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Abstract

Background:

There is increasing evidence that menstrual changes (MC) should be considered as an adverse event resulting from COVID-19 vaccination. However, the contributing health factors are still poorly understood.

Objectives:

The aim was to analyze the characteristics and factors influencing MC after the administration of the severe acute respiratory syndrome coronavirus 2 vaccine.

Design & Methods:

A retrospective observational study of currently menstruating women (N = 14,550) in Spain was conducted during the month of December 2021 using an online survey. Among others, general characteristics of the menstrual cycle, medical history, and adverse events following vaccination—including MC—were recorded. Bivariate logistic regression analysis was performed to determine the influencing factors.

Results:

45.0%—50.9% of respondents reported experiencing mostly transient MC after both doses of COVID-19 vaccine, including different spotting (41.6%—49.0%), longer periods (26.5%—29.5%) and/or heavier flow (28.7%—31.6%). Binary logistic regression analysis showed that some of the inter-individual factors that may be involved in this unexpected event are age (dose 2: adjusted odds ratio (aOR): 1.02; 95% confidence intervals (CI): 1.02–1.03), heavy menstrual flow (dose 1: aOR: 1.12; 95% CI: 1.00–1.25), use of short- (dose 1: aOR: 1.38; 95% CI: 1.24–1.54) to medium-term contraception (dose 1: aOR: 1.31; 95% CI: 1.09–1.57), number of previous pregnancies (dose 1: aOR: 1.11; 95% CI: 1.03–1.19), pre-existing diagnoses of certain clinical conditions—including endometriosis (dose 1: aOR: 1.33; 95% CI: 1.11–1.59)—and suffering from other vaccine adverse events.

Conclusion:

Currently menstruating women may experience MC after COVID-19 vaccination. Further research is warranted to address the influencing factors, considering their heterogeneity according to the geographical background of the target population. This kind of evidence could prove instrumental in the context of future viral outbreaks, helping healthcare professionals to provide scientifically up-to-date information to patients for making informed decisions regarding their well-being, particularly in societies where menstruation remains a taboo subject.

Plain Language Summary

COVID-19 vaccination in currently menstruating women

Health-related factors such as age, heavy menstrual flow, contraception, previous pregnancy, endometriosis, and other vaccine-related adverse events may underlie menstrual changes after COVID-19 vaccination.

Keywords

SARS-CoV-2 vaccine COVID-19 reproductive health pregnancy endometriosis contraception adverse effects

Background

The menstrual cycle is a sign of a woman’s health and fertility. It is controlled by the complex functioning of the hypothalamic-pituitary-gonadal axis, which can be affected by a variety of stressors.^1,2 Nevertheless, any abnormal changes should not be ignored,^3,4 even in the context of the coronavirus disease (COVID-19) pandemic, probably the most challenging public health emergency faced by modern society.

Over the past 4 years, a significant number of studies^1,2,5–19 and reports from national public health surveillance agencies^20–24 have claimed that menstrual changes (MC) should be considered as adverse events resulting from COVID-19 vaccination. While the vaccine against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was undoubtedly one of the most significant preventive strategies to halt the progression of the pandemic, it is crucial that we continue to analyze the factors influencing the occurrence of related side effects. This will allow us to understand how this, and other new vaccines, may affect the reproductive health of both sexes and, consequently, to provide scientifically based medical advice to those patients who seek help for this unexpected health event.

To date, several transient MC have been described in currently menstruating women after receiving the SARS-CoV-2 vaccine, including those related to menstrual cycle length^{5,6–9,12,14,15,17–19}—including period length,^5,10,16,17 menstrual flow,^{5,6,10–12,14–17} and other menstrual irregularities.^{2,5,6,12,13,15,16,18} In the context of limited understanding of menstrual physiology, it is conceivable that neuro-immune-endocrine interactions may underpin the observed phenomenon.^5,25,26 However, little is known about the health factors that may be associated with its occurrence. The expected outcomes of this study include improving outbreak preparedness, strengthening evidence-based clinical practice, and fostering trust by validating and responding to patient-reported side effects. The aim of this study is to contribute to the existing body of scientific knowledge by analyzing the characteristics of this unexpected event in one of the most affected countries, Spain, and the factors that may have influenced it.

Materials and methods

Experimental design

An online survey (Microsoft Forms^®; Microsoft Corporation, Washington, DC, USA) was used to conduct a retrospective, observational study of adult Spanish women. The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the University of Extremadura (ref. 180/2021). The reporting of this study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement.²⁷ Accordingly, a completed STROBE checklist is provided as a Supplemental Material to ensure comprehensive and transparent reporting of the study design, methods, results, and interpretation.

Recruitment, data collection, and participants

The online survey was launched in Spain in December 2021. Recruitment was done through social networks using the snowball method. Only participants who agreed to be contacted by the research group by email for additional data collection gave electronic informed consent. A total of 17,512 people were recruited within 15 days irrespective of their menstrual status. From this larger sample, the research team identified data from a subpopulation of currently menstruating women as being of interest and thus selected them for the present analysis (N = 14,550). Inclusion criteria were women: (1) over 18 years of age, (2) being currently menstruating women, and (3) who had received at least one dose of COVID-19 vaccine (Figure 1). Exclusion criteria included age <18 years, unvaccinated, formerly menstruating women and incomplete surveys.

Figure 1.

STROBE flow diagram.

Survey information

Based on the study by Lee et al.,¹⁰ the research team designed a customized questionnaire with 56 multiple-choice and text-entry questions divided into 6 sections regarding (1) the general characteristics of the menstrual cycle—or its absence and the cause, (2) SARS-CoV-2 infection, (3) COVID-19 vaccine, (4) menstrual experiences both after the SARS-CoV-2 infection and COVID-19 vaccination compared to the expected period symptoms—for example, shorter/longer/same, heavier/lighter/same. . ., (5) other MC—for example, spotting, breast pain, hot flashes, premenstrual syndrome, and other bleeding, (6) time between infection/vaccination and MC, (7) duration of MC, (8) adverse events of each vaccine dose, (9) reproductive history, (10) medical history, and (11) demographics. Some modifications were made to the original survey instrument to adapt it to the national context, including the two most used contraceptives in Spain: combined hormonal contraceptives and nonhormonal intrauterine device. The survey took 20–25 min to complete.

