What Do We Know About Abnormal Uterine Bleeding Following Vaccination Against Covid-19 After Two and A Half Years Of Experience? A Systematic Review and Meta- Analysis

What Do We Know About Abnormal Uterine Bleeding Following Vaccination Against Covid-19 After Two and A Half Years Of Experience? 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A Systematic Review and Meta- Analysis Nataly Zilberman Sharon, Ron Maymon, Ran Svirsky, Ilya Novikov, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3759326/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 Vaccination against COVID-19 is regarded to be most effective in preventing morbidity and mortality and limiting the spread of the virus. Publications in the media and scientific journals have raised a concern regarding abnormal uterine bleeding as a COVID-19 vaccine side effect. We aimed to estimate the prevalence of abnormal uterine bleeding after vaccination against COVID-19. Medline, PubMed, Embase and Google Scholar databases were searched between January 2020 and June 2022. English-language studies, including at least four women with abnormal uterine bleeding. Eligible studies were assessed for risk of bias using the Newcastle-Ottawa Scale. Data synthesis was accomplished by meta-analysis of frequencies of the abnormal uterine bleeding rate and meta-regression of the rate adjusted for the type of vaccine and number of vaccinations. There were 12 eligible studies including a total of 1,132,613 women of whom 41,344 (31.3%, 95CI 11.7-55.1%) reported abnormal bleeding following vaccination. Nine studies included clinical presentation: the abnormality was menorrhagia in 38%, irregular menses in 28%, intermenstrual bleeding in 12% and delayed menses in 9%). There was considerable heterogeneity in results which could not be accounted for by the method of recording abnormal bleeding, vaccine manufacturer or number of doses. COVID-19 vaccine carries the inconvenience of abnormal uterine bleeding in almost one-third of the women vaccinated, mostly irregular menses or menorrhagia. In addition, the innovative vaccine mechanism could be used against various potential targets, further highlighting the need to assess vaccine side effects. Health sciences/Medical research Health sciences/Medical research/Epidemiology Abnormal uterine bleeding COVID-19 Side effects Vaccine Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The COVID-19 pandemic has led to considerable morbidity and mortality worldwide, with a current count of 533 million infected and 6.34 million deaths [ 1 ]. Social distancing and quarantines have significantly reduced the infection rate. This solution creates a burden on the economy as well as on physical and psychosocial well-being [ 2 – 6 ]. Social distancing is not considered a long-term solution, but the absence of effective treatment led to the prompt development of vaccines [ 7 , 8 ]. Currently, vaccination is the leading cause of the worldwide decline in morbidity and mortality decline and is considered a cost-effective tool against COVID-19 strains [ 9 ]. Monitoring for adverse effects has focused on reactogenicity, cardiovascular side effects and anaphylactic response to the vaccine. Common short term side effects include injection site symptoms, fever, headache, fatigue and myalgia [ 10 , 11 ]. Vaccination against COVID-19 in children and adolescents has been shown to be both effective and safe, even when there are underlying comorbidities [ 12 – 15 ]. Children and adolescents usually suffer from the same short term side effects as adults [ 14 ]. The reported association between the COVID-19 vaccine and myocarditis [ 16 , 17 ] is predominantly in young men, with a median age of 24. The clinical course is often mild with favorable prognosis, as reported also in children and adolescents [ 16 , 18 ]. Side effects can lead to hesitation regarding vaccination even when safety is not an issue, threatening vaccine effectiveness [ 19 ]. In menopausal women, abnormal uterine bleeding related to vaccine may lead to unnecessary diagnostic procedure [ 20 , 21 ]. Addressing possible side effects and providing evidence-based information to the public is essential to reduce stress related to the encounter with a side effect and increase willingness to be vaccinated. Abnormal uterine bleeding has been reported as a vaccine side effect to public health databases, as well as by the media [ 22 – 24 ]. Even though not included in initial safety studies of the vaccine or vaccine adverse effects databases, abnormal uterine bleeding was reported actively by vaccinated individuals worldwide [ 25 – 27 ]. A few mechanisms have been suggested for the phenomenon, including vaccine-related stress, immune-induced thrombocytopenia, protein similarity and ACE2 related side effects [ 28 – 30 ]. Emerging evidence suggests that abnormal uterine bleeding is short lasting, not requiring treatment or hospitalization [ 21 – 36 ]. Nevertheless, abnormal uterine bleeding disrupts daily tasks, hindering normal activities and inducing associated stress. The public has not yet been assured regarding the nature of abnormal uterine bleeding post COVID-19 vaccination and confusion about the COVID-19 side effects remains [ 37 , 38 ]. This systematic review aims to assess and evaluate the rate of abnormal uterine bleeding post COVID-19 vaccination as well as clinical presentation. We hypothesize that abnormal uterine bleeding post COVID-19 vaccine will be observed with a similar occurrence pattern as that of fertility age. Materials and methods Data sources A literature review was carried out by searching in the Medline, PubMed, Embase and Google Scholar databases for cohort studies or case series related to abnormal uterine bleeding following COVID-19 vaccination. The following MeSH (Medical Subject Headings) was used with text words and abbreviations for the terms: “COVID-19 vaccine”, “SARS-CoV-2 vaccine”, “Corona vaccine”, “abnormal uterine bleeding”, “dysfunctional uterine bleeding”, ”menstrual cycle”, “metrorrhagia”, “menorrhagia”, “intermenstrual bleeding”, and “menses”. The search protocol was registered on PROSPERO (CRD42022324122; www.crd.york.ac.uk/PROSPERO) in line with current recommendations and reported as per PRISMA 2009 guidelines (www. Prisma statement.org). Study selection Peer-reviewed articles in English language journals published between May 2020 and June 2022 were selected. Only those including at least four cases of abnormal uterine bleeding among a cohort of vaccinated women were included. Those with selective recruitment or duplication of previously published data were excluded. Study appraisal Full texts of eligible studies were assessed independently by two reviewers (R.S. and N.Z.S.) for content: author, date of publication, country, and study characteristics, including the method of information gathering on abnormal uterine bleeding. The latter was obtained either retrospectively from a questionnaire following a fixed number of cycles since the vaccination or prospectively using a diary. For each study we obtained the number of vaccinated participants and of which, those who reported of abnormal uterine bleeding. In addition to extracting the total number of women and those having abnormal bleeding, the following data was sought: clinical presentation of abnormal bleeding, the vaccine manufacturer, number of vaccine doses, and gynecological background. The summarized results were cross checked, and any inconsistencies were discussed among reviewers and a consensus was reached. Quality assessment The quality of the included studies was assessed using the Newcastle-Ottawa Scale (NOS) for cohort studies. The NOS judges each study by three categories: selection of study group, comparability of the groups and the ascertainment of the outcome of interest. Each item was scored “low”, “medium” or “high” risk for bias. No study was excluded based on the risk for bias. Two independent reviewers (R.S. and N.Z.S.) undertook the quality assessment. Discrepancies were resolved with evaluation from the third reviewer (R.M.). Data synthesis Two types of statistical analysis were performed: (1) meta-analysis of frequencies of the abnormal uterine bleeding rate and (2) meta-regression of the rate adjusted for the type of vaccine and number of vaccinations. Meta-analysis was performed for pooling results overall and from sub-groups of studies and to evaluate the variation in study outcomes between studies using the “metaprop” (StataCorp) routine. A random effects model was used and exact confidence intervals for proportion in pooling the results to adjust for variations among studies. Statistical heterogeneity was assessed with the I 2 statistic (the proportion of variation in study estimates because of heterogeneity rather than sampling error) and the p-value derived for the hypothesis of no heterogeneity. The results were presented as: number of studies, pooled rate estimated by random effects model with its 95% confidence interval (CI). Meta-regression used data from studies with the available information on potential co-variables. Fixed effect multiple logistic regression of grouped data was used to obtain the upper bound (i.e., optimistic) estimation of the p-value. All analyses were done using STATA SE software. All tests are two-sided. P-values <0.05 were considered statistically significant. However, due to very limited number of studies, p-values between 0.05 and 0.10 were described as potentially interesting. No corrections were performed for multiple comparisons since this was a hypothesis generating type of study. Results Study selection and characteristics Out of 506 publications identified 12 studies were eligible (Figure 1). Ten included women from a single country, one from a region and one worldwide. In four studies information on abnormal bleeding was derived from a diary and in eight from a questionnaire (Table 1). A total of 1,132,613 women were included. Table 1 | Eligible studies: design features and abnormal uterine bleeding rate Study Published Country Data Cycles Total no. of subjects Rate n subjects n affected 95% CI Alghamdi 86 8 October 2021 Saudi Arabia Question. NK 2,874 0.9% 2,874 25 0.6-1.3% Male 26 15 November 2021 UK Question. 10 1,273 54.8% 1,273 698 52.0-57.6% Alvergne 77 6 December 2021 UK Question. 3 4,989 20.0% 4,989 998 18.9-21.1% Trogstad 87 14 January 2022 Norway Question. 1 5,688 59.7% 5,688 3,398 58.4-62.0% KMN Lee 36 11 February 2022 USA Question. 