Menstrual characteristics and associations with sociodemographic factors and self-rated health in Spain: a cross-sectional study.

Data from 19,358 women and PWM were included. Mean age was 33.8 ( SD = 8.7) . Most identified as women (96.6%) while 3.4% as non-binary/other and 0.8% identified as trans. Also, most participants were born in Spain (93.4%) and held Spanish nationality (95.8%). Over half (65.0%) were working at the time of the research, and 70.7% had completed university studies. 35.3% reported being caregivers of someone else (e.g., children). Almost half reported financial problems in the 12 months prior to the study (42.8%). Most participants indicated their self-rated health to be good (45.9%), very good (38.5%) or excellent (6.5%). See Table 1 for more details. Table 1 Participants’ sociodemographic characteristics, menstrual characteristics, and health conditions (N = 19,358) Variable N (%) Age M (SD) = 33.8 (8.7)  18–25 4155 (21.5%)  26–35 6753 (34.9%)  36–45 6587 (34.0%)  46–55 1863 (9.6%) Gender  Women 18,706 (96.6%)  Non-binary/other 652 (3.4%) Trans  Yes 158 (0.8%)  No 19,055 (98.4%)  Don’t know 145 (0.8%) Country of birth  Spain 17,745 (93.4%)  Other 1264 (6.6%) Administrative situation  Spanish nationality 18,492 (95.8%)  Permanent residency 619 (3.2%)  Temporal residency 145 (0.8%)  No permit/in process 43 (0.2%) Employment situation  Working full-time/part-time 12,574 (65.0%)  Self-employed 1834 (9.5%)  Studying full-time/part-time 4600 (23.8%)  Unemployment, COVID-19, retirement and other benefits 1687 (8.7%)  Unpaid carer/houseworker 1020 (5.3%) Completed education  Primary education 214 (1.1%)  Secondary education 5450 (28.2%)  University education 13,664 (70.7%) Caregiver  No 12,464 (64.7%)  Yes 6789 (35.3%) Financial problems < 12 months  Always/Many times 2245 (11.8%)  Some/A few times 5878 (31.0%)  Never 10,847 (57.2%) Self-rated health  Excellent 1262 (6.5%)  Very good 7430 (38.5%)  Good 8865 (45.9%)  Fair 1603 (8.3%)  Poor 153 (0.8%) Age at menarche M (SD) = 12.4 (1.5)  ≤10 1491 (7.8%)  11–12 9124 (47.8%)  13–15 7956 (41.7%)  ≥16 499 (2.6%) Menstrual bleeding abundance   80 ml 3923 (20.3%) Menstrual blood clots  Yes 6957 (35.9%)  No 12,401 (64.1%) Menstrual bleeding duration   7 days 1239 (6.4%) Menstrual cycle duration   35 days 1707 (9.0%) Menstrual pain  Low intensity 5874 (31.0%)  Moderate intensity 8792 (46.3%)  High intensity 4308 (22.7%) Menstrual pain management  Use of analgesics 12,978 (68.4%)  Use of natural remedies 9886 (52.1%)  Yoga/Meditation or physical activity 160 (0.8%)  Resting, self-care (including through nutrition) 299 (1.6%)  Sex (including masturbation) 59 (0.3%)  Cannabis, cannabidiol (CBD), or alcohol use 44 (0.2%)  Alternative medicine 20 (0.1%)  Accessing the emergency room 14 (0.1%)  Physiotherapy/Osteotherapy 16 (0.1%)  Transcutaneous electric stimulation 7 (0.0%)  Cannot afford menstrual pain management products 40 (0.2%)  I do not do anything 3719 (19.6%)  No menstrual pain 1520 (8.0%)  Do not know what to do 526 (2.8%) Premenstrual symptoms  Always/Many times 12,009 (68.2%)  Some/A few times 4751 (27.0%)  Never 844 (4.8%) Gynaecological and systemic health conditions  Anaemia 5958 (30.8%)  Iron deficiency 8601 (44.4%)  Uterine myomas 1454 (7.5%)  Endometrial polyps 989 (5.1%)  Endometriosis/adenomyosis 925 (4.8%)  Polycystic ovary syndrome 3072 (15.9%)  Premenstrual syndrome/dysphoric premenstrual disorder 2629 (13.6%)  Gynaecological cancers (ovary or fallopian tube, uterine and cervical) 107 (0.6%)  Breast cancer 74 (0.4%)  No diagnosis 5704 (29.5%) The percentages of some variables are above 100% as participants could choose multiple category for some questions Participants’ sociodemographic characteristics, menstrual characteristics, and health conditions (N = 19,358) The percentages of some variables are above 100% as participants could choose multiple category for some questions Mean age at menarche was 12.4 ( SD = 1.5 ) and was most commonly reported between 11-12 years old (47.8%) and 13–15 years old (41.7%) (see Fig. 1 ). Menstrual bleeding abundance was between 25 and 80 ml in 62.4% of women and PWM. Over a third indicated having menstrual blood clots (35.9%). The duration of menstrual bleeding was between 2 and 7 days for most (92.0%); 6.4% experienced bleeding for over 7 days. As for menstrual cycle duration, the most common was between 21 and 35 days (83.0%). Moderate intensity menstrual pain was reported by 46.3% of women and PWM, followed by high intensity (22.7%). Menstrual pain was mostly managed by using analgesics (68.4%) and natural remedies (52.1%); 19.6% reported not doing anything to manage menstrual pain. Premenstrual symptoms were experienced always/many times by 68.2% of women and PWM; 27% reported premenstrual symptoms in some or a few menstrual cycles. Self-reports of lifetime gynaecological and systemic health conditions were predominantly of iron deficiency (44.4%), anaemia (30.8%), polycystic ovary syndrome (15.9%), premenstrual syndrome or dysphoric premenstrual disorder (13.6%), uterine myomas (7.5%) and endometriosis or adenomyosis (4.8%). See Table 1 for further information. Fig. 1 Distribution of age at menarche ( N  = 19,070) Distribution of age at menarche ( N  = 19,070) When stratifying analyses by age, we observed an age gradient in age at menarche, with early menarche (≤10) and between 11 and 12 years being more common in the 18–25 years old group (9.2 and 51.7%, respectively) than the other age groups, and especially compared to participants aged 46–55 (6.5 and 40.9% respectively). Menstrual bleeding abundance between 25 and 80 ml was most common among younger age groups, while lighter ( 80 ml) abundance was more frequently reported as age