Analysis of Women's Menstrual Changes after COVID-19 Infection: a Descriptive Study

Analysis of Women's Menstrual Changes after COVID-19 Infection: a Descriptive Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis of Women's Menstrual Changes after COVID-19 Infection: a Descriptive Study Ruxue Han, Xiaolin Jiang, Xin Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3281461/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: In December, 2019, a pneumonia associated with the 2019 novel coronavirus (2019-nCoV) emerged in Wuhan, China. Since December 2022, China has adjusted anti-epidemic policies and a large-scale COVID-19 infection has emerged. We aimed to explore the menstrual changes of women before and after infection with the COVID-19. Methods: This study was designed as a descriptive, cross-sectional study. We collected data from participants infected with COVID-19 from January 1, 2023 to March 1, 2023 by issuing electronic questionnaires. Women were invited to fill out the questionnaire about their menstrual characteristic after COVID-19 infection. Results: A total of 884 women with COVID-19 infections participated in the study. 662(74.9%) participants experienced changes in one or more of menstrual characteristics. Cycle length seemed to be the characteristic most likely to change (47.6%), followed by menstrual flow (41.7%), duration of menstrual periods (29.5%), degree of dysmenorrhea (29.0%) and intermenstrual bleeding (14.9%). The main clinical manifestations were menstruation delayed (26.3%), menstrual flow decreased (25.5%), dysmenorrhea relief (21.9%) and menstruation prolonged (21.0%). And we found new intermenstrual bleeding in 8.4% participants after COVID-19 infection. The menstrual change rate of the irregular menstrual group was significantly higher than that of the regular menstrual group (73.0% vs. 62.3%, P＜0.001). Conclusions: COVID-19 infection may cause menstrual changes in most women. It is important to be aware of the menstrual changes after COVID-19 infection and to inform women about this issue. COVID-19 menstruation abnormal uterine bleeding Background Since December 2019, cases of pneumonia caused by a new type of coronavirus have been found in Wuhan, Hubei Province, China, and have subsequently appeared in other parts of China and many countries around the world [ 1 ] . As an acute respiratory infectious disease, the disease has been included in the Class B infectious disease stipulated in the "Law of the People's Republic of China on the Prevention and Control of Infectious Diseases", and is managed as a Class A infectious disease in January 20, 2020. On February 11, 2020, the World Health Organization named it 2019 novel coronavirus pneumonia (COVID-19). Since December 2022, China has gradually adjusted and liberalized its anti-epidemic policies, and on December 29 it was officially adjusted to Class B infectious disease and managed as Class B. Due to its strong transmissibility and reduced pathogenicity, most people have experienced novel coronavirus infection. Participants with COVID-19 have multisystem complications in addition to respiratory symptoms, such as cardiovascular and digestive system problems [ 2 , 3 ] , but the impact of COVID-19 on reproductive and endocrine systems in women of reproductive age is currently unclear. There are no clinical data on the impact of COVID-19 on ovarian function in women of reproductive age. In today's society, women's reproductive health has become more and more important, and the world has called for attention to the impact of COVID-19 on the reproductive system. In China, with more and more infections in the country, the impact on female reproduction is also under constant concern. Abnormal uterine bleeding (AUB) is a common symptom and sign in gynecology. As a general term, AUB refers to abnormal bleeding from the uterine cavity that is inconsistent with any one of the cycle frequency, regularity, menstrual length, and menstrual bleeding volume of normal menstruation [ 4 ] . FIGO System 2, published in 2011, focused on classifications of AUB etiology into structural and nonstructural entities using the PALM-COEIN (polyp[s], adenomyosis, leiomyoma, malignancy, coagulopathy, ovulatory dysfunction, endometrial disorders, iatrogenic, and not yet classified) classification system. AUB is a common cause of gynecological outpatient visits. However, some participants lack the correct definition of AUB and ignore its risk. In the past, participants lacked a correct definition of AUB and ignored its risk. With the improvement of medical care and participants' emphasis on health, menstrual problems have become a common reason for participants to visit gynecological clinics. In clinical work and online forums, we found that many female participants of childbearing age complained of different menstrual changes, including irregular menstrual cycles, increased or decreased menstrual flow, and new symptoms of dysmenorrhea. Although China's epidemic policy has been adjusted for a short time, COVID-19 has a high infection rate among the population. There is currently no evidence to prove that the COVID-19 infection is related to changes in menstrual conditions. Studies around the world have not found that the COVID-19 will cause damage to the female reproductive system. This study aims to explore the menstrual changes of women before and after infection with the COVID-19. Method Study design and participants This study is a retrospective cross-sectional study. We collected data from participants infected with COVID-19 from January 1, 2023 to March 1, 2023 by issuing electronic questionnaires. Inclusion criteria: (1) having menstrual cramps; (2) voluntarily filling out the questionnaire. Exclusion criteria: (1) Use estrogen-progestin drugs in the past three months; (2) Suffer from endocrine diseases that affect menstruation, organic diseases that seriously affect menstruation. A total of 1033 questionnaires were collected, 38 of them had no evidence of COVID-19 infection, 43 of them had taken oral hormone drugs in the past three months, and 70 of them had endocrine diseases that may affect menstruation (There were 2 participants taking hormone drugs because of hyperandrogenism). The above questionnaires were all excluded. Finally, a total of 884 participants were included in this study. This study was approved by Institutional Review Board of Peking University People's Hospital (2023PHB034-001). Written informed consent as exempted in accordance with the urgent situation and the Ethics Committee's rules. A case could be diagnosed as COVID-19 infected by reverse transcription polymerase chain reaction (RT-PCR) of SARS-CoV-2, or self-test rapid diagnostic tests (RDTs) positive result. Assessment tools All women used a mobile phone or tablet to scan a WeChat QR Code to access the mobile questionnaire survey system. The study complied with the terms of service for the WeChat social media application software. All participants voluntarily filled out the questionnaire after informed consent. Outcomes The general contents of the questionnaire include age, menarche age, AUB-related diseases, COVID-19 vaccination status, last menstrual period, time and method of diagnosis of COVID-19. In addition, participants were required to select symptoms after COVID-19 infection and menstrual cycle, length of menstrual period, menstrual flow, dysmenorrhea, and intermenstrual bleeding status before and after COVID-19 infection. Definition The normal menstrual bleeding patterns need to meet the following three characteristics at the same time: normal frequency (21 ~ 35 days), normal duration (3 ~ 7 days) and normal flow volume (patient determined). A discrepancy in any one of the items will be defined as irregular periods. Statistical analysis SPSS 25.0 (SPSS Inc., Chicago, IL) was used to perform the statistical analyses of the questionnaire data. Continuous data were expressed as means ± standard deviations (SD). Categorical data were expressed as frequency (percentage) and analyzed using the chi-square test. Results Baseline characteristics of the participants According to the completion of the questionnaire, only 38 of the 1033 (3.7%) women who filled out the questionnaire were not infected with the COVID-19. Due to the large number of infections, we could not set up a control group to observe the menstrual changes of women who were not infected with COVID-19 and could not make the comparisons between the two groups. However, of the 38 participants who were not infected with COVID-19, the vast majority (37/38, 97.4%) did not complain of menstrual changes. A total of 884 women with COVID-19 infections participated in the study. Demographics, baseline characteristics are shown in Table 1 . The average age of the participants was 35.56 years (ranging from 18–58). The average age at menarche was 13.32 years (ranging from 9–19). Most participants (83.5%, 738/884) had no AUB-related diseases. AUB-related diseases included leiomyoma (100/884, 11.3%), endometrial polyps (38/884, 4.3%), adenomyosis (29/884, 3.3%), abnormal coagulation (4/884, 0.5%) and malignant or atypical hyperplasia of endometrium (2/884, 0.2%). 