U

P‑R‑O‑C‑E‑E‑D‑I‑N‑G‑S

(8:37 a.m.)

ADMINISTRATIVE MATTERS

MEMBER FREAS: Mr. Chairman, members of

the Committee, invited speakers, and members of the

public, I would like to welcome all of you to this,

our 97th meeting of the Vaccines and Related

Biological Products Advisory Committee meeting.

I am Bill Freas. I am the Acting

Executive Secretary for today. At this time, before

the meeting, begins, I would like to go around and

introduce to the public the members seated at the head

table.

We will start on the right side of the

table. That is the audience's right‑hand side. And

I will read the names of the people at the table.

Would the members please raise their hands as I call

their names?

David Markovitz, Professor, Division of

Infectious Diseases, University of Michigan Medical

Center.

Next is Dr. Walter Dowdle, Senior Public

Health Consultant, The Task Force for Child Survival

and Development.

Next is Dr. Judith Goldberg, Director,

Division of Biostatistics, New York University School

of Medicine.

Next is Dr. Ruth Karron, Associate

Professor, Johns Hopkins School of Hygiene and Public

Health.

Next is Dr. Walter Royal, Associate

Professor of Medicine, Morehouse School of Medicine.

Next is Dr. Monica Farley, Professor of

Medicine, Emory University School of Medicine.

Next is Dr. Pamela McInnes, Deputy

Director, Division of Microbiology and Infectious

Diseases, NIH.

Next is Ms. Cindy Lyn Province, Associate

Director, Bioethics Center of St. Louis.

Next is Dr. Bruce Gellin, Director,

National Vaccine Program.

In the empty chair, we will soon be joined

by Dr. Stephen Phillips, Director, Deployment Medicine

and Surveillance, Office of Assistant Secretary of

Defense.

Next I would like to introduce you to the

Chair of this Committee, Dr. Gary Overturf, Professor

of Medicine, University of New Mexico School of

Medicine.

Coming around the table, we have Dr.

Philip LaRussa, Professor of Clinical Pediatrics,

Columbia‑Presbyterian Hospital.

Next we have Dr. Martin Myers,

Co‑Director, Public Health Policy and Education,

University of Texas Medical Branch.

Next we have Dr. Bonnie Word, Assistant

Professor of Pediatrics, Baylor College of Medicine.

Next we have Dr. Peter Palese, Chairman

and Professor, Department of Microbiology, Mt. Sinai

School of Medicine.

Next we have Dr. Arnold Monto, Professor,

The University of Michigan.

Next we have Dr. Ted Eickhoff, Professor

of Medicine, University of Colorado Health Sciences

Center.

Next we have our nonvoting industry

representative, Dr. Michael Decker, Vice President,

Scientific and Medical Affairs of Aventis Pasteur.

Next we have a nonvoting participant, Dr.

Nancy Cox, Chief of the Influenza Branch, Centers for

Disease Control and Prevention.

Next we have Dr. Roland Levandowski from

the FDA.

Dr. Richard Whitley, University of

Alabama, member of this Committee, has recused himself

from today's participation.

I would like to thank the members for

attending. There is one other person I would like to

introduce at this time. Many people have asked me,

"When are you going to get a real executive secretary

for this Advisory Committee?" I would like to

introduce this morning Christine Walsh, who will be

the next Executive Secretary for this Committee at the

next meeting, which will be announced later. It will

be either in March or May. We have a teleconference

scheduled for March the 17th at this time.

I would now like to read the conflict of

interest statement into the record. Before I do that,

I would like to ask the members of the public if they

could put their cell phones on silence mode, it would

be appreciated.

"The following announcement addresses the

conflict of interest issues associated with the

Vaccines and Related Biological Products Advisory

Committee meeting on February 18th and 19th, 2004.

The Director of the Center of Biologics Evaluation and

Research has appointed Drs. Walter Dowdle, Ted

Eickhoff, Bruce Gellin, Judith Goldberg, Pamela

McInnes, Arnold Monto, Martin Myers, and Stephen

Phillips as temporary voting members for this meeting.

"Based on the agenda, it has been

determined that there are no specific products being

approved at this meeting. The Committee participants

have been screened for their financial interests. To

determine if any conflicts of interest existed, the

agency reviewed the agenda and all relevant financial

interests reported by the meeting participants.

"The Food and Drug Administration prepared

general matters waivers for participants who required

a waiver under 18 U.S. Code 208. Because general

topics impact on many entities, it is not prudent to

recite all potential conflicts of interest as they

apply to each member. FDA acknowledges that there may

be potential conflicts of interest, but because of the

general nature of the discussions before the

Committee, these potential conflicts of interest are

mitigated.

"We would like to note for the record that

Dr. Michael Decker is a nonvoting industry

representative for this Committee acting on behalf of

a regulated industry. Dr. Decker's appointment is not

subject to 18 U.S. Code 208. He is employed by

Aventis and, thus, has a financial interest in his

employer. In addition, in the interest of fairness,

FDA is disclosing that his employer, Aventis, is a

manufacturer of a product that could be affected by

today's discussions.

"With regards to FDA's invited guest

speakers, the agency has determined that the services

of these speakers are essential. The following

interests are being made public to allow meeting

participants to objectively evaluate any presentation

and/or comment made by the speakers.

"Dr. Antoine Flahault is employed by the

World Health Organization Collaborating Center for

Electronic Disease Surveillance in France. He has

associations with firms that could be affected by the

Committee discussion.

"Dr. Maria Zambon is employed by the

Respiratory Virus Unit, Health Protection Agency in

England. Her agency's laboratory conducts tests on

licensed influenza vaccines. Her employers

collaborates with firms that could be affected by the

Committee discussions.

"In addition, there are vaccine

manufacturers making industry presentations. These

speakers have financial interests associated with

their employer and with other regulated firms. They

were not screened for conflict of interest.

"Members and consultants are aware of the

need to exclude themselves from the discussions

involving specific products or firms for which they

have not been screened for conflict of interest.

Their exclusion will be noted in the public record.

"With respect to all other meeting

participants, we ask in the interest of fairness that

you address any current or previous financial

involvement with any firm or product you wish to

comment upon. Waivers are available by written

request under the Freedom of Information Act."

So ends the reading of the conflicts of

interest statement. Dr. Overturf, I turn the meeting

over to you.

CALL TO ORDER

CHAIRMAN OVERTURF: Good morning. I would

like to welcome everybody to this meeting of the

VRBPAC Advisory Committee February 18th and 19th.

Dr. Midthun, would you like to make

presentations to retiring members?

PRESENTATION OF PLAQUES TO RETIRING MEMBERS

DR. MIDTHUN: Good morning. I would like

to ask Dr. Judith Goldberg to please come up to the

podium. I would like to thank her for her many years

of service on this Advisory Committee. She has always

been here with great dedication, always has prepared

extremely well, and provided excellent input to us.

We are really going to miss her and really appreciate

all that she has given to us.

I think I am supposed to stand over here

so we can get with the picture of the plaque. Thank

you. And we also have a letter here for you from Mr.

Peter Pitts, who is our Associate Commissioner for

External Relations. So thank you so much.

(Applause.)

DR. GOLDBERG: I just want to thank all of

you because it has really been a privilege to serve on

this Committee. I have enjoyed every minute of it,

and I have learned a tremendous amount.

DR. MIDTHUN: Thank you so much.

CHAIRMAN OVERTURF: I think we will

proceed. As you know, there has been a great deal of

interest in influenza, for those of you who forgot

about last winter. So this year I think will be a

striking update of last year. So that I think we will

begin with the data that is going to be presented by

Dr. Roland Levandowski and his associates. Thank you.

DR. LEVANDOWSKI: Great. Thanks very

much, Dr. Overturf.

OPEN SESSION

STRAIN SELECTION FOR INFLUENZA VIRUS VACCINE

FOR THE 2004‑2005 SEASON

INTRODUCTION

DR. LEVANDOWSKI: I am going to try to

give a somewhat extended review of what has been

happening this last season. Generally I do give a

fairly brief review, but today I am going to be going

into a little bit more detail.

There is an awful lot that is going on.

What we would like to try to cover, just as a

reminder, the real business for today is what is first

on this list of topics for us to take a look at. We

are really here today to make the recommendations for

the strains that should be used in next year's

trivalent vaccine, for the 2004‑2005 trivalent vaccine

for the H1N1, H3N2, and Influenza B viruses.

We do also on this program, as you will

see from the agenda, have some other items that we

wanted to bring to the attention of the Committee. In

particular, there are some items that we have that we

would like to bring to the attention of the Committee

just mainly for information for things that are

happening.

There has been a lot of interest this year

about the effectiveness of vaccines. This has been a

discussion point at this Committee on many occasions

in the past. In fact, I can't remember one when it

hasn't been discussed in recent memory of mine.

So we have several speakers: Dr. Carolyn

Bridges from CDC, Col. James Neville from the

Department of Defense, and Dr. Antoine Flahault from

the Institut National de la Sante et de la Recherche

Medicale, who will be talking about some studies that

they have ongoing to look at vaccine effectiveness.

Some of these are still in progress, but we will at

least get to hear what is happening to try to look at

this in an ongoing manner.

I also wanted to bring to the attention of

the Committee what is happening with H5 avian

influenza in Asia. At this time last year, you might

remember we were talking about what is happening with

SARS. So we know that the Committee will be very

interested to hear this information. We also want to

bring it to the attention of the Committee because

there may need to be some activities that go on at a

later date. We just wanted to have them informed as

much as we can at this point.

Finally, on the agenda, we have something

that we would like to have some discussion with the

Committee. This relates to a point that was brought

up last year about use of tissue culture isolates from

field laboratories for preparation of influenza

vaccines.

You will see on the agenda that tomorrow

toward the conclusion of the meeting we have a couple

of presentations by one of my colleagues, Zhiping Ye,

Center for Biologics, and also Dr. Phil Minor from

NIBSC, to talk about our perspectives on what the

issues might be with issue of tissue culture isolates.

So this is the main business. Again, this

is the question that we are asking the Committee to

give us recommendations today. We are asking for a

vote on this. This is the abbreviated version of the

question, what strain should be recommended for the

antigenic composition of the 2004‑2005 influenza virus

vaccine for use in the United States?

Just by way of review, this was the

recommendation that was made by the Committee last

year at this time. It was for a trivalent vaccine

that would contain an A/New Caledonia/20/99 H1N1‑like

strain. Actually, it was A/New Caledonia/20/99.

It would also contain a B/Hong

Kong/330/01‑like strain. And in our case, the strains

that were used for vaccine preparation were the actual

strain, B/Hong Kong/330/2001 or B/Hong Kong/1434/2002.

The H3N2 component that was recommended based on all

of the information was for an A/Panama/2007/99‑like

strain.

Why do we change strains for influenza

vaccines? Well, we do it because the vaccine efficacy

is really related to two things. One is how much

antigen is present in the vaccine and then, very

importantly, what the match of the vaccine,

hemagglutinin and neuraminidase, are with the wild

type circulating strains. This has been very clear to

us since the earliest days of influenza vaccine use.

You might remember that influenza viruses

were first isolated in the mid '30s, human influenza

viruses, mid '30s to early '40s. And it was very

quickly that some vaccines were prepared as whole

virus vaccines.

The first vaccine was licensed in the

United States in 1945. And within two years, by 1947,

it was pretty clear that when there were antigenic

changes occurring by way of mutation in the viruses,

that there was reduced vaccine effectiveness. That

led to setting up the global surveillance system.

What we know from that period of time

onward is that there have been continuous antigenic

changes in the hemagglutinin and neuraminidase of both

influenza A and influenza B viruses.

These are the questions that are asked in

order to answer the question for recommendations. I

will just go over these a little bit with you. First

of all, we want to know, are there new either drifted

or shifted antigen influenza viruses present? Drift

is point mutation occurring in the viruses. And shift

would be exchange of an entire gene segment.

I guess I should remind you that influenza

viruses have a segmented genome. There are eight gene

segments for either influenza A or influenza B. These

can reassort in nature to put new hemagglutinin and

neuraminidases into human influenza viruses. That

usually results in a pandemic. But, anyway, the

question is, are there new influenza viruses present?

This is the purpose that surveillance

serves. It provides us with that information as to

whether there are new viruses that are occurring.

Mainly we are interested in, are they new in terms of

their antigenic properties, mainly for the

hemagglutinin but also for the neuraminidase?

It is also from surveillance that we get

the viruses that are used for vaccine preparation. So

without that underpinning, there really isn't anything

that we would be able to accomplish.

The question to be answered if there are

new viruses, ‑‑ and they almost always are new viruses

that are being identified because of the continuity of

evolution of the viruses ‑‑ are they spreading in

people?

It is not unusual to see that there are

influenza viruses that are really wildly different,

but it turns out they are one off. So that occurs

from time to time. And it takes a while, in fact, to

have an understanding as to whether these new viruses

really have any significance or a potential impact

that we need to take into consideration for vaccine

preparation.

If there are new viruses spreading, then

we also want to know whether our current vaccines are

going to have any likelihood of having effectiveness

against these new strains. And, for that purpose, we

look at responses from people who have been immunized

with the current vaccines. Often the case is that

although there are some new viruses that are

spreading, the current vaccines actually make

antibodies that cross‑react fairly well.

And while the differences you will see for

these two different activities are somewhat

complementary, there are thousands of influenza

viruses that are examined with a relatively small

number ‑‑ it is not entirely small, but it is a

relatively small number of sera that are used to

categorize them. With the human serologies, we are

looking at the reverse, where we have several hundred

different sera from people who have been infected or

immunized and looking at a relatively small, select

group of these viruses that have been identified in

surveillance.

And then, last but not least, if it is

true that there are new viruses, they are spreading,

the current vaccines don't look like they produce very

good antibody responses to those new strains, then we

still need to know, "Can we do something about it?

And are there any strains that are suitable for use in

vaccines?"

And so, to answer the questions, last

year, just to review what we did, were there new

influenza A, H1N1, viruses? No. The answer was no.

The HA of all of the strains was very similar to the

vaccine strain.

For H3N2, the answer, however, was yes.

There were quite a few strains that were identified.

Although most of these strains were originally very

much like the current vaccine strain, there were some

strains that were identified early in 2003 that were

antigenically distinguishable. And it was a

relatively small proportion to begin with, but that is

not unusual either that it starts out small and

quickly snowballs.

However, after collecting information and

analyzing, it wasn't really until February of last

year that it was clear that there was a cluster that

were antigenically and genetically related that seemed

to be the ones that were most likely to spread

further.

For influenza B viruses, again, the answer

was really no. There weren't really any new viruses

that were found. The majority of the strains were

very similar to what was in the vaccine, but there

were a small number of strains that are different.

There actually have been two hemagglutinin

lineages for influenza B viruses co‑circulating for at

least the past 15 years. One or the other of these

hemagglutinin lineages has tended to be the

predominant strain. We had just left a period of time

where for about ten years, the strains in the

so‑called B/Yamagata/16/88 lineage were the ones that

were predominant, particularly outside of Asia.

For the last two years, however, the

strains that have been predominating are on the other

HA lineage. They are in the B/Victoria/2/87

hemagglutinin lineage. And that is what we have

currently in our vaccine. But there was a small

proportion of viruses last year that were identified

that were in the B/Yamagata/16/88 lineage. That was

being paid attention to, but it was not sufficient to

think that there was something really happening there.

Were these viruses spreading? For the

H3N2, as I mentioned, the answer was yes. By the time

the Committee was meeting in February or March, it was

pretty clear that there were some of these viruses

found on several continents, including Asia, Europe,

and North America. Were these viruses inhibited by

the current vaccines? And the answer to that was

partially no.