3 39,129 53.2% 39,129 20,831 52.7-53.7% Lagana 35 18 February 2022 Italy Question. 1 164 60.0% 164 98 51.8-67.3% Muhaidat 34 28 March 2022 MENA Question. 2 2,269 66.3% 2,269 1,504 64.3-68.2% Von Woon 33 30 March 2022 UK Diary 6 79 73.4% 79 58 62.2-82.7% Edelman 32 1 April 2022 USA Diary 3 2,403 5.2% 2,403 125 4.3-6.2% Zhang 88 7 April 2022 Worldwide Diary 11 1,061,886 1.2% 1,061,886 13,118 1.2-1.3% Sualeh 89 14 May 2022 Pakistan Question. 4 384 38.0% 384 146 33.1-43.1% Rogers 31 16 May 2022 UK Diary 8 11,475 3.0% 11,475 344 2.7-3.3% The clinical criteria for identifying abnormal uterine bleeding were similar between the studies. The age range included years when fertility is expected, except for one which also included some women in the expected menopausal period [36]. Mean age was available for nine studies and ranged from 21 to 36; mean parity was included in 5 studies with range 0.4-1.74. Some studies also reported for women with abnormal bleeding, those with a previous menstrual abnormality or hormonal use. Quality assessment of included studies Table 2 shows the risk of bias among the studies, overall and according to three sub-categories. Only one study had a high risk and at most two had high risk in a sub-category. Table 2 | Risk of bias using the Newcastle-Ottawa Scale (NOS) for cohort studies: number according to sub-categories and overall Risk of bias Sub-category Overall Selection of groups Comparability of groups Outcome ascertainment High 1 2 1 1 Medium 7 5 9 8 Low 4 5 2 3 Synthesis of results In total 41,344 women reported abnormal uterine bleeding after the COVID-19 vaccine (31.3%, 95%CI 11.7-55.1%). Nine studies included information on the clinical presentation of abnormal uterine bleeding (Table 3). In most papers, the clinical presentation was reported per woman rather than per cycle. Excluding those with type unknown or other, the most common types of menses were in 41,344 women, of whom 15,751 (38%) suffered from menorrhagia, 11,734 (28%) had irregular menses, 5156 (12%) women reported intermenstrual bleeding and 3714 (9%) had delayed menses. Table 3 | Clinical presentation of abnormal uterine bleeding** in 9 studies Study Type of menstrual abnormality Irregular Intermenstrual bleed Menorrhagia Hypomenorrhea Amenorrhea Delayed Male 26 698 (41%) 0 (0%) 412 (24%) 180 (11%) 0 (0%) 392 (23%) Trogstad 87 1209 (38%) 0 (0%) 1420 (44%) 0 (0%) 0 (0%) 567 (18%) KMN Lee 36 4358 (23%) 1350 (7%) 13262 (70%) 0 (0%) 0 (0%) 0 (0%) Lagana 35 94 (51%) 0 (0%) 28 (15%) 37 (20%) 0 (0%) 26 (14%) Muhaidat 34 680 (52%) 48 (4%) 424 (32%) 0 (0%) 151 (12%) 0 (0%) Von Woon 33 53 (62%) NK 12 (14%) 20 (23%) 0 (0%) 0 (0%) Edelman 32 NK NK 125 (100%) NK NK NK Zhang 88 4626 (33%) 3759 (27%) 28 (<1%) 1043 (8%) 1655 (12%) 2698 (20%) Sualeh 89 16 (13%) 0 (0%) 40 (32%) 35 (28%) 0 (0%) 32 (26%) *First author Figure 2 shows the rate of abnormal uterine bleeding in each study and overall. There was highly statistically significant heterogeneity (I 2 =99.99%, P<0.00001). One source of homogeneity is the method used to collect information on menstrual bleeding. There was a highly statistically significant difference in the rate between the diary and questionnaire studies. The pooled rate was 9.7% (95%CI 5.7-14.6%) in the former and 41.3% (95%CI 24.1-59.8%) in the latter, P <0.000. Nevertheless, there remained very high heterogeneity with those using a diary (Figure 3) or a questionnaire (Figure 4). In the 5 studies reporting hormonal treatment among women with abnormal bleeding following vaccination the proportion was 6%, 9%, 19%, 28% and 59%, a mean of 22%. In the 7 studies reporting abnormal uterine bleeding or menstrual abnormality in previous cycles among those affected after vaccination, the proportion was 0%, 0%, 14%, 19%, 23%, 25% and 63%, a mean of 16%. Comment Principal findings Abnormal uterine bleeding post COVID-19 vaccination was identified in almost a third (31.3%, 95CI 11.7%-55.1%) of vaccinated women, being a common side effect of the vaccine and possibly a cause of vaccine hesitancy. Strength and limitations The novelty of this current review and meta-analysis is the attempt to estimate the extent of abnormal uterine bleeding side effect after COVID-19 vaccine. We used statistical methods in order to overcome methodological differences between the included studies in order to consider them equally. The main limitation of this study is that in most of the studies information on abnormal uterine bleeding was obtained retrospectively from questionnaires. In the questionnaire studies, abnormal uterine bleeding was diagnosed by the patient, as a change from the familiar menstrual bleeding in length, intensity or occurrence of the bleeding. The population included in the various studies originated from different countries and it is possible that ethnic or social differences would influence the reporting of symptoms. Another possible influence might originate from various management policies of COVID-19 virus outbreaks, quarantine policies and avoidance from medical centers during outbreaks. Interpretation and comparison with existing literature SARS-CoV-2 virus was defined as a pandemic by the WHO at March 2020 [39], causing the largest pandemic described since the flu pandemic in 1918. SARS-CoV-2 virus spreads by respiratory droplets, causing systemic symptoms of fever, myalgia and fatigue along the common respiratory symptoms such as dyspnea and cough [40]. Abdominal pain, vomiting and diarrhea are also considered quite common. COVID-19 is known to cause a variety of complications ranging from sepsis, acute respiratory distress syndrome and disseminated intravascular coagulopathy to neurological, renal and hepatic injury [41]. SARS-CoV-2 virus invades the cell via angiotensin-converting enzyme-2 receptor (ACE2). ACE2 is found in the female reproductive tract as well as ovarian granulosa cells, implying that SARS-CoV-2 might target female reproduction [42]. Recent literature suggests that the ovaries and fertility of reproductive age women is not impaired by the SARS-CoV-2 [43-45]. Non-the-less, changes in the function of angiotensin II and ACE2 have the ability to cause menstrual irregularities [28]. Angiotensin II promotes vasoconstriction of the spiral arteries and influences myometrial activity, consequently inducing menstruation [46]. Menstrual cycle is considered the fifth vital sign, indicating a woman’s well-being as well as being a marker of reproductive health [47, 48]. Menstrual cycle disorders serves as an indication for disease, as well as a prognostic factor for metabolic disorders such as diabetes mellitus and dyslipidemia [49], cardiovascular disease and related mortality [50-52], along with overall premature mortality (age<70 years) [53]. The menstrual cycle is controlled by the hypothalamic–pituitary–ovarian (HPO) axis, and influenced by immune status and chronic illnesses, psychological illness and stress, medications, body mass index, smoking, socioeconomic status and educational level [54-57]. The menstrual cycle usually begins when a woman is at 12-15 years of age (median 12.2 years) and lasts until menopause at 51 years of age [58, 59]. Cycle length varies between 24-38 days, with up to 8 bleeding days in the reproductive years (21-35). Menstrual disorders can present as irregular menses, frequent or infrequent menses, heavy or light menstrual bleeding, intermenstrual bleeding or prolonged bleeding [60]. Abnormal uterine bleeding is reported to be 3-30% during the reproductive years [60]; age-specific rates are not available. It is estimated that one-third of women suffer from abnormal uterine bleeding at cycles during their reproductive lives [61]. This is a similar rate to that estimated in the current analysis for the small number of cycles (up to 11) following vaccination, thus confirming our hypothesis. Considerable statistical heterogeneity in the abnormal uterine bleeding rate after COVID-19 vaccination was observed since most studies had a narrow confidence interval but the variance between studies was high. An explanation was sought and one factor was the difference in rate for diary versus questionnaire studies. In general diary studies are superior to questionnaire studies in the level of reporting and validity of the side effect reported, with less recall bias [62]. However, in our analysis the reported rate was lower in the diary studies and there was significant heterogeneity within the two types of study design. About one-half of women suffering from abnormal uterine bleeding do not reach out for medical attention [63, 64]. Abnormal uterine bleeding can range from mild to severe and has a significant impact on a women’s quality of life [65]. In some religious societies such as Judaism or Islam abnormal uterine bleeding prevents intimacy between partners and might impede fertility [66, 67]. Abnormal uterine bleeding was reported after infection with SARS-CoV-2 [29,68-71] And there are reports of more premenstrual symptoms along with prolonged menses, missed periods (infrequent menses), light and heavy menstrual bleeding. The reports after COVID-19 infection are similar to those reported after COVID-19 vaccination, suggesting a common mechanism may be responsible for the effect. The hypothalamic-pituitary-ovarian axis is susceptible to stress [72, 73]. In addition, acute stress enhances sympathetic activity in different brain areas and especially the amygdala, causing pregnanolone fluctuations, potentially leading to menstrual irregularities [74]. COVID-19 pandemic induced considerable stress that was previously linked to menstrual changes [66, 75, 76]. Stress -related changes could potentially explain symptoms also after SARS-CoV-2 vaccine. The current study estimates that there is abnormal uterine bleeding after vaccination in almost one-third of vaccinated females. Individual studies also report that this is transient and mild [31-36]. The current analysis found no specific vaccine manufacturer to influence the abnormal bleeding rate. There is evidence suggesting that abnormal uterine bleeding following vaccination against SARS-CoV-2 is more common in women who were previously sick with COVID-19 [35, 77]. Composition of the uterine microbiome is researched as another possible cause for the phenomenon [78, 79]. Abnormal uterine bleeding was reported previously after vaccinations against the papilloma virus, hepatitis B and typhoid [80-82]. One of the suggested mechanisms is immune mediated vaccine induced thrombocytopenia [83]. Immune induced thrombocytopenia was reported after COVID-19 vaccine as well as thrombotic thrombocytopenia [84]. Those thrombocytopenia’s usually present with a clinical picture that includes more than abnormal uterine bleeding, such as easy bruising and bleeding from mucosal surfaces. It seems unlikely that thrombocytopenia is the explanation for abnormal uterine bleeding associated with COVID-19 vaccine given the clinical features described. This assumption was declined in the context of abnormal uterine bleeding seen after human papilloma virus vaccine [85]. Conclusions Our analysis estimates that COVID-19 vaccine carries an inconvenient side effect of abnormal uterine bleeding in almost one-third of women vaccinated. Other studies in the literature indicate that this is mostly short lasting and mild. The consensus remains that COVID-19 vaccination has a substantial protective value against the disease, limiting the disease extent and severity. Misinformation regarding the menstrual side effects of the vaccine could limit acceptance as opposed to evidence-based information. We highly recommend for continuation of COVID-19 vaccine as indicated and enabling access to accurate information regarding potential side effect. The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request. Declarations The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. The authors have no relevant financial or non-financial interests to disclose. References Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Inf Dis. 20:533-4. doi: 10.1016/S1473-3099(20)30120-1 Chung PC, Chan TC. 