increased. Longer menstrual bleeding duration (> 7 days) was more commonly indicated by 46–55 (8.8%) and 18–25 (7.1%) aged participants. Shorter bleeding duration ( 35 days) were most common among participants aged 18–35. Differences in menstrual pain levels were evident by age groups, with high intensity menstrual pain increasing as age decreased (30.2% in 18–25 age group vs 15.0% in 46–55 age group). The use of analgesics and natural remedies for pain management also decreased as age increased. Disparities in premenstrual symptoms reporting by age were unclear and not as pronounced. Moreover, reports of uterine myomas, endometrial polyps and endometriosis significantly increased with age. Self-rated health hardly varied amongst age groups, with poorer health slightly increasing with age. Refer to Table 2 for more details. Table 2 Participants’ sociodemographic characteristics, menstrual characteristics and health conditions, stratified by age ( N  = 19,385) Variable Age p value 18–25 26–35 36–45 46–55 N (%) N (%) N (%) N (%) Gender  Women 3904 (94.0%) 6537 (96.8%) 6443 (97.8%) 1822 (97.8%) < 0.001  Non-binary/other 251 (6.0%) 216 (3.2%) 144 (2.2%) 41 (2.2%) Trans  Yes 107 (2.6%) 38 (0.6%) 10 (0.2%) 3 (0.2%) < 0.001  No 3986 (95.9%) 6663 (98.7%) 6553 (99.5%) 1853 (99.5%)  Don’t know 62 (1.5%) 52 (0.8%) 24 (0.4%) 7 (0.4%) Country of birth  Spain 3859 (94.4%) 6232 (93.6%) 5988 (92.8%) 1666 (92.0%) 0.001  Other 231 (5.6%) 423 (6.4%) 466 (7.2%) 144 (8.0%) Administrative situation  Spanish nationality 3989 (96.3%) 6428 (95.5%) 6294 (95.8%) 1781 (96.0%) < 0.001  Permanent residency 102 (2.5%) 209 (3.1%) 241 (3.7%) 67 (3.6%)  Temporal residency 42 (1.0%) 79 (1.2%) 19 (0.3%) 5 (0.3%)  No permit/in process 9 (0.2%) 17 (0.3%) 15 (0.2%) 2 (0.1%) Employment situation  Working full-time/part-time 1319 (31.7%) 5014 (74.2%) 4811 (73.0%) 1430 (76.8%) < 0.001  Self-employed 70 (1.7%) 671 (9.9%) 868 (13.2%) 225 (12.1%) < 0.001  Studying full-time/part-time 3112 (74.9%) 1116 (16.5%) 320 (4.9%) 52 (2.8%) < 0.001  Unemployment, COVID19, retirement and other benefits 293 (7.1%) 661 (9.8%) 594 (9.0%) 139 (7.5%) < 0.001  Unpaid carer/houseworker 134 (3.2%) 341 (5.0%) 448 (6.8%) 97 (5.2%) < 0.001 Completed education  Primary education 40 (1.0%) 29 (0.4%) 93 (1.4%) 52 (2.8%) < 0.001  Secondary education 2377 (57.3%) 1264 (18.7%) 1333 (20.3%) 476 (25.6%)  University education 1733 (41.8%) 5450 (80.8%) 5149 (78.3%) 1332 (71.6%) Caregiver  No 3987 (96.8%) 5416 (80.7%) 2520 (38.4%) 541 (29.1%) < 0.001  Yes 130 (3.2%) 1295 (19.3%) 4048 (61.6%) 1316 (70.9%) Financial problems < 12 months  Always/Many times 505 (12.8%) 978 (14.7%) 611 (9.4%) 151 (8.2%) < 0.001  Some/A few times 1399 (35.6%) 2164 (32.4%) 1820 (27.9%) 495 (26.9%)  Never 2029 (51.6%) 3533 (52.9%) 4090 (62.7%) 1195 (64.9%) Self-rated health  Excellent 289 (7.0%) 470 (7.0%) 393 (6.0%) 110 (5.9%) < 0.001  Very good 1661 (40.4%) 2789 (41.4%) 2387 (36.3%) 593 (31.9%)  Good 1789 (43.5%) 2948 (43.7%) 3188 (48.5%) 940 (50.6%)  Fair 374 (9.1%) 492 (7.3%) 550 (8.4%) 187 (10.1%)  Poor 29 (0.7%) 42 (0.6%) 55 (0.8%) 27 (1.5%) Age at menarche [M (SD)] 12.2 (1.4) 12.3 (1.5) 12.5 (1.5) 12.6 (1.6) < 0.001  ≤10 372 (9.2%) 540 (8.1%) 460 (7.1%) 119 (6.5%) < 0.001  11–12 2092 (51.7%) 3299 (49.5%) 2980 (45.7%) 753 (40.9%)  13–15 1507 (37.2%) 2660 (40.0%) 2894 (44.4%) 895 (48.6%)  ≥16 78 (1.9%) 159 (2.4%) 188 (2.9%) 74 (4.0%) Menstrual bleeding abundance  < 25 ml 664 (16.0%) 1198 (17.7%) 1103 (16.7%) 370 (19.9%) 0.001  25–80 ml 2822 (67.9%) 4370 (64.7%) 3894 (59.1%) 988 (53.0%)  80 ml 736 (17.7%) 1233 (18.3%) 1513 (23.0%) 441 (23.7%) < 0.001 Menstrual blood clots  No 2585 (62.2%) 4293 (63.6%) 4284 (65.0%) 1239 (66.5%) 0.002  Yes 1570 (37.8%) 2460 (36.4%) 2303 (35.0%) 624 (33.5%) Menstrual bleeding duration  < 2 days 33 (0.8%) 98 (1.5%) 129 (2.0%) 56 (3.0%)  7 days 294 (7.1%) 395 (5.9%) 388 (5.9%) 162 (8.8%) Menstrual cycle duration  < 21 days 311 (7.7%) 437 (6.6%) 588 (9.0%) 191 (10.6%)  35 days 574 (14.2%) 690 (10.4%) 275 (4.2%) 168 (9.3%) Menstrual pain  Low intensity (1–3) 733 (18.1%) 1677 (25.3%) 2574 (39.8%) 890 (48.8%) < 0.001  Moderate intensity (4–7) 2102 (51.8%) 3245 (49.0%) 2784 (43.0%) 661 (36.2%)  High intensity (8–10) 1224 (30.2%) 1700 (25.7%) 1111 (17.2%) 273 (15.0%) Menstrual pain management  Use of analgesics 3079 (75.9%) 4607 (69.6%) 4167 (64.4%) 1125 (61.6%) < 0.001  Use of natural remedies 2458 (60.6%) 3999 (60.4%) 2861 (44.2%) 568 (31.1%) < 0.001  Yoga/Meditation or physical activity 24 (0.6%) 71 (1.1%) 55 (0.8%) 10 (0.5%) 0.027  Resting, self-care (including through nutrition) 34 (0.8%) 97 (1.5%) 141 (2.2%) 24 (1.5%) < 0.001  Sex (including masturbation) 8 (0.2%) 30 (0.5%) 19 (0.3%) 2 (0.1%) 0.037  Cannabis, cannabidiol (CBD), or alcohol use 6 (0.1%) 24 (0.4%) 13 (0.2%) 1 (0.1%) 0.031  Alternative medicine 2 (0.0%) 19 (0.3%) 20 (0.3%) 3 (0.2%) 0.033  Accessing the emergency room 6 (0.1%) 4 (0.1%) 2 (0.0%) 2 (0.1%) 0.164  Physiotherapy/Osteotherapy 1 (0.0%) 5 (0.1%) 9 (0.1%) 1 (0.1%) 0.234  Transcutaneous electric stimulation 1 (0.0%) 4 (0.1%) 2 (0.0%) 0 (0.0%) 0.595  Cannot afford menstrual pain management products 17 (0.4%) 13 (0.2%) 7 (0.1%) 3 (0.2%) 0.008  I do not do anything 823 (20.3%) 1176 (17.8%) 1342 (20.7%) 378 (20.7%) < 0.001  I experience no menstrual pain 218 (5.4%) 422 (6.4%) 642 (9.9%) 238 (13.0%) < 0.001  I do not know what to do 200 (4.9%) 227 (3.4%) 88 (1.4%) 11 (0.6%) < 0.001 Premenstrual symptoms  Always/Many times 2552 (68.2%) 4331 (70.5%) 4068 (67.7%) 1058 (61.9%) < 0.001  Some/A few times 1011 (27.0%) 1562 (25.4%) 1641 (27.3%) 537 (31.4%)  Never 179 (4.8%) 250 (4.1%) 302 (5.0%) 113 (6.6%) Gynecological and systemic health conditions  Anemia 987 (23.8%) 2047 (30.3%) 2287 (34.7%) 637 (34.2%) < 