There were a number of strains that were

very much like the Panama/2007/99 vaccine strain.

Those were very well‑inhibited. But for the group

that we are now calling A/Fujian‑like strains, some of

these were relatively well‑inhibited by current

vaccines and some were not. It was not a very

homogeneous situation.

Then, to answer the question, were strains

suitable for manufacturing available? The answer was

actually no. And it related to the fact that all of

this information was coming out just at the time that

decisions need to be made in order to prepare a

vaccine. I will give a little bit of explanation

shortly about why that is true, why the timing was

off.

Sort of in a nutshell here, the

manufacturing does depend on having an egg‑adapted

strain that will grow well. It could be either

wild‑type or a high growth reassortant for the

influenza A viruses. Generally it needs to be a high

growth reassortment.

The fact that these first Fujian‑like

strains were first identified in February made this

difficult. The first egg isolate of an A Fujian‑like

strain wasn't until April. The first high growth

reassortant wasn't prepared until toward the end of

June. That timing also is fairly typical for dealing

with new influenza viruses as they are appearing.

So the implications of the strain

selection from last year were that the preparation of

vaccines this current year was very much on schedule.

I will provide some information about that. And the

supply of vaccine matched the demand that was expected

by the previous year's experiences.

There were some other implications. One,

this year there was an early widespread appearance of

drift variant of A Fujian‑like viruses in the United

States. There were reports of mortality in children.

Now, that significantly increased vaccine demand.

And although there were several million

doses of vaccine, both of inactivated and live

vaccine, that were still available in mid November.

And it appeared that we were headed toward a situation

where a lot of vaccine would not be used again, which

has frequently been the case in the past, not just the

year before but for many years running. The amount of

vaccine that was available was not sufficient to avoid

some spot shortages that occurred after the

Thanksgiving holiday in the United States.

And then again, the effectiveness of the

vaccines against this drift variant has been

questioned. So there are some studies that are

ongoing. We are going to be hearing about those.

So for the United States, we have three

licensed influenza vaccine manufacturers. Two of them

produce inactivated vaccine: Aventis Pasteur and

Evans Vaccines. Evans is now part of Chiron. So I

have to be careful. It is hard for me to keep up with

the changes that occur business‑wise, but these two

companies have been licensed in the United States for

quite some time, as you can see here.

And last year, between the two companies,

there was production of about 83 million doses of

vaccine. Put that into a little bit of perspective.

Inactivated vaccines around 1990, there were

approximately 25 to 30 million doses produced per

year. So over the decade of the '90s, vaccine

production had increased substantially.

And our license manufacturer for live

attenuated vaccine is MedImmune. They were licensed,

as you might recall, in June of 2003. And they

produced about four million doses of vaccine for use.

The timelines for vaccine production are

shown here. And it is a little bit of a pyramid

scheme. What most everybody is interested in or what

gets the most visibility is vaccine use, which occurs

in the fall through the early winter. But supporting

that, underneath that, is all the work that the

manufacturers have to do to prepare the vaccine. And,

even before that, surveillance and other activities

are required. We are right here in February to March.

So we are right down here in this period of time. It

is early days for vaccine preparation, and it is

months away from vaccine use.

As I mentioned, without surveillance, we

would not have strains for use in vaccine production

in the first place. And there is a lot of work that

goes on between surveillance and trying to develop new

strains throughout the year, although there may be

periods of time when there is more activity than

others. But there is some activity going on pretty

much all the time.

Recommendations are generally made by the

WHO for the Northern Hemisphere and for the winter

months here and for the Southern Hemisphere and for

the winter months there. But these recommendations

are important so that the manufacturers know what they

should do.

Too, as there are reference strains that

are getting worked, the manufacturers throughout the

year are working on their seed viruses, which were

proprietary to them. They worked with the virus to

make sure that it is going to be appropriate for the

manufacturing conditions. And although there may be

some early seed viruses that are used in production,

there is some continuous work that goes on to try to

make that better so that manufacturing can be smoothed

out. I will show a little bit of information about

that, too.

Production of the monovalent components of

the vaccine takes many, many months. And, really, it

starts maybe earlier than January. Manufacturers may

be working at risk before recommendations are made to

produce monovalent components. They don't do this

without some education. They are paying close

attention to the surveillance that is being reported

throughout the year by WHO and our colleagues at CDC.

Once all three of the components are

present, trivalent vaccine can be produced, but you

will see that there is still overlap. There is still

for quite a long time, actually, many years, work

being done with the monovalent vaccines. And so there

is some vaccine that starts to come out, but it

doesn't all come out at one time. And that vaccine

uses the desired goal at the top again.

So to try to also give some understanding

about how long it takes, when there is a new strain

that is recommended, when there is a new reference

virus that is identified, for the point of time that

that new reference is identified to the time that that

is available for sending out to manufacturers to

develop their seed viruses, for the period of several

weeks, part of this is trying to understand, is this

the best strain that is available or are these the

best strains that are available for producing the

vaccine? It takes some analysis. It takes some

collaboration between the WHO centers to come to that

understanding.

Part of the time for influenza A viruses

and probably for influenza B viruses in the future is

preparing the high growth reassortants that make it

more expeditious for producing the vaccine. At the

same time that that is happening, reference reagents

for standardization of the vaccine need to be

prepared. And this is true not only for inactivated

vaccines, but it may be necessary for the live

vaccines also.

Potency testing for these is dependent

upon having antisera that can be used for the tests

that are done to try to standardize in terms of

potency.

Once the manufacturer has a seed virus

prepared, then they can start to manufacture. As I

mentioned, there may be manufacturing at risk when the

strains are not changed. Those strains can be

prepared in advance of this meeting if the

manufacturers so desire, but they really can't do

anything until they have in their hand something that

is appropriate for making a seed virus. And they

can't manufacture all the strains without having those

available.

So this just shows manufacturing three

different strains. And I am showing down here vaccine

release. There are activities that go on between the

manufacturer and regulatory authorities to try to make

sure that these seed viruses: first, are appropriate

for use, that they maintain their antigenic

characteristics; and, second, that other kinds of

qualities are maintained.

Now, this bar is about three weeks long

for each of the monovalents. That is an

approximation. Most of that time I think you will

hear from our colleagues from industry relates to

quality control, not necessarily interaction with

regulatory authorities but just needing to meet their

own good manufacturing practices and be sure that the

vaccines are going to meet all of the specifications

that are set for them.

Once the three strains are produced, then

it is possible to go ahead and formulate vaccine and

to fill it and to send it out. You see, each of these

points have bars that are about three weeks long as

well. And there are some release activities that go

on for the trivalent vaccines.

This duration of time here again is not so

much the actual physical manipulation of the vaccine.

It has to do with the quality control measures that

need to be met and some very important ones, like

sterility for inactivated vaccines, for example.

So once that has all happened, then, of

course, the vaccine can be distributed. But, you see,

this time line up here, for a new strain, I am

indicating about 20 weeks. I think that is a

reasonable estimate. If you put that in months, that

is about five months from the time of the first

appearance of the new strain until there is really the

possibility of having a product in hand that can be

used.

That is just the first. Once the first

comes out, then, of course, it just keeps rolling.

Again, to try to put this into some more perspective,

it seems to be cut off a little bit on the boundaries.

That is okay.

I went back and reviewed monovalent

vaccines that were produced for inactivated vaccine

over the last several years. What I am trying to show

you here is that when strains are changed and those

are shown across the bottom, the relative proportion

‑‑ this should add up to 100 percent for all 3 strains

‑‑ of the strains that are produced really tends to

favor the new strains that are added. So you can see

between 1998 and 1999, there was a new influenza B

virus. And although 38 percent of all of the

monovalent concentrates produced in 1998 were

influenza B, over half of them, about 55 percent, were

influenza B.

You see that with other changes. In 2000,

we added a new H1N1 and H3N2. And you can see in both

instances, the amount of effort, the relative amount

of effort, in terms of the number of monovalent

concentrates that had to be produced was mostly

devoted toward the new strains. You can see that all

the way across here, that when new strains were added,

that there needs to be an adjustment that it is the

early work that has to be done by the manufacturers to

figure out how best to get things growing.

Once they get it organized, you can see

that it is possible that things may even out a little

bit more between the three strains, but the strain

that has changed, the strains that are changed are the

ones that are the most difficult in terms of overall

production, at least for the first year.

In terms of timing of these things, this

is what we see in terms of submission to the Center

for Biologics for Release. I am showing the number of

lots here against the month for both the monovalent

vaccine and the trivalent.

What I really want to point out to you on

this slide, the numbers aren't so important. It is

the overall pattern. You see that there is kind of

this buildup of more and more monovalent concentrates

coming in up until about August‑September. And then

it starts to wane.

And this relates to the point at which

manufacturers when they are trying to meet the need

for vaccine in October and November have already

planned out how they are going to be putting together

how they are going to be manufacturing the vaccine

components, when they need to have them on tap and so

on. And so they come to a decision point about August

or September where they have to decide whether it is

worthwhile for them to continue manufacturing or not.

There is a lot of effort and money that

goes into that continuation. And it is possible for

them to do so. They could keep going if they knew

that there would be demand, for example, within our

current system. It is possible to make more vaccine.

It doesn't have to stop right here, but it does

because there is a target that has been developed from

sales and demand. And it is really kind of a

practical decision.

You see that there still is overlap

between the preparation of the trivalent vaccine and

preparation of the monovalent right on out to the end

of the overall campaign for the year.

For this year, because the strains were

the same as the previous year and the demand was

fairly well‑understood at the beginning of the year,

it was possible to get everything ready.

This curve shows cumulative percent of all

the lots that are submitted to us for release from

June to December. Two thousand was the year that we

had the shortages or delays that were concerning. And

this was an atypical year in that the point at which

50 percent of the vaccine that was available was

shifted out substantially from where it normally is.

These curves over here are more typical of

what we would be seeing. And generally 100 percent of

the vaccine in the past has been out by about October.

And that is where we are here. This is the red color

here, the diamonds. The red diamonds are this year,

2003.

So you can see that vaccine was being

produced fairly consistently throughout this period of

time and very expeditiously met the overall goal for

this production campaign without any delays.

So why are influenza vaccines important?

Well, they are important because influenza has a lot

of economic consequences, the lost work, school time,

and so on. We know that morbidity is high,

particularly in the very young.

Pneumonia and Influenza, that is the only

category that is in the top ten causes of death in the

United States, the only infectious diseases category

that is in the top ten causes of death in the United

States. And this is for ages overall. It is not for

a specific age group but for the ages overall.

We know from other statistics that we can

expect somewhere between 20,000 and 40,000 deaths in

a typical year related to influenza. That is

generally in the elderly. And we know that pandemics

cause even more.

I just wanted to read a couple of things

that were from some publications that sort of put this

into perspective. So I am quoting here. It says,

"Early apprehension was increased by the fact that

when the first indications of the outbreak were

observed in the country, the influenza had already

attained epidemic proportions in England.

The sharp rise in influenza deaths,

however, was found not to be due to virulence of the

causative organism but to a high case incidence. The

term "lightening influenza" was used in newspaper

reports.

Also, the epidemic caused by influenza A

viruses was unusually severe for the inner pandemic

period. The attack rate in children was much higher

than for adults. At least 30 percent of children

under 5 years of age were ill. And most were taken to

medical care facilities.

Over 320 children per day crowded into the

pediatric outpatient clinic at Ben Taub Hospital. So

you might think that was this year, but the first one

is from 1943 and the second one is from 1975.

I just wanted to try to remind everybody

that what we are dealing with here is something that

maybe has been a little bit forgotten but that we

should remember that influenza is a very serious

disease.

And to try to highlight that more, I have

got some other slides here that I have taken from some

of the older literature. This is data from

door‑to‑door surveillance activities in Baltimore that

were undertaken during and after the pandemic of 1918

to try to get some information about what was

happening. Unfortunately, my legend is cut off over

here. The red one is 1918. The purplish one down

here is 1919. The green one is 1928 to '29. And the

black one down here is '40 to '41. This is 1943 to

1944.

That quote that I was reading partly

related to this. There was what was seen as a

relatively large incidence of case attack rate in

children predominantly during that period of time.

There was a large fear that this was the return of the

1918 pandemic strain. So you can see, by comparison,

it wasn't quite as high an attack rate, but it was

much higher than what had been seen in some of these

intervening years. So there was a lot of concern

about that.

I think what it indicates to us is that

attack rates can be higher or lower. It is sort of

interesting that in the case of both of these years,

there is sort of a relative disproportionality in

terms of younger children and then sort of young

adults. It caught my attention because I think that

may relate a little bit to what we are seeing this

year as well. I think we are seeing more activity,

and I think we will hear more about that.

So in terms of pneumonia ‑‑ and these were

cases per 10,000 shown on the other slide, and it is

the same scale here, but the numbers are drastically

different. So this is the pneumonia cases in those

same surveys.

You can see from 1918 to 1919, this very

much parallels the mortality curve, where there was a

kind of instead of a U‑shape, where it is very high at

both ends in the very young and the very old, there

was this extra peak occurring in young otherwise

healthy adults. And there is a small echo of that in

the year following 1918, during 1919 and 1920.

What I am showing here is that this blue

down here again is 1943, where there is a huge number

of cases occurring, but the amount of pneumonia that

was being identified in Baltimore was relatively low,

particularly in young healthy adults. There was a

little bit of an increase more in elderly than in

children, but the young children were affected as

well.

These are some data from 1975‑1976 in

Houston during an A/Victoria/3/75 epidemic of

influenza. I am showing this. Again, I am trying to

use that same scale. This is hospitalizations per

10,000. These are the ages of the individuals.

I guess what I should have said in my

previous two slides is that a lot of this, of course,

1918, some of this could have been something other

than flu. We didn't really have virologic

capabilities until later, but it is based on the

sharpness of the peak of the epidemic. And it is

probably true a little bit here for these data as

well.

These are not all virus isolates. These

are clinical studies that were done to try to define

what was happening in the epidemic. But this is

during an epidemic in Houston. Again, you see this

U‑shaped curve, where the hospitalizations are most

marked for the very young and the very old. In fact,

the number of hospitalizations in this particular

instance appear to be probably more than in the

elderly. But you see some level of hospitalization

during this relatively severe influenza season in all

age groups.

Different from that, however, is what has

been seen for mortality in some of these epidemics.

This is a different age 3 and 2 epidemic in Houston,

encompassing Harris County, Texas, the statistics,

health statistics, from there. Again, these are

deaths per 10,000 at different ages.

Here you can see that, really, it is the

elderly who are most markedly affected. They are the

ones who die when they become ill and develop

pneumonia. But there are deaths that are reported in

all ages. It is kind of a small number here in the

young adults. And there are quite a few more seen in

young children but not nearly what we see in the

elderly.

So from this information, the effects

here, we know that influenza attack rates are often

highest in children who are less than ten years ago.

There is serious illness in all of the age brackets,

with the young and the old most affected. And the

mortality is generally highest in the elderly,

although it is also seen in young children. And in

some instances, it seems to parallel the incidence of

pneumonia during the period of time that the influenza

epidemic is occurring.

So a brief history of influenza vaccine

efficacy. In 1941, there was a request to license the

first inactivated vaccine in the United States, but

the regulatory authorities at the time, who were part

of NIH under the Public Health Service Act, thought

that it was best to get efficacy. That wasn't really

required, I believe. It was mainly safety data that

were needed. But there was a request to show that the

vaccine would actually be efficacious.

