Risk factors for preterm labor: An Umbrella Review of meta-analyses of observational studies

Risk factors for preterm labor: An Umbrella Review of meta-analyses of observational studies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Risk factors for preterm labor: An Umbrella Review of meta-analyses of observational studies Ioannis Mitrogiannis, Evangelos Evangelou, Athina Efthymiou, Theofilos Kanavos, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2639005/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Preterm birth defined as delivery before 37 gestational weeks, is a leading cause of neonatal and infant morbidity and mortality. Understanding its multifactorial nature may improve prediction, prevention and the clinical management. We performed an umbrella review to summarize the evidence from meta-analyses of observational studies on risks factors associated with PTB, evaluate whether there are indications of biases in this literature and identify which of the previously reported associations are supported by robust evidence. We included 1511 primary studies providing data on 170 associations, covering a wide range of comorbid diseases, obstetric and medical history, drugs, exposure to environmental agents, infections and vaccines. Only seven risk factors provided robust evidence. The results from synthesis of observational studies suggests that sleep quality and mental health, risk factors with robust evidence should be routinely screened in clinical practice, should be tested in large randomized trial. Identification of risk factors with robust evidence will promote the development and training of prediction models that could improve public health, in a way that offers new perspectives in health professionals. Health sciences/Health care/Disease prevention/Preventive medicine Health sciences/Medical research/Epidemiology Health sciences/Risk factors Health sciences/Health care/Public health/Epidemiology Figures Figure 1 Introduction Preterm Birth (PTB) is defined as delivery before 37 gestational weeks and is a leading cause of infant morbidity and mortality [ 1 – 4 ]. 15 million babies are estimated to be born preterm every year and the PTB rate ranges between 5–18% worldwide [ 3 ] (PTB rates in USA:12–13% [ 1 , 2 ]; in Europe is 5–9% [ 2 ]). Advances in neonatology and the administration of corticosteroids before birth have improved significantly the prognosis of babies born preterm. In contrast, although vigorous research, costing millions of dollars, was carried out during the last 40 years, focusing in the prediction and prevention of preterm birth its incidence remains relatively unchanged. The most probable explanation is that preterm birth is a syndrome, rather than a single disease and many different causes may be responsible [ 161 ]. Numerous systematic reviews and meta-analyses have assessed various, non-genetic risk factors of preterm labor. Several environmental and clinical parameters such as present pregnancy characteristics, previous pregnancy history [ 4 ], infections [ 7 , 8 ], environmental exposures, pharmaceutical factors [ 9 , 10 ] and surgical interventions have been proposed as plausible factors related to PTB. Identifying robust risk factors for PTB should either help us define a study population for specific interventions, allocate available resources effectively and allow risk-specific treatment and understanding the mechanism leading to PTB [ 1 ]. However, the exact causes of this syndrome are still mostly unknown and the contribution of every risk factor in terms of prevention is still questionable. To our knowledge there is no previous effort to summarize existing evidence of meta-analyses of non-genetic risk factors for PTB. We conducted an umbrella review across published meta-analyses of observational studies with the goal to map the existing evidence and critically evaluate the reported associations applying stringent criteria that assess potential systematic biases and we highlight previously studied associations that provide robust evidence of association. Results Description of Eligible meta-analyses The search identified 2769 items, of which 2239 were excluded after review of the title and abstract (Fig. 1 , PRISMA Flowchart). Of the remaining 530 articles that were reviewed in full text, eight articles did not report the appropriate information for the calculation of excess of statistical significance (either because the total sample size was missing or the study-specific relative risk estimates were missing), and 98 articles were excluded because a larger systematic review or meta-analysis investigating the same risk factor was available. From the 223 comparisons, we further excluded the ones that included one or two studies (53 comparisons). Therefore, 219 articles were analyzed, of which 133 were systematic reviews without any quantitative component and 86 were meta-analyses. The 86 eligible meta-analyses [ 26 – 109 , 116 – 118 ] included data on 170 comparisons and 1511 primary studies. Summary Effect-sizes And Significant Findings Three to 152 studies, with a median of nine studies, were included per meta-analysis. The median number of case and control subjects in each study was 88 and 529, respectively. The median number of case and control subjects in each meta-analysis was 98 and 807, respectively. The number of cases was greater than 1000 in 98 comparisons. Overall, 578 (45%) individual studies observed nominally statistically significant results. 40 meta-analyses used the Newcastle–Ottawa Scale to assess qualitatively the included primary studies. One meta-analysis used assessment criteria for non-randomized observational studies adapted from Duckitt and Harrington, 3 meta-analyses used the Methodological Index for Non-Randomized Studies (MINORS) and 38 meta-analyses used other assessment tools. Four meta-analyses did not perform any quality assessment. Details of the 170 comparisons that included 1511 individual study estimates are summarized in Supplemental Table 1. Of the 170 comparisons, 100(58,8%) had nominally statistically significant findings at P < 0.05 using the random-effects model, of which 94 reported an increased risk and six a decreased risk for preterm birth (preconception care vs no care, magnesium supplementation vs placebo ,single vs double embryo transfer, high gestational weight gain vs normal gestational weight gain, IPI following miscarriage 6m, greenery including only a 100-m NDVI buffer). Of these, a total of 62(36,5%) associations presented statistically significant effect at P < 0.001, while only 41 (24,1%) remained significant after the application of a more stringent P-value threshold of P 75%) heterogeneity estimates (Supplemental Table 1). When calculating the 95% PIs, the null value was excluded in only thirty two (18,8%) comparisons. Small-study Effects Evidence for statistically significant small-study effects (Egger test P < 0.10 and random-effects summary estimate larger compared with the point estimate of the largest study in the meta-analysis) was identified in 41 (24,1%) comparisons (Supplemental Table 1). Test Of Excess Statistical Significance Evidence of excess-statistical-significance bias were observed in 12(7%) associations, with statistically significant (P < 0.05) excess of positive studies under any of the three assumptions for the plausible effect size, i.e. the fixed-effects summary, random-effects summary or results of the largest study (Supplemental Table 1). In addition, the observed and expected number of positive studies showed that, overall, the excess of positive results was driven by meta-analyses with large estimates of heterogeneity (I2 > 50%). Grading Of Evidence The summary of the epidemiological credibility for 170 associations of risk factors for PTB is shown in Supplemental Table 1. Seven of the 170 associations (4.1%) were supported by robust evidence (fetus with isolated single umbilical artery, maternal personality disorder, sleep-breathing disorder, prior induced termination of pregnancy with vacuum aspiration, low gestational gain weight and interpregnancy interval following miscarriage less than 6 months) (Supplemental Table 4). 26 associations (15.3%) were supported by highly suggestive evidence (Intimate partner violence, cancer survivors, placenta previa, velamentous cord insertion, African/Black ethnicity, Aboriginal ethnicity, first trimester bleeding, unmarried women, obstetric cholestasis, severe maternal morbidity (hemorrhagic and hepatic disorders), Body Mass Index (BMI) > 40 kg/m 2 , cocaine exposure, endometriosis, prior surgical termination of pregnancy, maternal age > = 45 years, pregnancy with chronic kidney disease, underweight women, LEEP, LLETZ for CIN, any type of treatment for CIN with a cone depth of ≥ 10-12mm compared to untreated CIN, any type of treatment for CIN with a cone depth of ≥ 15-17mm compared to untreated CIN and PCOS). 16 associations (9,4%) were supported by suggestive evidence. Regarding the environmental risk factors, higher residential greenness did not technically qualify to be categorized as robust evidence because the random effects p-value was 3.25 x 10 − 6 but fulfilled all other criteria.. The rest of the associations regarding different levels of exposure to air pollutants (PM 2,5, NO 2 ) in all windows of exposure were classified as weak. Discussion In this umbrella review we evaluated the current evidence, derived from meta-analyses of observational studies on the association between various risk factors and PTB. Overall, from the 170 associations that have been examined, only a minority had strongly significant results with no suggestion of bias, as can be inferred by substantial heterogeneity between studies, small study effects, and excess significance bias. Seven risk factors were supported by robust evidence, including amphetamine exposure, isolated single umbilical artery, maternal personality disorder, sleep disordered breathing measured with objective assessment, prior induced termination of pregnancy with vacuum aspiration compared to no termination, low gestational weight gain compared to normal weight gain, and interpregnancy interval following miscarriage less than 6 months. Several others had highly suggestive evidence including intimate partner violence and unmarried women, cancer survivors, Black race, placental complications, hemorrhagic and hepatic disorders, endometriosis, chronic kidney disease and treatments for CIN. Interpretation In The Light Of Evidence Apart from risk factors that have been well incorporated in the clinical screening system, we identified a few that are not receiving the attention they should during pregnancy follow up despite the fact that they demonstrate robust evidence. The World Health Organization (WHO) encourages women who experienced a previous miscarriage to wait for a minimum of 6 months before the next conception to achieve optimal outcome and reduce obstetric complications such as preterm birth [ 119 ]. Contrary to the findings of the research on which WHO based its recommendations, some studies reported that the risk of adverse obstetric outcomes including preterm birth is lower in women who conceived less than 6 months after a pregnancy loss [ 120 , 125 , 126 ], while synthesizing all available data provided the same conclusion [ 105 ]. This meta-analysis included eight studies, performed two analyses: one including the study of Conde Agudelo 2004 [ 121 ] and one excluding it, and robust results were obtained after excluding the study. While this was a large retrospective study on which the WHO guidelines for delaying pregnancy for at least 6 months [ 119 ] are based, it did not differentiate between induced and spontaneous abortions and used data from many countries where induced abortion is illegal[ 121 ], therefore should be interpreted with caution. After a miscarriage, there is a very small burden on the folate reserve and thus miscarriage is not very likely to lead to folate deficiency in the postpartum period, so miscarriage and delivery later in pregnancy can have differential effects on subsequent pregnancy. This could explain the reduced risk of adverse outcomes in a short IPI after a miscarriage [ 122 ] but not after delivery. In support of this hypothesis, there is evidence to suggest that late miscarriages (after 12 weeks of gestation) are associated with worse outcomes in the subsequent pregnancy [ 123 ]. In addition, most women who attempt another pregnancy soon after a miscarriage are likely to be motivated to take better care of their health and consequently result in better pregnancy outcomes [ 124 ]. Another plausible reason may be that those who conceive soon after a miscarriage are naturally more fertile and younger and consequently have better pregnancy outcomes. Another association with robust evidence was pregnant women with sleep breathing disorders. This meta-analysis clearly demonstrated the increased risk profile of women who experience SBD not only for preterm birth but for other pregnancy outcomes. Regarding plausible mechanisms, the association between SDB and intermittent maternal hypoxia as well as the link with conditions synonymous with impaired placental function such as pre-eclampsia suggest a multifactorial cause, with both physiologic changes associated with pregnancy and placental dysfunction involved. This robust association has clear implications for obstetric practice. First, given the rapidly increasing worldwide obesity rates, SDB is likely to become