BACKGROUND: A seroprevalence survey carried out in four counties in the Tampa Bay area of Florida provided an estimate of cumulative incidence of infection due to the 2009 influenza A (H1N1) as of the end of that year's pandemic in the four counties from which seroprevalence data were obtained Methods. Excess emergency department (ED) visits for influenza-like illness (ILI) during the pandemic period (compared to four non-pandemic years) were estimated using the ESSENCE-FL syndromic surveillance system for the four-county area. RESULTS: There were an estimated 44 infections for every ILI ED visit. Age-specific ratios rose from 19.7 to 1 for children aged <5 years to 143.8 to 1 for persons aged >64 years. CONCLUSIONS: These ratios provide a way to estimate cumulative incidence. These estimated ratios can be used in real time for planning and forecasting, when carrying out timely seroprevalence surveys is not practical. Syndromic surveillance data allow age and geographic breakdowns, including for children.
BACKGROUND: A seroprevalence survey carried out in four counties in the Tampa Bay area of Florida provided an estimate of cumulative incidence of infection due to the 2009 influenza A (H1N1) as of the end of that year's pandemic in the four counties from which seroprevalence data were obtained Methods. Excess emergency department (ED) visits for influenza-like illness (ILI) during the pandemic period (compared to four non-pandemic years) were estimated using the ESSENCE-FL syndromic surveillance system for the four-county area. RESULTS: There were an estimated 44 infections for every ILI ED visit. Age-specific ratios rose from 19.7 to 1 for children aged <5 years to 143.8 to 1 for persons aged >64 years. CONCLUSIONS: These ratios provide a way to estimate cumulative incidence. These estimated ratios can be used in real time for planning and forecasting, when carrying out timely seroprevalence surveys is not practical. Syndromic surveillance data allow age and geographic breakdowns, including for children.
Reliable information about the cumulative incidence of influenza, available in near
real time as the epidemic is unfolding, would be extremely helpful in assessing the
likely near-term course of an epidemic or pandemic, and in supporting
decision-making about the public health response. If the reproductive number R0 of
the epidemic is known, it is possible to estimate the cumulative incidence of
infection at which the epidemic will peak and start its descent. Such information
could be used for example to inform decisions about whether to deploy or to phase
out temporary treatment facilities. Ideally such information would be obtained from
repeated seroprevalence surveys, but these are likely to be resource-intensive and
slow, and cannot be performed at all unless a reliable laboratory test is available
early in the epidemic to detect infection with the agent responsible.During the 2009 Florida pandemic of illness due to the 2009 pandemic influenza A
(H1N1) virus (pH1N1), the Florida Department of Health (FDOH) relied on emergency
department (ED) visits for influenza-like illness (ILI) as an indicator of the
impact of influenza by geographic area and age, but had no way to infer the weekly
or cumulative number of influenza infections in the community from these data in
real time. This project was undertaken to develop a method to use ED visits for ILI
to estimate the number of influenza infections in the community, in a more timely
way than by carrying out a serosurvey (which may in any case not be practical if
appropriate laboratory tests are not available to detect infection with the pandemic
strain.)In late November and early December, 2009, the Centers for Disease Control and
Prevention and the Florida Department of Health (DOH) collaborated on a serologic
survey of antibodies to the 2009 influenzaA/H1N1 virus, among both children and
adults. The methods and results of that survey are described in detail in a
previously published paper 1 . Briefly, the
seroprevalence survey enrolled subjects from a four county region in central Florida
-- Hillsborough, Manatee, Pasco, and Pinellas Counties, -- which include the Tampa
Bay region and the cities of Tampa, St. Petersburg and Clearwater. Leftover sera
were obtained from 657 adult blood donors over the four day period from November 30,
2009 to December 3, 2009, and from 219 children seeking medical care for various
conditions, over the 48 day period from November 14, 2009 to December 31, 2009. All
personal identifiers were removed from the specimens before delivery to DOH. Age,
date of specimen collection, gender, and zip code were retained for all subjects.