They were set to do the study. They had

all of the materials in place and the desire to do the

study in 1942, but this often happens to those people

who are trying to study influenza. There was no

epidemic that year. So it was not possible to do the

studies. Instead, there were some challenge studies

that were done at the time, which demonstrated that

these vaccines were effective against influenza A and

Those studies were published as well as

the information from the studies that were done later

from large‑scale field studies. Those were done in

1943 through 1945. And they were done with bivalent

vaccine using influenza A and B viruses.

The first vaccine was licensed in the

United States in 1945. And, as I mentioned before, it

was very shortly after that that it was recognized

that antigenic drift could reduce the effectiveness of

vaccines and the Global Surveillance System was

inaugurated to try to identify changes that were

occurring and to be able to make alterations in

influenza vaccine as necessary.

So the first studies that were done by

Tommy Francis and Jonas Salk and others with the armed

forces and a special commission that was set up to

investigate influenza, the studies that I am going to

be talking about were done as randomized

placebo‑controlled field efficacy studies between 1943

and 1945. The vaccines that were used at that time

were whole virus, formal and inactivated. They were

highly formalin‑inactivated reactogenic.

A large percentage of the people who got

the vaccines felt ill for a couple of days. Some of

them went to infirmary. The antigens that were

contained in the vaccine, it was actually trivalent

vaccine. It had two H1N1 components: A/Puerto

Rico/8/34 and A/Weiss/43. There was an influenza B

component. It was B/Lee/40.

The studies were done at the Army

specialized training program centers around the

country at the time. These were located in a number

of universities and medical schools. In these

particular studies, there were more than 10,000

participants.

What they were looking at, mainly they

were looking at the clinical endpoint. It was

influenza illness. It was most important. They did

have the capability. And they were using it during

the studies to identify infection by culture. They

also could look for serologies. But, really, the

endpoint here was the illness.

Illness was supposed to be characterized

by symptoms that included abrupt onset fever,

myalgias, cough, sore throat, and nasal symptoms. And

the cases were further categorized by illness

severity. Those who had a temperature over 100 by

whatever the going criteria were, they were sent to

the infirmary for hospitalization to get them away

from the rest of the men in the barracks where they

were staying. They also, of course, did X‑rays when

they wanted to look for pneumonia.

There were some differences between the

multiple centers in terms of the way the actual study

was run. So that in some instances, hospitalization

could have been for lesser fever, but generally this

is what was followed.

I am showing the two different studies

that were done here looking at influenza A and

influenza B. Again, this was a clinical measurement.

It was clinical influenza that resulted in febrile

illness that needed hospitalization. There were

approximately 12,000 individuals who were randomized

to get either the vaccine, the trivalent vaccine, or

somewhat identical placebo.

The number of cases that occurred was

substantially higher in those who got the placebo than

those who got the vaccine. If you calculate

protective effectiveness from that, it works out to be

about 69 percent protective effectiveness against the

clinical febrile illness requiring hospitalization.

A similar study was done for influenza B.

The numbers here are approximations because the people

involved in these programs at the time were sort of

going in and out. There was a lot of personnel

transfer in motion. So they did the best they could

to try to determine what the denominators were here,

but it really is kind of an estimate.

The number of cases, it is firmer. Again,

you can see that for the vaccine, there were

substantially fewer cases than in the placebo group.

Translated, it would be a protective effectiveness,

around 88 percent.

There were some subanalyses that were done

in this set of studies that have been published also.

In one subset at the University of Michigan, they

tried to look at the effect on illness, different

levels of severity. They looked at people who had any

kind of respiratory illness. This included the common

cold or what they called the common cold. It was

illness that was obvious, but it wasn't severe enough

to result in hospitalization. And it didn't have

other symptoms that they thought would be more typical

of the syndrome that we call influenza with all of

those symptoms that I listed early on.

We know very clearly that influenza

infection can cause what seems to be a common cold in

some people, and we know just as well that other viral

infections can cause what seems to be influenza by its

clinical manifestations with fever, myalgias, and so

on. So it is a very nonspecific indicator.

For those that they thought were more

likely to have influenza based on the symptomatology,

again, that is based on the clinical symptoms but

being more typical for influenza syndrome. These

inpatients had fever. Then, of course, they were also

looking at pneumonia.

What you see across the bottom here if you

try to figure out a protective effectiveness, you can

see that there is increasing effectiveness of the

vaccine against the more severe forms of illness.

It is very difficult to show vaccine

effectiveness when it is diluted by many different

types of respiratory viruses, none of which were known

at the time. They were identified specifically at the

time these studies were being done, but they were, the

giants whose shoulders we stand on were, very much

aware of the fact that there were other etiologic

agents out there that needed to be categorized. You

can see that it is very difficult to show that.

If you go to the most severe forms of

illness, it is a lot easier to try to show that there

is some effect. They commented that throughout the

study, there were no cases of pneumonia in anybody who

got the vaccine. There were ‑‑ this is a relatively

small number ‑‑ only four cases of pneumonia in the

recruits who got the placebo. That does fit, however.

These studies were done in 1943 and 1944, when I

showed that there was very low incidence of pneumonia,

even though there was a high attack rate for

influenza.

So there were some other observations they

made from this. One of them was that in these kinds

of studies, the placebo group was actually diluted by

having an immunized cohort that may have been able to

reduce transmission in the placebo group. This was in

one of the first thoughts about herd immunity.

The differences in the attack rates

between vaccine and placebo were really greatest at

the peak of the epidemic. And as the epidemic

receded, it was harder and harder to be able to show

anything.

One corollary to this part was that there

was at least one center where there was an attempt to

immunize in the face of what seemed to be the

developing epidemic. And they noted that it was very

clear‑cut that during the first week, they couldn't

really show any difference in the attack rate in the

placebo or the vaccinated individuals, but after one

week, it was very clear who had been immunized. There

seemed to be a big difference, even after that

one‑week period of time.

I mention this just because we often talk

about needing two weeks after immunization, somebody

who is immunologically primed. Of course, these

individuals all were immunologically prime by previous

exposures. But there may be protective effects that

are kicking in, even in an earlier period of time.

So, to add a little bit of information

about some of the other studies that had been done, I

wanted to concentrate on a few studies. These are

selected by me to make some points about the effect of

vaccine when there is antigenic drift that is

occurring.

This first study that I want to talk a

little bit about was done in Texas in 1976. It was

the Houston family study for this particular

publication. There were 37 families who had 155

members of different ages, ranging from infants up to

about mid '40s.

The A/Port Chalmers/1/73‑like viruses had

caused an epidemic in 1975. And so these individuals

who were not immunized specifically had antibodies

that were directed against or should have had some

possibility of having antibodies directed against Port

Chalmers, but then the following year, A

Victoria/3/75‑like viruses caused an epidemic. These

viruses, the A Victoria/3/75 viruses, are really drift

variants of the previous strains.

At the time, it was noted that this was a

very dramatic difference in terms of antigenic

characteristics between these two viruses. I don't

know whether it is fair to say so, but it probably was

at least as different as what we are seeing this past

year with the Panama‑like strains and the Fujian‑like

strains and possibly more because, actually, it was

remarkable and there was a lot of comment about how

different those strains were.

They were able to use virus isolation and

serologies to try to document infection. As I

mentioned, there was no vaccine used. Now, what I am

showing here are the preexposure hemagglutination

inhibition titers from the people who were in the

study. They were able to get blood from 154. They

tested them for antibodies to both Port Chalmers 73

and Victoria 75 strains.

What you can see here, I think, is that

there was some proportion who had, really, very low

antibodies in both of these groups. These are the

same people, of course. So you see that some higher

frequency of those who they tested for antibodies to

Victoria were more likely. They were more likely not

to have antibodies is what I am trying to say.

If you look at the distribution from low

antibody to high antibody, you can see that in

comparison to Port Chalmers, there is a shift toward

lower antibody titers for Victoria strains. And that

is what we are usually dealing with when we are

looking at our serology. So this is very similar.

They were able to do some other things to

look at infection and illness. I think that what you

can see is that there is a relation between antibody

presence and protection. As you get higher antibody

titers, there are fewer and fewer people who are

infected. The same thing is true if you look at

clinical illness. Those who have higher antibody

titers are less likely to be clinically ill.

What this also says is that the number of

people who are infected who are relatively

asymptomatic is fairly high compared to the numbers

that we would recognize as having had influenza. So

it is something else to keep in mind in terms of

trying to make sense out of what is there.

So another study done by the same group in

1986. Again, it was the family study, Houston family

study. They had 98 families enrolled with 192

children who are between 3 and 18 years old.

These children were randomized. I am not

sure that is quite the right word. They were groups

that got either placebo or inactivated trivalent

vaccine or a live attenuated bivalent vaccine.

Each of those vaccines contained an

A/Chile/1/83‑like H1N1 virus. These children all got

a single dose of vaccine. That particular year, the

H1N1 virus was a new one that had appeared only in

March, was first identified in March.

And you might remember that that was the

year that there was a supplemental vaccine that was

produced for A/Taiwan/1/86. It was not used in this

study, but it was recognized that the Taiwan/1/86

virus was substantially different from Chile so much

so that it was thought that for younger people, that

an A/Taiwan vaccine would be a good idea.

Again, they had virus isolation and

serology to try to document infection. What they

showed was both of these vaccines had protective

effect against infection with A/Taiwan/1/86. And this

is infection and not illness that we are looking at

now. So it is the measurement that would pick up more

individuals.

Anyway, you see this is fairly similar.

It was about 52 percent protective effect on this

drift variant from the live attenuated vaccine in use

at that time and about 61 percent for the inactivated

vaccine.

The authors commented that they thought,

actually, in the younger age group, the live

attenuated vaccine performed better than the

inactivated vaccine and vice versa for the inactivated

vaccine, which you can see here. Nevertheless, both

of these vaccines were substantially better than no

vaccine in terms of what happened to the placebo

group.

So, finally, one more drift variant story,

a nursing home in Colorado in 1987 during an outbreak.

There was an outbreak that was caused by an H3N2 drift

variant.

The vaccine strain at the time was

A/Leningrad/6/86. The viruses that were being

isolated have names. They are Colorado, of course.

They were all similar to this reference strain, the

Sichuan/2/87 strain. And that strain was different

enough that the following year, it was included in the

vaccine for use. So that there was some difference.

I am not sure that the difference between

Sichuan/2/87 and Leningrad/6/86 is the same as the

Victoria/Port Chalmers difference, but there was still

enough that it was thought a good idea to change the

vaccine after those had appeared.

Not everybody in this nursing home was

immunized, but they immunized a very high percentage

of them after the outbreak started. The outbreak

itself had a peak that occurred about two weeks after

the immunization campaign.

This analysis was done retrospectively,

but they were able to get pretty good documentation

from the nursing home records about who had fever; as

measured by thermometer, who had illnesses, what kind

of illnesses they were. Of course, pneumonia and

death were pretty obvious because the residents needed

for the treatments. In a subset, they were able to

confirm that infection had occurred because of H3N2.

But generally this again was a clinical observation.

This just shows the epidemic, how it

occurred. When it was first recognized, it was a

fairly sharp epidemic. There were about five of the

residents of the nursing home who were infected. The

following week, vaccine was given to all who wanted it

or could receive it. There were a number of

individuals who refused the vaccine, and there were a

number of individuals who had other ongoing illnesses

that were thought to be contraindications to getting

the vaccine.

So the numbers peaked around week four

here on the epidemic. And then it kind of quickly

tapered off afterward. What you can see is that

although there are quite a few cases in both the

immunized and the unimmunized populations, looking at

vaccine effectiveness, as calculated by the authors,

they mainly were looking at febrile upper respiratory

illness.

They excluded a number of individuals who

had been immunized in that two‑week interval. From

the time they immunized until two weeks, they excluded

those from their true analysis. And that was true for

both vaccinees and non‑vaccinees. There were some

other exclusions as well.

Looking at the incidence, these are

numbers and not percents here. Looking at the number

of febrile upper respiratory illnesses that occurred,

the proportion was significantly less in those who

received vaccine. And protective effectiveness

against febrile upper respiratory illness in that

group was calculated to be about 65 percent.

There were no pneumonias in those who got

vaccine and there were no deaths in those who got

vaccine; whereas, there were pneumonias and deaths in

those who did not. This isn't really randomized

prospectively. So there may be some other reasons for

that. But if you look at all of the residents all

together, you can see that all together, the residents

who got vaccine, there were no pneumonias and no

deaths; whereas, there was a substantial number of

both incidence of pneumonia and death in the residents

who were not immunized.

Some facts that I guess we could take from

those studies are that the vaccine protective effect

is a lot more obvious for severe forms of illness and

for complications that are related to influenza and

infection.

The vaccine shifts the spectrum of disease

toward the less severe consequences and milder

illness. Whatever you are looking at, they have to

keep that in mind. And higher antibody titers are

more likely to result in protection from clinical

illness.

Also, for infection, this is not an

absolute. There is in my own mind not an absolute

number to use for this, but it is pretty clear that

the more antibody you have, the better.

The vaccine administered in an ongoing

epidemic still may reduce illness, pneumonia, and

death, even when there is antigenic drift that has

occurred.

Turning away from that at the moment,

these are the recommendations that were made by the

World Health Organization for influenza vaccine

composition for the Northern Hemisphere for 2004‑2005.

The recommendations from there that are

based on the information that was available to WHO

last week were to continue to use an A/New

Caledonia/20/99 H1N1‑like virus, to use an

A/Fujian/411/2002 H3N2‑like virus, and to use a

B/Shanghai/361/2002‑like virus for the B strain.

Again, the question for the Committee,

"What strain should be recommended for the antigenic

composition of the 2004‑2005 influenza virus vaccine?

Should it be based on the epidemiology and antigenic

characteristics of the viruses, the serologic

responses, and availability of candidate strains?"

All the information that we are going to

be presenting apart from the vaccine effectiveness

studies this morning will relate directly to answering

this set of questions by the Committee. And I think

I can stop there and ask if there are any questions.

CHAIRMAN OVERTURF: Were there any

questions for Dr. Levandowski? Yes? Please identify

yourself.

DR. MARKOVITZ: David Markovitz. I am

speaking from the somewhat claustrophobic right

corridor of the table, where it is difficult to see or

breathe. So if you will excuse me, I am not

articulate.

My question is this. You showed a graph

that implied that the vaccine manufacturing process

was quite effective this year but, yet, also alluded

to some early gaps, early in the season, which we all

noticed just in our hospitals or reading the

newspaper. What is your overall assessment of how the

manufacturing process kicked in this year?

DR. LEVANDOWSKI: Well, I am not sure what

gaps you are referring to, but from the point of view

of production and vaccine release this year, it went

about as smoothly as it ever goes, which means that

manufacturers were busy producing monovalent vaccines

and busy producing trivalent vaccines and having those

released and being able to get them into distribution.

I don't know that I have any other

information. From our perspective, things went

extremely well.

DR. MARKOVITZ: I guess at our hospital,

in the fall, for example, people were not able to get

vaccine. I don't know what the cause of that was.

Was that just too early in the year? Did they strike

too soon or what? Is that just an anecdotal

observation from what I have seen? I believe I read

about that nationally, too, I thought.

DR. LEVANDOWSKI: Again, from our

perspective, I don't understand it. I don't have any

information that relates directly to distribution. We

don't get involved with distribution per se at FDA.

It is more understanding that the vaccines meet their

specifications and that they are okay to go out.

What we saw was a steady stream of vaccine

preparation release. And maybe this is a question for

the manufacturers and not for me because I don't know

what issues there might have been for them in terms of

distribution.

DR. MARKOVITZ: I wasn't really thinking

of distribution. I was thinking more of manufacture.

