Prevalence of chronic pelvic pain and associated factors among indigenous women of reproductive age in Ecuador

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This number is estimated to be higher in developing countries, with a significant negative personal and socioeconomic impact on women. The lack of data on this condition in several countries, particularly those in development and in socially and biologically vulnerable populations such as the indigenous, makes it difficult to guide public policies. Objectives: To evaluate the prevalence of chronic pelvic pain (dysmenorrhea, dyspareunia, non-cyclical pain) and identify which variables are independently associated with the presence of the condition in indigenous women from Otavalo-Ecuador. Design: A cross-sectional study was carried out including a sample of 2429 women of reproductive age between 14-49 years old, obtained from April 2022 to March 2023. A directed questionnaire was used, collected by bilingual interviewers (Kichwa and Spanish) belonging to the community itself; the number of patients was selected by random sampling proportional to the number of women estimated by sample calculation. Data are presented as case prevalence, odds ratio, and 95% confidence interval, with p < 0.05. Results: The prevalence of primary dysmenorrhea, non-cyclic pelvic pain, and dyspareunia was, respectively, 26.6%, 8.9%, and 3.9%.all forms of chronic pain were independently associated with each other. Additionally, dysmenorrhoea was independently associated with hypertension, intestinal symptoms, miscegenation, long cycles, previous pregnancy, use of contraceptives and pear body shape. Urinary symptoms, late menarche, exercise, and pear body shape were associated with non-cyclic pelvic pain. And, urinary symptoms, previous pregnancy loss, late menarche, hormone usage, and pear body shape were associated with dyspareunia. Conclusion: The prevalence of primary dysmenorrhea and non-cyclical chronic pelvic pain was notably high, in contrast with the frequency of reported dyspareunia. Briefly, our results suggest an association between dysmenorrhoea and conditions related to inflammatory and/or systemic metabolic disorders, including a potential causal relationship with other manifestations of pelvic pain, and between non-cyclical pelvic pain and signs/symptoms suggesting central sensitization. The report of dyspareunia may be influenced by local cultural values and beliefs. Dyspareunia Ecuador Indigenous Kichwa Non-Cyclic Pelvic Pain Prevalence Primary Dysmenorrhoea Risk Factor Introduction Chronic pelvic pain (CPP) is a complex and debilitating medical condition that affects people around the world regardless of gender or age, although it is most frequently reported in people assigned female at birth (women henceforth) and throughout their reproductive years ( 1 ). Most recent estimates suggest a prevalence between 5% and 26% ( 2 ). However, it seems to have significant differences between developing and developed countries ( 3 ). A significant portion of women sufferers has a detrimental impact on their life trajectory, with reduced quality of life, depressive symptoms, anxiety ( 4 ), decreased workplace productivity ( 5 ), and diminished sexual satisfaction ( 6 ). Additionally, it is associated with a significant economic burden on women's lives and the community as a whole ( 7 , 8 )⁠. While it is possible to alleviate the symptoms of patients to a great extent, and even suppress them, recurrence frequently occurs over time. This is a source of dissatisfaction not only for the patients themselves but also for the healthcare professionals who care for them. Treatment often is not curative, and personalized care within a multidisciplinary perspective is very important ( 9 )⁠. The International Association for the Study of Pain (IASP) defines chronic pelvic pain as " chronic or persistent pain perceived in structures related to the pelvis of either men or women. It is often associated with negative cognitive, behavioral, sexual, and emotional consequences and with symptoms suggestive of lower urinary tract, sexual, bowel, pelvic floor, or gynecological dysfunction ". In this definition, continuous or recurrent pain such as dysmenorrhea and dyspareunia are also included ( https://www.iasp-pain.org/publications/free-ebooks/classification-of-chronic-pain-second-edition-revised/ , accessed in 10/01/2024). The ReVITALize, an initiative led by the Royal College of Obstetricians and Gynaecologists (RCOG), proposes a very similar definition ( https://www.acog.org/practice-management/health-it-and-clinical-informatics/revitalize-gynecology-data-definitions , accessed in 10/01/2024). Dysmenorrhoea is the most common cause of cyclic CPP. The prevalence varies between 50% and 75%, with approximately 20% of women reporting severe associated pain ( 10 ). It is defined as a painful menstrual cycle with a cramping sensation in the lower abdomen immediately before or during the menstruation period. Usually, it is classified as primary or secondary dysmenorrhoea. Each of them has its own characteristics. Primary dysmenorrhoea typically begins near menarche, as soon as ovulatory cycles are established, and is not associated with any obvious pelvic pathology. Secondary dysmenorrhoea typically starts some time after menarche and is associated with an underlying medical condition, appearing even in a woman's fourth or fifth decade of life. Primary dysmenorrhoea has become a progressively growing area of interest in recent years ( 11 ). Firstly, because it has a significant impact on the lives of young women ( 12 ), being associated with lower quality of life ( 13 ), worse psychological well-being ( 14 ), poorer sleep quality ( 15 ), inattention and problems with hyperactivity and impulsivity ( 16 ), and lower frequency and academic performance ( 17 ). Secondly, because the literature has shown evidence that dysmenorrhoea may be a general risk factor for future chronic pain, both pelvic and extra-pelvic ( 18 , 19 ), as well as other conditions such as ischemic heart disease ( 20 ). Painful sexual intercourse, or dyspareunia, is also a common and overlooked health issue among women. It has been reported by approximately 8–26% of sexually active women ( 3 , 21 ), of whom one-quarter experienced symptoms very often or always for ≥ 6 months, usually linked to poorer sexual, physical, relational, and mental health ( 22 ), including depression, anxiety, low self-esteem, negative body image ( 23 ), and symptom hypervigilance ( 24 ). A precise and widely accepted classification of the condition is lacking, which results in a poorly understood pathophysiology. Generally, it can also be divided into superficial and deep dyspareunia depending on the depth within the vagina where it is perceived ( 25 ). With regard to the cause, it may be associated with specific pathologies such as endometriosis ( 26 ), but often does not appear to be clearly linked to a disease, but rather associated with conditions such as sexual abuse, fear of physical abuse ( 27 ), or even partners' cognitive responses ( 28 ). Over the past two decades, the number of publications considering the topic has significantly increased. However, data are still scarce for some populations, especially those with unique social and cultural values. Despite the available evidence suggesting that the epidemiology, perception, tolerance and treatment of pain are influenced by multiple biological, psychological, emotional, psychosocial and cognitive factors, as well as those related to racial, cultural and ethnic origin ( 29 , 30 ), there are still gaps in knowledge and understanding on this subject in indigenous population whose number in the world is estimated to be around 370–500 million people. While this number constitutes about 5–6% of the global population, it corresponds to 15% of those living in extreme poverty and has a life expectancy roughly 20 years lower than the non-indigenous population ( 31 ). There is also a higher incidence of diseases such as diabetes, cancer, mental disorders, tuberculosis among indigenous populations ( 32 ). Access to healthcare services has been one of the most significant social determinants in this population. They face various barriers, including living in remote areas, having limited financial resources, and having distinct cultural values ( 33 ). And throughout history, they have suffered deep discrimination based on ethnicity, poverty, and language compared to their non-indigenous counterparts, possibly stemming from a history of colonization, discrimination, marginalization, subjugation, and dispossession. This vulnerability became even more evident during the COVID-19 pandemic ( 34 ). A high prevalence of chronic pain has been reported in the indigenous population ( 35 ), but there is, in fact, limited evidence of the true impact of this condition on the health of this population ( 36 ). Similarly, pain management has generally been guided by their own cultural values ( 37 ), although these findings may be described in more details in the near future ( 38 ). This same aspect of vulnerability has been observed in Ecuador among the Kichwa population, particularly in young women of reproductive age ( 39 ). Considering the specificities of indigenous women, the wide global distribution of chronic pelvic pain, and its negative impact on people's quality of life, this study aims to identify the prevalence of chronic pelvic pain and describe the associated factors in this population. Materials and methods Study design A cross-sectional study was carried out that included women of reproductive age between 14 and 49 years of age, between April 2022 and March 2023 in Otavalo, Ecuador, which has a population largely made up of the indigenous group. The study was approved by the Ethics Committee for Research in Human Beings of the Central University of Ecuador. The human research parameters of the Declaration of Helsinki were met, including the signing of the respective informed consent or assent, which were applied in Kichwa language, respecting the culture of the community and allowing a better understanding of the text. Women of reproductive age between 14 and 49 years old at the date of the interview, residing in the Otavalo canton, were included. Pregnant and lactating women were not eligible to participate. Interviewer training and data management Four interviewers who were not linked to the municipality's health care programs were trained. The interviewers were trained and supervised in-person and coordinated by the researcher responsible for the study. Doubts were resolved through virtual meetings through the web or in-person when necessary. The collection of information was carried out with the assistance of trained bilingual interviewers (Kichwa and Spanish), belonging to the community, knowledgeable about its habits and values. Additionally, the interviewers were trained to be able to resolve any doubts that the women interviewed may have. Before the start of the field work, a pre-test of the questionnaire was carried out through an interview with 50 women living in the area on questions selected by random draw. All forms were transferred to a Research Electronic Data Capture (REDCap) electronic database ( 40 ) and confirmed in a second analysis by a second researcher independent of the interviewer. Research location The study was carried out in the Otavalo canton, belonging to the Imbabura province, located in the northern inter-Andean region of Ecuador. According to the 2022 census, it had a population of 114,303 inhabitants, of which 51.9% (59,288) were women, with an estimated percentage of women of reproductive age (between 14 and 49 years) of 48.4% of the total of women, the majority of whom (97.5%) are indigenous and mestizos ( https://citypopulation.de/en/ecuador/admin/imbabura/1004__otavalo/ , accessed in 10/01/2024). Data source and measurement methods The information was obtained through an interview conducted in a confidential and private environment, during preventive medicine appointments. The choice of the participant was made through a systematic sampling proportional to the number of residences in the parish and the number of women estimated by the size of the sample (Supplementary Table 1). Data collection was facilitated by and conducted in compliance with the World Endometriosis Research Foundation Endometriosis Phenome and Biobanking Harmonisation Project (WERF EPHect), whose aim is "to enable large-scale, cross-centre, epidemiologically robust research into the causes of endometriosis, novel diagnostic methods, and better treatments through the development of an international consensus on: standardized detailed clinical and personal phenotyping (phenome) data to be collected from women with endometriosis and controls, to improve patient and disease characterisation; and standard operating procedures (SOPs) for banking of biological samples from women with endometriosis and controls, with respect to collection, transport, processing, and long-term storage" ( 41 – 44 ). We used the Spanish version, for which the respective access was obtained. Semantic adaptations were validated through a preliminary test. Study data were collected and managed using REDCap electronic data capture tools hosted at Ribeirão Preto Medical School of the University of São Paulo, Brazil [ https://redcap.fmrp.usp.br/ ]. REDCap (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies, providing 1) an intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for data integration and interoperability with external sources ( 45 , 46 ). Sample size The sample calculation, according to the study design, was based on the following expression \(n=\frac{{{Z}_{\alpha /2}^{2} (1-p)}_{}^{}}{{{\epsilon }_{r}^{2} P}_{}^{}}\) \(\) where \({Z}_{\alpha /2}\) represents the critical value that corresponds to the desired degree of confidence, \(\rho\) represents the prevalence of pelvic pain, and \(\epsilon\) represents the relative margin of error of the estimation. A systematic sampling was carried out, with a final calculation of at least 2401 women of reproductive age, between 14 and 49 years old, who reside in the Otavalo canton. Bias and minimization methods Potential biases were associated with data collection, data transfer to the database, and data analysis. To minimize them, we proceeded as follows: 5% of the women in each parish were re-interviewed so that the accuracy of the information provided could be verified. Any disagreement was confirmed with the participant and the information confirmed in the last interview was considered. All forms were transferred to the