This project was deemed not to be human subjects research by the Florida Department
of Health Institutional Review Board. Serologic results were adjusted to reflect
estimated proportions of the population who might have received 2009 H1N1 vaccine
before their blood specimen was obtained, and to reflect estimated sensitivity of
the assay used to detect antibodies to the pandemic virus. The results used here
reflect a hemagglutination inhibition assay titer of ≥40.As judged by multiple surveillance systems available to the Florida Department of
Health 2, the period when sera were obtained
for the serosurvey coincided with the effective end of the pandemic in Florida. Very
little H1N1influenza activity was detected after the first week of December. The
vaccine campaign was just getting started in Florida at the time of this serosurvey;
the vast majority of doses administered were given after this time period. Thus the
period when the sera were obtained was close to optimal to obtain a good estimate of
cumulative immunity due to wild virus infection among Florida residents.Data from Florida facilities participating in CDC’s National Respiratory and Enteric
Virus Surveillance System (NRVESS) indicated that during the September-November
pandemic period, as during the same period in other years, few infections due to
respiratory viruses other than influenza were detected. The exception was the normal
RSV season in central and southern Florida, which showed a steadily rising number of
detections of Respiratory Syncyctial Virus (RSV) from late August through the end of
December.
Methods
Visits to emergency departments for ILIIn 2009, information about ED visits in Florida was available daily in an electronic
format from 135 out of Florida’s 205 licensed acute care hospitals, which account
for more than 70% of the state’s ED visit volume. They are included in Florida’s
Electronic Surveillance System for the Early Notification of Community-Based
Epidemics (ESSENCE-FL). For this analysis, historical ED visit data provided by
facilities enrolled in ESSENCE-FL since 2009 have been included to more closely
simulate the situation in which a jurisdiction has 100% coverage of emergency
department visits in its syndromic surveillance system.The Agency for Healthcare Administration (AHCA) maintains records of emergency
department visits across the state of Florida, based on required reporting by
licensed hospitals. An aggregated data set of all ED visits by facility, month of
visit, and age group (<5 years, 5-17 years, 18-24 years, 25-49 years, 50-64
years, and >65 years) for May through December 2009 for
hospitals in the 4-county project area was obtained from AHCA. These aggregated data
were used to assess the coverage proportion of ESSENCE-FL within the four-county
area. The ratios of AHCA aggregated visit counts by facility to the total ED visit
data from ESSENCE-FL reporting facilities in the four counties by month and by age
group were used to estimate the completeness of ESSENCE data compared to AHCA data,
and to adjust ILI visit counts to account for ED visits not captured in ESSENCE-FL
(Table 1). This completeness ranged from 74.2% for children under age 5 to 91.9% for
persons aged 18 to 24 years, and was 85.2% overall.Syndrome and sub-syndrome categories in ESSENCE-FL are based upon the text of the
patient’s chief complaint. Records are binned into sub-syndromes using weighted
keyword matching 3. The combinations of
weights are logically equivalent to a decision algorithm based on these chief
complaint text elements. The ILI syndrome is created by the combination of Influenza
or (Fever and (Cough or Sore Throat)) sub-syndromes, so that patient records with
chief complaints of fever and cough, fever and sore throat, influenza, flu, flulike,
H1N1, swine flu, and avian flu, or combinations of these, are included. Records with
chief complaints that include flu shot, stomach flu, vaccination, or immunization
are excluded. The free-text terms H1N1 and swine flu were added to the ESSENCE-FL
sub-syndrome definitions in April 2009 and remained in place throughout the rest of
the period covered by this analysis.ESSENCE-FL visit data for ILI were extracted by facility and by age group for Week 21
2009 through Week 44 2009 for patients who resided in one of the four project
counties. Week 21, 2009, was the first week where public health laboratory data
indicated that >50% of influenza confirmations were pH1N1. Week 44, 2009, was 14
days before median date of serum collection, allowing time for antibody development.
