Introduction
The H1N1 2009 influenza (pH1N1) pandemic has resulted in over 209,000 laboratory-confirmed
cases and over 3,205 deaths worldwide as of 11 September 2009 (http://www.who.int/csr/don/2009_09_11/en/index.html,
accessed 14 September 2009), but national and international authorities have acknowledged
that these counts are substantial underestimates, reflecting an inability to identify,
test, confirm, and report many cases, especially mild cases. Severity of infection
may be measured in many ways, the simplest of which is the case-fatality ratio (CFR),
the probability that an infection causes death. Other measures of severity, which
are most relevant to the burden a pandemic exerts on a health care system, are the
case-hospitalization and case-intensive care ratios (CHR and CIR, respectively), the
probabilities that an infection leads to hospitalization or intensive care unit (ICU)
admission. In the absence of a widely available and validated serologic test for infection,
it is impossible to estimate these quantities directly, and in this report we instead
focus on the probabilities of fatality, hospitalization, and ICU admission per symptomatic
case; we denote these ratios sCFR, sCHR, and sCIR respectively.
Although it is difficult to assess these quantities, estimates of their values and
associated uncertainty are important for decision-making, planning, and response during
the progression of this pandemic. Initially, some national and international pandemic
response plans were tied partly to estimates of the CFR, but such plans had to be
modified in the early weeks of this pandemic, as it became clear that the CFR could
not at that time be reliably estimated [1]. Costly measures to mitigate the pandemic,
such as the purchase of medical countermeasures and the use of disruptive social distancing
strategies may be acceptable to combat a more severe pandemic but not to slow a milder
one. While past experience [2] and mathematical models [3]–[5] suggest that between
40% and 60% of the population will be infected in a pandemic with a reproduction number
similar to those seen in previous pandemics, the number of deaths and the burden on
the health care system also depend on the age-specific severity of infection, which
varies by orders of magnitude between pandemics [6] and even between different waves
in the same pandemic [7]. Reports from the Southern Hemisphere suggest that a relatively
small fraction of the population experienced symptomatic pH1N1 infection (7.5% in
New Zealand, for example [8]), although these numbers are considered highly uncertain
[8]. On the other hand, primary care utilization for influenza-like illness (ILI)
has been considerably higher than in recent years [8], and anecdotal reports in the
Southern Hemisphere have indicated that some intensive care units (ICUs) have been
overwhelmed and surgery postponed due to a heavy burden of pH1N1 cases [9],[10].
The problem of estimating severity of pH1N1 infection includes the problem of estimating
how many of the infected individuals in a given population and time period subsequently
develop symptoms, are medically attended, hospitalized, admitted to ICU, and die due
to infection with the virus. No large jurisdiction in the world has been able to maintain
an accurate count of total pH1N1 cases once the epidemic grew beyond hundreds of cases,
because the effort required to confirm and count such cases is proportionate to the
size of the exponentially growing epidemic [11], making it impossible to reliably
estimate the frequency of an event (e.g., death) that occurs on the order of 1 in
1,000 patients or fewer. As a result, simple comparisons of the number of deaths to
the number of cases suffer from underascertainment of cases (making the estimated
ratio too large), and underascertainment of deaths due to inability to identify deaths
caused by the illness and due to delays from symptom onset to death (making the estimated
ratio too small) [1]. Imperfect ascertainment of both numerator and denominator will
lead to biased estimates of the CFR. Estimating the number of persons at these varying
levels of severity therefore depends on estimating the proportion of true cases that
are recognized and reported by existing surveillance systems. Similar problems affect
estimates of key parameters for other diseases, such as HIV. In HIV, a solution to
this problem—which now forms the basis for the UK's annual HIV prevalence estimates
published by the Health Protection Agency [12],[13]—has been to synthesize evidence
from a variety of sources that together provide a clearer picture of incidence, prevalence,
and diagnosis probabilities. This synthesis is performed within a Bayesian framework
that allows each piece of evidence, with associated uncertainties, to be combined
into an estimate of the numbers of greatest interest [14],[15].
Here we use a similar framework to synthesize evidence from two cities in the United
States—New York and Milwaukee—together with estimates of important detection probabilities
from epidemiologic investigations carried out by the US Centers for Disease Control
and Prevention (CDC) and other data from CDC. We estimate the severity of pH1N1 infection
from data from spring–summer 2009 wave of infections in the United States. The New
York City and Milwaukee health departments pursued differing surveillance strategies
that provided high-quality data on complementary aspects of pH1N1 infection severity,
with Milwaukee documenting medically attended cases and hospitalizations, and New
York documenting hospitalizations, ICU/ventilation use, and fatalities. These are
the numerators of the ratios of interest.
The denominator for these ratios is the number of symptomatic pH1N1 cases in a population,
which cannot be assessed directly. We use two different approaches to estimate this
quantity. In the first (Approach 1), we use self-reported rates of patients seeking
medical attention for ILI from several CDC investigations to estimate the number of
symptomatic cases from the number of medically attended cases, which are estimated
from data from Milwaukee. In the second (Approach 2), we use self-reported incidence
of ILI in New York City, and making the assumption that these ILI cases represent
the true denominator of symptomatic cases, we directly estimate the ratio between
hospitalizations, ICU admissions/mechanical ventilation, and deaths (adjusting for
ascertainment) in New York City. Each of these two methods provides estimates for
the general population, and also for broad age categories 0–4, 5–17, 18–64, and 65+
years. The result of each approach is a tiered severity estimate of the pandemic.