0.001  Iron deficiency 1413 (34.0%) 2860 (42.4%) 3355 (50.9%) 973 (52.2%) < 0.001  Uterine myomas 17 (0.4%) 234 (3.5%) 786 (11.9%) 417 (22.4%) < 0.001  Endometrial polyps 43 (1.0%) 222 (3.3%) 502 (7.6%) 222 (11.9%) < 0.001  Endometriosis/adenomyosis 60 (1.4%) 307 (4.5%) 421 (6.4%) 137 (7.4%) < 0.001  Polycystic ovary syndrome 464 (11.2%) 1312 (19.4%) 1089 (16.5%) 207 (11.1%) < 0.001  Premenstrual syndrome/dysphoric premenstrual disorder 410 (9.9%) 952 (14.1%) 971 (14.7%) 296 (15.9%) < 0.001  Gynaecological cancers (ovary or fallopian tube, urine and cervical) 2 (0.0%) 37 (0.5%) 54 (0.8%) 14 (0.8%) < 0.001  Breast cancer 1 (0.0%) 6 (0.1%) 36 (0.5%) 31 (1.7%) < 0.001  No diagnosis 1846 (44.4%) 1983 (29.4%) 1463 (22.2%) 412 (22.1%) < 0.001 Participants’ sociodemographic characteristics, menstrual characteristics and health conditions, stratified by age ( N  = 19,385) An age gradient was identified for menstrual abundance, with the odds of light menstrual flow (< 25 ml) being significantly higher among participants aged 46–55 (aOR: 1.56, 95% CI, 1.33–1.83). Similarly, light menstruations were more common as educational attainment decreased (eg., aOR primary education : 1.86, 95% CI, 1.35–2.57). The odds for menstrual abundance over 80 ml also decreased with education (eg., aOR primary education : 0.68, 95% CI, 0.47–0.98). The odds for abundant menstrual flow (> 80 ml) were significantly higher among caregivers (aOR: 1.38, 95% CI, 1.27–1.51). However, caregivers were less likely to report menstrual blood clots (aOR: 0.86, 95% CI, 0.80–0.93). A gradient was also identified regarding financial problems in the 12 months preceding data collection, as more severe financial difficulties were significantly associated with higher odds for abundant menstrual flow (> 80 ml) (aOR: 1.19, 95% CI, 1.06–1.34) and menstrual blood clots (aOR: 1.21, 95% CI, 1.10–1.33). The odds for a light menstrual flow ( 80 ml) (aOR poor self-rated health : 2.08, 95% CI, 1.43–3.01), and menstrual blood clots (aOR poor self-rated health : 2.90, 95% CI, 2.05–4.10) were higher as self-rated health worsened. See Table 3 for more details. Table 3 Associations between menstrual bleeding abundance, menstrual blood clots, sociodemographic characteristics and self-rated health ( N  = 18,839) Menstrual bleeding abundance* Menstrual blood clots*  80 ml aOR (95%CI) p value aOR (95%CI) p value aOR (95%CI) p value aOR (95%CI) p value Age  18–25 1.00 1.00 1.00 1.00  26–35 1.29 (1.15–1.45) < 0.001 0.84 (0.77–0.92) < 0.001 0.96 (0.86–1.07) 0.418 0.94 (0.87–1.03) 0.205  36–45 1.28 (1.13–1.45) < 0.001 0.72 (0.65–0.79) < 0.001 1.12 (0.99–1.26) 0.067 0.94 (0.86–1.04) 0.242  46–55 1.56 (1.33–1.83) < 0.001 0.58 (0.51–0.66) < 0.001 1.13 (0.97–1.31) 0.125 0.89 (0.78–1.02) 0.084 Completed education  University education 1.00 1.00 1.00 1.00  Secondary education 1.32 (1.21–1.45) < 0.001 0.87 (0.81–0.93) < 0.001 0.96 (0.88–1.05) 0.365 0.97 (0.90–1.04) 0.416  Primary education 1.86 (1.35–2.57) < 0.001 0.64 (0.49–0.85) 0.002 0.68 (0.47–0.98) 0.038 0.82 (0.61–1.11) 0.200 Caregiver  No 1.00 1.00 1.00 1.00  Yes 0.88 (0.80–0.97) 0.008 0.84 (0.78–0.90) < 0.001 1.38 (1.27–1.51) < 0.001 0.86 (0.80–0.93) < 0.001 Financial problems < 12 months  Never 1.00 1.00 1.00 1.00  Some/A few times 1.03 (0.94–1.12) 0.528 0.97 (0.91–1.04) 0.464 1.04 (0.95–1.12) 0.398 1.08 (1.01–1.15) 0.035  Always/Many times 1.08 (0.96–1.22) 0.219 0.84 (0.76–0.93) 0.001 1.19 (1.06–1.34) 0.003 1.21 (1.10–1.33) < 0.001 Self-rated health  Excellent 1.00 1.00 1.00 1.00  Very good 0.83 (0.72–0.97) 0.019 1.13 (0.99–1.27) 0.064 0.98 (0.84–1.15) 0.839 1.24 (1.08–1.41) 0.002  Good 0.77 (0.66–0.89) 0.001 1.03 (0.91–1.16) 0.649 1.15 (0.99–1.34) 0.075 1.51 (1.33–1.73) < 0.001  Fair 0.64 (0.53–0.79) < 0.001 0.94 (0.81–1.10) 0.450 1.51 (1.25–1.81) < 0.001 2.05 (1.74–2.40) < 0.001  Poor 0.73 (0.47–1.15) 0.172 0.49 (0.35–0.70) < 0.001 2.08 (1.43–3.01) < 0.001 2.90 (2.05–4.10) < 0.001 *  80 ml are three separate variables with yes/no values, as a participant may have a ‘yes’ value in one or more of these variables. All the models presented in this table are logistic regression models Associations between menstrual bleeding abundance, menstrual blood clots, sociodemographic characteristics and self-rated health ( N  = 18,839) *  80 ml are three separate variables with yes/no values, as a participant may have a ‘yes’ value in one or more of these variables. All the models presented in this table are logistic regression models As reported in Table 4 , the odds for short menstruation duration (< 2 days) (aOR age 46–55 : 4.80, 95% CI, 2.91–7.93) and menstrual cycles (< 21 days) (aOR age 46–55 : 1.47, 95% CI, 1.18–1.83) were higher as age increased. A similar gradient was found by completed education; menstrual duration of 2 days or less (aOR primary education : 2.87, 95% CI, 1.42–5.79) and menstrual cycles that were 21 days or shorter (aOR primary education : 2.97, 95% CI, 2.06–4.28) were more likely among participants with completed primary education. The odds for long menstruations (> 7 days) were also higher as educational attainment decreased (aOR primary education : 2.05, 95% CI, 1.35–3.11). Caregivers had higher odds for reporting menstrual duration of 7 days or over (aOR: 1.24, 95% CI, 1.07–1.43). The odds for long menstruations (aOR always/many times : 1.45, 95% CI, 1.22–1.74) and short menstrual cycles (aOR always/many times : 1.43, 95% CI, 1.21–1.68) increased as women and PWM reported more financial difficulties. Poor self-rated health was also associated with shorter (aOR: 2.89, 95% CI, 1.30–6.39) and longer (aOR: 6.23, 95% CI, 3.70–10.47) menstruations, and shorter (aOR: 2.15, 95% CI, 1.30–3.55) and longer (aOR: 1.89, 95% CI, 1.10–3.23) menstrual cycles. Table 4 