DR. LEVANDOWSKI: Well, that is an

important part of manufacturing: getting the vaccine

out to where it is supposed to be used. But, again,

that is not a part that FDA really interacts with

directly as the vaccine is being prepared. It is more

of the release specifications being met for the

vaccine and that all of the good manufacturing

practices have been met to make sure that the vaccine

is ready to go.

Again, I would have to say that this year

‑‑ and the graph I showed I think is an indication of

that ‑‑ all the vaccine that came through FDA came

through at a very early time point.

CHAIRMAN OVERTURF: Dr. Myers?

DR. MYERS: Roland, the data sometimes is

presented for children less than five and sometimes

broken down by a year or less than five. The data you

showed is striking for the morbidity being for

children less than a year of age. Is there any

protective effectiveness data for that population?

DR. LEVANDOWSKI: There is relatively

little direct information. Again, I would have to ask

some of our other colleagues out there what they know.

I don't know that there has been a specific study to

look at vaccine effectiveness in children who are less

than one year of age.

I think we do have some understanding that

immunogenicity may be decreased in the very young

children as well. I think that is partly reflected in

the setting, the cutoff at six months for use of

vaccines, if there is an understanding that maybe the

vaccines won't be so immunogenic in those children and

maybe the reactogenicity is a bigger concern than any

clinical benefit that they might get.

There is, however, relatively little for

inactivated vaccines. There is more recently for live

attenuated vaccines. In children 15 to 72 months of

age, the studies that were done by MedImmune show a

very high level of vaccine effectiveness, efficacy

actually, in those children.

CHAIRMAN OVERTURF: Dr. Gellin?

DR. GELLIN: Well, two questions. The

first is you made a comment in 1986 that there was a

supplemental vaccine produced. Could you give us a

little more insight into what that was?

DR. LEVANDOWSKI: Right. The vaccine was

made for the A/Taiwan/1/86 strain. That virus was

first identified, I believe, in March of that year.

It was at a very late point in time. There was a

recommendation that a supplemental vaccine be

prepared. And the manufacturers did that, but the

timing for it was not available until late November

anyplace.

And because of the way it came out, there

was a lot of confusion, part of the confusion because

I was in clinical practice at the time trying to

figure out what to do with the vaccine late November

and early December. There was a lot of confusion on

the part of practitioners about what to do with it.

Not much of it got used. Most of it was subsequently

discarded. It had to be thrown away, basically. That

strain was what was used in the vaccine, then, the

following year for the trivalent vaccine.

DR. GELLIN: So it was a monovalent

product?

DR. LEVANDOWSKI: Yes. Sorry. It was a

monovalent supplemental vaccine, right, that one year.

DR. GELLIN: The second question was, in

your chart about the efforts, intensive efforts, that

go into when they are changed each year and then the

subsequent graph about the delay in production.

In 1992 to 2000, there were two changes.

And that was the year that there was a delay in the

release of the vaccine that was used. I was wondering

if they were related.

DR. LEVANDOWSKI: It is partly related.

Some manufacturers had some difficulty with

replication of the H3 strain early on, but that was

worked out, as it usually is. Manufacturers are

actually quite resourceful at making things work.

If you will look at that chart that you

are talking about ‑‑ I am not sure I can get to it

because of the touch pad here on the computer. It is

not that friendly. If you look at that graph that you

are referring to, you can see that most of the effort

actually goes into producing the H1 strain. And

although the H3 is the one that got all the notoriety

for being difficult to work with, at least initially,

the H1 strain took up more manufacturing time in terms

of number of monovalents.

The way I presented that information, you

can't directly equate that with the overall amount of

vaccine that is being produced because their

variability, lot sizes are variable from manufacturer

to manufacturer and even within a manufacturer. So it

is not like you have one box that has 100 units in it.

You have a box that might have ten units. You have a

box that might have 150 units. It is not that direct.

But I just tried to give some impression

as to the overall effort. It is actually the H1

strains that have been more difficult the last few

years in terms of overall manufacturing effort.

CHAIRMAN OVERTURF: Dr. Monto?

DR. MONTO: I would comment about the

nursing home and how to interpret drift in terms of

nursing home outbreaks. We have had a surveillance

going on in a number of Michigan nursing homes for a

number of years now. Two years ago, with a rather

wimpy A/H3N2 outbreak with a non‑drifted variant, we

had confirmed transmission in 26 percent of our homes.

This year it is going to be higher. And

we had an outbreak in December. It is going to be in

the 30 percent range, we think, once we finish the

analysis.

What I am saying is that you really have

to look at what happens in nursing homes, even in a

non‑drifted year, in terms of putting things into

context because the vaccine really does not protect

all that well against just influenza‑like illness,

even laboratory‑confirmed, even in a non‑drifted year,

in this population. Our nursing homes were typically

80 percent and many of them 90 percent vaccinated.

CHAIRMAN OVERTURF: Dr. Farley?

MEMBER FARLEY: To me, one of the most

striking features of this year's influenza profile was

the early onset of disease. Given the manufacturing

timetable that you presented, if there were any

indication to attempt to begin immunizations earlier

in the fall or late summer even, is it even possible

within the constraints of the timetable?

DR. LEVANDOWSKI: I think the answer, is

it possible, yes, I think it is. I mean, I think that

this year the vaccine was prepared at a very early

point. If you have that graph? Again, I am not sure

I can get to it easily here because of the computer

system.

There was a substantial amount of vaccine

in trivalent form. That graph that I was showing was

for trivalent vaccine. There was a substantial amount

that had been released for distribution, even in the

summer months. So it is possible that it could have

been.

This year, although there was an early

epidemic of influenza, although it started early,

there was a substantial amount of vaccine that could

have been available around the country at that point

based on the manufacturing timelines for this past

year.

If we had made a strain change this last

year, I don't know that that would have been true. I

am not sure that there would have been further delays.

Given the timelines for preparation of seed viruses

and so on this past year, I would assume that we would

not have been seeing vaccine early in the summer but

probably the first vaccines might have been available

September, rather than in July.

So, again, from my perspective, I think

manufacturing went very efficiently and was early and

on time with a total amount that was intended for

production for this year based on what the demand

parameters were that the manufacturers understood for

all of the vaccines.

CHAIRMAN OVERTURF: Dr. LaRussa?

MEMBER LaRUSSA: Just to carry that a

little further, can you just sort of estimate?

According to one of your slides, the high growth

reassortants were available in June for the Fujian

strain. If you had decided to go ahead and make a

monovalent vaccine, what would be the earliest that

would have been available?

DR. LEVANDOWSKI: Well, that is what I was

trying to get to with that other slide that I had

about manufacturing timelines, the 20 weeks. If you

take about six weeks for development of the reference

virus at that point, what would that be? That is

about three and a half months.

So from June until sometime late September

probably by the time we would be seeing any vaccine

produced that was trivalent; whereas, with the

manufacturing system as it was, the first trivalent

vaccines were actually coming in end of June,

beginning of July.

CHAIRMAN OVERTURF: We have time for about

one more question. Any other questions?

(No response.)

CHAIRMAN OVERTURF: I think we will go

ahead and proceed, then, with the discussion regarding

vaccine effectiveness. I guess Dr. Carolyn Bridges is

going to make that presentation.

VACCINE EFFECTIVENESS

DR. BRIDGES: Good morning. Today I am

going to be discussing some studies that have been

done on vaccine effectiveness of the inactivated

influenza vaccine this year that CDC collaborated on.

I will be discussing a little bit of

background about the flu season, most of which Roland

has already covered. Then I will be describing two of

those studies, where we have preliminary results; and

then listing some other studies that are currently in

progress; and then end with some final remarks.

As Roland stated, influenza activity

started earlier than usual this year. And children

appeared to be disproportionately affected compared

with recent years. There were widely publicized

reports of pediatric deaths that received quite a bit

of attention.

There is also unprecedented demand for

vaccine more than in some recent years. And there was

discussion at the HHS level about additional vaccine

purchase. Although, as Roland says, the amount of

vaccine that was purchased or was manufactured was

equal to the demand from the previous year.

In addition to these pediatric deaths,

there is also a drifted variant of influenza H3N2,

which predominated, which was different from the

vaccine strain. And influenza vaccine effectiveness

was questioned.

In order to assess the effectiveness of

this year's inactivated vaccine, several studies were

initiated simultaneously using various age groups and

looking at different outcomes.

Preliminary results are available from two

of those studies, which were conducted in the State of

Colorado. One of those studies was a retrospective

cohort study among health care workers. The principal

investigator for that study is Dr. Nidhi Jain.

The other one is a case cohort and

subsequent case control study among persons aged 50 to

64 years who have laboratory‑confirmed influenza. And

the co‑PIs for that study are Drs. Marika Iwane and

Guillermo Herrera.

This is some surveillance data from the

Children's Hospital in Denver, Colorado. And, as you

will look at the left scale, it goes from zero to 400.

This is a scale for influenza A viruses. On the

right‑hand of the graph, it is a scale for RSV and

influenza B, which goes from zero to 40. So there is

a tenfold difference in the scales.

As you can see, influenza A activity began

early in November. It peaked towards the end of

November and then started on this decline. There were

very few influenza B viruses and very few cases of RSV

that were identified at the Children's Hospital among

hospitalized as well as outpatients.

This is the remainder of their

surveillance data. Again notice the scale, zero to

20. So there also was very little in the way of

paraflu, adeno, rhinovirus, or pertussis that was

identified.

Incidentally, of the respiratory specimens

that were tested in the hospital laboratory, the

percent of specimens that tested positive for

influenza at the peak was around 60 to 70 percent. So

this is a very high percent positive rate from

respiratory specimens.

We decided to conduct a study among health

care workers at the Children's Hospital because the

staff provided a large cohort for rapid analysis. And

we knew that a large number was needed if we were

going to look at nonspecific outcome.

Also, this cohort has substantial

opportunities for exposure to influenza as they had

many hospitalized patients who were influenza‑positive

and they had conducted the bulk of their inactivated

influenza vaccine campaign in the month of October.

Influenza‑like illness was the outcome.

This has been used, as Roland has described, in many

prior studies, although we clearly understand that

this underestimates the vaccine effectiveness that

could be seen looking at more specific outcomes, such

as laboratory‑confirmed influenza.

We thought that this may provide us with

a reasonable estimate or reasonable chance of finding

vaccine effectiveness because influenza was so

predominant as the cause of flu‑like illness in the

population based on the Children's Hospital

surveillance data. Preliminary results of this study

were published in the January 16th MMWR.

The objective of this study was to

estimate the effectiveness of the 2003‑04 inactivated

influenza vaccine in preventing influenza‑like

illness, or ILI, among adults working at the

Children's Hospital in Denver, Colorado.

This is a retrospective cohort study. A

questionnaire was distributed via e‑mail and also

paper surveys to approximately 3,100 employees. This

is an anonymous survey, and very limited demographic

information could be collected. Information was

collected on age group, sex, whether they had patient

contact, whether they had one or more high‑risk

conditions, whether they are vaccinated, and the

timing of their vaccination, illness onset and

symptoms, and whether they had physician visits or

were influenza‑tested. We also asked about missed

workdays from flu‑like illness. The questionnaire was

distributed from December 11th through December 17th.

The ILI definition used was self‑reported

fever plus either cough or sore throat, which is

similar to the CDC surveillance case definition.

Illnesses were counted if they began on or after

November 1st or through the date of survey completion.

We conducted two different types of

analysis, one a categorical analysis and the other a

person‑time analysis. For the categorical analysis,

we estimated vaccine effectiveness against ILI only

among persons who were vaccinated before November 1st

and compared those with those who were never

vaccinated.

We looked at two different vaccination

definitions for persons who became ill less than two

weeks after being vaccinated. In one instance, we

categorized those persons as being unvaccinated. In

the second analysis, we excluded them from the

analysis.

For the person‑time analysis, again, the

person‑time began November 1st and ended on the date

of survey completion. We did not exclude persons who

were vaccinated during the illness period for this

analysis, but an individual could contribute both

vaccinated and unvaccinated time if they got

vaccinated during the period of interest of November

1 through survey completion date.

The outcome for this study was ILI

incidence density rate. And, similar to the first

study for those who became ill one to 13 days after

vaccination, we either counted that time as being

unvaccinated time in one analysis or we excluded those

person‑days from the analysis.

This graph shows the number of

influenza‑like illness cases among staff. Those are

in the short light blue bars; the number of

laboratory‑confirmed influenza cases among patients at

the Children's Hospital, which are the tall purple

bars; and the line graph shows the percentage of the

cohort that was included in the study, the percent of

the cohort that was vaccinated by time.

So as of November 1st, 54 percent of the

persons who answered the questionnaire were

vaccinated. An additional 24 percent were vaccinated

during the outbreak period of November 1 and later.

So of 3,100 persons to whom the

questionnaire was distributed, 1,886, or 61 percent,

completed the survey. Half of those completed it

online and half of those completed it by paper.

Persons were excluded if they did not

report their vaccination status as being yes or no or

if they did not report date of vaccination. We also

excluded persons who did not report whether or not

they had an illness or if they did not report date of

illness onset.

This is the demographic information from

the persons who completed the questionnaire. It

includes all persons vaccinated or unvaccinated,

regardless of the timing of their vaccination.

Persons who were vaccinated tended to be

in the older age group. And a higher proportion of

them, though not statistically significant, were

female.

In addition, physicians and nurses were

more likely to be vaccinated than persons in neither

of those occupation categories. And people who had

patient contact also were more likely to be

vaccinated. There is a considerable correlation

between these two variables.

As I mentioned, we asked people if they

had been tested for influenza and also what the

results of that testing were. Twenty‑eight people who

had influenza‑like illness reported being tested for

influenza. And of those, 13, or 46 percent, reported

that they were positive.

Now let's skip to the results. This is

for the categorical analysis, where persons were

counted as unvaccinated if they became ill in the less

than 14 days prior to vaccination.

So of the vaccinated persons, about 15

percent of them developed influenza‑like illness

compared to almost 17 percent among the unvaccinated.

The vaccine effectiveness adjusted for age group,

high‑risk condition, and patient contact was

approximately 14 percent with confidence intervals

that included zero.

If we looked at the additional vaccination

definition, where we excluded persons who became ill

in the 14 days after vaccination, that eliminated 9

people from the analysis. This analysis, then, again,

15 percent of vaccinated were ill and approximately 15

percent of unvaccinated were ill, with an adjusted

vaccine effectiveness estimate of approximately 3

percent, again, with confidence intervals that

included zero.

In addition to the categorical analysis,

we conducted a person‑time or survival analysis. In

this analysis, again, people were not excluded if they

got vaccinated during the outbreak period.

Again, looking at the two different

definitions for vaccination, vaccine effectiveness for

both of these, the point estimates were negative, but

the confidence intervals were very wide and were not

statistically significant from zero.

There are a number of limitations to this

study. One, the response rate was only 61 percent.

And there was certainly a possibility for responses by

us. Secondly, vaccination and influenza‑like illness

status were self‑reported. And vaccination was

self‑selected. So there may have been some

confounding by indication for those two who were

vaccinated.

In addition, there was a high vaccination

rate among the respondents. And this decreased our

power to detect a lower vaccine effectiveness

estimate. However, the vaccination rate that we found

in this cohort was very similar to what the hospital

estimated for their entire population. They estimated

that they had approximately 75 to 80 percent of their

employees vaccinated.

In addition, because the influenza season

was early, some persons were vaccinated during the

peak influenza activity. In addition, we used a

nonspecific case definition, which likely

underestimated true vaccine effectiveness if one were

to look at laboratory‑confirmed flu.

The conclusions from this study are that

we were unable to demonstrate vaccine effectiveness

against ILI and this study when it drifted, influenza

H3N2 virus predominated. However, vaccine

effectiveness is likely to be higher against

laboratory‑confirmed influenza and against

influenza‑related hospitalizations and deaths.

