COVID-19 vaccination and menstrual cycle changes: A United Kingdom (UK) retrospective case-control study

  1. Alexandra Alvergne
  2. Gabriella Kountourides
  3. M. Austin Argentieri
  4. Lisa Agyen
  5. Natalie Rogers
  6. Dawn Knight
  7. Gemma C Sharp
  8. Jacqueline A Maybin
  9. Zuzanna Olszewska

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Background

There has been increasing public concern that COVID-19 vaccines cause menstrual cycle disturbances, yet there is currently limited data to evaluate the impact of vaccination on menstrual health. Our objectives were (1) to evaluate the prevalence of menstrual changes following vaccination against COVID-19, (2) to test potential risk factors for any such changes, and (3) to identify patterns of symptoms in participants’ written accounts.

Methods

We performed a secondary analysis of a retrospective online survey titled “The Covid-19 Pandemic and Women’s Reproductive Health”, conducted in March 2021 in the UK before widespread media attention regarding potential impacts of SARS-CoV-2 vaccination on menstruation. Participants were recruited via a Facebook ad campaign in the UK and eligibility criteria for survey completion were age greater than 18 years, having ever menstruated and currently living in the UK. In total, 26,710 people gave consent and completed the survey. For this analysis we selected 4,989 participants who were pre-menopausal and vaccinated. These participants were aged 28 to 43, predominantly from England (81%), of white background (95%) and not using hormonal contraception (58%).

Findings

Among pre-menopausal vaccinated individuals (n=4,989), 80% did not report any menstrual cycle changes up to 4 months after their first COVID-19 vaccine injection. Current use of combined oral contraceptives was associated with lower odds of reporting any changes by 48% (OR = 0.52, 95CI = [0.34 to 0.78], P <0.001). Odds of reporting any menstrual changes were increased by 44% for current smokers (OR = 1.44, 95CI = [1.07 to 1.94], P <0.01) and by more than 50% for individuals with a positive COVID status [Long Covid (OR = 1.61, 95CI = [1.28 to 2.02], P <0.001), acute COVID (OR = 1.54, 95CI = [1.27 to 1.86], P <0.001)]. The effects remain after adjusting for self-reported magnitude of menstrual cycle changes over the year preceding the survey. Written accounts report diverse symptoms; the most common words include “cramps”, “late”, “early”, “spotting”, “heavy” and “irregular”, with a low level of clustering among them.

Conclusions

Following vaccination for COVID-19, menstrual disturbance occurred in 20% of individuals in a UK sample. Out of 33 variables investigated, smoking and a previous history of SARS-CoV-2 infection were found to be risk factors while using oestradiol-containing contraceptives was found to be a protective factor. Diverse experiences were reported, from menstrual bleeding cessation to heavy menstrual bleeding.

Article activity feed

  1. PREreview

    This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/6515250.

    Review of: [Alvergne, A., G. Kountourides, M. A. Argentieri, L. Agyen, N. Rogers, D. Knight, G. C. Sharp, J. A. Maybin, and Z. Olszewska. December 6, 2021. COVID-19 vaccination and menstrual cycle changes: A United Kingdom (UK) retrospective case-control study. medRxiv. https://doi.org/10.1101/2021.11.23.21266709]

     

    This review was written collaboratively by undergraduates at Mount Holyoke College (MA, USA) who selected this preprint for an assignment in a course on peer review taught by Dr. Rebeccah S. Lijek, Assistant Professor of Biological Sciences. Student-reviewers who give their permission to list their names or initials are: [Amanda Kearney, Olive Aries, Soli Guzman, Emilia Fallman, Rameen Farrukh] 

     

    Disclosures: The reviewers declare no conflict of interest and have no personal or financial relationship with the study's authors. The reviewers did not analyze the validity of the statistical analysis as these were beyond the scope of their expertise. This review represents a general consensus of this group of student reviewers comments, though not every reviewer listed agrees with all of these opinions. We thank the authors for sharing their manuscript as a preprint.

     

    Summary

    The manuscript by Alvergne et al. used data from a previous survey to gain insight into how the COVID-19 vaccine was affecting people's menstrual cycle. The survey was released before the media discussed how menstrual cycles were being affected by vaccines which allowed for a non-biased collection of data. They found that: there was an increased risk of reporting menstrual changes after having a COVID-19 infection and receiving a COVID-19 vaccine; using contraceptives containing oestradiol would reduce the risk of reporting changes after a Covid-19 vaccine; and smoking was the only variable associated with changes to the menstrual cycle post-vaccination. The manuscript provides a rich background for how different vaccines have interacted with the menstrual cycle, and that there is limited research on its relationship with the COVID-19 vaccine. Instead of a hypothesis the manuscript focuses on documenting any menstrual changes after an individual received their COVID-19 vaccine, what could make them more at risk to experience these changes, and the intensity of these changes. This research provides crucial knowledge for the general public as menstruation affects a large portion of the population and the COVID-19 vaccine has become a central part of society in the past two years. This is a well-written and researched manuscript whose approach allows for a deeper understanding of the relationship between the COVID-19 vaccine and the menstrual cycle! We believe there are a few portions that require some improvements and we have provided suggestions that could help improve them.