REDCap and confirmed in a second analysis by a second researcher independent of the interviewer. Data analysis was performed by one of the researchers without access to the interviews and by a statistician with no prior knowledge of the clinical data or characterization data of the participants. Primary outcomes and characterisation features The primary outcomes were: primary dysmenorrhea, non-cyclic pelvic pain, and persistent dyspareunia. For evaluating this last, we only considered women who had previously had sexual intercourse. We consider the following features as independent variables: age, education level (higher: post-secondary education, or certain college or professional schools, university, graduate education; intermediate: secondary/junior education or bachelor's degree; minimal or none: primary education or none), body mass index (BMI), obesity (BMI > 30), exercise (minimum of 2h per week), body type (pear, straight, hourglass, apple), pain catastrophizing scale ( 47 ), ethnicity (unmixed or mixed), hormone usage, menstrual irregularity on the last 3 months, duration of menstrual flow, volume of bleeding, length of the menstrual cycle, coitarche, previous pregnancy, subfertility, age of the first pregnancy, pregnancy while teenage, number of pregnancies, cesarean section, previous pregnancy loss (spontaneous abortion, stillbirth, induced abortion, ectopic pregnancy), alcoholism according alcohol misuse concept ( https://www.nhs.uk/conditions/alcohol-misuse/ ) , previous or current smoking, anxiety, depression, diabetes, hypertension, polycystic ovary syndrome, intestinal symptoms, urinary symptoms, migraine, endometriosis, previous abdominal or pelvic surgery, familial history of pelvic pain, other pains (low back, joint, ovulation, legs, urinating, defecating). Statistical methods All exploratory and statistical analyzes were implemented in Python ( 48 ) using the Colab platform ( 49 ), which is a hosted Jupyter Notebook service that requires no setup to use and provides free access to computing resources, including GPUs and TPUs. Statsmodels, SciPy, and Scikit-learn were the packages utilized. Initially, an exploratory data analysis was carried out, considering the measures of central position (mean and median) and dispersion (standard deviation and range). For the qualitative variables, the absolute and relative frequencies were estimated. Student's t test was used for univariate analyses to compare means between the binary outcomes: dysmenorrhea, non-cyclic pelvic pain, and dyspareunia. Fisher's exact test or Chi-square test for qualitative variables was used when appropriate. For analyzing dyspareunia, only women previously ever sexually active were included. Before conducting multivariate analysis, a correlogram was created using bivariate Spearman correlation to assess monotonic relationships (whether linear or not) between each pair of variables. Those with a correlation coefficient greater than 0.60 were either avoided or cautiously considered. To determine which variables were associated with each of the results, log-binomial regression models were constructed. We initially employed three different strategies for variable selection, in addition to the background acknowledgment: including variables with differences between groups in univariate analysis (p < 0.20), forward and backward stepwise selection. The model that best fit our data was the full model followed by augmented backward elimination. Ultimately, all models were adjusted by age, race, and hormone usage. For the choice of the best of them, the following principles were considered: Log-likelihood, Bayesian (BIC), and Akaike information criterion (AIC). We presented the results using odds ratio and confidence interval. For dysmenorrhoea, we have also presented a prevalence ratio, because of its prevalence in the population. Results The final sample included 2,429 women of childbearing age between 14 and 49 years of age residing in Otavalo. The prevalence of primary dysmenorrhea, non-cyclic pelvic pain, and dyspareunia was, respectively, 26.6%, 8.9%, and 3.9%. Table 1 shows the characterization of the interviewed participants and the results of the univariate analysis. Table 1 Characteristics of the interviewed participants according to the primary outcomes. Features Primary dysmenorrhoea 26.6% (647/2429) Non-cyclic pelvic pain 8.9% (216/2429) Dyspareunia 3.9% (80/2061) Yes No p-value Yes No p-value Yes No p-value Age (years), mean ± sd 24.5 ± 8.3 28.6 ± 9.3 < 0.001 30.0 ± 9.4 27.3 ± 9.2 < 0.001 31.5 ± 8.1 28.6 ± 9.1 0.006 Education level, % (n) minimum 12.8 (83/647) 25.6 (456/1782) < 0.001 25.9 (56/216) 21.8 (483/2213) 0.112 17.5 (14/80) 24.7 (510/2061) 0.201 medium 66.5 (430/647) 57.5 (1025/1782) -- 53.2 (115/216) 60.6 (1340/2213) -- 57.5 (46/80) 56.4 (1163/2061) -- high 20.7 (134/647) 16.9 (301/1782) -- 20.8 (45/216) 17.6 (390/2213) -- 25.0 (20/80) 18.8 (388/2061) -- BMI (Kg.m -2 ), mean ± sd 24.7 ± 4.8 25.3 ± 4.7 0.007 26.3 ± 5.4 25.0 ± 4.7 30), % (n) 8.7 (56/647) 13.1 (234/1782) 0.002 18.5 (40/216) 11.3 (250/2213) 0.003 17.5 (14/80) 13.1 (271/2061) 0.243 Exercise, % (n) 70.6 (457/647) 59.9 (1067/1782) < 0.001 16.2 (129/216) 63.0 (1395/2213) 0.339 72.5 (58/80) 60.9 (1255/2061) 0.046 Body type, % (n) pear 29.2 (189/647) 53.7 (957/1782) < 0.001 31.5 (68/216) 48.7 (1078/2213) < 0.001 28.7 (23/80) 48.3 (995/2061) < 0.001 straight 23.8 (154/647) 18.1 (322/1782) -- 26.4 (57/216) 18.9 (419/2213) -- 18.8 (15/80) 19.4 (399/2061) -- hourglass 30.3 (196/647) 11.8 (210/1782) -- 23.1 (50/216) 16.1 (356/2213) -- 37.5 (30/80) 14.8 (306/2061) -- apple 16.7 (108/647) 16.4 (293/1782) -- 19.0 (41/216) 16.3 (360/2213) -- 15.0 (12/80) 17.5 (361/2061) -- Catastrophizing, mean ± sd 10.8 ± 11.1 7.3 ± 9.0 < 0.001 11.4 ± 12.1 7.9 ± 9.4 < 0.001 13.8 ± 12.4 8.0 ± 9.6 < 0.001 Unmixed ethnicity % (n) 40.2 (260/647) 49.0 (873/1782) < 0.001 42.1 (91/216) 47.1 (1042/2213) 0.175 37.5 (30/80) 45.9 (946/2061) 0.169 Menarche (years), mean ± sd 13.3 ± 1.4 13.1 ± 1.5 0.002 12.9 ± 1.4 13.2 ± 1.5 0.023 13.0 ± 1.4 13.1 ± 1.5 0.484 Hormone usage, % (n) 3.4 (22/647) 6.2 (111/1782) 0.006 5.6 (12/216) 5.5 (121/2213) 0.876 3.8 (3/80) 6.3 (130/2061) 0.480 Menstrual irregularity, % (n) 24.1 (156/647) 25.5 (454/1782) 0.525 30.6 (66/216) 24.6 (544/2213) 0.059 33.8 (27/80) 25.3 (521/2061) 0.091 Menstrual flow (days), mean ± sd 4.9 ± 1.9 4.7 ± 1.9 0.006 5.2 ± 2.9 4.7 ± 1.7 < 0.001 4.9 ± 2.1 4.7 ± 1.9 0.360 Prolonged menstrual bleeding, % (n) 1.9 (12/647) 1.7 (30/1782) 0.728 4.2 (9/216) 1.5 (33/2213) 0.010 3.8 (3/80) 1.8 (38/2061) 0.196 Average bleeding, % (n) spotting 26.6 (172/647) 15.5 (276/1782) < 0.001 23.6 (51/216) 17.9 (397/2213) 0.015 27.5 (22/80) 16.9 (349/2061) 0.050 light 49.8 (322/647) 59.0 (1052/1782) -- 46.3 (100/216) 57.6 (1274/2213) -- 43.8 (35/80) 57.4 (1184/2061) -- moderate 21.0 (136/647) 23.6 (420/1782) -- 27.3 (59/216) 22.5 (497/2213) -- 26.2 (21/80) 23.4 (482/2061) -- heavy 2.6 (17/647) 1.9 (34/1782) -- 2.8 (6/216) 2.0 (45/2213) -- 2.5 (2/80) 2.2 (46/2061) -- Menstrual cycle length, % (n) normal 88.4 (572/647) 85.0 (1515/1782) < 0.001 81.9 (177/216) 86.3 (1910/2213) 0.195 83.8 (67/80) 85.5 (1763/2061) 0.891 38 days 6.6 (43/647) 11.8 (211/1782) -- 13.0 (28/216) 10.2 (226/2213) -- 12.5 (10/80) 10.8 (223/2061) -- Ever sexually active, % (n) 85.0 (550/647) 89.3 (1591/1782) 0.006 95.4 (206/216) 87.4 (1935/2213) < 0.001 100.0 (80/80) 100.0 (2061/2061) -- Previous pregnancy, % (n) 41.6 (269/647) 67.7 (1206/1782) < 0.001 72.2 (156/216) 59.6 (1319/2213) < 0.001 78.8 (63/80) 68.2 (1406/2061) 0.049 Subfertility, % (n) 1.4 (9/647) 0.6 (11/1782) 0.075 2.3 (5/216) 0.7 (15/2213) 0.027 5.0 (4/80) 0.8 (16/2061) 0.006 Age of first pregnancy (years), mean ± sd 20.3 ± 3.8 20.1 ± 4.1 0.459 19.9 ± 4.0 20.2 ± 4.0 0.329 19.8 ± 3.5 20.2 ± 4.1 0.434 Teenage pregnancy, % (n) 19.8 (128/647) 35.4 (631/1782) < 0.001 38.4 (83/216) 30.5 (676/2213) 0.021 38.8 (31/80) 35.1 (723/2061) 0.551 Number of pregnancies, median (Q1-Q3) 0 (0 to 1) 1 (0 to 2) < 0.001 2 (0 to 3) 1 (0 to 2) < 0.001 2 (1 to 3) 1 (0 to 2) 0.025 Cesarean section, % (n) 6.2 (40/647) 7.6 (136/1782) 0.250 12.0 (26/216) 6.8 (150/2213) 0.008 8.8 (7/80) 8.1 (167/2061) 0.834 Cesarean section, median (Q1-Q3) 0 (0 to 0) 0 (0 to 0) 0.372 0 (0 to 0) 0 (0 to 0) 0.002 0 (0 to 0) 0 (0 to 0) 0.955 Previous pregnancy loss, % (n) 3.1 (20/647) 3.6 (65/1782) 0.617 7.4 (16/216) 3.1 (69/2213) 0.003 13.8 (11/80) 3.5 (73/2061) < 0.001 Pregnancy loss, median (Q1-Q3) 0 (0 to 0) 0 (0 to 0) 0.723 0 (0 to 0) 0 (0 to 0) 0.001 0 (0 to 0) 0 (0 to 0) < 0.001 Alcoholism, % (n) 0.8 (5/647) 0.7 (12/1782) 0.786 0.5 (1/216) 0.7 (16/2213) 1.000 0.0 (0/80) 0.8 (17/2061) -- Smoking, % (n) 0.5 (3/647) 0.4 (7/1782) 0.732 0.5 (1/216) 0.4 (9/2213) 0.607 1.2 (1/80) 0.4 (9/2061) 0.317 Anxiety, % (n) 0.6 (4/647) 0.4 (8/1782) 0.532 0.9 (2/216) 0.5 (10/2213) 0.290 0.0 (0/80) 0.5 (11/2061) -- Depression, % (n) 0.6 (4/647) 1.0 (17/1782) 0.620 0.5 (1/216) 0.9 (20/2213) 1.000 1.2 (1/80) 0.9 (18/2061) 0.516 Diabetes, % (n) 0.6 (4/647) 0.5 (9/1782) 0.755 0.5 (1/216) 0.5 (12/2213) 1.000 1.2 (1/80) 0.6 (12/2061) 0.391 Hypertension, % (n) 1.4 (9/647) 0.7 (12/1782) 0.133 1.4 (3/216) 0.8 (18/2213) 0.426 0.0 (0/80) 1.0 (21/2061) -- Polycystic ovary syndrome, % (n) 1.9 (12/647) 2.0 (36/1782) 0.87 4.2 (9/216) 1.8 (39/2213) 0.034 2.5 (2/80) 2.1 (44/2061) 0.689 Intestinal symptoms, % (n) 51.5 (333/647) 29.6 (528/1782) < 0.001 44.0 (95/216) 34.6 (766/2213) 0.007 51.2 (41/80) 34.4 (709/2061) 0.003 diarrhea 2.6 (17/647) 3.1 (56/1782) 0.592 6.0 (13/216) 2.7 (60/2213) 0.012 2.5 (2/80) 3.2 (65/2061) 1.000 constipation 4.2 (27/647) 3.9 (69/1782) 0.725 5.1 (11/216) 3.8 (85/2213) 0.359 7.5 (6/80) 3.9 (80/2061) 0.134 Urinary symptoms, % (n) 35.1 (227/647) 27.8 (495/1782) 0.001 44.0 (95/216) 28.3 (627/2213) < 0.001 66.2 (53/80) 29.3 (604/2061) < 0.001 Migraine, % (n) 1.1 (7/647) 1.1 (19/1782) 1.000 1.9 (4/216) 1.0 (22/2213) 0.283 1.2 (1/80) 1.1 (23/2061) 0.601 Endometriosis, % (n) 0.3 (2/647) 0.2 (4/1782) 0.66 0.9 (2/216) 0.2 (4/2213) 0.093 0.0 (0/80) 0.2 (5/2061) -- Previous abdominal/pelvic surgery, % (n) 17.8 (115/647) 23.3 (416/1782) 0.003 31.5 (68/216) 20.9 (463/2213) 0.001 36.2 (29/80) 23.6 (486/2061) 0.016 Primary dysmenorrhoea, % (n) -- -- -- 41.7 (90/216) 25.2 (557/2213) < 0.001 53.8 (43/80) 24.6 (507/2061) < 0.001 Non-cyclic pelvic pain, % (n) 13.9 (90/647) 7.1 (126/1782) < 0.001 -- -- -- 30.0 (24/80) 8.8 (182/2061) < 0.001 Dyspareunia, % (n) 6.6 (43/647) 2.1 (37/1782) < 0.001 11.1 (24/216) 2.5 (56/2213) < 0.001 -- -- -- Familial history of pelvic pain, % (n) 6.6 (43/647) 6.8 (121/1782) 1.000 10.6 (23/216) 6.4 (141/2213) 0.022 10.0 (8/80) 6.4 (131/2061) 0.240 Other pains, % (n) 8.0 (52/647) 12.7 (227/1782) 0.001 16.7 (36/216) 11.0 (243/2213) 0.018 5.0 (4/80) 12.8 (264/2061) 0.038 Notes: Q1 = first quartile (25%), Q3 = third quartile (75%). Among the women with primary dysmenorrhoea, 13.9% and 6.6% had, respectively, non-cyclic pelvic pain and dyspareunia. Among those with non-cyclic pelvic pain, 41.7% and 11.1% had, respectively, primary dysmenorrhoea and dyspareunia. Finally, among those with dyspareunia, 53.8% and 30.0% had, respectively, primary dysmenorrhoea and non-cyclic pelvic pain. Non-cyclical pelvic pain, dyspareunia, and intestinal symptoms were positively associated with primary dysmenorrhea. We observed the same direction of the association with hypertension. On the other hand, indigenous people without miscegenation (unmixed), long cycles, previous pregnancy (and if in adolescence), use of contraceptives and pear body shape were negatively associated with this outcome. Table 2 shows these results in detail. Table 2 Features independently associated with primary dysmenorrhoea in the population predominantly indigenous of Otavalo, Ecuador. Features OR (PR) CI lower CI higher adjusted p-value Hypertension 4.5 (2.1) 1.67 (0.87) 12.25 (4.88) .003 (0.154) Dyspareunia 3.1 (3.2) 1.83 (2.08) 5.14 (4.92) < .001 (< .001) Non-cyclic pelvic pain 2.0 (2.0) 1.40 (1.52) 2.72 (2.54) < .001 (< .001) Intestinal symptoms 1.8 (1.7) 1.47 (1.57) 2.22 (1.93) < .001 (< .001) Menarche 1.1 1.03 1.09 < .001 Catastrophizing 1.0 1.03 1.05 < .001 Age 1.0 0.93 0.96 < .001 Teenage pregnancy 0.7 (0.6) 0.54 (0.47) 0.97 (0.66) .030 (< .001) Unmixed ancestry 0.7 (0.8) 0.56 (0.74) 0.84 (0.91) 38 days 0.7 (0.6) 0.47 (0.41) 0.98 (0.77) .041 (0.001) Previous pregnancy 0.7 (0.6) 0.49 (0.56) 0.93 (0.68) .018 (< .001) Hormone usage 0.6 (0.6) 0.34 (0.35) 0.95 (0.85) .030 (0.013) Pear body shape 0.3 (0.5) 0.28 (0.48) 0.42 (0.62) < .001 (< .001) Notes: OR = odds ratio; PR = prevalence ratio; CI = confidence interval. Dyspareunia, pain at ovulation, primary dysmenorrhoea, and urinary symptoms were positively associated with non-cyclic pelvic pain. On the other hand, late menarche, exercise and pear body shape were negatively associated with this outcome. Table 3 shows these results in detail. Table 3 Factors independently associated with non-cyclical pelvic pain in the population predominantly indigenous of Otavalo, Ecuador. Features OR CI lower CI higher adjusted p-value Dyspareunia 2.7 1.56 4.622 < .001 Pain at ovulation 2.2 1.03 4.80 .042 Muscle/joint pain 2.2 1.00 4.90 .050 Primary dysmenorrhoea 2.0 1.43 2.77 < .001 Previous pregnancy 1.6 0.97 2.49 .069 Urinary symptoms 1.5 1.11 2.04 .009 Teenage pregnancy 1.1 0.76 1.52 .690 Catastrophizing 1.0 1.01 1.03 .003 Age 1.0 0.99 1.03 .268 Hormone usage 1.0 0.50 1.82 .888 Unmixed ancestry 0.9 0.68 1.22 .520 Menarche 0.8 0.75 0.82 < .001 Exercise 0.7 0.55 0.98 .039 Pear body shape 0.5 0.38 0.72 < .001 Notes: OR = odds ratio; CI = confidence interval.Notes: OR = odds ratio; CI = confidence interval. Urinary symptoms, pregnancy loss, primary dysmenorrhoea, and non-cyclic pelvic pain were positively associated with dyspareunia. On the other hand, late menarche, hormone usage and pear body shape were negatively associated with this outcome. Table 4 shows these results in detail. Table 4 Factors independently associated with dyspareunia in the population predominantly indigenous of Otavalo, Ecuador. Features OR CI lower CI higher adjusted p-value Urinary symptoms 3.3 2.02 5.28 < .001 Pregnancy loss 3.2 1.51 6.87 .002 Primary dysmenorrhoea 2.8 1.68 4.63 < .001 Non-cyclic pelvic pain 2.1 1.22 3.64 .008 Catastrophizing 1.0 1.00 1.04 .030 Age 1.0 0.99 1.04 .317 Menarche 0.7 0.64 00.74 < .001 Hormone usage 0.6 0.19 2.10 .447 Pear body shape 0.6 0.34 0.95 .030 Supplementary table 2 shows the full logistic regression models for each one of the primary outcomes. Discussion The results of this study revealed that within this population, more than a quarter of women, up to two years after menarche, experienced primary dysmenorrhea, approximately 