>> All right.
Good afternoon.
I'm Tom Shimabukuro.
I'm with the Immunization
Safety Office
and the Vaccine Safety Team,
on the Vaccine Planning Unit.
And this afternoon I'm going
to be giving you an overview
of vaccine safety monitoring.
I'll cover some of the basic
issues, and then I'll touch
on some of our plans
and some of our systems,
and some of our populations.
And I just want to mention
that I'm presenting on behalf
of our partners, FDA, DOD,
Indian Health Service and VA.
Next slide.
So this is a schematic,
this is actually posted
on the CDC website,
and it describes the
vaccine life cycle,
this describes the
traditional pathway,
but I think the concepts
are also relevant.
And safety is a priority
during all phases
of vaccine development,
approval and use.
Start off with basic research,
discovery, pre-clinical work,
and then move to the
phase clinical trials.
Once the phase 3 is completed,
BLA is submitted, FDA reviews,
and there's an approval.
And shortly thereafter,
there's an ACIP review
and recommendation.
And then we move into
what's called phase 4.
And traditionally,
CDC and FDA do this,
do most of the safety
monitoring.
There are other federal
partners that participate
in this monitoring,
and monitoring
for COVID vaccine will
be a coordinated effort
by multiple federal agencies.
Next slide.
So I just want to mention the
rationale for post licensure,
or post authorization
safety and monitoring.
So the safety standards
for vaccines are high.
Vaccines are for
primary prevention.
They're being given to many,
to generally healthy people,
at least people who do
not have the disease,
so it's not for treatment,
and therefore the tellability
for risk is lower than, say,
for drugs or for other things
that are used to treat illness.
Furthermore, the pre-licensure
trials are not optimal
for detecting rare,
adverse events.
The numbers enrolled
are too small,
even with large clinical
trials, like the ones for COVID
where you have 30,000
individuals.
They're not optimal for
monitoring vaccine safety
in a real world environment,
or for assessing safety
and special populations.
Often there, groups like
pregnant women, and individuals
with certain preexisting
medical conditions are excluded,
or at least excluded in the
initial clinical trials.
And then finally,
evaluating adverse events
with the late onset.
As you heard previously, for
some potential adverse events,
like vaccine enhanced
disease, I mean, they're,
they have to monitor out
months to possibly years.
Next slide.
So I want to touch on the
roles and responsibilities
in vaccine safety monitoring.
And there are really
two players here.
There's the manufacturers and
there's the U.S. Government.
Vaccine safety monitoring,
as far as government,
is really a federal
responsibility.
So next slide.
So manufacturers have
phase 4 responsibilities
for their individual products.
These are based on standard
regulatory obligations
as specified by the FDA.
They can be guided by results
from the clinical trials.
They're conducted or managed
by the manufacturer's
pharmacal vigilance programs,
with regulatory oversight by FDA
that may include post
marketing commitments,
post marketing requirements,
and pregnancy registries.
It also includes adverse
event monitoring and reporting
of adverse events to the Vaccine
Adverse Event reporting system.
Next slide.
The U.S. government
has a responsibility
for public safety.
And many of these
requirements are laid
out in the 1986 National
Childhood Vaccine Injury Act,
which authorized the creation
of a Vaccine Adverse
Event Reporting System,
which I'll touch on later.
So U.S. Government
monitoring is independent
from the manufacturers.
There's no financial
stake, there's less real
or perceived conflict
of interest.
And this type of monitoring is
important for public confidence.
Monitoring covers all
vaccines from all manufacturers
in a comprehensive and
integrated fashion.
And the U.S. government
manages large data systems
that are standing
long term investments
in public health surveillance.
Like VAERS, the Vaccine
Safety Data Link and CMS,
all which I'll cover
in a little bit.
And then finally,
surveillance data
from VAERS are made
publicly available.
This is available
online to anyone.
And surveillance findings
from phase 4 monitoring
by the government are presented
at Federal Advisory
Committee meetings
in a transparent manner.
Next slide.
So manufacturers
play a critical role
in post authorization
safety mining,
however we can't
get all the answers
from manufacturing monitoring.
The U.S. government maintains
it has constant access
to the largest, most robust
and most sophisticated
electronic monitoring
systems available.
And the systems and methods
that we use are complimentary.
Government agencies can freely
cooperate and collaborate,
share information, leverage
expertise in other agencies,
and support each others
surveillance efforts,
and to really act in a
coordinated and integrated way.
Next slide.
I'll get into our
safety monitoring systems
and populations.
Next slide.
The first system
I'll touch upon,
I'm sure you are
all familiar with,
is the Vaccine Adverse
Event Reporting System.
This is our national spontaneous
reporting system that's
co-managed by CDC and the FDA.
Next slide.
So VAERS essentially has the
entire U.S. population under,
as a covered population, all 320
million or so U.S. residents.
This includes individuals of all
ages, races, from all states,
territories, healthy people,
and those with comorbidities.
In recent years,
VAERS has received
around 60,000 reports
per year, a small number
of foreign reports, most U.S.
reports, and that averages
out to about 1,000
reports per week.
Next slide.
So specifically focusing on
older adults, I want to talk
about some of the active
surveillance systems
in the U.S. government's
arsenal.
I'll start off with the
FDA's Centers for Medicaid
and Medicare Services
Data Monitoring.
This data monitoring
system includes 55
to 60 million persons 65 and
older, and this is about 92%
of the U.S. older adults.
The CDC's Vaccine Safety
Data Link is a collaboration
between 8 integrated healthcare
systems, and this has data
on about 1.8 million persons
65 years and older per year.
