Associations between endometriosis and adverse pregnancy and perinatal outcomes: a population-based cohort study

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Methods A population-based retrospective cohort study was conducted among 468,778 eligible women who contributed 912,747 singleton livebirths between 1980 and 2015 in Western Australia (WA). We used probabilistically linked perinatal and hospital separation data from the WA data linkage system’s Midwives Notification System and Hospital Morbidity Data Collection databases. We used a doubly robust estimator by combining the inverse probability weighting with the outcome regression model to estimate adjusted risk ratios (RR) and 95% confidence intervals (CIs). Results There were 19,476 singleton livebirths among 8,874 women diagnosed with endometriosis. Using a doubly robust estimator, we found pregnancies in women with endometriosis to be associated with an increased risk of preeclampsia with RR of 1.18, 95% CI 1.11–1.26, placenta previa (RR, 1.59, 95% CI 1.42–1.79) and preterm birth (RR 1.45, 95% CI 1.37–1.54). The observed association persisted after stratified by the use of Medically Assisted Reproduction, with a slightly elevated risk among pregnancies conceived spontaneously. Conclusions In this large population-based cohort, endometriosis is associated with an increased risk of preeclampsia, placenta previa, and preterm birth, independent of the use of Medically Assisted Reproduction. This may help to enhance future obstetric care among this population. endometriosis preeclampsia placenta previa preterm birth medically assisted reproduction What Does This Study Adds To The Clinical Work Women with endometriosis have a greater risk of preeclampsia, placenta previa, and preterm birth, which cannot be explained by the use of medically assisted reproduction. These findings may inform future obstetric care among this population. Introduction Endometriosis is a chronic inflammatory condition affecting women, where endometrial cells normally lining up the uterine cavity are found outside the uterus. Endometriosis can cause a variety of and sometimes unspecific symptoms with no to severe cyclic pain episodes, dyspareunia, dysmenorrhea, and subfertility. 1 , 2 The disease highly affects the quality of life, and productivity, and causes high treatment and societal costs. 3 It often takes 8–12 years from symptom onset to surgical diagnosis 4 – 6 , leading to varying prevalence estimates (5–50% in infertile women, up to 75% in cases with chronic pain). 7 , 8 In Australia, 11% of reproductive age women are affected with prevalence ranging from 2 to 11% in asymptomatic women. 9 Three-quarters of women with mild to moderate endometriosis can achieve pregnancy spontaneously, despite an increased risk of subfertility. 10 The association between endometriosis and adverse pregnancy outcomes has drawn more attention in recent years with fairly consistent evidence of increased risks for caesarean section, preterm birth, and stillbirth. 11 , 12 However, the link with gestational diabetes, preeclampsia, or intrauterine growth restriction remains less clear due to heterogeneity in study designs and methodologies used in previous studies. 13 – 21 In epidemiology, it remains challenging to study the direct impact of endometriosis on pregnancy outcomes and underlying mechanisms are not well understood. Much of the existing research on this topic comes from small cohort studies at infertility clinics or single surgical centres, 22 which can produce results that are misinterpreted as evidence of no association rather than a lack of evidence for any association. Moreover, data limited to clinical settings are prone to selection bias as these participants may have better access to care, which may be linked to other health behaviours that affect pregnancy outcomes. 23 Classical study designs adopted by studies that do not have access to a wide range of potential risk factors may also be prone to residual confounding. To address some of these limitations, we used a ‘doubly robust estimator’ to estimate the association between endometriosis and adverse pregnancy outcomes. This approach offers an opportunity to achieve unbiased inference while accounting for selection effects by combining inverse probability weighting and regression adjustment and allows for a causal interpretation of the results. 24 , 25 Findings from this approach can be directly interpreted as the risk of adverse pregnancy outcomes given that women had endometriosis as compared to the counterfactual scenario that they had no endometriosis. This causal interpretation is usually not possible from classical epidemiological approaches. This study aimed to estimate the effect ( average treatment effect) of endometriosis on adverse pregnancy and perinatal outcomes using a large population-based cohort in Western Australia (WA). Methods Study design We conducted a population-based, longitudinal cohort study including all women 15 to 49 years of age with a singleton pregnancy in the period of 1980 to 2015 in WA. Data sources and study population We obtained maternal, infant and birth information from the Midwives Notification System, a validated database 26 that includes >99% of births in WA of at least 20 weeks’ gestation or birthweight of 400 g or more if the gestational age was unknown. 27 We sourced hospitalization records from the Hospital Morbidity Data Collection, which includes information on all hospitalizations from public, private and day procedure facilities in the state with International Classification of Diseases (ICD-9/10 th revision-Australian Modification) coded diagnoses. 28 Data sources have been described in detail elsewhere. 29 Data were probabilistically linked using best practice protocols through the WA Data Linkage Branch. 30 From a total of 487,297 women (964,015 births) during the study period, we sequentially excluded multiple gestations, stillbirths, and pregnancies with missing information for gestational age, outcomes, maternal age, and socioeconomic status (SES). This resulted in 468,778 eligible women who contributed to 912,747 singleton pregnancies included in the analytic cohort (Fig. S1). Exposure assessment We identified all women with a principal or additional diagnosis of endometriosis from the hospital separation data using the International Classification of Diseases (ICD)-AM (Australian Modification) diagnostic codes consistent with ICD-9: 617.0-617.9; ICD-10: N80.0-N80.9 and Australian Classification of Health Interventions (ACHI) for endometriosis-related procedures (codes are shown in Table S5). Women were categorized as having endometriosis if they had hospital admission or surgical procedure coded as a diagnosis of endometriosis. We included women diagnosed before and after pregnancy in the primary analysis because recent studies documented a diagnosis delay of 8 to 12 years. 4-6 This approach has been adopted by other recent studies. 15, 19 Outcomes The outcomes of interest were ascertained from the Midwives’ Notifications System and hospital separation data in the state, with the diagnostic codes consistent with preeclampsia (ICD-9/ICD-9-CM: 642.4, 642.5, 642.7, ICD-10-AM: O14, O11) and placenta previa, with or without haemorrhage (ICD-9/ICD-9-CM: 641.0-641.1, ICD-10-AM: O44.-). The onset of preeclampsia at the gestational age between 20-34 weeks and after 34 weeks of gestation was classified as early or late-onset preeclampsia, respectively. Preterm birth was defined as birth before 37 completed weeks of gestation, categorized into moderate preterm birth (gestational week 32-36) and very preterm birth (prior to 32 gestational weeks). Further, we also categorized preterm birth into spontaneous (due to spontaneous onset of labour) and medically indicated (due to elective cesarean section, or induction of labour). The details of ICD codes used to define variables for analysis are presented in Table S5. Covariates Information on potential confounding factors including the calendar year of birth of the child (categorical variable), maternal age group (15-24, 25-29, 30-34, 35-39, 40-49 years), parity (0, 1, 2, ≥3), smoking during pregnancy (Yes vs No), race/ethnicity (Caucasian versus non-Caucasian), and socioeconomic status (SES) was obtained from the databases. SES was measured using Socio-Economic Indexes for Areas (SEIFA). Specifically, we used the Index of Relative Socio-economic Disadvantage level at the time of birth of the child. These scores were obtained from the Australian Bureau of Statistics 31 and categorized into quintiles. To assess the potential impact of Medically Assisted Reproduction (MAR) or infertility treatment on perinatal outcomes, we identified pregnancies with MAR procedure (using ACHI) or the following ICD diagnostic codes; ICD-9: 628.0-628.9, V26.1-V26.9, and ICD-10-AM: N97.0-97.9; Z31.1-Z31.9. These codes cover ART (assisted reproductive technology) techniques, intrauterine insemination, and ovulation induction and might include some spontaneously conceived pregnancies in couples with fertility issues. 32 Statistical analysis We first estimated the unadjusted relative risks (RRs) with 95% confidence intervals (CI) using Generalized Linear Models (GLM) fitted using a Poisson distribution with a log link function. Next, we estimated the causal effect ( average treatment effect ) of endometriosis on adverse pregnancy outcomes using the potential outcome approach, which allows for the estimation of causal effects in large observational data. 33 We specifically used a doubly robust estimation 34 by combining the inverse probability of treatment weighting (IPTW, weight each person by the inverse of their propensity score) and the outcome regression model. The doubly robust estimation allows us to estimate the unbiased average causal effect when either the outcome regression model (traditional way of obtaining treatment effect) or the propensity score model (treatment selection model) is correctly specified. 24, 25 To estimate the adjusted RRs with 95% CI for each outcome, we fitted the exposure model with maternal age, birth year, SES, ethnicity/race, and MAR treatment, and the outcome model with maternal age, birth year, SES, ethnicity/race and parity. As preeclampsia and placenta previa may influence the risk of preterm birth, gestational age (37 weeks of gestation) was also included in the exposure model as a covariate for all outcomes included except for preterm birth. Robust (sandwich) variance estimation was used to account for the effect of repeated pregnancies per mother. 34 To examine the influence of MAR on the association between endometriosis and adverse pregnancy outcomes, we included a sub-analysis stratified by MAR status (Table 3). To check the covariate balance after propensity score matching using IPTW, we performed diagnostics including standardized differences in means of all covariates (Fig. S2). The confounders included in the treatment weighting were decided based on prior knowledge as well as consideration of Directed Acyclic Graphs (DAGs) (Fig. S3). Missing data For the main results, we conducted a complete case analysis as the proportion of missing data was small (<3%, range 0.6% for gestational age to 1.8% for SES). Sensitivity analysis To check the robustness of our findings, we conducted several sensitivity analyses. Firstly, to ascertain the sensitivity of our result to higher-order parity, we restricted the analysis to primiparous women. Secondly, to limit the possibility of misclassification bias, we conducted an analysis restricted to women (i) with a principal diagnosis of endometriosis, a diagnosis established to be chiefly responsible for occasioning an episode 28 ; (ii) with any diagnosis of endometriosis prior to the birth of the child to ensure endometriosis was present during pregnancy; (iii) with endometriosis diagnosis before delivery and up to five years after delivery; and (iv) considering endometriosis diagnosis at more than one-time point during five years look-up period. Thirdly, to explore the potential influence of maternal smoking during pregnancy, which was routinely collected in the Midwifery notification from 1997 onwards, we conducted a separate analysis adjusting for smoking. Next, we compared the effect of endometriosis on preterm birth (very preterm vs moderate). Fifth, we undertake a causal mediation analysis based on the counterfactual framework using a parametric regression approach 35 to estimate the natural direct effect of endometriosis compared with the natural indirect effect through MAR. Finally, to assess the extent of unmeasured confounding, we calculated E-values, which represent the minimum strength of association on the risk ratio scale, that any unmeasured confounder would need to have with both endometriosis and each outcome to fully explain away the observed association, conditional on the measured covariates. 