Safety monitoring of bivalent, quadrivalent, and 9-valent human papillomavirus vaccination in Japan: The vaccine effectiveness, networking, and universal safety (VENUS) study

Chieko Ishiguro; Hiroya Morita; Wataru Mimura; Shinya Tsuzuki; Junko Hirashima-Terada; Yoshinori Takeuchi; Futoshi Oda; Megumi Maeda; Haruhisa Fukuda

doi:10.1080/21645515.2026.2688606

. 2026 Jul 7;22(1):2688606. doi: 10.1080/21645515.2026.2688606

Safety monitoring of bivalent, quadrivalent, and 9-valent human papillomavirus vaccination in Japan: The vaccine effectiveness, networking, and universal safety (VENUS) study

Chieko Ishiguro ^a,^✉,^*, Hiroya Morita ^a,^*, Wataru Mimura ^a, Shinya Tsuzuki ^b,^c, Junko Hirashima-Terada ^d, Yoshinori Takeuchi ^a,^e, Futoshi Oda ^f, Megumi Maeda ^f, Haruhisa Fukuda ^f

^aLaboratory of Clinical Epidemiology, Department of Data Science, Center for Clinical Sciences, Japan Institute for Health Security, Tokyo, Japan

^bDisease Control and Prevention Center, Center Hospital of the National Center for Global Health and Medicine, Japan Institute for Health Security, Tokyo, Japan

^cFaculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium

^dDivision of Respiratory Medicine, Center Hospital of the National Center for Global Health and Medicine, Japan Institute for Health Security, Tokyo, Japan

^eDepartment of Data Science, School of Data Science, Yokohama City University, Yokohama, Japan

^fDepartment of Health Care Administration and Management, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan

^✉

CONTACT Chieko Ishiguro ishiguro.ch@jihs.go.jp Laboratory of Clinical Epidemiology, Department of Data Science, Center for Clinical Sciences, Japan Institute for Health Security, 1-21-1 Toyama, Shinjuku-Ku, Tokyo 162-8655, Japan.

Co-first author.

Roles

Chieko Ishiguro: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Visualization, Writing – original draft, Writing – review & editing

Hiroya Morita: Data curation, Formal analysis, Methodology, Writing – original draft

Wataru Mimura: Methodology, Resources, Software, Supervision, Writing – review & editing

Shinya Tsuzuki: Investigation, Methodology, Supervision, Writing – review & editing

Junko Hirashima-Terada: Methodology, Supervision, Writing – review & editing

Yoshinori Takeuchi: Conceptualization, Methodology, Supervision, Writing – review & editing

Futoshi Oda: Data curation, Investigation, Resources, Writing – review & editing

Megumi Maeda: Data curation, Investigation, Resources, Writing – review & editing

Haruhisa Fukuda: Conceptualization, Funding acquisition, Project administration, Supervision, Writing – review & editing

PMCID: PMC13349031 PMID: 42415471

ABSTRACT

Safety data on human papillomavirus (HPV) vaccination in the Japanese population remain limited owing to the lack of healthcare databases available for vaccine safety assessment. In this study, we assessed the risk of adverse events of special interest (AESIs) following HPV vaccination among females aged 12–26 y using medical claims data linked to routine or catch-up vaccination records provided by municipalities. We conducted a population-based cohort study and self-controlled case series (SCCS) from April 2015 to March 2023 for bivalent/quadrivalent vaccines and from April 2023 to March 2024 for the 9-valent vaccine. All females eligible for the vaccination program were included in the cohort study, whereas only those who experienced AESIs were included in the SCCS. The observation period was classified according to vaccination status as unvaccinated and post-vaccination risk periods following the first, second, and third doses. Adjusted rate ratios with 95% confidence intervals were estimated for the cohort and SCCS. In the bivalent/quadrivalent vaccines analysis cohort (25131 females), 1763, 1492, and 975 received the first, second, and third doses, respectively. In the 9-valent vaccine analysis cohort (38970 females), 3931, 2389, and 934 received the first, second, and third doses, respectively. Rates of 54 AESIs were calculated during unvaccinated periods. AESIs with feasible quantitative analyses for either the bivalent/quadrivalent or 9-valent vaccines included migraine, hypotension, asthma, polycystic ovary syndrome, postural orthostatic tachycardia syndrome, hypothyroidism, hyperthyroidism, and epilepsy. No statistically significant increased risk following HPV vaccination was observed for these AESIs. Larger datasets are needed to assess rarer AESIs.

KEYWORDS: HPV, vaccines, safety, routinely collected health data, post-authorization phase, pharmacovigilance, pharmacoepidemiology, adverse events of special interest, medical claims data, vaccination records

Introduction

Human papillomavirus (HPV) vaccines prevent HPV infection, a causative factor of cervical and other cancers.¹ In Japan, a national routine immunization program for bivalent and quadrivalent HPV vaccines for girls aged 12–16 y was introduced in 2013. However, safety concerns arising from case reports of adverse events, amplified by intensive media coverage, led the Ministry of Health, Labor and Welfare (MHLW) to suspend proactive recommendations just 2 months after program launch.² This decision caused HPV vaccine coverage to fall to among the lowest levels worldwide by 2020.³ In 2022, the MHLW resumed its active recommendation based on accumulated evidence on the effectiveness and safety of HPV vaccines, supported by extensive international data.⁴ Routine immunization with the 9-valent (nonavalent) vaccine was subsequently introduced in 2023. Nevertheless, as of the end of September 2024, first-dose HPV vaccination coverage among females born in fiscal year (FY, April – March) 2007—who completed their four-year routine vaccination eligibility period in FY 2023—remained at 45.2%.^5,6 The World Health Organization (WHO) has established a global public health target of achieving HPV vaccination coverage in 90% of girls 15 y of age.⁷ To support this goal, building robust domestic evidence is essential for transparent and trustworthy risk communication with the public in Japan.