10% reported non-cyclical pelvic pain, and almost 4% suffered from dyspareunia. All chronic pain conditions were independently associated with one another. Considering that primary dysmenorrhea occurs temporally before the others, although it cannot be definitively confirmed in this study's design, it is plausible to hypothesize about a potential causal or facilitating effect of primary dysmenorrhea on other chronic pains, a notion already discussed in the literature ( 18 ). The prevalence of primary dysmenorrhea identified falls within the range reported worldwide, but interestingly, it is nearly double the rate we previously identified in the non-indigenous population living in an urban area in the capital of Ecuador ( 50 ). In that population, the observed rate of hormonal contraceptive use was considered low, at around 25%, while in this indigenous population, the usage rate is only 5.5%, ranging from 3% among those with primary dysmenorrhea to 6% among those without this condition. We believe that this difference can be due to cultural and religious reasons. Considering that these medications are associated with a significant improvement in dysmenorrhea symptoms ( 51 ), the low frequency of usage, in our view, may be a crucial factor contributing to the increased reporting of menstrual pain by these women. On the other hand, there is a lower prevalence of primary dysmenorrhea among indigenous women without a history of interbreeding in the family. Although our study cannot deeply discuss this difference, we can propose at least two hypotheses. The first is that there may be a specific racial and/or genomic characteristic of this population. However, this is purely speculative, as genomic data on indigenous populations are still limited ( 52 ). The second hypothesis we consider is socio-cultural significance. Menstruation in the indigenous community that maintains its deep-rooted beliefs is often characterized as "private women's business." It is sometimes seen as a sign of impurity. Stigma, secrecy, and shame associated with discussing menstruation can reduce symptom reporting among indigenous women with more conservative cultural values and taboos ( 53 ). This may make dysmenorrhea less likely to be reported in this group. Another point that captures our attention is the association of primary dysmenorrhea with systemic arterial hypertension, although the confidence interval of the prevalence ratio has included the null value. The link with adverse cardiovascular events has already been identified by other researchers ( 20 , 54 ), and this may perhaps be attributed to a systemic inflammatory status observed in these women, which, however, requires more detailed evaluation ( 55 ). Studies have shown an association between cardiovascular events not only with a higher amount of body fat but also with its distribution ( 56 , 57 ). The "pear" shape, with a more homogeneous distribution of fat tissue primarily on the hips, has been associated with this ( 58 ). In parallel, the relationship between BMI and dysmenorrhea is controversial. Longitudinal studies with large cohorts have shown evidence of a U-shaped relationship between these conditions ( 59 ), suggesting a more significant connection with body constitution than weight itself. Finally, a large British cohort has demonstrated significant associations between body shape and inflammatory and metabolic biomarkers ( 60 ). In our study, the prevalence of non-cyclical pelvic pain was similar to that observed in Latin American countries such as Brazil, where it's close to 10% ( 61 , 62 ), and in urban communities of Ecuador, where it is 8.9% ( 50 ). It was positively associated with various other painful conditions, reinforcing the link between the condition and nociplastia ( 63 ), and perhaps reflecting the clinical expression of central sensitization that commonly occurs in this group of patients ( 64 ). These findings are also supported by the apparent protective effect identified in the practice of physical exercise and non-cyclical pelvic pain. Recent literature has shown that physical exercise can strengthen the modulation promoted by the central nervous system ( 65 , 66 ), reducing pain sensitization ( 67 ), and there is a direct inverse relationship between measures of physical activity and chronic pain levels ( 68 ). The observed prevalence of dyspareunia in this population was significantly lower than that previously reported in the urban population of Ecuador ( 50 ). The exact reason for this difference remains uncertain. Regardless of the low prevalence of dyspareunia in the studied population, what is equally remarkable is the fact that virtually all the women who reported pain during sexual intercourse did not discontinue intercourse for that reason. Taken together, we believe that this finding could be attributed to the patriarchal structure of indigenous society, where "male" attitudes prevail, exacerbating the social and biological vulnerability to which indigenous women are historically subjected. They have often been tied to familial and communal roles, hindering their ability to express their desires and preferences ( http://repositorio.utn.edu.ec/handle/123456789/6165 ) ( 69 ). Despite the absence of affirmative responses regarding the presence of intrauterine infections, 66.3% of the women reported frequent and concurrent urinary symptoms, which could potentially be linked to infectious processes secondary to Neisseria gonorrhoeae and Chlamydia trachomatis , causative bacterial agents of urethritis and pelvic inflammatory disease ( 70 ). An underdiagnosis of pelvic inflammatory disease might also explain the association with previous pregnancy losses, as there is a connection between intrauterine infection and abortions ( 71 ). The findings concerning urinary discomfort contrast with those related to intrauterine infections, which exhibit a notably low prevalence. We did not observe any association between pain and psychological symptoms, smoking, alcoholism, or violence, as we had observed in other studies conducted in Ecuador and Brazil. One possible justification for this could be the low reported prevalence of these conditions in this specific community, which is close to or less than 1% for each. Strengthens and Limitations Our study's strength lies in the inclusion of a large and representative cohort of the Kichwa indigenous population. Furthermore, it was conducted with meticulous methodological rigor, granting it robust inferential power. However, there are certain limitations associated with both the characteristics of the population and the analysis itself. Ethnic self-identification, defined as "the right of every person to freely and voluntarily decide whether or not to belong to a nationality or people" may have also altered the population distribution of the women studied, as each individual can choose to identify themselves accurately or erroneously with a particular nationality or people, even if they do not genuinely belong. The results of this study may also be subject to biases primarily related to the indigenous and Andean worldviews. These worldviews, shaped by beliefs, values, and knowledge systems, play a pivotal role in the social life of these human groups and define their cultural identity. It is crucial to emphasize the social and biological vulnerability of indigenous women, who unfortunately remain entrenched in economic, social, and cultural inequalities, where patriarchal social structures persist. Even though all our interviewers were women representing the local indigenous community, which facilitated the feasibility of the research, some topics are still considered taboo, particularly concerning sexual activity, illicit substance use, tobacco, alcohol, psychological symptoms, and violence. We believe this might have influenced the identification of independently associated factors and, in some way, hindered the formulation of education and healthcare policies tailored to indigenous women, especially with regard to dyspareunia. Regarding the analysis, certain aspects are inherent to logistic regression models. For our study, the backward stepwise approach proved to be the most suitable model for the data. It may be influenced by the relationship between the number of candidate variables and the sample size, but this was not a concern given our relatively large cohort. Including all variables would add significant complexity and could potentially compromise the model's generalizability. To mitigate this, we conducted a correlogram and aimed to avoid including highly correlated variables in the simulated models. Nonetheless, this was done judiciously to prevent the premature exclusion of relevant variables. It is also challenging to ensure that all potential combinations of predictors have been tested. The significance of the p-value does not always equate to clinical relevance, making the interpretation of the effect (in this case, odds ratio) crucial. Moreover, it is impossible to establish a causal relationship between the outcomes and associated variables, which is a limitation inherent to cross-sectional studies. On the other hand, backward elimination allows for the advantage of initially considering the effects of all variables simultaneously, which is especially important in cases of potential collinearity, as mentioned earlier. Other factors that balance its limitations include its ease of application, objectivity, reproducibility, interpretability, and the enhanced generalization achieved by reducing the number of predictor variables. Conclusions The prevalence of primary dysmenorrhea and chronic pelvic pain in Kichwa women from Otavalo was notably high, while the frequency of reported dyspareunia was comparatively low, at 26.6%, 8.9%, and 3.9% respectively. There exists a direct correlation between the outcomes of all forms of chronic pelvic pain. We have identified a significant association between primary dysmenorrhoea and conditions related to inflammatory and/or systemic metabolic disorders, warranting special attention. Similarly, a connection was established between non-cyclical pelvic pain and signs/symptoms that could collectively signify central sensitization and nociplastia. Furthermore, dyspareunia was additionally linked to conditions frequently associated with infections, which, in turn, were not reported by the participants. Taking into account the temporal relationship between the conditions, the study suggested a potential causal association between primary dysmenorrhea and the development of dyspareunia and acyclic pelvic pain in the future. We consider welcoming and offering additional care opportunities to these women to be essential. In conclusion, considering that the concepts of health and illness in this population differ from typical Western ideals ( 72 ), public policies should take into account the provision of education and healthcare services in alignment with the values and principles of the local community. Declarations Acknowledgments We acknowledge the Central University of Ecuador and the University of São Paulo for their academic support. We also acknowledge the Coordination for the Improvement of Higher Level Personnel - Brazil (CAPES) and its Academic Excellence Program (PROEX). Finally, we acknowledge the Ministry of Public Health of Ecuador for its support during the execution of the field work. Finally, we acknowledge Paccha Sofía Morales Picuasí, Nina Pakari Ruiz Morales, Mónica Vanessa Pijuango Cotacachi and María Fernanda Saransig Gualsaquí, full representatives of Otavalo Kichwa women, who participated as interviewers in this project. Ethics approval and consent to participate The study was approved by the Ethics Committee for Research in Human Beings of the Central University of Ecuador. All participants and/or their parents or legal representatives gave their written consent prior to being enrolled. The human research parameters of the Declaration of Helsinki were followed. Declaration of interest statement The authors declare no conflict of interest. The authors received no financial support for the research and/or authorship. Funding details This work was supported by the Central University of Ecuador (UCE), the University of São Paulo (USP), the Coordination for the Improvement of Higher Level Personnel - Brazil (CAPES) and its Academic Excellence Program (PROEX), and the Ministry of Public Health of Ecuador. Disclosure statement of competing interests The authors report there are no competing interests to declare. Data availability statement The data that support the findings of this study are available from the corresponding author, [OBPN], upon reasonable request. Author's contributions JAVC: Conception, design, acquisition of data, interpretation of data, drafting the article, final approval. CYLMVR: Conception, interpretation of data, revising the article, final approval. SCM: Analysis and interpretation of data, revising the article, final approval. FJCR: Analysis and interpretation of data, revising the article, final approval. AAN: Interpretation of data, revising the article, final approval. 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Does aerobic exercise effect pain sensitisation in individuals with musculoskeletal pain? A systematic review. BMC Musculoskelet Disord. 2022;23(1):113. Fjeld MK, Årnes AP, Engdahl B, Morseth B, Hopstock LA, Horsch A, et al. Consistent pattern between physical activity measures and chronic pain levels: the Tromsø Study 2015 to 2016. Pain. 2023;164(4):838–47. Segarra J, Argudo MVF, Espinoza E del CP, Crespo B, Brito DAJ, Cisneros MAC, et al. Percepciones sobre la salud sexual y reproductiva de las mujeres indígenas Kichwas y Shuaras. Ecuador, 2016. At: https://www.semanticscholar.org/paper/Percepciones-sobre-la-salud-sexual-y-reproductiva-y-Segarra-Argudo/fbfbb450d9d5186ed361bff2dd64a0c5b2518d60 Darville T. Pelvic Inflammatory Disease Due to Neisseria gonorrhoeae and Chlamydia trachomatis: Immune Evasion Mechanisms and Pathogenic Disease Pathways. J Infect Dis. 2021;224(Suppl 2):S39–46. Patel SV, Baxi RK, Kotecha PV, Mazumdar VS, Mehta KG, Diwanji M. Association between pelvic inflammatory disease and abortions. Indian J Sex Transm Dis AIDS. 2010;31(2):127–8. Bautista-Valarezo E, Duque V, Verdugo Sánchez AE, Dávalos-Batallas V, Michels NRM, Hendrickx K, et al. Towards an indigenous definition of health: an explorative study to understand the indigenous Ecuadorian people's health and illness concepts. Int J Equity Health. 2020;19(1):101. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3903885","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271217233,"identity":"4d192ecc-7f0c-4f4d-83ff-d427634b04d4","order_by":0,"name":"José Antonio Vargas-Costales","email":"","orcid":"","institution":"Universidad Central del Ecuador","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"José","middleName":"Antonio","lastName":"Vargas-Costales","suffix":""},{"id":271217234,"identity":"8281ca4b-bfaf-4b53-9793-a65d8e26bd9d","order_by":1,"name":"Carmen Yolanda de las Mercedes Villa Rosero","email":"","orcid":"","institution":"Universidad Central del Ecuador","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Carmen","middleName":"Yolanda de las Mercedes Villa","lastName":"Rosero","suffix":""},{"id":271217235,"identity":"d718f62b-7bab-4714-b2fb-d2099f15764a","order_by":2,"name":"Suleimy Cristina Mazin","email":"","orcid":"","institution":"Ribeirao Preto Medical School of the University of São Paulo USP","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Suleimy","middleName":"Cristina","lastName":"Mazin","suffix":""},{"id":271217236,"identity":"fc91a313-7968-4484-b91e-dc17df784e5c","order_by":3,"name":"Francisco José Candido-dos-Reis","email":"","orcid":"","institution":"Ribeirao Preto Medical School of the University of São Paulo USP","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"José","lastName":"Candido-dos-Reis","suffix":""},{"id":271217237,"identity":"638e214f-3c60-404d-914a-cd45bcbe0a82","order_by":4,"name":"Antonio Alberto Nogueira","email":"","orcid":"","institution":"Ribeirao Preto Medical School of the University of São Paulo USP","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"Alberto","lastName":"Nogueira","suffix":""},{"id":271217238,"identity":"5524e967-7d6d-4ecd-9761-a227c87bd568","order_by":5,"name":"Julio Cesar Rosa-e-Silva","email":"","orcid":"","institution":"Ribeirao Preto Medical School of the University of São Paulo USP","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Julio","middleName":"Cesar","lastName":"Rosa-e-Silva","suffix":""},{"id":271217239,"identity":"5ea95bcc-82fb-495c-80eb-016a99ee63a6","order_by":6,"name":"Omero Benedicto Poli-Neto","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYFAC5oYDIIofRDA2EKWFEaTFgEGygRQtDCAtBgeI1WLO3th4uKDij5zxjfRnDxh33COsxbLnYMPhGWcMjM1u5JgbMJ4pJqzF4EZiw2HeNoPEbTdy2CQY2xKI1fLPIHHzjPRnpGhpMEjcIJFgRpwWsF94jhkbS5x5Y26QeIYILebszYc/89TIyfG3A0Ps4w5iHIbEZmMgQgO6llEwCkbBKBgF2AAAY0k8c8v8DBcAAAAASUVORK5CYII=","orcid":"","institution":"Ribeirao Preto Medical School of the University of São Paulo USP","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Omero","middleName":"Benedicto","lastName":"Poli-Neto","suffix":""}],"badges":[],"createdAt":"2024-01-27 19:29:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3903885/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3903885/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50809825,"identity":"9eb1357e-4619-4b77-9f7d-ee867091b63a","added_by":"auto","created_at":"2024-02-07 17:32:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":526992,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3903885/v1/91ab5701-848b-4769-b400-12f246ed7279.pdf"},{"id":50809528,"identity":"84168fe0-02ba-415b-b70e-96a4b69d81a5","added_by":"auto","created_at":"2024-02-07 17:24:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":43784,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3903885/v1/889c7aa79f7578eb46d38e9a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence of chronic pelvic pain and associated factors among indigenous women of reproductive age in Ecuador","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic pelvic pain (CPP) is a complex and debilitating medical condition that affects people around the world regardless of gender or age, although it is most frequently reported in people assigned female at birth (women henceforth) and throughout their reproductive years (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Most recent estimates suggest a prevalence between 5% and 26% (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). However, it seems to have significant differences between developing and developed countries (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA significant portion of women sufferers has a detrimental impact on their life trajectory, with reduced quality of life, depressive symptoms, anxiety (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), decreased workplace productivity (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), and diminished sexual satisfaction (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Additionally, it is associated with a significant economic burden on women's lives and the community as a whole (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)⁠. While it is possible to alleviate the symptoms of patients to a great extent, and even suppress them, recurrence frequently occurs over time. This is a source of dissatisfaction not only for the patients themselves but also for the healthcare professionals who care for them. Treatment often is not curative, and personalized care within a multidisciplinary perspective is very important (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)⁠.\u003c/p\u003e \u003cp\u003eThe International Association for the Study of Pain (IASP) defines chronic pelvic pain as \"\u003cem\u003echronic or persistent pain perceived in structures related to the pelvis of either men or women. It is often associated with negative cognitive, behavioral, sexual, and emotional consequences and with symptoms suggestive of lower urinary tract, sexual, bowel, pelvic floor, or gynecological dysfunction\u003c/em\u003e\". In this definition, continuous or recurrent pain such as dysmenorrhea and dyspareunia are also included (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iasp-pain.org/publications/free-ebooks/classification-of-chronic-pain-second-edition-revised/\u003c/span\u003e\u003cspan address=\"https://www.iasp-pain.org/publications/free-ebooks/classification-of-chronic-pain-second-edition-revised/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed in 10/01/2024). The ReVITALize, an initiative led by the Royal College of Obstetricians and Gynaecologists (RCOG), proposes a very similar definition (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.acog.org/practice-management/health-it-and-clinical-informatics/revitalize-gynecology-data-definitions\u003c/span\u003e\u003cspan address=\"https://www.acog.org/practice-management/health-it-and-clinical-informatics/revitalize-gynecology-data-definitions\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed in 10/01/2024).\u003c/p\u003e \u003cp\u003eDysmenorrhoea is the most common cause of cyclic CPP. The prevalence varies between 50% and 75%, with approximately 20% of women reporting severe associated pain (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). It is defined as a painful menstrual cycle with a cramping sensation in the lower abdomen immediately before or during the menstruation period. Usually, it is classified as primary or secondary dysmenorrhoea. Each of them has its own characteristics. Primary dysmenorrhoea typically begins near menarche, as soon as ovulatory cycles are established, and is not associated with any obvious pelvic pathology. Secondary dysmenorrhoea typically starts some time after menarche and is associated with an underlying medical condition, appearing even in a woman's fourth or fifth decade of life. Primary dysmenorrhoea has become a progressively growing area of interest in recent years (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Firstly, because it has a significant impact on the lives of young women (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), being associated with lower quality of life (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), worse psychological well-being (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), poorer sleep quality (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), inattention and problems with hyperactivity and impulsivity (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), and lower frequency and academic performance (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Secondly, because the literature has shown evidence that dysmenorrhoea may be a general risk factor for future chronic pain, both pelvic and extra-pelvic (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), as well as other conditions such as ischemic heart disease (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePainful sexual intercourse, or dyspareunia, is also a common and overlooked health issue among women. It has been reported by approximately 8\u0026ndash;26% of sexually active women (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), of whom one-quarter experienced symptoms very often or always for \u0026ge;\u0026thinsp;6 months, usually linked to poorer sexual, physical, relational, and mental health (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), including depression, anxiety, low self-esteem, negative body image (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), and symptom hypervigilance (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). A precise and widely accepted classification of the condition is lacking, which results in a poorly understood pathophysiology. Generally, it can also be divided into superficial and deep dyspareunia depending on the depth within the vagina where it is perceived (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). With regard to the cause, it may be associated with specific pathologies such as endometriosis (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), but often does not appear to be clearly linked to a disease, but rather associated with conditions such as sexual abuse, fear of physical abuse (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), or even partners' cognitive responses (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOver the past two decades, the number of publications considering the topic has significantly increased. However, data are still scarce for some populations, especially those with unique social and cultural values. Despite the available evidence suggesting that the epidemiology, perception, tolerance and treatment of pain are influenced by multiple biological, psychological, emotional, psychosocial and cognitive factors, as well as those related to racial, cultural and ethnic origin (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), there are still gaps in knowledge and understanding on this subject in indigenous population whose number in the world is estimated to be around 370\u0026ndash;500\u0026nbsp;million people. While this number constitutes about 5\u0026ndash;6% of the global population, it corresponds to 15% of those living in extreme poverty and has a life expectancy roughly 20 years lower than the non-indigenous population (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). There is also a higher incidence of diseases such as diabetes, cancer, mental disorders, tuberculosis among indigenous populations (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Access to healthcare services has been one of the most significant social determinants in this population. They face various barriers, including living in remote areas, having limited financial resources, and having distinct cultural values (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). And throughout history, they have suffered deep discrimination based on ethnicity, poverty, and language compared to their non-indigenous counterparts, possibly stemming from a history of colonization, discrimination, marginalization, subjugation, and dispossession. This vulnerability became even more evident during the COVID-19 pandemic (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). A high prevalence of chronic pain has been reported in the indigenous population (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), but there is, in fact, limited evidence of the true impact of this condition on the health of this population (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Similarly, pain management has generally been guided by their own cultural values (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), although these findings may be described in more details in the near future (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). This same aspect of vulnerability has been observed in Ecuador among the Kichwa population, particularly in young women of reproductive age (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Considering the specificities of indigenous women, the wide global distribution of chronic pelvic pain, and its negative impact on people's quality of life, this study aims to identify the prevalence of chronic pelvic pain and describe the associated factors in this population.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eA cross-sectional study was carried out that included women of reproductive age between 14 and 49 years of age, between April 2022 and March 2023 in Otavalo, Ecuador, which has a population largely made up of the indigenous group. The study was approved by the Ethics Committee for Research in Human Beings of the Central University of Ecuador. The human research parameters of the Declaration of Helsinki were met, including the signing of the respective informed consent or assent, which were applied in Kichwa language, respecting the culture of the community and allowing a better understanding of the text. Women of reproductive age between 14 and 49 years old at the date of the interview, residing in the Otavalo canton, were included. Pregnant and lactating women were not eligible to participate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eInterviewer training and data management\u003c/h2\u003e \u003cp\u003eFour interviewers who were not linked to the municipality's health care programs were trained. The interviewers were trained and supervised in-person and coordinated by the researcher responsible for the study. Doubts were resolved through virtual meetings through the web or in-person when necessary.\u003c/p\u003e \u003cp\u003eThe collection of information was carried out with the assistance of trained bilingual interviewers (Kichwa and Spanish), belonging to the community, knowledgeable about its habits and values. Additionally, the interviewers were trained to be able to resolve any doubts that the women interviewed may have. Before the start of the field work, a pre-test of the questionnaire was carried out through an interview with 50 women living in the area on questions selected by random draw.