Menstrual health and health-related quality of life in Spain: a multicenter cross-sectional study

This cross-sectional study employs a critical gender perspective and health equity principles. This approach understands how gender norms and power dynamics may shape menstrual health. This perspective guided the entire research process, including the definition of the study population, explicitly including women and PWM regardless of gender identity, to capture a broader range of menstrual experiences. It also informed the selection and operationalisation of variables, including dimensions of menstrual health that reflect social determinants of health (e.g., menstrual poverty). Given the cross-sectional design of the study, causal inferences cannot be drawn. Findings should be interpreted as associations between variables. The STROBE guidelines for reporting cross-sectional studies were followed [ 51 ] (Additional File 1). Thirty-two primary healthcare centres took part in this research across five regions in Spain during May and September 2023. The number of healthcare centres that participated varied by region: Andalusia ( N  = 7), the Basque Country ( N  = 6) the Canary Islands ( N  = 6), Catalonia ( N  = 7), and Galicia ( N  = 6). These five regions were included to capture territorial diversity within Spain, as they differ in terms of population density, sociodemographic composition, healthcare organisation, and other factors. Catalonia and the Basque Country are regions with higher levels of economic resources, in comparison to others. Moreover, Catalonia has implemented specific menstrual health actions such as “Plan for Menstrual and Climacteric Equity 2023–2025”, that aimed to promote menstrual equity through menstrual education and the provision of free reusable menstrual products. Preliminary calculations indicated that a minimum of 1,070 women and PWM were necessary, with a total of 36 participants from each healthcare centre. The sample size was determined based on an OR = 1.5, a statistical power of 80%, an alpha risk of 5%, and an interclass correlation of 0.01 between healthcare centres. The PASS software was used to conduct sample size calculations, as described in the PASS 2022 Power Analysis and Sample Size Software (NCSS, LLC, 2022, Kaysville, Utah, USA). The study employed non-probabilistic, convenience-based, and purposive sampling. Healthcare centres were selected to achieve diversity across different socioeconomic levels. When healthcare centres accepted their participation, healthcare professionals were invited to recruit participants. The research team provided the recruiters (healthcare professionals) with a recruitment poster, a participant information leaflet to invite potential participants, and a recruitment manual containing essential information to adhere to during the recruitment process. The research team supplied recruiters with an Excel spreadsheet detailing the number of participants required for each sociodemographic profile. This spreadsheet was created to ensure a diverse range of participants. The researchers sought to guarantee a diverse participant pool in terms of age (18–25, 26–35, 36–45, and over 46 years), migration status (born in Spain; born outside Spain), LGTBIQ+ community, and people with recognised disabilities (in the Spanish context, “recognised disability” refers to an officially certified disability status with a disability degree of 33% or higher). The inclusion criteria for the study were as follows: (1) women and PWM with active menstrual cycles, who had experienced at least one menstruation within the last six months, (2) aged 18 years or older, (3) residing in one of the five participating regions, (4) being users of a participating healthcare centre. Exclusion criteria were: (1) women and PWM who had not experienced any menstruation within the last six months (e.g., being pregnant or breastfeeding, amenorrhea, etc.) and (2) being in the menopausal stage or with ovarian failure (absence of menstruation in the last 12 months). When women and PWM accepted to participate, the recruiters gave access to an online questionnaire administered via the LimeSurvey platform ( https://www.limesurvey.org ). The participants completed the questionnaire on their own in approximately 20 min. For those participants who had difficulties filling the online version, an alternative paper-based questionnaire was provided to help them to complete the questionnaire. The research team provided biweekly updates to each healthcare centre on the progress of recruitment and data collection, in order to ensure the diversity of participants’ profiles. The questionnaire was composed of a combination of validated scales and ad hoc questions developed by the research team (Additional File 2). The use of ad hoc questions responded to the exploratory nature of the study and to the limited availability of validated instruments covering broader social dimensions of menstrual health, for example, menstrual stigma, menstrual poverty, menstrual education and social participation during menstruation. The questionnaire was also employed in another study conducted by the same research team, which had a different objective and is currently being under review for publication. Four meetings were held to develop and devise the questionnaire, which was also tested in all regions before data collection. 1,431 collected questionnaires met the study’s inclusion criteria. Then, 1,381 participants have all relevant data for the variables (e.g., all items of EQ-5D-5 L scale). Complete-case analysis was performed during the analyses. Sociodemographic variables were: age, region of residence in Spain, gender identity, sexual orientation, country of birth, administrative situation, completed education, employment situation, access to public healthcare card, financial constraints, informal caregiving, experiences of discrimination, and recognised disabilities. See Additional File 2 for an overview of the different categories of each variable. Self-reported menstrual health variables were: age at menarche, menstrual bleeding abundance, presence of menstrual blood clots, menstrual bleeding duration, menstrual cycle duration, intensity of menstrual pain, presence of self-reported gynaecological and systemic health conditions (referring to health conditions, general and specific to the reproductive system, that can be associated to menstrual health, for example, anaemia, iron deficiency, endometrial polyps, among others), use of hormonal contraception, type of hormonal contraception methods, frequency of premenstrual symptoms, impact of premenstrual symptoms on daily life, comfortability to change menstrual products at home, comfortability to change menstrual products at work, comfortability to change menstrual products at public spaces, access to menstrual education, menstrual-related experiences of taboo, stigma, and discrimination, satisfaction with menstrual healthcare consultations, frequency of menstrual poverty, and social participation impairment during menstruation (see Additional File 2). We used some items of the CVM-22 scale [ 47 ] to construct the “social participation impairment during menstruation” variable. The research team’s original intention to utilise the full scale in the study was subsequently modified. This decision was made in order to avoid redundancy with other scales that had already been included. Data on menstrual health and sociodemographic characteristics were collected through ad hoc questions developed by the research team, based on previous menstrual health research and the team expertise in the field. The researchers evaluated the frequency of premenstrual symptoms