Vaccination programs are essential for reducing the burden of infectious diseases. However, their success depends on both vaccine effectiveness and the availability of reliable data to support ongoing safety assessments in the post-approval phase. In the last decade, secondary use of healthcare data has played an increasingly important role in vaccine pharmacovigilance. These databases complement the limitations of spontaneous reporting systems^8–11 by enabling quantitative analyses comparing vaccinated and unvaccinated populations or risk and non-risk periods to monitor and assess safety signals. To monitor the safety of HPV vaccines, several nationwide studies using large-scale healthcare databases have been conducted across multiple countries.^12–16 In contrast, Japan has faced persistent challenges in evaluating vaccine safety owing to the lack of healthcare databases available for vaccine safety monitoring.¹⁷

Recently, efforts have been made in Japan to address the lack of databases enabling quantitative evaluation of vaccine safety. In 2021, the Vaccine Effectiveness, Networking, and Universal Safety (VENUS) study was launched as a pilot system for vaccine safety monitoring. This initiative uses a resident-based database linking existing health insurance claims data with the immunization registry from 13 municipalities.¹⁸ This study aimed to address the gap in domestic evidence and contribute to HPV vaccine safety signal monitoring by utilizing data from the VENUS study. Specifically, we calculated the incidence rates of adverse events of special interest (AESIs) during unvaccinated periods and following HPV vaccination. We also conducted comparative evaluations using a cohort design and a self-controlled case series (SCCS) design to assess AESIs following administration of the bivalent, quadrivalent, and 9-valent HPV vaccines. In addition, our study aimed to provide valuable insights to strengthen the development of a future vaccine monitoring system based on the secondary use of healthcare data in Japan.

Materials and methods

Data sources

We used data from the VENUS study.^18,19 The VENUS study is based on a municipality-level database comprising several linked data sources. First, the National Health Insurance System covers residents aged ≤74 y who are self-employed, part-time employed, retired, or engaged in agriculture, forestry, or fishery industries, as well as their dependent family members. The National Health Insurance claims data include information on outpatient visits and hospital admissions, including age, sex, diagnoses, medical examinations, and treatments. Diagnoses were coded according to the International Classification of Diseases, 10th Revision (ICD-10) codes, and medications were established using the national code for claims data. Second, the vaccination registry contained records of vaccine types and administered dates for residents who received routine or catch-up immunizations. Third, the basic residential registry included residential history data. These data sets were linked at the individual level using anonymous residential identifiers. VENUS data have contributed to vaccine pharmacovigilance in the signal evaluation phase by assessing detected signals through spontaneous reporting systems.^20,21

All data were anonymized, and relevant ordinances and regulations for the protection of personal information were followed. This study was approved by the Kyushu University Institutional Review Board for Clinical Research (approval No. 2021–399). In accordance with Japanese ethical guidelines, the requirement for informed consent was waived, as this was a secondary analysis of routinely collected, anonymized data from the municipalities.

National routine/catch-up vaccination programme for HPV in Japan

The national routine HPV vaccination program targets females in grades 6–10 (approximately 12–16 y old) under the Immunization Act in Japan.²² However, because the government’s proactive recommendations were suspended from June 2013 to March 2022, a time-limited catch-up program was introduced in April 2022 under the Enforcement Order of the Immunization Act.²³ This program targets females born between FY 1997 and FY 2007. Before March 2023, Cervarix® (bivalent) and Gardasil® (quadrivalent) vaccines were used, each administered in a three-dose schedule. In April 2023, Silgard 9® (9-valent vaccine) was introduced into the national immunization program, with age-specific dosing schedules consisting of two doses (at 0 and 6 months) for those initiating vaccination before 15 y of age and three doses (at 0, 2, and 6 months) for those initiating at ≥15 y of age. Although quadrivalent and 9-valent vaccines have been approved for boys, Japan’s national routine vaccination program does not cover boys’ vaccinations as of December 2025.

Target population & study design

We adopted a population-based cohort design, which is a pharmacoepidemiological method commonly used in vaccine safety evaluations.^20,24,25 Analyses were performed separately for the bivalent/quadrivalent vaccines (FY2015–FY2022) and the 9-valent vaccine (FY2023). Although the cohort design allowed estimation of population-level risks, it may be susceptible to residual confounding. Therefore, we additionally conducted an SCCS analysis, which inherently controls for time-invariant confounders, to assess the robustness of the findings.^26,27

For the bivalent/quadrivalent vaccines, data from five municipalities (one in the Kanto region, one in the Kansai region, and three in the Kyushu region) were available. Of these, two municipalities had populations exceeding 200,000. The target population comprised females eligible for the routine immunization program (FY2015–FY2022) and catch-up immunization program (FY2022) who were residents of participating municipalities and enrolled in the National Health Insurance system during the study period. For each individual, the cohort entry date (CED) was defined as the latest of the following: 1 April of the fiscal year in which the participant reached the eligible age or 1 y after the first recorded medical claim. Eligible age for the routine and catch-up vaccination programs was defined as 12 y (FY2015–FY2022) and 16 y (FY2022), respectively. Accordingly, participants were required to have at least 1 y of continuous health insurance coverage before the CED to ensure adequate baseline assessment. Females whose enrollment history ended before the CED were excluded. To capture only the first occurrence of each outcome, individuals who had already experienced the outcome (defined below) during the year before the CED were excluded. Follow-up continued until the earliest of the following: the date of the last insurance claim or 31 March 2023.