\u003c/p\u003e \u003cp\u003eAll forms were transferred to a Research Electronic Data Capture (REDCap) electronic database (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) and confirmed in a second analysis by a second researcher independent of the interviewer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eResearch location\u003c/h2\u003e \u003cp\u003eThe study was carried out in the Otavalo canton, belonging to the Imbabura province, located in the northern inter-Andean region of Ecuador. According to the 2022 census, it had a population of 114,303 inhabitants, of which 51.9% (59,288) were women, with an estimated percentage of women of reproductive age (between 14 and 49 years) of 48.4% of the total of women, the majority of whom (97.5%) are indigenous and mestizos (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://citypopulation.de/en/ecuador/admin/imbabura/1004__otavalo/\u003c/span\u003e\u003cspan address=\"https://citypopulation.de/en/ecuador/admin/imbabura/1004__otavalo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed in 10/01/2024).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData source and measurement methods\u003c/h2\u003e \u003cp\u003eThe information was obtained through an interview conducted in a confidential and private environment, during preventive medicine appointments. The choice of the participant was made through a systematic sampling proportional to the number of residences in the parish and the number of women estimated by the size of the sample (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eData collection was facilitated by and conducted in compliance with the World Endometriosis Research Foundation Endometriosis Phenome and Biobanking Harmonisation Project (WERF EPHect), whose aim is \"to enable large-scale, cross-centre, epidemiologically robust research into the causes of endometriosis, novel diagnostic methods, and better treatments through the development of an international consensus on: standardized detailed clinical and personal phenotyping (phenome) data to be collected from women with endometriosis and controls, to improve patient and disease characterisation; and standard operating procedures (SOPs) for banking of biological samples from women with endometriosis and controls, with respect to collection, transport, processing, and long-term storage\" (\u003cspan additionalcitationids=\"CR42 CR43\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). We used the Spanish version, for which the respective access was obtained. Semantic adaptations were validated through a preliminary test.\u003c/p\u003e \u003cp\u003eStudy data were collected and managed using REDCap electronic data capture tools hosted at Ribeir\u0026atilde;o Preto Medical School of the University of S\u0026atilde;o Paulo, Brazil [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://redcap.fmrp.usp.br/\u003c/span\u003e\u003cspan address=\"https://redcap.fmrp.usp.br/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. REDCap (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies, providing 1) an intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for data integration and interoperability with external sources (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSample size\u003c/h2\u003e \u003cp\u003eThe sample calculation, according to the study design, was based on the following expression \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(n=\\frac{{{Z}_{\\alpha /2}^{2} (1-p)}_{}^{}}{{{\\epsilon }_{r}^{2} P}_{}^{}}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\)\u003c/span\u003e\u003c/span\u003e where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{\\alpha /2}\\)\u003c/span\u003e\u003c/span\u003e represents the critical value that corresponds to the desired degree of confidence, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho\\)\u003c/span\u003e\u003c/span\u003e represents the prevalence of pelvic pain, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\epsilon\\)\u003c/span\u003e\u003c/span\u003e represents the relative margin of error of the estimation. A systematic sampling was carried out, with a final calculation of at least 2401 women of reproductive age, between 14 and 49 years old, who reside in the Otavalo canton.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBias and minimization methods\u003c/h2\u003e \u003cp\u003ePotential biases were associated with data collection, data transfer to the database, and data analysis. To minimize them, we proceeded as follows: 5% of the women in each parish were re-interviewed so that the accuracy of the information provided could be verified. Any disagreement was confirmed with the participant and the information confirmed in the last interview was considered. All forms were transferred to the REDCap and confirmed in a second analysis by a second researcher independent of the interviewer. Data analysis was performed by one of the researchers without access to the interviews and by a statistician with no prior knowledge of the clinical data or characterization data of the participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePrimary outcomes and characterisation features\u003c/h2\u003e \u003cp\u003eThe primary outcomes were: primary dysmenorrhea, non-cyclic pelvic pain, and persistent dyspareunia. For evaluating this last, we only considered women who had previously had sexual intercourse.\u003c/p\u003e \u003cp\u003eWe consider the following features as independent variables: age, education level (higher: post-secondary education, or certain college or professional schools, university, graduate education; intermediate: secondary/junior education or bachelor's degree; minimal or none: primary education or none), body mass index (BMI), obesity (BMI\u0026thinsp;\u0026gt;\u0026thinsp;30), exercise (minimum of 2h per week), body type (pear, straight, hourglass, apple), pain catastrophizing scale (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e), ethnicity (unmixed or mixed), hormone usage, menstrual irregularity on the last 3 months, duration of menstrual flow, volume of bleeding, length of the menstrual cycle, coitarche, previous pregnancy, subfertility, age of the first pregnancy, pregnancy while teenage, number of pregnancies, cesarean section, previous pregnancy loss (spontaneous abortion, stillbirth, induced abortion, ectopic pregnancy), alcoholism according alcohol misuse concept (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nhs.uk/conditions/alcohol-misuse/\u003c/span\u003e\u003cspan address=\"https://www.nhs.uk/conditions/alcohol-misuse/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, previous or current smoking, anxiety, depression, diabetes, hypertension, polycystic ovary syndrome, intestinal symptoms, urinary symptoms, migraine, endometriosis, previous abdominal or pelvic surgery, familial history of pelvic pain, other pains (low back, joint, ovulation, legs, urinating, defecating).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical methods\u003c/h2\u003e \u003cp\u003eAll exploratory and statistical analyzes were implemented in Python (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e) using the Colab platform (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), which is a hosted Jupyter Notebook service that requires no setup to use and provides free access to computing resources, including GPUs and TPUs. Statsmodels, SciPy, and Scikit-learn were the packages utilized.\u003c/p\u003e \u003cp\u003eInitially, an exploratory data analysis was carried out, considering the measures of central position (mean and median) and dispersion (standard deviation and range). For the qualitative variables, the absolute and relative frequencies were estimated. Student's t test was used for univariate analyses to compare means between the binary outcomes: dysmenorrhea, non-cyclic pelvic pain, and dyspareunia. Fisher's exact test or Chi-square test for qualitative variables was used when appropriate. For analyzing dyspareunia, only women previously ever sexually active were included.\u003c/p\u003e \u003cp\u003eBefore conducting multivariate analysis, a correlogram was created using bivariate Spearman correlation to assess monotonic relationships (whether linear or not) between each pair of variables. Those with a correlation coefficient greater than 0.60 were either avoided or cautiously considered.\u003c/p\u003e \u003cp\u003eTo determine which variables were associated with each of the results, log-binomial regression models were constructed. We initially employed three different strategies for variable selection, in addition to the background acknowledgment: including variables with differences between groups in univariate analysis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.20), forward and backward stepwise selection. The model that best fit our data was the full model followed by augmented backward elimination. Ultimately, all models were adjusted by age, race, and hormone usage. For the choice of the best of them, the following principles were considered: Log-likelihood, Bayesian (BIC), and Akaike information criterion (AIC). We presented the results using odds ratio and confidence interval. For dysmenorrhoea, we have also presented a prevalence ratio, because of its prevalence in the population.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe final sample included 2,429 women of childbearing age between 14 and 49 years of age residing in Otavalo. The prevalence of primary dysmenorrhea, non-cyclic pelvic pain, and dyspareunia was, respectively, 26.6%, 8.9%, and 3.9%. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the characterization of the interviewed participants and the results of the univariate analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the interviewed participants according to the primary outcomes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFeatures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003ePrimary dysmenorrhoea\u003c/p\u003e \u003cp\u003e26.6% (647/2429)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eNon-cyclic pelvic pain\u003c/p\u003e \u003cp\u003e8.9% (216/2429)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eDyspareunia\u003c/p\u003e \u003cp\u003e3.9% (80/2061)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (years), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eminimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.8 (83/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.6 (456/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.9 (56/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.8 (483/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.5 (14/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.7 (510/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.5 (430/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.5 (1025/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.2 (115/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60.6 (1340/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e57.5 (46/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e56.4 (1163/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.7 (134/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.9 (301/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.8 (45/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.6 (390/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25.0 (20/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.8 (388/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBMI (Kg.m\u003csup\u003e-2\u003c/sup\u003e), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity (BMI\u0026thinsp;\u0026gt;\u0026thinsp;30), % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.7 (56/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.1 (234/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.5 (40/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.3 (250/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.5 (14/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.1 (271/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.6 (457/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.9 (1067/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.2 (129/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63.0 (1395/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72.5 (58/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e60.9 (1255/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody type, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.2 (189/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.7 (957/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.5 (68/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e48.7 (1078/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e28.7 (23/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e48.3 (995/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003estraight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.8 (154/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.1 (322/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.4 (57/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.9 (419/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18.8 (15/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.4 (399/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehourglass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.3 (196/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.8 (210/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.1 (50/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.1 (356/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e37.5 (30/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14.8 (306/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eapple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.7 (108/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.4 (293/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.0 (41/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.3 (360/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.0 (12/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17.5 (361/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCatastrophizing, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmixed ethnicity % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.2 (260/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.0 (873/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.1 (91/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.1 (1042/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e37.5 (30/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e45.9 (946/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMenarche (years), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHormone usage, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4 (22/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2 (111/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.6 (12/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.5 (121/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.8 (3/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.3 (130/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMenstrual irregularity, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.1 (156/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.5 (454/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.6 (66/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.6 (544/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33.8 (27/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25.3 (521/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMenstrual flow (days), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eProlonged menstrual bleeding, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9 (12/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7 (30/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.2 (9/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.5 (33/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.8 (3/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.8 (38/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage bleeding, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003espotting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.6 (172/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.5 (276/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.6 (51/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.9 (397/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27.5 (22/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.9 (349/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.8 (322/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.0 (1052/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46.3 (100/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57.6 (1274/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43.8 (35/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e57.4 (1184/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emoderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.0 (136/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.6 (420/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.3 (59/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.5 (497/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.2 (21/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.4 (482/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eheavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6 (17/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9 (34/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8 (6/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.0 (45/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.5 (2/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.2 (46/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenstrual cycle length, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.4 (572/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.0 (1515/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.9 (177/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86.3 (1910/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e83.8 (67/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e85.5 (1763/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;24 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9 (32/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.1 (56/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.1 (11/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.5 (77/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.8 (3/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.6 (75/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;38 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.6 (43/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.8 (211/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.0 (28/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.2 (226/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.5 (10/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10.8 (223/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEver sexually active, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.0 (550/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.3 (1591/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95.4 (206/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e87.4 (1935/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100.0 (80/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100.0 (2061/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious pregnancy, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.6 (269/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.7 (1206/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72.2 (156/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e59.6 (1319/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e78.8 (63/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e68.2 (1406/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubfertility, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4 (9/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6 (11/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.3 (5/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7 (15/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.0 (4/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8 (16/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge of first pregnancy (years), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTeenage pregnancy, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.8 (128/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.4 (631/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.4 (83/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.5 (676/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.8 (31/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e35.1 (723/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNumber of pregnancies, median (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0 to 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0 to 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (0 to 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (0 to 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2 (1 to 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 (0 to 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCesarean section, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.2 (40/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.6 (136/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.0 (26/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.8 (150/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.8 (7/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.1 (167/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCesarean section, median (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0 to 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0 to 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0 to 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0 to 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0 to 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0 (0 to 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePrevious pregnancy loss, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1 (20/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.6 (65/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.4 (16/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.1 (69/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.8 (11/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.5 (73/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePregnancy loss, median (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0 to 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0 to 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0 to 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0 to 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0 to 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0 (0 to 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcoholism, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8 (5/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7 (12/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5 (1/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7 (16/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0 (0/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8 (17/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5 (3/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4 (7/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5 (1/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4 (9/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.2 (1/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.4 (9/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6 (4/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4 (8/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9 (2/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5 (10/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0 (0/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.5 (11/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6 (4/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (17/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5 (1/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.9 (20/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.2 (1/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.9 (18/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6 (4/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5 (9/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5 (1/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5 (12/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.2 (1/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.6 (12/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4 (9/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7 (12/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.4 (3/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.8 (18/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0 (0/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.0 (21/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePolycystic ovary syndrome, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9 (12/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0 (36/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.2 (9/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.8 (39/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.5 (2/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.1 (44/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntestinal symptoms, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.5 (333/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.6 (528/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.0 (95/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.6 (766/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e51.2 (41/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e34.4 (709/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ediarrhea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6 (17/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.1 (56/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.0 (13/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.7 (60/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.5 (2/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.2 (65/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econstipation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2 (27/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9 (69/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.1 (11/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.8 (85/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.5 (6/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.9 (80/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary symptoms, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.1 (227/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.8 (495/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.0 (95/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.3 (627/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e66.2 (53/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e29.3 (604/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMigraine, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1 (7/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1 (19/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.9 (4/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0 (22/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.2 (1/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.1 (23/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometriosis, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3 (2/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2 (4/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9 (2/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2 (4/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0 (0/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2 (5/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePrevious abdominal/pelvic surgery, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.8 (115/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.3 (416/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.5 (68/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.9 (463/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36.2 (29/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.6 (486/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePrimary dysmenorrhoea, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.7 (90/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.2 (557/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e53.8 (43/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.6 (507/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNon-cyclic pelvic pain, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.9 (90/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.1 (126/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.0 (24/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.8 (182/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDyspareunia, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.6 (43/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1 (37/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.1 (24/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.5 (56/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFamilial history of pelvic pain, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.6 (43/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.8 (121/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.6 (23/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.4 (141/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.0 (8/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.4 (131/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOther pains, % (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0 (52/647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.7 (227/1782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.7 (36/216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.0 (243/2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.0 (4/80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12.8 (264/2061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eNotes: Q1\u0026thinsp;=\u0026thinsp;first quartile (25%), Q3\u0026thinsp;=\u0026thinsp;third quartile (75%).