The recommendations that were published in

the MMWR were to continue vaccination, particularly of

high‑risk persons, their contacts and health care

workers because H1N1 and influenza B may circulate

later in the flu season and because based on

historical information, vaccine is expected to protect

against influenza‑related complications and

laboratory‑confirmed influenza.

The study pointed out that retrospective

analyses and sort of on‑the‑fly analyses are very

difficult to do during the middle of flu season and

that prospective annual vaccine effectiveness studies

against laboratory‑confirmed influenza are needed for

more accurate yearly assessment of vaccine

effectiveness and to assess the impact of the vaccine

program.

I am now going to move on to the case

cohort analysis for persons 50 to 64 years of age.

This study was initiated to estimate the effectiveness

of the inactivated vaccine against

laboratory‑confirmed influenza in persons 50 to 64

years of age. And this was initiated at the same time

as the health care worker study.

For the cases, these all were

laboratory‑confirmed cases that were identified

through the surveillance system in the Colorado

Department of Health. Colorado requires that persons

with laboratory‑confirmed influenza are reported to

the state Health Department. However, they are not

required to report contact information. They report

age, gender, and name but not phone number or address

or county many of the times.

Over 10,000 cases were reported to

Colorado by the end of December. And over 500 of

those cases were in persons 50 to 64 years of age.

However, more serious cases of influenza were more

likely to be reported.

The cases were interviewed by phone. And

they were excluded if they did not recall being tested

for influenza or they denied being ill. In that case,

we may have had the wrong person when we tried to hunt

down a phone number based on name only.

Information was collected on demographics,

illness onset, and duration, vaccination, and timing

of vaccination, health care provider visits, and

hospitalization.

For the cohort, the cohort was considered

the Colorado population aged 50 to 64 years. To

estimate the coverage in Colorado among persons 50 to

64 years, we looked at the Colorado behavior risk

factor surveillance survey. This is an annually

conducted survey that is state‑based. And for a

number of years, the question has been asked, "Have

you had a flu shot in the past 12 months?"

We looked at data from 2001 through 2003

for the limited 2003 data that was available. We used

a screening method or case cohort method to estimate

vaccine effectiveness, which was estimate to be

approximately equal to one minus the relative risk.

The confidence intervals calculation included the

variance of the various aspects and coverage

estimates. So the confidence intervals were

considerably wider.

Again, similar to the other study, we used

two different classifications of vaccination for

persons who were ill in the two weeks following

vaccination. In one sense, they were counted as

unvaccinated. And then they were also in a second

analysis excluded.

Vaccine effectiveness was calculated for

the different possible cohort coverage rates in a

sensitivity analysis. Because of the difficulty in

determining the vaccination rate of the cohort,

because the outbreak happened during the typical time

where vaccine is administered, we have also initiated

a case control study.

So controls are being recruited that will

be age group frequency matched. We are attempting to

recruit three controls per case. And they are being

recruited through random digit dialing. Hopefully

this will provide a more accurate assessment of

vaccine effectiveness.

So among the cases, there were

approximately 574 cases reported to the Colorado

Department of Health. Of those, interviews were

completed on 56 percent. Among those where there was

not an interview completed, for most of those persons,

it was an inability to contract the person or for most

of those inability to identify a telephone number. If

there were 1,200 John Smiths in Colorado, it was

difficult to identify who that name and age may have

belonged to. However, those who were interviewed and

those non‑interviewed did not differ by gender or age.

So this graph shows the number of cases by

week of their illness onset. As you can see, most of

these started again in early November and a cumulative

percentage of these cases that were vaccinated, which

is represented by the red line.

Of the cases, approximately 329 were

included. As you may have noticed from the slide

earlier, there were 330, but the vaccination status on

one of those persons couldn't be confirmed.

Of the 329, approximately 50 percent were

high‑risk and 50 percent were not high‑risk using our

vaccination definition of counting them as

unvaccinated if they became ill less than 2 weeks

after the vaccination date.

Among all persons in this category, 42

percent were vaccinated, 52 percent of those were

high‑risk, and 32 percent were non‑high‑risk who were

vaccinated. A high proportion was hospitalized.

Forty‑eight percent of high‑risk people were

hospitalized, and 17 percent of those classified as

non‑high‑risk were hospitalized.

We found very similar numbers when we did

a second analysis and excluded people if they were

vaccinated one to 13 days before illness onset.

Again, about 50 percent were high‑risk. And

vaccination rate was about 56 percent among

non‑high‑risk and 36 percent among non‑high‑risk

persons. And, again, high rates of hospitalization,

about a third.

I would like to look at the results of the

cohort. So this is the overall Colorado population 50

to 64. We had a number of different estimates. The

middle estimate was using the 2002 BRFSS final

weighted data. And the estimate, vaccine estimate, is

45 percent.

The high estimate may be the 2003

unweighted monthly data from BRFSS. So this is

preliminary data. And the December estimate was 52

percent.

A low estimate would be the 2002 Western

regional national health interview survey data for

persons 50 to 64. That data would suggest that the

vaccination for high‑risk be 40 percent and

non‑high‑risk 32 percent.

However, we know that Colorado generally

has substantially higher vaccination rates in all age

groups compared to most of the other Western region,

particularly California, which tends to weight that

estimate down quite a bit.

This is some preliminary results from the

controls that have been recruited from the case

control study. Of 304 persons who have interviews

completed and data entered, 26 percent of those have

high‑risk conditions. And vaccination rate for those

for this flu seasons, for 2003‑04, was 58 percent

among non‑high‑risk and 61 percent among high‑risk, so

considerably higher than the previous behavioral risk

factor surveillance survey estimates. We hope to have

recruitment through about 1,100 persons completed by

early March.

So if one considers this range of vaccine

coverage estimates in the cohort, we then use the

screening method to estimate what the vaccine

effectiveness may be in the overall cohort of 50 to

64‑year‑olds.

If you look at the non‑high‑risk group,

where among the cases, the vaccination rate was 32.5

percent, assuming the vaccination coverage for the

cohort ranges somewhere between 40 and 60 percent,

then the overall vaccine effectiveness may range

somewhere between 28 percent and 68 percent.

When you look at the high‑risk group,

there is substantially more content by indication. I

think it is very difficult to assess these results.

In general, it looks like vaccine effectiveness is

lower. I think that is very difficult to assess.

Again, looking at the second definition

for vaccination for cases being excluded if they

became ill in that two‑week period after vaccination,

the estimates are somewhat lower, but the

non‑high‑risk, the estimate is somewhere between 16

and 63 percent, and for the high‑risk, again, most of

the estimates are negative. That I think is very,

very difficult to assess.

The limitation of this study is that these

lab‑reportable cases over‑represent sicker and

hospitalized patients and certainly over‑represent

high‑risk persons. The case cohort method provided

for a very wide confidence intervals of the vaccine

effectiveness estimate, which gives us quite a bit of

degree of uncertainty.

However, overall the vaccination rate at

the time of onset of cases of illness is unknown due

to the early season. So we don't really know what the

vaccination rate is, really, for the controls at the

time of the outbreak. So use of the historical

vaccine coverage estimates would overestimate overall

vaccine effectiveness.

So the case control study will help us in

determining what the vaccination rate was during the

time of the outbreak. So that should give us a little

bit more confidence.

The conclusions from our case cohort study

are that vaccine effectiveness among the non‑high‑risk

is likely somewhere between 16 and 68 percent. It is

a very wide estimate.

The vaccine effectiveness among high‑risk

persons is difficult to interpret because of content

by indication. And further analysis is pending for

the case control study.

I would like to acknowledge our

collaborators at the Children's Hospital, the

University of Colorado, the Colorado Department of

Public Health and Environment, and colleagues at CDC.

I just wanted to show you some other

studies that are currently in progress on which CDC is

collaborating. As I mentioned, the Colorado case

control study, we hope to have some results from that

by the summer.

There is also a cohort study being done

among children 6 to 23 months using Colorado HMO

database, which will look at ILI as an outcome. In

addition, the vaccine safety datalink sites are going

to be looking at vaccine effectiveness among 6 to

23‑month‑olds with outcome of hospitalized

influenza‑like illness.

And in Georgia, a study is ongoing to look

at the effectiveness in children 6 months to 4 years

with laboratory‑confirmed influenza as outpatients.

The new vaccine surveillance network in

Rochester, Cincinnati, and Vanderbilt will be using

the case cohort method to look at vaccine

effectiveness for influenza, hospitalization, and ED

and outpatient visits.

There is an ongoing study in Iowa among

college students, which is a cohort study to estimate

effectiveness against influenza in outpatients.

Among all of these studies, one of the

issues has been for persons who are tested for

influenza. What is the bias in terms of who gets

tested? And are people being tested based on their

vaccination status.

The new vaccine surveillance network is an

active prospective surveillance system. And children

who come into that system who are enrolled, there is

no bias in terms of their vaccination status. So this

may provide us with one of the more accurate

estimates.

So our results, we feel, are compatible

with many prior studies and years with the suboptimal

vaccine match, where low or no vaccine effectiveness

against ILI can be demonstrated. However, there is

likely to be vaccine effectiveness against

laboratory‑confirmed influenza as our case cohort

study suggests. These studies also illustrate the

difficulty in estimating vaccine effectiveness in

retrospective studies.

Our resources should be devoted to annual

assessment of vaccine effectiveness and to the

vaccine's health impact. We hope to have a pediatric

assessment using the new vaccine surveillance network.

And we would like to be able to use that system for

yearly estimates in children.

I will stop there.

CHAIRMAN OVERTURF: Yes, Dr. Myers?

DR. MYERS: Carolyn, I guess my conclusion

is it is hard to do studies with vaccine effectiveness

in the midst of the epidemic. So I think it is great

that the CDC is trying to do that.

On the first study, I am not so troubled

by your using ILI in association with a lot of

clinical isolates of influenza as your endpoint, but

I am troubled by the apples and oranges of the groups

that you evaluated.

You showed a statistical likelihood for

people who are likely to be exposed to get vaccine.

So the doctors and the nurses or people who had

patient contact were more likely to get the vaccine.

So I would have liked to have seen and

maybe you have available of that subgroup looking at

vaccine effectiveness for the people who had patient

contact during that interval.

DR. BRIDGES: You know, we really thought

a lot about asking questions about exposure, more

exposure, variables. But because there was so much

influenza circulating in the community, we thought

that that was extremely difficult to do, particularly

given that many people affected with influenza may

shed virus but are clinically asymptomatic.

We did adjust in our analysis for patient

contact. We adjusted for occupation in one analysis,

which I didn't present. And it didn't make any

difference.

We did a subgroup analysis. Also, it

didn't make any difference in terms of being able to

find vaccine effectiveness.

CHAIRMAN OVERTURF: Dr. Palese?

DR. PALESE: In terms of your first study,

where influenza‑like illness is being used as an

endpoint and there was no vaccine effectiveness

demonstrated, is it known what percentage the Fujian

strain made up in terms of influenza activity?

In other words, what is the reason for the

ineffectiveness of the vaccine? Do we know that 80

percent was Fujian there? You said it was

predominantly Fujian, but do we know? Do we have

precise values in terms of how much Fujian was and how

much H3 basically was occurring at the time in that

location?

DR. BRIDGES: Very few isolates were

antigenically characterized from Colorado by CDC.

They obviously characterized a huge number of isolates

but relatively few isolates from every state. Some of

those isolates have overworked Fujian. I think it is

very difficult to assess what the proportion of H3N2

viruses in Colorado were Fujian based on the small

number of isolates that were characterized.

DR. PHILLIPS: Wouldn't they be interested

to know why the vaccine was not effective?

DR. BRIDGES: Nationally I think Ann Moen

will present this. About 80 percent. Is that right?

Seventy‑five or 80 percent of the H3N2 viruses were

Fujian‑like.

CHAIRMAN OVERTURF: Dr. Monto?

DR. MONTO: I want to congratulate you all

for putting together the case cohort study in

Colorado. I know how difficult it is in the midst of

an outbreak trying to identify the places where you

can get useful data, which may have important public

health impact.

I just want to recall that the multiple

studies that were put together during the period when

HCFA questioned the use of influenza vaccine for older

individuals, the 65‑plus, that there the endpoint was

hospitalization for pneumonia, influenza indications

in the course of an influenza season and not

laboratory‑confirmed influenza.

I would assume that many of your

confirmations here are in the more severe individuals.

And controlling for confounding because we all know

that the people with undermined conditions are both

more likely to get vaccinated and more likely to be

hospitalized, the effectiveness there was from 31 to

maybe 50‑55 percent, fairly wide.

And these were all non‑drifted years. So

this really is not all that incompatible. You would

have a better endpoint here in terms of laboratory

confirmation. So it is all in the same ballpark and,

thus, suggests that there was some degree of vaccine

effectiveness in the past year.

In terms of the other study, I want to ask

you a question and make a comment, namely since the

outbreak was so abrupt and you have a fairly large

population group, could you look at the peak couple of

weeks of the outbreak in terms of the reported illness

and also in terms of the ILI case definition?

We have done some studies which we are

going to be publishing suggesting that sore throat

actually is a negative predictor in a febrile

individual of the isolation of an influenza virus,

which suggests that it may be appropriate to change

the very long used ILI case definition, but this has

been present in two very large studies of antivirals

in which laboratory confirmation was made before

treatment began. Sore throat clearly predicts against

the presence of influenza virus.

DR. BRIDGES: Thanks, Arnold.

We also looked at analysis from just

November 15th on, which was more of the peak week. We

didn't look at a two‑week interval, but we did narrow

it down to about a four‑week interval and still didn't

find effectiveness.

In addition, we looked at several other

outcomes that I didn't present today. One of those

was looking at a definition where persons had fever

for three days, plus two or more respiratory symptoms.

In addition, we looked at influenza

associated with loss of work, physician visits, and we

also looked at days in bed to try and find a more

severe outcome. And with none of those were we able

to demonstrate effectiveness.

CHAIRMAN OVERTURF: I will take just two

more questions: Dr. Karron and Dr. Goldberg.

MEMBER KARRON: Given information that has

been available in the past about potential increased

efficacy of live attenuated vaccine in children

against drifted strains, do you know of any studies

that are currently being conducted, either

head‑to‑head comparisons to look this year at the

efficacies or just live attenuated vaccine versus

non‑vaccinated children?

DR. BRIDGES: I don't know if there is

someone from the manufacturers that could address that

question, I think. I see Kathy Coelingh in the back.

Kathy Coelingh?

MS. COELINGH: I am Kathy Coelingh from

MedImmune Vaccines. There are some ongoing studies

that are being conducted. None of those data will be

available for several months, head‑to‑head comparisons

during the first year.

MEMBER KARRON: Did you do some studies to

assess the effective coverage probabilities in one of

these studies? Did you look at different models to

try and assess the different levels of

non‑responsiveness in the different groups and whether

you can model this out just to get an idea of whether

that is what is causing the problem?

The definition problem you addressed in

several ways, but there is a whole other piece about

what happened to the non‑responsiveness. You made

different classes of assumptions about them. Have you

done that?

MS. COELINGH: We haven't done that, but

that is something that we easily can do.

CHAIRMAN OVERTURF: Carolyn, if you could

just stay there? I am going to have Col. Neville come

forward and go ahead and set up his presentation. We

can take one more.

DR. DOWDLE: Thank you very much. I will

congratulate you for some remarkable analysis in a

very short period of time.

On the first study, in the 28 staff

members, as I recall, that reported being tested for

influenza, which was considerably less than the

percentage among the patients, if I recall correctly,

the question is, did you go further, do any further

analysis, of those who had reported influenza‑like

illness and were tested?