For the 9-valent vaccines, data from eight municipalities (one in the Kanto region, one in the Tokai region, one in the Kansai region, one in the Chugoku region, and four in the Kyushu region) were available. Of these, two municipalities had populations exceeding 200,000. The target population comprised females eligible for the routine immunization program (FY2023) and catch-up immunization program (FY2023), who were residents of participating municipalities and enrolled in the National Health Insurance system during the study period. For each individual, the CED was defined as the latest of the following: 1 April 2023, or 1 y after the first recorded claim. Accordingly, participants were required to have at least 1 y of continuous insurance coverage before the CED to ensure adequate baseline assessment. Females whose enrollment history ended before the CED and those with a recorded outcome during the year before the CED were also excluded. Follow-up continued until the earliest of the following: the date of the last insurance claim or 31 March 2024.

Supplementary Figure 1 shows the study design diagram based on the HARPER guideline for the harmonized protocol template to enhance reproducibility.²⁸ Cohort and SCCS analyses were conducted for each target population. The SCCS analysis was restricted to individuals who experienced each outcome.

Exposure

Based on the vaccination history of the bivalent/quadrivalent or 9-valent HPV vaccine, the observation period from the CED to the end of follow-up for the study population was categorized into four periods as follows: the unvaccinated period, risk period after the first dose (risk period 1), risk period after the second dose (risk period 2), and risk period after the third dose (risk period 3). Each risk period lasted up to 180 d after vaccination, but ended earlier if a subsequent dose was administered. In this study, the period before the first dose during the observation period was defined as the control period. In this SCCS analysis, the control period comprised all observation time excluding the risk and pre-exposure periods. Pre-exposure periods were defined as 14 d preceding each vaccination to minimize potential bias from healthcare-seeking behaviors before vaccination. As vaccination status was treated as a time-varying exposure, the same individual could contribute person-time to multiple risk and control periods during the observation period (Supplementary Figure 2).

Outcome

Based on previously conducted nationwide studies in various countries,^12–16 66 diseases were identified as AESIs of HPV vaccines in this study: Pemphigus vulgaris, Pemphigus foliaceus, Vitiligo, Pemphigoid, Psoriasis, Erythema nodosum, Localized scleroderma, Addison’s disease, Type 1 diabetes, Hypothyroidism, Hashimoto’s disease, Graves’ disease, Hyperthyroidism, Guillain – Barré syndrome, Transverse myelitis, Other demyelinating diseases, Other encephalitis, myelitis, encephalomyelitis, Optic neuritis, Epilepsy, Acute disseminated encephalomyelitis, Myositis, Localized lupus erythematosus, Myasthenia gravis, Multiple sclerosis, Narcolepsy, Bell’s palsy, Paralysis, Neuralgia and neuritis, Extrapyramidal symptoms and movement disorders, Intracerebral hemorrhage, Migraine, Crohn’s disease, Ulcerative colitis, Peptic ulcer, Celiac disease, Primary biliary cholangitis, Pancreatitis, Hypotension, Raynaud’s disease, Venous thromboembolism, Acute rheumatic fever, Polyarteritis nodosa, Henoch – Schönlein purpura, Idiopathic thrombocytopenic purpura, Pernicious anemia, Autoimmune hemolytic anemia, Vasculitis, Rheumatoid arthritis, Ankylosing spondylitis, Juvenile arthritis, Behçet’s syndrome, Systemic lupus erythematosus, Wegener’s granulomatosis, Sarcoidosis, Sjögren’s syndrome, Polymyositis/Dermatomyositis, Reiter’s disease, Systemic sclerosis (Scleroderma), Tuberculosis, Kawasaki disease, Asthma, Autism, Polycystic ovary syndrome, Postural orthostatic tachycardia syndrome (POTS), Chronic fatigue syndrome, Complex Regional Pain Syndrome. Among these, Guillain – Barré syndrome, acute disseminated encephalomyelitis, and idiopathic thrombocytopenic purpura are recognized as serious adverse reactions on Japanese product labels. Claim-based algorithms for these AESIs were adapted from previous studies (Supplementary Table 1) and confirmed by two physicians, coauthors of this study. For each outcome-specific analysis, the first onset of the respective AESI during follow-up was defined as the incident outcome event, with subsequent occurrences of the same AESI not considered.

Statistical analysis

Descriptive analysis of baseline characteristics, including birth year, cohort entry year, municipality, medical care visits, insurance type, and pediatric comorbidity index²⁹ (code was developed based on the referenced methodology; Supplementary Table 2), in each vaccination status group was expressed as person-years and proportions in the bivalent/quadrivalent and 9-valent vaccine analysis cohorts. Incidence rates of AESIs were calculated for each vaccination status in both cohorts.