And then the VA Data Warehouse
and Electronic Health
Records has
about 1.56 million
persons 65 years and older
who were typically
vaccinated for flu,
and we anticipate would be
a priority group for the VA.
So these are EHR
based, or claims based,
or encounter based systems,
which we do active surveillance.
So we have complete, or
near complete, information,
depending on the
system, on a population,
therefore we can calculate
rates and assess risk.
Next slide.
Moving into the age group
of adults and children,
and I'm just, just to reinforce,
CDC's Vaccine Safety Data Link
has 8 million persons in the age
of 19 to 64 years old,
and 2.3 million persons
under 18 years old.
And again, we routinely
conduct active surveillance
in the Vaccine Safety Data
Link each season for flu,
and when new vaccines
are recommended, licensed
and recommended for use.
Next slide.
So I want to touch on a
couple of FDA's systems
that cover adults and children.
First is FDA's Biologic
Effectiveness Safety System,
or BESS, that has data
from claims EHR systems,
and the FDA's Post licensure
Rapid Immunization Safety
Monitoring Program, or PRISM,
that's part of the
FDA's Sentinel Program.
And that has EHR data from large
health insurers with claims data
and access to medical charts
on about 100 million persons.
Next slide.
Some of the populations of our
partners, our federal partners,
the Department of Defense
does theirs monitoring
in collaboration with CDC
through a VAERS data
sharing agreement.
There's roughly 1.4
million active duty
and 860,000 reserves.
Most of, the majority of which
are less than 30 years old,
but this also includes
dependents and beneficiaries
if seen in DOD healthcare
facilities.
DOD also has the
capability or the capacity
to do active surveillance.
And for COVID, the DOD
Immunization healthcare Division
plans to collaborate with the
Armed Forces Health Surveillance
Division to monitor
safety in DOD EHR systems.
And they're mainly using the
Defense Medical Surveillance
System and the DOD Personnel
and Readiness COVID registry.
Next slide.
Lastly, I want to mention a
new collaboration between CDC
and the Indian Health Service.
We are currently entering
into a VAERS data
monitoring collaboration
through a VAERS data
sharing agreement.
And the IHS population
is mainly American Indian
and Alaskan Native
patients seen in IHS
and Tribal healthcare
facilities.
We are working with
IHS to identify reports
in their patient population,
and for individuals
vaccinated in their facilities.
And the analysis for the
IHS data will be conducted
by the National Pharmacy
and Therapeutics Committee
and the IHS division
of epidemiology.
Next slide.
So I want to touch on case
review and inquiry response.
This is also a component of our
monitoring and surveillance.
Next slide.
But it also provides a service
to providers and individuals
who have questions and
concerns about vaccine safety.
So the first system I'll mention
is CDC's Clinical Immunization
Safety Assessment Project,
and the CISA Project assists
U.S. healthcare providers
with complex fact and safety
questions about their patients
by conducting in depth
clinical case reviews.
And CISA also plans to
establish a call service
for clinician assistance
during the COVID response.
CDC's Immunization Safety Office
manages an inquiry response
program, and we respond to
vaccine safety inquiries
and questions from the public,
including patients, parents,
healthcare providers, and public
health partners and others.
And DOD runs a couple
of services,
the Regional Vaccine Safety
Hubs, for case evaluation,
and the Vaccine Adverse
Event Clinical System.
And DOD, through these systems,
evaluates and tracks cases
of adverse events
following immunization in DOD
and DOD affiliated populations.
Next slide.
So I want to touch on
some new initiatives
for enhanced monitoring programs
to meet the challenge
of COVID19.
Next slide.
So one of the challenges
we have identified is
that during the early phase
of a national COVID19
vaccination program,
the initial doses may be
distributed to specific groups,
such as healthcare personnel
and other essential workers.
And in this scenario, activities
to enhance normal public
health monitoring systems will
be necessary.
So potential solutions
to address this challenge
include active surveillance
in early recipients
through smart phone
and email based web surveys
with directed reporting to VAERS
for clinically important
or clinically significant
adverse events.
This is really a combination
of active surveillance
for react antigenicity and then
enhanced passive surveillance
as well.
And also, vaccination capture
and enhanced passive
surveillance
through other data sources
through healthcare facilities
with partners within CDC and
other government agencies.
Next slide.
I just want to outline
our current plan to do,
to conduct smart phone based
monitoring in early recipients
of COVID vaccine, and again,
this is in the scenario
where there may be
limited amounts
of vaccine doses available,
and those would be prioritized,
or delivered, to
certain workers,
like healthcare workers,
and other essential workers.
So CDC currently is
establishing a program
to identify these potential
early recipients of vaccine,
and to register them
in anticipation
of scheduling vaccination,
getting vaccination,
and communicating with
them during vaccination,
such as reminder recall.
We plan to piggyback on this
process to send text messages,
really starting right when they
get vaccination, frequently,
early on, to assess
react antigenicity.
And also to ask some
specific questions,
both in the first week or
so, and out to 6 weeks,
about any clinically
important adverse health events
that the individual feels may
be related to vaccination.
Depending on the answers
to these text messages,
which can either
be through a text,
or through a web based survey,
or through an email survey,
we would direct these
individuals
to report medically important
adverse events to VAERS,
which ultimately gets
sent to the CDC and FDA
through the normal
VAERS process.
So through this, we would
actually have both numerator
and denominator data on
these early recipients,
and we think this will be
a good way to, early on,
characterize the basic safety
profile of COVID vaccines
in a real world environment.
Next slide.