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COVID-19: Current understanding of its pathophysiology, clinical presentation and treatment. Postgrad Med J. 2021;97(1147):312-320. doi: 10.1136/postgradmedj-2020-138577 Jing Y, Run-Qian L, Hao-Ran W, Hao-Ran C, Ya-Bin L, Yang G, et al. Potential influence of COVID-19/ACE2 on the female reproductive system. Mol Hum Reprod. 2020;26:367-73. doi: 10.1093/molehr/gaaa030 Mohr-Sasson A, Haas J, Abuhasira S, Sivan M, Doitch Amdurski H, Dadon T, et al. The effect of Covid-19 mRNA vaccine on serum anti-Müllerian hormone levels. Hum Reprod. 2022;37:534-41. 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. Reprod Biomed Online. 2022;45:153-8. Bentov Y, Beharier O, Moav-Zafrir A, Kabessa M, Godin M, Greenfield CS, et al. Ovarian follicular function is not altered by SARS-CoV-2 infection or BNT162b2 mRNA COVID-19 vaccination. Hum Reprod. 2021;36(9):2506-3. doi: 10.1093/humrep/deab182 Vaz-Silva J, Carneiro MM, Ferreira MC, Pinheiro SV, Silva DA, Silva-Filho AL, et al. The vasoactive peptide angiotensin-(1-7), its receptor Mas and the angiotensin-converting enzyme type 2 are expressed in the human endometrium. Reprod Sci. 2009;16:247-56. doi: 10.1177/1933719108327593 American Academy of Pediatrics Committee on Adolescence; American College of Obstetricians and Gynecologists Committee on Adolescent Health Care, Diaz A, Laufer MR, Breech LL. Menstruation in girls and adolescents: using the menstrual cycle as a vital sign. Pediatrics. 2006;118:2245-2250. doi: 10.1542/peds.2006-2481 ACOG Committee Opinion No. 651: Menstruation in girls and adolescents: using the menstrual cycle as a vital sign. Obstet Gynecol. 2015;126:e143-e146. doi: 10.1097/AOG.0000000000001215 Rostami Dovom M, Ramezani Tehrani F, Djalalinia S, Cheraghi L, Behboudi Gandavani S, Azizi F. Menstrual cycle irregularity and metabolic disorders: a population-based prospective study. PLoS One. 2016;11(12):e0168402. doi: 10.1371/journal.pone.0168402 Solomon CG, Hu FB, Dunaif A, Rich-Edwards JE, Stampfer MJ, Willett WC, et al. Menstrual cycle irregularity and risk for future cardiovascular disease. J Clin Endocrinol Metab. 2002;87(5):2013-7. doi: 10.1210/jcem.87.5.8471 Gast GC, Grobbee DE, Smit HA, Bueno-de-Mesquita HB, Samsioe GN, van der Schouw YT. Menstrual cycle characteristics and risk of coronary heart disease and type 2 diabetes. Fertil Steril. 2010;94(6):2379-81. doi: 10.1016/j.fertnstert.2010.03.044 Wang ET, Cirillo PM, Vittinghoff E, Bibbins-Domingo K, Cohn BA, Cedars MI. Menstrual irregularity and cardiovascular mortality. J Clin Endocrinol Metab. 2011;96:E114-8. doi: 10.1210/jc.2010-1709 Wang YX, Arvizu M, Rich-Edwards JW, Stuart JJ, Manson JE, Missmer SA, et al. Menstrual cycle regularity and length across the reproductive lifespan and risk of premature mortality: prospective cohort study. BMJ. 2020;371:m3464. doi: 10.1136/bmj.m3464 Jung EK, Kim SW, Ock SM, Jung KI, Song CH. Prevalence and related factors of irregular menstrual cycles in Korean women: the 5th Korean National Health and Nutrition Examination Survey (KNHANES-V, 2010-2012). J Psychosom Obstet Gynaecol. 2018;39:196-202. doi: 10.1080/0167482X.2017.1321631 Rowland AS, Baird DD, Long S, Wegienka G, Harlow SD, Alavanja M, et al. Influence of medical conditions and lifestyle factors on the menstrual cycle. Epidemiology. 2002;13:668-74. doi: 10.1097/00001648-200211000-00011 Yamamoto K, Okazaki A, Sakamoto Y, Funatsu M. The relationship between premenstrual symptoms, menstrual pain, irregular menstrual cycles, and psychosocial stress among Japanese college students. J Physiol Anthropol. 2009;28:129-36. doi: 10.2114/jpa2.28.129 Kwak Y, Kim Y, Baek KA. Prevalence of irregular menstruation according to socioeconomic status: A population-based nationwide cross-sectional study. PLoS One. 2019;14:e0214071. doi: 10.1371/journal.pone.0214071 Biro FM, Pajak A, Wolff MS, Pinney SM, Windham GC, Galvez MP, et al. Age of menarche in a longitudinal US cohort. J Pediatr Adolesc Gynecol. 2018;31:339-45. doi: 10.1016/j.jpag.2018.05.002 McKinlay SM. The normal menopause transition: an overview. Maturitas. 1996;23:137-45. doi: 10.1016/0378-5122(95)00985-x Munro MG, Critchley HOD, Fraser IS; FIGO Menstrual Disorders Committee. The two FIGO systems for normal and abnormal uterine bleeding symptoms and classification of causes of abnormal uterine bleeding in the reproductive years: 2018 revisions. Int J Gynaecol Obstet. 2018;143:393-408. doi: 10.1002/ijgo.12666. Erratum in: Int J Gynaecol Obstet. 2019;144:237. Liu Z, Doan QV, Blumenthal P, Dubois RW. A systematic review evaluating health-related quality of life, work impairment, and health-care costs and utilization in abnormal uterine bleeding. Value Health. 2007;10:183-94. doi: 10.1111/j.1524-4733.2007.00168.x Verbrugge LM. Health diaries. Med Care. 1980;18(1):73-95. doi: 10.1097/00005650-198001000-00006 Harlow SD, Campbell OM. Epidemiology of menstrual disorders in developing countries: a systematic review. BJOG. 2004;111:6-16. doi: 10.1111/j.1471-0528.2004.00012.x Fraser IS, Mansour D, Breymann C, Hoffman C, Mezzacasa A, Petraglia F. Prevalence of heavy menstrual bleeding and experiences of affected women in a European patient survey. Int J Gynaecol Obstet. 2015;128:196-200. doi: 10.1016/j.ijgo.2014.09.027 Frick KD, Clark MA, Steinwachs DM, Langenberg P, Stovall D, Munro MG; STOP-DUB Research Group. Financial and quality-of-life burden of dysfunctional uterine bleeding among women agreeing to obtain surgical treatment. Womens Health Issues. 2009;19(1):70-8. doi: 10.1016/j.whi.2008.07.002 Haimov-Kochman R, Hurwitz A. [Religious (Halachic) infertility]. Harefuah. 2009;148:271-4. Hebrew. Tan DA, Haththotuwa R, Fraser IS. Cultural aspects and mythologies surrounding menstruation and abnormal uterine bleeding. Best Pract Res Clin Obstet Gynaecol. 2017;40:121-33. doi: 10.1016/j.bpobgyn.2016.09.015 Phelan N, Behan LA, Owens L. The impact of the COVID-19 pandemic on women's reproductive health. Front Endocrinol (Lausanne). 2021;12:642755. doi: 10.3389/fendo.2021.642755 Medina-Perucha L, López-Jiménez T, Holst AS, Jacques-Aviñó C, Munrós-Feliu J, Martínez-Bueno C, et al. Self-reported menstrual alterations during the COVID-19 syndemic in Spain: A cross-sectional study. Int J Womens Health. 2022;14:529-44. doi: 10.2147/IJWH.S354655 Khan SM, Shilen A, Heslin KM, Ishimwe P, Allen AM, Jacobs ET, et al. SARS-CoV-2 infection and subsequent changes in the menstrual cycle among participants in the Arizona CoVHORT study. Am J Obstet Gynecol. 2022;226:270-3. doi: 10.1016/j.ajog.2021.09.016 Lebar V, Laganà AS, Chiantera V, Kunič T, Lukanović D. The effect of COVID-19 on the menstrual cycle: A systematic review. J Clin Med. 2022;11:3800. doi: 10.3390/jcm11133800 Huhmann K. Menses requires energy: a review of how disordered eating, excessive exercise, and high stress lead to menstrual irregularities. Clin Ther. 2020;42:401-7. doi: 10.1016/j.clinthera.2020.01.016 Morrison AE, Fleming S, Levy MJ. A review of the pathophysiology of functional hypothalamic amenorrhoea in women subject to psychological stress, disordered eating, excessive exercise or a combination of these factors. Clin Endocrinol (Oxf). 2021;95:229-38. doi: 10.1111/cen.14399 Ossewaarde L, Hermans EJ, van Wingen GA, Kooijman SC, Johansson IM, Bäckström T, et al. Neural mechanisms underlying changes in stress-sensitivity across the menstrual cycle. Psychoneuroendocrinology. 2010;35:47-55. doi: 10.1016/j.psyneuen.2009.08.011 Takmaz T, Gundogmus I, Okten SB, Gunduz A. The impact of COVID-19-related mental health issues on menstrual cycle characteristics of female healthcare providers. J Obstet Gynaecol Res. 2021;47:3241-9. doi: 10.1111/jog.14900 Ozimek N, Velez K, Anvari H, Butler L, Goldman KN, Woitowich NC. Impact of stress on menstrual cyclicity during the coronavirus disease 2019 pandemic: A survey study. J Womens Health (Larchmt). 2022;31:84-90. doi: 10.1089/jwh.2021.0158 Alvergne A, Kountourides G, Argentieri MA et al. COVID-19 vaccination and menstrual cycle changes: A United Kingdom (UK) retrospective case-control study. medRxiv. Published online November 23, 2021. Winters AD, Romero R, Gervasi M, Gomez-Lopez N, Tran MR, Garcia-Flores V, et al. Does the endometrial cavity have a molecular microbial signature? Sci Rep. 2019;9:9905. https://doi.org/10.1038/s41598-019-46173-0 Minakshi R, Rahman S, Ayaggari A, Dutta D, Shankar A. Understanding the Trauma of Menstrual Irregularity After COVID Vaccination: A Bird's-Eye View of Female Immunology. Front Immunol. 