Harm assessment of HPV vaccine and“frailty exclusion bias” or“healthy vaccinee effect”:
A theoretical basis and practical influenceson Nagoya City Study

2016.5.11

Rokuro Hama M.D.
NPO Japan Institute of Pharmacovigilance
Med Check TIP

Frailty exclusion bias
orHealthy-vaccinee effects

A theoretical basis and practicalinfluences on Nagoya City StudyAbstract (1)• It is essential to take “Frailty exclusion bias” or “Healthy-vaccinee
effects” into account in assessing efficacy and/or harm of vaccine byobservational studies.
• Theoretically, proportion (or odds of frailty) increases in non
vaccinated and decreases in vaccinated as the coverageincreases.
• Consequently, even if a vaccine has no efficacy and no harm, odds
ratio for having symptoms/diseases in vaccinated compared with
non-vaccinated become less than 1.0.
• If vaccine coverage increase, due to this type of bias, a vaccine
without efficacy looks like effective and a harmful vaccine looks likesafe.
• Theoretically, if the coverage of vaccine increased up to 80 to 90 %,the bias get extremely serious.
• In fact, symptoms such as “difficulty in simple calculation” or “needstick or wheel chair” etc were reported as if these increase by 30 to
40 % every year but these were due to extremely high vaccine
coverage namely 88 % to 90 %.
• In order to eliminate this type of bias, health status prior tovaccination should be adjusted.

Abstract(2)• No information for health status prior to vaccination was collectedin Nagoya City Study. However, it may be possible to reduce bias by
the following methods:• Odds ratio for each symptom and the 95% confidence interval(95%CI) should be calculated by using the number of subjects,
number of responders, number of women with symptoms, by
symptom, by birth year (age)• These odds ratios are the ones comparing those within the sameage. Hence no age adjustment is needed.
• Theoretically the frailty exclusion bias is the least in the girls who
were born in 2000 (about age 15-years) because of the least
coverage (15%). Odds ratio in the women with other ages, should
be adjusted by the the most reliable odds ratio with the least
coverage of vaccination (15%).
• The odds ratios obtained by the methods above may be far morereliable than the interim results. However, it is still biased by
exclusion of frailty.

Conclusion• The preliminary data in the Nagoya City Studysuggest the highly possibility of harmful effectof HPV vaccine.
• We recommend that Nagoya City should
withdraw the interim report and disclose raw
data so that the third party could analyse thedata.
• We also recommend that Nagoya City should
also re-analyse the data by themselves using
appropriate methods.

Observational studies comparing cohorts vaccinated with
HPV vaccine and non-vaccinatedPapers that claimed “no association”

1.Siegrist CA, Lewis EM, Eskola J, Evans SJ, Black SB. Human Papilloma Virus Immunization in Adolescent and Young
Adults: A Cohort Study to Illustrate What Events Might be Mistaken for Adverse Reactions. Pediatr Infect Dis J
2007;26: 979-84
2.Gee J, Naleway A, Shui I, et al. Monitoring the safety of quadrivalent human papillomavirus vaccine: Findings fromthe Vaccine Safety Datalink. Vaccine 2011:29: 8279-82.
3.Arnheim-Dahlström L, Pasternak B et al. Autoimmune, neurological, and venous thromboembolic adverse events
after immunisation of adolescent girls with quadrivalent human papillomavirus vaccine in Denmark and Sweden:
cohort study. BMJ. 2013; 347: f5906.
4.Donegan K, Beau-Lejdstrom R, King B, Seabroke S et al. Bivalent human papillomavirus vaccine and the risk offatigue syndromes in girls in the UK. Vaccine 2013; 31: 4961-7
5.Scheller NM, Arnheim-Dahlström L et al. Quadrivalent HPV vaccination and risk of multiple sclerosis and otherdemyelinating diseases of the central nervous system. JAMA. 2015;313:54-61

Papers that reported association or with data suggesting association
3.Arnheim-Dahlström (having data suggesting association)

6. Geier DA, Geier MR. A case-control study of quadrivalent human papillomavirus vaccine-associated autoimmune
adverse events. Clin Rheumatol. 2015;34:1225-31.
7. Baril L, Rosillon D, Willame C, Angelo MG, Zima J, van den Bosch JH et al Risk of spontaneous abortion and otherpregnancy outcomes in 15-25 year old women exposed to human papillomavirus-16/18 AS04-adjuvanted vaccine in
the United Kingdom.. Vaccine. 2015:33(48): 6884-91 [Epub 2015 Jul]
8.Agence nationale de sécurité des medicaments et des produits de santé. Vaccins anti-HPV et risque de maladies
autoimmunes : étude pharmacoépidémiologique.
http://ansm.sante.fr/content/download/80841/1023043/version/1/file/Ansm_Gardasil-Hpv2_Rapport_Septembre-2015.pdf(explanation about the paper 1 and 4～8 will be on the slides37～42)

9. Interim report of Nagoya City Study will be criticized in the slides 9～34 precisely.

Papers claiming “no association” without
taking “frailty exclusion bias” into account(2) Paper by Gee et al• In the (2) Gee article, selection criteria for thecontrol group are not clearly stated.
• This alone is enough to make the study unreliable.
• Additionally, it is likely that outpatients who
consulted for any reasons other than vaccination
were selected for the control group.
• This may mean that the control group included many
patients with infections, increasing the incidence ofautoimmune diseases at the start of follow-up.
• Therefore, they are unsuited as a control group of
healthy vaccinated people.

