Pre-pregnancy or first-trimester risk scoring to identify women at high risk of preterm birth

A Scoping Review of Preterm Birth Risk Factors

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. KEY POINTS: · Preterm birth is difficult to predict.. · Preterm birth has many diverse risk factors.. · Holistic approaches may yield new insights..

Risk Scoring Systems for Preterm Birth and Their Performance: A Systematic Review

Introduction: Nowadays, the risk stratification of preterm birth (PTB) and its prediction remain a challenge. Many risk factors associated with PTB have been identified, and risk scoring systems (RSSs) have been developed to face this challenge. The objectives of this systematic review were to identify RSSs for PTB, the variables they consist of, and their performance. Materials and methods: Two databases were searched, and two authors independently performed the screening and eligibility phases. Records studying an RSS, based on specified variables, with an evaluation of the predictive value for PTB, were considered eligible. Reference lists of eligible studies and review articles were also searched. Data from the included studies were extracted. Results: A total of 56 studies were included in this review. The most frequently incorporated variables in the RSS included in this review were maternal age, weight, history of smoking, history of previous PTB, and cervical length. The performance measures varied widely among the studies, with sensitivity ranging between 4.2% and 92.0% and area under the curve (AUC) between 0.59 and 0.95. Conclusions: Despite the recent technological and scientifical evolution with a better understanding of variables related to PTB and the definition of new ultrasonographic parameters and biomarkers associated with PTB, the RSS's ability to predict PTB remains poor in most situations, thus compromising the integration of a single RSS in clinical practice. The development of new RSSs, the identification of new variables associated with PTB, and the elaboration of a large reference dataset might be a step forward to tackle the problem of PTB.

Predicting Spontaneous Pre-term Birth Risk Is Improved When Quantitative Ultrasound Data Are Included With Historical Clinical Data

OBJECTIVE

Predicting women at risk for spontaneous pre-term birth (sPTB) has been medically challenging because of the lack of signs and symptoms of pre-term birth until interventions are too late. We hypothesized that prediction of the sPTB risk level is enhanced when using both historical clinical (HC) data and quantitative ultrasound (QUS) data compared with using only HC data. HC data defined herein included birth history prior to that of the current pregnancy as well as, from the current pregnancy, a clinical cervical length assessment and physical examination data.

METHODS

The study population included 248 full-term births (FTBs) and 26 sPTBs. QUS scans (Siemens S2000 and MC9-4) were performed by registered diagnostic medical sonographers using a standard cervical length approach. Two cervical QUS scans were conducted at 20 ± 2 and 24 ± 2 wk of gestation. Multiple QUS features were evaluated from calibrated raw radiofrequency backscattered ultrasonic signals. Two statistical models designed to determine sPTB risk were compared: (i) HC data alone and (ii) combined HC and QUS data. Model comparisons included a likelihood ratio test, cross-validated receiver operating characteristic area under the curve, sensitivity and specificity. The study's birth outcomes were only FTBs and sPTBs; medically induced pre-term births were not included.

DISCUSSION

Combined HC and QUS data identified women at risk of sPTB with better AUC (0.68, 95% confidence interval [CI]: 0.57-0.78) compared with HC data alone (0.53, 95% CI: 0.40-0.66) and HC data + cervical length at 18-20 wk of gestation (average AUC = 0.51, 95% CI: 0.38-0.64). A likelihood ratio test for significance of QUS features in the classification model was highly statistically significant (p < 0.01).

CONCLUSION

Even with only 26 sPTBs among 274 births, value was added in predicting sPTB when QUS data were included with HC data.

Beyond Medically Complex Pregnancy: A Scoping Review to Understand How Complexity in Pregnancy is Conceptualized

BACKGROUND

The goal of this scoping review was to better understand how complexity in pregnancy is conceptualized. Specific objectives were to (1) identify factors that are conceptualized in the literature as complicating or impacting pregnancy; and (2) summarize tools and programs that have been implemented to support pregnant people with complex care needs.

METHODS

Electronic databases were searched from January 2000 to July 2020 and supplemented by bibliographic searches and citation chaining, to identify articles that described at least one nonmedical and one medical risk factor during pregnancy. We focused on complexity prior to the onset of labor and only included primary studies conducted in middle- or high-income countries. More than 6000 records were screened independently by 3 reviewers at the abstract and title level.