2022;13:906091. doi: 10.3389/fimmu.2022.906091 Suzuki S, Hosono A. No association between HPV vaccine and reported post-vaccination symptoms in Japanese young women: Results of the Nagoya study. Papillomavirus Res. 2018;5:96-103. doi: 10.1016/j.pvr.2018.02.002 Shingu T, Uchida T, Nishi M, Hayashida K, Kashiwagi S, Hayashi J, et al. Menstrual abnormalities after hepatitis B vaccine. The Kurume Medical Journal 1982;29:123-5. ‏ Lamb AR. Experiences with prophylactic typhoid vaccination: its effect on menstruation. Arch Intern Med. 1913;12:565-77. ‏ Perricone C, Ceccarelli F, Nesher G, Borella E, Odeh Q, Conti F, et al. Immune thrombocytopenic purpura (ITP) associated with vaccinations: a review of reported cases. Immunol Res. 2014;60(2-3):226-35. doi: 10.1007/s12026-014-8597-x Saluja P, Gautam N, Yadala S, Venkata AN. Thrombotic thrombocytopenic purpura (TTP) after COVID-19 vaccination: A systematic review of reported cases. Thromb Res. 2022;214:115-21. doi: 10.1016/j.thromres.2022.04.020 Genovese C, LA Fauci V, Squeri A, Trimarchi G, Squeri R. HPV vaccine and autoimmune diseases: systematic review and meta-analysis of the literature. J Prev Med Hyg.. 2018;59:E194-E199. doi: 10.15167/2421-4248/jpmh2018.59.3.998 Alghamdi AN, Alotaibi MI, Alqahtani AS, Al Aboud D, Abdel-Moneim AS. BNT162b2 and ChAdOx1 SARS-CoV-2 Post-vaccination side-effects among Saudi vaccinees. Front Med. (Lausanne). 2021;8:760047. doi: 10.3389/fmed.2021.760047 Trogstad L. Increased occurrence of menstrual disturbances in 18-to 30-year-old women after covid-19 vaccination. Available at SSRN 3998180. Published online 2022. Zhang B, Yu X, Liu J, Liu P. COVID-19 vaccine and menstrual conditions in female: data analysis of the Vaccine Adverse Event Reporting System. Res Sq. Published online April 7, 2022. doi:10.21203/rs.3.rs-1388159/v2 Sualeh M, Uddin MR, Junaid N, Khan M, Pario A. Impact of COVID-19 vaccination on the menstrual cycle: a cross-sectional study from Karachi, Pakistan. Published online 2022. 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. 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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-3759326","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":264544038,"identity":"7a17f1c7-2226-418c-ae85-5213ee9af535","order_by":0,"name":"Nataly Zilberman 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University","correspondingAuthor":false,"prefix":"","firstName":"Howard","middleName":"","lastName":"Cuckle","suffix":""},{"id":264544043,"identity":"67fab6af-40a2-4565-9ed4-10a6901fdd4e","order_by":5,"name":"Osnat Levtzion-Korach","email":"","orcid":"","institution":"Hospital Management, Shamir (Assaf Harofeh) Medical Center, Tel Aviv University","correspondingAuthor":false,"prefix":"","firstName":"Osnat","middleName":"","lastName":"Levtzion-Korach","suffix":""}],"badges":[],"createdAt":"2023-12-15 14:59:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3759326/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3759326/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49040610,"identity":"40520c6f-6831-4a30-b42b-c623903e9e81","added_by":"auto","created_at":"2024-01-02 04:58:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26909,"visible":true,"origin":"","legend":"\u003cp\u003eflow chart of study selection.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3759326/v1/2f01ae32bace2b9ffb30498f.png"},{"id":49040989,"identity":"5a6d4ee8-85bb-4a5e-a058-c041f3e79c84","added_by":"auto","created_at":"2024-01-02 05:06:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":42218,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot comparing the abnormal uterine bleeding rate between studies and overall I\u003csup\u003e2\u003c/sup\u003e=99.989%, p\u0026lt;0.000.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3759326/v1/cd2022075bbb08f86899d867.png"},{"id":49040448,"identity":"327d611a-f2d3-41e3-82dc-74a7be0055bd","added_by":"auto","created_at":"2024-01-02 04:50:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":29858,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot comparing the abnormal uterine bleeding rate in questionnaire studies (overall I\u003csup\u003e2\u003c/sup\u003e=99.9136%, p\u0026lt;0.000).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3759326/v1/061e99d107787bdbca749e4d.png"},{"id":49040450,"identity":"0f66c033-e751-4958-a67b-43903be1e716","added_by":"auto","created_at":"2024-01-02 04:50:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":47622,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot comparing the abnormal uterine bleeding rate in questionnaire studies (overall I\u003csup\u003e2\u003c/sup\u003e=99.9136%, p\u0026lt;0.000).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3759326/v1/1469fab1353100c005cbd5f5.png"},{"id":64173623,"identity":"fe3455bd-f160-4e38-b610-0cd36db76f67","added_by":"auto","created_at":"2024-09-09 12:04:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":746865,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3759326/v1/d5ec9270-22bf-4b95-8924-50788eae0223.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"What Do We Know About Abnormal Uterine Bleeding Following Vaccination Against Covid-19 After Two and A Half Years Of Experience? A Systematic Review and Meta- Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe COVID-19 pandemic has led to considerable morbidity and mortality worldwide, with a current count of 533\u0026nbsp;million infected and 6.34\u0026nbsp;million deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Social distancing and quarantines have significantly reduced the infection rate. This solution creates a burden on the economy as well as on physical and psychosocial well-being [\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Social distancing is not considered a long-term solution, but the absence of effective treatment led to the prompt development of vaccines [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, vaccination is the leading cause of the worldwide decline in morbidity and mortality decline and is considered a cost-effective tool against COVID-19 strains [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Monitoring for adverse effects has focused on reactogenicity, cardiovascular side effects and anaphylactic response to the vaccine. Common short term side effects include injection site symptoms, fever, headache, fatigue and myalgia [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Vaccination against COVID-19 in children and adolescents has been shown to be both effective and safe, even when there are underlying comorbidities [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Children and adolescents usually suffer from the same short term side effects as adults [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The reported association between the COVID-19 vaccine and myocarditis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] is predominantly in young men, with a median age of 24. The clinical course is often mild with favorable prognosis, as reported also in children and adolescents [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSide effects can lead to hesitation regarding vaccination even when safety is not an issue, threatening vaccine effectiveness [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In menopausal women, abnormal uterine bleeding related to vaccine may lead to unnecessary diagnostic procedure [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Addressing possible side effects and providing evidence-based information to the public is essential to reduce stress related to the encounter with a side effect and increase willingness to be vaccinated.\u003c/p\u003e \u003cp\u003eAbnormal uterine bleeding has been reported as a vaccine side effect to public health databases, as well as by the media [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Even though not included in initial safety studies of the vaccine or vaccine adverse effects databases, abnormal uterine bleeding was reported actively by vaccinated individuals worldwide [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A few mechanisms have been suggested for the phenomenon, including vaccine-related stress, immune-induced thrombocytopenia, protein similarity and ACE2 related side effects [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEmerging evidence suggests that abnormal uterine bleeding is short lasting, not requiring treatment or hospitalization [\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Nevertheless, abnormal uterine bleeding disrupts daily tasks, hindering normal activities and inducing associated stress. The public has not yet been assured regarding the nature of abnormal uterine bleeding post COVID-19 vaccination and confusion about the COVID-19 side effects remains [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. This systematic review aims to assess and evaluate the rate of abnormal uterine bleeding post COVID-19 vaccination as well as clinical presentation. We hypothesize that abnormal uterine bleeding post COVID-19 vaccine will be observed with a similar occurrence pattern as that of fertility age.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eData sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA literature review was carried out by searching in the Medline, PubMed, Embase and Google Scholar databases for cohort studies or case series related to abnormal uterine bleeding following COVID-19 vaccination. The following MeSH (Medical Subject Headings) was used with text words and abbreviations for the terms: “COVID-19 vaccine”, “SARS-CoV-2 vaccine”, “Corona vaccine”, “abnormal uterine bleeding”, “dysfunctional uterine bleeding”, ”menstrual cycle”, “metrorrhagia”, “menorrhagia”, “intermenstrual bleeding”, and “menses”. The search protocol was registered on PROSPERO (CRD42022324122; www.crd.york.ac.uk/PROSPERO) in line with current recommendations and reported as per PRISMA 2009 guidelines (www. Prisma statement.org).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeer-reviewed articles in English language journals published between May 2020 and June 2022 were selected. \u0026nbsp;Only those including at least four cases of abnormal uterine bleeding among a cohort of vaccinated women were included. Those with selective recruitment or duplication of previously published data were excluded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy appraisal\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFull texts of eligible studies were assessed independently by two reviewers (R.S. and N.Z.S.) for content: author, date of publication, country, and study characteristics, including the method of information gathering on abnormal uterine bleeding. The latter was obtained either retrospectively from a questionnaire following a fixed number of cycles since the vaccination or\u0026nbsp;prospectively using a diary. For each study we obtained the number of vaccinated participants and of which, those who reported of abnormal uterine bleeding. In addition to extracting the total number of women and those having abnormal bleeding, the following data was sought:\u0026nbsp;clinical presentation of abnormal bleeding, the vaccine manufacturer, number of vaccine doses, and gynecological background.