Statistical analysis

Participants were grouped according to the occurrence (MC subgroup) or non-occurrence (n-MC subgroup) of MC after COVID-19 vaccination. Quantitative variables were expressed as median and interquartile range, while qualitative variables were expressed as number of participants and frequency (%). Statistical analysis includes chi-square, Mann-Whitney U, and McNemar mid-p tests. Subsequently, bivariate logistic regression analysis was performed to investigate possible associations between the occurrence of MC following vaccination (dependent variable) and the independent variables that were significant (p ⩽ 0.05) in the previous analyses. Results were presented as adjusted odds ratios with 95% confidence intervals (CI). The above analyses were performed with the Statistical Package for Social Sciences (SPSS v.25; IBM, New York, NY, USA) for Windows. Statistical significance was set at p ⩽ 0.05.

Results

Anthropometric characteristics and medical history

65.2% of the study participants (N = 14,550, age: 33.0 (28.0–38.0)) had normal weight (body mass index, BMI: 22.8 (20.7–25.7)). Focusing on medical history, 23.0% had a diagnosis of non-autoimmune conditions and 8.9% of autoimmune diseases, both cases mainly related to thyroid. Regarding gynecological history, most women reported regular menstrual cycle (87.7%) and menstruation lasting 3–5 days (60.6%) with moderate flow (58.6%). Hormonal contraception (14.5%) was the most used method. 32.3% had a diagnosis of gynecological disease, the most common being polycystic ovary syndrome (43.6%) and endometriosis (12.4%; Table 1).

Table 1.

Anthropometric characteristics and medical history of the study population (currently menstruating women, N = 14,550).

Variable	Category	Total (N = 14,550)
Age (years)^a	—	33.0 (28.0–38.0)
Weight (kg)^a	—	62.0 (55.0–70.0)
Height (cm)^a	—	165.0 (160.0–169.0)
BMI^a,b	—	22.8 (20.7–25.7)
	Underweight	762 (5.2)
	Normal weight	9482 (65.2)
	Pre-obesity/overweight	2880 (19.8)
	Obesity I	982 (6.7)
	Obesity II	307 (2.1)
	Obesity III	137 (1.0)
Medical history^a,b
Autoimmune diseases
Diagnosis	Yes	1299 (8.9)
Diagnosis	No	13,251 (91.1)
Comorbidity	Yes	127 (0.9)
Comorbidity	No	1172 (8.1)
Types	Thyroid	529 (3.6)
	Gastrointestinal	317 (2.2)
	Dermatological	231 (1.6)
	Rheumatic/articular	90 (0.6)
	None of the above	194 (1.3)
Other clinical conditions
Diagnosis	Yes	3340 (23.0)
Diagnosis	No	11,210 (77.0)
Comorbidity	Yes	605 (4.2)
Comorbidity	No	2735 (18.8)
Types	Thyroid	13 (9.6)
	Gynecological	925 (6.4)
	Respiratory	455 (3.1)
	Neurological/mental	396 (2.7)
	Gastrointestinal	232 (1.6)
	HPV	179 (1.2)
	Cardiovascular	90 (0.6)
	Rheumatic/articular	94 (0.6)
	Dermatological	78 (0.5)
	Cancer	62 (0.4)
	None of the above	674 (4.6)
Allergies
Diagnosis	Yes	4848 (33.3)
Diagnosis	No	9702 (66.7)
COVID-19
Diagnosis	Yes	2404 (16.5)
Diagnosis	No	12,146 (83.5)
Gynecological history^a,b
Menarcheal Age	—	12.0 (11.0–14.0)
Menstrual cycle
Length	Regular	12,755 (87.7)
Length	Irregular	1975 (12.3)
Period length	1–3 days	2252 (15.5)
	3–5 days	8830 (60.7)
	>5 days	3338 (22.9)
	Others	130 (0.9)
Period flow	Light	2707 (18.6)
	Moderate	8520 (58.6)
	Heavy	3323 (22.8)
Current/past use of contraceptives
Time of use	<10 years	1926 (13.2)
Time of use	⩾10 years	562 (3.9)
Types	None	12,009 (82.5)
	Hormonal	2112 (14.5)
	IUD (nonhormonal)	429 (2.9)
Reproduction
Have you been pregnant?	Yes	4715 (32.4)
Have you been pregnant?	No	9834 (67.6)
No. of pregnancies	0–2	3634 (25.0)
No. of pregnancies	>2	1056 (7.3)
No. of children	1–2	3706 (25.5)
No. of children	>2	263 (1.8)
Diseases
Diagnosis	Yes	4696 (32.3)
Diagnosis	No	9854 (67.7)
Comorbidity	Yes	847 (18.0)
Comorbidity	No	3849 (82.0)
Types	PCOS	2074 (43.6)
	Endometriosis	582 (12.4)
	Menorrhagia	1595 (11.0)
	Fibroids	351 (7.5)
	Adenomyosis	75 (1.6)
	Uterine bleeding	76 (0.5)
	None of the above	954 (20.3)

BMI: body mass index; HPV: human papillomavirus; IUD: intrauterine device; PCOS: polycystic ovary syndrome.

Values are expressed as median (interquartile range).

Values are expressed as n (%).

Impact of vaccination on the menstrual health of currently menstruating women

On the other hand, most participants received the Pfizer-BioNTech Company^® (New York, USA) vaccine. Moderna Spikevax^® (Cambridge, USA) and Oxford-AstraZeneca^® (Cambridge, UK) vaccines followed (Table 2).

Table 2.

COVID-19 vaccination of the study population (currently menstruating women, N = 14,550), according to the dose received.

Variable	Category	Dose 1 (n = 14,550)	Dose 2 (n = 12,563)
Vaccination	Yes	14,550 (100.0)	12,563 (86.3)
Vaccination	No	0 (0)	1987 (13.7)
Brand	Pfizer-BioNTech	9509 (65.4)	8540 (68.0)
	Moderna	2643 (18.2)	2352 (18.7)
	Oxford-AstraZeneca	1873 (12.9)	1562 (12.4)
	None of the above	525 (3.6)	109 (0.9)

Values are expressed as n (%).