RESULTS

Fourteen articles met inclusion criteria. Eight studies described antenatal risk scoring systems, including the Florida Healthy Start Prenatal Risk Screen, the Kindex risk screening tool, the prenatal event history calendar, and the Rotterdam Reproductive Risk Reduction score card. We abstracted 85 medical factors and 25 nonmedical factors from the literature. Nonmedical factors that were conceptualized as complicating pregnancy or birth could be grouped into 4 domains: characteristics of the childbearing person (7 factors), socioeconomic conditions (7 factors), family and social life (5 factors), and psychoemotional health (6 factors).

DISCUSSION

We found limited scholarly research and few assessment tools that broaden the discussion of complexity in pregnancy beyond medical multimorbidity. Multiple dimensions of health should be integrated into a complexity framework for pregnancy that account for the diverse contexts and needs of pregnant people. An important part of this process is the development of a shared language to describe complexity that is strength based and acknowledges how environments, health care encounters, and the larger sociocultural context can affect pregnant people's medical status in pregnancy.

Development of prognostic model for preterm birth using machine learning in a population-based cohort of Western Australia births between 1980 and 2015

Preterm birth is a global public health problem with a significant burden on the individuals affected. The study aimed to extend current research on preterm birth prognostic model development by developing and internally validating models using machine learning classification algorithms and population-based routinely collected data in Western Australia. The longitudinal retrospective cohort study involved all births in Western Australia between 1980 and 2015, and the analytic sample contains 81,974 (8.6%) preterm births (< 37 weeks of gestation). Prediction models for preterm birth were developed using regularised logistic regression, decision trees, Random Forests, extreme gradient boosting, and multi-layer perceptron (MLP). Predictors included maternal socio-demographics and medical conditions, current and past pregnancy complications, and family history. Class weight was applied to handle imbalanced outcomes and stratified tenfold cross-validation was used to reduce overfitting. Close to half of the preterm births (49.1% at 5% FPR, 95% CI 48.9%,49.5%) were correctly classified by the best performing classifier (MLP) for all women when current pregnancy information was available. The sensitivity was boosted to 52.7% (95% CI 52.1%,53.3%) after including past obstetric history in a sub-population of births from multiparous women. Around half of the preterm birth can be identified antenatally at high specificity using population-based routinely collected maternal and pregnancy data. The performance of the prediction models depends on the available predictor pool that is individual and time specific.

Dense phenotyping from electronic health records enables machine learning-based prediction of preterm birth

BACKGROUND

Identifying pregnancies at risk for preterm birth, one of the leading causes of worldwide infant mortality, has the potential to improve prenatal care. However, we lack broadly applicable methods to accurately predict preterm birth risk. The dense longitudinal information present in electronic health records (EHRs) is enabling scalable and cost-efficient risk modeling of many diseases, but EHR resources have been largely untapped in the study of pregnancy.

METHODS

Here, we apply machine learning to diverse data from EHRs with 35,282 deliveries to predict singleton preterm birth.

RESULTS

We find that machine learning models based on billing codes alone can predict preterm birth risk at various gestational ages (e.g., ROC-AUC = 0.75, PR-AUC = 0.40 at 28 weeks of gestation) and outperform comparable models trained using known risk factors (e.g., ROC-AUC = 0.65, PR-AUC = 0.25 at 28 weeks). Examining the patterns learned by the model reveals it stratifies deliveries into interpretable groups, including high-risk preterm birth subtypes enriched for distinct comorbidities. Our machine learning approach also predicts preterm birth subtypes (spontaneous vs. indicated), mode of delivery, and recurrent preterm birth. Finally, we demonstrate the portability of our approach by showing that the prediction models maintain their accuracy on a large, independent cohort (5978 deliveries) from a different healthcare system.

CONCLUSIONS

By leveraging rich phenotypic and genetic features derived from EHRs, we suggest that machine learning algorithms have great potential to improve medical care during pregnancy. However, further work is needed before these models can be applied in clinical settings.

PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks

BACKGROUND

Early identification of pregnant women at risk for preterm birth (PTB), a major cause of infant mortality and morbidity, has a significant potential to improve prenatal care. However, we lack effective predictive models which can accurately forecast PTB and complement these predictions with appropriate interpretations for clinicians. In this work, we introduce a clinical prediction model (PredictPTB) which combines variables (medical codes) readily accessible through electronic health record (EHR) to accurately predict the risk of preterm birth at 1, 3, 6, and 9 months prior to delivery.

METHODS

The architecture of PredictPTB employs recurrent neural networks (RNNs) to model the longitudinal patient's EHR visits and exploits a single code-level attention mechanism to improve the predictive performance, while providing temporal code-level and visit-level explanations for the prediction results. We compare the performance of different combinations of prediction time-points, data modalities, and data windows. We also present a case-study of our model's interpretability illustrating how clinicians can gain some transparency into the predictions.

RESULTS

Leveraging a large cohort of 222,436 deliveries, comprising a total of 27,100 unique clinical concepts, our model was able to predict preterm birth with an ROC-AUC of 0.82, 0.79, 0.78, and PR-AUC of 0.40, 0.31, 0.24, at 1, 3, and 6 months prior to delivery, respectively. Results also confirm that observational data modalities (such as diagnoses) are more predictive for preterm birth than interventional data modalities (e.g., medications and procedures).

CONCLUSIONS

Our results demonstrate that PredictPTB can be utilized to achieve accurate and scalable predictions for preterm birth, complemented by explanations that directly highlight evidence in the patient's EHR timeline.

Risk of early birth by body mass index in a propensity score-matched sample: A retrospective cohort study

OBJECTIVE

Evaluate the risk of preterm (<37 weeks) or early term birth (37 or 38 weeks) by body mass index (BMI) in a propensity score-matched sample.

DESIGN

Retrospective cohort analysis.

SETTING

California, USA.

POPULATION

Singleton live births from 2011-2017.

METHODS

Propensity scores were calculated for BMI groups using maternal factors. A referent sample of women with a BMI between 18.5 and <25.0 kg/m² was selected using exact propensity score matching. Risk ratios for preterm and early term birth were calculated.

MAIN OUTCOME MEASURES

Early birth.

RESULTS

Women with a BMI <18.5 kg/m² were at elevated risk of birth of 28-31 weeks (relative risk [RR] 1.2, 95% CI 1.1-1.4), 32-36 weeks (RR 1.3, 95% CI 1.2-1.3), and 37 or 38 weeks (RR 1.1, 95% CI 1.1-1.1). Women with BMI ≥25.0 kg/m² were at 1.2-1.4-times higher risk of a birth <28 weeks and were at reduced risk of a birth between 32 and 36 weeks (RR 0.8-0.9) and birth during the 37th or 38th week (RR 0.9).

CONCLUSION

Women with a BMI <18.5 kg/m² were at elevated risk of a preterm or early term birth. Women with BMI ≥25.0 kg/m² were at elevated risk of a birth <28 weeks. Propensity score-matched women with BMI ≥30.0 kg/m² were at decreased risk of a spontaneous preterm birth with intact membranes between 32 and 36 weeks, supporting the complexity of BMI as a risk factor for preterm birth.

Association of Alcohol Use Diagnostic Codes in Pregnancy and Offspring Conotruncal and Endocardial Cushion Heart Defects

Background

The pathogenesis of congenital heart disease (CHD) remains largely unknown, with only a small percentage explained solely by genetic causes. Modifiable environmental risk factors, such as alcohol, are suggested to play an important role in CHD pathogenesis. We sought to evaluate the association between prenatal alcohol exposure and CHD to gain insight into which components of cardiac development may be most vulnerable to the teratogenic effects of alcohol.

Methods and Results

This was a retrospective analysis of hospital discharge records from the California Office of Statewide Health Planning and Development and linked birth certificate records restricted to singleton, live‐born infants from 2005 to 2017. Of the 5 820 961 births included, 16 953 had an alcohol‐related International Classification of Diseases, Ninth and Tenth Revisions (ICD‐9; ICD‐10) code during pregnancy. Log linear regression was used to calculate risk ratios (RR) for CHD among individuals with an alcohol‐related ICD‐9 and ICD10 code during pregnancy versus those without. Three models were created: (1) unadjusted, (2) adjusted for maternal demographic factors, and (3) adjusted for maternal demographic factors and comorbidities. Maternal alcohol‐related code was associated with an increased risk for CHD in all models (RR, 1.33 to 1.84); conotruncal (RR, 1.62 to 2.11) and endocardial cushion (RR, 2.71 to 3.59) defects were individually associated with elevated risk in all models.