Papers claiming “no association” without taking“frailty exclusion bias” into account(3) Paper by Arnheim-Dahlström et al• A cohort study following-up approximately 1.0 million girls aged
10-17 years old between 2006 and 2010, utilizing a database in
Sweden and Denmark. Some 300,000 girls received at least one
dose of Gardasil (average 2.35 doses), and were observed for 180
days after inoculation.
• After adjusting for their age, educational background of parents,
and the year of inoculation, incidences of 53 neurologicaldisorders, autoimmune diseases and venous thrombosis wereanalysed, and the risk ratio (RR) with the control was calculated.
• As a result, among 29 diseases analysed, 23 autoimmune
diseases appeared in 5 or more vaccinees. Of these, there was no
significant difference for 20 diseases.
• Even though a “frailty exclusion bias” was not taken into account,the incidence was significantly higher in the Gardasil group for 3
diseases, namely Behcet’s disease (RR=3.37), Raynaud's disease(RR=1.67), and type I diabetes mellitus (RR=1.29).

Nagoya City Survey• Subjects: about 70,000 women who were born in thefiscal year 1994 to 2000: they were in the 3rd grade ofjunior high school (about 14or 15 year old) to 21year-old at September 2015
• Questionnaire survey asking:1) Experience of HPV vaccine injection at least once.
2) Whether they have 24 different symptoms and othersymptoms or not.
Hence, this survey may be considered as a retrospective
cohort study comparing present symptoms between
HPV vaccinee group and non-vaccinee group.

Nagoya City Survey (2) Preliminary report
Nagoya City Survey on HPV vaccine:preliminary report of analysis
Table 1 Response rate of questionnaire
Distributed

not delivered net distributed response

rate

Table 2 Proportion of vaccinated and non-vaccinatednon-vaccinated Vaccinated

(%)

Total

unknown grand total

Nagoya City Survey (3)Birth year (age) and vaccination coverage (%)
Table 5 birth year (fiscal year) and vaccination coverage
Birth year
2000
approximate age 15

1999
16

1998
17

1997
18

1996
19

1995
20

1994
21

Vaccine ＋－
Total
Coverage (%)

Coverage of vaccination among those born in 1994 through 1997 were very highwith more than 85%. However, the coverage decreased subsequently and was
only 15 % among those born In 2000.
Comment by Hama: Coverage is extremely high (88 to 90 %) among those who wereborn in 1994-96. These must have been heavily influenced by “frailty exclusion bias”.

Nagoya City Survey (4)Odds ratio with symptoms by vaccination status
Table 3 cross tabulation by (vaccinated vs non-vaccinated) and (symptom positive vs negative)
30,279: excluding unknown vaccination status
Non-vaccinated Vaccinated Symptom
Symptom
Symptom unknown Odds(－)(＋)(－) (＋)
ratio

95% CI

Irregular menstrual cycle 01
Abnormal menstrual volume 02
Arthralgia 03
Severe headache 04
Fatigue 05
Easy to be tired 06
Unable to concentrate 07
Abnormal visual field 08
Too glaring 09
Sudden visual acuity decrease 10

The red marked items are those with significantly more symptoms in vaccinated andthe green marked items are those with significantly less symptoms in vaccinated.

Table 3 continued: Odds ratio with symptoms by vaccination status
Non-vaccinatedSymptom(－)(＋)

Vaccinated Symptom
Symptom unknown Odds(－) (＋)
ratio

95% CI

Dizziness 11
Cold foot 12
Difficult to sleep 13
Sleep too long 14
Rough skin 15
Hyperventilation 16
Difficult to remember 17
Difficulty insimplecalculation 18
Difficulttoremembersimple kanji19
Involuntary move 20
Cannot walk normally 21
Need stick or wheel chair 22
Sudden loss of power 23
Weakness in extremities 24
Others 1(free writing) 25
Others 2 ‘free writing) 26
The red marked items are those with significantly more symptoms in vaccinated andthe green marked items are those with significantly less symptoms in vaccinated.