Comparative analysis of the occurrence of MC and other adverse events after COVID-19 vaccination

Compared to the first dose (45.0%), a significantly higher percentage (50.9%) of women experienced MC after the second dose (Figure 2(a)–(g)), as well as the simultaneous occurrence of two or more symptoms (Figure 2(b)), mainly spotting. Focusing on changes in menstrual bleeding, there were also significant differences between doses. For example, after the second dose, more women reported longer periods (Figure 3(a)), and heavier flow (Figure 3(b)). In addition, 30.6% of women reported that these changes had lasted “to date” (versus 17.1% for the first dose; Figure 3(d)).

Figure 2.

Comparison between vaccine doses of frequencies (%) for variables related to MC (currently menstruating women, n = 12,563). (a) Occurrence of MC; (b) number of symptoms related to the MC; (c) spotting; (d) different spotting; (e) breast pain; (f) hot flushes; (g) premenstrual syndrome.

Figure 3.

Comparison between vaccine doses of frequencies (%) for variables related to menstrual bleeding changes (currently menstruating women, n = 12,563). (a) Length; (b) flow; (c) time between vaccination and period; (d) duration of menstrual bleeding changes.

The occurrence of two or more adverse events was also significantly more frequent after the second dose (Figure 4(b)). The most common were fatigue (Figure 4(d)), fever (Figure 4(e)), and headache (Figure 4(f)).

Figure 4.

Comparison between vaccine doses of frequencies (%) for variables related to other adverse events (currently menstruating women, n = 12,563). (a) Occurrence of other adverse events; (b) number of adverse events; (c) arm pain; (d) fatigue; (e) fever; (f) headache; (g) nausea, (h) breast lumps; (i) swollen glands; (j) others adverse events.

Factors associated with the occurrence or not of MC after COVID-19 vaccination

Table 3 shows the comparative analysis between the subgroups. Compared to the n-MC subgroup, women who experienced MC after receiving the two doses were heavier, with significant differences in the BMI category “pre-obesity/overweight.” These women were also significantly older than those who did not report MC after dose 2. The prevalence of some non-autoimmune clinical conditions was significantly higher in the MC subgroup according to the dose considered, such as rheumatic/articular and other conditions (dose 1), infectious (dose 2), and neurological/mental and gastrointestinal disorders (doses 1 & 2). In addition, allergies were observed to be more common in this subgroup compared to the n-MC subgroup (dose 1). For COVID-19, significant differences between subgroups were observed for both diagnosis (dose 2) and non-common vaccine brands (doses 1 & 2).

Table 3.

Differences in the study variables according to the dose received and the occurrence or not of MC after COVID-19 vaccination (currently menstruating women: n = 14,550 (dose 1); n = 12,563 (dose 2)).