36 All analyses were performed using Stata version 16.1 (Stata Corporation, College Station, Texas, USA). Results Cohort characteristics In total, we included 912,747 eligible singleton births with a gestational age of 20-44 weeks from women (n=468,778) aged 15-49 years in the study period between 1980 and 2015 in WA. In these pregnancies 8,874 women (1.9%) had a diagnosis of endometriosis, corresponding to 19,476 pregnancies (2.1%). Women with endometriosis were on average of advanced age at the time of birth (>35 years), Caucasian, and had a higher proportion of medically assisted reproduction compared to women without endometriosis. Socio-economic status, parity, and ethnicity were similar among exposed and non-exposed groups (Table 1). The prevalence of pregnancy complications was higher among pregnancies of women with endometriosis compared to women without endometriosis (preeclampsia, 7.5% vs 6.1%; preterm birth, 10.2% vs 7.0%; placenta previa, 1.9% vs 1.0%; Table 2). Using doubly robust estimation, pregnancies in women with a diagnosis of endometriosis were associated with a higher risk of preeclampsia (RR 1.18, 95% CI 1.11- 1.26), placenta previa (RR 1.59, 95% CI 1.42- 1.79), and preterm birth (RR 1.45, 95% CI 1.37- 1.54). This risk associated with endometriosis was higher for medically indicated preterm birth (RR 1.74, 95% CI 1.58- 1.93) compared to spontaneous preterm birth (RR 1.40, 95% CI 1.27- 1.56) (Table 2). Furthermore, endometriosis was associated with both moderate and very preterm birth with a stronger association observed for very preterm birth (Table S3). In a stratified analysis based on MAR status, the higher risk of placenta previa and preterm birth persisted regardless of conception mode with the strongest effect estimates for the non-MAR group (RR 1.77, 95 % CI 1.50- 2.08 for placenta previa and RR 1.67, 95% CI 1.55-1.80 for preterm birth) and slightly attenuated effect estimates among the MAR group. However, for preeclampsia, the observed association disappeared when stratified by MAR status (Table 3). Results of the sensitivity analyses The results restricted to nulliparous women were consistent with the main finding with a slight attenuation (Table S2; Model 2). Findings from the analyses restricted to a subset of the study population with different exposure definitions were very similar to those reported in the main analyses (Table S2; Model 3-6). Analyses restricted to pregnancies from women with endometriosis diagnosed before delivery also resulted in slightly higher risk estimates for all adverse pregnancy outcomes evaluated (Table S2; Model 4). Additionally, the pattern of the association between endometriosis and adverse pregnancy outcomes was similar when further adjusted to smoking status (Table S2; Model 7). Our mediation analyses suggest that the percentage of endometriosis effect on preterm birth and placenta previa that was mediated through MAR was 8% and 3% respectively. (Table S4). The E-values for the observed RRs varied from 1.64 to 2.87 for these three adverse pregnancy outcomes (Table S6). Discussion Principal findings To our knowledge, this is the first population-based retrospective cohort study to examine the association between endometriosis with adverse pregnancy outcomes using the potential outcome framework. Using a large (~ 1 million births) cohort in WA, we observed a higher risk of preeclampsia, placenta previa, and preterm birth among pregnancies in women with endometriosis as compared to women without endometriosis. The associations persisted after stratification for conception mode (MAR or natural conception, non-MAR), with an elevated risk among the non-MAR group meaning the risks observed were attenuated among pregnancies conceived by MAR. The risk for adverse pregnancy outcomes was higher (approximately 24%, 56%, and 85% of increased risk of preeclampsia, preterm birth, and placenta previa respectively) when we restricted our sample to women with endometriosis as the principal diagnosis code, suggesting probably more severe disease. These observed associations were not mediated through MAR. Strengths and limitations Our cohort was based on longitudinally linked, highly reliable sources of population-based perinatal information ascertained from hospital separations and midwives’ notifications. We also included sensitivity analysis to check the robustness of our result. Our cohort is less prone to exposure misclassification bias since the hospital morbidity data collection (source data for our exposure) contains records for all hospital separations of admitted patients from all public and private hospitals in WA. Furthermore, our study restricted the analysis to singleton pregnancies, which improved generalizability to other similar cohorts. We only had information on the diagnosis of endometriosis for women who have been hospitalized during the study period. Such data may likely represent more severe stages of endometriosis. The clinical routines and obstetric care have changed through the years and the diagnosis and awareness regarding endometriosis have evolved. However, to minimize this bias our model included the birth year of the child as a covariate. In our main analysis, we included all women with any diagnosis of endometriosis (principal and additional diagnosis). This could have introduced non-differential misclassification bias (i.e., independent of the outcome), and therefore will potentially bias the results towards the null. To limit this possible misclassification, we included a sensitivity analysis restricted to an exposure defined as a principal diagnosis of endometriosis, which indicated higher risk estimates as compared to the main result. We opted to include women with a diagnosis of endometriosis before and after pregnancy to account for the diagnostic delay. 4, 5 This could induce similar misclassification bias and attenuation of the association. Indeed, our sensitivity analysis restricted to a diagnosis of endometriosis before delivery consistently suggested higher effect estimates as compared to the main analysis. Though the validity of the diagnosis of endometriosis in the hospital separation database remains unknown, previous analysis of the same database suggested that endometriosis is reliably recorded in the hospital separation data. 37 Moreover, while the use of ICD and procedure codes ensures that those classified as having endometriosis are likely true cases, there is the possibility that the comparison group may have undiagnosed endometriosis. Our cohort had a relatively small number of events for stillbirths to be considered as an outcome. We, therefore, excluded pregnancies resulting in stillbirths from our analysis. This may have introduced a livebirth bias in the association between endometriosis and adverse pregnancy outcomes. However, a previous simulation study indicated that the magnitude of this bias is small. 38 Interpretation The reported association between endometriosis and preeclampsia have been mixed, with some suggesting no association 14 or decreased risk, 17 possibly owing to heterogeneity in exposure or outcome definition and study population (selection bias) not taking MAR into account. 20, 39 Our study observed a modest association between endometriosis and preeclampsia, which is consistent with previous studies, 12, 13, 15, 20, 21 The attenuated risk in women who received MAR treatment is supported by other studies that did not find an association or a reduced association between endometriosis and preeclampsia or hypertension in pregnancy in women with an endometriosis and MAR procedure. 40, 41 Our study also found an increased risk of placenta previa in pregnancies among women with endometriosis, which is consistent with other research. 20 It has been suggested that the association may be confounded by the increased use of MAR in women with endometriosis. In our study, the association persisted even after stratification by MAR status, with a stronger association observed in non-MAR women which is consistent with other studies. 40 In our study, for women using MAR, the precision of the effect estimates was reduced likely because of the small sample size. For preterm birth, we observed higher risk estimates for very preterm deliveries compared to moderate preterm deliveries, and an association between endometriosis and both spontaneous and medically indicated preterm birth, with a stronger association for medically indicated preterm birth. This could imply that pregnancies from women with endometriosis are more likely to be induced or delivered through a cesarean section before gestational week 37. This finding is consistent with previous research and the association seems independent of MAR. 20, 41, 42 A smaller protective effect was also observed in a Canadian study. 12 Endometriosis may be associated with adverse pregnancy outcomes through various mechanisms, including effects on the uterine environment, progesterone signalling, and the remodelling of the spiral artery. 18, 43, 44 These factors may play a role in the association with preeclampsia, preterm birth and intrauterine growth restriction. 45-47 Endometriotic lesions in the uterus may also reduce uterine contractility and cause abnormal implantation, leading to placenta previa. 39 In our sub-analysis of the timing of preeclampsia, we found an elevated risk for early onset compared to late-onset preeclampsia, which can be accounted for inadequate and incomplete trophoblast invasion of maternal spiral arteries. 43 MAR treatment itself has shown to be a risk factor for adverse pregnancy outcomes, with mixed results for women with endometriosis. 47 In MAR treatment, the effects caused by endometriosis such as inflammatory processes and regulatory disbalances are suppressed offering a better pregnancy environment and could explain the attenuation in the risks seen in our study in the MAR pregnancies group. 48 Women conceiving following MAR might also have support from better obstetric care and closer screening for adverse outcomes. In this study, a stronger association between endometriosis and adverse pregnancy outcomes was observed, but residual and/or unmeasured confounding could not be completely ruled out. Nevertheless, the E-values for the observed RRs (ranging from 1.64 to 2.87) indicated that substantial confounding would need to explain away these associations (Table S6). Systematic reviews that examined the risk factors for endometriosis, for example, reported RRs ranging from 1.63 for smoking to 1.87 for overweight – lower than that of the E-values. 