19(2.1%) participants had more than one comorbidity. Table 1 Demographics, baseline characteristics of females with COVID-19. Data are n (%) unless specified otherwise. COVID-19, coronavirus disease 2019. Participants(n = 884) Age, years Mean ± SD 35.56 ± 8.03 Range 18–58 50 19(2.1%) Age of Menarche,years Mean ± SD 13.32 ± 1.51 Range 9–19 14 167(18.9%) AUB-related disease Endometrial polyps 38(4.3%) Leiomyoma 100(11.3%) Adenomyosis 29(3.3%) Malignant or atypical hyperplasia of endometrium 2(0.2%) Abnormal coagulation 4(0.5%) None 738(83.5%) More than one comorbidity 19(2.1%) 615 (69.6%) participants were diagnosed by RDTs and 269 (30.4%) by RT-PCR of SARS-CoV-2. Most of the participants (706/884, 79.8%) received at least three doses of vaccine. Only 49 (5.5%) had not been vaccinated. We counted the signs and symptoms associated with COVID-19 infection and found that fever (833/884, 94.2%), shortness of breath (676/884, 76.5%) and insomnia (671/884, 75.9%) were the most common symptoms. More than half of the participants' temperature rose above 38.5°C. Only 24 participants (2.7%) had CT-confirmed pneumonia. Other symptoms included sore throat (358/884, 40.5%), asthenia (239/884, 27.0%) and cough (171/884, 19.3%). 126(14.3%) participants had other symptoms. (Table 2 ) Table 2 Clinical characteristics of females with COVID-19 infection. Data are n (%) unless specified otherwise. COVID-19, coronavirus disease 2019. RT-PCR, reverse transcription polymerase chain reaction. RDTs, rapid diagnostic tests. Participants(n = 884) Confirmed diagnosis RT-PCR 269 (30.4%) RDTs 615 (69.6%) Vaccination status: 1 dose 14 (1.6%) 2 doses 115 (13.0%) 3 doses (booster 1 dose) 634 (71.7%) 4 doses (booster 2 doses) 72 (8.1%) Not vaccinated 49 (5.5%) Signs and symptoms of COVID-19 Fever 833(94.2%) <38.5℃ 313(35.4%) ≥38.5℃ 520(58.8%) Cough 171(19.3%) Sore throat 358(40.5%) Shortness of breath 676(76.5%) Insomnia 671(75.9%) Asthenia 239(27.0%) Pneumonia on CT 24(2.7%) Other symptoms 126(14.3%) Menstrual characteristics before COVID-19 infection The menstrual characteristics before COVID-19 infection was shown in Table 3 . The menstrual cycle of most participants(836/884, 94.6%) was within the normal range(21–35 days).10(1.1%)participants showed their ranges of menstruation were frequent (35days). 765(86.5%) women had durations of 3–7 days. 20(2.3%) participants’ menstruation lasted less than 3 days and 99(11.2%) more than 7 days. We asked the participants to self-evaluate their menstrual flow.53(6.0%) participants thought their vaginal bleeding was less than normal, which meant they had no need to use sanitary napkins during menstruation. 699(79.1%) participants thought their menstrual blood loss were within normal range, using no more than 20 daily sanitary napkins during menstruation. And 132(14.9%) participants were suffering from heavy menstrual bleeding, which meant they need to change daily or night sanitary napkins frequently during menstruation, and the number was more than 20 pieces. Before COVID-19 infection, 591(66.9%) participants considered they had normal menstrual bleeding patterns while 293(33.1%) did not. Table 3 Menstrual characteristics before COVID-19 infection Participants(n = 884) Cycle length 35d 38(4.3%) Duration of menstrual periods 7d 99(11.2%) Menstrual flow a Light 53(6.0%) Normal 699(79.1%) Heavy 132(14.9%) Dysmenorrhea b None 406(45.9%) Mild 342(38.7%) Moderate 126(14.3%) Severe 10(1.1%) Intermenstrual bleeding Yes 125(14.1%) No 759(85.9%) Data are n (%) unless specified otherwise. a. Definition: Hypomenorrhea: No need to use sanitary napkins during menstruation. Normal: Using no more than 20 daily sanitary napkins during menstruation. Heavy menstrual bleeding: Using more than 20 daily sanitary napkins during menstruation. b. Definition: Mild: The pain did not affect daily study and life, and anodyne was not required. Moderate: The pain required anodyne and could be relieved. Severe: Anodyne could not relieve pain. 406(45.9%) participants never had dysmenorrhea. Among participants with dysmenorrhea, the rates of self-assessed mild, moderate and severe were 38.7% (342/884), 14.3% (126/884) and 1.1% (10/884), respectively. There were 14.1% (125/884) participants suffering intermenstrual bleeding before COVID-19 infection. Changes of menstrual characteristics after COVID-19 infection Only 222 (25.1%) of the 884 participants included in this study did not have any menstrual characteristics changes after COVID-19 infection. 662(74.9%) participants had one or more menstrual characteristics changes. Based on participants’ self-assessments, we found that change incidence rates of cycle length, duration of menstrual periods and menstrual flow were 47.6%(421/884), 29.5%(261/884) and 41.7%(369/884),respectively(Table 4 ). 189(21.4%) participants’ menstruation interval shortened by 1 or 2 weeks. Menstrual bleeding in 232(26.3%) participants was later than scheduled after infection with COVID-19, and 45(5.1%) of them were delayed by more than 3 weeks. The durations of menstrual periods were shortened by 3 days or more in 76(8.6%) participants and prolonged in 112(12.7%). 73(8.3%) participants suffered from dripping problem. 225(25.5%) participants considered their menstrual flow to be significantly reduced compared with that before infection with COVID-19, while 144(16.3%) participants felt menstrual flow increased. There were 752(85.1%) participants had no change in intermenstrual bleeding before and after infection, in which 685(77.5%) participants had no intermenstrual bleeding before and after infection and 67(7.6%) participants had. 74(8.4%) participants were free of intermenstrual bleeding before infection but developed bleeding after infection. In contrast, 58(6.6%) participants no longer had intermenstrual bleeding after COVID-19 infection. The changes of dysmenorrhea were more complicated. 371(42.0%) participants never had dysmenorrhea before or after COVID-19 infection. The degree of dysmenorrhea did not change in 257(29.1%) participants. 35(4.0%) participants had no dysmenorrhea before COVID-19 infection, but experienced dysmenorrhea of varying degrees after infection, including 31(3.5%) with mild dysmenorrhea, 3(0.3%) with moderate dysmenorrhea, and 1(0.1%) with severe dysmenorrhea. Among the participants who had dysmenorrhea before infection, 27(3.1%) had aggravated pain and 194(21.9%) had decreased dysmenorrhea grade. Table 4 Changes of cycle length, duration of menstrual periods, menstrual flow after COVID-19 infection. Participants(n = 884) Cycle length Interval shortened by 2 weeks 59(6.7%) Interval shortened by 1 week 130(14.7%) No change 463(52.4%) Interval prolonged by 1 week 143(16.2%) Interval prolonged by 2 weeks 44(5.0%) Interval prolonged more than 3 weeks 45(5.1%) Duration of menstrual periods Shortened by 3 days or more 76(8.6%) No change 623(70.5%) Prolonged by 3 days or more 112(12.7%) Dripping 73(8.3%) Menstrual flow Decrease 225(25.5%) No change 515(58.3%) Increase 144(16.3%) Data are n (%) unless specified otherwise. 62.3% (368/591) participants with previously regular menstruation believed that they had experienced menstrual changes after COVID-19 infection. While the menstrual change rate reached 73.0% (214/293) in participants with abnormal menstrual bleeding patterns before. The menstrual change rate of the irregular menstrual group was significantly higher than that of the regular menstrual group (73.0% vs. 62.3%, P<0.001). Discussion This is a descriptive study on the epidemiology and clinical menstrual characteristics of 884 women who were infected with COVID-19. This is the study with the largest number of participants in women's menstrual changes after COVID-19 infection in China. Unexpectedly, only 222 (25.1%) of the 884 participants included in this study did not have any menstrual characteristics changes after COVID-19 infection. The top three changes in menstrual characteristics were cycle length, menstrual flow and duration of menstrual periods, the incidence rates were 47.6%, 41.7% and 29.5%. It appears that women abnormal menstrual bleeding patterns are more likely to experience menstrual changes after COVID-19 infection. At present, there are few studies on menstrual changes before and after COVID-19 infection. The sample size of existing studies is generally small, and there is a lack of large-sample studies describing the characteristics of menstrual changes. We compared the results of this study with those of previous studies. Kezhen Li et al. [ 5 ] conducted a retrospective, cross-sectional study in which clinical and laboratory data from 177 women of child-bearing age diagnosed with COVID-19 were retrospectively reviewed. Of 177 participants with menstrual records, 45(25%) participants presented with menstrual volume changes, and 50(28%) participants had menstrual cycle changes, mainly a decreased volume (20%) and a prolonged cycle (19%). Compared with Li's study, the proportion of changed cycle length and menstrual volume were both higher in our