Variable	Category	Dose 1				Dose2
Variable	Category	n-MC (n = 7855)	MC (n = 6695)	χ²	p value	n-MC (n = 6170)	MC (n = 6393)	χ²	p value
Age (years)^a	—	33.0 (28.0–38.0)	33.0 (28.0–39.0)	—	0.24	32.0 (27.0–38.0)	33.0 (28.0–39.0)*	—	0.001
Weight (kg)^a	—	61.0 (55.0–70.0)	62.0 (55.5–70.0)*	—	0.001	61.0 (55.0–70.0)	62.0 (56.0–71.0)*	—	0.001
Height (cm)^a	—	165.0 (160.0–169.0)	165.0 (160.0–169.0)	—	0.92	165.0 (160.0–169.0)	165.0 (160.0–169.0)	—	0.20
BMI^a,b	—	22.6 (20.6–25.5)	23.0 (20.8–25.9)*	—	0.001	22.6 (20.6–25.5)	22.9 (20.8–26.0)*	—	0.001
	Underweight	419 (5.3)	330 (4.9)	16.28	0.006	320 (5.2)	322 (5.0)	23.11	0.001
	Normal weight	5153 (65.6)	4212 (62.9)^•			4086 (66.2)	3985 (62.3)*
	Pre-obesity/overweight	1460 (18.6)	1384 (20.7)^•			1115 (18.1)	1347 (21.1)*
	Obesity I	500 (6.4)	468 (7.0)			402 (6.5)	449 (7.0)
	Obesity II	172 (2.2)	135 (2.0)			129 (2.1)	136 (2.1)
	Obesity III	70 (0.9)	67 (1.0)			59 (1.0)	63 (1.0)
Medical history^b
Autoimmune diseases
Types	Thyroid	293 (3.7)	236 (3.5)	0.04	0.84	236 (3.8)	225 (3.5)	1.00	0.32
	Dermatological	128 (1.6)	103 (1.5)	0.01	0.91	107 (1.7)	101 (1.6)	0.43	0.51
	Gastrointestinal	169 (2.2)	148 (2.2)	0.51	0.48	135 (2.2)	146 (2.3)	0.27	0.60
	Rheumatic/articular	54 (0.7)	36 (0.5)	0.96	0.33	44 (0.7)	40 (0.6)	0.32	0.57
	None of the above	110 (1.4)	84 (1.3)	0.25	0.62	83 (1.3)	89 (1.4)	0.10	0.75
Other clinical conditions
Types	Gynecological	481 (6.1)	444 (6.6)	2.24	0.14	380 (6.2)	435 (6.8)	0.32	0.57
	Rheumatic/articular	227 (2.9)	228 (3.4)^•	7.30	0.007	33 (0.5)	54 (0.8)	2.23	0.14
	Thyroid	170 (2.2)	151 (2.3)	1.32	0.25	136 (2.2)	141 (2.2)	1.35	0.25
	Neurological/mental	164 (2.1)	232 (3.5)*	12.97	0.001	24 (0.4)	33 (0.5)^†	5.83	0.02
	Respiratory	7 (2.1)	7 (3.3)	0.00	0.99	189 (3.1)	206 (3.2)	0.78	0.38
	Gastrointestinal	102 (1.3)	130 (1.9)	3.52	0.06	78 (1.3)	127 (2.0)^†	5.32	0.02
	HPV	99 (1.3)	80 (1.2)	2.20	0.14	75 (1.2)	75 (1.2)	1.13	0.29
	Blood	68 (0.9)	54 (0.8)	1.72	0.19	53 (0.9)	54 (0.8)	0.63	0.43
	Food intolerance/allergy	66 (0.8)	80 (1.2)	1.35	0.25	52 (0.8)	80 (1.3)	2.28	0.12
	Cardiovascular	51 (0.6)	39 (0.6)	1.70	0.20	30 (0.5)	45 (0.7)	1.04	0.31
	Cancer	41 (0.5)	21 (0.3)^•	6.65	0.01	25 (0.4)	30 (0.5)	0.03	0.96
	Dermatological	37 (0.5)	41 (0.6)	0.20	0.70	27 (0.4)	42 (0.7)	1.26	0.26
	Infectious	29 (0.4)	41 (0.6)	2.06	0.15	15 (0.2)	40 (0.6)^•	7.74	.005
	Kidney	28 (0.4)	32 (0.5)	0.26	0.61	24 (0.4)	33 (0.5)	0.32	0.57
	None of the above	338 (4.3)	336 (5.0)	0.02	0.89	271 (4.4)	330 (5.2)	0.15	0.70
Allergies	—	2553 (32.5)	2295 (34.3)^†	5.14	0.02	2030 (32.9)	2196 (34.4)	2.95	0.09
COVID-19
Diagnosis	—	1268 (16.1)	1136 (17.0)	1.79	0.18	460 (7.5)	390 (6.1)^•	9.14	.003
Vaccine brand	Pfizer-BioNTech	5189 (66.1)	4320 (64.5)	10.22	0.02	4161 (67.4)	4379 (68.5)	9.68	0.02
	Moderna	1433 (18.2)	1210 (18.1)			1141 (18.5)	1211 (18.9)
	Oxford-AstraZeneca	979 (12.5)	894 (13.4)			802 (13.0)	760 (11.9)
	None of the above	254 (3.2)	271 (4.0)^†			66 (1.1)	43 (0.7)^†
Gynecological history^a,b
Menarcheal age	—	12.0 (11.0–14.0)	12.0 (11.0–14.0)	—	0.34	12.0 (11.0–14.0)	12.0 (11.0–13.0)^•	—	.006
Menstrual cycle
Length	Regular	6934 (88.3)	5821 (86.9)^†	5.91	0.02	5419 (87.8)	5614 (87.8)	0.001	0.98
Length	Irregular	921 (11.7)	874 (13.1)^†			751 (12.2)	779 (12.2)
Period length	1–3 days	1201 (15.3)	1051 (15.7)	13.70	0.003	963 (15.6)	963 (15.1)	14.351	0.002
	3–5 days	4852 (61.8)	3978 (59.4)^•			3819 (61.9)	3841 (60.1)^•
	>5 days	1746 (22.2)	1592 (23.8)^•			1347 (21.8)	1518 (23.7)^•
	None of the above	56 (0.7)	74 (1.1)^•			41 (0.7)	71 (1.1)^•
Period flow	Light	1464 (18.6)	1243 (18.6)	14.97	0.001	1176 (19.1)	1142 (17.9)	23.37	0.001
	Moderate	4692 (59.7)	3828 (57.2)*			3689 (59.8)	3668 (57.4)*
	Heavy	1699 (21.6)	1624 (24.3)*			1305 (21.2)	1583 (24.8)*
Agreement between observed/expected period dates	Yes	6236 (79.4)	3363 (50.2)*	1373.7	0.001	4724 (76.6)	3232 (50.6)*	923.6	0.001
Agreement between observed/expected period dates	No	1604 (20.4)	3319 (49.6)*			1430 (23.2)	3153 (49.3)*
Current/past use of contraceptives
Duration	<10 years	929 (11.8)	997 (14.9)^†	3.99	0.05	754 (12.2)	894 (14.0)^•	8.94	0.01
Duration	⩾10 years	298 (3.8)	264 (3.9)^†			239 (3.9)	256 (4.0)
Types	None	6600 (84.0)	5409 (80.8)*	28.36	0.001	5515 (83.5)	5216 (81.6)*	13.30	0.001
	Hormonal	1057 (13.5)	1055 (15.8)*			864 (14.0)	960 (15.0)
	IUD (nonhormonal)	198 (2.5)	231 (3.5)*			151 (2.4)	217 (3.4)*
Reproduction
Have you ever been pregnant?	Yes	2446 (31.1)	2269 (33.9)*	12.46	0.001	1867 (30.3)	2180 (34.1)*	21.21	0.001
Have you ever been pregnant?	No	5408 (68.8)	4426 (66.1)*			4303 (69.7)	4213 (65.9)*
No. of pregnancies	0–2	7278 (92.7)	6190 (92.5)	0.21	0.65	5736 (93.0)	5900 (92.3)	1.67	0.20
No. of pregnancies	>2	563 (7.2)	493 (7.4)			425 (6.9)	478 (7.5)
No. of children	1–2	1896 (24.1)	1810 (27.0)	1.55	0.21	1450 (23.5)	1746 (27.3)	0.47	0.49
No. of children	>2	145 (1.8)	118 (1.8)			95 (1.5)	126 (2.0)
Diseases
Types	PCOS	1049 (13.4)	998 (14.9)	1.03	0.31	822 (13.3)	967 (15.1)	1.94	0.16
	Menorrhagia	791 (10.1)	754 (11.3)	0.73	0.39	609 (9.9)	736 (11.5)	2.91	0.09
	Endometriosis	267 (3.4)	315 (4.7)*	10.27	0.001	211 (3.4)	291 (4.6)^•	6.09	0.01
	Fibroids	199 (2.5)	152 (2.3)	3.23	0.07	161 (2.6)	154 (2.4)	2.12	0.15
	Adenomyosis	45 (0.6)	30 (0.4)	1.91	0.17	33 (0.5)	31 (0.5)	0.50	0.48
	Uterine bleeding	31 (0.4)	45 (0.7)^†	3.95	0.05	22 (0.4)	46 (0.7)^•	6.10	0.01
	None of the above	513 (6.5)	441 (6.6)	1.37	0.24	409 (6.6)	413 (6.5)	2.75	0.10
Adverse events^b
Types	Arm pain	6031 (76.8)	5243 (78.3)*	54.43	0.001	4407 (71.4)	4773 (74.7)^•	8.92	0.003
	Fatigue	3088 (39.3)	3534 (52.8)*	175.67	0.001	3279 (53.1)	4143 (64.8)*	91.80	0.001
	Headache	1815 (23.1)	2300 (34.4)*	162.63	0.001	2076 (33.6)	2893 (45.3)*	127.04	0.001
	Fever	1255 (16.0)	1488 (22.2)*	59.76	0.001	1931 (31.3)	2484 (38.9)*	38.78	0.001
	Nauseas	377 (4.8)	643 (9.6)*	104.37	0.001	481 (7.8)	951 (14.9)*	125.21	0.001
	Swollen glands	22 (0.3)	51 (0.8)*	14.44	0.001	476 (7.6)	814 (12.7)*	69.16	0.001
	Breast lumps	30 (0.4)	172 (2.6)*	115.30	0.001	41 (0.7)	221 (3.5)*	108.28	0.001
	None of the above	469 (6.0)	548 (8.2)*	16.87	0.001	419 (6.8)	600 (9.4)*	17.56	0.001

