Near-Real Time Assessment of Seasonal and Pandemic Influenza Vaccine Coverage, Using Private Sector Surveillance Systems Lone Simonsen, George Washington University and SDI Laurel Edelman, Lindsay Jones, Glenna Rohlfing, Farid Khan, Joel Greenspan, SDI 44th National Immunization Conference, Atlanta April 19, 2010

2 Background and Objective  CDC Sources of Influenza Vaccine Coverage data  BRFSS estimates  Random digit phone  National H1N1 Flu Survey (NHFS)  Random digit phone, ~1,500/week  Special survey for season  Could these be augmented with private sector data?  Improve reporting timeliness  Automatize  Flexibility (age, geography, gender, risk groups)  Pandemic AND seasonal influenza coverage simultaneously  Dose uptake patterns at high spatial/temporal granularity

3 Two Private Sector Data Sources  SDI Sentinel ILI Surveillance Network  Collects clinical, demographic and virological data from  ~15,000 patients with influenza-like illness (ILI) seen in ~400 primary physicians offices across the US each season.  A convenience sample, represents 44 US States  Medical Claims CMS-1500 data  Influenza administration by  CPT procedure code for injectable and inhaled vaccine  Administration code  Can also track disease patterns (ICD9 coded)

4 1. SDI Sentinel ILI Surveillance Network  Physicians testing 2-4 patients/week from Oct-May with ILI (Influenza-Like Illness)  ILI Case Definition: Fever+Resp symptoms  Data Collected on forms  Demographics  medical history and treatment  current symptoms  vaccination status (received in ANY setting)  Influenza laboratory confirmation  Quidel Quickvue Influenza A + B test kit.

5 SDI Sentinel Patients, season: As of April 16, ~12,000 ILI patients 2 nd pandemic wave Sept-Nov 2009

6 SDI Sentinel Patients Under Study, by age and week 2 nd pandemic wave Sept-Nov 2009

7 Generating Vaccination Coverage Estimates using SDI Sentinel Surveillance System  Define population under study  All ILI patients who test lab-negative for influenza virus as population “proxy”  Stratifications  Age groups: Any  Spatial and Temporal patterns  Pandemic versus Seasonal  Injectable (TIV) versus Inhaled (LAIV)  High risk status  Validation  Compare coverage estimates with BRFSS for back years

8 Validation: Comparing SDI sentinel coverage estimates to CDC-BRFSS, season High risk defined as “any mention of chronic conditions” by SDI

9 VALIDATION: Comparing NHFS and SDI, 1+ dose coverage around Jan SourceTypeKids <5 years Kids 5-18 years Adults years Seniors 65+ years NHFS Period: Dec27-Jan4 N=~1.500 persons Response rate: 43-57% 33% [22-44%] 28% [22-35%] 19% [ 15-23%] 11% [ 7-16%] SDI Sentinel Period: Jan 2010 N=1028 patients 26%18%10%12% SDI estimates well within the CDC-NHFS 95%CI for all age groups NHFS data from MMWR Jan 22, 2010

10 Pandemic Vaccine Coverage, SDI Sentinel

11 Seasonal and Pandemic Vaccine Combinations Relative Proportions of LAIV vs TIV received, Seasonal Vaccine, by Age

12 2. Using CMS-1500 Office Visit Claims Data to track influenza vaccine administration in physician offices Doctor sees patient, codes diagnoses and procedures “Switch” directs claim to correct payer SDI De-identified CMS records Synthetic ID patient key Payer processes claim Timeliness: 1-2 weeks lag Sample Size: ~50% of all outpatient visits, 300,000 physicians Representativeness: All States, Ages, Medicare, Medicaid, Private payers $ Analyze CPT (procedure) codes listed for Vaccine and administration submits claim electronically

13 Using SDI projected CMS-1500 data to study patterns in vaccine uptake in physician’s offices From CDC-SDI Collaboration D.S.I.P.H.E.R

14 Pandemic Vaccine Uptake Pattern >=95% of vaccinations achieved AFTER the 2nd pandemic wave 2nd Pandemic Wave Period Sept-Nov2009

15 Conclusion  Private sector has much to offer in terms of tracking vaccine dose administrations and vaccine coverage  SDIs Sentinel ILI Surveillance Network can be used to track age-specific National and Regional influenza vaccine coverage RECEIVED IN ANY SETTING in a timely manner  SDIs large sample of CMS-1500 claims data can be used to track NEAR-REAL TIME patterns in influenza vaccine DOSE uptake ADMINISTERED IN PHYSICIANS OFFICES, with tremendous temporal/spatial resolution

16 Acknowledgments  Thanks to CDC colleagues who have been working alongside SDI investigators to develop the best use of claims data to track influenza and immunizations  Eric Kasowski, Dan Jernigan, CDC Influenza Division  Gary Euler and colleagues, CDC Immunization Division  Funding: CDC Contracts  We also acknowledge Roche for funding parts of SDIs Sentinel Influenza Network