Nagoya City Survey (5)Birth year (age) and odds ratio of symptoms comparedwith those born in 2000 (Non-vaccinated only)
Table 4: Birth year (age) and risk ratio of symptoms (Non-vaccinated only)
Birth year
approximate age

00
15

99
16

98
17

97
18

96
19

95
20

94 Increment
21 Per year

Irregular menstrual cycle 01
Abnormal menstrual volume 02
Arthralgia 03
Severe headache 04
Fatigue 05
Easy to be tired 06
Unable to concentrate 07
Abnormal visual field 08
Too glaring 09
Sudden visual acuity decrease 10
Odds ratios of positive symptom by various birth year compared with those born in 2000 are
shown. The red marked odds ratios are significant (95% confidence intervals are not shown inthis table). All symptoms except “sudden visual acuity decrease” increased as age increased.

Table 4 (continued): Birth year and odds ratio of symptoms
compared with those born in 2000 (Non-vaccinated only)
Birth year
approximate age

00
15

99
16

98
17

97
18

96
19

95
20

94 Increment
Increment
21 PerPeryearyear

Dizziness 11
Table 4 (continued) Cold foot 12
Difficult to sleep 13
Sleep too long 14
Rough skin 15
Hyperventilation 16
Difficult to remember 17
Difficulty in simple calculation 18
Difficult toremembersimplekanji 19
Involuntary move 20
Cannot walk normally 21
Need stick or wheel chair 22
Sudden loss of power 23
Weakness in extremities 24
Others 1 (free writing) 25
Others 2 (free writing) 26
Odds ratios of positive symptom by various birth year compared with those born in 2000 are
shown. The red marked odds ratios are significant (95% confidence intervals are not shown inthis table). All symptoms except “sudden visual acuity decrease” increased as age increased.

Nagoya City Survey (6) Age adjusted odds ratio
Table 6: Age adjusted odds ratio(odds ratio in the Table 3 were adjusted by age using logistic regression analysis

Before adjustment
OR
95%CI
Irregular menstrual cycle 01
Abnormal menstrual volume 02
Arthralgia 03
Severe headache 04
Fatigue 05
Easy to be tired 06
Unable to concentrate 07
Abnormal visual field 08
Too glaring 09
Sudden visual acuity decrease 10

age adjusted
OR
95%CI

Table 6 (continued): Age adjusted odds ratio
Before adjustment
OR
95%CI
Dizziness 11
Cold foot 12
Difficult to sleep 13
Sleep too long 14
Rough skin 15
Hyperventilation 16
Difficult to remember 17
Difficulty in simple calculation 18
Difficult toremembersimplekanji 19
Involuntary movement 20
Cannot walk normally 21
Need stick or wheel chair 22
Sudden loss of power 23
Weakness in extremities 24
Others 1 (free writing) 25
Others 2 (free writing) 26

age adjusted
OR
95%CI

Nagoya City Survey(7) Conclusion• Of the 24 symptoms, there was no symptom that wassignificantly more reported among those vaccinated, after
age adjustment.
• In the contrarily, there were several symptoms that weresignificantly less reported among those vaccinatedcompared with non-vaccinated.
• These might have occurred because those who had
symptoms did not receive vaccine and it may be difficult toconsider that HPV vaccination reduced such symptoms.
(This point is deleted in the formally disclosed preliminary
report on Dec 14 2015)• This is the statistical analysis hence, the causality of
individual case should be assessed very carefully.

Nagoya City Survey(8)the most serious limitations• Highly biased by “the frailty exclusion bias”• They misunderstand the increase of odds ratio of
positive symptoms among those born in 1994 to
1997 due to “the frailty exclusion bias” derived by
the very high coverage of vaccination as theincrease of age.
• They yield very strange results in that significantly
low odds ratio in the vaccinated compared to thenon-vaccinated by adjusting age.

Theoretical basis of “frailty exclusion bias” (1) no exclusion
Fig 1 No exclusion of frailty

・Imagine a population in which high risk people (the frailty) are living by
the proportion of "a".
・People are vaccinated by the coverage "c".
・If the frailty (people with high risk) or the healthy are equally vaccinated,
and the vaccine do not cause any adverse effect, the odds of frailty is
a/b for both in the vaccinated and non-vaccinated.
・Hence the odds ratio of frailty in vaccinated compared with
non-vaccinated is 1.0.

Theoretical basis of “frailty exclusion bias” (2) with exclusion
Fig 2: If frail people were excluded for vaccination

・ If the frailty were excluded for vaccination by the proportion of "e“ and if thevaccine do not cause any adverse effect, odds of frailty is (ac-ace)/(bc + ace))among vaccinated and (ad + ace)/(bd - ace)) among non-vaccinated.
・ Hence the odds ratio of frailty in vaccinated compared with non-vaccinated= ((ac - ace)/(bc + ace))／((ad + ace)／(bd - ace))
・ Unless "e" is 0, odds ratio of frailty in the vaccinated compared with unvaccinatedwill be always less than 1.0 theoretically.
・ This is the theoretical basis of "frailty exclusion bias“, "frailty selection bias"
or "healthy vaccinee effect".