In 2009, week 21 was the week ending May 30, and week 44 was the week ending
November 7. Data were also obtained for weeks 21 through 44 of calendar years 2007,
2008, 2010 and 2011. Data are shown in the Figure as counts by week, with each
year's MMWR weeks superimposed. This allows a visual assessment of the year-to-year
similarity of these data in the four non-pandemic years, and the striking magnitude
and timing of ILI visits during the pandemic period of 2009.For each week from 21 to 44 in each of these five years, for each of the six age
groups used in this analysis, the percentage of ED visits assigned to the ILI
syndrome was ascertained. (Percentages were used rather than absolute numbers of ED
visits for ILI, as the number of ESSNCE-FL participating hospitals was steadily
rising during the period 2007-2009. Data before 2007 were and are too sparse to use
as part of the baseline.) These percentages for the years 2007, 2008, 2010, and 2011
were averaged to obtain a baseline to which the pandemic year could be compared. For
each week and age group, the excess ILI visit % was calculated by subtracting this
baseline value from the 2009 value. For each week and age group, the excess ILI
visit % was then expressed as a percentage of the 2009 percent ILI, to obtain the
percentage of ILI visits that were excess visits. The actual number of 2009 ED
visits for ILI, by week by age group, was multiplied by this percentage to obtain an
estimated excess number of visits (compared to the expected number). Finally, all
the excess ILI counts by week for each age group were summed to obtain an estimated
number of excess ED visits for ILI by age group for the pandemic period from week 21
to week 44.This analysis is based on routinely collected public health surveillance data,
collected by the Florida Department of Health as authorized by law. Personal
identifiers are not accessible to Florida Department of Health staff in ESSENCE-FL.
Activities carried out as part of the Department’s surveillance function are not
submitted to the Department’s Institutional Review Board for approval.Estimates of Infected IndividualsThe proportion of people infected with pH1N1 by age group was determined based on
serology results from the CDC’s Tampa Bay area seroprevalence survey 1. Serologic results were adjusted to reflect
estimated proportions of the population who would have received 2009 pH1N1 vaccine
before their blood specimen was obtained, and to reflect estimated sensitivity of
the assay used to detect antibodies to the pandemic virus. The results used here
reflect a hemagglutination inhibition assay titer of ≥ 1:40. This proportion was
then multiplied by age-specific 2009 population estimates for the four counties
obtained from Florida CHARTS 4 to provide an
estimate of infected individuals within the four-county region.
Results
Comparisons by age group for May through December 2009 showed a range for
completeness of ESSENCE visit data of 74.2 to 91.9% and no trend over time (data not
shown).The Figure shows the number of ILI visits by week for the state of Florida for
calendar years 2007, 2008, 2009, 2010, and 2011. Weeks 17 and 18 of 2009 immediately
followed the announcement of the detection of a novel influenza virus in California
and Mexico. During those two weeks, the vast majority of influenza isolates in the
Florida Bureau of Laboratories, for the state as a whole and for our four-county
Tampa Bay region, were for other viruses (seasonal A/H3N2, seasonal A/H1N1, and B).
The proportion of influenza isolates that were pH1N1 first reached 50% in week
21.Table 1. Estimation of Ratio of Number of Persons Infected with
2009 H1N1 Virus to Excess Number of Emergency Department Visits, in
Pinellas, Hillsborough, Manatee and Pasco Counties, Autumn
2009.Applying the results of the seroprevalence survey to the four-county population (see
Table), there were an estimated 722,966 persons infected with pH1N1 virus by early
December. There were 18,405 ILI visits at Tampa Bay emergency departments from weeks
21 through 44, 2009. Of these, 13,764 (or 74.8%) were excess visits compared to our
baseline from 2007, 2008, 2010 and 2011. Since 85.3% of all ED visits during May
through December of 2009 were captured by ESSENCE, we estimated that there would
have been a total of 13,764/0.853 or 16,136 excess visits in all Tampa Bay
facilities. Similar calculations were done for each age group. Thus the estimated
ratio of number of ED visits to number of people infected with pH1N1 was 16,136 to
722,966, or 1 to 44.0. This ratio varies from 1 to 19.7 in children aged 1 to 4
years, to 1 to 143.8 in persons aged 65 and over. The proportion of infections by
age group is generally similar to the proportion of excess ED visits, with some
variation. For example, 13.1% of excess ED visits were for children under age 5,
while 9.7% of estimated infections were in that age group. For persons aged 65 years
and older, 2.3% of excess ED visits and 6.5% of infections were in that age
group.