Methods
Methods Overview
The overall goal of this study was to estimate, for each symptomatic pH1N1 case, the
probability of hospitalization, ICU admission or mechanical ventilation, or death,
overall and by age group. The challenge is that in any population large enough to
have a significant number of patients with these severe outcomes, there is no reliable
measure of the number of symptomatic pH1N1 cases. This problem was approached in two
ways. Approach 1 was to view the severity of infection as a “pyramid” [16], with each
successive level representing greater severity; to estimate the ratio of the top level
to the base (symptomatic cases), we estimated the ratios of each successive level
to the one below it (Figure 1, left side). Thus we broke down (for example) the sCFR
(Figure 1, black), i.e., the probability of death per symptomatic case, into components
for which data were available – the probability of a case coming to medical attention
given symptomatic infection (CDC survey data); the probability of being hospitalized
given medical attention (Milwaukee data); and the probability of dying given hospitalization
(New York data, including a correction for those who died of pH1N1 but were not hospitalized).
Approach 2 was to use the self-reported incidence of ILI from a telephone survey in
New York City as the estimate of total symptomatic pH1N1 disease, and the total number
of confirmed deaths in New York City as the estimate of the deaths (after accounting
for imperfect ascertainment, in this case due to possibly imperfect viral testing
sensitivity). In each case, prior distributions were used to quantify information
on the probability that cases at each level of severity were detected; these prior
distributions reflected the limited data available on detection probabilities and
associated uncertainty.
10.1371/journal.pmed.1000207.g001
Figure 1
Diagram of two approaches to estimating the sCFR.
Approach 1 used three datasets to estimate successive steps of the severity pyramid.
Approach 2 used self-reported ILI for the denominator, and confirmed deaths for the
numerator, both from New York City. Both approaches used prior distributions, in some
cases informed by additional data, to inform the probability of detecting (confirming
and reporting) cases at each level of severity (not shown in the diagram; see Text
S1). The Bayesian evidence synthesis framework was used as a formal way to combine
information and uncertainty about each level of severity into a single estimate and
associated uncertainty that reflected all of the uncertainty in the inputs.
All of these estimates were combined within a Bayesian evidence synthesis framework.
This framework permits the estimation of probabilities for the quantities of interest
(the sCFR, sCIR, and sCHR) and associated uncertainty (expressed as credible intervals
[CIs]). These credible intervals appropriately reflect the combined uncertainties
associated with each of the inputs to the estimate—mainly, the true numbers of cases
at each level of severity, after accounting for imperfect detection—as well as the
uncertainties due to sampling error (chance).
Study Populations
Data were obtained from enhanced pandemic surveillance efforts by the City of Milwaukee
Health Department and the New York City Department of Health and Mental Hygiene (DOHMH).
Details of testing policies, data acquisition, and analysis are given in Text S1.
All data were analyzed first in aggregate and then by age category.
Milwaukee Data
Between April 6 and July 16, 2009, Milwaukee recorded 3,278 confirmed cases and four
deaths due to pH1N1, reflecting sustained efforts to test patients reporting ILI and
their household contacts from the start of the epidemic in April until mid-July. On
April 27, Milwaukee initiated protocols including recommendations for testing persons
with influenza symptoms and travel history to areas reporting novel H1N1 cases, using
a reverse transcriptase polymerase chain reaction (RT-PCR) test specific for pH1N1.
By May 7, Milwaukee issued testing guidance updated to recommend testing persons with
moderate to severe symptoms, except that testing continued to be recommended for health
care workers with mild, moderate or severe symptoms. We used a line list dated July
21, and in a preliminary analysis examined the frequency of hospitalization among
cases by “episode date” (the earliest date in their case report). The proportion of
confirmed cases hospitalized was stable around 3% up to May 20, after which it increased
markedly to 6%–8% in the following weeks. We judged that this change reflected reduced
testing of mild cases and limited our analysis (used to inform the ratio of hospitalizations
to medically attended cases) to the 763 cases with an episode date up to or including
May 20. While Milwaukee data were not the main source of estimates of ICU admission
or death probabilities, we did employ hospitalized cases up to an episode date of
June 14 to contribute to estimates of the ratio of deaths or ICU admissions to hospitalizations,
since these should not be affected by failure to test mild cases.
New York Case Data
New York City maintained a policy from April 26 to July 7, 2009 of testing hospitalized
patients with ILI according to various criteria. These criteria evolved up to May
12, from which point they remained as follows: all hospitalized ILI patients received
a rapid influenza antigen test. Those patients who tested positive on rapid test (which
is known to have low sensitivity for seasonal influenza [17] and for pH1N1 [18]),
and any patient in the ICU or on a ventilator, regardless of rapid test result, received
RT-PCR tests for pH1N1. We obtained a line list of confirmed or probable hospitalized
cases dated July 7, and found in a preliminary analysis that all patients in this
line list had a date (onset or admission) in their record no later than June 30, 7
d prior to the date of the line list. Given that >90% of hospitalizations were reported
in New York within 7 d, we used this entire line list without accounting for delays
in reporting of hospitalizations. Also, given that 98% of admissions occurred after
May 12, we did not attempt to account for changes in testing practices before May
12. This line list included a field indicating whether the patient had been admitted
to the ICU or ventilated; patients were not followed up after admission to determine
if this status changed. However, a chart review of 99 hospitalized cases indicated
that none had been admitted to the ICU after admission, so no effort was made to account
for this limitation.
Separately, we obtained a list of 53 patients whose deaths were attributed to pH1N1,
of whom 44 (83%) had been hospitalized before dying. All patients with known influenza
or unexplained febrile respiratory illness at the time of death had postmortem samples
and/or samples taken before they died sent for PCR testing.