Associations between menstrual bleeding duration, menstrual cycle duration, sociodemographic characteristics, and self-rated health Menstrual bleeding duration* N = 18,818 Menstrual cycle duration* N = 18,581  7 days  35 days aOR (95%CI) p value Ref aOR (95%CI) p value aOR (95%CI) p value Ref aOR (95%CI) p value Age  18–25 1.00 1.00 1.00 1.00  26–35 2.25 (1.45–3.51) < 0.001 0.82 (0.69–0.98) 0.026 0.98 (0.83–1.16) 0.801 0.69 (0.60–0.79) < 0.001  36–45 3.12 (1.99–4.91) < 0.001 0.77 (0.64–0.94) 0.009 1.23 (1.03–1.46) 0.022 0.27 (0.23–0.32) < 0.001  46–55 4.80 (2.91–7.93) < 0.001 1.15 (0.91–1.46) 0.231 1.47 (1.18–1.83) 0.001 0.64 (0.52–0.79) < 0.001 Completed education  University education 1.00 1.00 1.00 1.00  Secondary education 1.49 (1.14–1.95) 0.003 1.15 (1.00–1.32) 0.052 1.70 (1.51–1.93) < 0.001 0.98 (0.86–1.10) 0.714  Primary education 2.87 (1.42–5.79) 0.003 2.05 (1.35–3.11) 0.001 2.97 (2.06–4.28) < 0.001 1.00 (0.57–1.75) 0.993 Caregiver  No 1.00 1.00 1.00 1.00  Yes 1.02 (0.78–1.33) 0.893 1.24 (1.07–1.43) 0.004 1.09 (0.96–1.25) 0.179 0.92 (0.80–1.05) 0.221 Financial problems < 12 months  Never 1.00 1.00 1.00 1.00  Some/A few times 1.06 (0.81–1.38) 0.681 1.25 (1.09–1.43) 0.001 1.31 (1.16–1.48) < 0.001 1.01 (0.90–1.14) 0.821  Always/Many times 1.19 (0.83–1.70) 0.352 1.45 (1.22–1.74) < 0.001 1.43 (1.21–1.68) < 0.001 1.07 (0.91–1.26) 0.428 Self-rated health  Excellent 1.00 1.00 1.00 1.00  Very good 0.70 (0.45–1.08) 0.103 1.56 (1.13–2.17) 0.008 0.91 (0.72–1.15) 0.445 0.85 (0.68–1.05) 0.128  Good 0.67 (0.44–1.03) 0.067 1.97 (1.42–2.72) < 0.001 1.02 (0.81–1.28) 0.876 1.08 (0.88–1.34) 0.469  Fair 0.89 (0.52–1.52) 0.675 3.08 (2.16–4.39) < 0.001 1.29 (0.98–1.70) 0.069 1.59 (1.24–2.05) < 0.001  Poor 2.89 (1.30–6.39) 0.009 6.23 (3.70–10.47) < 0.001 2.15 (1.30–3.55) 0.003 1.89 (1.10–3.23) 0.021 *Multinomial logistic regression models Associations between menstrual bleeding duration, menstrual cycle duration, sociodemographic characteristics, and self-rated health *Multinomial logistic regression models Odds for moderate (aOR aged 46–55 : 0.36, 95% CI, 0.31–0.42) and high (aOR aged46–55 : 0.33, 95% CI, 0.27–0.40) intensity menstrual pain decreased as age increased. They were also lower among caregivers (aOR high intensity pain : 0.39, 95% CI, 0.35–0.44). Instead, the odds for both moderate (aOR always/many times : 1.21, 95% CI, 1.07–1.37) and high (aOR always/many times : 1.87, 95% CI, 1.63–2.14) intensity menstrual pain were higher as participants reported more financial problems in the 12 months preceding the study. A gradient was also found regarding self-rated health. The worse health was perceived, the higher the odds for moderate (aOR poor self-rated health : 2.70, 95% CI, 1.61–4.52) and high (aOR poor self-rated health : 8.33, 95% CI, 4.97–13.94) intensity menstrual pain. See Table 5 for further details. Table 5 Associations between menstrual pain, sociodemographic characteristics, and self-rated health (N = 18,467) Menstrual pain* Low intensity Moderate intensity High intensity Ref aOR (95%CI) p value aOR (95%CI) p value Age  18–25 1.00 1.00  26–35 0.75 (0.67–0.84) < 0.001 0.74 (0.65–0.84) < 0.001  36–45 0.50 (0.45–0.57) < 0.001 0.44 (0.39–0.51) < 0.001  46–55 0.36 (0.31–0.42) < 0.001 0.33 (0.27–0.40) < 0.001 Completed education  University education 1.00 1.00  Secondary education 1.06 (0.97–1.15) 0.200 1.19 (1.07–1.31) <0.001  Primary education 1.04 (0.75–1.45) 0.815 1.10 (0.73–1.65) <0.645 Caregiver  No 1.00 1.00  Yes 0.61 (0.56–0.66) < 0.001 0.39 (0.35–0.44) < 0.001 Financial problems < 12 months  Never 1.00 1.00  Some/A few times 1.21 (1.12–1.31) < 0.001 1.46 (1.33–1.61) < 0.001  Always/Many times 1.21 (1.07–1.37) 0.002 1.87 (1.63–2.14) < 0.001 Self-rated health  Excellent 1.00 1.00  Very good 1.39 (1.21–1.60) < 0.001 1.41 (1.18–1.68) < 0.001  Good 1.88 (1.64–2.16) < 0.001 2.00 (1.67–2.38) < 0.001  Fair 2.63 (2.17–3.18) < 0.001 4.36 (3.49–5.45) < 0.001  Poor 2.70 (1.61–4.52) < 0.001 8.33 (4.97–13.94) < 0.001 *Multinomial logistic regression models Associations between menstrual pain, sociodemographic characteristics, and self-rated health (N = 18,467) *Multinomial logistic regression models Lastly, being a caregiver appeared to be a protective factor for experiencing premenstrual symptoms always/many times (aOR: 0.60, 95% CI, 0.50–0.71) and sometimes (aOR: 0.70, 95% CI, 0.58–0.84). On the other hand, risk factors were reporting financial difficulties (< 12 months) (aOR always/many times : 2.80, 95% CI, 2.06–3.79) and worsened self-rated health (aOR poor self-rated health : 3.34, 95% CI, 1.20–9.31). Refer to Table 6 for more information. Table 6 Associations between premenstrual symptoms, sociodemographic characteristics, and self-rated health ( N  = 17,158) Premenstrual symptoms* Always/many times Some/a few times Never aOR (95%CI) p value aOR (95%CI) p value Ref Age  18–25 1.00 1.00  26–35 1.21 (0.97–1.51) 0.085 1.13 (0.90–1.43) 0.290  36–45 1.23 (0.97–1.57) 0.093 1.17 (0.91–1.51) 0.211  46–55 0.89 (0.66–1.19) 0.415 1.04 (0.77–1.41) 0.802 Completed education  University education 1.00 1.00  Secondary education 0.82 (0.69–0.98) 0.030 0.89 (0.74–1.07) 0.228  Primary education 0.90 (0.41–1.96) 0.789 1.20 (0.54–2.67) 0.656 Caregiver  No 1.00 1.00  Yes 0.60 (0.50–0.71) < 0.001 0.70 (0.58–0.84) < 0.001 Financial problems < 12 months  Never 1.00 1.00  Some/A few times 2.06 (1.72–2.47) < 0.001 1.63 (1.35–1.96) < 0.001  Always/Many times 2.80 (2.06–3.79) < 0.001 1.48 (1.08–2.04) 0.015 Self-rated health  Excellent 1.00 1.00  Very good 1.50 (1.18–1.91) 0.001 1.38 (1.08–1.78) 0.011  Good 2.40 (1.88–3.06) < 0.001 1.81 (1.40–2.34) < 0.001  Fair 4.66 (3.02–7.20) < 0.001 2.53 (1.61–3.98) < 0.001  Poor 3.34 (1.20–9.31) 0.021 1.22 (0.40–3.69) 0.722 *Multinomial logistic regression models Associations between premenstrual symptoms, sociodemographic characteristics, and self-rated health ( N  = 17,158) *Multinomial logistic regression models