Discussion
During future influenza epidemics and pandemics, these estimates would allow the FDOH
to make daily estimates of the cumulative number of infected persons with influenza
by age group. Mathematical models indicate that the prevalence of immunity at which
the epidemic will peak and start to decline is a function of the effective R0,
the average number of people infected by each infected person 5. Being able to estimate cumulative
population prevalence in real time thus provides information to those organizing the
response to the epidemic, to address questions such as “How soon will the epidemic
peak and start to decline” and “Is it necessary to plan for healthcare services for
many more cases than we are seeing now?” Methods are available to estimate the
effective R0 of an epidemic from prospectively collected influenza
surveillance data 5, though they are subject
to error, and to estimate the equilibrium seroprevalence using the R0
value 6.During the fall of 2009, ED visit data suggested a disproportionate impact of this
influenza epidemic on children aged less than 5 years old, with 18.0% of visits
occurring in this group. Different decisions might have been made about priority
groups for vaccine as it became available had it been known that only 13.1% of
excess ED visits and 9.7% of infections were in this age group.These estimates are subject to several limitations. As discussed in reference 1, estimated prevalence of antibodies due to
wild virus infection from the serosurvey are subject to error. Future seasonal or
pandemic influenza viruses may differ in average illness severity from that observed
here, and thus the ratio of ED visits to infections may be higher or lower; and the
distribution of severity by age may differ. Future versions of ESSENCE or other
syndromic surveillance systems may change how they identify ED visits as being due
to ILI, and thus reduce the generalizability of these results. Ratios based on
cumulative infection percentages might not apply earlier in the pandemic. This
method depends on having available stable baseline values of ED visits for ILI in
non-pandemic years available, so that excess ILI visits may be estimated compared to
expected values for each combination of week and age group.During the pandemic period, essentially no other influenza viruses besides pH1N1 were
being isolated in the Florida Bureau of Laboratories. Laboratories doing routine
testing for RSV, and participating in the National Respiratory and Enteric Viruses
Surveillance System (NREVSS) did show the expected seasonal increase in RSV
detections during the period of the pH1N1 pandemic. When baseline ED visits for ILI
from the non-pandemic years were compared to observed ILI visits for children under
age 5, almost half of the total visits were accounted for by the baseline.In a seasonal influenza epidemic, a high prevalence of partial pre-existing immunity
to circulating viruses might alter the distribution of severity by age group
compared to the 2009 pandemic, in which most people over age 65 had pre-existing
immunity but younger people did not. This could limit the generalizability of these
ratios during seasonal influenza.In spite of these limitations, the FDOH plans to use the observed ratios between
number of ED visits for influenza-like illness and number of infected persons,
derived from this analysis, to assist in understanding the epidemiology of future
outbreaks and pandemics of influenza. Similar analyses of data from other
jurisdictions that carried out both a serosurvey and syndromic during the 2009
pandemic, or during other pandemics and during non-pandemic years, would help refine
these estimates and help determine whether these estimated ratios are
reproducible.
Authors: Chad M Cox; Kate Goodin; Emily Fisher; Fatimah S Dawood; Janet J Hamilton; German F Leparc; Monica Gray; Linda Nelson; Rebekah H Borse; James A Singleton; Carrie Reed; Amanda L Balish; Jacqueline M Katz; Richard S Hopkins; Alicia M Fry Journal: PLoS One Date: 2011-12-20 Impact factor: 3.240
Authors: Richard S Hopkins; Catherine C Tong; Howard S Burkom; Judy E Akkina; John Berezowski; Mika Shigematsu; Patrick D Finley; Ian Painter; Roland Gamache; Victor J Del Rio Vilas; Laura C Streichert Journal: Public Health Rep Date: 2017 Jul/Aug Impact factor: 2.792
Authors: Kyle S Hickmann; Geoffrey Fairchild; Reid Priedhorsky; Nicholas Generous; James M Hyman; Alina Deshpande; Sara Y Del Valle Journal: PLoS Comput Biol Date: 2015-05-14 Impact factor: 4.475
Authors: Howard Burkom; Wayne Loschen; Richard Wojcik; Rekha Holtry; Monika Punjabi; Martina Siwek; Sheri Lewis Journal: JMIR Public Health Surveill Date: 2021-06-21