Association Between Vaccination for Human Papillomavirus (HPV) and Autonomic Dysfunction and Menstrual Irregularities: A Self-Controlled Case Series Analysis.

IF 1.9 Q3 PHARMACOLOGY & PHARMACY
Linda Wastila, Yu-Hua Fu, Chih Chun Tung, Danya M Qato
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引用次数: 0

Abstract

Background: Limited research has addressed safety concerns related to vaccination against the human papillomavirus (HPV).

Objective: To investigate the association between receipt of HPV vaccination and autonomic dysfunction and menstrual irregularities in girls and young women.

Methods: Using a 25% random sample of IQVIA PharMetrics® Plus for Academics claims database from 2016 to 2020, we conducted a self-controlled case series study in commercially insured girls and young women receiving their first HPV vaccine dose (analyses conducted between March 2024 and April 2025). Incidence rate ratios (IRRs) and 95% confidence intervals (CIs) were estimated for two outcomes-autonomic dysfunction and menstrual irregularities. We conducted further analyses stratified by number of HPV vaccine doses received per beneficiary and by age (9-17 years vs. 18-26 years), as well as adjusted for age as a time-varying covariate. The IRRs were estimated over a maximum risk case post-vaccination period of 36 months compared to a 6-month within-person control pre-vaccination period.

Results: There were 1654 individuals in the autonomic dysfunction cohort and 3140 individuals in the menstrual irregularities cohort. When adjusted for age, HPV vaccination was associated with elevated IRRs for autonomic dysfunction (IRR 1.23; 95% CI 1.08-1.41) and menstrual irregularities (IRR 1.30; 95% CI 1.18-1.43). IRRs for individual outcomes varied by age group, with the younger cohort showing a significantly higher age-adjusted IRR than the older cohort for menstrual irregularities (IRR 1.51; 95% CI 1.33-1.72 vs. IRR 1.15; 95% CI 0.99-1.33, respectively). Although the risk of experiencing autonomic dysfunction was not significant in the adjusted younger cohort, young women aged 18-26 years had a heightened age-adjusted risk (IRR 1.40; 95% CI 1.12-1.75). Findings from the dose-response analysis were inconclusive.

Conclusions: HPV vaccination is associated with elevated risks of autonomic dysfunction and menstrual irregularities, which vary by age. Further research is needed to identify additional risk factors associated with HPV vaccination safety.

人乳头瘤病毒(HPV)疫苗接种与自主神经功能障碍和月经不规则之间的关系:一项自我控制的病例系列分析。
背景:有限的研究已经解决了与人乳头瘤病毒(HPV)疫苗接种相关的安全问题。目的:探讨女童和青年妇女接种HPV疫苗与自主神经功能障碍和月经不规律的关系。方法:使用IQVIA PharMetrics®Plus for Academics索赔数据库2016年至2020年的25%随机样本,我们对商业保险的女孩和年轻女性进行了自我控制的病例系列研究(分析于2024年3月至2025年4月进行)。对自主神经功能障碍和月经不规律这两个结果的发生率比(IRRs)和95%置信区间(CIs)进行了估计。我们进行了进一步的分析,按每个受益人接受的HPV疫苗剂量和年龄(9-17岁vs. 18-26岁)分层,并根据年龄作为时变协变量进行了调整。与接种疫苗前6个月的个人对照相比,在接种疫苗后36个月的最大风险病例期间估计了irr。结果:自主神经功能障碍组1654例,月经不规律组3140例。经年龄调整后,HPV疫苗接种与自主神经功能障碍的IRR升高相关(IRR 1.23;95% CI 1.08-1.41)和月经不规律(IRR 1.30;95% ci 1.18-1.43)。个体结果的IRR因年龄组而异,年轻队列显示月经不规则的年龄调整IRR明显高于年长队列(IRR 1.51;95% CI 1.33-1.72, IRR 1.15;95% CI分别为0.99-1.33)。虽然经历自主神经功能障碍的风险在调整后的年轻队列中并不显著,但18-26岁的年轻女性的年龄调整风险较高(IRR 1.40;95% ci 1.12-1.75)。剂量-反应分析的结果尚无定论。结论:HPV疫苗接种与自主神经功能障碍和月经不规律的风险升高有关,其随年龄而异。需要进一步的研究来确定与HPV疫苗接种安全性相关的其他危险因素。
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来源期刊
Drugs - Real World Outcomes
Drugs - Real World Outcomes PHARMACOLOGY & PHARMACY-
CiteScore
3.60
自引率
5.00%
发文量
49
审稿时长
8 weeks
期刊介绍: Drugs - Real World Outcomes targets original research and definitive reviews regarding the use of real-world data to evaluate health outcomes and inform healthcare decision-making on drugs, devices and other interventions in clinical practice. The journal includes, but is not limited to, the following research areas: Using registries/databases/health records and other non-selected observational datasets to investigate: drug use and treatment outcomes prescription patterns drug safety signals adherence to treatment guidelines benefit : risk profiles comparative effectiveness economic analyses including cost-of-illness Data-driven research methodologies, including the capture, curation, search, sharing, analysis and interpretation of ‘big data’ Techniques and approaches to optimise real-world modelling.
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