more prevalent in the pregnant population and is worthy of being screened for. Second, the increased risk for both adverse intrapartum and perinatal outcomes demonstrated in this review strongly support the need for increased surveillance of this cohort. Third, public health education programs must take into account the specific maternal and perinatal risks and promote education about the significance of obstructive sleep apnea symptoms and the need for women to discuss this with their obstetric caregivers. In alignment with this suggestion, women with personality disorders could be identified early through mental health screening, where targeted health interventions and multidisciplinary management can be implemented in order to reduce poor outcomes for the baby/child and woman. This early identification and support also have the potential to enable the prevention of maladaptive development trajectories within the mother infant relationship [ 128 , 129 ]. Regarding induced termination of pregnancy with vacuum aspiration, our results should be interpreted with caution because it is unclear whether two of the five included studies come from the same population, therefore the variance of the pooled estimate may be artificially narrower. Furthermore, it is important that clinical examination and medical history includes risk factors which are not well known, identified in meta-analysis with highly suggestive evidence. To be more specific regarding highly suggestive evidence, there were a few that are well known and used to classify pregnancies as high risk for PTB such as therapies for cervical intraepithelial neoplasia, advanced maternal age, placental pathology, race, first trimester bleeding and maternal comorbidities. There were also included factors that are not routinely screened in the obstetric population such as intimate partner violence, cancer survivors and being unmarried. When it comes to intimate partner violence exposure during pregnancy, this meta-analysis included 30 studies examining the risk of PTB [ 28 ]. Two possible pathways have been described which could lead to adverse perinatal outcomes [ 140 , 141 ]. One is the direct exposure to violence consisted of either physical assault directly to the abdomen or sexual abuse. Direct exposure has been associated with pregnancy complications such as premature rupture of membranes, uterine contractions and placental damage, too[ 140 , 142 ]. On the other hand, indirect exposure to violence trigger biological mechanisms, such as smoking, alcohol or drug use, inadequate prenatal care and weight gain that contribute to adverse birth outcomes [ 140 , 145 – 156 ]. Women with history of abuse by their partner is believed to have less support, lower levels of self-esteem and higher levels of stress, too [ 140 , 142 , 145 , 153 , 160 ]. All these factors contribute to the indirect mechanism “theory” associated to preterm birth. As a result, healthcare professionals/institutes follow screening protocols in some nations or clinical guidelines, in order to detect and take care of these cases [ 157 – 159 ]. Another association that demonstrates highly suggestive evidence is pregnant women, whom survived cancer. This meta-analysis included fourteen studies which described the incidence of PTB [ 30 ]. Regarding plausible mechanisms, it is believed that radiotherapy treatment protocols for cancer, especially irradiation of the abdomen is harmful both for the uterine vasculature and the uterus muscular development. This leads to a reduction in uterine elasticity and uterine volume [ 143 , 144 ]. Uterine volume can also be smaller due to hormonal deficiency, caused by ovarian failure [ 144 ]. This could lead to preterm delivery. However, there is a possible association between the dosage of radiotherapy and risk of PTB, something which still has not been examined due to the obscuring of pooling dosages in previous studies. Higher radiations doses may reflect to higher risk of PTB. In addition, we should highlight the fact that the population of cancer survivors following advancing treatment grows and the prevalence of PTB cases in these groups is going to rise, in regards. Maternal marital status plays also a role in PTB, but healthcare professionals rarely consider it as a risk factor. This meta-analysis consists of 21 studies comparing unmarried women to married ones, identifying an increased risk of PTB [ 43 ]. Regarding the ways in which unmarried women are associated to PTB, it is suggested that the quality of relationship between biological maternal and paternal figures is more important than their legal status [ 136 , 137 ]. Moreover, a biological father might be more caring or supportive of the birth compared to another family member or partner. Mother psychosocial stress level depends on the support that she receives from her familiar environment [ 138 , 139 ], but a variety of other factors should be taken into consideration before interpreting these results. With regards to health practitioner’s point of view, the importance of obtaining social history information during clinical exam, lies in identifying pregnancies at risk for PTB and offering new perspectives. This information should be focused on rarely screened factors in every-day routine, which support highly suggestive evidence. Regarding environmental risk factors, increased residential greenness was associated with a protective effect on the risk of PTB. Although this finding was categorized as having suggestive evidence, the p-value of the random effect estimate was very close to the stringent threshold of < 10 − 6. Acknowledging the detrimental projected effect of climate change in greenness and given that it is one of the few protective risk factors for PTB, serious efforts should be made to maintain and grow residential greenness. Possible mechanisms include among others amelioration of the effects of air pollutants, reduction of stress and increase in physical activity [ 116 ]. There were also suggestive evidence for early pregnancy exposure to PM 2.5 and the risk of PTB. This association has been debated in the literature with conflicting results about the timing and magnitude of effect and is less robust than other associations that have been shown to have strong evidence for associations [ 130 ] such as birthweight. In the current umbrella review, we applied a transparent and replicable set of criteria and statistical tests to evaluate and categorize the level of existing observational evidence. Although, 58,8% of associations in the included meta-analyses report a nominally (P < 0.05) statistically significant random-effects summary estimate, when stringent P value was considered (P < 10 − 6 ), the proportion of significant associations decreased to 24,1%. 94 (55,3%) associations had large or very large heterogeneity, while when we calculated the 95% prediction intervals, which further account for heterogeneity, we found that the null value was excluded in less than half of the associations. Only seven (4.1%) of the assessed risk factors found to provide robust evidence, indicating that several published meta-analyses of observational studies in the field could be susceptible to biases and the reported associations in the existing studies are often exaggerated. The ability to modify those factors, mainly those related to mental health and sleep quality screening, through screening and clinical interventions or public health policy measures remains to be established. Furthermore, there is no guarantee that even a convincing observational association for a modifiable risk factor would necessarily translate into large preventive benefits for preterm birth if these risk factors were to be modified [ 93 ]. With obesity becoming a global epidemic, the assessment of the strength of the evidence supporting the impact of overweight and obesity in sleep breathing disorders could allow the identification of women at high risk for adverse outcomes and allow better prevention. Obesity is generating an unfavorable metabolic environment from early gestation; therefore, initiation of interventions for weight loss during pregnancy might be belated to prevent or reverse adverse effects, which highlights the need of weight management strategies before conception [ 68 , 103 , 104 , 131 ]. PTB does not only increase the risk for maternal and infant complications, but also significantly increases a woman’s risk of cardiovascular disease (CVD) after pregnancy, therefore primary prevention [ 12 , 132 – 134 ] is extremely important. Our assessment has certain limitations. Umbrella reviews focus on existing systematic reviews and meta-analyses and therefore some studies may have not been included either because the original systematic reviews did not identify them, or they were too recent to be included. In the current assessment we used all available data from observational studies, therefore the meta-analysis estimates may partly reflect the biases from which the original studies suffer from. Statistical tests of bias in the body of evidence (small study effect and excess significance tests) offer hints of bias, not definitive proof thereof, while the Egger test is difficult to interpret when the between-study heterogeneity is large. These tests have low power if the meta-analyses include less than 10 studies and they may not identify the exact source of bias [ 23 , 25 , 135 ]. More specifically, in our study, all robust evidence applied to meta-analyses with less than 10 studies, therefore the results of publication bias should be interpreted with caution. Furthermore, we did not appraise the quality of the individual studies on our own, since this should be included in the original meta-analysis and it was beyond the scope of the current umbrella review. However, we recorded whether and how they performed a quality assessment of the synthesized studies. Lastly, we cannot exclude the possibility of selective reporting for some associations in several studies. For example, perhaps some risk factors were more likely to be reported, if they had statistically significant results. Conclusion The present umbrella review of meta-analyses identified 170 unique risk factors for preterm birth. Our analysis identified seven risk factors with robust evidence and strong epidemiological credibility pertaining to isolated single umbilical artery, amphetamine exposure, maternal personality disorder, sleep breathing disorders, induced termination of pregnancy with vacuum aspiration, low gestational weight gain and interpregnancy interval following miscarriage of less than 6 months. As previously suggested, the use of standardized definitions and protocols for exposures, outcomes, and statistical analyses may diminish the threat of biases, allow for the computation of more precise estimates and will promote the development and training of prediction models that could promote public health. Methods We conducted an umbrella review which is a comprehensive and systematic approach that collects and critically evaluates all systematic reviews and meta-analyses performed on a specific research topic [ 11 ]. We used previously described, standardized methods that have been already used in previously published umbrella reviews referring to risk factors related to various outcomes [ 13 – 16 ] and have been elaborated below. A protocol for this umbrella review was registered in the International prospective register of systematic review (PROSPERO 2021 CRD42021227296) Search Strategy Two researchers (A.E., I.M.) independently searched PubMed database from inception to December 2020, in order to identify systematic reviews and meta-analyses of studies that examine the association between risk factors and preterm birth. The search strategy included combinations of the Medical Subject Headings (MESH) terms, key words and word variants for terms “preterm birth” AND (“systematic review” OR “meta-analysis”). Titles and abstracts were screened and potentially eligible articles were retrieved for full text evaluation. A detailed description of our search strategy is provided in the supplement (Supplemental Table 3). Eligibility Criteria And Data Extraction We included systematic reviews with meta-analyses investigating the association between various types of exposures and PTB. Specifically, we included studies with singleton pregnancies and studies where PTB was evaluated as primary outcome. Case report or series and individual participant data meta-analyses were excluded. We also excluded studies that set time limits on time span or were performed on a restricted setting (i.e. conducted for one specific country). Furthermore, we excluded studies that assessed PTB as a secondary outcome, studies including multiple pregnancies, and studies that assessed genetic or over -omics features as risk factor for PTB. All studies were compared to avoid the possibility of duplicate or overlapping samples. If more than one meta-analysis referring to the same research question were eligible, the one with the largest amount of component studies with data on individual studies’ effect sizes retained for the main analysis Publications whom the estimates of the studied associations, such as relative risks (RR) and 95% confidence intervals (CIs), were not reported or could not be retrieved/calculated were excluded from the analysis. For the non-environmental risk factors, we also excluded meta-analyses that did not provide the number of cases in the exposed and non-exposed groups, which is used for the calculation of the excess significance tests. For the environmental risk factors, since most commonly they report the results as per unit(s) increase in exposure and everyone is exposed, we included them even if they did not report the number of cases and total sample size. Eligible articles were screened by four independent reviewers (AE/IM and EB/TK). Any disagreement between reviewers was resolved by consensus or after evaluation of a third author (SP or EE). The data of eligible studies were extracted in a predefined data extraction form recording for each study the first author, journal, year of publication, the examined risk factors and the number of reviewed studies. Either the study specific relative risk estimates (risk ratio, odds ratio, hazard ratio, incidence rate ratio) and the confidence intervals were extracted or the mean and the standard deviation for continuous outcomes were also noted in this form. We also extracted exposed and control group used; outcome assessed; study population; exposure characteristics; number of studies in the meta-analysis; meta-analysis metric and method; effect estimate with the corresponding 95% confidence interval; number of cases and total sample size; I2 metric and the corresponding χ2 p-value for the Q test; and Egger’s regression P-value. Assessment Of Summary Effect And Heterogeneity We re-calculated summary effects and 95% Confidence Intervals (CIs) for each meta-analysis via fixed and random effects model [ 17 , 18 ]. 