Conclusions

Alcohol‐related diagnostic codes in pregnancy were associated with an increased risk of an offspring with a CHD, with a particular risk for endocardial cushion and conotruncal defects. The mechanistic basis for this phenotypic enrichment requires further investigation.

Residential particulate matter, proximity to major roads, traffic density and traffic volume as risk factors for preterm birth in California

BACKGROUND

While pollution from vehicle sources is an established risk factor for preterm birth, it is unclear whether distance of residence to the nearest major road or related measures like major road density represent useful measures for characterising risk.

OBJECTIVE

To determine whether major road proximity measures (including distance to major road, major road density and traffic volume) are more useful risk factors for preterm birth than other established vehicle-related measures (including particulate matter <2.5 μm in diameter (PM_2.5 ) and diesel particulate matter (diesel PM)).

METHODS

This retrospective cohort study included 2.7 million births across the state of California from 2011-2017; each address at delivery was geocoded. Geocoding was used to calculate distance to the nearest major road, major road density within a 500 m radius and major road density weighted by truck volume. We measured associations with preterm birth using risk ratios adjusted for target demographic, clinical, socioeconomic and environmental covariates (aRRs). We compared these to the associations between preterm birth and PM_2.5 and diesel PM by census tract of residence.

RESULTS

Findings showed that whereas higher mean levels of PM_2.5 and diesel PM by census tract were associated with a higher risk of preterm birth, living closer to roads or living in higher traffic density areas was not associated with higher risk. Residence in a census tract with a mean PM_2.5 in the top quartile compared with the lowest quartile was associated with the highest observed risk of preterm birth (aRR 1.04, 95% CI 1.04, 1.05).

CONCLUSIONS

Over a large geographical region with a diverse population, PM2.5 and diesel PM were associated with preterm birth, while measures of distance to major road were not, suggesting that these distance measures do not serve as a proxy for measures of particulate matter in the context of preterm birth.

Predictive Models for Very Preterm Birth: Developing a Point-of-Care Tool

OBJECTIVE

The objective of this study was to create three point-of-care predictive models for very preterm birth using variables available at three different time points: prior to pregnancy, at the end of the first trimester, and mid-pregnancy.

STUDY DESIGN

This is a retrospective cohort study of 359,396 Ohio Medicaid mothers from 2008 to 2015. The last baby for each mother was included in the final dataset. Births prior to 22 weeks were excluded. Multivariable logistic regression was used to create three models. These models were validated on a cohort that was set aside and not part of the model development. The main outcome measure was birth prior to 32 weeks.

RESULTS

The final dataset contained 359,396 live births with 6,516 (1.81%) very preterm births. All models had excellent calibration. Goodness-of-fit tests suggested strong agreement between the probabilities estimated by the model and the actual outcome experience in the data. The mid-pregnancy model had acceptable discrimination with an area under the receiver operator characteristic curve of approximately 0.75 in both the developmental and validation datasets.

CONCLUSION

Using data from a large Ohio Medicaid cohort we developed point-of-care predictive models that could be used before pregnancy, after the first trimester, and in mid-pregnancy to estimate the probability of very preterm birth. Future work is needed to determine how the calculator could be used to target interventions to prevent very preterm birth.

KEY POINTS

· We developed predictive models for very preterm birth.. · All models showed excellent calibration.. · The models were integrated into a risk calculator..

Patient and provider perspectives on preterm birth risk assessment and communication

OBJECTIVE

To describe and compare how obstetric patients and care providers view preterm birth risk assessment and communication.

METHODS

We conducted eight focus groups with obstetric patients (n = 35) and 16 qualitative interviews with obstetric providers. Grounded theory was used to identify and analyze themes.

RESULTS

Patients' knowledge about preterm birth varied greatly. Similar benefits and risks of preterm birth risk counseling were discussed by patients and providers with notable exceptions: patients cited preparedness as a benefit and providers cited maternal blame, patient alienation, and estimate uncertainty as potential risks. Most patients expressed a desire to know their personalized preterm birth risk during pregnancy. Providers differed in whether they offer universal versus selective, and quantitative versus qualitative, preterm birth risk counseling. Many providers expressed concern about discussing social and structural risk factors for preterm birth.