\u0026nbsp;The summarized results were cross checked, and any inconsistencies were discussed among reviewers and a consensus was reached.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe quality of the included studies was assessed using the Newcastle-Ottawa Scale (NOS) for cohort studies. The NOS judges each study by three categories: selection of study group, comparability of the groups and the ascertainment of the outcome of interest. Each item was scored “low”, “medium” or “high” risk for bias. No study was excluded based on the risk for bias. Two independent reviewers (R.S. and N.Z.S.) undertook the quality assessment. Discrepancies were resolved with evaluation from the third reviewer (R.M.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData synthesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo types of statistical analysis were performed: (1) meta-analysis of frequencies of the abnormal uterine bleeding rate and (2) meta-regression of the rate adjusted for the type of vaccine and number of vaccinations.\u003c/p\u003e\n\u003cp\u003eMeta-analysis was performed for pooling results overall and from sub-groups of studies and to evaluate the variation in study outcomes between studies using the “metaprop” (StataCorp) routine. A random effects model was used and exact confidence intervals for proportion in pooling the results to adjust for variations among studies. Statistical heterogeneity was assessed with the I\u003csup\u003e2\u003c/sup\u003e statistic (the proportion of variation in study estimates because of heterogeneity rather than sampling error) and the p-value derived for the hypothesis of no heterogeneity. \u0026nbsp;The results were presented as: number of studies, pooled rate estimated by random effects model with its 95%\u0026nbsp;confidence interval (CI).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMeta-regression used data from studies with the available information on potential co-variables. Fixed effect multiple logistic regression of grouped data was used to obtain the upper bound (i.e., optimistic) estimation of the p-value.\u003c/p\u003e\n\u003cp\u003eAll analyses were done using STATA SE\u0026nbsp;software. All tests are two-sided. P-values \u0026lt;0.05 were considered statistically significant. However, due to very limited number of studies, p-values between 0.05 and 0.10 were described as potentially interesting. No corrections were performed for multiple comparisons since this was a hypothesis generating type of study.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eStudy selection and characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOut of 506 publications identified 12 studies were eligible (Figure 1). \u0026nbsp;Ten included women from a single country, one from a region and one worldwide. \u0026nbsp;In four studies information on abnormal bleeding was derived from a diary and in eight from a questionnaire (Table 1). \u0026nbsp;A total of 1,132,613 women were included. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 | \u0026nbsp;Eligible studies: design features and abnormal uterine bleeding rate\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"786\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.786802030456853%\" style=\"width: 13.0915%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.48223350253807%\" style=\"width: 12.776%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePublished\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.913705583756345%\" style=\"width: 11.5142%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.375634517766498%\" style=\"width: 10.0946%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eData\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.121827411167512%\" style=\"width: 7.571%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal no. of subjects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 6.7823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e\u003cstrong\u003en subjects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.771573604060913%\" style=\"width: 9.306%;\"\u003e\n \u003cp\u003e\u003cstrong\u003en affected\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.152284263959391%\" style=\"width: 8.0442%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.786802030456853%\" style=\"width: 13.0915%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlghamdi\u003csup\u003e86\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.48223350253807%\" style=\"width: 12.776%;\"\u003e\n \u003cp\u003e8 October 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.913705583756345%\" style=\"width: 11.5142%;\"\u003e\n \u003cp\u003eSaudi Arabia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.375634517766498%\" style=\"width: 10.0946%;\"\u003e\n \u003cp\u003eQuestion.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.121827411167512%\" style=\"width: 7.571%;\"\u003e\n \u003cp\u003eNK\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e2,874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 6.7823%;\"\u003e\n \u003cp\u003e0.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e2,874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.771573604060913%\" style=\"width: 9.306%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.152284263959391%\" style=\"width: 8.0442%;\"\u003e\n \u003cp\u003e0.6-1.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.786802030456853%\" style=\"width: 13.0915%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003csup\u003e26\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.48223350253807%\" style=\"width: 12.776%;\"\u003e\n \u003cp\u003e15 November 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.913705583756345%\" style=\"width: 11.5142%;\"\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.375634517766498%\" style=\"width: 10.0946%;\"\u003e\n \u003cp\u003eQuestion.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.121827411167512%\" style=\"width: 7.571%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e1,273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 6.7823%;\"\u003e\n \u003cp\u003e54.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e1,273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.771573604060913%\" style=\"width: 9.306%;\"\u003e\n \u003cp\u003e698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.152284263959391%\" style=\"width: 8.0442%;\"\u003e\n \u003cp\u003e52.0-57.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.786802030456853%\" style=\"width: 13.0915%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlvergne\u003csup\u003e77\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.48223350253807%\" style=\"width: 12.776%;\"\u003e\n \u003cp\u003e6 December 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.913705583756345%\" style=\"width: 11.5142%;\"\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.375634517766498%\" style=\"width: 10.0946%;\"\u003e\n \u003cp\u003eQuestion.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.121827411167512%\" style=\"width: 7.571%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e4,989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 6.7823%;\"\u003e\n \u003cp\u003e20.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e4,989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.771573604060913%\" style=\"width: 9.306%;\"\u003e\n \u003cp\u003e998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.152284263959391%\" style=\"width: 8.0442%;\"\u003e\n \u003cp\u003e18.9-21.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.786802030456853%\" style=\"width: 13.0915%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrogstad\u003csup\u003e87\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.48223350253807%\" style=\"width: 12.776%;\"\u003e\n \u003cp\u003e14 January 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.913705583756345%\" style=\"width: 11.5142%;\"\u003e\n \u003cp\u003eNorway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.375634517766498%\" style=\"width: 10.0946%;\"\u003e\n \u003cp\u003eQuestion.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.121827411167512%\" style=\"width: 7.571%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e5,688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 6.7823%;\"\u003e\n \u003cp\u003e59.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e5,688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.771573604060913%\" style=\"width: 9.306%;\"\u003e\n \u003cp\u003e3,398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.152284263959391%\" style=\"width: 8.0442%;\"\u003e\n \u003cp\u003e58.4-62.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.786802030456853%\" style=\"width: 13.0915%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKMN Lee\u003csup\u003e36\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.48223350253807%\" style=\"width: 12.776%;\"\u003e\n \u003cp\u003e11 February 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.913705583756345%\" style=\"width: 11.5142%;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.375634517766498%\" style=\"width: 10.0946%;\"\u003e\n \u003cp\u003eQuestion.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.121827411167512%\" style=\"width: 7.571%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e39,129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 6.7823%;\"\u003e\n \u003cp\u003e53.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e39,129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.771573604060913%\" style=\"width: 9.306%;\"\u003e\n \u003cp\u003e20,831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.152284263959391%\" style=\"width: 8.0442%;\"\u003e\n \u003cp\u003e52.7-53.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.786802030456853%\" style=\"width: 13.0915%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLagana\u003csup\u003e35\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.48223350253807%\" style=\"width: 12.776%;\"\u003e\n \u003cp\u003e18 February 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.913705583756345%\" style=\"width: 11.5142%;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.375634517766498%\" style=\"width: 10.0946%;\"\u003e\n \u003cp\u003eQuestion.