In the quantitative analysis of the cohort analysis, incidence rate ratios (IRRs) and 95% confidence intervals (CIs) were estimated for each outcome during the periods of assessment following the first, second, and third doses compared with the control periods. Poisson regression with time-dependent exposure was used, adjusting for baseline covariates (municipality, insurance type, pediatric comorbidity index, and number of medical care visits in the year prior to CED) and time-dependent covariates (age and calendar year). Generalized estimating equations were employed, and 95% CIs for the IRRs were calculated using robust variance estimators to account for the clustering by the contribution of the same individual across multiple observation periods. In the SCCS analysis, within-individual IRRs and 95% CIs were estimated using conditional Poisson regression models, with age and calendar year treated as time-varying covariates. Quantitative analysis was not conducted for AESIs with fewer than three cases in each risk period because of instability resulting from sparse data. As sensitivity analyses, we additionally conducted analyses using 30- and 90-d risk periods. All statistical analyses were performed using R version 4.4.2 (R Foundation for Statistical Computing, Vienna, Austria).

Results

Cohort identification and baseline characteristics

In the analysis for the bivalent/quadrivalent vaccines, a total of 25,131 females (55,558 person-years) were identified as eligible for inclusion in the study population (Figure 1). Among them, 1763, 1492, and 975 received the first, second, and third doses of the vaccine, respectively. Table 1 presents the distribution of the baseline characteristics at the CED and person-years of follow-up by exposure period. Vaccinated individuals were more likely to have been born in or after 2003 and have more recent CEDs.

An infographic flowchart showing the identification of study populations for the 2- and 4-valent HPV vaccine analysis and the 9-valent HPV vaccine analysis using the VENUS database. The infographic flowchart includes two sections describing the study population selection process. The first section shows the selection of the study population for safety monitoring of the 2- and 4-valent HPV vaccines using the VENUS database from April 2015 to March 2023. The database includes 51,218 females born between 1997 and 2010. The cohort entry date is defined as the latest of the following three dates: one year after enrollment in National Health Insurance, April 1 of the fiscal year in which the individual turned 12 or 16 years old, or the municipality’s data start date. Individuals whose enrollment ended before cohort entry or who had a prior medical history for each outcome were excluded, resulting in 25,131 females (56,298 person-years) for the cohort study analysis. The second section shows the selection of the study population for safety monitoring of the 9-valent HPV vaccine using the VENUS database from April 2023 to March 2024. The database includes 53,201 females born between 1997 and 2011. The cohort entry date and exclusion criteria are defined similarly, resulting in 38,970 females (33,258 person-years) for the cohort study analysis. Both sections also include self-controlled case series analyses involving females who experienced an outcome after the cohort entry date. — Flowchart of participant selection for the study cohort.

Table 1.

Characteristics of the study population in the cohort study of bivalent and quadrivalent HPV vaccines^a.

	Unvaccinated period	Risk period 1	Risk period 2	Risk period 3
Total follow-up time, person-years	54,398 (100)	359 (100)	472 (100)	328 (100)
Birth fiscal year
1997–1998	1734 (3.2)	13 (3.7)	14 (3.0)	3 (1.0)
1999–2000	4299 (7.9)	22 (6.0)	22 (4.6)	4 (1.1)
2001–2002	10,865 (20.0)	32 (9.0)	33 (7.1)	11 (3.2)
2003–2004	13,492 (24.8)	55 (15.2)	83 (17.5)	73 (22.1)
2005–2006	11,113 (20.4)	103 (28.8)	157 (33.2)	145 (44.2)
2007–2008	9593 (17.6)	87 (24.2)	107 (22.7)	66 (20.3)
2009–2010	3301 (6.1)	47 (13.2)	56 (11.8)	27 (8.1)
Cohort entry fiscal year
2015	785 (1.4)	1 (0.3)	1 (0.3)	1 (0.2)
2016	12,317 (22.6)	21 (5.7)	22 (4.7)	10 (3.1)
2017	6454 (11.9)	23 (6.4)	35 (7.5)	37 (11.4)
2018	15,107 (27.8)	100 (27.9)	153 (32.4)	139 (42.4)
2019	4869 (9.0)	41 (11.4)	59 (12.4)	42 (12.7)
2020	5802 (10.7)	54 (15.0)	66 (14.0)	45 (13.6)
2021	3586 (6.6)	48 (13.5)	58 (12.4)	36 (10.9)
2022	5479 (10.1)	71 (19.7)	77 (16.2)	19 (5.7)
Municipality
A	2873 (5.3)	9 (2.6)	12 (2.5)	6 (2.0)
B	2255 (4.1)	13 (3.5)	14 (3.1)	7 (2.2)
C	25,685 (47.2)	216 (60.1)	304 (64.4)	238 (72.6)
D	17,762 (32.7)	77 (21.4)	96 (20.3)	58 (17.7)
E	5824 (10.7)	44 (12.3)	46 (9.8)	18 (5.5)
Medical care visit^b
0–4	15,126 (27.8)	86 (24.1)	108 (22.9)	65 (19.9)
5–12	23,222 (42.7)	159 (44.3)	199 (42.1)	148 (45.1)
≥13	16,049 (29.5)	114 (31.7)	165 (35.0)	115 (35.0)
Insurance type
Social insurance	4090 (7.5)	40 (11.3)	43 (9.0)	19 (5.7)
Single parent^c	634 (1.2)	7 (2.0)	6 (1.3)	3 (0.8)
Disabilities ≥ 18^d	76 (0.1)	1 (0.3)	1 (0.2)	1 (0.2)
Disabilities < 18^e	3 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)
National health insurance	43,231 (79.5)	278 (77.3)	381 (80.7)	268 (81.7)
Public assistance	6365 (11.7)	33 (9.1)	42 (8.8)	38 (11.6)
Pediatric comorbidity index
0	30,554 (56.2)	204 (56.7)	255 (54.0)	181 (55.2)
1	12,313 (22.6)	86 (23.8)	119 (25.2)	84 (25.7)
≥2	11,530 (21.2)	70 (19.4)	98 (20.8)	63 (19.2)