49, 50 In general, findings from our sensitivity analyses were remarkably similar to those reported for the main analysis and collectively support the hypothesis that endometriosis is associated with adverse pregnancy outcomes independent of MAR. Therefore, knowledge of a patient's endometriosis history may inform targeted prenatal care and reduce unfavourable pregnancy outcomes. Future studies would benefit from elucidating the potential mechanism that might explain how endometriosis affects implantation, placentation, and fetal growth and identifying potential interventions to decrease the risk of adverse perinatal outcomes. Conclusion In conclusion, regardless of the use of medically assisted reproduction, endometriosis is associated with an increased risk of preeclampsia, placenta previa, and preterm birth. These findings offer new insight into the causal association between endometriosis on adverse pregnancy outcomes, taking MAR into account. This may help to enhance future obstetric care among this population. Declarations Acknowledgements The authors would like to thank the Data Linkage Branch (Department of Health Western Australia) as well as the Data custodian for the Midwives Notification System and Hospital Morbidity Data Collection for providing data for this project. Author Contributions ATG: project development, data management and analysis, initial data interpretation, wrote the first draft of the manuscript. VRM, BD, GAT, and GP: substantial contributions to the statistical analyses, data interpretation, and critical revisions of the subsequent drafts of the manuscript for important intellectual content. All authors read and approved the final manuscript. Funding GP was supported with funding from the National Health and Medical Research Council Project and Investigator Grants #1099655 and #1173991, and the Research Council of Norway through its Centres of Excellence funding scheme #262700. GAT was supported with funding from the National Health and Medical Research Council Investigator Grant #1195716. The funders had no role in the analysis, interpretations of the results, writing of the reports, and the decision to submit the paper for possible publication. Competing interests The authors have no potential conflicts of interest to disclose. Ethical Approval This study was conducted in accordance with the principles of the Declaration of Helsinki. This research was approved by the Human Research Ethics Committee (HREC approval 2016/51) from the Department of Health, WA. The Ethics Committee approval was accepted on 14 September 2016. Consent to participate Consent for the study was obtained from the data custodians. As the study was based on routinely collected de-identified linked administrative data, individual consent from the participants was not obtained. Availability of data and material No additional data are available. References Macer ML, Taylor HS. Endometriosis and infertility: a review of the pathogenesis and treatment of endometriosis-associated infertility. Obstetrics and Gynecology Clinics. 2012;39(4):535-49. Farquhar C. Endometriosis. Bmj. 2007 Feb 3;334(7587):249-53. Nnoaham KE, Hummelshoj L, Webster P, d'Hooghe T, de Cicco Nardone F, de Cicco Nardone C, et al. 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Health Outcomes and Systematic Analyses (HOUSSAY) Project. Association between endometriosis and preterm birth in women with spontaneous conception or using assisted reproductive technology: a systematic review and meta-analysis of cohort studies. Reprod Sci. 2018;25:311-9. Fiorentino G, Cimadomo D, Innocenti F, Soscia D, Vaiarelli A, Ubaldi FM, et al. Biomechanical forces and signals operating in the ovary during folliculogenesis and their dysregulation: implications for fertility. Hum Reprod Update. 2022. Joshi NR, Miyadahira EH, Afshar Y, Jeong J-W, Young SL, Lessey BA, et al. Progesterone resistance in endometriosis is modulated by the altered expression of microRNA-29c and FKBP4. The Journal of Clinical Endocrinology & Metabolism. 2017;102(1):141-9. Kunz G, Beil D, Huppert P, Leyendecker G. Structural abnormalities of the uterine wall in women with endometriosis and infertility visualized by vaginal sonography and magnetic resonance imaging. Human Reproduction. 2000;15(1):76-82. Brosens I, Pijnenborg R, Benagiano G. Defective myometrial spiral artery remodelling as a cause of major obstetrical syndromes in endometriosis and adenomyosis. Placenta. 2013;34(2):100-5. Vigano P, Corti L, Berlanda N. Beyond infertility: obstetrical and postpartum complications associated with endometriosis and adenomyosis. Fertil Steril. 2015 Oct;104(4):802-12. Pirtea P, de Ziegler D, Ayoubi JM. Effects of endometriosis on assisted reproductive technology: gone with the wind. Fertility and Sterility. 2021;115(2):321-2. Bravi F, Parazzini F, Cipriani S, Chiaffarino F, Ricci E, Chiantera V, et al. Tobacco smoking and risk of endometriosis: a systematic review and meta-analysis. BMJ open. 2014;4(12):e006325. Jenabi E, Khazaei S, Veisani Y. The association between body mass index and the risk of endometriosis: A meta-analysis. Journal of Endometriosis and Pelvic Pain Disorders. 2019;11(2):55-61. Tables Table 1. Maternal characteristics according to endometriosis status for women delivering singleton births during 1980-2015 in WA (n=912,747 pregnancies). Characteristics Total Endometriosis No endometriosis N=912,747 (n=19,476) (n=893,271) Maternal age, y 15-24 225,204 (24.7) 4,855 (24.9) 220,349 (24.7) 25-29 292,416 (32.0) 6,037 (31.0) 286,379 (32.1) 30-34 260,947 (28.6) 5,334 (27.4) 255,613 (28.6) 35-39 113,489 (12.4) 2,731 (14.0) 110,758 (12.4) 40-49 20,691 (2.3) 519 (2.7) 20,172 (2.3) SES in quintiles <20th percentile 181,980 (19.9) 3,860 (19.8) 178,120 (19.9) 20-39th percentile 182,472 (20.0) 4,015 (20.6) 178,457 (20.0) 40-59th percentile 182,685 (20.0) 3,765 (19.3) 178,920 (20.0) 60-79th percentile 182,731 (20.0) 3,964 (20.4) 178,767 (20.0) >=80th percentile 182,879 (20.0) 3,872 (19.9) 179,007 (20.0) Time period of birth 1980-1984 102,077 (11.2) 1,494 (7.7) 100,583 (11.3) 1985-1989 115,682 (12.7) 2,843 (14.6) 112,839 (12.6) 1990-1994 121,186 (13.3) 3,791 (19.5) 117,395 (13.1) 1995-1999 121,670 (13.3) 3,639 (18.7) 118,031 (13.2) 2000-2004 119,334 (13.1) 2,982 (15.3) 116,352 (13.0) 2005-2009 141,603 (15.5) 2,681 (13.8) 138,922 (15.6) 2010-2015 191,195 (20.9) 2,046 (10.5) 189,149 (21.2) Parity first birth 368,504 (40.4) 7,516 (38.6) 360,988 (40.4) 2 nd birth 309,240 (33.9) 6,557 (33.7) 302,683 (33.9) 3 rd birth 148,061 (16.2) 3,375 (17.3) 144,686 (16.2) >=4 birth 86,869 (9.5) 2,028 (10.4) 84,841 (9.5) Missing 73 (0.0) 0 (0.0) 73 (0.0) MAR No 890,327 (97.5) 16,198 (83.2) 874,129 (97.9) Yes 22,420 (2.5) 3,278 (16.8) 19,142 (2.1) Smoking during pregnancy No 427,544 (46.8) 7,782 (40.0) 419,762 (47.0) Yes 80,709 (8.8) 1,539 (7.9) 79,170 (8.9) Missing 404,494 (44.3) 10,155 (52.1) 394,339 (44.1) Ethnicity (race) Caucasian 759,584 (83.2) 17,734 (91.1) 741,850 (83.0) Non-Caucasian 153,163 (16.8) 1,742 (8.9) 151,421 (17.0) MAR: Medically Assisted Reproduction; SES: Socio-economic status Table 2. Crude and adjusted Risk ratio (RR) and 95 % CI for each adverse pregnancy outcome for women with endometriosis among 912,747 singleton births in WA, 1980-2015. Outcomes No endometriosis n=893,271 (%) Endometriosis n=19,476 (%) Unadjusted RR (95 % CI) Adjusted RR (95 % CI) using doubly robust estimation Preeclampsia 54,098 (6.1) 1,468 (7.5) 1.24 (1.18, 1.31) 1.18 (1.11, 1.26) Early 4,749 (0.6) 157 (0.8) 1.52 (1.30, 1.78) 1.50 (1.12, 2.02) Late 32,240 (3.6) 823 (4.3) 1.17 (1.09, 1.25) 1.06 (0.92, 1.21) Placenta previa 8,901 (1.0) 364 (1.9) 1.88 (1.69, 2.08) 1.59 (1.42, 1.79) Preterm birth 62,706 (7.0) 1,989 (10.2) 1.45 (1.39, 1.52) 1.45 (1.37, 1.54) Spontaneous 36,034 (4.0) 978 (5.0) 1.27 (1.16, 1.38) 1.40 (1.27, 1.56) Indicated 26,671 (3.0) 1,011 (5.2) 1.62 (1.48, 1.78) 1.74 (1.58, 1.93) Doubly robust estimation: the exposure model included maternal age, birth year, SES, ethnicity/race, and MAR treatment, and the outcome model included maternal age, birth year, SES, parity, and ethnicity/race. The outcome model for preeclampsia and placenta previa also included gestational age. SES: Socio-economic status; MAR: Medically Assisted Reproduction; RR: Risk Ratio; CI: Confidence interval; n: Total number of pregnancies from women with endometriosis Table 3. Crude and adjusted Risk ratio (RR) and 95 % CI for each adverse pregnancy outcome for women with endometriosis stratified by MAR status among 912,747 singleton births in WA, 1980-2015 MAR (n=22, 420) Non-MAR (n=890,327) Outcome Number PE (%) Crude RR (95 % CI) Adjusted RR (95 % CI) Number PE (%) Crude RR (95 % CI) Adjusted RR (95 % CI) P-value PE Endometriosis <0.001 Yes 3,278 214 (6.5) 0.99 (0.86, 1.14) 0.86 (0.69, 1.07) 16,198 1,254 (7.7) 1.28 (1.21, 1.35) 1.22 (1.10, 1.35) No 19,142 1,256 (6.6) 874,129 52, 842 (6.0) PP Number PP (%) Crude RR (95 % CI) Adjusted RR (95 % CI) Number PP (%) Crude RR (95 % CI) Adjusted RR (95 % CI) <0.001 Endometriosis Yes 3278 82 (2.5) 1.20 (0.95, 1.52) 1.11 (0.83, 1.46) 16,198 282 (1.7) 1.79 (1.59, 2.01) 1.77 (1.50, 2.08) No 19142 400 (2.1) 874,129 8,501 (1.0) PTB Number PTB (%) Crude RR (95 % CI) Adjusted RR (95 % CI) Number PTB (%) Crude RR (95 % CI) Adjusted RR (95 % CI) <0.001 Endometriosis Yes 3278 416 (12.7) 1.21 (1.09, 1.33) 1.26 (1.09, 1.45) 16,198 1,573 (9.7) 1.40 (1.33, 1.47) 1.67 (1.55, 1.80) No 19142 2,011 (10.5) 874,129 60,695 (6.9) All adjusted RRs presented in this table are estimated using a doubly robust estimation: the exposure model included maternal age, birth year, SES, and ethnicity/race, and the outcome model included maternal age, birth year, SES, parity, and ethnicity/race. The outcome model for preeclampsia and placenta previa also included gestational age. PE: preeclampsia; PP: placenta previa; PTB: Preterm birth; SES: Socio-economic status; MAR: Medically Assisted Reproduction; RR: Risk Ratio; CI: Confidence interval; n: Total number of pregnancies from women with endometriosis; a P-value for interaction test Supplementary Files SupplementaryAppendix.docx Cite Share Download PDF Status: Published Journal Publication published 20 Mar, 2023 Read the published version in Archives of Gynecology and Obstetrics → Version 1 posted Reviewers agreed at journal 11 Jan, 2023 Reviewers invited by journal 11 Jan, 2023 Editor invited by journal 10 Jan, 2023 Editor assigned by journal 10 Jan, 2023 First submitted to journal 10 Jan, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2462392","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":166760691,"identity":"ea983627-f182-429d-b4f2-0f8de19e096f","order_by":0,"name":"Amanuel Tesfay Gebremedhin","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-2459-1805","institution":"Curtin University School of Population Health","correspondingAuthor":true,"prefix":"","firstName":"Amanuel","middleName":"Tesfay","lastName":"Gebremedhin","suffix":""},{"id":166760692,"identity":"abdce72f-8be5-42e3-b2bd-1efdbca4abb8","order_by":1,"name":"Vera R Mitter","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Vera","middleName":"R","lastName":"Mitter","suffix":""},{"id":166760693,"identity":"4eb19421-32d6-4e47-ac70-b607d15e03e2","order_by":2,"name":"Bereket Duko","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Bereket","middleName":"","lastName":"Duko","suffix":""},{"id":166760694,"identity":"1d71af67-4480-4d55-a2a0-3663de69e6f9","order_by":3,"name":"Gizachew A Tessema","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Gizachew","middleName":"A","lastName":"Tessema","suffix":""},{"id":166760695,"identity":"199a6984-9831-42b9-babb-d8ffaf602874","order_by":4,"name":"Gavin F Pereira","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Gavin","middleName":"F","lastName":"Pereira","suffix":""}],"badges":[],"createdAt":"2023-01-10 10:10:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2462392/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2462392/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00404-023-07002-y","type":"published","date":"2023-03-20T20:06:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":44723200,"identity":"9f0a1f33-6e4d-4cf1-aa99-049349d497cc","added_by":"auto","created_at":"2023-10-16 20:14:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":555095,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2462392/v1/3b626c77-9fc3-41d8-b0bf-783ab0b1e02c.pdf"},{"id":31489894,"identity":"0ab2d788-cc26-4c6e-b4eb-49e05255327d","added_by":"auto","created_at":"2023-01-12 16:03:17","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7203132,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryAppendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-2462392/v1/0f41600e2989f4e4c3314307.docx"}],"financialInterests":"","formattedTitle":"Associations between endometriosis and adverse pregnancy and perinatal outcomes: a population-based cohort study","fulltext":[{"header":"What Does This Study Adds To The Clinical Work","content":"\u003cp\u003eWomen with endometriosis have a greater risk of preeclampsia, placenta previa, and preterm birth, which cannot be explained by the use of medically assisted reproduction. These findings may inform future obstetric care among this population. \u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eEndometriosis is a chronic inflammatory condition affecting women, where endometrial cells normally lining up the uterine cavity are found outside the uterus. Endometriosis can cause a variety of and sometimes unspecific symptoms with no to severe cyclic pain episodes, dyspareunia, dysmenorrhea, and subfertility.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e The disease highly affects the quality of life, and productivity, and causes high treatment and societal costs.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e It often takes 8\u0026ndash;12 years from symptom onset to surgical diagnosis \u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, leading to varying prevalence estimates (5\u0026ndash;50% in infertile women, up to 75% in cases with chronic pain).