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.121827411167512%\" style=\"width: 7.571%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 6.7823%;\"\u003e\n \u003cp\u003e60.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.771573604060913%\" style=\"width: 9.306%;\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.152284263959391%\" style=\"width: 8.0442%;\"\u003e\n \u003cp\u003e51.8-67.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.786802030456853%\" style=\"width: 13.0915%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMuhaidat\u003csup\u003e34\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.48223350253807%\" style=\"width: 12.776%;\"\u003e\n \u003cp\u003e28 March 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.913705583756345%\" style=\"width: 11.5142%;\"\u003e\n \u003cp\u003eMENA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.375634517766498%\" style=\"width: 10.0946%;\"\u003e\n \u003cp\u003eQuestion.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.121827411167512%\" style=\"width: 7.571%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e2,269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 6.7823%;\"\u003e\n \u003cp\u003e66.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e2,269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.771573604060913%\" style=\"width: 9.306%;\"\u003e\n \u003cp\u003e1,504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.152284263959391%\" style=\"width: 8.0442%;\"\u003e\n \u003cp\u003e64.3-68.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.786802030456853%\" style=\"width: 13.0915%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVon Woon\u003csup\u003e33\u003c/sup\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.48223350253807%\" style=\"width: 12.776%;\"\u003e\n \u003cp\u003e30 March 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.913705583756345%\" style=\"width: 11.5142%;\"\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.375634517766498%\" style=\"width: 10.0946%;\"\u003e\n \u003cp\u003eDiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.121827411167512%\" style=\"width: 7.571%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 6.7823%;\"\u003e\n \u003cp\u003e73.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.771573604060913%\" style=\"width: 9.306%;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.152284263959391%\" style=\"width: 8.0442%;\"\u003e\n \u003cp\u003e62.2-82.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.786802030456853%\" style=\"width: 13.0915%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEdelman\u003csup\u003e32\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.48223350253807%\" style=\"width: 12.776%;\"\u003e\n \u003cp\u003e1 April 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.913705583756345%\" style=\"width: 11.5142%;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.375634517766498%\" style=\"width: 10.0946%;\"\u003e\n \u003cp\u003eDiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.121827411167512%\" style=\"width: 7.571%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e2,403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 6.7823%;\"\u003e\n \u003cp\u003e5.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e2,403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.771573604060913%\" style=\"width: 9.306%;\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.152284263959391%\" style=\"width: 8.0442%;\"\u003e\n \u003cp\u003e4.3-6.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.786802030456853%\" style=\"width: 13.0915%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZhang\u003csup\u003e88\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.48223350253807%\" style=\"width: 12.776%;\"\u003e\n \u003cp\u003e7 April 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.913705583756345%\" style=\"width: 11.5142%;\"\u003e\n \u003cp\u003eWorldwide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.375634517766498%\" style=\"width: 10.0946%;\"\u003e\n \u003cp\u003eDiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.121827411167512%\" style=\"width: 7.571%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e1,061,886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 6.7823%;\"\u003e\n \u003cp\u003e1.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e1,061,886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.771573604060913%\" style=\"width: 9.306%;\"\u003e\n \u003cp\u003e13,118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.152284263959391%\" style=\"width: 8.0442%;\"\u003e\n \u003cp\u003e1.2-1.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.786802030456853%\" style=\"width: 13.0915%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSualeh\u003csup\u003e89\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.48223350253807%\" style=\"width: 12.776%;\"\u003e\n \u003cp\u003e14 May 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.913705583756345%\" style=\"width: 11.5142%;\"\u003e\n \u003cp\u003ePakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.375634517766498%\" style=\"width: 10.0946%;\"\u003e\n \u003cp\u003eQuestion.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.121827411167512%\" style=\"width: 7.571%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 6.7823%;\"\u003e\n \u003cp\u003e38.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.771573604060913%\" style=\"width: 9.306%;\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.152284263959391%\" style=\"width: 8.0442%;\"\u003e\n \u003cp\u003e33.1-43.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.786802030456853%\" style=\"width: 13.0915%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRogers\u003csup\u003e31\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.48223350253807%\" style=\"width: 12.776%;\"\u003e\n \u003cp\u003e16 May 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.913705583756345%\" style=\"width: 11.5142%;\"\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.375634517766498%\" style=\"width: 10.0946%;\"\u003e\n \u003cp\u003eDiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.121827411167512%\" style=\"width: 7.571%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e11,475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 6.7823%;\"\u003e\n \u003cp\u003e3.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\" style=\"width: 10.4101%;\"\u003e\n \u003cp\u003e11,475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.771573604060913%\" style=\"width: 9.306%;\"\u003e\n \u003cp\u003e344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.152284263959391%\" valign=\"bottom\" style=\"width: 8.0442%;\"\u003e\n \u003cp\u003e2.7-3.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;The clinical criteria for identifying abnormal uterine bleeding were similar between the studies. The age range included years when fertility is expected, except for one which also included some women in the expected menopausal period [36]. Mean age was available for nine studies and ranged from 21 to 36; mean parity was included in 5 studies with range 0.4-1.74. \u0026nbsp;Some studies also reported for women with abnormal bleeding, those with a previous menstrual abnormality or hormonal use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality assessment of included studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 shows the risk of bias among the studies, overall and according to three sub-categories. Only one study had a high risk and at most two had high risk in a sub-category.\u003c/p\u003e\n\u003cp\u003eTable 2 | Risk of bias using the Newcastle-Ottawa Scale (NOS) for cohort studies: number according to sub-categories and overall\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"546\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.582417582417584%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eRisk of bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"63.73626373626374%\" colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003eSub-category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.681318681318682%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eSelection\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eof groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eComparability\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eof groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eOutcome ascertainment\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.582417582417584%\" valign=\"bottom\"\u003e\n \u003cp\u003eHigh\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.245421245421245%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.245421245421245%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.245421245421245%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.681318681318682%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.582417582417584%\" valign=\"bottom\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.245421245421245%\" valign=\"bottom\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.245421245421245%\" valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.245421245421245%\" valign=\"bottom\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.681318681318682%\" valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.582417582417584%\" valign=\"bottom\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.245421245421245%\" valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.245421245421245%\" valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.245421245421245%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.681318681318682%\" valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSynthesis of results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn total 41,344 women reported abnormal uterine bleeding after the COVID-19 vaccine (31.3%, 95%CI 11.7-55.1%). Nine studies included information on the clinical presentation of abnormal uterine bleeding (Table 3). \u0026nbsp;In most papers, the clinical presentation was reported per woman rather than per cycle. Excluding those with type unknown or other, the most common types of menses were in 41,344 women, of whom 15,751 (38%) suffered from menorrhagia, 11,734 (28%) had irregular menses, 5156 (12%) women reported intermenstrual bleeding and 3714 (9%) had delayed menses.