So other potential
data sources to assist
with vaccine safety monitoring
include State Immunization
Information Systems to
capture denominator data
for adverse event rates.
We are looking at
capturing information
on telehealth encounters in
CDC's Vaccine Safety Data Link.
And we understand with COVID19
there has been a fundamental
change in the way
healthcare encounters incur.
And we want to make sure that
we are able to capture data
to the extent possible from
these telehealth encounters
in our surveillance systems.
We also are able to
gauge healthcare provider
and general public concerns
through our CISA inquiries
and through our Vaccine Safety
Inquiry Response Program,
which I mentioned previously.
We do conduct schematic
analysis on what types
of concerns providers
have and what types
of concerns the general public
have, so we get an idea,
it's kind of a pulse check out
there on what the public thinks
about vaccine safety and what
concerns are rising to the top.
And also, our partners
at FDA plan
to develop new electronic health
data, electronic data sources
through EHR partners that
they are working with.
Next slide.
So I want to quickly touch on
signal detection and assessment.
Next slide.
So the CIOMS defines a
signal as information from one
or multiple sources,
which suggests a new
potentially causal association,
or a new aspect of
a known association
between an intervention and
event, or set of related events,
either adverse of beneficial,
that is judged to be
of sufficient likelihood to
justify verificatory action.
For safety, we're really
looking at adverse events,
not beneficial events.
Next slide.
So I'm going to focus first
on spontaneous reporting,
I'm going to focus on VAERS.
So there are traditional methods
we use for signal detection
and signal assessment.
And VAERS clinical review
of individual reports is
an important component
of what we do.
We plan to identify a select
group of adverse events
of special interest up front,
but we review any report
with any outcome, or we
monitor all reports in VAERS.
And the clinical reviews are
really to verify the diagnosis
in the onset interval,
characterize clinical
and laboratory features
and identify other
potential risk factors.
We also do aggregate
report review in VAERS,
that's really looking
at automated data,
so we get case counts,
frequency of adverse events,
and reporting trends over
time, and reporting rates.
We use statistical data
mining methods in VAERS
to detect disproportional
reporting
of specific vaccine
adverse events, combinations
in the VAERS database, and two
methods we use are empirical
basing and data mining,
which is conducted by FDA,
and proportional
reporting ratio analysis.
And these generate
statistical signals
when pre-specified
thresholds are reached.
And one of the first
things we do
when we detect a
statistical signal is to go in
and do this clinical review
of individual reports.
Again, to verify the diagnosis,
to check on the onset interval,
look for biological plausibility
and to characterize the reports.
Next slide.
The timeliness for signal
detection assessment,
of course, is important.
I just want to mentioned
that CDC
and FDA receive update
VAERS data sets daily.
So on a daily basis, both CDC
and FDA get what is
essentially an updated version
of the entire database,
because it's a dynamic database
and it does update.
And this is from the beginning
until the current day,
so 1990 all the way
to the present,
each day we get an
updated database
from our VAERS contractor.
The processing action for
VAERS reports as they come
in are a review and MedDRA
Coding of the symptoms.
These are by certified
MedDRA coders.
There's also redaction
of personally Identifile
information.
There's quality assurance
and there's preparation
for posting on the secure VPN.
These are individual
reports that get posted
on the secure VPN for
investigator access.
On the processing times
for COVID vaccines
for death reports will be 1
day, for reports classified
as serious, within 3 days,
and for reports classified
as nonserious within 5 days.
Next slide.
So I want to touch on approaches
for monitoring NDHR
administrative
and claims based data.
And, when I talk about
his I'm really talking
about near real time
sequential monitoring.
And, the example
you're probably familiar
with is rapid psychoanalysis in
the Vaccine Safety Data link.
So for rapid psychoanalysis,
the data are refreshed weekly
in high volume situations.
So a high volume situation
that you're familiar
with is influenza
vaccine where you have 100
or so million doses I a span
of a couple months in the Fall.
In this case we refresh
the data every week.
The prespecified
outcomes monitored are,
prespecified outcomes
are monitored.
So these are outcomes that
are identified in advance.
RCA as a surveillance
activity is not the same
as an epidemiologic study.
It's designed to detect
statistical signals
which ware values
above prespecified
statistical thresholds.
When a statistical
signal does occur,
assessment requires a series
of evaluations using traditional
epidemiologic methods.
Not all statistical signals are
indicative of an increased risk
or a vaccine safety problem.
They need to be assessed.
In short confirmation
and diagnosis to confirm
or exclude cases as a true
incident cases is a key part
of statistical signal
assessment.
And, I will say, in VSD our
ability to pull the charts
and review them is rapid.
Typically we can
do a rapid review
of a chart within 1 to 3 days.
A more detailed chart analysis
would sometimes require us going
out and getting additional
information
and apply case definitions
can generally be conducted
within a week.
So the statistical signal
assessment process is
fairly rapid.
Next slide.
Next slide.
So I want to, in closing, I
want to emphasize a few points.
And, I just got done
talking about the timelines.
But, I'll emphasize it again.
Real time or near real time
safety monitoring will be
critical during the earl stages
of the COVID-19 vaccination
program.
And, this is to characterize
the safety of profile,
safety profile of
COVID-19 vaccines quickly
in a real world environment
and to rapidly assess
COVID-19 vaccine safety
in risk based priority
groups such as older adults
and individuals with certain
preexisting health conditions.
During a broad based vaccination
program, large amounts
of COVID-19 vaccine
are anticipated
to be administered during
a short period of time.
And, it's important to have
established high functioning
systems and validated methods
in place to rapidly detect
and assess potential
safety signals
so public health action
can be taken if necessary.