New York Telephone Survey Data
To estimate levels of ILI in New York City, DOHMH conducted 1,006 surveys between
May 20 and May 27, 2009, and 1,010 between June 15 and June 19. Interviews lasted
5 min and were conducted with households in both English and Spanish. The survey used
a random-digit dialing (RDD) telephone sampling methodology to obtain data from a
random sample of residential households in New York City. A nonrandom individual from
each selected household was interviewed and provided information about all household
members. Sampled numbers were dialed between five and 15 times to contact and interview
a household, or until the sampled number was determined to be nonworking.
To account for this design, the data were weighted to the 2007 American Community
Survey (ACS); respondents were weighted to householders by borough, age, gender, and
race/ethnicity, and the population was weighted by age to the borough of residence.
The survey's RDD sampling methodology gave a useful overview of ILI in the community,
but it has limitations. The design does not include individuals living in households
only reachable by cellular telephone but not by a landline telephone number, and it
omitted those living in group or institutional housing. Although households were randomly
selected, for the sake of efficiency the interviewed adult was not. Instead, an available
adult in the household provided information about all household members and themselves,
which may have introduced bias. The results of the survey are being compiled for publication
elsewhere. Here, we use summaries of these results by age group (see Text S1) as one
means to provide denominators of symptomatic cases.
Data on Detection Probabilities from CDC Investigations
Sources of data include two community surveys on ILI and health-seeking behavior,
and two field investigations conducted during early outbreaks of pH1N1 in the US.
These sources are described in further detail elsewhere [19], but are summarized here
briefly. In 2007, the Behavioral Risk Factor Surveillance Survey (BRFSS), an RDD telephone
survey, included a module on ILI in nine states. This module included questions to
assess the incidence of ILI, health-seeking behavior, physician diagnosis of influenza,
and treatment of influenza with antiviral medications during the annual 2006–2007
influenza season. In May 2009, following the emergence of pH1N1, an RDD telephone
survey sampled similar to the BRFSS was conducted in the same nine states using only
the ILI module from the 2007 BRFSS and limited demographic questions. In addition,
some data were available from field investigations conducted during large outbreaks
of pH1N1 in one community in Chicago and a university campus in Delaware. Investigations
of these outbreaks consisted of household interviews in a Chicago neighborhood and
an online survey of students and faculty in Delaware. These data were used to inform
detection probabilities. In addition, these data were used to inform a prior distribution
on the ratio between symptomatic and medically attended cases, cM
|S
: these surveys estimated that between 42% and 58% of symptomatic ILI patients sought
medical attention [19].
Analysis
Estimation of the probabilities of primary interest, cH
|S
, cI
|S
, and cD
|S
, respectively the sCHR, sCIR, and sCFR, was undertaken using a Bayesian evidence
synthesis framework [14]. Details are given in Text S1, and a schematic illustration
of the model is given in Figure 2. Briefly, in this framework, prior information about
the quantities of interest (including the uncertainty associated with this prior information)
is combined with the information coming from the observed cases at each severity level
to derive a posterior distribution on these quantities. This posterior distribution
fully reflects all information about the quantities of interest that is contained
in the prior distribution and the observed data. Specifically, it was assumed that
detected cases O at each level of severity—medically attended (M), hospitalized (H),
ICU-admitted (I), and fatal (D)—represented binomially distributed samples from the
true number of cases N at the corresponding level of severity, in the given location
(New York, abbreviated N or Milwaukee, abbreviated W), with probability equal to the
probability of detection at each level (d). The probability d for each level was informed
by evidence on the probability of testing at each level of severity (which may have
depended on the sensitivity of the rapid test if this was required for PCR testing)
and the sensitivity of the PCR test (Table 1). Thus, for example, we defined the probability
of detecting a hospitalized case in New York as dHN = dHN1dHN2
, where dHN1
was the probability of performing an RT-PCR–based test and dHN2
was the sensitivity of that test. Hence, the observed number of hospitalized patients
in New York, OHN
, was assumed to be distributed as Binomial(NHN,dHN
).
10.1371/journal.pmed.1000207.g002
Figure 2
Schematic illustration of the relationship between the observed data (rectangles)
and the conditional probabilities (blue circles).
The key quantities of interest, sCHR, sCIR, and sCFR, are products of the relevant
conditional probabilities. (A) Approach 1, synthesizing data from New York City and
Milwaukee. Note that cM
|S
(double circle) is informed by prior information [19] rather than observed data. (B)
Approach 2, using data from New York City only, including the telephone survey. Variables:
cD
|M
: the ratio of non-hospitalized deaths to medically-attended cases; cD
|H
: the ratio of deaths to hospitalized cases; cI
|H
: the ratio of cases admitted to intensive care or using mechanical ventilation to
hospitalized cases; cH
|M
: the ratio of hospitalized cases to medically attended cases; cM
|S
: the ratio of medically attended cases to symptomatic cases; cD
|S
: the ratio of deaths to symptomatic cases; cH
|S
: the ratio of hospitalized cases to symptomatic cases.
10.1371/journal.pmed.1000207.t001
Table 1
Detection probabilities and their prior distributions.