\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e In Australia, 11% of reproductive age women are affected with prevalence ranging from 2 to 11% in asymptomatic women.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Three-quarters of women with mild to moderate endometriosis can achieve pregnancy spontaneously, despite an increased risk of subfertility.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe association between endometriosis and adverse pregnancy outcomes has drawn more attention in recent years with fairly consistent evidence of increased risks for caesarean section, preterm birth, and stillbirth.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e However, the link with gestational diabetes, preeclampsia, or intrauterine growth restriction remains less clear due to heterogeneity in study designs and methodologies used in previous studies.\u003csup\u003e\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17 CR18 CR19 CR20\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e In epidemiology, it remains challenging to study the direct impact of endometriosis on pregnancy outcomes and underlying mechanisms are not well understood. Much of the existing research on this topic comes from small cohort studies at infertility clinics or single surgical centres,\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e which can produce results that are misinterpreted as evidence of no association rather than a lack of evidence for any association. Moreover, data limited to clinical settings are prone to selection bias as these participants may have better access to care, which may be linked to other health behaviours that affect pregnancy outcomes.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Classical study designs adopted by studies that do not have access to a wide range of potential risk factors may also be prone to residual confounding.\u003c/p\u003e \u003cp\u003eTo address some of these limitations, we used a \u0026lsquo;doubly robust estimator\u0026rsquo; to estimate the association between endometriosis and adverse pregnancy outcomes. This approach offers an opportunity to achieve unbiased inference while accounting for selection effects by combining inverse probability weighting and regression adjustment and allows for a causal interpretation of the results.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Findings from this approach can be directly interpreted as the risk of adverse pregnancy outcomes given that women had endometriosis as compared to the counterfactual scenario that they had no endometriosis. This causal interpretation is usually not possible from classical epidemiological approaches. This study aimed to estimate the effect (\u003cem\u003eaverage treatment effect)\u003c/em\u003e of endometriosis on adverse pregnancy and perinatal outcomes using a large population-based cohort in Western Australia (WA).\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy design\u003c/h2\u003e\n\u003cp\u003eWe conducted a population-based, longitudinal cohort study including all women 15 to 49 years of age with a singleton pregnancy in the period of 1980 to 2015 in WA. \u003c/p\u003e\n\u003ch2\u003eData sources and study population \u003c/h2\u003e\n\u003cp\u003eWe obtained maternal, infant and birth information from the Midwives Notification System, a validated database \u003csup\u003e26\u003c/sup\u003e that includes \u0026gt;99% of births in WA of at least 20 weeks\u0026rsquo; gestation or birthweight of 400 g or more if the gestational age was unknown.\u003csup\u003e27\u003c/sup\u003e We sourced hospitalization records from the Hospital Morbidity Data Collection, which includes information on all hospitalizations from public, private and day procedure facilities in the state with International Classification of Diseases (ICD-9/10\u003csup\u003eth\u003c/sup\u003e revision-Australian Modification) coded diagnoses.\u003csup\u003e28\u003c/sup\u003e Data sources have been described in detail elsewhere.\u003csup\u003e29\u003c/sup\u003e Data were probabilistically linked using best practice protocols through the WA Data Linkage Branch.\u003csup\u003e30\u003c/sup\u003e \u003c/p\u003e\n\u003cp\u003eFrom a total of 487,297 women (964,015 births) during the study period, we sequentially excluded multiple gestations, stillbirths, and pregnancies with missing information for gestational age, outcomes, maternal age, and socioeconomic status (SES). This resulted in 468,778 eligible women who contributed to 912,747 singleton pregnancies included in the analytic cohort (Fig. S1). \u003c/p\u003e\n\u003ch2\u003eExposure assessment \u003c/h2\u003e\n\u003cp\u003eWe identified all women with a principal or additional diagnosis of endometriosis from the hospital separation data using the International Classification of Diseases (ICD)-AM (Australian Modification) diagnostic codes consistent with ICD-9: 617.0-617.9; ICD-10: N80.0-N80.9 and Australian Classification of Health Interventions (ACHI) for endometriosis-related procedures (codes are shown in Table S5). Women were categorized as having endometriosis if they had hospital admission or surgical procedure coded as a diagnosis of endometriosis. We included women diagnosed before and after pregnancy in the primary analysis because recent studies documented a diagnosis delay of 8 to 12 years.\u003csup\u003e4-6\u003c/sup\u003e This approach has been adopted by other recent studies.\u003csup\u003e15, 19\u003c/sup\u003e\u003c/p\u003e\n\u003ch2\u003eOutcomes\u003c/h2\u003e\n\u003cp\u003eThe outcomes of interest were ascertained from the Midwives\u0026rsquo; Notifications System and hospital separation data in the state, with the diagnostic codes consistent with preeclampsia (ICD-9/ICD-9-CM: 642.4, 642.5, 642.7, ICD-10-AM: O14, O11) and placenta previa, with or without haemorrhage (ICD-9/ICD-9-CM: 641.0-641.1, ICD-10-AM: O44.-). The onset of preeclampsia at the gestational age between 20-34 weeks and after 34 weeks of gestation was classified as early or late-onset preeclampsia, respectively. Preterm birth was defined as birth before 37 completed weeks of gestation, categorized into moderate preterm birth (gestational week 32-36) and very preterm birth (prior to 32 gestational weeks). Further, we also categorized preterm birth into spontaneous (due to spontaneous onset of labour) and medically indicated (due to elective cesarean section, or induction of labour). The details of ICD codes used to define variables for analysis are presented in Table S5.\u003c/p\u003e\n\u003ch2\u003eCovariates\u003c/h2\u003e\n\u003cp\u003eInformation on potential confounding factors including the calendar year of birth of the child (categorical variable), maternal age group (15-24, 25-29, 30-34, 35-39, 40-49 years), parity (0, 1, 2, \u0026ge;3), smoking during pregnancy (Yes vs No), race/ethnicity (Caucasian versus non-Caucasian), and socioeconomic status (SES) was obtained from the databases. SES was measured using Socio-Economic Indexes for Areas (SEIFA). Specifically, we used the Index of Relative Socio-economic Disadvantage level at the time of birth of the child. These scores were obtained from the Australian Bureau of Statistics \u003csup\u003e31\u003c/sup\u003e and categorized into quintiles.\u003c/p\u003e\n\u003cp\u003eTo assess the potential impact of Medically Assisted Reproduction (MAR) or infertility treatment on perinatal outcomes, we identified pregnancies with MAR procedure (using ACHI) or the following ICD diagnostic codes; ICD-9: 628.0-628.9, V26.1-V26.9, and ICD-10-AM: N97.0-97.9; Z31.1-Z31.9. These codes cover ART (assisted reproductive technology) techniques, intrauterine insemination, and ovulation induction and might include some spontaneously conceived pregnancies in couples with fertility issues.\u003csup\u003e32\u003c/sup\u003e\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eWe first estimated the unadjusted relative risks (RRs) with 95% confidence intervals (CI) using Generalized Linear Models (GLM) fitted using a Poisson distribution with a log link function. Next, we estimated the causal effect (\u003cem\u003eaverage treatment effect\u003c/em\u003e) of endometriosis on adverse pregnancy outcomes using the potential outcome approach, which allows for the estimation of causal effects in large observational data.\u003csup\u003e33\u003c/sup\u003e We specifically used a \u003cem\u003edoubly robust estimation\u003c/em\u003e\u003cem\u003e\u003csup\u003e34\u003c/sup\u003e\u003c/em\u003e by combining the inverse probability of treatment weighting (IPTW, weight each person by the inverse of their propensity score) and the outcome regression model. The doubly robust estimation allows us to estimate the unbiased average causal effect when either the outcome regression model (traditional way of obtaining treatment effect) or the propensity score model (treatment selection model) is correctly specified.\u003csup\u003e24, 25\u003c/sup\u003e To estimate the adjusted RRs with 95% CI for each outcome, we fitted the \u003cem\u003eexposure\u003c/em\u003e \u003cem\u003emodel\u003c/em\u003e with maternal age, birth year, SES, ethnicity/race, and MAR treatment, and the \u003cem\u003eoutcome\u003c/em\u003e \u003cem\u003emodel\u003c/em\u003e with maternal age, birth year, SES, ethnicity/race and parity. As preeclampsia and placenta previa may influence the risk of preterm birth, gestational age (\u0026lt;32, 32-36, \u0026gt;37 weeks of gestation) was also included in the \u003cem\u003eexposure\u003c/em\u003e model as a covariate for all outcomes included except for preterm birth. Robust (sandwich) variance estimation was used to account for the effect of repeated pregnancies per mother.\u003csup\u003e34\u003c/sup\u003e To examine the influence of MAR on the association between endometriosis and adverse pregnancy outcomes, we included a sub-analysis stratified by MAR status (Table 3). To check the covariate balance after propensity score matching using IPTW, we performed diagnostics including standardized differences in means of all covariates (Fig. S2). The confounders included in the treatment weighting were decided based on prior knowledge as well as consideration of Directed Acyclic Graphs (DAGs) (Fig. S3). \u003c/p\u003e\n\u003ch2\u003eMissing data\u003c/h2\u003e\n\u003cp\u003eFor the main results, we conducted a complete case analysis as the proportion of missing data was small (\u0026lt;3%, range 0.6% for gestational age to 1.8% for SES). \u003c/p\u003e\n\u003ch2\u003eSensitivity analysis\u003c/h2\u003e\n\u003cp\u003eTo check the robustness of our findings, we conducted several sensitivity analyses. Firstly, to ascertain the sensitivity of our result to higher-order parity, we restricted the analysis to primiparous women. Secondly, to limit the possibility of misclassification bias, we conducted an analysis restricted to women (i) with a principal diagnosis of endometriosis, a diagnosis established to be chiefly responsible for occasioning an episode\u003csup\u003e28\u003c/sup\u003e ; (ii) with any diagnosis of endometriosis prior to the birth of the child to ensure endometriosis was present during pregnancy; (iii) with endometriosis diagnosis before delivery and up to five years after delivery; and (iv) considering endometriosis diagnosis at more than one-time point during five years look-up period. Thirdly, to explore the potential influence of maternal smoking during pregnancy, which was routinely collected in the Midwifery notification from 1997 onwards, we conducted a separate analysis adjusting for smoking. Next, we compared the effect of endometriosis on preterm birth (very preterm vs moderate). Fifth, we undertake a causal mediation analysis based on the counterfactual framework using a parametric regression approach\u003csup\u003e35\u003c/sup\u003e to estimate the natural direct effect of endometriosis compared with the natural indirect effect through MAR. Finally, to assess the extent of unmeasured confounding, we calculated E-values, which represent the minimum strength of association on the risk ratio scale, that any unmeasured confounder would need to have with both endometriosis and each outcome to fully explain away the observed association, conditional on the measured covariates.\u003csup\u003e36\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses were performed using Stata version 16.1 (Stata Corporation, College Station, Texas, USA).