using an ad hoc 12-item scale focused on emotional shifts and physical changes experienced 1–2 weeks before menstruation. Each item on the 12-item premenstrual symptom frequency scale had six possible response options ranging from ‘never’ to ‘always’ (using a Likert-type scale). In the analysis process, we assigned a value from 0 to 5 to each item and we calculated the sum of the results, resulting the total score ranging from 0 to 60. The variable was then re-coded into three categories, with low frequency of premenstrual symptoms corresponding to a score of 0–20, moderate frequency corresponding to a score of 21–40, and high frequency corresponding to a score of 41–60. The division into categories (for the variable premenstrual symptoms: 0–20, 21–40, and 41–60) was defined based on the research team’s expertise in menstrual health research, the distribution of responses in the study sample, and the aim of facilitating interpretability of results in this exploratory analysis. Other variables followed the same process of re-codification and re-categorisation during the analysis process. Access to menstrual education was assessed using two ad hoc questions. The categorisation of this variable resulted in low (0–3), moderate (4–6), and high (7–10). Menstrual-related taboo, stigma, and discrimination were assessed using a four-item scale, and then rated as low (0–3), moderate (4–6) and high (7–20). Menstrual poverty encompassed a three-item scale and was categorised into never (0), low (1–3], moderate (4–6] and high (7–15). Social participation was assessed based on responses to 9 items from the CVM-22 scale, which pertained to reduced productivity during menstruation, work absenteeism due to menstruation, cancelled social plans during menstruation 1 , and one question regarding the impact of premenstrual symptoms on daily life. Social participation then was categorised into mildly affected (0–8), moderately affected (9–16), significantly affected (17–24), and highly affected (25–33). HRQoL was assessed using the Spanish version of the validated EQ-5D-5 L scale [ 44 – 46 , 52 – 54 ] and the value set of the Spanish population. The EQ-5D-5 L consists of the descriptive system and the EQ visual analogue scale (EQ VAS) regarding health status on the day completing the questionnaire. The descriptive system includes questions on five dimensions (one question for each dimension): mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Each dimension has five possible responses, ranging from no problems, slight problems, moderate problems, severe problems and extreme problems. The participant was required to select the most appropriate statement for each dimension. This resulted in a 1-digit number. The five dimensions’ digits are combined to form a 5-digit number that describes the participant health status. Then, a utility score is generated, which quantitatively represents a person’s health status on a scale where 1 corresponds to a perfect health status and 0 equals death. Some statuses may have negative values (indicating a perceived quality of life worse than death). The EQ VAS captures self-assessed health on scale, ranging from 0 (“The worst health you can imagine”) to 100 (“The best health you can imagine”). A validated question on self-rated health was also collected (“In general, how would you rate your health?”); responses were categorised using a 5-point Likert scale (excellent; very good; good; fair; poor). Internal consistency was assessed for all composite variables developed in this study. The “Premenstrual symptoms” and “Impact of premenstrual symptoms on daily life” scales showed excellent reliability (Cronbach’s α = 0.92 for both). The “Menstrual poverty” variable, constructed from three items, demonstrated good internal consistency (Cronbach’s α = 0.81). The “Menstruation-related experiences of taboo, stigma and discrimination” composite, comprising four items, showed acceptable reliability (Cronbach’s α = 0.67), which is considered adequate for short exploratory scales. Finally, the “Access to menstrual education” composite was derived from two items, which presented a low inter-item correlation (Spearman–Brown coefficient = 0.39), suggesting limited internal consistency. Therefore, findings related to this variable should be interpreted with caution. Each variable was subjected to descriptive statistics to detect any non-symmetrical data patterns. Categorical variables were presented as frequencies (percentages) and continuous variables as mean (standard deviation). To assess differences between groups, Chi-square tests with Rao–Scott adjustments and design-adjusted ANOVA and t-tests were performed to account for the multilevel modelling. Linear regression models were employed to evaluate associations between HRQoL (measured using the EQ-5D-5 L and EQ-VAS) and both sociodemographic factors and menstrual health variables. A separate statistical model was estimated for each menstrual health exposure of interest (11 models in total), with all models adjusted for the same theoretically relevant covariables identified. To account for multiple testing, we applied the Benjamini-Hochberg procedure to control the false discovery rate (FDR) at 0.05 across the primary exposure test. The selection of adjustment variables and the hypothesised relationships among them and the dependent variables are illustrated using a DAG-style conceptual framework presented in the supplementary material (Fig.  1 ). All models incorporated the multilevel modelling through robust standard errors clustered at the healthcare centre level, to consider intraclass correlation. Sensitivity analyses were conducted using the EQ-VAS scale. Additional analyses using the original continuous versions of composite menstrual health variables were also performed and are presented in Additional File 3, yielding similar findings to the main analyses. Statistical significance was set at 0.05, with all tests conducted as 2-tailed analyses. The selection of adjustment variables in the linear regression models was informed by prior knowledge and evidence from the literature regarding potential confounders and determinants of both exposure and outcome. No automated or data-driven selection methods (e.g., stepwise procedures) were applied. Instead, all variables considered theoretically relevant were included and entered simultaneously into the regression models to ensure consistent adjustment across analyses. Those deemed to be theoretically relevant were incorporated into the models. Model assumptions were verified before model interpretation. Normality and homoscedasticity of residuals were assessed through visual inspection of residual-versus-fitted and Q–Q plots. Multicollinearity was evaluated using variance inflation factors (VIFs). Robust standard errors clustered by healthcare centre were applied to account for potential heteroscedasticity and within-cluster correlation. Standardised effect sizes (Cohen’s d) were calculated by dividing each regression coefficient by the observed standard deviation of the EQ-5D-5 L utility score in the study population (SD = 0.132). The descriptive analyses were conducted using SPSS software, version 25.0, developed by IBM Corp (based in Armonk, NY), and the models were calculated using Stata/MP software, version 17.0, developed by StataCorp LLC, which is based in Texas. Fig. 1 Conceptual framework of the relationships between sociodemographic characteristics, menstrual characteristics, menstrual social and structural factors, gynaecological and systemic health conditions, use of hormonal contraception, social participation during menstruation and health-related quality of life. Note. This conceptual