Detection Probability
Components
Distributions
Rationale
dM
Medically attended illness
dM
1 probability of testing, follow-up, and reporting among medically attended patients
Uniform (0.2,0.35)
Data from CDC epi-aids in Delaware and Chicago [19]
dM
= dM
1
dM
2
dM
2 PCR test sensitivity
Uniform (0.95,1)
Assumption [19]
dHW
Hospitalization (Milwaukee)
dHW1
probability of testing, follow-up, and reporting among hospitalized patients
Uniform (0.2,0.4)
Assumption [19]
dHW
= dHW
1
dHW
2
dHW
2 PCR test sensitivity
Uniform (0.95,1)
Assumption [19]
dIW
ICU admission (Milwaukee)
dIW
1 probability of testing, follow-up and reporting among hospitalized patients
Uniform (0.2,0.4)
Assumption [19]
dIW
= dIW
1
dIW
2
PCR test sensitivity
Uniform (0.95,1)
Assumption [19]
dDW
Deaths (Milwaukee)
PCR test sensitivity and other detection
Beta (45,5)
Assumption [19] (mean 0.9, standard deviation 0.05)
dHW
Hospitalization (New York City)
dHN
1 probability of performing PCR (rapid A positive or ICU/ventilated)
0.27+0.73 (Uniform (0.2,0.71))
27% of cases were ICU-admitted so received PCR test; remainder were tested if rapid
A positive, which has a sensitivity of 0.2 [17] to 0.71 (sensitivity among ICU patients
in NYC)
dHN
= dHN
1
dHN
2
dHN
2 PCR test sensitivity
Uniform (0.95,1)
Assumption [19]
dIN
ICU/ventilation (New York City)
PCR test sensitivity
Uniform (0.95,1)
Assumption [19]
dDN
Deaths (New York City)
PCR test sensitivity and other detection
Beta (45,5)
Assumption [19] (mean 0.9, standard deviation 0.05)
We noted that the ratios cH
|S
, cI
|S
, and cD
|S
can be built up multiplicatively from simpler components: for instance, the ratio
of deaths to symptomatic infections may be expressed as cD
|S
= cD
|H
cH
|M
cM
|S
, the product of the ratios of deaths:hospitalizations, of hospitalizations:medically
attended cases, and of medically attended cases:symptomatic cases. These ratios of
increasing severity are similar to conditional probabilities but are not strictly
so in all cases, since for example some deaths in New York City occurred in persons
who were not hospitalized. For this reason we model deaths separately among hospitalized
and nonhospitalized patients, i.e., cD
|S
= cD
|H
cH
|M
cM
|S
+ cD
|M
cM
|S
. For each observed level of severity (medically attended, hospitalized, ICU, death),
the true number of cases was modeled as a binomial sample from the true number of
cases at an appropriate lower level, hence
where the first subscript indicates severity and the second indicates the population
(New York, Milwaukee to May 20, Milwaukee to June 14).
In Approach 1 (New York and Milwaukee data combined), for the unobserved level of
severity (symptomatic cases) we used a prior distribution of cM
|S
∼ Beta(51.5,48.5) to represent uncertainty between 42% and 58% [19]; this distribution
has 90% of its mass in this range, with a mean of 0.515. The main analysis of this
first approach was performed using prior information to inform the detection probabilities.
An additional “naïve” analysis was performed, in which the detection probabilities
d were set equal to 1 at all levels of severity. Our prior distributions for the number
of symptomatic cases in New York (overall and by age) were taken as ranging uniformly
between zero and the proportion reporting ILI in the telephone survey (with the upper
bound of that distribution itself having a prior distribution reflecting the confidence
bounds of the survey results; details in Text S1). For Milwaukee, the prior distribution
on symptomatic cases was taken as uniform between 0 and 25% of the population.
In Approach 2 (New York case data and telephone survey data), we made the assumption
that self-reported ILI cases represented symptomatic pH1N1 infection, and used the
mean and 95% confidence intervals from that survey to define a prior distribution
on the number of symptomatic cases overall and by age group. We then used observed
hospitalizations, ICU/ventilator use, and fatalities along with prior distributions
on detection probabilities as above to inform estimates of true numbers of hospitalizations,
ICU/ventilator use, and fatalities, and these in turn were used to estimate sCHR,
sCIR, and sCFR.
The evidence was synthesized through a full probability model in a Bayesian framework,
implemented in the OpenBUGS software [20], which uses Markov chain Monte Carlo to
sample from the posterior distribution.
Results
Table 2 shows the numbers of medically attended cases, hospitalizations, ICU admissions,
and deaths in the two cities, with the Milwaukee data separated into the period (to
May 20) for which we believe medically attended cases were consistently detected,
and the period (to June 14) for which we consider only hospitalized cases, ICU admissions,
and deaths.
10.1371/journal.pmed.1000207.t002
Table 2
Cases at each level of severity.
Location
Age Group
Severity
Medically Attended
Hospitalized
ICU-Admitted
Dead
to May 20
to May 20
to Jun 14
to Jun 14
to Jun 14
Milwaukee
0–4
126 (16%)
7 (28%)
27 (18%)
5 (20%)
0
5–17
470 (60%)
6 (24%)
29 (20%)
7 (26%)
2 (50%)
18–64
189 (24%)
12 (48%)
87 (59%)
14 (52%)
2 (50%)
65+
3 (0.4%)
0
4 (3%)
1 (4%)
0
Total
788
25
147
25
4
New York
Age Group
Medically Attended
Hospitalized
ICU-Admitted
Dead (total)/Dead but not hospitalized
0–4
—
225 (23%)
44 (17%)
2 (4%)/2
5–17
—
197 (20%)
51 (20%)
2 (4%)/1
18–64
—
518 (52%)
147 (57%)
46 (87%)/6
65+
—
56 (6%)
15 (6%)
3 (6%)/0
Total
—
996
257
53/9
Approach 1
We considered two alternatives to estimate the ratios of interest from the combined
New York and Milwaukee data, using self-reported rates of seeking medical attention
to establish the denominator. First, we obtained a naïve estimate of the ratios of
deaths to hospitalizations, ignoring differences in detection across levels of severity;
and second, we obtained an estimate that incorporated evidence and expert opinion
on the detection probabilities at each level of severity.