Next slide.
So I just want to
briefly touch on process.
So as you know, there is an ACIP
COVID-19 Vaccines Work Group
that's led by, chaired
by Dr. Beth Bell.
The mission of this group is
really to advise on the planning
and use of vaccines and
advise on all components
of program implementation
during large scale COVID-19,
during a large scale
COVID-19 immunization program.
Also to review
post-authorization
or post-approval vaccine
safety surveillance data.
So there is a ACIP COVID-19
Vaccine Safety Technical
Subgroup, which is a subgroup
of the COVID Work Group that's
chaired by Dr. Grace Lee.
And, the purpose of this
technical subgroup is to advise
on the safety of COVID-19
candidates and development
and safety monitoring
of vaccines authorized
or approved for use.
And, one of the key things
we'll be doing during the
implementation of a
vaccination program is review
post-authorization
approval, post-authorization
or post-approval vaccine
safety surveillance data.
Next slide.
So to close, multiple U.S.
Government agencies will use
complimentary systems
and methods
to monitor COVID-19 vaccines.
The current monitoring systems
that I've described
have the capacity
to effectively monitor COVID-19
vaccine safety both under UA
and in a post-licensure
scenario.
The analytic methods have
been validated through years
of development and refinement.
Data refresh and updates are
timely and analysis occur
in near real time and new
data sources will contribute
to COVID-19 vaccine safety
monitoring, especially early
in the vaccination program.
Next slide.
Just want to preview
a few topics
for future presentations
to ACIP.
We plan to get into more detail
on our COVID-19 vaccine safety
monitoring plans and methods
to include vaccine safety
outcomes and adverse events
of special interests for COVID
monitoring and also the process
of reviewing and
presenting safety data
as it becomes available during
the implementation of a program.
That's my final slide and
I'm happy to take questions.
>> Thank you very much for
that excellent presentation.
Are there any questions or
comments from the committee?
Dr. Hunter please.
>> Thanks for that
helpful presentation.
I'm just wondering if
you can comment now
or in the future presentations
about how often potential
signals turn
out to not be concerning or not
be biologically plausible cause
and effect relationships?
>> I have in the past used
a number that about 90%
of statistical signals
that we detect
and are monitoring do not turn
out to be true signals
after assessment.
I think that's somewhat
arbitrary.
But, I will say that most of
the signals that we detect
after we assess them,
and like I said
that involves both doing a
quality check on the actual data
and assessing biological
plausibility,
most turn out to be
not true signals.
>> Thank you.
That's very helpful.
>> Dr. Bailing.
>> Thank you for that
fantastic presentation
about all the complimentary
systems to detect
and verify things for states.
I wanted to ask a question
about the active surveillance
in early recipients
which is really exciting.
And, one of my question is.
As people are thinking about
launching their flu vaccine
in [inaudible] including
in healthcare workers,
is there any work towards
testing this system
and seeing how it works?
>> You mean testing
it in for flu risk.
>> In healthcare workers yeah.
>> For flu?
>> Yeah so that you know how
the system works before the,
before COVID starts.
>> I'd have to refer to
the immunization program
on the tech's monitoring
part of that.
Because we're, the safety
component's piggybacking
on the actual techs monitoring.
I will say for other, for
our other systems I do think
that there is an
opportunity in some
of our other data sources
working with our other partners
within CDC, there is an
opportunity to do that.
So some of the other, and
I'll just mention NHSN as one
of the systems that
we're working with.
I do think that there's
an opportunity
to evaluate how well we're
collecting vaccine exposure
and to, and to also work
on education and outreach
on directing individuals
who have adverse events
or healthcare providers
who evaluate them
to report to VAERS.
So I think there's an
opportunity to do that.
>> Dr. Fry.
>> Thank you.
Thanks for that great
presentation.
It was very interesting.
I also had a question about
the active surveillance
and early COVID vaccine
recipients.
And, was curious to know how
many individuals you anticipate
following and why you would
choose a certain number
of individuals for example?
So I can better understand
whether you'll be capturing any
SAE's because you know we need
to follow typically thousands
if not more of people to
find some of the more,
the rarer complications.
Thank you.
>> I believe the answer to that
question is we would follow
as many people as we are able
to, as we are able to capture
in the, in the initial,
you know,
registration process
for vaccination.
So there isn't a limit.
We're not, we're not saying
we need to hit this many.
I think we would, the system is
capable of sending text messages
and implementing the online
survey in as many people,
as many people as we can
send the messages out to.
I do not believe there is
a hard limit and certainly
for safety we're not saying
we need to meet this number
and that would be good enough.
We're interested in
getting as much data
on these early recipients
as possible.
>> Dr. Heather I see that
your hand is still up.
Did you have a follow
up question?
>> No sorry I'll get it down.
>> Dr. Bernstein.
>> Yeah. Thanks Tom.
This was a wonderful overview
of critically important process.
I just had a question
around the case reviews
and inquiry response.
You mentioned about planning
to establish a call service
for clinician assistance.
Is that intended to be
retrospective of prospective
or how what kinds of
calls are you expecting
and how what kind
of availability?
Will that be 24/7 or otherwise?
>> It's intended
to be prospective.
You know, if clinicians have
vaccine safety questions either
specific or general
or on topics.
I'd have to get back
to you on the coverage.
But it's intended to be
available as a service
for clinicians as they need it
if they have a vaccine safety
question and want to talk
to a CDC subject matter
expert on vaccine safety.
>> And, given the number of
vaccines that are going to be,
that are planned
to be administered
in such a short period of time,
it's will be a great
service I think
to clinicians at point of care.
