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Published in final edited form as: Am J Perinatol. 2023 Sep 25;41(Suppl 1):e2804–e2817. doi: 10.1055/s-0043-1775564

A Scoping Review of Preterm Birth Risk Factors

Irene D Tang 1, Daniel Mallia 2, Qi Yan 3, Itsik Pe’er 4, Anita Raja 2, Ansaf Salleb-Aouissi 4, Ronald Wapner 3
PMCID: PMC11891747  NIHMSID: NIHMS2040664  PMID: 37748506

Abstract

Preterm birth is a major cause of neonatal morbidity and mortality, but its etiology and risk factors are poorly understood. We undertook a scoping review to illustrate the breadth of risk factors for preterm birth that have been reported in the literature. We conducted a search in the PubMed database for articles published in the previous 5 years. We determined eligibility for this scoping review by screening titles and abstracts, followed by full-text review. We extracted odds ratios and other measures of association for each identified risk factor in the articles. A total of 2,509 unique articles were identified from the search, of which 314 were eligible for inclusion in our final analyses. We summarized risk factors and their relative impacts in the following categories: Activity, Psychological, Medical History, Toxicology, Genetics, and Vaginal Microbiome. Many risk factors for preterm birth have been reported. It is challenging to synthesize findings given the multitude of isolated risk factors that have been studied, inconsistent definitions of risk factors and outcomes, and use of different covariates in analyses. Novel methods of analyzing large datasets may promote a more comprehensive understanding of the etiology of preterm birth and ability to predict the outcome.

Keywords: preterm birth, risk factor, scoping review, genetics, microbiome

Preterm birth, defined as delivery occurring before 37 weeks of gestation, is the leading cause of neonatal morbidity and mortality in the United States, with increasingly poor outcomes for preterm babies delivered at earlier gestational ages.1,2 Previous preterm birth is considered the most important risk factor for preterm birth; however, a large proportion of births are to nulliparous women, for whom no birth history is available.3,4 In addition, many preterm births occur in women with no symptoms or clinical risk factors.5 Predictive factors have been insufficient to identify high-risk first pregnancies, limiting the ability to perform interventions to prevent preterm births.

The current literature on factors associated with preterm birth is wide-ranging but disjointed. The types of risk factors identified include environmental exposures,6 psychological and sociodemographic factors,7–11 medical and nonmedical substance use,12–15 obstetric history,16 maternal medical conditions,17,18 and current pregnancy conditions (including nutrition, biomarkers, and microbiome data).19–22 Despite the multitude of published risk factors, the latest Cochrane review on the topic found that there are no trials using risk score systems for prevention of preterm birth.23 Integrating results from various studies could enhance further research and the development of predictive models for preterm birth.

It has also been difficult to interpret findings about risk factors for preterm birth due to our lack of understanding about the biological mechanisms of preterm birth. Clinically, preterm birth is often categorized into two subtypes—spontaneous (following spontaneous onset of labor or membrane rupture) or medically indicated (when delivery is induced due to maternal or fetal indications)—but some research suggests that these outcomes have overlapping etiologies.24 Underlying these clinical outcomes, preterm birth may arise from any of four major pathways, which converge onto a final process of rupture of membranes or uterine contractions.25 Among the many avenues of preterm birth research, genetic studies may be especially valuable for enhancing our understanding of these biological pathways.26 In addition, our understanding of how risk factors contribute to the various mechanisms of preterm birth could benefit from being as granular as possible in describing the various preterm birth outcomes associated with each factor.

To improve our ability to predict risk for preterm birth, especially in the absence of information about previous birth outcomes, it is important to reevaluate our approach.