BMI: body mass index; HPV: human papillomavirus; IUD: intrauterine device; MC: menstrual changes subgroup; n-MC: non-menstrual changes subgroup; PCOS: polycystic ovary syndrome.

Values are expressed as median (interquartile range).

Values are expressed as n (%).

p ⩽ 0.001. ^•p ⩽ 0.01. ^†p ⩽ 0.05 versus n-MC.

Focusing on the data on gynecological history (Table 3), menarcheal age was significantly lower in MC women compared to those in the n-MC subgroup (dose 2). Regarding menstrual cycle characteristics, a significantly higher percentage of women with irregular length (dose 1), period length of more than 5 days or other (doses 1 & 2), and heavy flow (doses 1 & 2) was observed in the MC subgroup compared to the n-MC subgroup. Moreover, significant differences between the subgroups were detected for both doses in terms of duration of use and type of contraceptive, as well as previous pregnancies, which were more common in the MC subgroup. Endometriosis and uterine bleeding were also more frequently observed in this subgroup. Finally, those who experienced MC after both doses were more likely to experience adverse events.

Binary logistic regression analysis for dose 1 (Table 4) showed that factors influencing MC after COVID-19 vaccination in currently menstruating women may include heavy menstrual flow, the phase of the menstrual cycle at the time of vaccination, heavy period flow, the presence of certain clinical conditions―rheumatic/articular, neurological/mental or gastrointestinal non-autoimmune conditions, and endometriosis―use of short- to medium-term contraception, having been pregnant more than two times, and experiencing vaccine adverse events such as fatigue, headache, nausea, swollen glands, breast lumps, and others.

Table 4.

Factors associated with the occurrence of MC after receiving the first dose of the COVID-19 vaccine: binary logistic regression.

Dose 1
Variable	Category	MC (n = 6693), aOR (95% CI)	p value
Other clinical conditions
Rheumatic/articular	Yes	1.58 (1.00–2.49)	0.05
Rheumatic/articular	No	1
Neurological/mental	Yes	1.48 (1.19–1.85)	0.001
Neurological/mental	No	1
Gastrointestinal	Yes	1.38 (1.04–1.83)	0.03
Gastrointestinal	No	1
Menstrual cycle
Period flow	Moderate	1.00 (0.90–1.09)	0.82
	Heavy	1.12 (1.00–1.25)	0.05
	Light	1
Agreement between observed/expected period dates	No	3.86 (3.60–4.16)	0.001
Agreement between observed/expected period dates	Yes	1
Past use of contraception	<10 years	1.38 (1.24–1.54)	0.001
	⩾10 years	1.31 (1.09–1.57)	0.004
	Never	1
No. of pregnancies	⩾2	1.11 (1.03–1.19)	0.009
No. of pregnancies	0–2	1
Endometriosis	Yes	1.33 (1.11–1.59)	0.002
Endometriosis	No	1
Adverse events
Fatigue	Yes	1.53 (1.41–1.65)	0.001
Fatigue	No	1
Headache	Yes	1.32 (1.21–1.44)	0.001
Headache	No	1
Nauseas	Yes	1.45 (1.25–1.70)	0.001
Nauseas	No	1
Swollen glands	Yes	4.05 (2.41–6.82)	0.001
Swollen glands	No	1
Breast lumps	Yes	5.70 (3.80–8.55)	0.001
Breast lumps	No	1
None of the above	Yes	1.20 (1.04–1.37)	0.01
	No	1

Reference subgroup n-MC. aOR: adjusted odd radio; MC: menstrual changes subgroup; 95% CI: confidence interval.

On the other hand, the age, phase of the menstrual cycle at the time of vaccination, previous diagnosis of an infectious disease, experiencing MC after the first dose, and suffering from some adverse events―for example, fatigue, fever, headache, nausea, swollen glands, breast lumps, and others―may be some of the factors associated with MC after receiving the second dose (Table 5).

Table 5.

Factors associated with the occurrence of MC after receiving the second dose of the COVID-19 vaccine: binary logistic regression.

Dose 2
Variable	Category	MC (n = 6393), aOR (95% CI)	p value
Age	—	1.02 (1.02–1.03)	<0.001
Infectious diseases	Yes	2.66 (1.31–5.40)	0.007
Infectious diseases	No	1
Menstrual changes after dose 1	Yes	12.48 (11.41–13.67)	<0.001
Menstrual changes after dose 1	No	1
Agreement between observed/expected period dates	No	2.66 (2.42–2.91)	<0.001
Agreement between observed/expected period dates	Yes	1
Adverse events
Fatigue	Yes	1.31 (1.18–1.45)	<0.001
Fatigue	No	1
Fever	Yes	1.13 (1.02–1.26)	0.02
Fever	No	1
Headache	Yes	1.15 (1.03–1.28)	0.01
Headache	No	1
Nausea	Yes	1.45 (1.25–1.69)	<0.001
Nausea	No	1
Swollen glands	Yes	1.34 (1.15–1.57)	<0.001
Swollen glands	No	1
Breast lumps	Yes	3.27 (2.21–4.82)	<0.001
Breast lumps	No	1
None of the above	Yes	1.28 (1.08–1.50)	0.003
	No	1

Reference subgroup n-MC. aOR: adjusted odd radio; MC: menstrual changes subgroup; 95% CI: confidence interval.