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^aThese data represent person-time (%). Individuals may contribute person-time to multiple periods.

^bNumber of medical care visits during the 1 y prior to the cohort entry date.

^cSingle parent:Medical subsidy for single-parent families.

^dDisabilities ≥ 18: Assistance with medical expenses for people with severe physical/intellectual disabilities.

^eDisabilities < 18: Medical services and financial supports for persons under 18 y with disabilities.

In the analysis of the 9-valent vaccine, a total of 38,970 females (33,043 person-years of follow-up) were identified as eligible for inclusion in the study population (Figure 1). Among them, 3931, 2389, and 934 received the first, second, and third vaccine doses, respectively. Table 2 shows the distribution of baseline characteristics at the CED and person-years of follow-up, stratified by exposure period.

Table 2.

Characteristics of the study population in the cohort study of 9-valent HPV vaccines^a.

	Unvaccinated period	Risk period 1	Risk period 2	Risk period 3
Total follow-up time, person-years	31,007 (100)	1200 (100)	649 (100)	186 (100)
Birth fiscal year
1997–1998	2384 (7.7)	15 (1.2)	16 (2.5)	8 (4.5)
1999–2000	1959 (6.3)	21 (1.7)	23 (3.5)	10 (5.5)
2001–2002	2190 (7.1)	44 (3.7)	42 (6.5)	17 (9.2)
2003–2004	2232 (7.2)	37 (3.1)	36 (5.6)	14 (7.7)
2005–2006	3524 (11.4)	51 (4.2)	45 (6.9)	15 (7.8)
2007–2008	6223 (20.1)	419 (34.9)	305 (46.9)	96 (51.7)
2009–2010	7979 (25.7)	449 (37.4)	139 (21.4)	20 (10.7)
2011	4517 (14.6)	165 (13.7)	44 (6.7)	5 (2.7)
Cohort entry fiscal year
2023	31,007 (100.0)	1200 (100.0)	649 (100.0)	186 (100.0)
Municipality
A	1149 (3.7)	28 (2.3)	10 (1.6)	1 (0.6)
B	1054 (3.4)	22 (1.8)	6 (1.0)	1 (0.7)
C	7440 (24.0)	135 (11.2)	95 (14.7)	31 (16.7)
E	2397 (7.7)	125 (10.4)	37 (5.7)	5 (2.7)
F	2002 (6.5)	42 (3.5)	23 (3.5)	7 (3.8)
G	148 (0.5)	1 (0.1)	0 (0.0)	0 (0)
H	2872 (9.3)	84 (7.0)	60 (9.2)	23 (12.6)
I	13,944 (45.0)	766 (63.8)	418 (64.3)	117 (62.8)
Medical care visit^b
0–4	13,896 (44.8)	592 (49.3)	354 (54.5)	103 (55.2)
5–12	10,063 (32.5)	351 (29.3)	163 (25.1)	47 (25.2)
≥13	7049 (22.7)	257 (21.4)	133 (20.4)	36 (19.5)
Insurance type
Social insurance	12,281 (39.6)	774 (64.5)	370 (57.0)	91 (49.1)
Single parent^c	1207 (3.9)	42 (3.5)	24 (3.7)	6 (3.1)
Disabilities ≥ 18^d	253 (0.8)	3 (0.2)	3 (0.5)	1 (0.4)
Disabilities < 18^e	0 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)
National health insurance	15,437 (49.8)	356 (29.6)	238 (36.7)	84 (45.1)
Public assistance	1829 (5.9)	26 (2.2)	13 (2.1)	4 (2.2)
Pediatric comorbidity index
0	20,713 (66.8)	883 (73.5)	479 (73.8)	140 (75.2)
1	3987 (12.9)	146 (12.2)	67 (10.4)	14 (7.5)
≥2	6307 (20.3)	171 (14.3)	102 (15.8)	32 (17.3)

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^aThese data represent person-year (%). Individuals may contribute person-time to multiple periods.

^bNumber of medical care visits during the 1 y prior to the cohort entry date

^cSingle parent: Medical subsidy for single-parent families

^dDisabilities ≥ 18: Assistance with medical expenses for people with severe physical/intellectual disabilities

^eDisabilities < 18: Medical services and financial supports for persons under 18 y with disabilities.

