1 Using ESSENCE-FL and a serosurvey to estimate total influenza infections, 2009 Richard S. Hopkins, MD, MSPH Kate Goodin, MPH Mackenzie Weise, MPH Aaron Kite-Powell, MS Janet Hamilton, MPH CSTE annual meeting Omaha, Nebraska June, 2012

2 Outline Quick review of 2009 H1N1 epidemic in FL Missing information during response: cumulative number of infected people Seroprevalence survey – Cox et al  Number infected, by age group at end of pandemic Total ED visits for ILI, by age group ILI illnesses reported by BRFSS respondents, by age group Ratio of infections to ED visits Ratio of infections to reported illnesses in BRFSS How these ratios can be used going forward

3 Percentage of Visits for Influenza-like Illness (ILI) Reported by the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet), National Summary and Previous Two Seasons First cases detected 2- PCR testing available at CDC 3- Vaccine available

4 Florida Influenza Surveillance Systems ESSENCE ILI chief complaint  hospital admissions for ILI through EDs Florida Pneumonia and Influenza Mortality Surveillance System (FPIMSS) (modeled on 122 Cities system) Notifiable disease reporting (through Merlin)  Novel flu  Pediatric influenza deaths County influenza activity code reporting Influenza and ILI outbreak reporting (through EpiCom) Florida ILINet sentinel surveillance Bureau of Laboratories viral surveillance

5 Influenza-Like Illness in ESSENCE --Timely, accurate characterization of 75% of all emergency department room visits --Added discharge disposition as a field, 87 hospitals now reporting

6 County Flu Activity Reporting Week 3, 2010Week 17, 2009Week 37, 2009

7 Bureau of Laboratories Viral Surveillance --Novel H1N1 consistently sensitive to oseltamivir and zanamivir --Almost all influenza circulating in Florida was H1N1

8 What we were missing In September-November 2009, the DOH incident commander wanted to know answers to questions like: How big is this going to get? Have we reached the peak yet? How long is it going to last? Will the burden on EDs and hospitals remain tolerable?

9 Influenza modeling A key parameter for any influenza epidemic is R: the average number of new infections resulting from each infection For any given value of R, you can calculate the final cumulative % infected – higher R means higher cumulative % R can be estimated from epidemiologic data early in the epidemic (slope of curve) We could use a tool that would tell us how close we are to the eventual predicted prevalence

10 Seroprevalence study We requested this from CDC Chad Cox led CDC team that visited FL Chose Tampa Bay area as project site because of good availability of blood bank specimens for adults – all specimens from a four-day range Getting leftover blood specimens for children was more of a challenge, resulting in spread-out specimen collection range

11 Adult samples collected Pediatric collection time frame * Includes Hillsborough, Manatee, Pinellas and Pasco counties Percentage of ED Visits for Influenza-like Illness, Tampa Bay* Florida,

12 Summary Location – Tampa Bay (Pinellas, Pasco, Hillsborough and Manatee counties) Sample Population – Anonymous blood samples from clinical laboratories and blood bank Number Sampled total samples, 219 from children aged <18 years Testing type – HI assay ≥20-40

H1N1 Seroprevalence by Age Group, Unadjujsted Tampa Bay Florida, Nov-Dec 2009

14 Adjusted prevalences In the final analysis, the prevalence of antibody was adjusted to account for the small proportion who would have received vaccine before serum was obtained Also adjusted for estimated sensitivity of the assay in detecting antibody in infected people

15 Estimated number of infected people Applying seroprevalence from Tampa Bay to whole state Age groupPopulation % infected with pH1N Florida population Estimated number infected with pH1N1 < 5 years 28% 1,136, ,305 5 to 17 years 53% 3,013,5711,597, to 24 years 47% 1,685, , to 49 years 19% 6,082,0231,155, to 64 years 11% 3,598, ,820 > 64 years 9 % 3,302, ,235 Total 25%18,819,0004,704,750

16 Estimating total ED visits for ILI Age group% of ED visits at ESSENCE EDs # of ILI visits at ESSENCE EDs Adjusted estimate of ILI visits < 5 years ,490 37,591 5 to 17 years ,985 53, to 24 years ,524 21, to 49 years ,683 44, to 64 years ,441 10,209 > 64 years ,303 4,476 Total ,426170,674

17 Ratios of pH1N1 infections to ED visits for ILI Age group < 5 years 8.5 to 1 5 to 17 years 30.0 to 1 18 to 24 years 36.5 to 1 25 to 49 years 26.0 to 1 50 to 64 years 39.0 to 1 > 64 years 66.0 to 1 Total 27.5 to 1

18 Additional observations 22.3% of ED visits were for children under age 5, while only 6.7% of estimated infections were in that age group.  Were children sicker than adults?  Were parents were more likely to take children to ED than themselves? For persons aged 50 to 64 years, 6.0% of ED visits and 8.4% of infections were in that age group.

19 Similar calculations from BRFSS During October through December, FL BRFSS asked respondents about ILI, defined as fever and (cough or sore throat) Date of onset was assigned based on date of interview and respondent’s time interval (last week, last 2 weeks, last 3 to 4 weeks) Ratios calculated for seroprevalence to reported ILI

20 Ratios of pH1N1 infections to BRFSS report of ILI Age group < 5 years no data 5 to 17 years no data 18 to 24 years 13.1 to 1 25 to 49 years 10.7 to 1 50 to 64 years 3.6 to 1 > 64 years 11.6 to 1 Total 8.7 to 1

21 Why such high ratios? Most infections were apparently quite mild Most ill people do not make a healthcare visit, much less visit an ED For our purposes, it doesn’t matter – antibodies resulting from mild infections are probably just as good at stopping the spread of a pandemic as those from severe illness

22 Epidemic curve from ESSENCE data alone

23 New estimates of total infections by week using these ratios

24 How will we use these results? We intend to publish them in the peer-reviewed literature We hope others will do similar analyses If faced with another influenza epidemic or pandemic, we can use these ratios to estimate the cumulative number of infections Combined with mathematical models, the cumulative # of infections can help tell us whether we are near or past the peak of the epidemic

25 Acknowledgements County health department epidemiologists Hospitals participating in ESSENCE CDC seroprevalence study team, led by Dr. Chad Cox