95% prediction intervals (PI) were also computed for the summary random-effects estimates, which further account for between-study heterogeneity indicating the uncertainty for the effect that would be expected in a new study examining the same correlation [ 19 , 20 ]. A PI describes the variability of the individual study estimates around the summary effect size and represents the range in which the effect estimate of a new study is expected to lie. The largest study considered as the most precise with a difference between the point estimate and the upper or lower 95% confidence interval less than 0.20. If the largest study presented a statistically significant effect, then we recorded this as a part of the grading criteria. Between study heterogeneity was assessed and P-value of the χ 2 -based Cochran Q test and the I 2 metric for inconsistency (reflecting either diversity or bias) was reported, too. I 2 metric were used to indicate the ratio of between study-variance over the sum of within and between-study variances, ranging from 0–100% [ 21 ]. Values exceeding 50% or 75% are usually considered to represent large or very large heterogeneity, respectively. 95% Confidence intervals were calculated as per Ioannidis et al. [ 22 ]. Assessment Of Small-study Effect Small studies tend to give substantially larger estimates of effect size when compared to larger studies. We evaluated the evidence of the presence of the small study effect, in order to identify publication and other selective reporting biases. They can also reflect genuine heterogeneity, chance, or other reasons for differences between small and large studies [ 23 ]. We evaluated whether smaller (less precise) studies lead to inflated effect estimates comparted to than larger studies. We used the regression asymmetry test proposed by Egger, that examines the potential existence of small study effects via funnel plot asymmetry [ 24 ]. Egger’s test fits a linear regression of the study estimates on their standard errors weighted by their inverse variance. Indication of small study effects based on the Egger’s asymmetry test was claimed when P-value ≤ 0.10. This is considered as an indication of publication bias, Indication of small study effects based on the Egger’s asymmetry test was claimed when P-value ≤ 0.10 and the random effects summary estimate was larger compared to the point estimate of the largest (most precise) study in the meta-analysis. Excess Statistical Significance Evaluation The excess significant test was applied to evaluate the existence of relative excess of significant findings in the published literature for any reason (e.g. publication bias, selective reporting of outcomes or analyses). The number of expected positive studies is estimated by a chi-squared-based test and being compared to the observed number of studies with statistically significant results (P < 0.05) [ 25 ]. A binomial test evaluated whether the number of positive studies in a meta-analysis was too large according to the power that these studies have to detect plausible effects at α = 0.05. In brief, observed versus expected studies for each meta-analysis were compared separately and this comparison also extended to groups of many meta-analysis after summing the observed and expected studies from each meta-analysis. The power of each component study was calculated using the fixed-effects summary, the random effects summary, or the effect size of the largest study (smallest SE) as the plausible effect size [ 15 ]. An algorithm using non-central t distribution was used to calculate the power of each study [ 26 ]. Excess statistical significance for single meta-analyses was claimed at P < 0.10 (one-sided P expected as previously proposed), given the power to detect a specific excess will be low, especially with few positive studies.[ 25 ] Grading Of Evidence We followed a 4-level grading (robust, highly suggestive, suggestive and weak) to evaluate the strength of the evidence based on the following criteria: number of cases, summary random-effects P-value, between-studies heterogeneity, 95% PI, small study effects bias and excess statistical significance[ 110 ]. This grading approach based on these parameters was used because it allows for an objective, standardized classification of the level of evidence and has been previously shown that provides consistent results with other more subjective grading schemes [ 111 , 112 ]. As most of the environmental risk factors included meta-analyses did not report the number of cases or the sample size of the studies included, we were unable to estimate the power of each meta-analysis and the excess significance test for these factors so we did not include excess statistical significance in the grading of these evidence. Briefly, meta-analyses were considered to be supported by robust evidence if: the association was supported by more than 1000 cases, a highly significant association (the random effects model had a P-value ≤ 10 − 6 , a threshold that is considered to substantially reduce false positive findings) [ 113 , 114 , 115 ], there was absence of high heterogeneity based on I2 < 50%, the 95% PI excluded the null value, and there was no evidence of small study effects or excess statistical significance. Highly suggestive evidence required more than 1000 cases, a highly significant association (a random-effects P-value ≤ 10 − 6 ), and the largest study in the meta-analysis was nominally significant. Associations based on meta-analyses a random-effects P-value ≤ 10 − 3 and included more than 1000 cases [ 113 , 114 , 115 ] were graded as suggestive evidence. The remaining nominally significant associations were graded as weak evidence (P < 0.05). We need to highlight that this specific grading scheme focuses on the reduction of false positive findings and the evaluation of potential biases in the studied associations. Therefore, the set of criteria used here is not ideal for a detailed evaluation of non-significant associations and to distinguish insufficient evidence from robust evidence of no association. That would require a different approach and another set of criteria altogether that would focus on the power of the meta-analyses to observe a significant effect, which was beyond the scope of our review. Statistical analyses were performed using STATA version 14 (StataCorp, Texas, USA) Declarations Reporting summary : Further information on research design is available in the Nature Research Reporting Summary linked to this article. Data availability : Relevant data to our study are mainly included in the article, tables and supplemental material. However, we will share the original dataset after reasonable requests. Code availability: The statistical code supporting the findings of our study will be available upon reasonable requests Acknowledgments Stefania Papatheodorou is supported by the National Institute of Environmental Health Sciences of the National Institutes of Health under Award Number R01ES034038. Author Contributions GM, SP, EE, AE conceptualized the idea for the manuscript. All authors contributed to the methods for the paper. SP, IM drafted the manuscript under the supervision of EE and GM. All authors approved the manuscript. SP is the guarantor of this manuscript and is responsible for the overall content. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. Competing Interests The authors declare no competing interests. Correspondence and requests for materials should be addressed to SP. Reprints and permissions information is available at www.nature.com/reprints. 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Moyer, Virginia A. and U.S. Preventive Services Task Force. “Screening for Intimate Partner Violence and Abuse of Elderly and Vulnerable Adults: U.S. Preventive Services Task Force Recommendation Statement.” Annals of Internal Medicine 158, no. 6 (March 19, 2013): 478–86. https://doi.org/10.7326/0003-4819-158-6-201303190-00588. Muhajarine, N., and C. D’Arcy. “Physical Abuse during Pregnancy: Prevalence and Risk Factors.” CMAJ: Canadian Medical Association Journal = Journal de l’Association Medicale Canadienne 160, no. 7 (April 6, 1999): 1007–11. Romero, Roberto, Sudhansu K. Dey, and Susan J. Fisher. “Preterm Labor: One Syndrome, Many Causes.” Science (New York, N.Y.) 345, no. 6198 (August 15, 2014): 760–65. https://doi.org/10.1126/science.1251816. Additional Declarations There is NO Competing Interest. 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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-2639005","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":182415670,"identity":"97a8ba1d-ea93-49a1-9804-5d958a108e07","order_by":0,"name":"Ioannis Mitrogiannis","email":"","orcid":"https://orcid.org/0000-0003-3904-7175","institution":"General Hospital of Arta","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ioannis","middleName":"","lastName":"Mitrogiannis","suffix":""},{"id":182415671,"identity":"e2e1bb14-ebb9-444b-b5d0-045a0e3740e9","order_by":1,"name":"Evangelos Evangelou","email":"","orcid":"","institution":"University of Ioannina","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Evangelos","middleName":"","lastName":"Evangelou","suffix":""},{"id":182415672,"identity":"77dd82ae-e848-4434-ad8d-564501f3a65e","order_by":2,"name":"Athina Efthymiou","email":"","orcid":"","institution":"Guy’s and St Thomas’ NHS Foundation Trust","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Athina","middleName":"","lastName":"Efthymiou","suffix":""},{"id":182415673,"identity":"55e05ed3-51aa-472e-9d2d-da8db11a42c4","order_by":3,"name":"Theofilos Kanavos","email":"","orcid":"https://orcid.org/0000-0002-4430-0588","institution":"University of Ioannina","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Theofilos","middleName":"","lastName":"Kanavos","suffix":""},{"id":182415674,"identity":"16584067-9b84-4c12-b19c-ab5cbb8cc2dc","order_by":4,"name":"Effrosyni Birbas","email":"","orcid":"https://orcid.org/0000-0002-5263-9207","institution":"University of Ioannina Medical School","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Effrosyni","middleName":"","lastName":"Birbas","suffix":""},{"id":182415675,"identity":"737f1d6c-dec1-4bef-9ba4-f4b5c3e0dbe5","order_by":5,"name":"George Makrydimas","email":"","orcid":"","institution":"University Hospital of Ioannina","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"George","middleName":"","lastName":"Makrydimas","suffix":""},{"id":182415676,"identity":"8918f97a-9464-44aa-80f7-d045e6093dc7","order_by":6,"name":"Stefania Papatheodorou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIie3OMUvDQBTA8RcC7XLQNaXWfIULB8lS9atcCKRLBsHBbhYcuhTnV5oPIRQ6Hxzo0g9wYAalcFMGQRAFQe8EXdoT3RzuP73h/XgPwOf7vwnoRQChHT4jAMH0R8EF9PHPhKrfknh5qe9fX5pDdjfT21No4gwL/djCaHgt9hPa3GTJnGuWNpuMIeikViVb1FAyJ4l4GhEu87WqOgMCMkAiWGiG3EViHD/337i8WOFYW3KC5PbJkHcnAVWlA3OF23OW5Nid2yvCSWizOWcHpUxQVRkjVBaGnAU1LdjC9dhytn5oRzLumce2ZCKPMOyuoJ0cD69cj0XQ+RrNQL+vO9Z3ic/n8/n29AFJz2HGnnHdzAAAAABJRU5ErkJggg==","orcid":"","institution":"Harvard Chan School of Public Health, Harvard University, Cambridge, MA, USA","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Stefania","middleName":"","lastName":"Papatheodorou","suffix":""}],"badges":[],"createdAt":"2023-02-28 14:22:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2639005/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2639005/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":34262185,"identity":"6f0de73d-ce86-4be2-8b9a-9caa68dd64fc","added_by":"auto","created_at":"2023-03-14 22:56:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56575,"visible":true,"origin":"","legend":"\u003cp\u003eFlow Diagram for the selection of included studies\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2639005/v1/f3eca1a8ffd2c68df7cb9fea.png"},{"id":36646226,"identity":"9502cdce-d66b-4133-98a7-b3bc41205813","added_by":"auto","created_at":"2023-05-05 13:01:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":446585,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2639005/v1/1c1df04e-0ab0-4993-a3ba-5793f7093a76.pdf"},{"id":34262186,"identity":"2b55fdcf-1440-4a75-98a6-87f21b3e62f3","added_by":"auto","created_at":"2023-03-14 22:56:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":193426,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-2639005/v1/b79f7b0be7d9fcb59bf70a72.