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eCohort characteristics\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn total, we included 912,747 eligible singleton births with a gestational age of 20-44 weeks from women (n=468,778) aged 15-49 years in the study period between 1980 and 2015 in WA. In these pregnancies 8,874 women (1.9%) had a diagnosis of endometriosis, corresponding to 19,476 pregnancies (2.1%). Women with endometriosis were on average of advanced age at the time of birth (\u0026gt;35 years), Caucasian, and had a higher proportion of medically assisted reproduction compared to women without endometriosis. Socio-economic status, parity, and ethnicity were similar among exposed and non-exposed groups (Table 1).\u003c/p\u003e\n\u003cp\u003eThe prevalence of pregnancy complications was higher among pregnancies of women with endometriosis compared to women without endometriosis (preeclampsia, 7.5% vs 6.1%; preterm birth, 10.2% vs 7.0%; placenta previa, 1.9% vs 1.0%; Table 2).\u003c/p\u003e\n\u003cp\u003eUsing doubly robust estimation, pregnancies in women with a diagnosis of endometriosis were associated with a higher risk of preeclampsia (RR 1.18, 95% CI 1.11- 1.26), placenta previa (RR 1.59, 95% CI 1.42- 1.79), and preterm birth (RR 1.45, 95% CI 1.37- 1.54). This risk associated with endometriosis was higher for medically indicated preterm birth (RR 1.74, 95% CI 1.58- 1.93) compared to spontaneous preterm birth (RR 1.40, 95% CI 1.27- 1.56) (Table 2). Furthermore, endometriosis was associated with both moderate and very preterm birth with a stronger association observed for very preterm birth (Table S3). In a stratified analysis based on MAR status, the higher risk of placenta previa and preterm birth persisted regardless of conception mode with the strongest effect estimates for the non-MAR group (RR 1.77, 95 % CI 1.50- 2.08 for placenta previa and RR 1.67, 95% CI 1.55-1.80 for preterm birth) and slightly attenuated effect estimates among the MAR group. However, for preeclampsia, the observed association disappeared when stratified by MAR status (Table 3).\u003c/p\u003e\n\u003cp\u003eResults of the sensitivity analyses\u003c/p\u003e\n\u003cp\u003eThe results restricted to nulliparous women were consistent with the main finding with a slight attenuation (Table S2; Model 2). Findings from the analyses restricted to a subset of the study population with different exposure definitions were very similar to those reported in the main analyses (Table S2; Model 3-6). Analyses restricted to pregnancies from women with endometriosis diagnosed before delivery also resulted in slightly higher risk estimates for all adverse pregnancy outcomes evaluated (Table S2; Model 4). Additionally, the pattern of the association between endometriosis and adverse pregnancy outcomes was similar when further adjusted to smoking status (Table S2; Model 7). Our mediation analyses suggest that the percentage of endometriosis effect on preterm birth and placenta previa that was mediated through MAR was 8% and 3% respectively. (Table S4). The E-values for the observed RRs varied from 1.64 to 2.87 for these three adverse pregnancy outcomes (Table S6).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrincipal findings\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo our knowledge, this is the first population-based retrospective cohort study to examine the association between endometriosis with adverse pregnancy outcomes using the potential outcome framework. Using a large (~ 1 million births) cohort in WA, we observed a higher risk of preeclampsia, placenta previa, and preterm birth among pregnancies in women with endometriosis as compared to women without endometriosis. The associations persisted after stratification for conception mode (MAR or natural conception, non-MAR), with an elevated risk among the non-MAR group meaning the risks observed were attenuated among pregnancies conceived by MAR. The risk for adverse pregnancy outcomes was higher (approximately 24%, 56%, and 85% of increased risk of preeclampsia, preterm birth, and placenta previa respectively) when we restricted our sample to women with endometriosis as the principal diagnosis code, suggesting probably more severe disease. These observed associations were not mediated through MAR.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStrengths and limitations \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur cohort was based on longitudinally linked, highly reliable sources of population-based perinatal information ascertained from hospital separations and midwives\u0026rsquo; notifications. We also included sensitivity analysis to check the robustness of our result. Our cohort is less prone to exposure misclassification bias since the hospital morbidity data collection (source data for our exposure) contains records for all hospital separations of admitted patients from all public and private hospitals in WA.\u0026nbsp;Furthermore, our study restricted the analysis to singleton pregnancies, which improved generalizability to other similar cohorts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe only had information on the diagnosis of endometriosis for women who have been hospitalized during the study period. Such data may likely represent more severe stages of endometriosis. The clinical routines and obstetric care have changed through the years and the diagnosis and awareness regarding endometriosis have evolved. However, to minimize this bias our model included the birth year of the child as a covariate.\u0026nbsp;In our main analysis, we included all women with any diagnosis of endometriosis (principal and additional diagnosis). This could have introduced non-differential misclassification bias (i.e., independent of the outcome), and therefore will potentially bias the results towards the null. To limit this possible misclassification, we included a sensitivity analysis restricted to an exposure defined as a principal diagnosis of endometriosis, which indicated higher risk estimates as compared to the main result. We opted to include women with a diagnosis of endometriosis before and after pregnancy to account for the diagnostic delay.\u003csup\u003e4, 5\u003c/sup\u003e This could induce similar misclassification bias and attenuation of the association. Indeed, our sensitivity analysis restricted to a diagnosis of endometriosis before delivery consistently suggested higher effect estimates as compared to the main analysis.\u0026nbsp;Though the validity of the diagnosis of endometriosis in the hospital separation database remains unknown, previous analysis of the same database suggested that endometriosis is reliably recorded in the hospital separation data.\u003csup\u003e37\u003c/sup\u003e Moreover, while the use of ICD and procedure codes ensures that those classified as having endometriosis are likely true cases, there is the possibility that the comparison group may have undiagnosed endometriosis.\u0026nbsp; Our cohort had a relatively small number of events for stillbirths to be considered as an outcome. We, therefore, excluded pregnancies resulting in stillbirths from our analysis. This may have introduced a livebirth bias in the association between endometriosis and adverse pregnancy outcomes. However, a previous simulation study indicated that the magnitude of this bias is small.\u003csup\u003e38\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eInterpretation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe reported association between endometriosis and preeclampsia have been mixed, with some suggesting no association\u003csup\u003e14\u003c/sup\u003e or decreased risk,\u003csup\u003e17\u003c/sup\u003e possibly owing to heterogeneity in exposure or outcome definition and study population (selection bias) not taking MAR into account.\u003csup\u003e20, 39\u003c/sup\u003e Our study observed a modest association between endometriosis and preeclampsia, which is consistent with previous studies,\u0026nbsp;\u003csup\u003e12, 13, 15, 20, 21\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe attenuated risk in women who received MAR treatment is supported by other studies that did not find an association or a reduced association between endometriosis and preeclampsia or hypertension in pregnancy in women\u0026nbsp;with an endometriosis and MAR procedure.\u003csup\u003e40, 41\u003c/sup\u003e Our study also found an increased risk of placenta previa in pregnancies among women with endometriosis, which is consistent with other research.\u003csup\u003e20\u003c/sup\u003e It has been suggested that the association may be confounded by the increased use of MAR in women with endometriosis. In our study, the association persisted even after stratification by MAR status, with a stronger association observed in non-MAR women\u0026nbsp;which is\u0026nbsp;consistent with other studies.\u003csup\u003e40\u003c/sup\u003e In our study, for women using MAR, the precision of the effect estimates was reduced likely because of the small sample size.\u0026nbsp;For preterm birth, we observed higher risk estimates for very preterm deliveries compared to moderate preterm deliveries, and an association between endometriosis and both spontaneous and medically indicated preterm birth, with a stronger association for medically indicated preterm birth.\u0026nbsp;This could imply that pregnancies from women with endometriosis are more likely to be induced or delivered through a cesarean section before gestational week 37.\u0026nbsp;This finding is consistent with previous research and the association seems independent of MAR.\u0026nbsp;\u003csup\u003e20, 41, 42\u003c/sup\u003e A smaller protective effect was also observed in a Canadian study.\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eEndometriosis may be associated with adverse pregnancy outcomes through various mechanisms, including effects on the uterine environment, progesterone signalling, and the remodelling of the spiral artery.\u003csup\u003e18, 43, 44\u003c/sup\u003e These factors may play a role in the association with preeclampsia, preterm birth and intrauterine growth restriction.\u003csup\u003e45-47\u003c/sup\u003e Endometriotic lesions in the uterus may also reduce uterine contractility and cause abnormal implantation, leading to placenta previa.\u003csup\u003e39\u003c/sup\u003e In our sub-analysis of the timing of preeclampsia, we found an elevated risk for early onset compared to late-onset preeclampsia, which can be accounted for inadequate and incomplete trophoblast invasion of maternal spiral arteries.\u003csup\u003e43\u003c/sup\u003e MAR treatment itself has shown to be a risk factor for adverse pregnancy outcomes, with mixed results for women with endometriosis.\u003csup\u003e47\u003c/sup\u003e In MAR treatment, the effects caused by endometriosis such as inflammatory processes and regulatory disbalances are suppressed offering a better pregnancy environment and could explain the attenuation in the risks seen in our study in the MAR pregnancies group.\u003csup\u003e48\u003c/sup\u003e Women conceiving following MAR might also have support from better obstetric care and closer screening for adverse outcomes.\u003c/p\u003e\n\u003cp\u003eIn this study, a stronger association between endometriosis and adverse pregnancy outcomes was observed, but residual and/or unmeasured confounding could not be completely ruled out. Nevertheless, the E-values for the observed RRs (ranging from 1.64 to 2.87) indicated that substantial confounding would need to explain away these associations (Table S6). Systematic reviews that examined the risk factors for endometriosis, for example, reported RRs ranging from 1.63 for smoking to 1.87 for overweight \u0026ndash; lower than that of the E-values.\u0026nbsp;\u003csup\u003e49, 50\u003c/sup\u003e In general, findings from our sensitivity analyses were remarkably similar to those reported for the main analysis and collectively support the hypothesis that endometriosis is associated with adverse pregnancy outcomes independent of MAR. Therefore, knowledge of a patient\u0026apos;s endometriosis history may inform targeted prenatal care and reduce unfavourable pregnancy outcomes. Future studies would benefit from elucidating the potential mechanism that might explain how endometriosis affects implantation, placentation, and fetal growth and identifying potential interventions to decrease the risk of adverse perinatal outcomes.