Discussion

Currently menstruating women may experience MC, most commonly in the 2 weeks after receiving the COVID-19 vaccine. Inter-individual factors influencing this unexpected event may include age, heavy menstrual flow, use of short- to medium-term contraception, number of previous pregnancies, pre-existing diagnoses of certain clinical conditions―including endometriosis―and suffering from other vaccine adverse events.

Our findings add to a growing body of research showing that currently menstruating women may experience MC. As in our study, other authors have found changes in menstrual bleeding patterns, reporting longer periods^5,20 and/or heavier flow^{5,6,10–12,14–17} than usual in vaccinated women. Although evidence suggests that these MC are mostly transient in nature,^{5,7–9,12,15,17,18,20} a non-negligible percentage of women reported suffering from them for more than 12 months.

Consistent with previous studies,^{5,12,14,16–18,20} the occurrence of MC seems to be significantly higher after the second dose of the vaccine. This was also observed in our study of formerly menstruating women.²⁵ For Male et al.,²⁶ the interval between doses (3–4 weeks in the case of Spain)²⁸ may explain this additive effect, suggesting the importance of the phase of the menstrual cycle at the time of vaccination, later confirmed by binary logistic regression. Further research is needed, however, because of conflicting results.^8,12 It should be remembered that the menstrual cycle is controlled by a complex hormonal process that varies at different stages and can be influenced by several internal and external factors.^3–5 In the past, vaccinations against other viruses^29–32 have also been associated with MC. Thus, human papilloma vaccine has been linked to premature ovarian insufficiency and increased number of hospital visits for abnormal amount of menstrual bleeding.³³ On the other hand, viral infections have also been reported to induce reproductive alterations.^34–36 In the case of SARS-CoV-2 infection, similar percentages of vaccinated women with MC were observed regardless of prior infection history^5–13; however, Muhaidat et al.⁵ found a significant association between post-vaccination menstrual abnormalities and the severity of COVID-19. For their part, Li et al.³⁷ conclude that the average sex hormone concentrations and the ovarian reserve did not significantly change in those COVID-19 women of child-bearing age who experienced temporary MC. Our recent research indicate that SARS-CoV-2 infection may also result in menstrual-related disturbances in formerly menstruating women, including unexpected vaginal bleeding and spotting. Our analysis revealed that perimenopausal status and a history of menorrhagia are associated with an increased likelihood of experiencing these events.³⁸ Taken together, this evidence would support the theory proposed by other researchers^5,25,26 about the role of unknown immunological mechanisms as triggers of MC resulting from both infection and vaccination. The interaction between the immune and neuroendocrine systems has been shown to control some of the processes of the menstrual cycle, including the cyclic build-up and breakdown of the uterine endometrium.^6–8 In this sense, thrombocytopenia^39,40 and changes in lymphocyte subpopulation patterns³⁴ may be involved. Furthermore, from our point of view, the possible involvement of oxidative events in this phenomenon should not be ignored. Unfortunately, our knowledge of the basic uterine and menstrual physiology is not enough to understand more complex processes of this kind, as demonstrated by the increasing evidence about an unknown internal circamonthly timing system.⁴¹

Binary logistic regression analysis showed that other inter-individual factors may be involved in the occurrence of MC following COVID-19 vaccination in currently menstruating women, such as age, heavy menstrual flow, use of short- to medium-term contraception, number of previous pregnancies, pre-existing diagnoses of certain clinical conditions―including endometriosis―and suffering from other vaccine adverse events. Several of these factors were also reported by Lee et al.¹⁰ A previous study in Spanish women² also points to age as one of the factors influencing this event. But again, the current scientific evidence is mixed. Other authors observed that the association between vaccination and MC did not differ according to age, use of contraceptives/hormones, or history of gynecological disease(s),^5,16 but found a significant association with common vaccine side effects,^5,11 in contrast to Alvergne et al.⁸ From our perspective, these factors likely reflect distinct neuro-immune-endocrine contexts that may be involved in the occurrence of this vaccine side effect. Further research is warranted to clarify these complex interactions, considering their heterogeneity according to the geographical background of the target population. Moreover, sociodemographic factors may also underlie the difference in MC rates between countries. Thus, the country of residence could be an influencing factor, both for the predominant use of certain brands of vaccines—and the corresponding schedule—and for cultural reasons that may discourage young women from seeking help.^5,42 That aside, most studies^{5,7–9,12,14,15} seem to agree that the occurrence of MC does not seem to be brand-specific and therefore independent of the technological approach (adenovirus-vectored or mRNA vaccines), suggesting shared immunological mechanisms.^7,8

Given the current evidence, it is necessary to study in depth the impact of the vaccine, not only at reproductive age, but also throughout the different stages of a woman’s life, taking in account the potential influence of inter-individual factors (weight, genetic, medical history, smoking. . .).^2,5,26 Further research must be also focused on determine the underlying physiological mechanisms of this unexpected event, including hemostatic—and inflammatory—changes to the endometrium after the acute immune response to the vaccine against SARS-CoV-2 virus. Furthermore, other unknown aspects such as the influence of the vaccine administration during the different menstrual phases and the potential effect of the vaccine adjuvants must be also dilucidated. From our point of view, the hypothesis of pandemic-related stress as an influencing factor on MC after vaccination could be discarded,^1,7 but not on coronavirus disease.⁴³

Limitations

Certain limitations of the current study merit mention. While the sample analyzed was large enough to be statistically representative, a priori sample size calculation was not performed as the prevalence of this unexpected event was unknown. The experimental design may also have influenced the detection of significant differences, and causal relationships cannot be properly determined. The online questionnaire was not pre-validated due to the unexpected health crisis, but it gave us the opportunity to sample a target population in real time and investigate this unexpected event before it was reflected in official reports. In any case, although this type of data is potentially subject to errors such as recall bias or self-selection, self-reports are considered the gold standard for menstrual cycle data and are useful for quickly identifying potential signals or rare adverse events. Finally, we would like to point out that, as in similar studies, our results may not be applicable to countries other than Spain for some of the reasons mentioned above. In the view of foregoing, we agree that a longitudinal and multinational study could help to establish the cause-effect relationship and to clarify the factors influencing the occurrence of changes in the menstrual cycle after COVID-19 vaccination.