Influence of frailty exclusion bias on non-vaccinated group
Odd of frailty in the case “excluded” versus “not excluded”
12
11
10
9
8
7
6
5
4
3
2
1
0

B：odds of coverage (vaccinated/non)e: e:proportion excluded
e=0.9
e=0.7
e=0.5
e=0.3
e=0.1
e=0.01e=0

Odds of frailty(excluded/not-excluded）

Odds of frailty(excluded/not-excluded)

A：percent of coverage

0 10 20 30 40 50 60 70 80 90 100

c: percent coverage

%

12
11
10
9
8
7
6
5
4
3
2
1
0

e:proportion excluded
e=0.9
e=0.7
e=0.5
e=0.3
e=0.1
e=0

0 1 2 3 4 5 6 7 8 9 10 11 12

Odds of coverage (vaccinated/non)％If the coverage is 15%(odds=0.18）( ), influence of exclusion on the non-vaccinated groupis small in any proportion of exclusion . However, if the coverage is nearly 90 % (odds = 9)
as in the Nagoya City Survey, the influence is extreme (odds ratio increase to 5(), if
one half of frailty were excluded (e=0.5)

Proportion of women aged 22 year or more withpositive symptoms will increase impossibly highif the increase were simply due to age
Ratio of frailty proportion for ages more than
21-years compared with those born in 2000

10000018.Difficulty in simple
calculation (38.9%）

10000

21.Cannot walk normally(37.9%)
22.Need stick or wheel chair（30.9%)

1000

19.Difficult to remember
simple kanji (29.7%)

100

23.Sudden loss of power(20.9%)

10

24.Weakness in extremities(11.0%)

1

15171921232527293133353739414345474951

age(increment per year)

year

If the apparent increasing tendency of positive symptoms were simply
due to age, the proportion of women with positive symptoms have toincrease dramatically high: That’s impossible.

Example of influence：24. Weakness in extremities

Vaccination coverage and odds ratio of positive symptom in
non-vaccinated （compared to those born in 2000: coverage=15%)
HPV vaccination coverage(A) shown in percent coverage(B) shown in odds of coverage
Pattern is similar to slide 22-B
Odds ratio of positive symptom

Odds ratio of positive symptom

Pattern is similar to slide 22-A

0

20

40

60

n=7, Spearman‘s Rankcorrelation coefficient=0.991, p=0.0028

80 100

HPV vaccine percent coverage (%)

HPV vaccine odds of coverage

. Difficulty in simple calculation

Example of influence：18

Vaccination coverage and odds ratio of positive symptom in
non-vaccinated （compared to those born in 2000: coverage=15%)
Pattern is similar to slide 22-A

6

6
Odds ratio of positive symptom

Odds ratio of positive symptom

HPV vaccination coverage(A) shown in percent coverage(B) shown in odds of coverage

5
4
3
2
1

5
4
3
2
1
0

0
0

20

40

60

80

100

Percent coverage (%)

Pattern is similar to slide 22-B

Spearman‘s rank correlation
coefficient , p=0.048
0
2
4
6
8
10
Odds of coverage (vaccinated/non)Example of influence：Symptoms (18, 19, 21, 24)

Vaccination coverage and odds ratio of positive symptom in
non-vaccinated （compared to those born in 2000: coverage=15%)
HPV vaccination coverage(A) shown in percent coverage(B) shown in odds of coverage
8

18.Difficultyin simple
calculation

7
6

19.Difficult toremembersimple kanji

5
4
3

21.Cannot
walk normally

2
1

24.Weakness
in extremities

0
0

20

40

60

80

Percent coverage

100

Pattern is similar to slide 22-B
8
odd ratio of positive symptoms

odd ratio of positive symptoms

Pattern is similar to slide 22-A

18.Difficulty insimplecalculation
19.Difficult toremembersimple kanji
21.Cannot
walk normally

7
6
5
4
3
2

24.Weakness
in extremities

1
0
0

2

4

6

8

10

Odds of coverageodds ratio of
positive symptoms

Vaccination coverage and odds ratio of positive symptom in
non-vaccinated （compared to those born in 2000: coverage=15%)
Graphic illustration of the Table 4 of Nagoya City survey
3
Pattern is similar to slide 22-A
2
1
0

0

20

odd ratio of
positive symptoms

8
6

40

60

80

vaccination percent coverage (%)

100

Pattern is similar to slide 22-A

4
2
0

0

20

40

60

80

vaccination percent coverage (%)

100

1. Irregular menstrual cycle
2. Abnormal menstrual volume
3. Arthralgia
4. Severe headache
5. Fatigue
6. Easy to be tired
7. Unable to concentrate
8. Abnormal visual field
9. Too glaring
10.Sudden visual acuity decrease
11.Dizziness12.Cold foot13.Difficult to sleep
14.Too long sleep
15.Rough skin
16.Hyperventilation
17.Difficult to remember
18.Difficulty in simple calculation
19.Difficult to remember simple kanji
20.Involuntary movement
21.Cannot walk normally
22.Need stick or wheel chair
23.Sudden loss of power
24.Weakness in extremities

Odds ratios of positive symptoms go up to 3 to 7 for those born in 1994 to 96 in table 4.
The major reason may be due to frailty exclusion bias rather than age.