Descriptive analysis for incidence rates

In the analysis of the bivalent/quadrivalent vaccine, the study population size varied across AESI-specific analyses because individuals with a history of the relevant AESI before the CED were excluded. Of the 66 AESIs, 13 had no cases during follow-up. Supplementary Table 3 presents incidence rates for the remaining 53 AESIs, each with at least one case observed during any period. Among these, 29 AESIs occurred only during the control period; 19 AESIs occurred in control and risk periods, but had fewer than three cases in any risk period; and five AESIs (migraine, hypotension, asthma, polycystic ovary syndrome, and POTS) occurred in control and risk periods, with at least one risk period including three or more cases. Among the 53 AESIs observed during the control period, asthma had the highest incidence rate (327.07 per 10,000 person-years). The incidence rates of Guillain – Barré syndrome and acute disseminated encephalomyelitis were 0.18 per 10,000 person-years, whereas that of idiopathic thrombocytopenic purpura was 0.92 per 10,000 person-years. Similar trends were observed in the sensitivity analyses using either 30- or 90-d risk periods (Supplementary Tables 5 and 6).

In the analysis of the 9-valent vaccine, 20 of the 66 AESIs had no observed cases during follow-up. Supplementary Table 4 presents the incidence rates of the remaining 46 AESIs, each with at least one observed case during any period. Among these, 28 AESIs occurred exclusively during the control period; 10 AESIs occurred in control and risk periods but had fewer than three cases in any risk period; and eight AESIs (hypothyroidism, hyperthyroidism, epilepsy, migraine, hypotension, asthma, polycystic ovary syndrome, and POTS) occurred in control and risk periods, with at least one risk period including three or more cases. Similar trends were observed in the sensitivity analyses using either 30- or 90-d risk periods (Supplementary Tables 5 and 6).

Quantitative analysis for safety

In the analysis of the bivalent/quadrivalent vaccine, quantitative analysis was conducted for the five AESIs in the cohort and SCCS analyses: migraine (risk period 1–3), hypotension (risk period 1–3), asthma (risk period 1–3), polycystic ovaries (risk period 1), and POTS (risk period 2–3). Table 3 presents the adjusted IRRs and corresponding 95% CIs derived from the cohort and SCCS analyses. No significant increases in risk were observed based on the adjusted IRRs in either the cohort or SCCS analyses.

Table 3.

Incidence rate ratios in the cohort study and self-controlled case series.

		Bivalent or quadrivalent		9-valent
		Cohort study	SCCS	Cohort study	SCCS
AESI	Risk period	Adjusted IRR (95% CI)	Adjusted IRR (95% CI)	Adjusted IRR (95% CI)	Adjusted IRR (95% CI)
Hypothyroidism	1	–	–	1.61 (0.57, 3.61)	0.84 (0.19, 3.71)
	2	–	–	–	–
	3	–	–	–	–
Hyperthyroidism	1	–	–	1.91 (0.46, 5.26)	3.58 (0.27, 46.9)
	2	–	–	–	–
	3	–	–	–	–
Epilepsy	1	–	–	1.03 (0.25, 2.75)	0.74 (0.09, 6.03)
	2	–	–	–	–
	3	–	–	–	–
Migraine	1	0.55 (0.14, 1.42)	0.66 (0.20, 2.18)	1.18 (0.69, 1.89)	1.68 (0.76, 3.72)
	2	1.37 (0.68, 2.43)	1.53 (0.73, 3.18)	1.38 (0.75, 2.30)	2.18 (0.81, 5.90)
	3	0.59 (0.15, 1.53)	0.67 (0.20, 2.28)	1.16 (0.36, 2.73)	3.34 (0.70, 16.0)
Hypotension	1	1.09 (0.34, 2.54)	1.20 (0.42, 3.46)	1.55 (0.93, 2.43)	1.18 (0.52, 2.70)
	2	1.20 (0.47, 2.45)	0.76 (0.27, 2.18)	1.28 (0.63, 2.28)	1.75 (0.56, 5.49)
	3	1.67 (0.66, 3.43)	1.56 (0.62, 3.95)	1.18 (0.29, 3.13)	2.91 (0.45, 18.9)
Asthma	1	1.08 (0.49, 2.02)	1.59 (0.73, 3.47)	1.01 (0.70, 1.41)	1.14 (0.66, 1.96)
	2	1.16 (0.60, 2.00)	1.38 (0.68, 2.79)	1.11 (0.71, 1.64)	1.30 (0.67, 2.51)
	3	1.11 (0.48, 2.16)	1.30 (0.55, 3.06)	1.20 (0.57, 2.17)	2.29 (0.84, 6.23)
Polycystic ovary syndrome	1	3.73 (0.91, 10.0)	2.15 (0.48, 9.65)	2.22 (0.67, 5.36)	1.19 (0.21, 6.75)
	2	–	–	–	–
	3	–	–	–	–
Postural orthostatic tachycardia syndrome	1	–	–	1.87 (0.79, 3.76)	1.61 (0.33, 7.91)
	2	1.60 (0.49, 3.78)	1.31 (0.36, 4.70)	–	–
	3	2.27 (0.70, 5.40)	1.71 (0.53, 5.54)	–	–

Open in a new tab

AESI, adverse events of special interest; IRR, incidence rate ratio; SCCS, self-controlled case series; CI, confidence interval.