One framework for addressing complex research topics is that of a scoping study, where all literature relevant to a broad question is collected to produce a comprehensive summaryand illustrate gaps.27 We undertook a scoping review to identify risk factors for preterm birth in the following categories: Activity, Psychological, Medical History, Toxicology, Genetics, and Vaginal Microbiome. These are categories that have been shown to be pertinent to preterm birth in the related literature and we illustrate the relative impacts of specific risk factors that have been studied. We aim to describe risk and protective factors that are independent of previous birth history in people with asymptomatic, singleton pregnancies in the United States. With the exception of genetic studies, we limit our review of other categories to studies done in the United States because psychosocial factors often contribute to preterm birth in ways dependent on national context.

Materials and Methods

Search Strategy

We searched the PubMed database using the following search terms: (“Premature Birth”[majr] OR preterm birth [ti] OR premature birth[ti]) AND (“Risk Factors”[Mesh] OR risk[tiab] OR odds[tiab]) NOT “review”[Publication Type]. We conducted the search on June 26, 2020 and limited the results to articles published in the last 5 years. Additional articles that met inclusion criteria were identified from the reference lists of included studies.

Study Selection

We screened studies by titles and abstracts. We then determined article eligibility through full-text review. Full inclusion and exclusion criteria can be found in Table 1. We did not restrict eligibility to studies of nulliparous women, as the majority of studies did not limit their samples. Studies including nonnulliparous women were included when features related to prior birth were not the only studied risk factors.

Table 1.

Inclusion and exclusion criteria

Inclusion criteria Exclusion criteria

  • Available in English

  • Human subjects

  • More than 100 participants

  • Association studies, meta-analyses

  • Continental U.S.-based populationa

  • Uncomplicated pregnancies (study did not exclusively recruit people with symptoms suggestive of preterm birth or who already had a preterm birth-related intervention)

  • Presence of control group without the risk factor of interest

  • Effect sizes for individual preterm birth risk reported

  • Categorical preterm birth outcome usedb

  • Review articles, case reports, illustrations, mediation analyses

  • Sample of multiple pregnancies only

  • Sample of high-risk women based on obstetric history

  • Maternal or fetal structural anomalies (e.g., isolated single umbilical artery)

  • Risk factors unavailable in nulliparous women (e.g., interpregnancy interval, prior preterm birth)

  • Risk factors not generalizable (e.g., statewide initiatives, historical events, treatment at specific institutions)

  • or clinically relevant (e.g., environmental pollutant levels)

  • Results duplicated in a meta-analysis
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a

No geographic restriction was applied to studies of genetic risk factors. Meta-analyses or multicenter studies including at least one study site in the United States were included.

b

Additional outcomes were kept for genetic studies (e.g., family history of preterm birth, continuous gestational age outcome) due to the potential commonalities in genetic risk factors.

Data Extraction

We extracted the following information from each study: first author, year of publication, study design, study population (including whether the sample was restricted to nulliparous women), predictive factors (including their timing relative to the pregnancy, if relevant), preterm birth and subtype outcomes, odds ratios or other measures of association with 95% confidence intervals from the best fit or most fully adjusted model, and adjusted factors. Since the purpose of our scoping review is to identify potential risk factors, nonsignificant findings were not included. For ease of interpretation, categorical variables were preferentially extracted over continuous variables, and the most clinically relevant divisions of categories were extracted. Stratified and subgroup analyses were not extracted, but significant effect modification and interactions were noted. Within studies, risk estimates for factors that met our exclusion criteria were not extracted.

For studies on genetic risk factors, we collected results for a wider range of outcomes related to preterm birth (e.g., family history of preterm birth, continuous gestational age outcome). We extracted p-values for single-nucleotide polymorphisms significantly associated with each outcome when the reference number (rsID) for the polymorphism was provided or could be found based on chromosome location information. We extracted risk estimates from discovery analyses only.

Statistical Analysis

We grouped results by the gestational age outcomes used by each study to allow for meaningful comparisons. In this review, we present results for preterm birth at less than 37 weeks of gestation, the most commonly studied outcome. Many but not all articles also used an outcome of preterm birth at less than 32 weeks of gestation, the upper limit of early preterm birth as defined by the World Health Organization.28 When the gestational age threshold for preterm birth was not specified, it was assumed to be 37 weeks.