This is a cross-sectional study, part of the “Equity and Menstrual Health in Spain” project. This study adopts a critical and feminist perspective to public health research, and critically questions androcentrism and systemic sociopolitical inequities of health that impact women and PWM [ 53 , 54 ]. An online questionnaire was devised by the research team, given the lack of standardized measures available in our context. The team consists of interdisciplinary experts including psychologists, medical doctors, public health professionals, midwives. The questionnaire was developed during several meetings and it was piloted before data collection. Data were collected between 24th of March and 8th of July 2021 using the Lime Survey platform ( https://www.limesurvey.org ), a secure web-based software designed to securely conduct online surveys. The questionnaire included 58 questions and took around 20 minutes to complete. Although most data collection was done online, data were also collected face-to-face to ensure the participation of vulnerable groups. Face-to-face data collection ( N  = 78) took place at sexual and reproductive health centres, a service for sex workers, and a food bank in the Barcelona area. Participants were women and PWM aged 18–55 who lived in Spain at the time of data collection. Main exclusion criteria were having entered menopause. Participants taking hormonal contraception were excluded from the analyses for this article ( N  = 3465). At least 1535 participants were required, based on sample size calculations. These were performed for the “Equity and Menstrual Health in Spain” project, considering a “menstrual hygiene management” variable. Maximum indetermination of the main variable (proportion of 50%) was assumed. These assumptions were in order to obtain a precision of 2.5% in the confidence intervals. These estimates have been calculated assuming an alfa risk of 5%. PASS software was used for the sample size calculations [PASS 15 Power Analysis and Sample Size Software (2017). NCSS, LLC. Kaysville, Utah, USA]. Sampling was non-probabilistic and purposive. Recruitment strategies included dissemination of the survey in social media, primary healthcare centres, sexual and reproductive healthcare centres, non-governmental and other local organisations. Snowballing techniques were also used. Menstrual characteristics were: age at menarche (≤10; 11–12; 13–15; ≥16), menstrual bleeding abundance ( 80 ml), menstrual blood clots (yes, no), menstrual bleeding duration ( 7 days), menstrual cycle duration ( 35 days), menstrual pain (low intensity; moderate intensity; high intensity), menstrual pain management, and premenstrual symptoms (always/many times; some/a few times; never). Reports on premenstrual symptoms were collected through a question on experiences of emotional fluctuations (e.g., sadness or irritability) and physical changes (e.g., tiredness or liquid retention) in the week/2 weeks preceding menstrual bleeding. Gynaecological and systemic health conditions were also collected: anaemia; iron deficiency; uterine myomas; endometrial polyp; endometriosis/adenomyosis; polycystic ovary syndrome; premenstrual syndrome/dysphoric premenstrual disorder; gynaecological cancers (ovary or fallopian tube cancer; uterine cancer; breast cancer); and no diagnoses. Data on menstrual bleeding abundance was collected by asking participants the number of menstrual products used per menstruation [light bleeding, < 25 ml per menstruation (≤6 regular absorbency tampons or pads, or less than 1 full 20 ml menstrual cup); moderate bleeding, 25-80 ml per menstruation (7–15 or 7–19 regular absorbency tampons or pads respectively, or between 1 and 4 full 20 ml menstrual cups); heavy bleeding, > 80 ml per menstruation (≥16 or ≥ 20 regular absorbency tampons or pads respectively, or more than 4 full 20 ml menstrual cups)] [ 55 ]. Sociodemographics included: age (18-35, 36-45, 46-55), gender (woman, non-binary/other), trans (yes, don’t know, no), country of birth (Spain; other countries), administrative situation (Spanish nationality; permanent residence; temporal residence; no permit), employment status (working full or part time; studying full or part time; self-employed; unemployed, COVID19 or other benefits; unpaid carer/houseworker), educational attainment (no education, primary education, secondary education, university education), financial constraints < 12 months (always/many times, some/a few times, never), caregiver (yes, no). Self-rated health was categorized using a 5-point Likert scale (excellent, very good, good, fair, poor), based on participants’ responses to the following validated question: “In general, how would you say your health is?” More details on the questionnaire can be found in the Supplementary File 1 . Descriptive statistics were calculated for each variable to identify asymmetric distributions. Age and age at menarche were analyzed as means (Standard Deviation (SD)) based on the normality of the distribution, and categorical variables were described as percentages. Descriptive statistics were calculated to characterize sociodemographic characteristics, menstrual characteristics, and gynaecological and systemic health conditions. Chi-square tests were used to assess differences between socioeconomic variables, menstrual characteristics and gynaecological and systemic health conditions, according to age. Logistics and multinomial logistic regression models were constructed to compare odds of menstrual characteristics dependent variables (menstrual bleeding abundance, menstrual blood clots, menstrual bleeding duration, menstrual cycle duration, menstrual pain, and premenstrual symptoms) based on independent variables (age, completed education, being a caregiver (yes/no), experiencing financial constraints in the last 12 months (always or many times/some or a few times/never), and self-rated health). Analyses were adjusted by age, educational attainment, financial constraints < 12 months, caregiver and self-rated health. These variables were chosen based on preliminary analyses. Statistical significance was set at 0.05. Analyses were conducted using SPSS 25.0 (SPSS Inc., Armonk, NY: IBM Corp), and Stata/MP 17.0 (StataCorp LLC, TX).