Conclusions

The findings of this study reinforce previous evidence and underscore the need for further research. Vaccine clinical trials should systematically account for potential menstrual side effects and the factors that may influence their occurrence, regardless of individuals’ reproductive status. Expanding research in this area would contribute directly to achieving the WHO Sustainable Development Goals, particularly goals 3 (“Good health and well-being”) and 5 (“Gender equality”). Moreover, generating robust evidence on this subject is vital for preparedness in the face of future viral outbreaks. Ensuring that healthcare professionals are equipped with up-to-date, evidence-based information is key to empowering patients to make informed decisions that support their quality of life. It also helps prevent the dismissal or gaslighting of individuals who report unexpected vaccine-related side effects.

Supplemental Material

sj-docx-2-whe-10.1177_17455057251406958 – Supplemental material for Characteristics and health factors influencing menstrual changes after COVID-19 vaccination: A Spanish retrospective observational study in currently menstruating women

Supplemental material, sj-docx-2-whe-10.1177_17455057251406958 for Characteristics and health factors influencing menstrual changes after COVID-19 vaccination: A Spanish retrospective observational study in currently menstruating women by Miriam Al-Adib, Ana B. Rodríguez and Cristina Carrasco in Women's Health

Supplemental Material

sj-pdf-1-whe-10.1177_17455057251406958 – Supplemental material for Characteristics and health factors influencing menstrual changes after COVID-19 vaccination: A Spanish retrospective observational study in currently menstruating women

Supplemental material, sj-pdf-1-whe-10.1177_17455057251406958 for Characteristics and health factors influencing menstrual changes after COVID-19 vaccination: A Spanish retrospective observational study in currently menstruating women by Miriam Al-Adib, Ana B. Rodríguez and Cristina Carrasco in Women's Health

Footnotes

Acknowledgements

The research team would like to thank all the participants who have collaborated in the study in an unselfish manner.

ORCID iDs

Miriam Al Adib Mendiri

Ana Beatriz Rodríguez Moratinos

Cristina Carrasco

Ethics considerations

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of University of Extremadura (ref. 180/2021).

Consent to participate

An electronic informed consent was only obtained from those who agree to be contacted via email by the research group for additional data collection.

Consent for publication

Not applicable.

Author contributions

Miriam Al-Adib: Conceptualization; Methodology; Writing – review & editing; Visualization; Funding acquisition.

Ana B. Rodríguez: Conceptualization; Methodology; Investigation; Visualization; Supervision; Writing – review & editing; Funding acquisition.

Cristina Carrasco: Conceptualization; Methodology; Investigation; Visualization; Supervision; Writing – original draft; Writing – review & editing; Funding acquisition; Formal analysis; Project administration.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Regional Government of Extremadura-ERDF funds (grant BBB021-GR21042); Cumlaude Lab under research agreement with the University of Extremadura (ref. 431/22). Neither the government agency nor the sponsoring company have played a role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical restrictions.

Supplemental material

Supplemental material for this article is available online.

References

Edelman

Boniface

Benhar

, et al. Association between menstrual cycle length and coronavirus disease 2019 (COVID-19) vaccination: a U.S. cohort. Obstet Gynecol 2022; 139(4): 481–489.

Baena-García

Aparicio

Molina-López

, et al. Premenstrual and menstrual changes reported after COVID-19 vaccination: the EVA project. Womens Health (Lond) 2022; 18: 17455057221112237.

Critchley

HOD

Babayev

Bulun

, et al. Menstruation: science and society. Am J Obstet Gynecol 2020; 223(5): 624–664.

Sharp

Fraser

Sawyer

, et al. The COVID-19 pandemic and the menstrual cycle: research gaps and opportunities. Int J Epidemiol 2022; 51(3): 691–700.

Muhaidat

Alshrouf

Azzam

, et al. Menstrual symptoms after COVID-19 vaccine: a cross-sectional investigation in the MENA region. Int J Womens Health 2022; 14: 395–404.

Rodríguez Quejada

Toro Wills

Martínez-Ávila

, et al. Menstrual cycle disturbances after COVID-19 vaccination. Womens Health (Lond) 2022; 18: 17455057221109375.

Wang

Mortazavi

Hart

, et al. A prospective study of the association between SARS-CoV-2 infection and COVID-19 vaccination with changes in usual menstrual cycle characteristics. Am J Obstet Gynecol 2022; 227(5): 739.e1–739.e11.

Alvergne

Woon

Male

Effect of COVID-19 vaccination on the timing and flow of menstrual periods in two cohorts. Front Reprod Health 2022; 4: 952976.

Edelman

Boniface

Male

, et al. Association between menstrual cycle length and COVID-19 vaccination: global, retrospective cohort study of prospectively collected data. BMJ Med 2022; 1(1): e000297.

10.

Lee

KMN

Junkins

Luo

, et al. Investigating trends in those who experience menstrual bleeding changes after SARS-CoV-2 vaccination. Sci Adv 2022; 8(28): eabm7201.

11.

Dabbousi

El Masri

El Ayoubi

, et al. Menstrual abnormalities post-COVID vaccination: a cross-sectional study on adult Lebanese women. Ir J Med Sci 2022; 26: 1–8.

12.

Laganà

Veronesi

Ghezzi

, et al. Evaluation of menstrual irregularities after COVID-19 vaccination: results of the MECOVAC survey. Open Med (Wars) 2022; 17(1): 475–484.

13.

Abdollahi

Naseh

Kalroozi

, et al. Comparison of side effects of COVID-19 vaccines: Sinopharm, AstraZeneca, Sputnik V, and Covaxin in women in terms of menstruation disturbances, hirsutism, and metrorrhagia: a descriptive-analytical cross-sectional study. Int J Fertil Steril 2022; 16(3): 237–243.