>> Dr. Arthur.
>> Thank you very much.
Actually this piggybacks
off of the last question.
Great presentation,
thank you doctor.
Will you be planning, as part
of a communication strategy
for the roll out leading
up to the vaccine,
to do educational
activities for clinicians
on the various safety
monitoring system.
We found that many doctors
don't necessarily understand how
to use theirs.
And, it probably would be good
given how many immunizers there
will be for the vaccine to have
some general education before
launch on the various
systems that you report to.
>> The short answer
to that is yes we plan
to do outreach and education.
I think for providers,
it's largely around,
as far as an active role that
they can take it's around VAERS
but certainly around
educating healthcare providers
on vaccine safety monitoring
and how thorough the
U.S. Government is
and how serious we take it and
how transparent we want to be.
So yes we do plan
to do education
and outreach both specific,
specific meaning what can you do
or what can I do
and general as far
as this is how we
monitor vaccine safety.
>> Ms. McDally.
>> Thank you.
Can you explain how you received
post-licensure safety monitoring
information from
the manufacturers
and also what the timing
of that looks like?
>> So the post-licensure,
most of the post-licensure
safety monitoring data we
receive from the manufacturers
comes through VAER.
So the manufacturers
are required by FDA
to submit a report a VAERS
report for any adverse event
that comes to their attention.
That reporting process
goes through FDA
and then funnels into VAERS.
As far as other, as far as
other phase 4 safety information
that the manufacturers
communicate to FDA,
I'd have to refer to FDA.
I can only really talk
about adverse event reports.
I know that there is a
reporting requirement
that any adverse event get
ultimately reported to VAERS.
So both CDC and FDA have access
to those manufacturer reports.
>> Thank you.
>> Are there any additional
comments on that from FDA?
>> It's fine if there are not.
We just wanted to make sure you
had an opportunity to state.
>> No. The FDA manufacturers
are required
to submit yearly reports to
FDA that include safety data
from spontaneous reports that
they may receive from patients
or healthcare providers
as well as results
of post marketed studies
that they conduct,
not just in the U.S.
but also worldwide.
So FDA does review those
reports and examines the data
to inform any regulatory
actions that we might take over.
>> Thank you Dr. Vic.
Are there any other
questions or comments
from the voting members?
Very good.
Thank you Dr. Shimabukuro.
We will now go on to Dr.
McClun for her presentation
on epidemiology of individuals
that increase risk
for COVID-19 disease.
Dr. McClun go forward.
Please.
>> Good afternoon.
Next slide.
First I'll present
a brief update
to the overall U.S.
COVID-19 epidemiology.
And, then epidemiology among
individuals at increase risk
of severe COVID-19 disease
including older adults ages 65
years or older and adults with
underlying medical conditions.
Next slide.
Next. As of August 23rd, a total
of 5.6 million cases
have been reported
to CDC this map shows
accumulative case counts
by county with a darker red
representing larger number
of cases.
Next slide.
This figure shows the
trends in the number
of COVID cases reported per day
in the U.S. through August 23rd
with a 7 day moving
average in red.
Nationally cases
peaked in mid July
and have been decreasing
over the past month.
But, daily case counts
reported remain higher
than was seen before
the increases in June.
Next slide.
This figure shows the
number of specimen tested
for SARS-CoV-2 using a molecular
assay and reported to CDC
by public health labs.
The number specimens
tested are in the bar graphs
and the percent positives
are shown on the line graph.
Nationally the percentage of
specimens testing positive
for SARS-CoV-2 have continued
to decrease since mid July.
In this past week, Week 33,
the overall percent positive
at public health labs was 6.6%.
Next slide.
This figure shows
the specimens tested
and percent positive among
commercial labs reporting
to CDC.
Note the x axis is different
from the previous slide
as the number tested at these
labs is substantially higher.
The percentage of
specimens testing positive
at commercial labs has also
been decreasing since mid July.
In this past week, Week 33,
the percent positive was 6.3%.
Next. As of August 23rd, a
total of 176,223 deaths due
to COVID-19 have
been reported to CDC.
This map shows accumulative
number of deaths by county
with a darker purple
representing the larger number
of deaths.
Next slide.
This figure shows the
trends in the number
of COVID deaths reported
per day in the U.S.
with a 7 day moving
average, again, in red.
Nationally the number of
deaths peaked at the end
of April then declined
through the end of June,
began to increase again in July.
And, for the past month,
the number of deaths have
remained relatively stable
at approximately
1,000 deaths per day.
Next. Next.
Adults age 65 years and
older and people of any age
with certain underlying medical
conditions are at increased risk
for severe illness
from COVID-19.
Next. Severe illness is
defined as hospitalization,
need for ICU care, need
for intubation or death.
Next. In the United
States adults,
older adults age 65 years
or older represent 16%
of COVID-19 cases, shown in
the green lines, but nearly 80%
of COVID-19 deaths
shown in the blue lines.
This figure shows case
level data reported
by health departments to CDC
for approximately 4.2 million
cases and 131,000 deaths.
Adults age 65 years and older
are highlighted in the red box.
And, as you can see,
the percentage
of deaths increases with age.
Next. Much of the data I'll
be presenting today comes
from COVID-NET.
I know that the committee has
seen this slide several times.
But, I'm including it today for
those who may be less familiar.
COVID-NET conducts
hospitalization surveillance
with 14 states representing
about 10%
of the U.S. population.
Patients must be a resident
of the surveillance area
and have a positive SARS CoV
test within 14 days prior to
or during hospitalization.