The last few years have been crucial to draw attention towards the need to consider menstruation and the menstrual cycle as vital signs for the health of women and people who menstruate (i.e., gender non-confirming menstruators) (PWM). Menstrual health has been recently defined in an attempt to approach and conceptualize menstrual health in a holistic manner, as it also considers the access to accurate menstrual education, menstrual products and menstrual management facilities and services, a timely diagnosis for menstrual-related health conditions, having stigma and discrimination-free menstrual experiences, and being able to decide whether to participate in civil, cultural, economic, social and political spheres throughout the menstrual cycle [ 1 ]. Menstrual health is closely related to menstrual inequity, which refers to the systematic and avoidable differences in menstrual access and experiences, based on the intersection of social inequities of health amongst individuals and communities [ 2 , 3 ]. The relationship between social and economic inequities and negative health outcomes [ 4 , 5 ] and the feminization of poverty [ 6 ] is well-established. A growing literature, especially from Global South contexts but increasingly from the Global North, highlights that menstrual equity and health are especially compromised among socioeconomically vulnerable women and PWM, such as those living in situations of financial hardship [ 2 , 7 , 8 ], homelessness [ 9 ], displacement [ 10 ] and migration [ 11 ]. Socioeconomic inequities and a limited and inadequate access to healthy menstrual management can have a profound impact on reproductive [ 12 ], emotional [ 13 , 14 ] and general [ 15 ] health outcomes. It is thus imperative to conduct research on menstrual health that considers social inequities of health and, particularly, socioeconomic factors. Besides, little is known about menstrual health patterns among women and PWM, as most research in Spain has focused on investigating menstrual disorders and specific populations [ 16 , 17 ]. Menstrual characteristics encompass age at menarche, menstrual bleeding duration, menstrual cycle duration, menstrual bleeding abundance, premenstrual symptoms, menstrual pain, and menstrual blood clots. Previous evidence has demonstrated linkages between sociodemographic variables and menstrual characteristics [ 18 , 19 ]. The decrease in age of menarche has been attributed to population changes in nutrition, physical activity and body fat [ 20 – 22 ], the exposure to endocrine disruptor chemicals [ 23 – 25 ], climate change [ 26 ], psychosocial stressors [ 27 , 28 ], socioeconomic factors (e.g., family composition and income or place of residence) [ 20 , 21 , 28 – 30 ], factors related to race/ethnicity [ 22 ], among other factors [ 31 – 34 ]. Similarly, higher age, lower education level [ 19 ], as well as living in deprived areas are linked with experiences of heavy menstrual bleeding [ 35 ]. Menstrual cycle duration variates depending on age, ethnicity, and body weight [ 36 ]. Many sociodemographic characteristics have been reported related to menstrual pain, such as age [ 37 , 38 ], body mass index [ 37 , 39 ], low socioeconomic status [ 37 , 38 , 40 ], and family history of dysmenorrhea [ 41 ]. Premenstrual symptoms were more common in women with higher educational status [ 42 ] and experiencing stress [ 43 ]. Menstrual blood clots (those greater than 1 in. = 2,5 cm) are used as indicators of heavy menstrual bleeding [ 18 , 44 , 45 ] as well as a sign of adenomyosis [ 46 ]. On the other hand, self-rated health is a known proxy for health status [ 47 , 48 ] and an indicator for health equity [ 48 – 51 ]. Considering that self-rated health is mediated by social, cultural, economic and political factors, it is necessary to contextualize the understanding of how self-rated health may be associated with health outcomes. In the area of menstrual health and equity research, Teperi and Rimpelä already suggested in 1989 that poor self-rated health was a determinant of menstrual pain in Finland [ 52 ]. However, to the authors’ knowledge, menstrual health research has not yet further explored the potential association between menstrual characteristics and self-rated health. Having identified this gap the current article explores the intersection between menstrual characteristics, sociodemographic factors, and self-rated health. Understanding menstrual characteristics in context is necessary to menstrual health and equity research, particularly to highlight the needs of most vulnerable populations. This is particularly relevant at a time when menstrual policymaking is rapidly increasing and, often, failing to implement evidence-based policies. In order not to become tokenistic, policies should consider and respond to the needs of different groups of women and PWM. The main aim of this study was to investigate the associations between menstrual characteristics, sociodemographic factors and self-rated health among women and PWM aged 18–55 in Spain.

