Determining the impact of COVID-19 vaccination on the menstrual cycle

点击翻译按钮获取中文摘要

Project Summary This supplement to the parent award R01HD089957 will use several large preexisting US-based datasets to determine the impact of COVID-19 vaccine on menstrual health. While the parent award focuses on menstrual cycle changes with direct measures of ovulation and obesity as the main exposure, this supplement will focus on the COVID-19 vaccine as the main exposure, menstrual cycle changes, and indirect measures of ovulation. We will utilize similar scientific methods approved of in Specific Aims 1 and 2 of the parent award including prospective tracking of the menstrual cycle using validated measures and the inclusion of reproductive-age women of varying body mass index with regular menstrual cycles not using hormonal contraception. Public concern is mounting regarding the possible association between COVID-19 and menstrual health. This concern could lead to vaccine hesitancy for individuals and their families; a threat to achieving sufficient rates of vaccination and enabling windows of opportunity for the development of additional virulent variants. Menstrual cyclicity is an overt sign of health and fertility. Thus, our ability to gain a greater understanding of whether or not an association exists between COVID-19 vaccine and menstrual health is critical for the physical and mental well-being of those who menstruate, their community, and our greater public health. The goal of this supplement “COVID19 vaccination and Menstruation” is to determine if COVID-19 vaccination causes menstrual disturbances. Our primary outcome is the within-woman difference in mean menstrual cycle length (in days) pre- and post-vaccination or unvaccinated as well as secondary outcomes related to menstrual cycle timing and severity of vaccine response and changes in menstrual characteristics (e.g. flow, length, pain, associated symptoms, intermenstrual bleeding). We will perform a retrospective analysis of prospectively tracked menstrual cycle data utilizing validated measures pre- and post-COVID-19 vaccination or over similar time period for the unvaccinated group. We will develop a multi-variable logistic regression model considering all available potential confounders including but not limited to vaccine type, BMI, age, race/ethnicity, life stressors, etc. Our team has received the commitment of two leading online menstrual cycle tracking platforms with the built-in user approval to provide this de-identified data for research purposes plus the ability to survey these users to obtain additional data. As of June 2021, they report a cumulative number of active US reproductive age users with ‘natural’ cycles (no hormonal contraception) of approximately 2 million.