CONCLUSION

While many patients desired knowing their personalized preterm birth risk, prenatal care providers' disclosure practices vary because of uncertainty of estimates, concerns about negative consequences and challenges of addressing systemic inequities and social determinants of health.

PRACTICE IMPLICATIONS

Given the existing asymmetry of information about preterm birth risk, providers should consider patient preferences regarding and potential benefits and risks of such disclosure in their practice.

Maternal Pre-conception Body Mass Index and Fasting Plasma Glucose With the Risk of Pre-term Birth: A Cohort Study Including 4.9 Million Chinese Women

Background: To evaluate the associations of pre-conception body mass index (BMI), fasting plasma glucose (FPG) alone and their combination with pre-term birth (PTB) risk.

Methods: We conducted a population-based retrospective cohort study with 4,987,129 reproductive-aged women, who participated in National Free Pre-Pregnancy Checkups Project in 2013–2016 and had a singleton delivery before December 2017 in China. All data analyses were conducted in 2018–2021.

Results: A total of 339,662 (6.81%) women had pre-term deliveries. Compared with women with normal weight and normal glucose, underweight and normal weight were associated with PTB among hypoglycemia women, the adjusted odd ratios (aORs) were 1.24 (95% CI: 1.05–1.48) and 1.16 (95% CI: 1.07–1.25), respectively; underweight, overweight and obesity were associated with PTB among women with normal glucose, the aORs were 1.09 (95% CI: 1.08–1.10), 1.06 (95% CI: 1.05–1.07) and 1.08 (95% CI: 1.05–1.12), respectively; all the BMI groups were significantly associated with PTB among women with pre-diabetes or diabetes (P < 0.05). The dose-response relationships of BMI with PTB varied in different FPG level, with U-shaped curve in normal glucose and pre-diabetes women, J-shaped in diabetes women, L-shaped in hypoglycemia women. For FPG with PTB, the dose-response relationships were U-shaped in normal weight, overweight, and obesity women, and L-shaped in underweight women.

Conclusion: We found that the associations of PTB with BMI varied with levels of FPG, and associations of PTB with FPG varied with levels of BMI. There was a synergistic effect on PTB risk due to abnormal weight and glycemia besides a conventional main effect derived from either of them. Achieving desirable weight and glucose control before conception should be advised.

Association between maternal prepregnancy body mass index and risk of preterm birth in more than 1 million Asian American mothers

BACKGROUND

Asian Americans are among the fastest growing subpopulations in the United States. However, evidence about maternal prepregnancy body mass index (BMI) and preterm birth among Asian Americans is lacking.

METHODS

This population-based study used nationwide birth certificate data from the US National Vital Statistics System 2014 to 2018. All Asian American mothers who had a singleton live birth were included. According to Asian-specific cutoffs, maternal prepregnancy BMI was classified into underweight (BMI < 18.5 kg/m2 ), normal weight (BMI 18.5-22.9 kg/m2 ), overweight (BMI 23.0-27.4 kg/m2 ), class I obesity (BMI 27.5-32.4 kg/m2 ), class II obesity (BMI 32.5-37.4 kg/m2 ), and class III obesity (BMI ≥37.5 kg/m2 ). Preterm birth was defined as gestational age less than 37 weeks. Multivariable logistic regression models were used to estimate the odds ratio (OR) of preterm birth.

RESULTS

We included 1 081 341 Asian American mother-infant pairs. The rate of preterm birth was 6.51% (n = 70 434). The rate of maternal prepregnancy overweight and obesity was 46.80% (n = 506 042). Compared with mothers with normal weight, the adjusted OR of preterm delivery was 1.04 (95% CI, 1.01-1.07) for underweight mothers, 1.18 (95% CI, 1.16-1.20) for overweight mothers, 1.41 (95% CI, 1.37-1.44) for mothers with class I obesity, 1.69 (95% CI, 1.63-1.76) for mothers with class II obesity, and 1.78 (95% CI, 1.66-1.90) for mothers with class III obesity. Similar patterns of associations were observed in Asian American mothers across different country origins.