Odds ratio of frailty compared with no exclusion
In case when a=0.001(0.1%). However, if a=<0.01 (1%), the results are almost similar

Odds ratio of frailty

1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Proportion of exclusion (e)

0.8

0.9

1

e=0
e=0.01

1.0
0.9

e=0.1

0.8
0.7
0.6
0.5

▼

e=0.5

0.4
0.3
0.2
0.1

e=0.7
e=0.9

▼

0.0

0 10 20 30 40 50 60 70 80 90 100
Percent coverage%

Odds ratio of frailty(vaccinated/non)Odds ratio of frailty(vaccinated/non)Theoretical influence on the odds ratio of frailty by
the percent coverage and the proportion of exclusion(vaccine has no efficacy nor harm)
1.0

e=0.5

0.9
0.8
0.7
0.6
0.5

▼

0.4
0.3
0.2
0.1

▼

0.0

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Proportion of exclusion (e)If e=0.5, odds ratio of frailty of vaccinated compared to non-vaccinated is calculated less than
0.5 (▼), even if coverage is low (15%). If coverage increase up to 90%, odds ration fell to lessthan 0.1(▼). Apparent odds ratio in vaccinated compared with non-vaccinated is calculated
one tenth although the vaccine has no harmful effect.

Influence of frailty exclusion bias：How harmfula vaccine must be in order to be odds ratio = 1.0
Odds ratio of vaccinatedwith positive symptoms

16
c=0.9
c=90%

14
12

c=0.7
c=70%

10
8

c=50%
c=0.5

6
4

c=15%
c=0.15

2
0

0

0.1

0.2
0.3
0.4
0.5
Proportion of exclusion (e)0.6

If coverage is 90％ (c=0.9), 5.5 times more diseases (symptoms) areneeded when e=0.3(→), and 11 times more diseases are needed when e=0.5(→) in order to be 1.0 of odds ratio.

Influence of frailty exclusion bias： comparison of
apparent odds ratio and extent of harm to be 1.0 of odds ratio
Apparent odds ratio by harmless vaccine
Odds ratio in vaccinated (post/pre)Apparent odds ratio (vaccinated/non)

1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2

16

Extent of harm to be
1.0 of apparent odds ratio

14
12
10
8
6
4
2

0.1

0

0.0

0

0.1 0.2 0.3 0.4 0.5 0.6

0

0.1 0.2 0.3 0.4 0.5 0.6

Proportion of exclusion (e)Proportion of exclusion (e)Note: see the foot note of slide 36

How can we know from the raw dataof Nagoya City Survey:• Odds ratio should be adjusted by the health status before vaccination
instead of age, because the age difference may be very small if any.
• However, Nagoya City Survey did not collect such information.
• The second best estimation may be as follows:• Odds ratio and its 95 % confidence interval in vaccinated compared to
non-vaccinated should be calculated by birth year for each 24 symptom(see slide 39）.
• The odds ratios calculated by birth year is not needed for age
adjustment.
• Odds ratio among those born in 2000 (OR2000) may be the least biasedby the frailty exclusion bias, as the coverage is the least (15%).
• Then, odds ratios among other ages should be adjusted by the OR2000.
• Note that odds ratio adjusted by OR2000 is still biased (see slides 35） and
we should consider that the true odds ratio may be higher.
• Hence if odds ratio by these methods show statistical increase, weshould at least consider that HPV vaccine increased symptoms invaccinated women.

Necessary data for calculation of odd ratio by birth year
Example：24. Weakness in extremities

1) Data in the above table for symptoms 1～25(26) are necessary.
2) Odds ratios for each birth year should be adjusted by OR2000 .
3) Odds ratio having at least one of the symptoms No8 or No17～No26
which are considered several typical symptoms after HPV vaccination
should be calculated.

Conclusion• It is highly suspected that the interim report ofNagoya City Survey indicates harmful effect of HPV
vaccines.
• We recommend that Nagoya City withdraw theinterim report and disclose raw data
• so that the third party could analyse the data.
• We also recommend that Nagoya City itself
reanalyse the data by appropriate methods.