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Risk factors for preterm labor: An Umbrella Review of meta-analyses of observational studies","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePreterm Birth (PTB) is defined as delivery before 37 gestational weeks and is a leading cause of infant morbidity and mortality [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. 15\u0026nbsp;million babies are estimated to be born preterm every year and the PTB rate ranges between 5\u0026ndash;18% worldwide [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] (PTB rates in USA:12\u0026ndash;13% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]; in Europe is 5\u0026ndash;9% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]). Advances in neonatology and the administration of corticosteroids before birth have improved significantly the prognosis of babies born preterm. In contrast, although vigorous research, costing millions of dollars, was carried out during the last 40 years, focusing in the prediction and prevention of preterm birth its incidence remains relatively unchanged. The most probable explanation is that preterm birth is a syndrome, rather than a single disease and many different causes may be responsible [\u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e161\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNumerous systematic reviews and meta-analyses have assessed various, non-genetic risk factors of preterm labor. Several environmental and clinical parameters such as present pregnancy characteristics, previous pregnancy history [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], infections [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], environmental exposures, pharmaceutical factors [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and surgical interventions have been proposed as plausible factors related to PTB. Identifying robust risk factors for PTB should either help us define a study population for specific interventions, allocate available resources effectively and allow risk-specific treatment and understanding the mechanism leading to PTB [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, the exact causes of this syndrome are still mostly unknown and the contribution of every risk factor in terms of prevention is still questionable.\u003c/p\u003e \u003cp\u003eTo our knowledge there is no previous effort to summarize existing evidence of meta-analyses of non-genetic risk factors for PTB. We conducted an umbrella review across published meta-analyses of observational studies with the goal to map the existing evidence and critically evaluate the reported associations applying stringent criteria that assess potential systematic biases and we highlight previously studied associations that provide robust evidence of association.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDescription of Eligible meta-analyses\u003c/h2\u003e \u003cp\u003eThe search identified 2769 items, of which 2239 were excluded after review of the title and abstract (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, PRISMA Flowchart). Of the remaining 530 articles that were reviewed in full text, eight articles did not report the appropriate information for the calculation of excess of statistical significance (either because the total sample size was missing or the study-specific relative risk estimates were missing), and 98 articles were excluded because a larger systematic review or meta-analysis investigating the same risk factor was available. From the 223 comparisons, we further excluded the ones that included one or two studies (53 comparisons). Therefore, 219 articles were analyzed, of which 133 were systematic reviews without any quantitative component and 86 were meta-analyses. The 86 eligible meta-analyses [\u003cspan additionalcitationids=\"CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39 CR40 CR41 CR42 CR43 CR44 CR45 CR46 CR47 CR48 CR49 CR50 CR51 CR52 CR53 CR54 CR55 CR56 CR57 CR58 CR59 CR60 CR61 CR62 CR63 CR64 CR65 CR66 CR67 CR68 CR69 CR70 CR71 CR72 CR73 CR74 CR75 CR76 CR77 CR78 CR79 CR80 CR81 CR82 CR83 CR84 CR85 CR86 CR87 CR88 CR89 CR90 CR91 CR92 CR93 CR94 CR95 CR96 CR97 CR98 CR99 CR100 CR101 CR102 CR103 CR104 CR105 CR106 CR107 CR108\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e, \u003cspan additionalcitationids=\"CR117\" citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e] included data on 170 comparisons and 1511 primary studies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSummary Effect-sizes And Significant Findings\u003c/h3\u003e\n\u003cp\u003eThree to 152 studies, with a median of nine studies, were included per meta-analysis. The median number of case and control subjects in each study was 88 and 529, respectively. The median number of case and control subjects in each meta-analysis was 98 and 807, respectively. The number of cases was greater than 1000 in 98 comparisons. Overall, 578 (45%) individual studies observed nominally statistically significant results. 40 meta-analyses used the Newcastle\u0026ndash;Ottawa Scale to assess qualitatively the included primary studies. One meta-analysis used assessment criteria for non-randomized observational studies adapted from Duckitt and Harrington, 3 meta-analyses used the Methodological Index for Non-Randomized Studies (MINORS) and 38 meta-analyses used other assessment tools. Four meta-analyses did not perform any quality assessment. Details of the 170 comparisons that included 1511 individual study estimates are summarized in Supplemental Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eOf the 170 comparisons, 100(58,8%) had nominally statistically significant findings at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 using the random-effects model, of which 94 reported an increased risk and six a decreased risk for preterm birth (preconception care vs no care, magnesium supplementation vs placebo ,single vs double embryo transfer, high gestational weight gain vs normal gestational weight gain, IPI following miscarriage\u0026thinsp;\u0026lt;\u0026thinsp;6m vs\u0026thinsp;\u0026gt;\u0026thinsp;6m, greenery including only a 100-m NDVI buffer). Of these, a total of 62(36,5%) associations presented statistically significant effect at P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, while only 41 (24,1%) remained significant after the application of a more stringent P-value threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;10\u003csup\u003e\u0026ndash;6\u003c/sup\u003e (Supplemental Table\u0026nbsp;2)\u003c/p\u003e\n\u003ch3\u003eBetween-study Heterogeneity And Prediction Intervals\u003c/h3\u003e\n\u003cp\u003eForty-five (26,5%) comparisons had large (I2\u0026thinsp;\u0026ge;\u0026thinsp;50% and \u0026le;\u0026thinsp;75%) and forty-nine comparisons (28,8%) had very large (I2\u0026thinsp;\u0026gt;\u0026thinsp;75%) heterogeneity estimates (Supplemental Table\u0026nbsp;1). When calculating the 95% PIs, the null value was excluded in only thirty two (18,8%) comparisons.\u003c/p\u003e\n\u003ch3\u003eSmall-study Effects\u003c/h3\u003e\n\u003cp\u003eEvidence for statistically significant small-study effects (Egger test P\u0026thinsp;\u0026lt;\u0026thinsp;0.10 and random-effects summary estimate larger compared with the point estimate of the largest study in the meta-analysis) was identified in 41 (24,1%) comparisons (Supplemental Table\u0026nbsp;1).\u003c/p\u003e\n\u003ch3\u003eTest Of Excess Statistical Significance\u003c/h3\u003e\n\u003cp\u003eEvidence of excess-statistical-significance bias were observed in 12(7%) associations, with statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) excess of positive studies under any of the three assumptions for the plausible effect size, i.e. the fixed-effects summary, random-effects summary or results of the largest study (Supplemental Table\u0026nbsp;1). In addition, the observed and expected number of positive studies showed that, overall, the excess of positive results was driven by meta-analyses with large estimates of heterogeneity (I2\u0026thinsp;\u0026gt;\u0026thinsp;50%).\u003c/p\u003e\n\u003ch3\u003eGrading Of Evidence\u003c/h3\u003e\n\u003cp\u003eThe summary of the epidemiological credibility for 170 associations of risk factors for PTB is shown in Supplemental Table\u0026nbsp;1. Seven of the 170 associations (4.1%) were supported by robust evidence (fetus with isolated single umbilical artery, maternal personality disorder, sleep-breathing disorder, prior induced termination of pregnancy with vacuum aspiration, low gestational gain weight and interpregnancy interval following miscarriage less than 6 months) (Supplemental Table\u0026nbsp;4). 26 associations (15.3%) were supported by highly suggestive evidence (Intimate partner violence, cancer survivors, placenta previa, velamentous cord insertion, African/Black ethnicity, Aboriginal ethnicity, first trimester bleeding, unmarried women, obstetric cholestasis, severe maternal morbidity (hemorrhagic and hepatic disorders), Body Mass Index (BMI)\u0026thinsp;\u0026gt;\u0026thinsp;40 kg/m\u003csup\u003e2\u003c/sup\u003e, cocaine exposure, endometriosis, prior surgical termination of pregnancy, maternal age\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;45 years, pregnancy with chronic kidney disease, underweight women, LEEP, LLETZ for CIN, any type of treatment for CIN with a cone depth of \u0026ge;\u0026thinsp;10-12mm compared to untreated CIN, any type of treatment for CIN with a cone depth of \u0026ge;\u0026thinsp;15-17mm compared to untreated CIN and PCOS). 16 associations (9,4%) were supported by suggestive evidence.\u003c/p\u003e \u003cp\u003eRegarding the environmental risk factors, higher residential greenness did not technically qualify to be categorized as robust evidence because the random effects p-value was 3.25 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e but fulfilled all other criteria.. The rest of the associations regarding different levels of exposure to air pollutants (PM\u003csub\u003e2,5,\u003c/sub\u003e NO\u003csub\u003e2\u003c/sub\u003e) in all windows of exposure were classified as weak.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this umbrella review we evaluated the current evidence, derived from meta-analyses of observational studies on the association between various risk factors and PTB. Overall, from the 170 associations that have been examined, only a minority had strongly significant results with no suggestion of bias, as can be inferred by substantial heterogeneity between studies, small study effects, and excess significance bias. Seven risk factors were supported by robust evidence, including amphetamine exposure, isolated single umbilical artery, maternal personality disorder, sleep disordered breathing measured with objective assessment, prior induced termination of pregnancy with vacuum aspiration compared to no termination, low gestational weight gain compared to normal weight gain, and interpregnancy interval following miscarriage less than 6 months. Several others had highly suggestive evidence including intimate partner violence and unmarried women, cancer survivors, Black race, placental complications, hemorrhagic and hepatic disorders, endometriosis, chronic kidney disease and treatments for CIN.\u003c/p\u003e\n\u003ch3\u003eInterpretation In The Light Of Evidence\u003c/h3\u003e\n\u003cp\u003eApart from risk factors that have been well incorporated in the clinical screening system, we identified a few that are not receiving the attention they should during pregnancy follow up despite the fact that they demonstrate robust evidence. The World Health Organization (WHO) encourages women who experienced a previous miscarriage to wait for a minimum of 6 months before the next conception to achieve optimal outcome and reduce obstetric complications such as preterm birth [\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e]. Contrary to the findings of the research on which WHO based its recommendations, some studies reported that the risk of adverse obstetric outcomes including preterm birth is lower in women who conceived less than 6 months after a pregnancy loss [\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e, \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e, \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e], while synthesizing all available data provided the same conclusion [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e]. This meta-analysis included eight studies, performed two analyses: one including the study of Conde Agudelo 2004 [\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e] and one excluding it, and robust results were obtained after excluding the study. While this was a large retrospective study on which the WHO guidelines for delaying pregnancy for at least 6 months [\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e] are based, it did not differentiate between induced and spontaneous abortions and used data from many countries where induced abortion is illegal[\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e], therefore should be interpreted with caution. After a miscarriage, there is a very small burden on the folate reserve and thus miscarriage is not very likely to lead to folate deficiency in the postpartum period, so miscarriage and delivery later in pregnancy can have differential effects on subsequent pregnancy. This could explain the reduced risk of adverse outcomes in a short IPI after a miscarriage [\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e] but not after delivery. In support of this hypothesis, there is evidence to suggest that late miscarriages (after 12 weeks of gestation) are associated with worse outcomes in the subsequent pregnancy [\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e]. In addition, most women who attempt another pregnancy soon after a miscarriage are likely to be motivated to take better care of their health and consequently result in better pregnancy outcomes [\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e]. Another plausible reason may be that those who conceive soon after a miscarriage are naturally more fertile and younger and consequently have better pregnancy outcomes.