CONCLUSIONS

Among Asian American mothers, maternal prepregnancy overweight or obesity, defined by Asian-specific, lower BMI cutoffs, was significantly associated with an increased risk of preterm birth. The risk of preterm birth increased with increasing obesity severity. These findings highlight the importance of using Asian-specific BMI cutoffs in assessing risk of preterm birth among Asian American mothers.

Risk and Protective Factors for Preterm Birth Among Black Women in Oakland, California

This project examines risk and protective factors for preterm birth (PTB) among Black women in Oakland, California. Women with singleton births in 2011-2017 (n = 6199) were included. Risk and protective factors for PTB and independent risk groups were identified using logistic regression and recursive partitioning. Having less than 3 prenatal care visits was associated with highest PTB risk. Hypertension (preexisting, gestational), previous PTB, and unknown Women, Infant, Children (WIC) program participation were associated with a two-fold increased risk for PTB. Maternal birth outside of the USA and participation in WIC were protective. Broad differences in rates, risks, and protective factors for PTB were observed.

Cervicovaginal fluid cytokines as predictive markers of preterm birth in symptomatic women

Objective

Here, we investigated whether cytokines in the cervicovaginal fluid (CVF) can be predictive markers of preterm birth (PTB).

Methods

A multi-center prospective cohort study was conducted on 59 singleton pregnant women hospitalized for preterm labor (PTL) and/or preterm premature rupture of membranes (pPROM) between 22 weeks and 36 weeks 6 days of gestation from 2014 to 2015. The levels of 13 inflammatory cytokines (macrophage inflammatory protein [MIP]-1α, MIP-1β, tumor necrosis factor [TNF]-α, interleukin [IL]-1β, IL-6, IL-8, IL-17α, granulocyte colony stimulating factor [G-CSF], IL-7, IL-4, IL-5, IL-10, and IL-13) were measured using a multiplex bead-based immunoassay and that of fetal fibronectin (fFN) was measured using enzyme-linked immunosorbent assay (ELISA). Statistical analyses were performed using Student’s t-test, Mann-Whitney U test, Pearson’s correlation, and receiver operating characteristic (ROC) curve analysis in SPSS version 20.0.

Results

Among the 13 cytokines assessed, the levels of 3 cytokines (MIP-1α, IL-6, and IL-7) were negatively correlated with gestational age at delivery (P=0.028, P=0.002, and P=0.018, respectively). Sensitivities of MIP-1α, IL-6, and IL-17α were 70%, 80%, and 75%, respectively, and their specificities were 57%, 65%, and 69%, respectively. The sensitivity and specificity of fFN were 33% and 95%, respectively.

Conclusion

In symptomatic women diagnosed with PTL and/or pPROM, cytokines from cervicovaginal fluid, especially IL-6 and IL-17α, could be better predictive markers of PTB than fFN.

Association between maternal sleep duration and quality, and the risk of preterm birth: a systematic review and meta-analysis of observational studies

Background

To assess the association of sleep duration and quality with the risk of preterm birth.

Methods

Relevant studies were retrieved from the PubMed and Web of Science databases up to September 30, 2018. The reference lists of the retrieved articles were reviewed. Random effects models were applied to estimate summarized relative risks (RRs) and 95% confidence intervals (CIs).

Results

Ten identified studies (nine cohort studies and one case-controlled study) examined the associations of sleep duration and quality with the risk of preterm birth. As compared with women with the longest sleep duration, the summary RR was 1.23 (95% CI = 1.01–1.50) for women with the shortest sleep duration, with moderate between-study heterogeneity (I² = 57.4%). Additionally, as compared with women with good sleep quality, the summary RR was 1.54 (95% CI = 1.18–2.01) for women with poor sleep quality (Pittsburgh Sleep Quality Index > 5), with high between-study heterogeneity (I² = 76.7%). Funnel plots as well as the Egger’s and Begg’s tests revealed no evidence of publication bias.

Conclusions

This systematic review and meta-analysis revealed that short sleep duration and poor sleep quality may be associated with an increased risk of preterm birth. Further subgroup analyses are warranted to test the robustness of these findings as well as to identify potential sources of heterogeneity.