framework presents the main hypothesized determinants of menstrual health and health-related quality of life (HRQoL). Arrows indicate the dominant hypothesized causal directions between variables, although they do not necessarily imply measured associations. Internal relationships within blocks (e.g., among sociodemographic characteristics) are not depicted. This framework informed the selection of adjustment variables for statistical models Conceptual framework of the relationships between sociodemographic characteristics, menstrual characteristics, menstrual social and structural factors, gynaecological and systemic health conditions, use of hormonal contraception, social participation during menstruation and health-related quality of life. Note. This conceptual framework presents the main hypothesized determinants of menstrual health and health-related quality of life (HRQoL). Arrows indicate the dominant hypothesized causal directions between variables, although they do not necessarily imply measured associations. Internal relationships within blocks (e.g., among sociodemographic characteristics) are not depicted. This framework informed the selection of adjustment variables for statistical models

The analysis encompassed a total of 1,381 women and PWM, with a mean age of 34.63 years (SD = 10.06). 99.4% of participants self-identified as women; 1.0% of participants self-identified as trans. The majority identified as heterosexual (85.9%). 3.3% of participants reported having a recognised disability. Also, most participants held the Spanish nationality (91.7%) and were born in Spain (84.3%). Almost half of the women and PWM had completed university studies (47.6%), and 70.2% were employed or self-employed at the time of data collection. Besides, 42.7% reported being informal caregivers. A total of 41.4% of participants indicated that they had experienced financial constraints. Most of the women and PWM self-rated their health as ‘good’ (45%), ‘very good’ (33.6%), ‘fair’ (13.3%). 25% and 8.5% of participants a self-reported diagnosis of anxiety and depression, respectively (Table  1 ). Table 1 Health-related quality of life (HRQoL), obtained by EQ-5D-5 L utility score and EQ-VAS score, stratified by sociodemographic characteristics ( N  = 1,381) EQ-5D-5 L utility score EQ-VAS score N (%) Mean (SD*) p -value Mean (SD*) p -value Total sample 1,381 (100%) 0.880 (0.130) 73.63 (22.6) Age 0.225 1 0.521 1 Mean (SD) 34.63 (10.06)  18–25 333 (24.1%) 0.882 (0.130) 73.3 (23.3)  26–35 414 (30.0%) 0.890 (0.116) 73.9 (23.0)  36–45 367 (26.6%) 0.883 (0.142) 73.8 (23.1)  ≥ 46 267 (19.3%) 0.862 (0.141) 73.5 (20.4) Gender 0.345 1 0.438 1  Women 1373 (99.4%) 0.881 (0.132) 73.7 (22.5)  Non-binary/Gender fluid 5 (0.4%) 0.869 (0.121) 61.2 (33.9)  Men 3 (0.2%) 0.780 (0.083) 53.0 (39.5) Trans 0.013 2 < 0.018 2  No 1367 (99.0%) 0.882 (0.131) 73.9 (22.4)  Yes 14 (1.0%) 0.774 (0.204) 49.0 (32.1) Sexual orientation 0.111 2 0.018 2  Heterosexual 1186 (85.9%) 0.885 (0.129) 74.2 (22.5)  No heterosexual 195 (14.1%) 0.855 (0.145) 70.1 (22.7) Country of birth 0.381 2 0.662 2  Spain 1164 (84.3%) 0.882 (0.133) 73.8 (22.2)  Other countries 217 (15.7%) 0.873 (0.125) 72.9 (24.5) Nationality 0.582 2 0.361 2  Spanish 1267 (91.7%) 0.880 (0.134) 73.5 (22.6)  Others 114 (8.3%) 0.889 (0.105) 75.1 (23.1) Administrative situation 0.319 1 0.149 1  Spanish nationality 1255 (90.9%) 0.880 (0.134) 73.5 (22.6)  Permanent residency 66 (4.8%) 0.868 (0.129) 73.3 (22.9)  Temporal residency 22 (1.6%) 0.923 (0.067) 82.0 (16.5)  No permit/In process 38 (2.8%) 0.901 (0.067) 73.8 (24.6) Completed education 0.004 1 0.251 1  No formal education 12 (0.9%) 0.849 (0.186) 66.3 (17.4)  Primary education 94 (6.8%) 0.831 (0.181) 67.5 (25.5)  Secondary education 617 (44.7%) 0.863 (0.139) 71.7 (24.0)  University education 658 (47.6%) 0.905 (0.109) 76.5 (20.5) Employment status  Salaried/Self-employed 970 (70.2%) 0.891 (0.121) 0.024 2 75.0 (21.5) < 0.036 2  Student 235 (17.0%) 0.881 (0.122) 0.993 2 73.4 (21.4) 0.582 2  Unemployed (without benefits) / Unpaid caregiving work 169 (12.2%) 0.833 (0.168) 0.005 2 69.9 (23.6) 0.062 2  Unemployed (receiving benefits) / Pension 86 (6.2%) 0.812 (0.183) 0.037 2 63.6 (29.0) 0.031 2 Informal caregiver 0.042 2 0.395 2  Yes 589 (42.7%) 0.864 (0.147) 72.8 (22.7)  No 792 (57.3%) 0.894 (0.118) 74.3 (22.5) Recognised disability 0.034 2 0.006 2  Yes 45 (3.3%) 0.753 (0.207) 61.8 (24.0)  No 1336 (96.7%) 0.885 (0.127) 74 (22.4) Public healthcare card 0.369 2 0.040 2  Yes 1358 (98.3%) 0.881 (0.132) 73.8 (22.6)  No 23 (1.7%) 0.860 (0.099) 66.0 (21.4) Financial constraints 0.035 1 0.038 1  Always/Almost always 175 (12.7%) 0.800 (0.170) 63.8 (25.6)  Frequently/Sometimes 396 (28.7%) 0.861 (0.142) 72.6 (23.1)  Almost never/Never 810 (58.7%) 0.908 (0.106) 76.3 (21.0) Experiences of discrimination  Gender 380 (27.5%) 0.872 (0.117) 0.145 2 73.6 (20.5) 0.989 2  Age 117 (8.5%) 0.859 (0.140) 0.225 2 74.7 (19.5) 0.473 2  Others discrimination 225 (16.3%) 0.826 (0.149) 0.016 2 67 (24.2) 0.059 2  Never 836 (60.5%) 0.895 (0.129) 0.023 2 74.7 (23.0) 0.059 2 Self-rated health 0.012 1* 0.003 2  Excellent 99 (7.2%) 0.952 (0.092) 85.9 (20.8)  Very good 464 (33.6%) 0.925 (0.091) 80.6 (20.1)  Good 622 (45%) 0.879 (0.111) 72.0 (20.7)  Fair 183 (13.3%) 0.767 (0.160) 58.5 (22.9)  Poor 13 (0.9%) 0.446 (0.264) 25.4 (15.7) Self-reported mental health diagnoses  Depression 117 (8.5%) 0.701 (0.194) < 0.001 2 54.1 (24.1) 0.001 2  Anxiety 345 (25.0%) 0.788 (0.158) 0.002 2 64.1 (24.6) 0.001 2  Bipolar disorder 9 (0.7%) 0.772 (0.226) 0.022 2 58.8 (30.2) 0.179 2  Obsessive compulsive disorder 13 (0.9%) 0.714 (0.097) 0.001 2 54.9 (19.4) 0.076 2  Eating disorders (e.g., anorexia, bulimia) 22 (1.6%) 0.790 (0.159) 0.011 2 65.6 (21.3) 0.156 2  Substance abuse disorders 5 (0.4%) 0.692 (0.309) 0.276 2 68.4 (20.3) 0.524 2  Schizophrenia or other psychotic disorders 5 (0.4%) 0.928 (0.100) 0.349 2 64 (35.5) 0.592 2  Personality disorders (e.g., borderline personality disorder) 5 (0.4%) 0.867 (0.070) 0.614 2 45.2 (36.7) 0.037 2  No mental health diagnoses 942 (68.2%) 0.918 (0.097) 0.001 2 78 (20.3) 0.006 2 The percentages of some variables are above 100% as participants could choose multiple categories for some questions 1 ANOVA (design-adjusted) 2 T-test(design-adjusted) * SD Standard Deviation Health-related quality of life (HRQoL), obtained by EQ-5D-5 L utility score and EQ-VAS score, stratified by sociodemographic characteristics ( N  = 1,381) The percentages of some variables are above 100% as participants could choose multiple categories for some questions 1 ANOVA (design-adjusted) 2 T-test(design-adjusted) * SD Standard Deviation Approximately half of the women and PWM reported experiencing a menstrual bleeding abundance of 25–80 ml (49.7%), while 51.3% reported having menstrual blood clots sometimes. Most participants had a menstrual bleeding duration of 3–7 days (85.6%). A significant proportion of women and PWM reported experiencing moderate intensity menstrual pain (43.1%). Furthermore, 52.6% reported moderate frequency of premenstrual symptoms. Around 23.7% of women and PWM reported experiencing high frequency of menstrual-related experiences of taboo, stigma and discrimination. It is noteworthy that 21.6% of participants experienced moderate-high levels of menstrual poverty. Social participation due to menstruation was moderately affected in 42.9% of the participants (Table  2 ). Table 2 Health-related quality of life (HRQoL), obtained by EQ-5D-5 L utility score and EQ-VAS score, stratified by menstrual health variables ( N  = 1,381) EQ-5D-5 L utility score EQ-VAS score N (%) Mean (SD*) p -value Mean (SD*) p -value Menstrual bleeding abundance 0.064 1 0.262 1   80 ml 179 (13.0%) 0.843 (0.140) 70.5 (20.9) Menstrual blood clots 0.005 1 0.221 1  Yes, always 413 (29.9%) 0.861 (0.138) 73.9 (20.1)  Yes, sometimes 709 (51.3%) 0.879 (0.134) 71.7 (24.4)  No, never 259 (18.8%) 0.918 (0.108) 78.7 (20.3) Menstrual bleeding duration 0.630 1 0.008 1  ≤ 2 days 87 (6.3%) 0.863 (0.155) 65.2 (27.3)  3–7 days 1182 (85.6%) 0.883 (0.130) 74.7 (21.9)  ≥ 8 days 112 (8.1%) 0.872 (0.136) 68.8 (24.3) Menstrual cycle duration 0.009 1 0.026 1  ≤ 21 days 285 (20.6%) 0.867 (0.137) 69.4 (25.0)  22–35 days 947 (68.6%) 0.888 (0.129) 75.4 (21.6)  ≥ 36 days 149 (10.8%) 0.859 (0.135) 70.7 (22.8) Intensity of menstrual pain 0.004 1 0.037 1  Low 477 (34.5%) 0.906 (0.120) 77.2 (20.7)  Moderate 595 (43.1%) 0.878 (0.130) 72.5 (22.6)  Severe 309 (22.4%) 0.847 (0.145) 70.3 (24.7) Self-reported gynaecological and systemic health conditions  Anaemia 184 (13.3%) 0.867 (0.131) 0.161 2 69.1 (23.4) 0.058 2  Iron deficiency 287 (20.8%) 0.841 (0.147) 0.035 2 69.4 (22.5) 0.109 2  Uterine myomas 80 (5.8%) 0.865 (0.146) 0.204 2 74.8 (19.1) 0.328 2  Endometrial polyps 27 (2.0%) 0.834 (0.160) 0.393 2 65.3 (22.1) 0.008 2  Endometriosis/adenomyosis 28 (2.0%) 0.859 (0.120) 0.520 2 64.8 (25.4) 0.172 2  Polycystic ovary syndrome 123 (8.9%) 0.850 (0.152) 0.034 2 70.8 (23.4) 0.220 2  Premenstrual syndrome/dysphoric premenstrual disorder 88 (6.4%) 0.861 (0.128) 0.104 2 73.4 (21.5) 0.878 2  Gynaecological (ovary, cervical and uterus) and breast cancers 3 (0.2%) 0.659 (0.199) 0.087 2 50.7 (1.2) < 0.001 2  No diagnoses 800 (57.9%) 0.899 (0.121) 0.005 2 75.8 (21.9) 0.010 2 Use of hormonal contraception 0.159 2 0.025 2  Yes 518 (37.5%) 0.876 (0.134) 72.1 (23.5)  No 863 (62.5%) 0.884 (0.131) 74.5 (22.0) Type of hormonal contraception methods  Oral contraceptive pills 312 (22.6%) 0.872 (0.138) 0.459 2 71.2 (23.6) 0.147 2  Vaginal ring 39 (2.8%) 0.896 (0.135) 0.459 2 72.9 (23.5) 0.826 2  Hormonal intrauterine device 68 (4.9%) 0.888 (0.114) 0.858 2 76.0 (21.0) 0.307 2  Copper intrauterine device 67 (4.9%) 0.884 (0.143) 0.822 2 75.3 (22.3) 0.626 2  Contraceptive patch 6 (0.4%) 0.787 (0.185) 0.336 2 65.3 (22.5) 0.486 2  Contraceptive implant 17 (1.2%) 0.889 (0.096) 0.666 2 68.1 (29.5) 0.284 2  Contraceptive injection 4 (0.3%) 0.878 (0.072) 0.852 2 59.3 (41.5) 0.258 2 Frequency of premenstrual symptoms 0.017 1 < 0.014 1  Low 445 (32.2%) 0.939 (0.095) 80.2 (21.1)  Moderate 727 (52.6%) 0.866 (0.122) 71.4 (21.9)  Severe 209 (15.1%) 0.808 (0.176) 67.2 (24.7) Impact of premenstrual symptoms on daily life 0.021 1 0.262 1  No impact 133 (9.6%) 0.924 (0.121) 82.4 (18.7)  Low 420 (30.4%) 0.922 (0.106) 77.5 (21.7)  Moderate 636 (46.1%) 0.858 (0.136) 70.2 (23.2)  High 192 (13.9%) 0.838 (0.143) 70.5 (22.1) Comfort changing menstrual products at home 0.284 1 0.004 1  Sometimes/Rarely/Never 200 (14.5%) 0.863 (0.150) 0.064 #2 68.2 (27.6) 0.007 #2  Always/Often/Frequently 1157 (83.8%) 0.884 (0.129) 74.6 (21.4)  Non applicable 24 (1.7%) 0.886 (0.133) 71.3 (26.8) Comfort changing menstrual products at work 0.271 1 0.016 1  Sometimes/Rarely/Never 554 (40.1%) 0.871 (0.133) 0.106 #2 71.6 (23.9) 0.006 # 2  Always/Often/Frequently 717 (51.9%) 0.890 (0.128) 75.3 (21.5)  Non applicable 110 (8%) 0.870 (0.149) 73.3 (22.5) Comfort changing menstrual products at public spaces 0.277 1 0.806 1  Sometimes/Rarely/Never 842 (61%) 0.875 (0.130) 0.129 #2 73.3 (22.7) 0.543 #2  Always/Often/Frequently 486 (35.2%) 0.892 (0.134) 74.3 (22.4)  Non applicable 53 (3.8%) 0.878 (0.139) 73.7 (23.3) Access to menstrual education 0.064 1 0.039 1  Low 130 (9.4%) 0.867 (0.128) 69.4 (25.8)  Moderate 443 (32.1%) 0.865 (0.136) 71.2 (22.1)  High 808 (58.5%) 0.892 (0.130) 75.7 (22.1) Menstrual-related experiences of taboo, stigma and discrimination 0.043 1 0.146 1  Low 537 (38.9%) 0.898 (0.118) 75.4 (22.6)  Moderate 517 (37.4%) 0.878 (0.136) 73.5 (22.3)  High 327 (23.7%) 0.857 (0.144) 71.0 (22.9) Satisfaction with menstrual healthcare consultations (< 6 months) 0.218 1 0.198 1  Satisfied 810 (58.7%) 0.877 (0.138) 0.324 # 2 72.5 (23.3) 0.539 # 2  Not satisfied 159 (11.5%) 0.868 (0.128) 70.3 (24.2)  Non applicable 412 (29.8%) 0.893 (0.121) 77.2 (20.0) Menstrual poverty 0.204 1 0.219 1  Never 809 (58.6%) 0.904 (0.115) 75.7 (22.2)  Low 274 (19.8%) 0.870 (0.130) 73.6 (21.2)  Moderate 196 (14.2%) 0.825 (0.157) 69.4 (23.1)  High 102 (7.4%) 0.832 (0.159) 65.3 (25.5) Social participation impairment during menstruation 0.048 1 0.126 1  Highly affected 142 (10.3%) 0.817 (0.164) 67.1 (26.0)  Moderately high affected 245 (17.7%) 0.831 (0.149) 68.5 (23.4)  Moderately affected 593 (42.9%) 0.882 (0.124) 72.7 (22.1)  Mildly affected 401 (29%) 0.932 (0.095) 80.5 (19.6) The percentages of some variables are above 100% as participants could choose multiple categories for some questions 1 ANOVA (design-adjusted) 2 T-test(design-adjusted) * SD Standard Deviation # Analyses excluding “Non-applicable” category Health-related quality of life (HRQoL), obtained by EQ-5D-5 L utility score and EQ-VAS score, stratified by menstrual health variables ( N  = 1,381) The percentages of some variables are above 100% as participants could choose multiple categories for some questions 1 ANOVA (design-adjusted) 2 T-test(design-adjusted) * SD Standard Deviation # Analyses excluding “Non-applicable” category In relation to HRQoL, the mean EQ-5D-5 L index was 0.88 (SD = 0.13), and the mean EQ-VAS index was 73.63 (SD = 22.60). Most participants reported no problems in the dimensions of mobility, self-care and usual activities of the EQ-5D-5 L (92.3% mobility; 96.7% self-care; 83.5% usual activities). Different results were shown for the dimensions of pain/discomfort and anxiety/depression: a significant proportion of participants showed slight (35%) and moderate (9.4%) problems of pain/discomfort, and slight (33.7%) and moderate (11.3%) problems of anxiety/depression (Table 1, Additional File 4). Bivariate analyses were conducted to explore associations between HRQoL (measured with EQ-5D-5 L utility scores and EQ-VAS scores) and sociodemographic characteristics and menstrual health (Tables  1 and 2 ). Sensitivity analyses were conducted using EQ-VAS scores (Tables  1 and 2 ). Regarding sociodemographic characteristics, those with who identified as trans showed a lower EQ-5D-5 L utility score (M[SD] = 0.774 [0.204]). A gradient was found between completed education and the EQ-5D-5 L utility score, with a lower utility score among those with a lower level of completed education. A gradual reduction in the EQ-5D-5 L utility score was observed as reports of financial problems increased. A similar pattern was observed with self-rated health, wherein participants who self-reported better health had higher EQ-5D-5 L utility scores. Participants who self-reported mental health diagnoses (e.g., depression or anxiety) reported a lower EQ-5D-5 L utility score (M[SD] depression = 0.701 [0.194]; M[SD] anxiety = 0.788 [0.158]). More details are available in Table  1 . EQ-5D-5 L utility score was significantly higher among participants who never reported having menstrual blood clots (M[SD] = 0.918 [0.108]). Those having ≤ 21 days (M[SD] = 0.867 [0.137]) or ≥ 36 days (M[SD] = 0.859 [0.135]) of menstrual cycle duration had a lower EQ-5D-5 L utility score. A gradient was found between the intensity of menstrual pain and the EQ-5D-5 L utility score, with a lower utility score as menstrual pain intensity increased. Also, a higher frequency of premenstrual symptoms was associated with a lower EQ-5D-5 L utility score (M[SD] = 0.808 [0.176]). Similar findings were obtained for the impact of premenstrual symptoms on daily life. The more experiences of menstrual taboo, stigma and discrimination, the lower the EQ-5D-5 L utility score. The same gradient was found