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, regardless of the use of medically assisted reproduction, endometriosis is associated with an increased risk of preeclampsia, placenta previa, and preterm birth. These findings offer new insight into the causal association between endometriosis on adverse pregnancy outcomes, taking MAR into account. This may help to enhance future obstetric care among this population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Data Linkage Branch (Department of Health Western Australia) as well as the Data custodian for the Midwives Notification System and Hospital Morbidity Data Collection for providing data for this project.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003eATG: project development, data management and analysis, initial data interpretation, wrote the first draft of the manuscript. VRM, BD, GAT, and GP: substantial contributions to the statistical analyses, data interpretation, and critical revisions of the subsequent drafts of the manuscript for important intellectual content. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e GP was supported with funding from the National Health and Medical Research Council Project and Investigator Grants #1099655 and #1173991, and the Research Council of Norway through its Centres of Excellence funding scheme #262700. GAT was supported with funding from the National Health and Medical Research Council Investigator Grant #1195716. The funders had no role in the analysis, interpretations of the results, writing of the reports, and the decision to submit the paper for possible publication. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors have no potential conflicts of interest to disclose.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u0026nbsp;\u003c/strong\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki. This research was approved by the Human Research Ethics Committee (HREC approval 2016/51) from the Department of Health, WA. The Ethics Committee approval was accepted on 14 September 2016.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u0026nbsp;\u003c/strong\u003e Consent for the study was obtained from the data custodians. As the study was based on routinely collected de-identified linked administrative data, individual consent from the participants was not obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e No additional data are available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMacer ML, Taylor HS. Endometriosis and infertility: a review of the pathogenesis and treatment of endometriosis-associated infertility. Obstetrics and Gynecology Clinics. 2012;39(4):535-49.\u003c/li\u003e\n\u003cli\u003eFarquhar C. Endometriosis. Bmj. 2007 Feb 3;334(7587):249-53.\u003c/li\u003e\n\u003cli\u003eNnoaham KE, Hummelshoj L, Webster P, d\u0026apos;Hooghe T, de Cicco Nardone F, de Cicco Nardone C, et al. Reprint of: Impact of endometriosis on quality of life and work productivity: a multicenter study across ten countries. 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Persistent high rates of hysterectomy in Western Australia: a population‐based study of 83 000 procedures over 23 years. BJOG: An International Journal of Obstetrics \u0026amp; Gynaecology. 2006;113(7):804-9.\u003c/li\u003e\n\u003cli\u003eLiew Z, Olsen J, Cui X, Ritz B, Arah OA. Bias from conditioning on live birth in pregnancy cohorts: an illustration based on neurodevelopment in children after prenatal exposure to organic pollutants. International journal of epidemiology. 2015;44(1):345-54.\u003c/li\u003e\n\u003cli\u003eLeone Roberti Maggiore U, Ferrero S, Mangili G, Bergamini A, Inversetti A, Giorgione V, et al. A systematic review on endometriosis during pregnancy: diagnosis, misdiagnosis, complications and outcomes. Hum Reprod Update. 2016 Jan-Feb;22(1):70-103.\u003c/li\u003e\n\u003cli\u003eEpelboin S, Labrosse J, Fauque P, Levy R, Gervoise-Boyer MJ, Devaux A, et al. Endometriosis and assisted reproductive techniques independently related to mother-child morbidities: a French longitudinal national study. Reprod Biomed Online. 2021 Mar;42(3):627-33.\u003c/li\u003e\n\u003cli\u003eIbiebele I, Nippita T, Baber R, Torvaldsen S. Pregnancy outcomes in women with endometriosis and/or ART use: a population-based cohort study. Human Reproduction. 2022.\u003c/li\u003e\n\u003cli\u003eP\u0026eacute;rez-L\u0026oacute;pez F, Villagrasa-Boli P, Mu\u0026ntilde;oz-Olarte M, Morera-Grau \u0026Aacute;, Cruz-Andr\u0026eacute;s P, Hernandez A. Health Outcomes and Systematic Analyses (HOUSSAY) Project. Association between endometriosis and preterm birth in women with spontaneous conception or using assisted reproductive technology: a systematic review and meta-analysis of cohort studies. Reprod Sci. 2018;25:311-9.\u003c/li\u003e\n\u003cli\u003eFiorentino G, Cimadomo D, Innocenti F, Soscia D, Vaiarelli A, Ubaldi FM, et al. Biomechanical forces and signals operating in the ovary during folliculogenesis and their dysregulation: implications for fertility. Hum Reprod Update. 2022.\u003c/li\u003e\n\u003cli\u003eJoshi NR, Miyadahira EH, Afshar Y, Jeong J-W, Young SL, Lessey BA, et al. Progesterone resistance in endometriosis is modulated by the altered expression of microRNA-29c and FKBP4. The Journal of Clinical Endocrinology \u0026amp; Metabolism. 2017;102(1):141-9.\u003c/li\u003e\n\u003cli\u003eKunz G, Beil D, Huppert P, Leyendecker G. Structural abnormalities of the uterine wall in women with endometriosis and infertility visualized by vaginal sonography and magnetic resonance imaging. Human Reproduction. 2000;15(1):76-82.\u003c/li\u003e\n\u003cli\u003eBrosens I, Pijnenborg R, Benagiano G. Defective myometrial spiral artery remodelling as a cause of major obstetrical syndromes in endometriosis and adenomyosis. Placenta. 2013;34(2):100-5.\u003c/li\u003e\n\u003cli\u003eVigano P, Corti L, Berlanda N. Beyond infertility: obstetrical and postpartum complications associated with endometriosis and adenomyosis. Fertil Steril. 2015 Oct;104(4):802-12.\u003c/li\u003e\n\u003cli\u003ePirtea P, de Ziegler D, Ayoubi JM. Effects of endometriosis on assisted reproductive technology: gone with the wind. Fertility and Sterility. 2021;115(2):321-2.\u003c/li\u003e\n\u003cli\u003eBravi F, Parazzini F, Cipriani S, Chiaffarino F, Ricci E, Chiantera V, et al. Tobacco smoking and risk of endometriosis: a systematic review and meta-analysis. BMJ open. 2014;4(12):e006325.\u003c/li\u003e\n\u003cli\u003eJenabi E, Khazaei S, Veisani Y. The association between body mass index and the risk of endometriosis: A meta-analysis. Journal of Endometriosis and Pelvic Pain Disorders. 2019;11(2):55-61.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Maternal characteristics according to endometriosis status for women delivering singleton births during 1980-2015 in WA (n=912,747 pregnancies).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEndometriosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo endometriosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003eN=912,747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e(n=19,476)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e(n=893,271)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003eMaternal age, y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003e15-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e225,204 (24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e4,855 (24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e220,349 (24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003e25-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e292,416 (32.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e6,037 (31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e286,379 (32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003e30-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e260,947 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e5,334 (27.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e255,613 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003e35-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e113,489 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e2,731 (14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e110,758 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003e40-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e20,691 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e519 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e20,172 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003eSES in quintiles\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003e\u0026lt;20th percentile\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e181,980 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e3,860 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e178,120 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003e20-39th percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e182,472 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e4,015 (20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e178,457 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003e40-59th percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e182,685 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e3,765 (19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e178,920 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003e60-79th percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e182,731 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e3,964 (20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e178,767 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003e\u0026gt;=80th percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e182,879 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e3,872 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e179,007 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003eTime period of birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003e1980-1984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e102,077 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e1,494 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e100,583 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003e1985-1989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e115,682 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e2,843 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e112,839 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003e1990-1994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e121,186 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e3,791 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e117,395 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003e1995-1999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e121,670 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e3,639 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e118,031 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003e2000-2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e119,334 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e2,982 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e116,352 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003e2005-2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e141,603 (15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e2,681 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e138,922 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003e2010-2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e191,195 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e2,046 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e189,149 (21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003eParity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003efirst birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e368,504 (40.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e7,516 (38.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e360,988 (40.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e309,240 (33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e6,557 (33.