The naïve estimate would suggest a median (95% CI) ratio of deaths to hospitalizations
(cD
|H
) of 4.3% (95% CI 3.2%–5.5%), of ICU admissions to hospitalizations (cI
|H
) of 25% (95% CI 22%–27%), and of hospitalizations to medically attended cases (cH
|M
) of 3.1% (95% CI 2.0%–4.4%). The ratio of deaths outside of hospitals to medically
attended cases (cD
|M
) is estimated to be 0.03% (95% CI 0.01%–0.06%). Incorporating the prior evidence
that 42%–58% of symptomatic ILI is medically attended, this would imply a naïve estimate
of the sCFR (cD
|S
= cD
|H
cH
|M
cM
|S
+ cD
|M
cM
|S
) of 0.081% (95% CI 0.049%–0.131%), a corresponding estimate of the sCIR (cI
|S
= cI
|H
cH
|M
cM
|S
) of 0.38% (95% CI 0.24%–0.58%), and an estimate of the sCHR (cH
|S
= cH
|M
cM
|S
) of 1.55% (95% CI 0.98%–2.32%). If one assumes that detection probabilities are no
worse at higher levels of severity than at lower levels, then these figures would
be reasonable upper bounds on the symptomatic CFRs and CIRs.
Incorporating prior evidence of the detection probabilities at each level of severity,
and thus accommodating structural and statistical uncertainties in these probabilities,
we estimated that ratio of deaths to hospitalizations (cD
|H
) of 2.7% (95% CI 1.8%–3.8%) of ICU admissions to hospitalizations (cI
|H
) of 17% (95% CI 12%–21%) and of hospitalizations to medically attended cases (cH
|M
) of 2.9% (95% CI 1.6%–5.0%). The ratio of deaths outside of hospitals to medically
attended cases (cD
|M
) is estimated to be 0.02% (95% CI 0.01%–0.04%).
Table 3 shows the estimates for the quantities of primary interest, overall and by
age group, in the analysis that incorporated prior evidence of detection probabilities.
Here, the posterior median estimate for the sCFR is 0.048% (95% CI 0.026%–0.096%)
and for the sCIR is 0.239% (95% CI 0.134%–0.458%). The sCHR is estimated as 1.44%
(95% CI 0.83%–2.64%).
10.1371/journal.pmed.1000207.t003
Table 3
Posterior median (95% CI) estimates of the sCFR, sCIR, and sCHR, by age group, based
on a combination of data from New York City and Milwaukee, and survey data on the
frequency of medical attendance for symptomatic cases.
Age
sCFR
sCIR
sCHR
0–4
0.026% (0.006%–0.092%)
0.321% (0.133%–0.776%)
2.45% (1.10%–5.56%)
5–17
0.010% (0.003%–0.031%)
0.106% (0.043%–0.244%)
0.61% (0.27%–1.34%)
18–64
0.159% (0.066%–0.333%)
0.542% (0.230%–1.090%)
3.00% (1.35%–5.92%)
65+
0.090% (0.008%–1.471%)
0.327% (0.035%–4.711%)
1.84% (0.21%–25.38%)
Total
0.048% (0.026%–0.096%)
0.239% (0.134%–0.458%)
1.44% (0.83%–2.64%)
Estimates of each of these severity measures vary dramatically by age group, with
the lowest severity by each measure in the 5–17 year age group. Comparing the two
groups for which we have the most data, the relative risk of death for a symptomatic
18–64-year-old compared to a symptomatic 5- to 17-year-old is 15 (95% CI 5–57). The
corresponding relative risks of ICU admission and hospitalization are 5 (95% CI 2–13)
and 5 (95% CI 2–12) respectively. The Bayesian framework provides a natural way to
estimate confidence (measured as the posterior probability) that one rate is higher
than another. The probability that severity is higher in the 18- to 64-y age group
than in the 5–17 age group is >99.9%, for each of fatality, ICU admission, and hospitalization
respectively. The data are too sparse to say with confidence whether adults over 65
or under 65 have greater severity. For example, among the four age groups, the symptomatic
case-fatality ratio is highest in the 18- to 64-y age group with posterior probability
62.%, and in those 65 and over with probability 38%. The symptomatic case-ICU admission
ratio is highest in 18- to 64-year-olds with posterior probability 51% and in those
over 65 with posterior probability 38%. The sCHR is highest in 18- to 64-year-olds
with posterior probability 37% and in those over 65 with posterior probability 37%.
Approach 2
Table 4 shows the estimates for the sCFR, sCIR, and sCHR, by age group, when self-reported
ILI is used as the denominator for total symptomatic cases. Overall these estimates
are: sCFR = 0.007% (95% CI 0.005%–0.009%), sCIR = 0.028% (95% CI 0.022%–0.035%)
and sCHR = 0.16% (95% CI 0.12%–0.26%). Compared to Approach 1, these estimates are
nearly an order of magnitude smaller, and the age distribution differs. The relative
risks for each severity in the 18- to 64-year-old group compared to the 5- to 17-year-old
group are 7 (95% CI 3–25) for fatalities, 1.5 (95% CI 0.9–2.5) for ICU admissions,
and 1.4 (95% CI 0.9–2.1) for hospitalizations. The CFR is highest in the 18–64 y group
with posterior probability 52%. In contrast to Approach 1, the CIR is highest among
0- to 4-year-olds, with posterior probability 79%, and the CHR is highest among 0-
to 4-year-olds, with posterior probability 99%.
10.1371/journal.pmed.1000207.t004
Table 4
Posterior median (95% CI) estimates of the sCFR, sCIR, and sCHR, by age group, using
self-reported ILI as the denominator of symptomatic cases.