for menstrual poverty, with participants reporting higher levels of menstrual poverty showing poorer EQ-5D-5 L utility score. A similar pattern was observed for social participation, as those with less affectation on their social participation during menstruation had higher EQ-5D-5 L utility scores. Further details can be found in Table  2 . Bivariate analyses were also performed for each of the five dimensions of EQ-5D-5 L (mobility; self-care; usual activities; pain/discomfort; anxiety/depression) (Tables 2, 3, 4, 5 and 6, Additional File 4). Menstrual health variables were predominantly associated with dimensions of usual activities, pain/discomfort, and anxiety/depression. For example, the greater frequency and impact of premenstrual symptoms, were associated with greater problems in usual activities, pain/discomfort, and anxiety/depression. Conversely, a limited number of menstrual health variables were significantly associated with the mobility and self-care dimension. The β coefficients of linear regression models between menstrual health variables and EQ-5D-5 L utility scores are shown in Table  3 . The scores were adjusted for sociodemographic variables and other confounders (age, completed education, employment status, financial constraints, informal caregiver, sexual orientation, self-reported gynaecological and system health conditions, and the use of hormonal contraception). EQ-5D-5 L utility score was significantly lower for > 80 ml menstrual bleeding abundance, compared to 25-80 ml bleeding abundance (β= -0.034; 95%CI= -0.056 to -0.012; p  = 0.004). Those who reported always (β= -0.045; 95%CI= -0.071 to -0.019; p  = 0.002) and sometimes having blood clots (β= -0.035; 95%CI= -0.055 to -0.014; p  = 0.002) had a significant reduction on EQ-5D-5 L utility score, compared to those who never had menstrual blood clots. EQ-5D-5 L utility score was significantly lower for high intensity of menstrual pain (β= -0.047; 95%CI= -0.068 to -0.025; p  < 0.001), compared to low intensity of menstrual pain. Those reporting moderate (β= -0.064; 95%CI= -0.078 to -0.050, p  < 0.001) and high frequency of premenstrual symptoms (β= -0.144; 95%CI= -0.139 to -0.090, p  < 0.001) had a significantly lower EQ-5D-5 L utility score, compared to those with low frequency of premenstrual symptoms. A moderate (β= -0.059; 95%CI= -0.086 to -0.031, p  < 0.001) and a high (β= -0.071; 95%CI= -0.102 to -0.040, p  < 0.001) impact of premenstrual symptoms on daily life were associated with a lower EQ-5D-5 L utility score, compared to those who reported no impact of premenstrual symptoms in their daily life. Table 3 Associations between menstrual health and health-related quality of life (HRQoL), using the EQ-5D-5 L utility score ( N  = 1,381) HRQoL (EQ-5D-5 L utility score) β coefficient (95%CI*) p -value Menstrual abundance  25–80 ml Reference 0.015/0.020**  80 ml -0.034 (-0.056 to -0.012) 0.004 Menstrual blood clots  No, never Reference 0.003/0.006**  Yes, sometimes -0.035 (-0.055 to -0.014) 0.002  Yes, always -0.045 (-0.071 to -0.019) 0.002 Menstrual bleeding duration  3–7 days Reference 0.455/0.449**  ≤2 days -0.015 (-0.045 to 0.015) 0.314  ≥8 days 0.012 (-0.013 to 0.036) 0.347 Menstrual cycle duration  22–35 days Reference 0.164/0.180**  ≤21 days -0.005 (-0.026 to 0.015) 0.602  ≥36 days -0.020 (-0.040 to 0.001) 0.059 Intensity of menstrual pain  Low Reference 0.001/0.001***  Moderate -0.022 (-0.040 to 0.004) 0.019  High -0.047 (-0.068 to -0.025) < 0.001 Frequency of premenstrual symptoms  Low Reference < 0.001 / <0.001  Moderate -0.064 (-0.078 to -0.050) < 0.001  High -0.114 (-0.139 to -0.090) < 0.001 Impact of premenstrual symptoms on daily life  No impact Reference < 0.001/ <0.001  Light -0.007 (-0.031 to 0.017) 0.555  Moderate -0.059 (-0.086 to -0.031) < 0.001  High -0.071 (-0.102 to -0.040) < 0.001 Access to menstrual education  High Reference 0.005/0.008  Moderate -0.020 (-0.031 to -0.008) 0.002  Low -0.016 (-0.037 to- 0.005) 0.131 Menstrual-related experiences of taboo, stigma and discrimination  Low Reference 0.016/0.020  Moderate -0.018 (-0.031 to -0.004) 0.013  High -0.030 (-0.052 to -0.007) 0.011 Menstrual poverty  Never Reference 0.001/0.001  Low -0.014 (-0.036 to 0.007) 0.177  Moderate -0.054 (-0.078 to -0.029) 0.018  High -0.028 (-0.061 to 0.005) 0.096 Social participation impairment during menstruation  Mildly affected Reference < 0.001/<0.001  Moderately affected -0.045 (-0.062 to -0.029) < 0.001  Moderately high affected -0.091 (-0.116 to -0.066) < 0.001  Highly affected -0.104 (-0.128 to -0.080) < 0.001 An independent statistical model was estimated for each of the eleven variables presented The linear regression models were adjusted for the variables: age, completed education, employment status, financial constraints, informal caregiver, sexual orientation, self-reported gynaecological and systemic health conditions, and use of hormonal contraception * CI confidence interval ** Adjusted p -values (q-values) were calculated using the Benjamini–Hochberg procedure to control the false discovery rate (FDR = 0.05) Associations between menstrual health and health-related quality of life (HRQoL), using the EQ-5D-5 L utility score ( N  = 1,381) An independent statistical model was estimated for each of the eleven variables presented The linear regression models were adjusted for the variables: age, completed education, employment status, financial constraints, informal caregiver, sexual orientation, self-reported gynaecological and systemic health conditions, and use of hormonal contraception * CI confidence interval ** Adjusted p -values (q-values) were calculated using the Benjamini–Hochberg procedure to control the false discovery rate (FDR = 0.05) A gradual reduction in EQ-5D-5 L score was observed with an increase in menstrual-related experiences of taboo, stigma, and discrimination. Moderate (β= -0.018; 95%CI= -0.031 to -0.004, p  = 0.013) and high (β= -0.030; 95%CI= -0.052 to -0.007; p  = 0.011) experiences of taboo, stigma and discrimination related to menstruation were associated with lower EQ-5D-5 L utility scores, compared to those with low experiences. A moderate frequency of menstrual poverty (β= -0.054; 95%CI= -0.078 to -0.029; p  = 0.018) was associated with a significant reduction in the EQ-5D-5 L score, compared to those who never experienced menstrual poverty. The EQ-5D-5 L score significantly lower in a gradual manner as social participation during menstruation became more affected. When expressed as standardised effect sizes (Cohen’s d), statistically significant associations ranged from small to large in magnitude (d= -0.26 to -0.86). For example, heavy menstrual bleeding (> 80 ml) was associated with lower EQ-5D-5 L utility score (β= −0.034), corresponding to Cohen’s d= -0.26. High frequency of premenstrual symptoms (β= -0.114) corresponded to Cohen’s d=-0.86. The linear regression models between menstrual health variables and the EQ-VAS score were also conducted to assess the robustness of the previous findings. The results were largely consistent, but some differences were observed. Menstrual-related experiences of taboo, stigma and discrimination did not show a significant association with EQ-VAS score while it was significant in EQ-5D-5 L score. In contrast, menstrual bleeding duration and menstrual cycle duration showed significant associations with the EQ-VAS score but they were not associated with EQ-5D-5 L score (Table 7, Additional File 4). Sensitivity analyses using continuous scores for menstrual health variables showed associations with EQ-5D-5 L utility scores in the same direction as the main categorical analyses, supporting the robustness of the findings (Additional File 3).