study. However, the main clinical manifestations were decreased volume and prolonged cycle in two studies. Another study, which also conducted a menstrual survey of hospitalized participants after infection with COVID-19, suggested a menstrual change rate of 37.3% (59/158) [ 6 ] . However, this study could not reflect the changes in menstrual characteristics of individual participants before and after COVID-19 infection. Another descriptive, cross-sectional study [ 7 ] involving 241 women infected with COVID-19 suggested that 86 (35.7%) patients experienced various changes in their menstrual patterns in the first three cycles after infection. The major menstrual changes in this study were delayed cycle (17.4%), heavier menstrual bleeding (7.4%) and longer period (4.1%). The results of menstrual cycle prolongation and menstrual period prolongation are the same as that of our study but the proportion is lower. However, in terms of menstrual flow, more participants in this study showed increased rather than decreased menstrual flow. A cross-sectional study from Jordan and Iraq showed that 47.2% (228/483) patients suffered from a change in the cycle length and the amount of blood loss [ 8 ] . What's more, they found new intermenstrual bleeding in 10 (4.1%) patients after COVID-19 infection while this proportion reached 8.4% in our study. A cross sectional study reported an exacerbation of dysmenorrhea after COVID-19 infection, which is thought to be related to anxiety [ 9 ] . Menstruation is the cyclic, orderly sloughing of the uterine lining on account of the interactions of hormones produced by the hypothalamic-pituitary-ovarian (HPO) axis. SARS-CoV-2 infection can affect endocrine glands through various mechanisms including direct impacts, indirect damage through immune response and/or activation of HPO axis by the inflammatory status. The menstruation can be affected by different factors including infections, stress, weight changes, the use of medication and lifestyle changes, all of which are likely to change during COVID-19 infection. The current study found that coronaviruses enter hosts cells by binding of the viral spike (S) protein to cell receptors and, upon S protein priming, by host cell proteases [ 10 ] . SARS-CoV-2 binds to the cell by the angiotensin-converting enzyme 2 (ACE2) receptor and the cell protease type II transmembrane serine protease (TMPRSS2) and for virus–cell fusion, which indicates that the co-expression of ACE2 and TMPRSS2 can predict the potential of cells to be prone to infection [ 11 ] . However, no co-expression of ACE2 with TMPRSS2 expression was found in the myometrium, uterus, ovaries or fallopian tubes when investigating published scRNA-seq datasets and reproductive tissue from patients undergoing hysterectomy, which suggest that female reproductive system is unlikely to be susceptible to infection by SARS-CoV2 [ 12 ] . Oocytes seem to have the receptor/protease machinery to be susceptible to SARS-CoV-2 infection, but viral RNA in oocytes has not been detected so far [ 13 ] . Some studies have implicated cytokines (e.g., interleukin-6, interleukin-8, and tumor necrosis factor-alpha) as mediators of the inflammatory response to COVID-19, which can trigger a procoagulant state and may be a way to affect menstruation indirectly. The increased social distance, the surge in the number of infections and the economic losses during COVID-19 pandemic could cause severe stress, anxiety and depression [ 14 ] , which were thought to cause menstrual disturbances [ 6 , 15 – 17 ] . There have even been studies suggesting that the high prevalence of menstrual cycle irregularities in the general population during the COVID-19 pandemic is not related to a COVID-19 diagnosis but to anxiety, depression and/or stress levels [ 18 , 19 ] . Research has found thyroid dysfunction seems to be related to the severity of SARS-CoV-2 infection, with non-thyroidal illness syndrome, characterized by normal thyroid functions, decreased free T3 and thyroiditis as the most common clinical manifestations [ 20 ] , which could also cause menstrual changes. One study found no difference in follicular-phase serum levels of FSH, LH, estradiol and AMH levels between women hospitalized for COVID-19 and controls and most patients’ menstruations could return to the regularity before infection by 1 or 2 months [ 5 ] . In 132 women with unexplained infertility before and after COVID-19 reported no statistically significant differences in terms of serum levels of AMH, FSH, LH, FSH/LH ratio or estradiol levels [ 21 ] . But there is also study that came to the opposite conclusion. Significantly lower serum AMH levels were reported in 78 women with COVID-19 as compared with 151 healthy age-matched controls, with higher FSH, prolactin and testosterone levels [ 22 ] . There are certain limitations to our study. As with the majority of questionnaire-based research, researchers are reliant upon true responses from the participants and accuracy when recall is required. Some previous studies have demonstrated measurement error when using self-report data regarding the menstrual cycle [ 23 ] . In addition, there could be an element of bias within our sample. Our study was recruited through social media. Though we inform that all women with COVID-19 infection could complete the questionnaire regardless of the presence or absence of menstrual changes, those who have changes to their menstrual cycle during the pandemic were more likely to have complete the survey. What’s more, due to the huge difference in numbers between the infected and uninfected groups, we were unable to perform a valid correlation analysis. Finally, our data only provides a small insight into the changes experienced by females during the initial onset of COVID-19 pandemic; it does not provide any longitudinal data that may document the rise and fall of menstrual changes, which would be the focus of our investigations when we follow up participants. Conclusions In conclusion, COVID-19 infection may cause menstrual changes in most women, with menstruation delayed, menstruation prolonged, menstrual flow decreased and dysmenorrhea relief as the most common clinical manifestations. Some women developed new intermenstrual bleeding. It is important to be aware of the menstrual changes after COVID-19 infection and to inform women about this issue. In the next step, we will follow up the menstrual status of the women when they were infected with COVID-19 for more than 3 months. Declarations Ethics approval and consent to participate This study was approved by the Medical Ethics Committee of Peking University People's Hospital, ethics number (2015PHB087-01). Written informed consent as exempted in accordance with the urgent situation and the Ethics Committee's rules. All methods were carried out in accordance with relevant institutional guidelines and regulations. Consent for publication n/a Availability of data and materials The datasets analysed during the current study are available from the corresponding author on reasonable request. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding n/a Author Contributions Ruxue Han: Data curation, Formal analysis, Methodology, Validation, Writing- Original draft preparation, Writing- Reviewing and Editing Xiaolin Jiang: Data curation, Formal analysis, Methodology, Validation, Resources, Writing-Original draft preparation Xin Yang: Conceptualization, Methodology, Resource, Supervision, Writing- Reviewing and Editing Acknowledgements n/a References Hui DS, E I A, Madani TA, et al. The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health - The latest 2019 novel coronavirus outbreak in Wuhan, China [J]. Int J Infect Dis. 2020;91:264–6. Chen N, Zhou M, Dong X, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study [J]. Lancet. 2020;395(10223):507–13. Guan WJ, Ni ZY, Hu Y, et al. Clinical Characteristics of Coronavirus Disease 2019 in China [J]. N Engl J Med. 2020;382(18):1708–20. Marnach ML, Laughlin-Tommaso SK. Evaluation and Management of Abnormal Uterine Bleeding [J]. Mayo Clin Proc. 2019;94(2):326–35. Li K, Chen G, Hou H, et al. Analysis of sex hormones and menstruation in COVID-19 women of child-bearing age [J]. Reprod Biomed Online. 2021;42(1):260–7. Muharam R, Agiananda F, Budiman YF, et al. Menstrual cycle changes and mental health states of women hospitalized due to COVID-19 [J]. PLoS ONE. 2022;17(6):e0270658. Taşkaldıran I, Vuraloğlu E, Bozkuş Y et al. Menstrual Changes after COVID-19 Infection and COVID-19 Vaccination [J]. Int J Clin Pract, 2022, 2022: 3199758. Al-Najjar MaA, Al-Alwany RR, Al-Rshoud FM, et al. Menstrual changes following COVID-19 infection: A cross-sectional study from Jordan and Iraq [J]. PLoS ONE. 2022;17(6):e0270537. Aolymat I, Khasawneh AI, Al-Tamimi M. COVID-19-Associated Mental Health Impact on Menstrual Function Aspects: Dysmenorrhea and Premenstrual Syndrome, and Genitourinary Tract Health: A Cross Sectional Study among Jordanian Medical Students [J]. Int J Environ Res Public Health, 2022, 19(3). Laporte M, Naesens L. Airway proteases: an emerging drug target for influenza and other respiratory virus infections [J]. Curr Opin Virol. 