By the way, was this serology? Was this

isolation?

DR. BRIDGES: These were all rapid tests.

As far as we know, it is anecdotal. We didn't ask

people to specifically to antidote. The hospital

epidemiologists and laboratories were testing some of

the staff in the lab. And also physically they used

the staff for the advantage of testing.

We did look to see if we could find

something very different in terms of the vaccine

effectiveness point estimate among those 20 people.

And in looking at that, we have estimates of zero,

point estimates.

DR. DOWDLE: Thank you.

CHAIRMAN OVERTURF: You get the last

question.

MEMBER DECKER: And a couple of segues, I

think. First, I don't find your presentation and the

handouts. Will it be made available to the Committee?

DR. BRIDGES: The case cohort, the

information on the health care workers study was

published in the MMWR. And that is in the handout.

MEMBER DECKER: Right. The other one.

DR. BRIDGES: The other one is very

preliminary. So at some point, when we have more

analysis, we will be able to provide that.

MEMBER DECKER: All right. And the second

question is, if I understood your table correctly, the

first availability of the data that looks at

hospitalization or mortality is by vaccination status,

which I think is the real outcome of interest because

I think that is why the country hires the vaccines, to

prevent hospitalization and death and not

influenza‑like illness.

The first data on that will be available

at the end of this year at the sites? Is that right?

DR. BRIDGES: Right. The NVSN sites, we

want to have some data, hopefully by the end of the

year. For the case control data, because we have such

a high proportion of those persons who were

hospitalized, maybe also we are able to look at

hospitalization outcomes for the 50 to 64‑year‑olds.

CHAIRMAN OVERTURF: I think we will

proceed at this time. Thank you very much.

Dr. Neville is going to make a

presentation from DOD.

COL. NEVILLE: Thank you very much. I

appreciate the invitation.

I am here because the Air Force is the

executive agency in the DOD for influenza

surveillance. And the Air Force has been conducting

systematic influenza surveillance since the late

1970s. In 1999, it became the DOD program.

My organization is the Air Force Institute

for Operational Health, Brooks City, based in San

Antonio. We collaborate with the Naval Health

Research Center you see here in San Diego. In

addition the Army, this here, you will see in a minute

conducts clinical and virology services at the medical

centers. And the DOD oversees labs participating in

febrile respiratory illness projects.

The overall program is conducted under the

auspices and guidance of the DOD global emerging

infections surveillance and response system, or

DOD‑GEIS.

Later you will hear from Linda Canas this

afternoon, who will present her annual summary of the

data from the DOD lab‑based surveillance program. I

won't present her data here, but, rather, my intention

is to describe other aspects of the DOD's involvement

in influenza surveillance with an emphasis on tests to

meet vaccine effectiveness, which, of course, received

attention this year.

DOD typically does not conduct influenza

vaccine effectiveness research, although this year

several efforts were made, as I will describe.

I will briefly overview the surveillance

programs that exist in the DOD and describe some of

the data that was available for estimating vaccine

effectiveness. Those include a Navy estimate, an Army

estimate, and Air Force estimate. These are the three

that I am aware of in DOD.

All three medical services and also the

Coast Guard contributed to influenza surveillance in

the Department of Defense. The Air Force program is

laboratory‑based, radiology‑focused, its main purpose

being the collection of respiratory pathogens from

military populations around the world that are

considered good sentinel sites. Don't worry about the

map details here. You will see it again this

afternoon from Linda Canas.

Selection factors for several sites

include overseas location, major ports of entry, and

the history of participation in the program. There

are 27 sites from all 3 services and the Coast Guard.

In addition, the overseas laboratory

network supplies specimens from various countries

where those programs are active. Some of the overseas

labs sends isolates to us in San Diego. Some of them

process their own or ship to the regional health

organization labs.

AFIOH in San Antonio has the only

full‑service clinical virology laboratory in the Air

Force. The Navy's febrile respiratory illness

surveillance program is managed by the Naval Health

Research Center. It focuses on eight military

training centers.

On‑site investigators at FRI visits,

febrile respiratory illness visits, they all take the

denominator data and collect specimens of

systematically health care‑seeking trainees who meet

an FRI case definition, which is a temperature greater

than 100.4 and a cough or a sore throat. we may have

to change that maybe. We will see.

Since these trainee populations are

well‑characterized and monitored, attack rates can be

established with a high degree of confidence. NHRC

has the name "Respiratory Virology Lab."

U.S. Army has six regional medical

centers, each with clinical diagnostic virology

capability. While there is no systematic influenza

surveillance program per se in the Army, clinical

results are characterized as needed. And local and

regional preventative medicine staff track FRI rates

and etiologies as needed.

Special operating investigations also are

conducted as needed, more of which I will describe

briefly here in a minute. I should mention that all

three services have agreements or a plan in place for

sharing laboratory services in situations where demand

outstrips capacity, such as how major febrile

respiratory illness operates.

I will briefly describe some of the

relevant data sources that have been identified from

the DOD. Certainly these are not all unique data

sources, but in the context of an urgent public health

effort to estimate the effectiveness of the vaccine in

the face of this nationally occurring disease, these

are the data sources that we used both to monitor

disease activity and to attempt the evaluation of

vaccine effectiveness.

The first is the local public health

officer reports, which is a surprising part of the

report. The primary responsibility for response and

monitoring rests at the local level in DOD. Reports

include routine reports of reportable diseases and

evidence of unusual patterns of disease or outbreaks

which might require special responses or assistance.

Our medical surveillance currently takes

advantage of data collection systems, largely not

designed specifically for surveillance activities.

Among this existing data sources is familiar IC‑9

coding for ambulatory visits.

Many of you may be familiar with the

electronic surveillance system for the early

notification of community‑based epidemics, or ESSENCE,

which monitors ambulatory visits at every DOD health

care facility.

These data can be manipulated or used in

several ways. Here this chart may be hard to see.

This data accumulates all of the Pacific Rim DOD

medical care facilities and the percent of visits for

ILI, influenza‑like illness, which is comprised of

about 30 different codes, IC‑9 codes, that make this

ILI family. So this can be monitored on a daily

basis.

Facility‑specific data can also be

monitored. And a similar approach is used to monitor

diseases and non‑battle injuries for deployed

locations. Data collection at those locations is

often more lacking for a variety of reasons.

Laboratory specimens, submissions, and the

results can also be used to identify surges in

activity generally or at specific sites. This is an

example from last year where an outbreak occurred in

the Tidewater region of Virginia. Percent of total

commissions gives a good indication of pathogen

activity as well as illness occurrence. This is

similar to data displayed by CDC.

Officers. This tracks vaccination

delivery. These here just show the Air Force's data

because that is what it was most able to meet.

Annual influenza vaccination is mandatory

for military members, barring contraindications, of

course. High vaccination rates are achieved fairly

quickly.

Again, this may be a little difficult to

see from the back reading from the handouts. Eighty

percent coverage was achieved by the 2nd of December

in this past year, 2003 and by the 6th of December,

2002.

Because influenza vaccination is mandatory

and it is a command program, it is not a medical

service program, it is a commend program, analyzing

vaccine effectiveness among active duty personnel

during this time period would be a fairly complex

undertaking.

Because such a small portion of service

members remain unvaccinated comparing attack rates

between vaccinated and unvaccinated groups is not very

efficient.

Now to the vaccine effectiveness data.

First, the Navy's. As I described earlier, the Naval

Health Research Center in San Diego monitors FRI

illness, febrile respiratory illness, on basic

trainees of all three services. They have used

existing data here to estimate influenza vaccine

effectiveness.

Their calculation is based on data for

four representative centers, training bases: Fort

Leonardwood, Fort Jackson, Marine Corps Recruit Depot

in San Diego, and Lackland Air Force Base in Texas.

For the month of December 2003,

influenza‑specific attack rates were calculated based

on culture‑proven influenza cases identified in the

ongoing surveillance program.

All trainees are routinely vaccinated upon

arrival of basic training, especially during this

time, the early fall. For this analysis, they were

considered unvaccinated or non‑immune for the first

two weeks after vaccination. So for an 8‑week

training course, for example, 75 percent of the

person‑time, they are considered immune or vaccinated.

So just to display the data that NHRC

worked with, these numbers are for their overall

surveillance program, not just for the sites that they

focused on.

Only influenza A has been identified so

far this season as a ton of data, which was about a

week and a half ago. All of the isolates that they

sequenced were of the Fujian strain.

The dark red segments are influenza

isolates from trainees who had been vaccinated at

least two weeks prior to the onset of illness. As you

can see, the number of influenza cases is dreadfully

small, but there were no isolates segmented away.

This is the size of the calculation.

Because of the specific calculations they are showing,

viewing the vaccine effectiveness estimate of 9.9

percent, it is basic trainees. These data are not yet

published. I don't have other parameters, like age

ranges and gender and so forth, at this time.

The Army's Fort Lee is an Army post,

design post, in southeast of Richmond, Virginia. It

is home of the Army's quartermaster center and school.

It is not a basic training base, but it is a basic.

And that is specialty training.

At any one time, there are a little over

3,000 students present. And courses at that school

are of varying length, from 4 to 12 weeks. Students

arrive weekly for different courses. They are

organized into companies of 200 or so trainees. They

are housed in dormitory rooms of three to four people

each. And so it is a fairly complex model of exposure

in populations at risk.

An influenza outbreak began on the 31st of

October to find out it was in the students and staff

of the 23rd quartermaster brigade, which was a

training. Also, that epidemic occurred here. So the

31st of October, that is when that outbreak began.

Massive vaccination campaign began.

EPICON is the term for epidemiology

consulting team, the Army's Center for Health

Promotion. So the next outside team came and did some

work here. A team came back later in December to get

more information to try to evaluate this vaccine

effectiveness.

Fort Lee had just done their routine

annual influenza immunization campaign. And few were

immunized when the outbreak started. So the medical

personnel on the post responded with a rigorous case

isolation protocol and mass immunization campaign that

reportedly reached about 95 percent of the target

population, which in this case is the trainees.

Data for two different cohorts were

analyzed, primarily based on data availability. One

cohort arrived early November, and the other cohort

arrived later in November. These two different case

definitions now that we see, in a minute we will see

four different analyses.

There are two case definitions. The loose

case definition and any ILI‑related IC‑9 code, a

visit, a health care visit, with a diagnosis of an ILI

code, and also a tight case definition that documented

febrile illness with sore throat or cough or

lab‑confirmed influenza infection.

There are few limitations here for us.

One of those is that it occurred right after an

epidemic, an influenza epidemic, in that training

post, which means that the threat of a subsequent

outbreak may have been modified somewhat by the

population on the study, being surrounded by people

who had achieved the natural immunity grasp.

So two different cohorts are listed,

population size for each, 67 and 463 there. The

vaccine effectiveness point estimates range from 40.5

to 100. And here there were no cases among the group

considered to be immune. Obviously those confidence

intervals are pretty well. And we put zero. So it's

hard to reach any conclusions of effectiveness

evaluation.

Now, the Air Force is here. The Services

Branch of AFIOH has a retrospective force underway

using telephone surveys to collect data on household

contacts of influenza‑infected index cases. They

identify influenza‑exposed cohort by identifying index

cases, who are persons whose cultures have tested

positive for influenza in our own lab.

We included only Air Force personnel for

simplicity and excluded persons from the Air Force

Academy and those who are deployed, in deployed

locations, because our real target is household

members.

We contacted the sponsor of each positive

culture, which may be the person with the culture or

in post housing, and sought voluntary participation in

the survey and took some surveillance within two weeks

of the index case as well as vaccination status.

The secondary attack rate among vaccinated

and unvaccinated is used to estimate vaccine

effectiveness or is being used to estimate vaccine

effectiveness.

There are almost 2,500 specimens in our

lab this year as of a couple of weeks ago. Of those,

859 were positive for influenza. And of those, 114

were considered eligible for this. Among those

eligible, there should be about 400 family members of

those.

So as of February 12th, last Thursday, we

had collected data on 219 of these family members.

And the preliminary estimate of vaccine effectiveness

is 40.1 percent. We feel this could be a greatly

recurring annual study because it is relatively

efficient to do.

Plan B, we would like to do the same study

with a little more effort to validate some of hte

data, like looking at medical records, linking the Air

Force's immunization tracking, electronic tracking,

system to validate vaccine delivery and so on.

So, to summarize, NHRC had a vaccine

effectiveness of 91 percent. CHPPM's vaccine

effectiveness was 40.1 percent, although our concern

was pretty wide. It included zero. Our vaccine point

estimate in the Air Force was preliminary point

estimate is 40 percent, but that it gave us

collections that were none.

With that, I will be happy to answer any

questions.

CHAIRMAN OVERTURF: You didn't mention,

out of those 240 isolates, what the type was.

COL. NEVILLE: Every isolate we got was an

A, an influenza A. And every one that we have

sequenced is consistent with Fujian strain. I don't

know that we sequenced, but it might be something like

20‑30 percent.

CHAIRMAN OVERTURF: Are there any more

questions?

DR. MYERS: I missed it, the NHRC Fujian

portion.

COL. NEVILLE: Every one that they

sequenced as well as those after the first isolate

that they sequenced ‑‑

PARTICIPANT: They have sequenced about 20

percent, sir.

COL. NEVILLE: Pardon me?

PARTICIPANT: They sequenced about 20

percent.

COL. NEVILLE: About 20 percent.

DR. GELLIN: Can you comment about the

regularly recurring annual study?

COL. NEVILLE: Well, competing interests,

I suppose, supplies and manpower and stuff like that.

We were able to achieve pretty rapid IRB permission to

do this next year. It's on the chart. We will do it

again. And it is relatively easy and cheap.

But there is another operator, another war

somewhere and competing priorities, then yes. We plan

to, sir.

CHAIRMAN OVERTURF: Dr. LaRussa?

MEMBER LaRUSSA: Would you just remind me?

The Air Force study, was it everyone who had the

influenza culture?

COL. NEVILLE: The sampling is designed

primarily for collecting pathogens. So not every

person who shows up at a clinic, even if the symptoms

are right, is sampled. It's just the clinician in the

local public health system.

We try to get them to send us six a week,

but that is the target. So if they have not much

disease, they don't get that many out of these. And

we may get a lot more than that.

So I don't know what portion of people

presenting to a clinic with febrile respiratory

illness is sampled. It is variable.

CHAIRMAN OVERTURF: Any other questions?

(No response.)

CHAIRMAN OVERTURF: We are on time. We

will hear the INSERM presentation.

DR. FLAHAULT: Ladies and gentlemen, first

of all, I would like to thank the adviser very much

for inviting me to present this work. This work is

followed by INSERM in France, which is the national

institute, the French NIH, I will say, and by the

Universite Pierre et Marie Curie, which is a

university of Paris.

Of course, I will not come back too long

to the strain selection for the influenza vaccine,

just to say that in Europe, that was, of course, the

same recommendation. And vaccine which was not

created in Europe also used Panama strain, rather than

the Moscow, and used several other strains of the B

virus for the epidemic current season. Eventually it

is a main circulating strain. As you say, it was also

in Europe, the Fujian strain.

So it is a question of which of those is

during, at the beginning of the epidemic, and not

after the epidemic was, how effective was the vaccine

against clinical disease? So it was nearly impossible

to try to provide some answer due to certain

difficulties but also to measure it for sure during

that time in Europe as well as in the U.S.A.

Also in Europe, we had a very early

epidemic. It was just above the man, the average,

epidemic, but as an early epidemic, it was not so easy

to determine.

The question you had in the previous talk

was about the particular rates you found in your

revised studies and also in the comprehensive review,

which was done by Roland at the first talk.