Age
sCFR
sCIR
sCHR
0–4
0.004% (0.001%–0.011%)
0.044% (0.026%–0.078%)
0.33% (0.21%–0.63%)
5–17
0.002% (0.000%–0.004%)
0.019% (0.013%–0.027%)
0.11% (0.08%–0.18%)
18–64
0.010% (0.007%–0.016%)
0.029% (0.021%–0.040%)
0.15% (0.11%–0.25%)
65+
0.010% (0.003%–0.025%)
0.030% (0.016%–0.055%)
0.16% (0.10%–0.30%)
Total
0.007% (0.005%–0.009%)
0.028% (0.022%–0.035%)
0.16% (0.12%–0.26%)
Discussion
We have estimated, using data from two cities on tiered levels of severity and self-reported
rates of seeking medical attention, that approximately 1.44% of symptomatic pH1N1
patients during the spring in the US were hospitalized; 0.239% required intensive
care or mechanical ventilation; and 0.048% died. Within the assumptions made in our
model, these estimates are uncertain up to a factor of about 2 in either direction,
as reflected in the 95% credible intervals associated with the estimates. These estimates
take into account differences in detection and reporting of cases at different levels
of severity, which we believe, based on some evidence, to be more complete at higher
levels of severity. Without such corrections for detection and reporting, estimates
are approximately two-fold higher for each level of severity. Using a second approach,
which uses self-reported rates of influenza-like illness in New York City to estimate
symptomatic infections, we have estimated rates approximately an order of magnitude
lower, with a symptomatic sCHR of 0.16%, an sCIR of 0.028%, and an sCFR of 0.007%.
In both approaches, the sCFR was highest in adults (in Approach 1, 18–64 y, while
Approach 2 cannot distinguish whether it is higher in that group or in those 65y and
older) and lowest in school-aged children (5–17 y). Data on children 0–4 and adults
65 and older were relatively sparse, making statements about their ordering more difficult.
Nonetheless, these findings, along with surveillance data on the age-specific rates
of hospitalization and death in this pandemic (http://www.cdc.gov/vaccines/recs/ACIP/downloads/mtg-slides-oct09/12-2-flu-vac.pdf),
indicate that the burden of hospitalization and mortality in this pandemic falls on
younger individuals than in seasonal influenza [21]. A shift in mortality toward nonelderly
persons has been observed in previous pandemics and the years that immediately followed
them [22].
These estimates are valuable for attempting to project, in approximate terms, the
possible severity of a fall–winter wave of pH1N1, under the assumption that the virus
does not change its characteristics. In the 1957 and 1968 pandemics, it appears that
perhaps 40%–60% of the population was serologically infected, and that of those, 40%–60%
were symptomatic [2],[23]–[25]. Current estimates of the transmission of pH1N1 range
between about 1.4 and about 2.2, consistent with estimates of the reproduction numbers
from prior pandemics [26]–[30]. To convert our estimates into population impacts,
one needs to make an assumption about the attack rate and its age distribution. For
each 10% of the US population symptomatically infected (with the same age distribution
observed in the spring wave), our Approach 1 estimates suggest that approximately
7,800–29,000 deaths (3–10 per 100,000 population), 40,000–140,000 intensive care admissions
(13–46 per 100,000 population), and 250,000–790,000 hospitalizations (170–630 per
100,000 population) will occur. These estimates scale up or down in proportion to
the attack rate; for example, they should be doubled if 20% of the population were
symptomatic, producing for example 15,000–58,000 deaths (6–20 per 100,000 population).
Approach 2 suggests much smaller figures (for each 10% of the population symptomatic)
of 1,500–2,700 deaths (0.5–0.9 per 100,000), 6,600–11,000 ICU admissions/uses of mechanical
ventilation (22–35 per 100,000), and 36,000–78,000 hospitalizations (12–26 per 100,000).
Again, these numbers should be scaled in proportion to the attack rate.
To date, symptomatic attack rates seem to be far lower than 25% in both the completed
Southern Hemisphere winter epidemic and the autumn epidemic in progress in the US;
severe outcomes seem to be considerably less numerous than those described for Approach
1 with a 25% attack rate. In New Zealand, just under 2% of the population consulted
a general practitioner (GP) for ILI during the winter wave of the pandemic (http://www.moh.govt.nz/moh.nsf/indexmh/influenza-a-h1n1-update-138-180809),
which is consistent with an attack rate significantly lower than 25%, though somewhat
higher than the GP consultation rate observed in severe seasonal flu outbreaks such
as those in 2003 and 2004 (http://www.surv.esr.cri.nz/PDF_surveillance/Virology/FluWeekRpt/2004/FluWeekRpt200444.pdf).
The level of severity estimated for the United States reflects in part the availability
of antiviral treatment and other medical interventions that will not be available
in all populations. Oseltamivir use was common in Milwaukee (Milwaukee Department
of Health, unpublished data), and although the health care system was put under strain
in both cities studied, there was no shortage of intensive care or other life-saving
medical resources. In a situation of greater stress on the health system, as has been
observed in certain locations in the Southern Hemisphere ([9],[10]; http://www.capegateway.gov.za/eng/your_gov/3576/news/2009/aug/185589),
or in areas that lack a high-quality health care system, severity might increase in
proportion to decreased availability of adequate medical attention. Worryingly, our
estimates of the proportion of symptomatic cases requiring mechanical ventilation
or ICU care was approximately 4–5× our estimate of the sCFR. It is possible that a
substantial proportion of those admitted to ICUs could have died without intensive
care. In populations without widespread access to intensive care, our results suggest
that the same burden of disease could lead to a death rate 4–5× higher. Likewise,
a change in the virus to become more virulent or resistant to existing antiviral drugs,
or the emergence of more frequent bacterial coinfections, could increase the severity
of infection compared to that observed so far.