2017;24:16–24. Hoffmann M, Kleine-Weber H, Schroeder S, et al. SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor [J]. Cell. 2020;181(2):271–280e278. Goad J, Rudolph J, Rajkovic A. Female reproductive tract has low concentration of SARS-CoV2 receptors [J]. PLoS ONE. 2020;15(12):e0243959. Barragan M, Guillén JJ, Martin-Palomino N, et al. Undetectable viral RNA in oocytes from SARS-CoV-2 positive women [J]. Hum Reprod. 2021;36(2):390–4. Czepczor-Bernat K, Swami V, Modrzejewska A et al. COVID-19-Related Stress and Anxiety, Body Mass Index, Eating Disorder Symptomatology, and Body Image in Women from Poland: A Cluster Analysis Approach [J]. Nutrients, 2021, 13(4). Demir O, Sal H, Comba C. Triangle of COVID, anxiety and menstrual cycle [J]. J Obstet Gynaecol. 2021;41(8):1257–61. Nguyen BT, Pang RD, Nelson AL, et al. Detecting variations in ovulation and menstruation during the COVID-19 pandemic, using real-world mobile app data [J]. PLoS ONE. 2021;16(10):e0258314. Anto-Ocrah M, Valachovic T, Chen M, et al. Coronavirus Disease 2019 (COVID-19)-Related Stress and Menstrual Changes [J]. Obstet Gynecol. 2023;141(1):176–87. Takmaz T, Gundogmus I, Okten SB, et al. The impact of COVID-19-related mental health issues on menstrual cycle characteristics of female healthcare providers [J]. J Obstet Gynaecol Res. 2021;47(9):3241–9. Medina-Perucha L, López-Jiménez T, Holst AS, et al. Self-Reported Menstrual Alterations During the COVID-19 Syndemic in Spain: A Cross-Sectional Study [J]. Int J Womens Health. 2022;14:529–44. Lui DTW, Lee CH, Chow WS, et al. Thyroid Dysfunction in Relation to Immune Profile, Disease Status, and Outcome in 191 Patients with COVID-19 [J]. J Clin Endocrinol Metab. 2021;106(2):e926–35. Madendag IC, Madendag Y, Ozdemir AT. COVID-19 disease does not cause ovarian injury in women of reproductive age: an observational before-and-after COVID-19 study [J]. Reprod Biomed Online. 2022;45(1):153–8. Ding T, Wang T, Zhang J, et al. Analysis of Ovarian Injury Associated With COVID-19 Disease in Reproductive-Aged Women in Wuhan, China: An Observational Study [J]. Front Med (Lausanne). 2021;8:635255. Small CM, Manatunga AK, Marcus M. Validity of self-reported menstrual cycle length [J]. Ann Epidemiol. 2007;17(3):163–70. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3281461","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":232820180,"identity":"f706c23a-d782-499d-b079-d236dc29a191","order_by":0,"name":"Ruxue Han","email":"","orcid":"","institution":"Peking University People’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ruxue","middleName":"","lastName":"Han","suffix":""},{"id":232820182,"identity":"6b5088b8-ee65-4b50-be8e-205ccd4509d2","order_by":1,"name":"Xiaolin Jiang","email":"","orcid":"","institution":"Peking University People’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaolin","middleName":"","lastName":"Jiang","suffix":""},{"id":232820183,"identity":"b67f9874-1273-40e7-ae2d-32eaaf89d162","order_by":2,"name":"Xin Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDACZijNz8zY/OADgwQJWiTbm48ZziBKCwwYnDmWIM1DlMrjvMckfu6oZWC4kWNgbPPHIo+/gfnhoxt4tEg286VJ9p45zsA4I8fgcW6bRLHEATZj4xw8WviZecwkeNuOMTBLAG3JbZBIbDjAwyaNTwsbUIvkX6AWNqAWaYs/EonzCWkB2SLN21bDwMMD9D5QY+IGQlokm3mMrWXbDjBIsAMDubdNInHjYQJ+MTh/xvDm27Y6BvvDwKj88acucd7x5oeP8WkBAhZg9B2ub4DzmXErhSv5wMBQR1jZKBgFo2AUjFwAANc2RrDjF422AAAAAElFTkSuQmCC","orcid":"","institution":"Peking University People’s Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2023-08-21 07:29:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3281461/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3281461/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60643738,"identity":"97e344a0-554e-4f76-9821-9c1ffacb5bac","added_by":"auto","created_at":"2024-07-19 04:18:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":478016,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3281461/v1/a9b0bec7-845f-4dce-93f7-e0892a2ead09.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of Women's Menstrual Changes after COVID-19 Infection: a Descriptive Study","fulltext":[{"header":"Background","content":"\u003cp\u003eSince December 2019, cases of pneumonia caused by a new type of coronavirus have been found in Wuhan, Hubei Province, China, and have subsequently appeared in other parts of China and many countries around the world\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. As an acute respiratory infectious disease, the disease has been included in the Class B infectious disease stipulated in the \"Law of the People's Republic of China on the Prevention and Control of Infectious Diseases\", and is managed as a Class A infectious disease in January 20, 2020. On February 11, 2020, the World Health Organization named it 2019 novel coronavirus pneumonia (COVID-19). Since December 2022, China has gradually adjusted and liberalized its anti-epidemic policies, and on December 29 it was officially adjusted to Class B infectious disease and managed as Class B. Due to its strong transmissibility and reduced pathogenicity, most people have experienced novel coronavirus infection. Participants with COVID-19 have multisystem complications in addition to respiratory symptoms, such as cardiovascular and digestive system problems\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e, but the impact of COVID-19 on reproductive and endocrine systems in women of reproductive age is currently unclear. There are no clinical data on the impact of COVID-19 on ovarian function in women of reproductive age. In today's society, women's reproductive health has become more and more important, and the world has called for attention to the impact of COVID-19 on the reproductive system. In China, with more and more infections in the country, the impact on female reproduction is also under constant concern.\u003c/p\u003e \u003cp\u003eAbnormal uterine bleeding (AUB) is a common symptom and sign in gynecology. As a general term, AUB refers to abnormal bleeding from the uterine cavity that is inconsistent with any one of the cycle frequency, regularity, menstrual length, and menstrual bleeding volume of normal menstruation\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. FIGO System 2, published in 2011, focused on classifications of AUB etiology into structural and nonstructural entities using the PALM-COEIN (polyp[s], adenomyosis, leiomyoma, malignancy, coagulopathy, ovulatory dysfunction, endometrial disorders, iatrogenic, and not yet classified) classification system. AUB is a common cause of gynecological outpatient visits. However, some participants lack the correct definition of AUB and ignore its risk. In the past, participants lacked a correct definition of AUB and ignored its risk. With the improvement of medical care and participants' emphasis on health, menstrual problems have become a common reason for participants to visit gynecological clinics. In clinical work and online forums, we found that many female participants of childbearing age complained of different menstrual changes, including irregular menstrual cycles, increased or decreased menstrual flow, and new symptoms of dysmenorrhea.\u003c/p\u003e \u003cp\u003eAlthough China's epidemic policy has been adjusted for a short time, COVID-19 has a high infection rate among the population. There is currently no evidence to prove that the COVID-19 infection is related to changes in menstrual conditions. Studies around the world have not found that the COVID-19 will cause damage to the female reproductive system. This study aims to explore the menstrual changes of women before and after infection with the COVID-19.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eStudy design and participants\u003c/p\u003e \u003cp\u003eThis study is a retrospective cross-sectional study. We collected data from participants infected with COVID-19 from January 1, 2023 to March 1, 2023 by issuing electronic questionnaires. Inclusion criteria: (1) having menstrual cramps; (2) voluntarily filling out the questionnaire. Exclusion criteria: (1) Use estrogen-progestin drugs in the past three months; (2) Suffer from endocrine diseases that affect menstruation, organic diseases that seriously affect menstruation. A total of 1033 questionnaires were collected, 38 of them had no evidence of COVID-19 infection, 43 of them had taken oral hormone drugs in the past three months, and 70 of them had endocrine diseases that may affect menstruation (There were 2 participants taking hormone drugs because of hyperandrogenism). The above questionnaires were all excluded. Finally, a total of 884 participants were included in this study.\u003c/p\u003e \u003cp\u003e This study was approved by Institutional Review Board of Peking University People's Hospital (2023PHB034-001). Written informed consent as exempted in accordance with the urgent situation and the Ethics Committee's rules.\u003c/p\u003e \u003cp\u003eA case could be diagnosed as COVID-19 infected by reverse transcription polymerase chain reaction (RT-PCR) of SARS-CoV-2, or self-test rapid diagnostic tests (RDTs) positive result.