Chart reviews are conducted
and data include underlying
medical conditions.
Next slide.
Older adults age 65 years
and older have the
highest cumulative rate
of COVID-19 associated
hospitalizations.
This figure shows accumulative
rate of hospitalizations
by week reported to COVID-NET.
As of Week 33, which
is August 15th,
hospitalization rate among older
adults is almost 4 times the
rate of adults age
18 to 49 years.
Next. This figure shows the
percent of severe outcomes
by age groups in adults reported
to COVID-NET through
August 15th.
Adults age 50 years and older,
seen in the black and blue bars,
are more likely to have severe
outcomes during hospitalization
compared to adults age 18
to 49 years in the gray bar.
Of note, 25% of adults 65
years or older, in blue,
die during hospitalization
compared to 2% to 10%
of the younger age groups.
Next slide.
Among 2,491 adults with COVID-19
associated hospitalizations
reported to COVID-NET
Between March 1 and
and May 2 older age was the
strongest independent risk
factor for in hospital death.
This figure shows
adjusted rate ratios
in 95% confidence intervals
in a multivariable model
in risk of hospital death.
And, I will be coming back
to this figure later
in the presentation.
As you can see in the red box,
not only is age the
strongest risk factor,
the risk increases
with increasing age.
Hospitalized adults age 85 years
or older had 11 times the risk
of in hospital death compared to
hospitalized 18 to 39 year olds.
Next slide.
These data are from a recently
published multicenter U.S.
cohort study including
65 hospitalizations
across the United States.
Among 2,215 adults with COVID-19
associated ICU admission
between March 4 and April 4
older age was the strongest
independent risk factor for in
hospital death within 28 days
of admission, even after
adjusting for patient
and hospital level
characteristics.
Similar to COVID-NET findings,
as you can see in the figure,
with increasing age, the odds
of in hospital death increased.
Adults 80 years or
older admitted
to the ICU had 11 times
odds of death compared to 18
to 39 year olds admitted
to the ICU.
I will also be returning to this
model later in the presentation.
Next slide.
Next.
This figure shows COVID-NET
data through August 15th
through selected underlying
medical conditions among adults
age 18 years or older with
COVID-19 hospitalizations.
Some conditions listed in
this figure include multiple
conditions for instance
cardiovascular disease includes
coronary artery disease
and congestive heart failure
among other conditions.
The most common underlying
conditions here were
hypertension, obesity, diabetes,
and cardiovascular disease.
Next.
The most common underlying
medical conditions seen
in the previous slide among
hospitalized adults varies
by age group.
You can see obesity in
red was in the top 1
to 2 conditions reported for
younger and middle age adults
but only reported in 34%
of adults 65 and older.
Hypertension in blue was the
most common among middle aged
and 65 and older adults.
And, diabetes was the most
common condition in all groups.
Next. From COVID-NET over 60% of
hospitalized adults age 18 years
and older had 3 or more
of the selected underlying
medical conditions
seen previously.
Only 12% of hospitalized
adults had no underlying
medical condition.
Next slide.
This figure shows the number
of conditions by outcome,
death defer mechanical
ventilation or ICU admission
with each outcome
adding to 100%.
Of hospitalized adults age 65
years or older nearly 80% had 3
or more medical conditions
and approximately 70%
of adults requiring
intubation or ICU admission.
Next slide.
This figure shows the
same data by age group
with each age group
adding to 100%.
Of hospitalized adults 65
years or older 80% had 3
or more underlying medical
conditions versus 60% of 50
to 64 year olds and less the
40% of 18 to 49 year olds.
Next slide.
Although we know underlying
medical conditions are common
among hospitalized adults,
our underlying medical
conditions independently
associated with COVID-19
associated hospitalizations
among adults in the
general population
of 18 years and older.
A recent analysis
by the COVID-NET investigations
group combined population based
data from COVID-NET and
the Behavioral Risk Factor
Surveillance System, or BRFSS
to answer this question.
COVID-NET includes community
dwelling adults at its residence
of the catchment area prior
to their hospitalization
with chart abstracted data on
underlying medical conditions.
The analysis included about
5,000 individuals hospitalized
from March 1st to June 23rd.
BRFSS is an annual
cross sectional survey
on health behaviors
and self-reported underlying
medical conditions among
community dwelling adults or
residents in all 50 states, DC,
and 3 U.S. Territories.
The data was weighted
to be represented
of the population residing in
the COVID-NET catchment area.
Next slide.
For the statistical analysis,
prevalence of underlying medical
conditions was calculated among
the COVID-NET hospitalized
adults,
the COVID-NET catchment
area, and nationwide.
Unadjusted and adjusted
rate ratios
and 95% confidence intervals for
hospitalization were calculated
for each medical condition
and the models were adjusted
for age, sex, and
race and ethnicity.
Next slide.
The overall prevalence
of underlying medical conditions
was greater among COVID-19
hospitalized cases
from COVID-NET shown
in the blue bars compared to the
COVID-NET catchment area show
in the yellow bars and
the United States shown
in the orange bar.
COVID-NET catchment area
estimates were similar
or slightly lower than
nationwide estimates.
Next slide.
This figure shows
adjusted rate ratios
in 95% confidence intervals
for hospitalization
by medical condition.
The magnitude of risk for
hospitalization was greatest
for adults with severe obesity,
chronic kidney disease,
and diabetes.
Adults with these conditions
have 3 to 4 times the risk
for hospitalization compared
to hospitalized adults
without these conditions.
Adults with hypertension
and obesity had almost
3 times the risk
for hospitalization compared to
adults without those conditions.