Observational studies comparing cohorts vaccinated with
HPV vaccine and non-vaccinated (reprint)
Papers that claimed “no association”

1.Siegrist CA, Lewis EM, Eskola J, Evans SJ, Black SB. Human Papilloma Virus Immunization in
Adolescent and Young Adults: A Cohort Study to Illustrate What Events Might be Mistaken for
Adverse Reactions. Pediatr Infect Dis J 2007;26: 979-84
2.Gee J, Naleway A, Shui I, et al. Monitoring the safety of quadrivalent human papillomavirus
vaccine: Findings from the Vaccine Safety Datalink. Vaccine 2011:29: 8279-82.
3.Arnheim-Dahlström L, Pasternak B et al. Autoimmune, neurological, and venous
thromboembolic adverse events after immunisation of adolescent girls with quadrivalent
human papillomavirus vaccine in Denmark and Sweden: cohort study. BMJ. 2013; 347: f5906.
4.Donegan K, Beau-Lejdstrom R, King B, Seabroke S et al. Bivalent human papillomavirus vaccine
and the risk of fatigue syndromes in girls in the UK. Vaccine 2013; 31: 4961-7
5.Scheller NM, Arnheim-Dahlström L et al. Quadrivalent HPV vaccination and risk of multiple
sclerosis and other demyelinating diseases of the central nervous system. JAMA. 2015;313:54-61

Papers that reported association or with data suggesting association

3.Arnheim-Dahlström (having data suggesting association)
6. Geier DA, Geier MR. A case-control study of quadrivalent human papillomavirus vaccineassociated autoimmune adverse events. Clin Rheumatol. 2015;34:1225-31.
7. Baril L, Rosillon D, Willame C, Angelo MG, Zima J, van den Bosch JH et al Risk of spontaneousabortion and other pregnancy outcomes in 15-25 year old women exposed to humanpapillomavirus-16/18 AS04-adjuvanted vaccine in the United Kingdom.. Vaccine. 2015:33(48):6884-91 [Epub 2015 Jul]

(explanation about the paper 1 and 4～7 will be on the slides38～41)

Papers claiming “no association” without
taking “frailty exclusion bias” into account(2) Paper by Gee et al• In the (2) Gee article, selection criteria for thecontrol group are not clearly stated.
• This alone is enough to make the study unreliable.
• Additionally, it is likely that outpatients who
consulted for any reasons other than vaccination
were selected for the control group.
• This may mean that the control group included many
patients with infections, increasing the incidence ofautoimmune diseases at the start of follow-up.
• Therefore, they are unsuited as a control group of
healthy vaccinated people.

Papers claiming “no association” without taking“frailty exclusion bias” into account(3) Paper by Arnheim-Dahlström et al• A cohort study following-up approximately 1.0 million girls aged
10-17 years old between 2006 and 2010, utilizing a database in
Sweden and Denmark. Some 300,000 girls received at least one
dose of Gardasil (average 2.35 doses), and were observed for 180
days after inoculation.
• After adjusting for their age, educational background of parents,
and the year of inoculation, incidences of 53 neurologicaldisorders, autoimmune diseases and venous thrombosis wereanalysed, and the risk ratio (RR) with the control was calculated.
• As a result, among 29 diseases analysed, 23 autoimmune
diseases appeared in 5 or more vaccinees. Of these, there was no
significant difference for 20 diseases.
• Even though a “frailty exclusion bias” was not taken into account,the incidence was significantly higher in the Gardasil group for 3
diseases, namely Behcet’s disease (RR=3.37), Raynaud's disease(RR=1.67), and type I diabetes mellitus (RR=1.29).

A paper confused prevalence with incidence (Siegrist et al)• Siegrist et al used a prevalence as follows as the control of incidence after
HPV vaccination.
• Denominator: the number of health insured females aged 9-18 and 19-30
years old
• Numerator: the number of persons who consulted a doctor (outpatient,emergency and hospitalization) at least once in 2005.
“prevalence”= a proportion of persons having a disease among certain
population at a certain point in time.
• “incidence rate” = newly occurred diseases among certain population
within a certain period of time.
• Since autoimmune diseases do not easily remit, “prevalence” is about 1030 times higher than “incidence rate” in general. For instance, the ratio ofprevalence to incidence of MS is about twenty on average.
• Siegrist et al reported prevalence of MS = 5.1 per 100,000 incidence =0.25/100,000 person-years (py) 0.03 per 100,000 person-6 weeks.
 1/3,300,000 person-6 weeks. In Japan, incidence of MS is about 1/10 ofthat in the Western countries.
• In Japan, three cases with MS were already reported.
• The Japanese MHLW calculated that 30 cases are expected to occur in 3.3
million persons inoculated with HPV vaccine in Japan based on theprevalence reported by Siegristself-controlled case series (SCCS)
is also problematic• 4.Donegan et al• 5.Scheller et alboth used the methods of self-controlled case series (SCCS)
・The self-controlled case series (SCCS) method was developed to investigate
associations between acute outcomes and transient exposures, using only
data on cases (individuals who have experienced the outcome of interest)
・Double peaked trends were suggested and incidence rates of chronic
diseases including autoimmune diseases and mortality rates seem toincrease after 3.5 years from the first inoculation.
●Donegan used the incidence 1year after inoculation as control and reported
no association: incident rate ratio (IRR) 1.07 (95%CI: 0.57-2.00, p=0.84).
・ However, sensitivity analysis by the prolonged time window (18 months)
yielded IRR 1.47 (p=0.25). What might be the estimated risk ratio if the time
window had been prolonged to 24 and/or 48 months in Donegan’s study?