\u003c/p\u003e \u003cp\u003eAssessment tools\u003c/p\u003e \u003cp\u003eAll women used a mobile phone or tablet to scan a WeChat QR Code to access the mobile questionnaire survey system. The study complied with the terms of service for the WeChat social media application software. All participants voluntarily filled out the questionnaire after informed consent.\u003c/p\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003cp\u003eThe general contents of the questionnaire include age, menarche age, AUB-related diseases, COVID-19 vaccination status, last menstrual period, time and method of diagnosis of COVID-19. In addition, participants were required to select symptoms after COVID-19 infection and menstrual cycle, length of menstrual period, menstrual flow, dysmenorrhea, and intermenstrual bleeding status before and after COVID-19 infection.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDefinition\u003c/strong\u003e \u003cp\u003eThe normal menstrual bleeding patterns need to meet the following three characteristics at the same time: normal frequency (21\u0026thinsp;~\u0026thinsp;35 days), normal duration (3\u0026thinsp;~\u0026thinsp;7 days) and normal flow volume (patient determined). A discrepancy in any one of the items will be defined as irregular periods.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSPSS 25.0 (SPSS Inc., Chicago, IL) was used to perform the statistical analyses of the questionnaire data. Continuous data were expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SD). Categorical data were expressed as frequency (percentage) and analyzed using the chi-square test.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics of the participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the completion of the questionnaire, only 38 of the 1033 (3.7%) women who filled out the questionnaire were not infected with the COVID-19. Due to the large number of infections, we could not set up a control group to observe the menstrual changes of women who were not infected with COVID-19 and could not make the comparisons between the two groups. However, of the 38 participants who were not infected with COVID-19, the vast majority (37/38, 97.4%) did not complain of menstrual changes. A total of 884 women with COVID-19 infections participated in the study. Demographics, baseline characteristics are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The average age of the participants was 35.56 years (ranging from 18\u0026ndash;58). The average age at menarche was 13.32 years (ranging from 9\u0026ndash;19). Most participants (83.5%, 738/884) had no AUB-related diseases. AUB-related diseases included leiomyoma (100/884, 11.3%), endometrial polyps (38/884, 4.3%), adenomyosis (29/884, 3.3%), abnormal coagulation (4/884, 0.5%) and malignant or atypical hyperplasia of endometrium (2/884, 0.2%). 19(2.1%) participants had more than one comorbidity.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDemographics, baseline characteristics of females with COVID-19. Data are n (%) unless specified otherwise. COVID-19, coronavirus disease 2019.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eParticipants(n\u0026thinsp;=\u0026thinsp;884)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge, years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35.56\u0026thinsp;\u0026plusmn;\u0026thinsp;8.03\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRange\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u0026ndash;58\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9(1.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u0026ndash;30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e265(30.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30\u0026ndash;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e356(40.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40\u0026ndash;50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e235(26.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19(2.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge of Menarche,years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRange\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u0026ndash;19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56(6.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u0026thinsp;~\u0026thinsp;14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e661(74.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e167(18.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAUB-related disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEndometrial polyps\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38(4.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLeiomyoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100(11.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdenomyosis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29(3.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMalignant or atypical hyperplasia of endometrium\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2(0.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAbnormal coagulation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4(0.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e738(83.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMore than one comorbidity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19(2.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e615 (69.6%) participants were diagnosed by RDTs and 269 (30.4%) by RT-PCR of SARS-CoV-2. Most of the participants (706/884, 79.8%) received at least three doses of vaccine. Only 49 (5.5%) had not been vaccinated. We counted the signs and symptoms associated with COVID-19 infection and found that fever (833/884, 94.2%), shortness of breath (676/884, 76.5%) and insomnia (671/884, 75.9%) were the most common symptoms. More than half of the participants' temperature rose above 38.5\u0026deg;C. Only 24 participants (2.7%) had CT-confirmed pneumonia. Other symptoms included sore throat (358/884, 40.5%), asthenia (239/884, 27.0%) and cough (171/884, 19.3%). 126(14.3%) participants had other symptoms. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eClinical characteristics of females with COVID-19 infection. Data are n (%) unless specified otherwise. COVID-19, coronavirus disease 2019. RT-PCR, reverse transcription polymerase chain reaction. RDTs, rapid diagnostic tests.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eParticipants(n\u0026thinsp;=\u0026thinsp;884)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConfirmed diagnosis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRT-PCR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e269 (30.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRDTs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e615 (69.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVaccination status:\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 dose\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14 (1.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 doses\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e115 (13.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 doses (booster 1 dose)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e634 (71.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 doses (booster 2 doses)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e72 (8.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNot vaccinated\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e49 (5.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSigns and symptoms of COVID-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFever\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e833(94.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;38.5℃\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e313(35.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;38.5℃\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e520(58.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCough\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e171(19.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSore throat\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e358(40.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShortness of breath\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e676(76.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInsomnia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e671(75.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAsthenia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e239(27.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePneumonia on CT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24(2.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther symptoms\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e126(14.