In the analysis of the 9-valent vaccine, quantitative analyses were conducted for eight AESIs in the cohort and SCCS analyses: hypothyroidism (risk period 1), hyperthyroidism (risk period 1), epilepsy (risk period 1), migraine (risk period 1–3), hypotension (risk period 1–3), asthma (risk period 1–3), polycystic ovaries (risk period 1), and POTS (risk period 1). Table 3 presents the adjusted IRRs and corresponding 95% CIs derived from the cohort and SCCS analyses. No significant increases in risk were observed based on the adjusted IRRs in either the cohort or SCCS analyses.

Discussion

This study evaluated various AESIs following HPV vaccination using an individually linkable healthcare database of females eligible for national routine or catch-up vaccination programs in five and eight municipalities in Japan between April 2015 and March 2024. Among the 66 AESIs examined, incidence rates during the control period were estimable for 54 AESIs in either the bivalent/quadrivalent or 9-valent vaccine cohorts. However, only five AESIs in the bivalent/quadrivalent cohort and eight in the 9-valent cohort had sufficient case numbers for quantitative analysis. In both cohorts, adjusted IRRs derived from the cohort and SCCS analyses showed no statistically significant association between HPV vaccination and these outcomes.

Previous large-scale database studies conducted in several countries have reported no association between HPV vaccination and a range of autoimmune, neurological, or other serious health events.^12–16 Although only a limited number of events could be quantitatively assessed in our study, we observed no increased risk of hypothyroidism, hyperthyroidism, epilepsy, migraine, hypotension, asthma, polycystic ovaries, or POTS, which is consistent with previous findings. However, in the cohort analysis of bivalent/quadrivalent vaccines, the point estimates for polycystic ovary syndrome following the first dose exceeded 3, although the 95% CI crossed 1. This evaluation may reflect detection bias: some individuals may have received HPV vaccination in gynecology clinics and undergone concurrent consultations, increasing the likelihood of diagnosing preexisting conditions after vaccination compared with the unvaccinated period. The dataset used in this study did not allow identification of the clinical department where the vaccination was administered, precluding further assessment of this potential bias. In the additional analysis with SCCS of the 9-valent vaccine, the point estimate for hyperthyroidism after the first dose, migraine after the third dose, and hypotension after the third dose exceeded 3 or were approximately 3. However, the CI was extremely wide, and the analysis was unstable because there were only three or four cases.

Official documents, including HPV vaccine product labels issued by health and regulatory authorities such as the WHO,^30,31 the U.S. Food and Drug Administration,^32,33 and the European Medicines Agency,^34,35 warn of known adverse reactions, including injection-site reactions (e.g., pain, swelling, and erythema), systemic symptoms (e.g., headache, fatigue, and fever), syncope, and hypersensitivity. Concurrently, various other adverse events of unknown causality, which were the focus of this study, are also described and undergo continuous monitoring. Although this study could quantitatively analyze a limited subset of AESIs, the findings represent a meaningful contribution to global safety monitoring. Among the target outcomes, Guillain – Barré syndrome, acute disseminated encephalomyelitis, and idiopathic thrombocytopenic purpura are listed as serious adverse reactions on Japanese product labels, for which a causal relationship with HPV vaccination has already been recognized. However, we could not quantitatively estimate the increased risk of these events because they are extremely rare, and the number of cases that occurred during the post-vaccination risk period was zero or one in this study.

The incidence rates observed during the control period were consistent with previously reported background incidence rates for females of similar age groups in Japan and other countries.^36–38 Although quantitative comparisons between the exposed and unexposed periods were not feasible for many outcomes, background rates were presented for 54 AESIs. These estimates provide important information for future vaccine safety surveillance. For example, in observed-to-expected (O/E) analyses using the spontaneous reporting rate as the observed rate, background rates can be used to calculate the expected rate.^39,40 O/E analyses are an important tool in vaccine pharmacovigilance, particularly during mass vaccination programs.⁴¹ Furthermore, because background incidence rates for globally used vaccines are increasingly shared through international networks such as ACCESS and the Global Vaccine Data Network to facilitate international comparisons through O/E analyses, the baseline epidemiological data generated in this study may contribute substantially to these global surveillance efforts.^36,37

Notably, quantitative evaluation of multiple AESIs using a fixed study design, as conducted in this study, serves primarily to detect or strengthen safety signals and does not constitute formal signal verification. Vaccine safety surveillance typically involves two stages: signal detection and signal verification. The present study corresponds to the former. Signal detection can be conducted in a hypothesis-free, comprehensive manner without specifying AESIs in advance; however, it can also be performed, as in our study, by predefining multiple AESIs and applying a fixed design. Any signals identified using this approach require further verification in future studies. In particular, residual confounding by unmeasured factors (e.g. time-dependent underlying health status) cannot be fully excluded, even with the SCCS methodology. When databases are used for the confirmatory evaluation of causality, it is recommended to apply designs and statistical methods tailored to each AESI within the framework of target trial emulation.⁴²

The historical decline in HPV vaccine coverage in Japan, driven by media-amplified safety concerns, highlights the critical need to build robust domestic evidence to support transparent and trustworthy risk communication. Although our findings are important for addressing public anxiety, scientific evidence alone is insufficient to improve vaccine uptake without effective dissemination strategies. Recent global studies have emphasized that social media discourse and public sentiment substantially influence vaccination behavior; for example, exposure to positive testimonials and credible health messages on digital platforms is associated with lower vaccine refusal rates.^43–45 Integrating the domestic safety evidence provided by the VENUS study with such digital communication strategies may be essential for overcoming real-world vaccine hesitancy and achieving national immunization goals in Japan.