\u003c/p\u003e \u003cp\u003eAnother association with robust evidence was pregnant women with sleep breathing disorders. This meta-analysis clearly demonstrated the increased risk profile of women who experience SBD not only for preterm birth but for other pregnancy outcomes. Regarding plausible mechanisms, the association between SDB and intermittent maternal hypoxia as well as the link with conditions synonymous with impaired placental function such as pre-eclampsia suggest a multifactorial cause, with both physiologic changes associated with pregnancy and placental dysfunction involved. This robust association has clear implications for obstetric practice. First, given the rapidly increasing worldwide obesity rates, SDB is likely to become more prevalent in the pregnant population and is worthy of being screened for. Second, the increased risk for both adverse intrapartum and perinatal outcomes demonstrated in this review strongly support the need for increased surveillance of this cohort. Third, public health education programs must take into account the specific maternal and perinatal risks and promote education about the significance of obstructive sleep apnea symptoms and the need for women to discuss this with their obstetric caregivers. In alignment with this suggestion, women with personality disorders could be identified early through mental health screening, where targeted health interventions and multidisciplinary management can be implemented in order to reduce poor outcomes for the baby/child and woman. This early identification and support also have the potential to enable the prevention of maladaptive development trajectories within the mother infant relationship [\u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e, \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e]. Regarding induced termination of pregnancy with vacuum aspiration, our results should be interpreted with caution because it is unclear whether two of the five included studies come from the same population, therefore the variance of the pooled estimate may be artificially narrower.\u003c/p\u003e \u003cp\u003eFurthermore, it is important that clinical examination and medical history includes risk factors which are not well known, identified in meta-analysis with highly suggestive evidence. To be more specific regarding highly suggestive evidence, there were a few that are well known and used to classify pregnancies as high risk for PTB such as therapies for cervical intraepithelial neoplasia, advanced maternal age, placental pathology, race, first trimester bleeding and maternal comorbidities. There were also included factors that are not routinely screened in the obstetric population such as intimate partner violence, cancer survivors and being unmarried. When it comes to intimate partner violence exposure during pregnancy, this meta-analysis included 30 studies examining the risk of PTB [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Two possible pathways have been described which could lead to adverse perinatal outcomes [\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e, \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e141\u003c/span\u003e]. One is the direct exposure to violence consisted of either physical assault directly to the abdomen or sexual abuse. Direct exposure has been associated with pregnancy complications such as premature rupture of membranes, uterine contractions and placental damage, too[\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e, \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e142\u003c/span\u003e]. On the other hand, indirect exposure to violence trigger biological mechanisms, such as smoking, alcohol or drug use, inadequate prenatal care and weight gain that contribute to adverse birth outcomes [\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e, \u003cspan additionalcitationids=\"CR146 CR147 CR148 CR149 CR150 CR151 CR152 CR153 CR154 CR155\" citationid=\"CR145\" class=\"CitationRef\"\u003e145\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e156\u003c/span\u003e]. Women with history of abuse by their partner is believed to have less support, lower levels of self-esteem and higher levels of stress, too [\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e, \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e142\u003c/span\u003e, \u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e145\u003c/span\u003e, \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e153\u003c/span\u003e, \u003cspan citationid=\"CR160\" class=\"CitationRef\"\u003e160\u003c/span\u003e]. All these factors contribute to the indirect mechanism \u0026ldquo;theory\u0026rdquo; associated to preterm birth. As a result, healthcare professionals/institutes follow screening protocols in some nations or clinical guidelines, in order to detect and take care of these cases [\u003cspan additionalcitationids=\"CR158\" citationid=\"CR157\" class=\"CitationRef\"\u003e157\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR159\" class=\"CitationRef\"\u003e159\u003c/span\u003e]. Another association that demonstrates highly suggestive evidence is pregnant women, whom survived cancer. This meta-analysis included fourteen studies which described the incidence of PTB [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Regarding plausible mechanisms, it is believed that radiotherapy treatment protocols for cancer, especially irradiation of the abdomen is harmful both for the uterine vasculature and the uterus muscular development. This leads to a reduction in uterine elasticity and uterine volume [\u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e143\u003c/span\u003e, \u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e144\u003c/span\u003e]. Uterine volume can also be smaller due to hormonal deficiency, caused by ovarian failure [\u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e144\u003c/span\u003e]. This could lead to preterm delivery. However, there is a possible association between the dosage of radiotherapy and risk of PTB, something which still has not been examined due to the obscuring of pooling dosages in previous studies. Higher radiations doses may reflect to higher risk of PTB. In addition, we should highlight the fact that the population of cancer survivors following advancing treatment grows and the prevalence of PTB cases in these groups is going to rise, in regards. Maternal marital status plays also a role in PTB, but healthcare professionals rarely consider it as a risk factor. This meta-analysis consists of 21 studies comparing unmarried women to married ones, identifying an increased risk of PTB [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Regarding the ways in which unmarried women are associated to PTB, it is suggested that the quality of relationship between biological maternal and paternal figures is more important than their legal status [\u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e, \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e]. Moreover, a biological father might be more caring or supportive of the birth compared to another family member or partner. Mother psychosocial stress level depends on the support that she receives from her familiar environment [\u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e, \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e], but a variety of other factors should be taken into consideration before interpreting these results. With regards to health practitioner\u0026rsquo;s point of view, the importance of obtaining social history information during clinical exam, lies in identifying pregnancies at risk for PTB and offering new perspectives. This information should be focused on rarely screened factors in every-day routine, which support highly suggestive evidence.\u003c/p\u003e \u003cp\u003eRegarding environmental risk factors, increased residential greenness was associated with a protective effect on the risk of PTB. Although this finding was categorized as having suggestive evidence, the p-value of the random effect estimate was very close to the stringent threshold of \u0026lt;\u0026thinsp;10\u0026thinsp;\u0026minus;\u0026thinsp;6. Acknowledging the detrimental projected effect of climate change in greenness and given that it is one of the few protective risk factors for PTB, serious efforts should be made to maintain and grow residential greenness. Possible mechanisms include among others amelioration of the effects of air pollutants, reduction of stress and increase in physical activity [\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e]. There were also suggestive evidence for early pregnancy exposure to PM\u003csub\u003e2.5\u003c/sub\u003e and the risk of PTB. This association has been debated in the literature with conflicting results about the timing and magnitude of effect and is less robust than other associations that have been shown to have strong evidence for associations [\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e] such as birthweight.\u003c/p\u003e \u003cp\u003eIn the current umbrella review, we applied a transparent and replicable set of criteria and statistical tests to evaluate and categorize the level of existing observational evidence. Although, 58,8% of associations in the included meta-analyses report a nominally (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) statistically significant random-effects summary estimate, when stringent P value was considered (P\u0026thinsp;\u0026lt;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), the proportion of significant associations decreased to 24,1%. 94 (55,3%) associations had large or very large heterogeneity, while when we calculated the 95% prediction intervals, which further account for heterogeneity, we found that the null value was excluded in less than half of the associations. Only seven (4.1%) of the assessed risk factors found to provide robust evidence, indicating that several published meta-analyses of observational studies in the field could be susceptible to biases and the reported associations in the existing studies are often exaggerated.\u003c/p\u003e \u003cp\u003eThe ability to modify those factors, mainly those related to mental health and sleep quality screening, through screening and clinical interventions or public health policy measures remains to be established. Furthermore, there is no guarantee that even a convincing observational association for a modifiable risk factor would necessarily translate into large preventive benefits for preterm birth if these risk factors were to be modified [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. With obesity becoming a global epidemic, the assessment of the strength of the evidence supporting the impact of overweight and obesity in sleep breathing disorders could allow the identification of women at high risk for adverse outcomes and allow better prevention. Obesity is generating an unfavorable metabolic environment from early gestation; therefore, initiation of interventions for weight loss during pregnancy might be belated to prevent or reverse adverse effects, which highlights the need of weight management strategies before conception [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e, \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e]. PTB does not only increase the risk for maternal and infant complications, but also significantly increases a woman\u0026rsquo;s risk of cardiovascular disease (CVD) after pregnancy, therefore primary prevention [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR133\" citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e] is extremely important.