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eMenstrual characteristics before COVID-19 infection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe menstrual characteristics before COVID-19 infection was shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The menstrual cycle of most participants(836/884, 94.6%) was within the normal range(21\u0026ndash;35 days).10(1.1%)participants showed their ranges of menstruation were frequent (\u0026lt; 21 days) and 38(4.3%) were infrequent (\u0026gt;35days). 765(86.5%) women had durations of 3\u0026ndash;7 days. 20(2.3%) participants\u0026rsquo; menstruation lasted less than 3 days and 99(11.2%) more than 7 days. We asked the participants to self-evaluate their menstrual flow.53(6.0%) participants thought their vaginal bleeding was less than normal, which meant they had no need to use sanitary napkins during menstruation. 699(79.1%) participants thought their menstrual blood loss were within normal range, using no more than 20 daily sanitary napkins during menstruation. And 132(14.9%) participants were suffering from heavy menstrual bleeding, which meant they need to change daily or night sanitary napkins frequently during menstruation, and the number was more than 20 pieces. Before COVID-19 infection, 591(66.9%) participants considered they had normal menstrual bleeding patterns while 293(33.1%) did not.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMenstrual characteristics before COVID-19 infection\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eParticipants(n\u0026thinsp;=\u0026thinsp;884)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCycle length\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;21d\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10(1.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21\u0026thinsp;~\u0026thinsp;25d\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130(14.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26\u0026thinsp;~\u0026thinsp;28d\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e323(36.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29\u0026thinsp;~\u0026thinsp;32d\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e296(33.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33\u0026thinsp;~\u0026thinsp;35d\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87(9.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;35d\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38(4.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eDuration of menstrual periods\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;3d\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20(2.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u0026thinsp;~\u0026thinsp;7d\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e765(86.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;7d\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99(11.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMenstrual flow\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLight\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53(6.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNormal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e699(79.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeavy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e132(14.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDysmenorrhea\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e406(45.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMild\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e342(38.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e126(14.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSevere\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10(1.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntermenstrual bleeding\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e125(14.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e759(85.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\"\u003eData are n (%) unless specified otherwise.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\"\u003ea. Definition: Hypomenorrhea: No need to use sanitary napkins during menstruation. Normal: Using no more than 20 daily sanitary napkins during menstruation. Heavy menstrual bleeding: Using more than 20 daily sanitary napkins during menstruation.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\"\u003eb. Definition: Mild: The pain did not affect daily study and life, and anodyne was not required. Moderate: The pain required anodyne and could be relieved. Severe: Anodyne could not relieve pain.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e406(45.9%) participants never had dysmenorrhea. Among participants with dysmenorrhea, the rates of self-assessed mild, moderate and severe were 38.7% (342/884), 14.3% (126/884) and 1.1% (10/884), respectively. There were 14.1% (125/884) participants suffering intermenstrual bleeding before COVID-19 infection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChanges of menstrual characteristics after COVID-19 infection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnly 222 (25.1%) of the 884 participants included in this study did not have any menstrual characteristics changes after COVID-19 infection. 662(74.9%) participants had one or more menstrual characteristics changes. Based on participants\u0026rsquo; self-assessments, we found that change incidence rates of cycle length, duration of menstrual periods and menstrual flow were 47.6%(421/884), 29.5%(261/884) and 41.7%(369/884),respectively(Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). 189(21.4%) participants\u0026rsquo; menstruation interval shortened by 1 or 2 weeks. Menstrual bleeding in 232(26.3%) participants was later than scheduled after infection with COVID-19, and 45(5.1%) of them were delayed by more than 3 weeks. The durations of menstrual periods were shortened by 3 days or more in 76(8.6%) participants and prolonged in 112(12.7%). 73(8.3%) participants suffered from dripping problem. 225(25.5%) participants considered their menstrual flow to be significantly reduced compared with that before infection with COVID-19, while 144(16.3%) participants felt menstrual flow increased. There were 752(85.1%) participants had no change in intermenstrual bleeding before and after infection, in which 685(77.5%) participants had no intermenstrual bleeding before and after infection and 67(7.6%) participants had. 74(8.4%) participants were free of intermenstrual bleeding before infection but developed bleeding after infection. In contrast, 58(6.6%) participants no longer had intermenstrual bleeding after COVID-19 infection. The changes of dysmenorrhea were more complicated. 371(42.0%) participants never had dysmenorrhea before or after COVID-19 infection. The degree of dysmenorrhea did not change in 257(29.1%) participants. 35(4.0%) participants had no dysmenorrhea before COVID-19 infection, but experienced dysmenorrhea of varying degrees after infection, including 31(3.5%) with mild dysmenorrhea, 3(0.3%) with moderate dysmenorrhea, and 1(0.1%) with severe dysmenorrhea. Among the participants who had dysmenorrhea before infection, 27(3.1%) had aggravated pain and 194(21.9%) had decreased dysmenorrhea grade.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eChanges of cycle length, duration of menstrual periods, menstrual flow after COVID-19 infection.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eParticipants(n\u0026thinsp;=\u0026thinsp;884)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCycle length\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInterval shortened by 2 weeks\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e59(6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInterval shortened by 1 week\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e130(14.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo change\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e463(52.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInterval prolonged by 1 week\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e143(16.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInterval prolonged by 2 weeks\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e44(5.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInterval prolonged more than 3 weeks\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e45(5.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDuration of menstrual periods\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShortened by 3 days or more\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e76(8.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo change\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e623(70.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProlonged by 3 days or more\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e112(12.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDripping\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e73(8.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMenstrual flow\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e225(25.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo change\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e515(58.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e144(16.