Estimates of severity for an infection such as influenza are fraught with uncertainties
[1]. Our analysis has accounted for many of these uncertainties, including imperfect
detection and reporting of cases, bias due to delays between events (such as the delay
from illness onset to death), and the statistical uncertainties associated with limited
numbers of cases, hospitalizations, and deaths. Another major source of difficulty
is the spatial and temporal variation in reporting effort for mild and severe cases;
for example, most jurisdictions in the US stopped reporting mild cases on or before
the second week of May, but this change varied by jurisdiction. We have attempted
to avoid this difficulty by focusing on individual jurisdictions—New York and Milwaukee—for
which the approach to reporting was relatively stable over time. One limitation is
that Milwaukee changed its guidance during our surveillance period from testing of
all symptomatic cases to testing of all symptomatic health care workers but only moderate-to-severe
cases in non-health care workers. We believe that testing policies did not change
dramatically during this period, because the proportion of hospitalized cases remained
fairly constant; however, the sample size before this change in guidance was small.
Thus, our estimates should be seen as being the risk of severe outcome among persons
with symptoms, possibly biased somewhat toward those with more severe symptoms.
Despite our efforts to account for sources of uncertainty, several others remain and
have not been accounted for in our analysis. First, we have assumed that for each
level of severity (from medically attended up to fatal), case reporting was equal
across age groups; for example, we assumed that medically attended cases were as likely
to be reported for young children as for adults. It is possible that this is not the
case, for example that mild cases were more likely to come to medical attention if
they occurred in children than if they occurred in adults. If this were true, our
conclusion that severity was higher in adults than children could be partly a result
of differential reporting.
Second, the overall estimates of severity (not stratified by age group) reflect the
age composition of cases in the sample we studied, especially the age composition
of the lowest level of severity examined, medically attended illness. Among medically
attended cases in Milwaukee, 60% were in the 5–17 y age group, the one in which severe
outcomes were the least likely. A preponderance of cases within this age group may
be typical of the early part of influenza epidemics, and while it has been argued
that there is a shift from younger to older age groups in seasonal influenza [31]
as the epidemic progresses, there is evidence, at least from the 1957 pandemic, that
attack rates remain higher in children than adults throughout the course of the epidemic
[2]. Since severity of pH1N1 influenza appears to be considerably higher in adults,
a shift in the burden of disease from children to adults as the epidemic progresses
would lead to an increase in average severity.
We note that the association between age and severity may also affect observed trends
in the characteristics of cases. The World Health Organization has noted worldwide
a shift from younger to older mean age among confirmed cases (http://www.who.int/csr/disease/swineflu/notes/h1n1_situation_20090724/en/index.html).
If severity is lowest among children, this upward shift in age distribution may partially
reflect a shift toward detection of more severe cases, rather than a true shift in
the ages of those becoming infected.
Third, the symptomatic CFR, CIR, and CHR are dependent upon our estimates of the true
number of symptomatic cases, N
iSk
, and hence are sensitive to the choice of prior distribution for these, as well as
to our prior assumptions on the detection probabilities. In particular, if the probability
that symptomatic patients seek medical attention and are confirmed is lower than we
assume in our prior distributions, then there are more cases than are inferred by
our model, and severity is correspondingly lower than our estimates. If the probability
of detecting severe outcomes (hospitalizations, deaths, ICU) is lower than our prior
distributions reflect, then there are more severe outcomes than our model infers,
so severity is correspondingly higher.
Finally, the small sample sizes in some age groups, the over-65 year olds in particular,
lead to large uncertainty in the age-specific estimates. This level of uncertainty
is reflected in the wide 95% credible intervals for the estimates.
Our two approaches yield estimates that differ by almost an order of magnitude in
the severity of the infection, on each of the three measures considered. How should
planners evaluate these contrasting estimates? The lower estimates, using the denominator
of self-reported ILI in New York City, may reasonably be considered lower bounds on
the true ratios. ILI is thought to be relatively rare in May–June, hence true ILI
was probably largely attributable to pH1N1 during this period in New York City. However,
self-reported ILI is notoriously prone to various biases, most of which suggest that
true rates are probably lower than self-reported rates. A previous telephone survey
conducted in New York City found that 18.5% of New Yorkers reported ILI in the 30
d prior to being surveyed in late March 2003 [32], which represented a period of above-baseline
but declining influenza activity nationally and no known influenza outbreaks in New
York City [32]. The survey was repeated in October–November 2003, prior to the appearance
of significant influenza activity, and 20.8% reported ILI in the 30 d prior [32].
If these surveys represent a baseline level of self-reported ILI in the absence of
significant influenza activity, then the approximately 12% self-reported ILI in the
telephone survey is substantially lower than this out-of-season baseline, suggesting
that it likely overstates the total burden of symptomatic pH1N1 disease. The lower
estimates are also broadly consistent with estimates from New Zealand, which has experienced
a nearly complete influenza season [8], and from Australia (http://www.health.gov.au/internet/main/publishing.nsf/Content/cda-surveil-ozflu-flucurr.htm/FILE/ozflu-no14-2009.pdf).
