencyPre-clinicalPre-diagnostic Nurse hotline callIndividualCall typePre-clinicalPre-diagnostic Chief complaint / Reason for Visit IndividualText, briefClinicalPre-diagnostic EMS callIndividualRun typeClinicalPre-diagnostic TemperatureIndividualVital signClinicalPre-diagnostic Radiology ReportIndividualText, narrativeClinicalPre-diagnostic Chest X-rayIndividualCPT codeClinicalPre-diagnostic Diagnosis codeIndividualICD9 codeClinicalDiagnostic Progress NoteIndividualText, narrativeClinicalDiagnosis

EHR Syndromic Surveillance The Primary Care Information Project (PCIP) uses different EHR data sources to conduct & pilot its syndromic surveillance activities Some syndromes tracked using EHR data are: –Influenza-like Illness (ILI) –Fever –Gastrointestinal Illness (GI) Case definitions for these syndromes based upon text in these structured fields: –Chief Complaint –Measured Temperature –Diagnosis (ICD-9 CM Code) 9

System Screenshot 10

Aggregate Level Syndromic Data Only “Count” Data is collected 11

Data processing and syndrome coding 12 Respiratory conditions %Macro Resp; *Respiratory; IF CC=:'COUGH' OR CC=:'COUGHING' OR CC=:'SOB' OR CC=:'DIFFICULTY BREATHING' OR CC='BREATHING PROBLEMS' OR CC=:'SHORTNESS OF BREA' OR CC=:'DIFF BREA' CC='URI' OR THEN RESP=1; ELSE DO; RESP= MisspellingINDEX(CC,"COUG") + INDEX(CC,"COUH") + Shortness of breath INDEX(CC,"S.O.B") + INDEX(CC,"SOB") + INDEX(CC,"S O B") + INDEX(CC,"S O B") + INDEX(CC,"S.OB"); Difficulty breathing INDEX(CC,"BREAT") + INDEX(CC,"BEATH") + INDEX(CC,"DIB") + INDEX(CC,"D I B") + INDEX(CC,"D.I.B") + INDEX(CC,"BRATHING") + INDEX(CC,"DIFF BR") + INDEX(CC,"DIFF, BR") + Upper respiratory infection INDEX(CC,"URI ") + INDEX(CC,"URI/") + INDEX(CC,"URI;") + INDEX(CC,"U R I") + INDEX(CC,"URI,") + INDEX(CC,"U.R.I") +

Analysis: Calculate Baseline Approaches: Moving average, regression, time series methods. Length of baseline: Years, months, days Expected disease level Long: Seasonal, secular, environmental (e.g. heat, pollen) Short: Day of week, weekend/weekday, holidays, reporting failures Adjustments 13

Analysis: Test Observed vs. Expected 14 Significance tests Predetermined number of standard deviations Crossing statistical thresholds  Signal

Analysis: Test Observed vs. Expected 15

Challenges of Outbreak Detection 16 Natural occurring vs. simulations Measuring Accuracy Surge easier to detect than slow building outbreaks Outbreak types Smaller is harder to detect Size Earlier is harder to detect Timeliness

Electronic Health Record Syndromic Surveillance During 2009 Pandemic H1N1 in NYC

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Monday – Memorial Day 28

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H1N1 in New York City: Where did patients seek treatment? Emergency Departments or Primary Care Clinics?

Objective To determine whether the timing of the increase in patient visits was different at emergency departments from primary care clinics during the spring 2009 H1N1 influenza outbreak across the 5 boroughs of NYC 31

Study Sites 58 Primary Care Providers (PCP): –9 Institute for Family Health (IFH) clinics –49 practices enrolled in the NYCDOHMH PCIP (30 visits/day) v 50 Emergency Departments –247 visits/day 32

Methods Influenza-like Illness (ILI) as a broad estimate of H1N1 33 Fever + respiratory related reason for visit or diagnosis PCP Chief complaint of fever + a sore throat or cough, or a chief complaint of flu ED

Methods 34 City-wide By borough to see if there was a geographic difference Compared number of days to a significant increase at EDs to PCP clinics using a log- rank test 4/24-5/8 5/14-6/4 Two Waves:

ED, IFH and PCIP ILI Visits 35

ED, IFH and PCIP ILI Visits

Facilities with a significant increase in ILI volume 38

Results, April 24-May 8 39 Median Days to Increase in ILI Facilities with Increase in ILI 1-sided log rank BoroughEDPCPEDPCPp-value All41243/50 (86%)36/58 (62%)< Bronx5128/9 (88%)10/17 (59%)0.045 Brooklyn31412/15 (80%)6/9 (67%)0.025 Manhattan41313/15 (87%)11/19 (58%)0.008 Queens378/8 (100%)6/7 (86%)0.007 Staten Island14102/3 (67%)3/3 (100%)0.902

Results, May 14-June 4 40 Median Days to Increase in ILI Facilities with Increase in ILI 1-sided log rank BoroughEDPCPEDPCPp-value All 4847/50 (94%)50/58 (86%)< Bronx 169/9 (100%)16/17 (82%)0.004 Brooklyn 41213/15 (87%)7/9 (78%)0.039 Manhattan 4714/15 (93%)17/19 (89%)0.016 Queens 488/8 (100%)5/7 ( 71%)0.091 Staten Island 583/3 (100%) 0.012

Findings Emergency Departments experienced an increase in patients with ILI before Primary Care Providers PCPs were vastly under-utilized during the outbreak NYCDOHMH changed messaging to encourage visiting PCPs instead of EDs for mild illness 41

Future of Syndromic Surveillance 42 Capability to submit syndromic data to health departments Meaningful Use Regional Health Information Organizations (RHIOS), Hubs Data Validation and Quality Assurance

Online Resources CDC Flu Surveillance Distribute 43