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\"\u003eData are n (%) unless specified otherwise.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e62.3% (368/591) participants with previously regular menstruation believed that they had experienced menstrual changes after COVID-19 infection. While the menstrual change rate reached 73.0% (214/293) in participants with abnormal menstrual bleeding patterns before. The menstrual change rate of the irregular menstrual group was significantly higher than that of the regular menstrual group (73.0% vs. 62.3%, P\u0026lt;0.001).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is a descriptive study on the epidemiology and clinical menstrual characteristics of 884 women who were infected with COVID-19. This is the study with the largest number of participants in women's menstrual changes after COVID-19 infection in China. Unexpectedly, only 222 (25.1%) of the 884 participants included in this study did not have any menstrual characteristics changes after COVID-19 infection. The top three changes in menstrual characteristics were cycle length, menstrual flow and duration of menstrual periods, the incidence rates were 47.6%, 41.7% and 29.5%. It appears that women abnormal menstrual bleeding patterns are more likely to experience menstrual changes after COVID-19 infection.\u003c/p\u003e \u003cp\u003eAt present, there are few studies on menstrual changes before and after COVID-19 infection. The sample size of existing studies is generally small, and there is a lack of large-sample studies describing the characteristics of menstrual changes. We compared the results of this study with those of previous studies. Kezhen Li et al.\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e conducted a retrospective, cross-sectional study in which clinical and laboratory data from 177 women of child-bearing age diagnosed with COVID-19 were retrospectively reviewed. Of 177 participants with menstrual records, 45(25%) participants presented with menstrual volume changes, and 50(28%) participants had menstrual cycle changes, mainly a decreased volume (20%) and a prolonged cycle (19%). Compared with Li's study, the proportion of changed cycle length and menstrual volume were both higher in our study. However, the main clinical manifestations were decreased volume and prolonged cycle in two studies. Another study, which also conducted a menstrual survey of hospitalized participants after infection with COVID-19, suggested a menstrual change rate of 37.3% (59/158)\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. However, this study could not reflect the changes in menstrual characteristics of individual participants before and after COVID-19 infection. Another descriptive, cross-sectional study\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e involving 241 women infected with COVID-19 suggested that 86 (35.7%) patients experienced various changes in their menstrual patterns in the first three cycles after infection. The major menstrual changes in this study were delayed cycle (17.4%), heavier menstrual bleeding (7.4%) and longer period (4.1%). The results of menstrual cycle prolongation and menstrual period prolongation are the same as that of our study but the proportion is lower. However, in terms of menstrual flow, more participants in this study showed increased rather than decreased menstrual flow. A cross-sectional study from Jordan and Iraq showed that 47.2% (228/483) patients suffered from a change in the cycle length and the amount of blood loss\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. What's more, they found new intermenstrual bleeding in 10 (4.1%) patients after COVID-19 infection while this proportion reached 8.4% in our study. A cross sectional study reported an exacerbation of dysmenorrhea after COVID-19 infection, which is thought to be related to anxiety\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMenstruation is the cyclic, orderly sloughing of the uterine lining on account of the interactions of hormones produced by the hypothalamic-pituitary-ovarian (HPO) axis. SARS-CoV-2 infection can affect endocrine glands through various mechanisms including direct impacts, indirect damage through immune response and/or activation of HPO axis by the inflammatory status. The menstruation can be affected by different factors including infections, stress, weight changes, the use of medication and lifestyle changes, all of which are likely to change during COVID-19 infection. The current study found that coronaviruses enter hosts cells by binding of the viral spike (S) protein to cell receptors and, upon S protein priming, by host cell proteases\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. SARS-CoV-2 binds to the cell by the angiotensin-converting enzyme 2 (ACE2) receptor and the cell protease type II transmembrane serine protease (TMPRSS2) and for virus\u0026ndash;cell fusion, which indicates that the co-expression of ACE2 and TMPRSS2 can predict the potential of cells to be prone to infection\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. However, no co-expression of ACE2 with TMPRSS2 expression was found in the myometrium, uterus, ovaries or fallopian tubes when investigating published scRNA-seq datasets and reproductive tissue from patients undergoing hysterectomy, which suggest that female reproductive system is unlikely to be susceptible to infection by SARS-CoV2\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Oocytes seem to have the receptor/protease machinery to be susceptible to SARS-CoV-2 infection, but viral RNA in oocytes has not been detected so far\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Some studies have implicated cytokines (e.g., interleukin-6, interleukin-8, and tumor necrosis factor-alpha) as mediators of the inflammatory response to COVID-19, which can trigger a procoagulant state and may be a way to affect menstruation indirectly. The increased social distance, the surge in the number of infections and the economic losses during COVID-19 pandemic could cause severe stress, anxiety and depression\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, which were thought to cause menstrual disturbances\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. There have even been studies suggesting that the high prevalence of menstrual cycle irregularities in the general population during the COVID-19 pandemic is not related to a COVID-19 diagnosis but to anxiety, depression and/or stress levels\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Research has found thyroid dysfunction seems to be related to the severity of SARS-CoV-2 infection, with non-thyroidal illness syndrome, characterized by normal thyroid functions, decreased free T3 and thyroiditis as the most common clinical manifestations\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, which could also cause menstrual changes. One study found no difference in follicular-phase serum levels of FSH, LH, estradiol and AMH levels between women hospitalized for COVID-19 and controls and most patients\u0026rsquo; menstruations could return to the regularity before infection by 1 or 2 months\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. In 132 women with unexplained infertility before and after COVID-19 reported no statistically significant differences in terms of serum levels of AMH, FSH, LH, FSH/LH ratio or estradiol levels\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. But there is also study that came to the opposite conclusion. Significantly lower serum AMH levels were reported in 78 women with COVID-19 as compared with 151 healthy age-matched controls, with higher FSH, prolactin and testosterone levels\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThere are certain limitations to our study. As with the majority of questionnaire-based research, researchers are reliant upon true responses from the participants and accuracy when recall is required. Some previous studies have demonstrated measurement error when using self-report data regarding the menstrual cycle\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. In addition, there could be an element of bias within our sample. Our study was recruited through social media. Though we inform that all women with COVID-19 infection could complete the questionnaire regardless of the presence or absence of menstrual changes, those who have changes to their menstrual cycle during the pandemic were more likely to have complete the survey. What\u0026rsquo;s more, due to the huge difference in numbers between the infected and uninfected groups, we were unable to perform a valid correlation analysis. Finally, our data only provides a small insight into the changes experienced by females during the initial onset of COVID-19 pandemic; it does not provide any longitudinal data that may document the rise and fall of menstrual changes, which would be the focus of our investigations when we follow up participants.