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e302,683 (33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003e3\u003csup\u003erd\u0026nbsp;\u003c/sup\u003ebirth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e148,061 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e3,375 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e144,686 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003e\u0026gt;=4 birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e86,869 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e2,028 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e84,841 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e73 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e73 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003eMAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e890,327 (97.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e16,198 (83.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e874,129 (97.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e22,420 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e3,278 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e19,142 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003eSmoking during pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e427,544 (46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e7,782 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e419,762 (47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e80,709 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e1,539 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e79,170 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e404,494 (44.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e10,155 (52.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e394,339 (44.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003eEthnicity (race)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e759,584 (83.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e17,734 (91.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e741,850 (83.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.509933774834437%\"\u003e\n \u003cp\u003eNon-Caucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.70860927152318%\"\u003e\n \u003cp\u003e153,163 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.543046357615893%\"\u003e\n \u003cp\u003e1,742 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.52980132450331%\"\u003e\n \u003cp\u003e151,421 (17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMAR: Medically Assisted Reproduction;\u003cem\u003e\u0026nbsp;\u003c/em\u003eSES: Socio-economic status\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Crude and adjusted Risk ratio (RR) and 95 % CI for each adverse pregnancy outcome for women with endometriosis among 912,747 singleton births in WA, 1980-2015.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"674\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 15.2362%;\" valign=\"top\" width=\"19.555555555555557%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3543%;\" valign=\"top\" width=\"19.703703703703702%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo endometriosis n=893,271 (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6378%;\" valign=\"top\" width=\"16%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEndometriosis n=19,476 (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 12.2835%;\" valign=\"top\" width=\"15.703703703703704%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnadjusted RR (95 % CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5094%;\" valign=\"top\" width=\"29.037037037037038%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted RR (95 % CI) using doubly robust estimation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 15.2362%;\" valign=\"top\" width=\"0%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreeclampsia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3543%;\" valign=\"top\" width=\"0%\"\u003e\n \u003cp\u003e54,098 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6378%;\" valign=\"top\" width=\"0%\"\u003e\n \u003cp\u003e1,468 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 13.7008%;\" valign=\"top\" width=\"0%\"\u003e\n \u003cp\u003e1.24 (1.18, 1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.252%;\" valign=\"top\" width=\"0%\"\u003e\n \u003cp\u003e1.18 (1.11, 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 15.2362%;\" valign=\"top\" width=\"0%\"\u003e\n \u003cp\u003eEarly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3543%;\" valign=\"top\" width=\"0%\"\u003e\n \u003cp\u003e4,749 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6378%;\" valign=\"top\" width=\"0%\"\u003e\n \u003cp\u003e157 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 13.7008%;\" valign=\"top\" width=\"0%\"\u003e\n \u003cp\u003e1.52 (1.30, 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.252%;\" valign=\"top\" width=\"0%\"\u003e\n \u003cp\u003e1.50 (1.12, 2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 15.2362%;\" valign=\"top\" width=\"19.555555555555557%\"\u003e\n \u003cp\u003eLate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3543%;\" valign=\"top\" width=\"19.703703703703702%\"\u003e\n \u003cp\u003e32,240 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6378%;\" valign=\"top\" width=\"16%\"\u003e\n \u003cp\u003e823 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 13.7008%;\" valign=\"top\" width=\"17.48148148148148%\"\u003e\n \u003cp\u003e1.17 (1.09, 1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.252%;\" valign=\"top\" width=\"25.333333333333332%\"\u003e\n \u003cp\u003e1.06 (0.92, 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 15.2362%;\" valign=\"top\" width=\"0%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlacenta previa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3543%;\" valign=\"top\" width=\"0%\"\u003e\n \u003cp\u003e8,901 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6378%;\" valign=\"top\" width=\"0%\"\u003e\n \u003cp\u003e364 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 13.7008%;\" valign=\"top\" width=\"0%\"\u003e\n \u003cp\u003e1.88 (1.69, 2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.252%;\" valign=\"top\" width=\"0%\"\u003e\n \u003cp\u003e1.59 (1.42, 1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 15.2362%;\" valign=\"top\" width=\"19.555555555555557%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreterm birth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3543%;\" valign=\"top\" width=\"19.703703703703702%\"\u003e\n \u003cp\u003e62,706 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6378%;\" valign=\"top\" width=\"16%\"\u003e\n \u003cp\u003e1,989 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 13.7008%;\" valign=\"top\" width=\"17.48148148148148%\"\u003e\n \u003cp\u003e1.45 (1.39, 1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.252%;\" valign=\"top\" width=\"25.333333333333332%\"\u003e\n \u003cp\u003e1.45 (1.37, 1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4.252%;\" valign=\"top\" width=\"5.62962962962963%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9843%;\" valign=\"top\" width=\"13.925925925925926%\"\u003e\n \u003cp\u003eSpontaneous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3543%;\" valign=\"top\" width=\"19.703703703703702%\"\u003e\n \u003cp\u003e36,034 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6378%;\" valign=\"top\" width=\"16%\"\u003e\n \u003cp\u003e978 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 13.7008%;\" valign=\"top\" width=\"17.48148148148148%\"\u003e\n \u003cp\u003e1.27 (1.16, 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.252%;\" valign=\"top\" width=\"25.333333333333332%\"\u003e\n \u003cp\u003e1.40 (1.27, 1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4.252%;\" valign=\"top\" width=\"5.62962962962963%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9843%;\" valign=\"top\" width=\"13.925925925925926%\"\u003e\n \u003cp\u003eIndicated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3543%;\" valign=\"top\" width=\"19.703703703703702%\"\u003e\n \u003cp\u003e26,671 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6378%;\" valign=\"top\" width=\"16%\"\u003e\n \u003cp\u003e1,011 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 13.7008%;\" valign=\"top\" width=\"17.48148148148148%\"\u003e\n \u003cp\u003e1.62 (1.48, 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.252%;\" valign=\"top\" width=\"25.333333333333332%\"\u003e\n \u003cp\u003e1.74 (1.58, 1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDoubly robust estimation: the \u003cem\u003eexposure\u003c/em\u003e model included maternal age, birth year, SES, ethnicity/race, and MAR treatment, and the \u003cem\u003eoutcome\u003c/em\u003e model included maternal age, birth year, SES, parity, and ethnicity/race. The outcome model for preeclampsia and placenta previa also included gestational age. \u003cem\u003eSES: Socio-economic status; MAR: Medically Assisted Reproduction; RR: Risk Ratio; CI: Confidence interval; n: Total number of pregnancies from women with endometriosis\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Crude and adjusted Risk ratio (RR) and 95 % CI for each adverse pregnancy outcome for women with endometriosis stratified by MAR status among 912,747 singleton births in WA, 1980-2015\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"1008\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"9.433962264150944%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" width=\"42.50248262164846%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMAR (n=22, 420)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" width=\"41.112214498510426%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-MAR (n=890,327)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.951340615690169%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"9.433962264150944%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.05759682224429%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.334657398212512%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePE (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.711022840119165%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude RR (95 % CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.399205561072492%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted RR (95 % CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.05759682224429%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.440913604766633%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePE (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.512413108242304%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude RR (95 % CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.101290963257199%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted RR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95 % CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.951340615690169%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"4.766633565044687%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"10.724925521350546%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEndometriosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.334657398212512%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.711022840119165%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.399205561072492%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.05759682224429%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.440913604766633%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.512413108242304%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.101290963257199%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"6.951340615690169%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.286839145106861%\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.861642294713161%\"\u003e\n \u003cp\u003e3,278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.573678290213723%\"\u003e\n \u003cp\u003e214 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.398200224971879%\"\u003e\n \u003cp\u003e0.99 (0.86, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.310461192350957%\"\u003e\n \u003cp\u003e0.86 (0.69, 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.861642294713161%\"\u003e\n \u003cp\u003e16,198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.561304836895388%\"\u003e\n \u003cp\u003e1,254 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.173228346456693%\"\u003e\n \u003cp\u003e1.28 (1.21, 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.973003374578179%\"\u003e\n \u003cp\u003e1.22 (1.10, 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.286839145106861%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.861642294713161%\"\u003e\n \u003cp\u003e19,142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.573678290213723%\"\u003e\n \u003cp\u003e1,256 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.398200224971879%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.310461192350957%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.861642294713161%\"\u003e\n \u003cp\u003e874,129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.561304836895388%\"\u003e\n \u003cp\u003e52, 842 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.173228346456693%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.973003374578179%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" width=\"4.766633565044687%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.667328699106256%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.05759682224429%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.334657398212512%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePP (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.711022840119165%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude RR (95 % CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.399205561072492%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted RR (95 % CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.05759682224429%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.440913604766633%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePP (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.512413108242304%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude RR (95 % CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.101290963257199%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted RR (95 % CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" width=\"6.951340615690169%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"12.148481439820022%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEndometriosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.573678290213723%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.398200224971879%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.310461192350957%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.861642294713161%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.561304836895388%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.173228346456693%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.973003374578179%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.286839145106861%\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.861642294713161%\"\u003e\n \u003cp\u003e3278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.573678290213723%\"\u003e\n \u003cp\u003e82 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.398200224971879%\"\u003e\n \u003cp\u003e1.20 (0.95, 1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.310461192350957%\"\u003e\n \u003cp\u003e1.11 (0.83, 1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.861642294713161%\"\u003e\n \u003cp\u003e16,198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.561304836895388%\"\u003e\n \u003cp\u003e282 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.173228346456693%\"\u003e\n \u003cp\u003e1.79 (1.59, 2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.973003374578179%\"\u003e\n \u003cp\u003e1.77 (1.50, 2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.286839145106861%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.861642294713161%\"\u003e\n \u003cp\u003e19142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.573678290213723%\"\u003e\n \u003cp\u003e400 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.398200224971879%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.310461192350957%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.861642294713161%\"\u003e\n \u003cp\u003e874,129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.561304836895388%\"\u003e\n \u003cp\u003e8,501 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.173228346456693%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.973003374578179%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" width=\"4.766633565044687%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePTB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.667328699106256%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.05759682224429%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.334657398212512%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePTB (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.711022840119165%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude RR (95 % CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.399205561072492%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted RR (95 % CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.05759682224429%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.440913604766633%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePTB (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.512413108242304%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude RR (95 % CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.101290963257199%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted RR (95 % CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" width=\"6.951340615690169%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"12.148481439820022%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEndometriosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.573678290213723%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.398200224971879%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.310461192350957%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.861642294713161%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.561304836895388%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.173228346456693%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.973003374578179%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.286839145106861%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.861642294713161%\"\u003e\n \u003cp\u003e3278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.573678290213723%\"\u003e\n \u003cp\u003e416 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.398200224971879%\"\u003e\n \u003cp\u003e1.21 (1.09, 1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.310461192350957%\"\u003e\n \u003cp\u003e1.26 (1.09, 1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.861642294713161%\"\u003e\n \u003cp\u003e16,198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.561304836895388%\"\u003e\n \u003cp\u003e1,573 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.173228346456693%\"\u003e\n \u003cp\u003e1.40 (1.33, 1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.973003374578179%\"\u003e\n \u003cp\u003e1.67 (1.55, 1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.286839145106861%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.861642294713161%\"\u003e\n \u003cp\u003e19142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.573678290213723%\"\u003e\n \u003cp\u003e2,011 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.398200224971879%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.310461192350957%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.861642294713161%\"\u003e\n \u003cp\u003e874,129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.561304836895388%\"\u003e\n \u003cp\u003e60,695 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.173228346456693%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.973003374578179%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAll adjusted RRs presented in this table are estimated using a doubly robust estimation: the \u003cem\u003eexposure\u003c/em\u003e model included maternal age, birth year, SES, and ethnicity/race, and the \u003cem\u003eoutcome\u003c/em\u003e model included maternal age, birth year, SES, parity, and ethnicity/race. The \u003cem\u003eoutcome\u003c/em\u003e model for preeclampsia and placenta previa also included gestational age. \u003cem\u003ePE: preeclampsia; PP: placenta previa; PTB: Preterm birth; SES: Socio-economic status; MAR: Medically Assisted Reproduction; RR: Risk Ratio; CI: Confidence interval; n: Total number of pregnancies from women with endometriosis;\u003c/em\u003e\u003csup\u003ea\u003c/sup\u003eP-value for interaction test\u0026nbsp;\u003c/p\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":"archives-of-gynecology-and-obstetrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arch","sideBox":"Learn more about [Archives of Gynecology and Obstetrics](https://www.springer.com/journal/404)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/arch/default.aspx","title":"Archives of Gynecology and Obstetrics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"endometriosis, preeclampsia, placenta previa, preterm birth, medically assisted reproduction","lastPublishedDoi":"10.21203/rs.3.rs-2462392/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2462392/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo examine the association between endometriosis and adverse pregnancy and perinatal outcomes (preeclampsia, placenta previa, and preterm birth).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA population-based retrospective cohort study was conducted among 468,778 eligible women who contributed 912,747 singleton livebirths between 1980 and 2015 in Western Australia (WA). We used probabilistically linked perinatal and hospital separation data from the WA data linkage system\u0026rsquo;s Midwives Notification System and Hospital Morbidity Data Collection databases. We used a doubly robust estimator by combining the inverse probability weighting with the outcome regression model to estimate adjusted risk ratios (RR) and 95% confidence intervals (CIs).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThere were 19,476 singleton livebirths among 8,874 women diagnosed with endometriosis. Using a doubly robust estimator, we found pregnancies in women with endometriosis to be associated with an increased risk of preeclampsia with RR of 1.18, 95% CI 1.11\u0026ndash;1.26, placenta previa (RR, 1.59, 95% CI 1.42\u0026ndash;1.79) and preterm birth (RR 1.45, 95% CI 1.37\u0026ndash;1.54). The observed association persisted after stratified by the use of Medically Assisted Reproduction, with a slightly elevated risk among pregnancies conceived spontaneously.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn this large population-based cohort, endometriosis is associated with an increased risk of preeclampsia, placenta previa, and preterm birth, independent of the use of Medically Assisted Reproduction. This may help to enhance future obstetric care among this population.\u003c/p\u003e","manuscriptTitle":"Associations between endometriosis and adverse pregnancy and perinatal outcomes: a population-based cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-01-12 16:03:12","doi":"10.21203/rs.3.rs-2462392/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2023-01-11T06:22:35+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-01-11T06:18:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Archives of Gynecology and Obstetrics","date":"2023-01-10T21:52:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-01-10T15:05:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Archives of Gynecology and Obstetrics","date":"2023-01-10T05:10:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"archives-of-gynecology-and-obstetrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arch","sideBox":"Learn more about [Archives of Gynecology and Obstetrics](https://www.springer.com/journal/404)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/arch/default.aspx","title":"Archives of Gynecology and Obstetrics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"48b2fd39-21cc-4d14-90b2-b0eb644fc95b","owner":[],"postedDate":"January 12th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T20:10:48+00:00","versionOfRecord":{"articleIdentity":"rs-2462392","link":"https://doi.org/10.1007/s00404-023-07002-y","journal":{"identity":"archives-of-gynecology-and-obstetrics","isVorOnly":false,"title":"Archives of Gynecology and Obstetrics"},"publishedOn":"2023-03-20 20:06:53","publishedOnDateReadable":"March 20th, 2023"},"versionCreatedAt":"2023-01-12 16:03:12","video":"","vorDoi":"10.1007/s00404-023-07002-y","vorDoiUrl":"https://doi.org/10.1007/s00404-023-07002-y","workflowStages":[]},"version":"v1","identity":"rs-2462392","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2462392","identity":"rs-2462392","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}