A strength of this study is that the outcomes were ascertained using claims data, thereby minimizing recall bias. The evaluated outcomes were events in which patients sought medical care and a claim was submitted for a confirmed diagnosis (suspected diagnoses were excluded), providing information independent of vaccination status.

This study has some limitations. First, the generalizability of the findings may be limited because the cohort consisted primarily of National Health Insurance enrollees from selected municipalities in western Japan, which may not be representative of all young females in Japan. Second, although we examined a wide range of AESIs, the statistical power for rare events was limited. Therefore, the lack of statistical significance for rare events should not be interpreted as definitive evidence for the absence of risk. Third, although we analyzed the 9-valent vaccine separately, we could not analyze the bivalent and quadrivalent vaccines separately because some municipalities did not record these vaccine types. The first, second, and third limitations may be addressed in the near future because a nationwide vaccination registry covering the entire population is currently being developed as a national project, which will enable linked analyses with the national claims database once completed.^46,47 Fourth, the validity of the claims-based algorithms used to identify each AESI in this study is unknown. Ideally, a validation study for each claim-based algorithm should be conducted beforehand.⁴⁸ In Japan, the number of validation studies is very limited.⁴⁹ The Claims data Learning & Enhancing for Algorithm Refinement (CLEAR) study, which is a database platform to enable the systematic, low-cost implementation of validation studies in Japan, will contribute to the development of a library of validated claims-based algorithms for AESIs in vaccine monitoring.⁵⁰

In conclusion, the quantitative analyses comparing vaccinated and unvaccinated groups did not observe a statistically significant increase in the risk of hypothyroidism, hyperthyroidism, epilepsy, migraine, hypotension, asthma, polycystic ovary syndrome, or POTS. For other AESIs, the case numbers were insufficient for quantitative assessment; however, the incidence rates during the unvaccinated periods provide valuable background data to inform future vaccine pharmacovigilance efforts. The key limitations identified here offer valuable insights to inform the development of an advanced vaccine safety monitoring system with healthcare databases in Japan.

Supplementary Material

Supplemental File_Rivsed_20260524_resubmit.docx

KHVI_A_2688606_SM2554.docx^{(463KB, docx)}

Acknowledgments

CI drafted the study protocol and wrote the manuscript. HM analyzed the data and wrote the manuscript. FO, MM and HF acquired and cleaned the VENUS data. YT provided statistical expertise. JH-T and TS provided clinical inputs on the outcome definitions and interpretation of the results. All the authors critically reviewed and revised the study protocol, interpreted the results, revised the manuscript for important intellectual content, and approved the final version for submission.

Biographies

Chieko Ishiguro, MPH, PhD, is the head of the Clinical Epidemiology Laboratory of the Japan Institute for Health Security. Her expertise lies in vaccine and drug pharmacoepidemiology and regulatory science, with a particular focus on enhancing pharmacovigilance and drug development using real-world data, such as claims data and medical records.

Hiroya Morita, Master of Medical Science. He is a research student at the Japan Institute for Health Security. His research interests include methodological aspects of pharmacoepidemiology, causal inference, and practical implementation of analyses in real-world pharmacoepidemiologic studies.

Funding Statement

This study was supported by the Japan Agency for Medical Research and Development [grant numbers JP24fk0108709 and JP24mk0121297] and the Japan Science and Technology Agency FOREST Program [grant number JPMJFR205J]. The funders played no role in the design, data collection, data analysis, interpretation, or writing of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

The authors used ChatGPT (version 5.5) to correct grammatical errors and improve the readability of the manuscript. After using this tool, the authors carefully reviewed and edited the content to ensure accuracy and coherence, taking full responsibility for the final version of the publication.

Data availability

Data cannot be shared for privacy or ethical reasons.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/21645515.2026.2688606.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental File_Rivsed_20260524_resubmit.docx

KHVI_A_2688606_SM2554.docx^{(463KB, docx)}

Data Availability Statement

Data cannot be shared for privacy or ethical reasons.

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PERMALINK

Safety monitoring of bivalent, quadrivalent, and 9-valent human papillomavirus vaccination in Japan: The vaccine effectiveness, networking, and universal safety (VENUS) study

Chieko Ishiguro

Hiroya Morita

Wataru Mimura

Shinya Tsuzuki

Junko Hirashima-Terada

Yoshinori Takeuchi

Futoshi Oda

Megumi Maeda

Haruhisa Fukuda

Roles

ABSTRACT

Introduction

Materials and methods

Data sources

National routine/catch-up vaccination programme for HPV in Japan

Target population & study design

Exposure

Outcome

Statistical analysis

Results

Cohort identification and baseline characteristics

Figure 1.

Table 1.

Table 2.

Descriptive analysis for incidence rates

Quantitative analysis for safety

Table 3.

Discussion

Supplementary Material

Acknowledgments

Biographies

Funding Statement

Disclosure statement

Data availability

Supplementary material

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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