Validating community concerns of menstrual changes associated with COVID-19 vaccination using a self-controlled case series analysis of real-world data

Objectives

Stories of menstrual changes occurring post COVID-19 vaccination have abounded, with many affected persons expressing frustration their concerns were not being heard. In an era where misinformation is rampant and can fuel vaccine hesitancy it is imperative to address and validate community concerns. We aimed to investigate evidence of increased menstrual disturbances associated with COVID-19 vaccination.

Methods

We adopted a two-pronged approach; firstly, scrutinising social-media for discussions on menstrual changes associated with COVID-19 vaccination using our deep learning framework VaxPulse. Secondly, we analysed a large de-identified Australian general practice dataset to validate any evidence of increased menstrual disturbance presentations for females aged 15–49 years post-COVID-19 vaccination from 1 January 2021 to 28 March 2023, stratified by vaccine platform (adenovirus vector, mRNA, or protein-subunit). We used a self-controlled case series (SCCS) analysis to determine the relative incidence (RI) with 95 % confidence interval (CI) at six weeks post-vaccination and monitored thereafter until a return to baseline for minimum 2 consecutive weeks.

Results

Examining Reddit and Twitter (now X) data, we identified 70,977 posts discussing menstrual cycle irregularities with two prominent peaks since the global COVID-19 vaccine rollout. The SCCS analysis demonstrated increased presentations with a menstrual disturbance diagnosis associated with mRNA COVID-19 vaccination (RI: 1.14, 95 %CI: 1.07, 1.22, P < 0.001). The increase in presentations was still evident at 7 weeks but dissipated by 13 weeks post vaccination. (RI:1.03, 95 %CI: 0.91, 1.16, P = 0.20).

Conclusion

This study demonstrated a transient increase in menstrual change presentations for up to three months following COVID-19 mRNA vaccination. These findings affirm community concerns raised on social media and are important to ensure people who are vaccinated or are considering future vaccines feel heard, supported, and validated. Our analyses highlight the value of using large real-world datasets to gather reliable evidence for public health decision-making.