     

    Strengths: 

    • The paper is novel and investigates a relevant and important topic that will help scientists better understand the health implications of vaccines on menstrual cycles and fertility. 
    • The data is improved by the number of variables (33), which makes it a more holistic view to determine if the COVID-19 vaccine is the root cause of menstrual cycle changes, as there are many ways that menstrual cycles can be altered throughout someone's life. 
    • The paper does a good job of highlighting the necessary background information and history of vaccine influence on menstrual cycles such as the polio vaccine. It builds on this work by providing a more comprehensive study. 
    • The paper began this work prior to media attention surrounding how the COVID-19 vaccine may affect menstrual cycles, limiting bias. 
    • This research uses previous knowledge to expand upon how the vaccine is currently affecting individuals. At the end of the manuscript, the readers are provided with a question to further expand the research by focusing on the body's immune response to a iCOVID-19 nfection and how that might affect the way they react to the vaccine. This is a fair follow-up question and we appreciate how in the manuscript there are justifications for why a researcher should look into it. 
    • Figure 1 was helpful in understanding the evolution of the selection process, and easy to understand. 
    • Figure 2 provided a lot of clarity on the full demographics of the study, and was definitely important to include for both general readers and future researchers. 
    • Figure 3 was well executed and demonstrated how the 5 variables that were affected the most behaved through each model. 
    • Figure 4 was also well-executed and demonstrated how the manuscript checked if the data it obtained could reliably allow us to predict reports of a menstrual change.

     

    Major issues: 

    • A justification should be added as to why only a subset of data was analyzed and what the requirements were. The current justifications are concentrated in the discussion section with a few scattered earlier in the paper. The justifications and limitations in "Strengths and weaknesses of the study in relation to other studies" section need to be clearly defined before the methods and results to better contextualize them.
    • The paper would be strengthened by analyzing the effects of the COVID-19 vaccine on menstruation after the first dose of the vaccine and comparing the effects or lack thereof after the second dose of the vaccine. This would allow for a larger portion of the public, the addition of 8,539 individuals who had some form of vaccination, to be added to data set. 
    • In keeping the manuscript gender and sex inclusive, "female reproductive health" and other phrases along those lines should be reframed. Sex is not binary (Roberts 2007; Fausto-Sterling 1993), and thus maintaining that binary within studies can be confusing at best and harmful at worst. The abstract is a good model of how to keep language gender- and sex-inclusive. This can be done by, for example, consistently replacing "women" and "female" with "people who menstruate". 
    • Roberts, C. (2007). Messengers of Sex: Hormones, Biomedicine and Feminism (Cambridge Studies in Society and the Life Sciences). Cambridge: Cambridge University Press. doi:10.1017/CBO9780511489174
    • Fausto-Sterling, A. (1993). The fives sexes: Why male and female are not enough. The Sciences, Vol 33(2). 20-24.  https://doi.org/10.1002/j.2326-1951.1993. tb03081.x
    • There should be a disclaimer either in the "Strengths and weaknesses of the study in relation to other studies" or when BMI is first introduced that explains BMI's limitations. It is increasingly evident that BMI is not a reliable measurement of health, and is actually based on anti-Black, fatphobic conceptions of health (Gee et al 2008, Hicken et al 2018, Humphreys 2010). When discussing future studies there should be an inclusion about other types of measurements that can be used instead such as waist-to-height ratio (Ashwell et al. 2012), waist circumference measurements (Lissner et al 2001), and heart rate (Kristal-Boneh et al 1995)
    • Ashwell, M., P. Gunn, and S. Gibson. (2012) Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obesity Reviews 13:275–286.
    • Gee, G. C., A. Ro, A. Gavin, and D. T. Takeuchi. (2008). Disentangling the Effects of Racial and Weight Discrimination on Body Mass Index and Obesity Among Asian Americans. American Journal of Public Health 98:493–500.
    • Hicken, M. T., H. Lee, and A. K. Hing. (2018). The weight of racism: Vigilance and racial inequalities in weight-related measures. Social Science & Medicine 199:157–166.
    • Humphreys, S.. (2010). The unethical use of BMI in contemporary general practice. British Journal of General Practice 60:696–697.
    • Lissner, L., C. Björkelund, B. L. Heitmann, J. C. Seidell, and C. Bengtsson. (2001) Larger Hip Circumference Independently Predicts Health and Longevity in a Swedish Female Cohort. Obesity Research 9:644–646.
    • Kristal-Boneh E, Raifel M, Froom P, and J. Ribak. (1995)  Heart rate variability in health and disease. Scand J Work Environ. 21 :85 - 9
    • The "Strengths and weaknesses of the study in relation to other studies" section of this study should include the fact that the study was conducted via a Facebook survey. This method brings up concerns because Facebook is only accessible to a certain subset of the population (people with access to the internet, people who have a basic understanding of how to use social media, etc). In addition, Facebook algorithms are often biased as to who gets shown which ads, which introduces skew into the population sampling
    • In the "Unanswered questions and future research" ways to create an improved survey for the future should be added. Our suggestions include:
    • To gather a greater number of participants taking the survey and a more diverse sample size that better reflects the general population we suggest recruiting participants through multiple avenues, such as letter mail (and making sure they are being sent to diverse urban and rural areas) and physical posters, as well other types of social media platforms such as Instagram, Twitter, TikTok, and email. 
    • If a similar survey is used, add a second portion to the question "have you noticed any changes to your menstrual cycles since you got vaccinated?" (which had four choice-answers: YES, NO, Yes less disrupted, Yes more disrupted). We would suggest a Likert scale answer: "On a scale of 1-10 rate how disrupted your menstrual cycle was with 1 being the least/minimal, 5 being neutral/no changes, and 10 being the highest/maximum." This allows respondents to contribute nuanced responses and allows the researchers to understand how severe the changes were. 
    • For a future study, a similar survey could be run focusing on the possible effects of the booster vaccines on menstruation and then comparing that data to the past data from the first vaccine and see if there are any overall trends or changes