Next slide.
The magnitude of risk
for COVID-19 associated
hospitalization was greatest
for adults 65 years and older
for all underlying
medical conditions.
And, this table shows
the same data
as the previous slide
but by age group.
And, compared to adults age
18 to 44 years, adults 64
and older had 2 to
4 times the risk
of hospitalization
depending on the condition.
And, although of smaller
magnitude, adults age 45
to 64 also had an increased risk
for hospitalization compared
to the younger group
for all conditions.
Next slide.
The magnitude of risk for
hospitalization increased
with the number of
underlying medical conditions
with the greatest
risk among adults
with 3 or more conditions.
This table shows the unadjusted
and adjusted rate ratios
for number of medical conditions
and the COVID-19
associated hospitalization.
In the adjusted model,
any number of conditions
increased for hospitalization.
But, adults with 3 or more
conditions had 5 times the risk
compared to adults
with no condition.
Next slide.
To summarize this analysis
from COVID-NET BRFSS,
accounting for age,
race, ethnicity, and sex,
higher hospitalization rates
were observed for adults
with underlying medical
conditions
in the general population.
Adults with think of the
COVID-NET catchment area,
adults with 3 or more medical
conditions had the highest
hospitalization risk.
And certain conditions
had higher risks
as well including severe obesity
and chronic kidney disease
with almost 4 times the
risk compared to adults
without those conditions,
and diabetes, obesity,
and hypertension with 3 times
the risk compared to adults
without the condition.
Also accounting for the presence
of underlying medical
conditions,
higher hospitalization rates
were observed in adults 65 years
or older compared to
younger age groups.
Next slide.
So the previous analysis
focused on risk
for hospitalization
in the population.
Now we are back to the previous
solution COVID-NET multivariable
model for risk of
in hospital death.
The red box is now highlighting
that, in addition to age,
certain underlying medical
conditions were independent risk
factors for in hospital
death with each having 1.2
to 1.4 times the risk of death
compared to hospitalized adults
without those conditions.
Next slide.
And, now coming back
to the large multicenter U.S.
cohort study among adults
with COVID-19 associated ICU
admission shown previously.
You can now see that,
in the same model,
certain underlying medical
conditions are also independent
risk factors for death
within 28 days of admission.
Again, even after
adjusted for a patient
in a hospital level
characteristics,
the odds of death after
ICU admission increased 1.5
to 2.2 times for individuals
with severe obesity, BMI,
greater than 40,
coronary artery disease,
and cardiovascular
condition and active cancer.
Next slide.
This figure shows
data on a number
of underlying medical conditions
among COVID-19 deaths reported
by supplementary U.S. case
based surveillance data that's
reported to CDC.
This is different from
COVID-Net surveillance.
Among convenient sample of
about 10,000 COVID-19 deaths
that occurred from February
12th through April 24th
by 16 health jurisdictions,
76% decedents had
at least 1 underlying
medical conditions.
And, the majority of decedents
of any age had multiple
conditions including adults less
than 65 years shown in
gray and adults 65 years
or older shown in blue.
Overall, the most common
underlying conditions reported
in this group were
cardiovascular disease,
diabetes, chronic kidney disease
and chronic lung disease.
Next slide.
So in addition to
COVID-19 surveillance
of severe COVID-19 disease,
CDC has an ongoing
evidence informed process
to assess the risk for severe
COVID-19 disease for individuals
with underlying medical
conditions.
This includes a comprehensive
ongoing literature review
on underlying conditions
with an integral database
to track both published peer
reviewed and preprint articles.
Collaboration with subject
matter experts across the agency
and monthly updates to the
CDC website, which listed
at the bottom of this slide.
Next slide.
The list of underlying
conditions is organized
in 2 tiers based on
the level of evidence.
The conditions that
are consider associated
with increased risk are informed
by strong evidence defined
as consistent evidence coming
from multiple smaller studies
or a strong association
from a larger study.
Conditions listed in the
second tier might be associated
with increased risk are informed
by mixed or limited evidence.
Mixed evidence is defined
as multiple studies
that reach different
conclusions.
And limited evidence
is considered that from
which a small number
of small reports.
Specific evidence for each
condition is on the CDC website.
Next slide.
This is CDC's list of conditions
that are considered associated
with increased risk
for severe disease.
They're listed alphabetically
and include cancer,
chronic kidney disease, COPD,
and immunocompromised state
from solid organ
transplant, obesity,
serious heart condition,
sickle cell disease,
and type 2 diabetes.
Next slide.
This is a list of conditions
that might be associated
with increased risk
for severe disease.
These conditions are also
listed alphabetically
and include asthma,
cerebrovascular disease,
hypertension, and a
number of other conditions
that you can see listed here.
Next slide.
Of note, nationally, 41% of
adults in the United States have
at least 1 underlying medical
condition that puts them at risk
for severe COVID-19 disease.
By county, the prevalence varies
from almost 1 in 4 to as many
as 2/3rds of adults having
at least 1 underlying
medical condition.
And, 1/2 of U.S. counties,
almost 50% of adults
are estimated
to have an underlying medical
condition, as you can see
in the figure up on the slide.
Next. In summary, as of August
23rd, over 5.6 million cases
of COVID-19 been diagnosed
and over 176,000 COVID-19
associated deaths have been
reported in the United States.
Older adults age 65 years or
older have the highest risk
of severe COVID-19 disease.
And, within this age group, risk
increases with increasing age.
Adults with underlying
medical conditions also are
at increased risk for
severe COVID-19 disease.
Obesity, diabetes
and cardiovascular disease
are common conditions observed
across data sources.