The results indicated a significant association between most menstrual health variables and EQ-5D-5 L, suggesting that the more severe or frequent menstrual-related challenges are, the greater the decline in HRQoL. Moreover, the present study has demonstrated how certain sociodemographic variables (e.g., low educational level or financial constraints), were associated with poorer menstrual health and HRQoL, this being consistent with previous literature [ 55 – 58 ]. Despite the importance of social determinants of health, HRQoL related to menstruation has largely addressed from a clinical and pharmacological perspective. It is therefore pertinent to incorporate approaches that engage with social determinants of health to move beyond the medicalisation of (menstrual) experiences of women and PWM [ 59 , 60 ]. Moreover, an intersectional perspective is essential to fully understand the following discussion of patterns of menstrual health with HRQoL [ 21 , 58 , 61 ], as social and structural determinants and experiences of privilege and discrimination shape how menstrual symptoms and conditions affect HRQoL. Experiencing menstrual poverty was associated with a reduction in HRQoL, a finding that is similar in other contexts [ 38 , 39 ]. The lack of access to menstrual products can contribute to physical discomfort and increased risk of infections [ 62 ], that can have a detrimental effect on diverse domains of HRQoL, including in the emotional domain of HRQoL [ 9 , 10 , 16 ]. These findings must be understood within a broader context, as menstrual poverty reflects systemic socio-economic challenges faced by women and PWM, which can also result in diminished HRQoL [ 63 ]. The relationship between (menstrual) poverty and HRQoL should be understood considering the ‘feminisation of poverty’, whereby women are more likely to experience poverty due to systemic gender inequities (e.g., greater unpaid caregiving responsibilities) that constrain economic autonomy [ 64 ]. These gender inequities are closely linked to (menstrual) poverty, potentially limiting access to menstrual products, with implications in HRQoL. Menstrual-related experiences of taboo, stigma and discrimination can also have a detrimental effect on psychological well-being [ 65 ] and HRQoL. As already discussed, these experiences should be understood in the context of social determinants of health - such as gender, race and socioeconomic status, among others– that shape how women and PWM experience and navigate menstruation. Gender norms and expectations have the potential to expose women and PWM to social judgement and the concealment of menstruation [ 66 ]. Besides, experiences of racism may further deepen experiences of menstrual stigma and discrimination [ 18 ]. These broader social and structural inequities may limit social participation [ 67 ], contributing to feelings of isolation and exclusion, which our study shows are associated with lower HRQoL [ 68 ]. Our research also highlights that a greater access to menstrual education is associated with better HRQoL. Menstrual education can increase self-awareness, thereby enabling women and PWM to better manage the potential changes during the menstrual cycle, which may have a positive association on their HRQoL. However, research on the underlying mechanisms of this relationship remains scarce. Most studies have focused on menstrual education for young people [ 15 ], emphasising its role in managing the menstrual cycle [ 69 ], and addressing menstrual taboos, stigma and discrimination [ 70 ]. While policy and community actions have promoted menstrual education in Spain [ 50 , 71 ], primarily in school environments [ 71 ], it is crucial that future research and actions extend their focus about menstrual education and HRQoL in other life stages (e.g., perimenopause). This study has also demonstrated a decrease of HRQoL among women and PWM experiencing menstrual-related symptoms (HMB, menstrual blood clots, premenstrual symptoms, and menstrual pain). Previous studies show that heavy menstrual bleeding (HMB) is associated with lower HRQoL [ 29 , 30 , 36 , 37 ]. Swedish research indicates that women with HMB exhibit diminished HRQoL across all SF-36 domains [ 29 ], while other studies highlight impact on social life, relationships, and daily activities, often related to fear of leakage in public [ 5 , 21 ]. Limited social participation may further contribute to lower HRQoL [ 67 , 68 ]. Menstrual blood clots, frequently indicative of HMB, have also been associated to fatigue and anxiety, potentially diminishing HRQoL [ 72 ]. Menstrual pain is another major factor affecting HRQoL, interfering with daily activities, work, and social participation [ 25 , 28 , 73 ]. Its normalisation in healthcare can delay diagnosis of underlying conditions, such as endometriosis, perpetuating discomfort and further impacting HRQoL [ 33 , 35 ]. Premenstrual symptoms, both physical and emotional, also impact daily life and HRQoL [ 74 ], particularly in the mental health domain. Much of the existing literature focuses on clinical diagnoses such as PMS or PMDD [ 75 , 76 ], which may reinforce psychiatric framings of menstrual experiences and limit understanding of their broader social and emotional consequences. In our study, menstrual health variables were found to have a strong association with usual activities, pain/discomfort, and anxiety/depression dimensions of the EQ-5D-5 L. The impact of menstruation on daily activities, social relations, emotional and mental health, and pain has been highlighted in previous literature [ 5 , 10 , 14 ]. However, the mobility and self-care dimensions were not as strongly associated with most of menstrual health variables. This could be explained by the conceptualisation of these dimensions in the EQ-5D-5 L scale, as the mobility dimension was only focused on walking problems, and the self-care dimension included only cleaning and dressing issues. At the policy level, menstrual health strategies in Spain have been primarily oriented towards biomedical and clinical aspects, with limited consideration of social inequities shaping menstrual health experiences [ 50 ]. However, recent policy developments suggest a gradual shift towards a more comprehensive understanding of menstrual health. Addressing menstrual inequities requires recognising the “conditions of possibility” that shape people’s lived experiences, ensuring genuine and equitable access to education, healthcare, menstrual products and other resources for menstrual care, and spaces for social participation [ 21 ]. Effective policies should therefore move beyond individual-focused interventions and engage with structural determinants to promote equity and avoid tokenism. In this way, policies and clinical practices may work in synergy to address menstrual inequities and improve HRQoL for all women and PWM. Recent policy discussions in Spain, including work-related initiatives such as menstrual leave, reflect a growing recognition of menstruation as a matter of public and occupational health, acknowledging its impact in multiple domains of life. However, while these initiatives represent an important step in recognising the impact of menstruation beyond a purely biomedical focus, their implementation and impact should be monitored to ensure equitable access across women and PWM. Within this context, the present study provides evidence to support the expansion of policy and practice beyond visible domains such as workplace absence, by addressing less visible dimensions of HRQoL, particularly emotional aspects that emerge from the findings. Beyond statistical significance, the magnitude of the observed effects warrants consideration. Although some estimated effects (e.g., β ≈ −0.02 to − 0.05) were statistically significant, their magnitude was modest. The effects of some variables, such as premenstrual symptoms (β = −0.144), were substantially larger than that of most other exposures, reflecting a stronger influence on HRQoL. When interpreted in absolute terms, changes around − 0.03 in EQ-5D-5 L were close to the lower bound of commonly reported minimally important differences. A recent systematic review showed that MID estimates for the EQ-5D-5 L utility index vary widely across populations and methods, and that no single universal threshold can be assumed [ 77 ]. Larger absolute changes observed for some menstrual characteristics exceed most published MID estimates, suggesting a more clearly clinically relevant reduction in HRQoL. These considerations highlight that, while most associations are relatively small, they remain meaningful for interpreting the practical impact of the studied exposures. From a methodological perspective, the use of purposive sampling through healthcare centres should be considered when interpreting the findings. This