\u003c/p\u003e \u003cp\u003eOur assessment has certain limitations. Umbrella reviews focus on existing systematic reviews and meta-analyses and therefore some studies may have not been included either because the original systematic reviews did not identify them, or they were too recent to be included. In the current assessment we used all available data from observational studies, therefore the meta-analysis estimates may partly reflect the biases from which the original studies suffer from. Statistical tests of bias in the body of evidence (small study effect and excess significance tests) offer hints of bias, not definitive proof thereof, while the Egger test is difficult to interpret when the between-study heterogeneity is large. These tests have low power if the meta-analyses include less than 10 studies and they may not identify the exact source of bias [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e]. More specifically, in our study, all robust evidence applied to meta-analyses with less than 10 studies, therefore the results of publication bias should be interpreted with caution. Furthermore, we did not appraise the quality of the individual studies on our own, since this should be included in the original meta-analysis and it was beyond the scope of the current umbrella review. However, we recorded whether and how they performed a quality assessment of the synthesized studies. Lastly, we cannot exclude the possibility of selective reporting for some associations in several studies. For example, perhaps some risk factors were more likely to be reported, if they had statistically significant results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present umbrella review of meta-analyses identified 170 unique risk factors for preterm birth. Our analysis identified seven risk factors with robust evidence and strong epidemiological credibility pertaining to isolated single umbilical artery, amphetamine exposure, maternal personality disorder, sleep breathing disorders, induced termination of pregnancy with vacuum aspiration, low gestational weight gain and interpregnancy interval following miscarriage of less than 6 months. As previously suggested, the use of standardized definitions and protocols for exposures, outcomes, and statistical analyses may diminish the threat of biases, allow for the computation of more precise estimates and will promote the development and training of prediction models that could promote public health.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe conducted an umbrella review which is a comprehensive and systematic approach that collects and critically evaluates all systematic reviews and meta-analyses performed on a specific research topic [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. We used previously described, standardized methods that have been already used in previously published umbrella reviews referring to risk factors related to various outcomes [\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and have been elaborated below.\u003c/p\u003e \u003cp\u003eA protocol for this umbrella review was registered in the International prospective register of systematic review (PROSPERO 2021 CRD42021227296)\u003c/p\u003e\n\u003ch3\u003eSearch Strategy\u003c/h3\u003e\n\u003cp\u003eTwo researchers (A.E., I.M.) independently searched PubMed database from inception to December 2020, in order to identify systematic reviews and meta-analyses of studies that examine the association between risk factors and preterm birth. The search strategy included combinations of the Medical Subject Headings (MESH) terms, key words and word variants for terms \u0026ldquo;preterm birth\u0026rdquo; AND (\u0026ldquo;systematic review\u0026rdquo; OR \u0026ldquo;meta-analysis\u0026rdquo;). Titles and abstracts were screened and potentially eligible articles were retrieved for full text evaluation. A detailed description of our search strategy is provided in the supplement (Supplemental Table\u0026nbsp;3).\u003c/p\u003e\n\u003ch3\u003eEligibility Criteria And Data Extraction\u003c/h3\u003e\n \u003cp\u003eWe included systematic reviews with meta-analyses investigating the association between various types of exposures and PTB. Specifically, we included studies with singleton pregnancies and studies where PTB was evaluated as primary outcome.\u003c/p\u003e \u003cp\u003eCase report or series and individual participant data meta-analyses were excluded. We also excluded studies that set time limits on time span or were performed on a restricted setting (i.e. conducted for one specific country). Furthermore, we excluded studies that assessed PTB as a secondary outcome, studies including multiple pregnancies, and studies that assessed genetic or over -omics features as risk factor for PTB. All studies were compared to avoid the possibility of duplicate or overlapping samples. If more than one meta-analysis referring to the same research question were eligible, the one with the largest amount of component studies with data on individual studies\u0026rsquo; effect sizes retained for the main analysis\u003c/p\u003e \u003cp\u003ePublications whom the estimates of the studied associations, such as relative risks (RR) and 95% confidence intervals (CIs), were not reported or could not be retrieved/calculated were excluded from the analysis. For the non-environmental risk factors, we also excluded meta-analyses that did not provide the number of cases in the exposed and non-exposed groups, which is used for the calculation of the excess significance tests. For the environmental risk factors, since most commonly they report the results as per unit(s) increase in exposure and everyone is exposed, we included them even if they did not report the number of cases and total sample size.\u003c/p\u003e \u003cp\u003eEligible articles were screened by four independent reviewers (AE/IM and EB/TK). Any disagreement between reviewers was resolved by consensus or after evaluation of a third author (SP or EE). The data of eligible studies were extracted in a predefined data extraction form recording for each study the first author, journal, year of publication, the examined risk factors and the number of reviewed studies. Either the study specific relative risk estimates (risk ratio, odds ratio, hazard ratio, incidence rate ratio) and the confidence intervals were extracted or the mean and the standard deviation for continuous outcomes were also noted in this form. We also extracted exposed and control group used; outcome assessed; study population; exposure characteristics; number of studies in the meta-analysis; meta-analysis metric and method; effect estimate with the corresponding 95% confidence interval; number of cases and total sample size; I2 metric and the corresponding χ2 p-value for the Q test; and Egger\u0026rsquo;s regression P-value.\u003c/p\u003e\n\u003ch3\u003eAssessment Of Summary Effect And Heterogeneity\u003c/h3\u003e\n\u003cp\u003eWe re-calculated summary effects and 95% Confidence Intervals (CIs) for each meta-analysis via fixed and random effects model [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. 95% prediction intervals (PI) were also computed for the summary random-effects estimates, which further account for between-study heterogeneity indicating the uncertainty for the effect that would be expected in a new study examining the same correlation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A PI describes the variability of the individual study estimates around the summary effect size and represents the range in which the effect estimate of a new study is expected to lie.\u003c/p\u003e \u003cp\u003eThe largest study considered as the most precise with a difference between the point estimate and the upper or lower 95% confidence interval less than 0.20. If the largest study presented a statistically significant effect, then we recorded this as a part of the grading criteria.\u003c/p\u003e \u003cp\u003eBetween study heterogeneity was assessed and P-value of the χ\u003csup\u003e2\u003c/sup\u003e-based Cochran Q test and the I\u003csup\u003e2\u003c/sup\u003e metric for inconsistency (reflecting either diversity or bias) was reported, too. I\u003csup\u003e2\u003c/sup\u003e metric were used to indicate the ratio of between study-variance over the sum of within and between-study variances, ranging from 0\u0026ndash;100% [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Values exceeding 50% or 75% are usually considered to represent large or very large heterogeneity, respectively. 95% Confidence intervals were calculated as per Ioannidis et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eAssessment Of Small-study Effect\u003c/h3\u003e\n\u003cp\u003eSmall studies tend to give substantially larger estimates of effect size when compared to larger studies. We evaluated the evidence of the presence of the small study effect, in order to identify publication and other selective reporting biases. They can also reflect genuine heterogeneity, chance, or other reasons for differences between small and large studies [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. We evaluated whether smaller (less precise) studies lead to inflated effect estimates comparted to than larger studies. We used the regression asymmetry test proposed by Egger, that examines the potential existence of small study effects via funnel plot asymmetry [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Egger\u0026rsquo;s test fits a linear regression of the study estimates on their standard errors weighted by their inverse variance. Indication of small study effects based on the Egger\u0026rsquo;s asymmetry test was claimed when P-value\u0026thinsp;\u0026le;\u0026thinsp;0.10. This is considered as an indication of publication bias, Indication of small study effects based on the Egger\u0026rsquo;s asymmetry test was claimed when P-value\u0026thinsp;\u0026le;\u0026thinsp;0.10 and the random effects\u003c/p\u003e \u003cp\u003esummary estimate was larger compared to the point estimate of the largest (most precise) study in the meta-analysis.\u003c/p\u003e\n\u003ch3\u003eExcess Statistical Significance Evaluation\u003c/h3\u003e\n\u003cp\u003eThe excess significant test was applied to evaluate the existence of relative excess of significant findings in the published literature for any reason (e.g. publication bias, selective reporting of outcomes or analyses). The number of expected positive studies is estimated by a chi-squared-based test and being compared to the observed number of studies with statistically significant results (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. A binomial test evaluated whether the number of positive studies in a meta-analysis was too large according to the power that these studies have to detect plausible effects at α\u0026thinsp;=\u0026thinsp;0.05. In brief, observed versus expected studies for each meta-analysis were compared separately and this comparison also extended to groups of many meta-analysis after summing the observed and expected studies from each meta-analysis. The power of each component study was calculated using the fixed-effects summary, the random effects summary, or the effect size of the largest study (smallest SE) as the plausible effect size [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. An algorithm using non-central t distribution was used to calculate the power of each study [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Excess statistical significance for single meta-analyses was claimed at P\u0026thinsp;\u0026lt;\u0026thinsp;0.10 (one-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, with observed\u0026thinsp;\u0026gt;\u0026thinsp;expected as previously proposed), given the power to detect a specific excess will be low, especially with few positive studies.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\n\u003ch3\u003eGrading Of Evidence\u003c/h3\u003e\n\u003cp\u003eWe followed a 4-level grading (robust, highly suggestive, suggestive and weak) to evaluate the strength of the evidence based on the following criteria: number of cases, summary random-effects P-value, between-studies heterogeneity, 95% PI, small study effects bias and excess statistical significance[\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. This grading approach based on these parameters was used because it allows for an objective, standardized classification of the level of evidence and has been previously shown that provides consistent results with other more subjective grading schemes [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e]. As most of the environmental risk factors included meta-analyses did not report the number of cases or the sample size of the studies included, we were unable to estimate the power of each meta-analysis and the excess significance test for these factors so we did not include excess statistical significance in the grading of these evidence.\u003c/p\u003e \u003cp\u003eBriefly, meta-analyses were considered to be supported by robust evidence if: the association was supported by more than 1000 cases, a highly significant association (the random effects model had a P-value\u0026thinsp;\u0026le;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, a threshold that is considered to substantially reduce false positive findings) [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e, \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e, \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e], there was absence of high heterogeneity based on I2\u0026thinsp;\u0026lt;\u0026thinsp;50%, the 95% PI excluded the null value, and there was no evidence of small study effects or excess statistical significance. Highly suggestive evidence required more than 1000 cases, a highly significant association (a random-effects P-value\u0026thinsp;\u0026le;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), and the largest study in the meta-analysis was nominally significant. Associations based on meta-analyses a random-effects P-value\u0026thinsp;\u0026le;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and included more than 1000 cases [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e, \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e, \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e] were graded as suggestive evidence. The remaining nominally significant associations were graded as weak evidence (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We need to highlight that this specific grading scheme focuses on the reduction of false positive findings and the evaluation of potential biases in the studied associations. Therefore, the set of criteria used here is not ideal for a detailed evaluation of non-significant associations and to distinguish insufficient evidence from robust evidence of no association. That would require a different approach and another set of criteria altogether that would focus on the power of the meta-analyses to observe a significant effect, which was beyond the scope of our review.