Associations between unstable housing, obstetric outcomes, and perinatal health care utilization

BACKGROUND

While there is a growing interest in addressing social determinants of health in clinical settings, there are limited data on the relationship between unstable housing and both obstetric outcomes and health care utilization.

OBJECTIVE

The objective of the study was to investigate the relationship between unstable housing, obstetric outcomes, and health care utilization after birth.

STUDY DESIGN

This was a retrospective cohort study. Data were drawn from a database of liveborn neonates linked to their mothers' hospital discharge records (2007-2012) maintained by the California Office of Statewide Health Planning and Development. The analytic sample included singleton pregnancies with both maternal and infant data available, restricted to births between the gestational age of 20 and 44 weeks, who presented at a hospital that documented at least 1 woman as having unstable housing using the International Classification of Diseases, ninth edition, codes (n = 2,898,035). Infants with chromosomal abnormalities and major birth defects were excluded. Women with unstable housing (lack of housing or inadequate housing) were identified using International Classification of Diseases, ninth edition, codes from clinical records. Outcomes of interest included preterm birth (<37 weeks' gestational age), early term birth (37-38 weeks gestational age), preterm labor, preeclampsia, chorioamnionitis, small for gestational age, long birth hospitalization length of stay after delivery (vaginal birth, >2 days; cesarean delivery, >4 days), emergency department visit within 3 months and 1 year after delivery, and readmission within 3 months and 1 year after delivery. We used exact propensity score matching without replacement to select a reference population to compare with the sample of women with unstable housing using a one-to-one ratio, matching for maternal age, race/ethnicity, parity, prior preterm birth, body mass index, tobacco use during pregnancy, drug/alcohol abuse during pregnancy, hypertension, diabetes, mental health condition during pregnancy, adequacy of prenatal care, education, and type of hospital. Odds of an adverse obstetric outcome were estimated using logistic regression.

RESULTS

Of 2794 women with unstable housing identified, 83.0% (n = 2318) had an exact propensity score-matched control. Women with an unstable housing code had higher odds of preterm birth (odds ratio, 1.2, 95% confidence interval, 1.0-1.4, P < .05), preterm labor (odds ratio, 1.4, 95% confidence interval, 1.2-1.6, P < .001), long length of stay (odds ratio, 1.6, 95% confidence interval, 1.4-1.8, P < .001), emergency department visits within 3 months (odds ratio, 2.4, 95% confidence interval, 2.1-2.8, P < .001) and 1 year after birth (odds ratio, 2.7, 95% confidence interval, 2.4-3.0, P < .001), and readmission within 3 months (odds ratio, 2.7, 95% confidence interval, 2.2-3.4, P < .0014) and 1 year after birth (odds ratio, 2.6, 95% confidence interval, 2.2-3.0, P < .001).

CONCLUSION

Unstable housing documentation is associated with adverse obstetric outcomes and high health care utilization. Housing and supplemental income for pregnant women should be explored as a potential intervention to prevent preterm birth and prevent increased health care utilization.

Maternal dyslipidemia and risk for preterm birth

Maternal lipid profiles during pregnancy are associated with risk for preterm birth. This study investigates the association between maternal dyslipidemia and subsequent preterm birth among pregnant women in the state of California. Births were identified from California birth certificate and hospital discharge records from 2007-2012 (N = 2,865,987). Preterm birth was defined as <37 weeks completed gestation and dyslipidemia was defined by diagnostic codes. Subtypes of preterm birth were classified as preterm premature rupture of membranes (PPROM), spontaneous labor, and medically indicated, according to birth certificate data and diagnostic codes. The association between dyslipidemia and preterm birth was tested with logistic regression. Models were adjusted for maternal age at delivery, race/ethnicity, hypertension, pre-pregnancy body mass index, insurance type, and education. Maternal dyslipidemia was significantly associated with increased odds of preterm birth (adjusted OR: 1.49, 95%CI: 1.39, 1.59). This finding was consistent across all subtypes of preterm birth, including PPROM (adjusted OR: 1.54, 95%CI: 1.34, 1.76), spontaneous (adjusted OR: 1.51, 95%CI: 1.39, 1.65), and medically indicated (adjusted OR: 1.454, 95%CI: 1.282, 1.649). This study suggests that maternal dyslipidemia is associated with increased risk for all types of preterm birth.