Within gestational age categories, the definitions used for subtypes of preterm birth varied by article. While some articles separated preterm prelabor rupture of membranes and spontaneous labor with intact membranes, others combined them in a single category of spontaneous preterm birth. For studies that used the two distinct outcomes, we calculated a pooled odds ratio for spontaneous preterm birth by averaging the log-odds ratios for each outcome, weighted by standard error as derived from the confidence intervals.

We transformed log association measures and β coefficients by exponentiation so they could be directly compared with odds ratios, the most common measure of association provided. Because preterm birth is a rare outcome, other measures of association (e.g., relative risk, hazard ratio) are comparable to odds ratios and were not transformed. For consistency within categories, we switched exposure and reference groups as needed by calculating the reciprocal of the effect size. When multiple articles studied the same risk factor for a particular preterm birth outcome, we report only the largest of the studies with overlapping samples to prevent data duplication.

In the genetic results, p-values given as ranges were recorded as the maximum value of the range (e.g., <0.01 was recorded as 0.01). Chromosome and base position were obtained for each rsID based on the hg38 reference genome database.

Results

Identified Studies

We identified 2,509 unique articles for potential inclusion. A total of 314 articles were eligible for our final analyses, but many studied overlapping samples and are not reported here (Fig. 1).

Fig. 1.

Fig. 1

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Flowchart of included articles.

Activity

Fig. 2 shows the impacts of behavioral factors on preterm birth. Healthy diet and physical activity were found to have significant protective effects.29–34 The results also suggested that certain physical activities may have a stronger protective effect in African Americans than in the general population. Sleep impairments were associated with increased risk for preterm birth.35–37

Fig. 2.

Fig. 2

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Activity-related risk factors for preterm birth. Specific study populations are denoted in brackets. denotes nulliparous sample or results from analysis of nulliparous women only. OR, odds ratio; PR, prevalence ratio; RR, risk ratio.

Genetics

Genetic results from the two genome-wide association studies found in our search are shown in Table 2. Ten loci were reported to be associated with preterm birth at genome-wide significance level, including EBF1, AGTR2, EEFSEC, WNT4, ADCY5, RAP2C, DUSP4-LOC101929470, COL24A1, LRRC28-MEF2A, and HGF-CACNA2D1.38,39 In addition, the majority of the literature contains candidate gene studies that showed 25 additional preterm birth-associated loci that did not reach genome-wide significance level, including AKAP10, ATG16L1, DNMT3B, FLT1, FNDC5, FSHR, IL10, IL16, IL1B, IL1R2, IL4, LEPR, LIFR-AS1, MMP1, MMP2, OGG1, PER3, PGR, PLA2G4D, PRKCA, RLN2, SKA2, TIMP2, TLR4, and VDR.40–56 Of these, rs1800872 in IL10 has notably been associated with various preterm birth outcomes in multiple studies with different sample populations: specifically, with preterm labor associated with preterm premature rupture of membranes at 26 to 34 weeks of gestation in Zaporizhzhia women, with preterm birth at 24 to 37 weeks in Korean women, and with preterm birth before 37 weeks in Indian women.43,54,56

Table 2.