\u003c/p\u003e\n\u003cp\u003eTable 3 | Clinical presentation of abnormal uterine bleeding** in 9 studies\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"726\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.75515818431912%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"86.24484181568089%\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of menstrual abnormality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.440191387559809%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIrregular\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.28548644338118%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntermenstrual bleed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.629984051036683%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMenorrhagia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.819776714513555%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypomenorrhea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.992025518341308%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmenorrhea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.832535885167465%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDelayed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.75515818431912%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003csup\u003e26\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.729023383768913%\"\u003e\n \u003cp\u003e698 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.082530949105916%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.480055020632737%\"\u003e\n \u003cp\u003e412 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.23108665749656%\"\u003e\n \u003cp\u003e180 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.929848693259972%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.792297111416781%\"\u003e\n \u003cp\u003e392 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.75515818431912%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrogstad\u003csup\u003e87\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.729023383768913%\"\u003e\n \u003cp\u003e1209 (38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.082530949105916%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.480055020632737%\"\u003e\n \u003cp\u003e1420 (44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.23108665749656%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.929848693259972%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.792297111416781%\"\u003e\n \u003cp\u003e567 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.75515818431912%\"\u003e\n \u003cp\u003e\u003cstrong\u003eKMN Lee\u003csup\u003e36\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.729023383768913%\"\u003e\n \u003cp\u003e4358 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.082530949105916%\"\u003e\n \u003cp\u003e1350 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.480055020632737%\"\u003e\n \u003cp\u003e13262 (70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.23108665749656%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.929848693259972%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.792297111416781%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.75515818431912%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLagana\u003csup\u003e35\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.729023383768913%\"\u003e\n \u003cp\u003e94 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.082530949105916%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.480055020632737%\"\u003e\n \u003cp\u003e28 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.23108665749656%\"\u003e\n \u003cp\u003e37 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.929848693259972%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.792297111416781%\"\u003e\n \u003cp\u003e26 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.75515818431912%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMuhaidat\u003csup\u003e34\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.729023383768913%\"\u003e\n \u003cp\u003e680 (52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.082530949105916%\"\u003e\n \u003cp\u003e48 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.480055020632737%\"\u003e\n \u003cp\u003e424 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.23108665749656%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.929848693259972%\"\u003e\n \u003cp\u003e151 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.792297111416781%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.75515818431912%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVon Woon\u003csup\u003e33\u003c/sup\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.729023383768913%\"\u003e\n \u003cp\u003e53 (62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.082530949105916%\"\u003e\n \u003cp\u003eNK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.480055020632737%\"\u003e\n \u003cp\u003e12 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.23108665749656%\"\u003e\n \u003cp\u003e20 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.929848693259972%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.792297111416781%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.75515818431912%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEdelman\u003csup\u003e32\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.729023383768913%\"\u003e\n \u003cp\u003eNK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.082530949105916%\"\u003e\n \u003cp\u003eNK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.480055020632737%\"\u003e\n \u003cp\u003e125 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.23108665749656%\"\u003e\n \u003cp\u003eNK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.929848693259972%\"\u003e\n \u003cp\u003eNK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.792297111416781%\"\u003e\n \u003cp\u003eNK\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.75515818431912%\"\u003e\n \u003cp\u003e\u003cstrong\u003eZhang\u003csup\u003e88\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.729023383768913%\"\u003e\n \u003cp\u003e4626 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.082530949105916%\"\u003e\n \u003cp\u003e3759 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.480055020632737%\"\u003e\n \u003cp\u003e28 (\u0026lt;1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.23108665749656%\"\u003e\n \u003cp\u003e1043 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.929848693259972%\"\u003e\n \u003cp\u003e1655 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.792297111416781%\"\u003e\n \u003cp\u003e2698 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.75515818431912%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSualeh\u003csup\u003e89\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.729023383768913%\"\u003e\n \u003cp\u003e16 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.082530949105916%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.480055020632737%\"\u003e\n \u003cp\u003e40 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.23108665749656%\"\u003e\n \u003cp\u003e35 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.929848693259972%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.792297111416781%\"\u003e\n \u003cp\u003e32 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*First author\u003c/p\u003e\n\u003cp\u003eFigure 2 shows the rate of abnormal uterine bleeding in each study and overall. \u0026nbsp;There was highly statistically significant heterogeneity (I\u003csup\u003e2\u003c/sup\u003e=99.99%, P\u0026lt;0.00001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne source of homogeneity is the method used to collect information on menstrual bleeding. \u0026nbsp; There was a highly statistically significant difference in the rate between the diary and questionnaire studies. \u0026nbsp;The pooled rate was 9.7% (95%CI 5.7-14.6%) in the former and 41.3% (95%CI 24.1-59.8%) in the latter, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.000. \u0026nbsp;Nevertheless, there remained very high heterogeneity with those using a diary (Figure 3) or a questionnaire (Figure 4).\u003c/p\u003e\n\u003cp\u003eIn the 5 studies reporting hormonal treatment among women with abnormal bleeding following vaccination the proportion was 6%, 9%, 19%, 28% and 59%, a mean of 22%. In the 7 studies reporting abnormal uterine bleeding or menstrual abnormality in previous cycles among those affected after vaccination, the proportion was 0%, 0%, 14%, 19%, 23%, 25% and 63%, a mean of 16%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eComment\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrincipal findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbnormal uterine bleeding post COVID-19 vaccination was identified in almost a third (31.3%, 95CI 11.7%-55.1%) of vaccinated women, being a common side effect of the vaccine and possibly a cause of vaccine hesitancy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrength and limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe novelty of this current review and meta-analysis is the attempt to estimate the extent of abnormal uterine bleeding side effect after COVID-19 vaccine. We used statistical methods in order to overcome methodological differences between the included studies in order to consider them equally. The main limitation of this study is that in most of the studies information on abnormal uterine bleeding was obtained retrospectively from questionnaires. In the questionnaire studies, abnormal uterine bleeding was diagnosed by the patient, as a change from the familiar menstrual bleeding in length, intensity or occurrence of the bleeding. The population included in the various studies originated from different countries and it is possible that ethnic or social differences would influence the reporting of symptoms. Another possible influence might originate from various management policies of COVID-19 virus outbreaks, quarantine policies and avoidance from medical centers during outbreaks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation and comparison with existing literature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSARS-CoV-2 virus was defined as a pandemic by the WHO at March 2020\u0026nbsp;[39],\u003csup\u003e\u0026nbsp;\u003c/sup\u003ecausing the largest pandemic described since the flu pandemic in 1918. SARS-CoV-2 virus spreads by respiratory droplets, causing systemic symptoms of fever, myalgia and fatigue along the common respiratory symptoms such as dyspnea and cough\u0026nbsp;[40]. Abdominal pain, vomiting and diarrhea are also considered quite common. COVID-19 is known to cause a variety of complications ranging from sepsis, acute respiratory distress syndrome and disseminated intravascular coagulopathy to neurological, renal and hepatic injury\u0026nbsp;[41].