recruitment strategy facilitated access to participants already engaged with healthcare services and may have favoured the inclusion of women and PWM with higher educational level or more stable socioeconomic positions. For example, approximately half of participants of this study had completed university studies and 70.2% were employed. This is particularly relevant when considering social and emotional dimensions of HRQoL, which may be more pronounced in vulnerable population groups. Moreover, some menstrual health variables were constructed ad hoc due to the limited availability of validated instruments addressing broader social dimensions of menstrual health. In addition, some composite variables were categorised to facilitate interpretability in this exploratory study. Although most composite variables showed acceptable internal consistency, the “access to menstrual education” variable presented lower reliability and should therefore be interpreted with caution. Nevertheless, additional analyses using continuous versions of the variables yielded similar findings. The present study has yielded findings that provide data regarding the relationship between menstrual health and HRQoL in Spain. This relationship has been examined through the utilisation of a broad definition of menstrual health [ 2 ], which allows a deeper exploration of various issues related to menstruation. This study provides that menstrual health should not be considered as an independent health process, but rather in connection with other health aspects, such as HRQoL. Another strength of the study lies in the hybrid format and self-administered questionnaire, which facilitated the collection of data from a substantial number of women and PWM from diverse regions across Spain. Also, the interdisciplinary research team has facilitated the incorporation of diverse perspectives throughout the entire research process, from conception to interpretation of the study results. Some limitations have been identified. The use of non-validated ad hoc variables was another limitation of the study, limiting the comparability with other studies. However, these variables allowed the capture relevant dimensions of menstrual health that did not have validated scales yet. The study used non-probabilistic, convenience/purposive sampling through healthcare centres. As a result, the sample may over-represent individuals already engaged with healthcare services and those with higher education or employment. This sampling approach limits the generalisability of the findings to the broader population of women and PWM in Spain. Therefore, the observed associations may not fully reflect those in more socioeconomically diverse or underserved populations. Therefore, results should be interpreted as associations observed within this centre-based sample rather than population-level estimates. Nonetheless, efforts were made to enhance diversity by recruiting from healthcare centres serving areas with different socio-economic profiles. Moreover, causal interpretations of the findings cannot be established, given the cross-sectional design of the study. The EQ-5D-5 L instrument has limited sensitivity to social and context-specific dimensions of health. As a result, it may not fully capture the broader social impacts of menstrual health on HRQoL. These narrow conceptualisations may have constrained the findings of this research. For this reason, it is pertinent to review the operationalisation of HRQoL and further refine and create scales that are socially sensitive, comprehensive, and address all aspects of menstrual health to better assess HRQoL. Previous menstrual studies have used diverse scales to assess HRQoL, encompassing both general (e.g., SF-36) and more specific (e.g., PERIOD-QOL) scales, with limited studies employing the EQ-5D-5 L. Nonetheless, this scale is quick to administer, comparable, provides an overview of HRQoL, and is therefore useful in this study to provide the first approximation of the relationship between HRQoL and menstrual health in Spain. Moreover, the use of only a subset of items from the CVM-22 validated scale may have reduced comparability with studies applying the complete instrument. Certain limitations can be identified in using only quantitative methods in assessing HRQoL, as it may narrow down a broad question about women and PWM’s experiences. The application of quantitative methods it is useful for describing the general health of the population, for developing public health strategies, and for determining the potential cost-benefits and where to allocate health resources. Consequently, there is a compelling need for the integration of qualitative research methodologies within mixed-method studies to obtain a more comprehensive understanding of HRQoL. This study offers significant implications for research, policy, and practice by providing evidence of menstrual health association with HRQoL. In particular, the strongest associations were observed in the EQ-5D-5 L domains of pain/discomfort, usual activities, pain/discomfort. It is crucial that future research continue to focus on understanding the relationship between dimensions of major impact (e.g., anxiety/depression, pain) and menstrual health. These studies should adopt a multidimensional approach to menstrual health, moving beyond only symptom assessment to examine the impact on other spheres (e.g., emotional health). The results of this study also provide an opportunity to investigate the specific population groups that manifest poorer HRQoL (e.g., women and PWM with high frequency and high impact of premenstrual symptoms, women and PWM experiencing menstrual poverty). This study could also inform menstrual policymaking in Spain. For example, to prioritise HRQoL domains that have shown to be more related to menstrual health (e.g., anxiety/depression domain, pain) in policy agendas. Although legislation about menstrual leaves has been implemented in Spain, this legislation does not explicitly address the impact of menstrual health on different domains of HRQoL. These findings also underscore the need for equity-focused menstrual health policies that address both clinical and social determinants of health. The present study also has implications for healthcare practice. It is imperative that healthcare institutions and professionals acknowledge the relationship between menstrual health and HRQoL. This includes recognising all the domains of HRQoL (including physical, emotional, social impacts) of menstrual health and moving beyond strictly a biomedical perspective. Healthcare professionals should consider routine assessment of menstrual-related symptoms and their impact on daily functioning and emotional well-being, particularly in relation to pain and mental health symptoms. This could be achieved through continued menstrual health training and updates for healthcare professionals (e.g., midwives, gynaecologists, family doctors).

This study reveals an association between worse menstrual health and a deterioration in HRQoL, particularly in domains of usual activities, anxiety/depression, and pain/discomfort of the EQ-5D-5 L scale. Furthermore, socio-demographic characteristics significantly impact menstrual health and HRQoL. However, the relationship between menstrual health, sociodemographic characteristics and HRQoL requires further exploration in future research. These findings highlight the need to move beyond a purely biomedical approach of menstrual health in clinical practice and health planning and to better integrate its broader impact on daily life. These results support the importance of routinely considering menstrual health in healthcare assessments, particularly in relation to general pain, emotional health, and limitations in daily activities among women and PWM. At the public health policy, these findings reinforce the need to design and integrate strategies that address both clinical and non-clinical needs. Moreover, subsequent research should concentrate on understanding the HRQoL of specific population groups (e.g., women and PWM with high frequency of premenstrual symptoms or those with high levels of menstrual poverty). Furthermore, it is crucial to delve deeper into the mechanisms underlying the relationship between of menstrual health and HRQoL, especially among the social issues (e.g., menstrual taboo, stigma and discrimination), that explain their association with HRQoL. The development and refinement of HRQoL measures that incorporate social aspects and are context specific, as well as the combination of qualitative and quantitative research in HRQoL field, is crucial in future research. This will provide more complete evidence for appropriate policies and actions, which can be applied in educational, healthcare, social and community contexts.