Genetic risk factors for preterm birth

SNP Chr BP Test allelea Ref. allele MAF Effect direction p-Value Gene Ethnicity Outcomeb Ref.
rs2963463 5 158468041 T C 0.27 risk 7.70E-24 EBF1 European GA Zhanq et al 201738
rs2946171 5 158494932 G T 0.22 risk 8.1 OE-21 EBF1 European GA
rs5950491 X 116013381 A C 0.42 risk 6.60E-16 AGTR2 European GA
rs201226733 X 116033519 D I 0.42 risk 7.20E-16 AGTR2 European GA
rs200745338 3 128150621 I D 0.24 protective 7.50E-16 EEFSEC European GA
rs2955117 3 128162770 A G 0.28 protective 9.50E-15 EEFSEC European GA
rs56318008 1 22143914 T C 0.14 protective 3.40E-14 WNT4 European GA
rsl2037376 1 22135618 A G 0.15 protective 5.60E-14 WNT4 European GA
rs9861425 3 123354036 C A 0.46 risk 4.20E-10 ADCY5 European GA
rs200879388 X 132166543 D I 0.35 risk 3.40E-09 RAP2C European GA
rs4383453 3 123349512 A G 0.2 risk 3.70E-08 ADCY5 European GA
rs149014416 8 29812253 A AG 0.03 risk 1.10E-08 DUSP4-LOC101929470 African American Overall PTB Hong et al 201739
rs11161721 1 86022231 A C 0.22 risk 1,80E-08C COL24A1 African American Overall PTB
rs8029754 15 99438954 G A 0.21 risk 1.90E-08 LRRC28-MEF2A African American GA
rs1558001 7 81777764 T C 0.21 risk 3.00E-08 HGF-CACNA2D1 African American sPTB
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Abbreviations: GA, gestational age; MAF, minor allele frequency; PTB, preterm birth; sPTB, spontaneous preterm birth.

a

Test allele is the minor allele. Test allele is used for MAF and effect direction.

b

GA is gestational age, PTB is preterm birth, and sPTB is spontaneous preterm birth.

c

This p-value is for the interaction association between rs11161721 and prepregnancy body mass index category on overall PTB.

Medical History

Fig. 3 displays the effects of past medical and surgical history prior to pregnancy. Of the cancers studied, cervical cancer diagnosed within a year before conception appears to confer the highest risk, although interpretation is limited by the large confidence intervals.57–59 For adolescent or young adult cancer survivors compared with those with no cancer, there was significant effect modification by early prenatal care (likelihood ratio test, p=0.032).57 Prior bariatric operation and cervical excision were both associated with moderate increases in risk for preterm birth.60,61 There were small but significant increases in preterm birth risk with an increasing number of previous pregnancies, including pregnancies that resulted in abortion.62,63

Fig. 3.

Fig. 3

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Medical history risk factors for preterm birth. OR, odds ratio; RR, risk ratio.

Psychological

Findings related to psychological characteristics are displayed in Fig. 4. Overall, experiences of abuse and psychiatric disorders were associated with increased risk for preterm birth.11,64–73 One study of Black women found a significant interaction between depressive symptoms and support from the father of the baby (p=0.04).74 Interestingly, depression was found to have a protective effect in a study of teenage mothers.70 Other notable results were an increased risk of preterm birth among members of the military who reported greater acceptance of pregnancy and preparation for labor as well as several risk factors related to experiencing racism and stress.75–79

Fig. 4.

Fig. 4

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Psychological risk factors for preterm birth. Specific study populations are denoted in brackets. OR, odds ratio; PR, prevalence ratio; RR, risk ratio.

Toxicology

Risks associated with various toxins are shown in Fig. 5. Both drinking and smoking during pregnancy were associated with increased risk for preterm birth; interestingly, drinking and smoking preconception resulted in protective effects.63,66,78,80–86 The use of other substances contributed varying degrees of risk, with the greatest risks conferred by amphetamine, cocaine, and opioid use.73,80,87–89 Although nitrites are considered a toxin, intake from plant sources was associated with a protective effect.15

Fig. 5.

Fig. 5

Fig. 5

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Toxicology risk factors for preterm birth. Specific study populations are denoted in brackets. † denotes reference group was first trimester low-intensity smokers. * denotes nulliparous sample or results from analysis of nulliparous women only. OR, odds ratio; RR, risk ratio.

