# Association between COVID-19 Vaccines and Menstrual Disorders: Retrospective Cohort Study of Women Aged 12–55 Years Old in Catalonia, Spain

## TL;DR

This study found that some COVID-19 vaccines were linked to increased menstrual issues, while others showed a protective effect.

## Contribution

The study is the first to analyze the association between specific vaccine types and menstrual disorders using a large real-world dataset.

## Key findings

- AstraZeneca® and Janssen® vaccines were associated with higher odds of heavy menstrual bleeding and amenorrhea.
- Moderna® vaccine showed a protective effect against menstrual disorders between the second and third doses.
- The incidence of menstrual disorders increased with the second and third vaccine doses.

## Abstract

During the rapid development of COVID-19 vaccines, concerns emerged about potential adverse effects on menstrual health. This study examines the association between COVID-19 vaccination—considering the number of doses and vaccine type—and menstrual disorders, specifically heavy menstrual bleeding (HMB) and amenorrhea (AM). Utilizing electronic health records from the Sistema d’Informació per al Desenvolupament de la Investigació en Atenció Primària (SIDIAP) database in Catalonia, Spain, the retrospective cohort included 1,172,621 vaccinated women aged 12–55 with no prior menstrual disorders observed from 27 December 2020 to 30 June 2023. The incidence rate of HMB and AM increased with the second and third doses of the vaccine. Notably, the AstraZeneca® and Janssen® vaccines were associated with higher odds of HMB (OR: 1.765, CI: 1.527–2.033; OR: 2.155, CI: 1.873–2.476, respectively) and AM (OR: 1.623, CI: 1.416–1.854; OR: 1.989, CI: 1.740–2.269, respectively) from the first to the second dose compared to Pfizer/BioNTech®. Conversely, the Moderna® vaccine appeared to offer a protective effect against HMB (OR: 0.852, CI: 0.771–0.939) and AM (OR: 0.861, CI: 0.790–0.937) between the second and third doses. These results were adjusted for potential confounders, such as age, previous COVID-19 infection, and other relevant covariates.