And, of importance,
multimorbidity increases risk
of severe COVID-19 disease.
Surveillance and projects
ongoing are ongoing to continue
to monitor COVID-19 associated
hospitalizations and death
and to identify person
at higher risk
for severe COVID-19 disease.
Next slide.
I'd like to take a moment to
acknowledge the contribution
of the COVID-NET Hospitalization
Surveillance Team
and the Community Interventions
and Critical Population
Task Force that helped
with these slides
today, thank you.
>> Thank you Dr. McClun.
This is now open for
questions or for discussion.
Any questions for the members?
I'm not seeing any.
I think it's a tribute to
your excellent presentation.
Let me ask, oh wait,
spoke too soon.
Dr. Gluckman, please go forward.
Dr. Gluckman?
Grace, Dr. Lee.
Oh.
>> [Inaudible] about
the patterns
of mechanical ventilation
of March through August.
One question I had is that
you notice these things shift
in the use of mechanical
ventilation and the frequency
of that status, since
it appears that,
that may actually increase
their risk of mortality.
And, my second question is
in your presentation you kind
of demonstrated that
there's a lesser relationship
between the presence of chronic
conditions in the young people
who have severe COVID.
Is there any hypothesis as
to why that might be less
of a factor to young people?
>> This is Sarah Oliver.
Could you repeat
the second question?
I apologize that we just
didn't quite catch that one.
>> Sure, they demonstrated the
correlation between the presence
of chronic conditions
and severe COVID.
You showed a significant
increase in older patients,
but the younger patients, it
seemed like a higher proportion
or many of those
patients had either 1
or 2 chronic conditions or none.
So can you, do you
have any hypothesis
about why some young people
may develop serious COVID
without coexisting
chronic conditions?
>> Thanks.
So regarding the first question,
COVID-NET doesn't specifically
look at kind of an association
with ventilation and death.
And, so I don't know
that we can use
that data to comment on that.
Regarding the second question, I
think it probably just has more
to do with the fact that the
younger population just is
generally healthier and
has, we showed that,
that they're less likely to have
that multimorbidity as opposed
to the older population.
So I think it's likely
an interplay
with a younger healthier
population as a whole
that we're seeing there.
>> Dr. Lee please.
>> Thank you.
I just have, it's
actually a minor question.
Which is there's been a
41% estimate of greater
than 1 medical condition.
I might have missed it, but
does that include the 2 slides
that show the risk
factors that are associated
versus might be associated?
Are both included?
>> That's a good question.
This is Nancy.
I think those only include
the ones that are associated
with COVID-19 disease.
But, we will check on that.
>> Dr. Musinao was
you going to add cont?
>> No I was just going
to ask a question.
>> Go ahead.
>> Hi. Two questions maybe.
One, does COVID-NET
have data on treatment?
And, are you able to correct
for sort of treatments
that might also be having
an impact on outcome?
And, second are there other
data from other countries
that are finding the
same things and can sort
of corroborate our
findings here in terms
of the trends that you found?
>> Sure. I can comment
on the global question.
So I, there is, has been
recently published a global med
analysis on comorbidities
and mortality.
And, the findings are similar
to what we are seeing
in the United States.
And, we can make
that paper available.
And, then Shika are
you on the line
to comment a little bit more
about the COVID-NET treatment
in the extent to which
you guys have that?
>> Yeah. Can you hear me?
>> Yes we can, great, thanks.
>> So we do collect data
on treatments in COVID-NET.
We collect data on from
[inaudible] convalescent plasma
and other medications.
We've been adapting that as
the pandemic has been evolving.
And, we have an other field, and
so we continue to collect new
and emerging treatments.
We haven't yet thought through a
method for specifically looking
at the impact of
treatment on outcomes.
I think that's something
we're interested
in looking at in the future.
One we're still collecting data,
so we don't have complete
treatment data on all cases.
And, then two just
thinking through the biases
about who is receiving
treatment versus who's not.
I think we need to think through
that before we do that analysis.
>> Thanks Shika.
>> Thank you.
Dr. Bell.
>> Thank you.
I just have sort of a
general technical question
about COVID-NET which is
the extent to which data
in COVID-NET can be
disaggregated according
to evaluate kind of
regional differences in some
of the outcomes that
we're looking at?
>> Yeah. I can, I'll
comment first and then see
if Shika has anything to add.
But, a do know that it's
across a geographically divers,
so it's 10 study states but it's
geographically kind of spread
across the U.S. representing
about 10%
of the U.S. population.
And, the BRFSS data was matched
to the same communities as,
you know, the COVID-NET
catchment areas.
Shika, do you have
anything else to say?
>> Sure. I'll just add that
there are 4 additional sites.
So we have select counties,
about 99 counties
across the 14 states.
We don't we haven't
done specific state
by state analyses.
We could potentially try to
group some regions together
and do those types of analyses
but we haven't done that yet.
Thank you.
>> Dr. Bernstein.
>> Yes. Thank you.
You may have said this,
I may have missed it.
But, in the COVID-NET
Hospitalization Surveillance
Team, what are their plans
for pediatric population
under the age of 18?
>> This is Nancy.
COVID-NET Surveillance does
capture all COVID-19 associated
hospitalizations and all ages.
And, that data is
publicly available.
I did not present data
on children today.
But, it is available.
>> Thank you.
>> Are there any other
questions or comments
from the voting members?
Seeing none, we're scheduled
for a break until 20 till.
But, I think what we'll
do is we'll go and, yeah,
we'll give you, we'll
give you 12 minutes
and say start at 10 till.