Vaginal Microbiome

The composition of the vaginal microbial population varies considerably across individuals and time points. It is commonly dominated by Lactobacilli and is typically classified into Community State Types (CSTs), primarily by the particular strain that is most dominant in the sample.90 Multiple studies reported differences in vaginal microbiome CSTs between preterm birth and full-term mothers,91,92 but others reported race as a confounder.93,94 Meta-analysis of these and other studies confirmed the association between CSTs and preterm birth.95,96

Discussion

Research on risk factors for preterm birth is extensive but disjointed. While many risk factors have been reported, their relative contributions to preterm birth are unclear. Despite extensive research efforts, there are few clinically applicable prediction models or risk assessments for preterm birth.81 The purpose of this scoping review is to summarize recent research and highlight opportunities to improve the integration of future studies into the existing fund of knowledge about preterm birth.

We were able to identify a wide range of specific risk factors within the following categories: Activity, Genetics, Medical History, Psychology, Toxicology, and Vaginal Microbiome. However, this scoping review also revealed several limitations and avenues for further exploration. For example, this review focused on the outcomes of spontaneous and indicated preterm birth before 37 weeks of gestation, but some risk factors may only be apparent when examining more specific outcomes, such as preterm birth at earlier gestational ages or spontaneous birth following preterm premature rupture of membranes. Although many independent criteria were presented in this review, risk factors may interact to produce greater effects on preterm birth.

Demographic risk factors were not presented here because of the abundance of diverse results reported in the literature, making consolidation difficult. Many large cohorts have shown that age contributes to preterm birth risk with a nonlinear pattern—with extremes of both old and young age being identified as risk factors—but we were unable to provide a quantitative summary due to different age categories being used across studies.82,97–100 Racial and ethnic disparities in preterm birth rates have been extensively illustrated in the literature, but the underlying etiologies for these disparities are complex and remain unaddressed.7 While various community-level factors and individual psychosocial stressors have been shown to contribute to these disparities in preterm birth, they do not fully account for the inequalities seen.7,8,67,73,101 Further research that examines a more comprehensive group of risk factors is needed to provide more insight into these findings.

The field of preterm birth research and the ability to use newer technologies such as machine learning may benefit from synthesis of the existing literature; however, several challenges exist. One limitation is the heterogeneity of research methods. The use of different covariates in analyses prevents the application of robust statistical methods such as meta-analysis to consolidate studies of common risk factors. In addition, definitions of preterm birth outcomes are not standardized. Different studies use thresholds of 32 or 34 weeks for early preterm birth. Some include premature rupture of membranes in the definition of spontaneous preterm labor, and others use it as a unique category.

Including more granular outcomes may enhance understanding of the different etiologies leading to preterm birth, as outcomes such as delivery for various indications may result from distinct pathways to preterm birth.24 Future research could also examine how interventions modify risk trajectories as patients progress throughout pregnancy. In addition, a more holistic approach may better elucidate the multifactorial risk factors for preterm birth, including the mechanisms underlying the so far unexplained racial disparities in pregnancy outcomes.7 For example, instead of selecting covariates a priori, methods such as machine learning could approach preterm birth research without making assumptions about the most important risk factors.

Conclusion

The ability to predict risk for preterm birth is vital. Risk stratification allows health care practitioners to increase prevention and monitoring when appropriate, while also reducing the costs and burden of surveillance for low-risk patients. Continued research is needed to clarify the most clinically relevant and actionable risk factors for preterm birth. While a wide range of risk factors for preterm birth have been studied, innovative methods are needed to integrate past findings and improve preterm birth predictions and interventions.

Key Points.

  • Preterm birth is difficult to predict.

  • Preterm birth has many diverse risk factors.

  • Holistic approaches may yield new insights.

Acknowledgments

Nicole Krenitsky and Ronald Andrade assisted with data collection and preparation. Alisa Leschenko and Cassan-dra Marcussen helped with synthesizing results. Maria Andrikopoulou provided additional guidance and review.

Funding

Research reported in this study was supported by the National Library Of Medicine of the National Institutes of Health under Award Number: R01LM013327. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

Conflict of Interest

None declared.

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