So what is an appropriate protective rate

is difficult to answer. It has been proposed by the

weekly epidemiological record of the WHO that against

ILI, clinical disease, at least, that should be

between 15 and 80 vaccines.

How can we measure vaccine effectiveness

in the flu season? We have seen several methods also,

of course, particular efficacy, which is mainly used

for trial vaccines. And the clinical effectiveness is

usually observed by the so‑called screening methods in

the cohorts that are the case controls and the case

cohorts and all of these kinds of models, which, in

fact, try to compare attack rates in vaccinated versus

unvaccinated.

The vaccine effectiveness with the

so‑called screening methods is this formula, which is

1 minus the ratio of attack rate in vaccinated

population and of the attack rate in unvaccinated

population.

To assess vaccine effectiveness in real

time in France, we used the screening methods because

they are efficient, because cases are drawn from our

sentinel system, which is a very clinic surveillance

system, which provides some figures in real time or

close to a real‑time basis. I say "closely" because

we have all of the consolidated figures each week.

We chose the controls of the cohort from

a regular poll which had been collected by the French

Social Security using a private company specializing

in these kinds of polls for studying and estimating

the vaccine coverage in France for years.

It had been set up in our communities 20

years ago in November '84. It continues on the clinic

surveillance of 12 conditions: ID, of course, but

also acute diarrhea, measles, mumps, chicken pox,

spinal meningitis, viral hepatitis, and also other

infectious conditions, such as asthma.

It can hold a network of 1,200 sentinel

GPs, which are virtually unpaid and all private

practitioners, as they provide information on a

numbering basis. As I told you, we have re‑created a

ton of information which is widely available on the

internet, including the vaccine effectiveness.

So cases are ILI reporting ID cases, which

are only clinical cases with high fever and myalgia,

upper respiratory symptoms, as you mentioned. What is

at work is in each case is individually described with

the age in year, either in months for children;

gender; vaccine status. That is the trademark, only

the vaccine or not, during the preceding four; and

also hospitalization, the case of and with many

details of hospitalization, such as condition dates

and so on, in the case of. It is a mix for France.

Controls. As I told you, we conducted a

poll, which has been done annually for estimating in

terms of vaccine compliance. This is a national

survey. It was a representative sample of the French

population. We obtained data until ten years ago

conducted by a private organization.

As I told you, we have to make

assumptions. These are the figures which were 65

years or less, between 60 persons and 70 persons now.

Of course, we did not have the level of the

cooperation of the current season. So we assume it

was constant, it was the same as the year before.

For the others, the illness in 50 and 64,

there is a rate of conditions being low in France. It

is assumed to be 11 percent for the current season as

it was for the preceding season.

This slide is just to show you some

existing tools for monitoring that we have in France.

We have an early warning system based on accepting

what it takes to detect epidemic in time and to

measure the excess morbidity of influenza.

Also, we have a set of predictors. The

size of influenza epidemics prior to its occurrence

has been presented in our application 5. It predicted

fairly well the level of influenza activity in last

September for the current system. We predicted a

sweep on two million cases. And eventually the size

was Sweden in person‑time, which is a country of 16

million persons.

We also have set up a forecasting

time‑space dynamic of influenza epidemics broadcasted

on French TV, which was for broadcasting three weeks

ahead the times of influenza. These maps are

broadcast on the French TV each week now during the

epidemic, of course. It was actually this content in

France.

As I told you, we have now set up a field

vaccine effectiveness, which is run for years for

measles. It has been published in 1993 and which is

on the routine basis since 1998 in France for

influenza and clinical disease. We also have some

maps showing the current evolution of the epidemic

week after week in the country.

So the available material for influenza

effectiveness assessment is to have this contingency

table for each age strata and each age group strata.

We need to have the number of vaccinated ID cases,

vaccinated ID cases, and say which coverage in the age

strata.

In fact, we did not choose the screening

methods as it was presented because it is too

simplistic. If we want to adjust an age group, we

need to use this for vaccine efficacy which has been

proposed by Greenland in statistics medicine in '86

and which also is used to compute the 95 confidence

interval, which is very easy to meet demands in our

system.

The results of these measures, the ongoing

measurements, are this one. You have here the figure

of this year, which is vaccine efficacy/effectiveness

of 60 percent, which computes along with the score to

77.

Of course, the confidence interval may be

large. And if the point estimate, the confidence

interval, which is very important, which was very

important to our public health authority, is to see

that in 2003‑2004, the level of vaccine effectiveness

was pretty similar to that of the other years with the

exception of the year '97‑'98 where when the H3N2

Sydney strain was circulating and it was very

well‑known that this strain was not included in the

vaccine and the vaccine was clearly not protective

against this strain and the vaccine effectiveness

using the same methods was 26 persons developed, 13 to

39.

We presented this measure on the first

week of the epidemic. And we completed this series

during the whole epidemic using two relative figures,

but it did not vary a lot. Of course, the confidence

interval was much larger at the beginning of the

epidemic, but the vaccine effectiveness was about the

same.

We put all of the data each week on the

Web site, which is now available also available. Many

things are in French. We have set up for WHO since we

have been designated five years or six years ago as a

collaborating center for epidemic disease

surveillance. We have set up for them the FluNet

System, which is still based in Paris, our unit, but

we put the move, really, in 2004 for Geneva. It was

set up in 1997 trying to connect all of the network of

412 centers and also the center between WHO, which was

very important.

Because of that, it is possible to have

some very interesting findings. The first thing is

that there are similar patterns in the Northern

Hemisphere in terms of morbidity. It really begins in

the U.S. early. It begins also in Europe very early.

The duration of epidemics was the same in '98 and '99

presenting the figures, but also for the four

following winters.

The virus circulation with the FluNet,

which is reported in FluNet, you can see that in the

Southern Hemisphere, not for Hong Kong, which is more

a subtropical area, but in the temperate zone of the

Southern Hemisphere, you have a very good coherence of

time occurrence of isolates reported in the

hemisphere. Of course, in the Southern Hemisphere,

there is a lag of six months, which is also very, very

good evidence.

We have tried to study the mortality in

France, in the U.S., and in Australia. We have seen

that the average duration of activity with the same

kind of model organization using a periodic regression

model for estimating the size of epidemics to show

that the average is really very, very similar in

France and in the U.S. It is a little shorter in

Australia.

Excess mortality is highly correlated

between France. So we can say between Europe and the

U.S. It is not correlated between the South and the

North Hemisphere as in Europe or in the U.S. The

correlation between France and hte U.S. was open to 60

to 66.

To understand if the peak is happening

here the same date in France and in the U.S. and also

in Australia, we can see this is a way to get the

analysis. We show very, very high coincidence in the

phase of rapidity.

And we can see also a high correlation

between the peak date in the U.S. and the peak date in

France, showing that the peak occurred at the same

time in both countries. So we see a peak between the

U.S. and France in the last 20 years. It was opened

five weeks, so very, very short.

There is a strong coherence in Northern

Hemisphere, the temperate zone at least. It may

suggest the starting date, date of peak, duration of

influenza epidemic are similar in the U.S. and Europe.

Also size and severity of influenza incidence are

proportionally similar in the U.S. and as for Europe.

So we can probably assume that virus circulation and

probably also vaccine effectiveness among clinical

disease as we see in France is probably similar. And

this is going to be around 60 persons in the U.S. as

well as Europe. That was around 60 persons using the

method reviews, the general practitioners, the

clinical nurse, and so on.

So I want to take this opportunity to

thank all of my team, which worked with us on this

thing. Tank you very much for your attention.

(Applause.)

CHAIRMAN OVERTURF: Are there any

questions? Yes, Michael?

MEMBER DECKER: I just wanted the same

question I had for Dr. Bridges. Will you make a copy

of your presentation? Will it be available?

DR. FLAHAULT: Yes, of course. I just did

not provide that. In fact, I was a little embarrassed

because we wanted to publish the material. And we

know that FDA will publish it on the Web site. So we

will wait for the presentation to provide all of the

figures and charts with that.

CHAIRMAN OVERTURF: Dr. Farley?

MEMBER FARLEY: Yes. I am wondering what

predicts an early onset of a flu season.

DR. FLAHAULT: In fact, we did not try to

predict. We did not include that in our predictions.

So we only predicted the size in terms of morbidity

and mortality of the epidemic in France.

We did not predict the onset. We only

predict the onset with the other model, which has been

very recently published in the American Journal of

Epidemiology by Cecile Viboud, a nurse, which is

predictions three weeks ahead. So that is providing

a very big warning for the beginning. And after we

predict three weeks.

CHAIRMAN OVERTURF: Yes, Dr. Monto?

DR. MONTO: I think your studies show the

idea of long‑term ability to study influenza vaccine

effectiveness from year to year because then you

really can put what you observe into context.

What I would like to ask you about is the

variation in peak time in influenza incidence in

various countries in Europe. I had better be careful

what I say. France is a relatively small country.

And the peak occurs over a reasonably short time

period in the entire country.

I understand there were different times,

just as there were different times of peak occurrence

in Europe. And we have the same situation in the

United States. How well does your model predict

occurrence of peaks in western and central Europe, for

example?

DR. FLAHAULT: It is not so easy to have

precise data and available data on that. In fact, we

felt that the distribution of the peak was much wider

than it was, effectively, either in France, within

France. We used to say that the peak was moving from

a region to another one. The reasons were so that, in

fact, there is a good coincidence everything is done

within ten weeks.

So ten weeks may be not exactly

coincident, but all activity is done in ten weeks.

And all of the peaks are done within three weeks, so

very, very short. So within Europe, we need to have

the same kind of system or at least the same kind, not

the same system but to know what we know, if anything,

about the peak.

And in terms of mortality data, when we

have them, we can say that the peak of mortality is

really, really coincidental. And so I don't know what

is your experience in the United States between the

states of the United States, but I am not so sure it

is a very wide distribution of peak in your country,

too.

CHAIRMAN OVERTURF: I assume that the

reason for no breakdown in the age group from 15 to 60

or 65 is because there is no recommendation for

vaccine routinely to be given. I assume that the

rates of immunization in 50‑year‑olds is the same as

it is in 15‑year‑olds, which is 10 percent. Is that

true or not?

DR. FLAHAULT: Yes, not completely true

but approximately true. That is right. In France, it

is free of charge to be immunized after 65 and plus,

and it is not reimbursed before. It is not completely

exact because in several companies, you may have free

of charge vaccination. And because of that, in the

same age group, in some particular situation, you may

have some higher figures, but in children at least,

the figure ‑‑ not in children. It is very low, but

between 15 and 30 is very low, too.

CHAIRMAN OVERTURF: There are no European

countries that are considering immunization of

children that you know?

DR. FLAHAULT: All of us are considering

immunization of children, but we are still waiting for

the product, which is still not yet marketed, also to

have some recommendation of regulatory authority with

work on that in the future, we are sure.

CHAIRMAN OVERTURF: Dr. Gellin?

DR. GELLIN: We are enlightened by this

series of vaccine‑effective studies. Do you or does

anybody know if there are similar studies in the

Southern Hemisphere?

DR. FLAHAULT: I am sorry. Can you repeat

that?

DR. GELLIN: Does anyone know if there are

vaccine‑effectiveness studies in the Southern

Hemisphere?

DR. FLAHAULT: No, I am not aware of that.

CHAIRMAN OVERTURF: Yes, Dr. LaRussa?

MEMBER LaRUSSA: Do you plan to have any

data on the importance of influenza‑like illness in

children?

DR. FLAHAULT: We collect influenza‑like

illness in children. In France, general practitioners

are taking main charge of the presence of the children

in terms of immunization, in terms of like illness.

So we have these down. They are not immunized at all.

So we did not compute any vaccine effectiveness. So

the measurement of the coverage, vaccine coverage,

only concerns age 15‑plus.

CHAIRMAN OVERTURF: Any further questions?

(No response.)

CHAIRMAN OVERTURF: Well, I would like to

thank all of the presenters thus far. We have a

little extra time for a break. We are to be back here

by 11:30. And we will hear about U.S. surveillance.

(Whereupon, the foregoing matter went off

the record at 11:15 a.m. and went back on

the record at 11:36 a.m.)

CHAIRMAN OVERTURF: Ann Moen is going to

present the data on the U.S. influenza surveillance.

U.S. SURVEILLANCE

MS. MOEN: Good morning. I am going to

spend about the next 20 minutes giving you an overview

of the U.S. influenza surveillance data for the

current 2003‑2004 season.

This first slide depicts the general

schematic of the U.S. influenza surveillance season

and the major components of that under which CDC

collects data on a weekly basis from October to May

each year.

There are four major components that we

collect data on weekly. The first is virologic data,

which we collect from the system of laboratories in

all 50 states. And then there are sentinel providers

that we collect influenza‑like illness on throughout

the 50 states. That is data reported weekly to CDC.

Each week, the state and territorial

epidemiologists in each state report to CDC the level

of influenza activity in their state based on defined

criteria. And then the 122 cities' mortality system

we receive vital statistics, registrars' reports to

CDC on a weekly basis, reporting influenza mortality

due to P and I.

There are some other various forms of data

that come in sporadically, such as institutional

outbreaks and sometimes cruise ships. This year we

had some extra data we collected on pediatric

mortality, which I will talk about later.

All of this information flows into CDC

through the state health departments. And we work

very closely with our partners in the states. Then

the data is analyzed weekly at CDC and then published

in reports that go back out to public health

officials, physicians, the medics, and the public.

This slide shows the virologic data that

is collected through the WHO and national respiratory

and enteric virus surveillance system collaborating

laboratories. There are approximately 120

laboratories throughout the United States that collect

virologic data. They report to us weekly the number

of respiratory specimens that they tested and the

percent that were positive for influenza.

This graph shows just the positive

samples, and the yellow bar shows the influenza A

viruses, which were unsubtyped. And that smaller

subset, which were subtyped, the influenza A(H3N2)

viruses, are shown in red. There is a smattering of

B viruses down here, just a small handful, shown in

green that you may or may not be able to see. And

then the blue line shows the percent of overall

respiratory specimens that tested positive for

influenza.

To give you an idea of the magnitude of

the data collected through that system, of the

viruses, the total specimens tested by the WHO in

NREVSS laboratories in the U.S. for the week ending

February 7th, there were over 92,000 respiratory

specimens tested for influenza. And of these, 22,419

were positive for flu.

All of the laboratories in this

surveillance system type the influenza viruses. Of

those positive for influenza, 22,286, or 99.4 percent,

were influenza A viruses. And then there was a small

handful of influenza B viruses, just 133.

Now, a smaller subset of these 120 or so

labs also do subtyping of influenza viruses. And of

the 22,000‑some viruses positive for influenza A,

5,862 were subtyped. Of those, 99.9 percent were

influenza A, H3N2, viruses with just one influenza A,

H1 virus, which was detected from a military base in

Virginia, I believe, and was probably associated with

travel.

So of all of these viruses collected

through this system, a subset is sent to CDC for

further characterization. This afternoon, you will

hear about the antigenic and genetic characterization

in detail.

In reference to an earlier question about

the percentage of Fujian‑like viruses, I believe for

the week ending February 7th, approximately 82 percent

of them were antigenically similar to Fujian viruses.

And 18 percent were Panama‑like. You will hear those

details later.

So this slide shows the percentage of

estimates testing positive for influenza. The red

line shows this year's data. You can see that it

peaked at about 36 percent positive around week 51‑52.

For comparison here, I have shown the data

from the 1999‑2000 season in green, which is the most

recent moderately severe influenza AH2 H3N2 season

that we have had. And then, for further comparison,

the purple line shows the percent of positive

specimens tested through this system of labs for the

2002‑2003 season.