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, COVID-19 infection may cause menstrual changes in most women, with menstruation delayed, menstruation prolonged, menstrual flow decreased and dysmenorrhea relief as the most common clinical manifestations. Some women developed new intermenstrual bleeding. It is important to be aware of the menstrual changes after COVID-19 infection and to inform women about this issue. In the next step, we will follow up the menstrual status of the women when they were infected with COVID-19 for more than 3 months.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Medical Ethics Committee of Peking University People's Hospital, ethics number (2015PHB087-01). Written informed consent as exempted in accordance with the urgent situation and the Ethics Committee's rules. All methods were carried out in accordance with relevant institutional guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003en/a\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003en/a\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRuxue Han: Data curation, Formal analysis, Methodology, Validation, Writing- Original draft preparation, Writing- Reviewing and Editing\u003c/p\u003e\n\u003cp\u003eXiaolin Jiang: Data curation, Formal analysis, Methodology, Validation, Resources, Writing-Original draft preparation\u003c/p\u003e\n\u003cp\u003eXin Yang: Conceptualization, Methodology, Resource, Supervision, Writing- Reviewing and Editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003en/a\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHui DS, E I A, Madani TA, et al. The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health - The latest 2019 novel coronavirus outbreak in Wuhan, China [J]. Int J Infect Dis. 2020;91:264\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen N, Zhou M, Dong X, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study [J]. Lancet. 2020;395(10223):507\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuan WJ, Ni ZY, Hu Y, et al. Clinical Characteristics of Coronavirus Disease 2019 in China [J]. N Engl J Med. 2020;382(18):1708\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarnach ML, Laughlin-Tommaso SK. Evaluation and Management of Abnormal Uterine Bleeding [J]. Mayo Clin Proc. 2019;94(2):326\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi K, Chen G, Hou H, et al. Analysis of sex hormones and menstruation in COVID-19 women of child-bearing age [J]. Reprod Biomed Online. 2021;42(1):260\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuharam R, Agiananda F, Budiman YF, et al. Menstrual cycle changes and mental health states of women hospitalized due to COVID-19 [J]. PLoS ONE. 2022;17(6):e0270658.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaşkaldıran I, Vuraloğlu E, Bozkuş Y et al. Menstrual Changes after COVID-19 Infection and COVID-19 Vaccination [J]. Int J Clin Pract, 2022, 2022: 3199758.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Najjar MaA, Al-Alwany RR, Al-Rshoud FM, et al. Menstrual changes following COVID-19 infection: A cross-sectional study from Jordan and Iraq [J]. PLoS ONE. 2022;17(6):e0270537.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAolymat I, Khasawneh AI, Al-Tamimi M. COVID-19-Associated Mental Health Impact on Menstrual Function Aspects: Dysmenorrhea and Premenstrual Syndrome, and Genitourinary Tract Health: A Cross Sectional Study among Jordanian Medical Students [J]. Int J Environ Res Public Health, 2022, 19(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaporte M, Naesens L. Airway proteases: an emerging drug target for influenza and other respiratory virus infections [J]. Curr Opin Virol. 2017;24:16\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoffmann M, Kleine-Weber H, Schroeder S, et al. SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor [J]. Cell. 2020;181(2):271\u0026ndash;280e278.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoad J, Rudolph J, Rajkovic A. Female reproductive tract has low concentration of SARS-CoV2 receptors [J]. PLoS ONE. 2020;15(12):e0243959.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarragan M, Guill\u0026eacute;n JJ, Martin-Palomino N, et al. Undetectable viral RNA in oocytes from SARS-CoV-2 positive women [J]. Hum Reprod. 2021;36(2):390\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCzepczor-Bernat K, Swami V, Modrzejewska A et al. COVID-19-Related Stress and Anxiety, Body Mass Index, Eating Disorder Symptomatology, and Body Image in Women from Poland: A Cluster Analysis Approach [J]. Nutrients, 2021, 13(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemir O, Sal H, Comba C. Triangle of COVID, anxiety and menstrual cycle [J]. J Obstet Gynaecol. 2021;41(8):1257\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen BT, Pang RD, Nelson AL, et al. Detecting variations in ovulation and menstruation during the COVID-19 pandemic, using real-world mobile app data [J]. PLoS ONE. 2021;16(10):e0258314.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnto-Ocrah M, Valachovic T, Chen M, et al. Coronavirus Disease 2019 (COVID-19)-Related Stress and Menstrual Changes [J]. Obstet Gynecol. 2023;141(1):176\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakmaz T, Gundogmus I, Okten SB, et al. The impact of COVID-19-related mental health issues on menstrual cycle characteristics of female healthcare providers [J]. J Obstet Gynaecol Res. 2021;47(9):3241\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMedina-Perucha L, L\u0026oacute;pez-Jim\u0026eacute;nez T, Holst AS, et al. Self-Reported Menstrual Alterations During the COVID-19 Syndemic in Spain: A Cross-Sectional Study [J]. Int J Womens Health. 2022;14:529\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLui DTW, Lee CH, Chow WS, et al. Thyroid Dysfunction in Relation to Immune Profile, Disease Status, and Outcome in 191 Patients with COVID-19 [J]. J Clin Endocrinol Metab. 2021;106(2):e926\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMadendag IC, Madendag Y, Ozdemir AT. COVID-19 disease does not cause ovarian injury in women of reproductive age: an observational before-and-after COVID-19 study [J]. Reprod Biomed Online. 2022;45(1):153\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing T, Wang T, Zhang J, et al. Analysis of Ovarian Injury Associated With COVID-19 Disease in Reproductive-Aged Women in Wuhan, China: An Observational Study [J]. Front Med (Lausanne). 2021;8:635255.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmall CM, Manatunga AK, Marcus M. Validity of self-reported menstrual cycle length [J]. Ann Epidemiol. 2007;17(3):163\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, menstruation, abnormal uterine bleeding","lastPublishedDoi":"10.21203/rs.3.rs-3281461/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3281461/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e In December, 2019, a pneumonia associated with the 2019 novel coronavirus (2019-nCoV) emerged in Wuhan, China. Since December 2022, China has adjusted anti-epidemic policies and a large-scale COVID-19 infection has emerged. We aimed to explore the menstrual changes of women before and after infection with the COVID-19.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This study was designed as a descriptive, cross-sectional study. We collected data from participants infected with COVID-19 from January 1, 2023 to March 1, 2023 by issuing electronic questionnaires. Women were invited to fill out the questionnaire about their menstrual characteristic after COVID-19 infection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 884 women with COVID-19 infections participated in the study. 662(74.9%) participants experienced changes in one or more of menstrual characteristics. Cycle length seemed to be the characteristic most likely to change (47.6%), followed by menstrual flow (41.7%), duration of menstrual periods (29.5%), degree of dysmenorrhea (29.0%) and intermenstrual bleeding (14.9%). The main clinical manifestations were menstruation delayed (26.3%), menstrual flow decreased (25.5%), dysmenorrhea relief (21.9%) and menstruation prolonged (21.0%). And we found new intermenstrual bleeding in 8.4% participants after COVID-19 infection. The menstrual change rate of the irregular menstrual group was significantly higher than that of the regular menstrual group (73.0% vs. 62.3%, P＜0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e COVID-19 infection may cause menstrual changes in most women. It is important to be aware of the menstrual changes after COVID-19 infection and to inform women about this issue.\u003c/p\u003e","manuscriptTitle":"Analysis of Women's Menstrual Changes after COVID-19 Infection: a Descriptive Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-19 09:55:13","doi":"10.21203/rs.3.rs-3281461/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c072d091-638f-4070-863c-28ad8ba76c41","owner":[],"postedDate":"September 19th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-19T04:10:31+00:00","versionOfRecord":[],"versionCreatedAt":"2023-09-19 09:55:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3281461","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3281461","identity":"rs-3281461","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}