14.

Al-Mehaisen

A Mahfouz

Khamaiseh

, et al. Short term effect of corona virus diseases vaccine on the menstrual cycles. Int J Womens Health 2022; 14: 1385–1394.

15.

Sualeh

Uddin

Junaid

, et al. Impact of COVID-19 vaccination on menstrual cycle: a cross-sectional study from Karachi, Pakistan. Cureus 2022; 14(8): e28630.

16.

Trogstad

Laake

Robertson

, et al. Heavy bleeding and other menstrual disturbances in young women after COVID-19 vaccination. Vaccine 2023; 41(36): 5271–5282.

17.

Almomani

Hajjo

Qablan

, et al. A cross sectional study confirms temporary post-COVID-19 vaccine menstrual irregularity and the associated physiological changes among vaccinated women in Jordan. Front Med 2023; 10: 01211283.

18.

Mahfouz

Abdelmageed

Alqassim

, et al. Menstrual irregularities associated with COVID-19 vaccines among women in Saudi Arabia: a survey during 2022. Open Med (Wars) 2023; 18(1): 20230804.

19.

Hosoya

Piedvache

Nakamura

, et al. Prolongation of the menstrual cycle after receipt of the primary series and booster doses of mRNA coronavirus disease 2019 (COVID-19) vaccination. Obstet Gynecol 2024; 143(2): 284–293.

20.

Trogstad

Laake

Robertson

, et al. Increased incidence of menstrual changes among young women after coronavirus vaccination. Norwegian Institute of Public Health, https://www.fhi.no/en/studies/ungvoksen/increased-incidence-of-menstrual-changes-among-young-women/ (2021).

21.

Vaccine Adverse Event Reporting System (VAERS). NOT-HD-21-035: notice of special interest (NOSI) to encourage administrative supplement applications to investigate COVID-19 vaccination and menstruation (admin supp clinical trial optional). United States Government, https://grants.nih.gov/grants/guide/notice-files/NOT-HD-21-035.html (2021).

22.

Therapeutic Goods Administration (TGA). COVID-19 vaccine safety report. Australia Government, https://www.tga.gov.au/news/covid-19-vaccine-safety-reports/covid-19-vaccine-safety-report-06-10-2022 (2022).

23.

Medicines and Healthcare Products Regulatory Agency (MHRA). Coronavirus vaccine—summary of yellow card reporting. United Kingdom, https://www.gov.uk/government/publications/coronavirus-covid-19-vaccine-adverse-reactions/coronavirus-vaccine-summary-of-yellow-card-reporting (2022).

24.

Spanish Agency of Medicines and Medical Devices (AEMPS). 17th COVID-19 vaccines pharmacovigilance report. Government of Spain, https://www.aemps.gob.es/informa/17o-informe-de-farmacovigilancia-sobre-vacunascovid-19/ (2022).

25.

González

Al-Adib

Rodríguez

, et al. Factors associated with menstrual-related disturbances following SARS-CoV-2 vaccination: a Spanish retrospective observational study in formerly menstruating women. Women Health 2025; 65: 167–181.

26.

Male

Effect of COVID-19 vaccination on menstrual periods in a retrospectively recruited cohort. medRxiv 2021; 2021: 2021.11.15.21266317.

27.

von Elm

Altman

Egger

, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol 2008; 61(4): 344–349.

28.

Ministry of Health. COVID-19 vaccination strategy. Government of Spain, https://www.vacunacovid.gob.es (2022).

29.

Lamb

AR.

Experiences with prophylactic typhoid vaccination: its effect on menstruation. Arch Intern Med 1913; XII: 565–577.

30.

Shingu

Uchida

Nishi

, et al. Menstrual abnormalities after hepatitis B vaccine. Kurume Med J 1982; 29: 123–125.

31.

Suzuki

Hosono

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.

32.

Christianson

Wodi

Talaat

, et al. Primary ovarian insufficiency and human papilloma virus vaccines: a review of the current evidence. Am J Obstet Gynecol 2020; 222: 239–244.

33.

Gong

Tang

, et al. Human papillomavirus vaccine-associated premature ovarian insufficiency and related adverse events: data mining of vaccine adverse event reporting system. Sci Rep 2020; 10: 10762.

34.

Kurmanova

Lokshin

VN.

Reproductive dysfunctions in viral hepatitis. Gynaecol. Endocrinol 2016; 32(2): 37–40.

35.

Giakoumelou

Wheelhouse

Cuschieri

, et al. The role of infection in miscarriage. Hum Reprod Update 2015; 22(1): 116–133.

36.

Schoenbaum

Hartel

, et al. HIV infection, drug use, and onset of natural menopause. Clin Infect Dis 2005; 41(10): 1517–1524.

37.

Chen

Hou

, et al. Analysis of sex hormones and menstruation in COVID-19 women of child-bearing age. Reprod Biomed Online 2021; 42(1): 260–267.

38.

González

Al-Adib

Miriam

Rodríguez

, et al. COVID-19 and menstrual-related disturbances: a Spanish retrospective observational study in formerly menstruating women. Front Glob Womens Health 2024; 5: 1393765.

39.

Perricone

Ceccarelli

Nesher

, et al. Immune thrombocytopenic purpura (ITP) associated with vaccinations: a review of reported cases. Immunol Res 2014; 60(2–3): 226–235.

40.

Merchant

COVID-19 post-vaccine menorrhagia, metrorrhagia or postmenopausal bleeding and potential risk of vaccine-induced thrombocytopenia in women. BMJ 2021; 18: n958.

41.

Ecochard

Stanford

Fehring

, et al. Evidence that the woman’s ovarian cycle is driven by an internal circamonthly timing system. Sci Adv 2024; 10(15): eadg9646.

42.

Liaquat

Huda

Azeem

, et al. Post-COVID-19 vaccine-associated menstrual cycle changes: a multifaceted problem for Pakistan. Ann Med Surg (Lond) 2022; 78: 103774.

43.

Demir

Sal

Comba

Triangle of COVID, anxiety and menstrual cycle. J Obstet Gynaecol 2021; 41(8): 1257–1261.

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