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the women with primary dysmenorrhoea, 13.9% and 6.6% had, respectively, non-cyclic pelvic pain and dyspareunia. Among those with non-cyclic pelvic pain, 41.7% and 11.1% had, respectively, primary dysmenorrhoea and dyspareunia. Finally, among those with dyspareunia, 53.8% and 30.0% had, respectively, primary dysmenorrhoea and non-cyclic pelvic pain.\u003c/p\u003e \u003cp\u003eNon-cyclical pelvic pain, dyspareunia, and intestinal symptoms were positively associated with primary dysmenorrhea. We observed the same direction of the association with hypertension. On the other hand, indigenous people without miscegenation (unmixed), long cycles, previous pregnancy (and if in adolescence), use of contraceptives and pear body shape were negatively associated with this outcome. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows these results in detail.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFeatures independently associated with primary dysmenorrhoea in the population predominantly indigenous of Otavalo, Ecuador.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeatures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (PR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCI lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI higher\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eadjusted p-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.5 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.67 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.25 (4.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.003 (0.154)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyspareunia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.83 (2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.14 (4.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-cyclic pelvic pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40 (1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.72 (2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntestinal symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.47 (1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.22 (1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenarche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatastrophizing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTeenage pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54 (0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.030 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmixed ancestry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56 (0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84 (0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001 (0.021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCycle length\u0026thinsp;\u0026gt;\u0026thinsp;38 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47 (0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.041 (0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49 (0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93 (0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.018 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHormone usage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34 (0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95 (0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.030 (0.013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePear body shape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28 (0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42 (0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: OR\u0026thinsp;=\u0026thinsp;odds ratio; PR\u0026thinsp;=\u0026thinsp;prevalence ratio; CI\u0026thinsp;=\u0026thinsp;confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDyspareunia, pain at ovulation, primary dysmenorrhoea, and urinary symptoms were positively associated with non-cyclic pelvic pain. On the other hand, late menarche, exercise and pear body shape were negatively associated with this outcome. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows these results in detail.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors independently associated with non-cyclical pelvic pain in the population predominantly indigenous of Otavalo, Ecuador.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeatures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCI lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI higher\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eadjusted p-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyspareunia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain at ovulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle/joint pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary dysmenorrhoea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTeenage pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.690\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatastrophizing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHormone usage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmixed ancestry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.520\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenarche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePear body shape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: OR\u0026thinsp;=\u0026thinsp;odds ratio; CI\u0026thinsp;=\u0026thinsp;confidence interval.Notes: OR\u0026thinsp;=\u0026thinsp;odds ratio; CI\u0026thinsp;=\u0026thinsp;confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUrinary symptoms, pregnancy loss, primary dysmenorrhoea, and non-cyclic pelvic pain were positively associated with dyspareunia. On the other hand, late menarche, hormone usage and pear body shape were negatively associated with this outcome. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows these results in detail.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors independently associated with dyspareunia in the population predominantly indigenous of Otavalo, Ecuador.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeatures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCI lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI higher\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eadjusted p-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary dysmenorrhoea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-cyclic pelvic pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatastrophizing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenarche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e00.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHormone usage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.447\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePear body shape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.030\u003c/p\u003e \u003c/td\u003e\u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSupplementary table 2 shows the full logistic regression models for each one of the primary outcomes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of this study revealed that within this population, more than a quarter of women, up to two years after menarche, experienced primary dysmenorrhea, approximately 10% reported non-cyclical pelvic pain, and almost 4% suffered from dyspareunia. All chronic pain conditions were independently associated with one another. Considering that primary dysmenorrhea occurs temporally before the others, although it cannot be definitively confirmed in this study's design, it is plausible to hypothesize about a potential causal or facilitating effect of primary dysmenorrhea on other chronic pains, a notion already discussed in the literature (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe prevalence of primary dysmenorrhea identified falls within the range reported worldwide, but interestingly, it is nearly double the rate we previously identified in the non-indigenous population living in an urban area in the capital of Ecuador (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). In that population, the observed rate of hormonal contraceptive use was considered low, at around 25%, while in this indigenous population, the usage rate is only 5.5%, ranging from 3% among those with primary dysmenorrhea to 6% among those without this condition. We believe that this difference can be due to cultural and religious reasons. Considering that these medications are associated with a significant improvement in dysmenorrhea symptoms (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e), the low frequency of usage, in our view, may be a crucial factor contributing to the increased reporting of menstrual pain by these women.\u003c/p\u003e \u003cp\u003eOn the other hand, there is a lower prevalence of primary dysmenorrhea among indigenous women without a history of interbreeding in the family. Although our study cannot deeply discuss this difference, we can propose at least two hypotheses. The first is that there may be a specific racial and/or genomic characteristic of this population. However, this is purely speculative, as genomic data on indigenous populations are still limited (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). The second hypothesis we consider is socio-cultural significance. Menstruation in the indigenous community that maintains its deep-rooted beliefs is often characterized as \"private women's business.\" It is sometimes seen as a sign of impurity. Stigma, secrecy, and shame associated with discussing menstruation can reduce symptom reporting among indigenous women with more conservative cultural values and taboos (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). This may make dysmenorrhea less likely to be reported in this group.\u003c/p\u003e \u003cp\u003eAnother point that captures our attention is the association of primary dysmenorrhea with systemic arterial hypertension, although the confidence interval of the prevalence ratio has included the null value. The link with adverse cardiovascular events has already been identified by other researchers (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e), and this may perhaps be attributed to a systemic inflammatory status observed in these women, which, however, requires more detailed evaluation (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Studies have shown an association between cardiovascular events not only with a higher amount of body fat but also with its distribution (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). The \"pear\" shape, with a more homogeneous distribution of fat tissue primarily on the hips, has been associated with this (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn parallel, the relationship between BMI and dysmenorrhea is controversial. Longitudinal studies with large cohorts have shown evidence of a U-shaped relationship between these conditions (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e), suggesting a more significant connection with body constitution than weight itself. Finally, a large British cohort has demonstrated significant associations between body shape and inflammatory and metabolic biomarkers (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our study, the prevalence of non-cyclical pelvic pain was similar to that observed in Latin American countries such as Brazil, where it's close to 10% (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e), and in urban communities of Ecuador, where it is 8.9% (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). It was positively associated with various other painful conditions, reinforcing the link between the condition and nociplastia (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e), and perhaps reflecting the clinical expression of central sensitization that commonly occurs in this group of patients (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). These findings are also supported by the apparent protective effect identified in the practice of physical exercise and non-cyclical pelvic pain. Recent literature has shown that physical exercise can strengthen the modulation promoted by the central nervous system (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e), reducing pain sensitization (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e), and there is a direct inverse relationship between measures of physical activity and chronic pain levels (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe observed prevalence of dyspareunia in this population was significantly lower than that previously reported in the urban population of Ecuador (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). The exact reason for this difference remains uncertain. Regardless of the low prevalence of dyspareunia in the studied population, what is equally remarkable is the fact that virtually all the women who reported pain during sexual intercourse did not discontinue intercourse for that reason. Taken together, we believe that this finding could be attributed to the patriarchal structure of indigenous society, where \"male\" attitudes prevail, exacerbating the social and biological vulnerability to which indigenous women are historically subjected. They have often been tied to familial and communal roles, hindering their ability to express their desires and preferences (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://repositorio.utn.edu.ec/handle/123456789/6165\u003c/span\u003e\u003cspan address=\"http://repositorio.utn.edu.ec/handle/123456789/6165\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the absence of affirmative responses regarding the presence of intrauterine infections, 66.3% of the women reported frequent and concurrent urinary symptoms, which could potentially be linked to infectious processes secondary to \u003cem\u003eNeisseria gonorrhoeae\u003c/em\u003e and \u003cem\u003eChlamydia trachomatis\u003c/em\u003e, causative bacterial agents of urethritis and pelvic inflammatory disease (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e). An underdiagnosis of pelvic inflammatory disease might also explain the association with previous pregnancy losses, as there is a connection between intrauterine infection and abortions (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). The findings concerning urinary discomfort contrast with those related to intrauterine infections, which exhibit a notably low prevalence.