Papers that reported association or with data suggesting
association: 6. Geier et al and 7. Baril et alboth are free from frailty exclusion bias

6. Geier et al
・Using the vaccine adverse event reporting system (VAERS) database,
Geier et al reported positive and significant associations between
HPV vaccines use and serious autoimmune adverse events (SAAE):
・odds ratios (95%CI) were:
gastroenteritis: 4.6 (1.3-18.5), arthritis: 2.5 (1.4-4.3),
SLE: 5.3 (1.5-20.5), vasculitis: 4.0 (1.01-16.4),
alopecia: 8.3 (4.5-15.9) and CNS conditions: 1.8 (1.04-2.9).
・In addition, those who had positive rheumatoid factor, antinuclear
antibody or antiphospholipid antibodies were significantly more
likely to be exposed to HPV vaccine than the unexposed (OR:4.8,
95%CI=2.7-8.7, p<0.0001).

7. Baril et al denied association but data shows association
Purpose: To assesse the risk of spontaneous abortion after inadvertent
exposure to HPV-16/18-vaccine during pregnancy
Participants：Pregnant women aged 15～25.
Exposed：women who had the first day of gestation between 30 days
before and 45 days (90 days for the extended exposure period) after
any HPV-16/18-vaccine dose.
Non-exposed: women who had the first day of gestation 4-18 months
after the last dose
Results：the hazard ratio (HR) (95%CI) adjusted for age at first day ofgestation was 1.30 (0.79-2.12) No evidence of risk of abortion.
Problems：
1)Dose-response was observed: 1 dose  HR 1.11 (0.64-1.91)
2 dose  HR 2.55 (1.09-5.93, p=0.03)
2)The first day of gestation between 4 to 18 months after the last doseof vaccine may never be free from risk of spontaneous abortion.
3)Considering the association with anti-phospholipid antibodies,4)HR of 2.55 should be considered as underestimation for
spontaneous abortion.

8. French National Agency for Medicines and Health Products Safety :A Cohort study on autoimmune conditions following HPV vaccination
http://ansm.sante.fr/content/download/80841/1023043/version/1/file/Ansm_Gardasil-Hpv2_Rapport_Septembre-2015.pdf

Participants and methods: A retrospective cohort study utilizing database of national
health system. 2.25 million girls aged 13 to 16 years between 2008 to 2012 were
included : 0.84 million (37.3%) were exposed with HPV vaccine and 1.41 million were notexposed.
Inclusion criteria: At least one visit for medical care during 2 years prior to inclusion, no
HPV vaccination nor history of autoimmune diseases before inclusion. Vaccine coverages
were variable with the highest in 2008 (45%) and the lowest in 2012 (15%).
Available information：Reimbursement data for medical care (medication, other cares),hospitalisation (including purpose of hospitalisation), comorbidities, chronic illness etc.
Follow-up period:2008.1.1(or birthday of 13 year)～2013.12.31(or birthday of 17 year):Median duration of follow-up: vaccinated girls: 19.8m（1.4million person-years）, nonvaccinated girls: 25.3m (4.7million person-years).
Outcome events (14 autoimmune diseases: AD): 1) Central demyelinating diseases(multiple sclerosis etc), 2) Guillain-Barres syndrome, 3) SLE, 4) DLE, 5) vasculitis,
6)
rheumatic arthritis, 7) Myositis/dermatomyositis, 8) Shogren Syndrome, 9) ITP,
10)
Type 1 diabetes, 11) Thyroiditis, 12) Pancreatitis, 13) IBD, 14) Celiac disease
Analysis methods：Hazard ratios of any 14 AD or of individual AD were estimated by Cox
regression analysis adjusting age, year of vaccination as time dependent variable.
Geometric data, other diseases, level of care before inclusion, vaccination other than HPV
vaccine were also adjusted. Subgroup analysis and sensitivity analysis were performed tomake sure of the robustness of the results.
42

Table 3: Characteristics of cohort according to exposure of HPV vaccine
Inclusion
year
2008
2009
2010
2011
2012
total

total
N
1,096,378
290,252
285,188289,457
291,441
2,252,716

%
100
100
100
100
100
100

vaccinatedN%
N 842,120 100
Consultation≧4/year
403,877 48.0
person-year 1,400,000 100
Hospitalization *a
195,936 14.1
Outcome Events
after inclusion

vaccinatedN%497,275
45.4
125,671
43.3
99,91935.0
75,19226.0
44,06315.1
842,120
37.4

non-vaccinatedN%
599,10354.6
164,58156.7
185,26965.0
214,265
74.0
247,37884.9
1,410,596
62.6

non-vaccinatedodds ratio (OR)N%
95%CI NNTH
OR
1,410,596 100
LL
UL
347,940 24.7 2.81 2.80 2.83 44,700,000 100
207,812 4.4 3.54 3.52 3.56 10*a: odds of hospitalisation during two years prior to inclusion were slightly frequent in the
HPV vaccinated group (OR=1.11 (1.10, 1.12). However after receiving HPV vaccine,
hospitalization frequency increased greatly (OR= 3.5), and one among 10 girls/year
vaccinated were necessary to hospitalise additionally compared to those without vaccine.

