The risk factors of preterm birth: A multicentre <scp>case–control</scp> survey in China in 2018

Zhang, Yi‐Jie; Shen, Jieru; Lin, Sam Bill; Lu, Chengqiu; Jiang, Hong; Sun, Yi; Cheng, Xiuyong; Wang, Nana; Cui, Shudong; Liu, Xiuqin; Huang, Lie; Lin, Xin-Zhu; Zhao, Guoying; Yang, Ling; Chen, Chao

doi:10.1111/jpc.16002

Cited by 7 publications

(7 citation statements)

References 26 publications

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“…In this study, multiple pregnancy had the highest odds (aOR=10.57) for preterm delivery, which is consistent with previous studies (17)(18). Uterine overdistension is believed to be the causative mechanism for the high rate of spontaneous preterm births (19).…”

Section: Discussionsupporting

confidence: 92%

Risk factors of preterm birth in Sri Lanka: case-control study

Peiris,

Jayaratne,

Seneviratne

2023

J Coll Community Phys Sri Lanka

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Introduction: Preterm birth is a major challenge as a contributor to neonatal and child mortality in low- and middle-income countries (LMIC). Many countries toil to prevent and care for preterm babies. Country-specific solid data on risk factors of preterm births are crucial to formulate preventive strategies. Objectives: To determine the antenatal risk factors of preterm births in Sri Lanka Methods: An unmatched case-control study was conducted in seven Government hospitals with obstetric and neonatal care facilities in Colombo District in Sri Lanka. Live-born, spontaneous or provider-initiated, 374 preterm babies (<37 & >24 weeks of gestation) as cases and live-born 374 term babies as controls were recruited from mothers who had a dating scan before 22 weeks. A risk factor profile worked out by extensive literature search and consultation of experts was subjected to multivariable analysis to identify risk factors. Results: Risk factors identified were: multiple pregnancy (adjusted odds ratio (aOR)=10.57; 95% CI: 3.48, 32.08); bleeding/spotting during pregnancy (aOR=3.51; 95% CI: 1.77, 6.98), past preterm birth (aOR= 2.83; 95% CI: 1.09, 7.36), recent stressful life event (aOR=2.62; 95% CI: 1.43, 4.81), higher gravidity (aOR=2.58; 95% CI: 1.35, 4.9), dissatisfaction with self-assessed own health (aOR=2.54; 95% CI: 1.52, 4.22), pregnancy induced hypertension (aOR=2.25; 95% CI: 1.16, 4.38), no antenatal dental assessment (aOR=2.16; 95% CI: 1.23, 3.81), unsatisfactory oral hygiene (aOR=2.01; 95% CI: 1.33, 3.04), long standing hours during 3rd trimester (aOR=1.91; 95% CI: 1.24, 2.94) and cooking using firewood (aOR=1.51; 95% CI: 1.01, 2.25). Sexual abstinence (aOR=0.46; 95% CI: 0.26, 0.81) was a protective factor. Conclusions & Recommendations: Modifiable and unmodifiable factors for preterm births were identified. Special care during pregnancy and early admission is recommended for multiple pregnancy, those had spotting during pregnancy and those had past preterm delivery. Avoiding stress during pregnancy and emphasizing oral hygiene are recommended.

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Section: Discussionsupporting

confidence: 92%

Risk factors of preterm birth in Sri Lanka: case-control study

Peiris,

Jayaratne,

Seneviratne

2023

J Coll Community Phys Sri Lanka

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“…Nevertheless, the study also has some limitations. First, the NVSS database did not have information about the causes of infertility ( 33 ), placental abnormalities ( 34 ), polyhydramnios ( 35 ), and the type of IVF ( 36 - 38 ), which might be important for studying the risk of preterm birth ( 35 - 37 ). Second, although we excluded some participants who lacked information on preterm birth, IVF, eclampsia, newborn data, mothers, and fathers in the NVSS database, we were not sure whether these missing cases affected the results of this study.…”

Section: Discussionmentioning

confidence: 99%

Eclampsia, in vitro fertilization and the risk of preterm birth: a retrospective cohort study based on 2,880,759 samples

Wang¹,

Yin²,

Xue³

et al. 2023

Transl Pediatr

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Background Eclampsia and in vitro fertilization (IVF) are independent risk factors for preterm birth. Understanding the combined effects of multiple risk factors for preterm birth is critical to making accurate and personalized risk predictions. This study aimed to explore the interaction between eclampsia and IVF on the risk of preterm birth. Methods A total of 2,880,759 eligible participants from Birth Data Files in the National Vital Statistics System (NVSS) database 2019 were enrolled in this retrospective cohort study. Some characteristics were collected, such as maternal age, prepregnancy body mass index (BMI), history of preterm birth, paternal age, race, newborn sex. Preterm birth was defined as <37 weeks gestation. Univariate and multivariate logistic regression models were utilized to assess the associations between eclampsia, IVF and preterm birth. The odds ratio (OR) and 95% confidence interval (CI) were calculated in this study. Relative excess risk due to interaction (RERI), attributable proportion (AP) and synergy index (S) were adopted to evaluate the interaction between eclampsia and IVF on the risk of preterm birth. Results The age of mothers and fathers, the number of multiple births, the proportion of mothers with a history of preterm birth, pregnancy infections, eclampsia and IVF among the preterm birth group were all higher than those among the non-preterm birth group. The incidence of preterm birth in eclampsia and IVF population was approximately 37.31% and 22.96%, separately. After adjusting some covariates, subjects with both eclampsia and IVF had a higher risk of preterm birth (OR =9.197, 95% CI: 6.795–12.448, P<0.001). Furthermore, the results (RERI =3.426, 95% CI: 0.639–6.213, AP=0.374, 95% CI: 0.182–0.565, S =1.723, 95% CI: 1.222–2.428) suggested that the interaction between eclampsia and IVF on preterm birth was statistically significant, indicating a synergistic interaction. Conclusions Eclampsia and IVF might interact in a synergistic manner to increase the risk of preterm birth. Awareness of the risk profile associated with preterm birth is crucial for pregnant woman with IVF to implement dietary and lifestyle modifications.