The higher estimates, on the other hand, were obtained using ratios of hospitalizations
to confirmed medically attended cases and self-reported rates of seeking medical attention
for ILI, which have been consistently measured in the range of about 40%–60%. It is
possible that the special efforts of the New York City health department to identify
pH1N1-related fatalities (including those not hospitalized) provides a fuller picture
of the total number of deaths from this infection. Interestingly, New York City reports
about the same number of hospitalizations for our study period (996) as New Zealand
reports up to mid-August (972), but 3.5× as many deaths (53 versus 16) [8]. If this
discrepancy reflects more complete ascertainment of deaths in New York City, it may
account for much of the difference between our higher estimates of case-fatality ratios
and those from New Zealand. Given the number of uncertainties cataloged above (which
apply also to other jurisdictions within and outside the US), we believe that our
two approaches probably bracket the reasonable range of severity for the US spring
wave.
Age-specific severity patterns as estimated here are largely consistent with those
one would obtain by simply comparing the incidence of confirmed cases, hospitalizations,
and deaths in the US as a whole for a similar period [19], although the estimates
for persons over age 65 are highly uncertain, with 95% credible intervals spanning
several orders of magnitude, due to the very small number of individuals in our sample
from that age group.
The estimates provided here may be compared to those for seasonal influenza. Compared
to seasonal influenza, these estimates (assuming a 25% symptomatic attack rate) suggest
a number of deaths in the US that could range from about half the number estimated
for an average year to nearly twice the number estimated for an average year [33]
(Approach 1), or a range about 10-fold lower than that (Approach 2); however, the
deaths would be expected to occur in younger age groups, compared to the preponderance
of deaths in persons over 65 in seasonal influenza. Such a shift in age distribution
is typical for pandemics and the years that follow them [22]. Under Approach 1, and
assuming a typical pandemic symptomatic attack rate of 25%, the estimated number of
hospitalizations for an autumn–winter pandemic wave is considerably more than the
approximately 300,000 estimated for typical seasonal influenza [34], whereas Approach
2 suggests a number between 1/3 and 2/3 of that observed in typical seasonal influenza.
It should be noted that most hospitalizations, and about 90% of deaths attributed
to seasonal influenza, are categorized as respiratory and circulatory, not including
the more specific diagnoses of pneumonia and influenza; that is, they are due to myocardial
infarction, stroke, and other proximate causes, but are nonetheless likely initially
caused by influenza infection [35]. The deaths included in our study may have reflected
more directly influenza-related causes and may not reflect these indirect causes of
influenza-related death. Indeed, it is unclear whether the proportion of indirect
respiratory and circulatory causes of death and hospitalization will be as high in
this pandemic year, given the younger ages involved in most severe cases. Given these
differences between the estimates here based on virologically confirmed deaths and
the ecological statistical approach to estimating influenza-attributable deaths and
hospitalizations for seasonal influenza, it will be difficult to interpret comparisons
between the two types of estimates until (after the pandemic has finished) comparisons
can be made between the ecological and the confirmed-case approach to estimating burden
of hospitalization and deaths.
Our estimate of the sCFR is lower than those provided by Garske et al. [16], which
ranges from 0.11% to 1.47% overall, and between 0.59% and 0.78% in the US, but which
was based on confirmed plus probable (rather than symptomatic) cases. Nishiura et
al. [36] estimate that between 0.16% and 4.48% of confirmed cases in the United States
and Canada were fatal. Our Approach 1 includes a probability of approximately 1/8
(∼50% probability of symptomatic patients seeking care × ∼28% probability of testing
and report for a symptomatic × ∼97% test sensitivity, with associated ranges for each;
Table 1) to convert symptomatic into medically attended cases, and this factor accounts
for most of the difference between our estimates and the earlier estimates based on
confirmed or confirmed plus probable cases. Wilson and Baker [37], on the other hand,
use a denominator of infections (rather than symptomatic or confirmed cases) and estimate
a range of CFR from 0.0004% up to 0.6%. Our estimates fall in the middle part of this
range. More recently, Baker et al. [8] used their estimates of the total incidence
of symptomatic disease in New Zealand to estimate an sCFR of 0.005%, equal to the
lower end of the credible interval for our Approach 2 estimate, and considerably below
our Approach 1 estimate. The generally downward trend in the estimates of severity
reflects early ascertainment of more severe cases (e.g., mainly hospitalized cases
in the early Mexican outbreak); the authors of each of these earlier reports recognized
and discussed the issue of ascertainment and its potential biasing effect on severity
estimates.
While we have been careful to highlight uncertainties in the estimates of severity,
our results are sufficiently well-resolved to have important implications for ongoing
pH1N1 pandemic planning. The estimated severity indicates that a reasonable expectation
for the autumn–winter pandemic wave in the US is a death toll less than or equal to
that which is typical for seasonal influenza, though possibly with considerably more
deaths in younger persons. If attack rates in the autumn match those of prior pandemics
and hospitalization rates are comparable to our estimates using Approach 1, the surge
of ill individuals and subsequent burden on hospitals and intensive care units could
be large. However, using Approach 2, estimates of hospitalizations and ICU admissions
are considerably lower. Either set of estimates places the epidemic within the lowest
category of severity considered in pandemic planning conducted prior to the appearance
of pH1N1 in the United States, which considered CFRs up to 0.1% (http://www.flu.gov/professional/community/community_mitigation.pdf).
Continued close monitoring of severity of pandemic (H1N1) 2009 influenza is needed
to assess how patterns of hospitalization, intensive care utilization, and fatality
are varying in space and time and across age groups. Increases in severity might reflect
changes in the host population—for example, infection of persons with conditions that
predispose them to severe outcomes—or changes in the age distribution of cases—for
example a shift toward adults, in whom infection is more severe. Changes in severity
might also reflect changes in the virus or variation in the access and quality of
care available to infected persons.
Supporting Information
Text S1
Supplementary methods.
(0.43 MB DOC)
Click here for additional data file.