\u003c/p\u003e \u003cp\u003eWe did not observe any association between pain and psychological symptoms, smoking, alcoholism, or violence, as we had observed in other studies conducted in Ecuador and Brazil. One possible justification for this could be the low reported prevalence of these conditions in this specific community, which is close to or less than 1% for each.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStrengthens and Limitations\u003c/h2\u003e \u003cp\u003eOur study's strength lies in the inclusion of a large and representative cohort of the Kichwa indigenous population. Furthermore, it was conducted with meticulous methodological rigor, granting it robust inferential power. However, there are certain limitations associated with both the characteristics of the population and the analysis itself.\u003c/p\u003e \u003cp\u003eEthnic self-identification, defined as \"the right of every person to freely and voluntarily decide whether or not to belong to a nationality or people\" may have also altered the population distribution of the women studied, as each individual can choose to identify themselves accurately or erroneously with a particular nationality or people, even if they do not genuinely belong.\u003c/p\u003e \u003cp\u003eThe results of this study may also be subject to biases primarily related to the indigenous and Andean worldviews. These worldviews, shaped by beliefs, values, and knowledge systems, play a pivotal role in the social life of these human groups and define their cultural identity. It is crucial to emphasize the social and biological vulnerability of indigenous women, who unfortunately remain entrenched in economic, social, and cultural inequalities, where patriarchal social structures persist. Even though all our interviewers were women representing the local indigenous community, which facilitated the feasibility of the research, some topics are still considered taboo, particularly concerning sexual activity, illicit substance use, tobacco, alcohol, psychological symptoms, and violence. We believe this might have influenced the identification of independently associated factors and, in some way, hindered the formulation of education and healthcare policies tailored to indigenous women, especially with regard to dyspareunia.\u003c/p\u003e \u003cp\u003eRegarding the analysis, certain aspects are inherent to logistic regression models. For our study, the backward stepwise approach proved to be the most suitable model for the data. It may be influenced by the relationship between the number of candidate variables and the sample size, but this was not a concern given our relatively large cohort. Including all variables would add significant complexity and could potentially compromise the model's generalizability. To mitigate this, we conducted a correlogram and aimed to avoid including highly correlated variables in the simulated models. Nonetheless, this was done judiciously to prevent the premature exclusion of relevant variables.\u003c/p\u003e \u003cp\u003eIt is also challenging to ensure that all potential combinations of predictors have been tested. The significance of the p-value does not always equate to clinical relevance, making the interpretation of the effect (in this case, odds ratio) crucial. Moreover, it is impossible to establish a causal relationship between the outcomes and associated variables, which is a limitation inherent to cross-sectional studies.\u003c/p\u003e \u003cp\u003eOn the other hand, backward elimination allows for the advantage of initially considering the effects of all variables simultaneously, which is especially important in cases of potential collinearity, as mentioned earlier. Other factors that balance its limitations include its ease of application, objectivity, reproducibility, interpretability, and the enhanced generalization achieved by reducing the number of predictor variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe prevalence of primary dysmenorrhea and chronic pelvic pain in Kichwa women from Otavalo was notably high, while the frequency of reported dyspareunia was comparatively low, at 26.6%, 8.9%, and 3.9% respectively. There exists a direct correlation between the outcomes of all forms of chronic pelvic pain. We have identified a significant association between primary dysmenorrhoea and conditions related to inflammatory and/or systemic metabolic disorders, warranting special attention. Similarly, a connection was established between non-cyclical pelvic pain and signs/symptoms that could collectively signify central sensitization and nociplastia. Furthermore, dyspareunia was additionally linked to conditions frequently associated with infections, which, in turn, were not reported by the participants. Taking into account the temporal relationship between the conditions, the study suggested a potential causal association between primary dysmenorrhea and the development of dyspareunia and acyclic pelvic pain in the future. We consider welcoming and offering additional care opportunities to these women to be essential. In conclusion, considering that the concepts of health and illness in this population differ from typical Western ideals (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e), public policies should take into account the provision of education and healthcare services in alignment with the values and principles of the local community.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the Central University of Ecuador and the University of S\u0026atilde;o Paulo for their academic support. We also acknowledge the Coordination for the Improvement of Higher Level Personnel - Brazil (CAPES) and its Academic Excellence Program (PROEX).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, we acknowledge the Ministry of Public Health of Ecuador for its support during the execution of the field work.\u003c/p\u003e\n\u003cp\u003eFinally, we acknowledge Paccha Sof\u0026iacute;a Morales Picuas\u0026iacute;, Nina Pakari Ruiz Morales, M\u0026oacute;nica Vanessa Pijuango Cotacachi and Mar\u0026iacute;a Fernanda Saransig Gualsaqu\u0026iacute;, full representatives of Otavalo Kichwa women, who participated as interviewers in this project.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee for Research in Human Beings of the Central University of Ecuador. All participants and/or their parents or legal representatives gave their written consent prior to being enrolled. The human research parameters of the Declaration of Helsinki were followed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest. The authors received no financial support for the research and/or authorship.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Central University of Ecuador (UCE), the University of S\u0026atilde;o Paulo (USP), the Coordination for the Improvement of Higher Level Personnel - Brazil (CAPES) and its Academic Excellence Program (PROEX), and the Ministry of Public Health of Ecuador.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement of competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report there are no competing interests to declare.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author, [OBPN], upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026apos;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJAVC: Conception, design, acquisition of data, interpretation of data, drafting the\u003c/p\u003e\n\u003cp\u003earticle, final approval.\u003c/p\u003e\n\u003cp\u003eCYLMVR: Conception, interpretation of data, revising the article, final approval.\u003c/p\u003e\n\u003cp\u003eSCM: Analysis and interpretation of data, revising the article, final approval.\u003c/p\u003e\n\u003cp\u003eFJCR: Analysis and interpretation of data, revising the article, final approval.\u003c/p\u003e\n\u003cp\u003eAAN: Interpretation of data, revising the article, final approval.\u003c/p\u003e\n\u003cp\u003eJCRS: Interpretation of data, revising the article, final approval.\u003c/p\u003e\n\u003cp\u003eOBPN : Conception, design, management, acquisition of data, analysis and interpretation of data,\u003c/p\u003e\n\u003cp\u003edrafting and revising the article, final approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAyorinde AA, Bhattacharya S, Druce KL, Jones GT, Macfarlane GJ. 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Associations of body shape index (ABSI) and hip index with liver, metabolic, and inflammatory biomarkers in the UK Biobank cohort. Sci Rep. 2022;12(1):8812.\u003c/li\u003e\n\u003cli\u003eSilva GP de OG da, Nascimento AL do, Michelazzo D, Alves Junior FF, Rocha MGMG, Silva JCRE, et al. High prevalence of chronic pelvic pain in women in Ribeir\u0026atilde;o Preto, Brazil and direct association with abdominal surgery. Clin Sao Paulo Braz. 2011;66(8):1307\u0026ndash;12.\u003c/li\u003e\n\u003cli\u003eCoelho LSCSC, Brito LMOMO, Chein MBCBC, Mascarenhas TSS, Costa JPLPL, Nogueira AAA, et al. Prevalence and conditions associated with chronic pelvic pain in women from S\u0026atilde;o Lu\u0026iacute;s, Brazil. Braz J Med Biol Res. 2014;47(9):818\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eTill SR, Schrepf A, Clauw DJ, Harte SE, Williams DA, As-Sanie S. Association Between Nociplastic Pain and Pain Severity and Impact in Women With Chronic Pelvic Pain. 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Int J Equity Health. 2020;19(1):101.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Dyspareunia, Ecuador, Indigenous, Kichwa, Non-Cyclic Pelvic Pain, Prevalence, Primary Dysmenorrhoea, Risk Factor","lastPublishedDoi":"10.21203/rs.3.rs-3903885/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3903885/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eChronic pelvic pain is a common disease that affects approximately 4% of women of reproductive age in developed countries. This number is estimated to be higher in developing countries, with a significant negative personal and socioeconomic impact on women. The lack of data on this condition in several countries, particularly those in development and in socially and biologically vulnerable populations such as the indigenous, makes it difficult to guide public policies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives:\u003c/strong\u003e To evaluate the prevalence of chronic pelvic pain (dysmenorrhea, dyspareunia, non-cyclical pain) and identify which variables are independently associated with the presence of the condition in indigenous women from Otavalo-Ecuador.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign: \u003c/strong\u003eA cross-sectional study was carried out including a sample of 2429 women of reproductive age between 14-49 years old, obtained from April 2022 to March 2023. A directed questionnaire was used, collected by bilingual interviewers (Kichwa and Spanish) belonging to the community itself; the number of patients was selected by random sampling proportional to the number of women estimated by sample calculation. Data are presented as case prevalence, odds ratio, and 95% confidence interval, with p \u0026lt; 0.05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe prevalence of primary dysmenorrhea, non-cyclic pelvic pain, and dyspareunia was, respectively, 26.6%, 8.9%, and 3.9%.all forms of chronic pain were independently associated with each other. Additionally, dysmenorrhoea was independently associated with hypertension, intestinal symptoms, miscegenation, long cycles, previous pregnancy, use of contraceptives and pear body shape. Urinary symptoms, late menarche, exercise, and pear body shape were associated with non-cyclic pelvic pain. And, urinary symptoms, previous pregnancy loss, late menarche, hormone usage, and pear body shape were associated with dyspareunia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe prevalence of primary dysmenorrhea and non-cyclical chronic pelvic pain was notably high, in contrast with the frequency of reported dyspareunia. Briefly, our results suggest an association between dysmenorrhoea and conditions related to inflammatory and/or systemic metabolic disorders, including a potential causal relationship with other manifestations of pelvic pain, and between non-cyclical pelvic pain and signs/symptoms suggesting central sensitization. The report of dyspareunia may be influenced by local cultural values and beliefs.\u003c/p\u003e","manuscriptTitle":"Prevalence of chronic pelvic pain and associated factors among indigenous women of reproductive age in Ecuador","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-07 17:24:42","doi":"10.21203/rs.3.rs-3903885/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-18T18:01:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-23T11:56:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90230b05-70e0-42fa-8208-199c5a96e395","date":"2024-02-18T22:58:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48a56883-0ff9-4e64-ac2d-6541997f653f","date":"2024-02-18T19:19:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-18T17:45:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-18T17:37:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-02-05T20:22:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-05T20:20:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2024-01-27T19:25:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6bf73bc7-0f51-4394-99ac-7078e7486970","owner":[],"postedDate":"February 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-07T04:16:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-07 17:24:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3903885","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3903885","identity":"rs-3903885","version":["v1"]},"buildId":"omnImTCwR2MFx8CMYfrG7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}