What this slide does a very good job of

showing is that the season did come earlier this year

and started very early and peaked earlier than most of

the previous seasons. The 1999‑2000 season was

considered early at the time. And this season was

even earlier than that.

This slide showed the percentage of visits

for influenza‑like illness reported by sentinel

providers through our national system of sentinel

providers. Each week, a series of sentinel providers

in the U.S. report the total number of patients seen

in their practice and then the total number of patient

visits for influenza‑like illness using a case

definition of fever of greater than or equal to 100

and cough or sore throat with no other known cause of

illness.

The smooth white line across the center at

2.5 percent is a national baseline. And you can see

that this season that started early peaked around

eight percent. And it has currently come back down

under the baseline. We are currently at the end of

last week around 1.5 percent.

Again, for comparison, the 1999

season‑2000 season is shown in green. And then the

much milder season that we had last year is shown in

purple, where there wasn't that much time spent above

the national baseline.

Just to give you an idea of the numbers of

sentinel physicians in this program, we currently have

1,931 sentinel providers that are enrolled in this

program. And they voluntarily report this information

to CDC on a weekly basis. Of the providers enrolled

in this program this year, 1,141 of them have been

regularly reporting so far, which is considered

reporting more than half of the weeks.

From these sentinel physicians, we

received almost 20,000 reports to the end of last

week, which was for over 5 and a half million patient

visits. And of these, about 170,000 of them were for

reports of influenza‑like illness.

This slide shows the third major component

of the influenza surveillance data collected at CDC.

It represents the pneumonia and influenza mortality

data for the 122 cities. Each week the vital

registrars in the 122 cities report the total number

of death certificates filed and then the number of

death certificates that have either pneumonia or

influenza listed somewhere on them so that we can get

a percentage of deaths due to P and I.

This data represents about a third of the

mortality data for the U.S. The 122 cities represent

about a third of all deaths in the U.S. On this

slide, you can see the bottom smooth line is the

seasonal baseline that is calculated using a

mathematical modeling method. And the upper smooth

line is the epidemic threshold, which is 1.645

standard deviations above the seasonal baseline. Then

the red jagged line shows the influenza mortality due

to pneumonia and influenza.

You can see for the current season that we

have peaked at about 10.3 percent and just for the

week ending 2‑7 came down to about 8.7 percent is

still above the epidemic threshold. Any time the red

line crosses the epidemic threshold, then we consider

that there is excess death due to influenza and

pneumonia.

For comparison on this graph, you can also

see last season, which was much milder and barely up

above the epidemic threshold. Then you can also see

the 1999‑2000 H3N2 season, which peaked at 11.2

percent over here. So it would sort of show that this

season wasn't greater in magnitude in terms of

mortality than previous H3N2 seasons.

This slide shows the fourth major

component of our influenza surveillance system, where

we have received weekly influenza activity estimates

reported, as assessed and reported, by state and

territorial epidemiologists.

The white color shows no report. The

yellow color is no activity. Green represents

sporadic activity. Purple or the light purple

represents local activity. Blue is the regional. And

red represents widespread.

This is the very first week that we have

started reporting data for this season. And you can

see that already in Texas, local activity was being

shown.

The next series of slides is going to take

you on a quick trip through the weekly reports as

reported and assessed by the state and territorial

epidemiologists. You will see how the season

progressed.

By the middle of November, there was quite

a bit of activity reported, especially in the western

half of the United States. And by the beginning of

December, there was widespread, lots of influenza

activity throughout the United States, where we peaked

about the middle of December or towards the end of

December.

Then you can see the activity continued to

decline, and there is still as of the week ending

February 7th some activity or relatively a bit more

activity in the East, where the outbreaks and the

epidemics started just a bit later.

This year we made some changes in the

activity reports from the state and territorial

epidemiologists. In previous years, the activity

levels were assessed at four levels: none, sporadic,

regional, or widespread. These were based on a

percentage of the population and counties. And then

the criteria used was either outbreaks of

culture‑confirmed influenza or influenza‑like illness.

In response to some comments from states,

we worked closely with our partners in the states to

make the criteria by which reporting was done more

defined so that it could be more uniform from state to

state.

So for this current year, five levels of

activity reported: none, sporadic, local, regional,

or widespread. And this was based on state‑defined

regions within the states. The defined criteria used

a combination of influenza‑like illness and outbreaks

and laboratory data. There were specified time frames

for which the lab confirmation needed to take place,

though hopefully we will be able to assess this at the

end of the season and see if states are happier with

this local new definition for providing activity

reports.

So, just to take you back to the peak

week, though, for the week ending December 20th, 351,

there were 49 states reporting either regional or

widespread activity in contrast to the previous

1999‑2000 H3N2 season, when there were 45 states

reporting either regional or widespread activity.

You can see that that season peaked about

three and a half weeks later than the current season.

This is in contrast to last year, where at the peak of

the epidemic, there were 34 states reporting regional

or widespread activity. And that peaked at the

beginning of March.

I also want to spend a couple of minutes

talking about influenza‑associated death among

children less than 18 years of age. Because of the

early attention that pediatric mortality received, CDC

requested reports of influenza death in children by

sending out health alert networks, publishing in the

MMWR and Epi‑X.

As of the end of last week, we had 134

influenza‑associated deaths that were lab‑confirmed

reported to CDC. The median age was 3.36 years with

the range of 2 weeks to 17 years.

Of these, 82 children were less than 5

years of age, 36 children were 6 to 23 months of age.

And of the 134 children, 32 of these children had

underlying medical conditions.

Of the available vaccination histories, it

is shown that 76 children were unvaccinated, 38 had

missing or unknown vaccination histories, 20 had some

sort of vaccination, but only 3 of these were

vaccinated according to the recommendations. It was

pretty much equal opportunity with about half and half

male and female.

This slide shows the epidemic curve by day

and week of the influenza impact in children. You can

see or you will see that the peak here correlates with

the peak of the nationwide aggregate data.

So the question is, is the 2003‑2004

different in impact among children? Well, this has

not been a normal part of our regular surveillance

systems. The influenza‑associated deaths are not

reportable conditions in the U.S. So the average

annual number of influenza death is unknown. There is

no baseline data.

There was a study that looked at ten

years, from 1990 to 1999, and estimated the annual

average of 92 deaths, respiratory and circulated

deaths, occurring among children less than 5 years of

age. This estimate is based on mathematical modeling,

and it is not counting laboratory‑confirmed

fatalities.

Studies to determine if hospitalization

increased in children are ongoing. And discussions

are underway with our state partners to consider

making laboratory‑confirmed deaths in children or

reportable conditions.

This slide, in summary, shows some of the

main components of our influenza surveillance system

overlay so you can see how they correlate. The red

line shows the percent of visits for influenza‑like

illness. The blue line shows the number of states

reporting widespread or regional influenza activity.

The yellow line shows the pediatric deaths. And the

green line shows the percent of positive isolates as

tested by the WHO collaborating and NREVSS

laboratories.

You can see that the ILI, the state

reports, and the influenza mortality, pediatric

mortality all nicely correlate and peak around week

51. The virologic data seems to precede the other

indicators a bit. And it may be due to some early and

very heavy reporting by some of the states who tested

a lot of respiratory specimens in the West and

submitted a lot of specimens that were positive.

I think I will stop there and take any

questions.

CHAIRMAN OVERTURF: Are there any

questions? Dr. Myers?

DR. MYERS: Ann, in those pediatric

deaths, particularly the ones in young children, were

there risk factors other than age? And did you look

specifically under one year of age?

MS. MOEN: They have got age data on all

of the children. The data are being analyzed. The

form that they used is to collect as much information

as they could on children.

But some of the information was hard to

collect because we had reporting of a lot of death

before they decided to collect certain information.

They are going back and trying to fill in some of that

information. Hopefully more full information will be

published.

CHAIRMAN OVERTURF: I believe from the

published data already, about 50 percent of the

children did have underlying illness, though. Isn't

that correct?

MS. MOEN: That was through a few weeks

ago. This is the most current data as of the end of

last week. So the underlying illness for this is a

bit lower.

CHAIRMAN OVERTURF: Dr. Monto?

DR. MONTO: One of the questions we are

always asked is whether the deaths in children that

have been in the newspapers, et cetera, and locally is

something new or is something that has been around for

a while that we haven't recognized.

One thing that was striking to us in

southeastern Michigan is that we had several deaths of

children last year. And some of them were type A H1.

And some of them were type B. And none of them were

A H3N2, which is the subtype we generally associate

with severity.

My question is, how much is the use of

rapid testing to identify influenza virus associated

with the correlation between children's deaths during

an influenza outbreak and identification of influenza

as the etiologic agent?

MS. MOEN: I am not sure how many of these

children were tested by rapid test or influenza

culture. So I can't answer that question.

CHAIRMAN OVERTURF: There is a question

from the audience. Yes?

DR. RUBEN: Fred Ruben of Aventis Pasteur.

I just wondered, Ann. We would normally

think of deaths during influenza periods as associated

with pneumonia and influenza. My understanding from

a presentation by Tim Uyeki was that these weren't all

pneumonia and influenza‑like type deaths, that there

were other attributes, like neurologic conditions and

so forth. Could you comment on that?

MS. MOEN: I think that is true. Some of

them were sudden. It runs the gamut. There were some

that were quite rapid and not associated with

long‑term pneumonia illness.

And there were definitely some excess or

additional cases of pediatric encephalopathies

associated with some of these cases. And we

additionally have been collecting information on other

severe cases of influenza associated with

encephalopathies that may not have resulted in death.

CHAIRMAN OVERTURF: Dr. Gellin?

DR. GELLIN: Ann, you commented early on

about the surveillance, the virologic surveillance,

system. I don't have a context for how many samples

normally go through that system. I guess the question

is akin to the rapid diagnostics. With the increasing

availability of rapid diagnostics, is there a problem

in the system with getting enough viral cultures?

MS. MOEN: I think right now it is

something we remain concerned about. We know there is

a good useful rapid test on occasion, but we also need

to maintain viral isolation so that we can

characterize the viruses.

I don't know what percentage of these

tests were rapid positives, but those are also

reported. An in some cases, say in a year like this

year, when there weren't very many B's, if we're just

getting mostly rapid test reports of B's, that doesn't

leave us very many isolates.

But so far, I mean, the 92,000 that have

been year to date this year is almost equivalent to

the total number of respiratory specimens that were

looked at last year. So I think we are getting more

isolates so far this year.

CHAIRMAN OVERTURF: Dr. Myers?

DR. MYERS: I guess after the presentation

we had just before the break, the obvious question is

to ask, as the numerator data, the case selection

method that France is using, is available to us

through our sentinel physician system, has CDC

considered the possibility of doing an electronic

surveillance analogous to what is done in France,

which, really, I was impressed by the fact that it

gives real‑time data?

MS. MOEN: Yes, their system is

impressive. Right now our sentinel physicians don't

collect vaccination data or patient‑level data. We

get only age data on the sentinel physicians. So we

can't look at vaccination right now.

And on a wide scale, I don't know how well

we would be able to convince all of these providers to

report that level of detail gratis, as they are doing

now.

But it would be great if the U.S. could

implement some way to have annual estimates of vaccine

effectiveness based on surveillance data.

CHAIRMAN OVERTURF: Yes?

DR. ROYAL: Are you able to comment more

specifically on what is known about some of the

neurologic complications these children have

developed, either pathologic findings or other autopsy

abnormalities?

MS. MOEN: No. I wouldn't be the best

person to comment on that.

CHAIRMAN OVERTURF: Is there anyone

present who can comment on that?

(No response.)

CHAIRMAN OVERTURF: Are there any further

questions for Dr. Moen?

(No response.)

CHAIRMAN OVERTURF: We only have three

minutes left before the scheduled break for lunch. I

think Dr. Cox was going to go early, but I don't think

we have time for Dr. Cox. So I think we will just

wait until after that time.

We are scheduled to reconvene at 1:00

o'clock. So I will adjourn the meeting until that

time.

(Whereupon, at 11:59 a.m., the foregoing

matter was recessed for lunch, to

reconvene at 1:00 p.m. the same day.)

DR. FREAS: I think we're ready to resume

the meeting.

CHAIRMAN OVERTURF: At this time of the

meeting, there's time set aside for public comment.

We've heard of nobody who wants to make public

comment, but at this time I would encourage members in

the audience or others who want to make public comment

to step forward to a microphone, identify themselves.

Before you comment, I need to make one

statement which I need to read for the FDA. "Both the

Food and Drug Administration and the public believe in

a transparent process for information gathering and

decision making. To ensure such transparency at the

open public hearing session of the Advisory Committee

Meeting, FDA believes that it's important to

understand the context of an individual's

presentation. For this reason, the FDA encourages

you, the open public hearing speaker at the beginning

of your written or oral statement to advise Committee

of any financial relationship that you may have with

any company or any group that is likely to be impacted

by the topic of this meeting. For example, the

financial information may include the company's or

group's payment of your travel, lodging or other

expenses in connection with your attendance at the

meeting. Likewise, the FDA encourages you at the

beginning of your statement to advise the Committee if

you do not have any such financial relationships. If

you choose not to address this issue of financial

relationships at the beginning of your statement, it

will not preclude you from speaking."

MS. BARR: Thank you. My name is Geeta

Barr. I'm with the National Vaccine Information

Center. And I have no financial conflicts of

interest.

And this question is addressed to Ann

Moen. During the question and answer period after

your presentation, something came up regarding flu

related deaths in children, especially in regards to

neurological complications, encephalopathy and you

mentioned encephalopathy has been associated as a

cause of complication with these flu related deaths.

Is there or will there be available data

on how many of these children were vaccinated?

MS. MOEN: I gave the vaccination data for

the kids that were vaccinated. I believe it was 76 or

78 of the children were unvaccinated and then there

was missing data on I believe it was 38 of the

children and vaccinated children were approximately 20

with three of them vaccinated according to the

recommendations.

MS. BARR: Thank you. And I had one other

comment.

Dr. Levandowski mentioned in his

introduction that protection from being vaccinated has

been noted as early as one week following vaccination

and considering this point, is it really appropriate

in many of the effectiveness studies since they have

considered subjects as unvaccinated for the two weeks

following vaccination?

CHAIRMAN OVERTURF: Dr. Levandowski, would

you like to address that issue?

DR. LEVANDOWSKI: All right, I'll try. I

was just ‑‑ I think I was trying to point out in that

early study in 1943, it was commented that they could

tell the difference between the people who were

vaccinated and unvaccinated as early as one week

later.

Traditionally, we think about protection

as taking two weeks, mainly because it takes at least

that long for peak antibody titers to be achieved

after immunization and somebody who has been

immunologically primed and we believe strongly, at

least for the inactivated influence of vaccines, it's

the antibodies' direction against hemagglutinin and

that they are the most important of the protection.

I'm sorry if that seems a little confusing, but I

don't think we actually know exactly when protection

kicks in for any one person and on a population basis,

we would, I think, normally be expecting it should be

at least within about two weeks, but there may be some

effect earlier or it might be later in some

individuals.

MS. BARR: Thank you.

DR. FREAS: Mr. Chairman, just for

clarification, I would like to say that the open

public hearing, it really is to address the Committee

and make comments before the Committee. We would

appreciate you holding questions until the end of the

discussion or the end of all presentations just in

case those questions may be answered in subsequent

presentations.

Thank you.

CHAIRMAN OVERTURF: Are there any

additional public comments?

(Pause.)

I think we will proceed then. Dr. Cox

will present the information on the world surveillance

of strain characterization.

DR. COX: Okay, I want to start out my

presentation by reminding you that CDC houses one of

four WHO collaborating centers for influenza and one