\u003c/p\u003e \u003cp\u003eStatistical analyses were performed using STATA version 14 (StataCorp, Texas, USA)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eReporting summary\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Further information on research design is available in the Nature Research Reporting Summary linked to this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e: Relevant\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003edata to our study are mainly included in the article, tables and supplemental material. However, we will share the original dataset after reasonable requests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u0026nbsp;\u003c/strong\u003eThe statistical code supporting the findings of our study will be available upon reasonable requests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStefania Papatheodorou is supported by the National Institute of Environmental Health Sciences of the National Institutes of Health under Award Number R01ES034038.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGM, SP, EE, AE conceptualized the idea for the manuscript. All authors contributed to the methods for the paper. SP, IM drafted the manuscript under the supervision of EE and GM. All authors approved the manuscript. SP is the guarantor of this manuscript and is responsible for the overall content.\u0026nbsp;The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence\u0026nbsp;\u003c/strong\u003eand requests for materials should be addressed to\u0026nbsp;SP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReprints and permissions information\u003c/strong\u003e is available at www.nature.com/reprints.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eInstitute of Medicine (US) Committee on Understanding Premature Birth and Assuring Healthy Outcomes. \u003cem\u003ePreterm Birth: Causes, Consequences, and Prevention\u003c/em\u003e. Edited by Richard E. Behrman and Adrienne Stith Butler. The National Academies Collection: Reports Funded by National Institutes of Health. Washington (DC): National Academies Press (US), 2007. http://www.ncbi.nlm.nih.gov/books/NBK11362/.\u003c/li\u003e\n\u003cli\u003eGoldenberg, Robert L., Jennifer F. Culhane, Jay D. Iams, and Roberto Romero. \u0026ldquo;Epidemiology and Causes of Preterm Birth.\u0026rdquo; \u003cem\u003eLancet (London, England)\u003c/em\u003e 371, no. 9606 (January 5, 2008): 75\u0026ndash;84. https://doi.org/10.1016/S0140-6736(08)60074-4.\u003c/li\u003e\n\u003cli\u003e\u0026ldquo;Preterm Birth.\u0026rdquo; Accessed June 17, 2022. https://www.who.int/news-room/fact-sheets/detail/preterm-birth.\u003c/li\u003e\n\u003cli\u003eMenzies, Rebecca, Adrienne L. K. Li, Nir Melamed, Prakesh S. Shah, Daphne Horn, Jon Barrett, and Kellie E. Murphy. \u0026ldquo;Risk of Singleton Preterm Birth after Prior Twin Preterm Birth: A Systematic Review and Meta-Analysis.\u0026rdquo; \u003cem\u003eAmerican Journal of Obstetrics and Gynecology\u003c/em\u003e 223, no. 2 (August 2020): 204.e1-204.e8. https://doi.org/10.1016/j.ajog.2020.02.003.\u003c/li\u003e\n\u003cli\u003eEtwel, Fatma, Lauren H. Faught, Michael J. Rieder, and Gideon Koren. \u0026ldquo;The Risk of Adverse Pregnancy Outcome After First Trimester Exposure to H1 Antihistamines: A Systematic Review and Meta-Analysis.\u0026rdquo; \u003cem\u003eDrug Safety\u003c/em\u003e 40, no. 2 (February 2017): 121\u0026ndash;32. https://doi.org/10.1007/s40264-016-0479-9.\u003c/li\u003e\n\u003cli\u003eCoughlin, Catherine G., Katherine A. Blackwell, Christine Bartley, Madeleine Hay, Kimberly A. Yonkers, and Michael H. Bloch. \u0026ldquo;Obstetric and Neonatal Outcomes after Antipsychotic Medication Exposure in Pregnancy.\u0026rdquo; \u003cem\u003eObstetrics and Gynecology\u003c/em\u003e 125, no. 5 (May 2015): 1224\u0026ndash;35. https://doi.org/10.1097/AOG.0000000000000759.\u003c/li\u003e\n\u003cli\u003eWang, Anshi, Chang Liu, Yunan Wang, Aihua Yin, Jing Wu, Changbin Zhang, Mingyong Luo, et al. \u0026ldquo;Pregnancy Outcomes After Human Papillomavirus Vaccination in Periconceptional Period or During Pregnancy: A Systematic Review and Meta-Analysis.\u0026rdquo; \u003cem\u003eHuman Vaccines \u0026amp; Immunotherapeutics\u003c/em\u003e 16, no. 3 (March 3, 2020): 581\u0026ndash;89. https://doi.org/10.1080/21645515.2019.1662363.\u003c/li\u003e\n\u003cli\u003eNiyibizi, Joseph, Nad\u0026egrave;ge Zanr\u0026eacute;, Marie-H\u0026eacute;l\u0026egrave;ne Mayrand, and Helen Trottier. \u0026ldquo;Association Between Maternal Human Papillomavirus Infection and Adverse Pregnancy Outcomes: Systematic Review and Meta-Analysis.\u0026rdquo; \u003cem\u003eThe Journal of Infectious Diseases\u003c/em\u003e 221, no. 12 (June 11, 2020): 1925\u0026ndash;37. https://doi.org/10.1093/infdis/jiaa054.\u003c/li\u003e\n\u003cli\u003eLalani, S., A. 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A. \u0026ldquo;Integration of Evidence from Multiple Meta-Analyses: A Primer on Umbrella Reviews, Treatment Networks and Multiple Treatments Meta-Analyses.\u0026rdquo; \u003cem\u003eCMAJ: Canadian Medical Association Journal = Journal de l\u0026rsquo;Association Medicale Canadienne\u003c/em\u003e 181, no. 8 (October 13, 2009): 488\u0026ndash;93. https://doi.org/10.1503/cmaj.081086.\u003c/li\u003e\n\u003cli\u003eCatov, Janet M., Chun Sen Wu, Jorn Olsen, Kim Sutton-Tyrrell, Jiong Li, and Ellen A. Nohr. \u0026ldquo;Early or Recurrent Preterm Birth and Maternal Cardiovascular Disease Risk.\u0026rdquo; \u003cem\u003eAnnals of Epidemiology\u003c/em\u003e 20, no. 8 (August 2010): 604\u0026ndash;9. https://doi.org/10.1016/j.annepidem.2010.05.007.\u003c/li\u003e\n\u003cli\u003eTsilidis, Konstantinos K., Stefania I. Papatheodorou, Evangelos Evangelou, and John P. A. Ioannidis. \u0026ldquo;Evaluation of Excess Statistical Significance in Meta-Analyses of 98 Biomarker Associations with Cancer Risk.\u0026rdquo; \u003cem\u003eJournal of the National Cancer Institute\u003c/em\u003e 104, no. 24 (December 19, 2012): 1867\u0026ndash;78. https://doi.org/10.1093/jnci/djs437.\u003c/li\u003e\n\u003cli\u003eBelbasis, Lazaros, Vanesa Bellou, Evangelos Evangelou, John P. A. Ioannidis, and Ioanna Tzoulaki. \u0026ldquo;Environmental Risk Factors and Multiple Sclerosis: An Umbrella Review of Systematic Reviews and Meta-Analyses.\u0026rdquo; \u003cem\u003eThe Lancet. Neurology\u003c/em\u003e 14, no. 3 (March 2015): 263\u0026ndash;73. https://doi.org/10.1016/S1474-4422(14)70267-4.\u003c/li\u003e\n\u003cli\u003eBellou, Vanesa, Lazaros Belbasis, Ioanna Tzoulaki, Evangelos Evangelou, and John P. A. Ioannidis. \u0026ldquo;Environmental Risk Factors and Parkinson\u0026rsquo;s Disease: An Umbrella Review of Meta-Analyses.\u0026rdquo; \u003cem\u003eParkinsonism \u0026amp; Related Disorders\u003c/em\u003e 23 (February 2016): 1\u0026ndash;9. https://doi.org/10.1016/j.parkreldis.2015.12.008.\u003c/li\u003e\n\u003cli\u003eBelbasis, Lazaros, Vanesa Bellou, and Evangelos Evangelou. \u0026ldquo;Environmental Risk Factors and Amyotrophic Lateral Sclerosis: An Umbrella Review and Critical Assessment of Current Evidence from Systematic Reviews and Meta-Analyses of Observational Studies.\u0026rdquo; \u003cem\u003eNeuroepidemiology\u003c/em\u003e 46, no. 2 (2016): 96\u0026ndash;105. https://doi.org/10.1159/000443146.\u003c/li\u003e\n\u003cli\u003e\u0026ldquo;Quantitative Synthesis in Systematic Reviews - PubMed.\u0026rdquo; Accessed August 27, 2022. https://pubmed.ncbi.nlm.nih.gov/9382404/.\u003c/li\u003e\n\u003cli\u003eDerSimonian, R., and N. 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Eke. \u0026ldquo;A Systematic Review and Meta-Analysis of Velamentous Cord Insertion among Singleton Pregnancies and the Risk of Preterm Delivery.\u0026rdquo; \u003cem\u003eInternational Journal of Gynaecology and Obstetrics: The Official Organ of the International Federation of Gynaecology and Obstetrics\u003c/em\u003e 142, no. 1 (July 2018): 9\u0026ndash;14. https://doi.org/10.1002/ijgo.12489.\u003c/li\u003e\n\u003cli\u003eVahanian, Sevan A., Jessica A. Lavery, Cande V. Ananth, and Anthony Vintzileos. \u0026ldquo;Placental Implantation Abnormalities and Risk of Preterm Delivery: A Systematic Review and Metaanalysis.\u0026rdquo; \u003cem\u003eAmerican Journal of Obstetrics and Gynecology\u003c/em\u003e 213, no. 4 Suppl (October 2015): S78-90. https://doi.org/10.1016/j.ajog.2015.05.058.\u003c/li\u003e\n\u003cli\u003eButalia, S., L. Gutierrez, A. Lodha, E. Aitken, A. Zakariasen, and L. Donovan. \u0026ldquo;Short- and Long-Term Outcomes of Metformin Compared with Insulin Alone in Pregnancy: A Systematic Review and Meta-Analysis.\u0026rdquo; \u003cem\u003eDiabetic Medicine: A Journal of the British Diabetic Association\u003c/em\u003e 34, no. 1 (January 2017): 27\u0026ndash;36. https://doi.org/10.1111/dme.13150.\u003c/li\u003e\n\u003cli\u003eXiang, Li-Jie, Yan Wang, Guo-Yuan Lu, and Qin Huang. \u0026ldquo;Association of the Presence of Microangiopathy with Adverse Pregnancy Outcome in Type 1 Diabetes: A Meta-Analysis.\u0026rdquo; \u003cem\u003eTaiwanese Journal of Obstetrics \u0026amp; Gynecology\u003c/em\u003e 57, no. 5 (October 2018): 659\u0026ndash;64. https://doi.org/10.1016/j.tjog.2018.08.008.\u003c/li\u003e\n\u003cli\u003eChan, Y. Y., K. Jayaprakasan, A. Tan, J. G. Thornton, A. Coomarasamy, and N. J. 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Alzeidan, et al. \u0026ldquo;Systematic Review and Meta-Analysis of the Effectiveness of Pre-Pregnancy Care for Women with Diabetes for Improving Maternal and Perinatal Outcomes.\u0026rdquo; \u003cem\u003ePloS One\u003c/em\u003e 15, no. 8 (2020): e0237571. https://doi.org/10.1371/journal.pone.0237571.\u003c/li\u003e\n\u003cli\u003eLiu, Chenchen, Jiantao Sun, Yuewei Liu, Hui Liang, Minsheng Wang, Chunhong Wang, and Tingming Shi. \u0026ldquo;Different Exposure Levels of Fine Particulate Matter and Preterm Birth: A Meta-Analysis Based on Cohort Studies.\u0026rdquo; \u003cem\u003eEnvironmental Science and Pollution Research International\u003c/em\u003e 24, no. 22 (August 2017): 17976\u0026ndash;84. https://doi.org/10.1007/s11356-017-9363-0.\u003c/li\u003e\n\u003cli\u003eSchaaf, Jelle M., Sophie M. S. Liem, Ben Willem J. Mol, Ameen Abu-Hanna, and Anita C. J. Ravelli. \u0026ldquo;Ethnic and Racial Disparities in the Risk of Preterm Birth: A Systematic Review and Meta-Analysis.\u0026rdquo; \u003cem\u003eAmerican Journal of Perinatology\u003c/em\u003e 30, no. 6 (June 2013): 433\u0026ndash;50. https://doi.org/10.1055/s-0032-1326988.\u003c/li\u003e\n\u003cli\u003eShah, Prakesh S., Jamie Zao, Haydi Al-Wassia, Vibhuti Shah, and Knowledge Synthesis Group on Determinants of Preterm/LBW Births. \u0026ldquo;Pregnancy and Neonatal Outcomes of Aboriginal Women: A Systematic Review and Meta-Analysis.\u0026rdquo; \u003cem\u003eWomen\u0026rsquo;s Health Issues: Official Publication of the Jacobs Institute of Women\u0026rsquo;s Health\u003c/em\u003e 21, no. 1 (February 2011): 28\u0026ndash;39. https://doi.org/10.1016/j.whi.2010.08.005.\u003c/li\u003e\n\u003cli\u003eChakraborty, Joy, Joseph Cherng Kong, Wai Kin Su, Peter Gourlas, Christopher Gillespie, Timothy Slack, Bradley Morris, and Nicholas Lutton. \u0026ldquo;Safety of Laparoscopic Appendicectomy during Pregnancy: A Systematic Review and Meta-Analysis.\u0026rdquo; \u003cem\u003eANZ Journal of Surgery\u003c/em\u003e 89, no. 11 (November 2019): 1373\u0026ndash;78. https://doi.org/10.1111/ans.14963.\u003c/li\u003e\n\u003cli\u003eSaraswat, L., S. Bhattacharya, A. Maheshwari, and S. Bhattacharya. \u0026ldquo;Maternal and Perinatal Outcome in Women with Threatened Miscarriage in the First Trimester: A Systematic Review.\u0026rdquo; \u003cem\u003eBJOG: An International Journal of Obstetrics and Gynaecology\u003c/em\u003e 117, no. 3 (February 2010): 245\u0026ndash;57. https://doi.org/10.1111/j.1471-0528.2009.02427.x.\u003c/li\u003e\n\u003cli\u003eShah, Prakesh S., Jamie Zao, Samana Ali, and Knowledge Synthesis Group of Determinants of preterm/LBW births. \u0026ldquo;Maternal Marital Status and Birth Outcomes: A Systematic Review and Meta-Analyses.\u0026rdquo; \u003cem\u003eMaternal and Child Health Journal\u003c/em\u003e 15, no. 7 (October 2011): 1097\u0026ndash;1109. https://doi.org/10.1007/s10995-010-0654-z.\u003c/li\u003e\n\u003cli\u003eLadhani, Noor Niyar N., Prakesh S. Shah, Kellie E. Murphy, and Knowledge Synthesis Group on Determinants of Preterm/LBW Births. \u0026ldquo;Prenatal Amphetamine Exposure and Birth Outcomes: A Systematic Review and Metaanalysis.\u0026rdquo; \u003cem\u003eAmerican Journal of Obstetrics and Gynecology\u003c/em\u003e 205, no. 3 (September 2011): 219.e1-7. https://doi.org/10.1016/j.ajog.2011.04.016.\u003c/li\u003e\n\u003cli\u003eVeenendaal, M. V. E., A. F. M. van Abeelen, R. C. Painter, J. a. M. van der Post, and T. J. Roseboom. \u0026ldquo;Consequences of Hyperemesis Gravidarum for Offspring: A Systematic Review and Meta-Analysis.