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“…Variables related to the number of hospitals, beds, births, and geographic area were obtained from the 2017 Area Health Resource Files, publicly available from the US Health Resources and Services Administration . These covariates were selected a priori based on known associations with preterm birth or hypothesized to be related to an area’s tendency to adopt late preterm steroids into practice . Comparisons were made using Student t test or Wilcoxon rank-sum test, as appropriate.…”

Section: Methodsmentioning

confidence: 97%

“…18 These covariates were selected a priori based on known associations with preterm birth or hypothesized to be related to an area’s tendency to adopt late preterm steroids into practice. 19 , 20 , 21 , 22 Comparisons were made using Student t test or Wilcoxon rank-sum test, as appropriate. Then, a multivariable logistic regression was constructed using the aforementioned covariates to identify factors associated with faster adopter status in the postperiod.…”

Section: Methodsmentioning

confidence: 99%

Regional Variation in Antenatal Late Preterm Steroid Use Following the ALPS Trial

Freret,

Cohen,

Gyamfi-Bannerman

et al. 2024

JAMA Netw Open

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ImportanceThe publication of the Antenatal Late Preterm Steroids (ALPS) trial in February 2016 demonstrated that antenatal administration of betamethasone in the late preterm period (between 34 to 36 weeks of gestation) for individuals with a high risk of delivery decreased neonatal respiratory morbidity. National estimates have suggested the trial did change obstetric practice, but little is known if the evidence was adopted uniformly or equitably.ObjectiveTo assess regional variation in the use of late preterm steroids after the publication of the Antenatal Late Preterm Steroids (ALPS) Trial and to understand factors associated with a region’s pace of adoption.Design, Setting, and ParticipantsThis cross-sectional study used US natality data from February 2015 to October 2017 from hospital referral regions (HRRs) within the US. Inclusion criteria included live-born, nonanomalous, singleton, late preterm (34 to 36 completed weeks of gestation) neonates born to individuals without pregestational diabetes. This study was conducted from November 15, 2022, to January 13, 2023.Main Outcome and MeasuresHRRs were categorized as either a slower adopter or faster adopter of antenatal late preterm steroids based on the observed vs expected pace of antenatal steroid adoption in a 1-year period after the trial’s dissemination. Patient and regional factors hypothesized a priori to be associated with the uptake of late preterm steroids were compared between faster and slower adopters. Comparisons were made using Student t test or Wilcoxon rank-sum test, as appropriate. A multivariable logistic regression was constructed to identify factors associated with faster adopter status in the postperiod.ResultsThere were 666 097 late preterm births in 282 HRRs. The mean (SD) maternal age in HRRs was 27.9 (1.2) years. The median (IQR) percentage of births by race categories in HRRs for patients identifying as American Indian or Alaskan Native was 0.5% (0.2%-1.3%); Asian or Pacific Islander, 3.0% (1.7%-5.3%); Black, 12.9% (5.1%-29.1%); and White, 78.6% (66.6%-87.0%). The median percentage of births in HRRs to patients of Hispanic ethnicity was 11.2% (6.3%-27.4%). In this study, 136 HRRs (48.2%) were classified as faster adopters and 146 (51.8%) were classified as slower adopters. Faster adopters increased their steroid use by 12.1 percentage points (from 5.9% to 18.0%) compared with a 5.5 percentage point increase (from 3.7% to 9.2%) among slower adopters (P &lt; .001). Most examined patient and regional factors were not associated with a region’s pace of adoption, with the exception of the regional prevalence of prior preterm birth (adjusted odds ratio [aOR], 2.04 [95% CI, 1.48-2.82]) and the percentage of deliveries at 34 to 35 weeks of gestation (aOR, 0.68 [95% CI, 0.47-0.99]) compared with 36 weeks.Conclusions and RelevanceIn this cross-sectional study, there was widespread geographic variation in the adoption of antenatal steroid administration for late preterm births that largely remained unexplained by population factors. These findings should prompt further investigations to barriers to timely or equitable access to new evidence-based practices and guide future dissemination strategies with the goal of more uniform adoption.

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The risk factors of preterm birth: A multicentre case–control survey in China in 2018

Cited by 7 publications

References 26 publications

Risk factors of preterm birth in Sri Lanka: case-control study

Risk factors of preterm birth in Sri Lanka: case-control study

Eclampsia, in vitro fertilization and the risk of preterm birth: a retrospective cohort study based on 2,880,759 samples

Regional Variation in Antenatal Late Preterm Steroid Use Following the ALPS Trial

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