     

    Minor issues: 

    • Figure 5 is very well executed and clear to understand, but please provide more detail about  "Most Common Words" and "Most Common Pair of Adjacent Words." 
    • Figure 6 and Figure 7 are very similar and the manuscript would be improved by combining the concepts into one figure. 
    • The manuscript has a few discrepancies within the discussion that can be easily fixed. In the discussion, it states that a contraceptive containing oestradiol is correlated with 50% lower odds of there being a menstrual cycle change, meanwhile in the results it says 48%. For consistency's sake, the 50% in the discussion could be changed to 48%. 
    • In the "Missing Data" section, there is a small discussion about a forest imputation technique, from the R package "missRanger." For readers who are unfamiliar with this technique, we would suggest including a more detailed and concise description of the process as well as how it affects data and/or shows up in the conclusion. 
    • The discussion section should mention how smoking and previously contracting COVID-19 could increase a patient's odds of having menstrual changes after getting the vaccine and that patients on COCP have decreased odds, however these factors are not reliable for predicting whether or not an individual will have a change in their cycle.

     

     

     

     

     

    https://doi.org/10.1002/j.2326-1951.1993.tb03081.x

  2. Version published to 10.1101/2021.11.23.21266709v3 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.11.23.21266709: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Antibodies
    SentencesResources
    Second, COVID status was operationalized in two ways: (i) based on whether people thought they had had COVID, as widespread testing had not been available in the UK in the early months of the pandemic which fell within the survey period, leading to three categories: No COVID, acute COVID (symptoms lasting less than 28 days) and Long Covid (symptoms lasting more than 28 days) as well as (ii) based on a combination of testing and self-diagnosis, leading to three categories: No COVID (no tests or negative tests), COVID tested + (positive test) and “Self-diagnosed positive” (referring to individuals who had a suspected or clinically diagnosed COVID infection but had not obtained positive PCR, antigen or antibody tests).
    antigen
    suggested: None

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Strengths and weaknesses of the study: The analysis is drawing upon a survey not specifically designed to investigate the impact of COVID-19 vaccination on menstruation. It is retrospective in nature as well as sensitive to selection, recall and report biases and does not systematically assess the full spectrum of menstrual disturbance defined by the International Federation of Gynecology and Obstetrics Abnormal Uterine Bleeding System 1 [27]. We took several steps to limit selection bias during sampling (see methods) and the initial survey is broadly representative of people infected with COVID (8.9% with a positive PCR test compared to a national proportion of 6.6% at the time [28]). However, approximately 45% of the sample had received at least one dose of the vaccine, as compared to the national proportion of 59% by the time of the last survey entry [29]. In addition, menstrual changes may manifest later, and our study does not have the time depth to evaluate this possibility. However, among the studies of other vaccines conducted on a longer timescale, no effect was found by 6-9 months [14,30]. Strengths and weaknesses of the study in relation to other studies: While the survey is also sensitive to recall bias, it is limited as compared to more recent surveys [8] as the issue of menstrual disturbances was not reported by the British Broadcasting Corporation until May 13, 2021 [31], as compared to a flurry of attention in US media throughout April [1–3]. Reassuringly, rep...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  4. Version published to 10.1101/2021.11.23.21266709v2 on medRxiv
  5. Version published to 10.1101/2021.11.23.21266709v1 on medRxiv