Menstrual health extends beyond biological processes of menstruation, encompassing physical, psychological, and social well-being, that impact the overall health of women and people who menstruate (PWM) (e.g., non-binary, trans and intersex individuals who menstruate) [ 1 ]. Menstrual health has been defined as “a complete state of physical , mental and social well-being and not merely the absence of disease , in relation to the menstrual cycle” . This includes access to information, appropriate menstrual products, facilities and healthcare, experiences of menstruation free from stigma, and the ability to participate in daily life throughout the menstrual cycle [ 2 ]. Research has found that menstruation is closely intertwined with physical health [ 3 ], emotional and mental health [ 4 – 11 ], and social, economic, and community participation [ 5 , 12 – 14 ]. For example, the lack of menstrual information has been identified as a contributing factor to psychological stress [ 15 ]. Similarly, menstrual poverty has been associated with depression and anxiety symptoms [ 6 , 9 , 10 ]. Research on menstrual health in Spain has highlighted the widespread impact of social and structural factors on menstrual experiences [ 5 , 9 , 14 , 16 – 20 ]. From a critical gender perspective, these experiences are understood as being shaped not only by individual factors but also by gendered power relations and structural inequities that influence access to resources, information, and healthcare. A large cross-sectional study conducted across Spain found that some menstrual inequities were particularly pronounced among socioeconomically deprived participants, migrant populations, and non-binary or trans menstruators. Key findings included limited pre-menarche menstrual education, high prevalence of menstrual poverty, menstrual-related discrimination, and work and school absenteeism. Overall, this study demonstrated the role of social determinants in shaping menstrual experiences [ 14 ]. Qualitative research in Spain has provided insights into the social, cultural, and structural factors shaping menstrual experiences. A critical feminist study in Barcelona explored experiences of menstrual inequity among women and PWM, identifying barriers to managing menstruation in public spaces, menstrual poverty, limited education, and challenges accessing appropriate healthcare [ 5 ]. Besides, an intersectional study in Catalonia revealed that menstrual care is tied to socioeconomic vulnerability, housing, work, stigma, and intersecting axes of inequity such as gender, race, and class [ 21 ]. A recent study with Roma women highlighted that menarche and menstrual experiences are mediated by family and community networks, but limited access to accurate information reinforces cultural myths, taboos, and barriers to empowerment [ 18 ]. Another qualitative study explored how emotional and physical experiences of perimenopause are diverse, socially conditioned, and shaped by gender norms, limited information, and biomedicalised approaches to care [ 17 ]. These findings converge in highlighting that menstrual experiences are embedded in broader social and gendered structures. Menstrual health plays a crucial role influencing quality of life (QoL), defined by The World Health Organisation as “an individual’s perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals , expectations , standards and concerns” [ 22 ]. This is a broad concept that highlights how these experiences are shaped by the social context. It encompasses the individual’s overall well-being, including psychological and physical health, level of independence, and social relationships [ 22 ]. In health research, QoL is more commonly assessed as health-related quality of life (HRQoL) [ 23 ]. HRQoL is particularly important in menstrual research, as it captures the broader impact of menstrual health on the physical, mental, and social well-being of women and PWM. However, most studies have been focused in HRQoL relationship to specific menstrual conditions, including dysmenorrhea [ 24 – 28 ], heavy menstrual bleeding (HMB) [ 29 , 30 ], endometriosis [ 31 – 33 ], and polycystic ovary syndrome [ 34 , 35 ]. These studies consistently report lower HRQoL among women and PWM living with these conditions. Overall, research suggests that HMB negatively affects multiple domains of HRQoL, including social life, relationships, and daily activities, reducing social interactions and participation [ 36 , 37 ]. Moreover, available research links experiences of pain [ 38 ] and menstrual management [ 39 ] with poorer mental and psychological outcomes and lower HRQoL scores. Although previous findings must be interpreted within their specific contexts, international research is highly relevant for research in Spain. In fact, cross-national evidence further suggests that many menstrual experiences overlap across countries and appear to result in consistent findings [ 40 ]. While existing evidence provides important insights, HRQoL is rarely examined in relation to menstrual health (considering all dimensions of Hennegan et al. 2021 definition, for example, experiences of menstrual stigma, social participation during menstruation, etc.) and is more commonly assessed in relation to specific menstrual conditions (e.g., dysmenorrhea). This highlights a gap in the literature regarding the relationship between a broader conceptualisation of menstrual health and its association with HRQoL in Spain. Moreover, HRQoL is infrequently examined as a primary outcome and is more often addressed indirectly in relation to menstrual health. Overall, studies linking menstruation and HRQoL are scarce and often rely on a clinical or biomedical approaches, overlooking social inequities of health. In menstrual research, several instruments have been used to measure HRQoL, being the SF-36 (36-Item Short Form Health Survey) [ 41 , 42 ] the most frequently used scaledue to its measurement properties [ 27 , 41 ]. Other tools have been applied such as the WHOQOL-BREF (26-Item World Health Organisation Quality of Life) [ 43 ], and the EQ-5D, available in the EQ-5D-3 L and the EQ-5D-5 L versions [ 44 , 45 ]. These instruments asses different HRQoL domains, including physical health, mental health and social functioning. The specific domains covered vary across instruments. Although the three instruments are validated, the EQ-5D is widely used because of its short format and applicability across populations and health contexts [ 46 ]. Moreover, menstruation-specific instruments have been developed, such as PERIOD-QOL and CVM-22 [ 47 , 48 ]. Evidence on the relation between menstrual health and HRQoL is imperative for the design of evidence-based interventions and policies that effectively respond to menstrual health needs, considering the importance of enhancing HRQoL. In Spain, recent legislation (Ley Orgánica 1/2023) [ 49 ] recognises menstrual health and provides a legislative framework to develop and implement menstrual interventions [ 50 ]. However, the law does not specifically address interventions targeting HRQoL. Advancing to investigate the relationship between menstrual health and HRQoL, especially emotional and social dimensions, could support the expansion of the legislative framework and guide future policymaking. This study aims to analyse the relationship between menstrual health and self-reported health-related quality of life in women and PWM (≥ 18 years) in five regions of Spain (Andalusia, Basque Country, Canary Islands, Catalonia, and Galicia). We formulated the following hypotheses: (1) participants reporting menstrual health problems would have lower HRQoL scores than participants not reporting menstrual health problems, and (2) the association between menstrual health problems and HRQoL scores differ according to socio-demographic characteristics.

Additional file 1: STROBE Statement—checklist of items that should be included in reports of observational studies. Additional file 1: STROBE Statement—checklist of items that should be included in reports of observational studies. Additional file 2: Survey on menstrual health, mental health, and health related quality of life. Additional file 2: Survey on menstrual health, mental health, and health related quality of life. Additional file 3: Table 1, Associations between menstrual health using continuous scores and health-related quality of life (HRQoL), employing the EQ-5D-5 L utility score ( N  = 1,381). Additional file 3: Table 1, Associations between menstrual health using continuous scores and health-related quality of life (HRQoL), employing the EQ-5D-5 L utility score ( N  = 1,381). Additional file 4: Table 1, Frequencies and percentages reported by dimension and levels of EQ-5D-5 L ( N  = 1,381). Table 2, Frequencies and percentages of mobility dimension from EQ-5D-5 L stratified by sociodemographic characteristics and menstrual health variables ( N  = 1,381). Table 3, Frequencies and percentages of self-care dimension from EQ-5D-5 L stratified by sociodemographic characteristics and menstrual health variables ( N  = 1,381). Table 4, Frequencies and percentages of self-care dimension from EQ-5D-5 L stratified by sociodemographic characteristics and menstrual health variables ( N  = 1,381). Table 5, Frequencies and percentages of pain/discomfort dimension from Eq. 5D5L stratified by sociodemographic characteristics and menstrual health variables ( N  = 1,381). Table 6, Frequencies and percentages of anxiety/depression dimension from EQ-5D-5 L index stratified by sociodemographic characteristics and menstrual health variables ( N  = 1,381). Table 7. Frequencies and percentages of anxiety/depression dimension from EQ-5D-5 L index stratified by sociodemographic characteristics and menstrual health variables ( N  = 1,381). Additional file 4: Table 1, Frequencies and percentages reported by dimension and levels of EQ-5D-5 L ( N  = 1,381). Table 2, Frequencies and percentages of mobility dimension from EQ-5D-5 L stratified by sociodemographic characteristics and menstrual health variables ( N  = 1,381). Table 3, Frequencies and percentages of self-care dimension from EQ-5D-5 L stratified by sociodemographic characteristics and menstrual health variables ( N  = 1,381). Table 4, Frequencies and percentages of self-care dimension from EQ-5D-5 L stratified by sociodemographic characteristics and menstrual health variables ( N  = 1,381). Table 5, Frequencies and percentages of pain/discomfort dimension from Eq. 5D5L stratified by sociodemographic characteristics and menstrual health variables ( N  = 1,381). Table 6, Frequencies and percentages of anxiety/depression dimension from EQ-5D-5 L index stratified by sociodemographic characteristics and menstrual health variables ( N  = 1,381). Table 7. Frequencies and percentages of anxiety/depression dimension from EQ-5D-5 L index stratified by sociodemographic characteristics and menstrual health variables ( N  = 1,381).