\u0026rdquo; \u003cem\u003eBJOG: An International Journal of Obstetrics and Gynaecology\u003c/em\u003e 118, no. 11 (October 2011): 1302\u0026ndash;13. https://doi.org/10.1111/j.1471-0528.2011.03023.x.\u003c/li\u003e\n\u003cli\u003eChan, Y. Y., K. Jayaprakasan, A. Tan, J. G. Thornton, A. Coomarasamy, and N. J. Raine-Fenning. \u0026ldquo;Reproductive Outcomes in Women with Congenital Uterine Anomalies: A Systematic Review.\u0026rdquo; \u003cem\u003eUltrasound in Obstetrics \u0026amp; Gynecology: The Official Journal of the International Society of Ultrasound in Obstetrics and Gynecology\u003c/em\u003e 38, no. 4 (October 2011): 371\u0026ndash;82. https://doi.org/10.1002/uog.10056.\u003c/li\u003e\n\u003cli\u003eMarchenko, Alexander, Fatma Etwel, Olukayode Olutunfese, Cheri Nickel, Gideon Koren, and Irena Nulman. \u0026ldquo;Pregnancy Outcome Following Prenatal Exposure to Triptan Medications: A Meta-Analysis.\u0026rdquo; \u003cem\u003eHeadache\u003c/em\u003e 55, no. 4 (April 2015): 490\u0026ndash;501. https://doi.org/10.1111/head.12500.\u003c/li\u003e\n\u003cli\u003eKaplan, Y. C., J. Ozsarfati, F. Etwel, C. Nickel, I. Nulman, and G. Koren. \u0026ldquo;Pregnancy Outcomes Following First-Trimester Exposure to Topical Retinoids: A Systematic Review and Meta-Analysis.\u0026rdquo; \u003cem\u003eThe British Journal of Dermatology\u003c/em\u003e 173, no. 5 (November 2015): 1132\u0026ndash;41. https://doi.org/10.1111/bjd.14053.\u003c/li\u003e\n\u003cli\u003eConner, Shayna N., Victoria Bedell, Kim Lipsey, George A. Macones, Alison G. Cahill, and Methodius G. Tuuli. \u0026ldquo;Maternal Marijuana Use and Adverse Neonatal Outcomes: A Systematic Review and Meta-Analysis.\u0026rdquo; \u003cem\u003eObstetrics and Gynecology\u003c/em\u003e 128, no. 4 (October 2016): 713\u0026ndash;23. https://doi.org/10.1097/AOG.0000000000001649.\u003c/li\u003e\n\u003cli\u003eSobhy, S., Zoe Babiker, J. Zamora, K. S. Khan, and H. Kunst. \u0026ldquo;Maternal and Perinatal Mortality and Morbidity Associated with Tuberculosis during Pregnancy and the Postpartum Period: A Systematic Review and Meta-Analysis.\u0026rdquo; \u003cem\u003eBJOG: An International Journal of Obstetrics and Gynaecology\u003c/em\u003e 124, no. 5 (April 2017): 727\u0026ndash;33. https://doi.org/10.1111/1471-0528.14408.\u003c/li\u003e\n\u003cli\u003eWolf, Hanne T., Hanne K. Hegaard, Lene D. Huusom, and Anja B. Pinborg. \u0026ldquo;Multivitamin Use and Adverse Birth Outcomes in High-Income Countries: A Systematic Review and Meta-Analysis.\u0026rdquo; \u003cem\u003eAmerican Journal of Obstetrics and Gynecology\u003c/em\u003e 217, no. 4 (October 2017): 404.e1-404.e30. https://doi.org/10.1016/j.ajog.2017.03.029.\u003c/li\u003e\n\u003cli\u003eKim, Hyeong Ju, Jae-Hoon Kim, Doo Byung Chay, Joo Hyun Park, and Min-A. Kim. \u0026ldquo;Association of Isolated Single Umbilical Artery with Perinatal Outcomes: Systemic Review and Meta-Analysis.\u0026rdquo; \u003cem\u003eObstetrics \u0026amp; Gynecology Science\u003c/em\u003e 60, no. 3 (May 2017): 266\u0026ndash;73. https://doi.org/10.5468/ogs.2017.60.3.266.\u003c/li\u003e\n\u003cli\u003eCaissutti, Claudia, Alessandra Familiari, Asma Khalil, Maria E. Flacco, Lamberto Manzoli, Giovanni Scambia, Angelo Cagnacci, and Francesco D\u0026rsquo;antonio. \u0026ldquo;Small Fetal Thymus and Adverse Obstetrical Outcome: A Systematic Review and a Meta-Analysis.\u0026rdquo; \u003cem\u003eActa Obstetricia Et Gynecologica Scandinavica\u003c/em\u003e 97, no. 2 (February 2018): 111\u0026ndash;21. https://doi.org/10.1111/aogs.13249.\u003c/li\u003e\n\u003cli\u003eJarde, Alexander, Anne-Mary Lewis-Mikhael, Paul Moayyedi, Jennifer C. Stearns, Stephen M. Collins, Joseph Beyene, and Sarah D. McDonald. \u0026ldquo;Pregnancy Outcomes in Women Taking Probiotics or Prebiotics: A Systematic Review and Meta-Analysis.\u0026rdquo; \u003cem\u003eBMC Pregnancy and Childbirth\u003c/em\u003e 18, no. 1 (January 8, 2018): 14. https://doi.org/10.1186/s12884-017-1629-5.\u003c/li\u003e\n\u003cli\u003eGuillotin, Vivien, Alice Bouhet, Thomas Barnetche, Christophe Richez, Marie-Elise Truchetet, Julien Seneschal, Pierre Duffau, Estibaliz Lazaro, and F\u0026eacute;d\u0026eacute;ration Hospitalo-Universitaire Acronim. \u0026ldquo;Hydroxychloroquine for the Prevention of Fetal Growth Restriction and Prematurity in Lupus Pregnancy: A Systematic Review and Meta-Analysis.\u0026rdquo; \u003cem\u003eJoint Bone Spine\u003c/em\u003e 85, no. 6 (December 2018): 663\u0026ndash;68. https://doi.org/10.1016/j.jbspin.2018.03.006.\u003c/li\u003e\n\u003cli\u003eLiu, Na, Ping Li, Jie Wang, Dandan Chen, Weijia Sun, and Wei Zhang. \u0026ldquo;Effects of Home Visits for Pregnant and Postpartum Women on Premature Birth, Low Birth Weight and Rapid Repeat Birth: A Meta-Analysis and Systematic Review of Randomized Controlled Trials.\u0026rdquo; \u003cem\u003eFamily Practice\u003c/em\u003e 36, no. 5 (October 8, 2019): 533\u0026ndash;43. https://doi.org/10.1093/fampra/cmz009.\u003c/li\u003e\n\u003cli\u003eLiu, Liping, and Dan Sun. \u0026ldquo;Pregnancy Outcomes in Patients with Primary Antiphospholipid Syndrome: A Systematic Review and Meta-Analysis.\u0026rdquo; \u003cem\u003eMedicine\u003c/em\u003e 98, no. 20 (May 2019): e15733. https://doi.org/10.1097/MD.0000000000015733.\u003c/li\u003e\n\u003cli\u003eMatenchuk, Brittany, Rshmi Khurana, Chenxi Cai, Normand G. Boul\u0026eacute;, Linda Slater, and Margie H. Davenport. \u0026ldquo;Prenatal Bed Rest in Developed and Developing Regions: A Systematic Review and Meta-Analysis.\u0026rdquo; \u003cem\u003eCMAJ Open\u003c/em\u003e 7, no. 3 (September 2019): E435\u0026ndash;45. https://doi.org/10.9778/cmajo.20190014.\u003c/li\u003e\n\u003cli\u003eMohan, Manoj, Antoniou Antonios, Justin Konje, Stephen Lindow, Mohamed Ahmed Syed, and Anthony Akobeng. \u0026ldquo;Stillbirth and Associated Perinatal Outcomes in Obstetric Cholestasis: A Systematic Review and Meta-Analysis of Observational Studies.\u0026rdquo; \u003cem\u003eEuropean Journal of Obstetrics \u0026amp; Gynecology and Reproductive Biology: X\u003c/em\u003e 3 (July 2019): 100026. https://doi.org/10.1016/j.eurox.2019.100026.\u003c/li\u003e\n\u003cli\u003eThompson, Julie M., Stephanie M. Eick, Cody Dailey, Ariella P. Dale, Mansi Mehta, Anjali Nair, Jos\u0026eacute; F. 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Kilby. \u0026ldquo;Prognosis for the Co-Twin Following Single-Twin Death: A Systematic Review.\u0026rdquo; \u003cem\u003eBJOG: An International Journal of Obstetrics and Gynaecology\u003c/em\u003e 113, no. 9 (September 2006): 992\u0026ndash;98. https://doi.org/10.1111/j.1471-0528.2006.01027.x.\u003c/li\u003e\n\u003cli\u003eCarter, Ebony B., Lorene A. Temming, Jennifer Akin, Susan Fowler, George A. Macones, Graham A. Colditz, and Methodius G. Tuuli. \u0026ldquo;Group Prenatal Care Compared With Traditional Prenatal Care: A Systematic Review and Meta-Analysis.\u0026rdquo; \u003cem\u003eObstetrics and Gynecology\u003c/em\u003e 128, no. 3 (September 2016): 551\u0026ndash;61. https://doi.org/10.1097/AOG.0000000000001560.\u003c/li\u003e\n\u003cli\u003eLalani, S., A. J. Choudhry, B. Firth, V. Bacal, Mark Walker, S. W. Wen, S. Singh, A. Amath, M. Hodge, and I. 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Ness. \u0026ldquo;Prior Preterm Birth and Maternal Subclinical Cardiovascular Disease 4 to 12 Years after Pregnancy.\u0026rdquo; \u003cem\u003eJournal of Women\u0026rsquo;s Health (2002)\u003c/em\u003e 22, no. 10 (October 2013): 835\u0026ndash;43. https://doi.org/10.1089/jwh.2013.4248.\u003c/li\u003e\n\u003cli\u003eHobel, Calvin J., Siobhan M. Dolan, Niree A. Hindoyan, Nanbert Zhong, and Ramkumar Menon. \u0026ldquo;History of the Establishment of the Preterm Birth International Collaborative (PREBIC).\u0026rdquo; \u003cem\u003ePlacenta\u003c/em\u003e 79 (April 2019): 3\u0026ndash;20. https://doi.org/10.1016/j.placenta.2019.03.008.\u003c/li\u003e\n\u003cli\u003eTanz, Lauren J., Jennifer J. Stuart, Paige L. Williams, Eric B. Rimm, Stacey A. Missmer, Kathryn M. Rexrode, Kenneth J. Mukamal, and Janet W. Rich-Edwards. \u0026ldquo;Preterm Delivery and Maternal Cardiovascular Disease in Young and Middle-Aged Adult Women.\u0026rdquo; \u003cem\u003eCirculation\u003c/em\u003e 135, no. 6 (February 7, 2017): 578\u0026ndash;89. https://doi.org/10.1161/CIRCULATIONAHA.116.025954.\u003c/li\u003e\n\u003cli\u003eLau, Joseph, John P. A. Ioannidis, Norma Terrin, Christopher H. Schmid, and Ingram Olkin. \u0026ldquo;The Case of the Misleading Funnel Plot.\u0026rdquo; \u003cem\u003eBMJ (Clinical Research Ed.)\u003c/em\u003e 333, no. 7568 (September 16, 2006): 597\u0026ndash;600. https://doi.org/10.1136/bmj.333.7568.597.\u003c/li\u003e\n\u003cli\u003eBird, S. T., A. Chandra, T. Bennett, and S. M. Harvey. \u0026ldquo;Beyond Marital Status: Relationship Type and Duration and the Risk of Low Birth Weight.\u0026rdquo; \u003cem\u003eFamily Planning Perspectives\u003c/em\u003e 32, no. 6 (2000): 281\u0026ndash;87.\u003c/li\u003e\n\u003cli\u003eMacDonald, L. D., J. L. Peacock, and H. R. 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D., Louise E. Bath, and W. Hamish B. Wallace. \u0026ldquo;Radiation Damage to the Uterus -- Review of the Effects of Treatment of Childhood Cancer.\u0026rdquo; \u003cem\u003eHuman Fertility (Cambridge, England)\u003c/em\u003e 5, no. 2 (May 2002): 61\u0026ndash;66. https://doi.org/10.1080/1464727022000198942.\u003c/li\u003e\n\u003cli\u003eSanchez, Sixto E., Andrea V. Alva, Guillermo Diez Chang, Chungfang Qiu, David Yanez, Bizu Gelaye, and Michelle A. Williams. \u0026ldquo;Risk of Spontaneous Preterm Birth in Relation to Maternal Exposure to Intimate Partner Violence during Pregnancy in Peru.\u0026rdquo; \u003cem\u003eMaternal and Child Health Journal\u003c/em\u003e 17, no. 3 (April 2013): 485\u0026ndash;92. https://doi.org/10.1007/s10995-012-1012-0.\u003c/li\u003e\n\u003cli\u003eCokkinides, V. E., A. L. Coker, M. Sanderson, C. Addy, and L. 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World Health Organization, 2013. https://apps.who.int/iris/handle/10665/85240.\u003c/li\u003e\n\u003cli\u003eMoyer, Virginia A. and U.S. Preventive Services Task Force. \u0026ldquo;Screening for Intimate Partner Violence and Abuse of Elderly and Vulnerable Adults: U.S. Preventive Services Task Force Recommendation Statement.\u0026rdquo; \u003cem\u003eAnnals of Internal Medicine\u003c/em\u003e 158, no. 6 (March 19, 2013): 478\u0026ndash;86. https://doi.org/10.7326/0003-4819-158-6-201303190-00588.\u003c/li\u003e\n\u003cli\u003eMuhajarine, N., and C. D\u0026rsquo;Arcy. \u0026ldquo;Physical Abuse during Pregnancy: Prevalence and Risk Factors.\u0026rdquo; \u003cem\u003eCMAJ: Canadian Medical Association Journal = Journal de l\u0026rsquo;Association Medicale Canadienne\u003c/em\u003e 160, no. 7 (April 6, 1999): 1007\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eRomero, Roberto, Sudhansu K. Dey, and Susan J. Fisher. \u0026ldquo;Preterm Labor: One Syndrome, Many Causes.\u0026rdquo; \u003cem\u003eScience (New York, N.Y.)\u003c/em\u003e 345, no. 6198 (August 15, 2014): 760\u0026ndash;65. https://doi.org/10.1126/science.1251816.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-2639005/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2639005/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePreterm birth defined as delivery before 37 gestational weeks, is a leading cause of neonatal and infant morbidity and mortality. Understanding its multifactorial nature may improve prediction, prevention and the clinical management. We performed an umbrella review to summarize the evidence from meta-analyses of observational studies on risks factors associated with PTB, evaluate whether there are indications of biases in this literature and identify which of the previously reported associations are supported by robust evidence.\u003c/p\u003e\n\u003cp\u003eWe included 1511 primary studies providing data on 170 associations, covering a wide range of comorbid diseases, obstetric and medical history, drugs, exposure to environmental agents, infections and vaccines. Only seven risk factors provided robust evidence. The results from synthesis of observational studies suggests that sleep quality and mental health, risk factors with robust evidence should be routinely screened in clinical practice, should be tested in large randomized trial. Identification of risk factors with robust evidence will promote the development and training of prediction models that could improve public health, in a way that offers new perspectives in health professionals.\u003c/p\u003e","manuscriptTitle":"Risk factors for preterm labor: An Umbrella Review of meta-analyses of observational studies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-14 22:56:25","doi":"10.21203/rs.3.rs-2639005/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dedb18c1-afb4-43a8-b09d-e5bd6e789bbf","owner":[],"postedDate":"March 14th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":19817626,"name":"Health sciences/Health care/Disease prevention/Preventive medicine"},{"id":19817627,"name":"Health sciences/Medical research/Epidemiology"},{"id":19817628,"name":"Health sciences/Risk factors"},{"id":19817629,"name":"Health sciences/Health care/Public health/Epidemiology"}],"tags":[],"updatedAt":"2023-05-05T13:01:08+00:00","versionOfRecord":[],"versionCreatedAt":"2023-03-14 22:56:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2639005","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2639005","identity":"rs-2639005","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}