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSARS-CoV-2 virus invades the cell via angiotensin-converting enzyme-2 receptor (ACE2). ACE2 is found in the female reproductive tract as well as ovarian granulosa cells, implying that SARS-CoV-2 might target female reproduction\u0026nbsp;[42]. Recent literature suggests that the ovaries and fertility of reproductive age women is not impaired by the SARS-CoV-2\u0026nbsp;[43-45].\u003csup\u003e\u0026nbsp;\u003c/sup\u003eNon-the-less, changes in the function of angiotensin II and ACE2 have the ability to cause menstrual irregularities\u0026nbsp;[28]. Angiotensin II promotes vasoconstriction of the spiral arteries and influences myometrial activity, consequently inducing menstruation\u0026nbsp;[46].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMenstrual cycle is considered the fifth vital sign, indicating a woman\u0026rsquo;s well-being as well as being a marker of reproductive health\u0026nbsp;[47, 48]. Menstrual cycle disorders serves as an indication for disease, as well as a prognostic factor for metabolic disorders such as diabetes mellitus and dyslipidemia\u0026nbsp;[49], cardiovascular disease and related mortality\u0026nbsp;[50-52],\u003csup\u003e\u0026nbsp;\u003c/sup\u003ealong with overall premature mortality (age\u0026lt;70 years)\u0026nbsp;[53].\u003csup\u003e\u0026nbsp;\u003c/sup\u003eThe menstrual cycle is controlled by the hypothalamic\u0026ndash;pituitary\u0026ndash;ovarian (HPO) axis, and influenced by immune status and chronic illnesses, psychological illness and stress, medications, body mass index, smoking, socioeconomic status and educational level\u0026nbsp;[54-57].\u003csup\u003e\u0026nbsp;\u003c/sup\u003eThe menstrual cycle usually begins when a woman is at 12-15 years of age (median 12.2 years) and lasts until menopause at 51 years of age\u0026nbsp;[58, 59]. Cycle length varies between 24-38 days, with up to 8 bleeding days in the reproductive years (21-35). Menstrual disorders can present as irregular menses, frequent or infrequent menses, heavy or light menstrual bleeding, intermenstrual bleeding or prolonged bleeding\u0026nbsp;[60].\u003c/p\u003e\n\u003cp\u003eAbnormal uterine bleeding is reported to be 3-30% during the reproductive years\u0026nbsp;[60]; age-specific rates are not available. It is estimated that one-third of women suffer from abnormal uterine bleeding at cycles during their reproductive lives\u0026nbsp;[61]. This is a similar rate to that estimated in the current analysis for the small number of cycles (up to 11) following vaccination, thus confirming our hypothesis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsiderable statistical heterogeneity in the abnormal uterine bleeding rate after COVID-19 vaccination was observed since most studies had a narrow confidence interval but the variance between studies was high. An explanation was sought and one factor was the difference in rate for diary versus questionnaire studies. In general diary studies are superior to questionnaire studies in the level of reporting and validity of the side effect reported, with less recall bias\u0026nbsp;[62]. However, in our analysis the reported rate was lower in the diary studies and there was significant heterogeneity within the two types of study design.\u003c/p\u003e\n\u003cp\u003eAbout one-half of women suffering from abnormal uterine bleeding do not reach out for medical attention\u0026nbsp;[63, 64]. Abnormal uterine bleeding can range from mild to severe and has a significant impact on a women\u0026rsquo;s quality of life\u0026nbsp;[65].\u003csup\u003e\u0026nbsp;\u003c/sup\u003eIn some religious societies such as Judaism or Islam abnormal uterine bleeding prevents intimacy between partners and might impede fertility\u0026nbsp;[66, 67]. Abnormal uterine bleeding was reported after infection with SARS-CoV-2\u0026nbsp;[29,68-71]\u0026nbsp;And there are reports of more premenstrual symptoms along with prolonged menses, missed periods (infrequent menses), light and heavy menstrual bleeding. The reports after COVID-19 infection are similar to those reported after COVID-19 vaccination, suggesting a common mechanism may be responsible for the effect. The hypothalamic-pituitary-ovarian axis is susceptible to stress\u0026nbsp;[72, 73].\u003csup\u003e\u0026nbsp;\u003c/sup\u003eIn addition, acute stress enhances sympathetic activity in different brain areas and especially the amygdala, causing pregnanolone fluctuations, potentially leading to menstrual irregularities\u0026nbsp;[74]. COVID-19 pandemic induced considerable stress that was previously linked to menstrual changes\u0026nbsp;[66, 75, 76]. Stress -related changes could potentially explain symptoms also after SARS-CoV-2 vaccine. The current study estimates that there is abnormal uterine bleeding after vaccination in almost one-third of vaccinated females. \u0026nbsp;Individual studies also report that this is transient and mild\u0026nbsp;[31-36]. The current analysis found no specific vaccine manufacturer to influence the abnormal bleeding rate. There is evidence suggesting that abnormal uterine bleeding following vaccination against SARS-CoV-2 is more common in women who were previously sick with COVID-19\u0026nbsp;[35, 77].\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eComposition of the uterine microbiome is researched as another possible cause for the phenomenon\u0026nbsp;[78, 79].\u003c/p\u003e\n\u003cp\u003eAbnormal uterine bleeding was reported previously after vaccinations against the papilloma virus, hepatitis B and typhoid\u0026nbsp;[80-82]. One of the suggested mechanisms is immune mediated vaccine induced thrombocytopenia\u0026nbsp;[83].\u003csup\u003e\u0026nbsp;\u003c/sup\u003eImmune induced thrombocytopenia was reported after COVID-19 vaccine as well as thrombotic thrombocytopenia\u0026nbsp;[84].\u003csup\u003e\u0026nbsp;\u003c/sup\u003eThose thrombocytopenia\u0026rsquo;s usually present with a clinical picture that includes more than abnormal uterine bleeding, such as easy bruising and bleeding from mucosal surfaces. It seems unlikely that thrombocytopenia is the explanation for abnormal uterine bleeding associated with COVID-19 vaccine given the clinical features described. This assumption was declined in the context of abnormal uterine bleeding seen after human papilloma virus vaccine\u0026nbsp;[85].\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur analysis estimates that COVID-19 vaccine carries an inconvenient side effect of abnormal uterine bleeding in almost one-third of women vaccinated. Other studies in the literature indicate that this is mostly short lasting and mild.\u003c/p\u003e \u003cp\u003eThe consensus remains that COVID-19 vaccination has a substantial protective value against the disease, limiting the disease extent and severity. Misinformation regarding the menstrual side effects of the vaccine could limit acceptance as opposed to evidence-based information. We highly recommend for continuation of COVID-19 vaccine as indicated and enabling access to accurate information regarding potential side effect.\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.\u003c/em\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Inf Dis. 20:533-4. doi: 10.1016/S1473-3099(20)30120-1\u003c/li\u003e\n \u003cli\u003eChung PC, Chan TC. 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Published online 2022.\u003c/li\u003e\n\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":"Abnormal uterine bleeding, COVID-19, Side effects, Vaccine","lastPublishedDoi":"10.21203/rs.3.rs-3759326/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3759326/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVaccination against COVID-19 is regarded to be most effective in preventing morbidity and mortality and limiting the spread of the virus. Publications in the media and scientific journals have raised a concern regarding abnormal uterine bleeding as a COVID-19 vaccine side effect. We aimed to estimate the prevalence of abnormal uterine bleeding after vaccination against COVID-19.\u003c/p\u003e\n\u003cp\u003eMedline, PubMed, Embase and Google Scholar databases were searched between January 2020 and June 2022.\u003cstrong\u003e \u003c/strong\u003eEnglish-language studies, including at least four women with abnormal uterine bleeding. Eligible studies were assessed for risk of bias using the Newcastle-Ottawa Scale. Data synthesis was accomplished by meta-analysis of frequencies of the abnormal uterine bleeding rate and meta-regression of the rate adjusted for the type of vaccine and number of vaccinations.\u003c/p\u003e\n\u003cp\u003eThere were 12 eligible studies including a total of 1,132,613 women of whom 41,344 (31.3%, 95CI 11.7-55.1%) reported abnormal bleeding following vaccination. Nine studies included clinical presentation: the abnormality was menorrhagia in 38%, irregular menses in 28%, intermenstrual bleeding in 12% and delayed menses in 9%). \u0026nbsp;There was considerable heterogeneity in results which could not be accounted for by the method of recording abnormal bleeding, vaccine manufacturer or number of doses.\u003c/p\u003e\n\u003cp\u003eCOVID-19 vaccine carries the inconvenience of abnormal uterine bleeding in almost one-third of the women vaccinated, mostly irregular menses or menorrhagia. In addition, the innovative vaccine mechanism could be used against various potential targets, further highlighting the need to assess vaccine side effects.\u003c/p\u003e","manuscriptTitle":"What Do We Know About Abnormal Uterine Bleeding Following Vaccination Against Covid-19 After Two and A Half Years Of Experience? A Systematic Review and Meta- Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-02 04:50:27","doi":"10.21203/rs.3.rs-3759326/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":"1830d1fe-34c1-4693-8a34-624db7b61908","owner":[],"postedDate":"January 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":27879089,"name":"Health sciences/Medical research"},{"id":27879090,"name":"Health sciences/Medical research/Epidemiology"}],"tags":[],"updatedAt":"2024-09-09T11:56:01+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-02 04:50:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3759326","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3759326","identity":"rs-3759326","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}