This study aimed to investigate the associations between menstrual characteristics, sociodemographic factors and self-rated health among adult women and PWM in Spain. In our study, age at menarche was 12.4 (SD = 1.5) and most commonly reported between 11 and 12 (47.8%) and 13–15 (41.7%) years old. Menarche was reported before the age of 10 in 7.8% of our participants. These results are consistent with previous evidence, both in Spain [ 20 , 56 ] and other countries [ 57 – 59 ]. As participants’ age decreased menarche was reported to be at an earlier age, supporting the already evidenced decline in the onset of menstruation since the second half of the twentieth century [ 31 , 32 , 60 – 62 ]. The reason why age at menarche and its well-established onset decline matters, lays on the implications of early puberty. These include a higher risk of cardiovascular disease, mediated by increased body fat and other mechanisms [ 63 ], breast cancer [ 64 , 65 ], and the emotional and social impact of early menarche [ 66 , 67 ]. Moreover, it is imperative that the latter implications are further considered, especially as menarche can be understood as a rite of passage to adulthood, which often leads to the sexualization of girls and young menstruators and the constriction of the social and physical spaces they occupy in our and other sociocultural contexts [ 66 ]. On the other hand, 37.6% of women and PWM in our study reported menstrual abundance that could not be considered within healthy parameters; 17.2% bled less than 25 ml and 20.3% indicated bleeding more than 80 ml per menstruation. There were also common reports in our study regarding the presence of menstrual blood clots (35.9%) and, although not as frequent, menstrual durations longer than 7 days (6.4%). Heavy menstrual bleeding can be caused by processes interfering endocrine, paracrine and hemostatic functions of the endometrium and the myometrial contractility (e.g., endometrial polyps, adenomyosis, leiomyomas, coagulopathy, hyperplasia, polycystic ovarian syndrome) [ 68 ]. However, light menstrual bleeding sometimes is not clearly attributed to a specific cause [ 69 ]. Reports of heavy bleeding and menstrual blood clots appear to be lower than those identified in previous research [ 18 , 70 , 71 ]. This may be explained as the lack of access and adequacy of menstrual education considerably limit the resources of women and PWM to identify menstrual health factors (e.g., their bleeding patterns) [ 1 , 2 ]. Although parameters to calculate menstrual bleeding through calculating the number of menstrual products used were provided to participants in our study, future research should consider alternative ways of measuring bleeding patterns [ 55 ]. Heavy bleeding patterns may not only have important health implications, such as in the development of iron deficiency and anemia [ 72 – 74 ], but can have an impact on quality of life [ 75 ] and menstrual management. While managing menstruation can be generally challenging, mainly due to structural factors rooted in sociocultural androcentric perspectives and practices, heavy bleeders may encounter increased difficulties. For instance, these challenges may encompass needing adequate facilities in public spaces and changing menstrual products more often than other women and PWM. Considering that the lack of access to menstrual management spaces is a reality for most women and PWM in our study [ 2 ] and other contexts [ 76 , 77 ], it is imperative to explore the health and social implications of heavy bleeding (especially in public spaces) [ 76 ], and respond to the menstrual needs of women and PWM through research, advocacy, and policymaking [ 78 ]. A recent systematic literature review and meta-analysis including 38 studies conducted in a variety of countries from the Global South and North has identified the prevalence of dysmenorrhea to be 71.1% [ 79 ]. In our research, moderate and high intensity menstrual pain reports were also significantly high (46.3 and 22.7%, respectively). Menstrual pain, often caused by hyper-production of uterine prostaglandins, leads to elevated uterine tone and high uterine contractions. Although endocrine factors contribute to menstrual pain, other factors (e.g., age, childbearing, family history of dysmenorrhea, and mental health) play a role in the perception and the severity of pain [ 80 , 81 ]. For example, childbearing is associated with reduced menstrual pain. Uterine neurotransmitters dynamics change during pregnancy, and after that process, there is a partial regeneration of uterine nerve terminals that may explain the disappearance or reduction of menstrual pain after childbirth [ 82 , 83 ]. On the other hand, menstrual pain is still systematically normalized and often dismissed, even in healthcare settings [ 2 , 16 , 17 , 83 , 84 ], which may lead to delays on diagnosis of health conditions (e.g. endometriosis) and a poor quality of life [ 2 , 17 ], added to the emotional and social implications of experiencing pain [ 85 ]. The lack of a structural and social awareness of what a healthy menstrual cycle and menstruation may be has an impact on the few resources that many women and PWM have when it comes to dealing with pain management. Based on our results, menstrual pain was mostly managed by using analgesics (68.4%) and natural remedies (52.1%), while 19.6% reported not doing anything to manage it. Despite the wide variety of methods that can be used and be effective (e.g., physical activity) [ 86 ], medicalization is usually the most common strategy within healthcare services [ 87 ], especially via hormonal contraceptives or painkillers [ 2 , 88 ]. Narrowing down the options for menstrual pain management can greatly contribute to pathologizing the menstrual cycle and menstruation [ 89 ], rather than considering menstruation and the menstrual cycle as indicators of health [ 90 , 91 ]. Consistent with another study in the Spanish context [ 92 ], premenstrual symptoms reports were also high, since these were experienced by most women and PWM (68.2%) in most menstrual cycles. However, only 13.6% indicated a diagnosis of premenstrual syndrome or dysphoric premenstrual disorder. This may potentially be due to the normalization of premenstrual symptomatology among women, PWM and healthcare professionals. It also points towards the need to attend to premenstrual symptoms regardless of whether they fulfil a diagnostic criterion or not. Although these results do not provide enough information to assess to what extent these symptoms affect participants day-to-day, it is relevant to point towards the potential impact of premenstrual experiences on emotional and social health experienced by women and PWM. As for menstrual pain and other menstrual experiences, healthcare systems and professionals have often not paid enough consideration to premenstrual symptoms. One of the reasons for this is the ingrained stigmatization of menstruation [ 93 ] and “the menstruating woman”, portraited as irrational and monstruous [ 94 – 96 ]. The assumption that the bodies of women and PWM are pathological and tend to irrational emotions may have led to underestimation, minimization, and invalidation of (pre)menstrual experiences, maintaining the normalization of pain or fatigue, among other symptoms [ 95 ]. The estimated prevalence of self-reported gynaecological and systemic health conditions differs from previous evidence, since women and PWM taking hormonal contraception at the data collection were excluded from the analyses. This links with the abovementioned medicalization of menstrual related health issues [ 2 , 87 ], frequently treated by default with hormonal contraception [ 87 , 88 ]. Alternative approaches (e.g., natural remedies, nutrition, or physical activity) are rarely offered, partially as their adequacy and efficacy is often unknown by healthcare professionals, perpetuating a medication-based model to address menstrual issues [ 2 , 89 , 97 ]. Another explanation could be due to the lack of time health professionals often have to approach menstrual health in a more holistic way and to focus on menstrual education. This may however have important implications, as the neglect of menstrual-related symptomatology and its medicalisation are associated with late diagnosis and treatment of health conditions such as endometriosis [ 98 ] or ovarian cancer [ 99 ]. While the evidence on menstrual health and equity is growing, it is imperative to incorporate a critical perspective on how gender and other social inequities mediate and impact menstrual experiences, health, and equity [ 2 ]. Hence, beyond describing menstrual characteristics, this article aimed at identifying the associations between self-reported menstrual patterns and sociodemographic factors that represent axes of social inequities (i.e., age, educational attainment, caregiving, and financial situation). Other axes of inequity (i.e., gender identity, identification as trans, employment status, administrative status, and country of birth) were considered in primary analyses. However, preliminary findings were unsupportive of including these variables in further analyses. A potential reason could be the limited sample size available for certain participant groups (e.g., trans menstruators or those with no permit to reside in Spain). Despite these variables could not be included in our analyses, further research should actively investigate the associations of these axes of inequity with menstrual patterns. Intersectionality approaches could be particularly helpful to highlight social inequities of menstrual health [ 100 ]. An age gradient was observed for several menstrual experiences related to pain, bleeding abundance and menstrual cycle’s duration. As expected, the odds for lighter menstrual flow, shorter bleeding days and menstrual cycles, and moderate/high intensity pain were higher as participants were younger. This may be explained by ovarian maturation and low progesterone levels at a younger age. Elevated prostaglandin and diminished progesterone levels contribute to the inflammatory responses, triggering pain in the endometrium [ 101 ]. The shortening of menstrual cycles and bleeding duration among those over 40 years old can be explained by the decrease in oestrogen levels and diminished ovarian reserve [ 102 ]. Together with other axes of inequity, socioeconomic status is a well-known determinant of health [ 4 , 5 ], including of menstrual health [ 103 , 104 ]. In our research and previous literature, reporting financial hardship and lower educational attainment were risk factors for potentially unhealthy menstrual patterns [ 103 ]. Reporting financial constraints was associated with abundant flow (> 80 ml), blood clots, long menstruations (> 7 bleeding days), short menstrual cycles (< 21 days), moderate and high intensity pain, and premenstrual symptoms. As in our study, evidence has reported that heavy menstrual bleeding and menstrual pain can be associated with low socioeconomic status. Financial constraints can influence inadequate nutritional status, ultimately affecting menstrual cycle patterns [ 103 ]. Risk for light menstruations (< 25 ml), less bleeding days, short menstrual cycles (< 21 days) and menstruations that last over 7 days was higher among participants with lower educational attainment. Lower educational attainment tends to correlate with more precarious employment situations and elevated stress levels. Disruptions in hormonal equilibrium triggered by stress may result in changes in menstrual patterns [ 105 ]. These findings suggest the inherent relationship between social inequities and menstrual health and reinforce the need to deeply explore how socioeconomic contexts and stressors may have an impact on menstrual patterns and health (2,9106). An interesting finding was the role of identifying as a caregiver. Caregivers presented higher odds in abundant menstrual flow and longer menstruation days, which is consistent with previously evidence on caregiving as a factor of impaired health (e.g., mental health, chronic pain) [ 106 , 107 ]. However, caregiving was also found to be a protective factor for menstrual clots, reporting moderate and high intensity menstrual pain, and experiencing premenstrual symptoms. Caregivers’ age could explain these findings, as most informal caregivers in our study were between 26 and 55 years old. A complementary explanation could be related to a lack of awareness among participants on how to identify menstrual clots, although this could certainly apply to all participants in our study. In addition, the burden of care may limit embodied spaces and awareness to identify and validate experiences of pain and premenstrual symptoms (e.g., tiredness). Future gender-based research could investigate the complex intersections of care work and menstrual health. On the other hand, and as already stated, self-rated health is a widely used proxy for general health status and health inequities [ 47 – 50 ]. Our data highlight a gradient between poorer self-rated health and higher odds for reporting abundant bleeding (> 80 ml), menstrual blood clots, short ( 35 days) menstrual cycles, moderate and high intensity pain, and premenstrual symptoms. Therefore, these findings are suggestive of a link between general health status and poorer menstrual health patterns. In addition, they strengthen the inherent interconnection between social inequities and menstrual health. Main strengths of this research include its social relevance and innovation, as it pioneers in providing evidence on menstrual health and equity in Spain. Another strength is the large sample size included that despite not being representative to the population living in Spain, it includes women and PWM across the whole Spanish territory. Main limitations encompass the study not being representative of the menstruating populations living in Spain and the impact of the digital divide amongst vulnerable and hard-to-reach populations. Recall biases may be present in self-reported variables owing to the retrospective design of the study. Furthermore, using the amount of menstrual products used to determine the abundance of menstrual bleeding may be a limitation, as the frequency of menstrual product change can be influenced by other variables.

This study presents a detailed overview of menstrual characteristics among adult women and PWM in Spain. The odds for heavy menstrual bleeding, moderate/high intensity menstrual pain and experiencing premenstrual symptoms, among other menstrual characteristics, were higher in participants with less educational attainment, more financial hardship, and poorer self-rated health. In turn, increased age and identifying as a caregiver may be protective factors. This suggests the need to consider how social inequities may impact menstrual health, and the implications for menstrual management. Research highlighting the needs of vulnerable populations is imperative, alongside community-based actions and evidence-based policymaking. Menstrual inequities should be considered and addressed within interventions and public policies, considering menstruation as a vital sign of health and menstrual health as a public health issue. Adequate training to healthcare professionals is essential so that they have enough support to address menstrual health, attending to social inequities of health.

Additional file 1. Additional file 1.