Main results and comparison of incidence (/105py) in non-vaccinated:
Original data by French agency (FA) and re-analysis by Hama (RH)

Total person-years (py) of follow-up for vaccinated group was reported about 1.4 million. If
more accurate estimate(1,392,030) were used, re-calculated incidence rates per 100,000 py are
all the same as in the report by French agency. However, incidence rates in the non-vaccinatedgroup are greatly different, especially CNS demyelinating diseases, inflammatory bowel diseases(IBD) and at least one autoimmune disease (AD). It is very difficult to understand the reasons
why such large discrepancies occurred, but our calculation methods may be valid, because theincidence rates in vaccinated group are all the same as in the original report.

Reported results by French agency and opinion of WHO
Results reported by French Agency：
1. Any AD: Adjusted HR (HRa) =1.07 [95 %CI:0.99, 1.16] No difference.
But,
2. IBD: HRa=1.19 [1.02, 1.39]: significantly higher in the vaccinatedgroup until 3 months but decreased subsequently without significance.
3. GBS: HRa=4.00 [1.84, 8.69] significantly higher in the vaccinatedgroup until 3 months and decreased subsequently but still significant.
Risk of GBS is robust by sensitivity analysis and subgroup analysis.
Incidence of GBS attributable to HPV vaccine is １ to 2 per 100,000
person-years
4.This is the first study in which association between HPV vaccine andAD was observed.
WHO’s Opinion on the results of French study
This risk (of GBS) in the first few months after vaccination was verysmall (~1 per 100,000 vaccinated children) and has not been seen inother smaller studies.
45

Strength and Limitations of the French study (1)1. Vaccination coverage is the highest (45%) in 2008 and the lowest (15%) in
2012. HR was analysed by Cox regression analysis adjusting age andvaccinated year and health condition prior to inclusion. This means the“Frailty exclusion bias” was taken into account at least partially.
2. But it is unclear how it affected the results that girls with at least one visit
for medical care prior to inclusion were included.
3. If more accurately estimated person-year (1,392,030) is used, re-calculated
incidence rates per 100,000 py are all the same as in the table in the abstract
reported by the French agency. Hence this estimate and calculation ofincidence rate is correct.
4. However, incidence rates per 100,000 py in the non-vaccinated group are
greatly different: especially CNS demyelinating diseases (4.7 vs 5.8),
inflammatory bowel diseases (IBD) (13.8 vs 16.9) and at least one
autoimmune disease (AD) (63.4 vs 66.8).
5. If our estimate of incidence rate in the non-vaccinated group is correct,
OR for CNS demyelinating diseases may be nearly significant (P=0.07),
OR for IBD may increase much more up to 1.53 (P<0.001) andOR for having at least one autoimmune diseases= 1.13 (P<0.001).
6. Excess incidence rates (HPV vaccine attributable incidence rate/100,000py)
are: CNS demyelinating diseases (1.2), GBS (1.0), IBD (7.3) and any AD 8,3).

Strength and Limitations of the French study (2)5. These annual incidence rate may exceed the maximum benefit expected(but unproven) with HPV vaccine by preventing cervical cancer mortality.
6. Moreover, incidence rate per 100,000 py of CNS demyelinating diseases in
the non-vaccinated group (4.7 or 5.8) is very high compared with that ofmultiple sclerosis including optic neuritis (1.0) in the general population of
the same age group (women aged 15 to 24 years) in France [ref].
7. This suggest that non-vaccinated group had included frail girls at the timeof inclusion. Therefore “Frailty exclusion bias” or “healthy vaccinee effect”
may not be completely excluded in this French pharmacovigilance study.
(Even if it is taken into account that included girls were those who visited
medical facilities at least once , incidence rate of CNS demyelinating diseases
in the non-vaccinated group may be too high).
8. However, Risks were significant for 2 autoimmune diseases (ADs).
9. Moreover, frequent consultation (≧4/year): Odds ratio (OR) = 2.81
(2.80,2.83) and at least one hospitalization: OR = 3.54 (3.52, 3.56) (based on
person-year) should be taken into account. This is because high odds ratio forneed of frequent medical care and/or hospitalisation may be related to someadverse outcome of HPV vaccination other than autoimmune diseases.
10. More comprehensive approach for analysis is necessary, because
symptoms after HPV vaccination may be more complicated and difficult to be
described by classical diagnosis.